Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%.

优化污水收集对于水污染控制和废水处理厂的质量和效率提高至关重要。目前，城市排水管网正在升级，以提高其分类和收集能力。但是，缺乏有效的在线监视和识别技术。紫外可见吸收光谱探针由于其体积小，无试剂且检测速度快而被认为是一种潜在的监测方法。由于探头的性能参数（如光学分辨率，动态间隔和信噪比）较弱，并且污水的浊度较高，会提高噪声水平，因此有必要从浊度扰动的排水谱中提取形状特征以进行分类。在这项研究中，排水网络样本是在线收集和测试的，根据采样地点和环境状况分别标记了四种类型。采用导数光谱对形状特征进行放大，建立卷积神经网络算法进行非线性光谱分类。比较了输入和网络结构对分类准确性的影响。原始频谱，一阶导数频谱以及两者的组合被设置为三个不同的输入。具有或不具有卷积层的人工神经网络被设置为两种不同的网络结构。结果表明，结合一阶和零阶导数输入的卷积神经网络被认为对生活污水，混合雨水，雨水和工业污水具有最好的分类效果。工业废水的识别率为100％，