The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a...The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a medical examination system, which served as the cornerstone for the subsequent evolution of medical education. According to historical records, the Song government established dedicated medical departments, along with comprehensive systems encompassing medical professors, students, and examinations. By examining extant medical historical documents, such as Tai Yi Ju Zhu Ke Cheng Wen Ge(《太医局诸科程文格》 Examination Answers and Standards of the Imperial Medical Bureau), researchers and readers can obtain a comprehensive understanding of the medical system that prevailed in the Song dynasty. While the intricate details of medical education during this era are not explicitly documented in historical records, modern researchers have the opportunity to uncover the entire view of medical education, particularly the medical examination system, through rigorous analysis of these extant historical medical documents. Such studies offer valuable insights into the developmental trajectory of the ancient Chinese medical examination system and provide crucial references for contemporary medical education. By conducting in-depth literature research and analysis of Tai Yi Ju Zhu Ke Cheng Wen Ge, this study endeavors to reconstruct the authentic scenario of medical examinations in the Song dynasty, as presented in the document, for the benefit of modern readers and researchers.展开更多
铁路行业标准体系庞大、结构复杂且更新频繁,传统人工检索方式存在效率低、跨版本比对难等问题,制约了标准的贯彻实施与业务效率提升。为解决这一痛点,本文提出一种基于检索增强生成(Retrieval-augmented Generation,RAG)技术的铁路行...铁路行业标准体系庞大、结构复杂且更新频繁,传统人工检索方式存在效率低、跨版本比对难等问题,制约了标准的贯彻实施与业务效率提升。为解决这一痛点,本文提出一种基于检索增强生成(Retrieval-augmented Generation,RAG)技术的铁路行业标准智能问答系统。该系统结合大语言模型(Large Language Model,LLM)的语义理解与文本生成能力,依托向量数据库和知识图谱构建铁路标准知识库,实现标准文档的结构化和语义化管理。实验结果表明,相较于传统大模型,该系统在准确率、召回率、F1值及响应速度上均有显著提升,能满足设计、施工、运维等环节的标准快速查询、跨版本比对及版本追溯需求,有效提升了工作效率与决策质量,为铁路标准数字化转型提供了可行的技术路径。展开更多
文摘The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a medical examination system, which served as the cornerstone for the subsequent evolution of medical education. According to historical records, the Song government established dedicated medical departments, along with comprehensive systems encompassing medical professors, students, and examinations. By examining extant medical historical documents, such as Tai Yi Ju Zhu Ke Cheng Wen Ge(《太医局诸科程文格》 Examination Answers and Standards of the Imperial Medical Bureau), researchers and readers can obtain a comprehensive understanding of the medical system that prevailed in the Song dynasty. While the intricate details of medical education during this era are not explicitly documented in historical records, modern researchers have the opportunity to uncover the entire view of medical education, particularly the medical examination system, through rigorous analysis of these extant historical medical documents. Such studies offer valuable insights into the developmental trajectory of the ancient Chinese medical examination system and provide crucial references for contemporary medical education. By conducting in-depth literature research and analysis of Tai Yi Ju Zhu Ke Cheng Wen Ge, this study endeavors to reconstruct the authentic scenario of medical examinations in the Song dynasty, as presented in the document, for the benefit of modern readers and researchers.
文摘铁路行业标准体系庞大、结构复杂且更新频繁,传统人工检索方式存在效率低、跨版本比对难等问题,制约了标准的贯彻实施与业务效率提升。为解决这一痛点,本文提出一种基于检索增强生成(Retrieval-augmented Generation,RAG)技术的铁路行业标准智能问答系统。该系统结合大语言模型(Large Language Model,LLM)的语义理解与文本生成能力,依托向量数据库和知识图谱构建铁路标准知识库,实现标准文档的结构化和语义化管理。实验结果表明,相较于传统大模型,该系统在准确率、召回率、F1值及响应速度上均有显著提升,能满足设计、施工、运维等环节的标准快速查询、跨版本比对及版本追溯需求,有效提升了工作效率与决策质量,为铁路标准数字化转型提供了可行的技术路径。
