为了解决多个超高频RFID电子标签同时响应引发的“碰撞”现象，提高识别效率和准确性，确保巡检精确度，提出一种RFID技术下船舶电力计量设备巡检优化方法。针对船舶环境特点，从材料、安装方式和工作频率三方面选取超高频RFID电子标签。利用校验分组动态帧时隙RFID标签防撞算法优化手持RFID阅读器，识别电子标签并读取其内部存储的船舶电力计量设备信息，Web端则利用门控循环单元分析设备是否存在故障。实验结果表明，该方法能有效读取电子标签中的船舶电力计量设备信息，并准确检测设备故障。说明通过RFID技术在船舶电力计量设备巡检中的应用，成功解决了并发响应导致的碰撞问题，实现了电力计量设备的有效巡检，提高了船舶电力巡检的效率和精确度。
In order to solve the "collision" phenomenon caused by multiple ultra-high frequency RFID electronic tags responding simultaneously, improve recognition efficiency and accuracy, and ensure inspection accuracy, an optimization method for ship power metering equipment inspection under RFID technology is proposed. Select ultra-high frequency RFID electronic tags based on the characteristics of the ship environment, including materials, installation methods, and operating frequencies. Optimizing the handheld RFID reader using the verification group dynamic frame slot RFID tag collision avoidance algorithm, identifying electronic tags and reading their internal stored ship power metering equipment information, and using the gate control loop unit on the web end to analyze whether the equipment has faults. The experimental results show that this method can effectively read the information of ship power metering equipment in electronic tags and accurately detect equipment failures. The application of RFID technology in the inspection of ship power metering equipment has successfully solved the collision problem caused by concurrent responses, achieved effective inspection of power metering equipment, and improved the efficiency and accuracy of ship power inspection.
2025,47(4): 163-167 收稿日期：2023-11-20
DOI：10.3404/j.issn.1672-7649.2025.04.026
分类号：U665
基金项目：国家自然科学基金地区科学基金资助项目（61761004）
作者简介：李昊帅(1993-),女,硕士研究生,工程师,研究方向为RFID、区块链、智慧仓储
参考文献：
[1] 杨万勇, 甘辉兵, 刘泰, 等. 基于增强现实的船舶机舱智能巡检技术研究[J]. 中国造船, 2023, 64(5): 171-184.
YANG W Y, GAN H B, LIU T, et al. Research on intelligent inspection technology for marine engine room based on augmented reality[J]. Shipbuilding of China, 2023, 64(5): 171-184.
[2] 赵思沛, 史成军, 王浩亮, 等. 基于改进RRT算法的船舶机舱巡检机器人路径规划[J]. 船舶工程, 2022, 44(7): 109-114.
ZHAO S P, SHI C J, WANG H L, et al. Path planning of ship engine room inspection robot based on improved RRT algorithm[J]. Ship Engineering, 2022, 44(7): 109-114.
[3] 朱少斌, 许素安, 马宗彪, 等. 基于BSO-BPNN模型的电能计量装置异常诊断方法研究[J]. 中国测试, 2022, 48(1): 141-146.
ZHU S B, XU S A, MA Z B, et al. Research on abnormal diagnosis method of electric energy metering device based on BSO-BPNN[J]. China Measurement & Testing Technology, 2022, 48(1): 141-146.
[4] ZHANG D. Fault diagnosis of ship power equipment based on adaptive neural network[J]. International journal of emerging electric power systems, 2022, 23(6): 779-791.
[5] 王磊, 郝涌汀, 潘明然, 等. 电力巡检中改进YOLOv5s的缺陷检测算法研究[J]. 计算机工程与应用, 2024, 60(10): 256-265.
WANG L, HAO Y T, PAN M R, et al. Improved defect detection algorithm in power inspection based on YOLOv5s[J]. Computer Engineering and Applications, 2024, 60(10): 256-265.
[6] 黄冬梅, 徐琦, 孙锦中, 等. 基于改进混合粒子群算法和匹配理论的无人机电力巡检卸载策略[J]. 计算机应用研究, 2023, 40(7): 2111-2116.
HUANG D M, XU Q, SUN J Z, et al. Power inspection and unloading strategy of UAV based on improved hybrid particle swarm algorithm and matching theory[J]. Application Research of Computers, 2023, 40(7): 2111-2116.
[7] 杨帆, 朱力, 刁冠勋, 等. 面向电力设备数字孪生的RFID传感器与数据传输协议设计[J]. 高电压技术, 2022, 48(5): 1634-1643.
YANG F, ZHU L, DIAO G X, et al. Design of RFID sensor and data transmission protocol for digital twin of electrical equipment[J]. High Voltage Engineering, 2022, 48(5): 1634-1643.
[8] 尹衍楚, 邹永久, 杜太利, 等. 基于SSA-SVM算法的船舶LFCS故障诊断[J]. 计算机仿真, 2024, 41(1): 548-553.
YIN Y C, ZOU Y J, DU T L, et al. Fault diagnosis of ship low freshwater cooling system LFCS based on SSA-SVM algorithm[J]. Computer Simulation, 2024, 41(1): 548-553.
