Software process arrangement system and method based on large model

Abstract

The invention discloses a software process arrangement system and method based on a large model, and particularly relates to the technical field of software process arrangement. The intelligent process design module is used for realizing intelligent and low-threshold process design and optimization through natural language understanding and a visualization technology; the Agent collaborative execution module is used for dynamically distributing tasks, monitoring states and coordinating a plurality of Agents to efficiently complete process execution; and the knowledge learning enhancement module is used for constructing a domain knowledge system and providing intelligent support for process decision and optimization. According to the method, the demand described by a natural language of a user is converted into a structured process definition through the intelligent process design module, rapid design is carried out by utilizing a process pattern library and a visual editing tool, and meanwhile, compliance check and optimization suggestion are carried out, so that compliance and high efficiency of process design are ensured, and the process design efficiency is improved. And the Agent collaborative execution module dynamically allocates tasks to appropriate Agents according to process requirements, monitors the execution state in real time, and performs coordinated processing in case of abnormality to ensure efficient execution of the process.

Description

Technical Field

[0001] The present invention relates to the technical field of software process orchestration, and particularly to a software process orchestration system and method based on a large model. Background Art

[0002] As a core tool for enterprise digital transformation, software process orchestration systems are widely used in fields such as business process automation, IT service management, and cross-system integration. Traditional process orchestration systems mainly rely on manual rule configuration and predefined templates, and it is difficult to adapt to complex and changing business requirements. With the development of large language model technology, it has become possible to achieve intelligent process design through natural language interaction. Combining technologies such as knowledge graphs and multi-agent collaboration can significantly improve the automation level and decision-making ability of process orchestration.

[0003] The current mainstream process orchestration systems have the following limitations: in the process design stage, they overly rely on the structured thinking of technical personnel, and it is difficult to transform fragmented requirements described in natural language by users into executable process definitions. They lack built-in compliance verification mechanisms and optimization suggestion functions, resulting in low process design efficiency and easy generation of rule conflicts. During the task execution process, they adopt a static resource allocation strategy, and it is impossible to dynamically schedule Agent resources according to the real-time state. Exception handling relies on manual intervention, the knowledge support system is weak, there is a lack of a decision-making mechanism linked to the domain knowledge graph, and historical experience is difficult to effectively reuse. The human-computer interaction method is single, lacking intention understanding and multi-modal feedback capabilities, which affects the usability and user stickiness of the system. These defects lead to problems such as insufficient intelligence, slow response speed, and low fault tolerance rate in existing systems in complex business scenarios. Therefore, we provide a software process orchestration system and method based on a large model. Summary of the Invention

[0004] The purpose of the present invention is to solve the disadvantages existing in the prior art, and to propose a software process orchestration system and method based on a large model.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A software process orchestration system and method based on a large model, including an intelligent process design module for realizing intelligent and low-threshold process design and optimization through natural language understanding and visualization technology, an Agent collaborative execution module for dynamically allocating tasks, monitoring states, and coordinating multiple Agents to efficiently complete process execution, a knowledge learning enhancement module for constructing a domain knowledge system to provide intelligent support for process decision-making and optimization, and an intelligent interaction module for providing a natural and intuitive user operation experience through multi-modal interaction and intention understanding;

[0007] The intelligent process design module includes a natural language process parsing module for converting the process requirements described in natural language by users into structured process definitions, a process pattern library module for storing common process templates and best practices to support rapid reuse, a process visualization editing module for providing a graphical interface to support drag-and-drop process design, a process compliance checking module for automatically checking whether the process design complies with business rules and security specifications, and a process optimization suggestion module for providing process optimization suggestions based on historical data and AI models;

[0008] The Agent collaborative execution module includes an Agent capability registration module for managing the capability descriptions and interface information of all available Agents, a task allocation engine module for intelligently allocating tasks to appropriate Agents according to process requirements, an Agent status monitoring module for real-time monitoring of the execution status and resource usage of each Agent, an exception handling coordination module for coordinating relevant Agents to handle problems when process execution is abnormal, and an execution log analysis module for collecting and analyzing process execution logs to provide data support for optimization;

[0009] The knowledge learning and enhancement module includes a domain knowledge graph module for constructing and maintaining a knowledge system in a specific domain to support process decision-making, a historical case knowledge module for storing historical process cases to support case retrieval and reuse, a business rule engine module for managing business rules to support rule reasoning and decision-making in the process, a context management module for maintaining context information during process execution to support intelligent decision-making, and a knowledge update module for automatically updating and expanding system knowledge to keep the knowledge base up-to-date;

[0010] The intelligent interaction module includes a multi-modal interaction interface module for supporting various interaction methods such as voice, text, and image, an intention understanding engine module for accurately understanding the operation intentions and requirements of users, a process status visualization module for real-time display of process execution status and key metrics, an intelligent Q&A record module for answering users' questions about process design and execution, and a system feedback collection module for collecting users' feedback on process design and execution for system improvement.

[0011] The present invention is further configured as follows: The natural language process parsing module provides a reusable process template basis for the process pattern library module by converting user requirements into structured process definitions; The process pattern library module provides a reference for quickly designing processes for process visualization editing through the stored common process templates; The process visualization editing module provides a process plan to be verified for the process compliance checking module through the process designed by the graphical interface; The process compliance checking module provides basic data for optimization analysis for the process optimization suggestion module through the verified compliant process.

[0012] The present invention is further configured such that: the Agent capability registration module provides a basis for task allocation for the task allocation engine module by managing the capability descriptions of the Agents; the task allocation engine module provides a monitoring target for the execution status for the Agent status monitoring module through the intelligently allocated tasks; the Agent status monitoring module provides a basis for anomaly detection for the anomaly handling coordination module through the real-time monitored execution status; the anomaly handling coordination module provides data for problem handling for the execution log analysis module through the processed anomaly information.

[0013] The present invention is further configured such that: the domain knowledge graph module provides a knowledge basis for case retrieval for the historical case knowledge module through the constructed domain knowledge system; the historical case knowledge module provides a reference basis for rule reasoning for the business rule engine module through the stored process cases; the business rule engine module provides rule support for process decision-making for the context management module through the managed business rules; the context management module provides real-time data for knowledge expansion for the knowledge update module through the maintained context information.

[0014] The present invention is further configured such that: the multimodal interaction interface module provides raw data of user input for the intent understanding engine module through the supported multiple interaction methods; the intent understanding engine module provides key information for status display for the process status visualization module through the parsed user intent; the process status visualization module provides a real-time basis for question answering for the intelligent Q&A record module through the displayed execution status; the intelligent Q&A record module provides feedback data for system improvement for the system feedback collection module through the recorded user questions.

[0015] The present invention is further configured to include the following steps:

[0016] S1. Intelligent process construction and optimization;

[0017] S2. Multi-Agent task collaboration and execution;

[0018] S3. Domain knowledge construction and enhancement;

[0019] S4. Multimodal interaction and intelligent feedback.

[0020] The present invention is further configured such that in the step S1, intelligent process construction and optimization:

[0021] S1.1. Semantically understand the process requirements described in the user's natural language through a large model, extract key business elements, and convert them into structured process definitions;

[0022] S1.2. Based on the common templates and best practices in the process pattern library, intelligently match the user requirements and provide a reusable process framework;

[0023] S1.3. Support drag-and-drop operations through a graphical interface, allowing users to intuitively design and adjust process nodes;

[0024] S1.4. Automatically check the designed process using business rules and security specifications, identify and correct potential non-compliance issues;

[0025] S1.5. Combine historical data and AI models to analyze the performance bottlenecks of process design and provide optimization suggestions.

[0026] The present invention is further configured as follows: In the step S2, multi-Agent task collaboration and execution:

[0027] S2.1. Uniformly manage the ability descriptions and interface information of all available Agents, providing an accurate basis for ability matching for task allocation;

[0028] S2.2. Dynamically allocate tasks according to process requirements and Agent capabilities to ensure the best match between tasks and Agents;

[0029] S2.3. Real-time monitor the execution status and resource usage of each Agent, and promptly discover and handle abnormal situations;

[0030] S2.4. When an exception occurs during the process execution, coordinate relevant Agents to handle the problem and quickly resume the normal execution of the process;

[0031] S2.5. Collect and analyze process execution logs to identify problems and bottlenecks during execution.

[0032] The present invention is further configured as follows: In the step S3, domain knowledge construction and enhancement:

[0033] S3.1. Based on the knowledge system of a specific domain, construct a knowledge graph to support knowledge reasoning and intelligent recommendation in process decision-making;

[0034] S3.2. Store and manage historical process cases, support quick retrieval and reuse of cases, and provide a reference basis for process design;

[0035] S3.3. Through a business rule engine, reason and make decisions on the rules in the process to ensure the compliance and intelligence of process execution;

[0036] S3.4. During the process execution, dynamically maintain context information to support intelligent decision-making and process adjustment;

[0037] S3.5. Ensure the timeliness and comprehensiveness of the knowledge base by automatically updating and expanding system knowledge, and support the continuous optimization of the process.

[0038] The present invention is further configured such that in the step S4, multi-modal interaction and intelligent feedback:

[0039] S4.1. Provide various interaction methods such as voice, text, and image to meet the diverse operation needs of users;

[0040] S4.2. Accurately analyze the operation intention of the user through a large model to ensure that the system can accurately respond to the user's needs;

[0041] S4.3. Visualize and display the process execution status and key metrics in real time to help users intuitively understand the process progress;

[0042] S4.4. Answer the user's questions about process design and execution through an intelligent question-answering system and record the user's questions;

[0043] S4.5. Collect the feedback of users on process design and execution, analyze the user's needs and suggestions, and provide a basis for system improvement.

[0044] The beneficial effects of the present invention are as follows:

[0045] Through the intelligent process design module, the present invention converts the requirements described in the user's natural language into a structured process definition, and uses the process pattern library and visual editing tool for rapid design, while performing compliance checks and optimization suggestions to ensure the compliance and efficiency of the process design. The Agent collaborative execution module dynamically assigns tasks to appropriate Agents according to the process requirements, monitors the execution status in real time, and coordinates and processes in case of anomalies to ensure the efficient execution of the process. The knowledge learning enhancement module provides intelligent support for process decision-making through the domain knowledge graph, historical cases, and business rule engine, and continuously updates the knowledge base. The intelligent interaction module provides an intuitive user operation experience through multi-modal interaction and intention understanding, displays the process status in real time, and collects user feedback to continuously improve the system. Description of the Drawings

[0046] Figure 1 It is a schematic diagram of the modules of the software process orchestration system based on a large model in the present invention.

[0047] Figure 2 It is a schematic diagram of the process of the software process orchestration method based on a large model in the present invention. Specific Embodiments

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0049] Embodiment 1

[0050] AsFigure 1 As shown in Figure 1 , the large model-based software process orchestration system includes an intelligent process design module for realizing intelligent and low-threshold process design and optimization through natural language understanding and visualization technology, an Agent collaborative execution module for dynamically allocating tasks, monitoring status, and coordinating multiple Agents to efficiently complete process execution, a knowledge learning enhancement module for constructing a domain knowledge system to provide intelligent support for process decision-making and optimization, and an intelligent interaction module for providing a natural and intuitive user operation experience through multimodal interaction and intention understanding;

[0051] The intelligent process design module includes a natural language process parsing module for converting the process requirements described in natural language by users into structured process definitions, a process pattern library module for storing common process templates and best practices to support rapid reuse, a process visualization editing module for providing a graphical interface to support drag-and-drop process design, a process compliance check module for automatically checking whether the process design complies with business rules and security specifications, and a process optimization suggestion module for providing process optimization suggestions based on historical data and AI models;

[0052] The Agent collaborative execution module includes an Agent capability registration module for managing the capability descriptions and interface information of all available Agents, a task allocation engine module for intelligently allocating tasks to appropriate Agents according to process requirements, an Agent status monitoring module for real-time monitoring of the execution status and resource usage of each Agent, an exception handling coordination module for coordinating relevant Agents to handle problems when process execution is abnormal, and an execution log analysis module for collecting and analyzing process execution logs to provide data support for optimization;

[0053] The knowledge learning enhancement module includes a domain knowledge graph module for constructing and maintaining a domain-specific knowledge system to support process decision-making, a historical case knowledge module for storing historical process cases to support case retrieval and reuse, a business rule engine module for managing business rules to support rule reasoning and decision-making in the process, a context management module for maintaining context information during process execution to support intelligent decision-making, and a knowledge update module for automatically updating and expanding system knowledge to keep the knowledge base up-to-date;

[0054] The intelligent interaction module includes a multimodal interaction interface module for supporting multiple interaction methods such as voice, text, and image, an intention understanding engine module for accurately understanding the operation intentions and requirements of users, a process status visualization module for real-time display of process execution status and key metrics, an intelligent Q&A record module for answering users' questions about process design and execution, and a system feedback collection module for collecting users' feedback on process design and execution for system improvement;

[0055] The natural language process parsing module provides a reusable process template basis for the process pattern library module by transforming user requirements into structured processes; the process pattern library module provides a reference for quickly designing processes for process visual editing through common process templates stored; the process visual editing module provides a process solution to be verified for the process compliance checking module through the process designed by the graphical interface; the process compliance checking module provides basic data for optimization analysis for the process optimization suggestion module through the verified compliant process.

[0056] The Agent capability registration module provides a basis for task allocation for the task assignment engine module by managing the capability descriptions of Agents; the task assignment engine module provides a monitoring target for the execution status for the Agent status monitoring module through the intelligently assigned tasks; the Agent status monitoring module provides a basis for anomaly detection for the anomaly handling coordination module through the real-time monitored execution status; the anomaly handling coordination module provides data for problem handling for the execution log analysis module through the processed anomaly information.

[0057] The domain knowledge graph module provides a knowledge basis for case retrieval for the historical case knowledge module through the constructed domain knowledge system; the historical case knowledge module provides a reference basis for rule reasoning for the business rule engine module through the stored process cases; the business rule engine module provides rule support for process decision-making for the context management module through the managed business rules; the context management module provides real-time data for knowledge expansion for the knowledge update module through the maintained context information.

[0058] The multimodal interaction interface module provides raw data of user input for the intent understanding engine module through various supported interaction methods; the intent understanding engine module provides key information for status display for the process status visualization module through the parsed user intent; the process status visualization module provides a real-time basis for question answering for the intelligent Q&A record module through the displayed execution status; the intelligent Q&A record module provides feedback data for system improvement for the system feedback collection module through the recorded user questions.

[0059] In the above embodiments, the intelligent process design module converts the process requirements described in natural language by the user into a structured process definition, and uses the process pattern library and the visual editing tool for rapid design, while performing compliance checks and optimization suggestions. The Agent collaborative execution module dynamically assigns tasks to appropriate Agents according to the process requirements, monitors the execution status in real time, and coordinates the handling in case of anomalies to ensure the efficient execution of the process. The knowledge learning and enhancement module provides intelligent support for process decision-making through the domain knowledge graph, historical cases, and business rule engine, and continuously updates the knowledge base. The intelligent interaction module provides an intuitive user operation experience through multimodal interaction and intent understanding, displays the process status in real time, and collects user feedback to continuously improve the system.

[0060] Embodiment 2

[0061] As Figure 1-2 shown, the software process orchestration method based on the large model includes the following steps:

[0062] S1. Intelligent process construction and optimization;

[0063] S2. Multi-Agent task collaboration and execution;

[0064] S3. Domain knowledge construction and enhancement;

[0065] S4. Multimodal interaction and intelligent feedback;

[0066] In the step S1, intelligent process construction and optimization:

[0067] S1.1. Semantically understand the process requirements described in natural language by the user through the large model, extract key business elements, and convert them into a structured process definition;

[0068] S1.2. Based on the common templates and best practices in the process pattern library, intelligently match the user requirements and provide a reusable process framework;

[0069] S1.3. Support drag-and-drop operations through a graphical interface, allowing the user to intuitively design and adjust process nodes;

[0070] S1.4. Automatically check the designed process using business rules and security specifications, identify and correct potential non-compliance issues;

[0071] S1.5. Combine historical data and AI models to analyze the performance bottlenecks of the process design and provide optimization suggestions;

[0072] In the step S2, multi-Agent task collaboration and execution:

[0073] S2.1. Uniformly manage the ability descriptions and interface information of all available Agents to provide an accurate basis for ability matching in task allocation;

[0074] S2.2. Dynamically allocate tasks according to process requirements and Agent capabilities to ensure the best match between tasks and Agents;

[0075] S2.3. Monitor the execution status and resource usage of each Agent in real time, and promptly detect and handle abnormal situations;

[0076] S2.4. When an exception occurs during the process execution, coordinate relevant Agents to handle the problem and quickly resume the normal execution of the process;

[0077] S2.5. Collect and analyze process execution logs to identify problems and bottlenecks during execution;

[0078] In the above-mentioned step S3, Domain Knowledge Construction and Enhancement:

[0079] S3.1. Build a knowledge graph based on the knowledge system of a specific domain to support knowledge reasoning and intelligent recommendation in process decision-making;

[0080] S3.2. Store and manage historical process cases to support quick retrieval and reuse of cases, providing a reference basis for process design;

[0081] S3.3. Reason and make decisions on the rules in the process through a business rule engine to ensure the compliance and intelligence of process execution;

[0082] S3.4. Dynamically maintain context information during the process execution to support intelligent decision-making and process adjustment;

[0083] S3.5. Ensure the timeliness and comprehensiveness of the knowledge base by automatically updating and expanding system knowledge to support the continuous optimization of the process;

[0084] In the above-mentioned step S4, Multimodal Interaction and Intelligent Feedback:

[0085] S4.1. Provide various interaction methods such as voice, text, and image to meet the diverse operation needs of users;

[0086] S4.2. Accurately analyze the operation intentions of users through a large model to ensure that the system can accurately respond to user needs;

[0087] S4.3. Visualize and display the process execution status and key indicators in real time to help users intuitively understand the process progress;

[0088] S4.4. Answer users' questions about process design and execution through an intelligent question-answering system and record users' questions;

[0089] S4.5. Collect feedback from users on process design and execution, analyze user needs and suggestions, and provide a basis for system improvement.

[0090] In the above embodiments, through intelligent process and optimization, the process requirements described in natural language by users are transformed into structured process definitions, and designed using a process pattern library and a graphical interface. At the same time, compliance checks and optimization suggestions are carried out. In multi-Agent task collaboration and execution, the system dynamically allocates tasks according to process requirements and Agent capabilities, monitors the execution in real time, and coordinates and processes in case of exceptions to ensure the efficient execution of the process. The domain knowledge construction and enhancement module provides intelligent support for process decision-making through knowledge graphs, historical cases, and business rule engines, and continuously updates the knowledge base. The multi-modal interaction and intelligent feedback module provides an intuitive user operation experience through various interaction methods and intention understanding, displays the process status in real time, and collects user feedback to continuously improve the system.

[0091] Working principle: When in use, the present invention transforms the process requirements described in natural language by users into structured process definitions through an intelligent process design module. The system uses a large model for semantic understanding, extracts key business elements, and combines common templates and best practices in the process pattern library to intelligently match user needs and provide a reusable process framework. Users can perform drag-and-drop operations through a graphical interface to intuitively design and adjust process nodes. The system will also automatically check whether the process design complies with business rules and security specifications, and provide optimization suggestions in combination with historical data and AI models to ensure the compliance and efficiency of the process design.

[0092] In the process execution stage, the Agent collaboration execution module dynamically allocates tasks to appropriate Agents and monitors the execution status and resource usage of each Agent in real time. The system manages the capability descriptions and interface information of all available Agents through the Agent capability registration module, and the task allocation engine module performs intelligent task allocation according to process requirements and Agent capabilities. During the process execution, the system monitors the execution status in real time and coordinates relevant Agents to handle problems in case of exceptions to ensure the efficient execution of the process. The execution log analysis module collects and analyzes process execution logs, identifies problems and bottlenecks in the execution, and provides data support for subsequent optimization.

[0093] The knowledge learning enhancement module and the intelligent interaction module provide intelligent support and an intuitive user operation experience for the system. The domain knowledge graph module constructs a knowledge system for a specific domain, supporting knowledge reasoning and intelligent recommendation in process decision-making. The historical case knowledge module stores and manages historical process cases, providing a reference basis for process design. The business rule engine module reasons and makes decisions on the rules in the process, ensuring the compliance and intelligence of process execution. The intelligent interaction module provides various interaction methods such as voice, text, and image through multimodal interaction and intent understanding to meet the diverse operation needs of users. The system also displays the process execution status and key metrics in real time, and answers users' questions through an intelligent question-answering system, collects users' feedback, and provides a basis for system improvement.

[0094] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. A software process orchestration system based on a large model, characterized in that It includes an intelligent process design module for realizing intelligent and low-threshold process design and optimization through natural language understanding and visualization technologies, an Agent collaborative execution module for dynamically allocating tasks, monitoring status, and coordinating multiple Agents to efficiently complete process execution, a knowledge learning enhancement module for building a domain knowledge system to provide intelligent support for process decision-making and optimization, and an intelligent interaction module for providing a natural and intuitive user operation experience through multimodal interaction and intent understanding; The intelligent process design module includes a natural language process parsing module for converting the process requirements described in natural language by users into structured process definitions, a process pattern library module for storing common process templates and best practices to support rapid reuse, a process visualization editing module for providing a graphical interface to support drag-and-drop process design, a process compliance checking module for automatically checking whether the process design complies with business rules and security specifications, and a process optimization suggestion module for providing process optimization suggestions based on historical data and AI models; The Agent collaborative execution module includes an Agent capability registration module for managing the capability descriptions and interface information of all available Agents, a task allocation engine module for intelligently allocating tasks to appropriate Agents according to process requirements, an Agent status monitoring module for real-time monitoring of the execution status and resource usage of each Agent, an exception handling coordination module for coordinating relevant Agents to handle problems when process execution is abnormal, and an execution log analysis module for collecting and analyzing process execution logs to provide data support for optimization; The knowledge learning enhancement module includes a domain knowledge graph module for building and maintaining a domain knowledge system to support process decision-making, a historical case knowledge module for storing historical process cases to support case retrieval and reuse, a business rule engine module for managing business rules to support rule reasoning and decision-making in the process, a context management module for maintaining context information during process execution to support intelligent decision-making, and a knowledge update module for automatically updating and expanding system knowledge to keep the knowledge base up-to-date; The intelligent interaction module includes a multimodal interaction interface module for supporting multiple interaction methods such as voice, text, and image, an intent understanding engine module for accurately understanding the operation intentions and requirements of users, a process status visualization module for real-time displaying the process execution status and key metrics, an intelligent question answering record module for answering users' questions about process design and execution, and a system feedback collection module for collecting users' feedback on process design and execution for system improvement.

2. The software process orchestration system based on a large model according to claim 1, wherein The natural language process parsing module provides a reusable process template basis for the process pattern library module by converting user requirements into structured process definitions; The process pattern library module provides a reference for quickly designing processes for the process visualization editing module through the stored common process templates; The process visualization editing module provides a process solution to be verified for the process compliance checking module through the process designed by the graphical interface; The process compliance check module provides the basic data for optimization analysis to the process optimization suggestion module through the verified compliance process.

3. The software process orchestration system based on a large model according to claim 1, wherein The Agent capability registration module provides the basis for task assignment to the task assignment engine module by managing the capability descriptions of Agents; the task assignment engine module provides the monitoring target of the execution status to the Agent status monitoring module through the intelligently assigned tasks; the Agent status monitoring module provides the basis for anomaly detection to the anomaly handling coordination module through the real-time monitored execution status; the anomaly handling coordination module provides the data for problem handling to the execution log analysis module through the processed anomaly information.

4. The software process orchestration system based on a large model according to claim 1, wherein The domain knowledge graph module provides the knowledge basis for case retrieval to the historical case knowledge module through the constructed domain knowledge system; the historical case knowledge module provides the reference basis for rule reasoning to the business rule engine module through the stored process cases. The business rule engine module provides the rule support for process decision-making to the context management module through the managed business rules; the context management module provides the real-time data for knowledge expansion to the knowledge update module through the maintained context information.

5. The software process orchestration system based on a large model according to claim 1, wherein, The multimodal interaction interface module provides the original data of user input to the intent understanding engine module through the supported multiple interaction methods; the intent understanding engine module provides the key information for status display to the process status visualization module through the parsed user intent. The process status visualization module provides the real-time basis for question answering to the intelligent Q&A record module through the displayed execution status; the intelligent Q&A record module provides the feedback data for system improvement to the system feedback collection module through the recorded user questions.

6. A software process orchestration method based on a large model, characterized in that, Including the following steps: S1. Intelligent process construction and optimization; S2. Multi-Agent task collaboration and execution; S3. Domain knowledge construction and enhancement; S4. Multimodal interaction and intelligent feedback.

7. The software process orchestration method based on a large model according to claim 6, wherein: In the step S1, intelligent process construction and optimization: S1.

1. Semantically understand the process requirements described in natural language by the user through a large model, extract key business elements, and transform them into structured process definitions. S1.

2. Based on the common templates and best practices in the process pattern library, intelligently match the user requirements and provide a reusable process framework. S1.

3. Support drag-and-drop operations through a graphical interface, allowing users to intuitively design and adjust process nodes. S1.

4. Automatically check the designed process using business rules and security specifications, identify and correct potential non-compliance issues. S1.

5. Combine historical data and AI models to analyze the performance bottlenecks of the process design and provide optimization suggestions.

8. The software process orchestration method based on a large model according to claim 6, characterized in that: In the step S2, multi-Agent task collaboration and execution: S2.

1. Unifiedly manage the capability descriptions and interface information of all available Agents to provide an accurate basis for capability matching for task assignment. S2.

2. Dynamically assign tasks according to process requirements and Agent capabilities to ensure the best match between tasks and Agents. S2.

3. Real-time monitor the execution status and resource usage of each Agent, and promptly discover and handle abnormal situations. S2.

4. When an exception occurs during the process execution, coordinate relevant Agents to handle the problem and quickly resume the normal execution of the process; S2.

5. Collect and analyze the process execution logs to identify problems and bottlenecks during execution.

9. The software process orchestration method based on a large model according to claim 6, wherein: In step S3, Domain Knowledge Construction and Enhancement: S3.

1. Based on the knowledge system of a specific domain, construct a knowledge graph to support knowledge reasoning and intelligent recommendation in process decision-making; S3.

2. Store and manage historical process cases to support the rapid retrieval and reuse of cases, providing a reference basis for process design; S3.

3. Through a business rule engine, reason and make decisions on the rules in the process to ensure the compliance and intelligence of process execution; S3.

4. During the process execution, dynamically maintain context information to support intelligent decision-making and process adjustment; S3.

5. Ensure the timeliness and comprehensiveness of the knowledge base by automatically updating and expanding system knowledge, supporting the continuous optimization of the process.

10. The software process orchestration method based on a large model according to claim 6, wherein: In step S4, Multimodal Interaction and Intelligent Feedback: S4.

1. Provide multiple interaction methods such as voice, text, and image to meet the diverse operation needs of users; S4.

2. Through a large model, accurately parse the operation intention of users to ensure that the system can accurately respond to user needs; S4.

3. Visualize and display the process execution status and key indicators in real time to help users intuitively understand the process progress; S4.

4. Answer users' questions about process design and execution through an intelligent question-answering system and record users' questions; S4.

5. Collect users' feedback on process design and execution, analyze users' needs and suggestions, and provide a basis for system improvement.

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