基于STM32的数字孪生智慧水务管理系统设计

Design of Digital Twin Smart Water Management System Based on STM32

摘要:

针对传统水质监测系统在多参数同步检测、测量精度和远程交互方面的不足,设计并实现了一套基于STM32微控制器的智能水务监测系统；系统采用模块化硬件设计,集成PH传感器、温度传感器和浊度传感器,通过ADC转换和单总线通信采集数据；主控芯片STM32F103负责数据处理,ESP8266无线模块实现与管理端的连接,数据上传至AIRIOT物联网平台进行可视化展示、阈值报警和远程控制；测试结果表明,系统对PH、温度、浊度的测量误差分别控制在±0.5、±1℃和±5NTU范围内,数据上传响应时间短,连续运行稳定,功耗低于120mA；该系统具有低成本、模块化架构、物联网集成与低功耗优化等优势,为中小型水域管理提供了可行方案；未来可通过引入机器学习算法、扩展溶解氧与电导率等多参数传感器及集成太阳能供电,进一步提升系统智能化水平与适用场景,支持水资源可持续管理

Abstract:

To address the shortcomings of traditional water quality monitoring systems in multi-parameter synchronous detection, measurement accuracy, and remote interaction, an intelligent water quality monitoring system based on an STM32 microcontroller was designed and implemented. The system adopted a modular hardware design, integrating pH, temperature, and turbidity sensors. Data was collected via ADC conversion and single-bus communication. The STM32F103 master chip was responsible for data processing, while the ESP8266 wireless module enabled connection to the management platform. Data was uploaded to the AIRIOT IoT platform for visualization, threshold alarms, and remote control. Test results indicated that the system"s measurement errors for pH, temperature, and turbidity were controlled within ±0.5, ±1°C, and ±5 NTU, respectively. The system demonstrated short data upload response times, stable continuous operation, and power consumption below 120 mA. This system offers advantages of low cost, modular architecture, IoT integration, and low-power optimization, providing a feasible solution for small-to-medium water body management. Future work could introduce machine learning algorithms, expand multi-parameter sensors such as dissolved oxygen and conductivity, and integrate solar power supply to further enhance the system"s intelligence and applicability, supporting sustainable water resource management.