基于物联网的岩溶监测数据汇聚系统建设

杨辰; 毕奔腾

doi:10.11932/karst2026y011

基于物联网的岩溶监测数据汇聚系统建设

doi: 10.11932/karst2026y011

杨辰^{1, 2,},
毕奔腾^{1, 2, ,}

1.
中国地质科学院岩溶地质研究所/自然资源部、广西岩溶动力学重点实验室/联合国教科文组织国际岩溶研究中心, 广西桂林 541004
2.
广西平果喀斯特生态系统国家野外科学观测研究站, 广西平果 531406

基金项目: 广西自然科学基金项目（2025GXNSFBA069418）；中国地质科学院岩溶地质研究所基本科研业务费重大项目（2021004）；中国地质调查局地质调查项目（DD202610202624）

详细信息

作者简介:
杨辰（1989－），男，高级工程师，主要从事岩溶地质信息化建设及应用方面的研究工作。E-mail：ychen@mail.cgs.gov.cn

通讯作者:
毕奔腾（1990－），男，高级工程师，主要从事地质信息技术研究与应用。E-mail: bibenteng@mail.cgs.gov.cn。

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- 文章访问数: 35
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- 被引次数: 0
出版历程
- 收稿日期: 2025-07-31
- 录用日期: 2026-03-13
- 修回日期: 2026-03-06
- 网络出版日期: 2026-06-18

Construction of a karst monitoring data aggregation system based on the Internet of Things

YANG Chen^{1, 2
,},
BI Benteng^{1, 2
, ,}

1.
Institute of Karst Geology, CAGS/ Key Laboratory of Karst Dynamics, MNR & GZAR, Guilin/International Research Centre on Karst under the Auspices of UNESCO, Guangxi 541004, China
2.
Pingguo Guangxi, Karst Ecosystem, National Observation and Research Station, Pingguo, Guangxi 531406, China

摘要

摘要: 岩溶区因其“土在地表、水在地下”的特殊结构，对气候、水文等环境要素响应敏感，资源环境问题突出，实时监测岩溶关键带环境变化响应快速的水文过程、地球化学过程、生物过程(三大过程)对解决岩溶区资源环境问题至关重要。目前岩溶野外监测站点多侧重于设备端建设，存在类型多、协议多样、数据孤岛等问题，没有形成统一的数据系统。本研究旨在开发一套能够实时在线监测、统计分析、实现多元站点数据汇聚的岩溶监测数据系统。系统设计了支持多协议设备统一接入的数据集成架构，突破传感器及传输设备的协议限制；基于物联网、微服务架构与容器化等技术，结合Kubernetes 实现弹性伸缩与高可用部署。通过实际部署运行和接入监测设备验证，可实时汇聚监测设备数据、展示设备状态、异常预警，实现数据持久化与共享，提高了岩溶监测数据的时效性。
- 岩溶关键带 /
- 物联网 /
- 微服务架构 /
- 数据可视化 /
- 分布式系统
Abstract: As a vital component of the Earth's Critical Zone, the karst critical zone exhibits highly sensitive hydrological, geochemical, and biological processes in response to environmental changes. Effective monitoring is therefore crucial for addressing key resource and environmental challenges in karst regions, including soil erosion, drought and flood disasters, and ecological conservation. However, existing monitoring stations, often constructed during different periods with varying observation indicators, suffer from incompatible equipment, transmission protocols, and data platforms. This significantly impedes data timeliness, unified management, and data sharing.To systematically resolve these issues, this paper designs and implements a standardized data convergence system based on Internet of Things (IoT) technology and cloud-native architecture. Firstly, at the device access layer, the system utilizes the MQTT protocol as its core while maintaining compatibility with standard protocols like ModBus. It also provides a flexible and extensible SDK, enabling efficient and cost-effective integration of both existing and new heterogeneous monitoring devices, thereby achieving unified access and management of multi-protocol equipment. Secondly, at the system architecture level, it employs advanced microservices design and containerization technology. Core functions are modularized through RESTful APIs, and services are containerized and automatically orchestrated using Docker and Kubernetes. This cloud-native architecture endows the system with high elasticity, scalability, and reliability, enabling self-healing, elastic scaling, and load balancing. This effectively supports the processing of massive concurrent data streams and accommodates future business growth. Regarding data storage, the system innovatively adopts a hybrid multi-type database architecture. It integrates Redis, Apache Doris, MongoDB, and MinIO to handle different scenario-scaching, real-time analysis, document storage, and object storage, respectively-ensuring highly efficient and stable data read/write operations. Furthermore, the system implements three data snapshot collection rules-immediate, change-based, and scheduled-and supports the import of historical data, guaranteeing the continuity and integrity of monitoring data. Security is embedded throughout the device end, transmission layer, and platform layer, with comprehensive data protection ensured through mechanisms such as pre-authorization, TLS encryption, and access control.The system concretely implements a three-tier application structure: a management console, a large monitoring screen, and a mobile APP. The management console handles device management, intelligent operational maintenance, and system configuration; the large screen provides a global data overview based on map visualization; the mobile terminal enables users to access device status and data anytime, anywhere. Application examples demonstrate that the system has been successfully deployed, integrating numerous monitoring stations from several national and provincial field observation and research stations, including those in Pingguo, Guangxi, and Guilin. It has accumulated over 2 million data entries, achieved a station data completeness rate of 99.94%, and operates stably. Performance tests indicate that the system maintains millisecond-level response times under simulated scenarios of millions of concurrent device connections and queries on massive historical datasets, demonstrating excellent processing capability and stability.In conclusion, this system effectively addresses the long-standing challenges of equipment heterogeneity and data silos in karst monitoring. It establishes a unified, efficient, and reliable data convergence and management platform, providing a solid data foundation for scientific research, ecological protection, and disaster early warning in karst areas. Future work will focus on the deep integration of artificial intelligence algorithms and professional models to further enhance the system's intelligence in data quality control, intelligent early warning, and analytical evaluation.
- karst critical zone /
- Internet of Things /
- microservices architecture /
- data visualization /
- distributed system

HTML

图 1 RESTful API

Figure 1. RESTful API

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图 2 数据快照技术架构

Figure 2. Technical architecture of data snapshot

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图 3 系统架构图

Figure 3. System architecture diagram

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图 4 管理台-智慧运维

Figure 4. Management console-intelligent operations

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图 5 大屏首页

Figure 5. Large screen-homepage

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图 6 APP运行界面

Figure 6. APP operation interface

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表 1 LZK1数据采集统计

Table 1. LZK1 data collection statistics

统计时间计划采集数实际采集数缺失数据量数据完整率

2025年7月 4464 4464 0 100%

2025年6月 4464 4457 7 99.84%

2025年5月 4320 4319 1 99.97%

下载: 导出CSV

参考文献(26)

[1]	Lin, H. Earth's Critical Zone and hydropedology: concepts, characteristics, and advances, Hydrol. Earth Syst. Sci. , 14, 25−45, 2010.
[2]	蒲俊兵. 地球关键带与岩溶关键带: 结构、特征、底界[J]. 地质科技通报, 2022, 41(5): 230-241. doi: 10.19509/j.cnki.dzkq.2022.0191 PU Junbing. Earth's critical zone and karst critical zone: Structure, characteristic and bottom boundary[J]. Bulletin of Geological Science and Technology, 2022, 41(5): 230-241. doi: 10.19509/j.cnki.dzkq.2022.0191
[3]	陈喜, 张志才. 喀斯特地区地球关键带科学与生态水文学发展综述[J]. 中国岩溶, 2022, 41(3): 356-364. doi: 10.11932/karst20220303 CHEN Xi, ZHANG Zhicai. An overview on the development of science and ecological hydrology of the earth critical zones in karst area[J]. Carsologica Sinica, 2022, 41(3): 356-364. doi: 10.11932/karst20220303
[4]	章程, 蒋忠诚, Chris Groves, 袁道先. 岩溶IGCP国际合作30年与岩溶关键带研究展望[J]. 中国岩溶, 2019, 38(3): 301-306. ZHANG Cheng, JIANG Zhongcheng, Chris Groves, YUAN Daoxian. 30 years international cooperation with IGCP and perspectives of karst critical zone research[J]. Carsologica Sinica, 2019, 38(3): 301-306.
[5]	全国自然资源与国土空间规划标准化技术委员会. GB/T 43216 - 2023 岩溶关键带监测技术要求 [S]. 北京: 中国标准出版社, 2023.
[6]	赵恬茵, 吴媛媛, 孙连群, 高长春, 潘网生, 许玉凤, 程富东. 岩溶地区水土漏失的定量研究进展[J], 中国岩溶, 2023, 42(1): 61-70. ZHAO Tianyin, WU Yuanyuan, SUN Lianqun, GAO Changchun, PAN Wangsheng, XU Yufeng, CHENG Fudong. Review of the quantitative study on soil leakage in karst area[J]. Carsologica Sinica, 2023, 42(1): 61-70, 108.
[7]	李芹, 王宇, 张华, 等. 云南高原脆弱生态地质环境类型与特征及生态修复对策[J]. 中国岩溶, 2024, 43(6): 1261-1274. LI Qin, WANG Yu, ZHANG Hua, CHAI Zhengjiao, XIONG Bo. Types and characteristics of vulnerable eco-geological environments on the Yunnan plateau and countermeasures for ecological restoration[J]. Carsologica Sinica, 2024, 43(6): 1261-1274.
[8]	Zhang X, Deng L, Li N. Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things. Comput Intell Neurosci. 2022 May 18;2022: 2099268.
[9]	Bumpus P B , Kruse S E . Self-potential monitoring for hydrologic investigations in urban covered-karst terrain[J]. Geophysics, 2014, 79(6): B231-B242. DOI: 10.1190/geo2013-0354.1.
[10]	Mudarra M , Andreo B , Mudry J . Monitoring groundwater in the discharge area of a complex karst aquifer to assess the role of the saturated and unsaturated zones[J]. Environmental Earth Sciences, 2012, 65(8): 2321-2336. DOI: 10.1007/s12665-011-1032-x.
[11]	贾龙, 蒙彦, 雷明堂, 赵良杰, 王忠忠, 韩庆定, 石小龙, 戴建玲, 李录娟, 殷仁朝, 潘宗源, 吴远斌, 周富彪. 粤港澳大湾区岩溶水源地塌陷监测预警与安全开采[J/OL]. 中国地质, 1-19 [2025-10-29]. https://link.cnki.net/urlid/11.1167.p.20240515.1825.004. JIA Long, MENG Yan, LEI Mingtang, ZHAO Liangjie, WANG Zhongzhong, HAN Qingding, SHI Xiaolong, DAI Jianling, LI Lujuan, YIN Renchao, PAN Zongyuan, WU Yuanbin, ZHOU Fubiao. Safe exploitation of karst water source and collapse monitoring in the Guangdong-Hong Kong-Macao Greater Bay Area. Geology in China, 1-19 [2025-10-29]. https://link.cnki.net/urlid/11.1167.p.20240515.1825.004.
[12]	杨辰, 毕奔腾, 周立新. 岩溶野外科学观测研究站建设及数据共享研究[J]. 中国矿业, 2023, 32(S1): 114-116. doi: 10.12075/j.issn.1004-4051.20230018 YANG Chen, BI Benteng, ZHOU Lixin. Study on the construction and data sharing of karst field scientific observation and research stations[J]. China Mining Magazine, 2023, 32(S1): 114-116. doi: 10.12075/j.issn.1004-4051.20230018
[13]	王汉雨, 周永章, 许娅婷, 王维曦, 曹伟, 刘永强, 贺炬翔, 陆可飞. 基于微服务架构的城市土壤污染物联网监测及可视化系统研发[J]. 地学前缘, 2024, 31(4): 165-182. WANG Hanyu, ZHOU Yongzhang, XU Yating, WANG Weixi, CAO Wei, LIU Yongqiang, HE Juxiang, LU Kefei. IoT monitoring and visualization of urban soil pollution based on microservice architecture[J]. Earth Science Frontiers, 2024, 31(4): 165-182.
[14]	蒋宏毅, 李小军, 李丽. 基于MQTT协议的紧急地震信息发布系统应用研究[J]. 地震研究, 2025, 48(03): 440-449. JIANG Hongyi, LIXiaojun, LI Li. Research on the Application of Emergency Earthquake Information Release System Based on MQTT Protocol[J]. Journal of Seismological Research, 2025, 48(03): 440-449.
[15]	张奕鸣, 刘彩霞, 刘树新. RESTful API在5G应用场景中的安全威胁研究[J]. 计算机应用与软件, 2023, 40(02): 314-319. doi: 10.3969/j.issn.1000-386x.2023.02.049 ZHANG Yiming, LIU Caixia, LIU Shuxin. Security Threats of RESTful API in 5G Application Scenarios[J]. Computer Applications and Software, 2023, 40(02): 314-319. doi: 10.3969/j.issn.1000-386x.2023.02.049
[16]	Anderson C. Docker [software engineering]. IEEE Software, 2015, 32(3): 102−105.
[17]	Casalicchio E, Iannucci S. The state-of-the-art in container technologies: Application, orchestration and security. Concurrency andComputation: Practice and Experience, 2020, 32(17): e5668.
[18]	吴逸文, 张洋, 王涛, 王怀民. 从Docker容器看容器技术的发展: 一种系统文献综述的视角[J]. 软件学报, 2023, 34(12): 5527-5551. WU Yiwen , ZHANG Yang , WANG Tao , WANG Huaimin. Development Exploration of Container Technology Through Docker Containers: A Systematic Literature Review Perspective[J]. Journal of Software, 2023, 34(12): 5527-5551.
[19]	沈文枫, 邓荣, 刘政森, 杜雨航, 陆唯佳. 基于Kubernetes的工业AI项目开发平台[J]. 计算机应用与软件, 2024, 41(5): 15-20,48. doi: 10.3969/j.issn.1000-386x.2024.05.003 SHEN Wenfeng, DENG Rong, LIU Zhengsen, DU Yuhang, LU Weijia. An Industrial AI Project Development Platform Based on Kubernetes[J]. Computer Applications and Software, 2024, 41(5): 15-20,48. doi: 10.3969/j.issn.1000-386x.2024.05.003
[20]	扈静, 柏晨, 张玺, 葛茂根. 基于OPC UA的分布式数据采集处理系统架构研究[J]. 合肥工业大学学报(自然科学版), 2024, 47(08): 1028-1034. doi: 10.3969/j.issn.1003-5060.2024.08.004 HU Jing, BAl Chen, ZHANG Xi, GE Maogen. Research on the architecture of distributed data acquisition and processing system based on OPC UA[J]. Journal of Hefei University of Technology (Natural Science), 2024, 47(08): 1028-1034. doi: 10.3969/j.issn.1003-5060.2024.08.004
[21]	程远瑶, 王昕怡, 陈逸, 郭佳. 基于Redis的ATS缓存系统设计与实现[J]. 计算机应用与软件, 2023, 40(07): 111-115. CHENG Yuanyao , WANG Xinyi, CHEN Yi , GUO Jia. Design and Realization of ATS Catching System Based on Redis[J]. Computer Applications and Software, 2023, 40(07): 111-115.
[22]	许娜, 耿恒高, 徐传鹏, 彭蒙蒙, 彭苏萍, 何登科. 基于MongoDB的地震勘探数据管理系统的设计与实现[J]. 实验室研究与探索, 2022, 41(02): 251-260. doi: 10.19927/j.cnki.syyt.2022.02.054 XU Na, GENG Henggao, XU Chuanpeng, PENG Mengmeng, PENG Suping, HE Dengke. Design and Implementation of Seismic Exploration Data Management System Based on MongoDB[J]. Research and Exploration in Laboratory, 2022, 41(02): 251-260. doi: 10.19927/j.cnki.syyt.2022.02.054
[23]	邱宇. 基于MinI0分布式存储的微服务模块开发应用[J]. 互联网周刊, 2023(22): 38-40. QIU Yu. Development and Application of Microservices Modules Based on MinIO Distributed Storage[J]. The Internet Weekly, 2023(22): 38-40.
[24]	HuntP, KonarM, Junqueira F P, et al. ZooKeeper: wait-free coordination for Internet-scale systems. In: Proceedings of the USENIX Annual Technical Conference (USENIX ATC), 2010. 145-158.
[25]	史博轩, 章峰, 蒋文保. 基于ZooKeeper的全网统一信任锚模型研究[J]. 计算机应用研究, 2020, 37(12): 3722-3725. doi: 10.19734/j.issn.1001-3695.2019.08.0568 SHI Boxuan, ZHANG Feng, JIANG Wenbao. Research on unified trust anchor model based on ZooKeeper[J]. Application Research of Computers, 2020, 37(12): 3722-3725. doi: 10.19734/j.issn.1001-3695.2019.08.0568
[26]	唐瑛, 王宝会. 基于图神经网络的SSL/TLS加密恶意流量检测算法研究[J]. 计算机科学, 2024, 51(9): 365-370. doi: 10.11896/jsjkx.230800079 TANG Ying, WANG Baohui. Study on SSL/TLS Encrypted Malicious Traffic Detection Algorithm Based on Graph Neural Networks[J]. Computer Science, 2024, 51(9): 365-370. doi: 10.11896/jsjkx.230800079