IoT-based smart beehive health monitoring and early warning system
基于物联网的智能蜂箱健康监测与预警系统

摘要

目的 传统养蜂依靠人工开箱巡检，蜂箱内部环境难以实现实时监测，异常隐患处置滞后，人工运维成本较高。针对该行业痛点，研发一套智能蜂箱健康监测预警系统。方法 系统以STM32F103C8T6微控制器为主控单元，搭载DHT11温湿度传感器、光敏传感器、JW01 CO2气体传感器、火焰传感器完成蜂箱多源环境参数采集；借助ML307R 4G通信模块实现双向数据传输，有机发光二极管（OLED）显示屏完成本地数据可视化；手机App支持远程自定义告警阈值，联动风扇、雾化器、水泵实现蜂箱环境闭环调控与火情应急处置。完成硬件电路搭建、嵌入式程序开发，开展模块测试与整机联合验证。结果 系统可持续稳定采集蜂箱环境数据，及时识别参数越限，执行告警与调控动作，光照、火焰的识别时延及CO2、温湿度的执行机构响应时延分别为1s、0.8 s、1.5s、1.3s，实现24 h全天候自主运行，网络断开时仍可维持本地监测与保护功能。结论 本系统能够降低养蜂作业人工投入，提升蜂群生存保障能力，可为规模化智能养蜂提供软硬件技术支撑。

Abstract

Objective Traditional beekeeping relies on manual inspection, which cannot realize real time monitoring of the internal environment of beehives. The disposal of hidden dangers is delayed and manual operation maintenance cost is high. Aiming at these practical problems, this paper develops an intelligent beehive health monitoring and early warning system.
Methods The system uses an STM32F103C8T6 microcontroller as the main control unit. Multi-source environmental data are collected using a DHT11 temperature and humidity sensor, a photosensitive sensor, a JW01-CO2 sensor, and a flame sensor. An ML307R 4G module enables bidirectional data transmission, while an Organic Light‑Emitting Diode (OLED) display provides local data visualization. The mobile App supports remote configuration of alarm thresholds and enables the coordinated control of fans, a mist generator, and a water pump for environmental regulation and fire emergency response. Hardware circuits were assembled, embedded software was developed, and module-level tests and integrated system verification were conducted.
Results The system can continuously and stably collect beehive environmental data, identify abnormal parameters, and perform alarm and regulation actions. The recognition delay for light and flame, and the actuator response delays for CO₂, temperature and humidity are 1 s, 0.8 s, 1.5 s and 1.3 s respectively. The system can operate autonomously for 24 h and maintain local monitoring and protection functions even when the network is disconnected.
Conclusion   The proposed system reduces manual workload of beekeeping and improves the survival guarantee for bee colonies. It provides hardware software support for large scale intelligent beekeeping.