面向养殖水体分布差异的COD光谱法检测研究

面向养殖水体，传统光谱法对化学需氧量（Chemical Oxygen Demand，COD）检测模型构建的基础：源域（现有样本库）与目标域（检测地水体）间光谱数据独立同分布。但是当源域与目标域分布间存在差异时，由源域得到的低误差模型常在目标域上表现下滑。针对该问题，提出面向UV Vis光谱的域对抗训练网络（DAUVwpNet），将分布不同的源域和目标域数据映射至相同分布的特征空间中，使其在该空间的分布距离尽可能接近，从而在特征空间中对源域训练的目标函数也可以迁移至目标域上，以降低模型在目标域的误差。试验表明：面向同一批测试数据，DAUVwpNet的预测误差为0.78，要低于传统模型的预测误差（0.85）；DAUVwpNet预测值与实测值间相关系数为0.95，要高于传统模型的相关系数（0.89）。表明了该网络能够较好对齐两域特征空间数据分布，降低因分布差异带来的COD检测误差。

Research on COD spectrometric detection with differences in aquaculture water distribution

For aquaculture water, the traditional spectral method is based on the establishment of Chemical Oxygen Demand (COD) detection model based on the independent and identical distribution of spectral data between the source domain (the existing sample library) and the target domain (the water in the detection site). However, when the distribution of the source domain is different from that of the target domain, the low error model derived from the source domain often performs worse in the target domain. Aiming at this problem, this paper puts forward the UV Vis spectrum domain oriented training network (DAUVwpNet), the distribution of different source domain and target domain data mapping to the same feature space, make it as close as possible in the space distance, thus in the feature space to the source domain training objective function can also be migrated to the target domain in order to reduce the error of the model in the target domain. Experimental results show that for the same test data, the prediction error of DAUVWPNET is 0.78, which is lower than that of the traditional model (0.85). The correlation coefficient between the predicted and the measured value of DAUVwpPnet is 0.95, higher than that of the traditional model (0.89). It is shown that this network can efficiently align the distribution of characteristic spatial data of the two domains and reduce the COD detection error caused by the difference of distribution.