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<p>The prevalent cross-departmental data barriers and ineffective public safety risk management and control in smart city construction severely restrict governance efficiency. This study takes Hefei as an empirical case and innovatively proposes and validates a “technology-institution-data” ternary collaborative smart city public safety artificial intelligence optimization model. The core contribution lies in constructing a three-layer conceptual framework of “perception-decision-value”, systematically integrating real-time perception driven by the Internet of Things (IoT), intelligent decision-making guided by policy adaptation, and public value goals anchored by domain knowledge, effectively bridging the structural disconnection between the technological system and the governance system. This study uses AI technologies such as knowledge graph construction and rule reasoning to preprocess policy data, providing structured input for subsequent statistical modeling and achieving dual verification of technical methods and governance mechanisms. Empirical analysis shows that IoT infrastructure coverage is the foundation, but its effectiveness relies heavily on the transformation hub role of multi-source data collaboration and the dynamic adjustment function of policy adaptability. The study finds that inter-departmental collaboration resistance is the primary inhibitory factor of public safety resilience, and its negative impact far exceeds the positive effect of technology empowerment, highlighting the priority necessity of institutional reform in unleashing technological potential. The study also reveals the spatial heterogeneity pattern of governance efficiency, providing a basis for differentiated collaborative strategies in resource-constrained areas and urban-rural fringe areas. This model provides systematic theoretical support and practical pathways for breaking the dilemma of “emphasizing hardware over collaboration” in smart cities and achieving a transformation from passive defense to active resilience.</p>

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Artificial intelligence optimization model for public safety in smart cities based on multi-source data collaboration in the internet of things

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Han Zheng,
  • Qingguo Meng

摘要

The prevalent cross-departmental data barriers and ineffective public safety risk management and control in smart city construction severely restrict governance efficiency. This study takes Hefei as an empirical case and innovatively proposes and validates a “technology-institution-data” ternary collaborative smart city public safety artificial intelligence optimization model. The core contribution lies in constructing a three-layer conceptual framework of “perception-decision-value”, systematically integrating real-time perception driven by the Internet of Things (IoT), intelligent decision-making guided by policy adaptation, and public value goals anchored by domain knowledge, effectively bridging the structural disconnection between the technological system and the governance system. This study uses AI technologies such as knowledge graph construction and rule reasoning to preprocess policy data, providing structured input for subsequent statistical modeling and achieving dual verification of technical methods and governance mechanisms. Empirical analysis shows that IoT infrastructure coverage is the foundation, but its effectiveness relies heavily on the transformation hub role of multi-source data collaboration and the dynamic adjustment function of policy adaptability. The study finds that inter-departmental collaboration resistance is the primary inhibitory factor of public safety resilience, and its negative impact far exceeds the positive effect of technology empowerment, highlighting the priority necessity of institutional reform in unleashing technological potential. The study also reveals the spatial heterogeneity pattern of governance efficiency, providing a basis for differentiated collaborative strategies in resource-constrained areas and urban-rural fringe areas. This model provides systematic theoretical support and practical pathways for breaking the dilemma of “emphasizing hardware over collaboration” in smart cities and achieving a transformation from passive defense to active resilience.