Cloud phone-based application processing method and device, equipment and medium

Abstract

The present disclosure provides a cloud mobile phone-based application processing method and device, equipment and medium, relating to the technical field of cloud platforms. The method comprises: analyzing historical behavior data of a target object to obtain an interest application model; obtaining a candidate application program matched with the interest application model; determining a core function module based on the candidate application program; and transmitting the core function module to a cloud mobile phone in a block form for preloading. In this embodiment, the core function module of the application program is preloaded based on the user's usage preferences, which can avoid redundant transmission of complete application packages, optimize the application program startup process, improve the application startup speed, improve the running efficiency of the cloud mobile phone, and significantly reduce the user's waiting time.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, specifically to the field of cloud platform technology, and in particular to an application processing method, apparatus, device, and medium based on cloud mobile phones. Background Technology

[0002] With the rapid development of cloud phone technology, users have placed higher demands on the performance and user experience of cloud phones. However, existing technologies still have the following shortcomings: cloud phone storage space is limited, and pre-downloading complete application packages or frequent image updates can lead to excessive storage resource consumption. Furthermore, when users use cloud phones, application startup response times are long, operating efficiency is low, and the user experience is poor. Summary of the Invention

[0003] This invention provides an application processing method, apparatus, device, and medium based on cloud phones.

[0004] In a first aspect, embodiments of the present invention propose an application processing method based on a cloud phone, comprising: analyzing the historical behavior data of a target object to obtain an interest application model; obtaining candidate applications that match the interest application model; determining core functional modules based on the candidate applications; and transmitting the core functional modules to the cloud phone in a block format for preloading.

[0005] Furthermore, the analysis of the target object's historical behavioral data yields an interest application model, including:

[0006] Based on the user's historical behavior data, the applications used by the user are sorted according to the number of times they were launched and / or the duration of use.

[0007] The interest application model is generated based on the sorting results and a preset application list.

[0008] Furthermore, it also includes:

[0009] In response to determining that the cloud phone needs an image update, a differential algorithm is used to analyze the differences between the current image and the target image, and a differential file is generated.

[0010] The image file of the cloud phone is updated based on the differential file.

[0011] Furthermore, the step of using a differential algorithm to analyze the differences between the current image and the target image and generate a differential file includes:

[0012] Based on the componentized configuration of the cloud phone's image file, the component change information of the target image relative to the current image is analyzed using a differential algorithm;

[0013] The differential file is generated based on the component change information.

[0014] Furthermore, updating the cloud phone's image file based on the difference file includes:

[0015] The differential file is compressed using a compression algorithm;

[0016] Verify the integrity of the compressed differential file using a verification mechanism;

[0017] In response to determining the integrity of the compressed differential file, the image file of the cloud phone is updated based on the compressed differential file.

[0018] Furthermore, it also includes:

[0019] Perform security testing on the core functional modules;

[0020] The security test results are transmitted in an encrypted manner.

[0021] Furthermore, it also includes:

[0022] In response to receiving a startup command for the cloud phone, the applications in the cloud phone are loaded according to a preset priority, wherein the core functional modules that have been pre-loaded and their corresponding candidate applications have the highest priority.

[0023] Secondly, embodiments of the present invention propose an application processing device based on a cloud phone, comprising: an object behavior analysis module configured to analyze historical behavior data of a target object to obtain an interest application model; a candidate application acquisition module configured to acquire candidate applications that match the interest application model; a core function determination module configured to determine core function modules based on the candidate applications; and an application preloading module configured to transmit the core function modules to the cloud phone in a block format for preloading.

[0024] Thirdly, embodiments of the present invention provide a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0025] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause a computer to perform the method described in the first aspect or any of its corresponding embodiments.

[0026] Fifthly, embodiments of the present invention provide a computer program product including a computer program, which, when executed by a processor, can implement the application processing method based on a cloud phone as described in any of the implementations in the first aspect.

[0027] The application processing method, apparatus, device, and medium based on cloud phones provided in this invention preload the core functional modules of the application based on user preferences, which can avoid redundant transmission of the complete application package, optimize the application startup process, improve application startup speed, improve the operating efficiency of cloud phones, and significantly reduce user waiting time.

[0028] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0029] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0030] Figure 1 This is an exemplary system architecture to which this disclosure can be applied;

[0031] Figure 2 A flowchart illustrating an application processing method based on a cloud phone, provided in an embodiment of the present invention;

[0032] Figure 3 A flowchart illustrating another application processing method based on a cloud phone provided in an embodiment of the present invention;

[0033] Figure 4 A structural block diagram of an application processing device based on a cloud phone provided in an embodiment of the present invention;

[0034] Figure 5 This is a structural block diagram of a cloud phone system provided in an embodiment of the present invention;

[0035] Figure 6 This is a schematic diagram of the structure of an electronic device suitable for executing a cloud-based application processing method, provided as an embodiment of the present invention. Detailed Implementation

[0036] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments of the invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description. It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0037] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0038] Figure 1 An exemplary system architecture 100 is shown, to which embodiments of the cloud-based application processing methods, apparatuses, electronic devices, and computer-readable storage media disclosed herein can be applied.

[0039] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0040] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various applications for enabling information communication between the terminal devices 101, 102, and 103 and server 105 can be installed. These applications include instant messaging applications.

[0041] Terminal devices 101, 102, and 103 and server 105 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices, and can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here. When server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here.

[0042] Server 105 can provide various services through its built-in applications. It should be noted that the data or information required to provide these services can be obtained from terminal devices 101, 102, and 103 via network 104, or it can be pre-stored locally on server 105 through various means. Therefore, when server 105 detects that this data is already stored locally, it can choose to retrieve it directly from the local storage. In this case, the exemplary system architecture 100 may not include terminal devices 101, 102, and 103 and network 104.

[0043] Since running a cloud phone platform may require significant computing resources and power, the cloud phone-based application processing methods provided in the subsequent embodiments of this disclosure are generally executed by a server 105 with strong computing power and abundant computing resources. Correspondingly, the cloud phone-based application processing device is also generally located within the server 105. However, it should also be noted that when terminal devices 101, 102, and 103 also possess sufficient computing power and resources, they can also complete the aforementioned calculations performed by the server 105 through their installed applications, thereby outputting the same results as the server 105. Especially when multiple terminal devices with different computing capabilities exist simultaneously, but the relevant application determines that the terminal device has strong computing power and abundant remaining computing resources, the terminal device can perform the aforementioned calculations, thereby appropriately reducing the computing pressure on the server 105. Accordingly, the cloud phone-based application processing device can also be located within terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also exclude the server 105 and the network 104.

[0044] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0045] Please refer to Figure 2 , Figure 2 A flowchart of an application processing method based on a cloud phone provided in an embodiment of the present invention, wherein process 200 includes the following steps:

[0046] Step S11: Analyze the historical behavior data of the target object to obtain the interest application model.

[0047] In this embodiment of the application, the application processing method based on cloud phones (e.g.) Figure 1 The server 105 shown acquires historical behavior data of the target object and analyzes it to obtain the target object's application interest model. The target object can refer to various users of cloud phones (e.g., administrators, ordinary users, advanced users, etc.), and the historical behavior data refers to various historical behavior data generated when the target object uses the cloud phone. In this embodiment, the historical behavior data of the applications used by the target object (such as application launch frequency, usage duration, etc.) is analyzed first, and the application interest model of the target object is generated based on the analysis results. Furthermore, a list of popular applications can be combined to analyze the target object's interest in popular applications, thereby obtaining the application interest model.

[0048] For example, this interest application model can be represented by the following formula:

[0049] Wapp = α·start-up frequency + β·usage duration + γ·time decay factor

[0050] Where α represents the launch frequency weight (default value can be, for example, 0.5), reflecting how often users open the application; β represents the usage duration weight (default value can be, for example, 0.3), reflecting the depth of a single user's usage; and γ represents the time decay weight (default value can be, for example, 0.2), controlling the decay rate of historical behavior. Time decay factor = Δt is the number of days since the last use, and λ is the decay coefficient (the default value can be, for example, 0.1).

[0051] Based on this interest-based application model, the weight values ​​of the target object to various types of applications can be calculated and sorted from high to low to represent the order of the target object's interest in various types of applications from high to low.

[0052] Step S12: Obtain candidate applications that match the interest application model.

[0053] In this embodiment of the application, after determining the interest application model of the target object, the application whose weight value calculated for the interest application model is higher than a preset threshold or whose ranking is among the top few is used as the candidate application.

[0054] Step S13: Determine the core functional modules based on the candidate applications.

[0055] In this embodiment of the application, the core functional modules necessary for the runtime of the candidate application are determined as described above. For example, for a document application, the core functional module is a document editing module; for a social application, the core functional module is a chat core library; and for a game application, the core functional module is a rendering engine, etc.

[0056] Step S14: Transmit the core functional modules to the cloud phone in chunks for preloading.

[0057] In this embodiment, the core functional modules are divided into blocks as described above, and the divided core functional modules are transmitted to the cloud phone for preloading. In this embodiment, the block division can be based on functional modules or on file types. Blocking based on functional modules can be done by dividing into UI component blocks, basic logic blocks, dependency library blocks, resource configuration blocks, etc.; blocking based on file types can be done by dividing into code file blocks and resource file blocks, etc. The above block division methods are merely illustrative and are not intended to limit the invention. In practical applications, the block division method can be selected according to actual needs.

[0058] As an example, data showed that the target user frequently used a document editing application for file processing over the past three months, averaging about 40 minutes per day. They also opened a social chat application multiple times daily to communicate with colleagues and friends, while almost never using gaming applications. After cleaning and analyzing this historical behavioral data, a model of interest-based applications was constructed using machine learning algorithms. The results showed that the weight values ​​of document editing and social applications were significantly higher than preset thresholds. Based on this, the document editing application and the social chat application were identified as candidate applications. Subsequently, for the document editing application, its core functional module was determined to be the document editing module; for the social chat application, the chat core library was identified as the core functional module. Finally, the document editing module and the chat core library were divided into multiple data blocks and transmitted to a cloud phone via a secure and stable network channel. Upon receiving the data, the cloud phone immediately preloaded it, enabling the target user to load the core functions faster when subsequently opening these two applications on the cloud phone.

[0059] The cloud phone-based application processing method provided in this invention preloads the core functional modules of the application based on user preferences, which can avoid redundant transmission of the complete application package, optimize the application startup process, improve application startup speed, improve the operating efficiency of the cloud phone, and significantly reduce the user's waiting time.

[0060] Furthermore, embodiments of this application can also recommend more promising applications to the target audience based on the application usage patterns of similar user groups using an interest-based application model. Specifically, an in-depth analysis of the target audience's interest-based application model is conducted to extract their application usage characteristics, such as preferred application types and functional requirements. Then, a collaborative filtering algorithm is used to search for user groups with similar interests to the target audience in a large user database. By analyzing the application usage records of these similar user groups, applications used by them that the target audience has not yet encountered are identified, and these applications are considered as potential recommendations.

[0061] Next, the system acquires real-time information about the target user's current usage scenario. This can be determined through device sensor data (such as GPS positioning to determine movement and commuting scenarios, and detecting network connection type and environmental parameters to determine office or leisure scenarios) and user behavior (such as opening specific office software to determine an office scenario). The recommendation strategy is then dynamically adjusted based on the specific scenario. For example, when a commuting scenario is identified, lightweight entertainment applications that can be used without a network connection, such as single-player games and audiobook applications, are prioritized from potential recommendations. In an office scenario, the focus is on recommending applications related to improving work efficiency, such as file management tools and online collaboration platforms.

[0062] After recommending applications, user feedback data is continuously collected, including whether users have installed the recommended applications, whether they uninstalled them quickly after installation, and the duration of use. Based on this feedback data, the parameters of the collaborative filtering algorithm are adjusted to optimize the matching method for similar user groups and the weight calculation method for recommendations. Simultaneously, the feedback data is integrated into the target audience's interest application model, updating the weights of each application or application feature in the model, so that the interest application model more accurately reflects the target audience's true interests and needs.

[0063] In some optional implementations of this embodiment, after the core functional modules of the candidate application are preloaded, when the user issues a cloud phone startup command (e.g., clicking the start button or entering a startup command through the terminal device), the application in the cloud phone can be loaded according to a preset priority. Among them, the core functional modules and their corresponding candidate applications that have been preloaded have the highest priority, that is, the cloud phone prioritizes starting the candidate application and the core functional modules.

[0064] This process ensures fast startup of cloud phones, improves startup efficiency and application opening speed, and enhances the user experience.

[0065] In practical applications, to ensure the security of users using cloud phones, security tests can be performed on the aforementioned core functional modules, such as virus scanning and integrity verification. The security test results are then reported in an encrypted manner so that relevant personnel can be informed of the secure operation of the cloud phone in a timely manner.

[0066] Please refer to Figure 3 , Figure 3 A flowchart of another application processing method based on a cloud phone provided in an embodiment of the present invention, wherein process 300 includes the following steps:

[0067] Step S21: Analyze the historical behavior data of the target object to obtain the interest application model.

[0068] In this embodiment, the first step is to collect historical behavioral data of the target object. This data comes from a wide range of sources, including the target object's operation records, browsing history, and frequency of application downloads and usage on various application platforms. After collection, this data is cleaned to remove duplicate, erroneous, or irrelevant information to ensure the accuracy and usability of the data.

[0069] Next, data mining and machine learning techniques are used to analyze the cleaned data. For example, cluster analysis is used to categorize the behavior of the target object into different categories, and association rule mining is used to find potential relationships between different application usage behaviors.

[0070] Then, based on the analysis results, corresponding weights are assigned to various applications or application features to construct an interest application model that can reflect the interests and preferences of the target audience.

[0071] Step S22: Obtain candidate applications that match the interest application model.

[0072] In this embodiment, after determining the target object's interest application model, the weight values ​​of each application or application feature in the model are calculated. By pre-setting a threshold, applications with weight values ​​higher than the threshold are filtered out.

[0073] Simultaneously, all applications are sorted according to their weight values, and the top-ranked applications are selected. These filtered and top-ranked applications are candidate applications that match the interest application model. For example, if the target audience has a high interest weight for game applications, then popular game applications are likely to become candidate applications during the filtering and sorting process, thus accurately identifying applications that match the target audience's interests.

[0074] Step S23: Determine the core functional modules based on the candidate applications.

[0075] In this embodiment, firstly, the core functional modules differ for different types of applications. Taking document applications as an example, the document editing module is the core, as users primarily use the application to edit documents; for social applications, the chat core library is crucial, supporting real-time communication between users; and game applications rely on a rendering engine to present beautiful graphics and a smooth gaming experience. Through functional analysis of candidate applications, the core functional modules necessary for each application to run are determined.

[0076] Step S24: The core functional modules are transmitted to the cloud phone in chunks for preloading.

[0077] In this embodiment, the determined core functional modules are divided into multiple smaller blocks based on factors such as module size and functional relevance. This improves transmission efficiency and reduces errors and delays during transmission. Then, these divided core functional modules are sequentially transmitted to the cloud phone via a high-speed, stable network connection. During transmission, reliable transmission protocols and data verification mechanisms are employed to ensure data integrity and accuracy. Upon receiving these divided core functional modules, the cloud phone immediately performs a pre-loading operation. Pre-loading allows these modules to be loaded into the cloud phone's memory or cache in advance. When the user actually launches the corresponding application, these core functional modules can be quickly invoked, significantly shortening application startup time and improving the user experience.

[0078] The above steps S21-S24 and as follows Figure 2 The steps S11-S14 shown are the same. For the same parts, please refer to the corresponding parts of the previous embodiment. They will not be repeated here.

[0079] Step S25: In response to determining to update the cloud phone image, the difference between the current image and the target image is analyzed using a differential algorithm to generate a differential file.

[0080] In cloud phone scenarios, image updates are triggered in the following situations: regular maintenance windows, security vulnerability emergency response, feature iteration and user experience optimization, and user or administrator active triggering. By updating the image in the above processes, system performance can be improved, vulnerabilities can be fixed, or new requirements can be adapted.

[0081] Regular maintenance refers to pre-set periodic updates, or periodic cleanup of state-owned enterprise components or obsolete functions to optimize image size. Security vulnerability emergency response refers to the remediation of high-risk vulnerabilities, or the updating of encrypted data based on relevant requirements. Feature iteration and user experience optimization refers to the need to update the image for new feature releases, or to optimize system resource scheduling strategies, requiring corresponding image updates. User or administrator-triggered updates refer to image updates triggered by system administrators adjusting pre-installed applications, or user requests to revert to an older image due to compatibility issues.

[0082] Based on the above scenario requirements, an image update may be triggered. A differential algorithm (such as the BSDiff algorithm) is used to analyze the differences between the current image and the target image to be updated, and a differential file is generated.

[0083] In some optional implementations of this embodiment, the process of analyzing the differences between the current image and the target image using a differential algorithm to generate a differential file mainly includes:

[0084] Step 1: Based on the component-based configuration of the cloud phone's image file, use the differential algorithm to analyze the component change information of the target image relative to the current image.

[0085] In this embodiment, the component change information of the target image relative to the current image can be analyzed using a differential algorithm according to the componentized configuration of the cloud phone's image file.

[0086] Step 2: Generate differential files based on component change information.

[0087] In this embodiment, the image file to be updated can be split into multiple independent components (such as system framework components, application framework components, etc.) according to the component change information. The corresponding component files can be updated as needed, further avoiding the transmission and storage of redundant data.

[0088] Step S26: Update the cloud phone's image file based on the differential file.

[0089] In this embodiment of the application, after determining the differential file that needs to be updated, the image file of the cloud phone can be updated based on the differential file.

[0090] In some optional implementations of this embodiment, the process of updating the cloud phone's image file based on the differential file mainly includes:

[0091] Step 1: Compress the differential file using a compression algorithm.

[0092] In this embodiment, firstly, the storage location of the differential file needs to be determined. This could be a directory on the local disk or a specific path on a remote server. Then, file operation functions are used to load the differential file completely into memory. Next, the LZMA compression algorithm is used to process the differential file data in memory. In practice, dedicated LZMA libraries in programming languages ​​can be used, such as Python's lzma module or Java's org.tukaani.xz library.

[0093] The differential file data is input into the compression functions provided by these libraries. The algorithms analyze the data's repetition patterns, character frequencies, and other characteristics, and then use complex encoding methods to compress the data. After compression, the resulting compressed data is saved as a new file. The new file name can be appended with the ".lzma" suffix to the original differential file name to clearly identify that it is a file compressed using the LZMA algorithm, thereby effectively reducing the size of the updated file and facilitating subsequent transmission and storage.

[0094] Step 2: Verify the integrity of the compressed differential file using a verification mechanism;

[0095] In this embodiment, integrity verification of the compressed differential file begins. The first step is to select a reliable verification algorithm, such as SHA-256, which offers high security and a low probability of hash collisions. Next, the compressed differential file content is read block by block, and each block of data is input into the SHA-256 algorithm for hash calculation. The hash calculation status is continuously updated until the entire file is processed, yielding a final hash value. A correct hash value has already been pre-calculated during the differential file compression process and is securely stored in a configuration file, database, or other reliable storage medium.

[0096] Retrieve the correct hash value from the corresponding storage location, and then carefully compare it with the hash value just calculated. If the two hash values ​​are exactly the same, it indicates that the compressed differential file has not been missing, corrupted, or tampered with during transmission or storage; if they are different, it indicates that the file may have a problem, and the differential file needs to be retrieved again and the compression and verification steps repeated.

[0097] Step 3: In response to verifying the integrity of the compressed differential file, update the cloud phone's image file based on the compressed differential file.

[0098] Once the integrity of the compressed differential file is confirmed, the update operation for the cloud phone image file begins. First, the LZMA decompression library is used to decompress the differential file, restoring the compressed data to its original content. Then, the componentized structure of the cloud phone image is analyzed in depth, clarifying the storage location, data format, and interdependencies of each component (such as the system framework and application framework). Based on the change information recorded in the differential file, the data is accurately allocated to the corresponding components. For example, if the differential file records a modification to a configuration file in the system framework, the corresponding changes are updated in the configuration file of the system framework component; if an update involves a functional module of the application framework, the updated data for that module in the differential file replaces the corresponding location in the application framework. In this way, only the changed portions of the image file are updated, avoiding the retransmission and overwriting of the entire image file, greatly improving update efficiency while reducing data transfer volume.

[0099] By updating only the differential file during image updates, the amount of data transferred and stored can be effectively reduced. Furthermore, update efficiency is significantly improved, update time is shortened, and user experience is enhanced.

[0100] In addition, when updating the cloud phone image file, a component-based analysis is first performed on the cloud phone image file to clarify the dependencies and update order of each component. When an update is needed, a differential file is generated according to the component change information. Instead of simply overwriting, the current image file and the differential file are carefully compared to determine the specific components and data blocks that need to be updated. The content of the differential file is then precisely merged into the corresponding position in the current image file to achieve incremental updates.

[0101] Throughout the update process, the system records operation logs for each update in real time, detailing the update time, involved components, and executed steps, as well as the update status, such as "updating started," "updating in progress," and "updating complete." If a system crash or update failure occurs during the update process, the system immediately stops the update operation and, based on the recorded operation logs and status information, reverses the process to quickly roll back the cloud phone image file to the previous stable version, restoring it to its normal state before the update.

[0102] For critical components, such as the system core framework and security authentication modules, a pre-update phase is implemented before a full-scale update. This involves testing the update in a small-scale environment, such as a select few cloud phone instances or a simulated runtime environment, to comprehensively monitor the updated system's performance, stability, and compatibility. If issues are discovered during testing, they are promptly analyzed and fixed. Once the update is confirmed to be error-free, a full update of the critical components in all cloud phone image files is then performed, effectively reducing update risks.

[0103] In some optional implementations of this embodiment, various types of data can be continuously collected during the user's use of the cloud phone, including the name of the opened application, usage duration, operation frequency, operation path, and other information, to build a user behavior database.

[0104] Next, data analysis algorithms are used to mine and analyze the data in the database, identifying frequently used applications and related components, as well as components with lower usage frequency. For the identified frequently used components, they are placed at the top of the update priority queue when the cloud phone image file is updated, and these components are given priority for in-depth analysis, updates, and optimization. Through targeted code optimization, resource allocation, and other means, their performance and stability are effectively improved.

[0105] For infrequently used components, the update operation will not be performed immediately. Instead, the update task will be temporarily stored, and the update time will be delayed according to the importance of the component, or the corresponding update process will be triggered when the user has a need for it in a specific scenario.

[0106] In addition, the system monitors the user's network environment in real time, assessing network conditions by acquiring data such as network bandwidth, latency, and packet loss rate. When a poor network is detected, more efficient compression algorithms are automatically activated, such as block compression and intelligent encoding, to further reduce the size of differential files. During transmission, a more stable and reliable transmission protocol is selected, and data transmission strategies are dynamically adjusted to ensure that cloud phone image file updates can be completed smoothly, thus improving the user experience.

[0107] In some optional implementations of this embodiment, the aforementioned execution entity can also perform personalized configurations for different users. Specifically, it can configure the icon names, icon layouts, icon styles, usage habits, etc., in the user interface of different users through the cloud platform interface. This allows for adaptive adjustments when different user accounts are detected to ensure that when a user opens the cloud phone, it displays the user interface style that they prefer or are accustomed to, thus providing customized services to the user.

[0108] Further reference Figure 4 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of an application processing device based on a cloud phone, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0109] like Figure 4 As shown, the application processing device 400 based on a cloud phone in this embodiment may include: an object behavior analysis module 401, a candidate application acquisition module 402, a core function determination module 403, and an application preloading module 404. The object behavior analysis module 401 is configured to analyze historical behavior data of a target object to obtain an interest application model; the candidate application acquisition module 402 is configured to acquire candidate applications that match the interest application model; the core function determination module 403 is configured to determine core function modules based on the candidate applications; and the application preloading module 404 is configured to transmit the core function modules in chunks to the cloud phone for preloading.

[0110] In this embodiment, the specific processing of the object behavior analysis module 401, the candidate application acquisition module 402, the core function determination module 403, and the application preloading module 404 in the cloud phone-based application processing device 400, and their resulting technical effects, can be found in reference to [reference needed]. Figure 2 The relevant descriptions of steps 201-203 in the corresponding embodiments will not be repeated here.

[0111] This embodiment exists as a device embodiment corresponding to the above method embodiment. The application processing device based on cloud phones provided in this embodiment preloads the core functional modules of the application in advance based on user preferences, which can avoid redundant transmission of the complete application package, optimize the application startup process, improve the application startup speed, improve the operating efficiency of cloud phones, and significantly reduce the user's waiting time.

[0112] Further reference Figure 5 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a cloud phone system, such as... Figure 5 As shown, the cloud phone system includes a cloud platform 501 and a cloud phone 502. The cloud platform 501 can be used to execute the cloud phone-based application processing method described in any of the above embodiments. The cloud platform 501 includes a user behavior analysis module 5011, a pre-download management module 5012, an image optimization engine 5013, and a background configuration interface 5014. The cloud platform 501 communicates with the cloud phone 502 via a communication link. The cloud platform 501 is used to send pre-download instructions and image update packages to the cloud phone 502. The functions implemented by the user behavior analysis module 5011 can be found in the description of step 201 in any of the above embodiments; the functions implemented by the pre-download management module 5012 can be found in the description of steps 202-204 in any of the above embodiments; the functions implemented by the image optimization engine 5013 can be found in the description of steps 305-306 in any of the above embodiments; and the background configuration interface 5014 is used for configuring user information, system files, etc.

[0113] Cloud phone 502 includes: a pre-download component storage area 5021, a security detection module 5022, an image component library 5023, and a fast startup module 5024. The pre-download component storage area 5021 stores the core component modules pre-downloaded as determined by the cloud platform 501. The security detection module 5022 performs security checks on files and components transmitted from the cloud platform 501 to the cloud phone 502. The image component library 5023 stores image components in the cloud phone 502 that require image updates. The fast startup module 5024 enables fast startup based on the pre-loaded core functional modules. Cloud phone 502 reports the security detection results and user operation logs to the cloud platform 501.

[0114] According to embodiments of this disclosure, this disclosure also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to implement the cloud-based mobile phone application processing method described in any of the above embodiments.

[0115] According to embodiments of this disclosure, this disclosure also provides a readable storage medium storing computer instructions that enable a computer to implement the cloud-based application processing method described in any of the above embodiments when executed.

[0116] According to embodiments of this disclosure, this disclosure also provides a computer program product that, when executed by a processor, can implement the application processing method based on a cloud phone as described in any of the above embodiments.

[0117] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system).

[0118] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0119] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0120] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0121] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0122] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0123] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0124] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An application processing method based on cloud phones, characterized in that, include: Analyze the historical behavioral data of the target audience to obtain an interest application model; Obtain candidate applications that match the interest application model; By analyzing the functionality of candidate applications, the core functional modules necessary for each application to run were identified. The core functional modules are transmitted to the cloud phone in a chunk format for preloading, where the chunk format includes either function type or file type. It also includes: in response to determining that the cloud phone needs an image update, using a differential algorithm to analyze the differences between the current image and the target image, generating a differential file; and updating the image file of the cloud phone based on the differential file; The step of using a differential algorithm to analyze the differences between the current image and the target image and generate a differential file includes: analyzing the component change information of the target image relative to the current image according to the componentized configuration of the cloud phone's image file; and generating the differential file based on the component change information.

2. The method according to claim 1, characterized in that, The analysis of the target object's historical behavior data yields an interest application model, including: Based on users' historical behavior data, the applications used by users are sorted according to the number of times they were launched and / or the duration of use. The interest application model is generated based on the sorting results and a preset application list.

3. The method according to claim 1, characterized in that, The step of updating the cloud phone's image file based on the differential file includes: The differential file is compressed using a compression algorithm; Verify the integrity of the compressed differential file using a verification mechanism; In response to determining the integrity of the compressed differential file, the image file of the cloud phone is updated based on the compressed differential file.

4. The method according to claim 1, characterized in that, Also includes: Perform security testing on the core functional modules; The security test results are transmitted in an encrypted manner.

5. The method according to claim 1, characterized in that, Also includes: In response to receiving a startup command for the cloud phone, the applications in the cloud phone are loaded according to a preset priority, wherein the core functional modules that have been pre-loaded and their corresponding candidate applications have the highest priority.

6. An application processing device based on a cloud phone, characterized in that, include: The object behavior analysis module is configured to analyze the historical behavior data of the target object to obtain an interest application model. The candidate application acquisition module is configured to acquire candidate applications that match the interest application model; The core functionality determination module is configured to determine the core functional modules necessary for each application to run by analyzing the functionality of candidate applications. The application preloading module is configured to transmit the core functional modules to the cloud phone in a chunked manner for preloading, wherein the chunked manner includes functional type or file type. The application processing device also includes an update module, which, in response to determining that the cloud phone needs to be updated, analyzes the differences between the current image and the target image using a differential algorithm and generates a differential file; Update the cloud phone's image file based on the differential file; The update module is used to analyze the component change information of the target image relative to the current image according to the componentized configuration of the cloud phone image file using a differential algorithm; The differential file is generated based on the component change information.

7. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 5.

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