## 1 Introduction
[23] proposed an optical character recognition system to recognize characters printed on steel coils in the industry, in which the alignment and segmentation methods were used for filtering non-character components, and a CNN-based classification network was used for recognizing the characters. [PERSON] et al. [24] applied EfficientNet based on CNN to recognize characters of components mounted on the printed circuit board for decreasing defect detection. Most of methods are used to recognize English letters and digits in many areas. However, it is more difficult to recognize Chinese characters due to their complex and diverse features. The ResNet-based network [25] performs significantly in many applications and has extensive research in computer vision. In this article, we plan to explore a ResNet-based network to recognize Chinese characters for high recognition accuracy. The position, types, and timestamp of Chinese classes will be transmitted to cloud PLC that controls local PLC to conduct the multi-crane for completing material sorting. The control module plays a significant role in the industry, which aims to use PLCs [26] that sense the inputs, execute the program, and write the outputs. Traditional PLCs are connected to the equipment in the terminal, which cannot realize the decoupling of a traditional PLC and equipment. In addition, the data cannot be interconnected between different equipment due to different industrial control protocols. Many approaches were developed to break the traditional pyramid structure of the industrial internet. [PERSON] et al. (Ref. [27]) presented the architecture of a PLC programming environment that employed a virtual plant model consisting of virtual devices to support the specification of discrete event models. A hardware-in-the-loop simulation approach was proposed in the production system, which was directly used for the real plant with minor adaptations [28]. [PERSON] et al. [29] introduced cloud-based software PLCs to improve the performance of scalability and multi-tenancy, in which the equipment was controlled by the soft PLCs in the cloud that dynamically scaled and assigned workloads. [PERSON] et al. [30] showed a novel virtual-PLC approach to avoid significant perturbation of the remote attack in industrial control systems. [PERSON] et al. [31] presented a cloud PLC platform in human-machine interaction for real-time remote monitoring. However, the mentioned PLCs cannot be flexibly deployed in the cloud-terminal-edge. Many emerging technologies cannot be integrated into the industrial structure, such as artificial intelligence, big data, and 5G. In order to meet the requirement of flexibility and scalability in intelligent manufacturing, it is necessary to explore an integrated method to break the data island and improve the coordination between devices. The function of PLCs can be virtualized in the cloud, which not only decreases the cost of PLCs but also makes full use of computing resources to realize data intercommunication. Motived by the above methods, we developed a novel deep CNN-based multi-crane visual sorting system in intelligent manufacturing, which enables the accurate sorting of materials in real time. The system uses cloud PLC to control the collaborative work of a multi-crane in a 5G network for high reliability and low latency. The main contributions can be summarized as follows:  
1. We design a novel multi-crane visual sorting system that applies deep CNN algorithm for the material location and recognition, and propose a dynamic scheduling method of materials with cloud PLC in 5G network;
2. We apply the YOLOv5 algorithm to locate the position of materials in the industry and use a camera calibration method to realize coordinate conversion. Additionally, we explore a Chinese character recognition network (CCRNet) to significantly recognize the class of each object from the original image;
3.