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面向学科撤销与科研人员重分配的多阶段耦合决策框架

高飞1,陈董1,2,3,4,边帝行1,范文强1,刘起东1,2,3,4,吕培1,2,3,4,张朝阳1,2,3,4,徐明亮1,2,3,4   

  1. 1.郑州大学 计算机与人工智能学院 2.智能集群系统教育部工程研究中心 3.国家超级计算郑州中心 4.河南省大模型技术与新质软件工程研究中心
  • 收稿日期:2025-03-16 修回日期:2025-05-10 发布日期:2025-06-05 出版日期:2025-06-05
  • 通讯作者: 徐明亮
  • 作者简介:高飞(2001—)，男，河南洛阳人，硕士研究生，CCF会员，主要研究方向：自然语言处理；陈董(1994—)，男，河南郑州人，讲师，博士，主要研究方向：大模型决策、大小模型协同、跨媒体异常检测；边帝行(2002—)，男，河南郑州人，硕士研究生，主要研究方向：自然语言处理；范文强(1999—)，男，河南驻马店人，硕士研究生，主要研究方向：自然语言处理；刘起东(1993—)，男，河南新乡人，教授，博士，主要研究方向：强化学习、路径规划；吕培(1986—)，男，河南郑州人，教授，博士，主要研究方向：人工智能、虚拟现实、人机融合智能系统；张朝阳(1986—)，男，河南郑州人，教授，博士，主要研究方向：人工智能、虚拟现实、人机融合智能系统；徐明亮(1981—)，男，河南信阳人，教授，博士，主要研究方向：人工智能、大数据、机器人、工业软件。
  • 基金资助:
    国家自然科学基金资助项目（62325602，62036010，62276238，U24A20326）;河南省教育委员会基金资助项目（25HASTIT034）；河南省自然科学基金资助项目（232300421095）

Multi-stage coupled decision-making framework for discipline revocation and researcher reallocation#br#
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GAO Fei1, CHEN Dong1,2,3,4, BIAN Dixing1, FAN Wenqiang1, LIU Qidong1,2,3,4, LYU Pei1,2,3,4, ZHANG Chaoyang1,2,3,4, XU Mingliang1,2,3,4   

  1. 1.School of Computer Science and Artificial Intelligence, Zhengzhou University 2.Engineering Research Center of Intelligent Cluster System, Ministry of Education 3.National Supercomputing Center 4.Henan Province Large Model Technology and New Software Engineering Research Center
  • Received:2025-03-16 Revised:2025-05-10 Online:2025-06-05 Published:2025-06-05
  • About author:GAO Fei, born in 2001, M. S. candidate. His research interests include natural language processing. CHEN Dong, born in 1994, Ph.D., lecturer. His research interests include large model decision-making, collaboration between large and small models, cross-media anomaly detection. BIAN Dixing, born in 2001, M. S. candidate. His research interests include natural language processing. FAN Wenqiang, born in 1999, M. S. candidate. His research interests include natural language processing. LIU Qidong, born in 1993, Ph.D., professor. His research interests include reinforcement learning, path planning. LYU Pei, born in 1986, Ph.D., professor. His research interests include artificial intelligence, virtual reality, human-machine integrated intelligent systems. ZHANG Chaoyang, born in 1986, Ph.D., professor. His research interests include artificial intelligence, virtual reality, human-machine integrated intelligent systems. XU Mingliang, born in 1981, Ph.D., professor. His research interests include artificial intelligence, big data, robotics, industrial software.
  • Supported by:
    National Natural Science Foundation of China (62325602, 62036010, 62276238, U24A20326), Foundation of Henan Educational Committee (25HASTIT034), Natural Science Foundation of Henan (232300421095).

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摘要： 现有学科撤销与科研人员重分配依赖人工决策，难以有效统筹学科关联。在此背景下，拥有强大知识分析能力的大语言模型给基于学科撤销与科研人员重分配的学科优化提供了新的思路，但在以科研信息为代表的高校科研数据上也面临着专业术语难理解、长尾分布明显等挑战。针对上述挑战，提出一种面向撤销学科科研人员重分配的多阶段耦合决策框架MCRF（Multistage Coupled Redeployment Framework）。MCRF包含召回、语义增强、配对和重排4个阶段，能有效将困难问题分解为多个相对简单的子问题。首先，构建学科科研词云关联数据集，以缓解通用模型难以理解专用学术名词的问题；其次，设计了关联召回算法以快速召回科研信息的Top-K关联学科，进而降低整体决策时间开销；此外，引入了隐式优化模块，从而生成多样化的科研信息表述，确保尾部学科科研信息能与科研人员研究方向全面关联，并通过细粒度科研项目排序模型实现精准语义匹配。实验结果表明，在召回阶段，所提框架在数据集上的召回率达到了92%，在重排阶段准确率为95%，这些结果有效验证了MCRF在学科结构优化任务中的有效性。

关键词: 大语言模型, 科研词云, 学科结构优化, 科研信息, 语义匹配

Abstract: The existing discipline withdrawal and researcher redistribution rely on manual decision-making, which made it difficult to effectively coordinate discipline associations. In this context, large language models with strong knowledge analysis capabilities were utilized to provide new ideas for discipline optimization based on discipline withdrawal and researcher redistribution. However, university research data represented by scientific research information faced challenges such as difficult to understand professional terms and obvious long-tail distribution. In response to the above challenges, a multi-stage coupled decision framework MCRF (Multistage Coupled Redeployment Framework) for the redistribution of researchers in revoked disciplines was proposed. MCRF was composed of four stages: recall, semantic enhancement, pairing, and rearrangement, which effectively decomposed difficult problems into multiple relatively simple sub-problems. Firstly, a subject research word cloud association dataset was constructed to alleviate the problem where general models had difficulty understanding specialized academic terms; secondly, an association recall algorithm was designed to quickly recall Top-K related subjects of scientific research information, thereby reducing the overall decision-making time overhead; additionally, an implicit optimization module was introduced to generate diverse representations of scientific research information, ensuring that tail subject research information could be fully associated with researchers' research directions, and accurate semantic matching was achieved through a fine-grained scientific research project ranking model. The experimental results showed that in the recall stage, the recall rate of the proposed framework on the dataset reached 92%, and the accuracy rate in the rearrangement stage was 95%. These results effectively verify the effectiveness of MCRF in the task of subject structure optimization.

Key words: Large Language Model (LLM), scientific research word cloud, discipline structure optimization, research information；semantic matching

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