Zhong Yi, Ru Chen-Lei, Zhang Bo-Li, Cheng Yi-Yu
Department of Chinese Medicine Science & Engineering,Zhejiang University Hangzhou 310058,China.
State Key Laboratory of Component Chinese Medicine,Tianjin University of Traditional Chinese Medicine Tianjin 300193,China.
Zhongguo Zhong Yao Za Zhi. 2019 Dec;44(24):5269-5276. doi: 10.19540/j.cnki.cjcmm.20191203.302.
According to the requirements for developing the quality control technology in Chinese medicine( CM) manufacturing process and the practical scenarios in applying a new generation of artificial intelligence to CM industry,we present a method of constructing the knowledge graph( KG) for CM manufacture to solve key problems about quality control in CM manufacturing process.Based on the above,a " pharmaceutical industry brain" model for CM manufacture has been established. Further,we propose founding the KG-based methodology for quality control in CM manufacturing process,and briefly describe the design method,system architecture and main functions of the KG system. In this work,the KG for manufacturing Shuxuening Injection( SXNI) was developed as a demonstration study. The KG version 1. 0 platform for intelligent manufacturing SXNI has been built,which could realize technology leap of the quality control system in CM manufacturing process from perceptual intelligence to cognitive intelligence.
根据中药制造过程中质量控制技术发展的要求以及新一代人工智能应用于中药产业的实际情况，我们提出一种构建中药制造知识图谱（KG）的方法，以解决中药制造过程中质量控制的关键问题。基于此，建立了中药制造的“制药工业大脑”模型。此外，我们提出建立基于知识图谱的中药制造过程质量控制方法，并简要描述知识图谱系统的设计方法、系统架构和主要功能。在这项工作中，开发了用于生产舒血宁注射液（SXNI）的知识图谱作为示范研究。构建了用于SXNI智能制造的知识图谱1.0版平台，可实现中药制造过程质量控制系统从感知智能到认知智能的技术跨越。
