Application of Adaptive Artificial Bee Colony Algorithm in Reservoir Information Optimal Operation

Cui, Lijun

doi:10.31449/inf.v47i2.4031

Cited by 1 publication

(1 citation statement)

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“…Reservoir dispatching regulations are foundational documents for reservoir operation 1 . Water resource departments must strictly follow these regulations during operation to ensure the safety of hydraulic hub projects, thereby fully realizing the comprehensive benefits of flood control, power generation, navigation, and water resource utilization 2 .…”

Section: Introductionmentioning

confidence: 99%

Intelligent extraction of reservoir dispatching information integrating large language model and structured prompts

Yang,

Chen,

Zhu

et al. 2024

Sci Rep

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Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields.

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Section: Introductionmentioning

confidence: 99%