微型电脑应用

针对当前多传感数据融合算法性能不够理想导致室内空间适老化环境监测效果较差的问题，提出一种改进的多传感数据融合算法。提出一种变长数据融合算法(VLDFA)来消除冗余数据，对蜻蜓优化算法(DA)进行改进，结合改进DA和改进BP神经网络(BPNN),构建多传感数据融合模型。结果显示，该模型的精度超过98%。在温度误差与湿度误差监测比较中，所提出的模型的温度误差平均值为0.34℃,优于另外2种模型，表明该模型的多传感数据融合效果较为良好，能够很好地调整老年人居住环境。

The poor performance of current multi-sensor data fusion algorithms may lead to poor monitoring of indoor aging environments, therefore, an improved multi-sensor data fusion algorithm is proposed. A variable length data fusion algorithm(VLDFA) is proposed to eliminate redundant data. Strategies are proposed to improve the dragonfly algorithm(DA), and the improved DA is combined to improve the BP neural network(BPNN). A multi-sensor data fusion model is constructed. The results show that the accuracy of the model exceeds 98%. In the comparison of temperature error and humidity error monitoring, the average temperature error of the proposed model is 0.34 ℃, which is superior to the other two models. This indicates that the multi-sensor data fusion effect of the model is relatively good, and it can effectively adjust the living environment of the elderly.