CN118034742A - A natural language driven client application program seamless upgrade method and system - Google Patents

Abstract

The invention relates to a method and a system for non-inductive upgrading of a client application program driven by natural language. The method is realized by a dynamic plug-in pool, the plug-in pool comprises a plurality of functional plug-ins with independent version numbers, and the plug-ins can be dynamically updated through a server. When a client application is started, it queries the dynamic plug-in pool to obtain the version numbers of the functional plug-ins and compares these version numbers with the current version number of the client application. And loading the functional plug-in which meets the conditions according to the comparison result. Furthermore, the method supports interaction through natural language commands: when the user puts forward natural language commands, the system analyzes the commands and selects the function plug-in corresponding to the commands from the loaded function plug-ins to execute corresponding tasks. By the method, user experience can be improved, timely update of application program functions is ensured, and inconvenience caused by frequent manual update is avoided.

Description

A natural language driven client application program seamless upgrade method and system

Technical Field

The present invention relates to the technical field of client application upgrade, and in particular to a natural language driven client application seamless upgrade method and system.

Background technique

In the prior art, the update and upgrade of client applications (APPs) is a common and necessary process. Traditionally, APP updates usually require users to manually download and install the latest version. This method not only increases the user's operational burden, but in many cases, users may delay or ignore updates for various reasons, resulting in a decline in user experience and an increase in security risks. In addition, traditional update methods may result in user data loss or application settings reset, which further affects user trust and satisfaction with the APP.

As technology develops, especially with advances in natural language processing (NLP), users increasingly expect to be able to interact with applications in a more natural and intuitive way. However, incorporating these technologies into apps often requires frequent updates and upgrades, which conflicts with users' expectations for a seamless and low-intrusion experience.

Therefore, it is necessary to develop a new client application upgrade method.

Summary of the invention

The present application provides a natural language driven client application seamless upgrade method and system to improve the user experience during the client application upgrade process.

This application provides a natural language driven client application seamless upgrade method, including:

Providing and maintaining a dynamic plug-in pool, wherein the dynamic plug-in pool includes multiple functional plug-ins, each functional plug-in has an independent version number; each functional plug-in can be dynamically updated through a server;

When the client application is started, the dynamic plug-in pool is queried to obtain the version number of the functional plug-in in the dynamic plug-in pool;

Comparing the version number of the function plug-in with the version number of the client application, and loading the function plug-in that meets the predetermined conditions according to the comparison result;

After receiving the user's natural language command, the natural language command is parsed, and according to the parsing result, a function plug-in corresponding to the natural language command is selected from the loaded function plug-ins that meet the conditions and executed.

Furthermore, the dynamic plug-in pool serves all applications on the client and can dynamically adjust the provided plug-in collection according to the specific needs and characteristics of each client application.

Furthermore, the dynamic plug-in pool is deployed in the cloud.

Furthermore, providing and maintaining a dynamic plug-in pool includes:

Maintaining a historical version library for the functional plug-in in the dynamic plug-in pool, wherein the historical version library stores a previous stable version of the functional plug-in;

When the user encounters a preset situation when using the updated function plug-in, the rollback process is triggered, and the preset situation includes a function failure or performance degradation of the updated function plug-in;

The client application accesses the historical version library to roll back the updated functional plug-in to the previous stable version.

Furthermore, providing and maintaining a dynamic plug-in pool includes:

Collect user interaction data, including usage frequency, user feedback and usage patterns of each functional plug-in;

Analyze the collected interaction data to identify users’ usage habits and preferences;

Adjust the plug-in upgrade plan based on the analyzed user habits and preferences.

Furthermore, providing and maintaining a dynamic plug-in pool further includes:

Collect statistics on the analyzed user habits and preferences to identify user usage trends and user needs of functional plug-ins;

Guide the development and optimization of functional plug-ins based on identified user usage trends and user needs.

Furthermore, providing and maintaining a dynamic plug-in pool includes:

Before updating the functional plug-in, a series of security checks are performed, including verifying the plug-in's digital signature, scanning for potential malicious code, and performing vulnerability assessments;

Perform compatibility testing to ensure that the plug-in to be updated is compatible with the existing version of the client application, including testing the performance of the plug-in under different operating system versions and hardware configurations;

After confirming that the security check and compatibility test are successful, the update of the functional plug-in is allowed.

Furthermore, the selecting and executing a function plug-in corresponding to the natural language command from the loaded function plug-ins that meet the conditions according to the parsing result also includes:

Collect historical interaction data between the user and the client application, including commands issued by the user, function plug-ins used, and context information of the commands;

Analyze the historical interaction data to identify the user's usage habits and preferences;

According to the identified user habits and preferences, the response of the function plug-in is adjusted during the execution of the function plug-in.

This application provides a natural language driven client application seamless upgrade system, including:

Dynamic plug-in pool component, used to store and maintain multiple functional plug-ins, each plug-in has an independent version number and can be dynamically updated through connection with the server;

The client application program interface provides an interface for interacting with the dynamic plug-in pool component, which is used to query the dynamic plug-in pool when the client application starts, obtain the version number of the functional plug-in, and compare it with the current version number of the client application;

The plugin loading mechanism is used to determine the functional plugin that should be loaded into the client application based on the comparison result of the version number;

The natural language processing engine is used to parse the commands input by the user through natural language; according to the parsing results, the corresponding functional plug-in is selected and executed;

The server is used to provide the client with function plug-ins for updating.

The beneficial effects of the technical solution provided by this application include:

(1) Through the seamless upgrade method, users can enjoy the latest application features without going through the traditional download and installation update process, which significantly improves the user experience. (2) Since the update process is automatic, users do not need to perform any additional operations, which greatly reduces the user's operating burden, especially for users who are not familiar with technical operations. (3) The dynamic plug-in pool ensures that the application can receive the latest function updates and security patches in a timely manner, which helps maintain the security and functionality of the application. (4) By only loading qualified function plug-ins, unnecessary data downloads and storage usage are reduced, which is especially suitable for devices with limited storage space. (5) The integrated natural language processing mechanism enables users to interact with the application in a more natural way, enhancing the ease of use and interactivity of the application. (6) For developers, it is easier to push updates and new features without worrying that users will miss important features due to their unwillingness to update the application, thereby reducing the long-term maintenance cost of the application.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flowchart of a natural language driven client application seamless upgrade method provided in the first embodiment of the present application.

FIG2 is a schematic diagram of a natural language driven client application seamless upgrade system provided in a second embodiment of the present application.

Detailed ways

Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific implementation disclosed below.

The first embodiment of the present application provides a natural language driven client application seamless upgrade method. Please refer to Figure 1, which is a schematic diagram of the first embodiment of the present application. The following is a detailed description of the natural language driven client application seamless upgrade method provided in the first embodiment of the present application in conjunction with Figure 1. It should be pointed out that the client in this embodiment may include computing devices such as mobile phones, tablets, and desktops.

Natural language processing (NLP) plays a key role in this invention. In order to understand the essence of the present invention more easily, the following natural language processing is briefly described.

(1) Simplification and optimization of user interaction: Using natural language commands to start tasks is an efficient and intuitive way of interaction. In the context of unconscious upgrades, users may not fully understand the newly added features or changes. Through natural language commands, users can express their needs in a more natural and direct way, and the system provides services based on the currently available features.

(2) Intelligent function scheduling: Combining AI and natural language processing, the system can more intelligently understand user needs and schedule the most appropriate function (whether existing or newly upgraded). This method can hide the underlying technical details, and users do not need to know which version of the function is in service, achieving a truly imperceptible experience.

(3) Enhanced adaptability and scalability: As APP functions continue to iterate and upgrade, the use of natural language commands can better adapt to the increase and change of functions. Users do not have to remember specific operation steps or menu structures, but can describe the tasks they want to complete through natural language.

(4) Reduce user learning costs: For new users or users who do not frequently use the app, natural language commands reduce the time and effort they spend learning how to use new features. This is crucial to maintaining the app's appeal and improving user stickiness.

Step S101: providing and maintaining a dynamic plug-in pool, wherein the dynamic plug-in pool includes a plurality of functional plug-ins, each functional plug-in has an independent version number; and each functional plug-in can be dynamically updated through a server.

In step S101, the core is to provide and maintain a dynamic plug-in pool in the client application. This plug-in pool is part of the client, and it contains multiple functional plug-ins, each of which has an independent version number. The design of the dynamic plug-in pool enables the client application to dynamically update and load these plug-ins without a comprehensive update.

Regarding the specific implementation of the dynamic plug-in pool, first of all, it needs to be designed as a module or component of the client application. This component is responsible for managing all available functional plug-ins, including their loading, unloading, and updating operations. Functional plug-ins can be code libraries, user interface elements, or any other form of components. They are independent and have unique identifiers and version numbers.

In order to achieve dynamic updates of plugins, the plugin pool will periodically communicate with the remote server to check if a new version of the plugin is available. This is usually done through a background service of the client application, which runs automatically when the client application is running. When a new version of the plugin is detected, the client will download the update from the server and add it to the dynamic plugin pool. This process needs to take into account network conditions, the security of data transmission, and the smooth progress of the update.

Inside the client, the dynamic plug-in pool should be designed to flexibly handle the loading and unloading operations of various plug-ins. When a functional plug-in is updated, the new version of the plug-in should automatically replace the old version while maintaining compatibility and stability with the client application. In addition, considering that different users may receive updates at different times, the plug-in pool also needs to support version control and compatibility management to ensure that all users can smoothly transition to the new version of the plug-in.

Combined with the usage scenarios of natural language commands, the following are some specific examples of functional plug-ins that can be loaded and updated through the dynamic plug-in pool of the client application:

1. Weather query plug-in:

Example natural language command: "Show today's weather forecast".

Function: When the user asks about the weather through natural language, the plug-in is able to extract the user's location information (or use the location specified by the user) and display the current weather conditions and forecast.

2. Translation Assistant Plugin:

Example of a natural language command: "Translate 'hello' into English".

Function: When a user makes a translation request, this plugin can translate the specified text from one language to another.

3. Intelligent schedule management plug-in:

Example natural language command: "Schedule a meeting for tomorrow at 3pm".

Function: This plug-in helps users manage their schedules through natural language commands, such as adding, modifying or deleting calendar events.

4. Music playback control plug-in:

Example natural language command: "Play some relaxing music".

Function: When a user requests to play music, the plug-in can play music according to the user's preferences or specified categories.

5. Smart search plug-in:

Example of a natural language command: "Find nearby restaurants".

What it does: When users ask for specific information, like nearby restaurants or stores, the plugin provides relevant search results.

6. Personalized news reading plug-in:

Example natural language command: "Give me the latest news about technology."

Function: Provide customized news summaries or articles based on user interests or specific requests.

These functional plug-in examples show how to combine natural language commands with specific functions to provide an intuitive and easy-to-use user experience. Through the design of a dynamic plug-in pool, these functions can be updated or replaced as needed without major updates to the entire application, ensuring application flexibility and user convenience.

Furthermore, the dynamic plug-in pool serves all applications on the client and can dynamically adjust the provided plug-in collection according to the specific needs and characteristics of each client application.

The dynamic plug-in pool not only serves a single client application, but covers all applications on the client. The dynamic plug-in pool can dynamically adjust the provided plug-in collection according to the specific needs and characteristics of each client application.

The dynamic plug-in pool is designed as a multifunctional system that can serve all applications on the client. This means that it can not only store and manage plug-ins for a single application, but also cover various plug-ins required by multiple applications.

The plugin pool needs to be able to dynamically adjust to the unique needs and characteristics of each application. For example, if one application requires a specific data encryption plugin and another requires an advanced graphics processing plugin, the dynamic plugin pool should be able to identify these requirements and adjust its plugin library accordingly.

To manage such a diverse collection of plugins, the plugin pool requires an efficient management system that can track the version of each plugin, ensure compatibility, and handle different plugin requirements for different applications.

The specific implementation steps include:

1. Requirements analysis: First, perform a requirements analysis on all applications running on the client to determine what types of plug-ins they require.

2. Plug-in integration: Based on these analysis results, appropriate plug-ins are integrated into the dynamic plug-in pool. This includes ensuring the diversity and compatibility of plug-ins to meet the needs of different applications.

3. Dynamic response: When an application is started or running, the dynamic plug-in pool should be able to respond to specific requests of the application in real time and dynamically provide or adjust the required plug-ins.

4. Upgrade and maintenance: The plugin pool also needs to be regularly upgraded and maintained to ensure that all plugins are up to date and compatible with all served applications.

This approach improves the overall efficiency of client applications because there is no need to maintain a separate collection of plugins for each application.

By implementing this seamless upgrade method, the dynamic plug-in pool not only improves the flexibility and efficiency of client applications, but also provides users with a seamless and efficient experience. This approach allows different applications to share and utilize a centrally managed plug-in library while ensuring that each application can obtain plug-in support for its specific needs.

Furthermore, the dynamic plug-in pool is deployed in the cloud.

Cloud deployment of dynamic plugin pools first requires the establishment of a stable and reliable cloud infrastructure. This includes selecting a suitable cloud service provider, configuring servers, setting up data storage, and ensuring high availability and security of the network. The cloud infrastructure should be able to handle large amounts of data transfer and support high concurrent access.

In the cloud, a dynamic plug-in pool requires a complete plug-in management system. This system should be able to store various plug-ins, track their versions, and handle the upload, update, and deletion of plug-ins. Whenever a plug-in developer releases a new version, the system can receive and push the update to the plug-in pool.

The client application needs to be able to communicate with the dynamic plug-in pool in the cloud. This involves implementing an API or service interface in the client application to obtain the required plug-in information and download link from the cloud plug-in pool.

To optimize performance and reduce network latency, it is also critical to implement appropriate data synchronization and caching strategies. This may involve deploying multiple data centers in different geographical locations so that users can get data from the nearest server, thereby improving response speed and user experience.

By deploying the dynamic plug-in pool in the cloud, the seamless upgrade method provided by this embodiment not only achieves efficient plug-in management and distribution, but also provides a scalable, secure and high-performance solution. This deployment method makes plug-in updates transparent to end users and provides a powerful and flexible background support platform for application developers.

Furthermore, providing and maintaining a dynamic plug-in pool includes:

Maintaining a historical version library for the functional plug-in in the dynamic plug-in pool, wherein the historical version library stores a previous stable version of the functional plug-in;

When the user encounters a preset situation when using the updated function plug-in, the rollback process is triggered, and the preset situation includes a function failure or performance degradation of the updated function plug-in;

The client application accesses the historical version library to roll back the updated functional plug-in to the previous stable version.

First, the dynamic plug-in pool needs to include a historical version library, which is a storage area for saving historical versions of each functional plug-in, especially those verified stable versions. The historical version library should be able to store multiple versions so that you can fall back to a different historical state when necessary.

When updating a plugin, its previous stable version should be retained and stored in a historical repository. This process requires a clear version management strategy, how to decide which version is considered stable, and how long to retain the old version after the new version is released.

Preset conditions need to be clearly defined so that the system can recognize when the rollback process is triggered. These conditions may include functional failures, significant performance degradation, or user-reported issues. For example, if an updated plugin has severe performance issues or no longer responds to user commands, the system will identify it as a preset condition.

Once these preset situations are detected, the client application needs to be able to automatically access the historical version repository and initiate the rollback process. This process may involve automatically downloading the previous stable version, replacing the current faulty plug-in version, and reloading the plug-in to ensure the normal operation of the application.

By implementing these steps, the senseless upgrade method provided in this embodiment can provide a reliable fallback option when the user encounters a problem, thereby ensuring the stability and availability of the application. This strategy not only improves the user's trust in the application, but also ensures the security and reliability of the update process.

Furthermore, providing and maintaining a dynamic plug-in pool includes:

Collect user interaction data, including usage frequency, user feedback and usage patterns of each functional plug-in;

Analyze the collected interaction data to identify users’ usage habits and preferences;

Adjust the plug-in upgrade plan based on the analyzed user habits and preferences.

First, the types of user interaction data need to be defined and collected. This includes the usage frequency of each functional plug-in (i.e., how often users use a specific plug-in), direct user feedback on the plug-in (such as ratings, comments, or problem reports), and user usage patterns (e.g., in what situations do users tend to use a certain functional plug-in, the duration of use, etc.).

Next, implement a data collection mechanism that automatically records the above data as the user interacts with the client application. This may involve running a data collection module in the background of the application, which silently collects data without interfering with the user experience.

Analyzing the collected interaction data is key to identifying user usage habits and preferences. This usually involves using data analysis and machine learning techniques. For example, clustering algorithms can be applied to identify usage patterns, or classification algorithms can be used to determine user preferences.

Analysis involves setting up a data processing pipeline, including data cleaning (removing irrelevant or erroneous data), data transformation (converting data into a format suitable for analysis), and applying analytical models.

Finally, according to the analyzed user habits and preferences, the upgrade plan of the plug-in can be dynamically adjusted. This means that if the analysis shows that a plug-in is widely used by users and the feedback is positive, then the update and optimization of the plug-in should be given priority.

Tuning strategies should be designed to improve user satisfaction and the overall performance of your application. For example, for frequently used plugins, you can reduce the frequency of updates to reduce disruption, or update them when users are not active.

By implementing these steps, the senseless upgrade method provided by this embodiment not only makes the dynamic plug-in pool more intelligent and responsive to user needs, but also ensures the efficiency of the update process and the consistency of user experience. The implementation of this method enables the client application to continuously improve user satisfaction while also continuously optimizing and improving its functions.

Furthermore, providing and maintaining a dynamic plug-in pool further includes:

Collect statistics on the analyzed user habits and preferences to identify user usage trends and user needs of functional plug-ins;

Guide the development and optimization of functional plug-ins based on identified user usage trends and user needs.

First, the user interaction data collected from the dynamic plug-in pool needs to be aggregated and organized. This includes the frequency of users' use of each functional plug-in, the feedback they provide, and the usage patterns. By systematically organizing this data, we can lay the foundation for subsequent in-depth analysis.

Next, apply statistical analysis methods to identify trends in user usage of functional plugins. This may include identifying the most popular plugins, the most frequently used time periods, and the types of plugins that users prefer. The purpose of this step is to understand user behavior patterns and needs through data insights.

Based on the trend identification, further analysis is conducted on the specific needs of users. This includes analyzing user feedback to identify common problems, feature requests, or user suggestions for improvements to the plugin.

Based on the statistical and analytical results, provide guidance for the development and optimization of functional plug-ins. If the data shows that a functional plug-in is particularly popular, then you can consider adding more related functions or improving its performance. If user feedback indicates that a plug-in has problems or room for improvement, the development team can prioritize these issues.

In order to improve the overall quality and user satisfaction of the plugin, develop an optimization strategy based on user feedback and usage data. This may include adjusting the plugin's interface design, improving its responsiveness, or adding new features.

Think of this process as a continuous iterative cycle. As user behavior changes and new needs emerge, continuously update and adjust the development and optimization plan of functional plug-ins.

By adopting the method provided in this embodiment, it can be ensured that the dynamic plug-in pool is not just a passive function collection, but an intelligent system that can actively optimize and improve according to user behavior. This method will ultimately improve the user experience and functional efficiency of the client application, making it more in line with the actual needs and usage habits of users.

Furthermore, providing and maintaining a dynamic plug-in pool includes:

Before updating the functional plug-in, a series of security checks are performed, including verifying the plug-in's digital signature, scanning for potential malicious code, and performing vulnerability assessments;

Perform compatibility testing to ensure that the plug-in to be updated is compatible with the existing version of the client application, including testing the performance of the plug-in under different operating system versions and hardware configurations;

After confirming that the security check and compatibility test are successful, the update of the functional plug-in is allowed.

First, the digital signature of each functional plug-in to be updated needs to be verified. This step is to ensure that the source of the plug-in is credible and has not been tampered with by an unauthorized third party. Digital signature verification usually involves checking the validity of the plug-in manufacturer's certificate and signature.

Next, perform a malicious code scan. This process involves using anti-virus software or other security tools to check whether the plugin contains malicious code or software, such as viruses, trojans, or spyware.

Additionally, a vulnerability assessment of the plugin is required to identify and fix holes that could lead to security risks. This may involve running automated security scanning tools that check for common security holes and weaknesses.

Compatibility testing is performed to ensure that the plugin works properly on different versions of the operating system. This includes testing the performance of the plugin in both the latest and older versions of the operating system to ensure seamless use for all users.

Given the diversity of user devices, you also need to test the plugin's performance on different hardware configurations. This means checking how the plugin runs on devices with different memory sizes, processor types, and graphics processing power.

Plugin updates are only allowed if security checks and compatibility tests show that the plugin is safe and compatible. This confirmation process ensures the security and stability of the update.

Once confirmed, the dynamic plugin pool will automatically perform the plugin update process. This process should minimize the disruption to the user and is usually completed silently in the background.

By implementing the above steps, the seamless upgrade method provided by this embodiment not only ensures that the update of the functional plug-in is both safe and compatible, but also maintains the overall stability of the client application and the good user experience. The implementation of this method enables the client application to continuously upgrade and improve its functions in a safe and efficient manner, while reducing the possibility of security risks and compatibility issues.

In general, step S101 involves designing and implementing a dynamic plug-in pool within the client application, which enables the client to dynamically manage and update its functional components. In this way, the application can automatically update some functions without the user having to manually download the complete new version, thereby improving the user experience and reducing interference during the upgrade process.

Step S102: When the client application is started, the dynamic plug-in pool is queried to obtain the version number of the functional plug-in in the dynamic plug-in pool.

In step S102, a key process is provided, that is, when the client application is started, the dynamic plug-in pool is queried to obtain the version number of the functional plug-in. This step is the basis for ensuring that the client application can load and use the latest functional plug-in, which is crucial for achieving seamless upgrade.

Specifically, when the client application starts or at a predetermined time (for example, when checking for updates or when the application is activated), it will automatically perform a query operation on the dynamic plug-in pool. This query process involves interacting with the plug-in information in the plug-in pool stored locally on the client, with the goal of obtaining the latest version number of each plug-in. To achieve this goal, the client application needs to have a built-in query module that is responsible for initiating requests and receiving information about plug-in versions.

When performing a query, the query module will access the dynamic plug-in pool database or file system in the client, which stores basic information about each plug-in, including but not limited to the plug-in name, current version number, and last update time. The query module needs to process this data efficiently to ensure that the query process can be completed quickly even when a large number of plug-ins exist.

To ensure the accuracy of the information, the client application also needs to regularly receive the latest version information about the plug-in from the remote server, which may be achieved through polling or server push by the background service. Once the new version information is received, the client application will update the local dynamic plug-in pool to ensure the accuracy of the query results.

It is worth noting that this query process should be designed to be as efficient as possible to minimize the impact on application startup time or running performance.

In summary, step S102 not only requires the client application to be able to effectively query the plug-in version information in the dynamic plug-in pool, but also needs to consider the query efficiency, data accuracy, and the impact on application performance. The implementation of this step lays the foundation for subsequent plug-in loading and function execution, and is an important part of the seamless upgrade method.

Step S103: Compare the version number of the function plug-in with the version number of the client application, and load the function plug-in that meets the predetermined conditions according to the comparison result.

Step S103 is a crucial step in this embodiment, which involves comparing the version number of the function plug-in in the dynamic plug-in pool with the version number of the client application, and loading the function plug-in that meets the predetermined conditions according to the comparison result. This step ensures that the client application always runs the latest function plug-in that is compatible with its version, which is the key to achieving seamless upgrade.

When implementing step S103, a comparison mechanism is first required to compare the version numbers. This usually involves writing a version control module that is responsible for reading the current version number of the client application and comparing it with the version number of each functional plug-in queried from the dynamic plug-in pool. The version number is usually a string or number expressed in a certain format (such as major version number, minor version number, revision number), so the comparison mechanism needs to be able to correctly parse and compare these formatted version numbers.

The logic of a predetermined condition may be that only when the version number of the functional plug-in is greater than or equal to the version number of the client application, the plug-in is considered to meet the predetermined condition and needs to be loaded. This logic ensures that only plug-ins compatible with the current client version are loaded, avoiding potential problems caused by version mismatch.

Loading eligible feature plugins involves a dynamic plugin loading mechanism. This usually means that the client application needs to be able to load or update the plugin at runtime, rather than just at startup. Dynamically loading features may involve modifying the code or data structures in the client application's memory, so the loading process needs to be designed to ensure security and stability. In addition, in order not to interfere with the user experience, the plugin loading process should be as fast and non-intrusive as possible.

In terms of technical implementation, this may mean that the client application needs to include a plugin manager that manages the downloading, loading, and upgrading of plugins. This plugin manager needs to be able to handle network communication (to obtain plugin updates from the server), file operations (to install or update plugin files), and application state management (to ensure that plugin loading does not cause application crashes or data loss).

Finally, considering that different operating systems and platforms may have different limitations and features, the plugin loading mechanism also needs to consider cross-platform compatibility issues.

By implementing step S103, this embodiment can ensure that the client application can dynamically adapt to functional updates and achieve truly imperceptible upgrades, thereby providing users with a continuous optimization experience while maintaining the stability and security of the application.

Step S104: after receiving the user's natural language command, the natural language command is parsed, and according to the parsing result, a function plug-in corresponding to the natural language command is selected from the loaded function plug-ins that meet the conditions and executed.

Step S104 is the core part of the natural language driven client application non-sensing upgrade method provided in this embodiment, which involves receiving and processing the user's natural language commands, and executing the corresponding functional plug-ins according to these commands. The implementation of this step requires not only natural language processing (NLP) technology, but also the intelligent selection and execution of functional plug-ins. The user's natural language commands can be input by text or by voice. If it is voice input, voice-to-text conversion is required before client processing.

The client application needs to integrate a natural language processing engine. The main task of this engine is to understand the user's natural language input and convert it into actionable commands or queries. To achieve this, the natural language processing engine may use techniques such as lexical analysis, syntax analysis, intent recognition, and entity extraction. For example, when a user enters "play my favorite music", the NLP engine needs to recognize that "play music" is the user's intention, and "my favorite" is a specific description of the type of music.

Once the natural language command is parsed, the next step is to select a plugin corresponding to the parsed result from the already loaded functional plugins for execution. This selection process requires the participation of the plugin manager, which is responsible for monitoring all available plugins and their functions. The plugin manager will determine the most suitable plugin to execute the command based on the information provided by the NLP engine. For example, if the recognized command is to play music, the plugin manager will activate the music playback plugin.

Before activating the corresponding plug-in, a series of checks are required to ensure that the plug-in can be executed safely and effectively. This includes checking the plug-in version compatibility, the current application status, and whether the necessary resources are available. Once these checks are completed, the plug-in will be activated and perform the corresponding function.

In addition, to improve the user experience, the response when executing the plug-in should be fast and smooth. This may require optimizing the plug-in code and ensuring its efficient operation. At the same time, in order to deal with possible errors or abnormal situations, appropriate error handling mechanisms should be implemented to ensure the stability of the application and the continuity of the user experience.

Furthermore, the selecting and executing a function plug-in corresponding to the natural language command from the loaded function plug-ins that meet the conditions according to the parsing result also includes:

Collect historical interaction data between the user and the client application, including commands issued by the user, function plug-ins used, and context information of the commands;

Analyze the historical interaction data to identify the user's usage habits and preferences;

According to the identified user habits and preferences, the response of the function plug-in is adjusted during the execution of the function plug-in.

Considering that different users may have different usage habits and preferences, the implementation in step S104 should also be able to be personalized based on the user's historical interaction data. This means that the NLP engine and plug-in manager may need to have the ability to learn and adapt to user behavior.

As mentioned above, personalization is done based on the user's historical interaction data. This means using the user's past behavior and preferences to optimize the response of the natural language processing (NLP) engine and plugin manager. The purpose of this personalization is to provide an experience that better meets the needs of individual users. Here are some examples to illustrate this point:

Example 1: Music playback preferences

Suppose a user frequently uses voice commands to play a certain type of music, such as "play classical music." The NLP engine and plugin manager can record this preference and prioritize classical music in similar future requests. Therefore, the next time the user simply says "play music," the system may default to playing classical music based on historical data, rather than randomly selecting or asking for a specific genre.

Example 2: News reading habits

If a user frequently queries for news about technology, the system can learn this and personalize it in future queries. For example, the next time the user asks “show me some news,” the system might prefer to show technology news because it has learned the user’s interest in this type of news.

Example 3: Schedule Management

For users who frequently use the app to manage their schedules, the system can learn the user's scheduling patterns. For example, if the user often schedules team meetings on Monday mornings, when the user says "Schedule the next team meeting", the system may automatically propose to schedule the meeting on the next Monday morning instead of randomly selecting a time.

To achieve this personalization, the NLP engine and plugin manager need to have some form of learning capability, which usually involves data analysis and machine learning techniques. The system needs to be able to securely collect and analyze the user's historical interaction data to identify specific patterns and preferences. This may include recording the commands issued by the user, the plugins selected, and the context of the commands.

Importantly, this personalized adjustment needs to be carried out under the premise of respecting user privacy and data security. All data collection and processing should comply with relevant privacy protection regulations and ensure the security of user data.

By implementing this personalized adjustment, step S104 not only improves the quality of user experience, but also makes the client application more intelligent and adaptable to the actual needs of users.

By implementing step S104, this embodiment can provide a natural, intuitive and efficient way for users to control and use various functions of the client application through natural language commands, thereby greatly improving the user experience and making the application more intelligent and user-friendly.

In the above-mentioned embodiment, a natural language-driven client application seamless upgrade method is provided. Correspondingly, the present application also provides a natural language-driven client application seamless upgrade system. Please refer to Figure 2, which is a schematic diagram of an embodiment of a natural language-driven client application seamless upgrade system of the present application. Since this embodiment, that is, the second embodiment, is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described below is merely illustrative.

A natural language driven client application program seamless upgrade system provided in the second embodiment of the present application includes:

The dynamic plug-in pool component 201 is used to store and maintain multiple functional plug-ins, each of which has an independent version number and can be dynamically updated through a connection with a server;

The client application program interface 202 provides an interface for interacting with the dynamic plug-in pool component, and is used to query the dynamic plug-in pool when the client application is started, obtain the version number of the functional plug-in, and compare it with the current version number of the client application;

A plug-in loading mechanism 203 is used to determine the functional plug-in that should be loaded into the client application according to the comparison result of the version number;

The natural language processing engine 204 is used to parse the commands input by the user through natural language; according to the parsing results, the corresponding function plug-in is selected and executed;

The server 205 is used to provide the client application with a functional plug-in for updating.

A third embodiment of the present application provides an electronic device, the electronic device comprising:

processor;

The memory is used to store a program, and when the program is read and executed by the processor, the method for seamlessly upgrading the client application provided in the first embodiment of the present application is executed.

The fourth embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the client application program seamless upgrade method provided in the first embodiment of the present application is executed.

Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims (9)

1. A natural language driven client application seamless upgrade method, characterized by comprising: Providing and maintaining a dynamic plug-in pool, wherein the dynamic plug-in pool includes multiple functional plug-ins, each functional plug-in has an independent version number; each functional plug-in can be dynamically updated through a server; When the client application is started, the dynamic plug-in pool is queried to obtain the version number of the functional plug-in in the dynamic plug-in pool; Comparing the version number of the function plug-in with the version number of the client application, and loading the function plug-in that meets the predetermined conditions according to the comparison result; After receiving the user's natural language command, the natural language command is parsed, and according to the parsing result, a function plug-in corresponding to the natural language command is selected from the loaded function plug-ins that meet the conditions and executed. 2. According to the client application seamless upgrade method of claim 1, it is characterized in that the dynamic plug-in pool serves all applications on the client and can dynamically adjust the provided plug-in set according to the specific needs and characteristics of each client application. 3. The client application seamless upgrade method according to claim 1 or 2, characterized in that the dynamic plug-in pool is deployed in the cloud. 4. The client application program seamless upgrade method according to claim 1 or 2, characterized in that providing and maintaining a dynamic plug-in pool comprises: Maintaining a historical version library for the functional plug-in in the dynamic plug-in pool, wherein the historical version library stores a previous stable version of the functional plug-in; When the user encounters a preset situation when using the updated function plug-in, the rollback process is triggered, and the preset situation includes a function failure or performance degradation of the updated function plug-in; The client application accesses the historical version library to roll back the updated functional plug-in to the previous stable version. 5. The client application program seamless upgrade method according to claim 1, wherein providing and maintaining a dynamic plug-in pool comprises: Collect user interaction data, including usage frequency, user feedback and usage patterns of each functional plug-in; Analyze the collected interaction data to identify users’ usage habits and preferences; Adjust the plug-in upgrade plan based on the analyzed user habits and preferences. 6. The client application program seamless upgrade method according to claim 5, characterized in that the providing and maintaining a dynamic plug-in pool further comprises: Collect statistics on the analyzed user habits and preferences to identify user usage trends and user needs of functional plug-ins; Guide the development and optimization of functional plug-ins based on identified user usage trends and user needs. 7. The method for seamlessly upgrading a client application according to claim 1, wherein providing and maintaining a dynamic plug-in pool comprises: Before updating the functional plug-in, a series of security checks are performed, including verifying the plug-in's digital signature, scanning for potential malicious code, and performing vulnerability assessments; Perform compatibility testing to ensure that the plug-in to be updated is compatible with the existing version of the client application, including testing the performance of the plug-in under different operating system versions and hardware configurations; After confirming that the security check and compatibility test are successful, the update of the functional plug-in is allowed. 8. The method for seamlessly upgrading a client application according to claim 1, characterized in that the step of selecting and executing a function plug-in corresponding to the natural language command from the loaded function plug-ins that meet the conditions according to the analysis result further comprises: Collect historical interaction data between the user and the client application, including commands issued by the user, function plug-ins used, and context information of the commands; Analyze the historical interaction data to identify the user's usage habits and preferences; According to the identified user habits and preferences, the response of the function plug-in is adjusted during the execution of the function plug-in. 9. A natural language driven client application seamless upgrade system, characterized by comprising: Dynamic plug-in pool component, used to store and maintain multiple functional plug-ins, each plug-in has an independent version number and can be dynamically updated through connection with the server; The client application program interface provides an interface for interacting with the dynamic plug-in pool component, which is used to query the dynamic plug-in pool when the client application starts, obtain the version number of the functional plug-in, and compare it with the current version number of the client application; The plugin loading mechanism is used to determine the functional plugin that should be loaded into the client application based on the comparison result of the version number; The natural language processing engine is used to parse the commands input by the user through natural language; according to the parsing results, the corresponding functional plug-in is selected and executed; The server is used to provide the client with function plug-ins for updating.