Most sensors have been taken up, which resulted in a massive data size, with the continuously growing IoT (Internet of Things) devices and communications infrastructure in development. The inspection of the manufacturer to identify product defects is one of the most common examples. In order to develop an effective inspection system with greater precision, this paper has been proposed a Fog Computing based Hybrid Deep-Learning Framework (FC-HDLF), that can find possible defective products. Since a large number of assembly lines can occur in a single factory, one of the main problems is how these data are processed in real-time. The system can handle incredibly large amounts of data by discharging the load from the central servers to the fog nodes. In this paper, there are two obvious advantages. Next, the Convolutional Neural Network (CNN) model is adapted to the fog computing environment, which improves its calculation performance considerably. The other is that a model of control is built that can display the form and extent of the defect simultaneously. A decision-making framework for multi-agents is built to ensure a production process architecture to optimize production processes.

随着不断发展的IoT（物联网）设备和通信基础设施的发展，大多数传感器已被占用，从而导致海量数据。检查制造商以识别产品缺陷是最常见的例子之一。为了开发一种具有更高精确度的有效检查系统，本文提出了一种基于雾计算的混合深度学习框架（FC-HDLF），可以发现可能存在缺陷的产品。由于单个工厂中可能会出现大量装配线，因此主要问题之一是如何实时处理这些数据。该系统可以通过将负载从中央服务器释放到雾节点来处理大量数据。在本文中，有两个明显的优点。下一个，卷积神经网络（CNN）模型适用于雾计算环境，从而大大提高了其计算性能。另一个是建立了可同时显示缺陷形式和程度的控制模型。建立了用于多主体的决策框架，以确保生产过程架构可以优化生产过程。