The invention relates to a multi-time scale
convolutional neural network soft measurement method based on an attention mechanism, and belongs to the technical field of soft measurement. The method comprises the following steps: 1, determining an auxiliary variable, carrying out
data processing, selecting an easily-measured variable related to a difficult-to-measure parameter as the auxiliary variable of a soft measurement model, and collecting a
time sequence of the auxiliary variable and the difficult-to-measure parameter; abnormal value
elimination is carried out on the collected
time sequence; 2, selecting an attention mechanism and an attention area, and dividing the attention area according to the time
delay and the
effective time scale of each auxiliary variable relative to the difficult-to-measure parameters; 3, constructing the input of a soft measurement model, forming a matrix by the
time sequence of each auxiliary variable, and determining the input of the soft measurement model by combining the attention area of the attention mechanism; 4, establishing a time sequence
convolutional neural network soft measurement model; 5, training a time sequence
convolutional neural network soft measurement model; and 6, carrying out real-
time estimation on unmeasured parameters by utilizing the time sequence convolutional neural
network model trained in the step 5.