自动气象站数据采集器温度通道的环境温度补偿

针对自动气象站数据采集器温度通道容易受到环境温度影响限制测量精度的问题,对数据采集器进行了温度漂移检测实验并对实验数据进行了误差分析,提出了基于改进自适应遗传算法优化的最小二乘支持向量机(improved adaptive geneticalgorithm least squares support vector machine,IAGA-LSSVM)的温度补偿方法。改进的自适应遗传算法能够对最小二乘支持向量机拟合过程中的关键参数进行调整从而建立最优模型。与传统LS-SVM相比,IAGA-LSSVM对温度数据的建模均方根误差减小了0.007,有效提高了建模的精度。根据建立的最优函数模型对该数据采集器温度通道进行温度补偿结果表明,经该方法补偿后的数据采集器在任何温度环境下的温度测量误差均小于0.03℃,具有更高的测量精度和稳定性,有效提高了自动气象站的温度观测质量。同时,设计开发了温度补偿界面,为自动气象站观测数据校验和实际业务应用奠定了基础。

Environmental temperature compensation for the temperature channel of data-acquisition unit in automatic weather station

The output of data-acquisition unit temperature channel is easily affected by environmental temperature,which will limit the measurement accuracy of the data-acquisition unit.Aiming at this problem,a temperature compensation method based on improved adaptive genetic algorithm least squares support vector machines(IAGA-LSSVM) is proposed in this paper.This algorithm optimizes the parameters of LS-SVM based on IAGM and establishes the best model for the tested data of data-acquisition unit in temperature drift detection experiment.Compared with LS-SVM,the RMSE(root-mean-square error) of IAGA-LSSVM algorithm in training and prediction is decreased by 0.007,and the modeling accuracy is improved effectively.The temperature compensation of data-acquisition unit temperature channel is realized exactly based on the model.Compensation results show that the measurement error of data-acquisition unit temperature channel is less than 0.03 ℃ under any environmental temperatures,which indicates that the measurement accuracy and stability are both improved after temperature compensation.The proposed temperature compensation method improves the quality of AWS(antomatic weather station) temperature observation data effectively.And a temperature compensation interface window is also designed,which lays a foundation for the calibration and practical application of AWS observation data.