Because of big data on energy consumption, there is a lack of research on the discrete manufacturing system. The discrete manufacturing system has plenty of multi-source and heterogeneous data; it was challenging to collect real-time data. Recently, low carbon and green manufacturing is a hot field; especially, it can save electrical energy. This paper proposes a significant energy consumption data of a data-driven analysis framework, which promoting the energy efficiency of discrete manufacturing plant, equipment, and workshop production process. Firstly, put forward the evaluation standards of energy efficiency for discrete manufacturing shops. Then make energy-consumption data preprocessing. Efficiency optimization of big data mining method is put forward based on grid computing function. Design the discrete manufacturing system energy-consumption parameter values, then summarizes prediction algorithms and models in order to predict the results and the trends. Finally, introduce the application of a mobile phone shell manufacturing shop to verify the proposed framework. Further research will focus on energy-consumption data mining processing.

由于能耗大数据，对离散制造系统缺乏研究。离散制造系统拥有大量的多源异构数据；收集实时数据具有挑战性。低碳绿色制造是近期的热门领域；特别是，它可以节省电能。本文提出了一个数据驱动分析框架的显着能耗数据，促进离散制造工厂、设备和车间生产过程的能源效率。首先，提出离散制造车间能效评价标准。然后进行能耗数据预处理。提出了基于网格计算功能的大数据挖掘效率优化方法。设计离散制造系统能耗参数值，然后总结预测算法和模型，以预测结果和趋势。最后，介绍了一个手机壳制造车间的应用来验证所提出的框架。进一步的研究将集中在能耗数据挖掘处理上。