面向污水处理的轻量化多参数时序预测方法

doi:10.12141/j.issn.1000-565X.250132

摘要/Abstract

摘要：

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。

关键词: 多参数时序预测, 平稳小波变换, 协同注意力机制, 轻量化, 污水处理

Abstract:

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.

Key words: multivariate time series prediction, stationary wavelet transform, collaborative attention mechanism, lightweight, wastewater treatment

中图分类号:

TP277

唐莉丽, 刘乙奇. 面向污水处理的轻量化多参数时序预测方法[J]. 华南理工大学学报(自然科学版), 2026, 54(1): 60-69.

TANG Lili, LIU Yiqi. A Lightweight Multivariate Time Series Prediction Method for Wastewater Treatment[J]. Journal of South China University of Technology(Natural Science Edition), 2026, 54(1): 60-69.

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序号	变量符号	变量含义	单位
1	COD-in	进水化学需氧量	mg/L
2	NH+4-N	铵态氮浓度	mg/L
3	TN-in	进水总氮浓度	mg/L
4	pH-in	进水pH值
5	SS-in	进水悬浮固体浓度	mg/L
6	Q-in	进水流量	m³/d
7	Efflu-TN	出水总氮浓度	mg/L
8	Efflu-SS	出水悬浮固体浓度	mg/L
9	Efflu-COD	出水化学需氧量	mg/L

模型	步长	参数量
Autoformer	12	10 515 459
	24	10 515 459
	48	10 515 459
FEDformer	12	16 544 771
	24	16 806 915
	48	16 806 915
Informer	12	11 313 667
	24	11 313 667
	48	11 313 667
TimesNet	12	1 199 621 359
	24	1 199 622 523
	48	1 199 624 851
SimpleTM	12	10 052
	24	10 448
	48	11 240
SWT-CA	12	10 388
	24	10 592
	48	11 000

模型	预测变量	PCC	MSE	MAE	RMSSD
Autoformer	TN	0.640	0.713 1	0.630 5	1.988 5
	SS	0.599	0.505 3	0.533 5
	COD	0.617	0.770 1	0.678 5
FEDformer	TN	0.781	0.420 7	0.486 3	1.318 8
	SS	0.683	0.368 9	0.466 6
	COD	0.742	0.529 2	0.560 0
Informer	TN	0.834	0.326 6	0.410 0	1.081 5
	SS	0.739	0.348 2	0.464 8
	COD	0.809	0.406 6	0.478 7
TimesNet	TN	0.839	0.324 1	0.411 4	1.009 2
	SS	0.753	0.289 5	0.395 9
	COD	0.807	0.395 5	0.479 3
SimpleTM	TN	0.837	0.325 9	0.400 8	1.033 5
	SS	0.751	0.301 4	0.402 1
	COD	0.801	0.487 1	0.487 1
SWT-CA	TN	0.835	0.329 8	0.402 7	1.026 7
	SS	0.755	0.294 8	0.400 2
	COD	0.803	0.402 1	0.483 2

模型	预测变量	PCC	MSE	MAE	RMSSD
Autoformer	TN	0.674	0.624 3	0.583 2	1.799 3
	SS	0.631	0.438 0	0.495 9
	COD	0.635	0.736 9	0.679 9
FEDformer	TN	0.724	0.509 5	0.545 7	1.517 3
	SS	0.618	0.432 2	0.503 4
	COD	0.706	0.575 4	0.599 1
Informer	TN	0.709	0.534 9	0.556 3	1.621 3
	SS	0.660	0.440 8	0.522 7
	COD	0.734	0.645 4	0.625 9
TimesNet	TN	0.790	0.417 3	0.473 1	1.248 7
	SS	0.745	0.307 1	0.403 2
	COD	0.756	0.524 1	0.556 8
SimpleTM	TN	0.651	0.662 5	0.585 9	1.821 0
	SS	0.627	0.426 6	0.494 4
	COD	0.622	0.731 6	0.668 1
SWT-CA	TN	0.819	0.383 8	0.497 6	1.134 4
	SS	0.763	0.322 5	0.402 1
	COD	0.776	0.518 0	0.537 1

模型	预测变量	PCC	MSE	MAE	RMSSD
Autoformer	TN	0.446	1.043 8	0.768 3	2.591 4
	SS	0.526	0.547 5	0.564 3
	COD	0.488	1.000 1	0.811 0
FEDformer	TN	0.541	0.833 6	0.687 8	2.223 4
	SS	0.550	0.538 8	0.564 7
	COD	0.536	0.850 9	0.748 0
Informer	TN	0.626	0.674 8	0.635 7	1.905 1
	SS	0.638	0.490 2	0.558 0
	COD	0.611	0.739 9	0.673 6
TimesNet	TN	0.695	0.575 7	0.568 2	1.661 9
	SS	0.670	0.383 4	0.458 2
	COD	0.647	0.702 7	0.654 2
SimpleTM	TN	0.623	0.693 1	0.600 4	1.838 8
	SS	0.615	0.431 4	0.498 9
	COD	0.624	0.709 2	0.672 0
SWT-CA	TN	0.644	0.671 1	0.584 9	1.733 8
	SS	0.650	0.403 5	0.476 6
	COD	0.655	0.664 2	0.646 5