马永力，陈承，张小军，等. 尾矿库降雨监测技术研究进展与发展趋势[J].河南理工大学学报(自然科学版)，doi:10.16186/j.cnki.1673-9787.2025030067

MA Y L, CHEN C, ZHANG X J，et al．Research advances and development trends in rainfall monitoring technologies for tailings ponds［J］.Journal of Henan Polytechnic University( Natural Science)，doi:10.16186/j.cnki.1673-9787. 2025030067

尾矿库降雨监测技术研究进展与发展趋势(网络首发)

马永力^1,2，陈承^1,2，张小军³，刘彦志⁴，郭庆伟^1,2，张小波^1,2

（1.南昌大学 工程建设学院，江西 南昌 330031；2.水工岩土工程安全江西省重点实验室，江西 南昌 330031；3.江西省应急管理科学研究院，江西 南昌 330000；4.江西都昌金鼎钨钼矿业有限公司，江西 九江 332611）

摘要: [目的] 降雨监测是尾矿库安全防控体系的核心环节，其监测精度直接影响浸润线抬升、坝体渗流失稳等重大风险的研判效能。针对山谷型尾矿库地形复杂、局地降雨时空异质性显著的特点，系统梳理降雨监测技术发展脉络，旨在构建面向复杂地形条件的分布式精准监测技术体系，为提升尾矿库全生命周期安全管控水平提供支撑。[方法] 基于文献计量与案例分析方法，系统回顾国内外尾矿库降雨监测技术演进规律。重点解析地面雨量站网观测技术、天气雷达探测技术、卫星遥感反演技术三类主流方法的测量原理与工程适应性，通过对比揭示现有技术在山谷地形应用中存在的监测盲区大、时空分辨率低及反演精度差等共性问题。[结果] 创新提出山谷型尾矿库降雨分布式精准监测技术体系：研制压电式雨量传感器，解决传统翻斗式雨量计易堵塞、数据不连续的技术瓶颈；建立基于边界约束的传感网络优化布设模型，实现复杂地形下监测点位空间分布的动态匹配；开发融合边缘计算的智能网关，构建自适应降雨强度的监测数据采集传输机制。[结论] 系统揭示了尾矿库降雨监测技术发展规律，同时构建了具有工程实用价值的技术框架。随着多源传感融合、深度学习算法和数字孪生平台等新技术的集成应用，未来将形成集实时监测-智能分析-动态预警于一体的技术生态系统。研究成果对完善尾矿库风险防控理论体系、推动智慧矿山建设具有重要指导意义。

关键词: 尾矿库；降雨监测；压电式雨量计；分布式；边缘计算

中图分类号：TD926.4

doi: 10.16186/j.cnki.1673-9787.2025030067

基金项目: 国家重点研发计划项目（2023YFC3012200）

收稿日期：2025-03-29

修回日期：2025-05-19

网络首发日期：2025-06-12

Research advances and development trends in rainfall monitoring technologies for tailings ponds

Ma Yongli^{1, 2}, Chen Cheng^{1, 2}, Zhang Xiaojun³, Liu Yanzhi⁴, Guo Qingwei^{1, 2}, Zhang Xiaobo^{1, 2}

(1.School of Infrastructure Engineering, Nanchang University, Nanchang 330031, Jiangxi, China; 2. Jiangxi Provincial Key Laboratory of Hydraulic Geotechnical Engineering Safety, Nanchang 330031, Jiangxi, China; 3. Jiangxi Academy of Emergency Management Science, Nanchang 330000, Jiangxi, China; 4. Jiangxi Duchang Jinding Tungsten & Molybdenum Mining Co., Ltd., Jiujiang 332611, Jiangxi, China)

Abstract: [Objective] Rainfall monitoring constitutes a critical component of tailings pond safety prevention and control systems, as its accuracy directly affects the assessment of major risks such as phreatic line elevation and seepage-induced instability of dam structures. Targeting the complex terrain of valley-type tailings ponds and the significant spatiotemporal heterogeneity of localized rainfall, this study systematically reviews the developmental trajectory of rainfall monitoring technologies. The research aims to establish a distributed precision monitoring technology system tailored to complex topographic conditions, thereby enhancing the safety management of tailings ponds throughout their lifecycle. [Methods] Utilizing bibliometric analysis and case study methodologies, this paper systematically examines the evolution of rainfall monitoring technologies for tailings ponds both domestically and internationally. The measurement principles and engineering applicability of three mainstream techniques—ground-based rain gauge networks, weather radar detection, and satellite remote sensing inversion—are critically analyzed. Comparative evaluations reveal common limitations in valley terrain applications, including extensive monitoring blind spots, low spatiotemporal resolution, and poor inversion accuracy. [Results] An innovative distributed precision monitoring technology system for valley-type tailings ponds is proposed: (1) A piezoelectric rain gauge is developed to address the technical bottlenecks of traditional tipping-bucket sensors, such as susceptibility to clogging and discontinuous data acquisition; (2) A boundary-constrained optimization model for sensor network deployment is established, achieving dynamic spatial alignment of monitoring points with rainfall intensity gradients in complex terrains; (3) An edge computing-integrated intelligent gateway is designed to construct an adaptive data acquisition and transmission mechanism responsive to rainfall intensity variations. [Conclusion] This study not only systematically elucidates the developmental trends in tailings pond rainfall monitoring technologies but also constructs a practical technical framework. With the integration of emerging technologies such as multi-source sensor fusion, deep learning algorithms, and digital twin platforms, future systems will evolve into a holistic ecosystem encompassing real-time monitoring, intelligent analysis, and dynamic early warning capabilities. The findings provide critical theoretical and technical support for advancing risk prevention strategies and promoting the construction of smart mining infrastructures.

Key words: tailings ponds; rainfall monitoring; piezoelectric rain gauge; distributed; edge computing

CLC: