用群体智能与深度学习提升灾情响应速度和救援覆盖率。
SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning
- 结合粒子群优化与卷积神经网络，实时优化资源分配和路径规划。
- 在洪水与火灾场景中，响应速度提升40%，幸存者覆盖率达90%。
- 适合应急指挥、智能救援系统研发人员参考。
灾情响应需在混乱环境中快速适应地决策。SwarmFusion是一种新型混合框架，将粒子群优化与卷积神经网络结合，用于优化实时资源调配与路径规划。通过处理卫星、无人机及传感器的实时数据，该方法显著提升洪水与野火场景下的态势感知与运营效率。基于DisasterSim2025数据集的仿真显示，相比基线方法，响应时间最快提升40%，幸存者覆盖率达90%。这一可扩展的数据驱动方案为高时效性灾情管理提供革新解法，具备广泛危机应对应用潜力。
原文摘要 · Abstract (English)
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.
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