无人设备应用于物流基础设施水下检测的研究现状及热点——基于CiteSpace的可视化分析

Current Status and Hotspots in the Application of Unmanned Equipment for Underwater Inspection of Logistics Infrastructure—A Visualization Analysis Based on CiteSpace

摘要: 随着港口、航道、桥梁等物流基础设施持续运行，其水下结构状态直接影响物流运输安全与运行效率。传统人工水下检测存在作业风险高、效率有限、成本较高等问题，无人船、水下机器人等无人设备逐渐应用于基础设施检测。本文基于中国知网和万方数据库2002~2024年相关文献，运用CiteSpace对512篇有效文献进行可视化分析，从年度发文、作者与机构合作、关键词共现及聚类等方面梳理研究现状与热点。研究发现，无人船、传感器、深度学习、目标识别和水下检测是主要研究方向，研究正由单一设备应用向多传感器融合、智能识别和自主作业发展。从物流视角看，无人化水下检测可为港口、航道及桥梁等基础设施的状态感知、风险预警和预防性维护提供技术支撑，从而保障物流运输的连续性与安全性。

Abstract: With the continuous operation of logistics infrastructure such as ports, waterways, and bridges, the condition of their underwater structures directly affects the safety and efficiency of logistics transportation. Traditional manual underwater inspection involves high operational risks, limited efficiency, and relatively high costs. Unmanned vessels and underwater robots have therefore been increasingly applied to infrastructure inspection. Based on relevant literature from the CNKI and Wanfang databases from 2002 to 2024, this study uses CiteSpace to conduct a visual analysis of 512 valid publications, examining the research status and hotspots from the perspectives of annual publications, author and institutional collaboration, keyword co-occurrence, and clustering. The results show that unmanned vessels, sensors, deep learning, target recognition, and underwater inspection are the major research directions, with research developing from single-device applications toward multi-sensor fusion, intelligent recognition, and autonomous operations. From a logistics perspective, unmanned underwater inspection can provide technical support for condition monitoring, risk warning, and preventive maintenance of ports, waterways, and bridges, thereby helping ensure the continuity and safety of logistics transportation.