基于智能制造环境的机械自动化生产控制系统的设计

Design of Mechanical Automatic Production Control System Based on Intelligent Manufacturing Environment

在智能制造环境下,对机械自动化生产控制系统的设计进行了深入研究。首先,分析机械自动化生产控制系统所需要满足的需求。在此基础上,提出了一种运用大数据分析技术的机械自动化生产控制系统架构,并采用自适应优化算法对生产进行实时调度与优化。然后,设计了一种基于深度强化学习的故障预测模型,该模型能准确预测设备发生故障的概率并能提出维护策略。最后,通过仿真实验验证了该系统的有效性,其为智能制造领域提供了一种高效且可靠的控制系统解决方案。

In the context of intelligent manufacturing,in-depth research has been conducted on the design of mechanical automation production control systems.Firstly,analyze the requirements that the mechanical automation production control system needs to meet.On this basis,a mechanical automation production control system architecture using big data analysis technology is proposed,and adaptive optimization algorithms are used for real-time scheduling and optimization of production.Then,a fault prediction model based on deep reinforcement learning was designed,which can accurately predict the probability of equipment failure and propose maintenance strategies.Finally,the effectiveness of the system was verified through simulation experiments,providing an efficient and reliable control system solution for the field of intelligent manufacturing.