## 1 Introduction
Due to the time relevance character, algorithms that focus on analyzing the relationships between time relevance industrial data are more suitable for typical industrial process modeling. The emergence of the recurrent neural network (RNN) and the long short-term memory neural network (LSTM) improves the ability of neural network models to extract temporal information from temporal data. These two neural networks are also widely used in industrial forecasting. [PERSON] [17] proposed a new method for predicting renewable energy generation and power load in univariate and multivariate scenarios using the improved stacked gated recurrent unit neural network. The method uses various weather parameters to predict wind power generation and historical energy consumption data to predict power load. [PERSON] [18] proposed a model composed of LSTM and the genetic algorithm to predict short-term wind power generation. It also provides accurate, reliable, and robust predictions for the wind power of seven wind farms in Europe. [PERSON] [19] used RNN and LSTM to capture the dynamic trends of 15 environmental parameters, including particulate matter and pollutants in the atmosphere that cause long-term health hazards. In addition to these models, the attention models have also received attention from researchers. The idea of Attention originated from visual images in the 1990s. In 2014, Google Mind [20] team used the attention mechanism to classify images on the RNN model and achieved excellent results. Attentional mechanisms are used in many areas, such as the machine translation field [21], the image recognition field [22], and the language emotion analysis field [23]. In recent years, attention was also widely used in the prediction field. [PERSON] [24] proposed a new hybrid deep learning method based on bi-directional long short-term memory and a multi-head self-attention mechanism, which can accurately predict the marginal price of position and the system load one day ago. By analyzing the formation mechanism of NOx and the reaction mechanism of the SCR reactor, a sequence-to-sequence dynamic prediction model was proposed by [PERSON] [25], which can fit multivariable coupling, nonlinear, and large delay systems. [PERSON] [26]proposed a novel attention mechanism for selecting the relevant time series. [PERSON] used its frequency domain information for multivariate forecasting. Influenced by the successful application of temporal pattern attention [26], an improved attention layer is proposed in this paper. The model uses the Gaussian convolution kernels in the convolutional neural network, enabling the model to extract different inertial delay properties. Scaled dot-product attention [27] is used in the model for attention computation. The prediction method in this paper treats the input and output of the model and improves the way the model is trained. The main contributions of this paper are summarized as follows:  
(1) The delayed relationship between variables was considered in the correlation analysis. The best combination of variable inputs was selected through comparison experiments. (2) The differential values of the input variables are used as additional input features, which reduce the effect of the autocorrelation of the inputs on the model. The differential values in bed pressure are used as the predictive target for the model to improve the learning ability of the model for the target value. (3) The attention layer uses the Gaussian convolution kernel to extract the inertial and delay properties of the features. The convolution operation allows the model to learn different inertial and delay properties for different features. Thus, the prediction performance of the model is improved. (4) Based on the principle of the attention mechanism, the model is trained in segments. The first training segment enables the query vector in the attention layer to learn the target information well. The parameters of the attention layer are updated in the second segment training.