Research on Water Online Monitoring and Identification

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.