河北水利电力学院学报

河北水利电力学院学报 ›› 2024, Vol. 34 ›› Issue (4): 50-55.DOI: 10.16046/j.cnki.issn2096-5680.2024.04.009

• 计算机应用与自动控制专题 • 上一篇    下一篇

B/S架构下基于物联网的远程温室温度控制系统设计

王赵慧   

  1. 青岛酒店管理职业技术学院教务处,山东省青岛市李沧区九水东路599号 266100
  • 收稿日期:2023-12-04 修回日期:2024-06-17 出版日期:2024-12-31 发布日期:2025-01-14
  • 作者简介:王赵慧(1981-),女,山东菏泽人,副教授,主要从事计算机技术方面研究。E-mail:wzaohppp@126.com
  • 基金资助:
    山东省教育科学规划创新素养专项课题(2022CYB291)

Design of a High-precision Remote Greenhouse Control System Based on the Internet of Things under the B/S Architecture

WANG Zhaohui   

  1. Office of Teaching Affairs, Qingdao Hotel Management College, 266100, Qingdao, Shandong, China
  • Received:2023-12-04 Revised:2024-06-17 Online:2024-12-31 Published:2025-01-14

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摘要： 针对远程温室的温度控制问题,设计了一种基于温室信息采集传感器(温湿度传感器、光照传感器、CO2传感器与土壤水分传感器)与控制器(主控芯片为STM32F103VET6)的温度控制系统。先基于B/S架构构建温室控制网络软件框架,利用滤波算法去除采集温室信息中的噪声信息,结合控制器特点与PID控制理论,制定温室控制算法,执行制定算法实现了温室温度、湿度、光照、CO2浓度、土壤水分等环境因素的远程控制,实现温室控制系统的设计与运行。实验结果显示,设计系统应用后温室信息信噪比最大值为92.15dB,温度控制调节量计算结果与实际调节量保持一致,如何进一步降低网络延迟和提升对温室温度的控制精度,是未来系统重点优化和改进的方向。

关键词: 物联网, B/S架构, 智能温室, 远程控制, 数据分析

Abstract: The Internet of Things technology is adopted to build a high-precision temperature control problem for remote greenhouse. The network hardware architecture is designed and configured with a temperature control system based on greenhouse information acquisition sensors (temperature and humidity sensors, light sensors, CO2 sensors and soil moisture sensors) and controller (the main control chip is STM32F103VET6). Firstly, a greenhouse control network software framework was built based on B/S architecture, and noise information collected in greenhouse information was removed by filtering algorithm. Combined with controller characteristics and PID control theory, a greenhouse control algorithm was developed, and remote control of greenhouse temperature, humidity, light, CO2 concentration, soil moisture and other environmental factors and the design and operation of greenhouse control system were realized by implementing the developed algorithm. The experimental results show that the maximum signal to noise ratio of greenhouse information is 92.15dB after the design system is applied, and the calculated result of temperature control adjustment is consistent with the actual adjustment amount. How to further reduce the network delay and improve the control accuracy of greenhouse temperature is the key aspect of future system optimization and improvement.

Key words: Internet of Things, B/S architecture, intelligent greenhouse, remote control, data analysis

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