| CPC G06V 10/774 (2022.01) | 4 Claims |
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1. A learning data generation apparatus comprising:
a collection unit for collecting at least one among structured data and unstructured data; and
a processing unit for generating a learning data set for learning of an artificial intelligence model that predicts and diagnoses anomalies of a plant facility using at least one among the structured data and the unstructured data, wherein
the unstructured data is an external image of the plant facility acquired by an image sensor, and
when the learning data set is generated from the structured data, the processing unit extracts feature information of anomaly data by classifying the structured data into normal data corresponding to a normal pattern and anomaly data corresponding to an anomaly pattern by using a time domain analysis, extracts feature information of the anomaly data from the structured data through a frequency domain analysis in order to grasp a feature pattern that is unknown through the time domain analysis, analyzes a correlation between the anomaly data among the structured data by analyzing magnitude and frequency of occurrence by frequency for the extracted feature information of the anomaly data in consideration of a time domain and a frequency domain, and generates the learning data, after generating a plurality of matrix maps on the basis of the correlation between the analyzed anomaly data, by grouping the matrix maps by anomaly type, and
when the learning data set is generated from the unstructured data, the processing unit sets a region of interest in the external image, extracts features from the region of interest and stores the features in combination with location information of an object in the region of interest, classifies the extracted features into normal features and abnormal features, generates a labeling data set by performing labeling on the region of interest or the external image containing the anomaly features, and generates the learning data set on the basis of the labeling data set, and
wherein the processing unit generates the learning data set by fusing a learning data set based on the structured data and a learning data set based on the unstructured data.
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