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基于NB-IoT技术的土壤重金属检测仪设计与验证
引用本文:王蕾越, 战可涛, 尹亮. 基于NB-IoT技术的土壤重金属检测仪设计与验证[J]. 农业工程学报, 2021, 37(14): 221-227. DOI: 10.11975/j.issn.1002-6819.2021.14.025
作者姓名:王蕾越  战可涛  尹亮
作者单位:1.北京化工大学,北京 100029
基金项目:中央高校基本科研业务费专项资金资助(JD2120)
摘    要:为解决土壤重金属检测准确度较低、实时性差以及数据分散的问题,该研究设计了一种基于窄带物联网(NarrowBand Internet of Things,NB-IoT)技术的土壤重金属检测仪.自主研发高准确度能量色散X射线荧光光谱仪,将测量得到的数据通过NB-IoT实时上传到数据集成云平台.设计试验探究了该设备的最佳测试...

关 键 词:土壤  重金属  NB-IoT  快速检测  EDXRF  X射线  最佳测试时间  预热时间
收稿时间:2021-02-18
修稿时间:2021-05-25

Design and verification of soil heavy metal detector using NB-IoT technology
Wang Leiyue, Zhan Ketao, Yin Liang. Design and verification of soil heavy metal detector using NB-IoT technology[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(14): 221-227. DOI: 10.11975/j.issn.1002-6819.2021.14.025
Authors:Wang Leiyue  Zhan Ketao  Yin Liang
Affiliation:1.Beijing University of Chemical Technology, Beijing 100029, China
Abstract:Abstract: An Energy Dispersive X-Ray Fluorescence (EDXRF) monitor was here developed to detect the soil heavy metal using narrow band internet of things (NB-IoT). The scattered data was also real-time uploaded during detection. The instrument consisted of a TUB00050-AG2 X-ray tube, variable windows collimator, a Vitus H30 40mm2 detector, an LTC2269 analog to digital converter, and an NB-IoT communication module. The X-ray tube turned on the high voltage and filament current under the control of the main chip, thereby producing bremsstrahlung X-rays, where the electrons bombarded the silver target under a strong electric field. The X-ray was then converted to the object ray with the corresponding peak by the filter collimator. The object ray irradiated the center of the sample by adjusting the divergence angle through the collimator. The fluorescence ray was then reflected on the receiving surface of the silicon drift detector with Compton and Rayleigh scattered rays. The detector converted the photon of the incident ray into the pulse signal for the subsequent step rising signal with the preamplifier. The signal was amplified, held, and sampled to generate the spectrum, and then data and location information were uploaded to the NB-IoT module. The final content of each element was obtained for the spectrum resolution, deviation correction. There were no packets loss, and connections instability during 10000 times'' uploading simulated data, indicating low power consumption and stable signal in the NB-IoT module. Better repeatability, real-time detection, and data integration were achieved in the variable light window collimator and communication means with NB-IoT, compared with other similar devices. The NB-IoT base station can widely be expected to support many devices and cover a large area. A general communication protocol of the Internet of things, MQTT, was set up with an NB-IoT module between the platform and instrument. A wide range of expansion support can realize the integration of multiple instruments and various measurement data. An experiment was also designed to explore the best test time and preheating time of the instrument, where five durations were set. It was found that the instrument performed well, as the duration increased, but some elements became unstable when the duration reached 240s per sample. The best duration was determined to be 180s in this case. Consequently, the instrument presented the best repeatability, when the sample was preheated for more than 30min and the measurement time was 180s. Three instruments were also fabricated to verify the measurement accuracy of the instrument with the soil samples from the same batch in Sichuan Province. The collected soil was used to prepare the standard samples after drying, grinding, sieving and pressing. This instrument and Olympus Vanta Element-S were compared to measure each sample 5 times. The soil samples were also characterized in a laboratory chemical analysis. It was found that the detection presented a high accuracy for the Cd, Hg, As, Pb, Zn, Cu, and other five elements. Particularly, the measured value of Cr was much more approximate to the true one, compared with Olympus Vanta Element-S. The average relative errors of Cr, Cu, Pb and Zn were 4.6%, 7.5%, 3.8% and 14.2%, respectively, indicating high accuracy. The relative errors of the remaining three elements As, Cd, and Hg are 55.5%, 55.7%, and 37.2%, respectively. The errors are relatively large and will be significantly reduced as the detector accuracy improves in the future. The device can widely be expected to accurately, stably and real-time detect the content of heavy metals in soil. Subsequently, the data can be summarized to the cloud platform, indicating an excellent real-time performance and data integration.
Keywords:heavy metals   soils   NB-IoT   rapid detection of    EDXRF   X-ray   best test duration   warm-up time
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