Intelligent beehive IoT monitoring system based on STM32
基于STM32的智能蜂箱物联网监测系统

摘要

目的 针对传统蜂箱管理主要依赖人工巡查、难以连续获取蜂箱环境及蜂群活动信息的问题，设计一种基于多参数感知与物联网技术的智能蜂箱监测系统，实现蜂箱环境和蜂群活动状态的持续监测以及远程管理。方法 以STM32F103C8T6为核心控制器，构建由温湿度、二氧化碳（CO₂）、蜂箱重量和蜜蜂出勤监测模块组成的多参数感知系统，通过ESP8266无线通信模块实现数据上传，并利用OLED显示屏和微信小程序实现本地及远程监测；根据预设阈值驱动风扇、加热片、加湿器及蜂鸣器等执行设备，实现蜂箱环境自动调节和异常提醒。结果 系统能够完成温湿度、CO₂浓度、蜂箱重量和蜜蜂出勤次数等数据的采集、处理、显示与远程传输；测试结果表明，OLED能够稳定显示监测信息，阈值触发后执行设备能够按照设定逻辑完成相应动作，微信小程序能够接收蜂箱监测数据并实现远程控制。结论 所设计系统能够实现蜂箱多参数信息的持续感知、本地显示、远程传输及基本自动控制，为蜂群状态监测及后续基于多源时序数据的异常识别和健康预警研究提供了数据采集与物联网基础。

Abstract

Objective To address the limitations of traditional beehive management systems—which primarily rely on manual inspections and face challenges in continuously acquiring environmental and colony activity data—this study proposes an intelligent beehive monitoring system based on multi-parameter sensing and IoT technologies, enabling continuous monitoring and remote management of both the hive environment and colony activity status.
Methods The system uses an STM32F103C8T6 microcontroller as the core controller to build a multi-parameter sensing system comprising modules for monitoring temperature and humidity, carbon dioxide (CO₂) concentration, hive weight, and bee flight activity. Data are uploaded via an ESP8266 wireless communication module, while local and remote monitoring is facilitated using an OLED display and a WeChat Mini Program; based on preset thresholds, the system drives peripheral devices—such as fans, heating elements, humidifiers, and buzzers—to automatically adjust the hive environment and issue anomaly alerts.
Results The system is capable of collecting, processing, displaying, and remotely transmitting data including temperature and humidity, CO₂ concentration, hive weight, and bee flight activity counts; test results demonstrate that the OLED display provides stable visualization of monitoring information, that the triggered peripheral devices execute corresponding actions according to predefined logic, and that the WeChat Mini Program can receive hive monitoring data and enable remote control.
Conclusion   The designed system enables continuous sensing of multi-parameter hive data, local display, remote transmission, and basic automatic control, providing a robust foundation for data acquisition and IoT applications in colony status monitoring as well as subsequent research on anomaly detection and health early-warning systems based on multi-source time-series data.