ISSN 1003-8035 CN 11-2852/P

    地质灾害人工智能大语言模型研究展望

    Review on artificial intelligence-based large language models for geological hazards

    • 摘要: 当下,大语言模型技术迭代速度极快,正加速融入地质灾害防治领域。它不断拓展应用场景,突破了以往在数据分析和复杂建模能力上的局限,革新了传统研究范式。为进一步推动大语言模型等AI技术在地质灾害智慧防治方面取得新突破,文章全面梳理了大语言模型技术的演进特点,以及在多个领域的应用情况。首先,论述了小样本学习、多模态数据融合、模型轻量化与迁移应用,以及专家知识嵌入与人机协同等关键技术及其应用于地质灾害隐患智慧识别的主要思路与研发重点方向。在此基础上还提出基于“应用场景、关键问题、作用机制、数据模态、样本特征、模型研发、专家知识、人机协同”等核心要素的“AI+地质灾害”研究框架、技术思路与典型应用场景,凸显出AI技术在地质灾害领域中处理多维多尺度非线性复杂关系建模时的重要价值。通过以上分析,以促进大语言模型等AI技术从数据、模型、知识等更深层次,在更多场景中融入地质灾害防治工作,更好地借助AI技术,推动我国防灾减灾工作朝着精准化、智能化方向发展。

       

      Abstract: Currently, the technology of large language models is evolving rapidly and accelerating its integration in geological disaster prevention and control. It has been expanding the application scenarios and breaking the limitations in data analysis and complex modeling capabilities as well as innovating the traditional research paradigm. To further promote new breakthroughs in AI technologies in the intelligent prevention and control of geological disasters, this article reviews the evolution characteristics of large language model technology and the application scenarios in multiple fields, and also discusses the key technologies including small sample learning, multimodal data fusion, lightweight model and transfer application, as well as expert knowledge embedding and human-computer collaboration, which are also the main ideas and research focus directions for achieving intelligent identification of geological disaster hazards. The article also proposes an "AI + geological disasters" research framework, technical ideas and typical application scenarios based on core elements including "application scenarios, key issues, mechanism of action, data modalities, sample characteristics, model development, expert knowledge, and human-computer collaboration". This highlights the important value of AI technology in geological disasters research in solving the dealing with multi-dimensional, multi-scale, nonlinear and complex relationship modeling problems. The purpose of this article is to promote AI technologies to integrate into geological disaster prevention and control work at a deeper level, from data, models, and knowledge, and also better leverage AI technology to promote the development of disaster prevention and mitigation towards a greater precision and intelligence.

       

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