中国中药杂志

2020, v.45(02) 221-232

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中药工业大数据关键技术与应用
Key technologies and applications of industrial big data in manufacturing of Chinese medicine

徐冰;史新元;罗赣;林兆洲;孙飞;戴胜云;张志强;肖伟;乔延江;
XU Bing;SHI Xin-yuan;LUO Gan;LIN Zhao-zhou;SUN Fei;DAI Sheng-yun;ZHANG Zhi-qiang;XIAO Wei;QIAO Yan-jiang;Department of Chinese Medicine Information Science,Beijing University of Chinese Medicine;Beijing Key Laboratory for Production Process Control and Quality Evaluation of Traditional Chinese Medicine,Beijing Municipal Science & Technology Commission;Engineering Research Center of Key Technologies for Chinese Medicine Production and New Drug Development,Ministry of Education of People's Republi

摘要(Abstract):

在中药制造由工业2.0向更高水平迈进的过程中,部分信息化和数字化基础较好的企业已积累了轻量级工业大数据,成为企业资产的一部分。为促进中药工业大数据的应用,该研究针对当前中药制造过程中存在的Sigma差距和知识匮乏等问题,提出了以价值创造为导向的中药工业大数据三层架构设计原理,即数据集成层、数据分析层和应用场景层。在数据集成层,总结了以传感器为基础的中药关键质量属性感知关键技术。在数据分析层,提出了由模型构建、验证、配置和维护组成的模型生命周期,介绍了智慧中药系统(iTCM)算法库和模型库。针对中药制造过程质量传递结构特点,开发了"分块-集成"建模,递进建模和路径建模等系统建模关键技术;针对中药制造过程高度专业性,提出"数据+机制"双重驱动的高阶智能建模关键技术。最后结合中药注射剂、中药口服固体制剂和中药配方颗粒生产应用场景和需求,介绍了基于工业大数据的中药生产工艺诊断、质量传递规律解析、实时放行检验和制剂处方智能设计典型案例。对中药工业大数据这一可再生资源的利用,将有效促进中药制造知识积累和质量效益提升,为实现中药制造智能化奠定基础。
Along 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.

关键词(KeyWords): 中药;智能制造;工业大数据;架构设计;质量传递结构;系统建模;传感器
Chinese medicine;intelligent manufacturing;industrial big data;architecture design;quality transfer structure;system modeling;sensor

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基金项目(Foundation): 国家工信部智能制造综合标准化与新模式应用项目(KYYY20170820);; 国家“重大新药创制”科技重大专项(2018ZX09201010);; 国家自然科学基金项目(81403112)

作者(Author): 徐冰;史新元;罗赣;林兆洲;孙飞;戴胜云;张志强;肖伟;乔延江;
XU Bing;SHI Xin-yuan;LUO Gan;LIN Zhao-zhou;SUN Fei;DAI Sheng-yun;ZHANG Zhi-qiang;XIAO Wei;QIAO Yan-jiang;Department of Chinese Medicine Information Science,Beijing University of Chinese Medicine;Beijing Key Laboratory for Production Process Control and Quality Evaluation of Traditional Chinese Medicine,Beijing Municipal Science & Technology Commission;Engineering Research Center of Key Technologies for Chinese Medicine Production and New Drug Development,Ministry of Education of People's Republi

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