Automatic extraction technology of unstructured information from violation accident event database

In order to accurately extract information related to power operation violations and accidents from a large amount of unstructured text data, this paper proposes an unstructured information automatic extraction technology for the power operation violation accident event database. Using an improved K-means algorithm to cluster unstructured data in the violation accident event database, a database target retrieval model based on natural language semantics is used to extract real-time data from various preprocessed violation accident event databases according to user needs. The experimental results show that the proposed method has high accuracy and good efficiency in extracting unstructured data from various types of violation accident event databases. Efficiently extract important data such as accident time, location, cause, personnel involved, and equipment information from unstructured text from multiple sources, and convert it into structured format for storage in a database. This not only improves the efficiency of data processing, but also provides strong support for subsequent data analysis, fault diagnosis, safety supervision, etc.