为了对水处理过程中水质浊度进行实时、准确检测, 设计了基于红外光的散射浊度检测系统, 并提出一种聚类灰色融合的预测模型对水质浊度的变化趋势进行有效预测。利用890 nm波长的红外发光二极管作为发光器件, 光敏二极管作为接收器, 检测装置响应时间短, 零点误差小。采用灰色预测算法和聚类融合的方法对传感器所采集的数据进行处理, 将聚类融合处理后的数据作为灰色预测控制的输入数据, 灰色预测控制的输出数据与融合数据进行对比分析, 确定预测浊度值。通过实际项目进行了数据跟踪和运算, 聚类灰色融合算法的浊度预测输出值和实测值的平均误差值为0.008 7 NTU, 聚类灰色融合算法预测性能优于单一的灰色预测算法, 能够保证水质浊度参数的平稳, 满足了水质的要求。

In order to realize real-time and accurate detection of water turbidity in the water treatment process, the turbidity detection system was designed based on infrared light scattering and the turbidity forecasting model was put forward based on clustering grey fusion. The infrared light emitting diode with 890 nm wavelength was used as the light emitting device, the photosensitive diode was used as the receiver, and the response time of the detector was short, and the zero error was small. The data collected by the sensor was processed by the method of grey prediction algorithm and cluster fusion. The data processed by the cluster fusion were as the input data of the grey predictive control, and the output data of the grey predictive control and the fusion data were compared and analyzed. Data tracking and operation were carried out through the actual project. The average error of the measured value and the output value of the turbidity prediction is 0.008 7 NTU. Grey fusion algorithm is superior to the single grey prediction algorithm, to ensure that the water quality turbidity parameters are stable and meet the requirements of water quality, and ensures that the water quality turbidity parameters are more stable and meet the requirements of water quality.