Abstract: Under the background of rapid development of artificial intelligence, a group intelligent emergency decision-making method based on large language model and retrieval enhancement generation technology is proposed to address the problems of insufficient public participation and strong dependence on specialized knowledge in current emergency decision-making. It aims to integrate social media public data and domain knowledge base, construct a public-expert collaborative multi-attribute decision-making model, improve the scientific and response effectiveness of disaster response, and apply it to emergency management. Firstly, we use Python crawler tool to obtain public comments from microblogging platform to form the emergency disaster demand database; secondly, we integrate the emergency management professional database based on RAG technology to enhance the model generating ability, guide the topic classification through cue word engineering, construct the topic word co-occurrence network, adopt Louvain algorithm clustering, and combine with the expert checking and optimization, to generate attribute sets of emergency decision-making; and then, we integrate the importance and cohesiveness of the public-expert collaborative multi-attribute decision-making model, and apply it to the emergency management. , synthesize the importance and cohesion factors to construct the attribute weight measurement model; finally, consider the psychological behavior of decision makers, and use TODIM method to sort and optimize the alternative emergency solutions. Taking the 7-20 Henan rainstorm event as an example, the experimental results show that the method proposed in this paper is able to generate emergency decision-making topics that meet the public demand, and performs well in the consistency and diversity of the topics, which are 0.583 and 0.943, respectively, verifying the scientificity and effectiveness of the method proposed in this paper.

摘要： 在人工智能快速发展的背景下，针对当前应急决策中公众参与不足和专业知识依赖性强等问题，提出一种基于大语言模型与检索增强生成技术的群体智能应急决策方法。旨在整合社交媒体公众数据与领域知识库，构建“公众-专家”协同的多属性决策模型，提升灾害应对的科学性与响应效能，并将其应用于突发应急管理中。首先，利用Python爬虫工具从微博平台获取公众评论，形成应急灾害需求数据库；其次，基于RAG技术整合应急管理专业数据库以增强模型生成能力，通过提示词工程引导主题分类，构建主题词共现网络，采用Louvain算法聚类，结合专家校验优化，生成应急决策的属性集；然后，综合重要性和内聚性两方面因素，构建属性权重测度模型；最后，考虑决策者心理行为，采用TODIM方法对备选应急方案进行排序优化。以7•20河南暴雨事件为例，实验结果表明，本文所提方法能够生成符合公众需求的应急决策主题，在主题的一致性和多样性表现较好，分别为0.583和0.943，验证本文所提方法的科学性和有效性。