Abstract: The present invention discloses a method for constructing an intelligent computation engine of an artificial intelligence cross-platform model based on knowledge self-evolution. The method comprises: determining source and target moments; dividing a discrete manufacturing system data set; initializing a dynamic discrete manufacturing system model; preprocessing data and constructing a task pool; constructing a meta learning framework; migrating a trained neural network to new tasks; iterating until convergence and storing model parameters; and testing in a new environment. This invention shortens the convergence time of model parameters, significantly benefiting the training of dynamic discrete manufacturing models subject to temporal disturbances in actual production.

Abstract: Disclosed is an intelligent interactive decision-making method for a discrete manufacturing system. The method includes the following steps: step 1, establishing a production scheduling optimization model and strategy for discrete manufacturing for an actual application scene; step 2, training the scheduling strategy with existing production data on the basis of a deep reinforcement learning algorithm, and storing a state having a high reward in a training process in a memory; step 3, updating the state according to prior knowledge in the memory; step 4, inputting the updated state into a deep reinforcement learning network, obtaining a corresponding reward, and updating the memory according to the reward; and step 5, repeating step 4 until model parameters converge, and saving and putting the model into an actual production scene.

Abstract: Disclosed is a characterization method based on deep reinforcement learning for discrete manufacturing industry data. The method includes: collecting discrete manufacturing industry data, and creating a spatio-temporal database; dividing the discrete manufacturing industry data into a discrete feature and a continuous feature, creating a data coupling coding network, converting a coding vector in the coding network into a characterization vector, and creating a data characterization model; quantitatively characterizing discrimination of a data category by means of cluster evaluation indexes; and using weights of cluster evaluation indexes of different dimensions as dynamic rewards, creating a deep reinforcement learning model, and updating a neural network parameter of deep reinforcement learning through characterization of an interactive relation between a model and a discrete manufacturing decision-making analysis system.