As people pay more attention to environmental monitoring, Carbon dioxide (CO${}_2$) sensors are widely used. However, most of the infrared CO${}_2$ single-channel sensors are accompanied by low calibration efficiency and low accuracy. In order to save costs while improving calibration efficiency and accuracy, we proposed a fast calibration algorithm for Non-Dispersive Infrared (NDIR) single-channel carbon dioxide sensor based on deep learning. Firstly, we establish N network models which consist of N sensors by collecting m data points from different temperatures and concentrations. Secondly, we collect six data points from a new sensor which are measured at three temperatures and two concentrations. Thirdly, we choose multiple approximate models from N network models based on the matching of the data points. At last, we regard these models as the estimation model of the new sensor to calibrate the sensor concentration. This method eliminates the individual differences of a single model to a certain extent and achieves the purpose of rapid calibration. After comparing three kinds of neural networks and conducting relevant experiments, we chose BP neural network as the model, and set the number of selected models to three. The results show that the floating up and down by industry-standard 5% plus or minus 50 ppm calculation, the qualified rate of our method is up to 91.542% between 0 °C to 45 °C, and the qualified rate even reaches 99.063% between 20 °C to 35 °C. Compared with similar products, the qualified rate of our method in the calibration of carbon dioxide increases by 12.315% and 22.732% respectively.

随着人们对环境监测的日益重视，二氧化碳（CO${}_2$) 传感器被广泛使用。然而，大部分红外 CO${}_2$单通道传感器校准效率低，精度低。为了在提高标定效率和精度的同时节约成本，我们提出了一种基于深度学习的非色散红外（NDIR）单通道二氧化碳传感器快速标定算法。首先，我们通过收集来自不同温度和浓度的m个数据点，建立由N个传感器组成的N个网络模型。其次，我们从在三个温度和两个浓度下测量的新传感器收集六个数据点。第三，我们从N中选择多个近似模型基于数据点匹配的网络模型。最后，我们将这些模型作为新传感器的估计模型来校准传感器浓度。该方法在一定程度上消除了单个模型的个体差异，达到了快速标定的目的。在比较了三种神经网络并进行了相关实验后，我们选择了BP神经网络作为模型，并将选择的模型数量设置为三个。结果表明，按行业标准上下浮动5%正负50 ppm计算，我们方法在0℃～45℃之间的合格率高达91.542%，合格率甚至达到99.063% 20 °C 至 35 °C 之间。与同类产品相比，我们的方法校准二氧化碳的合格率提高了12.315%和22。