Machine-learning and water energy harvesting based wireless water consumption sensing system in buildings

Abstract

In recent years, smart wireless remote water meters have become popular, but this kind of smart water meter needs an external power supply, and every 100 to 200 installed this type of water meter, needs to set up an independent collector. This kind of water meter is expensive, and the external power supply will increase the energy consumption of the building, which is not conducive to the circuit safety of the old community. In order to reduce the burden of manual meter reading and reduce the building's "carbon emissions". A wireless water consumption sensing system for buildings based on machine learning and water energy collection is designed. Images are collected and recognized by visual sensors to realize the visual reading of water meter data. The system is powered by a 12V micro water generator and transferred to lithium battery energy storage to realize the self-energy supply of the whole system. The lightweight YOLOv5 was used to implement the model training process. Then, on the premise of the small sample data set, the data set was expanded by a data enhancement method which randomly changed the image brightness and hue. Finally, the training model is deployed on MCU through the TensorFlow Lite framework to realize edge AI detection. Experiments show that the size of the lightweight YOLOv5 is reduced by 87.1% and the mAP reaches 92.3%. The system can complete the water meter wireless meter reading and realize the system self-power stably. In addition, the system can be expanded for other meter reading tasks based on visual features.