污水处理过程中，由于工艺过程的复杂性、监测设备的不完备性和工作环境的恶劣性，导致重要的出水指标变量难以实现精准的监测；为此，文中提出了一种基于协同训练的集成自适应多输出软测量模型。首先，利用高斯过程回归(GPR)和相关向量机(RVM)两种不同类别的方法建立一个异构的软测量模型；然后，利用移动窗口（MW）和卡尔曼滤波（KF）同步对模型的结构和参数进行实时优化；最后，以一污水厂为对象进行实验，对模型的预测性能和自适应性进行验证。结果表明，文中提出的方法有效地提高了软测量模型的预测性能和自适应性。

In the wastewater treatment process, due to the complexity of the industry process, incomplete monitoring equipment and hostile working environment, it is difficult to achieve accurate and timely measurement of important effluent indices. To solve the problem, an ensemble adaptive soft sensor model based on semi-supervision learning was proposed. Firstly, Gaussian process regression (GPR) and Relevance vector machine (RVM) were used to establish a heterogeneous soft-sensor model. Then, the structure and parameters of the models  were optimized by using the moving window and the Kalman filter gain, respectively. Finally, the prediction performance and self-adaptability of the model were verified by experiments on a wastewater plant. The results demonstrate that the proposed method improves the prediction accuracy and self-adaptability of the soft sensor model.