Information exchange methods, devices, equipment, media, and program products

Abstract

This paper provides an information interaction method, apparatus, device, medium, and program product, relating to the field of computer processing technology. The method includes: receiving first information, which is a query message instructing a first intelligent system to perform a first task; displaying second information, provided by a second intelligent system, which instructs decision information in response to the first information; and displaying third information, which characterizes the response information of a third intelligent system to the decision information. This solves the technical problem of poor communication flexibility between the task scheduler and the task execution end, thereby improving the communication flexibility between intelligent systems, achieving efficient interactive response among multiple intelligent systems, ensuring efficient information transmission and accurate response during task execution, and ultimately improving the task execution efficiency and reliability of the intelligent system.

Description

Technical Field

[0001] This invention relates to the field of computer processing technology, and in particular to an information interaction method, apparatus, device, medium, and program product. Background Technology

[0002] With the continuous development of computer technology, the demand for using intelligent systems to complete various tasks is increasing. Summary of the Invention

[0003] This invention provides an information interaction method, apparatus, device, medium, and program product to enhance the communication flexibility between various intelligent systems, achieve efficient interactive response between multiple intelligent systems, ensure efficient information transmission and accurate response during task execution, and thereby improve the task execution efficiency and reliability of intelligent systems.

[0004] In one scenario, this paper provides an information exchange method, which includes: Receive first information, the first information being a query message used to instruct the first intelligent system to perform a first task; Display second information, provided by a second intelligent system, for indicating decision information in response to the first information; The third information is displayed, which is used to characterize the response information of the third intelligent system to the decision information.

[0005] In one instance, this document also provides an information interaction device, which includes: The first module is used to receive first information, which is a query message used to instruct the first intelligent system to perform a first task; The second module is used to display second information, which is provided by the second intelligent system and is used to indicate decision information in response to the first information. The third module is used to display third information, which is used to characterize the response information of the third intelligent system to the decision information.

[0006] In one instance, this document also provides an electronic device comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the information interaction method as described herein.

[0007] In one instance, this document also provides a storage medium containing computer-executable instructions that, when executed by a computer processor, are used to perform the information interaction methods as described herein.

[0008] In another scenario, this document also provides a computer program product, including a computer program that, when executed by a processor, implements the information interaction method as described herein.

[0009] The above method, by receiving first information—a query instructing a first intelligent system to execute a first task; displaying second information—provided by a second intelligent system and used to instruct a decision based on the first information; and displaying third information—characterizing the response of a third intelligent system to the decision—solves the technical problem of poor collaborative communication flexibility between the task scheduler and the task execution end. It achieves the technical effects of improving the communication and collaboration flexibility between intelligent systems, realizing efficient interactive response among multiple intelligent systems, ensuring efficient information transmission and accurate response during task execution, thereby improving the task execution efficiency and reliability of the intelligent system and meeting the various task needs of users. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the various scenarios described herein will become more apparent when considered in conjunction with the accompanying drawings and the specific examples below. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0011] Figure 1 This is a schematic diagram of the structure of an information processing system under one scenario. Figure 2 This is a flowchart illustrating an information interaction method in one scenario. Figure 3 This is a schematic diagram of an interface used to represent the first piece of information in one scenario. Figure 4 This is a schematic diagram of an interface used to represent the second information in another scenario; Figure 5 This is a flowchart illustrating another method of information exchange. Figure 6 This is a schematic diagram of an interface used to represent the fourth type of information in another scenario; Figure 7 This is a flowchart illustrating another method of information exchange. Figure 8 This is a flowchart illustrating another method of information exchange. Figure 9 This is a schematic diagram of an interface used to represent the sixth and seventh information in another scenario. Figure 10 This is a schematic diagram of an interface used to represent the sixth and seventh information in another scenario. Figure 11 This is a schematic diagram of an interface used to represent the sixth and seventh information in another scenario. Figure 12 This is a flowchart illustrating another method of information exchange. Figure 13 This is a schematic diagram of an interface used to represent the eighth piece of information in another scenario; Figure 14 This is a schematic diagram of an interface used to represent the eighth piece of information in another scenario; Figure 15 This is a schematic diagram of a system architecture used to characterize a method for implementing information interaction in one scenario. Figure 16 A flowchart illustrating an optional instance of an information interaction method in one scenario; Figure 17 This is a schematic diagram of the structure of an information interaction device in one scenario. Figure 18 This is a schematic diagram of the structure of an electronic device used in one scenario. Detailed Implementation

[0012] The situation will now be described in more detail with reference to the accompanying drawings. While some situations are shown in the drawings, it should be understood that the technical solutions can be implemented in various forms and should not be construed as limited to the situations described herein. Rather, these situations are provided to provide a more thorough and complete understanding of the technical solutions herein. It should be understood that the accompanying drawings and situations are for illustrative purposes only and are not intended to limit the scope of protection of the technical solutions.

[0013] It should be understood that the steps described in the method scenario may be performed in different orders and / or in parallel. Furthermore, the method scenario may include additional steps and / or omit the steps shown. The scope of this document is not limited in this respect.

[0014] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one situation" means "at least one situation"; the term "another situation" means "at least one additional situation"; the term "some situations" means "at least some situations". Definitions of other terms will be given in the following description.

[0015] It should be noted that the concepts of "first" and "second" mentioned are only used to distinguish different devices, modules or units, and are not used to limit the order of the functions performed by these devices, modules or units or their interdependencies.

[0016] It should be noted that the terms "one" and "more" used in this document are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0017] The names of messages or information exchanged between multiple devices in this document are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0018] It is understandable that before using the technical solutions disclosed in each scenario in this document, users should be informed of the type, scope of use, and usage scenarios of the personal information involved in this document in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0019] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as electronic devices, applications, servers, or storage media, that perform the operations described herein, based on the prompt message.

[0020] As an optional but not limiting approach, in response to a user's active request, sending a prompt message to the user can be done, for example, via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose whether to "agree" or "disagree" to provide personal information to the electronic device.

[0021] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the circumstances described herein. Other methods that comply with relevant laws and regulations may also be applied to the circumstances described herein.

[0022] It is understood that the data involved in the technical solutions in this article (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0023] In some cases, the provided solution can be applied to Figure 1The information processing system shown may include a client 101 and a server 102. The client 101 may include, but is not limited to, web applications such as browsers, applications (Apps), HyperText Markup Language (HTML) applications, lightweight applications (also known as mini-programs), or cloud applications. The client 101 may be deployed on an electronic device and relies on the operation of that device or certain applications on the device to implement its functions. The electronic device may be, for example, a device with a display screen that supports information browsing, such as a smartphone, tablet, personal computer, or other client terminal. For ease of understanding, Figure 1 The client is primarily represented in the form of a device. Other types of applications can also be configured on the electronic device, such as media content publishing applications, session applications, etc. Server 102 can be one or more servers providing various services. That is, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server; furthermore, it can be a server for a distributed system, a server integrating blockchain technology, a cloud server, or an intelligent cloud computing server or intelligent cloud host deployed with machine learning models, etc.

[0024] The information interaction method described herein allows interaction between client 101 and server 102, such as receiving or sending messages. For example, server 102 can receive first information received through client 101, provide second information through a second intelligent system, and send the second information to client 101 for display on a first interface. Server 102 can also provide third information based on the second information through a third intelligent system and send the third information to client 101 for display on a display interface.

[0025] It should be noted that the information interaction method can be executed by client 101, or by client 101 and server 102, with different functional parts of the corresponding information interaction device deployed on client 101 and server 102 respectively; wherein, the first module, second module, and third module of the device are deployed on client 101, and the intelligent system can be deployed on server 102, with client 101 and server 102 achieving data interaction and functional collaboration through network communication. It should be understood that... Figure 1 The number of clients and servers shown is for illustrative purposes only. Any number of clients and servers can be configured to meet specific implementation requirements.

[0026] It should also be noted that multiple intelligent systems with differentiated functions can be deployed within the intelligent system. The main differences between these systems lie in their built-in system prompts and execution command templates. Based on pre-configured differentiated command constraints, each intelligent system can adapt to its corresponding business process and accurately complete the corresponding operations it needs to perform. For example, each intelligent system can undertake different functions such as task execution and questioning, decision generation, and response information feedback.

[0027] To facilitate understanding of the overall execution logic of this technical solution, the types of intelligent systems involved in this solution are briefly described below: The intelligent systems include, but are not limited to: a first intelligent system for executing the original task and initiating questions when the task is blocked, a second intelligent system for receiving question information and outputting decision instructions, and a third intelligent system for generating corresponding response content based on the decision information.

[0028] For example, the prompt template for the first intelligent system may include: executing a specified task, promptly asking questions and reporting problems that cannot be solved independently. The prompt template for the second intelligent system may include: receiving questions from the task intelligent system, analyzing the problem, and providing corresponding decision-making solutions and processing guidance. The prompt template for the third intelligent system may include: generating corresponding feedback response content and completing information feedback based on the issued decision information.

[0029] It should be noted that the above are examples of prompt word templates for various intelligent systems. These prompt word templates can be dynamically adjusted and updated according to task type, business scenario requirements, and execution constraints.

[0030] Figure 2 This is a flowchart illustrating an information interaction method for one scenario. This method is applicable to intelligent system task information processing scenarios, particularly those involving collaborative task execution by multiple intelligent systems, cross-system information interaction, and dynamic decision-making responses. Examples include complex task breakdown and processing, intelligent system execution anomaly feedback, and multi-level intelligent system linkage information flow. This information interaction method can be executed by an information interaction device, which can be implemented in software and / or hardware, optionally through an electronic device such as a mobile terminal, PC, or server. Figure 2 As shown, this information interaction method may specifically include: S210. Receive first information, the first information being a query message used to instruct the first intelligent system to perform the first task.

[0031] In this paper, the first information can be received through a first interface. This first interface represents the information interaction interface associated with the intelligent system, used for receiving and displaying user input information, receiving, presenting, and transferring various types of intelligent system information. Optionally, the first interface may include at least one of the following: the main interface of an application (e.g., a conversation interface), a web-based dialogue interface, a mobile app interface, and a touchscreen of a smart device. The first interface can be used to receive query information from the first intelligent system performing a first task, and to display decision information from the second intelligent system in response to the query information, as well as response information from the third intelligent system in response to the decision information. In this paper, an intelligent system can represent a system capable of autonomous control based on a machine learning model. For example, it can represent a virtual object or physical entity capable of making decisions and autonomously executing actions based on a machine learning model to achieve a preset goal or complete a preset task. An intelligent system may include an automated program that understands the user's intent and can utilize models or invoke tools to complete various types of tasks. In some cases, examples of intelligent systems may include, but are not limited to: intelligent agents, robots, chatbots, digital avatars, intelligent customer service, digital assistants, etc. Alternatively, an intelligent system may also include intelligent roles implemented based on machine learning models. Intelligent systems can process user requests based on generative models (e.g., language models, multimodal models) to execute specified types of tasks. In some cases, intelligent systems may also involve virtual accounts with corresponding avatars or nicknames. Multiple intelligent systems with differentiated functions can be deployed within an intelligent system, each capable of autonomously completing tasks such as information reception, task parsing, task scheduling and allocation, multi-system collaborative interaction, and subsequent information feedback processing. The first intelligent system represents the entity performing the original task within the intelligent system and proactively generates and reports questions when execution is hindered or doubts arise.

[0032] The first information can be used to characterize the information generated and uploaded by the first intelligent system during task execution, reflecting questions, unresolved issues, and various inquiries arising during task execution. The first information can also be used to characterize user-inputted requirements, reflecting the user's desired task objectives, pending business requests, instructions requiring system response, and specific problems to be solved. The representation format of the first information is not limited and can include at least one data format such as natural language text, voice information, and structured instructions. The first information may include descriptive information related to task execution status, processing difficulties, resource requirements, and decision-making nodes. For example, the first information includes, but is not limited to, reasons for task obstruction, questions about execution parameters, confusion regarding step selection, data content to be verified, and inquiries about subsequent execution directions.

[0033] The first task executed by the first intelligent system can be used to represent various business matters initiated by the user and sent to the intelligent system, which are then assigned by the system and handled by the first intelligent system. That is, the first task can include all task types that the intelligent system has the processing capability to complete. In this document, the first task can include multiple types of tasks, optionally including, but not limited to, at least one of media data processing tasks, media data generation tasks, application invocation tasks, application generation tasks, and resource acquisition tasks. A media data processing task can be used to represent a task that processes existing media data, which can include at least one of parsing, editing, conversion, enhancement, recognition, and analysis. That is, a media data processing task can be used to process existing media content to meet user or system requirements. Media data can include, but is not limited to, text, images, audio, video, animation, and code. For example, a media data processing task can include tasks such as image cropping, audio noise reduction, video editing, and subtitle generation. A media data generation task can be used to represent a task that generates media data based on received instruction information. The difference between media data generation tasks and media data processing tasks lies in the fact that media data processing tasks modify existing media content, while media data generation tasks produce new media content from scratch. For example, media data generation tasks include: generating images or videos from text; generating articles, copy, and dialogue content based on a theme; automatically generating voiceovers and background music; and generating presentation files and posters. Application invocation tasks can be used to characterize tasks where an intelligent system invokes, starts, and controls third-party or internal programs based on instruction information. Application invocation tasks enable intelligent systems to have external capabilities, completing complex operations across applications and modules. For example, application invocation tasks may include: invoking a calendar to create an event; invoking a map for route planning; invoking a document editor to open or edit a document; and invoking query or utility interfaces. Application generation tasks can be used to characterize tasks where an intelligent system automatically generates runnable applications, mini-programs, scripts, interfaces, and / or functional modules based on user needs. For example, application generation tasks may include: generating mini-program front-end pages and logic; generating utility applications, form tools, or statistical panels; generating executable scripts or automated processes; and generating web pages or interactive interfaces. Resource acquisition tasks can be tasks undertaken by intelligent systems to acquire, retrieve, and organize resources from networks, databases, local storage, or third-party platforms based on user needs. For example, resource acquisition tasks may include: information retrieval tasks; data collection tasks; and tasks involving transferring preset resources to obtain corresponding resources.

[0034] In practical applications, users can input descriptive information about the first task into the intelligent system, specifying at least one of the following: task name, task objective, task execution content, task requirements, and constraints. This allows the intelligent system to identify and parse the task based on the user's input, trigger task execution, and then assign the parsed task to the first intelligent system for execution. If the first intelligent system encounters processing difficulties, missing information, or is unable to make autonomous decisions during task execution, it can provide feedback containing questions. For example, assuming the first intelligent system is executing a task generated by game software A (i.e., the first task) and there are issues such as unclear parameter configuration or uncertain generation logic, it can generate first information containing corresponding execution questions and pending confirmation points.

[0035] In this paper, the method of determining the first information may include at least one, and the specific description of this at least one method of determining the first information will be given below.

[0036] In one scenario, during the operation of the intelligent system, the first intelligent system can process the assigned first task. When it is unable to complete the task independently, has information doubts or processing obstacles, lacks sufficient reasoning to complete subsequent processes, or has further improvement needs such as output format optimization, method selection, or confirmation of detailed standards during task execution, the first intelligent system can generate corresponding question information as the first information and transmit this first information to the first interface for display.

[0037] In another scenario, the information output events of each intelligent system within the intelligent system can be detected in real time or periodically. When there are questions arising from task execution obstacles, or when there are needs such as optimizing output content, refining execution plans, or confirming presentation formats, the first intelligent system generates corresponding first information. The task question information output by the first intelligent system is obtained and displayed on the first interface.

[0038] In another scenario, the server can schedule internal information transmission links within the intelligent system to establish a directional data channel between the first intelligent system and the first interface. When the first intelligent system generates query requests during the task execution phase, including questions about execution obstacles, confirmation of parameter configurations, optimization of content output formats, and comparison of multiple implementation schemes, it can generate corresponding first information and push it to the first interface via the information transmission link.

[0039] For example, see Figure 3When a user inputs the first task, "Help me write some code in language A," the first intelligent system, in addition to performing the routine execution, can analyze the output version, text format, and display style of the code. It can then analyze the code and find out which versions can be output in multiple languages ​​for the user to choose from. At this point, it can generate the first information, including "What do you think if we expand this project to support outputting this code in different language versions, such as language B and language C?" Based on this first information, it can query the second intelligent system for the final decision.

[0040] Based on multi-dimensional triggering conditions, the system autonomously generates and reports primary information, covering feedback from points where task execution is hindered. It also fulfills various pre-emptive inquiry requests, such as solution selection, detailed verification, and content optimization. This process comprehensively retains all information awaiting confirmation throughout the entire task flow, ensuring that the subsequent secondary intelligent system can accurately perform decision analysis, thereby guaranteeing that the final task execution result highly aligns with the user's actual needs.

[0041] S220. Display second information, provided by a second intelligent system, to indicate decision information in response to the first information.

[0042] The second information can be used to characterize the decision guidance output by the second intelligent system in response to the questions posed by the first intelligent system. For example, the second information may include, but is not limited to, execution direction determination, parameter configuration specifications, output format requirements, and processing step limitations. The second intelligent system characterizes the intelligent system within the intelligent system that possesses information analysis and judgment, query response processing, and task execution decision-making coordination functions. The decision information can be used to characterize the processing basis and execution specifications determined in response to task execution questions, and may include query answers, execution constraints, and solution guidance generated by the second intelligent system based on the first information. For example, the decision information may include, but is not limited to, execution path selection, content output standards, explanations of matters to be avoided, and subsequent operation requirements. For instance, the first information may include questions about whether the task content output format is optimized; the second intelligent system can analyze this first information to generate second information indicating whether the intelligent system can optimize it.

[0043] Currently, after an intelligent system issues a task to a primary intelligent system, the primary intelligent system can usually only execute the task directly. If there are any doubts, it will blindly deduce and process the task based on its own inherent logic, which can easily lead to problems such as large deviations in task execution and insufficient accuracy of results.

[0044] In this paper, when the second intelligent system receives the first information, it can generate corresponding second information based on the first information. The second information can then be used to indicate the decision content for the question, thereby standardizing the subsequent task execution process of the first intelligent system, clarifying the execution standards and output requirements, and improving the task execution fit, result accuracy, and overall interaction rationality.

[0045] In one scenario, when the second intelligent system receives the first information reported by the first intelligent system, it can analyze and process the specific content of the first information based on its built-in judgment logic and decision rules corresponding to various types of question information, and generate the second information.

[0046] The second intelligent system can break down the first information into its components, extracting key questions (such as execution doubts, output optimization needs, and parameter confirmation requirements). It then matches these questions with pre-stored task processing rules, user constraints, and historical decision-making cases. Combined with the current task type being executed by the first intelligent system, it makes targeted decisions, generating second information containing specific execution specifications, answers, or solution choices. This second information addresses the questions, optimization requests, and execution obstacles encountered by the first intelligent system during task execution, ensuring the accuracy of its subsequent execution direction and that the overall operation meets task requirements.

[0047] In another scenario, the intelligent system can pre-configure multi-dimensional decision prompt templates for the second intelligent system. These templates can include various scenarios and corresponding processing solutions for the task execution process. When the second intelligent system receives the first information, it can quickly analyze and accurately decide on the first information based on the pre-configured decision prompt templates to generate the second information.

[0048] The second intelligent system can classify and identify the first information, such as classifying it as an execution obstacle, an optimization query, or a parameter configuration confirmation, and then call the corresponding decision prompt template. Based on the constraints and preset execution standards in the decision prompt template, the first information is analyzed and processed to generate second information containing decision conclusions, execution steps, and precautions. This allows for a rapid response to the query needs of the first intelligent system, ensuring that the decision content aligns with the actual needs of the first intelligent system, and preventing task execution interruptions caused by the first intelligent system waiting for a decision.

[0049] In another scenario, the second intelligent system can receive supplementary instructions from the user through the front-end interface of the intelligent system. When it receives the first information from the first intelligent system, it can analyze the user's actual needs based on the supplementary instructions, combine the user's instructions with the first information, and generate the second information.

[0050] The second intelligent system can synchronize the first information to the first interface, allowing the user to view and obtain supplementary instructions from the user regarding the question. It then integrates and analyzes the question content from the first information with the user's supplementary instructions, breaking down the user's needs and decision-making tendencies. Combining this with task execution guidelines, it generates second information that meets the user's needs and guides the subsequent operations of the first intelligent system, ensuring that the decision content is highly consistent with the user's expectations.

[0051] In another scenario, when the first intelligent system receives the first information, the second intelligent system can make a decision based on the relevant data of the first intelligent system's current task and user demand data, and generate the second information.

[0052] The second intelligent system can parse the question information in the first information and retrieve relevant information such as the task configuration and execution standards of the currently executing task from the first intelligent system. It can also obtain user demand data from user input. Based on the task configuration, execution standards, and user demand data of the currently executing task, it comprehensively compares them with the question information in the first information, analyzes reasonable solutions for the question information, and then generates second information containing decision-making basis, execution requirements, and feedback guidance. This ensures that the decision content conforms to system specifications and meets user needs, guarantees the accuracy of decision information, and avoids decision-making bias.

[0053] Through the aforementioned multiple methods of determining second information, the second intelligent system can flexibly adopt appropriate decision-making methods to generate second information based on the different types and complexities of the first information. This solves the doubts, optimization needs, and execution obstacles encountered by the first intelligent system in task execution, while ensuring the accuracy and relevance of the generated second information. It avoids problems such as task deviation and insufficient result accuracy caused by the first intelligent system blindly executing or failing to resolve doubts in a timely manner, which are common in related technologies. At the same time, it standardizes the subsequent execution process of the first intelligent system, clarifies the execution standards, and further improves the coherence and accuracy of the intelligent system's task execution, ensuring that the task execution results meet user needs.

[0054] Furthermore, the second information can be displayed on the first interface. The display format of the second information includes, but is not limited to, at least one of the following: interactive presentation with the first information, fixed display in the sidebar of the interface, or floating display as a pop-up window; it also supports displaying the content in a flat layout or folding it for storage.

[0055] For example, see Figure 4 The second intelligent system analyzes the first information and finds that the first task executed exceeds the scope of the user's task requirements. It can then provide a second message to refuse execution, such as "There is no need to output code in different language versions; just complete the original task."

[0056] The benefits of displaying the second piece of information are: it can intuitively present the decision guidance generated by the second intelligent system to the user, making it easy for the user to fully review the task execution basis and processing plan, effectively avoiding the problem that the subsequent intelligent system execution process deviates from the user's actual needs, while providing a basis for the task execution of the first intelligent system, ensuring that the overall task processing process is standardized and orderly, and further improving the matching degree between the task execution results and user needs.

[0057] S230. Display third information, which is used to characterize the response information of the third intelligent system to the decision information.

[0058] The third information can be used to characterize the execution results, execution feedback, or content improvement results generated by the third intelligent system based on the decision information. For example, the third information may include, but is not limited to, at least one of the following: execution result improvement content, decision execution details, content output content, question answers, and subsequent execution verification content. The third information may be displayed in at least one of the following forms: presented in a dialogue interaction with the second information, displayed in the sidebar of the interface, or displayed in a pop-up window.

[0059] The third intelligent system can be used to characterize the execution-type intelligent agents within the intelligent system that receive decision-making instructions. This third intelligent system can adapt instructions, generate content, and integrate results based on the decision information output by the second intelligent system, thereby outputting the corresponding final response result. For example, if the decision information specifies the target presentation format of the content output, the corresponding response information may include the task execution result generated by adapting to that target presentation format; if the decision information allows for the addition of multi-dimensional information elements, the corresponding response information may include the expanded and improved complete execution result; if the decision information specifies parameter configuration requirements, the corresponding response information may include the calibrated parameter details and corresponding operating status information.

[0060] In one scenario, the third intelligent system can analyze the decision information contained in the second information to determine the corresponding action direction, execution requirements, and content generation standards. Based on the analyzed information, it executes the corresponding action operation, generates response content that matches the decision information, and thus obtains the corresponding third information.

[0061] In another scenario, the third intelligent system can process the decision information by combining the task flow context information of the first intelligent system, improve the execution process based on the previous interaction information of the first intelligent system, and generate corresponding third information. The task flow context information may include the process data of the first intelligent system executing the task, the question information reported by the first intelligent system, and the initial task constraints, ensuring that the final generated content matches the original requirements.

[0062] In another scenario, the intelligent system pre-configures multi-dimensional response generation prompt word templates for the third intelligent system. Each template corresponds to a different type of decision-making scenario. When the third intelligent system acquires the second information, it can identify the category of the decision information and call the matching response generation prompt word template. Following the template's built-in content structure and generation specifications, it processes the decision information to generate the corresponding third information.

[0063] For example, assuming the second information includes outputting the result using the original language version without adding a language version, the third information generated by the third intelligent system after responding to the decision information may include the complete output content corresponding to the original language; assuming the second information includes allowing the addition of a language version, the third information generated by the third intelligent system after responding to the decision information may include the original language content and the extended output content of the newly added adapted language.

[0064] The above method, by receiving first information—a query instructing a first intelligent system to execute a first task; displaying second information—provided by a second intelligent system and used to instruct a decision based on the first information; and displaying third information—characterizing the response of a third intelligent system to the decision—solves the technical problem of poor collaborative communication flexibility between the task scheduler and the task execution end. It achieves the technical effects of improving the communication and collaboration flexibility between intelligent systems, realizing efficient interactive response among multiple intelligent systems, ensuring efficient information transmission and accurate response during task execution, thereby improving the task execution efficiency and reliability of the intelligent system and meeting the various task needs of users.

[0065] Optionally, the first information is received, including: Receive first input, which consists of natural language and is used to reflect the descriptive information entered by the user during the execution of the first task by the first intelligent system.

[0066] The first input can be used to represent the description provided by the user in natural language form when the intelligent system performs a task. The user-input description can be used to supplement or assist in the execution of the first task. This description can include various forms such as text descriptions and speech-to-text content, and is used to reflect the user's relevant intentions, status descriptions, or supplementary task information.

[0067] In one scenario, during the execution of a first task by the first intelligent system, descriptive information input by the user in natural language can be obtained through a text input box, and this descriptive information represents the first input.

[0068] In another scenario, when the first intelligent system needs supplementary information while performing its first task, it can receive natural language descriptions input by the user in the form of voice or text, and convert them into the first input in a unified format.

[0069] In another scenario, during the interaction phase where the first intelligent system performs the first task, the user's natural language input is detected in real time. Upon detecting valid descriptive information, it is considered that the first input has been received.

[0070] By receiving descriptive information from users in natural language during the execution of tasks by the first intelligent system, task execution can be made more aligned with the user's actual intentions, improving the accuracy and adaptability of task execution, and enhancing the user experience and task completion efficiency.

[0071] Optionally, the first information is received, including: The system receives a second input, which is a description of information fed back by the first intelligent system. The description reflects the questions asked by the first intelligent system in response to the first task.

[0072] The second input can be used to characterize the descriptive information fed back by the first intelligent system, which expresses its own execution questions. The feedback descriptive information can be used to characterize the explanatory or interrogative content proactively issued by the first intelligent system during task execution, aiming to reflect the current execution status and needs of the first intelligent system through the information content. The interrogative information may include, but is not limited to, questions to be confirmed, execution doubts, or inquiries requiring external answers encountered by the first intelligent system during task execution.

[0073] In one scenario, during the execution of the first task by the first intelligent system, descriptive information fed back by the first intelligent system when encountering execution obstacles or missing information can be received in real time, and this descriptive information can be used to represent the first input.

[0074] In one scenario, at each execution node of the first intelligent system advancing the first task, descriptive information fed back by the first intelligent system at each node is received, and the execution questions contained therein are integrated into a second input.

[0075] In one scenario, when it is detected that the first intelligent system is unable to complete a part of the first task autonomously, the system can receive descriptive information actively sent by the first intelligent system and organize the corresponding question information to generate a second input.

[0076] By receiving a second input containing question information from the first intelligent system, questions and issues requiring confirmation that arise during task execution can be captured in a timely manner. This prevents the intelligent system from stalling or making mistakes due to insufficient information, ensuring the smoothness of the task execution process and improving the accuracy of task processing and the reliability of inter-system collaborative execution.

[0077] Figure 5 This is a flowchart illustrating an information interaction method under one scenario. The technical solution in this scenario can be combined with other scenarios; for identical or related parts, descriptions of other scenarios can be used, and will not be repeated here. Figure 5 As shown, the method in this case may specifically include: S310. Receive first information, the first information being a query message used to instruct the first intelligent system to perform the first task.

[0078] S320, based on the second intelligent system and the first information, obtains processing instructions.

[0079] Among them, the processing instructions can be used to characterize the instructions generated by the second intelligent system, which are used to indicate the direction of information processing and task execution.

[0080] When the second intelligent system receives the first information, it can generate corresponding processing instructions in various ways.

[0081] In one scenario, the second intelligent system can perform semantic analysis on the first information, extract the key query content and demand orientation, match the corresponding processing logic according to the preset rule base, and then generate processing instructions for information query or result feedback.

[0082] In another scenario, the second intelligent system can use its own decision-making model to make a judgment based on the task execution status and problem type reflected in the first information, and combine it with historical processing records to determine the appropriate processing method, thereby generating corresponding processing instructions.

[0083] In another scenario, the second intelligent system can also classify and identify the first information, distinguishing whether it belongs to the user's supplementary description or the intelligent system's execution question, and construct corresponding instruction structures for different categories to generate processing instructions that meet the current description requirements.

[0084] In addition, the second intelligent system can combine the task context information contained in the first information, link relevant execution data for comprehensive analysis, determine the subsequent operation path based on the analysis results, and generate processing instructions for obtaining and displaying the response content.

[0085] S330: Based on the processing instructions, obtain the second information and display it.

[0086] The second information can be used to characterize the relevant content obtained in accordance with the processing instructions for answering questions or meeting needs.

[0087] In one scenario, matching content can be retrieved from a preset information database or data resource based on the information type and query conditions specified in the processing instruction. The retrieved results are then integrated to generate the second information. For example, the first information includes a question from the first intelligent system regarding whether to continue executing the task. The processing instruction requires a judgment based on a preset task rule base, which can retrieve matching content such as the current task status and termination conditions from the rule base. This information is then integrated to generate decision information indicating whether to continue or pause the task as the second information.

[0088] In another scenario, the relevant execution data can be statistically analyzed and summarized according to the calculation rules and logical flow indicated by the processing instructions, and corresponding second information can be generated based on the processing results. For example, the first information includes a question about resource allocation methods raised by the first intelligent system, the processing instructions requiring decisions based on task execution data, and the system performing statistical analysis on data such as task priority, execution time, and resource usage, and generating decision information as the second information to indicate the resource allocation scheme based on the analysis results.

[0089] In another scenario, based on the interactive requirements in the processing instructions, targeted responses can be provided to the questions or needs in the first information, and the standardized responses constitute the second information. For example, the first information includes a user's question about selecting a task execution path, the processing instructions requiring a clear execution decision, and the system providing a conclusive response based on the task scenario, generating decision information to indicate the execution path as the second information.

[0090] In another scenario, the system can filter, concatenate, and format multi-source information according to the display format and content scope defined by the processing instructions to generate second information that meets the requirements of the instructions. For example, the first information includes questions related to task execution exceptions. The processing instructions require the output of a processing solution according to a standardized decision format. The system filters key content from multi-source information such as exception type, processing strategy, and executing entity, concatenates it according to a fixed decision format, and generates decision information used to indicate the exception handling solution as the second information.

[0091] S340. Display third information, which is used to characterize the response information of the third intelligent system to the decision information.

[0092] The above method, by combining the second intelligent system with the first information generation and processing instructions before executing the second information generation and display, can make the information processing process more targeted, improve the accuracy of information matching, simplify the overall execution process, make question answering and demand response more efficient and intuitive, and effectively improve the smoothness of task processing and user experience.

[0093] Optionally, acquiring and displaying second information based on processing instructions includes: outputting and displaying second information based on processing instructions by a second intelligent system.

[0094] In one scenario, the second intelligent system can parse the processing instructions, generate corresponding second information according to the requirements of the instructions, and output the second information to the interactive interface for display.

[0095] In another scenario, the second intelligent system can also generate second information that meets the requirements according to the information format and output method specified by the processing instructions, and output and display it in the corresponding page area after completing the information verification.

[0096] In one scenario, the second intelligent system can also generate second information and output it in real time when the triggering conditions in the processing instructions are met, and simultaneously display the decision information on the interface.

[0097] By having the second intelligent system output and display second information based on processing instructions, the decision content in response to the question can be presented quickly and accurately, ensuring the timeliness and standardization of information transmission. This facilitates the relevant implementing entities to continue to advance the task based on the decision information, thereby improving the overall process coherence and processing efficiency.

[0098] It should be noted that the second intelligent system can analyze the key questions, execution concerns, and optimization needs contained in the first information reported by the first intelligent system, and generate corresponding decision content based on built-in decision rules to obtain the second information. For an example, see [link to example]. Figure 4 The second intelligent system can determine the decision information itself based on preset system constraints, and thus generate the second information. When parsing the first information, if the second intelligent system determines that the current question cannot be accurately decided using its internal logic, it can display a prompt to the user, waiting for further input. Upon receiving the fifth piece of information from the user, the second intelligent system combines the question information from the first information with the fifth piece of information input by the user to generate the second information. For example, when the first intelligent system reports a question about whether to expand the code language, the second intelligent system cannot determine this independently and can prompt the user to make a selection. The user inputs the fifth piece of information, allowing the addition of multiple language versions, and the second intelligent system, based on this user instruction, can generate the decision information "Allow the addition of multiple language versions." The advantage of this setup is that the second intelligent system can autonomously complete the decision output without human intervention, and can adapt to user needs by combining real-time input information to generate corresponding second information, effectively improving the comprehensiveness and adaptability of the decision content, and further ensuring the matching degree between the overall task execution result and the user's actual needs.

[0099] Optionally, based on the processing instruction, obtaining and displaying second information includes: in response to the processing instruction satisfying the first condition, displaying fourth information, the fourth information being used to prompt the user to input feedback information corresponding to the processing instruction; and displaying second information, the second information being used to instruct the second intelligent system to output a result based on the displayed fifth information and the first information, the fifth information representing the feedback information of the fourth information.

[0100] The first condition can be used to characterize a preset judgment rule or state requirement for determining whether user feedback is required. The fourth information can be used to characterize prompts to initiate interactive queries to the user, reminding the user to provide supplementary input, aiming to remind the user to input corresponding feedback content. User input can be used to characterize the operation selection, content supplementation, or confirmation information submitted by the user based on the prompts. The fifth information can be used to characterize the input information entered and submitted by the user based on the prompts of the fourth information, reflecting the user's supplementary needs, selection instructions, and constraints after the second intelligent system initiates queries to the user. Feedback information can be used to characterize the user's confirmation, modification, or supplementary response information to the processing instructions. The second information can be used to characterize the decision-making output results generated by the second intelligent system after combining feedback information and question information.

[0101] The first condition includes, but is not limited to, at least one of the following: the decision basis corresponding to the processing instruction is incomplete; the task scenario involved in the processing instruction exceeds the preset decision range; the task parameters associated with the processing instruction are ambiguous; there are multiple feasible solutions for the decision result corresponding to the processing instruction; the processing instruction involves a critical operation that requires user confirmation; or the risk level of the task corresponding to the processing instruction exceeds the automatic decision threshold.

[0102] It can be determined whether the processing instruction meets the first condition. If the processing instruction does not meet the first condition, it means that the second intelligent system itself can make a decision based on the first information, and the second information can be generated directly. If the processing instruction meets the first condition, it means that the second intelligent system itself may not be able to make a decision based on the first information, and the fourth information can be displayed to prompt the user to input the corresponding feedback information to assist in completing the decision.

[0103] There are several ways to generate fourth information: In one scenario, when the processing instruction representation cannot determine a suitable solution based on the internal rules of the intelligent system, it is determined that external information is needed. In this case, a fourth piece of information, such as a requirement prompt, can be generated and displayed to prompt the user to supplement the relevant requirement information. For example, for queries related to content layout optimization, there are general, fixed processing standards, and the second intelligent system can make decisions autonomously. However, for queries related to language expansion and personalized output requirements, there are no unified judgment criteria, and the system cannot make decisions autonomously. Therefore, interactive prompts are triggered to guide the user to input and select their requirements.

[0104] In another scenario, the second intelligent system can also combine the original constraints of the task, the question type of the first information, and the interaction context with the user to improve the prompt content and generate the fourth information, making the interaction direction of the fourth information more explicit.

[0105] In another scenario, the second intelligent system can also differentiate the output of fourth information based on the complexity and processing priority of the first information. For example, for simple questions, the prompts in the fourth information can be simplified, while for complex questions, the demand inquiry dimensions in the fourth information can be refined.

[0106] In another scenario, the second intelligent system can also perform semantic transformation on the reported questions, converting straightforward inquiries into flexible queries that cater to user experience, thus generating a fourth piece of information. This allows users to naturally respond with their actual needs without resorting to abrupt questions.

[0107] For example, see Figure 6 The second intelligent system cannot decide whether to expand the language versions, but it can display the fourth piece of information: "Which languages ​​do you usually use most when writing code?" If the user inputs "language A", then the fifth piece of information will include "language A".

[0108] Next, the user can complete the corresponding input based on the displayed fourth piece of information. When the second intelligent system receives the fifth piece of information from the user, it can combine the first and fifth pieces of information for decision analysis and processing, and then generate and display the corresponding second piece of information.

[0109] In another scenario, during the process of receiving the fifth information input by the user, the second intelligent system can also perform validity verification and standardization of the input content, such as removing invalid information, extracting valid demand elements, correcting text errors and omissions, dividing semantic paragraphs, and generating standardized and complete second information based on the filtered valid demand elements.

[0110] For example, if the second intelligent system analyzes that the fifth piece of information input by the user is the same as the requirement to generate language A code in the original task, then the second piece of information may include decision information on the requirement in the question rejection information.

[0111] In this paper, by displaying the fourth information when the processing instruction meets the first condition, the user is prompted to input the corresponding fifth information. After receiving the fifth information from the user, the second intelligent system combines the first and fifth information to complete the analysis and generate the second information. This enables the second intelligent system to proactively prompt the user to supplement the requirements when the internal analysis is insufficient, and to optimize the decision results by combining the acquired fifth information, thereby improving the adaptability between the second information and the user's actual needs.

[0112] Optionally, a fourth piece of information may be displayed, including: The fourth piece of information is displayed on the first interface; and / or, The fourth message is sent to at least one user terminal based on the second intelligent system.

[0113] The user terminal can be used to represent the terminal device or application used by the user that can receive system messages. For example, the user terminal may include, but is not limited to: desktop computers, laptops, smartphones, tablets, embedded interactive terminals, industrial control equipment, and software carriers such as web clients, mobile applications, desktop clients, and mini-programs running on the above devices.

[0114] In one scenario, the fourth piece of information can be displayed on the first interface currently in use, prompting the user to input the corresponding feedback information through a visual presentation of the interface, so that the prompting operation can be completed without having to jump to another page.

[0115] In another scenario, the second intelligent system can also send a fourth message to one or more designated user terminals based on the user's online status and communication permissions, so that the user can receive the prompt content in a timely manner on the corresponding terminal device.

[0116] In another scenario, the fourth information can also be displayed on the first interface and simultaneously sent to the relevant user terminal by the second intelligent system. By combining interface display with terminal push, it can be ensured that the user can effectively receive the prompt content.

[0117] By combining displaying the information on the first interface with sending it to the user's device, the timeliness of the prompts can be effectively improved, preventing users from missing key prompts and ensuring that subsequent feedback information can be obtained smoothly, thereby improving the smoothness and efficiency of the overall information processing flow.

[0118] Optionally, the first information includes a first identifier, which is used to indicate the communication relationship between the first intelligent system and the second intelligent system; the third information includes a second identifier, which is used to indicate the communication relationship between the third intelligent system and the second intelligent system; and the fourth information includes a third identifier, which is used to indicate the communication relationship between the second intelligent system and the user terminal.

[0119] The first identifier uniquely identifies and distinguishes the communication link, communication permissions, and interaction objects between the first and second intelligent systems. The second identifier identifies the communication correspondence between the third and second intelligent systems. The third identifier identifies the communication correspondence between the second intelligent system and its corresponding user terminal. The communication relationship characterizes the corresponding associations and transmission paths established between different systems or devices for data transmission and message interaction.

[0120] When the second intelligent system receives the first information, it can identify the first identifier contained within it to determine that the first information originates from the first intelligent system, establishing a communication correspondence between the two and ensuring accurate reception and targeted processing of the information. When the second intelligent system transmits or receives the third information to or from the third intelligent system, it can match the corresponding communication relationship using the second identifier carried in the third information, ensuring that data interaction with the third intelligent system accurately targets the intended object and avoids transmission confusion. When the second intelligent system generates and sends the fourth information to the user terminal, it can add a corresponding third identifier to the fourth information, clarifying the communication relationship with the target user terminal, ensuring that the prompt information is accurately sent to the designated user terminal and subsequent interactions are completed.

[0121] For example, see [link to previous article] Figure 4 The first identifier in the first information can be presented as the second intelligent system. The first intelligent system 1 (asking a question) represents a communication relationship where the first intelligent system 1 initiates a question to the second intelligent system. The second identifier in the second information can be represented by the second intelligent system. The first intelligent system 1 (decision-making) represents the third intelligent system. The second identifier can represent the communication relationship between the second intelligent system and the first intelligent system 1, which provides feedback on decision information.

[0122] By setting corresponding identifiers in different interactive information to clarify the communication relationship between various systems and user terminals, the accuracy of data transmission during multi-subject interaction can be effectively guaranteed, information mis-sending, omission, or object confusion can be avoided, the stability and reliability of multi-intelligent system collaborative work and user terminal interaction can be improved, and the efficiency of overall information processing and task collaboration can be increased.

[0123] Figure 7 This is a flowchart illustrating an information interaction method under one scenario. The technical solution in this scenario can be combined with other scenarios to further refine 230. For identical or related parts, descriptions of other scenarios can be used, and will not be repeated here. Figure 7 As shown, the method in this case may specifically include: S410. Receive first information, the first information being a query message used to instruct the first intelligent system to perform the first task.

[0124] S420. Display second information, provided by a second intelligent system, to indicate decision information in response to the first information.

[0125] S430. In response to the third intelligent system being the same as the first intelligent system, the third intelligent system executes the second information based on the first intelligent system to obtain the third information; or, in response to the third intelligent system being different from the first intelligent system, the third intelligent system feeds back the third information to display information used to characterize the first intelligent system continuing to execute the first task.

[0126] In one scenario, the third intelligent system and the first intelligent system are the same intelligent entity, eliminating the need for a separate intelligent processing unit. When the first intelligent system acquires the second information, it can execute tasks based on the decision requirements contained therein, thereby generating the corresponding third information.

[0127] For example, if the decision information output by the second intelligent system indicates that no language version expansion is required, the first intelligent system maintains the original generation logic and outputs the task result corresponding to the original language as the third information; if the decision information indicates that language version expansion is allowed, the first intelligent system performs language expansion processing based on the original task content and outputs the result information after multilingual expansion as the third information.

[0128] In another scenario, the third intelligent system and the first intelligent system are independent intelligent entities. The third intelligent system can receive the second information, execute decision information, and generate corresponding third information. The third intelligent system can send the acquired third information to the second intelligent system. Upon receiving the third information from the third intelligent system indicating completion, the second intelligent system can issue a running command to the first intelligent system that initiated the query, triggering the first intelligent system to resume operation and continue executing the previously unfinished original task. Information indicating the first intelligent system's continued task execution will be displayed. For example, the displayed information may include, but is not limited to, at least one of the following: status information identifying the first intelligent system's continued task execution, actual task execution information, and task execution flow information.

[0129] For example, after the third intelligent system completes the text generation based on the language decision information, it sends a completion notification to the second intelligent system. The first intelligent system receives the running instruction sent by the second intelligent system and continues to carry out the subsequent unfinished task process.

[0130] This paper proposes an architecture that is compatible with two types of intelligent systems: autonomous closed-loop operation and collaborative operation among different intelligent systems, adapting to task processing scenarios of varying complexity. Simultaneously, upon receiving third-party information indicating completion from a third intelligent system, the first intelligent system is awakened to continue task execution. This solves the problem of task flow stagnation after an intelligent system initiates an inquiry, enabling the original task to automatically resume operation after the decision is executed, ensuring the continuity and stability of the overall task execution process.

[0131] Figure 8This is a flowchart illustrating an information interaction method under one scenario. The technical solution in this scenario can be combined with other scenarios; for identical or related parts, descriptions of other scenarios can be used, and will not be repeated here. Figure 8 As shown, the method in this case may specifically include: S510, Receive the sixth information, which is used to indicate the processing requirements of the first task.

[0132] The sixth piece of information can be used to characterize adjustment instructions input by the user within the first interface, used to modify, update, reset, or constrain scheduling and / or task execution requirements within the intelligent system. Optionally, the sixth piece of information may include at least: parallel or serial scheduling instructions for multiple intelligent systems, task requirement change instructions, intelligent system start / stop control instructions, execution timing adjustment instructions, and task preemption switching instructions. Scheduling requirements can be used to characterize relevant requirements such as task allocation, flow order, call priority, and collaborative scheduling rules among multiple intelligent systems. Task requirements can be used to characterize relevant requirements for task processing in the first intelligent system, such as original business objectives, content generation requirements, output standards, and handling boundaries.

[0133] In this document, when a user needs to adjust scheduling requirements and / or task execution requirements, they can input the corresponding sixth information through the first interface, and the system will receive the sixth information through the first interface. Optionally, the input form of the sixth information by the user includes, but is not limited to, at least one of the following: text command input, voice interaction input, visual option selection input, parameter configuration adjustment input, command shortcut operation input, document import input, and image recognition input.

[0134] S520. Display the seventh information, which is provided by the second intelligent system and is used to indicate the response information of at least one fourth intelligent system to the sixth information.

[0135] The seventh piece of information can be generated and displayed by the second intelligent system, and can be used to characterize the adaptation results, execution responses, and status feedback information fed back by at least one fourth intelligent system in response to the sixth piece of information issued by the user. The fourth intelligent system can be used to characterize the execution unit used to receive demand adjustment instructions, perform adaptation processing, and feed back response results.

[0136] In one scenario, upon receiving the sixth piece of information input by the user, the system can identify the adjustment instructions within that information. If the instruction indicates a need for corresponding modifications and updates to the original task requirements, the second intelligent system can maintain the existing scheduling rules between intelligent systems and, based on the adjusted task requirements, distribute the corresponding task to the corresponding fourth intelligent system. For example, if the user input of the sixth piece of information includes adjusting the output format of the output content, the second intelligent system can issue the output format adjustment task to the fourth intelligent system.

[0137] In another scenario, when the adjustment instruction within the sixth information indicates the need for optimization and adjustment to meet the scheduling requirements of multiple intelligent systems, the second intelligent system can update the intelligent system call priorities, execution order, and collaborative flow logic, driving the corresponding fourth intelligent system to execute tasks according to the new intelligent system scheduling requirements. For example, if the user inputs the sixth information including using intelligent system 1 and intelligent system 2 to execute task A, the second intelligent system can distribute task A to intelligent system 1 and intelligent system 2, with intelligent system 1 and intelligent system 2 acting as the fourth intelligent system to execute the task.

[0138] In another scenario, upon acquiring the sixth piece of information, scheduling and task requirements can be simultaneously adjusted. The task execution standards and intelligent system collaborative scheduling logic are updated synchronously with the sixth piece of information, ensuring that subsequent processes adapt to the updated dual requirements. The second intelligent system allocates tasks to the fourth intelligent system based on the updated task and intelligent system scheduling relationship.

[0139] Next, when each of the fourth intelligent systems receives a task, it can send the task acceptance information as a response to the sixth information to the second intelligent system. Alternatively, when each of the fourth intelligent systems completes a task, it can send the task execution information as a response to the sixth information to the second intelligent system. The second intelligent system aggregates all response information to obtain the seventh information, which is then displayed on the first interface, intuitively presenting the response results corresponding to the requirement adjustment. The second intelligent system can also aggregate the corresponding response information according to the functional classification and response priority of each intelligent system, integrating it into the seventh information for display, clearly distinguishing the processing results of different intelligent system entities, making it easy for users to distinguish and view the response status of various requirement adjustments. When generating the seventh information, the second intelligent system can also differentiate the results by combining task execution status, such as displaying different response statuses like "adaptation completed," "pending processing," and "cannot be adjusted," showing the execution results fed back by the fourth intelligent system in the seventh information, allowing users to clearly understand whether the requirement adjustment has taken effect. For example, when a user issues a language modification command, the seventh information can indicate whether the requirement has been adapted and updated or is currently not supported for adjustment.

[0140] For example, see Figure 9 The user inputs the sixth piece of information, which includes "Dispatch an intelligent system to execute task A". After the first intelligent system 2 completes the task, it can return the seventh piece of information, "Task 1 has been completed and the execution result has been returned".

[0141] In this paper, by receiving the sixth piece of information, dynamic adjustment of various needs can be achieved, enabling the intelligent system to make individual adjustments or joint optimizations based on task requirements and intelligent system scheduling requirements, adapting to personalized user needs and improving the flexibility of task execution and intelligent system scheduling processes. Simultaneously, the second intelligent system can also summarize and display the seventh piece of information, providing complete feedback on the execution response results of each fourth intelligent system to the adjustment commands, forming timely feedback on user control commands, clearly presenting the demand adaptation status and task execution results, allowing users to intuitively understand the effectiveness of each adjustment command.

[0142] Based on the previous statement that users can input the sixth piece of information regarding adjustments to their needs through the first interface, this sixth piece of information can be input in multiple ways, including one of the following: Receive a third input, which instructs the concurrent invocation of multiple fourth intelligent systems to execute the first task; or, Receive a fourth input, which is used to demonstrate the changed task requirements; or, Receive the fifth input, which is used to instruct the first task to be stopped.

[0143] The third input represents a user-inputted invocation command, instructing the system to concurrently schedule multiple fourth intelligent systems to execute tasks synchronously. The fourth input represents a user-inputted request change command, instructing the system to modify or update existing task execution requirements. The fifth input represents a user-inputted termination command, instructing the system to pause or stop the currently running related task process.

[0144] The third, fourth, and fifth inputs may include at least one of the following: text command input, voice interaction input, visual option selection input, parameter configuration adjustment input, command shortcut operation input, document import input, and image recognition input. For example: When the input is a text command, the semantics of the user's input can be recognized and parsed to extract the scheduling type, task requirements, and control objectives, and then mapped to generate the corresponding sixth information. For example, if the user inputs the text command "Parallel start of multiple intelligent systems to process language adaptation tasks," parsing this command will yield the sixth information for parallel scheduling.

[0145] When the input is voice-interactive, the user's voice data can be parsed and semantically recognized to extract the user's control requirements and convert them into sixth information. For example, if the user voice inputs "change content output format requirement", the sixth information, a task requirement change category, can be generated after voice recognition.

[0146] When the input is a visual option and is selected, the system can identify the user's selected control option and generate the corresponding sixth piece of information based on the mapping relationship between preset options and adjustment requirements. For example, if the user selects the "Stop current task" option on the first interface, the system will match the termination command and generate the corresponding sixth piece of information.

[0147] When the input is a parameter configuration adjustment input, the system can obtain the user's modified operating parameters, task constraints, and scheduling configuration information, thus obtaining the sixth type of information in the parameter adjustment category. For example, if the user adjusts parameters such as the execution priority of the intelligent system or the language expansion range, the parameter modifications can be integrated into the sixth type of information in the requirement adjustment category.

[0148] When the input is a shortcut operation, the system can respond to the shortcut operation control triggered by the user, retrieve the built-in preset control commands, and quickly generate the corresponding sixth information. For example, if the user clicks the shortcut button "Batch Parallel Scheduling" on the interface, the preset commands can be invoked to generate the sixth information for parallel scheduling.

[0149] When the input is a document import, the system can read the content information from the user-imported document, parse the scheduling rules and task requirements recorded in the document, and extract the sixth piece of information. For example, if a user imports a configuration document containing scheduling methods and task constraint rules, the system can parse the document content to generate comprehensive control-related sixth piece of information.

[0150] When the input is an image recognition input, the control command information contained in the image can be extracted and converted to generate the corresponding sixth information. For example, if a user uploads a schematic diagram image containing command identifiers and requirement parameters, the image content can be recognized and parsed to generate the corresponding scheduling-type sixth information.

[0151] Next, upon receiving the user's third input, the corresponding sixth information can be obtained. Following the instructions in the sixth information, multiple fourth intelligent systems can be concurrently invoked, causing each fourth intelligent system to synchronously initiate its task execution process. For an example, see [link to example]. Figure 10 The user inputs "Intelligent System 1 and Intelligent System 2 shall execute the first task 1 in parallel", which is the third input including the sixth information, instructing Intelligent System 1 and Intelligent System 2 to process the task in parallel. The second intelligent system can schedule the first intelligent system 1 and the first intelligent system 2 to execute the first task 1.

[0152] When the user's fourth input is received, the corresponding sixth information can be obtained. Based on the instructions in the sixth information, the original task requirements are updated and modified, and the execution standards, output requirements, constraints, and other related configuration information of subsequent tasks are adjusted. Then, the subsequent fourth intelligent systems are driven to execute the first task according to the modified new requirements.

[0153] Upon receiving the user's fifth input, the system can retrieve the corresponding sixth information and trigger the task termination process. The system can interrupt the currently running task chain according to the instructions in the sixth information, stopping subsequent scheduling and execution actions of each intelligent system, and simultaneously reporting the task's stop status to avoid unnecessary operation and resource consumption. For example, if the user inputs the sixth information indicating termination during task execution, the intelligent system can pause the intelligent system scheduling and content generation process. See the example below. Figure 11 The user inputs "Terminate the tasks of intelligent system 1 and intelligent system 2", which is the third input including the sixth information, indicating that the execution of intelligent system 1 and intelligent system 2 should be stopped. The second intelligent system can stop the first intelligent system 1 and the first intelligent system 2 from executing the first task.

[0154] Furthermore, during the process of receiving various inputs and parsing the sixth information, the validity of input commands can be simultaneously verified, identifying the command's applicable scenario, execution permissions, and current task status. When the command is valid and the scenario matches, the corresponding control operation is executed; invalid or conflicting commands are intercepted and flagged, ensuring the overall scheduling process remains stable and controllable.

[0155] This paper describes how to achieve flexible control over the collaborative interaction of multiple intelligent systems by receiving a sixth message containing three types of control instructions: parallel scheduling, requirement change, and task termination. This message enables the system to perform operations such as parallel invocation of specified intelligent systems, dynamic updating of task constraints, and termination of the running process as needed. This allows the overall task chain to adaptively respond to user control requirements and adapt to diverse business task processing scenarios.

[0156] Furthermore, by combining the received sixth information, the second intelligent system can identify and analyze various user control needs, match the corresponding scheduling method based on the analysis results, and then execute the corresponding intelligent system scheduling operation. Depending on the differences in control needs, the intelligent system scheduling method that the second intelligent system can adopt may include any of the following: Multiple fourth-level intelligent systems are dispatched in parallel; Send a stop command to the intelligent system that is in operation; Issue instructions to intelligent systems that have stopped working, based on context information, to continue operating. The second intelligent system sends a message to the first part of the fourth intelligent system, waiting for the second part of the fourth intelligent system to complete its execution before proceeding; This enables the second intelligent system to send a task change instruction to the fourth intelligent system.

[0157] The intelligent system scheduling method can be used to characterize various scheduling operations performed by the second intelligent system based on the sixth information, including parallel scheduling, start / stop control, execution queuing, and task update scheduling methods. Context information can be used to characterize the intelligent system's task flow data, historical interaction records, completed node information, current running status, and process association data. The first part of the fourth intelligent system and the second part of the fourth intelligent system are used to characterize two types of execution units after grouping and dividing multiple fourth intelligent systems, and are used to implement the scheduling logic of sequential execution and waiting linkage.

[0158] In one scheduling scenario, after parsing the sixth piece of information, the second intelligent system can use a parallel distribution scheduling method to synchronously distribute the task to multiple fourth intelligent systems, enabling each intelligent system to simultaneously start its task processing flow. This parallel execution mode improves overall business processing efficiency. For example, the second intelligent system can simultaneously schedule multiple fourth intelligent systems to perform parallel tasks such as language adaptation, content format processing, and output parameter verification.

[0159] In another scheduling scenario, the second intelligent system can identify shutdown scheduling needs based on the sixth information, issue stop execution commands to each intelligent system currently in operation, control the interruption of task processing in the corresponding intelligent system, put it into a paused or shutdown state, and terminate subsequent related processes. For example, a shutdown command can be issued to an intelligent system that is performing content generation or language expansion tasks, causing it to stop working.

[0160] In another scheduling scenario, the second intelligent system can also combine the sixth information and process context information to issue a resumption command to the intelligent system that has stopped working. This command calls historical process data and context records to wake up the corresponding intelligent system, allowing it to continue running the previously unfinished task and achieve process resuming. For example, an intelligent system that has paused its task midway can resume language adaptation and related follow-up processing based on context information.

[0161] In another scheduling scenario, the second intelligent system can also execute a group queuing scheduling method based on the sixth information. This divides multiple fourth intelligent systems into a first part and a second part, issuing a waiting instruction to the first part of the fourth intelligent systems to temporarily suspend task initiation. Only after the second part of the fourth intelligent systems has completed its execution will the first part of the intelligent systems be triggered for subsequent processing, achieving orderly serial execution. For example, basic content generation can be completed first, followed by waiting for format verification before initiating the subsequent language extension processing flow.

[0162] In another scheduling scenario, after receiving the sixth piece of information, the second intelligent system can also issue task change control instructions to each of the fourth intelligent systems, updating the original task execution standards, output constraints, business requirements, and processing objectives, so that each intelligent system can adapt and adjust its execution logic according to the updated task requirements. For example, it can modify requirements such as the scope of language expansion or the format of content output, and simultaneously issue these instructions to each execution intelligent system to complete the adaptation update.

[0163] In this paper, the second intelligent system can match the corresponding scheduling method based on the sixth information to realize global scheduling and control such as parallel task distribution, intelligent system start and stop management, process breakpoint continuation, group serial queuing execution, and dynamic changes in task requirements. This makes the overall task chain run flexibly and controllably, with standardized execution timing, and can be dynamically adapted and adjusted according to user needs.

[0164] After completing the corresponding intelligent system scheduling and task control operations based on the sixth information, the corresponding seventh information can be generated and displayed on the first interface. The seventh information includes one of the following: Display information about multiple fourth-level intelligent systems performing the primary task; or, Demonstrate the revised task requirements for the fourth intelligent system; or, Display information indicating that at least one first-level intelligent system and / or third-level intelligent system has ceased operation.

[0165] The seventh piece of information can be used to present the status results after the execution of various control commands. Optionally, the representation of commands includes, but is not limited to, status identification signals, process trigger messages, task node notifications, interactive status markers, and command encoding information.

[0166] In one scenario, when a user inputs control commands for concurrently invoking multiple fourth intelligent systems, the second intelligent system can perform scheduling operations on the fourth intelligent systems. When multiple fourth intelligent systems execute a first task in parallel, they can provide feedback to the second intelligent system regarding the execution of that task. Upon receiving this information, the second intelligent system can integrate it into a seventh piece of information and display it. This information presents the running progress, processing status, and task execution overview of the multiple fourth intelligent systems executing the first task in parallel. For example, when multiple fourth intelligent systems simultaneously perform parallel tasks such as language adaptation, format verification, and parameter configuration, the seventh piece of information can be displayed, including the running status and processing results of each fourth intelligent system.

[0167] For example, see [link to previous article] Figure 10 It can display the seventh piece of information for parallel scheduling of the first intelligent system 1 and the first intelligent system 2.

[0168] In another scenario, when a user inputs a control command to change task requirements, the second intelligent system can allocate a fourth intelligent system based on the modified task requirements. When multiple fourth intelligent systems execute the modified task, they can provide feedback to the second intelligent system regarding the execution of the first task. Upon receiving this information, the second intelligent system can integrate it into a seventh piece of information and display it. This seventh piece of information can be used to present the execution information of the fourth intelligent systems that matches the modified task requirements, providing intuitive feedback on the task adaptation and subsequent execution status after the requirement adjustment. For example, after a user modifies task requirements such as language expansion or content output format, the seventh piece of information can be displayed, including the completion of the requirement change and the execution information of the intelligent systems.

[0169] In another scenario, when a user inputs a control command to stop task execution, the second intelligent system can trigger the termination of the current task of the first or fourth intelligent system, thereby displaying the corresponding seventh information. This seventh information can be used to provide feedback on the termination status of at least one of the first or third intelligent systems, presenting relevant operational information such as intelligent system shutdown, process interruption, and task completion. For example, when a user inputs a task stop command, the system can display seventh information including that the intelligent system executing the first task has paused its work. See also, for examples... Figure 11 It can display the seventh message indicating that the execution of the first intelligent system 1 and the first intelligent system 2 has been stopped.

[0170] In another scenario, the execution status of each intelligent system can be marked during the generation and display of the seventh information, distinguishing different execution status types such as parallel operation, demand adaptation and update, and process termination and shutdown, thereby improving the convenience of users to view information while providing complete feedback on the execution results.

[0171] By displaying the corresponding type of seventh information, the actual execution results of various control commands can be fully reflected. The parallel scheduling status, the adaptation effect of demand changes, and the task shutdown status are visualized, which improves the interactive effect of multi-intelligent system collaborative operation and makes it easier for users to grasp the overall task execution status in real time.

[0172] Figure 12 This is a flowchart illustrating an information interaction method under one scenario. The technical solution in this scenario can be combined with other scenarios; for identical or related parts, descriptions of other scenarios can be used, and will not be repeated here. Figure 12 As shown, the method in this case may specifically include: S610. Receive first information, the first information being a query message used to instruct the first intelligent system to perform the first task.

[0173] S620. Display second information, provided by a second intelligent system, for indicating decision information in response to the first information.

[0174] S630. Display third information, which is used to characterize the response information of the third intelligent system to the decision information.

[0175] S640: Receive a trigger operation for the first information and display the eighth information, wherein the eighth information includes at least one or more of the first information, the second information, and the third information.

[0176] Among them, the triggering operation can be used to retrieve the display of relevant information of the intelligent system, including but not limited to: interactive operations such as clicking, selecting, and touching performed on the corresponding identifier of the first intelligent system, and at least one of the triggering operations initiated on the dialogue-related information in the first interface.

[0177] In one scenario, when a trigger operation is detected, the generated first, second, and third information can be integrated into an eighth piece of information for display. For example, after a user triggers an operation, the system can display language-extended question information (i.e., the first information), decision information that does not support language extensions (i.e., the second information), and response information from the third intelligent system's decision-making process (i.e., the third information).

[0178] In another scenario, when a trigger operation is detected, the contents of the eighth information can be arranged according to the chronological order of the tasks executed by the first intelligent system. Information can be displayed hierarchically based on the generation sequence of question information reporting, decision information reception, and response information feedback, making the overall task flow clear and information retrieval more organized and orderly. For example, language-related question information, decision information, and response information can be arranged and displayed sequentially according to the event occurrence time.

[0179] In another scenario, the eighth piece of information can be highlighted. While preserving all interactive content, at least one type of key content from the inquiry, decision-making, and response information can be emphasized. This ensures information integrity and traceability while effectively improving the efficiency of information retrieval for users. For example, decision-making information that supports multilingual expansion and its corresponding response information can be highlighted.

[0180] Optionally, the eighth piece of information can be displayed on the second interface, allowing users to intuitively view the entire process of the task interaction. For example, the second interface may include a visual display interface independent of the first interface, such as a presentation format that is adjacent to or nested with the first interface, or any presentation format such as a partial display area within the first interface or a pop-up window on top of the first interface.

[0181] For example, see Figure 13Users can trigger dialogue messages or intelligent system identifiers from the first intelligent system 1 in the dialogue interface. In response to this trigger, the task execution process information of the first intelligent system 1 can be displayed in the second interface as the eighth piece of information. Alternatively, see [link to relevant documentation]. Figure 14 Users can trigger the first control in the interface to display a list of intelligent systems, which includes multiple intelligent system icons. When the user clicks the intelligent system icon of the first intelligent system 1, in response to this triggering operation, the task execution process information of the first intelligent system 1 can be displayed in the second interface as the eighth piece of information.

[0182] In this article, the eighth piece of information is displayed through response-triggered operations, which allows users to clearly understand the complete interaction chain from inquiry initiation, decision generation, and subject response. This makes it convenient for users to check and verify the interaction details of each link, and realizes full traceability of the collaborative process of multiple intelligent systems.

[0183] To meet the user's need to issue intervention commands and dynamically switch tasks during the execution of tasks in the intelligent system, optionally, the system can respond to the fifth intelligent system being rescheduled during task execution and execute the corresponding task based on the fifth intelligent system and the rescheduled task.

[0184] Among them, the rescheduling is related to the scheduling determined by the second intelligent system based on the received user input.

[0185] In this paper, the second intelligent system can issue various scheduling and control commands to one or more task execution intelligent systems based on user input. User input represents the interactive operation commands initiated by the user on the first interface, which are used to trigger related operations such as intelligent system scheduling, task switching, and dynamic process control. The fifth intelligent system is used to take over the tasks assigned by the second intelligent system and execute the corresponding task processing. Rescheduling represents a new round of scheduling commands subsequently issued by the second intelligent system when the fifth intelligent system is already in a task execution state. The current task represents the original task that the fifth intelligent system is currently running and processing.

[0186] In one scenario, when the fifth intelligent system receives a new round of rescheduling instructions while it is in the task execution phase, the second intelligent system can control the fifth intelligent system to interrupt the currently running task, terminate the original task flow, and switch to execute the new task corresponding to this rescheduling, thus achieving dynamic task preemption and switching. For example, if the fifth intelligent system was originally executing a language adaptation processing task and received a new scheduling instruction midway, it can pause the original language adaptation process and switch to executing the new task related to content verification in the new scheduling instruction.

[0187] In another scenario, the second intelligent system can also generate a corresponding rescheduling instruction and send it to the fifth intelligent system when it determines that a certain intelligent system needs to be rescheduled based on user input. The rescheduling instruction can carry a new task identifier and a switching priority, executing higher-priority tasks first. Each preemptive scheduling is associated with the user's control needs. For example, when the user inputs a new task type, the second intelligent system generates a scheduling instruction based on this input, triggering the fifth intelligent system to prioritize the execution of the new task.

[0188] In another scenario, when the fifth intelligent system switches tasks, it can retain the status of previously unfinished tasks, recording the current task execution progress and context information. This allows the interrupted tasks to be resumed based on the retained information when a scheduling request is received subsequently or when a new task carried out during a rescheduling is completed.

[0189] By responding to rescheduling during the operation of the fifth intelligent system, dynamic preemption and smooth switching during task execution are achieved. All new rounds of scheduling can be uniformly determined and issued by the second intelligent system in conjunction with user input, meeting the user's need for real-time control of task switching and improving the flexibility of the intelligent system in executing tasks.

[0190] Combining the various intelligent system scheduling and information interaction processes described above, this information interaction method can be applied to intelligent systems that include multiple intelligent systems. The second intelligent system generates decision information and then sends intelligent system scheduling instructions to at least one task execution intelligent system, so that at least one task execution intelligent system can execute the corresponding task based on the received intelligent system scheduling instructions. During the task execution process, at least one task execution intelligent system provides feedback to the second intelligent system with question information or task completion information.

[0191] Among them, the intelligent system can be used to characterize a collaborative system composed of multiple intelligent systems with independent processing functions. The second intelligent system, as the central intelligent system for overall coordination and scheduling, can be used for operations such as decision generation, instruction control, scheduling and distribution, information aggregation, and overall process coordination. The intelligent system scheduling instructions are used to characterize the control instructions issued by the second intelligent system to each executing intelligent system, indicating scheduling requirements such as task initiation, execution specifications, operating modes, and start / stop control. The task execution intelligent system can be used to characterize the execution entity that receives scheduling instructions and performs specific task processing.

[0192] At least one intelligent system for task execution includes a first intelligent system, a third intelligent system, a fourth intelligent system, and a fifth intelligent system. It should be noted that different types of intelligent systems for task execution each have their own functions and cooperate with each other. The first intelligent system, the third intelligent system, the fourth intelligent system, and the fifth intelligent system can undertake differentiated tasks such as original task flow, result generation, parallel processing, and dynamic preemption switching. They can all uniformly receive scheduling from the second intelligent system and provide bidirectional feedback information.

[0193] Question information can be used to characterize the queries reported by the task execution intelligent system to the second intelligent system during operation due to issues such as unclear requirements, missing parameters, or questions about scenario adaptation. Task completion information can be used to characterize the execution completion, result delivery, and status reporting information fed back to the second intelligent system by the task execution intelligent system after completing the corresponding task processing.

[0194] In one scenario, each task execution intelligent system can monitor its own execution status, missing information, or information blind spots in real time throughout the task's execution. When there are questions about requirements, unclear execution boundaries, or missing configurations, it can proactively report the corresponding questions to the second intelligent system, awaiting supplementary judgment and instruction updates, and providing feedback to ensure the accuracy of task execution. For example, if a task execution intelligent system has questions about output format optimization or language expansion scope, it can report the questions in real time and wait for a response from the second intelligent system.

[0195] In another scenario, after completing global analysis and generating corresponding decision information, the second intelligent system can issue intelligent system scheduling instructions to each task execution intelligent system based on system collaboration requirements. Upon receiving the instructions, each execution intelligent system can process the corresponding tasks according to the scheduling requirements. For example, in a business scenario, after the second intelligent system determines decision information related to language expansion, it issues scheduling instructions, and each execution intelligent system can sequentially initiate language expansion tasks such as content generation, format adaptation, and parameter verification.

[0196] In another scenario, after the task execution intelligent system completes the task processing, it can also promptly report the task completion information to the second intelligent system, including the execution results, running status, and process node status. This allows the second intelligent system to monitor the overall task progress and then carry out subsequent process scheduling or a new round of task distribution.

[0197] This information exchange method achieves unified decision-making and scheduling based on a second intelligent system. Each task execution intelligent system undertakes tasks on demand and reports query information and execution results in real time. This two-way information exchange mode of intelligent systems can improve the flexibility of communication and interaction between intelligent systems, realize efficient interactive response between multiple intelligent systems, ensure efficient transmission and accurate response of information during task execution, and thus improve the task execution efficiency and reliability of the intelligent system.

[0198] Figure 15 This is a schematic diagram of a system architecture used to characterize an information interaction method in one scenario. This information interaction method can be executed by an intelligent system, which can be integrated into various electronic devices and service carriers, such as, but not limited to, clients, mobile applications, cloud servers, embedded terminals, and web-based interactive terminals. The intelligent system includes a first intelligent system and a second intelligent system. Figure 15 As shown, the methods for performing information interaction based on intelligent systems may include: Users can interact with the intelligent system and input task requirements on the first interface.

[0199] The second intelligent system, acting as the task decision-making and orchestration entity, receives task requirement information input by the user, parses and identifies the task requirements within this information, breaks down the task based on the parsed requirements, and dispatches the corresponding execution task to the first intelligent system. Optionally, the second intelligent system can dispatch tasks to the first intelligent system in parallel, terminate the current task execution of the first intelligent system, or resume the task execution of the first intelligent system.

[0200] The first intelligent system acts as the main task executor. Each intelligent system has built-in independent generative dialogue capabilities and runtime environments to execute tasks dispatched by the second intelligent system, such as solution analysis, solution planning, task implementation, and experimental verification. During task execution, the first intelligent system may encounter situations where it cannot independently complete task stages, has questions about information, faces processing obstacles, or lacks sufficient reasoning basis. In such cases, it can generate questions and send them to the second intelligent system for inquiry.

[0201] When the second intelligent system receives a question, it can make a decision based on the question and generate decision information. If the second intelligent system cannot respond to the question, it can prompt the user to input a fourth piece of information related to the question, and generate decision information based on the fourth piece of information. The second intelligent system can send the decision information to the first intelligent system, which will then execute the task based on the decision information and finally submit the completed task back to the second intelligent system.

[0202] To further understand this information exchange method, please refer to the figure below. Figure 16 This is a flowchart illustrating an optional instance of an information interaction method in one scenario; such as... Figure 16 As shown, this information exchange method may include: When the first intelligent system is performing a task, it can send the question information to the information relay module (such as a message aggregation and relay component / system), and the information relay module can then send the question information to the second intelligent system.

[0203] If the second intelligent system fails to determine the decision information corresponding to the question, it can display the fourth information on the first interface, prompting the user to input the fifth information related to the question. The information relay module can send the fifth information input by the user to the second intelligent system to re-execute the decision. When the decision information corresponding to the question is determined, the corresponding first intelligent system can be scheduled to execute the task based on the decision information, and the task can be submitted upon completion, such as submitting task execution information.

[0204] The information relay module can use a min-heap data structure to sort various types of access information in an ordered manner according to both priority and timestamp. Optionally, the priority of each type of information, from high to low, can include: user input information, completion information of intelligent systems triggered by the user, system cancellation instructions, system error messages, questions from the first intelligent system, task completion information and status alarm information from the first intelligent system, timeout events, etc. Simultaneously, the information relay module can have a batch forwarding function. When multiple first intelligent systems complete tasks simultaneously, the module can integrate and package the corresponding events into a batch message and push it to the second intelligent system for processing, thereby reducing the frequency of interaction calls between intelligent systems.

[0205] It should be noted that the second intelligent system can generate multiple scheduling decisions during a single call, such as terminating the operation of a specified intelligent system first, and then dispatching corresponding tasks to other intelligent systems. The second intelligent system can store the generated multiple decision information into an execution queue in sequence, and execute them one by one according to the queue order; after the previous scheduling operation is completed, the next decision in the queue is called to continue processing.

[0206] The second intelligent system can also generate various scheduling instructions based on decisions. These scheduling instructions include, but are not limited to: intelligent system scheduling instructions for concurrently dispatching tasks, intelligent system scheduling instructions for forcibly stopping task execution, intelligent system scheduling instructions for resuming task execution, intelligent system scheduling instructions for waiting for task execution, and scheduling instructions for initiating queries to users.

[0207] For example, when the scheduling instructions include intelligent system scheduling instructions for concurrent task dispatch, the intelligent system can construct independent intelligent interaction sessions for each first intelligent system, and each first intelligent system executes task execution operations in parallel. When the target intelligent system to be scheduled (i.e., the fifth intelligent system) is running, the second intelligent system can prioritize terminating the current task of the target intelligent system. Upon receiving feedback information indicating task completion, it can restart the corresponding target intelligent system and issue a new task for execution.

[0208] When the scheduling instruction includes an intelligent system scheduling instruction for forcibly stopping the execution of a task, the second intelligent system can terminate the current session of the fourth intelligent system and all subtasks derived from the fourth intelligent system by canceling the execution context information (context) corresponding to the fourth intelligent system; after the terminated fourth intelligent system completes resource release and task exit, it sends an exit confirmation message to the second intelligent system.

[0209] When the scheduling instruction includes an intelligent system scheduling instruction for resuming task execution, user input information can be injected into the next round of task description information of the fifth intelligent system, and instructions can be issued through secondary scheduling so that the corresponding fifth intelligent system can continue the subsequent task execution process from the point of task interruption based on the existing session execution context information.

[0210] When the scheduling instructions include intelligent system scheduling instructions for waiting to execute tasks, the second intelligent system can control the target intelligent system to wait for certain intelligent systems to complete their execution before executing its own task.

[0211] When the scheduling instructions include those for initiating queries to the user, the second intelligent system can display prompts to the user in various forms, such as cards, terminal messages, pop-ups, etc. The information relay module sends the user's reply back to the second intelligent system, enabling the second intelligent system to perform intelligent system scheduling based on user input.

[0212] The following scenario example illustrates this information interaction method.

[0213] When the first intelligent system detects an expired login credential during webpage access, it can proactively send a query to the second intelligent system. After analysis and judgment, the second intelligent system confirms that the current anomaly requires user intervention and generates a fifth message prompting the user to input information. This fifth message is then pushed to the user via the application or terminal interface as a reminder. After the user completes account login and related operations, the corresponding interaction information (i.e., the fourth message) is routed to the second intelligent system with the highest priority via the information relay module. The second intelligent system then performs a reassignment operation on the first intelligent system, allowing the first intelligent system to resume subsequent execution from the point of task interruption based on the original session context information. Throughout this entire process, the other working intelligent systems within the intelligent system remain unaffected by this anomaly and maintain the normal operation of their original parallel tasks.

[0214] This paper proposes a bidirectional communication protocol to achieve stable interaction between multiple intelligent systems. The second intelligent system possesses five downlink operation semantics: dispatch, termination, resumption, waiting, and querying. The task execution intelligent system possesses two uplink operation semantics: result submission and message reporting. All interaction commands can be transmitted in a structured format, and the protocol layer can distinguish and identify various operation semantics. Based on this protocol, the working intelligent system implements query suspension and breakpoint resumption. This allows the task execution intelligent system to automatically enter a blocked waiting state after triggering a reporting signal during task execution. The second intelligent system can provide feedback on the processing result through a re-dispatch process, enabling the task execution intelligent system to retain its original session context and resume execution from the interrupted point. This process does not interfere with other parallel working intelligent systems, effectively ensuring efficient information transmission and accurate response during task execution, thereby improving the task execution efficiency and reliability of the intelligent systems.

[0215] Figure 17 This is a schematic diagram of the structure of an information interaction device in one scenario, such as... Figure 17 As shown, the device includes: a first module 710 for receiving first information, the first information being a query information used to instruct a first intelligent system to perform a first task; a second module 720 for displaying second information, the second information being provided by a second intelligent system and used to instruct decision information in response to the first information; and a third module 730 for displaying third information, the third information being used to characterize the response information of a third intelligent system in response to the decision information.

[0216] The aforementioned device, by receiving first information—a query message used to instruct a first intelligent system to execute a first task; displaying second information—provided by a second intelligent system and used to instruct decision-making information in response to the first information; and displaying third information—a response message from a third intelligent system to the decision-making information—solves the technical problem of poor collaborative communication flexibility between the task scheduling end and the task execution end. It achieves the technical effects of improving the communication and collaboration flexibility between various intelligent systems, realizing efficient interactive response between multiple intelligent systems, ensuring efficient information transmission and accurate response during task execution, thereby improving the task execution efficiency and reliability of the intelligent system and meeting the technical needs of users for various tasks.

[0217] In one scenario, the first module 710 includes: The first unit is used to receive a first input, which is composed of natural language and is used to reflect the descriptive information entered by the user during the execution of the first task by the first intelligent system.

[0218] In one scenario, the first module 710 includes: The second unit is used to receive a second input, which is a description information fed back by the first intelligent system. The description information is used to reflect the question information asked by the first intelligent system in response to the first task.

[0219] In one scenario, the second module 720 includes: The third unit is used to obtain processing instructions based on the second intelligent system and the first information; The fourth unit is used to obtain and display the second information based on the processing instructions.

[0220] In one case, the fourth unit includes: The fourth subunit is used to output and display the second information based on the processing instructions of the second intelligent system.

[0221] In one scenario, the fourth subunit includes: The fourth information display unit is used to display fourth information in response to the processing instruction satisfying the first condition. The fourth information is used to prompt the user to input feedback information corresponding to the processing instruction. The second information display unit is used to display second information, which is used to instruct the second intelligent system to output the results based on the displayed fifth information and the first information. The fifth information represents the feedback information of the fourth information.

[0222] In one scenario, the fourth information display unit is used to display the fourth information on the first interface; and / or to send the fourth information to at least one user terminal based on the second intelligent system.

[0223] In one scenario, the first information includes a first identifier, which indicates the communication relationship between the first intelligent system and the second intelligent system; the third information includes a second identifier, which indicates the communication relationship between the third intelligent system and the second intelligent system; and the fourth information includes a third identifier, which indicates the communication relationship between the second intelligent system and the user terminal.

[0224] In one embodiment, the device further includes: The fourth module is used to receive the sixth information, which is used to indicate the processing requirements of the first task. The fifth module is used to display the seventh information, which is provided by the second intelligent system, and is used to indicate the response information of at least one fourth intelligent system to the sixth information.

[0225] In one scenario, receiving the sixth information includes one of the following: Receive a third input, which instructs the concurrent invocation of multiple fourth intelligent systems to execute the first task; or, Receive a fourth input, which is used to demonstrate changes to the task requirements of the first task; or, Receive a fifth input, which is used to instruct the execution of the first task to be stopped.

[0226] In one scenario, the presentation of the seventh information includes one of the following: Display information about multiple fourth intelligent systems performing the first task; or, The fourth intelligent system is shown to execute the changed task requirements; or, Display information indicating that at least one first-level intelligent system and / or third-level intelligent system has ceased operation.

[0227] The aforementioned information interaction device can execute the information interaction method provided in any of the situations described herein, and has the corresponding functional modules and beneficial effects for executing the information interaction method.

[0228] It is worth noting that the various units and modules included in the above-mentioned information interaction device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of the present invention.

[0229] The following is for reference. Figure 18 This document illustrates a schematic diagram of an electronic device (e.g., a terminal device or server) 900 suitable for implementing the above-described methods. The terminal device referred to herein may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, personal digital assistants (PDAs), tablet computers (PADs), portable multimedia players (PMPs), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital televisions and desktop computers. Figure 18 The electronic device shown is merely an example and should not impose any limitations on the functionality and scope of use in the context of this article.

[0230] like Figure 18As shown, the electronic device 900 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage device 908 into a random access memory (RAM) 903. The RAM 903 also stores various programs and data required for the operation of the electronic device 900. The processing unit 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0231] Typically, the following devices can be connected to I / O interface 905: input devices 906 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 907 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 908 including, for example, magnetic tapes, hard disks, etc.; and communication devices 909. Communication device 909 allows electronic device 900 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 18 An electronic device 900 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0232] Specifically, according to the context of this document, the process described in the above-referenced flowchart can be implemented as a computer software program. For example, the technical solution of this document includes a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such a case, the computer program can be downloaded and installed from a network via communication device 909, or installed from storage device 908, or installed from ROM 902. When the computer program is executed by processing device 901, it performs the functions defined in the method of this document.

[0233] The names of messages or information exchanged between multiple devices in this document are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0234] The electronic device provided in this document and the information interaction method provided in the above-described technical solutions belong to the same inventive concept. Technical details not described in detail herein can be found in the above-described scenarios, and this scenario has the same beneficial effects as the above-described scenarios.

[0235] This article provides a computer storage medium on which a computer program is stored, which, when executed by a processor, implements the information interaction method provided in the above-described scenario.

[0236] It should be noted that the computer-readable medium mentioned above can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-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 a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM, also known as flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this document, 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. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0237] In some scenarios, clients and servers can communicate using any currently known or future-developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include Local Area Networks (LANs), Wide Area Networks (WANs), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0238] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0239] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: receive first information, the first information being a query information used to instruct a first intelligent system to perform a first task; display second information, the second information being provided by a second intelligent system and used to instruct decision information in response to the first information; and display third information, the third information being used to characterize the response information of a third intelligent system in response to the decision information.

[0240] Computer program code for performing the operations described herein can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0241] The flowcharts and block diagrams in the accompanying figures illustrate the architecture, functionality, and operation of possible implementations of the systems, methods, and computer program products according to the various scenarios described herein. In this respect, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the figures. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0242] The modules or units described herein can be implemented in software or hardware. The names of modules or units do not necessarily limit the module or unit itself; for example, the first module can be described as "the module for acquiring first information".

[0243] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include at least one of the following: Field-Programmable Gate Array (FPGA), Application-Specific Integrated Circuit (ASIC), Application-Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), etc.

[0244] In the context of this document, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory (flash memory), optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0245] The above description is merely a preferred embodiment and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure herein is not limited to technical solutions formed by specific combinations of the above-described technical features, but also includes other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed herein that have similar functions.

[0246] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain contexts, multitasking and parallel processing may be advantageous. Similarly, while some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of this paper. Certain features described in the context of a single case can also be implemented in combination within that single case. Conversely, various features described in the context of a single case can also be implemented individually or in any suitable sub-combination in multiple cases.

[0247] Although the subject matter has been described using a programming language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative forms of implementing the claims.

Claims

1. An information exchange method, comprising: Receive first information, the first information being a query message used to instruct the first intelligent system to perform a first task; Display second information, provided by a second intelligent system, for indicating decision information in response to the first information; The third information is displayed, which is used to characterize the response information of the third intelligent system to the decision information.

2. The information interaction method according to claim 1, wherein receiving the first information includes: The system receives a first input, which is composed of natural language and is used to reflect the descriptive information entered by the user during the execution of the first task by the first intelligent system.

3. The information interaction method according to claim 1, wherein receiving the first information includes: The system receives a second input, which is a description of information fed back by the first intelligent system. The description reflects the question information asked by the first intelligent system in response to the first task.

4. The information interaction method according to claim 1, wherein displaying the second information includes: Based on the second intelligent system and the first information, a processing instruction is obtained; Based on the processing instructions, the second information is obtained and displayed.

5. The method according to claim 4, wherein obtaining and displaying the second information based on the processing instruction includes: Based on the processing instructions, the second intelligent system outputs and displays the second information.

6. The information interaction method according to claim 5, wherein obtaining and displaying the second information based on the processing instruction includes: In response to the processing instruction satisfying the first condition, fourth information is displayed, which is used to prompt the user to input feedback information corresponding to the processing instruction; The system displays second information, which is used to instruct the second intelligent system to output results based on the displayed fifth information and the first information, wherein the fifth information represents the feedback information of the fourth information.

7. The information interaction method according to claim 6, wherein displaying the fourth information includes: The fourth information is displayed on the first interface; And / or, The fourth information is sent to at least one user terminal based on the second intelligent system.

8. The information interaction method according to any one of claims 1-7, wherein the first information includes a first identifier, the first identifier being used to indicate the communication relationship between the first intelligent system and the second intelligent system, the third information includes a second identifier, the second identifier being used to indicate the communication relationship between the third intelligent system and the second intelligent system, and the fourth information includes a third identifier, the third identifier being used to indicate the communication relationship between the second intelligent system and the user terminal.

9. The method according to claim 1, further comprising: Receive a sixth message, which is used to indicate adjustments to the processing requirements of the first task; The seventh piece of information is displayed, which is provided by the second intelligent system, and is used to indicate the response information of at least one fourth intelligent system to the sixth piece of information.

10. The method according to claim 9, wherein receiving the sixth information comprises one of the following: Receive a third input, which instructs the concurrent invocation of multiple fourth intelligent systems to execute the first task; or, Receive a fourth input, which is used to demonstrate changes to the task requirements of the first task; or, Receive a fifth input, which is used to instruct the execution of the first task to be stopped.

11. The method according to claim 9, wherein displaying the seventh information includes one of the following: Display information about multiple fourth intelligent systems performing the first task; or, The fourth intelligent system is shown to execute the changed task requirements; or, Display information indicating that at least one first-level intelligent system and / or third-level intelligent system has ceased operation.

12. An information interaction device, comprising: The first module is used to receive first information, which is a query message used to instruct the first intelligent system to perform a first task; The second module is used to display second information, which is provided by the second intelligent system and is used to indicate decision information in response to the first information. The third module is used to display third information, which is used to characterize the response information of the third intelligent system to the decision information.

13. An electronic device, the electronic device comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the information interaction method as described in any one of claims 1-11.

14. A storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the information interaction method as described in any one of claims 1-11.

15. A computer program product comprising a computer program that, when executed by a processor, implements the information interaction method as described in any one of claims 1-11.

Images