Application processing method and device based on cloud mobile phone, equipment and medium

Abstract

The invention provides an application processing method and device based on a cloud mobile phone, equipment and a medium, and relates to the technical field of cloud platforms. The method comprises the following steps: analyzing historical behavior data of a target object to obtain an interest application model; obtaining candidate application programs matched with the interest application model; determining a core function module based on the candidate application program; and transmitting the core function module to the cloud mobile phone in a block form for preloading. According to the embodiment, the core function module of the application program is preloaded in advance based on the use preference of the user, redundant transmission of a complete application package can be avoided, the application program starting process can be optimized, the application starting speed can be increased, the operation efficiency of the cloud mobile phone can be improved, and the waiting time of the user can be remarkably shortened.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to the field of cloud platform technology, and particularly to an application processing method, apparatus, device, and medium based on a cloud phone. Background Art

[0002] With the rapid development of cloud phone technology, users have put forward higher requirements for the performance and usage experience of cloud phones. However, the existing technologies still have the following deficiencies: the storage space of cloud phones is limited, and pre-downloading of complete application packages or frequent mirror updates will result in excessive occupation of storage resources. Moreover, when users use cloud phones, the application startup response time is long, the operation efficiency is low, and the operation experience is poor. Summary of the Invention

[0003] Embodiments of the present invention propose an application processing method, apparatus, device, and medium based on a cloud phone.

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

[0005] Further, the analyzing historical behavior data of the target object to obtain an interest application model includes:

[0006] Sorting the application programs used by the user according to the number of startups and / or usage duration based on the user historical behavior data;

[0007] Generating the interest application model based on the sorting result and a preset application program list.

[0008] Further, it further includes:

[0009] In response to determining to perform a mirror update on the cloud phone, analyzing the difference between the current mirror and the target mirror using a differential algorithm to generate a differential file;

[0010] Updating the mirror file of the cloud phone based on the differential file.

[0011] Further, the analyzing the difference between the current mirror and the target mirror using a differential algorithm to generate a differential file includes:

[0012] Analyzing the component change information of the target mirror relative to the current mirror using a differential algorithm according to the componentized configuration of the mirror file of the cloud phone;

[0013] Generating the differential file based on the component change information.

[0014] Further, updating the image file of the cloud phone based on the difference file includes:

[0015] Compressing the differential file using a compression algorithm;

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

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

[0018] Further, it also includes:

[0019] Performing a security detection on the core function module;

[0020] Transmitting the security detection result through an encryption method.

[0021] Further, it also includes:

[0022] In response to obtaining a startup instruction for the cloud phone, loading the application programs in the cloud phone according to a preset priority, where the core function module and the corresponding candidate application programs that have been pre-loaded have the highest priority.

[0023] In a second aspect, an application processing apparatus based on a cloud phone according to an embodiment of the present invention includes: 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 application programs that match the interest application model; a core function determination module configured to determine a core function module based on the candidate application programs; and an application pre-loading module configured to transmit the core function module to the cloud phone in a block form for pre-loading.

[0024] In a third aspect, an embodiment of the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the method according to the first aspect or any corresponding implementation manner thereof.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the method according to the first aspect or any corresponding implementation manner thereof.

[0026] In a fifth aspect, an embodiment of the present invention provides a computer program product including a computer program, and the computer program can implement the application processing method based on a cloud phone described in any implementation manner in the first aspect when executed by a processor.

[0027] The application processing method, device, equipment and medium based on cloud mobile phones provided by the embodiments of the present invention pre-load the core function modules of application programs in advance based on user usage preferences, which can avoid redundant transmission of complete application packages, optimize the application program startup process, improve the application startup speed, improve the operation efficiency of cloud mobile phones, and significantly reduce the waiting time of users.

[0028] It should be understood that the content described in this part does not identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. Description of the Drawings

[0029] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, purposes and advantages of the present disclosure will become more obvious:

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

[0031] Figure 2 is a flowchart of an application processing method based on cloud mobile phones provided by the embodiments of the present invention;

[0032] Figure 3 is a flowchart of another application processing method based on cloud mobile phones provided by the embodiments of the present invention;

[0033] Figure 4 is a structural block diagram of an application processing device based on cloud mobile phones provided by the embodiments of the present invention;

[0034] Figure 5 is a structural block diagram of a cloud mobile phone system provided by the embodiments of the present invention;

[0035] Figure 6 is a schematic structural diagram of an electronic device suitable for executing the application processing method based on cloud mobile phones provided by the embodiments of the present invention. Detailed Embodiments

[0036] The following describes exemplary embodiments of the present disclosure in conjunction with the accompanying drawings. Various details of the embodiments of the present invention are included to assist understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted below. It should be noted that, without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0037] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0038] Figure 1 Exemplary system architecture 100 is shown, in which embodiments of the application processing method, apparatus, electronic device, and computer-readable storage medium based on cloud phones according to the present disclosure can be applied

[0039] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0040] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various applications for realizing information communication between the two may be installed on the terminal devices 101, 102, 103 and the server 105, such as instant messaging applications, etc.

[0041] The terminal devices 101, 102, 103 and the server 105 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices with a display screen, including but not limited to smartphones, tablets, laptop portable computers, and desktop computers, etc.; when the terminal devices 101, 102, 103 are software, they may be installed in the above-listed electronic devices, and they may be implemented as multiple software or software modules, or may be implemented as a single software or software module, which is not specifically limited herein. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server; when the server is software, it may be implemented as multiple software or software modules, or may be implemented as a single software or software module, which is not specifically limited herein.

[0042] The server 105 can provide various services through various built-in applications. It should be noted that in addition to the data or information required to provide various services that can be obtained from the terminal devices 101, 102, 103 through the network 104, they can also be pre-stored locally in the server 105 in various ways. Therefore, when the server 105 detects that these data have been stored locally, it can choose to directly obtain these data from the local. In this case, the exemplary system architecture 100 may not include the terminal devices 101, 102, 103 and the network 104.

[0043] Since running a cloud mobile phone platform may require a large amount of computing resources and strong computing power, the application processing method based on cloud mobile phones provided in the subsequent embodiments of the present disclosure is generally executed by the server 105 with strong computing power and a large amount of computing resources. Correspondingly, the application processing device based on cloud mobile phones is generally also set in the server 105. However, it should also be noted that when the terminal devices 101, 102, and 103 also have sufficient computing power and computing resources, the terminal devices 101, 102, and 103 can also complete the above operations that were originally performed by the server 105 through the relevant applications installed thereon, and then output the same results as the server 105. Especially in the case where there are multiple terminal devices with different computing capabilities at the same time, when the relevant application determines that the terminal device where it is located has strong computing power and a large amount of remaining computing resources, the terminal device can be allowed to perform the above operations, thereby appropriately reducing the computing pressure on the server 105. Correspondingly, the application processing device based on cloud mobile phones can also be set in the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may not include the server 105 and the network 104 either.

[0044] It should be understood that Figure 1 the numbers of terminal devices, networks, and servers in

[0045] are merely illustrative. According to actual needs, there can be any number of terminal devices, networks, and servers. Figure 2 , Figure 2 Please refer to

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

[0047] In the embodiments of the present application, the historical behavior data of the target object is obtained from the application processing method based on cloud mobile phones (such as the server 105 shown in Figure 1 ), and the historical behavior data is analyzed to obtain the interest application model of the target object. Among them, the target object can refer to various users who use cloud mobile phones (such as administrators, ordinary users, senior users, etc.), and the historical behavior data refers to various historical behavior data generated when the target object uses the cloud mobile phone. In this embodiment, the historical behavior data of the application programs used by the target object (such as the startup frequency and usage duration of the application programs) is preferentially analyzed, and the interest application model of the target object is generated according to the analysis results. Further, the interest tendency of the target object for popular applications can also be analyzed in combination with the popular application list, so as to obtain the interest application model.

[0048] Exemplarily, the interest application model can be represented by the following formula:

[0049] W app = α · startup frequency + β · usage duration + γ · time decay factor,

[0050] where α represents the startup frequency weight (the default value can be, for example, 0.5), reflecting the frequency at which the user opens the application; β represents the usage duration weight (the default value can be, for example, 0.3), reflecting the depth of the user's single use; γ represents the time decay weight (the default value can be, for example, 0.2), controlling the decay rate of historical behavior. The time decay factor = e -λ·Δt , Δt is the number of days since the most recent use, and λ is the decay coefficient (the default value can be, for example, 0.1).

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

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

[0053] In the embodiment of the present application, after determining the interest application model of the target object as described above, the application programs whose weight values calculated for the interest application model are higher than the preset threshold or are ranked among the top several are used as the candidate application programs.

[0054] Step S13: Determine the core function modules based on the candidate application programs.

[0055] In the embodiment of the present application, for the candidate application programs as described above, determine the core function modules that are necessary for their operation. For example, for a document application program, its core function module is the document editing module; for a social application program, its core function module is the chat core library; for a game application program, its core function module is the rendering engine, etc.

[0056] Step S14: Transmit the core function modules to the cloud phone in a chunked form for preloading.

[0057] In the embodiment of the present application, the core function modules are chunked as described above, and the chunked core function modules are transmitted to the cloud phone for preloading. In this embodiment, the chunking process can be based on function modules or file types. Among them, chunking based on function modules can be divided according to UI component chunks, basic logic chunks, dependency library chunks, resource configuration chunks, etc.; chunking based on file types can be divided according to code file chunks and resource file chunks, etc. The above chunking methods are only examples and are not intended to limit the present invention. In actual applications, the chunking method can be selected according to actual needs.

[0058] As an example, it is collected that the target object frequently uses a certain document editing application for file processing within the recent three months, with an average daily usage duration of about 40 minutes. At the same time, the social chat application is opened multiple times a day to communicate with colleagues and friends, while the game applications are hardly used. After cleaning and analyzing these historical behavior data, a machine learning algorithm is used to construct an interest application model, and it is found that the weight values of document editing and social applications are much higher than the preset threshold. Based on this, the document editing application and the social chat application are determined as candidate application programs. Subsequently, for the document editing application, its core function module is identified as the document editing module; for the social chat application, the chat core library is determined as the core function module. Finally, the document editing module and the chat core library are split into multiple data chunks and transmitted to the cloud phone through a secure and stable network channel. The cloud phone immediately performs preloading after receiving them, so that when the target object subsequently uses the cloud phone to open these two applications, the core functions can be loaded faster.

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

[0060] In addition, the embodiment of the present application can also recommend more potential application programs for the target object based on the interest application model by referring to the application usage of similar user groups. Specifically, the interest application model of the target object is deeply analyzed to extract the characteristics of the target object in application usage, such as preferred application types, functional requirements, etc. Then, the collaborative filtering algorithm is used to find user groups with similar interests to the target object in a large user database. By analyzing the application usage records of these similar user groups, the application programs that they use and the target object has not come into contact with are mined, and these application programs are used as potential recommended objects.

[0061] Next, obtain the current usage scenario information of the target object in real time, which can be determined through the device's sensor data (such as using GPS positioning to determine whether it is in motion to identify the commuting scenario, detecting the network type and environmental parameters connected to the device to judge the office or leisure scenario) and the user's operation behavior (such as opening a specific office software to determine the office scenario). According to different scenarios, dynamically adjust the recommendation strategy. For example, when it is determined to be the commuting scenario, preferentially screen out lightweight entertainment applications that can be used without network connection from potential recommended objects, such as single-player games, audiobook applications, etc. for recommendation; in the office scenario, focus on recommending applications related to improving office efficiency, such as file management tools, online collaboration platforms, etc.

[0062] After recommending the application programs, continuously collect the feedback data of the user on these recommended application programs, including whether the user installs the recommended application program, whether it is uninstalled soon after installation, the duration of using the application program, etc. According to this feedback data, adjust the parameters of the collaborative filtering algorithm, optimize the matching method of similar user groups and the weight calculation method of recommendations. At the same time, integrate the feedback data into the interest application model of the target object, update the weights of each application or application feature in the model, so that the interest application model can more accurately reflect the real interests and needs of the target object.

[0063] In some alternative embodiments of this embodiment, after preloading the core function modules of the candidate application programs, when receiving the cloud phone startup instruction issued by the user (such as clicking the start button or inputting a startup command through the terminal device), the application programs in the cloud phone can be loaded according to the preset priority. Among them, the candidate application programs and their corresponding core function modules for which the core function modules have been preloaded have the highest priority, that is, the cloud phone starts the candidate application programs and core function modules first.

[0064] Through this process, it is possible to truly achieve the rapid startup of the cloud phone, improve the startup efficiency of the cloud phone and the opening speed of the application programs, and enhance the user experience.

[0065] In practical applications, in order to ensure the security of the user's use of the cloud phone, the above-mentioned core function modules can also be subjected to security detection, such as virus scanning and integrity verification, etc., and the security detection results are reported in an encrypted manner so that relevant personnel can timely learn about the safe operation status of the cloud phone.

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

[0067] Step S21, analyze the historical behavior data of the target object to obtain an interest application model.

[0068] In the embodiments of the present application, first, historical behavior data of the target object needs to be collected. These data sources are extensive, including the operation records, browsing history, download and usage frequencies of applications on various application platforms of the target object. After collection, these data are cleaned to remove duplicate, incorrect or irrelevant information to ensure the accuracy and availability of the data.

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

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

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

[0072] In the embodiments of the present application, after determining the interest application model of the target object, the weight values of each application or application feature in the model are calculated. By presetting a threshold, the application programs with weight values higher than the threshold are screened out.

[0073] At the same time, all applications are also sorted according to the weight values, and the top-ranked application programs are selected. These screened and top-ranked application programs are the candidate application programs that match the interest application model. For example, if the target object has a high interest weight in game applications, then in the screening and sorting process, popular game applications are very likely to become candidate application programs, so that applications that meet the interests of the target object can be accurately found.

[0074] Step S23: Determine the core function modules based on the candidate application programs.

[0075] In the embodiments of the present application, first, for different types of application programs, their core function modules are different. Taking document application programs as an example, the document editing module is the core, because users mainly use this application for document editing operations; for social application programs, the chat core library is the key, which supports the real-time communication function between users; while game application programs rely on the rendering engine to present beautiful pictures and smooth gaming experiences. By analyzing the functions of the candidate application programs, the core function modules required for each application program to run are determined.

[0076] Step S24: Transmit the core function modules to the cloud phone in a chunked form for preloading.

[0077] In the embodiments of the present application, the determined core function modules are segmented and divided into multiple small pieces according to factors such as the size and functional relevance of the modules. The advantage of this is that it can improve the transmission efficiency and reduce errors and delays during the transmission process. Then, through a high-speed and stable network connection, these segmented core function modules are sequentially transmitted to the cloud phone. During the transmission process, a reliable transmission protocol and data verification mechanism will be adopted to ensure the integrity and accuracy of the data. When the cloud phone receives these segmented core function modules, it will immediately perform a preloading operation. Preloading can load these modules into the memory or cache of the cloud phone in advance. When the user truly starts the corresponding application, these core function modules can be quickly called, thus greatly shortening the startup time of the application and enhancing the user experience.

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

[0079] Step S25: In response to determining to perform a mirror update on the cloud phone, use the differential algorithm to analyze the differences between the current mirror and the target mirror, and generate a differential file.

[0080] In the cloud phone scenario, mirror updates are triggered in the following situations: regular maintenance windows, security vulnerability emergency responses, function iterations and user experience optimizations, active triggers by users or administrators, etc. By performing mirror updates during the above processes, the system performance can be improved, vulnerabilities can be fixed, or new requirements can be adapted.

[0081] Among them, the regular maintenance window refers to pre-set periodic updates, or regular cleaning of obsolete components or abandoned functions to optimize the mirror volume. The security vulnerability emergency response refers to the repair of high-risk vulnerabilities, or the need to update encrypted data based on corresponding requirements. Function iterations and user experience optimizations refer to the need to update the mirror when new functions are released, or to update the corresponding mirror when optimizing the system resource scheduling strategy. Active triggers by users or administrators refer to the trigger of mirror updates by system administrators adjusting pre-installed applications, or users applying to roll back to the old version of the mirror due to compatibility issues, etc.

[0082] Based on the requirements of any of the above scenarios, a mirror update may be triggered. Use the differential algorithm (such as the BSDiff algorithm) to analyze the differences between the current mirror and the target mirror to be updated, and generate a differential file.

[0083] In some optional implementation manners of this embodiment, the process of using the differential algorithm to analyze the differences between the current mirror and the target mirror and generate a differential file mainly includes:

[0084] Step 1: According to 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, according to the component-based configuration of the cloud phone's image file, the differential algorithm can be used to analyze the component change information of the target image relative to the current image.

[0086] Step 2: Generate a differential file based on the component change information.

[0087] In this embodiment, the image file to be updated can be split into multiple independent components (such as the system framework component, application framework component, etc.) according to the component change information, and 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 the embodiment of the present application, after determining the differential file to be updated, the cloud phone's image file can be updated based on the differential file.

[0090] In some alternative embodiments 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, first, the storage location of the differential file needs to be determined, which may be a certain directory on the local disk or a specific path on a remote server. Then, with the help of the file operation function, the differential file is completely loaded into the memory. Next, the LZMA compression algorithm is selected to process the differential file data in the memory. In actual operation, a dedicated LZMA library in a programming language can be used, such as the lzma module in Python or the org.tukaani.xz library in Java.

[0093] The differential file data is input into the compression function provided by these libraries. The algorithm will analyze features such as the repeated patterns and character frequencies of the data and use complex encoding methods to compress the data. After compression, the generated compressed data is saved as a new file. The new file can be named by adding the.lzma suffix after the original differential file name to clearly identify that this is a file compressed by the LZMA algorithm, thus effectively reducing the volume of the updated file and facilitating subsequent transmission and storage.

[0094] Step 2: Use a verification mechanism to verify the integrity of the compressed differential file;

[0095] In this embodiment, the integrity verification of the compressed differential file is started. First step, select a reliable verification algorithm, such as SHA-256, which has high security and a low probability of hash collision. Then, read the content of the compressed differential file block by block, input each block of data into the SHA-256 algorithm for hash calculation, and continuously update the hash calculation state until the entire file is processed to obtain a final hash value. When the differential file is compressed and generated, a correct hash value has been pre-calculated and stored properly in a configuration file, a database, or other reliable storage media.

[0096] Obtain this 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 suffered from content loss, damage, or being tampered with during transmission or storage; if they are different, it means the file may have problems and the differential file needs to be re-obtained and the compression and verification steps need to be repeated.

[0097] Step three: In response to determining the integrity of the compressed differential file, update the mirror file of the cloud phone based on the compressed differential file.

[0098] When it is determined that the integrity of the compressed differential file is correct, the update operation of the cloud phone mirror file is started. First, use the LZMA decompression library to decompress the compressed differential file to restore the compressed data to the original differential file content. Then, deeply analyze the componentized structure of the cloud phone mirror, clarify the storage locations, data formats, and mutual dependencies of each component (such as the system framework, application framework, etc.). According to the change information recorded in the differential file, accurately allocate the data of the differential file to the corresponding components. For example, if the modification of a certain configuration file in the system framework is recorded in the differential file, update the corresponding modification content to the configuration file of the system framework component; if an update of a certain function module in the application framework is involved, replace the update data of this module in the differential file to the corresponding location in the application framework. In this way, only the part of the mirror file that has changed is updated, avoiding the re-transmission and overwriting of the entire mirror file, greatly improving the update efficiency and reducing the data transmission volume at the same time.

[0099] Through the above process, only the differential file is updated during mirror update, which can effectively reduce the data transmission volume and storage volume, and significantly improve the update efficiency, shorten the update time, and enhance the user experience.

[0100] In addition, when updating the cloud mobile phone image file, first perform component analysis on the cloud mobile phone image file to clarify the dependency relationships and update order of each component. When there is an update requirement, generate a differential file according to the component change information. Instead of simply overwriting directly, carefully compare the current image file with the differential file to determine the specific components and data blocks that need to be updated, and precisely merge the content in the differential file into the corresponding positions in the current image file to achieve incremental updates.

[0101] During the entire update process, the system will record the operation logs of each update in real time, detailedly record information such as the update time, the components involved, and the operation steps executed, and at the same time record the update status, such as starting the update, in the process of updating, update completed, etc. Once problems such as system crashes or update failures occur during the update process, the system immediately stops the update operation, and based on the recorded operation logs and status information, perform reverse operations to quickly roll back the cloud mobile phone image file to the previous stable version and restore it to the normal state before the update.

[0102] For some key components, such as the system core framework, security authentication module, etc., set a pre-update stage before the official comprehensive update. First, in a small-scale environment, such as selecting a small number of cloud mobile phone instances or a simulated operating environment, perform test updates on the key components, and comprehensively monitor the performance, stability, compatibility, etc. of the system after the update. If problems are found during the test process, analyze and repair them in a timely manner; if it is confirmed that the update is correct, then perform a comprehensive update on the key components in the image files of all cloud mobile phones, thereby effectively reducing the update risk.

[0103] In some alternative implementation manners of this embodiment, various types of data during the user's use of the cloud mobile phone can also be continuously collected, including information such as the names of opened application programs, usage duration, operation frequency, operation paths, etc., to construct a user usage behavior database.

[0104] Then, use data analysis algorithms to mine and analyze the data in the database to identify the application programs and related components that users often use, as well as the components with lower usage frequencies. For the components that are identified as often used by users, when updating the cloud mobile phone image file, place them at the forefront of the update priority queue, and give priority to deeply analyzing, updating, and optimizing these components. Through targeted code optimization, resource allocation, etc., ensure that their performance and stability are effectively improved.

[0105] For components that are not frequently used, the update operation will not be immediately executed. Instead, the update tasks will be temporarily stored, and the delayed update time will be set according to the importance of the components, or wait until the user generates a usage requirement in a specific scenario, and then trigger the corresponding update process.

[0106] In addition, the network environment of the user is monitored in real time, and the network condition is judged by obtaining data such as network bandwidth, latency, and packet loss rate. When a poor network is detected, more efficient compression algorithms are automatically enabled, such as using techniques like block compression and intelligent coding to further reduce the volume of the differential file; during the transmission process, a more stable and reliable transmission protocol is selected, and the data transmission strategy is dynamically adjusted to ensure the smooth completion of the update of the cloud mobile phone image file and improve the user experience.

[0107] In some alternative embodiments of this embodiment, the above execution entity can also perform personalized configuration for different users. Specifically, the icon names, icon layouts, icon styles, usage habits, etc. in the user interfaces of different users are configured through the cloud platform interface, so that when the accounts of different users are detected to be logged in, an adaptive adjustment can be made, enabling the user to have the user interface style that they prefer or are used to when opening the cloud mobile phone, and realizing the provision of customized services for the user.

[0108] Further referring to Figure 4 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an application processing device based on a cloud mobile phone. This device embodiment corresponds to Figure 2 the method embodiment shown, and this device can be specifically applied to various electronic devices.

[0109] As Figure 4 shown, the application processing device 400 based on a cloud mobile 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. Among them, the object behavior analysis module 401 is configured to analyze the historical behavior data of the target object to obtain an interest application model; the candidate application acquisition module 402 is configured to acquire candidate application programs that match the interest application model; the core function determination module 403 is configured to determine the core function module based on the candidate application programs; the application preloading module 404 is configured to transmit the core function module to the cloud mobile phone in a block form for preloading.

[0110] In this embodiment, in the application processing device 400 based on a cloud mobile phone, 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 and the technical effects brought by them can respectively refer to Figure 2 the relevant descriptions of steps 201-203 in the corresponding embodiments, which will not be elaborated 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 function modules of application programs in advance based on user usage preferences, which can avoid redundant transmission of complete application packages, optimize the application startup process, improve the application startup speed, enhance the operating efficiency of cloud phones, and significantly reduce the user's waiting time.

[0112] Further referring to Figure 5 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a cloud phone system, as shown in Figure 5 . The cloud phone system includes: a cloud platform 501 and a cloud phone 502. The cloud platform 501 can be used to execute the application processing method based on cloud phones described in any of the above embodiments. The cloud platform 501 includes: a user behavior analysis module 5011, a pre-download management module 5012, a mirror optimization engine 5013, and a background configuration interface 5014. Among them, the cloud platform 501 communicates with the cloud phone 502 through a communication link. The cloud platform 501 is used to send pre-download instructions and mirror update packages to the cloud phone 502. The functions implemented by the user behavior analysis module 5011 can be referred to the relevant description of step 201 in any of the above embodiments, the functions implemented by the pre-download management module 5012 can be referred to the relevant description of steps 202-204 in any of the above embodiments, the functions implemented by the mirror optimization engine 5013 can be referred to the relevant description of steps 305-306 in any of the above embodiments, and the background configuration interface 5014 is used to configure information such as user information and system files.

[0113] The cloud phone 502 includes: a pre-download component storage area 5021, a security detection module 5022, a mirror component library 5023, and a quick start module 5024. Among them, the pre-download component storage area 5012 is used to store the core component modules determined by the cloud platform 501 for pre-download, the security detection module 5022 is used to perform security detection on files, components, etc. transmitted by the cloud platform 501 to the cloud phone 502, the mirror component library 5023 is used to store the mirror components that need to be updated in the cloud phone 502, and the quick start module 5024 is used to perform quick start based on the pre-loaded core function modules. The cloud phone 502 is used to report the detection results of security detection and user operation logs to the cloud platform 501.

[0114] According to an embodiment of the present disclosure, the present 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, and when the instructions are executed by the at least one processor, the at least one processor can execute the application processing method based on cloud phones described in any of the above embodiments.

[0115] According to an embodiment of the present disclosure, the present disclosure also provides a readable storage medium storing computer instructions, which are used to enable a computer to implement the application processing method based on a cloud phone described in any of the above embodiments when executed.

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

[0117] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As Figure 6 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system).

[0118] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0119] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes to implement the method shown in the above embodiments.

[0120] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of a computer device presented by a kind of mini-program landing page, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided with respect to the processor 10, and these remote memories may be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, 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, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memory.

[0122] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0123] The embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention may be implemented in hardware, firmware, or may be implemented as computer code that can be recorded on a storage medium, or may be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and to be downloaded through a network and stored in a local storage medium, so that the methods described herein can be stored as such software processed on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0124] Although the embodiments of the present 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 present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for application processing based on cloud mobile phones, characterized in that, Including: Analyze the historical behavior data of the target object to obtain an interest application model; Obtain candidate application programs that match the interest application model; Determine the core function modules based on the candidate application programs; Transmit the core function modules to the cloud phone in a chunked form for preloading.

2. The method according to claim 1, wherein The analyzing the historical behavior data of the target object to obtain an interest application model includes: Based on the user's historical behavior data, sort the application programs used by the user according to the number of startups and / or usage duration; Generate the interest application model based on the sorting result and a preset list of application programs.

3. The method according to claim 1, characterized in that, It also includes: In response to determining to update the mirror of the cloud phone, use the differential algorithm to analyze the difference between the current mirror and the target mirror, and generate a differential file; [[ID=X]]Based on the differential file, update the mirror file of the cloud phone.

4. The method according to claim 3, wherein The using the differential algorithm to analyze the difference between the current mirror and the target mirror and generate a differential file includes: According to the componentized configuration of the mirror file of the cloud phone, use the differential algorithm to analyze the component change information of the target mirror relative to the current mirror; Generate the differential file based on the component change information.

5. The method according to claim 3, characterized in that, The updating the mirror file of the cloud phone based on the difference file includes: Use a compression algorithm to compress the differential file; Use a verification mechanism to verify the integrity of the compressed differential file; In response to determining the integrity of the compressed differential file, update the mirror file of the cloud phone based on the compressed differential file.

6. The method according to claim 1, wherein It also includes: Perform a security check on the core function modules; Transmit the security check results through an encryption method.

7. The method according to claim 1, wherein It also includes: In response to obtaining a startup instruction for the cloud phone, load the application programs in the cloud phone according to a preset priority, where the preloaded core function modules and the corresponding candidate application programs have the highest priority.

8. An application processing device based on a cloud phone, characterized in that, Including: An object behavior analysis module configured to analyze the historical behavior data of the target object to obtain an interest application model; A candidate application acquisition module configured to obtain candidate application programs that match the interest application model; A core function determination module configured to determine core function modules based on the candidate application programs; An application preloading module configured to transmit the core function modules to the cloud phone in a chunked form for preloading.

9. A computer device, characterized in that, Including: A memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

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