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作 者:刘昭阁 张瑞金[2] 李向阳[2] 乔立民 吴冲[2] LIU Zhaoge;ZHANG Ruijin;LI Xiangyang;QIAO Limin;WU Chong(School of Public Affairs,Xiamen University,Xiamen 361005,Fujian,China;School of Management,Harbin Institute of Technology,Harbin 150001,China;BKCC Co.,Ltd.,Beijing 100012,China)
机构地区:[1]厦门大学公共事务学院,福建厦门361005 [2]哈尔滨工业大学经济与管理学院,哈尔滨150001 [3]北京北科互联城市治理技术研究院有限公司,北京100012
出 处:《系统管理学报》2023年第6期1243-1254,共12页Journal of Systems & Management
基 金:国家自然科学基金大数据驱动的管理与决策研究重大研究计划项目(91746207);国家自然科学基金面上项目(71774043);教育部人文社会科学研究青年基金资助项目(22YJC630095)。
摘 要:为了系统客观地评价智慧城市转型下城市公共安全的大数据治理水平,提出了基于案例源证据推理的城市安全大数据治理能力成熟度评价方法。首先,以大数据治理活动为基本对象,从组织、规制、流程、技术等不同角度提炼关键过程域,同时定义各关键过程域的能力目标集合;其次,为避免专家打分的主观因素对评价结果的影响,将案例借鉴思想引入成熟度评价,在历史案例的结构化表达基础上,根据案例相似度检索案例中的评价证据,利用证据推理实现证据合成与成熟度评价。通过河南省濮阳市的城市智慧内涝防控用例分析了所提出基于案例方法的合理性。案例结果表明:所提出方法能够实现成熟度评价结果的智能化生成,其结果具有较强的区分性与场景适应性,有助于治理决策者精准认知当前城市安全大数据治理的薄弱环节,为治理更新完善提供决策支持。In order to systematically and objectively evaluate big data governance of government public services in the transformation of smart cities,a maturity evaluation method of city safety big data governance is proposed based on case source evidence reasoning.First,taking big data governance activities as the basic object,key process areas are extracted from different perspectives such as organization,regulation,process and technology,and the capacity goal sets of key process areas are defined.Then,in order to avoid the impact of subjective factors of expert scoring on the evaluation results,the case reference idea is introduced into the maturity evaluation.Based on the structural expression of the historical case source,the case-source evidence is retrieved according to case similarity,and synthesized to achieve capacity maturity evaluation using the evidence reasoning theory.The rationality of the proposed case-based method is analyzed by a case study of urban intelligent waterlogging prevention in Puyang,Henan Province.The result shows that the proposed method can realize the intelligent generation of maturity evaluation results,and the generated results have a strong differentiation and scenario adaptability,which helps governance decision-makers to accurately understand the weak points in the current city safety big data governance,and provides decision-making support for the updating and improvement of governance.
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