Wetland digital twin technology and its application

doi:10.13474/j.cnki.11-2246.2024.0206

Abstract

Abstract: Wetlands provide enormous ecological services function for nature, which is an essential component for maintaining regional and national ecological security and promoting sustainable development. Digital twin technology provides a new method and tool for wetland research, which can achieve comprehensive digital virtualization of wetlands, real-time visualization of the entire process and collaborative intelligence of management decisions. It not only helps to understand the operational mechanism of wetland ecosystems, but also promotes the intelligent process of wetland research. In this paper, the characteristics of wetlands and digital twin technology are combined to study and analyze the construction of wetland digital twin systems from four aspects: wetland information monitoring layer, wetland information processing layer, wetland model analysis layer, and wetland application interaction layer, so as to achieve a highly integrated wetland digital twin system with informatization, digitalization and intelligence. Then, the key technologies of establishing a digital twin system for wetlands are introduced, including real-time monitoring technology, high performance data processing technology, complex wetland models, and visual interaction technology. In addition, the application of digital twin technology in wetland research was explored from five aspects: dynamic monitoring of wetland resources, wetland classification, wetland ecosystem assessment, wetland tourism, wetland planning and management. What's more, the current existing problems in the construction of wetland digital twin were discussed, including the fusion of multi-source data, complex wetland modeling and optimization, and visualization of wetland multiple scenes. It is expected that relevant work can provide reference for the further development and implementation of digital twin in wetland research.

Key words: digital twin, wetland research, system architecture, wetland application, key technology

CLC Number:

P208

CAO Xuyue, SUN Yonghua, WANG Yanzhao, WANG Yihan, CHENG Xinglu, ZHANG Wangkuan. Wetland digital twin technology and its application[J]. Bulletin of Surveying and Mapping, 2024, 0(2): 32-38.

References

[1] 李浩,王昊琪,刘根,等.工业数字孪生系统的概念、系统结构与运行模式[J].计算机集成制造系统,2021,27(12):3373-3390.
[2] 聂蓉梅,周潇雅,肖进,等.数字孪生技术综述分析与发展展望[J].宇航总体技术,2022,6(1):1-6.
[3] 陶飞,刘蔚然,张萌,等.数字孪生五维模型及十大领域应用[J].计算机集成制造系统,2019,25(1):1-18.
[4] 赵建文,孟旭辉.数字孪生在煤矿电网中的应用研究[J].工矿自动化,2023,49(2):38-46.
[5] 杨鑫,海新权,杨玉婷.基于Meta分析的张掖黑河湿地生态系统服务价值评估[J].生态与农村环境学报,2023,39(1):60-68.
[6] 肖涛,石强胜,闻熠,等.湿地生态系统服务研究进展[J].生态学杂志,2022,41(6):1205-1212.
[7] CONVENTION ON WETLANDS.Global wetland outlook:special edition 2021[M].Gland,Switzerland:Secretariat of the Convention on Wetlands,2021.
[8] ROSEN R,VON WICHERT G,LO G,et al.About the importance of autonomy and digital twins for the future of manufacturing[J].IFAC-Papers Online,2015,48(3):567-572.
[9] GRIEVES M.Digital twin:manufacturing excellence through virtual factory replication[R].[S.l.]:BSDELMIA, 2015.
[10] SHAFTO M,CONROY M,DOYLE R,et al.Modeling,simulation,information technology and processing roadmap[R].[S.l.]:NASA,2010.
[11] TUEGEL E J,INGRAFFEA A R,EASON T G,et al.Reengineering aircraft structural life prediction using a digital twin[J].International Journal of Aerospace Engineering,2011,2011:1-5.
[12] 李欣,刘秀,万欣欣.数字孪生应用及安全发展综述[J].系统仿真学报,2019,31(3):385-392.
[13] 陶飞,刘蔚然,刘检华,等.数字孪生及其应用探索[J].计算机集成制造系统,2018,24(1):1-18.
[14] 张霖.关于数字孪生的冷思考及其背后的建模和仿真技术[J].系统仿真学报,2020,32(4):1-10.
[15] GXOKWE S,TIMOTHY D,DOMINIC M.Multispectral remote sensing of wetlands in semi-arid and arid areas:a review on applications,challenges and possible future research directions[J].Remote Sensing,2020,12(24):4190.
[16] 傅元,居万军,李莉,等.浅谈物联网技术在湿地环境监测的应用[J].信息系统工程,2020(12):25-26.
[17] 李思达,王新艳,张芳菲,等.湿地水动力研究中无人机遥感技术的应用进展、机遇与挑战[J].环境工程,2023,41(1):93-104.
[18] 石晓刚,张海燕,尹大东.基于GIS和物联网的湿地生态感知系统设计与实现[J].物联网技术,2023,13(5):44-46.
[19] 潘文静.金洞猛江河国家湿地公园监测预警信息系统构建[D].长沙:中南林业科技大学,2021.
[20] 邓朋浩.基于LoRa技术的滨海湿地水质参数在线监测系统研究[D].唐山:华北理工大学,2022.
[21] LI X,FENG M,RAN Y,et al.Big data in earth system science and progress towards a digital twin[J].Nature Reviews Earth & Environment,2023,4(5):319-332.
[22] 徐兴亚.数据同化技术在智慧水利中的研究与应用[C]//中国水利学会.中国水利学会2018学术年会论文集第五分册.北京:中国水利水电出版社,2018.
[23] 凌从高,穆溪,许敏,等.湿地生态系统实际蒸散发数据驱动估算模型研究[J].安徽农业大学学报,2022,49(5):771-779.
[24] 张楠,张曼胤,王贺年,等.水文模型在湿地景观规划设计中的应用研究[J].世界林业研究,2023,36(2):32-37.
[25] 夏鹏,宋伟凯,李俊辉,等.河湖湿地监测二维、三维一体可视化系统研究[J].经纬天地,2022(4):66-69.
[26] 杨一帆,邹军,石明明,等.数字孪生技术的研究现状分析[J].应用技术学报,2022,22(2):176-184.
[27] ZHANG S,XUE Y,ZHANG H,et al.Improved hungarian algorithm-based task scheduling optimization strategy for remote sensing big data processing[J].Geo-spatial Information Science,2023,1-14.
[28] 张玉超.计算机网络与云计算技术的应用[J].集成电路应用,2023,40(2):44-46.
[29] 李超,刘君武,王理,等.数字孪生在智慧城市中的应用[J].中国检验检测,2022(4):42-46.
[30] LIU M N,FANG S L,DONG H Y,et al.Review of digital twin about concepts,technologies,and industrial applications[J].Journal of Manufacturing Systems,2021,58:346-361.
[31] ZHU Z X,LIU C,XU X.Visualisation of the digital twin data in manufacturing by using Augmented Reality[J].Procedia CIRP,2019,81:898-903.
[32] 丽水市政府门户网站.全国首个“江浙之巅”国家公园数字孪生应用上线[EB/OL].(2023-02-17)[2023-07-09].http://www.lishui.gov.cn/art/2023/2/17/art_1229218389_57343676.html.
[33] 朱媛媛.【武汉】从百里荒湖到国际重要湿地:智慧沉湖的华丽转身[EB/OL].(2022-11-06)[2023-07-09].https://news.hubeidaily.net/mobile/945163.html.
[34] 孙晓倩.“荒湖蝶变”守护一江清水北上|山水里的中国[EB/OL].(2023-04-20)[2023-07-09].http://www.xinhuanet.com/science/2023-04/20/c_1310712393.htm.
[35] 丛毓,邹元春,吕宪国,等.湿地资源调查与湿地监测的比较研究[J].湿地科学,2021,19(3):277-284.
[36] 丛喜东,冯亚男,王佳佳,等.黑龙江省湿地水禽生物多样性资源监测数据管理系统的设计与实现[J].林业科技,2023,48(3):56-59.
[37] HALABISKY M,BABCOCK C,MOSKAL L M.Harnessing the temporal dimension to improve object-based image analysis classification of wetlands[J].Remote Sensing,2018,10(9):1-16.
[38] HU Y B,ZHANG J,MA Y,et al.Hyperspectral coastal wetland classification based on a multiobject convolutional neural network model and decision fusion[J].IEEE Geoscience and Remote Sensing Letters,2019,16(7):1110-1114.
[39] 钟燕飞,吴浩,刘寅贺.湿地遥感制图研究现状与展望[J].中国科学基金,2022,36(3):420-431.
[40] PASHAEI M,KAMANGIR H,STAREK M J,et al.Review and evaluation of deep learning architectures for efficient land cover mapping with UAS hyper-spatial imagery:a case study over a wetland[J].Remote Sensing,2020,12(6):959.
[41] 李心媛,贺智,楼桉君,等.双线性图卷积网络的环南海地区湿地遥感分类[J].测绘通报,2023(5):44-50.
[42] 张永彬,刘雅辉,刘明月,等.基于Relief F与卷积神经网络的湿地植物群落精细分类[J].测绘通报,2023(2):58-64.
[43] 吴志峰,曹峥,宋松,等.粤港澳大湾区湿地遥感监测与评估:现状、挑战及展望[J].生态学报,2020,40(23):8440-8450.
[44] 闫瑞华.中国国际重要湿地调查系统的设计与实现[J].湿地科学与管理,2022,18(5):14-18.
[45] 张凤阁.我国虚拟旅游研究:回顾与展望[J].科技创业月刊,2022(12):160-162.
[46] 裴玉.大庆湿地旅游虚拟现实系统设计[D].大庆:黑龙江八一农垦大学,2014.
[47] 何睦.江西省“智慧湿地”综合信息平台设计与案例实现[D].南昌:江西师范大学,2019.
[48] ECKHART M,EKELHART A.Towards security-aware virtual environments for digital twins[C]//CPSS'18:Proceedings of the 4th ACM Workshop on Cyber-Physical System Security.[S.l.]:ACM,2018.
[49] 刘梦瑶,胡海艳,王丽娜,等.城市湿地生态系统保护规划技术路径构建与实践:以湖北省潜江市湿地保护规划项目为例[J].规划师,2022,38(3):103-109.