Gauging Urban Security Advances: A Fuzzy Hierarchical TOPSIS Model with MOPSO Optimization

In the era of rapid urban digitalization, intelligent analysis systems are pivotal for urban safety management, yet their applicability lacks a robust evaluation framework. This study develops a comprehensive model integrating the Delphi method, Fuzzy Hierarchical TOPSIS, and MOPSO. Through two-round expert consultations, 36 indicators across four dimensions, technical performance, functionality, interaction modes, and cost-effectiveness, are identified. The MOPSO algorithm dynamically optimizes weights in the fuzzy TOPSIS framework, addressing static evaluation limitations and balancing conflicting objectives like reliability and cost-efficiency. Empirical results show a 12% average improvement in the closeness index and enhanced scenario adaptability, such as an 18% reduction in warning latency during emergencies. This hybrid approach bridges qualitative expert insights with quantitative optimization, offering a systematic tool for evaluating system applicability. The research enriches multi-criteria decision-making methodologies and supports evidence-based urban safety governance, facilitating resource allocation and sustainable planning.