面向工业产品产能预测的CNN-LSTM-Attention算法研究

Research on CNN-LSTM-Attention Algorithm for Industrial Product Capacity Prediction

摘要:

在复杂的工业生产环境中,产能预测对于优化生产流程、提高设备利用率、降低运营成本以及提升整体生产效率具有至关重要的作用。目前的研究大多采用长短时记忆网络(LSTM)和卷积神经网络(CNN)来提取产能数据中的时序和空间特征。然而,这些方法通常只侧重于单一特征的提取,未能充分考虑时序特征与空间特征之间的相互关联,导致模型的泛化能力较弱,预测精度无法满足实际工业需求。针对这些问题,本文提出了一种基于注意力机制的CNN-LSTM-Attention混合模型,以提升产能预测的准确性。该模型首先通过卷积神经网络提取产能数据中的空间特征,进而利用长短时记忆网络捕捉数据中的时序动态变化,最后引入注意力机制,以自动分配不同特征的重要性权重,实现对空间和时序特征的有效融合。通过这种方式,模型能够更精准地捕捉复杂的生产环境中的潜在模式,从而提升产能预测的效果。为了验证所提出模型的有效性,本文选取某实际工厂2018年至2022年的真实生产数据进行实验研究,使用RMSE、MSE等多项评估指标对预测精度进行衡量。实验结果表明,CNN-LSTM-Attention模型相比于传统方法在工业产品产能预测中表现出了显著的优势,尤其在短周期和长周期的预测中该模型的预测精度均高于0.9,证明了其在实际应用中的可行性与有效性。

Abstract:

In complex industrial production environments, capacity forecasting plays a crucial role in optimizing production processes, improving equipment utilization, reducing operating costs, and enhancing overall production efficiency. Currently, most research uses Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) to extract temporal and spatial features from production capacity data. However, these methods usually only focus on extracting a single feature and fail to fully consider the interrelationships between temporal and spatial features, resulting in weak generalization ability of the model and prediction accuracy that cannot meet practical industrial needs. In response to these issues, this article proposes a CNN-LSTM Attention hybrid model based on attention mechanism to improve the accuracy of capacity prediction. The model first extracts spatial features from production capacity data through convolutional neural networks, then uses long short-term memory networks to capture temporal dynamic changes in the data, and finally introduces attention mechanisms to automatically assign importance weights to different features, achieving effective fusion of spatial and temporal features. In this way, the model can more accurately capture potential patterns in complex production environments, thereby improving the effectiveness of capacity prediction. In order to verify the effectiveness of the proposed model, this paper selects real production data from a certain actual factory from 2018 to 2022 for experimental research, and uses multiple evaluation indicators such as RMSE and MSE to measure the prediction accuracy. The experimental results show that the CNN-LSTM Attention model exhibits significant advantages over traditional methods in industrial product capacity prediction, especially achieving an accuracy above 0.9 in both short and long cycle predictions, demonstrating its feasibility and effectiveness in practical applications.