[Application progress on data-driven technologies in intelligent manufacturing of traditional Chinese medicine extraction].

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.

[Construction of knowledge base of Chinese medicine manufacturing].

Chinese medicine pharmaceutical industry is in the process of digital and intelligent transformation. Intelligent methods are required for efficient analysis and mining of the valuable information in the history data including literature data, pharmaceutical big data, and expert knowledge. Therefore, it is urgent to establish a knowledge-driven intelligent system of pharmaceutical technologies of Chinese medicine for efficient supplying of high-quality Chinese medicinal products. The present study proposed the construction method of the knowledge base of Chinese medicine manufacturing, which was preliminarily established from literature mining, case-based reasoning, and real-time prediction based on vacuum belt drying process optimization. Integrating the technologies(such as deep learning, case-based reasoning, and simulation modeling), pharmaceutical mechanisms, and big data, the knowledge base of Chinese medicine manufacturing can realize knowledge automation and scientific decision-making. It provides an example for upgrading from experience-based manufacturing to intelligent Chinese medicine manufacturing.