优化自动化合规检查与本体学增强的自然语言处理:消防安全领域的案例
11st Fujian Normal University School of Geographical Sciences, Fuzhou, China; 2nd Fujian Normal University Institute of Geography, Fuzhou, China.
Journal of environmental management
|November 15, 2024
概括
本研究引入了一种用于解释消防安全法规的自动化框架. 它使用本体学和自然语言处理来有效地分析空间规则,改善建筑安全管理.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 消防安全合规性检查 (FSCC) 对高质量的消防工程和危险消除至关重要.
- 在消防法规中解释空间规则是由于手动,低效的翻译方法而具有挑战性.
- 现有的方法难以应对消防规范中空间限制的复杂性和体积.
研究的目的:
- 开发一个自动化框架来解释消防法规中复杂的空间规则.
- 提高消防安全合规检查的效率和准确性.
- 促进公众对建筑安全的更好理解和智能管理.
主要方法:
- 将本体学技术与自然语言处理 (NLP) 结合起来,用于规则解释.
- 使用预训练语言模型 (PLM) 来提取概念和关系.
- 将空间知识转化为语言不可知树结构,并将其映射到逻辑句子中.
主要成果:
- 在实体提取方面获得了F1得分86.27,空间关系提取方面获得了81.81.
- 在逻辑规则的正式化中显示了96.26%的准确性.
- 成功自动化了火灾空间规则的解释,提高了效率.
结论:
- 拟议的框架显著提高了对火灾空间规则的解释.
- 自动解释支持改进的消防安全管理和预测.
- 这项技术促进了智能建筑安全环境管理.
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