To address the challenge of identifying hidden danger in substations from unstructured text, a novel dynamic analysis method is proposed. We first extract relevant information from the unstructured text, and then leverages a flexible distributed search engine built on Elastic-Search to handle the data. Following this, the hidden Markov model is employed to train the data within the engine. The Viterbi algorithm is integrated to decipher the hidden state sequences, facilitating the segmentation and labeling of entities related to hidden dangers. The final step involves using the Neo4j graph database to dynamically create a knowledge graph that visualizes hidden dangers in the substation. The effectiveness of the proposed method is demonstrated through a case analysis from a specific substation with hidden dangers revealed in the text records.

翻译：针对从非结构化文本中识别变电站隐性隐患的挑战，提出了一种新颖的动态分析方法。首先从非结构化文本中提取相关信息，并利用基于Elastic-Search构建的灵活分布式搜索引擎处理数据。随后，采用隐马尔可夫模型对引擎内的数据进行训练，并集成维特比算法解码隐藏状态序列，以实现隐患相关实体的切分与标注。最后，利用Neo4j图数据库动态构建知识图谱，可视化展示变电站中的隐性隐患。通过某特定变电站文本记录中揭露的隐患案例分析，验证了所提方法的有效性。