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[Application progress on data-driven technologies in intelligent manufacturing of traditional Chinese medicine extraction].
- Source :
-
Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica [Zhongguo Zhong Yao Za Zhi] 2023 Nov; Vol. 48 (21), pp. 5701-5706. - Publication Year :
- 2023
-
Abstract
- The application of new-generation information technologies such as big data, the internet of things(IoT), and cloud computing in the traditional Chinese medicine(TCM)manufacturing industry is gradually deepening, driving the intelligent transformation and upgrading of the TCM industry. At the current stage, there are challenges in understanding the extraction process and its mechanisms in TCM. Online detection technology faces difficulties in making breakthroughs, and data throughout the entire production process is scattered, lacking valuable mining and utilization, which significantly hinders the intelligent upgrading of the TCM industry. Applying data-driven technologies in the process of TCM extraction can enhance the understanding of the extraction process, achieve precise control, and effectively improve the quality of TCM products. This article analyzed the technological bottlenecks in the production process of TCM extraction, summarized commonly used data-driven algorithms in the research and production control of extraction processes, and reviewed the progress in the application of data-driven technologies in the following five aspects: mechanism analysis of the extraction process, process development and optimization, online detection, process control, and production management. This article is expected to provide references for optimizing the extraction process and intelligent production of TCM.
Details
- Language :
- Chinese
- ISSN :
- 1001-5302
- Volume :
- 48
- Issue :
- 21
- Database :
- MEDLINE
- Journal :
- Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
- Publication Type :
- Academic Journal
- Accession number :
- 38114166
- Full Text :
- https://doi.org/10.19540/j.cnki.cjcmm.20230824.601