称重式蒸渗仪应用现状及智能化改造路径分析

Analysis of Application Status and Intelligent Transformation Path of Weighing Lysimeter

摘要: 称重式蒸渗仪作为监测土壤–植物–大气连续体水分运移的基准水文观测设备，其传统运行模式在蒸散发数据时效性、系统集成度及智能分析等层面存在局限，且受结构原理制约，难以同步满足高精度与高稳定性观测需求。本研究在剖析悬挂式、直称式及杠杆式等主流称重蒸渗仪的结构原理与应用局限性的基础上，提出一种整合物联网传感、边缘计算与云端处理的三层协同架构，实现水文蒸散发数据的高频采集、实时清洗与远程管理。引入多源数据融合模型，采用长短期记忆网络(LSTM)建立称重数据与环境因子的非线性映射关系，实现环境干扰的动态补偿与异常工况诊断。案例验证结果表明，该体系有助于抑制环境噪声与传感器漂移影响，提升监测数据的质量与运维效率，推动传统设备向网络化、智能化感知节点演进，以期为区域尺度蒸散发过程解析与水资源优化配置提供关键技术支撑。

Abstract: The weighing lysimeter, as a benchmark hydrological observation device for monitoring water movement within the soil-plant-atmosphere continuum, exhibits limitations in its traditional operational mode concerning the timeliness of evapotranspiration data, system integration, and intelligent analysis. Furthermore, constrained by its structural principles, it is challenging to simultaneously meet the demands for both high precision and high stability in observation. Based on an analysis of the structural principles and application limitations of mainstream weighing lysimeters (including hanging, direct-loading, and lever types), this study proposes a three-layer collaborative architecture integrating the Internet of Things (IoT) sensing, edge computing, and cloud processing. This architecture enables high-frequency data acquisition, real-time cleaning, and remote management. By introducing a multi-source data fusion model, a Long Short-Term Memory (LSTM) network establishes a nonlinear mapping relationship between weighing data and environmental factors, facilitating dynamic compensation for environmental interference and diagnosis of abnormal operating conditions. Case study validation results indicate that this system helps suppress the impact of environmental noise and sensor drift, thereby improving the quality of monitoring data and operational efficiency. It promotes the evolution of traditional devices towards networked, intelligent sensing nodes, aiming to provide key technological support for the analysis of regional-scale evapotranspiration processes and the optimal allocation of water resources.