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《科技研究与应用》( ISSN3081-135X、EISSN3081-1368 ) 发布者:Quest Press 发布日期:2025/10/9
10.12479/questpress-kjyjyyy.20250101 Open Access 下载4 浏览47

 

人工智能时代无人机与无人智能设备在应急救援中的应用、挑战与展望

毕敏杰
北海通航有限公司,广西北海,536000
摘要:在全球灾害频发与应急救援需求升级的背景下,人工智能(AI)技术与无人机(UAV)、无人地面车辆(UGV)、无人水面艇(USV)等无人智能设备的深度融合,正重塑应急救援的响应模式与效率边界。本文系统分析AI 赋能下无人智能设备在应急救援中的核心应用场景,包括灾害态势智能感知、受困人员精准定位、多设备协同作业及灾后重建辅助等。通过梳理2021—2024 年全球典型灾害案例(如2023 年土耳其-叙利亚地震、2024 年美国加州森林火灾),揭示AI 技术在提升设备自主性、优化救援决策、降低人员风险等方面的关键作用。例如,AI 驱动的多模态数据融合算法可将受困人员识别准确率提升至92%以上,比传统人工排查效率提升5 倍。同时,本文指出当前应用中存在的技术瓶颈(如复杂环境下AI 模型鲁棒性不足、设备续航与算力矛盾)、伦理风险(如数据隐私泄露、算法决策偏差)及监管空白(如跨区域协同标准缺失)。最后,从技术创新(如轻量化AI 模型研发、绿色能源与算力融合)、制度完善(如AI 应急救援伦理框架构建、国际协同机制建立)及生态培育(如复合型人才培养、政企协同研发)三个维度,提出未来发展路径。本研究为推动AI 与无人智能设备在应急救援领域的规模化、标准化应用提供理论参考与实践指引。
关健词:人工智能;无人智能设备;应急救援;灾害响应;多模态协同;智能决策
 
Applications,Challenges,and Prospects of Drones and Unmanned Intelligent Devices in Emergency Rescue in the Era of Artificial Intelligence
Minjie Bi
Beihai General Aviation Co.,Ltd.,Beihai,Guangxi,536000,China
Abstract:Against the backdrop of frequent global disasters and escalating demands for emergency rescue,the deep integration of artificial intelligence(AI) technology with unmanned intelligent devices—such as unmanned aerial vehicles(UAVs),unmanned ground vehicles(UGVs),and unmanned surface vessels(USVs)—is reshaping the response models and efficiency boundaries of emergency operations.This paper systematically examines the core applications of AI-powered unmanned devices in disaster scenarios,including intelligent situational awareness,precision localization of trapped victims,multi-device collaborative operations,and post-disaster reconstruction support.Through case studies of global disasters(e.g.,the 2023 Turkey-Syria earthquake and 2024 California wildfires),we demonstrate AI’s pivotal role in enhancing operational autonomy,optimizing decisionmaking, and mitigating human risks.For example,AI-driven multimodal data fusion algorithms achieve over 92%accuracy in victim identification,outperforming traditional manual searches by fivefold.The study also identifies critical challenges:technical limitations(e.g.,insufficient AI robustness in complex environments,trade-offs between device endurance and computing power),ethical concerns(e.g.,data privacy breaches,algorithmic biases),and regulatory gaps(e.g.,lack of standardized crossregional protocols).Future directions are proposed across three dimensions:(1)Technological innovation(e.g.,lightweight AI models,green energy-computing integration),(2)Institutional frameworks(e.g.,ethical guidelines for AI in rescue operations,international collaboration mechanisms),and(3)Ecosystem development(e.g.,interdisciplinary talent cultivation,public-private R&D partnerships).This research offers theoretical and practical insights to advance the scalable and standardized deployment of AI-driven unmanned systems in emergency rescue.
Keywords:artificial intelligence; unmanned intelligent evices; emergency rescue; disaster response; multimodal collaboration;intelligent decision-making
 
 
 
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