• Bing Xu, Xin-Yuan Shi, Gan Luo, Zhao-Zhou Lin, Fei Sun, Sheng-Yun Dai, Zhi-Qiang Zhang, Wei Xiao, and Yan-Jiang Qiao.
• Department of Chinese Medicine Information Science,Beijing University of Chinese Medicine Beijing 102400,China Beijing Key Laboratory for Production Process Control and Quality Evaluation of Traditional Chinese Medicine,Beijing Municipal Science & Technology Commission Beijing 102400,China Engineering Research Center of Key Technologies for Chinese Medicine Production and New Drug Development,Ministry of Education of People's Republic of China Beijing 102400,China.
• Zhongguo Zhong Yao Za Zhi. 2020 Jan 1; 45 (2): 221-232.

AbstractAlong with the striding of the Chinese medicine(CM) manufacturing toward the Industry 4.0, some digital factories have accumulated lightweight industrial big data, which become part of the enterprise assets. These digital assets possess the possibility of solving the problems within the CM production system, like the Sigma gap and the poverty of manufacturing knowledge. From the holistic perspective, a three-tiered architecture of CM industrial big data is put forward, and it consists of the data integration layer, the data analysis layer and the application scenarios layer. In data integration layer, sensing of CM critical quality attributes is the key technology for big data collection. In data analysis and mining layer, the self-developed iTCM algorithm library and model library are introduced to facilitate the implementation of the model lifecycle methodologies, including process model development, model validation, model configuration and model maintenance. The CM quality transfer structure is closely related with the connection mode of multiple production units. The system modeling technologies, such as the partition-integration modeling method, the expanding modeling method and path modeling method, are key to mapping the structure of real manufacturing system. It is pointed out that advance modeling approaches that combine the first-principles driven and data driven technologies are promising in the future. At last, real-world applications of CM industrial big data in manufacturing of injections, oral solid dosages, and formula particles are presented. It is shown that the industrial big data can help process diagnosis, quality forming mechanism interpretations, real time release testing method development and intelligent product formulation design. As renewable resources, the CM industrial big data enable the manufacturing knowledge accumulation and product quality improvement, laying the foundation of intelligent manufacturing.

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