The invention belongs to the technical field of IPv6
situation awareness, and relates to a power industry
structured text generation method based on an NLP technology, and the method comprises the steps: collecting sensor data, equipment logs, user reports and other multi-
source data from a power
system, and carrying out the preprocessing; next, key features are extracted by adopting primary
feature extraction and PCA dimension reduction technologies, standardized
processing is carried out on the key features,
feature data are labeled by utilizing power expert knowledge, the
labeled data are classified and sorted through a classification
algorithm, training is carried out by adopting a BERT model, and labeled key
feature data and text data are input; the method comprises the following steps: generating a text meeting the requirements of the power industry by using
supervised learning, generating a
structured text by iteratively optimizing a
feature extraction and classification strategy for multiple times and combining a pre-designed rule and a template, checking the text by using a
verification rule, carrying out quality evaluation on the text through a ROUGE evaluation index, further optimizing a model and generating the rule, and carrying out quality evaluation on the text. Therefore, the target of efficient and accurate power industry
text generation is achieved.