Intelligent factory production job scheduling method and system based on deep reinforcement learning

The invention relates to an intelligent factory production job scheduling method based on deep reinforcement learning, and the method comprises the following steps: S1, obtaining processing data of each process of each task on a corresponding machine, and carrying out preprocessing of the data, and forming a training set; S2, constructing a deep reinforcement learning DQN model, wherein the deep reinforcement learning DQN model comprises a DQN deep learning network structure and a DQN reinforcement learning module; S3, training the deep reinforcement learning DQN model to obtain a trained deep reinforcement learning DQN model; and S4, pre-processing to-be-produced task scheduling data, and inputting the pre-processed to-be-produced task scheduling data into the trained deep reinforcement learning DQN model to obtain a scheduling arrangement of a production task process. According to the invention, rapid and efficient scheduling of the current production operation can be realized. 本发明涉及一种基于深度强化学习的智能工厂生产作业调度方法，