The invention discloses a long sequence dual-prediction and informer-based high-frequency
time sequence data effective transmission method for an intelligent factory, and relates to the field of intelligent manufacturing, and the method comprises the steps: firstly, building a cloud-edge collaborative long sequence dual-prediction architecture, then deploying a trained long
sequence prediction model at an edge gateway and a
cloud server of the architecture, and finally constructing a long
sequence prediction model; and finally, reducing the transmission quantity of high-
frequency data on line by adopting a long-sequence dual-prediction method, and ensuring the precision of the data. According to the method, the structure of a traditional dual-prediction method is improved, and the reasoning frequency of the prediction model is reduced through long
sequence prediction, so that the application frequency of the traditional method is greatly improved, and it is possible that the method is used for reducing the transmission quantity of high-
frequency data needed in the intelligent manufacturing process. And meanwhile, the latest
deep learning model informer is introduced and combined to solve the problems of gradient disappearance and sharp increase of model reasoning time caused by long sequence prediction, so that the transmission quantity reduction proportion and the application frequency of the long sequence dual-prediction method are further improved.