专家观点 | 以“AI+场景”推动智慧应急走向实践

Core Insights - Emergency management is transitioning from passive response to proactive prevention, necessitating a new paradigm of smart emergency science to address complex challenges posed by climate change and urban governance [1][62] - The integration of AI and digital technologies into emergency management is crucial, with "AI + scenarios" serving as a practical bridge between scientific research and engineering practice [1][68] Group 1: Smart Emergency Science System Composition - Smart emergency science is an interdisciplinary field that combines information science, management science, engineering, and social sciences to fundamentally reshape traditional emergency management through data-driven approaches [3][64] - The transition from traditional emergency management, which relies on historical experience, to smart emergency management, which utilizes real-time data and predictive models, marks a significant paradigm shift [4][64] Group 2: Key Components of Smart Emergency Science - Data perception is foundational, focusing on integrated sensing networks and multi-source data fusion to monitor disaster elements and emergency resources comprehensively [5][65] - The smart emergency science system encompasses four key components: data intelligence, model intelligence, decision intelligence, and action intelligence, each contributing to a closed-loop system [6][65][66] Group 3: "AI + Scenarios" Implementation - "AI + scenarios" emphasizes the deep integration of AI technologies into specific emergency management contexts to address real pain points and create tangible value [8][68] - The approach shifts from a technology-driven model to one that is scenario-driven, defining specific emergency management challenges and developing tailored AI solutions [9][68] Group 4: Strategic Pathways for "AI + Scenarios" - The implementation of "AI + scenarios" requires breaking down broad goals into quantifiable, solvable scenario problems, such as predicting community evacuations during severe weather events [71] - Establishing cross-departmental data sharing and high-quality datasets is essential for training AI models effectively [71][72] Group 5: Challenges in Smart Emergency Management - Significant challenges include data silos, the scarcity of data for rare disaster scenarios, and the need for AI models to be robust and interpretable in high-stakes decision-making environments [72][73][74] - The complexity and uncertainty of real disaster scenarios necessitate AI systems that can adapt and function reliably under extreme conditions [75][76] Group 6: Frontiers of Research in Smart Emergency Science - Research directions include federated learning for data integration without sharing raw data, small-sample learning for rare disaster scenarios, and dynamic evolution of emergency knowledge graphs [78][79][80] - The development of digital twins for complex systems and disaster scenarios is crucial for high-fidelity simulations and effective emergency response planning [81]