# Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources
## 6 Conclusions and Future Work  
In the face of the current growing demand for personalized customization in the manufacturing field, the importance of dynamic reconfiguration of production lines is becoming more and more important. To solve the problem of knowledge reuse of manufacturing re-source processing experience in the production process, this paper builds on the existing cloud-edge collaboration architecture and deploys services for manufacturing process tasks on the edge side, reducing the system's dependence on the cloud; the task decomposition scheme makes knowledge reuse granular and enhances the versatility of knowledge; the knowledge matching algorithm has high accuracy and can well meet the needs of the existing manufacturing environment. In the case study, we manually configure part of the equipment on the production line to achieve the effect of learning sample input. After the knowledge extraction is completed on the edge side, the knowledge is learned and reused according to the requirements of each equipment processing task. The experimental results selected two groups of control groups for comparison. The experimental results well prove the effectiveness of our proposed scheme and reflect the accuracy of the learning process through the action reduction degree, and through the learning between different tasks, it reflects the improvement of the generality of knowledge by task decomposition.  
However, there are still some noteworthy issues in this study that need to be mentioned. First of all, regarding the establishment of the manufacturing process knowledge model, this article only proposes a reference plan for specific processing tasks and scenarios. If you want to apply it to large-scale practical applications, you need to establish a unified standard for the model. Secondly, in terms of knowledge matching, the initial primitive knowledge is heavily dependent, and when there is insufficient knowledge accumulation on the cloud and edge, new knowledge primitives need to be manually input. In the follow-up research, because of the above problems, there can be a clearer research plan: combining the existing knowledge graph research results, in the process of knowledge extraction, the existing sample data information is transformed into a semantic model and a knowledge graph. When a new data source is added, the mapping relationship of data nodes can be established based on the information on the existing knowledge graph, and a more complete knowledge system can be formed through the combination of ontology modeling and knowledge graphs. In terms of knowledge derivation, the method of entity alignment is adopted to quickly reuse knowledge and transfer knowledge between different fields and enhance the universality of knowledge.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Joint Fund of the National Natural Science Foundation of China and Guangdong Province (No. U1801264), and the Natural Science Foundation of Guangdong Province, China (No. 2021A1515011946).  
Not applicable.  
The authors thank Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing.  
The authors declare no conflict of interest.