Task processing method and electronic equipment

Abstract

The invention provides a task processing method and electronic equipment. The method comprises the following steps: acquiring demand information of a target task by utilizing a first intelligent agent; processing the first intelligent agent according to the demand information to obtain a second intelligent agent; wherein the second agent comprises at least one sub-agent different from the first agent; functional services provided by at least one sub-agent in the first agent and the second agent are different; and processing the corresponding sub-task in the target task by using at least one sub-agent in the second agent.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a task processing method and electronic device. Background Technology

[0002] Intelligent agents can be used to solve complex problems and perform specific tasks. However, when current intelligent agents handle complex tasks, they rely on predefined fixed structures and functions. As a result, intelligent agents are unable to adapt to complex and ever-changing task requirements and environments, leading to low efficiency in task processing or even failure to complete tasks. Summary of the Invention

[0003] The purpose of this application is to provide a task processing method and an electronic device.

[0004] The embodiments of this application adopt the following technical solution: a task processing method, comprising: The first intelligent agent is used to obtain the requirements information of the target task; The first intelligent agent is processed according to the demand information to obtain a second intelligent agent; wherein, the second intelligent agent includes at least one sub-intelligent agent that is different from the first intelligent agent; the functional services provided by the first intelligent agent and at least one sub-intelligent agent in the second intelligent agent are different; At least one of the sub-agents in the second agent is used to process the corresponding sub-task in the target task.

[0005] In some embodiments, processing the first agent according to the demand information to obtain the second agent includes at least one of the following: The first intelligent agent is updated according to the required information to obtain the second intelligent agent; Based on the required information, at least one third agent is invoked and combined to obtain the second agent, wherein the third agent provides different functional services from the first agent. Based on the required information, the sub-agents in the pre-built component library are invoked to obtain the second agent.

[0006] In some embodiments, the step of invoking a sub-agent from a pre-built component library to obtain the second agent based on the demand information includes: Based on the aforementioned requirement information, the reusability information of the component library is determined, and the reusability information is used to characterize the information of reusable sub-intelligent agents in the component library. The requirement information and the reusable information are input into the pre-built sub-agent design model to generate a sub-agent design scheme; Based on the aforementioned sub-agent design scheme, various sub-agents are generated to process the target task.

[0007] In some embodiments, the requirement information includes functional requirements; The step of determining the reusability information of the component library based on the demand information includes: Obtain the functional descriptions of each sub-agent in the component library; Based on the functional requirements and the functional descriptions of each sub-agent in the component library, a target sub-agent is determined. The target sub-agent is the sub-agent in the component library whose functional description overlaps with the requirement information. Based on the target sub-agent, the reusability information of the component library is determined.

[0008] In some embodiments, determining the target sub-agent based on the functional requirements and the functional descriptions of each sub-agent in the component library includes at least one of the following: In response to the fact that the similarity between the functional description of the sub-agent in the component library and the requirement information is greater than or equal to a preset similarity threshold, the target sub-agent is determined; In response to the fact that the similarity between the functional description of the sub-agent in the component library and the requirement information is less than the preset similarity threshold, the configuration parameters of the sub-agent are adjusted to generate the target sub-agent; Wherein, the similarity between the functional description of the target sub-agent and the requirement information is greater than or equal to the preset similarity threshold.

[0009] In some embodiments, processing the corresponding sub-task in the target task using at least one of the second intelligent agents includes at least one of the following: In response to the second intelligent agent including the first intelligent agent and the third intelligent agent, the target task is divided into a first sub-task corresponding to the first intelligent agent and a third sub-task corresponding to the third intelligent agent according to the functional descriptions of the first intelligent agent and the third intelligent agent; In response to the different functional services provided by the first intelligent agent and the various sub-intelligent agents in the second intelligent agent, the target task is divided into multiple sub-tasks corresponding to each of the various sub-intelligent agents according to the functional descriptions of each sub-intelligent agent in the second intelligent agent; In response to the different functional services provided by the first intelligent agent and the various sub-intelligent agents in the second intelligent agent, the target task is divided into multiple sub-tasks corresponding to the first intelligent agent and the various sub-intelligent agents according to the functional descriptions of the first intelligent agent and the various sub-intelligent agents in the second intelligent agent.

[0010] In some embodiments, generating sub-agents for processing the target task based on the sub-agent design scheme includes: Based on the sub-agent design scheme, a sub-agent code segment is determined, and the sub-agent code segment includes at least one of the following: the sub-agent code corresponding to the sub-agent reused in the component library, and the sub-agent code newly generated according to the requirement information; Based on preset code splicing rules, the sub-agent code fragments are spliced ​​together to generate complete sub-agent code; Based on the complete sub-agent code, the various sub-agents used to process the target task are generated.

[0011] In some embodiments, it also includes: Detect the reusability of the newly generated sub-agent based on the required information; Sub-agents that meet the preset reuse criteria are added to the component library.

[0012] In some embodiments, the method further includes at least one of the following: Based on the requirement information, the dependency relationship between the first intelligent agent and the second intelligent agent is determined, including a temporal relationship; based on the dependency relationship, a communication scheme between the first intelligent agent and the second intelligent agent is determined. Monitor the processing status and environmental status of the target task; update the sub-agent that processes the target task based on the processing status and environmental status.

[0013] This application also provides an electronic device, including: a main body; The main body is used to carry the first intelligent agent; The first intelligent agent is used to acquire the requirement information of the target task; the first intelligent agent is processed according to the requirement information to obtain the second intelligent agent; wherein, the second intelligent agent includes at least one sub-intelligent agent that is different from the first intelligent agent; the functional services provided by the first intelligent agent and at least one sub-intelligent agent in the second intelligent agent are different; the at least one sub-intelligent agent in the second intelligent agent is used to process the corresponding sub-task in the target task. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the task processing method for this application; Figure 2 This is a structural block diagram of the sub-agent generation module of this application; Figure 3 This is a flowchart of one embodiment of the task processing method of this application; Figure 4 This is a structural block diagram of the electronic device of this application. Detailed Implementation

[0016] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0017] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0018] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0019] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0020] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.

[0021] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0022] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0023] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0024] This application first introduces its application scenario. In the field of artificial intelligence, intelligent agent technology is widely used to build intelligent systems to solve complex problems and perform specific tasks. However, traditional intelligent systems are static systems. Static intelligent systems typically predefine fixed agent structures and functions, lacking flexibility and adaptability. When task requirements change, or when new, unforeseen problems need to be addressed, static intelligent systems struggle to dynamically adjust their structure, making it difficult for them to cope with or effectively address the aforementioned dynamically changing task requirements.

[0025] Unlike traditional static intelligent systems, this application provides a task processing method. This method can use a first intelligent agent to obtain the requirement information of the target task, and process the first intelligent agent according to the requirement information to obtain a second intelligent agent. The second intelligent agent includes sub-intelligent agents (sub-Agents) that are different from the first intelligent agent. When the requirement information changes, the processed second intelligent agent can also change accordingly, so that the second intelligent agent can adapt to the dynamically changing requirement information and thus solve the target task.

[0026] Combination Figure 1 The task processing method of this application includes: S10: Use the first intelligent agent to obtain the requirements information of the target task.

[0027] For example, the first intelligent agent can be understood as an intelligent entity that directly faces the user or task scenario and is able to obtain task requirement information. The requirement information of the target task may include the type, function, and configuration parameters of the sub-intelligent agents needed to solve the target task.

[0028] The target task requirements can be directly input into the first intelligent agent. This information must be directly recognizable by the agent and require no parsing. The requirements can be input in a standardized format, with no missing elements and no ambiguous expressions. For example, task type: Daily Sales Report; objective: Generate month-on-month analysis; input source: Amazon API + Shopify API; output: Spreadsheet (Excel) + WeChat push notification; time: 9 AM daily.

[0029] The target task requirements can also be obtained by the first intelligent agent after receiving the target task and parsing it. For example, a task receiving and analysis module can receive target task requests from users or external systems, such as natural language instructions, application programming interface (API) calls, event triggers, etc. Using natural language processing (NLP) techniques and task parsing algorithms, the target task can be deeply understood and decomposed to extract information such as task objectives, constraints, and key elements. This can be implemented using existing NLP engines or customized task parsers.

[0030] The first intelligent agent can acquire raw data information for the target task through relevant perception modules. The source of raw data information can be text commands directly input by the user (e.g., "Sync WeChat articles to Xiaohongshu"), voice descriptions (e.g., "Help me organize yesterday's meeting key points"), or interactive behaviors (e.g., uploading an Excel spreadsheet and marking it "Needs month-on-month analysis"). The source of raw data information can also be automatically monitored and acquired by the first intelligent agent, connecting to enterprise systems via APIs to capture potential demand signals (e.g., Amazon backend data updates, new email triggers, logistics system delay warnings). Raw data information can also be obtained by calling vector databases or user profiling systems to extract implicit needs (e.g., "This operations specialist needs to export last week's sales data every Monday"). The first intelligent agent can then analyze the above raw data information using natural language understanding and rule engines to obtain the demand information for the target task.

[0031] S20, the first intelligent agent is processed according to the demand information to obtain a second intelligent agent; wherein, the second intelligent agent includes at least one sub-intelligent agent that is different from the first intelligent agent; the functional services provided by the first intelligent agent and at least one sub-intelligent agent in the second intelligent agent are different.

[0032] For example, after obtaining the requirement information, the requirements and environmental status of the target task can be analyzed based on this information. After determining the requirements and environmental status of the target task, the first intelligent agent can be processed to obtain the second intelligent agent. For instance, the intelligent analysis module can call preset algorithms (e.g., natural language understanding, task decomposition, planning algorithms, etc.) to deeply understand the requirement information, identify the key elements of the target task, and the required functional modules. Based on the results of this in-depth understanding, the type, function, and configuration parameters of the sub-intelligent agents required to complete the target task can be dynamically identified. This process may include upgrading the first intelligent agent, querying the existing sub-intelligent agent library, combining functional modules, and conceiving new sub-intelligent agent functions. Decomposing successfully generated sub-intelligent agents that are considered to have general applicability or reusability can form reusable functional modules. The first intelligent agent can call the sub-intelligent agent generation module to generate responsive sub-intelligent agents based on the requirements of the identified sub-intelligent agents, and configure the attributes, functions, skills, and communication protocols of the sub-intelligent agents. The generation of sub-agents can be based on predefined templates, component libraries, or more advanced generative models (e.g., deep learning-based generative models).

[0033] The resulting second agent includes at least one sub-agent different from the first agent. This sub-agent can be newly generated, or it can be obtained by combining a newly generated sub-agent with some or all of the original sub-agents in the first agent. The second agent may include only sub-agents different from the first agent, or it may include at least some of the original sub-agents in the first agent. For example, the first agent includes sub-agents A, B, and C, and the second agent includes at least one sub-agent different from the first agent, designated as sub-agent D. The second agent can be sub-agent D, or it can be any one or more of sub-agents A, B, and C combined with sub-agent D.

[0034] The functional services provided by the first intelligent agent and at least one sub-agent in the second intelligent agent are different. For example, the first intelligent agent can provide a first functional service that cannot complete the target task or performs poorly in completing it (e.g., inefficiently). At least one sub-agent in the second intelligent agent can provide a second functional service that differs from the first and better meets the requirements for completing the target task. For instance, the first functional service provided by the first intelligent agent can complete the target task in 5 minutes, while the second intelligent agent providing the second functional service can also complete the target task in 10 seconds. The second functional service is significantly better than the first intelligent agent. For example, a meeting recorder needs to transcribe a 30-minute meeting recording. The first functional service provided by the first intelligent agent requires 5 minutes of sentence-by-sentence conversion, outputting continuous text with colloquial redundancy (such as "um" and "this"), homophone errors (such as recognizing "formulate" as "system"), and cannot distinguish between different speakers. In contrast, the second functional service provided by the second intelligent agent can accurately transcribe speech into text and provide structured organization. For the same meeting recording, the second intelligent agent can complete the conversion within 10 seconds, automatically filter out redundant spoken language, correct homophone errors, and can be formatted into segments according to "speaker, time, and core viewpoint". It can also extract key information such as "project deadline" and "to-do tasks" to generate meeting minutes that can be directly distributed.

[0035] S30, at least one of the sub-agents in the second agent is used to process the corresponding sub-task in the target task.

[0036] For example, the completion of a target task can be achieved by completing multiple sub-tasks. If one sub-task of the target task cannot be completed or its completion efficiency is very low, the target task will not be completed or its completion efficiency will be very low. For the first intelligent agent, one or more sub-tasks in the target task cannot be completed by the first intelligent agent, or although the first intelligent agent can complete each sub-task of the target task, its completion efficiency is very low. For the processed second intelligent agent, it includes at least one sub-intelligent agent that can handle the corresponding sub-tasks in the target task. Here, the corresponding sub-tasks may be sub-tasks in the target task that the first intelligent agent cannot complete or whose completion efficiency is very low. By having at least one sub-intelligent agent in the second intelligent agent handle the sub-tasks in the target task that the first intelligent agent cannot complete or whose completion efficiency is very low, the overall completion of the target task can be achieved or the overall completion efficiency of the target task can be improved.

[0037] Referring to the embodiment in step S20, when the second intelligent agent is sub-intelligent agent D, sub-intelligent agent D can complete the entire target task. When the second intelligent agent is a combination of sub-intelligent agent A, sub-intelligent agent B, sub-intelligent agent C, or sub-intelligent agent D, the second intelligent agent can be used only to complete sub-tasks that the first intelligent agent cannot complete. For example, the target task includes a first sub-task and a second sub-task. The first intelligent agent can only complete the first sub-task and cannot complete the second sub-task. When the second intelligent agent is sub-intelligent agent D, sub-intelligent agent D can complete both the first and second sub-tasks. When the second intelligent agent is a combination of sub-intelligent agent A, sub-intelligent agent B, sub-intelligent agent C, or sub-intelligent agent D, the second intelligent agent can be used only to complete the second sub-task. For another example, the target task includes a first sub-task and a second sub-task. The first intelligent agent can complete both the first and second sub-tasks, taking a total of 5 minutes, with the second sub-task accounting for approximately 98% of the time. Sub-agent D in the second intelligent agent can work independently or in cooperation with one or more of the sub-agents A, B, and C of the first intelligent agent to complete the target task in 10 seconds. Sub-agent D in the second intelligent agent can cooperate with one or more of the sub-agents A, B, and C to process the second sub-task, improve the processing efficiency of the second sub-task, and thus improve the processing efficiency of the entire target task.

[0038] The second intelligent agent obtained by processing the first intelligent agent in this embodiment can better complete the target task.

[0039] In some embodiments, step S20, which involves processing the first agent according to the demand information to obtain a second agent, includes at least one of the following: S201, Update the first intelligent agent according to the demand information to obtain the second intelligent agent; S202, based on the requirement information, at least one third intelligent agent is invoked and combined to obtain the second intelligent agent, wherein the third intelligent agent provides different functional services from the first intelligent agent; S203, based on the required information, call the sub-agents in the pre-built component library to obtain the second agent.

[0040] For example, the scenario where the first agent is updated based on demand information to obtain the second agent could be as follows: The main reason for the first agent's low efficiency in completing the target task is that a certain sub-agent within the first agent is of an outdated version (version 2.0), unable to meet the requirements of the target task for that sub-agent version (at least version 3.0). In this case, the sub-agent within the first agent can be upgraded to version 3.0 or higher, thereby upgrading the first agent and enabling the upgraded first agent (i.e., the second agent) to process the target task more efficiently. The upgrade process can involve upgrading all sub-agents of the first agent, or it can involve upgrading some (one or more) of the sub-agents within the first agent. The version upgrade process can be implemented through training or algorithm optimization, etc., and the specific upgrade method is not specifically required.

[0041] Based on the aforementioned requirements, the first agent invokes and combines at least one third agent to obtain a second agent. The third agent provides different functional services than the first agent. This can be because the first agent lacks a sub-agent to complete a specific sub-task of the target task. The first agent can generate a new sub-agent by invoking a sub-agent generation module or by searching for an existing sub-agent, which can be a third agent. The third agent can provide functional services different from those of the first agent; that is, the functional services provided by the first agent cannot complete the aforementioned sub-task of the target task, but the functional services provided by the third agent can complete the aforementioned sub-task of the target task. In other words, the combination of the first and third agents can complete the entire target task. By combining the third agent into the first agent to obtain the second agent, the second agent can meet the requirements for completing the target task. This combination can be done by combining the third agent into the first agent as a separate sub-agent, or by combining the third agent with one or more sub-agents in the first agent to form a new sub-agent.

[0042] The above-mentioned method of calling sub-agents from a pre-built component library based on requirement information to obtain a second agent can be exemplified by the following scenarios: During the analysis of requirement information and the generation of required sub-agents, the first agent can call sub-agents from the pre-built component library, and the called sub-agents can adapt to the requirements of the target task. The first agent calling a sub-agent from the pre-built component library can be understood as obtaining the second agent, or calling at least two sub-agents from the pre-built component library can combine to form the second agent. For example, if the completion of the target task requires the participation of a fourth agent, and the first agent does not have a fourth agent, but the fourth agent exists in the pre-built component library, then calling the fourth agent from the pre-built component library can be understood as obtaining the second agent. The second agent, possessing the fourth agent capable of completing the aforementioned target task, can thus complete the target task and meet its requirements. Alternatively, if the completion of the target task requires the participation of fourth agents A and B, and the first agent does not have fourth agents A and B, but fourth agents A and B exist in the pre-built component library. The fourth agent A and the fourth agent B are combined into a second agent to handle the target task.

[0043] In some embodiments, combined with Figure 2 The structural block diagram of the sub-agent generation module shown, wherein step S203, based on the requirement information, calls the sub-agents in the pre-built component library to obtain the second agent, may include: S2031, Based on the requirement information, determine the reusability information of the component library, wherein the reusability information is used to characterize the information of reusable sub-intelligent agents in the component library.

[0044] For example, the sub-agent requirement identification module can identify the functional requirements of the sub-agent based on the requirement information. The sub-agent generation module can receive the sub-agent functional requirements from the sub-agent requirement identification module and perform functional reusability testing to determine the reusable information of the component library. The component library can be an atomic unit agent library. Testing can be performed on existing atomic unit agent libraries and previously generated sub-agents to determine whether there are functional modules or sub-agent components that partially or completely correspond to the requirement information of the target task. For example, this can be achieved by constructing a knowledge base containing various sub-agent types, functional descriptions, skill lists, configuration parameters, etc. This knowledge base can be accessed by the first agent and can include sub-agent components or functional modules that can be accessed by the first agent. During the sub-agent requirement identification and generation phase, the first agent actively accesses this knowledge base to retrieve, match, and reuse existing sub-agent components or functional modules. Using the knowledge base and task analysis results, logical reasoning is performed to identify the sub-agent requirements. Perform algorithm planning, such as Hierarchical Task Network (HTN) planning and state-space planning.

[0045] The atomic unit agent library can store predefined, minimal, and reusable atomic unit agents. Each atomic unit agent can encapsulate a single, well-defined function, such as "Hypertext Transfer Protocol (HTTP) data request," "JSON (JavaScript Object Notation) data parsing," "image format conversion," and "text sentiment analysis." The atomic unit agents in the library can be viewed as pluggable functional components.

[0046] The generated sub-agents can be those that were previously successfully generated and considered to have generality and reusability. These sub-agents can be decomposed into functional modules for reuse.

[0047] The reusability information of the component library identified above can be output in the form of a functional reusability test report, which can record a list of components in the component library that are similar to or reusable to the current functional requirements, and assess the degree of reusability and adaptability.

[0048] S2032, The requirement information and the reusable information are input into the pre-built sub-agent design model to generate a sub-agent design scheme.

[0049] For example, in conjunction with the functional reusability test report output in step S2031, the requirement information and the functional reusability test report can be input into the pre-built sub-agent design model. The sub-agent design model can generate a sub-agent design scheme containing component reuse information based on the received sub-agent functional requirements and functional reusability test report. The design scheme will clearly define which components need to be reused, which functions need to be redesigned entirely, and how to combine them.

[0050] The pre-built sub-agent design model can be a trained sub-agent design scheme generation model. Training this model enables it not only to generate entirely new design schemes based on functional requirements, but also to prioritize the use of detected reusable components, effectively combining and integrating new functions and reusable components to form customized design schemes. The training samples for this model can come from existing tasks, sub-agent design schemes, and code examples. Through these samples, the model learns the correspondence between task requirements and sub-agent designs. Reusable components serve as reference templates during training, helping the model learn to prioritize the use of existing components and combine them to generate new sub-agents. Training data needs to include reuse scenarios, such as the correspondence between functional requirements, design schemes, and reused components: indicating which atomic unit agents or components of existing sub-agents should be reused under specific functional requirements, and how to combine new functions with reused components. For example, how the design scheme references and configures reused components, how to extend the functionality of reused components, and how to combine multiple reused components. For instance, a sub-agent design scheme might specify the need to generate a completely new sub-agent D, which can be used independently to complete the target task. Or, it might specify the need to generate sub-agents D and E, with sub-agent D combined with sub-agent E to complete the target task. Or, it might specify the need to reuse sub-agent F, generate sub-agent D, and combine sub-agent D with sub-agent F to complete the target task.

[0051] Model Type Selection: The model needs to be capable of processing reusable information and generating sub-agent design schemes. For example, graph neural networks (GCNs) or graph attention networks (GATs) can be chosen to obtain the connection relationships and combination structures between various sub-agents. Hybrid models can also be chosen, such as combining generative models (e.g., generative adversarial networks (GANs) or variational autoencoders (VAEs)) and retrieval models (e.g., similarity matching models). First, the retrieval model retrieves reusable components from a component library, and then the generative model further generates customized design schemes based on functional requirements and reusable components.

[0052] S2033, Based on the sub-agent design scheme, generate each sub-agent for processing the target task.

[0053] For example, based on the sub-agent design scheme of step S2032, various sub-agents are generated, each of which is used to process the target task. For instance, a completely new sub-agent D can be generated based on the sub-agent design scheme, and sub-agent D can independently complete the target task. Alternatively, sub-agents D and E can be generated based on the sub-agent design scheme, and the combination of sub-agents D and E can complete the target task. Or, sub-agent F can be reused based on the sub-agent design scheme to generate sub-agent D, and the combination of sub-agents D and F can complete the target task.

[0054] Once the sub-agents are generated, the first agent can call the sub-agent instantiation module for automatic deployment, and the scheduler will trigger execution to instantiate the sub-agents. During execution, the first agent is responsible for coordinating the sub-agents to complete their tasks and summarizing the results.

[0055] In some embodiments, the requirement information includes functional requirements; Step S2031, based on the requirement information, determines the reusability information of the component library, which may include: S20311, Obtain the functional descriptions of each sub-agent in the component library.

[0056] For example, the component library contains multiple types of sub-agents and related metadata. The storage format of the sub-agents can vary depending on their type. Other types of data, such as configuration samples, running logs, and reuse relationships, are automatically collected and updated by the system during operation.

[0057] Each sub-agent in the component library has a functional description corresponding to its capabilities. These descriptions can be presented in the form of documents, configuration files, or source code metadata. Taking a document-based functional description as an example, the document can record a list of the corresponding sub-agent's capabilities, including its core abilities, usage scenarios, and tool permissions. Users can locate the relevant section in the document by searching for the corresponding functional description, and view two categories of lists and detailed explanations. For example, a sub-agent that can generate images based on text descriptions might have the following functional description: It integrates the image generation capabilities of models such as Dali-E (DALL-E: a generative AI model from OpenAI, a text-to-image generation model that can create realistic or creative images based on text descriptions) and Hugging Face (an open-source community focused on natural language processing (NLP), computer vision, and other fields, providing a large number of pre-trained models), and can generate images based on text prompts using the `generate_image` command.

[0058] S20312, Based on the functional requirements and the functional descriptions of each sub-agent in the component library, a target sub-agent is determined. The target sub-agent is the sub-agent in the component library whose functional description overlaps with the requirement information.

[0059] For example, based on the requirements information, the functional descriptions of each sub-agent in the component library can be detected using keyword matching, semantic similarity calculation, etc. For instance, based on keyword matching, keywords in the functional requirements can be matched with the functional descriptions, tags, and metadata of the component library (which can be an atomic unit agent library) and the generated sub-agents to find functionally similar components. The sub-agent with the highest matching degree is then identified as the target sub-agent. Based on semantic similarity calculation, natural language processing (NLP) techniques, such as word vectors, sentence vectors, and semantic similarity models, can be used to calculate the semantic similarity between the functional requirements and the existing component functional descriptions, more accurately determining the degree of functional overlap. Higher semantic similarity indicates a higher degree of functional overlap, and the sub-agent with the higher degree of functional overlap is identified as the target sub-agent.

[0060] S20313, Based on the target sub-agent, determine the reusability information of the component library.

[0061] For example, based on the target sub-agent determined in step S20312, the functional description of the target sub-agent is used as reusable information of the component library. The reusable information can be output in the form of a functional reusability test report, but is not limited to.

[0062] In some embodiments, determining the target sub-agent based on the functional requirements and the functional descriptions of each sub-agent in the component library may include at least one of the following: One of these methods involves determining the target sub-agent in response to a similarity between the functional description of the sub-agent in the component library and the requirement information being greater than or equal to a preset similarity threshold.

[0063] Another step is to adjust the configuration parameters of the sub-agent in response to the fact that the similarity between the functional description of the sub-agent in the component library and the requirement information is less than the preset similarity threshold, and generate the target sub-agent; wherein the similarity between the functional description of the target sub-agent and the requirement information is greater than or equal to the preset similarity threshold.

[0064] For example, for directly reusable sub-agents, in response to the fact that the similarity between the functional description of the sub-agent in the component library and the required information is greater than or equal to a preset similarity threshold, an existing sub-agent can be directly reused as the target sub-agent. For example, the logic and interface of an existing atomic unit agent (such as an HTTP request module) can be directly reused.

[0065] In some embodiments, the target sub-agent that can be directly reused may contain only a single sub-agent, and the target sub-agent containing only a single sub-agent can complete the corresponding sub-task without making other adjustments.

[0066] In other embodiments, the target sub-agent that can be directly reused may include at least two sub-agents, and the at least two sub-agents may be combined to form a composite functional module (i.e., the target sub-agent) to complete the corresponding sub-task.

[0067] For sub-agents that require parameterized adjustment and reuse, the corresponding sub-tasks can be completed by fine-tuning configuration parameters (such as request frequency, application programming interface key (API key), model temperature, etc.).

[0068] In some embodiments, the target sub-agent for parameterized adjustment and reuse may contain only a single sub-agent. Before reuse, the similarity between the functional description and the requirement information of this single sub-agent is less than (e.g., close to) a preset similarity threshold. By fine-tuning some of its parameters, the similarity between the requirement information of the sub-agents in the component library and the preset similarity threshold can be made greater than or equal to the preset similarity threshold before reuse. That is, the target sub-agent containing only a single sub-agent needs to undergo parameter fine-tuning before it can process the corresponding sub-task.

[0069] In other embodiments, the target sub-agent of parameterized adjustment and reuse may include at least two sub-agents. The at least two sub-agents may be combined to synthesize a composite functional module. The similarity between the functional description of the composite functional module and the requirement information is less than (e.g. close to) a preset similarity threshold. By fine-tuning some or all of the parameters of the sub-agents, the composite functional module (target sub-agent) may be synthesized and combined again so that the similarity between the functional description of the synthesized composite functional module (target sub-agent) and the requirement information is greater than or equal to the preset similarity threshold, thus enabling it to complete the corresponding sub-task.

[0070] Regarding the above reuse methods, the system will automatically determine which reuse method to use when generating sub-agent design schemes.

[0071] For example, if the target sub-agent requires "web page sentiment analysis", the model that can be selected is: Directly reuse the "HTTP request agent"; Adjust the category labels for "Text Analysis Agent"; The two are combined to form a new sub-agent.

[0072] In some embodiments, the functional services of the second agent include at least one of the following: functional services corresponding to at least one sub-agent obtained from the first agent; and functional services corresponding to at least one newly generated sub-agent based on the requirement information.

[0073] For example, the functional services of the second intelligent agent including the functional services corresponding to at least one sub-intelligent agent derived from the first intelligent agent means that the second intelligent agent includes both at least one sub-intelligent agent derived from the first intelligent agent and newly generated sub-intelligent agents. In this case, the functional services of the second intelligent agent include the functional services corresponding to at least one sub-intelligent agent derived from the first intelligent agent and the functional services corresponding to the newly generated sub-intelligent agents. For instance, the target task requires reading video materials and performing basic editing (cutting, splicing, audio matching); rendering 4K quality special effects (adding transitions, color grading, 3D subtitles); and quickly exporting high-definition finished products (exporting 5GB 4K video within 10 minutes). The first intelligent agent includes a first sub-intelligent agent, a second sub-intelligent agent, and a third sub-intelligent agent. The first sub-intelligent agent includes a basic computing module (CPU is Intel Core i5-1240U) responsible for basic instruction execution; the second sub-intelligent agent includes a basic storage module (512GB SATA interface solid-state drive) for storing materials and system files; and the third sub-intelligent agent includes an integrated graphics module (CPU built-in UHD integrated graphics) for handling simple graphics display. The first agent cannot handle 4K image quality rendering and fast export. The second agent can reuse the first and second sub-agents of the first agent, and can also include newly generated fourth and fifth sub-agents. The fourth sub-agent is an independent graphics module (NVIDIA RTX 4050 discrete graphics card with CUDA acceleration cores, specifically for handling graphics-intensive tasks); the fifth sub-agent is a high-speed storage module (1TB NVMe protocol M.2 solid-state drive, with read and write speeds significantly faster than SATA hard drives).

[0074] The functional services of the second intelligent agent include the functional services corresponding to at least one newly generated sub-intelligent agent based on the demand information. The sub-intelligent agents included in the second intelligent agent can all be newly generated sub-intelligent agents. For example, the objective task is to realize coherent multi-turn dialogue in the large model of the notebook ontology. The core sub-tasks are single-turn question answering (answering an independent question raised by the user in a single turn) and multi-turn coherent response (remembering the key information of the previous round of dialogue and responding to subsequent related questions).

[0075] The first intelligent agent (the basic single-turn question-answering model) consists of a first sub-agent and a second sub-agent. The limitation of the first intelligent agent lies in the fact that its first sub-agent is a single-turn intent parsing module (only extracting the literal intent of the current question, without storing historical information); the second sub-agent is a basic response generation module (generating answers based on the current question and the local knowledge base, without historical association capabilities). Because it lacks a module for storing and associating historical information, the first intelligent agent cannot handle multi-turn coherent responses; it cannot remember the content of the previous dialogue. For example, if a user first asks, "Recommend a suitable laptop for office work," and the model recommends "ThinkPad X1," and then the user follows up with, "How long does its battery life last?", the first intelligent agent, lacking the information that "the previous recommendation was a ThinkPad X1," cannot provide a targeted response and can only give a general answer such as "Office laptops typically have a battery life of 6-10 hours."

[0076] The second intelligent agent does not reuse the first and second sub-agents of the first intelligent agent, but only includes newly generated sixth and seventh sub-agents. The sixth sub-agent is a dialogue memory module, specifically storing key information from multiple rounds of dialogue, such as products mentioned by the user, their needs, and preferences. The seventh sub-agent is a context association generation module, used to read the dialogue memory and generate a response by associating the current question with historical information. For example, if a user first asks, "Recommend a suitable laptop for office work," the seventh sub-agent generates the answer "ThinkPad X1, lightweight and portable with a good keyboard feel," while the sixth sub-agent stores key information such as "Recommended product: ThinkPad X1" and "User need: Office work." Then, if the user follows up with, "How long does its battery life last?", the seventh sub-agent first retrieves the product information from the previous round from the dialogue memory module, then combines it with the local knowledge base to generate "ThinkPad X1 has a battery life of up to 12 hours, meeting the needs of a full day of mobile office work," achieving a coherent response.

[0077] In some embodiments, processing the corresponding sub-task in the target task using at least one of the second intelligent agents includes at least one of the following: In response to the second intelligent agent including the first intelligent agent and the third intelligent agent, the target task is divided into a first sub-task corresponding to the first intelligent agent and a third sub-task corresponding to the third intelligent agent according to the functional descriptions of the first intelligent agent and the third intelligent agent.

[0078] For example, the second intelligent agent completely includes the first intelligent agent; any task that the first intelligent agent can perform, the second intelligent agent can also perform. For instance, if the first intelligent agent's functional description indicates that it can book airline tickets, the second intelligent agent can also perform the task of booking airline tickets. In addition, the second intelligent agent includes a third intelligent agent, which can perform another task that the first intelligent agent cannot perform. For example, continuing with the above embodiments, if the first intelligent agent cannot perform cost budgeting, the third intelligent agent's functional description indicates that it can perform cost budgeting. For example, if the target task is to book airline tickets for a user within a certain budget, then the target task can be broken down into booking airline tickets (the first sub-task corresponding to the first intelligent agent) and cost budgeting (the third sub-task corresponding to the third intelligent agent); the second intelligent agent, including both the first and third intelligent agents, can then perform the aforementioned target task.

[0079] In response to the different functional services provided by the first intelligent agent and the various sub-intelligent agents in the second intelligent agent, the target task is divided into multiple sub-tasks corresponding to each of the sub-intelligent agents according to the functional descriptions of each sub-intelligent agent in the second intelligent agent.

[0080] For example, the second intelligent agent does not include the first intelligent agent; the first intelligent agent is only used to analyze requirement information based on the target task. The functional services provided by the first intelligent agent are different from those provided by the various sub-intelligent agents in the second intelligent agent. Therefore, the target task can be broken down into multiple sub-tasks corresponding to each sub-intelligent agent in the second intelligent agent, so that each sub-intelligent agent in the second intelligent agent can complete its respective sub-task, thereby achieving the completion of the target task. For example, the first intelligent agent is independent of the second intelligent agent and can provide the functional service of requirement analysis. For example, the target task is "help me organize my weekly schedule," and the second intelligent agent includes sub-intelligent agent A, which can collect data; sub-intelligent agent B, which can classify information; and sub-intelligent agent C, which can set reminders. The target task can then be broken down into three sub-tasks, each corresponding to sub-intelligent agent A, sub-intelligent agent B, and sub-intelligent agent C, respectively. Sub-agent A reads all to-do items from the phone's calendar (regardless of title, filtered by time); all meeting invitations from WeChat / WeChat Work (filtered by time, no need to distinguish the sender); and ticketing information from 12306 / airline SMS messages (filtered by time). Sub-agent B receives the raw data collected by sub-agent A, categorizes it by meeting / work, transportation, and personal matters; and generates a tagged structured table. Table fields can include date, time, event name, tag, and source, facilitating subsequent reminder settings. Sub-agent C receives the structured list from sub-agent B and provides the following services: a pop-up window 30 minutes in advance and synchronized watch alarm (matching the user's need for watch synchronization for meetings); a pop-up window 1 hour in advance and associated map navigation (matching the user's need for navigation association for transportation); and a pop-up window 15 minutes in advance (matching the user's need for short reminders for personal matters).

[0081] In response to the different functional services provided by the first intelligent agent and the various sub-intelligent agents in the second intelligent agent, the target task is divided into multiple sub-tasks corresponding to the first intelligent agent and the various sub-intelligent agents according to the functional descriptions of the first intelligent agent and the various sub-intelligent agents in the second intelligent agent.

[0082] For example, if a target task requires collaboration between the first and second intelligent agents and their sub-agents, the target task can be broken down into sub-tasks. For instance, if the complexity of the target task exceeds the processing capacity of a single intelligent agent (detecting multi-stage or multimodal inputs), the task relies on multiple skills or data sources, or environmental changes occur, the existing combination of sub-agents may be unable to meet the current task requirements. The target task can be broken down into sub-tasks based on requirement information, the functional description of the first intelligent agent, and the functional description of at least one sub-agent in the second intelligent agent. Example scenario: In an intelligent assistant scenario, a user says, "Help me book a business trip to Shanghai tomorrow." The first intelligent agent detects that the requirement information for this task involves: 1. Time planning; 2. Flight and hotel booking; 3. Cost budgeting. The functional description of the first intelligent agent indicates that it can perform time planning, the functional description of sub-agent A in the second intelligent agent indicates that it can book flights and hotels, and the functional description of sub-agent B in the second intelligent agent indicates that it can budget costs. Sub-agents A and B can be sub-agents from a component library. Based on the functional descriptions of the first and second intelligent agents, the target task is broken down into three sub-tasks corresponding to the first intelligent agent and sub-agents A and B. The second intelligent agent can supplement the first intelligent agent and work together with it to process the target task.

[0083] In some embodiments, step S2033, based on the sub-agent design scheme, generates various sub-agents for processing the target task, which may include: S20331, Based on the sub-agent design scheme, determine the sub-agent code segment, wherein the sub-agent code segment includes at least one of the following: the sub-agent code corresponding to the sub-agent reused in the component library, and the sub-agent code newly generated according to the requirement information.

[0084] For example, the sub-agent design scheme can be preprocessed and converted into a numerical input format (e.g., feature vectors, graph structures, etc.) acceptable to the code / configuration generation model. Preprocessing methods can include feature extraction, data format conversion, and data normalization / standardization. The preprocessed sub-agent design scheme is then input into the code / configuration generation model. This model can be trained. The training objective can be to train the sub-agent code / configuration generation model to understand component reuse instructions in the sub-agent design scheme, correctly reuse specified atomic unit agents or functional modules of existing sub-agents, and simultaneously generate code or configuration for newly designed functional modules within the sub-agent design scheme, ultimately combining them into complete sub-agent code or configuration. The code / configuration generation model needs to possess the ability to reuse and combine code / configuration components.

[0085] S20332, Based on preset code splicing rules, splice the sub-agent code fragments to generate complete sub-agent code.

[0086] For example, based on preset code concatenation rules, sub-agent code fragments are concatenated to generate complete sub-agent code. A code fragment combination and concatenation model can be considered: the model learns the combination patterns and concatenation rules of code fragments, selects appropriate code fragments (including code from reused components and newly generated code) according to the design scheme, and concatenates them according to predefined rules to generate complete sub-agent code. Transformer-based models or Recurrent Neural Network (RNN) models can be used for code fragment selection and concatenation sequence generation. The model learns the inheritance mechanism and customization method of configuration parameters, inherits the configuration parameters of reused components according to the sub-agent design scheme, and generates configuration parameters for newly designed functional modules, ultimately merging them into a complete sub-agent configuration file. For example, GANs, VAEs, etc., can be used to generate models. For hybrid models, code fragment combination models and configuration parameter inheritance models can be combined to more comprehensively support the reuse and combination generation of code and configuration components. The code / configuration generation model can output complete sub-agent code.

[0087] This step can also perform post-processing, verification, and improvement on the generated sub-agent code or configuration to ensure its integrity, correctness, executability, and quality. Examples include code completion, error correction, consistent code style, configuration parameter verification, and performance optimization.

[0088] S20333, Based on the complete sub-agent code, generate the various sub-agents used to process the target task.

[0089] For example, the sub-agent generation model can automatically generate individual sub-agents for processing the target task based on the complete sub-agent code generated in step S20332.

[0090] In some embodiments, it also includes: S20334, Detect the reusability of the newly generated sub-intelligent agent based on the demand information.

[0091] S20335, add the sub-agents that meet the preset reuse criteria to the component library.

[0092] For example, newly generated sub-agents or their functional modules that are considered to have generality or reusability can be added to the atomic unit agent library as new atomic unit agents or reusable components to expand the atomic unit agent library and be reused in subsequent sub-agent generation processes.

[0093] In some embodiments, the method further includes at least one of the following: One of them is: based on the requirement information, determining the dependency relationship between the first agent and the second agent, the dependency relationship including a temporal relationship; and based on the dependency relationship, determining the communication scheme between the first agent and the second agent.

[0094] For example, the first and second intelligent agents need to collaborate to complete a target task. They may have certain dependencies in completing the task; the operation of the first intelligent agent (or the operation of at least some of its sub-agents) may require the results of the second intelligent agent (or at least some of its sub-agents). There may also be mutual dependencies between the first intelligent agent (or at least some of its sub-agents) and the second intelligent agent (or at least some of its sub-agents). Dependencies may also exist among the individual sub-agents within the second intelligent agent. These dependencies may include, but are not limited to, temporal relationships.

[0095] Based on the above dependencies, determine the communication scheme between the first agent and the second agent. For example, select a communication protocol (such as HTTP, WebSocket, gRPC, MQTT, etc.), and design a communication interface (such as API, message queue) and define a message format (such as JSON, Protobuf, etc.).

[0096] Based on the communication requirements, generate the communication module code or configuration for the sub-agents and integrate it into the sub-agent design scheme so that the generated sub-agents can communicate strictly according to the above communication requirements, ensuring accurate communication between the sub-agents and thus ensuring the successful completion of the target task.

[0097] Another aspect is: monitoring the processing status and environmental status of the target task; and updating the sub-agent that processes the target task based on the processing status and environmental status.

[0098] For example, the system senses the external environment and feeds back the environmental information to both the first and second agents. This information can be obtained from sensor data, user feedback, and system logs. Furthermore, during task execution, the system monitors the communication status between the sub-agents and dynamically adjusts communication strategies to adapt to environmental changes. For instance, various sensor interfaces, data acquisition modules, and user interfaces can be integrated to achieve environmental perception. Mechanisms such as message queues and callback functions are used for information feedback. If communication delays or packet loss are detected, the first agent can dynamically adjust the communication protocol or switch communication channels to ensure the continuity of task execution. Once the sub-tasks are completed, the task results can be output to the user / system.

[0099] For example, in a smart assistant scenario, a user says, "Plan a driving route from A to B for me." During or after the task is decomposed and executed, the user adds the instruction, "By the way, avoid highways." This new constraint changes the environmental state of the task execution, allowing the sub-agent handling the new task to be updated. For instance, before the user added the instruction, the sub-agent handling the task was agent A; after adding the instruction, it can be updated to agent B. Agent B can then plan a driving route from A to B while avoiding highways. After the new task is completed, a corresponding processing status is generated, indicating task completion. The task result can be displayed on the system for the user to listen to / view.

[0100] The flowchart of the task processing method of this application can be found in the flowchart of one embodiment of the task processing method. Figure 3 ).

[0101] This application also provides an electronic device, such as... Figure 4 As shown, an electronic device may include: a main body and a processor; The main body is used to carry the first intelligent agent.

[0102] The first intelligent agent is used to obtain the requirement information of the target task; the first intelligent agent is processed according to the requirement information to obtain the second intelligent agent; wherein the second intelligent agent includes at least one sub-intelligent agent that is different from the first intelligent agent; the functional services provided by the first intelligent agent and at least one sub-intelligent agent in the second intelligent agent are different; the at least one sub-intelligent agent in the second intelligent agent is used to process the corresponding sub-task in the target task.

[0103] The processor is also used to perform steps of the other methods described in the above embodiments.

[0104] Furthermore, one embodiment of this application also provides a storage medium storing a computer program, which, when executed by a processor, implements the task processing method described above. It should be understood that the various solutions in this embodiment have the corresponding technical effects in the above method embodiments, and will not be repeated here.

[0105] Furthermore, embodiments of this application also provide a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions that, when executed, cause at least one processor to perform a task processing method such as those described in the embodiments above.

[0106] It should be noted that the computer storage medium in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access storage media (RAM), read-only storage media (ROM), erasable programmable read-only storage media (EPROM or flash memory), optical fibers, portable compact disk read-only storage media (CD-ROM), optical storage media, magnetic storage media, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program configured for use by or in connection with an instruction execution system, system, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, antenna, optical fiber, RF, etc., or any suitable combination thereof.

[0107] Furthermore, those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0108] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] The foregoing has described in detail several embodiments of this application, but this application is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications based on the concept of this application, and all such variations and modifications should fall within the scope of protection claimed in this application.

Claims

1. A task processing method, comprising: The first intelligent agent is used to obtain the requirements information of the target task; The first intelligent agent is processed according to the demand information to obtain a second intelligent agent; wherein, the second intelligent agent includes at least one sub-intelligent agent that is different from the first intelligent agent; the functional services provided by the first intelligent agent and at least one sub-intelligent agent in the second intelligent agent are different; At least one of the sub-agents in the second agent is used to process the corresponding sub-task in the target task.

2. The task processing method according to claim 1, wherein processing the first agent based on the demand information to obtain the second agent includes at least one of the following: The first intelligent agent is updated according to the required information to obtain the second intelligent agent; Based on the required information, at least one third agent is invoked and combined to obtain the second agent, wherein the third agent provides different functional services from the first agent. Based on the required information, the sub-agents in the pre-built component library are invoked to obtain the second agent.

3. The task processing method according to claim 2, wherein the step of calling a sub-agent from a pre-built component library to obtain the second agent based on the requirement information includes: Based on the aforementioned requirement information, the reusability information of the component library is determined, and the reusability information is used to characterize the information of reusable sub-intelligent agents in the component library. The requirement information and the reusable information are input into the pre-built sub-agent design model to generate a sub-agent design scheme; Based on the aforementioned sub-agent design scheme, various sub-agents are generated to process the target task.

4. The task processing method according to claim 3, wherein the requirement information includes functional requirements; The step of determining the reusability information of the component library based on the demand information includes: Obtain the functional descriptions of each sub-agent in the component library; Based on the functional requirements and the functional descriptions of each sub-agent in the component library, a target sub-agent is determined. The target sub-agent is the sub-agent in the component library whose functional description overlaps with the requirement information. Based on the target sub-agent, the reusability information of the component library is determined.

5. The task processing method according to claim 4, wherein determining the target sub-agent based on the functional requirements and the functional descriptions of each sub-agent in the component library includes at least one of the following: In response to the fact that the similarity between the functional description of the sub-agent in the component library and the requirement information is greater than or equal to a preset similarity threshold, the target sub-agent is determined; In response to the fact that the similarity between the functional description of the sub-agent in the component library and the requirement information is less than the preset similarity threshold, the configuration parameters of the sub-agent are adjusted to generate the target sub-agent; in, The similarity between the functional description of the target sub-agent and the requirement information is greater than or equal to the preset similarity threshold.

6. The task processing method according to claim 2, wherein processing the corresponding sub-task in the target task using at least one sub-agent in the second agent includes at least one of the following: In response to the second intelligent agent including the first intelligent agent and the third intelligent agent, the target task is divided into a first sub-task corresponding to the first intelligent agent and a third sub-task corresponding to the third intelligent agent according to the functional descriptions of the first intelligent agent and the third intelligent agent; In response to the different functional services provided by the first intelligent agent and the various sub-intelligent agents in the second intelligent agent, the target task is divided into multiple sub-tasks corresponding to each of the various sub-intelligent agents according to the functional descriptions of each sub-intelligent agent in the second intelligent agent; In response to the different functional services provided by the first intelligent agent and the various sub-intelligent agents in the second intelligent agent, the target task is divided into multiple sub-tasks corresponding to the first intelligent agent and the various sub-intelligent agents according to the functional descriptions of the first intelligent agent and the various sub-intelligent agents in the second intelligent agent.

7. The task processing method according to claim 3, wherein generating each sub-agent for processing the target task based on the sub-agent design scheme comprises: Based on the sub-agent design scheme, a sub-agent code segment is determined, and the sub-agent code segment includes at least one of the following: the sub-agent code corresponding to the sub-agent reused in the component library, and the sub-agent code newly generated according to the requirement information; Based on preset code splicing rules, the sub-agent code fragments are spliced ​​together to generate complete sub-agent code; Based on the complete sub-agent code, the various sub-agents used to process the target task are generated.

8. The task processing method according to claim 7, further comprising: Detect the reusability of the newly generated sub-agent based on the required information; Sub-agents that meet the preset reuse criteria are added to the component library.

9. The task processing method according to claim 1, further comprising at least one of the following: Based on the requirement information, the dependency relationship between the first intelligent agent and the second intelligent agent is determined, including a temporal relationship; based on the dependency relationship, a communication scheme between the first intelligent agent and the second intelligent agent is determined. Monitor the processing status and environmental status of the target task; update the sub-agent that processes the target task based on the processing status and environmental status.

10. An electronic device, comprising: main body; The main body is used to carry the first intelligent agent; The first intelligent agent is used to obtain the requirement information of the target task; The first intelligent agent is processed according to the demand information to obtain a second intelligent agent; wherein, the second intelligent agent includes at least one sub-intelligent agent that is different from the first intelligent agent; the functional services provided by the first intelligent agent and at least one sub-intelligent agent in the second intelligent agent are different; at least one sub-intelligent agent in the second intelligent agent is used to process the corresponding sub-task in the target task.

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