在地震应急救援中,用于实体关系提取和时空决策知识图构建的多式模式深度学习
Shuai Liu1,2, Meng Huang3,4, Guang Yang3,4
1Institute of Disaster Prevention, Langfang, 065000, Hebei, China. liu145sh@163.com.
Scientific reports
|November 17, 2025
概括
本研究介绍了一种多式联网深度学习框架,用于地震救援,改进数据集成和决策支持. 该系统通过先进的信息处理和时空空间知识表示来提高灾难响应效率.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 地质物理学 地质物理学
背景情况:
- 地震应急响应在整合不同类型的数据方面面临挑战.
- 有效的决策支持系统对于及时有效的救援行动至关重要.
研究的目的:
- 开发一个新的框架,整合多式联网深度学习和时空空间知识表示,用于地震紧急救援.
- 在灾难场景中增强信息处理和决策支持能力.
主要方法:
- 开发了一个跨模态注意力融合网络 (CAFN),用于跨文本,视觉和时空模式的异质数据集成.
- 采用基于变压器的联合实体关系提取模型来识别灾害信息.
- 构建了一个时空知识图,用于动态紧急情况的专业推理.
主要成果:
- 实体关系提取模型获得了89.0%的F1得分,比传统方法提高了8.7%.
- 决策支持系统在资源分配和路线规划方面显示了94.0%的F1得分,比基于规则的系统提高了23.7%.
- 该框架有效地处理了复杂的多式联运信息,用于做出时间关键决策.
结论:
- 拟议的框架大大提高了地震紧急救援信息处理和决策支持.
- 多模式深度学习和时空知识表示对于改善灾难响应是有效的.
- 该系统为复杂,动态的紧急情况提供了强大的解决方案.
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