# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 1 Introduction  
In recent years, the manufacturing model has shown a shift in personalized customized production, leading to the need for manufacturing companies to complete more frequent manufacturing task adjustments in a shorter time-to-market cycle, which requires the manufacturing system to quickly complete the production line planning and scheduling layer refactoring. In the existing multi-product mixed-flow manufacturing production line, improving the production ramp-up time during the reconfiguration process will be a key factor in achieving production reconfiguration. However, traditional manufacturing resources are limited by their hardware computing capabilities, and a lot of human interventions are required in the process of complex task reconstruction, such as re-programming and commissioning. With the development of information technology, the Internet of Things [1, 2], cloud computing [3, 4], edge computing [5, 6], big data, and AI (artificial intelligence) [7] technologies have become more and more extensive in the manufacturing field [8]. In this context, the cloud-edge collaborative architecture has given stronger computing intelligence capabilities to edge nodes such as manufacturing resources [9], making it possible to share information and learn knowledge between manufacturing resources. On this basis, the study of task decomposition and task primitive modeling to build a resource knowledge base, to realize the sharing of processing experience between equipment will be able to effectively improve the ramp-up time of production [10]. To optimize the data transmission in the intelligent manufacturing process and meet the requirements of the complex environment, [PERSON] et al. [11] proposed a solution to optimize the edge computing network through software-defined network management physical device interfaces and prioritize different data streams. Combine path differences and time limits to obtain the best routing path. [PERSON] et al. [3] used a vertically integrated four-layerCaSF (cloud-assisted smart factory) architecture to combine artificial intelligence and cloud computing to improve the scalability and reconfigurability of smart factories. Based on the existing cloud computing, fog computing, and edge computing, [PERSON] et al. [12] proposed a hierarchical reference architecture for intelligent manufacturing and applied it to the digital twin workshop to better meet the needs of intelligent manufacturing applications. Aiming at the complex energy consumption problem of manufacturing clusters, [PERSON] et al. [13] used energy perception and load balancing methods to effectively schedule equipment in smart factories. [PERSON] [14] and others introduced an edge gateway that connects and manages plant equipment on the edge side of the cloud-side collaboration architecture and uses the edge gateway to respond to delay-sensitive applications in real-time. To realize the reuse of manufacturing process knowledge, [PERSON] et al. [15] proposed a metadata-based modeling method for manufacturing resource ontology, which solves the problem of heterogeneous description of manufacturing resource ontology in the cloud environment through the standardized description of the ontology. In terms of similar robot service configuration, [PERSON] et al. [16] proposed an ontology-based service configuration method, which develops reusable service configuration by combining ontology and standard network interfaces. [PERSON] [17] researched the search of semantic environment model and map knowledge base based on the information exchange network between robots and used the mobile operation control of robots to reuse service configuration. Through the analysis of the design process and the manufacturing process, [PERSON] et al. [18] proposed a scheme to use design experience and equipment failure experience as knowledge to realize experience reuse through ontology modeling.