Intelligent interactive decision-making method for discrete manufacturing system

An intelligent interactive decision-making method for a discrete manufacturing system, comprising 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 a 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 a 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 parameters of the production scheduling optimization model converge, and saving and putting the production scheduling optimization model into an actual production scene,
• wherein in step 3, updating the state according to the prior knowledge in the memory specifically comprises:
• collecting the production data of production cycles different from step 1 from a discrete manufacturing workshop put into production as a training and updating sample, obtaining a high-reward state sm most similar to st in the memory through a similarity computation, obtaining a weighted sum of the state and st, obtaining a new workshop state
• s
• t
• *
• ,
• and using
• s
• t
• *
• as input of a neural network R, wherein a specific formula is as follows:
• s
• t
• *
• =
• α
• ⁢
• s
• t
• +
• β
• ⁢
• s
• m
• in the formula,
• s
• t
• *
• denotes an updated new state, st denotes a workshop state currently input, sm denotes a high-reward state most similar to st from the memory, and α and β denote weight parameters, wherein a selection formula of sm is as follows:
• s
• m
• =
• arg
• ⁢
• max
• s
• i
• ⁢
• (
• s
• i
• ·
• s
• t
• 
• s
• i
• 
• ⁢
• 
• s
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• 
• )
• ,
• s
• i
• ∈
• D
• in the formula, si denotes a workshop state recorded in the memory D,
• wherein the high-reward state sm is used as the prior knowledge to generate the new workshop state st* with the high reward, such that an iterative convergence speed of the production scheduling optimization model is improved, and training time of the production scheduling optimization model for discrete manufacturing is reduced.