Reservoir flood control risk analysis and disposal plan generation system based on AI big model

Abstract

The present invention discloses a reservoir flood control risk analysis and disposal plan generation system based on AI big model, including a data processing module: extracting valuable information from water conservancy plans and reports in various formats, converting it into structured data, and performing cleaning and quality improvement to form a data set for model training; a model fine-tuning and training module: selecting a basic big model, and training an intelligent generation model for reservoir flood control risk analysis and emergency disposal through fine-tuning technology according to needs; a human-computer interaction module: realizing effective interaction between users and the system, including a question-and-answer system, allowing users to query reservoir information, input flood event data for risk analysis, and obtain disposal plans; the system solves the problem that the existing technology of flood control disposal plan generation uses virtual reality technology and case reasoning technology. Although these methods can improve the accuracy and effectiveness of the plan, they can only reason for specific reservoirs and events and lack universality and generalization capabilities.

Description

Technical Field

[0001] The present invention belongs to the technical field of flood control; in particular, it relates to a reservoir flood control risk analysis and disposal plan generation system based on an AI large model. Background Art

[0002] As the core flood control project in flood prevention tasks, reservoirs bear vital responsibilities for flood prevention and disaster reduction. Over the years, reservoir dispatchers have accumulated a large number of disposal plans in the process of reservoir operation and management. These plan texts record rich disposal experience and methods, which are of great significance for improving the intelligent level of reservoir flood control dispatching and management. In order to achieve rapid and intelligent disposal in different flood control scenarios, it is necessary to deeply explore the experience and methods in these texts so that they can quickly propose effective solutions based on real-time information, thereby providing a strong decision-making basis for reservoir flood control work.

[0003] However, current flood control solutions are mainly recorded in natural language, which is complex and difficult to be directly processed and understood by computers. How to use computer technology to effectively process these solution texts, extract key information and make inferences to generate practical solutions has become an urgent problem to be solved.

[0004] In existing technologies, virtual reality technology and case-based reasoning technology are used to intelligently generate flood control and disposal plans. These methods generate emergency plans by reviewing the disaster process and matching the similarities between target cases and historical cases. Although these methods can improve the accuracy and effectiveness of the plans, they have the following limitations: they can only reason about specific reservoirs and events, lack universality and generalization capabilities, and are difficult to deal with new problems that have never occurred. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a reservoir flood control risk analysis and disposal plan generation system based on an AI large model, so as to solve the technical problems that the existing flood control disposal plan generation adopts virtual reality technology and case reasoning technology. Although these methods can improve the accuracy and effectiveness of the plan, they can only reason for specific reservoirs and events, lack universality and generalization capabilities, and are difficult to deal with new problems that have never occurred.

[0006] The technical solution of the present invention is: A reservoir flood control risk analysis and disposal plan generation system based on AI big model, the system comprising: Data processing module: extract valuable information from water conservancy plans and reports in various formats, convert it into structured data, and perform cleaning and quality improvement to form a data set for model training; Model fine-tuning and training module: select the basic large model and train the intelligent generation model for reservoir flood control risk analysis and emergency response through fine-tuning technology according to the needs; Human-computer interaction module: realizes effective interaction between users and the system, including question-answering system, allowing users to query reservoir information, input flood event data for risk analysis, and obtain disposal plans.

[0007] The system further comprises: Knowledge enhancement and interface integration module: Enhance the model's knowledge base and response capabilities through plug-in interfaces and knowledge graph technology to ensure the accuracy and reliability of model output.

[0008] The implementation method of the data processing module includes: collecting original data related to the reservoir from various sources, including water conservancy plan documents, flood control plans and historical data; using LLM to extract structured information from the original data in PDF and Word formats, and then adjusting it to a format suitable for model training through a data cleaning process, and performing manual correction to ensure data quality; the data cleaning process includes: format adjustment and manual correction of the extracted structured data to form a training data set.

[0009] The implementation of the model fine-tuning and training module includes: selecting Llama, MixTRAL or ChatGLM as the model base for fine-tuning; according to different training strategies, selecting Llama, MixTRAL or ChatGLM as the model base for fine-tuning; according to different training strategies, including mixed data model training, general model training and hallucination correction assisted by vector database retrieval strategy, to obtain the best risk analysis and disposal plan generation capabilities; the fine-tuning uses lora to fine-tune the model.

[0010] The implementation method of the human-computer interaction module includes: using the langchain framework to build a multi-round dialogue system, and combining the fine-tuned model to provide accurate information responses and solutions.

[0011] The knowledge enhancement and interface integration module defines the interface for interaction with external systems to achieve real-time data exchange; it implements data exchange and command calls between modules within the system to ensure the smoothness of system collaboration.

[0012] The beneficial effects of the present invention are: Based on the open source large language model at home and abroad, this paper adopts advanced fine-tuning technology to develop an intelligent generation model specifically for reservoir flood control risk analysis and emergency response measures. The model uses the reservoir's flood control plan and emergency dispatch plan as the data basis, and through deep natural language processing technology, it realizes intelligent analysis of flood risk events and automatic generation of response plans.

[0013] The present invention can receive basic information of flood events input by users through human-computer dialogue, intelligently analyze the risk impact of the event on reservoir projects and upstream and downstream towns, and generate corresponding treatment measures and suggestions accordingly. The reservoir flood control risk analysis and disposal plan generation system of the present invention can effectively utilize the reasoning ability of the large model, perform effective and correct risk event disposal plan reasoning according to flood risk prompts, and provide more scientific and intelligent decision support for reservoir flood control management.

[0014] The present invention solves the technical problems that the existing flood control scheme generation adopts virtual reality technology and case reasoning technology. Although these methods can improve the accuracy and effectiveness of the scheme, they can only reason for specific reservoirs and events, lack universality and generalization ability, and are difficult to deal with new problems that have never occurred. DETAILED DESCRIPTION

[0015] A reservoir flood control risk analysis and disposal plan generation system based on AI big model, including: Data processing module: Responsible for quickly and efficiently extracting valuable information from water conservancy plans and reports in various formats (such as PDF and Word), converting it into structured data, and performing cleaning and quality improvement to form a data set suitable for model training.

[0016] Implementation method: By defining data extraction prompts related to the water conservancy industry, and using the large language model (LLM) for automated data extraction and structural processing. The data cleaning part will be combined with manual verification to ensure the accuracy and consistency of the data.

[0017] This module is responsible for collecting reservoir-related raw data from various sources, including but not limited to water conservancy program documents, flood control plans, historical data, etc.

[0018] Data extraction and cleaning: Use LLM to extract structured information from raw data in PDF and Word formats, then adjust it to a format suitable for model training (such as QA pairs) through a data cleaning process, and perform manual corrections to ensure data quality.

[0019] Raw data processing: Extract raw data from water scheme documents and reports, and use LLM to extract structured information based on industry-specific prompts.

[0020] Data cleaning: Adjust the format and manually correct the extracted structured data to form a high-quality training data set.

[0021] Feature retrieval vector database construction: Use m3e to build a feature retrieval vector database for documents, and use Faiss for fast feature vector retrieval to support the needs of model training and intelligent analysis.

[0022] Use m3e and Faiss to build a feature retrieval vector database for documents to provide richer background knowledge and precise data support for the model.

[0023] Model fine-tuning and training module: Select a suitable basic large model and, based on demand, use fine-tuning technology to train an intelligent generation model specifically for reservoir flood risk analysis and emergency response.

[0024] Select a suitable large model (such as Llama, MixTRAL, ChatGLM, etc.) as the model base according to your needs and perform fine-tuning.

[0025] Model training: Train the model according to different training strategies (including hybrid data models and general models) to obtain the best risk analysis and disposal plan generation capabilities.

[0026] Different strategies are used for model training, including mixed data model training, general model training, and hallucination correction assisted by vector feature library.

[0027] Hallucination correction: Combine the knowledge graph and feature vector library to correct the hallucinations that may be produced by the model to ensure the accuracy of the output results.

[0028] Implementation method: Use Lora to fine-tune the large model, and combine multiple data training strategies (mixed data model, general model) to train the model to improve the accuracy and generalization ability of the model.

[0029] In terms of model fine-tuning, the present invention considers mainstream fine-tuning technologies such as Adapter, Prefix Tuning, Prompt Tuning, P-Tuning, IA3, and LoRA. After comparative analysis, LoRA (Low-Rank Adaptation) is selected as the fine-tuning of the present invention for the following reasons: Parameter efficiency: Compared with other fine-tuning techniques, LoRA achieves fine-tuning by adding a low-rank matrix to the weights of the pre-trained model, which greatly reduces the number of additional parameters and improves parameter efficiency.

[0030] Model performance: LoRA can improve model performance through fine-tuning without destroying the original model architecture, and is particularly suitable for fine-tuning large language models.

[0031] Flexibility and generalization: LoRA provides better flexibility and generalization, and is particularly suitable for the diverse water conservancy data and complex disposal plan generation tasks that the present invention needs to process.

[0032] Note: Hybrid data model - all data are included in the training without differentiated extraction; general knowledge model - general data is extracted and included in the training, and reservoir-specific data is not included in the Lora training; feature vector library - reservoir-specific data is used to build a feature vector library (similar to the CV field, facial features in face recognition systems).

[0033] When necessary, vector features and graph data are called to assist in correcting the model output accuracy.

[0034] Human-computer interaction module: realizes effective interaction between users and the system, including a question-and-answer system that allows users to query reservoir information, input flood event data for risk analysis, and obtain customized disposal plans.

[0035] This module implements the interaction logic with users, including query response, event submission processing, etc.

[0036] This module uses the loaded model to perform intelligent analysis on input data and generate treatment plans for specific flood risks.

[0037] Implementation method: Use the langchain framework to build a multi-round dialogue system, combined with the fine-tuned model to provide accurate information responses and solution suggestions.

[0038] Knowledge enhancement and interface integration module: In order to deal with the illusion of accurate data in the final model, the knowledge base and response capabilities of the model are enhanced through plug-in interfaces and knowledge graph technology to ensure the accuracy and reliability of the model output. This module can determine whether to add or not based on the final model training evaluation results.

[0039] Interface integration for: External system interaction: Define interfaces for interaction with external systems (such as meteorological services, other emergency management systems, etc.) to achieve real-time exchange of data.

[0040] Internal module communication: realize data exchange and command calling between modules within the system to ensure the smoothness of system collaboration.

[0041] Technical implementation: Integrate specific external data sources and service interfaces, and build and maintain knowledge graphs for hallucination correction and providing richer and more accurate information.

Claims

1. A reservoir flood control risk analysis and disposal plan generation system based on AI big model, characterized by: The system comprises: Data processing module: extract valuable information from water conservancy plans and reports in various formats, convert it into structured data, and perform cleaning and quality improvement to form a data set for model training; Model fine-tuning and training module: select the basic large model and train the intelligent generation model for reservoir flood control risk analysis and emergency response through fine-tuning technology according to the needs; Human-computer interaction module: realizes effective interaction between users and the system, including question-answering system, allowing users to query reservoir information, input flood event data for risk analysis, and obtain disposal plans.

2. According to claim 1, a reservoir flood control risk analysis and disposal plan generation system based on AI large model is characterized by: The system further comprises: Knowledge enhancement and interface integration module: Enhance the model's knowledge base and response capabilities through plug-in interfaces and knowledge graph technology to ensure the accuracy and reliability of model output.

3. According to claim 1, a reservoir flood control risk analysis and disposal plan generation system based on AI large model is characterized by: The implementation method of the data processing module includes: collecting original data related to the reservoir from various sources, including water conservancy plan documents, flood control plans and historical data; using LLM to extract structured information from the original data in PDF and Word formats, and then adjusting it to a format suitable for model training through a data cleaning process, and performing manual correction to ensure data quality; the data cleaning process includes: format adjustment and manual correction of the extracted structured data to form a training data set.

4. According to claim 1, a reservoir flood control risk analysis and disposal plan generation system based on AI large model is characterized by: The implementation of the model fine-tuning and training module includes: selecting Llama, MixTRAL or ChatGLM as the model base for fine-tuning; according to different training strategies, including mixed data model training, general model training and hallucination correction assisted by vector database retrieval strategy, to obtain the best risk analysis and disposal plan generation capabilities; the fine-tuning uses lora to fine-tune the model.

5. According to claim 1, a reservoir flood control risk analysis and disposal plan generation system based on AI large model is characterized by: The implementation method of the human-computer interaction module includes: using the langchain framework to build a multi-round dialogue system, and combining the fine-tuned model to provide accurate information responses and solutions.

6. According to claim 2, a reservoir flood control risk analysis and disposal plan generation system based on AI large model is characterized by: The knowledge enhancement and interface integration module defines the interface for interaction with external systems to achieve real-time data exchange; it implements data exchange and command calls between modules within the system to ensure the smoothness of system collaboration.