“Utilizing Structured Prompt Language for Automated Entity-Relationship Extraction in Reservoir Dispatch”

In the field of reservoir dispatch, knowledge modeling plays a crucial role in extracting relevant information from texts to facilitate applications in this domain. By defining entities and relationships within reservoir dispatch texts, designing an ontology framework, and utilizing a structured prompt language, we can enhance the extraction of important triples related to reservoir dispatch.

The entity types identified in this study include dispatch procedures, regulation objects, dispatch requirements, dispatch mode, preconditions, dispatch measures, and dispatch goals. These entities are interconnected through relationships such as regulation, satisfaction, involvement, inclusion, take, and reach. By structuring this domain knowledge, we can provide a clear framework for large language models to extract information accurately.

To guide large language models in understanding and extracting reservoir dispatch information, a structured prompt language is developed. This language comprises labels such as Persona, Audience, Terminology, Instruction, Rule, Command, Format, and Example. These labels help establish a common language with the model, clarify specialized terminology, provide task instructions, impose rule constraints, define output formats, and offer examples for learning and guidance.

By incorporating artificial intelligence (AI) agents based on this structured prompt language, reservoir dispatch personnel can automate the extraction process from reservoir dispatch texts. The AI agent consists of modules for prompt setting, user information, API integration, and logging, enabling users to easily input texts, select appropriate models, and output extracted entities and relationships.

Using the Three Gorges-Gezhouba Water Conservancy Project dispatching procedure text as an example, we demonstrate the effectiveness of the AI agent in extracting triples related to reservoir dispatch. By showcasing examples of extracted triples, we highlight the value of domain knowledge modeling in enhancing information extraction and decision-making processes in reservoir management.

In conclusion, domain knowledge modeling, structured prompt language, and AI agents can significantly improve the efficiency and accuracy of extracting information from reservoir dispatch texts. By leveraging these tools and techniques, reservoir management professionals can better analyze, interpret, and utilize reservoir dispatch information to optimize water resource utilization and decision-making processes.