The invention relates to a carbon
crystallization defect treatment technology in carbon
recovery, and aims to improve the production efficiency and avoid the carbon
crystallization defect. According to a plurality of historical
production cycle production reports of carbon
recovery, obtaining abnormal data of carbon
crystal defects and integrating the abnormal data into a defect comparison table; in the current
production cycle, carbon
crystal growth data are monitored in real time, and abnormal
growth data are identified and integrated into a predicted abnormal group by constructing a two-dimensional coordinate
system, setting a
reference line and an early warning line and analyzing a production change curve; performing normalization,
time sequence feature extraction and other
processing on the production data on the super early warning curve, constructing a multivariable
feature vector, selecting an LSTM architecture
time sequence model, and training a
large model by using historical sample data; the trained
large model is used for predicting the carbon
crystal defects in the current
production cycle, whether the defects occur or not is judged according to comparison between the
prediction probability and a threshold value, if yes, an alarm is generated, and technicians analyze reasons and intervene according to the alarm, so that the defects are effectively avoided, and the production efficiency and quality are improved.