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推进卫生监测:创新实时下水道监测框架以实现有效的水位和井盖检测。

Advancing sanitary surveillance: Innovating a live-feed sewer monitoring framework for effective water level and chamber cover detections.

作者信息

Utepov Yelbek, Neftissov Alexandr, Mkilima Timoth, Shakhmov Zhanbolat, Akhazhanov Sungat, Kazkeyev Alizhan, Mukhamejanova Assel Toleubekovna, Kozhas Aigul Kenzhebekkyzy

机构信息

Department of Civil Engineering, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan.

Research and Innovation Center "Industry 4.0", Astana IT University, Astana, Kazakhstan.

出版信息

Heliyon. 2024 Mar 11;10(6):e27395. doi: 10.1016/j.heliyon.2024.e27395. eCollection 2024 Mar 30.

Abstract

Efficient sanitation system management relies on vigilant sewage surveillance to uphold environmental hygiene. The absence of robust monitoring infrastructure jeopardizes unimpeded conduit flow, leading to floods and contamination. The accumulation of harmful gases in sewer chambers, coupled with tampered lids, compounds sewer network challenges, resulting in structural damage, disruptions, and safety risks from accidents and gas inhalation. Notably, even vehicular transit is vulnerable, facing collisions due to inadequately secured manholes. The core objective of this research was to deconstruct and synthesize a prototype blueprint for a live-feed sewer monitoring framework (LSMF). This involves creating a data gathering nexus (DGN) and empirically assessing diverse wireless sensing implements (WSI) for precision. Simultaneously, a geographic information matrix (GIM) was developed with algorithms to detect sewer surges, blockages, and missing manhole covers. Three scrutinized sensors-the LiDar TF-Luna, laser TOF400 VL53L1X, and ultrasonic JSN-SR04T-were evaluated for their ability to measure water levels in sewer vaults. The results showed that the TF-Luna LiDar sensor performed favorably within the 1.0-5.0 m range, with a standard deviation of 0.44-1.15. The TOF400 laser sensor ranked second, with a more variable standard deviation of up to 104 as obstacle distance increased. In contrast, the JSN-SR04T ultrasonic sensor exhibited lower standard deviation but lacked consistency, maintaining readings of 0.22-0.23 m within the 2.0-5.0 m span. The insights from this study provide valuable guidance for sustainable solutions to sewer surveillance challenges. Moreover, employing a logarithmic function, TF-Luna Benewake exhibited reliability at approximately 84.5%, while TOF400 VL53L1X adopted an exponential equation, boasting reliability approaching approximately 89.6%. With this navigational tool, TF-Luna Benewake maintained accuracy within ±10 cm for distances ranging from 8 to 10 m, showcasing its exceptional performance.

摘要

高效的卫生系统管理依赖于对污水的严密监测,以维护环境卫生。缺乏强大的监测基础设施会危及排水管道的畅通,导致洪水和污染。污水井中有害气体的积聚,再加上井盖被篡改,使下水道网络面临的挑战更加复杂,导致结构损坏、中断以及因事故和吸入气体而带来的安全风险。值得注意的是,即使是车辆通行也很脆弱,由于检修孔固定不牢而面临碰撞风险。本研究的核心目标是解构并合成一个实时下水道监测框架(LSMF)的原型蓝图。这包括创建一个数据收集节点(DGN),并对各种无线传感设备(WSI)进行实证评估以确保精度。同时,开发了一个地理信息矩阵(GIM)并配备算法,以检测下水道涌水、堵塞和井盖缺失情况。对三个经过仔细审查的传感器——激光雷达TF-Luna、激光TOF400 VL53L1X和超声波JSN-SR04T——测量下水道拱顶水位的能力进行了评估。结果表明,TF-Luna激光雷达传感器在1.0 - 5.0米范围内表现良好,标准偏差为0.44 - 1.15。TOF400激光传感器排名第二,随着障碍物距离增加,标准偏差变化更大,高达104。相比之下,JSN-SR04T超声波传感器标准偏差较低,但缺乏一致性,在2.0 - 5.0米范围内读数保持在0.22 - 0.23米。本研究的见解为应对下水道监测挑战的可持续解决方案提供了有价值的指导。此外,采用对数函数时,TF-Luna Benewake的可靠性约为84.5%,而TOF400 VL53L1X采用指数方程,可靠性接近约89.6%。借助这个导航工具,TF-Luna Benewake在8至10米距离内保持精度在±10厘米以内,展现出其卓越性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01d7/10950577/f2e5c59e2780/gr1.jpg

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