基于多模式知识图和大型语言模型的水资源保护项目的风险决策生成方法研究
Libo Yang1,2, Yuan Li2, Junhua Tan2
1Advanced Research Institute for Digital-Twin Water Conservancy, North China University of Water Resources and Electric Power, Henan, Zhengzhou, China.
PloS one
|August 26, 2025
概括
这项研究引入了水资源保护项目风险的多模式知识图,通过大型语言模型加强决策,提高风险识别和评估的准确性.
科学领域:
- 土木工程
- 计算机科学
- 环境科学
背景情况:
- 传统的水资源保护项目风险知识图表受到数据模式和信息提取精度的限制.
- 有效的水资源保护风险管理需要整合多种数据来源进行全面分析.
研究的目的:
- 提出一个多式节水项目风险知识图.
- 通过使用多式联网大型语言模型来制定风险决策的协同策略.
- 提高水资源保护项目的风险评估的准确性和可靠性.
主要方法:
- 开发了一个综合视觉和文本数据的多式知识图.
- 改进了DenseNet模型的自我注意力和协调注意力,用于图像分类.
- 使用BERT-BiLSTM-CRF架构来提取文字风险实体.
- 实施了多个代理机构的搜索增强生成框架,用于决策.
主要成果:
- 增强的DenseNet模型在图像识别方面显示出更高的精度和回忆.
- 在BERTScore和ROUGE-L指标上实现了强的表现.
- 在风险决策输出中证明了可靠性和解释性.
结论:
- 拟议的多式联通知识图和生成策略有效地解决了传统方法的局限性.
- 这项工作为通过多式联网数据整合推进水资源保护项目的风险管理提供了新的视角.
- 这些发现支持在复杂的节水系统中更准确,更可靠的风险决策.
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