# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 2 System Architecture  
As the number of communication data nodes in the manufacturing system rises, the data volume increases and the equipment description becomes more detailed. This paper adopts the concept of cloud-edge collaboration and constructs data communication network architecture in a manufacturing environment to enable the reuse of multi-variety mixed-flow manufacturing and processing experience. The edge functions include constructing the processing actions' knowledge ontology, decomposing different task execution processes' actions, orderly combining the existing knowledge action primitives to obtain complex task-oriented processing procedures, storing the local resources and knowledge, and performing the matching for knowledge reuse. The cloud functions include deploying the overall manufacturing environment knowledge base, enabling the interaction between users and managers and the system, and supplementing and timely updating the edge-side knowledge.  
The system's framework is shown in Figure 1, which is composed of three parts: the manufacturing resource layer, the edge layer, and the cloud layer. The current manufacturing processes are shaped by the requirements regarding products' multi-variety, small batches, and high personalization and customization. In such multi-variety mixed-flow manufacturing systems, user needs are obtained directly through the cloud. After the user places an order on the web page, the order data is passed through the cloud, which, in turn, communicates with the edge server responsible for processing control. The edge server disassembles and refines the orders' processing tasks. After the processing procedure is determined, the specific execution plan is passed to the lowest layer (i.e., physical manufacturing resources). Once the manufacturing resource processing terminates, the processing completion status is set. The processing procedure's data is summarized, and the edge layer's and cloud layer's knowledge bases are updated. Compared with the existing cloud-edge collaboration three-tier architecture, to realize the knowledge sharing in the manufacturing environment better, we have deployed task-related functions on the edge side. In the data upload process, in addition to data preprocessing, the edge side will also decompose the processing tasks, extract reusable knowledge from the processing data and update the knowledge base. During the downward process of the control instruction, the edge side will decompose the task and find the knowledge primitives that can complete the task from the knowledge base. Effectively control the operation of manufacturing resources after the combination of knowledge primitives.  
Figure 1: Data communication network architecture based on cloud-edge collaboration.  
Manufacturing process data is characterized by large transmission volume [19], low latency, and high security. Using conventional cloud solutions for data communication results in high data transmission bandwidth costs and large delays. Therefore, the edge layer is deployed on the resource side to perform data preprocessing and caching, alleviating the transmission network's pressure. The edge layer is composed of two functional modules responsible for data transmission and data processing. The data transmission module includes intelligent edge gateways and wireless network cluster head nodes. The data processing module includes edge computing servers and edge knowledge bases, which perform processing tasks on the near-resource side, decompose and reorganize the equipment, and enable the equipment ontology modeling and knowledge extraction. The cloud-edge collaborative system architecture extracts knowledge that is suitable for manufacturing resources. The knowledge that is frequently used can be directly obtained on the edge side. Contrary, when the edge knowledge base does not find a matching task, the knowledge can be obtained through cloud communication for a wider search.