Distributed artificial intelligence system collaboration method and device based on software embedded deployment and electronic equipment

Abstract

The invention provides a distributed artificial intelligence system collaboration method and device based on software embedded deployment and electronic equipment, and relates to the technical field of distributed artificial intelligence, and the method comprises the steps: monitoring a software dynamic event of an operating system; implanting agent loading codes into a process space, a thread space and a memory space of the target software in a dynamic code injection mode, and synchronously deploying and loading a lightweight artificial intelligence model in the process space of the target software; and establishing an agent-software binding table, storing a mapping relationship between the software feature code and an agent internal storage address, and opening each software agent calling interface in the equipment. According to the method, an artificial intelligence micro model is deeply embedded into an existing software system, and an embedded distributed intelligent collaborative network is constructed; the dependence of a traditional AI architecture on an independent operation environment and the dependence of cooperative work on a preset protocol and a physical network are broken through; the situation that hardware equipment serves as an embedded carrier boundary of an embedded AI is broken, and each software carrier becomes an independent intelligent node.

Description

Technical Field

[0001] This invention relates to the field of distributed artificial intelligence technology, and in particular to a collaborative method, apparatus, and electronic device for distributed artificial intelligence systems based on software embedded deployment. Background Technology

[0002] Traditional artificial intelligence (AI) architectures are typically non-embedded deployments, and even embedded AI applications are limited to hardware-based deployments. In recent years, distributed AI has gained prominence, offering the advantage of multiple agents working independently and collaboratively to execute tasks. However, traditional distributed AI systems are generally non-embedded, failing to consider software as the embedded object and carrier. They lack the ability to deeply leverage existing software functionalities to simplify and optimize AI deployment, operation, and collaborative work, thus failing to fully utilize the advantages of organic integration and close collaboration between agents and software. Furthermore, communication and collaboration among multiple agents in traditional distributed AI systems rely on pre-defined networks and communication protocols, resulting in complex collaborative network structures, long-term monopolization of communication resources, high communication overhead, and highly complex interaction and coordination mechanisms, making it difficult to guarantee synchronization and consistency. Compared to the software-embedded distributed AI system proposed in this system, traditional distributed AI systems have higher deployment costs, more complex collaborative network structures, lower correlation, and poor fault tolerance.

[0003] Therefore, a collaborative method, device, and electronic device for distributed artificial intelligence systems based on software embedded deployment are proposed. Summary of the Invention

[0004] This specification provides a collaborative method, device, and electronic device for a distributed artificial intelligence system based on software embedded deployment. It deeply embeds artificial intelligence micro-models into existing software systems to construct an embedded distributed intelligent collaborative network. It breaks through the dependence of traditional distributed AI architectures on independent operating environments and excessive reliance on preset physical networks and protocols when working collaboratively. It breaks the boundaries of embedded AI based on embedded hardware devices, making each software carrier an independent intelligent node.

[0005] This specification provides a collaborative method for a distributed artificial intelligence system based on software embedded deployment, including: Monitor dynamic software events in the operating system; Intelligent agent loading code is injected into the process space, thread space, and memory space of the target software through dynamic code injection, and a lightweight artificial intelligence model is simultaneously deployed and loaded in the process space of the target software. Establish an agent-software binding table to store the mapping relationship between software signature codes and agent memory addresses, and open the calling interfaces of each software agent within the device.

[0006] Optional, also includes: The software dynamic events include software uninstallation events and software installation events; When the software uninstallation event is detected, the mapping relationship between the software and the agent and the training results of the agent are backed up. When the software installation event is detected, the memory address of the agent is matched with the software feature code, and the corresponding agent is embedded and loaded into the new target software to restore the corresponding training results.

[0007] Optional, also includes: The intelligent agent of the requesting software filters and listens to internal and external events by its host software. The artificial intelligence model in the requesting software intelligent agent performs artificial intelligence task processing on the internal and external events, specifically: When the AI ​​model and its training results in the AI ​​agent of the requesting party software cannot independently handle the AI ​​task processing of the internal and external events, the AI ​​agent of the requesting party software initiates a collaboration request to trigger a collaboration task. After initiating the collaboration request, the AI ​​agent of the requesting party software encapsulates the collaboration request into a first collaboration message and sends the first collaboration message to multiple points on the assisting party software on each assisting party's target device through the interaction function and communication behavior of the requesting party software. The requesting party software and the assisting party software are the same software client on different target devices.

[0008] Optional, also includes: The intelligent agent of the assisting party software on the assisting party target device filters its host software message communication API, listens to the first cooperation message, identifies the first cooperation message, and obtains the cooperation request. The assisting software agent responds to the collaboration request and queries the training results of the assisting software agent; If the first target training result is found in the training results of the assisting software agent, the first target training result is encapsulated into a second collaborative message, and the second collaborative message is sent back to the requesting software through the interactive functions and communication behaviors of its host software. The intelligent agent of the requesting party software filters its host software's message communication API, listens to the second collaborative message, identifies the second collaborative message, obtains the first target training result, and displays it.

[0009] Optional, also includes: If the first target training result is not found in the training results of the assisting software agent, the host software agent will use the collaboration request as a parameter and call the calling interface of other target software agents on the assisting local device to request the target software agent to participate in the collaboration.

[0010] The target software's agent queries its own training results. If a second target training result is obtained, it returns the second target training result to the assisting software's agent. The assisting software's agent encapsulates the second target training result into a third collaborative message and sends the third collaborative message back to the requesting software via the original path through the communication behavior of its host software's built-in interactive function. The requesting software's agent filters its host software's message communication API, listens to the third collaborative message, identifies the third collaborative message, obtains the second training result, and displays it. If no second target training result is obtained, the assisting software's agent and the target software's agent on other assisting devices that received the group collaboration request continue to execute the collaboration task according to the aforementioned optional method.

[0011] Optionally, the requesting and assisting intelligent agents encapsulate the cooperation request or training result into a cooperation message, and send the cooperation message to the other party through the communication behavior of the built-in interactive function of their host software, either to multiple points or to a specific point. This also includes: By simulating user input, keyboard API filtering, and message communication API filtering, the collaboration request or training result information is attached to the simulated original message or instant message message to form the first collaboration message, the second collaboration message, and the third collaboration message. By simulating user operations, including sending chat content to one or more contacts in the address book, sending SMS messages to one or more contacts, sending emails to one or more contacts in the email address book, and posting and replying to one or more comment sections, the collaboration message with the collaboration request or training result is sent to the other party in a hidden form in the background, either to multiple points or to a specific point. Determine whether the software interaction functions and communication capabilities of the requesting party meet preset values; When the interactive function and / or communication capability of the requesting party's software does not meet the preset value, the intelligent agent of the requesting party's software queries the communication capability rating table of each software on the requesting party's device and selects the software with the higher rating as the requesting party's agent software. The intelligent agent of the requesting party software takes the collaboration request as a parameter and transmits the collaboration request to the intelligent agent of the requesting party agent software by calling the calling interface of the requesting party agent software. The requesting agent software forwards the collaboration request to the assisting agent software by reusing the communication channel of its host software. After receiving the collaboration request, the assisting agent software uses the collaboration request as a parameter and requests the collaborating agent software to cooperate by calling the interface of the assisting agent software. This ensures that when direct communication between the requesting agent software and the assisting agent software is not smooth, they can still achieve roundabout communication and cooperation through the communication proxy mechanism. The requesting agent software and the assisting agent software are clients of the same software on different target devices.

[0012] Optional, also includes: A convergence algorithm is used to generate a collaborative path graph; Set a maximum forwarding hop threshold for collaboration requests; Perform a hop count threshold decrement operation on each forward; When the hop count threshold reaches zero or a valid response is received, the forwarding of the cooperation request is terminated.

[0013] This specification provides a collaborative device for a distributed artificial intelligence system based on software embedded deployment, comprising: The monitoring module is used to monitor dynamic software events in the operating system. The implantation module is used to implant intelligent agent loading code into the process space, thread space and memory space of the target software through dynamic code injection, and to synchronously deploy and load a lightweight artificial intelligence model in the process space of the target software. The association module is used to establish an agent-software binding table, store the mapping relationship between software feature codes and agent memory addresses, and open the calling interfaces of each software agent within the device.

[0014] Optional, also includes: The software dynamic events include software uninstallation events and software installation events; When the software uninstallation event is detected, the mapping relationship between the software and the agent and the training results of the agent are backed up. When the software installation event is detected, the memory address of the agent is matched with the software feature code, and the corresponding agent is embedded and loaded into the new target software to restore the corresponding training results.

[0015] Optional, also includes: The intelligent agent of the requesting software filters and listens to internal and external events by its host software. The artificial intelligence model in the requesting software intelligent agent performs artificial intelligence task processing on the internal and external events, specifically: When the AI ​​model and its training results in the AI ​​agent of the requesting party software cannot independently handle the AI ​​task processing of the internal and external events, the AI ​​agent of the requesting party software initiates a collaboration request to trigger a collaboration task. After initiating the collaboration request, the AI ​​agent of the requesting party software encapsulates the collaboration request into a first collaboration message and sends the first collaboration message to multiple points on the assisting party software on each assisting party's target device through the interaction function and communication behavior of the requesting party software. The requesting party software and the assisting party software are the same software client on different target devices.

[0016] Optional, also includes: The intelligent agent of the assisting party software on the assisting party target device filters its host software message communication API, listens to the first cooperation message, identifies the first cooperation message, and obtains the cooperation request. The assisting software agent responds to the collaboration request and queries the training results of the assisting software agent; If the first target training result is found in the training results of the assisting software agent, the first target training result is encapsulated into a second collaborative message, and the second collaborative message is sent back to the requesting software through the interactive functions and communication behaviors of its host software. The intelligent agent of the requesting party software filters its host software's message communication API, listens to the second collaborative message, identifies the second collaborative message, obtains the first target training result, and displays it.

[0017] Optional, also includes: If the first target training result is not found in the training results of the assisting software agent, the host software agent will use the collaboration request as a parameter and call the calling interface of other target software agents on the assisting local device to request the target software agent to participate in the collaboration.

[0018] The target software's agent queries its own training results. If a second target training result is obtained, it returns the second target training result to the assisting software's agent. The assisting software's agent encapsulates the second target training result into a third collaborative message and sends the third collaborative message back to the requesting software via the original path through the communication behavior of its host software's built-in interactive function. The requesting software's agent filters its host software's message communication API, listens to the third collaborative message, identifies the third collaborative message, obtains the second training result, and displays it. If no second target training result is obtained, the assisting software's agent and the target software's agent on other assisting devices that received the group collaboration request continue to execute the collaboration task according to the aforementioned optional method.

[0019] Optionally, the intelligent agents of both the requesting party and the assisting party encapsulate the cooperation request and training results into a cooperation message, and send the cooperation message to the other party through the communication behavior of the built-in interactive function of their host software, either to multiple points or to a specific point. This also includes: By simulating user input, keyboard API filtering, and message communication API filtering, the collaboration request or training result information is attached to the simulated original message or instant message message to form the first collaboration message, the second collaboration message, and the third collaboration message. By simulating user operations, including sending chat content to one or more contacts in the address book, sending SMS messages to one or more contacts, sending emails to one or more contacts in the email address book, and posting and replying to one or more comment sections, the collaboration message with the collaboration request or training result is sent to the other party in a hidden form in the background, either to multiple points or to a specific point. Determine whether the software interaction functions and communication capabilities of the requesting party meet preset values; When the interactive function and / or communication capability of the requesting party's software does not meet the preset value, the intelligent agent of the requesting party's software queries the communication capability rating table of each software on the requesting party's device and selects the software with the higher rating as the requesting party's agent software. The intelligent agent of the requesting party software takes the collaboration request as a parameter and transmits the collaboration request to the intelligent agent of the requesting party agent software by calling the calling interface of the requesting party agent software. The requesting agent software forwards the collaboration request to the assisting agent software by reusing the communication channel of its host software. After receiving the collaboration request, the assisting agent software uses the collaboration request as a parameter and requests the collaborating agent software to cooperate by calling the assisting agent software's API. This ensures that even when direct communication between the requesting agent software and the assisting agent software is not smooth, they can still achieve roundabout communication and collaboration through a communication proxy mechanism. The requesting agent software and the assisting agent software are the same software client on different target devices.

[0020] Optional, also includes: A convergence algorithm is used to generate a collaborative path graph; Set a maximum forwarding hop threshold for collaboration requests; Perform a hop count threshold decrement operation on each forward; When the hop count threshold reaches zero or a valid response is received, the forwarding of the cooperation request is terminated.

[0021] This specification also provides an electronic device, wherein the electronic device includes: A processor and a memory storing computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0022] This specification also provides a computer-readable storage medium that stores one or more programs that, when executed by a processor, implement any of the methods described above.

[0023] In this invention, AI capabilities are deconstructed into intelligent agent units bound to software carriers, leveraging a massive software installation base to achieve economies of scale. Process-level isolation technology is used to distribute resource consumption, completely resolving the resource bottleneck problem in AI deployment and enabling the system to scale horizontally. Embedded collaborative networks and application message filtering mechanisms are used to link intelligent perception, collaboration requests, and response feedback, simplifying the communication structure and process of multi-agent collaboration links and improving the real-time performance, synchronization, and consistency of AI perception and collaboration. Multi-agent communication shares the communication capabilities provided by the host software's existing communication behavior. Interaction between agents is achieved through protocol encapsulation technology, eliminating the need for additional creation and occupation of communication channels and other resources. Agents share communication with the software only when collaboration is needed; when collaboration is not triggered, the software can still exclusively occupy communication, avoiding long-term occupation of communication resources and significantly reducing the quantity and frequency of communication resource consumption. This design is naturally adaptable to various heterogeneous network environments, achieving zero-modification access in IoT and other scenarios, significantly reducing multi-agent collaboration overhead and optimizing and simplifying the collaboration structure. A central node-less design is adopted, with each agent independently identified by feature code hashing, and training data stored locally. This invention also employs an intelligent node communication proxy mechanism to achieve roundabout communication collaboration, enhancing the reliability and integrity of communication between nodes. This avoids node downtime due to incomplete or absent communication functions in the host software, significantly improving the fault tolerance and stability of the collaborative network. When a node failure is detected, the system quickly reconstructs the agent through a backup and recovery mechanism, ensuring high system availability. The AI ​​deployment and collaboration method in this invention is applicable to all types of software, not limited to software application areas or AI application environments, ensuring high system versatility. Based on software, this invention organically and tightly integrates distributed AI and embedded AI, fully leveraging their complementary advantages to significantly optimize system performance while comprehensively reducing system transformation costs. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A schematic diagram illustrating the principle of a collaborative method for a distributed artificial intelligence system based on software embedded deployment, provided in the embodiments of this specification; Figure 2 A schematic diagram of the structure of a collaborative device for a distributed artificial intelligence system based on software embedded deployment, provided as an embodiment of this specification; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification; Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification. Detailed Implementation

[0026] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0027] The following is in conjunction with the appendix Figure 1-4 Exemplary embodiments of the invention will be described more fully here. However, exemplary embodiments can be implemented in many forms and should not be construed as limiting the invention to the embodiments set forth herein. Rather, these exemplary embodiments are provided to make the invention more comprehensive and complete, and to facilitate a full communication of the inventive concept to those skilled in the art. The same reference numerals in the figures denote the same or similar elements, components, or parts, and therefore repeated descriptions of them are omitted.

[0028] Subject to the technical concept of this invention, the features, structures, characteristics or other details described in a particular embodiment may be combined in one or more other embodiments in a suitable manner.

[0029] In the description of specific embodiments, the features, structures, characteristics, or other details described in this invention are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that those skilled in the art can practice the technical solutions of this invention without one or more of the specific features, structures, characteristics, or other details.

[0030] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] The terms “and / or” or “and / or” include all combinations of any one or more of the listed items.

[0033] Figure 1 This specification provides a schematic diagram of a collaborative method for a distributed artificial intelligence system based on software embedded deployment, which may include: S110: Monitors dynamic software events in the operating system; S120: Implant agent loading code into the process space, thread space, and memory space of the target software through dynamic code injection, and simultaneously deploy and load a lightweight artificial intelligence model in the process space of the target software; S130: Establish an agent-software binding table to store the mapping relationship between software feature codes and agent memory addresses, and open the calling interfaces of each software agent within the device.

[0034] In the specific implementation of this specification, traditional distributed artificial intelligence systems are optimized and improved to establish a distributed artificial intelligence system that embeds, deploys, runs, and collaborates with software as the object. This system dynamically injects the agent code into the processes, threads, and memory spaces of various software within the operating system, embedding it into the corresponding software. Each agent runs independently along with its software carrier, processes data, and creates its own open calling interface.

[0035] Optional, also includes: The software dynamic events include software uninstallation events and software installation events; When the software uninstallation event is detected, the mapping relationship between the software and the agent and the training results of the agent are backed up. When the software installation event is detected, the memory address of the agent is matched with the software feature code, and the corresponding agent is embedded and loaded into the new target software to restore the corresponding training results.

[0036] In the specific implementation described in this specification, when new software is installed in the operating system, the embedded system actively synchronizes the embedded deployment of the new software. If all software on the device is uninstalled or the operating system is reinstalled (without retaining the original software), this system will retain the original software mapping relationships and training result records of each agent in the device's storage medium, so as to quickly and accurately restore the embedded loaded agents and their training results after the operating system and software are reinstalled. When collaboration is successful, all agents on the collaboration link save the training results of successful collaboration.

[0037] Optional, also includes: The intelligent agent of the requesting software filters and listens to internal and external events by its host software. The artificial intelligence model in the requesting software intelligent agent performs artificial intelligence task processing on the internal and external events, specifically: When the AI ​​model and its training results in the AI ​​agent of the requesting party software cannot independently handle the AI ​​task processing of the internal and external events, the AI ​​agent of the requesting party software initiates a collaboration request to trigger a collaboration task. After initiating the collaboration request, the AI ​​agent of the requesting party software encapsulates the collaboration request into a first collaboration message and sends the first collaboration message to multiple points on the assisting party software on each assisting party's target device through the interaction function and communication behavior of the requesting party software. The requesting party software and the assisting party software are the same software client on different target devices.

[0038] In the specific implementation of this specification, when a commonly used social media application receives a message, an artificial intelligence program needs to analyze and determine the security of the message, eliminating risks such as falsehood, malice, misleading information, cyberbullying, or exaggerated advertising. The software's intelligent agent filters and intercepts the message through its host software's message communication API (it can intercept the message and prompt before it is output, or it can not intercept it and prompt the user after the message is output). It first attempts to determine whether the message is safe. If it can determine the safety, it stops intercepting and prompts the user; if it cannot determine the safety, the intelligent agent sends a collaboration request to online users in the address book through simulated messages, keyboard API, message communication API filtering, etc. This request is sent in a hidden backend to the same social media client on the devices of the assisting users, seeking their intelligent agent's assistance.

[0039] Optional, also includes: The intelligent agent of the assisting party software on the assisting party target device filters its host software message communication API, listens to the first cooperation message, identifies the first cooperation message, and obtains the cooperation request. The assisting software agent responds to the collaboration request and queries the training results of the assisting software agent; If the first target training result is found in the training results of the assisting software agent, the first target training result is encapsulated into a second collaborative message, and the second collaborative message is sent back to the requesting software through the interactive functions and communication behaviors of its host software. The intelligent agent of the requesting party software filters its host software's message communication API, listens to the second collaborative message, identifies the second collaborative message, obtains the first target training result, and displays it.

[0040] Optional, also includes: If the first target training result is not found in the training results of the assisting software agent, the host software agent will use the collaboration request as a parameter and call the calling interface of other target software agents on the assisting local device to request the target software agent to participate in the collaboration.

[0041] The target software's agent queries its own training results. If a second target training result is obtained, it returns the second target training result to the assisting software's agent. The assisting software's agent encapsulates the second target training result into a third collaborative message and sends the third collaborative message back to the requesting software via the original path through the communication behavior of its host software's built-in interactive function. The requesting software's agent filters its host software's message communication API, listens to the third collaborative message, identifies the third collaborative message, obtains the second training result, and displays it. If no second target training result is obtained, the assisting software's agent and the target software's agent on other assisting devices that received the group collaboration request continue to execute the collaboration task according to the aforementioned optional method.

[0042] In the specific implementation of this specification, the interaction between intelligent agents is achieved through the network communication function of the software carrier's built-in interactive communication behavior and the mutual invocation between intelligent agents. After receiving a collaboration request, the intelligent agent of a user's assisting device in the address book filters and identifies the request information through the software's message communication API, responds, and attempts to check whether its own training results meet the response request. If the conditions are met, the training results are fed back to the social media agent on the requesting party's device via simulated messages, keyboard API, and message communication API filtering, and the user is notified. If the conditions are not met, the social media agent on the assisting party's device calls upon the agent of other software on that device (the function of mutual calling between multiple agents has been enabled during deployment) to determine the answer. If the search engine agent can provide a judgment result, it returns the result to the social media agent on that device, which then sends the result back to the social media agent on the requesting party's device via simulated messages, keyboard API, and message communication API filtering, and the user is notified. If the search engine agent on the assisting party's device does not receive a judgment result, it returns no result to the social media agent on that device, and then sends no result back to the social media agent on the requesting party's device, which then seeks help from the social media and search engine agents on other assisting party user devices that received the group collaboration request, and so on, until a collaboration judgment result is obtained.

[0043] Optionally, the intelligent agents of both the requesting party and the assisting party encapsulate the cooperation request and training results into a cooperation message, and send the cooperation message to the other party through the communication behavior of the built-in interactive function of their host software, either to multiple points or to a specific point. This also includes: By simulating user input, keyboard API filtering, and message communication API filtering, the collaboration request or training result information is attached to the simulated original message or instant message message to form the first collaboration message, the second collaboration message, and the third collaboration message. By simulating user operations, including sending chat content to one or more contacts in the address book, sending SMS messages to one or more contacts, sending emails to one or more contacts in the email address book, and posting and replying to one or more comment sections, the collaboration message with the collaboration request or training result is sent to the other party in a hidden form in the background, either to multiple points or to a specific point. Determine whether the software interaction functions and communication capabilities of the requesting party meet preset values; When the interactive function and / or communication capability of the requesting party's software does not meet the preset value, the intelligent agent of the requesting party's software queries the communication capability rating table of each software on the requesting party's device and selects the software with the higher rating as the requesting party's agent software. The intelligent agent of the requesting party software takes the collaboration request as a parameter and transmits the collaboration request to the intelligent agent of the requesting party agent software by calling the calling interface of the requesting party agent software. The requesting agent software forwards the collaboration request to the assisting agent software by reusing the communication channel of its host software. Upon receiving the collaboration request, the assisting agent software uses it as a parameter and calls the assisting agent software's API to request collaboration. This ensures that even when direct communication between the requesting agent software and the assisting agent software is disrupted, a roundabout communication collaboration can still be achieved through a communication proxy mechanism. The requesting agent software and the assisting agent software are the same software client on different target devices.

[0044] In the specific implementation of this specification, the information required for collaboration can be sent by attaching a specific message to the original message and simulating user operations such as sending chat messages or other common software communication events. For example, the agent of one of the collaborating parties can simulate user operations by sending chat content and private messages to chat friends and contacts in the address book, sending SMS messages to contacts in the address book, sending emails to contacts in the email address book, and posting or replying to comments in the comment section. This simulates the original message, and the collaboration request information or the training result information is attached to the original message and encapsulated into a collaboration message through keyboard API filtering, message API filtering, etc. The collaboration message with the collaboration request and training result is then sent to the other party in a hidden background form. If most of the software on the device does not have communication functions or has weak communication functions, the agent will conduct collaborative communication through a roundabout communication method. That is, the agent of software that does not have communication capabilities or has weak communication capabilities can automatically select software with strong communication capabilities on the device to communicate with, and open up new communication channels by leveraging its communication capabilities. If none of the software on the device has communication capabilities, the system will switch to a conventional distributed communication mechanism.

[0045] Optional, also includes: A convergence algorithm is used to generate a collaborative path graph; Set a maximum forwarding hop threshold for collaboration requests; Perform a hop count threshold decrement operation on each forward; When the hop count threshold reaches zero or a valid response is received, the forwarding of the cooperation request is terminated.

[0046] In the specific implementations described in this specification, the convergence algorithms include the shortest path method, the divide-and-conquer method, etc. The agent does not actively collect the training results of other agents; instead, it updates and saves its own training results, providing collaboration-related training results during cooperation.

[0047] In this invention, AI capabilities are deconstructed into intelligent agent units bound to software carriers, leveraging a massive software installation base to achieve economies of scale. Process-level isolation technology is used to distribute resource consumption, completely resolving the resource bottleneck problem in AI deployment and enabling the system to scale horizontally. Embedded collaborative networks and application message filtering mechanisms are used to link intelligent perception, collaboration requests, and response feedback, simplifying the communication structure and process of multi-agent collaboration links and improving the real-time performance, synchronization, and consistency of AI perception and collaboration. Multi-agent communication shares the existing communication capabilities provided by the host software. Interaction between agents is achieved through protocol encapsulation technology, eliminating the need for additional communication channel creation and resource occupation. Communication is triggered only when collaboration is needed, and the software can still exclusively occupy communication when collaboration is not triggered, avoiding long-term occupation of communication resources and significantly reducing the quantity and frequency of communication resource occupation. This design is naturally adaptable to various heterogeneous network environments, achieving zero-modification access in IoT and other scenarios, significantly reducing collaborative communication overhead and the complexity of the communication structure. A central node-less design is adopted, with each agent independently identified by feature code hashing, and training data stored locally. This invention also employs an intelligent node communication proxy mechanism to achieve roundabout communication collaboration, enhancing the reliability and integrity of communication between nodes. This avoids node downtime due to incomplete or absent communication functions in the host software, significantly improving the fault tolerance and stability of the collaborative network. When a node failure is detected, the system quickly reconstructs the agent through a backup and recovery mechanism, ensuring high system availability. The AI ​​deployment and collaboration method in this invention is applicable to all types of software, not limited to software application areas or AI application environments, ensuring high system versatility. Based on software, this invention organically and tightly integrates distributed AI and embedded AI, fully leveraging their complementary advantages to significantly optimize system performance while comprehensively reducing system transformation costs.

[0048] Figure 2 This specification provides a schematic diagram of a collaborative device for a distributed artificial intelligence system based on software embedded deployment, which may include: Monitoring module 10 is used to monitor dynamic software events of the operating system; The implantation module 20 is used to implant intelligent agent loading code into the process space, thread space and memory space of the target software through dynamic code injection, and to synchronously deploy and load a lightweight artificial intelligence model in the process space of the target software. The association module 30 is used to establish an agent-software binding table, store the mapping relationship between software feature codes and agent memory addresses, and open the calling interfaces of each software agent in the device.

[0049] Optional, also includes: The software dynamic events include software uninstallation events and software installation events; When the software uninstallation event is detected, the mapping relationship between the software and the agent and the training results of the agent are backed up. When the software installation event is detected, the memory address of the agent is matched with the software feature code, and the corresponding agent is embedded and loaded into the new target software to restore the corresponding training results.

[0050] Optional, also includes: The intelligent agent of the requesting software filters and listens to internal and external events by its host software. The artificial intelligence model in the requesting software intelligent agent performs artificial intelligence task processing on the internal and external events, specifically: When the AI ​​model and its training results in the AI ​​agent of the requesting party software cannot independently handle the AI ​​task processing of the internal and external events, the AI ​​agent of the requesting party software initiates a collaboration request to trigger a collaboration task. After initiating the collaboration request, the AI ​​agent of the requesting party software encapsulates the collaboration request into a first collaboration message and sends the first collaboration message to multiple points on the assisting party software on each assisting party's target device through the interaction function and communication behavior of the requesting party software. The requesting party software and the assisting party software are the same software client on different target devices.

[0051] Optional, also includes: The intelligent agent of the assisting party software on the assisting party target device filters its host software message communication API, listens to the first cooperation message, identifies the first cooperation message, and obtains the cooperation request. The assisting software agent responds to the collaboration request and queries the training results of the assisting software agent; If the first target training result is found in the training results of the assisting software agent, the first target training result is encapsulated into a second collaborative message, and the second collaborative message is sent back to the requesting software through the interactive functions and communication behaviors of its host software. The intelligent agent of the requesting party software filters its host software's message communication API, listens to the second collaborative message, identifies the second collaborative message, obtains the first target training result, and displays it.

[0052] Optional, also includes: If the first target training result is not found in the training results of the assisting software agent, the host software agent will use the collaboration request as a parameter and call the calling interface of other target software agents on the assisting local device to request the target software agent to participate in the collaboration.

[0053] The target software's agent queries its own training results. If a second target training result is obtained, it returns the second target training result to the assisting software's agent. The assisting software's agent encapsulates the second target training result into a third collaborative message and sends the third collaborative message back to the requesting software via the original path through the communication behavior of its host software's built-in interactive function. The requesting software's agent filters its host software's message communication API, listens to the third collaborative message, identifies the third collaborative message, obtains the second training result, and displays it. If no second target training result is obtained, the assisting software's agent and the target software's agent on other target devices that received the group collaboration request continue to execute the collaboration task according to the above optional method.

[0054] Optionally, the intelligent agents of both the requesting party and the assisting party encapsulate the cooperation request and training results into a cooperation message, and send the cooperation message to the other party through the communication behavior of the built-in interactive function of their host software, either to multiple points or to a specific point. This also includes: By simulating user input, keyboard API filtering, and message communication API filtering, the collaboration request or training result information is attached to the simulated original message or instant message message to form the first collaboration message, the second collaboration message, and the third collaboration message. By simulating user operations, including sending chat content to one or more contacts in the address book, sending SMS messages to one or more contacts, sending emails to one or more contacts in the email address book, and posting and replying to one or more comment sections, the collaboration message with the collaboration request or training result is sent to the other party in a hidden form in the background, either to multiple points or to a specific point. Determine whether the software interaction functions and communication capabilities of the requesting party meet preset values; When the interactive function and / or communication capability of the requesting party's software does not meet the preset value, the intelligent agent of the requesting party's software queries the communication capability rating table of each software on the requesting party's device and selects the software with the higher rating as the requesting party's agent software. The intelligent agent of the requesting party software takes the collaboration request as a parameter and transmits the collaboration request to the intelligent agent of the requesting party agent software by calling the calling interface of the requesting party agent software. The requesting agent software forwards the collaboration request to the assisting agent software by reusing the communication channel of its host software. Upon receiving the collaboration request, the assisting agent software uses it as a parameter and calls the assisting agent software's API to request collaboration. This ensures that even when direct communication between the requesting agent software and the assisting agent software is disrupted, a roundabout communication collaboration can still be achieved through a communication proxy mechanism. The requesting agent software and the assisting agent software are the same software client on different target devices.

[0055] Optional, also includes: A convergence algorithm is used to generate a collaborative path graph; Set a maximum forwarding hop threshold for collaboration requests; Perform a hop count threshold decrement operation on each forward; When the hop count threshold reaches zero or a valid response is received, the forwarding of the cooperation request is terminated.

[0056] The functions of the apparatus in this embodiment have been described in the above method embodiments. Therefore, for any parts not detailed in this embodiment, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0057] Based on the same inventive concept, embodiments of this specification also provide an electronic device.

[0058] The following describes embodiments of the electronic device of the present invention, which can be considered as specific implementations of the methods and apparatus embodiments of the present invention described above. Details described in the embodiments of the electronic device of the present invention should be considered as supplements to the methods or apparatus embodiments described above; details not disclosed in the embodiments of the electronic device of the present invention can be implemented with reference to the methods or apparatus embodiments described above.

[0059] Figure 3 This is a schematic diagram of an electronic device provided as an embodiment of this specification. Refer to the following... Figure 3 The electronic device 300 according to this embodiment of the present invention will be described. Figure 3 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0060] like Figure 3 As shown, the electronic device 300 is presented in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including storage unit 320 and processing unit 310), a display unit 340, etc.

[0061] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in the processing method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 310 can perform, for example... Figure 1 The steps are shown.

[0062] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache storage unit 3202, and may further include a read-only memory unit (ROM) 3203.

[0063] The storage unit 320 may also include a program / utility 3204 having a set (at least one) program module 3205, such program module 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0064] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0065] Electronic device 300 can also communicate with one or more external devices 400 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable viewers to interact with electronic device 300, and / or with any device that enables electronic device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. Network adapter 360 can communicate with other modules of electronic device 300 via bus 330. It should be understood that, although... Figure 3 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0066] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described in this invention can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the method described above according to this invention. When the computer program is executed by a data processing device, it enables the computer-readable medium to implement the method described above, i.e.: as... Figure 1 The method shown.

[0067] Figure 4 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification.

[0068] accomplish Figure 1 The computer program of the method shown can be stored on one or more computer-readable media. A computer-readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0069] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0070] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the audience's computing device, partially on the audience's device, as a standalone software package, partially on the audience's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the audience's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0071] In summary, the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that in practice, general-purpose data processing devices such as microprocessors or digital signal processors (DSPs) can be used to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0072] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0073] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0074] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A collaborative method for a distributed artificial intelligence system based on software embedded deployment, characterized in that, include: Monitor dynamic software events in the operating system; Intelligent agent loading code is injected into the process space, thread space, and memory space of the target software through dynamic code injection, and a lightweight artificial intelligence model is simultaneously deployed and loaded in the process space of the target software. Establish an agent-software binding table to store the mapping relationship between software signature codes and agent memory addresses, and open the calling interfaces of each software agent within the device.

2. The collaborative method for a distributed artificial intelligence system based on software embedded deployment as described in claim 1, characterized in that, Also includes: The software dynamic events include software uninstallation events and software installation events; When the software uninstallation event is detected, the mapping relationship between the software and the agent and the training results of the agent are backed up. When the software installation event is detected, the memory address of the agent is matched with the software feature code, and the corresponding agent is embedded and loaded into the new target software to restore the corresponding training results.

3. The collaborative method for a distributed artificial intelligence system based on software embedded deployment as described in claim 2, characterized in that, Also includes: The intelligent agent of the requesting software filters and listens to internal and external events by its host software. The artificial intelligence model in the requesting software intelligent agent performs artificial intelligence task processing on the internal and external events, specifically: When the artificial intelligence model and its training results in the intelligent agent of the requesting party software cannot independently handle the artificial intelligence task processing of the internal and external events, the intelligent agent of the requesting party software initiates a collaboration request to trigger a collaboration task. After the intelligent agent of the requesting party software initiates a collaboration request, it encapsulates the collaboration request into a first collaboration message and sends the first collaboration message to multiple points on the assisting party software on each assisting party's target device through the interaction function and communication behavior of the requesting party software; the requesting party software and the assisting party software are the same software client on different target devices.

4. The collaborative method for a distributed artificial intelligence system based on software embedded deployment as described in claim 3, characterized in that, Also includes: The intelligent agent of the assisting party software on the assisting party target device filters its host software message communication API, listens to the first cooperation message, identifies the first cooperation message, and obtains the cooperation request. The assisting software agent responds to the collaboration request and queries the training results of the assisting software agent; If the first target training result is found in the training results of the assisting software agent, the first target training result is encapsulated into a second collaborative message, and the second collaborative message is sent back to the requesting software through the interactive functions and communication behaviors of its host software. The intelligent agent of the requesting party software filters its host software's message communication API, listens to the second collaborative message, identifies the second collaborative message, obtains the first target training result, and displays it.

5. The collaborative method for a distributed artificial intelligence system based on software embedded deployment as described in claim 4, characterized in that, Also includes: If the first target training result is not found in the training results of the assisting software agent, the host software agent will use the collaboration request as a parameter and call the calling interface of other target software agents on the assisting local device to request the target software agent to participate in the collaboration. The target software's agent queries its own training results. If it obtains a second target training result, it returns the second target training result to the assisting software's agent. The agent of the assisting software encapsulates the training result of the second target into a third collaborative message, and sends the third collaborative message back to the requesting software along the original path through the communication behavior of the interactive function of its host software. The intelligent agent of the help request software filters the message communication API of its host software, listens to the third collaborative message, identifies the third collaborative message, obtains the second training result, and displays it. If the training result for the second target is not obtained, the assisting software agent and the target software agent on the other assisting devices that received the group collaboration request shall continue to perform the collaborative task in accordance with the above method.

6. The collaborative method for a distributed artificial intelligence system based on software embedded deployment as described in any one of claims 3-5, characterized in that, The requesting and assisting agents encapsulate the cooperation request or training result into a cooperation message, and send the cooperation message to the other party via multiple points or a fixed point through the communication behavior of their host software's built-in interactive functions. This also includes: By simulating user input, keyboard API filtering, and message communication API filtering, the collaboration request or training result information is attached to the simulated original message or instant message message to form the first collaboration message, the second collaboration message, and the third collaboration message. By simulating user operations, including sending chat content to one or more contacts in the address book, sending SMS messages to one or more contacts, sending emails to one or more contacts in the email address book, and posting and replying to one or more comment sections, the collaboration message with the collaboration request or training result is sent to the other party in a hidden form in the background, either to multiple points or to a specific point. Determine whether the software interaction functions and communication capabilities of the requesting party meet preset values; When the interactive function and / or communication capability of the requesting party's software does not meet the preset value, the intelligent agent of the requesting party's software queries the communication capability rating table of each software on the requesting party's device and selects the software with the higher rating as the requesting party's agent software. The intelligent agent of the requesting party software takes the collaboration request as a parameter and transmits the collaboration request to the intelligent agent of the requesting party agent software by calling the calling interface of the intelligent agent of the requesting party agent software; The requesting agent software forwards the collaboration request to the assisting agent software by reusing the communication channel of its host software. After receiving the collaboration request, the assisting agent software uses the collaboration request as a parameter and requests the collaborating agent software to cooperate by calling the assisting agent software's API. This ensures that even when direct communication between the requesting agent software and the assisting agent software is not smooth, they can still achieve roundabout communication and collaboration through a communication proxy mechanism. The requesting agent software and the assisting agent software are clients of the same software on different target devices.

7. The collaborative method for a distributed artificial intelligence system based on software embedded deployment as described in claim 6, characterized in that, Also includes: A convergence algorithm is used to generate a collaborative path graph; Set a maximum forwarding hop threshold for collaboration requests; Perform a hop count threshold decrement operation on each forward; When the hop count threshold reaches zero or a valid response is received, the forwarding of the cooperation request is terminated.

8. A collaborative device for a distributed artificial intelligence system based on software embedded deployment, characterized in that, include: The monitoring module is used to monitor dynamic software events in the operating system. The implantation module is used to implant intelligent agent loading code into the process space, thread space and memory space of the target software through dynamic code injection, and to synchronously deploy and load a lightweight artificial intelligence model in the process space of the target software. The association module is used to establish an agent-software binding table, store the mapping relationship between software feature codes and agent memory addresses, and open the calling interfaces of each software agent within the device.

9. An electronic device, wherein, The electronic device includes: A processor and a memory storing computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1-8.

10. A computer-readable storage medium, wherein, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-8.

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