CN113452379B - Section contour dimension reduction model training method and system and data compression method and system - Google Patents
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Abstract
The invention relates to a method and a system for training a section profile dimension reduction model, wherein the initial section profile dimension reduction model is constructed based on a noise reduction self-coding neural network, and the trained section profile dimension reduction model is obtained through optimization training, so that the dimension reduction model which accords with the actual production process of strip section profile data can be obtained. The invention also relates to a compression method of the strip section profile data, which compresses the section profile data in the production process of the strip by using the section profile dimensionality reduction model, meets the real-time compression requirement of the strip section profile data and is convenient for storage and transmission of the strip section profile data.
Description
Technical Field
The invention relates to the technical field of metallurgical rolling, in particular to a cross section profile dimensionality reduction model training method and system and a data compression method and system.
Background
With the rapid development of modern industry, the steel industry in China is transforming from the big iron and steel countries to the strong iron and steel countries, and the demand of users on high-quality strips is increasing day by day. The section shape is one of important quality indexes of strip products, and in order to improve the product quality, production enterprises often configure a convexity meter in a machine set to record and store strip profile data in real time so as to effectively control and trace back and detect the section shape of the strip in a later period. The profile data is a set of measurement data of the convexity meter on the strip thickness along the strip width direction, the dimensionality of the profile data is related to the strip width and the number of detection channels of the convexity meter, generally between 20-40, along with the rolling process, each roll of strip can generate hundreds of thousands of sets of profile data along the rolling direction, as the production is carried out continuously, a large amount of profile data can be generated, and meanwhile, the data can be stored in a production field only within a certain time, and meanwhile, the data volume is too large to be beneficial to remote transmission.
Therefore, a data compression method aiming at the strip section profile data needs to be established.
Disclosure of Invention
The invention aims to provide a cross section profile dimensionality reduction model training method and a cross section profile dimensionality reduction model training system, which can perform dimensionality reduction training on a large amount of cross section profile data by introducing a noise reduction self-coding neural network model.
The invention also provides a data compression method and a data compression system, which realize the compression of the strip section outline data through the section outline dimension reduction model, reduce the data volume and ensure the accuracy of the data.
In order to achieve the purpose, the invention provides the following scheme:
a cross section contour dimension reduction model training method comprises the following steps:
constructing an initial section contour dimension reduction model; the section contour dimension reduction model is constructed based on a self-coding neural network;
and carrying out optimization training on the initial section contour dimension reduction model to obtain a trained section contour dimension reduction model.
The invention also provides a cross section contour dimension reduction model training system, which comprises:
the model construction module is used for constructing an initial section contour dimension reduction model;
and the optimization training module is used for performing optimization training on the initial section contour dimension reduction model to obtain a trained section contour dimension reduction model.
The invention also provides a strip section contour data compression method, which comprises the following steps:
and compressing the strip section profile data by using the section profile dimension reduction model.
The invention also provides a strip section profile data compression system, which comprises:
and the data compression module is used for compressing the strip material section profile data by using the section profile dimension reduction model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the cross section profile dimensionality reduction model training method and system, and the data compression method and system, the cross section profile dimensionality reduction model is constructed by introducing the noise reduction self-coding neural network, the dimensionality reduction training can be carried out on a large amount of cross section profile data, the optimal compression dimensionality with the minimum distortion is obtained, real information is reserved to the greatest extent, the execution speed is high, the requirement for online use can be met, the storage and transmission efficiency of the strip cross section profile data is effectively improved, the monitoring and the tracing of strip product information are facilitated, and the important significance is achieved for improving the strip product quality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flowchart of a cross-sectional profile dimension reduction model training method according to an embodiment of the present invention;
FIG. 2 is a diagram of a training error loss of a cross-sectional profile dimensionality reduction model provided by an embodiment of the present invention;
FIG. 3 is a comparison graph of the original value of the profile of the cross section of the strip and the reconstructed value of the model provided by the embodiment of the invention;
FIG. 4(a) is a gray scale of the original value of the cross-sectional profile of the strip provided by the embodiment of the present invention, and (b) is a gray scale of the reconstructed value of the cross-sectional profile of the strip provided by the embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a noise-reducing self-coding neural network according to an embodiment of the present invention;
fig. 6 is a block diagram of a cross-sectional profile dimension reduction model training system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, with the rapid development of network technology and data acquisition and storage technology, data characteristics are becoming diversified. When large data is processed, not only are more strict requirements on acquisition, storage, bandwidth and the like provided, but also severe challenges are brought to tasks such as data analysis, compression, reconstruction and the like. Some theoretical and technical studies have been made on data compression, and image compression is more typical. For example, when a hyperspectral image is compressed by a tensor compression algorithm based on sparse representation, the method is superior to other algorithms in the aspects of original data information retention and denoising capability. The remote sensing image compression method based on the depth self-coding network is combined with the network structure characteristics of CNN and BNN, and a convolution self-coding and decoding network compression model added with binary noise is set up to be better than a traditional compression model in compression of the remote sensing image, and the image reconstruction effect is improved. The data dimension reduction method based on the sparse self-coding network abstracts and expresses face image data through a sparse coding theory, removes redundant information of images, reduces dimensions of the data, simultaneously retains main characteristics of faces, and obtains an identification rate close to 95%.
Therefore, the invention aims to provide a method and a system for training a section profile data dimension reduction model, a method and a system for data compression, which can well compress the dimension of the section profile data in the width direction of a strip material, greatly reduce the data volume, and have small error and low distortion rate between the section profile data after model reconstruction and the real section profile data.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1
As shown in fig. 1, the embodiment provides a cross-sectional profile dimension reduction model training method, which is characterized in that,
step 101: constructing an initial section contour dimension reduction model; the section contour dimension reduction model is constructed based on a self-coding neural network;
step 102: and carrying out optimization training on the initial section contour dimension reduction model to obtain a trained section contour dimension reduction model.
In order to make the dimension reduction model of the profile provided by the embodiment more conform to the profile data of the strip material in the production process, before the dimension reduction model of the initial profile is constructed, the dimension reduction model of the profile of the strip material in the production process is collected, and noise is added to the profile data of the strip material to obtain a sample data set. And carrying out pretreatment of standardizing and eliminating repeated values on the sample data set to obtain a processed sample data set, and carrying out optimization training on the initial section contour dimension reduction model according to the processed sample data set.
Specifically, in the rolling process, the section profile data of the strip is collected and divided into three areas, wherein the left area is the convexity at a distance of 3mm, 5mm, 7mm, 10mm, 15mm, 20mm, 25mm, 30mm, 50mm, 75mm, 100mm, 200mm and 300mm from the left side edge of the strip, namely WS3, WS5, WS7, WS10, WS15, WS20, WS25, WS30, WS50, WS75, WS100, WS125, WS200 and WS300, and the total number is 14 discrete values; the right region is the convexity at 3mm, 5mm, 7mm, 10mm, 15mm, 20mm, 25mm, 30mm, 50mm, 75mm, 100mm, 200mm, 300mm from the right edge of the strip, i.e. DS3, DS5, DS7, DS10, DS15, DS20, DS25, DS30, DS50, DS75, DS100, DS125, DS200, DS300, for a total of 14 discrete values; the convexity at the center point is WSC and DSC, totaling 2 discrete values. As the rolling process proceeds, T sets of profile values with a dimension of 30 were collected in the rolling direction, expressed as:
because the value of central zone all can be detected when the left and right sides is detected, and two times of repeated numerical values appear in WSC and DSC promptly, consequently eliminate the repeated numerical value and guarantee sample data and the degree of closeness of actual production data, the data after the removal are:
because the production process of the strip is uninterrupted, the section profile data of each roll of the strip is stored in one file, and n files are selected as a sample data set, wherein R is { D ═ D 1 ′,D 2 ′,D 3 ′…,D n ' }, as shown in Table 1.
Table 1 data set R partial data (50 groups)
TABLE 1 continuation
TABLE 1 continuation
In order to make the network better fit the target and accelerate the convergence speed, the strip section profile data should be standardized: all profile values C in the data set R divided by the maximum value C in R max I.e. by
(C is the original profile, C max Is the maximum value of the profile, C Sign board Normalized value) to [0, 1%]In the meantime. The normalized values for data set R are shown in table 2.
Table 2 data set R normalized partial data (50 groups)
TABLE 2 continuation
TABLE 2 continuation
Meanwhile, considering that the strip is in an unstable stage when rolling starts, the convexity meter cannot detect the profile of the section in certain channels, so that data is not completely lost, signals are interfered in the transmission process, so that the transmitted data is inaccurate, and finally the obtained data is noisy. In order to improve the accuracy of the cross-section profile dimension reduction model in practical application and improve the reconstruction capability of a self-encoder in the model, noise is added to the cross-section profile data of each roll of strip material in the embodiment. In order to fit a complex actual production working condition, gaussian noise is selected as noise, a noise reduction factor of the noise reduction self-encoder is set to be N, wherein N is 0.001 in the embodiment, and in subsequent model training, if a mean square error calculated by a trained model is large, the noise reduction factor can be adjusted.
After a sample data set which is subjected to the preprocessing of standardization and repeated value elimination and added with noise is obtained, the initial section contour dimension reduction model is subjected to optimization training by utilizing the sample data set.
First, as shown in fig. 5, constructing an initial profile dimension reduction model includes constructing an encoder and a decoder. Specifically, a noise reduction self-coding neural network is preset and comprises one to five layers which are connected in sequence, a coding part of the noise reduction self-coding device is formed from the first layer to the third layer, a decoding part is formed from the third layer to the fifth layer, and the connection mode of each layer of the self-coding device is full connection. The number of the neurons of the first layer inputlayer and the fifth layer outputlayer is consistent with the input variable of the strip section profile data, and the strip section profile data provided in the embodiment has C 1 -C 29 29 input variables are totally set, so that if the number of the neurons in the first layer and the fifth layer is set to be 29, the output variable C is set 1 '-C' 29 There are also 29. Setting the number of the neurons of the third layer encoder _3 as dimension l (1) of dimension reduction<l<28) The number of neurons in the second layer _2 and the fourth layer _4 is set as an initial value, and in this embodiment, the initial values of the second layer and the fourth layer are both set to 20.
Then, an activation function of each layer of the noise reduction self-coding neural network is selected, relu is selected from the activation functions of the first layer to the fifth layer, and tanh is selected as the activation function of the output layer. And finally, constructing an initial section profile dimension reduction model based on the noise reduction self-coding neural network.
And then training the initial section contour dimension reduction model: setting a reconstruction error function as a mean square error loss function mse, and then training the initial section contour dimension reduction model for preset iteration times to obtain a first model;
and judging whether the first model meets a preset precision threshold value or not according to the reconstruction error function, if so, taking the first model as a section contour dimension reduction model, otherwise, optimizing and adjusting parameters of the initial section contour dimension reduction model, and returning to the step of training the initial section contour dimension reduction model for preset iteration times.
Wherein, optimizing and adjusting the initial section contour dimension reduction model comprises:
(a) adjusting a network structure: adjusting the number of hidden layer layers of the initial section profile dimensionality reduction model and the number of neurons of each hidden layer;
(b) weight initialization: initializing the weight of each hidden layer by using a small random number;
(c) adjusting parameters: and adjusting batch training size batch-size, learning rate learning-rate and training step number Epoch of the initial cross-section contour dimension reduction model.
In order to better perform the training process, besides presetting the iteration number Epoch, the training parameters of the model also need to be set: determining the batch training size batch-size, setting the learning rate learning-rate, and selecting the optimizer. In this embodiment, the batch training size batch-size is specifically set to be 128, the learning rate learning-rate is 0.002, the iteration number Epoch of training is 150, and the optimizer chooses Adam.
Further, determining whether the first model meets a preset precision threshold according to the reconstruction error function includes:
taking partial data in the sample data set as training samples, and dividing the training samples into a training set and a verification set; and randomly extracting 90% of training samples as a training set train of the noise reduction self-coding neural network, wherein the training set train comprises 10000 groups, and the rest 10% of training samples are used as a verification set validity, and the total number of the verification sets is 1000 groups.
Training the initial section contour dimension reduction model by using the training set for preset iteration times to obtain a first model;
and judging whether the average error of the first model is smaller than a first preset threshold value according to the training error loss diagrams of the training set and the verification set, if so, the first model meets a preset precision threshold value, and otherwise, the first model does not meet the preset precision threshold value. As shown in fig. 2, the average error of the network is 0.002, in this embodiment, the first preset threshold is specifically set to be 0.01, and the first model satisfies the preset accuracy threshold.
In order to further improve the accuracy of model training and ensure the compression effect of the profile data, part of data in the sample data set except the training sample is used as a test set. And when the first model is smaller than a first preset threshold value, inputting the test set into the first model to obtain an output test set, and judging whether the mean square error of the test set and the output test set is smaller than a second preset threshold value, wherein if the mean square error of the test set and the output test set is smaller than the second preset threshold value, the first model meets a preset precision threshold value, and otherwise, the first model does not meet the preset precision threshold value.
In this embodiment, the input and output mean square error of the test set is calculated to be 8.33, the second preset threshold is 10, and the reconstruction accuracy of the model meets the preset condition and can reach a decimal point one bit. FIG. 3 is a comparison graph of the original value and the self-encoding reconstructed value of the strip section profile, and it can be seen from FIG. 3 that the position error with large curve fluctuation is large, but the compression precision is met. Fig. 4(a) (b) is a gray scale comparison graph of the reconstructed value and the original value of the section profile of a part of strip, and the degree of distortion of the noise reduction self-coding is very small by comparing the gray scales of a plurality of groups of section profile curves.
Therefore, after the initial cross-section profile dimension reduction model is trained, the trained cross-section profile dimension reduction model is obtained.
Example 2
As shown in fig. 6, the present embodiment provides a cross-sectional profile dimension reduction model training system, which includes:
the model building module M1 is used for building an initial section profile dimension reduction model;
an optimization training module M2, configured to perform optimization training on the initial profile dimensionality reduction model to obtain a trained profile dimensionality reduction model
Example 3
The embodiment provides a method for compressing strip section profile data, which uses the section profile dimension reduction model as described in embodiment 1, and the method includes:
and compressing the strip section profile data by using the section profile dimension reduction model.
Example 4
The embodiment provides a strip section profile data compression system, the system includes:
and the data compression module is used for compressing the strip material section profile data by using the section profile dimension reduction model.
The emphasis of each embodiment in the present specification is on the difference from the other embodiments, and the same and similar parts among the various embodiments may be referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (5)
1. A cross section contour dimension reduction model training method is characterized by comprising the following steps:
constructing an initial section contour dimension reduction model; the section contour dimension reduction model is constructed based on a self-coding neural network;
collecting strip section contour data in a production process, and adding noise into the strip section contour data to obtain a sample data set;
carrying out pretreatment of standardizing and eliminating repeated values on the sample data set to obtain a processed sample data set, and carrying out optimization training on the initial section contour dimension reduction model according to the processed sample data set;
carrying out optimization training on the initial section contour dimension reduction model to obtain a trained section contour dimension reduction model;
the optimization training of the initial section contour dimension reduction model comprises the following steps:
constructing a reconstruction error function;
training the initial section contour dimension reduction model for preset iteration times to obtain a first model;
judging whether the first model meets a preset precision threshold value or not according to the reconstruction error function, if so, taking the first model as a section contour dimension reduction model, otherwise, optimizing and adjusting parameters of the initial section contour dimension reduction model, and returning to the step of training the initial section contour dimension reduction model for preset iteration times;
the determining whether the first model meets a preset precision threshold according to the reconstruction error function includes:
taking partial data in the sample data set as training samples, and dividing the training samples into a training set and a verification set;
training the initial section contour dimension reduction model by using the training set for preset iteration times to obtain a first model;
judging whether the average error of the first model is smaller than a first preset threshold value according to the training error loss graphs of the training set and the verification set, if so, enabling the first model to meet a preset precision threshold value, otherwise, enabling the first model not to meet the preset precision threshold value;
the determining whether the first model meets a preset precision threshold according to the reconstruction error function further includes:
taking part of data in the sample data set except the training sample as a test set;
when the first model is smaller than a first preset threshold value, inputting the test set into the first model to obtain an output test set, and judging whether the mean square error of the test set and the output test set is smaller than a second preset threshold value, wherein if the mean square error of the test set and the output test set is smaller than the second preset threshold value, the first model meets a preset precision threshold value, otherwise, the first model does not meet the preset precision threshold value;
the optimizing and adjusting the parameters of the initial section profile dimension reduction model comprises the following steps:
adjusting the number of hidden layer layers of the initial section profile dimensionality reduction model and the number of neurons of each hidden layer;
initializing the weight of each hidden layer by using a small random number;
and adjusting the batch training size, the learning rate and the training steps of the initial section contour dimension reduction model.
2. The method for training the cross-sectional profile dimensionality reduction model according to claim 1, wherein the constructing the initial cross-sectional profile dimensionality reduction model comprises:
constructing a self-coding neural network comprising five layers, wherein the first layer to the fifth layer are connected in sequence in a full-connection mode;
setting the number of neurons of the first layer and the fifth layer according to the input variable of the strip section profile data;
setting the number of neurons in the third layer according to the dimensionality needing dimension reduction;
and setting the number of the neurons of the second layer and the fourth layer as an initial value.
3. The method for compressing the strip section profile data obtained by the training method of the section profile dimension reduction model according to claim 1, is characterized by comprising the following steps:
and compressing the strip section profile data by using the section profile dimension reduction model.
4. The system for compressing the strip section profile data obtained by the method for training the section profile dimension reduction model according to claim 1, is characterized by comprising the following steps:
and the data compression module is used for compressing the strip material section profile data by using the section profile dimension reduction model.
5. A system for training a cross-sectional profile dimension reduction model, the system comprising:
the model construction module is used for constructing an initial section contour dimension reduction model;
collecting strip section contour data in a production process, and adding noise into the strip section contour data to obtain a sample data set;
carrying out pretreatment of standardizing and eliminating repeated values on the sample data set to obtain a processed sample data set, and carrying out optimization training on the initial section contour dimension reduction model according to the processed sample data set;
the optimization training module is used for performing optimization training on the initial section contour dimension reduction model to obtain a trained section contour dimension reduction model;
the optimization training of the initial section contour dimension reduction model comprises the following steps:
constructing a reconstruction error function;
training the initial section contour dimension reduction model for preset iteration times to obtain a first model;
judging whether the first model meets a preset precision threshold value or not according to the reconstruction error function, if so, taking the first model as a section contour dimension reduction model, otherwise, optimizing and adjusting parameters of the initial section contour dimension reduction model, and returning to the step of training the initial section contour dimension reduction model for preset iteration times;
the determining whether the first model meets a preset precision threshold according to the reconstruction error function includes:
taking partial data in the sample data set as training samples, and dividing the training samples into a training set and a verification set;
training the initial section contour dimension reduction model by using the training set for preset iteration times to obtain a first model;
judging whether the average error of the first model is smaller than a first preset threshold value according to the training error loss graphs of the training set and the verification set, if so, enabling the first model to meet a preset precision threshold value, otherwise, enabling the first model not to meet the preset precision threshold value;
the determining whether the first model meets a preset precision threshold according to the reconstruction error function further includes:
taking part of data in the sample data set except the training sample as a test set;
when the first model is smaller than a first preset threshold value, inputting the test set into the first model to obtain an output test set, and judging whether the mean square error of the test set and the output test set is smaller than a second preset threshold value, wherein if the mean square error of the test set and the output test set is smaller than the second preset threshold value, the first model meets a preset precision threshold value, otherwise, the first model does not meet the preset precision threshold value;
the optimizing and adjusting the parameters of the initial section profile dimension reduction model comprises the following steps:
adjusting the number of hidden layer layers of the initial section profile dimensionality reduction model and the number of neurons of each hidden layer;
initializing the weight of each hidden layer by using a small random number;
and adjusting the batch training size, the learning rate and the training steps of the initial section contour dimension reduction model.
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