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Research on Water Online Monitoring and Identification

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DOI: 10.23977/isspj.2021.61001 | Downloads: 4 | Views: 118

Author(s)

Jiangxia Wang 1, Jiwen Chen 2

Affiliation(s)

1 Binhai Industrial Technology Research Institute of Zhejiang University, Tianjin 300345, China
2 CNOOC Energy Development Co., Ltd, Tianjin 300452, China

Corresponding Author

Jiwen Chen

ABSTRACT

Aiming at the monitoring of urban road water depth, based on narrow-band Internet of things, with the help of multi-sensor collaborative calibration, accurate real-time measurement of road water depth under complex outdoor conditions is realized. Combined with semi real-time image, it can realize the intuitive grasp of road water regime dynamic. The system is suitable for urban road water monitoring, risk warning and dispatching decision support under heavy rainfall. The real-time online water quality monitoring based on multi-sensor collaborative calibration collects semi real-time image data, real-time monitoring data of ultrasonic and capacitive liquid level meter, and the measurement is more accurate through multi-sensor collaborative calibration of camera, ultrasonic and capacitive liquid level meter; the online monitoring method based on convolution neural network model reasoning analysis is used for ponding image recognition to improve the urban intelligent drainage monitoring efficiency Test ability.

KEYWORDS

Multi sensor cooperation; Road water accumulation; Online monitoring

CITE THIS PAPER

Jiangxia Wang, Jiwen Chen, Research on Water Online Monitoring and Identification. Information Systems and Signal Processing Journal (2021) 6: 1-6. DOI: http://dx.doi.org/10.23977/isspj.2021.61001

REFERENCES

[1] Wang, L. (2005) Design of nitrogen bubble water level meter and monitoring software. Harbin University of science and technology.
[2] Hao, Y., Zhou, J.R., Cai, X. (2020) Application of convolutional neural network in petroleum exploration and development. Information system engineering, vol. 11 , pp. 138-140.
[3] Li, Y.F., Dong, H.B. (2018) Remote sensing image classification based on convolutional neural network. Journal of intelligent systems, vol. 13, no. 04, pp 550-556.
[4] Xu, Y.T. (2019) Design and implementation of rice variety identification system based on hyperspectral data. Heilongjiang University.
[5] Liu, J.C. (2019) Design and implementation of Road area water monitoring and early warning system based on multi technology. Inner Mongolia University.

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