To address the challenge of identifying and understanding hidden dangers in substations from unstructured text data, a novel dynamic analysis method is proposed. This approach begins by analyzing and extracting data from the unstructured text related to hidden dangers. It then leverages a flexible, distributed data search engine built on Elastic-Search to handle this information. 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 map that visualizes hidden dangers in the substation. This method's effectiveness is demonstrated through an example analysis using data from a specific substation's hidden dangers.

翻译：针对从非结构化文本数据中识别和理解变电站隐患这一挑战，提出了一种新型动态分析方法。该方法首先对与隐患相关的非结构化文本进行解析与数据提取，随后利用基于Elastic-Search构建的灵活分布式数据搜索引擎处理这些信息。在此基础上，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，从而实现隐患相关实体的切分与标注。最后，借助Neo4j图数据库动态构建可视化变电站隐患知识图谱。通过某变电站隐患数据的实例分析，验证了该方法的有效性。