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在技术快速演进和全球化风险加剧的背景下,构建适应人工智能时代的应急管理体系成为关键议题。本文从技术智能与人机交互两个维度构建了衔接技术功能逻辑与应用融合逻辑的整合性治理框架,并在此基础上对典型的科学实践进行了系统分析,揭示了人工智能在应急管理中的四类典型应用模式,即智能互动型、分析导向型、协作增强型与信息集成型。研究进一步指出,人工智能驱动的应急管理正在呈现一系列关键变革趋势,主要包括情境复杂性引发的维数灾难效应、多源数据支撑下的动态调度与资源优化需求,以及技术安全性和算法透明度方面的新挑战。为进一步提升应急管理体系的智能化和现代化水平,有必要在制度创新、人机协作和数据与技术治理等方面持续深化改革,并围绕人工智能的风险可控性、系统可用性与前沿演进等关键议题构建更加稳健的治理路径。
Abstract:In the context of rapid technological evolution and intensifying global risks,building an emergency management system that is adapted to the era of artificial intelligence( AI) has become a crucial issue. This paper developed an integrated governance framework that links technological functional logic and application integration logic from the two dimensions of technical intelligence and human AI interaction,and on this basis conducted a systematic analysis of representative scientific practices,identifying four typical application modes of AI in emergency management,namely intelligent interactive mode,analysis oriented mode,collaboration enhancing mode,and information integration mode. The study further indicates that AI driven emergency management was exhibiting a series of key transformative trends,mainly including the dimensional disaster effect triggered by increasing contextual complexity,the growing demand for dynamic scheduling and resource optimization supported by multi source data,and new challenges related to technological safety and algorithmic transparency. To further enhance the intelligence and modernization of the emergency management system,it is necessary to deepen reforms in institutional innovation,human AI collaboration,and data and technology governance,and to build more robust governance pathways around key issues such as AI risk-control ability,system usability,and frontier technological evolution.
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(1)资料来源:新华网,《寻找AI发展新路径-专访中国科学院院士鄂维南》,2024-08-20.
(1)指能支持新一代人工智能技术(深度学习、自然语言处理、增强学习等)的高性能计算平台。系统集成大量的计算资源和先进的数据处理能力,高效地处理和分析庞大数据集。
(1)“多模态交互”在人机交互领域中指的是一个系统或设备通过多种感官渠道(如视觉、听觉、触觉等)与用户进行交互的能力。这种交互方式允许用户通过自然的通信方式,如语音、手势、触摸等,与机器进行互动,从而提供更为直观和高效的用户体验。
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(1)资料来源:MIT News.Behind Covid-19 Vaccine Development,2021-05-18.
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(1)资料来源:UNELL.AI ISP Technology,2024-01-26.
(1)在危机情境中,AI的未来发展方向在于快速解析呼叫内容,识别事件类型、地理位置及呼叫者的紧迫级别。例如通过自动应答和引导流程,辅以情感识别,调整响应策略。实时语言转写与声音情感分析用以评估呼叫者情绪,当检测到高压力或恐慌时,自动优先转接人工。
基本信息:
DOI:10.20186/j.cnki.hustitama2022.2025.06.02
中图分类号:D63;TP18
引用信息:
[1]邹昀瑾,孔锋.探索人工智能驱动的应急管理:科学实践、变革趋势与前沿议题[J].信息技术与管理应用,2025,4(06):10-25.DOI:10.20186/j.cnki.hustitama2022.2025.06.02.
基金信息:
教育部人文社会科学研究青年基金项目“公共安全风险研判的人机协同机制研究”(25YJC630212); 中国高等教育学会2025年度高等教育科学研究规划课题重大项目“中国-东盟命运共同体建设背景下东南亚灾害风险评价及协同防控机制研究”(25DL0105); 北京市科技计划项目“北京青年顶尖科技人才引育留用机制研究”(Z251100007625008)
2025-12-15
2025-12-15