Process industry product quality short-term prediction method based on deep learning

The invention discloses a process industry product quality short-term prediction method based on deep learning, a prediction model of the method uses recently continuously monitored production process key data to predict product quality fluctuation in a period of time in the future, and the model is composed of parallel deep learning networks including an attention mechanism, a CNN and a BiLSTM. Aiming at the characteristics of nonlinearity, time dynamics, spatial correlation and the like of flow industrial process variable data, the deep learning method can better extract the characteristics in the data, can learn long-term and short-term time dependence, can better mine the implicit relationship in the data, greatly improves the prediction accuracy, and has better generalization ability. 本发明公开了一种基于深度学习的流程工业产品质量短时预测方法，该方法的预测模型利用近期连续监测的生产过程关键数据来预测未来一段时间的产品质量波动，模型由并行深度学习网络组成，包括注意力机制、CNN以及BiLSTM。本发明针对流程工业过程变量数据的非线性、时间动态性、空间相关性等特征，深度学习方法能够更好地提取数据中的特征，能够学习长期和短期的时间依赖，更好的挖掘数据中的隐性关系，大幅提升预测的精确率且具有更好的泛化能力。