Intelligent System for Water Conservancy Engineering Quality Inspection Based on Deep Learning and Internet of Things Technology

Quality inspection of water conservancy engineering is an important link to ensure engineering safety and sustainability. Traditional quality inspection methods have problems such as low efficiency and low accuracy. This study is based on CNN and Internet of Things technology to design and implement a smart system for water conservancy engineering quality inspection. Firstly, it constructed a CNN model suitable for water conservancy engineering, which achieved automatic recognition and classification of quality problems through learning and training a large amount of water conservancy engineering data. Secondly, IoT technology can be used to connect sensor devices with systems, achieving real-time data collection and transmission, and providing comprehensive quality monitoring and early warning functions. The experimental results prove that based on CNN and Internet of Things technology, the intelligent system for quality inspection of water conservancy engineering has achieved a good technical quality inspection. It can effectively handle the processing of massive water conservancy engineering data, and it is accurate to identify the potential quality defects and provide early warning and decision support.