Application scenario design and prospect of generative artificial intelligence(AI)in intelligent manufacturing and supply chain of traditional Chinese medicine
Intelligent manufacturing technologies,including databases,mathematical modeling,and information systems have played a significant role in process control,production management,and supply chain management in traditional Chinese medicine(TCM)industry.However,their ability to process and utilize unstructured data,such as research and development reports,batch pro-duction records,quality inspection records,and supplier documents,is relatively weak.For text,images,language,and other un-structured data,generative artificial intelligence(AI)technology has shown strong potential for development in extracting information,extracting knowledge,semantic retrieval,and content generation.Generative AI is expected to provide a feasible set of tools for the uti-lization of unstructured data resources in the TCM industry.Based on years of research and industrial application experience in TCM in-telligent manufacturing technology,this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data,analyzed the application value of generative AI in the TCM manufacturing process and supply chain,summarized four typical application scenarios,including intelligent pharmaceutical knowledge base/knowledge graph,intelligent on-the-job trai-ning,intelligent production quality control,and intelligent supply chain.Furthermore,this study also explained the data collection and processing,business process design,application potential,and value of each scenario based on industry demands.Finally,based on the integration of generative Al and TCM industrial models,the study proposed a preliminary concept of a smart industrial brain for TCM,aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.
artificial intelligencegenerative artificial intelligence(AI)traditional Chinese medicine(TCM)intelligent manu-facturingsupply chainlarge model
NETL
NSTL
万方数据
