中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (7): 151-156.doi: 10.16265/j.cnki.issn1003-3033.2017.07.027

• 公共安全 • 上一篇    下一篇

大数据时代城市公共安全风险演化与治理机制

曹策俊1,2, 李从东1,2 教授, 王玉2,3 教授, 屈挺2 教授, 张伟4   

  1. 1 天津大学 管理与经济学部, 天津 300072;
    2 暨南大学 物联网与物流工程研究院, 广东 珠海 519070;
    3暨南大学 国际商学院, 广东 珠海 519070;
    4 四川师范大学 商学院, 四川 成都 610101
  • 收稿日期:2017-04-17 修回日期:2017-06-16 发布日期:2020-11-26
  • 作者简介:李从东 (1962—),男,山西大同人,博士,教授,博士生导师,主要从事城市公共安全、大数据、应急管理、集成化管理等方面的研究。E-mail: licd@jnu.edu.cn。曹策俊 (1990—),男,四川南充人, 博士研究生,主要研究方向为城市公共安全、大数据、应急管理、灾害运作管理。E-mail: caocejun0601@tju.edu.cn。
  • 基金资助:
    国家自然科学基金资助(71672074, 71302153);广东省哲学社会科学“十二五”规划项目(GD15CGL07);广东省自然科学基金资助(2014A030313608);广州市科技计划科研资助项目(201607010012)。

Evolution and governance mechanism of urban public safety risk in big data era

CAO Cejun1,2, LI Congdong1,2, WANG Yu2,3, QU Ting2, ZHANG Wei4   

  1. 1 College of Management and Economics, Tianjin University, Tianjin 300072, China;
    2 Institute of Physical Internet, Ji'nan University, Zhuhai Guangdong 519070, China;
    3 School of International Business, Ji'nan University, Zhuhai Guangdong 519070, China;
    4 School of Business, Sichuan Normal University, Chengdu Sichuan 610101, China
  • Received:2017-04-17 Revised:2017-06-16 Published:2020-11-26

摘要: 为提高城市公共安全风险治理水平,减少社会损失,建立大数据时代风险演化和治理机制分析框架。在分析大数据时代城市公共安全风险特征、类型的基础上,构建以“点-链-网-超网”为主线的风险演化模式,结合初步危害分析,建立城市公共安全风险演化模型。基于系统视角,提出以前馈导控为主,反馈响应为辅的城市公共安全风险治理创新模式。结果表明:建立的城市公共安全风险演化模型,有助于精准预测风险要素状态的变化趋势,制定有针对性的治理措施,设计集成视角下的风险治理模式。

关键词: 大数据, 城市公共安全风险, 前馈导控, 演化模型, 治理模式

Abstract: To improve the governance level of urban public safety risks and reduce social losses, an analysis framework was developed to investigate evolution and governance mechanism of risk in big data era. Evolution modes of urban public safety risk were established from the perspective of point, chain, network, and super-network on the basis of analyzing characteristics and classifications of risks. An evolution model was built for urban public safety risks according to preliminary hazard analysis in big data era. An innovative governance mode of urban public safety risks was proposed from the integrated perspectives of both feedforward control and feedback response. Results indicate that the proposed evolution model of urban public safety risks is helpful to predict accurately the trend of status of risk elements over time, develop the targeted governance measures, and design governance mode of risk from an integrated viewpoint.

Key words: big data, urban public safety risk, feedforward control, evolutionary model, governance mode

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