水电工程施工安全隐患文本智能分类与知识挖掘北大核心CSCD

基于隐患排查信息的知识挖掘对于工程安全管理具有重要的支持作用。自然语言处理(natural language processing,NLP)技术是目前实现文本知识挖掘的重要方法,知识挖掘的深度和精度是该类方法的重要衡量指标。为了提升水电工程安全隐患文本知识挖掘效率,本文提出了一种结合文本分类与文本挖掘技术的隐患文本知识挖掘方法。该方法利用RoBERTa-wwm-CNN混合深度学习模型进行隐患文本快速智能分类,在此基础上,通过绘制隐患词云图实现不同种类隐患管理要点的可视化分析,以词共现网络构建为基础,分析隐患数据间的内在联系。将该方法应用于白鹤滩水电站安全隐患文本挖掘分析,与现有较先进的文本分类模型相比,本文所提模型精度有所提升,验证了所提模型的优越性。

Intelligent text classification and knowledge mining of hidden safety hazards in hydropower engineering construction

Knowledge mining based on hidden danger troubleshooting information plays an important role in supporting engineering safety management; the natural language processing (NLP) technology is an important method to realize text knowledge mining. The depth and accuracy of knowledge mining are the key indicators of such methods. This paper presents a new hidden hazard text knowledge mining method that combines text classification and text mining technology to improve its efficiency in application to hydropower projects. It uses the RoBERTa-wwm-CNN hybrid deep learning model to make a fast intelligent classification of hidden hazard texts. On this basis, it realizes a visual analysis of the key points of hidden danger management through drawing a nephogram for hidden hazard words, and analyzes inner links among latent danger data via constructing a word co-occurrence network. Application to a hydropower station for comparison with the existing advanced text classification models shows that our new model is better in accuracy and applicability.