基于EG-UIE的危化品事故处置措施实体识别方法研究OA

The emergency response to hazardous chemical accidents heavily relies on the rapid matching of historical cases with disposal strategies.However,existing accident reports and disposal plans are mostly un-structured texts,making precise and efficient strategy retrieval difficult in emergency situations,which restricts the speed of emergency response.The root cause lies in the cross-dimensional and multi-entity nested character-istics of hazardous chemical incident information,which prevent traditional methods from accurately identifying and mapping key disposal elements.To address these issues,a disposal measure entity recognition model was constructed based on the Universal Information Extraction(UIE)framework,integrating the Enhanced Lan-guage Representation with Informative Entities(ERNIE)pre-trained model and a Bidirectional Gated Recurrent Unit(BiGRU),effectively achieving cross-dimensional entity relationship mapping and nested entity recogni-tion.The model was trained and tested on a dataset comprising 1,074 entries from the Material Safety Data Sheet(MSDS)database and historical accident reports,achieving an overall precision(P),recall(R)and F1-score of 96.82%,96.98%,96.9%,respectively.The accuracy of this model demonstrated a distinct improve-ment compared to traditional models.This study applies entity extraction technology to the emergency response phase of hazardous chemical safety accidents,providing a foundation for the generation of accident disposal plans and emergency decision support,and offering crucial technical support for the future development of intel-ligent emergency decision support systems.