Method for predicting moisture of outlet material by using time convolutional neural network

Abstract

The invention discloses a method for predicting the moisture of an outlet material by using a time convolutional neural network, and the method comprises the steps: collecting the historical process data of a process manufacturing production line, and constructing a multivariate time series data set; preprocessing the multivariate time series data set to obtain preprocessed data, and dividing the preprocessed data into a training set and a test set according to an acquisition cycle sequence; introducing the time convolutional neural network into a dual attention mechanism to build a DA-TCN prediction model; training the DA-TCN model by using the training set to obtain a trained DA-TCN prediction model; and verifying the test set by using the trained prediction model DA-TCN to obtain a DA-TCN prediction model passing the verification for predicting the moisture of the outlet material. According to the method, the DA-TCN prediction model used for predicting the outlet material moisture is established by introducing the time convolutional neural network into a dual attention mechanism, and the method is further used for predicting the outlet material moisture in the sheet cut tobacco drying process.

Description

Technical Field

[0001] The invention relates to a method for predicting the moisture content of export materials by using a time convolutional neural network, and belongs to the technical field of time series prediction. Background Art

[0002] In modern complex process production, product quality prediction uses historical production data to mine the mapping relationship between various quality influencing factors and product performance. It is a reflection of technological innovation and an effective way to improve product quality, increase product stability, and reduce production costs. It plays a key role in process production.

[0003] The product quality of complex process production is closely related to process parameters, equipment operating parameters, real-time operating condition changes, etc. The complex nonlinear relationship between the moisture index of the export material and the process parameter index in the thin plate drying process affects the accurate prediction of the moisture index of the export material in the process production process, which in turn makes it difficult to improve the intelligent level of the production system in the process production workshop. The current product quality prediction methods for complex process production processes are usually complex in structure, and the implicit information and historical time series information in the historical production index data are difficult to be fully mined. Therefore, how to use the production data generated by complex production processes to accurately predict product quality is still a difficult problem that needs to be solved. Summary of the invention

[0004] The present invention provides a method for predicting the moisture content of outlet materials using a temporal convolutional neural network, which is used to introduce a dual attention mechanism into the temporal convolutional neural network to build a DA-TCN prediction model for predicting the moisture content of outlet materials, and further used for predicting the moisture content of outlet materials in a thin plate drying process.

[0005] The technical solution of the present invention is:

[0006] According to a first aspect of the present invention, a method for predicting the moisture content of export materials using a temporal convolutional neural network is provided, the method comprising the following steps:

[0007] S1. Collect historical process data of the process manufacturing production line and construct a multivariate time series data set; wherein the multivariate time series data set includes process parameter index data and export material moisture index data;

[0008] S2, preprocessing the multivariate time series data set to obtain preprocessed data, and dividing the preprocessed data into a training set and a test set according to the order of the acquisition cycle;

[0009] S3, introduce the dual attention mechanism into the temporal convolutional neural network to build the DA-TCN prediction model;

[0010] S4. Use the training set to train the DA-TCN model to obtain a trained DA-TCN prediction model; use the trained prediction model DA-TCN to verify the test set to obtain a verified DA-TCN prediction model for use in predicting the moisture content of export materials.

[0011] Furthermore, the preprocessing of the multivariate time series data set to obtain preprocessed data, and dividing the preprocessed data into a training set and a test set according to the order of the acquisition cycle, includes:

[0012] According to the material head and tail interception rules of the process production process, the process parameter data and quality indicator data are judged for the material head and tail, and the critical position of the material head and tail is determined to retain the data between the critical positions of the material head and tail;

[0013] Then, the data between the critical positions of the material head and the material tail are retained, and the abnormal data are eliminated by using the 3σ method, and normalized;

[0014] The normalized data is divided into training set and test set according to the preset ratio.

[0015] Furthermore, the method of using time convolutional neural network to predict the moisture content of the outlet material is used in the thin plate wire drying process. In the thin plate wire drying process, the rules for cutting the head and tail of the process production process are as follows:

[0016] If the reference value of the moisture content of the inlet material is ≤n1%, the position of the head and tail of the material is determined as follows: according to the batch order of the acquisition cycle, the first data of the outlet material moisture content greater than m1% in the current batch process data is used as the head node, and the data of the outlet material moisture content after a delay of t is used as the head critical position; the first data of the outlet material moisture content less than m1% in the current batch process data is used as the tail node, and the data after the tail node is moved forward by t is used as the tail critical position;

[0017] If the reference value of the moisture content of the inlet material is > n1% and ≤ n2%, the position of the head and tail of the material is determined as follows: according to the batch order of the acquisition cycle, the first data of the outlet material moisture content greater than m2% in the current batch process data is used as the head node, and the data after the head node is delayed t is used as the critical position of the head; the first data of the outlet material moisture content less than m2% in the current batch process data is used as the tail node, and the data after the tail node is moved forward t is used as the critical position of the tail;

[0018] If the reference value of moisture content of the inlet material is > n2%, the method for determining the position of the head and tail of the material is as follows: according to the batch order of the collection cycle, the first data of the outlet material moisture content greater than m3% in the current batch of process data is used as the head node, and the data after a delay of t from the head node is used as the head critical position; the first data of the outlet material moisture content less than m3% in the current batch of process data is used as the tail node, and the data after t from the tail node is used as the tail critical position.

[0019] Furthermore, the DA-TCN prediction model includes a feature attention mechanism FA, a temporal convolutional neural network TCN, a temporal attention mechanism TA, a ReLU layer, and a fully connected layer;

[0020] The pre-processed process production process parameter index characteristic variables and the export material moisture index characteristic variables are used as the input of the feature attention mechanism FA, and the feature weighted matrix X' is obtained by weighted summing the attention weights of each index feature and the input feature;

[0021] The obtained feature weighted matrix X' and the hidden layer output of the previous time step of the temporal convolutional neural network TCN are used as the hidden layer input of the next moment of the temporal convolutional neural network TCN. The dilated causal convolution in the temporal convolutional neural network TCN is used to realize the long sequence interval sampling of all indicators. The PReLU function is used to capture the long time series dependency between indicators. The feature extraction of all sequences in the input data is realized through the residual block of the temporal convolutional neural network TCN.

[0022] The output of the TCN hidden layer after iterating the residual block of the temporal convolutional neural network TCN to time t is used as the input of the temporal attention mechanism TA. The multivariate key temporal characteristics affecting the moisture index of export materials are determined by comparing the temporal attention weights of each historical moment in the process production process. All weights are weighted and summed with the output of the hidden layer at each historical moment to obtain the comprehensive temporal information; then, the predicted value of moisture of export materials is obtained through the ReLU layer and the fully connected layer.

[0023] According to a second aspect of the present invention, a device for predicting the moisture content of outlet materials using a time convolutional neural network is provided, comprising a module for executing a method for predicting the moisture content of outlet materials using a time convolutional neural network taking into account the current fed out by the grid-side converter GSC as described in any of the above items.

[0024] According to a third aspect of the present invention, there is provided a terminal device, comprising a memory, a processor, and a program stored in the memory and executable by the processor, wherein when the processor executes the program, the method for predicting the moisture content of export materials using a temporal convolutional neural network as described in any one of the above is implemented.

[0025] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for predicting the moisture content of export materials using a temporal convolutional neural network.

[0026] The beneficial effects of the present invention are:

[0027] 1. The present invention preprocesses the collected multivariate time series data, including screening the process data of the process production process and detecting and eliminating outliers using the 3σ method, so that the multivariate time series data is stable and reasonable; through normalization processing, all process data are mapped to the [0,1] interval to eliminate the influence of the characteristic dimension differences between the data and improve the calculation efficiency.

[0028] 2. In order to extract process parameter features more accurately, the present invention introduces a dual attention mechanism to determine the multivariate key time series features that affect product quality. Through the ability of adaptive feature extraction, the model is helped to simultaneously extract the time series relationship and the intrinsic connection between different feature variables, select key information for processing, strengthen the impact of process feature sequences and time series with higher correlation on the prediction results, and weaken the impact of sequences with lower correlation.

[0029] 3. The present invention constructs a DA-TCN prediction model. Compared with the traditional GRU model, TCN model, and LSTM-Attention model, the prediction accuracy of the present invention is more accurate, which realizes the accurate prediction of the process production process quality and provides a reference for the accurate prediction of the product quality of the process manufacturing production line.

[0030] From the above, it can be seen that the present invention aims at the problem that the complex nonlinear relationship between process parameters in the process production process makes it difficult to accurately predict the process quality. A process production quality prediction method based on DA-TCN is established, which provides a reference for the accurate prediction of product quality in complex process production processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the process of the present invention;

[0032] Figure 2 This is a structural diagram of the DA-TCN prediction model of the present invention;

[0033] Figure 3 This is a diagram of the internal unit structure of the temporal convolutional neural network TCN of the present invention;

[0034] Figure 4 This is the internal unit structure diagram of the feature attention mechanism FA of the present invention;

[0035] Figure 5 This is the internal unit structure diagram of the temporal attention mechanism TA of the present invention;

[0036] Figure 6 Part of the process data of the thin plate drying process of the process production of silk thread in the embodiment of the present invention;

[0037] Figure 7 It is a schematic diagram of a fitting curve comparing predicted values ​​and actual values ​​of the quality index of the thin-plate tofu baking process predicted on the test set using the DA-TCN prediction model in an embodiment of the present invention;

[0038] Figure 8 Schematic diagram of fitting curves comparing predicted values ​​and actual values ​​of various comparative experiments in the embodiments of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other arbitrarily without conflict.

[0040] Example 1: Figure 1-8 As shown, according to a first aspect of an embodiment of the present invention, a method for predicting the moisture content of export materials using a temporal convolutional neural network is provided, comprising the following steps:

[0041] S1. Collect historical process data of the process manufacturing production line and construct a multivariate time series data set; wherein the multivariate time series data set includes process parameter index data and export material moisture index data;

[0042] S2, preprocessing the multivariate time series data set to obtain preprocessed data, and dividing the preprocessed data into a training set and a test set according to the order of the acquisition cycle;

[0043] S3, introduce the dual attention mechanism into the temporal convolutional neural network to build the DA-TCN prediction model;

[0044] S4. Train the DA-TCN model using the training set to obtain a trained DA-TCN prediction model; verify the test set using the trained prediction model DA-TCN to obtain a verified DA-TCN prediction model;

[0045] S5. Based on the verified DA-TCN prediction model, the moisture content of the export materials is predicted based on the process data of the input process manufacturing production line.

[0046] Furthermore, the multivariate time series data set is preprocessed to obtain preprocessed data, and the preprocessed data is divided into a training set and a test set according to the order of the acquisition cycle, including: judging the head and tail of the process parameter data and the quality indicator data according to the head and tail interception rules of the process production process, determining the critical positions of the head and tail of the material, and retaining the data between the critical positions of the head and tail of the material; then using the 3σ method to eliminate abnormal data from the data retained between the critical positions of the head and tail of the material, and normalizing it; dividing the normalized data into a training set and a test set according to a preset ratio.

[0047] Furthermore, the normalized data is divided into a training set and a test set according to a preset ratio, specifically: the process data is divided into a ratio of 7:3, and each set of data consists of The composition includes m-1 dimensional process parameters and 1 dimensional quality index, wherein the quality index is the moisture content of the outlet material.

[0048] Furthermore, the method of using time convolutional neural network to predict the moisture content of the outlet material is used in the thin plate wire drying process. In the thin plate wire drying process, the rules for cutting the head and tail of the process production process are as follows:

[0049] If the reference value of the moisture content of the inlet material is ≤n1%, the position of the head and tail of the material is determined as follows: according to the batch order of the acquisition cycle, the first data of the outlet material moisture content greater than m1% in the current batch process data is used as the head node, and the data of the outlet material moisture content after a delay of t is used as the head critical position; the first data of the outlet material moisture content less than m1% in the current batch process data is used as the tail node, and the data after the tail node is moved forward by t is used as the tail critical position;

[0050] If the reference value of the moisture content of the inlet material is > n1% and ≤ n2%, the position of the head and tail of the material is determined as follows: according to the batch order of the acquisition cycle, the first data of the outlet material moisture content greater than m2% in the current batch process data is used as the head node, and the data after the head node is delayed t is used as the critical position of the head; the first data of the outlet material moisture content less than m2% in the current batch process data is used as the tail node, and the data after the tail node is moved forward t is used as the critical position of the tail;

[0051] If the reference value of moisture content of the inlet material is > n2%, the method for determining the position of the head and tail of the material is as follows: according to the batch order of the collection cycle, the first data of the outlet material moisture content greater than m3% in the current batch of process data is used as the head node, and the data after a delay of t from the head node is used as the head critical position; the first data of the outlet material moisture content less than m3% in the current batch of process data is used as the tail node, and the data after t from the tail node is used as the tail critical position.

[0052] Optionally, the parameters involved in the above may be as shown in Table 1 below:

[0053] Table 1

[0054]

[0055] n1: low-level standard of process quality. When the process quality reference value is less than or equal to n1, it means that the batch of materials belongs to low-level materials;

[0056] n2: high-level process quality standard. When the quality index setting value is greater than or equal to n2, it means that the moisture content of this batch of materials belongs to high-level materials.

[0057] When the process quality reference value of process production is between n1 and n2, it means that the batch of materials is at a medium or high level.

[0058] m1: standard value of quality index when the material is at a low level;

[0059] m2: Standard value of quality index when the material is at a medium or high level;

[0060] m3: Standard value of quality index when the material is at a high level;

[0061] t: The buffer time from the shutdown of production equipment to the full recovery of normal working conditions.

[0062] Complex process production processes usually have multi-batch characteristics. The process data collected from multiple batches are complex, large and have obvious differences. When the production equipment is in the start-up and stop states, the process data will generate critical values ​​for the beginning and end of the materials, thereby generating a large number of abnormal values. Therefore, before establishing a prediction model, it is necessary to analyze, screen and eliminate the data at the beginning and end of the materials according to the beginning and end interception rules of the process production process, so as to obtain an accurate data set.

[0063] Furthermore, if Figure 2-Figure 5 As shown, the DA-TCN prediction model includes a feature attention mechanism FA, a temporal convolutional neural network TCN, a temporal attention mechanism TA, a ReLU layer, and a fully connected layer;

[0064] The pre-processed process production parameter index characteristic variables and the export material moisture index characteristic variables are used as the input of the feature attention mechanism FA. The feature weighted matrix X' is obtained by weighted summing the attention weights of each indicator feature and the input feature. The feature weighted matrix reflects the coupling dependency relationship between different indicator feature variables.

[0065] The obtained feature weighted matrix X' and the hidden layer output of the previous time step of the temporal convolutional neural network TCN are used as the hidden layer input of the next moment of the temporal convolutional neural network TCN. The dilated causal convolution in the temporal convolutional neural network TCN is used to realize the long sequence interval sampling of all indicators. The PReLU function is used to capture the long time series dependency between indicators. The feature extraction of all sequences in the input data is realized through the residual block of the temporal convolutional neural network TCN.

[0066] The output of the TCN hidden layer after iterating the residual block of the temporal convolutional neural network TCN to time t is used as the input of the temporal attention mechanism TA. The multivariate key temporal characteristics affecting the moisture index of export materials are determined by comparing the temporal attention weights of each historical moment in the process production process. All weights are weighted and summed with the output of the hidden layer at each historical moment to obtain the comprehensive temporal information; then, the predicted value of moisture of export materials is obtained through the ReLU layer and the fully connected layer.

[0067] Furthermore, the preprocessed data is normalized, wherein the normalization formula is: Where X * is the normalized process data; X is the original process data; X max and X min are the maximum and minimum values ​​of the original data respectively.

[0068] Specifically:

[0069] The feature attention weights The calculation formula is as follows:

[0070]

[0071]

[0072] In the formula, is the value of the mth (1≤m≤24) indicator feature at time t (1≤t≤T); is the feature attention vector of the mth index; W e is the weight coefficient; b is the bias coefficient; σ(·) is the Sigmoid activation function. The Sigmoid activation function is used here to stabilize the output range between 0 and 1.

[0073] The feature weight matrix calculation formula is as follows:

[0074]

[0075] In the formula, Represents the attention weight value of the mth indicator; Represents the value of the mth indicator feature at time t (1≤t≤T).

[0076] The residual block calculation formula is as follows:

[0077] X h =δ(G(X h-1 )+X h-1 )

[0078] Where: δ(·) is the activation function; G(·) represents the transformation of DCC, weight normalization, activation function, and random dropout; X h-1 is the output of the h-1th residual block; X h is the output of the hth residual block.

[0079] The temporal attention weight calculation formula is as follows:

[0080]

[0081]

[0082] In the formula, is the temporal attention vector of each feature vector; is the output of the hidden layer of the TCN network at time t; W d is the weight coefficient; b d is the bias coefficient; ReLU(·) represents the activation function.

[0083] The calculation formula of the time series feature weighting matrix is ​​as follows:

[0084]

[0085] In the formula, in the formula, represents the temporal attention weight at time t; Represents the output of the hidden layer of the TCN network at time t.

[0086] Furthermore, the mean absolute error MAE, root mean square error RMSE, mean square error MSE, and goodness of fit R 2 As an evaluation index, the calculation formula is as follows:

[0087]

[0088] Where n is the number of samples; y i is the true value of the process quality indicator; is the mean value of process quality index; is the predicted value of the process quality index; reference goodness of fit R 2 To judge the model's fitting effect, R 2 The closer it is to 0, the worse the model fitting effect is.

[0089] According to a second aspect of an embodiment of the present invention, there is provided an apparatus for predicting the moisture content of export materials using a time convolutional neural network, including a module for executing a method for predicting the moisture content of export materials using a time convolutional neural network taking into account the feed-out current of the grid-side converter GSC as described in any one of the above items, specifically including: a first module for executing S1: collecting historical process data of a process manufacturing production line and constructing a multivariate time series data set; wherein the multivariate time series data set includes process parameter index data and export material moisture index data; a second module for executing S2: preprocessing the multivariate time series data set to obtain preprocessed data, and The preprocessed data is divided into a training set and a test set in a periodic order; the third module is used to execute S3: introduce a dual attention mechanism into the temporal convolutional neural network to build a DA-TCN prediction model; the fourth module is used to execute S4: train the DA-TCN model using the training set to obtain a trained DA-TCN prediction model; verify the test set using the trained prediction model DA-TCN to obtain a verified DA-TCN prediction model; the fifth module is used to execute S5: predict the moisture content of export materials based on the process data of the input process manufacturing production line according to the verified DA-TCN prediction model.

[0090] According to a third aspect of an embodiment of the present invention, there is provided a terminal device, comprising a memory, a processor, and a program stored in the memory and executable by the processor, wherein when the processor executes the program, the method for predicting the moisture content of export materials using a temporal convolutional neural network as described in any one of the above is implemented.

[0091] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for predicting the moisture content of export materials using a temporal convolutional neural network. In an exemplary embodiment, the above-mentioned computer-readable storage medium may include, but is not limited to: various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0092] Example 2: An optional implementation of the present invention is described in detail below, but the content of the invention is not limited to the scope of the embodiment.

[0093] The experiment was conducted on the thin-plate drying process in a certain process of silk production, and the moisture content of the exported materials, a quality indicator of this process, was predicted. Figure 2The process production process quality prediction method based on DA-TCN of the present invention comprises the following steps:

[0094] Step (1): Use a high-frequency sensor to collect and compile the original process data every 1 second and store it in the OPC server. Extract the historical process data of the thin plate drying process from 16:17:00 on October 17, 2022 to 14:40:00 on October 18, 2022 from the server as a data set. By evaluating the correlation between process parameters and quality indicators, select the 23-dimensional feature variables with the strongest correlation with the moisture content of the outlet material as process parameters, including the actual value of the moisture content of the inlet material, the humidity of the dehumidification wind, and the set value of the moisture content of the outlet material. , HT inlet material temperature, dehumidification air temperature, dehumidification air door opening setting value, dehumidification air door start and stop times, discharge hood negative pressure, hot air temperature setting value, front room air door actual value, ambient temperature, HT inlet steam flow, inlet thin plate steam pressure actual value, steam quality temperature, cylinder wall temperature setting value, cylinder wall temperature measurement average value, cylinder wall temperature pressure conversion value, thin plate condensate water temperature, radiator condensate water temperature, cylinder wall temperature (front) measurement value, cylinder wall temperature (middle) measurement value, cylinder wall temperature (back) measurement value, outlet material temperature, quality index is the actual value of outlet material moisture. There are 20107 groups of extracted raw data, some of which are as follows Figure 6 shown.

[0095] Step (2): When the tow-drying equipment is in the start-stop state, it will generate critical values ​​of the process data for the head and tail of the material, thereby generating a large number of abnormal values. According to the head and tail interception rules of the process production process, the head and tail characteristics of each batch of data are analyzed to determine the critical position of the head and tail of the material, and the data at the head and tail of the material are screened and eliminated. The data interception rules for the head and tail of the moisture content of the export material are shown in Table 2.

[0096] Table 2

[0097]

[0098] According to Table 2, the material head and tail are intercepted, and the data between the critical position of the material head and the critical position of the material tail are retained, and then the abnormal data are eliminated using the 3σ method.

[0099] Step (3): Apply the formula to the preprocessed data Normalization is performed, where X * is the process data after normalization; X is the original process data of the drying process; X max and X min are the maximum and minimum values ​​of the original data respectively.

[0100] Step (4): Divide the preprocessed data into a training data set and a test data set in a ratio of 7:3, where the training set has 14,074 data items and the test set has 6,033 data items.

[0101] Step (5): Build a DA-TCN prediction model consisting of a feature attention mechanism FA, a temporal convolutional neural network TCN, a temporal attention mechanism TA, a ReLU layer, and a fully connected layer, and set the model parameters. The basic parameter settings are shown in Table 3:

[0102] Table 3

[0103] Training parameters Parameter settings Number of features 24 Iterations 100 Time step 5 Activation Function ReLU Learning Rate 0.001 batch_size 128

[0104] Step (6): According to the divided data set, the DA-TCN prediction model is trained with the training set data as input. The TCN network in the model is used to capture the long-term temporal dependencies between indicators, and the feature extraction of all sequences in the multivariate time series data set is realized. The feature attention mechanism FA is introduced to enhance the expression of relevant features according to the adaptive weights, and reduce the influence of minor features on the model prediction results; the temporal attention mechanism TA is introduced to further determine the multivariate key temporal features that affect product quality; the activation function and the fully connected layer are used in the output layer to integrate the information of each layer, so that the network can better learn the nonlinear relationship of the feature vector.

[0105] Step (7): Use the divided test set data to verify the prediction performance of the DA-TCN prediction model. Use the mean absolute error (MAE), root mean square error (RMSE), and mean square error (MSE) as evaluation indicators to evaluate the prediction performance of the model. 2 To judge the model's fitting effect, R 2 The closer it is to 0, the worse the model fitting effect is.

[0106] The predicted results of the moisture content of the export materials obtained by the process production quality prediction method based on DA-TCN of the present invention are compared and analyzed with the actual value of the process quality. The comparison results are as follows: Figure 7 As shown, it can be seen from the figure that the process quality prediction method of the present invention has high accuracy.

[0107] In this embodiment, in order to verify that the DA-TCN prediction model has advantages in predicting the process quality of the silk thread thin plate drying process, a comparative experiment is designed to verify the prediction effect of the model, and the time series data of the same drying process is used for comparative analysis under the same experimental conditions. The comparative models are: GRU (gated recurrent unit), LSTM-Attention (prediction method of introducing attention mechanism in long short-term memory network), Attention-Seq2seq (Seq2seq network combined with attention mechanism method) and DA-LSTM network (prediction method combining long short-term memory network with the dual attention mechanism proposed in the present invention, i.e., feature attention mechanism FA, LSTM, temporal attention mechanism TA, ReLU layer, fully connected layer). The comparison results are shown in Table 4, and the fitting results are shown in Table 4. Figure 8 shown.

[0108] Table 4

[0109] Prediction Model MSE RMSE MAE <![CDATA[R 2 ]]> GRU 0.0038 0.0613 0.0479 0.901 LSTM-Attention 0.0014 0.0378 0.0293 0.960 Attention-Seq2seq 0.0013 0.0362 0.0280 0.962 DA-LSTM 0.0007 0.0272 0.0185 0.979 DA-TCN 0.0004 0.0212 0.0168 0.988

[0110] Combined with Table 4 Figure 8 It can be seen that compared with the single GRU network, the DA-TCN model uses the TCN network to predict the quality indicators of the drying process, and can model the long-term dependency relationship between the process parameters and quality indicators of the drying process in a shorter time. 2 The prediction effect was improved from 0.901 to 0.988. At the same time, compared with the LSTM-Attention, Attention-Seq2seq and DA-LSTM combination models, the DA-TCN prediction model combines the temporal convolutional neural network with the dual attention mechanism, which has certain advantages, and the prediction accuracy and fitting effect are better. 2 The mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) are 0.0004, 0.0212 and 0.0168 respectively. The results show that the process production quality prediction method based on DA-TCN of the present invention has a better prediction effect.

[0111] The specific implementation modes of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above implementation modes, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A method for predicting the moisture content of export materials using a temporal convolutional neural network, characterized in that: The method comprises the following steps: S1. Collect historical process data of the process manufacturing production line and construct a multivariate time series data set; wherein the multivariate time series data set includes process parameter index data and export material moisture index data; S2, preprocessing the multivariate time series data set to obtain preprocessed data, and dividing the preprocessed data into a training set and a test set according to the order of the acquisition cycle; S3, introduce the dual attention mechanism into the temporal convolutional neural network to build the DA-TCN prediction model; S4. Use the training set to train the DA-TCN model to obtain a trained DA-TCN prediction model; use the trained prediction model DA-TCN to verify the test set to obtain a verified DA-TCN prediction model for use in predicting the moisture content of export materials.

2. The method for predicting the moisture content of export materials using a temporal convolutional neural network according to claim 1, characterized in that: The preprocessing of the multivariate time series data set to obtain preprocessed data, and dividing the preprocessed data into a training set and a test set according to the order of the acquisition cycle, includes: According to the material head and tail interception rules of the process production process, the process parameter data and quality indicator data are judged for the material head and tail, and the critical position of the material head and tail is determined to retain the data between the critical positions of the material head and tail; Then, the data between the critical positions of the material head and the material tail are retained, and the abnormal data are eliminated by using the 3σ method, and normalized; The normalized data is divided into training set and test set according to the preset ratio.

3. The method for predicting the moisture content of export materials using a temporal convolutional neural network according to claim 2, characterized in that: The method of using time convolutional neural network to predict the moisture content of export materials is applied to the thin plate drying process. In the thin plate drying process, the rules for cutting the head and tail of the material in the process production process are as follows: If the reference value of the moisture content of the inlet material is ≤n1%, the position of the head and tail of the material is determined as follows: according to the batch order of the acquisition cycle, the first data of the outlet material moisture content greater than m1% in the current batch process data is used as the head node, and the data of the outlet material moisture content after a delay of t is used as the head critical position; the first data of the outlet material moisture content less than m1% in the current batch process data is used as the tail node, and the data after the tail node is moved forward by t is used as the tail critical position; If the reference value of the moisture content of the inlet material is greater than n1% and less than or equal to n2%, the position of the material head and tail is determined as follows: according to the batch order of the acquisition cycle, the first data of the outlet material moisture content greater than m2% in the current batch process data is used as the material head node, and the data after a delay of t from the material head node is used as the material head critical position; The first data of the outlet material moisture content less than m2% in the current batch process data is taken as the material tail node, and the data after the material tail node is moved forward by t is taken as the critical position of the material tail; If the reference value of the moisture content of the inlet material is greater than n2%, the position of the material head and tail is determined as follows: according to the batch order of the acquisition cycle, the first data of the outlet material moisture content greater than m3% in the current batch process data is used as the material head node, and the data after a delay of t from the material head node is used as the material head critical position; The first data of the outlet material moisture content less than m3% in the current batch process data is taken as the material tail node, and the data after the material tail node is moved forward by t is taken as the critical position of the material tail.

4. The method for predicting the moisture content of export materials using a temporal convolutional neural network according to claim 1, characterized in that: The DA-TCN prediction model includes a feature attention mechanism FA, a temporal convolutional neural network TCN, a temporal attention mechanism TA, a ReLU layer, and a fully connected layer; The pre-processed process production process parameter index characteristic variables and the export material moisture index characteristic variables are used as the input of the feature attention mechanism FA, and the feature weighted matrix X' is obtained by weighted summing the attention weights of each index feature and the input feature; The obtained feature weighted matrix X' and the hidden layer output of the previous time step of the temporal convolutional neural network TCN are used as the hidden layer input of the next moment of the temporal convolutional neural network TCN. The dilated causal convolution in the temporal convolutional neural network TCN is used to realize the long sequence interval sampling of all indicators. The PReLU function is used to capture the long time series dependency between indicators. The feature extraction of all sequences in the input data is realized through the residual block of the temporal convolutional neural network TCN. The output of the TCN hidden layer after iterating the residual block of the temporal convolutional neural network TCN to time t is used as the input of the temporal attention mechanism TA. The multivariate key temporal characteristics affecting the moisture index of export materials are determined by comparing the temporal attention weights of each historical moment in the process production process. All weights are weighted and summed with the output of the hidden layer at each historical moment to obtain the comprehensive temporal information; then, the predicted value of moisture of export materials is obtained through the ReLU layer and the fully connected layer.

5. A device for predicting the moisture content of export materials using a temporal convolutional neural network, characterized in that: It comprises a module for executing the method for predicting the moisture content of outlet materials by using a time convolutional neural network taking into account the current fed out by the grid-side converter GSC as described in any one of claims 1 to 4.

6. A terminal device, characterized in that: It includes a memory, a processor, and a program stored in the memory and executable by the processor. When the processor executes the program, it implements the method for predicting the moisture content of export materials using a temporal convolutional neural network as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for predicting the moisture content of export materials using a temporal convolutional neural network as described in any one of claims 1 to 4.

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