Cloud computer application store rapid upgrading method based on AI behavior prediction

Abstract

The invention discloses a cloud computer application store rapid upgrading method based on AI behavior prediction, and relates to the field of management operation support. The cloud computer application store client provides an application service, stores an application operation log according to a user operation by using an application management service, analyzes a user behavior according to the application operation log by using an AI model, generates a pre-downloading strategy according to an analysis result, stores the pre-downloading strategy by using the application management service, and stores the pre-downloading strategy according to the pre-downloading strategy; and the cloud computer application store client side updates the pre-downloading strategy and checks the pre-downloading strategy, requests the application management service to download the upgrade package if the check is passed, downloads the upgrade package and provides the upgrade package for the cloud computer application store client side, and the cloud computer application store client side sends the upgrade package to the cloud computer application store client side. And the cloud computer application store client stores the upgrade package and carries out upgrading.

Description

Technical Field

[0001] The present invention discloses a method for quickly upgrading a cloud computer application store based on AI behavior prediction, which relates to the field of management and operation support. Background Art

[0002] With the development of the cloud computer industry, cloud computer-specific application stores have gradually become popular, providing rich applications for cloud computer users. All cloud computer application stores on the market have the problems of slow application upgrade and download and poor user experience. The main scenario is that users are only prompted to upgrade the application when using the application, and they need to wait for the upgrade package to be downloaded and installed. A large number of users use the application store to upgrade applications at the same time period, causing network congestion on the server side. Summary of the Invention

[0003] In view of the problems of the prior art, the present invention provides a method for quickly upgrading a cloud computer application store based on AI behavior prediction, which has the characteristics of strong versatility and simple implementation, and has broad application prospects.

[0004] The specific solution proposed by the present invention is as follows:

[0005] The present invention provides a method for quickly upgrading a cloud computer application store based on AI behavior prediction: the cloud computer application store client provides application services, and uses the application management service to save the application operation log according to the user operation, uses the AI model to analyze the user behavior according to the application operation log, generates a pre-download strategy according to the analysis result, uses the application management service to save the pre-download strategy, and notifies the cloud computer application store client to update the pre-download strategy.

[0006] The cloud computer application store client updates the pre-download strategy and checks the pre-download strategy. If the check passes, it requests the application management service to download the upgrade package. The application management service downloads the upgrade package and provides it to the cloud computer application store client. The cloud computer application store client saves the upgrade package and performs the upgrade.

[0007] Further, for the method for quickly upgrading a cloud computer application store based on AI behavior prediction: the step of using the AI model to analyze the user behavior according to the application operation log and generating a pre-download strategy according to the analysis result includes:

[0008] Using the AI model to analyze the usage behaviors of all cloud computer users according to the application operation log to obtain the analysis result, and the analysis result includes: the off-peak time of each cloud computer user, the server network idle situation, and the cloud computer resource idle time.

[0009] According to the analysis result, a pre-download strategy is dynamically customized for each cloud computer application store client, and the pre-download strategy enables the cloud computer application store client to pre-download the application upgrade package for cloud computer users during the server network idle time and the cloud computer resource idle time.

[0010] Furthermore, a method for rapid upgrade of a cloud computer application store based on AI behavior prediction: Before using an AI model to analyze the usage behaviors of all cloud computer users according to application operation logs, it includes:

[0011] Obtain application operation logs, and extract the time spent by users browsing products and purchase preference data.

[0012] Perform data preprocessing and feature extraction.

[0013] Select a decision tree, support vector machine, neural network, or deep learning algorithm to establish an AI model according to the business requirements of the application service, and use the extracted feature data to train the AI model.

[0014] Evaluate and optimize the AI model.

[0015] Deploy the AI model on the cloud computer application store client.

[0016] Furthermore, a method for rapid upgrade of a cloud computer application store based on AI behavior prediction: The cloud computer application store client saves and upgrades upgrade packages, including:

[0017] Start the pre-download task: During the idle period of the server network, start the download task according to the pre-download strategy, monitor the download progress and speed to ensure that the download task can be completed on time.

[0018] Store and manage upgrade packages: Store the downloaded application upgrade packages in the specified location of the cloud computer, classify and manage the upgrade packages to facilitate subsequent application updates and deployments.

[0019] The present invention provides a rapid upgrade device for a cloud computer application store based on AI behavior prediction, including a cloud computer application store client management module, an application service management module, and an AI model analysis module.

[0020] The cloud computer application store client management module provides application services. The application service management module saves application operation logs according to user operations using application management services. The AI model analysis module analyzes user behaviors according to application operation logs using the AI model, generates a pre-download strategy based on the analysis results. The service management module saves the pre-download strategy using application management services and notifies the cloud computer application store client management module to update the pre-download strategy.

[0021] The cloud computer application store client management module updates the pre-download strategy and checks the pre-download strategy. If the check passes, it requests the application management service module to download the upgrade package. The application management service downloads the upgrade package and provides it to the cloud computer application store client management module. The cloud computer application store client management module saves the upgrade package and performs the upgrade.

[0022] Furthermore, the AI model analysis module of the cloud computer application store rapid upgrade device based on AI behavior prediction uses the AI model to analyze user behavior according to the application operation log, and generates a pre-download strategy according to the analysis result, including:

[0023] Using the AI model to analyze the usage behavior of all cloud computer users according to the application operation log, obtaining the analysis result, and the analysis result includes: the off-peak time of each cloud computer user, the server network idle situation, and the cloud computer resource idle time.

[0024] According to the analysis result, dynamically customize the pre-download strategy for each cloud computer application store client. The pre-download strategy enables the cloud computer application store client to pre-download the application upgrade package for cloud computer users during the server network idle time and the cloud computer resource idle time.

[0025] Furthermore, before the AI model analysis module of the cloud computer application store rapid upgrade device based on AI behavior prediction uses the AI model to analyze the usage behavior of all cloud computer users according to the application operation log, obtain the application operation log, and extract the time spent by the user browsing the product and the purchase preference data.

[0026] Perform data preprocessing and feature extraction.

[0027] Select a decision tree, support vector machine, neural network or deep learning algorithm to establish an AI model according to the business requirements of the application service, and use the extracted feature data to train the AI model.

[0028] Evaluate and optimize the AI model.

[0029] Deploy the AI model on the cloud computer application store client.

[0030] Furthermore, the cloud computer application store client management module of the cloud computer application store rapid upgrade device based on AI behavior prediction saves and upgrades the upgrade package, including:

[0031] Start the pre-download task: During the server network idle period, start the download task according to the pre-download strategy, monitor the download progress and speed, and ensure that the download task can be completed on time.

[0032] Store and manage the upgrade package: Store the downloaded application upgrade package in the specified location of the cloud computer, classify and manage the upgrade package, and facilitate subsequent application updates and deployments.

[0033] The beneficial effect of the present invention is:

[0034] Based on AI behavior prediction, rapid upgrade of the cloud computer application store can optimize the application upgrade process of the cloud computer application store and improve the user experience. Brief Description of the Drawings

[0035] Figure 1 It is a schematic flowchart of the method of the present invention. Detailed Embodiments

[0036] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it, but the embodiments cited do not limit the present invention.

[0037] Embodiment 1

[0038] The present invention provides a method for quickly upgrading a cloud computer application store based on AI behavior prediction: The cloud computer application store client provides application services, and uses the application management service to save application operation logs according to user operations. The AI model analyzes user behavior based on the application operation logs, and generates a pre-download strategy according to the analysis results, including: using the AI model to analyze the usage behaviors of all cloud computer users based on the application operation logs to obtain analysis results, and the analysis results include: the off-peak time of each cloud computer user, the server network idle situation, and the cloud computer resource idle time. According to the analysis results, a pre-download strategy is dynamically customized for each cloud computer application store client, and the pre-download strategy enables the cloud computer application store client to pre-download application upgrade packages for cloud computer users during the server network idle time and the cloud computer resource idle time. Before using the AI model to analyze the usage behaviors of all cloud computer users based on the application operation logs, it includes: obtaining the application operation logs, extracting the time spent by users browsing products and purchase preference data, performing data preprocessing and feature extraction, selecting a decision tree, support vector machine, neural network or deep learning algorithm to establish an AI model according to the business requirements of the application service, and using the extracted feature data to train the AI model, evaluate and optimize the AI model, and deploy the AI model on the cloud computer application store client.

[0039] Use the application management service to save the pre-download strategy and notify the cloud computer application store client to update the pre-download strategy.

[0040] The cloud computer application store client updates the pre-download strategy and checks the pre-download strategy. If the check passes, it requests the application management service to download the upgrade package. The application management service downloads the upgrade package and provides it to the cloud computer application store client. The cloud computer application store client saves the upgrade package and performs the upgrade.

[0041] Wherein the cloud computer application store client saves the upgrade package and performs the upgrade, including:

[0042] Start the pre-download task: During the server network idle period, start the download task according to the pre-download strategy, monitor the download progress and speed to ensure that the download task can be completed on time.

[0043] Store and manage upgrade packages: Store the downloaded application upgrade packages in the specified location of the cloud computer, classify and manage the upgrade packages to facilitate subsequent application updates and deployments.

[0044] Embodiment 2

[0045] The present invention provides a cloud computer application store rapid upgrade device based on AI behavior prediction, including a cloud computer application store client management module, an application service management module, and an AI model analysis module.

[0046] The cloud computer application store client management module provides application services. The application service management module uses the application management service to save application operation logs according to user operations. The AI model analysis module uses the AI model to analyze user behavior based on the application operation logs, generates a pre-download strategy according to the analysis results. The service management module uses the application management service to save the pre-download strategy and notifies the cloud computer application store client management module to update the pre-download strategy.

[0047] The cloud computer application store client management module updates the pre-download strategy and checks the pre-download strategy. If the check passes, it requests the application management service module to download the upgrade package. The application management service downloads the upgrade package and provides it to the cloud computer application store client management module. The cloud computer application store client management module saves the upgrade package and performs the upgrade.

[0048] Regarding the information interaction, execution process, etc. among the above-mentioned modules in the device, since they are based on the same concept as the method embodiment of the present invention, the specific content can be referred to the description in the method embodiment of the present invention and will not be elaborated here.

[0049] Similarly, the device of the present invention performs rapid upgrade of the cloud computer application store based on AI behavior prediction, which can optimize the application upgrade process of the cloud computer application store and improve the user experience.

[0050] It should be noted that not all steps and modules in the above-mentioned processes and device structures are necessary, and some steps or modules can be ignored according to actual needs. The execution order of each step is not fixed and can be adjusted according to needs. The system structure described in the above embodiments can be a physical structure or a logical structure. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or some components in multiple independent devices may be jointly implemented.

[0051] The above-described embodiments are only preferred embodiments given to fully illustrate the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or transformations made by those skilled in the art on the basis of the present invention are within the protection scope of the present invention. The protection scope of the present invention is subject to the claims.

Claims

1. A method for quickly upgrading a cloud computer application store based on AI behavior prediction, characterized in that The cloud computer application store client provides application services, and uses the application management service to save application operation logs according to user operations, uses the AI ​​model to analyze user behavior based on the application operation logs, generates a pre-download strategy based on the analysis results, uses the application management service to save the pre-download strategy, and notifies the cloud computer application store client to update the pre-download strategy. The cloud computer application store client updates the pre-download policy and checks the pre-download policy. If the check passes, it requests the application management service to download the upgrade package. The application management service downloads the upgrade package and provides it to the cloud computer application store client. The cloud computer application store client saves the upgrade package and performs the upgrade.

2. According to claim 1, a method for quickly upgrading a cloud computer application store based on AI behavior prediction is characterized by: The AI ​​model is used to analyze user behavior according to the application operation log, and a pre-download strategy is generated according to the analysis result, including: Use AI models to analyze the usage behavior of all cloud computer users based on application operation logs to obtain analysis results, including: peak hours of each cloud computer user, server network idleness, and cloud computer resource idle time. According to the analysis results, a pre-download strategy is dynamically customized for each cloud computer application store client. The pre-download strategy enables the cloud computer application store client to pre-download application upgrade packages for cloud computer users when the server network and cloud computer resources are idle.

3. According to claim 2, a method for quickly upgrading a cloud computer application store based on AI behavior prediction is characterized in that Before using AI models to analyze the usage behavior of all cloud computer users based on application operation logs, including: Obtain application operation logs and extract the time users spend browsing products and purchase preference data. Perform data preprocessing and feature extraction. According to the business needs of the application service, a decision tree, support vector machine, neural network or deep learning algorithm is selected to build an AI model, and the extracted feature data is used to train the AI ​​model. Evaluate and optimize AI models, Deploy AI models on the cloud computer application store client.

4. According to claim 1, a method for quickly upgrading a cloud computer application store based on AI behavior prediction is characterized in that The cloud computer application store client saves the upgrade package and performs the upgrade, including: Start pre-download tasks: When the server network is idle, start the download task according to the pre-download strategy, monitor the download progress and speed, and ensure that the download task can be completed on time. Storage and management of upgrade packages: Store the downloaded application upgrade packages in the designated location of the cloud computer, classify and manage the upgrade packages to facilitate subsequent application updates and deployment.

5. A cloud computer application store rapid upgrade device based on AI behavior prediction, characterized by Including cloud computer application store client management module, application service management module and AI model analysis module, The cloud computer application store client management module provides application services. The application service management module uses the application management service to save application operation logs according to user operations. The AI ​​model analysis module uses the AI ​​model to analyze user behavior according to the application operation logs and generates a pre-download strategy based on the analysis results. The service management module uses the application management service to save the pre-download strategy and notifies the cloud computer application store client management module to update the pre-download strategy. The cloud computer application store client management module updates the pre-download policy and checks the pre-download policy. If the check passes, it requests the application management service module to download the upgrade package. The application management service downloads the upgrade package and provides it to the cloud computer application store client management module. The cloud computer application store client management module saves the upgrade package and performs the upgrade.

6. The cloud computer application store rapid upgrade device based on AI behavior prediction according to claim 5 is characterized by: The AI ​​model analysis module uses the AI ​​model to analyze user behavior according to the application operation log, and generates a pre-download strategy according to the analysis results, including: Use AI models to analyze the usage behavior of all cloud computer users based on application operation logs to obtain analysis results, including: peak hours of each cloud computer user, server network idleness, and cloud computer resource idle time. According to the analysis results, a pre-download strategy is dynamically customized for each cloud computer application store client. The pre-download strategy enables the cloud computer application store client to pre-download application upgrade packages for cloud computer users when the server network and cloud computer resources are idle.

7. The cloud computer application store rapid upgrade device based on AI behavior prediction according to claim 6 is characterized in that AI The model analysis module uses AI models to analyze the usage behavior of all cloud computer users based on application operation logs, obtains application operation logs, extracts the time users spend browsing products, and purchase preference data, Perform data preprocessing and feature extraction. According to the business needs of the application service, a decision tree, support vector machine, neural network or deep learning algorithm is selected to build an AI model, and the extracted feature data is used to train the AI ​​model. Evaluate and optimize AI models, Deploy AI models on the cloud computer application store client.

8. The cloud computer application store rapid upgrade device based on AI behavior prediction according to claim 5 is characterized by: The cloud computer application store client management module saves the upgrade package and performs the upgrade, including: Start pre-download tasks: When the server network is idle, start the download task according to the pre-download strategy, monitor the download progress and speed, and ensure that the download task can be completed on time. Storage and management of upgrade packages: Store the downloaded application upgrade packages in the designated location of the cloud computer, classify and manage the upgrade packages to facilitate subsequent application updates and deployment.

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