面向发电领域文本数据结构化方法研究与应用

Research and Application of Text Data Structuring in Field of Power Generation

随着互联网的发展,发电领域从业者亟需利用智能化、自动化的手段来处理庞大的电力设备信息,而现有的标注工具难以支撑专业领域的文本数据标注工作以实现知识图谱的构建。因此基于深度学习设计了一种结合多种算法模型的文本数据结构化标注工具,优化语料标注过程,实现语料的精准标注,从而支持发电领域知识图谱的构建,优化电厂设备故障数据管理。基于真实电厂设备故障数据分析结果,验证了所提出的文本标注工具,为建立发电设备故障诊断可视分析系统提供极大可能性,有效地提高了电厂的设备管理能力和智能化层次。

With the development of the Internet,practitioners in the field of power generation urgently need to use intelligent and automated means to process huge power equipment information,and the existing annotation tools are difficult to support text in professional fields.Data annotation work to realize the construction of knowledge graph.Therefore,based on deep learning,this paper designs a text data structuring method that combines multiple algorithms to optimize the text labeling process and achieve accurate labeling of corpus.The construction of knowledge graphs in the field of power generation optimizes the management of power plant equipment failure data.Based on the analysis results of real power plant equipment fault data,it is verified that the proposed text data structuring method provides great possibilities for establishing a visual analysis system for power plant fault diagnosis,and effectively improves the equipment management ability.