Cloud resource customization methods, devices, and electronic equipment based on cloud and terminal interaction

Abstract

This application discloses a cloud resource customization method, apparatus, and electronic device based on cloud and terminal interaction. The method receives user information input by multiple users at their terminals, processes each user's information to obtain target user information for each user, then transmits this target user information to the cloud to generate analysis information for each user. Finally, it receives the analysis information from the cloud and generates customized cloud resource information for each user based on this analysis. This not only avoids privacy leaks but also provides users with resource support that matches their background, interests, and abilities.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus and electronic device for customizing cloud resources based on cloud and terminal interaction. Background Technology

[0002] With the rapid development of information technology, online education platforms have become an important carrier for resource customization. These platforms can leverage big data analytics and artificial intelligence algorithms to provide users with personalized resource recommendations and course customization services. However, in the process of customizing educational resources, data security has become a key factor restricting technological development. The collection and processing of large amounts of learners' personal information poses a serious threat to learners' privacy rights. Summary of the Invention

[0003] This application provides a cloud resource customization method, device, electronic device, and storage medium based on cloud and terminal interaction. It can not only avoid the leakage of user privacy, but also provide users with resource support that matches their background, interests, and abilities, and automatically push relevant in-depth learning materials, thereby improving the utilization efficiency of cloud resources.

[0004] Firstly, this application provides a cloud resource customization method based on cloud-terminal interaction, which is applied to a resource pool. The method includes:

[0005] Receive user information entered by multiple users at the terminal;

[0006] Process each user's information to obtain the target user information for each user;

[0007] The target user information for each user is transmitted to the cloud so that analytical information for each user can be generated in the cloud.

[0008] Receive analytics information for each user sent from the cloud;

[0009] Based on the analyzed information, customized information is generated for each user to tailor cloud resources.

[0010] Secondly, this application also provides a cloud resource customization method based on cloud-terminal interaction, which is applied in the cloud and includes:

[0011] Receive the target user information for each user transmitted from the resource pool;

[0012] Distributed processing technology is used to process the target user information of each user to obtain analysis information after analyzing each user;

[0013] Each user's analytics information is transmitted to the terminal to generate customized information for each user's cloud resources.

[0014] Thirdly, this application also provides a cloud resource customization device based on cloud and terminal interaction, which is applied to a resource pool. The device includes:

[0015] The first receiving unit is used to receive user information entered by multiple users at the terminal;

[0016] The first processing unit is used to process each user's information to obtain the target user information for each user;

[0017] The first transmission unit is used to transmit the target user information of each user to the cloud, so as to generate analysis information for each user in the cloud.

[0018] The second receiving unit is used to receive analysis information for each user sent from the cloud.

[0019] The generation unit is used to generate customized information for each user's cloud resources based on the analysis information.

[0020] The fourth aspect is a cloud resource customization device based on cloud and terminal interaction, which is applied in the cloud. The device includes:

[0021] The second receiving unit is used to receive the target user information of each user transmitted from the resource pool.

[0022] The second processing unit is used to process the target user information of each user using distributed processing technology to obtain the analysis information after analyzing each user.

[0023] The second transmission unit is used to transmit the analysis information of each user to the terminal, so as to generate customized information for each user to customize cloud resources in the terminal.

[0024] Fifthly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cloud resource customization method based on cloud and terminal interaction as provided in the first aspect above.

[0025] In a sixth aspect, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the cloud resource customization method based on cloud and terminal interaction provided in the first aspect.

[0026] In a seventh aspect, embodiments of this application also provide a computer program product, including a computer program or instructions, wherein the computer program or instructions are executed by a processor, and the cloud resource customization method based on cloud and terminal interaction provided in the first aspect is also provided.

[0027] This invention provides a cloud resource customization method, apparatus, and electronic device based on cloud and terminal interaction. The method receives user information input by multiple users at the terminal, processes each user information to obtain target user information for each user, then transmits the target user information of each user to the cloud to generate analysis information for each user in the cloud, and finally receives the analysis information of each user sent from the cloud and generates customized information for cloud resource customization for each user based on the analysis information. This not only avoids the leakage of user privacy, but also provides users with resource support that matches their background, interests, and abilities. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This application provides an architecture diagram of a cloud resource customization method based on cloud and terminal interaction, as illustrated in the embodiments of this application.

[0030] Figure 2 A flowchart illustrating the cloud resource customization method based on cloud-terminal interaction provided in this application embodiment;

[0031] Figure 3 A schematic block diagram of a cloud resource customization device based on cloud and terminal interaction provided in the embodiments of this application;

[0032] Figure 4 A schematic block diagram of the electronic device provided in this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0035] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0036] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0037] Furthermore, in this application, unless otherwise explicitly specified or limited in the embodiments, the terms "installation," "connection," "joining," and "fixing" appearing in the embodiments should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral part; it can also be a mechanical connection, an electrical connection, etc. Of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication between two components, or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific implementation.

[0038] Please see Figure 1 , Figure 1 This is an architecture diagram of a cloud resource customization method based on cloud-terminal interaction provided in this application embodiment. The cloud resource customization method based on cloud-terminal interaction provided in this application embodiment can be applied to the management platform where resource pool 102 resides, or to the cloud 101. This method is executed through application software installed in resource pool 102 or cloud 101. Meanwhile, resource pool 102 is located between cloud 101 and user terminal 103. Cloud 101 has multiple cloud servers, and resource pool 102 can be understood as an intermediate server between cloud 101 and user terminal 103.

[0039] It should be noted that the application scenarios described in the following embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0040] The following provides a detailed description of the cloud resource customization method based on cloud and terminal interaction provided in this application.

[0041] like Figure 2 As shown, the method includes the following steps S210~S250.

[0042] S210: Receive user information entered by multiple users at the terminal;

[0043] S220. Process each user's information to obtain the target user information for each user;

[0044] S230. Transmit the target user information of each user to the cloud so as to generate analysis information for each user in the cloud.

[0045] S240: Receive analysis information for each user sent from the cloud;

[0046] S250: Based on the analysis information, generate customized information for each user to customize cloud resources.

[0047] Specifically, user information can include the user's age, gender, geographical location, learning stage, interests, habits, and past learning achievements. Users can provide this information during registration or supplement it during use. Meanwhile, to ensure user information security, the resource pool can employ encryption technologies and security protocols to prevent information leakage during transmission and storage.

[0048] In this application, the resource pool can strictly process the collected user information, thereby removing sensitive information (such as ID card number, home address, etc.) and generating target user information while ensuring user privacy. This not only protects user privacy but also provides the necessary data foundation for subsequent analysis.

[0049] After the processed target user information is transmitted to a cloud server, the powerful data processing and analysis capabilities of the cloud can be leveraged to conduct a comprehensive analysis of the target user information, generating detailed analysis information for each user. Specifically, data mining techniques can be used to uncover potential patterns behind large amounts of data, helping to discover users' learning tendencies and thus identify their personalized learning needs, potential learning paths, and strengths and weaknesses. For example, by analyzing users' learning interests and past achievements, suitable learning directions and courses can be recommended.

[0050] The analysis information includes users' learning preferences, weaknesses, and suitable teaching and learning methods. After receiving the analysis information sent back from the cloud, this application generates customized educational resource recommendations for each user based on these analysis results, including textbooks, courses, learning tools, and supplementary materials. For example, the analysis information may reveal that a user is weak in mathematics but has a significant advantage in logical thinking; therefore, it will recommend corresponding math tutoring courses and logical thinking training resources.

[0051] The customized information aims to meet users' individual learning needs, improve learning efficiency, and help users achieve their learning goals. For example, for users who prefer visual learning, the system may recommend illustrated textbooks and video courses; while for users who prefer auditory learning, the system may recommend audio courses and lectures.

[0052] The method for customizing educational resources provided in this application offers users precise and personalized learning support, promoting educational equity and efficiency while preventing the leakage of user privacy. It not only meets the individualized needs of different users but also improves the efficiency of educational resource utilization and reduces waste. Simultaneously, it provides valuable data references for educational institutions and teachers, enabling them to develop more effective teaching strategies.

[0053] In some embodiments, processing each user information to obtain target user information for each user includes: processing each user information based on preset first information to obtain target user information for each user.

[0054] In this application, the customization of cloud resources can include the customization of educational resources. The first piece of information can be understood as pre-defined benchmark data or rules used when processing user information to filter and extract key elements directly related to cloud resource customization. This first piece of information includes basic learning characteristics, anonymized identity identifiers, learning behavior data, interest and preference tags, and external reference data. Basic learning characteristics can be understood as age, gender, learning stage (e.g., primary school, junior high school, high school, university), subject preferences, and learning habits (e.g., learning time preferences, learning style preferences). Anonymized identity identifiers are unique codes used to distinguish users (e.g., system-generated anonymous IDs), and do not include real names or ID numbers. Learning behavior data can be understood as users' historical operation records on the platform (e.g., course browsing, learning duration, test scores, interactive feedback, etc.). Interest and preference tags can be determined through user-selected or behaviorally analyzed interest areas (e.g., mathematics, programming, art) and learning styles (e.g., visual, auditory, hands-on). External reference data can be understood as information matched with educational standards or public datasets (e.g., regional differences in educational resources, subject teaching syllabus requirements, etc.).

[0055] Specifically, in the process of processing each user's information based on the preset first information to obtain each user's target user information, this application can perform data cleaning and screening, data desensitization and anonymization, data classification and standardization, feature integration and supplementation, and data verification and optimization. In this way, it can both meet the privacy protection requirements and provide a solid data foundation for personalized education services.

[0056] During the data cleaning and filtering process, missing values, outliers, or information unrelated to cloud resource customization (such as private data like phone numbers and home addresses) can be filtered out. At the same time, core fields can be extracted. Specifically, data directly related to learning and analysis (such as learning duration, academic performance, and interest tags) can be retained according to the rules defined in the first information.

[0057] During the data anonymization and desensitization process, sensitive information (such as user ID, geographical location, etc.) can be encrypted or anonymized to ensure that the user's real identity cannot be identified in reverse. In addition, it is necessary to comply with data protection regulations (such as GDPR, Personal Information Protection Act, etc.) to ensure the legality and compliance of the data processing process.

[0058] In the process of data classification and standardization, users' learning behaviors can be transformed into structured labels (such as weak mathematical foundation and strong logical thinking ability), and then normalization can be performed. Specifically, data of different dimensions can be standardized, such as converting learning time into relative indicators to avoid the impact of magnitude differences between different users on the analysis.

[0059] During the process of feature integration and supplementation, correlation analysis can be performed. Specifically, it can combine users' historical behavior and interest tags to generate comprehensive features (such as a preference for interactive learning and suitability for project-based teaching). Then, external data can be integrated, specifically by introducing publicly available educational resource data to supplement information on the educational characteristics or subject resource availability of the user's region.

[0060] During the data verification and optimization process, consistency checks can be performed to ensure that the processed target user information is logically consistent, such as matching subject preferences with historical learning records. Then, dynamic updates can be performed, specifically by setting data timeliness rules to periodically update target user information to reflect the user's latest learning status.

[0061] In some embodiments, based on the analysis information, custom information for cloud resource customization for each user is generated, including: determining a custom strategy for cloud resource customization for each user based on the analysis information for each user; and generating custom information for each user according to the custom strategy for each user.

[0062] Specifically, this application utilizes cloud-based analytics (such as learning preferences, ability levels, and knowledge gaps) to create personalized cloud resource customization plans for each user, ensuring that resource recommendations accurately match user needs and improve learning outcomes.

[0063] In this application, during the process of determining the customization strategy for cloud resources for each user based on the analysis information of each user, a personalized cloud resource customization plan can be formulated for each user according to the analysis information provided by the cloud, ensuring that the resource recommendation accurately matches the user's needs and improves the learning effect.

[0064] The analytical information sent from the cloud can include learning characteristics, behavioral preferences, target needs, and environmental characteristics. Learning characteristics include learning styles (such as visual and auditory), cognitive abilities (memory, comprehension, and application levels), and subject strengths and weaknesses. Behavioral preferences include commonly used learning tools, preferred learning times, and preferred interaction methods (such as video courses vs. text materials). Target needs include short-term learning goals (such as preparing for a certain exam) and long-term development directions (such as learning skills related to career inclination). Environmental characteristics include device type (mobile phone, computer), network conditions, and available learning time.

[0065] In the strategy formulation process, multidimensional matching, prioritization, dynamic adjustment, and personalized parameters can be used. Multidimensional matching can be understood as matching user analysis information with resource attributes in the educational resource library; for example, prioritizing video courses and charts for visual learners. Prioritization can be understood as determining the priority of resource recommendations based on the user's urgent needs (such as an upcoming exam) or long-term goals. Dynamic adjustment can be understood as setting strategy adjustment rules; for example, adjusting the recommendation strategy or providing alternatives when a user has not used recommended resources for several consecutive times. Personalized parameters can be understood as generating user-specific recommendation weights, such as the priority of knowledge point reinforcement and resource type preference coefficients.

[0066] In the process of generating customized information based on customized strategies, the strategies can be transformed into executable resource combinations and support services, forming a structured output. Customized information includes resource recommendation lists, learning path planning, and personalized support tools.

[0067] The resource recommendation list includes core resources, extended resources, and resource metadata. Core resources can be understood as learning materials that directly match the user's weaknesses or goals (such as specific chapter courses or specialized practice question banks). Extended resources can be understood as supplementary materials that help improve interest or ability (such as relevant popular science articles or advanced project case studies). Resource metadata can be understood as type (video, text), duration, difficulty level, and recommended usage scenarios (commuting, deep learning).

[0068] Learning path planning includes phase division and progress tracking. Phase division can be based on time or knowledge modules to divide learning steps, and progress tracking can be achieved based on preset checkpoints, such as triggering the next phase recommendation after completing a course.

[0069] Personalized support tools include learning reminders, adaptive assessments, and interactive support. Learning reminders can send reminders based on the user's preferred time periods. Adaptive assessments can generate personalized test questions regularly and dynamically adjust the difficulty of resources. Interactive support can recommend matching tutors or study groups and provide channels for answering questions.

[0070] This application determines a customized strategy for cloud resource customization for each user by analyzing the information of each user; based on the customized strategy for each user, it generates customized information for each user, and then outputs personalized solutions that can actually improve learning outcomes and meet the diverse needs of different users.

[0071] In some embodiments, determining a customization strategy for cloud resource customization for each user based on the analysis information of each user includes: obtaining second information matching each user based on the user information; and determining a customization strategy for cloud resource customization for each user based on the analysis information of each user and the corresponding second information.

[0072] In this application, the second information refers to external or internal data directly related to user analysis information, used to supplement and refine customized strategies. This second information includes educational resource attribute data, external reference data, dynamic user feedback, real-time environmental data, and other user group data. Educational resource attribute data includes metadata of resources in the platform's existing educational resource library (such as course difficulty, type, duration, applicable learning stage, etc.). External reference data includes educational standards (such as teaching syllabi, examination requirements), regional differences in educational resources, and industry demand data. Dynamic user feedback can be understood as user interaction data on previously recommended resources (such as usage frequency, ratings, completion rates, etc.). Real-time environmental data can be understood as factors that may affect resource access, such as the user's current device type, network conditions, and available time. Other user group data can be understood as common characteristics of similar user groups, such as common weaknesses among students of the same grade.

[0073] Specifically, in determining the customized strategy by combining analytical information and second information, this application can prioritize resource recommendations based on the user's urgent needs (such as exam countdowns) or long-term goals (such as career planning). For example, if a user is about to take an exam, courses covering the key points of the exam syllabus will be recommended first. Then, the user's learning preferences (such as visual learning) will be matched with resource types (videos, interactive exercises), and the difficulty of the resources will be considered in relation to the user's ability level to avoid recommending content that is too easy or too difficult. Finally, real-time feedback from the second information (such as a user's recent low rating of a resource) can be used to adjust the weight of resources in the strategy or replace them with alternative resources. For example, if a user frequently... Skip certain types of courses and adjust recommended resources to be similar but different in format, such as changing from videos to text and images; finally, recommend adapted resources based on user device type (e.g., short videos for mobile users, interactive simulation experiments for computer users), and consider network limitations, recommending lightweight resources or offline download options for users with low bandwidth. At the same time, generate user-specific learning pace control and customize the assessment frequency. This allows for a deep integration of the user's internal needs (analysis information) with external resources and environmental conditions (secondary information), ensuring that the customized strategy is both in line with the user's current state and flexible and forward-looking, providing core guidance for the subsequent generation of specific customized information.

[0074] In some embodiments, a customization strategy for cloud resource customization for each user is determined based on the analysis information and corresponding second information of each user, including: fusing the second information corresponding to each user with the analysis information to obtain target analysis information for analyzing each user; and determining a customization strategy for cloud resource customization for each user based on the target analysis information of each user.

[0075] In this application, the second information corresponding to each user is integrated with the analysis information to obtain the target analysis information for each user. In the process, multi-dimensional correlation matching can be performed, and weighted integration and dynamic updates can be carried out at the same time.

[0076] In the process of multidimensional correlation matching, user characteristics (such as weaknesses in subjects and learning styles) in the analysis information can be associated with resource attributes (difficulty and type) in the second information. For example, if the user analysis information shows that they are weak in math geometry, and the second information contains interactive geometry demonstration courses, then the association between the two can be used as a key recommendation candidate.

[0077] At the same time, during the weighted integration process, weights can be set according to the importance of the data. For example, the weight of a user's recent high-frequency use of a certain type of resource (such as video courses) is higher than that of historical data, or the priority of external reference data (such as exam syllabus) is higher than that of internal preferences.

[0078] During dynamic updates, the latest user behavior (such as resource interaction feedback) or environmental changes (such as device switching) can be integrated in real time to adjust the fusion results. For example, after a user switches to a mobile device, the resource compatibility parameters (such as support for offline downloads) in the second information are given higher weight.

[0079] In addition, when there is a contradiction between the analytical information and the second information (such as users prefer long videos but the device's network conditions are poor), the key constraints should be satisfied first (such as recommending short videos as an alternative).

[0080] In this application, during the process of determining a customized strategy for cloud resources for each user based on their target analysis information, the most suitable resource combination can be selected from the educational resource library based on the weaknesses or goals identified in the target analysis information. For example, for users with weak English listening skills, tiered listening practice combined with pronunciation correction tools can be recommended. Then, the workload can be allocated based on the user's available time (e.g., 1 hour per day), and a flexible adjustment mechanism can be set up (e.g., automatically extending or simplifying tasks if the user does not complete them). Adaptive tests can be inserted into the learning path to dynamically adjust subsequent resources (e.g., advancing to advanced courses if the test is passed, and reinforcing basic exercises if the test is failed). Finally, short courses can be pushed based on the user's preferred time slots (e.g., morning commute time), combined with gamification elements (e.g., points, badges) to increase participation. Furthermore, when a user rates a resource low or uses it infrequently, strategy adjustments can be triggered (e.g., changing the resource type or adjusting the difficulty).

[0081] In some embodiments, the present invention also provides a cloud resource customization method based on cloud and terminal interaction, which is applied to the cloud. The method includes: receiving target user information of each user transmitted from a resource pool; processing the target user information of each user using distributed processing technology to obtain analysis information after analysis of each user; and transmitting the analysis information of each user to the terminal to generate customization information for cloud resource customization for each user in the terminal.

[0082] Specifically, the cloud resource customization method based on cloud and terminal interaction in this application can utilize distributed processing technology to efficiently analyze user information and generate personalized cloud resource customization strategies when applied to the cloud. This ensures that educational resources can accurately match user needs, achieving high efficiency, accuracy, and dynamism in cloud resource customization. It not only meets users' personalized needs but also reduces system costs through large-scale processing, providing reliable technical support for educational informatization.

[0083] In this application, user information can be partitioned according to specific rules (such as user ID, subject category) and assigned to different computing nodes. The application can also mine user learning patterns (such as frequently accessed resource types, learning time preferences), assess knowledge mastery through historical scores and test data, identify weaknesses, and then analyze user preferences for resource types (such as videos, text, interactive questions) and interaction methods (such as self-study vs. collaborative learning). Secure communication protocols (such as HTTPS, MQTT) are used to ensure the secure transmission of analytical information between the cloud and the terminal.

[0084] In the cloud resource customization method based on cloud and terminal interaction provided in this embodiment of the invention, user information input by multiple users at the terminal is received and processed to obtain target user information for each user. Then, the target user information of each user is transmitted to the cloud to generate analysis information for each user. Finally, the analysis information of each user sent from the cloud is received, and customized information for cloud resource customization for each user is generated based on the analysis information. This not only avoids the leakage of user privacy, but also provides users with resource support that matches their background, interests and abilities, and automatically pushes relevant in-depth learning materials, thereby improving the efficiency of resource utilization.

[0085] In some embodiments, the present invention also provides a cloud resource customization device 300 based on cloud and terminal interaction, which is used to execute any embodiment of the aforementioned cloud resource customization method based on cloud and terminal interaction.

[0086] Specifically, please refer to Figure 3 , Figure 3 This is a schematic block diagram of a cloud resource customization device 300 based on cloud and terminal interaction provided in an embodiment of the present invention.

[0087] like Figure 3 As shown, the cloud resource customization device 300 based on cloud and terminal interaction provided in this application includes: a first receiving unit 310, a first processing unit 320, a first transmission unit 330, a second receiving unit 340, and a generation unit 350.

[0088] The first receiving unit 310 is used to receive user information input by multiple users at the terminal; the first processing unit 320 is used to process each user information to obtain target user information for each user; the first transmission unit 330 is used to transmit the target user information for each user to the cloud to generate analysis information for each user in the cloud; the second receiving unit 340 is used to receive the analysis information for each user sent from the cloud; and the generation unit 350 is used to generate customized information for cloud resource customization for each user based on the analysis information.

[0089] In some embodiments, the present invention also provides a cloud resource customization device based on cloud and terminal interaction, which is applied to the cloud and includes a second receiving unit, a second processing unit and a second transmission unit.

[0090] The second receiving unit is used to receive the target user information of each user transmitted from the resource pool; the second processing unit is used to process the target user information of each user using distributed processing technology to obtain the analysis information after analysis of each user; the second transmission unit is used to transmit the analysis information of each user to the terminal to generate customized information for cloud resource customization for each user in the terminal.

[0091] The cloud resource customization device 300 based on cloud and terminal interaction provided in this application embodiment can receive user information input by multiple users at the terminal, process each user information to obtain target user information for each user, and then transmit the target user information of each user to the cloud to generate analysis information for each user in the cloud. Finally, it receives the analysis information of each user sent by the cloud and generates customized information for cloud resource customization for each user based on the analysis information. This not only avoids the leakage of user privacy, but also provides users with resource support that matches their background, interests and abilities, and automatically pushes relevant in-depth learning materials, thereby improving the efficiency of resource utilization.

[0092] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the cloud resource customization device and its various units based on cloud and terminal interaction can be found in the corresponding descriptions in the foregoing method embodiments. For the sake of convenience and brevity, these details will not be repeated here.

[0093] The aforementioned cloud resource customization device based on cloud and terminal interaction can be implemented as a computer program, which can be used in, for example... Figure 4 It runs on the electronic device shown.

[0094] Please see Figure 4 , Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of the present invention.

[0095] See Figure 4 The device 400 includes a processor 402, a memory, and a network interface 405 connected via a system bus 401, wherein the memory may include a storage medium 403 and internal memory 404.

[0096] The storage medium 403 may store an operating system 4031 and a computer program 4032. When the computer program 4032 is executed, it enables the processor 402 to execute a cloud resource customization method based on cloud and terminal interaction.

[0097] The processor 402 provides computing and control capabilities to support the operation of the entire device 400.

[0098] The internal memory 404 provides an environment for the execution of the computer program 4032 in the non-volatile storage medium 403. When the computer program 4032 is executed by the processor 402, the processor 402 can execute a cloud resource customization method based on cloud and terminal interaction.

[0099] This network interface 405 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the device 400 to which the present invention is applied. The specific device 400 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0100] The processor 402 is used to run the computer program 4032 stored in the memory to perform the following functions: receiving user information input by multiple users at the terminal; processing each user information to obtain the target user information for each user; transmitting the target user information for each user to the cloud to generate analysis information for each user in the cloud; receiving the analysis information for each user sent from the cloud; and generating customized information for cloud resource customization for each user based on the analysis information.

[0101] In some embodiments, when the processor 402 processes each user's information to obtain the target user information for each user, it further implements the following steps: based on preset first information, it processes the user information of each user to obtain the target user information for each user.

[0102] In some embodiments, when the processor 402 generates customized information for cloud resource customization for each user based on the analysis information, it further implements the following steps: determining a customized strategy for cloud resource customization for each user based on the analysis information for each user; and generating customized information for each user according to the customized strategy for each user.

[0103] In some embodiments, when the processor 402 determines a customization strategy for cloud resource customization for each user based on the analysis information of each user, it further implements the following steps: obtaining second information matching each user based on the information of each user; and determining a customization strategy for cloud resource customization for each user based on the analysis information of each user and the corresponding second information.

[0104] In some embodiments, when the processor 402 determines a customization strategy for cloud resource customization for each user based on the analysis information and corresponding second information of each user, it further implements the following steps: fusing the second information corresponding to each user with the analysis information to obtain target analysis information for analyzing each user; and determining a customization strategy for cloud resource customization for each user based on the target analysis information of each user.

[0105] In some embodiments, the processor 402 is used to run a computer program 4032 stored in a memory to perform the following functions: receiving target user information of each user transmitted from the resource pool; processing the target user information of each user using distributed processing technology to obtain analysis information after analysis of each user; and transmitting the analysis information of each user to the terminal to generate customized information for cloud resource customization for each user in the terminal.

[0106] Those skilled in the art will understand that Figure 4 The embodiments of device 400 shown do not constitute a limitation on the specific configuration of device 400. In other embodiments, device 400 may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, in some embodiments, device 400 may include only memory and processor 402. In such embodiments, the structure and function of memory and processor 402 are similar to those shown. Figure 4 The embodiments shown are consistent and will not be described again here.

[0107] It should be understood that, in this embodiment of the invention, the processor 402 may be a Central Processing Unit (CPU), or it may be another general-purpose processor 402, a digital signal processor 402 (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 402 may be a microprocessor 402, or it may be any conventional processor 402, etc.

[0108] According to one aspect of this application, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following steps: receiving user information input by multiple users at a terminal; processing each user information to obtain target user information for each user; transmitting the target user information for each user to the cloud to generate analysis information for each user in the cloud; receiving the analysis information for each user sent from the cloud; and generating customized information for cloud resource customization for each user based on the analysis information.

[0109] According to one aspect of this application, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following steps: receiving target user information for each user transmitted from a resource pool; processing the target user information for each user using distributed processing technology to obtain analysis information for each user; and transmitting the analysis information for each user to a terminal to generate customized information for cloud resource customization for each user in the terminal.

[0110] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0111] In another embodiment of the present invention, a computer storage medium is provided. This storage medium can be a non-volatile computer-readable storage medium or a volatile storage medium. The storage medium stores a computer program 4032, which, when executed by a processor 402, performs the following steps: receiving user information input by multiple users at a terminal; processing each user information to obtain target user information for each user; transmitting the target user information for each user to the cloud to generate analysis information for each user in the cloud; receiving the analysis information for each user sent from the cloud; and generating customized information for cloud resource customization for each user based on the analysis information.

[0112] In another embodiment of the present invention, a computer storage medium is provided. This storage medium can be a non-volatile computer-readable storage medium or a volatile storage medium. The storage medium stores a computer program 4032, which, when executed by a processor 402, performs the following steps: receiving target user information for each user transmitted from a resource pool; processing the target user information for each user using distributed processing technology to obtain analysis information for each user; and transmitting the analysis information for each user to a terminal to generate customized information for cloud resource customization for each user in the terminal.

[0113] In some embodiments, when the processor executes program instructions to process each user's information and obtains each user's target user information, it further implements the following steps: based on preset first information, it processes each user's user information to obtain each user's target user information.

[0114] In some embodiments, when the processor executes program instructions to generate customized information for cloud resource customization for each user based on analysis information, it further implements the following steps: determining a customized strategy for cloud resource customization for each user based on the analysis information for each user; and generating customized information for each user according to the customized strategy for each user.

[0115] In some embodiments, when the processor executes program instructions to determine a customization strategy for cloud resource customization for each user based on the analysis information of each user, it further implements the following steps: obtaining second information matching each user based on the information of each user; and determining a customization strategy for cloud resource customization for each user based on the analysis information of each user and the corresponding second information.

[0116] In some embodiments, when the processor executes program instructions to determine a customization strategy for cloud resource customization for each user based on the analysis information and corresponding second information of each user, it further implements the following steps: fusing the second information corresponding to each user with the analysis information to obtain target analysis information for analyzing each user; and determining a customization strategy for cloud resource customization for each user based on the target analysis information of each user.

[0117] The storage medium can be any computer-readable storage medium that can store program code, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0120] The steps in the methods of this application embodiment can be adjusted, merged, or deleted according to actual needs. The units in the apparatus of this application embodiment can be merged, divided, or deleted according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0121] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods provided in the various embodiments of this application.

[0122] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for customizing cloud resources based on cloud-terminal interaction, characterized in that, Applied to a resource pool, the method includes: It can receive user information entered by multiple users at the terminal; the user information includes the user's age, gender, geographical location, learning stage, interests, habits, and past learning achievements; Each piece of user information is processed to obtain the target user information for each user; The target user information of each user is transmitted to the cloud, so as to generate analysis information for each user in the cloud. Receive analysis information for each user sent from the cloud; Based on the analysis information, customized information for cloud resource customization is generated for each user; the cloud resource customization is for educational resources. The process of processing each user information to obtain target user information for each user includes: processing the user information of each user based on preset first information to obtain target user information for each user; specifically, performing data cleaning and filtering, data desensitization and anonymization, data classification and standardization, feature integration and supplementation, and data verification and optimization; the first information includes basic learning features, anonymized identity identifiers, learning behavior data, interest and preference tags, and external reference data.

2. The cloud resource customization method based on cloud and terminal interaction according to claim 1, characterized in that, Based on the analyzed information, customized information for cloud resource customization is generated for each user, including: Based on the analysis information of each user, a customization strategy for cloud resource customization is determined for each user; Based on the user's customization strategy, generate customized information for each user.

3. The cloud resource customization method based on cloud and terminal interaction according to claim 2, characterized in that, The process of determining a customized strategy for cloud resource customization for each user based on the analysis information of each user includes: Based on each of the user information, obtain second information that matches each of the user information; Based on the analysis information and corresponding second information of each user, a customization strategy for cloud resource customization is determined for each user.

4. The cloud resource customization method based on cloud and terminal interaction according to claim 3, characterized in that, The step of determining a cloud resource customization strategy for each user based on the analysis information and corresponding second information includes: The second information corresponding to each user is fused with the analysis information to obtain the target analysis information for analyzing each user; Based on the target analysis information of each user, a customization strategy for cloud resource customization is determined for each user.

5. A method for customizing cloud resources based on cloud-terminal interaction, characterized in that, Applied to the cloud, the method includes: The resource pool receives target user information for each user transmitted from the resource pool; the resource pool executes the cloud resource customization method based on cloud and terminal interaction as described in any one of claims 1-4. Distributed processing technology is used to process the target user information of each user to obtain analysis information for each user. The analysis information of each user is transmitted to the terminal to generate customized information for cloud resource customization for each user in the terminal.

6. A cloud resource customization device based on cloud-terminal interaction, characterized in that, Applied to a resource pool, the device includes: The first receiving unit is used to receive user information input by multiple users at the terminal; the user information includes the user's age, gender, geographical location, learning stage, interests, habits, and past learning achievements; The first processing unit is used to process each user information to obtain target user information for each user; based on preset first information, it processes the user information of each user to obtain target user information for each user; specifically, it performs data cleaning and filtering, data desensitization and anonymization, data classification and standardization, feature integration and supplementation, and data verification and optimization; the first information includes basic learning features, anonymized identity identifiers, learning behavior data, interest and preference tags, and external reference data; The first transmission unit is used to transmit the target user information of each user to the cloud, so as to generate analysis information for each user in the cloud. The second receiving unit is used to receive analysis information of each user sent by the cloud. The generation unit is used to generate customized information for cloud resource customization for each user based on the analysis information; the cloud resource customization is the customization of educational resources.

7. A cloud resource customization device based on cloud and terminal interaction, characterized in that, The device, applied in the cloud, includes: The second receiving unit is used to receive the target user information of each user transmitted by the resource pool; the resource pool includes the cloud resource customization device based on cloud and terminal interaction as described in claim 6; The second processing unit is used to process the target user information of each user using distributed processing technology to obtain analysis information after analyzing each user. The second transmission unit is used to transmit the analysis information of each user to the terminal, so as to generate customized information for cloud resource customization for each user in the terminal.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the cloud resource customization method based on cloud and terminal interaction as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the cloud resource customization method based on cloud and terminal interaction as described in any one of claims 1 to 5.

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