您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（工学版）》

山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (2): 90-95.doi: 10.6040/j.issn.1672-3961.0.2023.046

• 机器学习与数据挖掘 • 上一篇    

基于岸基激光雷达的水位智能监测技术

陈晓燕1,齐明杰1,程之恒2,张昱3,庄绪彩2*,陈亮3,田源2   

  1. 1.济南市水利工程服务中心, 山东 济南 250099;2.山东大学齐鲁交通学院, 山东 济南 250002;3.山东省交通科学研究院, 山东 济南 250000
  • 发布日期:2024-04-17
  • 作者简介:陈晓燕(1982— ),女,山东梁山人,高级工程师,硕士,主要研究方向为水工结构工程、水利工程管理. E-mail:122473283@qq.com. *通信作者简介:庄绪彩(1999— ),女,山东日照人,硕士研究生,主要研究方向为车路协同. E-mail:202135442@mail.sdu.edu.cn
  • 基金资助:
    山东省重点研发计划(2020CXGC010118)

Intelligent technology for monitoring water levels based on shore-based LiDAR

CHEN Xiaoyan1, QI Mingjie1, CHENG Zhiheng2, ZHANG Yu3, ZHUANG Xucai2*, CHEN Liang3, TIAN Yuan2   

  1. 1. Jinan Water Engineering Service Center, Jinan 250099, Shandong, China;
    2. School of Qilu Transportation, Shandong University, Jinan 250002, Shandong, China;
    3. Shandong Transportation Research Institute, Jinan 250102, Shandong, China
  • Published:2024-04-17

PDF (PC)

113

摘要： 针对传统水位监测方法耗费人力物力极大且难以满足夜间、雨天等恶劣环境下实时监测的难题,基于激光雷达研发水位智能监测技术,通过在岸基搭建激光雷达数据采集平台,以相对水平角和垂直方位角为参数选择感兴趣区域;研发算法分析用户数据报协议(user datagram protocol, UDP),自行解析雷达数据,提取水面有效点云信息;拟合离散点云,构造水面方程,计算水位高度,并修正。对该算法进行实地试验,试验结果表明,本研究提出的水位智能监测技术可有效进行水位监测,平均绝对误差为0.057 m,均方根误差为0.073 5 m,平均百分比误差为7.588%。

关键词: 水位监测, 激光雷达, UDP, 最小二乘法, 点云

中图分类号: 

  • TP39
[1] 王宁,王伟,谌东海. 典型水利工程配套智慧管理系统设计研究[J]. 水电站机电技术, 2022, 45(12):28-31. WANG Ning, WANG Wei, SHEN Donghai. Design and research of supporting intelligent management system for typical water conservancy projects[J]. Electromechanical Technology of Hydropower Plants, 2022, 45(12):28-31.
[2] 杨林邦. 智慧水利水情监测与预警系统设计与实现[J].现代计算机, 2022, 28(22):80-85. YANG Linbang. Design and implementation of smart water conservancy monitoring and early warning system[J]. Modern Computers, 2022, 28(22):80-85.
[3] 曾德山. 智慧水利感知系统通信传输的设计与实现[J]. 数字通信世界, 2022(11):32-34. ZENG Deshan. Design and implementation of communication transmission in intelligent waterconservancy perception system[J]. The World of Digital Communication, 2022(11):32-34.
[4] 姜卫平,梁娱涵,余再康,等. 卫星定位技术在水利工程变形监测中的应用进展与思考[J]. 武汉大学学报(信息科学版), 2022, 47(10):1625-1634. JIANG Weiping, LIANG Yuhan, YU Zaikang, et al. Application progress and thinking of satellite positioning technology in deformation monitoring of hydraulic engineering[J]. Journal of Wuhan University(Information Science Edition), 2022, 47(10):1625-1634.
[5] 樊亚萍. 基于图像识别的水位监测系统的研究与实现[D]. 南京: 南京邮电大学, 2019. FAN Yaping. Research and implementation of water level monitoring system based on image recognition[D]. Nanjing: Nanjing University of Posts and Telecommunica-tions, 2019.
[6] WU Jianqing, XU Hao, ZHENG Jianying, et al. Automatic vehicle detection with roadside LiDAR data under rainy and snowy conditions[J]. IEEE Intelligent Transportation Systems Magazine, 2021, 13(1):197-209.
[7] L(¨overU)Bin, XU Hao, WU Jianqing, et al. Revolution and rotation-based method for roadside LiDAR data integration[J]. Optics & Laser Technology, 2019, 119:105571.
[8] WU Jianqing, TIAN Yuan, XU Hao, et al. Automatic ground points filtering of roadside LiDAR data using a channel-based filtering algorithm[J]. Optics & Laser Technology, 2019, 115:374-383.
[9] WU Jianqing, XU Hao, ZHENG Yichen, et al. A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data[J]. Accident Analysis & Prevention, 2018, 121:238-249.
[10] WU Jianqing, XU Hao, TIAN Yuan, et al. An automatic lane identification method for the roadside light detection and ranging sensor[J]. Journal of Intelligent Transportation Systems, 2020, 24(5):467-479.
[11] 王国军. 结构化道路下基于激光雷达的三维检测关键技术研究[D]. 长春: 吉林大学, 2021. WANG Guojun. Research on key technologies of the 3D detection based on LiDAR in structured roads[D]. Changchun: Jilin University, 2021.
[12] 王灿,孔斌,杨静,等. 基于三维激光雷达的道路边界提取和障碍物检测算法[J]. 模式识别与人工智能, 2020, 33(4):353-362. WANG Can, KONG Bin, YANG Jing, et al. An algorithm for road boundary extraction and obstacle detection based on 3D LiDAR[J]. Pattern Recognition and Artificial Intelligence, 2020, 33(4):353-362.
[13] WU Jianqing, XU Hao, ZHANG Yongsheng, et al. An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor[J]. Journal of Safety Research, 2020, 73:211-224.
[14] WU Jianqing, XU Hao, ZHAO Junxuan. Automatic lane identification using the roadside LiDAR sensors[J]. IEEE Intelligent Transportation Systems Magazine, 2020, 12(1):25-34.
[15] 吴建清,宋修广. 同步定位与建图技术发展综述[J]. 山东大学学报(工学版), 2021, 51(5):16-31. WU Jianqing, SONG Xiuguang. Review on development of simultaneous localization and mapping technology[J]. Journal of Shandong University(Engineering Edition), 2021, 51(5):16-31.
[16] WU Jianqing, XU Hao, ZHENG Yichen, et al. Automatic vehicle classification using roadside LiDAR data[J]. Transportation Research Record, 2019, 2673(6):153-164.
[17] JANNATA M S, SALAM R A, SUHENDI A. Study on the near-IR light detection and ranging(LiDAR)potential use as water level sensor[C] //IOP Conference Series: Earth and Environmental Science. Yogyakarta, Indonesia: IOP Publishing, 2021, 704(1):012-040.
[18] XU Nan, MA Yue, ZHANG Wenhao, et al. Monitoring annual changes of lake water levels and volumes over 1984-2018 using landsatimagery and ICESat-2 data[J]. Remote Sensing, 2020, 12(23):4004.
[19] MA Yue, XU Nan, SUN Jinyan, et al. Estimating water levels and volumes of lakes dated back to the 1980s using Landsat imagery and photon-counting LiDAR datasets[J]. Remote Sensing of Environment, 2019, 232(2):111287.
[20] NAWARAJ S, AARON R M, AARON R Y, et al. Groundwater level assessment and prediction in the Nebraska Sand Hills using LiDAR-derived lake water level[J]. Journal of Hydrology, 2021, 600:126582.
[21] PAUL Jonathan, BUYTAERT Wouter, SAH Neeraj. A technical evaluation of LiDAR-based measurement of river water levels[J]. Water Resources Research, 2020, 56(4):26810.
[1] 周勇,兰晓伟,吕斌,栗剑. 基于路侧激光雷达的雪天点云降噪算法[J]. 山东大学学报 (工学版), 2023, 53(4): 30-36.
[2] 王术剑,阎宗尧,刘世杰,田源. 路侧激光雷达与摄像机时间和空间同步方法[J]. 山东大学学报 (工学版), 2022, 52(6): 56-62.
[3] 周勇,吕琛,侯福金,郭鑫铭,宋修广. 基于坐标转换的多路侧激光雷达数据配准方法[J]. 山东大学学报 (工学版), 2022, 52(6): 41-49.
[4] 吴建清,宋修广. 同步定位与建图技术发展综述[J]. 山东大学学报 (工学版), 2021, 51(5): 16-31.
[5] 万鹏. 基于F-PointNet的3D点云数据目标检测[J]. 山东大学学报 (工学版), 2019, 49(5): 98-104.
[6] 刘世光,王海荣,刘锦. 快速四点一致性点云粗配准算法[J]. 山东大学学报 (工学版), 2019, 49(2): 1-7.
[7] 高艳普, 王向东, 王冬青. 多变量受控自回归滑动平均系统的极大似然辨识方法[J]. 山东大学学报(工学版), 2015, 45(2): 49-55.
[8] 叶雯,云挺*,业宁. 基于点云数据的立木树干局部曲面拟合及拓扑结构[J]. 山东大学学报(工学版), 2013, 43(2): 42-47.
[9] 刘菲菲,李建平,陈国强,罗斯特,李增勇*. 坐姿下人体振动特性建模及实验分析[J]. 山东大学学报(工学版), 2012, 42(4): 103-107.
[10] 李改1,2,3, 李磊2,3. 一种解决协同过滤系统冷启动问题的新算法[J]. 山东大学学报(工学版), 2012, 42(2): 11-17.
[11] 王育奎,徐帮树,李术才. 济南城市防洪汛情预警等级判定方法[J]. 山东大学学报(工学版), 2010, 40(4): 88-91.
[12] 李万军 赵东标 牛敏. 笔式加工轨迹的B样条曲线逼近[J]. 山东大学学报(工学版), 2010, 40(1): 59-62.
[13] 董增寿 刘明君. 锅炉氮氧化物排放量检测中PLS的应用改进[J]. 山东大学学报(工学版), 2010, 40(1): 126-128.
[14] 高洪霞,徐明铭,李可军,谭震宇. 配电系统电压跌落源定位新方法[J]. 山东大学学报(工学版), 2009, 39(5): 115-119.
[15] 孙殿柱,朱昌志,李延瑞 . 散乱点云边界特征快速提取算法[J]. 山东大学学报(工学版), 2009, 39(1): 84-86.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(工学版)》编辑部　
地址: 济南市山大南路27号（250100）　 电话: 0531-88366735　 E-mail: xbgxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发