中国人民警察大学学报

当今，世界百年未有之大变局加速演进，我国发展进入战略机遇和风险挑战并存、不确定难预料因素增多的时期，快速发展的一批新技术、新行业、新平台，催生了线上线下交互、形式形态各异、地区跨度极广、犯罪链条扩展迅速、打击处理困难的大量社会治理难题。这倒逼公安机关必须把大数据、人工智能等科技手段与公共安全治理深度融合，推动公共安全治理的精准化与科学化。传统治理模式以政府为主导，着重事后应对，存在对风险识别不充分、协同配合度不高等局限。“数治”通过将数据驱动、技术创新、协同治理、风险防控等与公共安全治理融合，优化治理体系与治理格局，但也面临着技术瓶颈、制度制约及伦理冲突等挑战。这就需要强化辩证思维、创新思维及系统思维，聚焦关键要素，构建高效、精准、协同的公共安全治理新局面。

Changes unseen in a century are unfolding across the world at a faster pace. China has entered a period of development in which strategic opportunities, risks, and challenges are concurrent and uncertainties and unforeseen factors are rising. The rapid emergence of a range of new technologies, industries, and platforms has given rise to a plethora of social governance challenges characterized by online-offline interaction, diverse forms and patterns, extensive geographical reach, rapidly expanding criminal chains, and difficulties in investigation and enforcement. This situation compels public security organs to deeply integrate big data, artificial intelligence, and other technological means with public security governance, thereby promoting its precision and scientific advancement in governance. The traditional governance model is government-led and focuses on post-incident response. It is often characterized by inadequate risk identification and low levels of coordination among stakeholders. Data-enabled governance can optimize the governance system and structure by integrating data-driven approaches, technological innovation, collaborative governance, and risk prevention and control into public security governance. However, it also faces challenges including technological bottlenecks, institutional constraints, and ethical conflicts. Addressing these requires greater emphasis on dialectical, innovative, and systems thinking, so as to focus on key elements to build a new paradigm of public security governance that is efficient, precise, and collaborative.