中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (S1): 139-145.doi: 10.16265/j.cnki.issn1003-3033.2026.S1.0021

• 公共安全与应急管理 • 上一篇    下一篇

数智化背景下我国安全应急产业关键技术识别与演进

梁本部1,2(), 李佩钊2,3, 詹莉4,**(), 李志1, 罗佳欣2,3   

  1. 1 武汉理工大学 法学院, 湖北 武汉 430070
    2 湖北知识产权研究中心, 湖北 武汉 430070
    3 武汉理工大学 管理学院, 湖北 武汉 430070
    4 武汉理工大学 本科生院, 湖北 武汉 430070
  • 收稿日期:2026-02-20 修回日期:2026-04-10 出版日期:2026-06-30
  • 通信作者:
    ** 詹 莉(1974—),女,湖北武汉人,硕士,副教授,主要从事应急产业等方面的研究。E-mail: 。
  • 作者简介:

    梁本部 (1992—),男,山东济宁人,博士,副教授,主要从事公共安全与应急管理、安全应急产业等方面的研究。E-mail:。

  • 基金资助:
    湖北省自然科学基金一般面上项目资助(2025AFB549); 中央高校基本科研业务费(y2024022); 中央高校基本科研业务费(W2025186); 中央高校基本科研业务费(104972026KFYzxk0014)

Identification and evolution of key technologies in China's safety and emergency industry in digital and intelligent era

Liang Benbu1,2(), Li Peizhao2,3, Zhan Li4,**(), Li Zhi1, Luo Jiaxin2,3   

  1. 1 School of Law, Wuhan University of Technology, Wuhan Hubei 430070, China
    2 Hubei Intellectual Property Research Center, Wuhan Hubei 430070, China
    3 School of Management, Wuhan University of Technology, Wuhan Hubei 430070, China
    4 Undergraduate School, Wuhan University of Technology, Wuhan Hubei 430070, China
  • Received:2026-02-20 Revised:2026-04-10 Published:2026-06-30

摘要:

为提升我国安全应急产业数智化转型成效,研究安全应急产业关键技术创新主题识别和演进趋势。基于2003—2024年36 971件安全应急产业专利大数据,构建关键技术指数;选择技术支撑、法律支撑及战略支撑3类共7项指标测度关键技术,借助XGBoost模型识别安全应急产业关键技术机会;运用潜在狄利克雷分配(LDA)等主题模型开展创新技术主题提取与演进分析。研究表明:安全应急产业关键数智技术可划分为10个主题聚类,呈现“技术集成、主动防御、处置协同”的逻辑关联特征;当前技术发展主题演进趋势包括:从依赖传统技术开展数据采集向依托传感器实现多模态数据融合演进,从聚焦突发事件的紧急应对向注重事前监测预警转型,从关注传统自然灾害防控向复合灾害治理韧性转变。

关键词: 数智化, 安全应急产业, 关键技术识别, 演进趋势, 潜在狄利克雷分配(LDA)

Abstract:

The identification of innovation themes of key technologies in China's safety and emergency industry (SEI) and their evolutionary trends was investigated to improve the effectiveness of the digital and intelligent transformation of the industry. Key technology indicators (KIs) were proposed based on a large-scale dataset of 36 971 patents in the SEI filed from 2003 to 2024. Seven indicators spanning three dimensions, namely, technological, legal and strategic support, were selected to measure the key technologies. The XGBoost model was employed to identify key technological opportunities in the SEI. Topic modeling methods, including LDA, were further applied to extract innovation themes of key technologies and analyze their evolutionary trends. The results show that key digital and intelligent technologies in the SEI can be categorized into 10 thematic clusters, exhibiting logical correlation characteristics of "technology integration, proactive prevention, and coordinated response". The current technological development is reflected in three major trends of innovation themes: the evolution from data acquisition relying on conventional technologies to multimodal data fusion enabled by sensors, the transformation from focusing on emergency response to sudden events to emphasizing proactive monitoring and early warning, and the shift from focusing on traditional natural disaster prevention and control to resilient management of composite disasters.

Key words: digital and intelligent transformation, safety and emergency industry, key technology identification, evolutionary trend, latent dirichlet allocation (LDA)

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