Dynamic arrangement and iterative optimization method for skill atlas in water conservancy field
By introducing technologies such as memory-driven rewriting, hybrid intent recognition, graph computation orchestration, and ReAct closed-loop feedback, the problems of scattered skill management and inflexible tool usage in water conservancy management have been solved, realizing efficient automation and intelligence of water conservancy business processes and improving the system's response accuracy and robustness.
Patent Information
- Application Number
- CN202511379072.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-25
- Publication Date
- 2026-01-13
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Current water conservancy management methods rely heavily on experience and post-event summaries, are labor-intensive, and are difficult to achieve ideal results. Furthermore, they lack unified skills management and dynamic tool scheduling capabilities. Existing artificial intelligence solutions face challenges such as a fragmented tool ecosystem, insufficient developer skills, inability to learn new information in real time, and difficulty in handling complex multi-step tasks.
By introducing technologies such as memory-driven rewriting, hybrid intent recognition, skill graph orchestration, parameter diagnosis, and multi-round feedback, and combining these with a water conservancy digital twin platform, efficient automation and intelligentization of water conservancy business processes can be achieved.
It improved the system's response accuracy and robustness, enhanced contextual understanding capabilities, increased tool invocation accuracy and system flexibility, optimized task decomposition and execution efficiency, reduced errors caused by incomplete information, and promoted model self-correction and task completion.
Smart Images
Figure CN121328674A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application discloses a dynamic arrangement and iterative optimization method for a skill map in the water conservancy field, and relates to the fields of artificial intelligence and information technology. BACKGROUND
[0002] With climate change and rapid development, water resources are increasingly scarce, and water security risks are increasing. The existing water management mode is mainly based on experience, and is mainly summarized after the event. It is labor-intensive, time-consuming and difficult to achieve ideal results. Emerging technologies such as the Internet of Things, cloud computing, artificial intelligence and digital twins are gradually maturing, making it a trend for the water industry to develop towards "intelligent water conservancy". At present, most water systems lack unified skill management and dynamic tool scheduling capabilities. Existing artificial intelligence solutions face problems such as fragmentation of tool ecology and lack of developer skills, and large language models themselves cannot learn new information in real time, making it difficult to handle multi-step complex tasks, and an additional arrangement layer is needed to manage multiple components. SUMMARY
[0003] The present application provides a dynamic arrangement and iterative optimization method for a skill map in the water conservancy field, which solves the problems of scattered skill management, inflexible tool calling, lack of multi-round context memory and feedback mechanism in the prior art. By introducing memory-driven rewriting, mixed intent recognition, skill map arrangement, parameter diagnosis, multi-round feedback, digital twin fusion and other technical means, the efficiency and intelligence of water business processes are realized, and the response accuracy and robustness of the system are improved.
[0004] The specific scheme provided by the present application is as follows:
[0005] The present application also provides a dynamic arrangement and iterative optimization method for a skill map in the water conservancy field, comprising:
[0006] Step 1: Memory-driven industry-specific problem rewriting: For the user's input question, perform dynamic memory retrieval, extract information related to the water conservancy theme from historical interaction data or professional knowledge base, and rewrite the user's question based on the NLR2s model,
[0007] Step 2: Hybrid intent recognition and tool calling routing: According to the input question after industry rewriting, perform intent recognition, convert natural language intent into recognizable operation targets, use a pluggable routing strategy, and intelligently select and call appropriate tools according to the intent recognition result,
[0008] Step 3: Dynamic skill map arrangement based on graph computing: Use graph computing technology to build a skill map in the water conservancy field, represent skill nodes and subtasks as directed acyclic graphs DAG, and combine routing strategies and intent recognition results to match skill nodes and subtasks according to task requirements to dynamically generate execution plans,
[0009] Step 4: Perform parameter self-diagnosis and multi-round clarification: Before calling the tool, check the integrity of the input information using the parameter integrity flag, and if the necessary parameters are incomplete, automatically start a multi-round clarification interaction to dynamically complete the missing information,
[0010] Step 5: Perform ReAct closed-loop feedback and domain iteration: Use the ReAct paradigm to combine model reasoning and action to form a multi-round closed-loop interaction, adjust the reasoning path according to the feedback in each iteration, and introduce an early termination mechanism to terminate the iteration when the model execution plan generates a result that meets the expectations.
[0011] Further, the step 2 of the method adopts the LLM and classifier dual fusion method for intent recognition, and converts the natural language intent into an identifiable operation target.
[0012] Further, the method further comprises step 6: performing water conservancy digital twin deep fusion: connecting the constructed skill map with the water conservancy digital twin platform, obtaining physical water conservancy facilities and environmental data in real time through bidirectional data flow, and feeding back the simulation results to the physical system to form a safe and traceable data interaction link, and closing the virtual-physical loop of digital twin for improving the accuracy and timeliness of scheme evaluation.
[0013] Further, the method further comprises step 7: memory-driven adaptive response: continuously updating the context memory during the interaction process, incorporating the newly obtained information into the memory storage, and combining the transfer learning technology to fine-tune the model, and adaptively optimizing the response strategy for specific water conservancy tasks and scenes, and enhancing the generalization ability.
[0014] The application also provides a water conservancy field skill map dynamic arrangement and iterative optimization system, which comprises a problem rewriting module, an intent recognition module, an arrangement module, an optimization module and a feedback iteration module,
[0015] The problem rewriting module performs memory-driven industry-specific problem rewriting: for the problem input by the user, dynamic memory retrieval is performed to extract information related to the water conservancy theme from historical interaction data or professional knowledge base, and the NLR2s model is used to rewrite the user's question into an industry-specific version,
[0016] The intent recognition module performs hybrid intent recognition and tool calling routing: according to the input problem after industry-specific rewriting, the natural language intent is converted into an identifiable operation target, and a pluggable routing strategy is used to intelligently select and call the appropriate tool according to the intent recognition result,
[0017] The orchestration module is based on a dynamic skill graph of graph calculation: a skill graph in the water conservancy field is constructed by using graph calculation technology, skill nodes and subtasks are expressed as a directed acyclic graph (DAG), and a skill node and a subtask are matched according to a task demand to dynamically generate an execution plan in combination with a routing strategy and an intention recognition result,
[0018] The optimization module performs parameter self-diagnosis and multi-round clarification: before calling a tool, the integrity of the input information is checked by using a parameter integrity flag, if necessary parameters are incomplete, multi-round clarification interaction is automatically started, and missing information is dynamically completed,
[0019] The feedback iteration module performs ReAct closed-loop feedback and field iteration: the ReAct paradigm is adopted, model reasoning and action are combined, multi-round closed-loop interaction is formed, the reasoning path is adjusted according to the feedback in each iteration, and an early termination mechanism is introduced, and when the model execution plan generates a result meeting the expectation, the iteration is terminated in advance.
[0020] Further, the intention recognition module in the system adopts an LLM and classifier dual-path fusion manner for intention recognition, and converts a natural language intention into an identifiable operation target.
[0021] Further, the system further comprises a fusion module, and the fusion module performs water conservancy digital twin deep fusion: the constructed skill graph is connected with a water conservancy digital twin platform, physical water conservancy facilities and environment data are obtained in real time through bidirectional data flow, meanwhile, simulation results are fed back to a physical system, a safe and traceable data interaction link is formed, a virtual-physical closed loop of digital twin is formed, and the accuracy and timeliness of scheme evaluation are improved.
[0022] Further, the system further comprises a response module, and the response module performs memory-driven adaptive response: in the interaction process, the context memory is updated, newly obtained information is included in the memory storage, a model is fine-tuned in combination with a transfer learning technology, a response strategy is adaptively optimized for specific water conservancy tasks and scenes, and the generalization ability is enhanced.
[0023] The present application has the following advantages:
[0024] (1) Compared with the traditional method, the present application combines memory driving and multi-round feedback mechanism, has stronger context understanding ability and coherent conversation memory, can more accurately grasp user demand and maintain consistency in multi-round interaction.
[0025] (2) Mixed intention recognition and pluggable routing strategy improve the accuracy and expansibility of tool calling, various water conservancy tools and skill modules can be accessed as needed, and the flexibility of the system is further enhanced.
[0026] (3) The dynamic skill graph orchestration based on graph computing optimizes task decomposition and parallel execution, and the fine-grained primitive-level planning improves the overall processing efficiency.
[0027] (4) The parameter self-diagnosis and multi-round clarification mechanism effectively reduces the error execution caused by incomplete information, and improves the reliability of the final result and the success rate of execution.
[0028] (5) The ReAct closed-loop feedback combined with the early stopping mechanism promotes the self-correction of the model during execution, accelerates the reasoning process and improves the task completion degree. BRIEF DESCRIPTION OF DRAWINGS
[0029] Figure 1 is a schematic diagram of the method flow of the present application. DETAILED DESCRIPTION
[0030] The present application will be further described below in conjunction with the drawings and specific examples, so that those skilled in the art can better understand the present application and implement it, but the examples are not limiting to the present application.
[0031] Example 1
[0032] The present application also provides a dynamic orchestration and iterative optimization method for skill graph in the field of water conservancy, comprising:
[0033] Step 1: Memory-driven industry-specific problem rewriting: For the user input problem, dynamic memory retrieval is performed to extract information related to the water conservancy theme from historical interaction data or professional knowledge base, and the NLR2s model is used to rewrite the user's question in an industry-specific manner. Among them, the NLR2s model is used to rewrite the user's question in an industry-specific manner, and the generative model is guided to reason in the water conservancy knowledge space. As shown in the related study, the enhanced dual memory mechanism can significantly improve the context tracking ability of multi-round dialogue.
[0034] Step 2: Hybrid intent recognition and tool calling routing: According to the input problem after industry-specific rewriting, the LLM and classifier dual-fusion method is used for intent recognition, the natural language intent is converted into a recognizable operation target, and the pluggable routing strategy is used to intelligently select and call the appropriate tool,
[0035] Step 3: Dynamic skill graph orchestration based on graph computing: The graph computing technology is used to construct the skill graph in the field of water conservancy, the skill nodes and sub-tasks are represented as directed acyclic graphs DAG, the routing strategy and the intent recognition result are combined to match the skill nodes and sub-tasks according to the task requirements, the execution plan is dynamically generated according to the task requirements, the decomposition and parallel processing of complex tasks are realized, and the flexibility and efficiency of task orchestration are improved.
[0036] Step 4: Perform parameter self-diagnosis and multi-round clarification: Before calling the tool, check the integrity of the input information using the parameter integrity flag. If the necessary parameters are incomplete, automatically start a multi-round clarification interaction to dynamically complete the missing information. At the same time, use the enhanced verification tool to verify the clarification results to ensure the accuracy and consistency of the parameters.
[0037] Step 5: Perform ReAct closed-loop feedback and domain iteration: Use the ReAct paradigm to combine model reasoning and action to form a multi-round closed-loop interaction. In each iteration, adjust the reasoning path based on feedback and introduce an early termination mechanism to terminate the iteration when the model execution plan generates results that meet expectations.
[0038] Step 6: Perform water conservancy digital twin deep fusion: Connect the constructed skill map with the water conservancy digital twin platform to obtain real-time physical water conservancy facility and environmental data through bidirectional data flow. At the same time, feed back the simulation results to the physical system to form a safe and traceable data interaction link, and form a virtual-physical closed loop of digital twin to improve the accuracy and timeliness of scheme evaluation.
[0039] Step 7: Perform memory-driven adaptive response: Continuously update the context memory during interaction, incorporate newly obtained information into memory storage, and fine-tune the model using transfer learning techniques to adaptively optimize response strategies for specific water conservancy tasks and scenarios, enhancing generalization capabilities.
[0040] After deep fusion with the water conservancy digital twin platform, the application can pre-optimize control strategies and find problems during the simulation stage, and significantly reduce resource consumption and risks during the application stage. Actual cases show that simulation optimization through digital twin technology can significantly reduce resource loss.
[0041] The memory-driven adaptive response and transfer learning of the application enable the system to continuously self-optimize to meet complex and changing water conservancy business needs.
[0042] Embodiment 2
[0043] The application also provides a dynamic arrangement and iterative optimization system for a water conservancy field skill map, which includes a problem rewriting module, an intent recognition module, an arrangement module, an optimization module, and a feedback iteration module,
[0044] The problem rewriting module performs memory-driven industry-specific problem rewriting: For user input problems, perform dynamic memory retrieval to extract information related to water conservancy topics from historical interaction data or professional knowledge bases, and rewrite user questions based on NLR2s models,
[0045] The intent recognition module performs hybrid intent recognition and tool calling routing: performing intent recognition on the input question after industry rewriting, converting the natural language intent into an identifiable operation target, using a pluggable routing strategy, intelligently selecting and calling appropriate tools according to the intent recognition result,
[0046] The arrangement module arranges based on the dynamic skill graph of graph calculation: using graph calculation technology to construct the skill graph in the water conservancy field, representing the skill nodes and sub-tasks as directed acyclic graphs DAG, combining the routing strategy and the intent recognition result to match the skill nodes and sub-tasks according to the task demand to dynamically generate an execution plan,
[0047] The optimization module performs parameter self-diagnosis and multi-round clarification: before calling the tool, the parameter integrity flag is used to check the integrity of the input information, and if the necessary parameters are incomplete, the multi-round clarification interaction is automatically started to dynamically complete the missing information,
[0048] The feedback iteration module performs ReAct closed-loop feedback and domain iteration: using the ReAct paradigm, combining model reasoning and action to form a multi-round closed-loop interaction, adjusting the reasoning path according to the feedback in each iteration, and introducing an early termination mechanism to terminate the iteration when the model execution plan generates a result that meets the expectation.
[0049] The information interaction and execution process between the modules in the above system are based on the same concept as the method embodiments of the present application, and the specific content can be referred to the description in the method embodiments of the present application, which will not be repeated here.
[0050] Similarly, the benefits of the system of the present application are:
[0051] (1) Compared with the traditional method, the present application combines memory driving and multi-round feedback mechanism, has stronger context understanding ability and coherent conversation memory, and can more accurately grasp the user demand and maintain the consistency of multi-round interaction.
[0052] (2) Hybrid intent recognition and pluggable routing strategy improve the accuracy and expandability of tool calling, various water conservancy tools and skill modules can be accessed as needed, further enhancing the flexibility of the system.
[0053] (3) The dynamic skill graph arrangement based on graph calculation realizes the optimization of task decomposition and parallel execution, and the fine-grained primitive-level planning improves the overall processing efficiency.
[0054] (4) The parameter self-diagnosis and multi-round clarification mechanism effectively reduces the error execution caused by incomplete information, and improves the reliability and execution success rate of the final result.
[0055] (5) ReAct closed-loop feedback combined with early termination mechanism promotes self-correction of the model during execution, speeds up the reasoning process and improves the task completion degree.
[0056] It should be noted that not all steps and modules in the above processes and system structures are necessary, and some steps or modules can be omitted according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in each of the above embodiments can be a physical structure or a logical structure, that is, some modules can be implemented by the same physical entity, or some modules can be implemented by multiple physical entities, or can be implemented by some components in multiple independent devices together.
[0057] The above-described embodiments are only preferred embodiments of the present application for fully illustrating the present application, and the protection scope of the present application is not limited thereto. Any equivalent replacement or transformation made by those skilled in the art based on the present application is within the protection scope of the present application. The protection scope of the present application is subject to the claims.
Claims
1. A dynamic arrangement and iterative optimization method for skill maps in the water conservancy field, characterized by: include: Step 1: Memory-driven industry-specific question rewriting: For the user's input question, perform dynamic memory retrieval to extract information related to the water conservancy topic from historical interaction data or professional knowledge base, and rewrite the user's question in an industry-specific way based on the NLR2s model. Step 2: Perform hybrid intent recognition and tool invocation routing: Based on the industry-specific rewritten input question, perform intent recognition, convert the natural language intent into a recognizable operation target, and use a pluggable routing strategy to intelligently select and invoke appropriate tools based on the intent recognition results. Step 3: Dynamic Skill Graph Orchestration Based on Graph Computation: A skill graph for the water conservancy field is constructed using graph computation technology. Skill nodes and subtasks are represented as directed acyclic graphs (DAGs). Combining routing strategies and intent recognition results, skill nodes and subtasks are matched according to task requirements to dynamically generate execution plans. Step 4: Perform parameter self-diagnosis and multiple rounds of clarification: Before calling the tool, use the parameter integrity flag to check the completeness of the input information. If any required parameters are found to be incomplete, automatically initiate multiple rounds of clarification interaction to dynamically complete the missing information. Step 5: Perform ReAct closed-loop feedback and domain iteration: Adopt the ReAct paradigm to combine model reasoning with action to form a multi-round closed-loop interaction. In each iteration, the reasoning path is adjusted according to the feedback, and an early stopping mechanism is introduced to terminate the iteration in advance when the model execution plan generates results that meet expectations.
2. The method according to claim 1, characterized in that: Step 2 employs a dual-path fusion approach of LLM and classifier for intent recognition, converting natural language intent into identifiable operational targets.
3. The method according to claim 1, characterized in that: It also includes step 6: Deep integration of water conservancy digital twin: Connect the constructed skill map with the water conservancy digital twin platform, obtain physical water conservancy facilities and environmental data in real time through bidirectional data flow, and feed back the simulation results to the physical system to form a safe and traceable data interaction link, and use the virtual-physical closed loop of digital twin to improve the accuracy and timeliness of solution evaluation.
4. The method according to claim 1, characterized in that: It also includes step 7: performing memory-driven adaptive response: continuously updating the context memory during the interaction process, incorporating newly acquired information into the memory storage, and combining transfer learning techniques to fine-tune the model, adaptively optimizing the response strategy for specific water conservancy tasks and scenarios, and enhancing generalization ability.
5. A dynamic arrangement and iterative optimization system for skill maps in the water conservancy field, characterized by: It includes a question rewriting module, an intent recognition module, an orchestration module, an optimization module, and a feedback iteration module. The question rewriting module performs memory-driven, industry-specific question rewriting: For user-input questions, it performs dynamic memory retrieval, extracting information related to the water resources topic from historical interaction data or a professional knowledge base, and then rewrites the user's question in an industry-specific manner based on the NLR2s model. The intent recognition module performs hybrid intent recognition and tool invocation routing: it recognizes the intent based on the industry-specific modified input question, converts the natural language intent into a recognizable operation target, and uses a pluggable routing strategy to intelligently select and invoke appropriate tools based on the intent recognition results. The orchestration module is based on dynamic skill graph orchestration using graph computing: it constructs a skill graph for the water conservancy field using graph computing technology, representing skill nodes and subtasks as directed acyclic graphs (DAGs). Combining routing strategies and intent recognition results, it dynamically generates execution plans by matching skill nodes and subtasks according to task requirements. The optimization module performs parameter self-diagnosis and multiple rounds of clarification: Before calling the tool, it checks the completeness of the input information using a parameter integrity flag. If any required parameters are found to be incomplete, it automatically initiates multiple rounds of clarification interaction to dynamically complete the missing information. The feedback iteration module performs ReAct closed-loop feedback and domain iteration: adopting the ReAct paradigm, it combines model reasoning with action to form a multi-round closed-loop interaction. In each iteration, the reasoning path is adjusted according to the feedback, and an early stopping mechanism is introduced to terminate the iteration in advance when the model execution plan generates results that meet expectations.
6. The system according to claim 5, characterized in that it is intended to... The recognition module uses a dual-path fusion approach of LLM and classifier to recognize intent, converting natural language intent into recognizable operational targets.
7. The system according to claim 5, characterized in that: It also includes a fusion module, which deeply integrates water conservancy digital twins: it connects the constructed skill map with the water conservancy digital twin platform, obtains physical water conservancy facilities and environmental data in real time through bidirectional data flow, and feeds back the simulation results to the physical system, forming a secure and traceable data interaction link. This virtual-physical closed loop of the digital twin is used to improve the accuracy and timeliness of solution evaluation.
8. The system according to claim 5, characterized in that: It also includes a response module, which performs memory-driven adaptive response: continuously updates the context memory during the interaction process, incorporates newly acquired information into the memory storage, and combines transfer learning technology to fine-tune the model, adaptively optimizes the response strategy for specific water conservancy tasks and scenarios, and enhances generalization ability.