The application discloses a
fermentation process soft measurement method based on
convolution ONLSTM and self-attention, and belongs to the technical field of soft measurement. The method considers the redundant information in the
fermentation process variables, proposes a convolutional ordered
neuron long short-
term memory network (ONLSTM) multilayer
time series prediction model with a self-attention mechanism, first extracts local features of input variables in the
fermentation process by using a CNN and reduces the dimensionality; then inputs the extracted features into a multilayer ONLSTM network for
time series feature extraction, judges the importance of each input variable through a level, and filters the redundant information in the characteristic variables; finally, the feature weights are dynamically adjusted in combination with the self-attention mechanism, the internal dependency relationship between the input variables is utilized,
high weight is given to the high correlation variables, the full connection layer
activation function is optimized, high-precision prediction of the dominant variables in the fermentation process is realized, and the method is verified to have high-precision prediction on the
penicillin concentration by taking the
penicillin fermentation process as an example.