面向电厂关键设备故障知识图谱构建的关系抽取方法研究

当今电厂面临着诸多挑战,包括电力设备种类繁多、设备数量庞大、故障类型众多、数据耦合关系复杂以及海量的故障信息数据等。知识图谱能够将各种信息整合、可视化呈现,并支持智能化应用,有助于人们更好地获取、管理和应用知识,从而提高效率、创造价值。运用知识图谱来分析电厂故障数据,有助于深入研究电厂设备故障情况。在构建知识图谱的过程中,关系抽取是关键步骤之一,其准确率直接影响最终知识图谱构建的质量。本文提出了一个面向电厂关键发电设备故障知识图谱构建的关系抽取工具,该工具能将故障信息中海量、异构的数据以及相关故障处理进行可视化表达,同时支持用户交互式地参与到关系抽取的过程中,通过迭代训练来优化关系抽取模型。在实验测试阶段,利用真实电厂设备故障数据进行验证,证明了该工具在显著提高关系抽取的准确率方面的有效性。因此,构建的知识图谱质量得以提升,为电厂管理人员更好地运维管理发电设备提供了重要支持,为管控电厂相关数据以及推动电厂完备建设提供有力支撑。

Research on relationship extraction method for constructing knowledge graph of key power plant generation equipment failure

Today''s power plants face many challenges, including a wide variety of power equipment, a large number of equipment, many types of faults, complex data coupling relationships, and massive fault information data. Knowledge graph can integrate various information, visualize and present, and support intelligent application, which helps people better acquire, manage and apply knowledge, thus improving efficiency and creating value. The use of knowledge graph to analyze power plant failure data helps to study the power plant equipment failure situation in depth. In the process of constructing knowledge graph, relation extraction is one of the key steps, and its accuracy directly affects the quality of the final knowledge graph construction. In this paper, we propose a relationship extraction tool for the construction of knowledge graph of key power plant equipment failures, which can visually represent the massive and heterogeneous data in the failure information as well as the related fault processing, and at the same time support the user to interactively participate in the process of relationship extraction, and optimize the relationship extraction model through iterative training. In the experimental testing phase, the validation using real power plant equipment fault data demonstrates the effectiveness of the tool in significantly improving the accuracy of relationship extraction. As a result, the quality of the constructed knowledge graph is improved, which provides important support for power plant managers to better operate, maintain and manage power generation equipment, and provides a strong support for controlling power plant related data as well as promoting the construction of a complete power plant.