2results about How to "High dynamic" patented technology

The present application relates to the technical field of illumination parameter detection method and automation device, disclose a kind of illumination big data monitoring method and its illumination Internet of Things system, set up illumination sensor group, multiple groups of environmental parameter sensor group under the illumination demand environment, real-time data value is collected and fused to obtain each environmental parameter real-time value, the ideal value of each real-time value is dynamically predicted to obtain illumination;Based on the ideal value of illumination and the real-time value of illumination, the real-time control quantity of illumination is obtained, based on the ideal value of illumination and the predicted value of illumination, the predicted control quantity of illumination is obtained, according to the real-time value of illumination and the actual control quantity of illumination, the disturbance quantity of illumination is obtained;According to the difference between the accumulation of the predicted control quantity of illumination and the real-time control quantity of illumination and the disturbance quantity of illumination as the actual control quantity of illumination.Compared with the prior art, the present application improves the accuracy of illumination adjustment, illumination adjustment benefit and illumination parameter monitoring effect.

This invention discloses a hierarchical dynamic scheduling method based on the policy optimization DDQN algorithm for solving the dynamic scheduling problem of a reentrant hybrid pipelined workshop with batch processing machines. First, the objective function and constraints of the scheduling problem are determined. Then, by introducing a self-attention mechanism, a hierarchical structure based on DDQN is proposed, constructing two agents: a batching agent and a scheduling agent, to solve the batching and scheduling subproblems respectively. Furthermore, to address the multi-stage batch processing and reentrant scheduling characteristics of the problem, a Markov decision process considering the characteristics of these two agents is designed, including state, action, and reward. Further, an action selection strategy based on masking combined with an ε-greedy strategy and a soft-start target network update strategy are proposed to improve efficiency and generalization ability. This invention demonstrates significant effectiveness in solving the dynamic scheduling problem of a reentrant hybrid pipelined workshop with batch processing machines.