随着铁路防洪安全巡检的常态化,传统的现场踏勘方法已无法满足日益增长的防洪隐患排查需求.旨在开发一种集成化的桌面端系统,以提高防洪隐患监测的效率和准确性.通过无人机多模态数据的集成管理和铁路基础地理信息的融合,解决了隐患定位、概况管理和展示汇报的问题;基于WPF框架,融合Cefsharp和Vue.js技术,可实现三维场景与数据操作的紧密结合;利用OpenGL的 3D GIS引擎,可提供实景三维模型、倾斜摄影原片、航飞视频等多模态数据的一体化展示与空间分析能力;系统设计分类的隐患标注功能,并通过离线的Spatialite数据库实现数据的持久化和导入导出.选取总里程300 km,包含148 个防洪重点工点,2 期无人机航飞数据的防洪隐患排查项目作为应用案例,验证了系统的有效性.研究结果表明,该系统能够通过关系数据库和对象存储的结合,实现无人机多模态数据的集成式管理,形成基于高精度实景三维场景的防洪隐患排查机制.
Railway Flood Hazard Investigation System Based on Multi-Modal Data from Unmanned Aerial Vehicles
The conventional on-site survey for railway flood safety is inadequate for the increasing demand of flood hazard assessment.This study introduced an integrated desktop system to enhance the efficiency and precision of flood hazard monitoring.By integrating drone multimodal data with railway geographic information,the system addressed the challenges of hazard identification,management,and reporting.Based on the WPF framework and integrated with the Cefsharp and Vue.js,it seamlessly combined 3D scenes with data operations and employed an OpenGL-based 3D GIS engine for multimodal data display and spatial analysis.A classified hazard marking function and an offline SpatiaLite database ensured data persistence and portability.Tested on a 300 km railway section with 148 key flood points and two phases of drone data,the system demonstrates effective multimodal data management and real scene visualization for hazard assessment.
railwayflood preventionUAVmulti-modal datageographic information system
NETL
NSTL
万方数据
