中国人民警察大学学报

大数据技术在社会治安智慧防控中扮演着核心角色，推动了防控模式的精细化和智能化。大数据赋能智慧防控的基本理念包括智能化、精准化、协同化和敏捷化。通过分析大数据在风险源头追溯、过程跟踪、趋势预测及人员画像等实践场景中的应用，揭示其在提升警务效率和效果方面的显著作用。然而，当前智慧防控仍面临数据壁垒、模型局限、人才短缺等困境。为此，必须优化大数据赋能智慧防控的实施路径，通过打破数据壁垒、完善预警监测模型、拓宽数据标签维度及打造数据研判人才梯队等措施推动治安防控从“被动响应”向“主动预防”转型，实现社会治理的智能化升级。

Big data technology plays a core role in smart prevention and control of public security, making the prevention and control model more refined and smarter. The basic concept of big data underpinning smart prevention and control includes intelligence, precision, collaboration, and agility. This paper explores the prominent value of big data in improving policing efficiency and governance effectiveness by analyzing the application of big data in practical scenarios such as risk source tracing, process tracking, trend prediction, and offender profiling. However, current smart prevention and control still face challenges such as data silos, model constraints, and talent shortages. To address these challenges, it is necessary to optimize the implementation path of big data-enabled smart prevention and control. Specific measures include breaking down data silos, improving early-warning and monitoring models, enriching data label dimensions, and building a talent pool for data analysis and assessment. These measures will facilitate the transformation of public security prevention and control from “passive response” to “active prevention” paradigm and achieve the intelligent upgrading of social governance.