在污水处理过程中，高效建模关键水质参数对于实现过程控制优化、异常检测和决策支持具有重要意义。然而，其过程数据普遍具有时序依赖性、多变量耦合性和工况非稳态性等特点，给精准建模带来了重大挑战。为解决这些问题，该文提出了一种基于平稳小波变换和协同注意力机制的轻量化多参数时序预测模型。该模型首先通过平稳小波变换对污水数据进行多尺度分解，提取不同尺度序列下的数据特征；然后，基于几何注意力与稀疏注意力构建协同注意力机制，高效捕捉关键水质参数之间的复杂耦合关系及时序特征；最后，通过双投影层将逆小波变换重构后的特征映射为最终预测结果。在东莞市某污水处理厂的实测数据集上进行模型训练与验证，并开展多步预测任务和部分数据可视化分析。实验结果表明：所提模型在24步多输出预测任务中，多输出均方根误差（RMSSD）相较于对比模型降低9.15%~37.70%；在其他预测任务中，精度仅次于参数规模更大的TimesNet。此结果体现了该模型在轻量化与高精度之间的有效平衡，验证了其在污水处理时序预测中的有效性。

In wastewater treatment processes, the efficient modeling of key water quality parameters is crucial for achieving process control optimization, anomaly detection, and decision support. However, the process data generally exhibits characteristics such as temporal dependence, multivariable coupling, and non-stationarity under varying operating conditions, posing significant challenges to accurate modeling. To address these issues, this paper proposes a Lightweight Multivariate Time Series Prediction Method for Wastewater Treatment based on the Stationary Wavelet Transform (SWT) and Collaborative Attention (CA) mechanism. This model first performed multi-scale decomposition on wastewater data and used the stationary wavelet transform to extract data features from sequences at different scales. Subsequently, a collaborative attention mechanism based on geometric attention and sparse attention was constructed to effectively capture the complex coupling relationships and temporal features among key water quality parameters. Finally, the features reconstructed via inverse wavelet transform were mapped to the final prediction results through a dual-prediction layer. The model was trained and validated on a measured dataset from a wastewater treatment plant in Dongguan, with multi-step prediction tasks and partial data visualization analyses conducted. Experimental results show that, in the 24-step multi-output prediction tasks, the proposed model achieves a reduction of 9.15% to 37.70% in multi-output root mean square deviation (RMSSD) compared to benchmark models. In other prediction tasks, its accuracy ranks second only to TimesNet, which has a significantly larger parameter scale. These results demonstrate an effective balance between lightweight design and high accuracy, thereby validating the efficacy of the proposed model for time-series prediction in wastewater treatment.