中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (7): 239-245.doi: 10.16265/j.cnki.issn1003-3033.2024.07.2014

• 应急技术与管理 • 上一篇    下一篇

基于知识图谱的地震救援装备智能管理方法

郭天颖(), 茆晓阳**(), 段齐骏, 马迪   

  1. 南京理工大学 设计艺术与传媒学院,江苏 南京 210094
  • 收稿日期:2024-01-10 修回日期:2024-04-20 出版日期:2024-07-28
  • 通信作者:
    ** 茆晓阳(1989—),男,宁夏吴忠人,博士,讲师,主要从事机器人、仿生设计、人工智能和知识系统等方面的研究。Email:
  • 作者简介:

    郭天颖 (2000—),女,江苏南京人,硕士研究生,研究方向为仿生机器人、知识图谱与知识推理。E-mail:

    段齐骏 教授;

    马迪 讲师

  • 基金资助:
    江苏省自然科学基金资助(BK20230927)

Intelligent management and scheduling approach for earthquake rescue equipment based on knowledge graph

GUO Tianying(), MAO Xiaoyang**(), DUAN Qijun, MA Di   

  1. School of Design Art and Media, Nanjing University of Science and Technology, Nanjing Jiangsu 210094,China
  • Received:2024-01-10 Revised:2024-04-20 Published:2024-07-28

摘要:

为提升地震救援人员的应急救援速度,适应多样化的搜救需求,提出一种基于知识图谱的智能化地震救援装备信息管理方法。首先,通过自顶向下的知识图谱构建方法,从多种信息源获取地震救援知识,并以此为基础进行知识建模;其次,用基于规则的方法抽取搜救知识,再基于余弦相似度进行知识融合,融合后的知识以资源描述框架(RDF)三元组的形式储存;然后,采用开源图数据库Neo4j将三元组组织成可视化的知识图谱;最后,基于知识图谱建立问答系统,方便用户以自然语言查询图谱上的知识。结果表明:知识图谱中包含灾害、次生灾害、环境因素、救援需求和救援装备5类实体及关系,且可以根据救援需求实现装备的快速匹配。基于知识图谱的方法可以实现对救援装备信息的管理与调度,有助于提升救援行动准备阶段的效率。

关键词: 知识图谱, 地震救援, 救援装备, 智能管理, 知识抽取, 知识融合, Neo4j

Abstract:

In order to assist earthquake rescue personnel in enhancing disaster response speed and adapting to diverse search and rescue needs, an intelligent management method for earthquake rescue equipment information based on a knowledge graph was proposed. Through the top-down knowledge graph construction method, earthquake rescue knowledge was first obtained from various information sources to serve as the basis for knowledge modeling. Next, a rule-based method was used to extract search and rescue knowledge, which was then integrated based on cosine similarity. The integrated knowledge was stored in the form of Resource Description Framework (RDF) triples. Subsequently, the open-source graph database Neo4j was employed to organize the triples into a visualized knowledge graph. Finally, a question-and-answer system was built based on the knowledge graph, allowing users to query the knowledge on the graph using natural language. The results indicate that the knowledge graph includes five categories of entities and relationships: disasters, secondary disasters, environmental factors, rescue needs, and rescue equipment. It facilitates quick matching of equipment based on rescue needs. The knowledge graph-based method can effectively manage and schedule rescue equipment information, improving the efficiency of the preparation phase of rescue operations.

Key words: knowledge graph, earthquake rescue, rescue equipment, intelligent management, knowledge extraction, knowledge fusion, Neo4j

中图分类号: