新型城市基础设施如何赋能韧性城市建设？有哪些典型经验做法？

Group 1: Infrastructure Challenges - Traditional infrastructure is facing systemic shortcomings in risk resilience and emergency response efficiency due to outdated design standards and insufficient disaster resistance [1] - Current infrastructure design standards are primarily based on historical disaster data, which are inadequate for the increasing frequency of extreme weather events [1] - Monitoring methods are lagging, with low coverage of intelligent monitoring systems, making it difficult to detect structural damage in aging infrastructure [1] Group 2: Advancements in Urban Infrastructure - The acceleration of new urban infrastructure construction, particularly through the application of artificial intelligence, is reshaping urban disaster prevention systems [2] - The integration of digital construction, smart facilities, and information platforms significantly enhances data collection, analysis, and forecasting capabilities for urban infrastructure [2] - Technologies like digital twins are enabling simulation-based urban governance, allowing managers to optimize emergency response plans in virtual environments [2] Group 3: Disaster Management in Zhejiang - Zhejiang faces significant risks from typhoons and secondary disasters, necessitating advanced disaster management strategies [3] - The province has developed the first national resilience disaster prevention model, integrating comprehensive risk assessment and decision support systems [3] - This model can provide real-time forecasts of various risks and assist in decision-making for population relocation and resource allocation [3] Group 4: Resilience of the Power Grid - The extensive power grid system in Zhejiang is vulnerable to natural disasters, making it crucial to enhance its disaster resilience [4] - The State Grid Zhejiang Electric Power Company has established a comprehensive disaster resilience model that incorporates advanced technologies for risk forecasting and assessment [4] - This model enables dynamic risk predictions for power grid equipment during disasters and supports optimal decision-making across various stages of disaster management [4]