AI Copilot method and system capable of being applied to multi-terminal application intellectualization

Abstract

The invention discloses an AI Copilot method and system capable of being applied to multi-terminal application intellectualization, and relates to the technical field of large language models.The AI Copilot method comprises the steps that an AI Native intelligent application is constructed through an AI Hub platform; sending, receiving and processing a message by using the Sidecar Message Service, and expanding a CMC application function based on the constructed AI Native intelligent application; and collecting user demand information, and carrying out related content introduction and intelligent navigation based on the collected user demand information. The AI Native intelligent application is constructed, the message processing mechanism is optimized, the intelligent navigation function is achieved, the Sidear Message Service is used for carrying out message transmission between different applications, intelligent processing of different types of messages is achieved, the real-time performance and expansibility of the system are improved, the requirements of a user in different scenes are effectively met, and through intelligent navigation, the user experience is improved. Therefore, the user can quickly find the required content and function in the use process of the multi-terminal equipment, and the problems of information overload and complicated operation are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of large language models, and in particular to an AI Copilot method and system that can be applied to intelligent multi-terminal applications. Background Art

[0002] In recent years, with the rapid development of artificial intelligence technology, AI has gradually been applied to various fields, including automated office, intelligent assistance and multi-terminal application systems. Especially in multi-terminal application systems, how to use AI technology to improve user experience, simplify operating procedures and intelligent navigation has become the focus of industry attention. Although traditional application systems have made significant improvements in user interaction and information transmission, there are still problems such as frequent manual operations and insufficient intelligence. In order to better solve these problems, the concept of AI Native intelligent application came into being. AI Native refers to the fundamental use of AI technology to build application systems, making these systems more intelligent and autonomous, thereby realizing functions such as automated task execution and personalized content recommendation. These intelligent applications are widely used in automated office systems, customer management systems and e-commerce platforms, and are committed to providing users with precise services through intelligent algorithms and big data analysis.

[0003] However, the existing multi-terminal application technology still has many shortcomings in terms of scalability and intelligence. First, multi-terminal applications often involve information synchronization and processing between different devices and platforms. Due to their complexity, traditional message transmission and processing mechanisms are difficult to meet the requirements of real-time and intelligence. Secondly, although many application systems have begun to use AI technology, they often only use AI as an additional function and cannot truly achieve deep integration of AI, which makes it impossible for application systems to respond and dynamically adjust in real time according to user needs. In addition, traditional multi-terminal applications lack intelligent navigation functions, and users cannot obtain continuous and consistent operating experience during cross-device use, further affecting user efficiency and satisfaction. Summary of the invention

[0004] In view of the problems existing in the above-mentioned existing AI Copilot method that can be applied to the intelligence of multi-terminal applications, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that multi-terminal applications are not intelligent enough, and the information synchronization and processing mechanisms between different devices and platforms are difficult to meet the requirements of real-time and intelligence. In addition, traditional multi-terminal applications lack intelligent navigation functions, and users cannot obtain continuous and consistent operating experience during cross-device use, which further affects user efficiency and satisfaction.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an AI Copilot method that can be applied to the intelligence of multi-terminal applications, which includes using the AI ​​Hub platform to build AI Native smart applications; using the SidecarMessage Service to send, receive and process messages, and expanding the CMC application functions based on the built AI Native smart applications; collecting user demand information, and providing relevant content introduction and intelligent navigation based on the collected user demand information.

[0007] As a preferred solution of the AI ​​Copilot method applicable to multi-terminal application intelligence of the present invention, wherein: the use of the AI ​​Hub platform to build an AI Native intelligent application includes:

[0008] Use the Kubernetes microservice framework to build AI Native intelligent applications;

[0009] Use the AI ​​Hub platform API and integration tools to create an AI Native smart application development environment, and use the AI ​​Hub platform's built-in interface to integrate the company's internal help center knowledge base into the AI ​​Native smart application;

[0010] Use the BERT model as the core engine of AI Native intelligent applications, collect question and answer data and module introduction data from the enterprise's internal help center knowledge base, and preprocess the collected question and answer data and module introduction data from the enterprise's internal help center knowledge base, including data cleaning and data standard version. Input the preprocessed question and answer data and module introduction data from the enterprise's internal help center knowledge base as training data into the BERT model, and adjust the BERT model through iterative training.

[0011] As a preferred solution of the AI ​​Copilot method applicable to multi-terminal application intelligence of the present invention, wherein: the use of Sidecar Message Service to send, receive and process messages includes:

[0012] Use Sidecar Message Service to publish messages and manage subscribers, and broadcast messages through the corresponding message channels in message topic management;

[0013] The AI ​​Native intelligent application receives broadcast messages, returns response data using a unified messaging channel, and distributes the response data to the caller function through message topic management. The caller function refers to a function that receives and processes messages and works in conjunction with the Sidecar Message Service.

[0014] As a preferred solution of the AI ​​Copilot method applicable to multi-terminal application intelligence of the present invention, the Sidecar Message Service includes:

[0015] Component container for subscribing to topic messages, hook for subscribing to topic messages, managing the lifecycle of Sidecar message instances, managing message channels, and managing message topic subscriptions;

[0016] The component container for subscribing to the topic message and the hook for subscribing to the topic message refer to the container and hook for directly consuming the message, which accelerates the subscription lifecycle management and message publishing of the message;

[0017] Managing the Sidecar message instance lifecycle refers to managing the instance of the Sidecar service for each application, and each application has only one instance;

[0018] The management message channel refers to the management of the communication channel established between the AI ​​Native intelligent application and the CMC application using the MessageChannel standard protocol;

[0019] Managing message topic subscription refers to managing the subscription of topic messages and the maintenance of message status.

[0020] As a preferred solution of the AI ​​Copilot method applicable to multi-terminal application intelligence described in the present invention, wherein: the AI ​​Native intelligent application based on the construction expands the CMC application function including:

[0021] Embed the Web application endpoint of the AI ​​Native smart application into the side panel of the CMC application through an iframe, wherein the CMC application includes a front end and a bank end, the front end includes a page and a side panel, the AI ​​Native smart application includes an AI Copilot end and an agent end, and the AI ​​Copilot end includes a Web application endpoint and a service endpoint;

[0022] Configure the open API interface in the AI ​​Hub, connect it to the open API interface of the CMC application bank, and communicate data with the CMC application through the Sidecar Message Service.

[0023] As a preferred solution of the AI ​​Copilot method applicable to multi-terminal application intelligence described in the present invention, wherein: the collection of user demand information includes:

[0024] Based on the expanded CMC application, users interact with the AI ​​Native smart application on the front-end side panel of the CMC application, ask questions to the AI ​​Native smart application, and input messages containing keywords, including relevant content introductions and page navigation requirements.

[0025] As a preferred solution of the AI ​​Copilot method applicable to multi-terminal application intelligence of the present invention, the introduction of relevant content and intelligent navigation based on the collected user demand information includes:

[0026] AI Native intelligent applications return corresponding answers and perform corresponding operations based on messages containing keywords in user demand information;

[0027] The performing of corresponding operations refers to introducing relevant contents and navigating pages;

[0028] The page navigation refers to the CMC application listening to AI Native smart application messages through the Sidecar Message Service, navigating to the corresponding URL address after receiving the message containing the navigation instruction and the corresponding valid URL, and generating the corresponding navigation page content introduction;

[0029] The subscription message and page information are automatically destroyed after the application exits.

[0030] Another object of the present invention is to provide an AI Copilot system applicable to multi-terminal application intelligence, which includes:

[0031] Application building modules, used to build AI Native intelligent applications;

[0032] Function expansion module, used to extend CMC application functions using Sidecar Message Service;

[0033] Data collection module, used to collect user information;

[0034] The operation execution module is used to execute corresponding operations according to user demand information.

[0035] A computer device includes: a memory and a processor; the memory stores a computer program, and the processor implements the steps of an AI Copilot method applicable to multi-terminal application intelligence when executing the computer program.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an AI Copilot method applicable to multi-terminal application intelligence.

[0037] The beneficial effects of the present invention are as follows: the present invention constructs AI Native intelligent applications, optimizes message processing mechanisms, and implements intelligent navigation functions. It uses Sidecar Message Service to transmit messages between different applications, realizes intelligent processing of different types of messages, improves the real-time and scalability of the system, and effectively meets the needs of users in different scenarios. Through intelligent navigation, it ensures that users can quickly find the required content and functions when using multiple devices, avoiding problems such as information overload and cumbersome operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 The figure is a flowchart of the AI ​​Copilot method that can be applied to the intelligence of multi-terminal applications.

[0040] Figure 2 This is a flowchart of data flow between AI Native intelligent applications and CMC applications.

[0041] Figure 3 This is a diagram of the calling process between the AI ​​Native intelligent application, the CMC application, and the Sidecar message service.

[0042] Figure 4 The following is a flowchart of the Sidecar message service life cycle.

[0043] Figure 5 This is a structural diagram of the AI ​​Copilot system that can be applied to the intelligence of multi-terminal applications. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0047] Example 1

[0048] Reference Figures 1 to 4 , which is the first embodiment of the present invention, and this embodiment provides an AI Copilot method applicable to multi-terminal application intelligence. The AI ​​Copilot method applicable to multi-terminal application intelligence includes: S1, using the AI ​​Hub platform to build an AI Native intelligent application;

[0049] Furthermore, using the AI ​​Hub platform to build AI Native intelligent applications includes:

[0050] Use the Kubernetes microservice framework to build AI Native intelligent applications;

[0051] Use the AI ​​Hub platform API and integration tools to create an AI Native smart application development environment, and use the AI ​​Hub platform's built-in interface to integrate the company's internal help center knowledge base into the AI ​​Native smart application;

[0052] Use the BERT model as the core engine of AI Native intelligent applications, collect question and answer data and module introduction data from the enterprise's internal help center knowledge base, and preprocess the collected question and answer data and module introduction data from the enterprise's internal help center knowledge base, including data cleaning and data standard version. Input the preprocessed question and answer data and module introduction data from the enterprise's internal help center knowledge base as training data into the BERT model, and adjust the BERT model through iterative training.

[0053] By using the API and integration tools of the AI ​​Hub platform, developers can quickly build and configure an AI Native application development environment. This process simplifies the development process, provides an automated tool chain, and reduces the complexity of application construction. At the same time, the company's internal help center knowledge base is integrated into the AI ​​Native smart application, allowing the application to directly access the rich data resources in the company's knowledge base, ensuring that users can get instant help and support in the application. This step integrates the company's internal data resources and simplifies the development process, thereby achieving the ability to quickly respond to user needs, improving development efficiency, and optimizing the use of internal corporate resources. In addition, by using the BERT model as the core engine of the AI ​​Native smart application, the system can deeply process and analyze the question and answer data and module introduction data of the company's internal help center, accurately identify and understand user questions, and match them to relevant knowledge base content, thereby improving the accuracy and response efficiency of the question and answer system.

[0054] S2. Use Sidecar Message Service to send, receive, and process messages, and expand CMC application functions based on the built AINative intelligent application;

[0055] Furthermore, using Sidecar Message Service to send, receive, and process messages includes:

[0056] Use Sidecar Message Service to publish messages and manage subscribers, and broadcast messages through the corresponding message channels in message topic management;

[0057] The AI ​​Native intelligent application receives broadcast messages, returns response data using a unified messaging channel, and distributes the response data to the caller function through message topic management. The caller function refers to a function that receives and processes messages and works in conjunction with the Sidecar Message Service.

[0058] By introducing the message publishing and subscription mechanism of Sidecar Message Service, the system realizes the standardization and efficiency of message transmission in multi-terminal applications. Message topic management provides a flexible message channel, so that each message can be accurately delivered to the corresponding recipient. This process ensures that the message can be received by all applications that have subscribed to the corresponding message topic through the broadcast mechanism, avoiding the delay and complexity of traditional point-to-point message transmission. Ultimately, it improves the message transmission efficiency and reduces the system communication overhead, so that the system has excellent performance in processing high concurrency and multi-device message transmission. In addition, after receiving the message, the AI ​​Native smart application returns the response data through a unified message channel. This mechanism simplifies the system's communication path and avoids redundant interactions between multiple independent channels. Through the unified channel, the system can effectively manage the reception and response of messages, and improve the reliability and consistency of communication.

[0059] Furthermore, Sidecar Message Service includes,

[0060] Component container for subscribing to topic messages, hook for subscribing to topic messages, managing the lifecycle of Sidecar message instances, managing message channels, and managing message topic subscriptions;

[0061] The component container for subscribing to the topic message and the hook for subscribing to the topic message refer to the container and hook for directly consuming the message, which accelerates the subscription lifecycle management and message publishing of the message;

[0062] Managing the Sidecar message instance lifecycle refers to managing the instance of the Sidecar service for each application, and each application has only one instance;

[0063] The management message channel refers to the management of the communication channel established between the AI ​​Native intelligent application and the CMC application using the MessageChannel standard protocol;

[0064] Managing message topic subscription refers to managing the subscription of topic messages and the maintenance of message status.

[0065] By introducing component containers and hooks for subscribing to topic messages, the system can consume messages directly, accelerating the lifecycle management of message subscription and publishing. Through this acceleration mechanism, the system can respond to user needs in a shorter time and reduce the time that messages stay in the system. This process effectively improves the real-time performance and response speed of the system, ensuring that users can obtain updated messages and content in a timely manner on multiple devices. In addition, by managing the Sidecar service instance of each application, it is ensured that each application only runs one Sidecar instance during its life cycle, which simplifies the system architecture, reduces the waste of system resources and the possibility of redundant configuration, and avoids conflicts and resource competition caused by multiple instances. In addition, the use of the Message Channel standard protocol to establish a communication channel ensures that messages between different applications can be efficiently and securely transmitted through standardized protocols, making cross-platform and cross-system message communication more reliable and controllable, and solving the problem of poor message transmission between different systems.

[0066] Furthermore, the AI ​​Native smart applications built on CMC have expanded their application functions to include:

[0067] Embed the Web application endpoint of the AI ​​Native smart application into the side panel of the CMC application through an iframe, wherein the CMC application includes a front end and a bank end, the front end includes a page and a side panel, the AI ​​Native smart application includes an AI Copilot end and an agent end, and the AI ​​Copilot end includes a Web application endpoint and a service endpoint;

[0068] Configure the open API interface in the AI ​​Hub, connect it to the open API interface of the CMC application bank, and communicate data with the CMC application through the Sidecar Message Service.

[0069] By embedding the Web application endpoint of the AI ​​Native smart application into the side panel of the CMC application, the problem of difficult integration between the AI ​​application and the traditional system in the original system is solved. The iframe embedding method effectively avoids complex front-end reconstruction or large-scale modification, and provides a lightweight integration solution. This step realizes the seamless connection between the AI ​​Native smart application and the CMC application, so that the AI ​​Copilot end can directly provide intelligent auxiliary services and real-time responses through the side panel of the CMC application. This process not only reduces the technical difficulty and development cost of integrating AI into the CMC system, but also enables users to enjoy the personalized functions provided by AI without leaving the CMC application, greatly improving the convenience and overall efficiency of the user experience. In addition, by configuring an open API interface, efficient data interaction and function expansion can be achieved between the AI ​​Native smart application and the CMC application bank end, so that different systems can maintain consistency when sharing data and calling functions, avoiding compatibility issues caused by different technologies in traditional systems.

[0070] S3. Collect user demand information, and introduce relevant content and provide intelligent navigation based on the collected user demand information;

[0071] Furthermore, collecting user demand information includes:

[0072] Based on the expanded CMC application, users interact with the AI ​​Native smart application on the front-end side panel of the CMC application, ask questions to the AI ​​Native smart application, and input messages containing keywords, including relevant content introductions and page navigation requirements.

[0073] Through the front-end side panel of the CMC application, users can directly interact with the AI ​​Native smart application and accurately express their needs by asking questions by entering keywords. By identifying and parsing keywords, accurate extraction of needs is achieved, enabling the system to obtain higher-quality user data, which in turn helps with subsequent personalized recommendations and operational navigation, improves the accuracy of demand information, and provides reliable data support for intelligent navigation. The system can use the built-in BERT model to perform semantic analysis on the keywords entered by users and quickly match them to the corresponding content and navigation paths. This not only reduces the tedious steps of users in the operation process, but also generates personalized navigation guidance through intelligent analysis of user intentions. Ultimately, this mechanism realizes the automatic adaptation of content and navigation needs, improves the system response speed and the smoothness of the user experience, enables users to obtain the required information in a short time, and quickly find the target page, significantly improving operational efficiency.

[0074] Furthermore, relevant content introduction and intelligent navigation based on the collected user demand information include:

[0075] AI Native intelligent applications return corresponding answers and perform corresponding operations based on messages containing keywords in user demand information;

[0076] The performing of corresponding operations refers to introducing relevant contents and navigating pages;

[0077] The page navigation refers to the CMC application listening to AI Native smart application messages through the Sidecar Message Service, navigating to the corresponding URL address after receiving the message containing the navigation instruction and the corresponding valid URL, and generating the corresponding navigation page content introduction;

[0078] The subscription message and page information are automatically destroyed after the application exits.

[0079] Through the collected user demand information, AI Native smart applications can not only return corresponding text or function responses, but also intelligently perform corresponding page navigation operations. After identifying the keywords in the user's needs, the system automatically generates navigation instructions and sends the instructions to related applications through Sidecar Message Service. This process improves the degree of automation of operations, so that users do not need to manually search and find specific page content, which improves the convenience and efficiency of operations. Through Sidecar Message Service, CMC applications can monitor and receive navigation instructions from AI Native smart applications in real time. CMC applications can automatically respond to user needs at any time and perform operations immediately after receiving navigation instructions. After receiving a message containing navigation instructions and a valid URL, the system can automatically navigate to the specified page and generate a corresponding content introduction, so that users can quickly understand the page content after reaching the target page, thereby reducing the time for information acquisition, providing users with a personalized and intuitive navigation experience, and avoiding users from getting lost in complex page hierarchies or unfamiliar functional modules. Finally, after the user exits the application, the automatic destruction mechanism improves resource utilization, ensures timely data cleanup and security, and enhances users' trust in the system.

[0080] Example 2

[0081] Reference Figure 5 , which is the second embodiment of the present invention. This embodiment is different from the previous embodiment and provides an AI Copilot system applicable to multi-terminal application intelligence, including:

[0082] Application building modules, used to build AI Native intelligent applications;

[0083] Function expansion module, used to extend CMC application functions using Sidecar Message Service;

[0084] Data collection module, used to collect user information;

[0085] The operation execution module is used to execute corresponding operations according to user demand information.

[0086] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0087] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0088] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0089] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An AI Copilot method applicable to intelligent multi-terminal applications, characterized in that: include, Use the AI ​​Hub platform to build AI Native intelligent applications; Use Sidecar Message Service to send, receive, and process messages, and expand CMC application functions based on the built AINative smart applications; Collect user demand information, and provide relevant content introduction and intelligent navigation based on the collected user demand information.

2. The AI ​​Copilot method applicable to multi-terminal application intelligence as claimed in claim 1, characterized in that: The use of the AI ​​Hub platform to build AI Native intelligent applications includes: Use the Kubernetes microservice framework to build AINative intelligent applications; Use the AIHub platform API and integration tools to create the AINative intelligent application development environment, and use the built-in interface of the AI ​​Hub platform to integrate the enterprise's internal help center knowledge base into the AINative intelligent application; The BERT model is used as the core engine of the AINative intelligent application. The question and answer data and module introduction data in the knowledge base of the internal help center of the enterprise are collected, and the collected question and answer data and module introduction data in the knowledge base of the internal help center of the enterprise are preprocessed, including data cleaning and data standard version. The preprocessed question and answer data and module introduction data in the knowledge base of the internal help center of the enterprise are input into the BERT model as training data, and the BERT model is adjusted through iterative training.

3. The AI ​​Copilot method applicable to multi-terminal application intelligence as claimed in claim 2, characterized in that: The use of Sidecar Message Service to send, receive, and process messages includes: Use Sidecar Message Service to publish messages and manage subscribers, and broadcast messages through the corresponding message channels in message topic management; AINative smart applications receive broadcast messages, use unified messaging channels to return response data, and distribute the response data to caller functions through message topic management. The caller function refers to the function that receives and processes messages and works in conjunction with the Sidecar Message Service.

4. The AI ​​Copilot method applicable to multi-terminal application intelligence as claimed in claim 3, characterized in that: The Sidecar Message Service includes: Component container for subscribing to topic messages, hook for subscribing to topic messages, managing the lifecycle of Sidecar message instances, managing message channels, and managing message topic subscriptions; The component container for subscribing to the topic message and the hook for subscribing to the topic message refer to the container and hook for directly consuming the message, which accelerates the subscription lifecycle management and message publishing of the message; Managing the Sidecar message instance lifecycle refers to managing the instance of the Sidecar service for each application, and each application has only one instance; The management message channel refers to the management of the communication channel established between the AINative intelligent application and the CMC application using the Message Channel standard protocol; Managing message topic subscription refers to managing the subscription of topic messages and the maintenance of message status.

5. The AI ​​Copilot method applicable to multi-terminal application intelligence as claimed in claim 4, characterized in that: The AINative intelligent application based on the construction expands the CMC application functions including: Embed the Web application endpoint of the AINative smart application into the side panel of the CMC application through an iframe. The CMC application includes a front end and a bank end. The front end includes a page and a side panel. The AINative smart application includes an AI Copilot end and an agent end. The AI ​​Copilot end includes a Web application endpoint and a service endpoint. Configure the open API interface in AIHub, connect it with the open API interface of the CMC application bank, and communicate data with the CMC application through the Sidecar Message Service.

6. The AI ​​Copilot method applicable to multi-terminal application intelligence as claimed in claim 5, characterized in that: The collecting of user demand information includes: Based on the expanded CMC application, users interact with the AI ​​Native smart application on the front-end side panel of the CMC application, ask questions to the AI ​​Native smart application, and input messages containing keywords, including relevant content introductions and page navigation requirements.

7. The AI ​​Copilot method applicable to multi-terminal application intelligence as claimed in claim 6, characterized in that: The related content introduction and intelligent navigation based on the collected user demand information includes: AINative intelligent applications return corresponding answers and perform corresponding operations based on messages containing keywords in user demand information; The performing of corresponding operations refers to introducing relevant contents and navigating pages; The page navigation refers to the CMC application listening to AINative smart application messages through the Sidecar Message Service, navigating to the corresponding URL address after receiving the message containing the navigation instruction and the corresponding valid URL, and generating the corresponding navigation page content introduction; The subscription message and page information are automatically destroyed after the application exits.

8. An AI Copilot system applicable to multi-terminal application intelligence based on the AI ​​Copilot method applicable to multi-terminal application intelligence as described in any one of claims 1 to 7, characterized in that: include, Application building modules, used to build AI Native intelligent applications; Function expansion module, used to extend CMC application functions using Sidecar Message Service; Data collection module, used to collect user information; The operation execution module is used to execute corresponding operations according to user demand information.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the AI ​​Copilot method applicable to multi-terminal application intelligence described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the AI ​​Copilot method applicable to multi-terminal application intelligence described in any one of claims 1 to 7 are implemented.

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