PDF(1864 KB)
Fog-IBDIS——基于雾计算的制造系统大数据集成方法
Junliang Wang, Peng Zheng, Youlong Lv, Jingsong Bao, Jie Zhang
工程（英文） ›› 2019, Vol. 5 ›› Issue (4) : 662-670.
PDF(1864 KB)
PDF(1864 KB)
Fog-IBDIS——基于雾计算的制造系统大数据集成方法
Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems
在工业领域，如何在多源工业数据的协作分析中保障源数据的私密性与安全性至关重要。本文提出了一种基于雾计算的工业大数据集成与共享方法（Fog-IBDIS），采用云端与边缘端协作的方式，实现工业数据的分布式本地处理，在多源数据的分析中保障源数据的私密性与安全性。首先，在云端设计了任务流图，将多源数据分析过程分解成多个子任务；其次，设计了子任务管理、编译和运行控制、数据集成传输、基本算法库和管理组件五个模块，实现子任务的本地边缘端处理；最后，本文以大型客机制造过程为例，对Fog-IBDIS的运行过程进行了验证，其通过边缘与云端的协作方式，将多来源数据分析任务分解至本地执行，通过中间结果的传输串联实现工业大数据的分析，可保障原始数据的私密性与安全性。
Industrial big data integration and sharing (IBDIS) is of great significance in managing and providing data for big data analysis in manufacturing systems. A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks. First, a task flow graph (TFG) is designed to model the data analysis process. The TFG is composed of several tasks, which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy. Second, the function of Fog-IBDIS to enable data integration and sharing is presented in five modules: TFG management, compilation and running control, the data integration model, the basic algorithm library, and the management component. Finally, a case study is presented to illustrate the implementation of Fog-IBDIS, which ensures raw data security by deploying the analysis tasks executed by the data generators, and eases the network traffic load by greatly reducing the volume of transmitted data.
Fog computing / Industrial big data / Integration / Manufacturing system
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