基于改进卡尔曼数据融合算法的温室物联网采集系统研究

Research on Internet of Things Acquisition System in Greenhouse Based on the Improved Kalman Data Fusion Algorithm

针对传统温室采集精度不高、布线复杂、能耗大等问题,研究了一种基于改进卡尔曼数据融合算法的温室物联网采集系统。系统硬件主要由CC2530芯片和STM32微处理器构成,运用罗曼诺夫斯基准则对采集的数据进行预处理后,再使用卡尔曼滤波算法在协调器节点处对数据进行静态融合。选取温度作为观测量进行仿真和实验,结果表明,使用改进卡尔曼数据融合算法处理后的温度误差仅为0.089℃,处理后的数据更加接近环境真实值。改进的卡尔曼数据融合算法可以有效地提高数据采集的精度和系统的稳定性,研究结果可为数据采集技术在农业智能化方面的应用提供参考。

In view of the traditional greenhouses’disadvantages such as low precision of collected data,complicated wiring and huge energy consumption,an internet of things acquisition system in greenhouse based on the improved Kalman data fusion algorithm is studied.Hardware of the system mainly includes CC2530 chip and STM32 microprocessor.It first makes pre-treatment on the collected data with Romanowsky criterion and then carries out static fusion of the data at the nodes of coordinator with Kalman filter algorithm.Temperature is selected to serve as the observing variable for simulation and test.As shown by the result,the error of temperature processed by Kalman data fusion algorithm is no more than 0.089℃and the processed data is closer to the actual value of the environment.The improved Kalman data fusion algorithm optimizes the data collection precision and system stability effectively.The research results could serve as reference for the application of data collection technology in agricultural intelligence.