摘要： 针对目前水产养殖过程中水质监测方式存在效率低、实时性差、工作区域受限制等缺点，设计了一种基于STM32与树莓派的水质监测无人艇系统。系统采用STM32单片机和树莓派4B作为控制器，结合GPS模块和电子罗盘实现无人艇的航行控制；利用摄像头、pH、浊度、温度传感器实时采集水体环境和水面图像数据；通过4G通信模块实现数据传输。在阿里云服务器上部署基于.NET Framework框架开发的上位机软件，实现对无人艇的指令下发以及可视化显示采集到的数据等功能。结果显示，养殖水质监测无人艇到达的实际测量区域与预设区域位置误差最大为4.3 m，最小为3.4 m，精度范围满足实际需求；水质测量平均用时在7 min28 s左右，相比传统人工检测方式，作业效率显著提升。研究表明，本系统运行稳定，操作方便，能够及时有效地获取养殖水域水体数据，提高了养殖人员的工作效率，降低了养殖成本，具有一定的应用前景。

关键词: 水产养殖, 水质监测, 无人艇, 控制系统, STM32, 树莓派

Abstract: For the current aquaculture process of water quality monitoring method has low efficiency, poor real-time, working area is restricted and other shortcomings, designed a water quality monitoring unmanned boat system based on STM32 and Raspberry Pi. The system uses STM32 single chip microcomputer and Raspberry Pi 4B as the controller, combines GPS module and electronic compass to realize the navigation control of the unmanned surface vehicle, uses camera, pH sensor, turbidity sensor and temperature sensor to collect water environment data and water surface image data in real time. Data transmission is realized through 4G communication module. Deploy the upper computer software developed based on the .NET Framework on Alibaba cloud server to implement the functions of issuing commands to unmanned surface vehicle and visualizing the collected data. The results show that the maximum and minimum positional errors between the actual measurement area and the preset area reached by the unmanned water quality monitoring boat for aquaculture are 4.3 meters and 3.4 meters, and the accuracy range meets the actual needs. The average time for water quality measurement is around 7 minutes and 28 seconds, which significantly improves the operational efficiency compared to traditional manual detection methods.The research shows that the system is stable, easy to operate, and can obtain the data of water body in breeding waters in a timely and effective manner, which improves the efficiency of breeding personnel and reduces the cost of breeding, and has certain application prospects.

Key words: aquaculture, water quality monitoring, unmanned surface vehicle (USV), control system, STM32, Raspberry Pi