Data management is one obstacle in the production sector to be reconfigured and adapted through optimum parameterization in industry cyber-physical systems. This paper presents an intelligent data management framework for a cyber-physical system (IDMF-CPS) with machine-learning methods. A training approach based on two enhanced training procedures, running concurrently to upgrade the processing and communication strategy and the predictive models, is contained in the suggested reasoning modules. The method described spreads computational and analytical engines in several levels and autonomous modules to enhance intelligence and autonomy for controlling and tracking behavior on the work floor. The appropriateness of the suggested solution is supported by rapid reaction time and a suitable establishment of optimal operating variables for the required quality during macro- and micro-operations.

数据管理是生产部门的一个障碍，需要通过工业网络物理系统中的最佳参数化进行重新配置和调整。本文提出了一种具有机器学习方法的网络物理系统 (IDMF-CPS) 的智能数据管理框架。建议的推理模块中包含基于两个增强训练程序的训练方法，同时运行以升级处理和通信策略以及预测模型。所描述的方法在多个级别和自主模块中传播计算和分析引擎，以增强智能和自主性，以控制和跟踪工作场所的行为。