标题： SwarmFusion：利用群体智能和深度学习革新灾难响应 显示英文标题

标题： SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning

摘要： 灾难响应需要在混乱环境中快速、灵活地做出决策。 SwarmFusion 是一种新型的混合框架，将粒子群优化与卷积神经网络相结合，以优化实时资源分配和路径规划。 通过处理实时卫星、无人机和传感器数据，SwarmFusion 提高了洪水和火灾场景中的态势感知和操作效率。 使用 DisasterSim2025 数据集进行的模拟表明，与基线方法相比，响应时间最多快 40%，幸存者覆盖率达到 90%。 这种可扩展的数据驱动方法为时间紧迫的灾难管理提供了变革性的解决方案，具有在多种危机场景中的应用潜力。 显示英文摘要

摘要： Disaster response requires rapid, adaptive decision-making in chaotic environments. SwarmFusion, a novel hybrid framework, integrates particle swarm optimization with convolutional neural networks to optimize real-time resource allocation and path planning. By processing live satellite, drone, and sensor data, SwarmFusion enhances situational awareness and operational efficiency in flood and wildfire scenarios. Simulations using the DisasterSim2025 dataset demonstrate up to 40 percentage faster response times and 90 percentage survivor coverage compared to baseline methods. This scalable, data-driven approach offers a transformative solution for time-critical disaster management, with potential applications across diverse crisis scenarios.