Abstract: The invention discloses a visualization method for process monitoring based on bi-kernel t-distributed stochastic neighbor embedding. It includes two steps of offline modeling and online monitoring. In offline modeling, standard t-SNE method is used to reduce the dimension of historical normal data. The mapping parameter matrix from the input kernel matrix to the feature kernel matrix is calculated. PCA is used to reduce the feature kernel matrix to two dimensions, and then the square Mahalanobis distance is calculated as a statistic and the control limit is solved. Online monitor and calculate the kernel function to between the collected data and the modeling data; and the obtained kernel vector is multiplied by the mapping parameter matrix to obtain the mapped feature kernel vector. PCA is used to reduce the dimension of the mapped feature kernel vector to obtain two-dimensional features for visualization. Draw the scatter diagram of the feature and observe whether it is within the ellipse control limit.

Abstract: The invention discloses a convolution self-encoding fault monitoring method based on batch imaging, and belongs to the technical field of batch process fault monitoring. The method comprises two steps of off-line modeling and on-line monitoring. The offline modeling step comprises the following steps: firstly, normalizing three-dimensional data of intermittent process; then, taking the two-dimensional array of each batch as an image to be directly input into a convolutional auto-encoder (CAE) to carry out deep unsupervised feature learning; and finally, constructing statistics and corresponding control limits for the features learned by CAE by utilizing a support vector machine. The online monitoring step includes: normalizing the collected data, and carrying out batch filling; inputting the normalized and filled batch graph into trained CAE to learn features; and calculating an online statistic, comparing online statistic with an offline control limit.

Abstract: Provided by the present invention are a novel GLP-1 fusion protein and a conjugate thereof, a pharmaceutical composition containing same, and a use thereof in reducing blood sugar or body weight, especially for treating diabetes, in particular type II diabetes.