In real-world Industrial Internet of Things (IIoT) environments,sensor signals are often contaminated by external noise,making it difficult to obtain clean data,which in turn affects the accuracy of data-driven time series prediction tasks.To address this issue,this paper proposes a novel time series prediction framework named MoCo-CBDAE-TCN-Transformer,which combines an improved Contrast Blind Denoising AutoEncoder (CBDAE) and a TCN-Transformer network.The framework enhances its ability to capture dynamic features of time series data and effectively utilizes historical negative sample information by introducing additional momentum encoders,dynamic queues,and Information Noise-Contrastive Estimation (InfoNCE) regularization.Without prior knowledge of noise or clean sensor data,this framework achieves blind denoising of sensor data by capturing and comparing temporal correlations and noise features.The denoised data are then used for time series prediction by a TCN-Transformer network,which combines the advantages of TCN's residual connections and dilated convolutions,as well as Transformer's attention mechanism,significantly improving prediction accuracy and efficiency.Finally,experimental results show that,compared with traditional noise removal methods and time series prediction models,the model designed in this paper achieves better noise removal effects and higher prediction accuracy through simulation verification on a public four-cylinder process dataset.Its real-time processing capability is suitable for deployment in actual industrial environments and provides an effective technical solution for IIoT data processing and analysis.