WO2023226227A1 - 用于电子级氢氟酸制备的自动配料系统及其配料方法 - Google Patents

用于电子级氢氟酸制备的自动配料系统及其配料方法 Download PDF

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WO2023226227A1
WO2023226227A1 PCT/CN2022/116222 CN2022116222W WO2023226227A1 WO 2023226227 A1 WO2023226227 A1 WO 2023226227A1 CN 2022116222 W CN2022116222 W CN 2022116222W WO 2023226227 A1 WO2023226227 A1 WO 2023226227A1
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density map
gaussian density
hydrofluoric acid
matrix
vector
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French (fr)
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林庆鑫
黄宗发
华钟
黄明新
邱秋生
袁海明
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福建龙氟新材料有限公司
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
    • B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
    • B01F35/20—Measuring; Control or regulation
    • B01F35/22—Control or regulation
    • B01F35/2201—Control or regulation characterised by the type of control technique used
    • B01F35/2207—Use of data, i.e. barcodes, 3D codes or similar type of tagging information, as instruction or identification codes for controlling the computer programs, e.g. for manipulation, handling, production or compounding in mixing plants
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present invention relates to the field of intelligent manufacturing, and more specifically, to an automatic batching system for preparing electronic grade hydrofluoric acid and a batching method thereof.
  • Electronic-grade hydrofluoric acid is a strong acidic cleaning agent and corrosive agent, mainly used in the production of very large-scale integrated circuits.
  • the semiconductor chip manufacturing industry and LCD manufacturing industry shift to my country, the domestic use of electronic grade hydrofluoric acid will surge, and the electronic grade hydrofluoric acid market will undoubtedly have broad development prospects.
  • electronic grade hydrofluoric acid can generally be prepared by distillation, subboiling distillation, gas absorption and other processes.
  • these preparation processes are complex and the production cost is high.
  • Existing improvements to electronic-grade hydrofluoric acid are mainly improvements to the equipment used to prepare electronic-grade hydrofluoric acid.
  • a multi-stage distillation tower is used to replace the first-stage distillation tower.
  • this physical structure level Improvement costs are high and the cycle is long. Therefore, an optimized automatic batching system for the preparation of electronic grade hydrofluoric acid is expected to optimize the preparation efficiency and purification accuracy of the intelligent manufacturing line of electronic grade hydrofluoric acid.
  • deep learning and neural networks have been widely used in computer vision, natural language processing, speech signal processing and other fields.
  • deep learning and neural networks have also shown that they are close to or even beyond human performance in areas such as image classification, object detection, semantic segmentation, and text translation.
  • the development of deep learning and neural networks provides technical support for the construction of intelligent manufacturing lines for electronic-grade hydrofluoric acid. That is, the development of deep learning and neural networks provides new solutions for the automatic batching and production of electronic-grade hydrofluoric acid. Solutions ideas and plans.
  • Embodiments of the present application provide an automatic batching system for the preparation of electronic grade hydrofluoric acid and a batching method thereof, which adopts an intelligent control method of artificial intelligence technology to dynamically adjust from the control end based on a global dynamic perspective.
  • the inflow rate of anhydrous hydrofluoric acid in the metering tank is used to optimize the preparation efficiency and purification accuracy of the intelligent manufacturing line for electronic grade hydrofluoric acid.
  • an automatic batching system for the preparation of electronic grade hydrofluoric acid which includes:
  • a control parameter data acquisition unit used to acquire the liquid level in the buffer tank at multiple predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still, and the hot water flowing into the tower still.
  • a first convolutional encoding unit configured to pass the liquid chromatograms of the products condensed by the condenser at the plurality of predetermined time points through a first convolutional neural network using a three-dimensional convolution kernel to obtain the first feature vector;
  • the second convolution encoding unit is used to combine the liquid level in the buffer tank, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower kettle at the plurality of predetermined time points, The temperature of the hot water flowing into the tower kettle and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension, and then passed through a second convolutional neural network to obtain a second feature vector;
  • a first Gaussian density map construction unit configured to construct a first Gaussian density map of the first feature vector, wherein the mean vector of the first Gaussian density map is the first feature vector, and the first Gaussian density
  • the value of each position in the covariance matrix of the graph corresponds to the variance between the eigenvalues of the corresponding two positions in the first eigenvector;
  • a second Gaussian density map construction unit is used to construct a second Gaussian density map of the second feature vector, wherein the mean vector of the second Gaussian density map is the second feature vector, and the second Gaussian density
  • the value of each position in the covariance matrix of the graph corresponds to the variance between the eigenvalues of the corresponding two positions in the second eigenvector;
  • a responsiveness estimation unit configured to calculate a responsiveness estimate of the first Gaussian density map relative to the second Gaussian density map to obtain a responsive Gaussian density map, where the mean vector of the responsive Gaussian density map is the first Gaussian density map.
  • the covariance matrix of the responsive density map is the covariance matrix of the first Gaussian density map multiplied by the inverse matrix of the covariance matrix of the second Gaussian density map;
  • Gaussian discretization unit used to perform Gaussian discretization processing on the responsive Gaussian density map to obtain a classification feature matrix
  • a feature matrix correction unit configured to perform balanced correction on the classification feature matrix based on the overall probability distribution of the classification feature matrix to generate a corrected classification feature map, wherein the correction based on the overall probability distribution of the classification feature matrix
  • the equalization correction is based on the difference between the natural exponential function value raised to the power of the feature value of each position in the classification feature map and the natural exponential function value raised to the power of the mean value of the feature values of all positions in the classification feature map.
  • a control result generation unit is used to pass the corrected classification feature map through a classifier to obtain a classification result, and the classification result is used to indicate that the inflow rate of anhydrous hydrofluoric acid in the metering tank should be increased or decreased.
  • the first convolution coding unit is further used to: use the first convolutional neural network of the three-dimensional convolution kernel to calculate the plurality of predetermined times.
  • the liquid chromatogram of the product condensed by the condenser at the point is processed to obtain the first characteristic vector; wherein, the formula is:
  • H j , W j and R j represent the length, width and height of the three-dimensional convolution kernel respectively
  • m represents the number of the (l-1)th layer feature map
  • b lj is the bias
  • f( ⁇ ) represents the activation function.
  • the second convolution encoding unit is further used to: combine the liquid level in the buffer tank at the plurality of predetermined time points, the metering The inflow rate of anhydrous hydrofluoric acid in the tank, the liquid level in the tower still, the temperature of the hot water flowing into the tower still and the temperature of the condenser are arranged in two dimensions according to the sample dimension and the time dimension.
  • parameter matrix using each layer of the second convolutional neural network to perform convolution processing, pooling processing along the feature matrix and activation processing on the input data in the forward pass of the layer to be processed by the second convolutional neural network
  • the last layer of the network generates the second feature vector, wherein the input of the first layer of the second convolutional neural network is the parameter matrix.
  • the first Gaussian density map construction unit is further used to: construct the first Gaussian density map of the first feature vector according to the following formula
  • x 1 represents the synthesized Gaussian vector
  • ⁇ 1 is the first eigenvector
  • the value of each position in ⁇ 1 corresponds to the variance between the eigenvalues of the corresponding two positions in the first eigenvector.
  • the second Gaussian density map construction unit is further used to: construct the second Gaussian density map of the second eigenvector with the following formula;
  • x 2 represents the synthesized Gaussian vector
  • ⁇ 2 is the second eigenvector
  • the value of each position in ⁇ 2 corresponds to the variance between the eigenvalues of the corresponding two positions in the second eigenvector.
  • the responsiveness estimation unit is further used to: calculate the responsiveness of the first Gaussian density map relative to the second Gaussian density map using the following formula Estimated to obtain said responsive Gaussian density map;
  • ⁇ represents the vector dot product
  • ⁇ -1 represents the reciprocal of the value at each position of the vector
  • the feature matrix correction unit is further used to perform balanced correction on the classification feature matrix based on the overall probability distribution of the classification feature matrix using the following formula To generate the corrected classification feature map;
  • m i,j are the eigenvalues corresponding to the i-th row and j-th column of the classification matrix, and is the global mean of the eigenvalues at each position of the classification matrix.
  • control result generation unit is further configured to: the classifier processes the corrected classification feature map according to the following formula to generate a classification result, where , the formula is: softmax ⁇ (W n ,B n ):...:(W 1 ,B 1 )
  • a batching method of an automatic batching system for the preparation of electronic grade hydrofluoric acid includes:
  • the liquid level in the buffer tank at the plurality of predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still, the water flowing into the tower still The temperature of the hot water and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension and then passed through the second convolutional neural network to obtain the second feature vector;
  • the covariance matrix of the responsive density map is the covariance matrix of the first Gaussian density map multiplied by the second Gaussian density map.
  • the classification feature matrix is subjected to a balanced correction based on the overall probability distribution of the classification feature matrix to generate a corrected classification feature map, wherein the balanced correction based on the overall probability distribution of the classification feature matrix is based on the classification Performed by the difference between the natural exponential function value raised to the power of the feature value of each position in the feature map and the natural exponential function value raised to the power of the mean of the feature values of all positions in the classification feature map; and
  • the corrected classification feature map is passed through a classifier to obtain a classification result, which is used to indicate that the inflow rate of anhydrous hydrofluoric acid in the metering tank should be increased or decreased.
  • the liquid chromatograms of the products condensed by the condenser at the multiple predetermined time points are passed through the third layer using a three-dimensional convolution kernel.
  • a convolutional neural network to obtain the first feature vector including: using the first convolutional neural network of the three-dimensional convolution kernel to perform liquid chromatography of the product condensed by the condenser at the plurality of predetermined time points. Process the graph to obtain the first feature vector;
  • H j , W j and R j represent the length, width and height of the three-dimensional convolution kernel respectively
  • m represents the number of the (l-1)th layer feature map
  • b lj is the bias
  • f( ⁇ ) represents the activation function.
  • the liquid level in the buffer tank at the plurality of predetermined time points and the inflow rate of anhydrous hydrofluoric acid in the metering tank are , the liquid level in the tower kettle, the temperature of the hot water flowing into the tower kettle and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and time dimension, and then passed through the second convolution neural
  • the network obtains the second feature vector, including: combining the liquid level in the buffer tank at the plurality of predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, and the liquid level in the tower still.
  • the temperature of the hot water flowing into the tower kettle and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension; using each layer of the second convolutional neural network in the layer In the forward pass, the input data is convolved, pooled along the feature matrix, and activated to generate the second feature vector by the last layer of the second convolutional neural network, where the second The input of the first layer of the convolutional neural network is the parameter matrix.
  • constructing the first Gaussian density map of the first feature vector includes: constructing the first Gaussian density map of the first feature vector according to the following formula Gaussian density plot;
  • x 1 represents the synthesized Gaussian vector
  • ⁇ 1 is the first eigenvector
  • the value of each position in ⁇ 1 corresponds to the variance between the eigenvalues of the corresponding two positions in the first eigenvector.
  • constructing the second Gaussian density map of the second eigenvector includes: constructing the second Gaussian density map of the second eigenvector according to the following formula Gaussian density plot;
  • x 2 represents the synthesized Gaussian vector
  • ⁇ 2 is the second eigenvector
  • the value of each position in ⁇ 2 corresponds to the variance between the eigenvalues of the corresponding two positions in the second eigenvector.
  • calculating the responsiveness estimate of the first Gaussian density map relative to the second Gaussian density map to obtain a responsive Gaussian density map includes: Calculate the responsiveness estimate of the first Gaussian density map relative to the second Gaussian density map using the following formula to obtain the responsive Gaussian density map;
  • ⁇ represents the vector dot product
  • ⁇ -1 represents the reciprocal of the value at each position of the vector
  • the classification feature matrix is balancedly corrected based on the overall probability distribution of the classification feature matrix to generate a corrected classification feature map, including: The following formula performs a balanced correction on the classification feature matrix based on the overall probability distribution of the classification feature matrix to generate the corrected classification feature map;
  • m i,j are the eigenvalues corresponding to the i-th row and j-th column of the classification matrix, and is the global mean of the eigenvalues at each position of the classification matrix.
  • the corrected classification feature map is passed through a classifier to obtain a classification result, and the classification result is used to represent anhydrous hydrofluoric acid in the metering tank.
  • the inflow rate should be increased or decreased, including: the classifier processes the corrected classification feature map with the following formula to generate a classification result, where the formula is: softmax ⁇ (W n ,B n ):...:(W 1 ,B 1 )
  • the weight matrix, B 1 to B n represents the bias matrix of the fully connected layer of each layer.
  • the automatic batching system and batching method for the preparation of electronic grade hydrofluoric acid provided by this application adopts the intelligent control method of artificial intelligence technology to dynamically control the situation from the control end based on the global dynamic perspective.
  • the inflow rate of anhydrous hydrofluoric acid in the metering tank is adjusted accordingly, thereby optimizing the preparation efficiency and purification accuracy of the intelligent manufacturing line for electronic grade hydrofluoric acid.
  • Figure 1 is an application scenario diagram of an automatic batching system for preparing electronic grade hydrofluoric acid according to an embodiment of the present application.
  • Figure 2 is a block diagram of an automatic batching system for preparing electronic grade hydrofluoric acid according to an embodiment of the present application.
  • Figure 3 is a flow chart of a batching method of an automatic batching system for electronic grade hydrofluoric acid preparation according to an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of a batching method of an automatic batching system for preparing electronic grade hydrofluoric acid according to an embodiment of the present application.
  • electronic grade hydrofluoric acid is a strong acidic cleaning agent and corrosive agent, mainly used in the production of very large-scale integrated circuits.
  • electronic grade hydrofluoric acid is a strong acidic cleaning agent and corrosive agent, mainly used in the production of very large-scale integrated circuits.
  • the semiconductor chip manufacturing industry and LCD manufacturing industry shift to my country, the domestic use of electronic grade hydrofluoric acid will surge, and the electronic grade hydrofluoric acid market will undoubtedly have broad development prospects.
  • electronic grade hydrofluoric acid can generally be prepared by distillation, subboiling distillation, gas absorption and other processes.
  • these preparation processes are complex and the production cost is high.
  • Existing improvements to electronic-grade hydrofluoric acid are mainly improvements to the equipment used to prepare electronic-grade hydrofluoric acid. For example, a multi-stage distillation tower is used to replace the first-stage distillation tower. However, this physical structure level Improvement costs are high and the cycle is long.
  • deep learning and neural networks have been widely used in computer vision, natural language processing, speech signal processing and other fields.
  • deep learning and neural networks have also shown that they are close to or even beyond human performance in areas such as image classification, object detection, semantic segmentation, and text translation.
  • the development of deep learning and neural networks provides technical support for the construction of intelligent manufacturing lines for electronic-grade hydrofluoric acid. That is, the development of deep learning and neural networks provides new solutions for the automatic batching and production of electronic-grade hydrofluoric acid. Solutions ideas and plans.
  • the oxidizing gas is a mixture of fluorine gas and nitrogen;
  • the inventor of the present application found that in the existing batching scheme, the anhydrous hydrofluoric acid in the metering tank flows through the primary filter at a predetermined rate and then enters the tower kettle.
  • This batching method does not fully consider the conditions in the metering tank.
  • the inflow rate of anhydrous hydrofluoric acid is closely related to the subsequent control parameters, which makes it difficult to improve the preparation efficiency and purification accuracy of the existing electronic-grade hydrofluoric acid. That is, if the inflow rate of anhydrous hydrofluoric acid in the metering tank can be dynamically adjusted based on a global dynamic perspective, the preparation efficiency and purification accuracy of the intelligent manufacturing line for electronic-grade hydrofluoric acid can be optimized. This can be achieved through artificial intelligence control technology based on deep neural networks.
  • multiple control parameters and a real-time result parameter of the smart production line of electronic grade hydrofluoric acid include the liquid level in the buffer tank and the inflow rate of anhydrous hydrofluoric acid in the metering tank. , the liquid level in the tower kettle, the temperature of the hot water introduced into the tower kettle and the temperature of the condenser.
  • the product after being condensed by the condenser is the ultrapure anhydrous hydrofluoric acid gas. Liquid chromatogram as the real-time result parameter.
  • a convolutional neural network model is used to calculate the control parameters in the smart production line of electronic grade hydrofluoric acid.
  • Multiple control parameters are encoded. Specifically, the liquid level in the buffer tank at multiple predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still, the temperature of the hot water flowing into the tower still, and The temperature of the condenser is arranged into a two-dimensional parameter matrix according to the sample dimension and time dimension.
  • the correlation between various control parameters at the same predetermined time point, the correlation between different control parameters at different predetermined time points, and the correlation between the same control parameter at different predetermined time points are established.
  • Convolutional neural networks have excellent performance in extracting local features. Therefore, using the convolutional neural network model can extract high-dimensional implicit correlations between various control parameters at the same predetermined time point in the parameter matrix, at different predetermined times. High-dimensional implicit correlations between different control parameters at points, and high-dimensional implicit correlations between the same control parameters at different predetermined time points to obtain the second feature vector.
  • the purpose of regulating the control parameters of the smart production line of electronic grade hydrofluoric acid is to obtain a product that meets the preset requirements. Therefore, the result data can be used to respond from real-time To evaluate the current control effect from different angles to improve the accuracy and precision of parameter control.
  • a convolutional neural network model using a three-dimensional convolution kernel is used to encode the liquid chromatogram of the product condensed by the condenser at the multiple predetermined time points to capture the time-series dimension of the product.
  • High-dimensional absolute features and high-dimensional absolute features that is, high-dimensional implicit feature representations of the absolute value and relative change value of the purity of hydrofluoric acid.
  • data enhancement is performed on the first feature vector and the second feature vector based on a Gaussian density map so that the positions and shapes of the data manifolds of the two in the high-dimensional feature space can be more accurately understood. for proximity.
  • a first Gaussian density map of the first feature map and a second density map of the second feature map are constructed, wherein the first Gaussian density map can be expressed as Wherein, ⁇ 1 is the first eigenvector, and the value of each position of ⁇ 1 is the variance of the eigenvalues of the corresponding two positions in the first eigenvector.
  • the second Gaussian density map can be expressed by the formula: Wherein, ⁇ 2 is the second eigenvector, and the value of each position of ⁇ 2 is the variance of the eigenvalues of the corresponding two positions in the second eigenvector.
  • a responsiveness estimate of the first Gaussian density map relative to the second Gaussian density map is further calculated to obtain a responsive Gaussian Density plot.
  • Gaussian discretization is performed on the responsive Gaussian density map to generate a classification feature matrix, and the corresponding control result can be obtained after passing the classification feature matrix through a classifier.
  • the classification feature matrix includes high-dimensional implicit correlations between multiple control parameters, high-dimensional correlation information of the multiple control parameters in the time series dimension, and high-dimensional change characteristics of the product in the time series dimension,
  • the product has high-dimensional correlation information with respect to the multiple control parameters. Therefore, classification by the classification feature matrix can improve the precision and accuracy of control.
  • the convolution kernel of the convolutional network acts as a filter to extract small-scale pixel-level correlation features from the source data, there will be disturbances in the feature vector as a probability distribution under the Gaussian density map.
  • the covariance matrix is the variance between the eigenvalues of each two positions, this will amplify the perturbation of individual probability distributions, and this perturbation may be used in calculating the responsiveness.
  • the density map and Gaussian discretization process are further amplified, thus affecting the balance of the overall probability distribution of the classification matrix.
  • m i,j is the eigenvalue corresponding to the i-th row and j-th column of the classification matrix, and is the global mean of the eigenvalues at each position of the classification matrix.
  • This correction method is to use the eigenvalue of a single position of the classification matrix obtained after Gaussian discretization as a single variable, and calculate the negative logarithm of the Cauchy loss form of the difference under the probability of belonging to the category, thereby converting the special distribution of a single eigenvalue Generalize the probability distribution to mask the perturbation of individual probability distributions within the overall distribution to improve the balance of the overall probability distribution of the classification matrix. In this way, the precision and accuracy of parameter control are further improved.
  • this application proposes an automatic batching system for the preparation of electronic grade hydrofluoric acid, which includes: a control parameter data acquisition unit for acquiring the liquid level in the buffer tank and the metering tank at multiple predetermined time points.
  • the inflow rate of anhydrous hydrofluoric acid, the liquid level in the tower still, the temperature of the hot water flowing into the tower still and the temperature of the condenser, and, after condensation by the condenser at the plurality of predetermined time points The liquid chromatogram of the product;
  • the first convolution coding unit is used to pass the liquid chromatogram of the product condensed by the condenser at the plurality of predetermined time points through the first volume using a three-dimensional convolution kernel convolution neural network to obtain the first feature vector;
  • a second convolution coding unit used to combine the liquid level in the buffer tank and the inflow rate of anhydrous hydrofluoric acid in the metering tank at the plurality of predetermined time points , the liquid level in the tower kettle, the temperature of the hot
  • the difference between the natural exponential function values is carried out; and, a control result generation unit is used to pass the corrected classification feature map through a classifier to obtain a classification result, the classification result is used to represent anhydrous hydrogen in the metering tank
  • the inflow rate of hydrofluoric acid should be increased or decreased.
  • FIG 1 illustrates an application scenario diagram of an automatic batching system for the preparation of electronic grade hydrofluoric acid according to an embodiment of the present application.
  • buffer tanks for example, T1-Tn as illustrated in Figure 1
  • T1-Tn as illustrated in Figure 1
  • the liquid level in B) as shown in Figure 1 the inflow rate of anhydrous hydrofluoric acid in the metering tank (for example, M as shown in Figure 1), the column still (for example, as shown in Figure 1
  • the liquid level in the schematic K) the temperature of the hot water flowing into the tower still and the temperature of the condenser, and through the camera deployed in the automatic batching system (for example, C as shown in Figure 1)
  • Liquid chromatograms of the product condensed by the condenser for example, N as shown in Figure 1 at the plurality of predetermined time points.
  • the obtained liquid level in the buffer tank at the plurality of predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still, and the temperature of the hot water flowing into the tower still are and the temperature of the condenser, and the liquid chromatograms of the products condensed by the condenser at the plurality of predetermined time points are input into a server deployed with an automatic batching algorithm for the preparation of electronic grade hydrofluoric acid (e.g. , the cloud server S) as shown in Figure 1, wherein the server can use an automatic batching algorithm for the preparation of electronic grade hydrofluoric acid to measure the liquid level in the buffer tank and the metering tank at the plurality of predetermined time points.
  • a server deployed with an automatic batching algorithm for the preparation of electronic grade hydrofluoric acid e.g. , the cloud server S
  • the server can use an automatic batching algorithm for the preparation of electronic grade hydrofluoric acid to measure the liquid level in the buffer tank and the meter
  • the inflow rate of anhydrous hydrofluoric acid, the liquid level in the tower still, the temperature of the hot water flowing into the tower still and the temperature of the condenser, and the condensation through the condenser at the plurality of predetermined time points The liquid chromatogram of the resulting product is processed to produce a classification result indicating whether the inflow rate of anhydrous hydrofluoric acid into the metering tank should be increased or decreased.
  • FIG. 2 illustrates a block diagram of an automated dosing system for electronic grade hydrofluoric acid preparation according to an embodiment of the present application.
  • an automatic batching system 200 for preparing electronic grade hydrofluoric acid according to an embodiment of the present application includes: a control parameter data acquisition unit 210 for acquiring the liquid level in the buffer tank at multiple predetermined time points.
  • the liquid chromatogram of the product condensed by the condenser; the first convolution encoding unit 220 is used to convert the liquid chromatogram of the product condensed by the condenser at the plurality of predetermined time points by using three-dimensional convolution
  • the first convolutional neural network of the kernel is used to obtain the first feature vector;
  • the second convolutional encoding unit 230 is used to combine the liquid level in the buffer tank and the absence of water in the metering tank at the plurality of predetermined time points.
  • the inflow rate of hydrofluoric acid, the liquid level in the tower still, the temperature of the hot water flowing into the tower still and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension.
  • the first Gaussian density map construction unit 240 is used to construct the first Gaussian density map of the first feature vector, wherein the mean value of the first Gaussian density map The vector is the first eigenvector, and the value of each position in the covariance matrix of the first Gaussian density map corresponds to the variance between the eigenvalues of the corresponding two positions in the first eigenvector;
  • the second Gaussian density The graph construction unit 250 is configured to construct a second Gaussian density map of the second feature vector, wherein the mean vector of the second Gaussian density map is the second feature vector, and the covariance of the second Gaussian density map is The value of each position in the variance matrix corresponds to the variance between the eigen
  • the responsiveness of the Gaussian density map is estimated to obtain a responsive Gaussian density map.
  • the mean vector of the responsive Gaussian density map is the point-by-point calculation of the mean vector of the first Gaussian density map and the vector of the second Gaussian density map.
  • the vector obtained by division, the covariance matrix of the responsive density map is the inverse matrix of the covariance matrix of the first Gaussian density map multiplied by the covariance matrix of the second Gaussian density map;
  • the Gaussian discrete unit 270 uses Perform Gaussian discretization processing on the responsive Gaussian density map to obtain a classification feature matrix;
  • the feature matrix correction unit 280 is used to perform balanced correction on the classification feature matrix based on the overall probability distribution of the classification feature matrix to generate The corrected classification feature map, wherein the balanced correction of the overall probability distribution based on the classification feature matrix is based on the natural exponential function value with the feature value of each position in the classification feature map as the power and the classification feature map with The mean value of the feature values of all positions in the value is the difference between the natural exponential function values raised to the power; and, the control result generation unit 290 is used to pass the corrected classification feature map through the classifier to obtain the classification result, so
  • the above classification results are used to indicate that the inflow rate of anhydrous hydrofluoric
  • control parameter data acquisition unit 210 and the first convolution encoding unit 220 are used to acquire the liquid level in the buffer tank and the water level in the metering tank at multiple predetermined time points.
  • the inflow rate of hydrofluoric acid, the liquid level in the tower still, the temperature of the hot water flowing into the tower still and the temperature of the condenser, as well as the products condensed by the condenser at the plurality of predetermined time points The liquid chromatogram of the product condensed by the condenser at the plurality of predetermined time points is passed through a first convolutional neural network using a three-dimensional convolution kernel to obtain a first feature vector.
  • the anhydrous hydrofluoric acid in the metering tank flows through the primary filter at a predetermined rate and then enters the tower still.
  • This batching method does not fully consider the anhydrous hydrofluoric acid in the metering tank.
  • the inflow rate of aqueous hydrofluoric acid is closely related to the subsequent control parameters, which makes it difficult to improve the preparation efficiency and purification accuracy of the existing electronic-grade hydrofluoric acid. That is, if the inflow rate of anhydrous hydrofluoric acid in the metering tank can be dynamically adjusted based on a global dynamic angle, the preparation efficiency and purification accuracy of the intelligent manufacturing line for electronic grade hydrofluoric acid can be improved. optimization. This can be achieved through artificial intelligence control technology based on deep neural networks.
  • dynamic control is carried out through the analysis of multiple control parameters and a real-time result parameter of the smart production line of electronic grade hydrofluoric acid.
  • the multiple control parameters include the liquid level in the buffer tank, the liquid level in the metering tank The inflow rate of anhydrous hydrofluoric acid, the liquid level in the tower still, the temperature of the hot water introduced into the tower still and the temperature of the condenser.
  • ultrapure anhydrous hydrofluoric acid gas is used The liquid chromatogram of the product condensed by the condenser is used as the real-time result parameter.
  • the purpose of regulating the control parameters of the smart production line of electronic grade hydrofluoric acid is to obtain a product that meets the preset requirements. Therefore, the result data can be used to Evaluate the current control effect from the perspective of real-time response to improve the accuracy and precision of parameter control.
  • a convolutional neural network model with a three-dimensional convolution kernel is further used to encode the liquid chromatogram of the product condensed by the condenser at the multiple predetermined time points to capture the time-series dimension of the product.
  • the high-dimensional absolute features and high-dimensional absolute features on that is, the high-dimensional implicit feature representation of the absolute value and relative change value of the purity of hydrofluoric acid.
  • the first convolutional coding unit is further configured to: use the first convolutional neural network of the three-dimensional convolution kernel to perform the described Process the liquid chromatogram of the product condensed by the condenser to obtain the first characteristic vector;
  • H j , W j and R j represent the length, width and height of the three-dimensional convolution kernel respectively
  • m represents the number of the (l-1)th layer feature map
  • b lj is the bias
  • f( ⁇ ) represents the activation function.
  • the second convolution encoding unit 230 is used to calculate the liquid level in the buffer tank and the anhydrous hydrofluoric acid in the metering tank at the plurality of predetermined time points.
  • the inflow rate, the liquid level in the tower still, the temperature of the hot water flowing into the tower still and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and time dimension, and then passed through the second Convolutional neural network to obtain the second feature vector. It should be understood that, considering that there is a correlation between the multiple control parameters in the smart production line of electronic grade hydrofluoric acid, the convolutional neural network model is used to predict the smart production line of electronic grade hydrofluoric acid.
  • the multiple control parameters in are encoded. Specifically, the liquid level in the buffer tank at multiple predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still, the temperature of the hot water flowing into the tower still, and The temperature of the condenser is arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension. Then, by constructing the parameter matrix, the correlation between various control parameters at the same predetermined time point is established, and the relationship between different control parameters at different predetermined time points is established. The correlation between them, as well as the correlation between the same control parameter at different predetermined time points.
  • the convolutional neural network has excellent performance in extracting local features
  • the high-dimensional implicit correlation between the various control parameters at the same predetermined time point in the parameter matrix can be extracted using the convolutional neural network model, High-dimensional implicit correlations between different control parameters at different predetermined time points, and high-dimensional implicit correlations between the same control parameters at different predetermined time points to obtain the second feature vector.
  • the second convolution coding unit is further used to: calculate the liquid level in the buffer tank at the plurality of predetermined time points, the anhydrous hydrogen level in the metering tank
  • the inflow rate of hydrofluoric acid, the liquid level in the tower still, the temperature of the hot water flowing into the tower still and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension; use
  • Each layer of the second convolutional neural network performs convolution processing, pooling processing along the feature matrix and activation processing on the input data in the forward pass of the layer to be processed by the last layer of the second convolutional neural network.
  • the second feature vector is generated, wherein the input of the first layer of the second convolutional neural network is the parameter matrix.
  • the first Gaussian density map construction unit 240 and the second Gaussian density map construction unit 250 are used to construct a first Gaussian density map of the first feature vector, where,
  • the mean vector of the first Gaussian density map is the first eigenvector, and the value of each position in the covariance matrix of the first Gaussian density map corresponds to the eigenvalues of the corresponding two positions in the first eigenvector.
  • a first Gaussian density map of the first feature map and a second density map of the second feature map are further constructed, wherein the first Gaussian density map is
  • the formula can be expressed as Wherein, ⁇ 1 is the first eigenvector, and the value of each position of ⁇ 1 is the variance of the eigenvalues of the corresponding two positions in the first eigenvector.
  • the second Gaussian density map can be expressed by the formula: Wherein, ⁇ 2 is the second eigenvector, and the value of each position of ⁇ 2 is the variance of the eigenvalues of the corresponding two positions in the second eigenvector.
  • the first Gaussian density map construction unit is further configured to: construct the first Gaussian density map of the first feature vector according to the following formula;
  • x 1 represents the synthesized Gaussian vector
  • ⁇ 1 is the first eigenvector
  • the value of each position in ⁇ 1 corresponds to the variance between the eigenvalues of the corresponding two positions in the first eigenvector.
  • the second Gaussian density map construction unit is further configured to: construct the second Gaussian density map of the second feature vector according to the following formula;
  • x 2 represents the synthesized Gaussian vector
  • ⁇ 2 is the second eigenvector
  • the value of each position in ⁇ 2 corresponds to the variance between the eigenvalues of the corresponding two positions in the second eigenvector.
  • the responsiveness estimation unit 260 and the Gaussian discrete unit 270 are used to calculate the responsiveness estimate of the first Gaussian density map relative to the second Gaussian density map to obtain Responsive Gaussian density map
  • the mean vector of the responsive Gaussian density map is a vector obtained by dividing the mean vector of the first Gaussian density map and the vector of the second Gaussian density map point by point
  • the responsiveness The covariance matrix of the density map is the covariance matrix of the first Gaussian density map multiplied by the inverse matrix of the covariance matrix of the second Gaussian density map
  • the responsive Gaussian density map is subjected to Gaussian discretization processing to Get the classification feature matrix.
  • the first Gaussian density map is further calculated relative to the second Responsiveness estimation of Gaussian density maps to obtain responsive Gaussian density maps. Then, Gaussian discretization is performed on the responsive Gaussian density map to generate a classification feature matrix, and the corresponding control result can be obtained after passing the classification feature matrix through a classifier.
  • the classification feature matrix includes high-dimensional implicit correlations between multiple control parameters, high-dimensional correlation information of the multiple control parameters in the time series dimension, and high-dimensional changes of the product in the time series dimension. Features, and high-dimensional correlation information of the product relative to the multiple control parameters. Therefore, classification by the classification feature matrix can improve the precision and accuracy of control.
  • the responsiveness estimation unit is further configured to: calculate the responsiveness estimate of the first Gaussian density map relative to the second Gaussian density map using the following formula to obtain the Responsive Gaussian density plot;
  • ⁇ represents the vector dot product
  • ⁇ -1 represents the reciprocal of the value at each position of the vector
  • the feature matrix correction unit 280 is used to perform balanced correction on the classification feature matrix based on the overall probability distribution of the classification feature matrix to generate a corrected classification feature map, where,
  • the balanced correction of the overall probability distribution based on the classification feature matrix is based on the natural exponential function value raised to the power of the feature value of each position in the classification feature map and the mean value of the feature values of all positions in the classification feature map. The difference between the values of a natural exponential function raised to a power is performed. It should be understood that since the convolution kernel of the convolution network acts as a filter to extract small-scale pixel-level correlation features from the source data, the feature vector will be perturbed as a probability distribution under a Gaussian density map.
  • the classification matrix obtained after Gaussian discretization is further modified.
  • the feature matrix correction unit is further configured to perform a balanced correction on the classification feature matrix based on the overall probability distribution of the classification feature matrix using the following formula to generate the corrected Classification feature map;
  • m i,j are the eigenvalues corresponding to the i-th row and j-th column of the classification matrix, and is the global mean of the eigenvalues at each position of the classification matrix.
  • the correction method is to use the eigenvalue of a single position of the classification matrix obtained after Gaussian discretization as a single variable to calculate the negative logarithm of the Cauchy loss form of the difference under the probability of belonging to the class,
  • the particularity of a single eigenvalue distribution is generalized, thereby masking the perturbation of individual probability distributions within the overall distribution, so as to improve the balance of the overall probability distribution of the classification matrix. In this way, the precision and accuracy of parameter control are further improved.
  • the control result generation unit 290 is used to pass the corrected classification feature map through a classifier to obtain a classification result, and the classification result is used to represent anhydrous hydrogen fluoride in the metering tank.
  • the acid inflow rate should be increased or should be decreased.
  • the corrected classification feature map is further passed through a classifier to obtain a classification result indicating that the inflow rate of anhydrous hydrofluoric acid in the metering tank should be increased or decreased.
  • the classifier processes the corrected classification feature map with the following formula to generate a classification result, where the formula is: softmax ⁇ (W n ,B n ):..
  • the automatic batching system 200 for the preparation of electronic grade hydrofluoric acid based on the embodiment of the present application has been clarified, which uses the intelligent control method of artificial intelligence technology to dynamically control the system from the control end based on the global dynamic perspective. Adjust the inflow rate of anhydrous hydrofluoric acid in the metering tank to optimize the preparation efficiency and purification accuracy of the intelligent manufacturing line for electronic grade hydrofluoric acid.
  • the automatic batching system 200 for the preparation of electronic grade hydrofluoric acid according to the embodiment of the present application can be implemented in various terminal devices, such as a server of the automatic batching algorithm for the preparation of electronic grade hydrofluoric acid, etc.
  • the automatic batching system 200 for preparing electronic grade hydrofluoric acid according to an embodiment of the present application can be integrated into a terminal device as a software module and/or a hardware module.
  • the automatic batching system 200 for the preparation of electronic grade hydrofluoric acid can be a software module in the operating system of the terminal device, or can be an application program developed for the terminal device; of course, the The automatic batching system 200 for preparing electronic grade hydrofluoric acid can also be one of the many hardware modules of the terminal equipment.
  • the automatic batching system 200 for the preparation of electronic grade hydrofluoric acid and the terminal device may also be separate devices, and the automatic batching system 200 for the preparation of electronic grade hydrofluoric acid may be Connect to the terminal device through a wired and/or wireless network, and transmit interactive information according to the agreed data format.
  • Figure 3 illustrates a flow chart of a dosing method of an automated dosing system for electronic grade hydrofluoric acid preparation.
  • the batching method of the automatic batching system for the preparation of electronic grade hydrofluoric acid according to the embodiment of the present application includes steps: S110, obtaining the liquid level in the buffer tank and the liquid level in the metering tank at multiple predetermined time points.
  • the inflow rate of anhydrous hydrofluoric acid, the liquid level in the tower still, the temperature of the hot water flowing into the tower still and the temperature of the condenser, and, after condensation by the condenser at the plurality of predetermined time points The liquid chromatogram of the product; S120, pass the liquid chromatogram of the product condensed by the condenser at the multiple predetermined time points through the first convolutional neural network using a three-dimensional convolution kernel to obtain the first Feature vector; S130, combine the liquid level in the buffer tank, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still, and the inlet at the multiple predetermined time points.
  • the temperature of the hot water in the tower kettle and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension, and then passed through the second convolutional neural network to obtain the second feature vector;
  • S140 construct the first A first Gaussian density map of a feature vector, wherein the mean vector of the first Gaussian density map is the first feature vector, and the values at each position in the covariance matrix of the first Gaussian density map correspond to the The variance between the eigenvalues of the corresponding two positions in the first eigenvector;
  • S150 construct a second Gaussian density map of the second eigenvector, wherein the mean vector of the second Gaussian density map is the second Eigenvector, the value of each position in the covariance matrix of the second Gaussian density map corresponds to the variance between the eigenvalues of the corresponding two positions in the second eigenvector;
  • S160 calculate the first Gaussian density map Relative to the responsiveness estimation of the
  • a vector obtained by point-by-point division of a vector, the covariance matrix of the responsive density map is the inverse matrix of the covariance matrix of the first Gaussian density map multiplied by the covariance matrix of the second Gaussian density map;
  • S170 perform Gaussian discretization processing on the responsive Gaussian density map to obtain a classification feature matrix;
  • S180 perform a balanced correction on the classification feature matrix based on the overall probability distribution of the classification feature matrix to generate a corrected classification feature map , wherein the balanced correction of the overall probability distribution based on the classification feature matrix is based on the natural exponential function value with the feature value of each position in the classification feature map as the power and the features of all positions in the classification feature map
  • the mean value of the value is the difference between the values of the natural exponential function whose power is the power; and, S190, pass the corrected classification feature map through the classifier to obtain the classification result, the classification result is used to indicate that there is no water in the metering tank
  • Figure 4 illustrates a schematic architectural diagram of a batching method of an automatic batching system for electronic grade hydrofluoric acid preparation according to an embodiment of the present application.
  • the liquid chromatogram of the product for example, P1 as illustrated in Figure 4
  • the liquid chromatogram of the product is passed through a first convolutional neural network (for example, CNN1 as illustrated in Figure 4) using a three-dimensional convolution kernel to obtain a first feature vector ( For example, VF1) as shown in Figure 4; then, the liquid level in the buffer tank at the multiple predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the column still
  • the liquid level in the column, the temperature of the hot water flowing into the tower still and the temperature of the condenser for example, P2 as shown in Figure 4) are arranged into a two-dimensional parameter matrix according to
  • a second convolutional neural network for example, CNN2 as shown in Figure 4
  • a second feature vector for example, VF2 as shown in Figure 4
  • construct a first Gaussian density map of the first feature vector for example, GD1 as illustrated in Figure 4
  • construct a second Gaussian density map of the second feature vector for example, as shown in Figure 4 GD2 as illustrated in Figure 4
  • Gaussian discretization is performed on the responsive Gaussian density map to obtain a classification feature matrix (for example, MF as shown in Figure 4); then, the overall classification feature matrix based on the classification feature matrix is performed on the classification feature matrix.
  • the liquid level in the buffer tank at multiple predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still, and the flow rate into the tower are obtained.
  • the liquid chromatogram of the product condensed by the condenser is passed through a first convolutional neural network using a three-dimensional convolution kernel to obtain a first feature vector.
  • the anhydrous hydrofluoric acid in the metering tank flows through the primary filter at a predetermined rate and then enters the tower still.
  • This batching method does not fully consider the anhydrous hydrofluoric acid in the metering tank.
  • the inflow rate of hydrofluoric acid is closely related to the subsequent control parameters, which makes it difficult to improve the preparation efficiency and purification accuracy of the existing electronic-grade hydrofluoric acid. That is, if the inflow rate of anhydrous hydrofluoric acid in the metering tank can be dynamically adjusted based on a global dynamic angle, the preparation efficiency and purification accuracy of the intelligent manufacturing line for electronic grade hydrofluoric acid can be improved. optimization. This can be achieved through artificial intelligence control technology based on deep neural networks.
  • dynamic control is carried out through the analysis of multiple control parameters and a real-time result parameter of the smart production line of electronic grade hydrofluoric acid.
  • the multiple control parameters include the liquid level in the buffer tank, the liquid level in the metering tank The inflow rate of anhydrous hydrofluoric acid, the liquid level in the tower still, the temperature of the hot water introduced into the tower still and the temperature of the condenser.
  • ultrapure anhydrous hydrofluoric acid gas is used The liquid chromatogram of the product condensed by the condenser is used as the real-time result parameter.
  • the purpose of regulating the control parameters of the smart production line of electronic grade hydrofluoric acid is to obtain a product that meets the preset requirements. Therefore, the result data can be used to Evaluate the current control effect from the perspective of real-time response to improve the accuracy and precision of parameter control.
  • a convolutional neural network model with a three-dimensional convolution kernel is further used to encode the liquid chromatogram of the product condensed by the condenser at the multiple predetermined time points to capture the time-series dimension of the product.
  • the high-dimensional absolute features and high-dimensional absolute features on that is, the high-dimensional implicit feature representation of the absolute value and relative change value of the purity of hydrofluoric acid.
  • step S130 the liquid level in the buffer tank at the plurality of predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still,
  • the temperature of the hot water flowing into the tower kettle and the temperature of the condenser are arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension, and then passed through the second convolutional neural network to obtain the second feature vector.
  • the convolutional neural network model is used to predict the smart production line of electronic grade hydrofluoric acid.
  • the multiple control parameters in are encoded.
  • the liquid level in the buffer tank at multiple predetermined time points, the inflow rate of anhydrous hydrofluoric acid in the metering tank, the liquid level in the tower still, the temperature of the hot water flowing into the tower still, and The temperature of the condenser is arranged into a two-dimensional parameter matrix according to the sample dimension and the time dimension. Then, by constructing the parameter matrix, the correlation between various control parameters at the same predetermined time point is established, and the relationship between different control parameters at different predetermined time points is established. The correlation between them, as well as the correlation between the same control parameter at different predetermined time points.
  • the convolutional neural network has excellent performance in extracting local features
  • the high-dimensional implicit correlation between the various control parameters at the same predetermined time point in the parameter matrix can be extracted using the convolutional neural network model, High-dimensional implicit correlations between different control parameters at different predetermined time points, and high-dimensional implicit correlations between the same control parameters at different predetermined time points to obtain the second feature vector.
  • a first Gaussian density map of the first feature vector is constructed, wherein the mean vector of the first Gaussian density map is the first feature vector, and the first The value of each position in the covariance matrix of the Gaussian density map corresponds to the variance between the eigenvalues of the corresponding two positions in the first eigenvector, and a second Gaussian density map of the second eigenvector is constructed, where, The mean vector of the second Gaussian density map is the second eigenvector, and the value of each position in the covariance matrix of the second Gaussian density map corresponds to the eigenvalues of the corresponding two positions in the second eigenvector.
  • the variance should be understandable, considering that the data modality, data scale and data volume are different between the multiple control parameters and the real-time result parameters, that is, the multiple control parameters are discrete data and the real-time result parameters are
  • the result parameter is image data. Therefore, in the embodiment of the present application, data enhancement is performed on the first feature vector and the second feature vector based on the Gaussian density map so that the data of the two are in the high-dimensional feature space. The positions and shapes of manifolds can be closer together.
  • a first Gaussian density map of the first feature map and a second density map of the second feature map are further constructed, wherein the first Gaussian density map is
  • the formula can be expressed as Wherein, ⁇ 1 is the first eigenvector, and the value of each position of ⁇ 1 is the variance of the eigenvalues of the corresponding two positions in the first eigenvector.
  • the second Gaussian density map can be expressed by the formula: Wherein, ⁇ 2 is the second eigenvector, and the value of each position of ⁇ 2 is the variance of the eigenvalues of the corresponding two positions in the second eigenvector.
  • a responsiveness estimate of the first Gaussian density map relative to the second Gaussian density map is calculated to obtain a responsive Gaussian density map, the mean of the responsive Gaussian density map
  • the vector is a vector obtained by point-by-point division of the mean vector of the first Gaussian density map and the vector of the second Gaussian density map
  • the covariance matrix of the responsive density map is the vector of the first Gaussian density map.
  • the covariance matrix is multiplied by the inverse matrix of the covariance matrix of the second Gaussian density map, and the responsive Gaussian density map is subjected to Gaussian discretization processing to obtain a classification feature matrix.
  • the first Gaussian density map is further calculated relative to the second Responsiveness estimation of Gaussian density maps to obtain responsive Gaussian density maps. Then, Gaussian discretization is performed on the responsive Gaussian density map to generate a classification feature matrix, and the corresponding control result can be obtained after passing the classification feature matrix through a classifier.
  • the classification feature matrix includes high-dimensional implicit correlations between multiple control parameters, high-dimensional correlation information of the multiple control parameters in the time series dimension, and high-dimensional changes of the product in the time series dimension. Features, and high-dimensional correlation information of the product relative to the multiple control parameters. Therefore, classification by the classification feature matrix can improve the precision and accuracy of control.
  • step S180 the classification feature matrix is subjected to a balanced correction based on the overall probability distribution of the classification feature matrix to generate a corrected classification feature map, wherein the overall probability based on the classification feature matrix
  • the balanced correction of the distribution is based on the difference between the natural exponential function value raised to the power of the feature value of each position in the classification feature map and the natural exponential function value raised to the power of the mean value of the feature values of all positions in the classification feature map. value to proceed.
  • the convolution kernel of the convolution network acts as a filter to extract small-scale pixel-level correlation features from the source data, the feature vector will be perturbed as a probability distribution under a Gaussian density map.
  • the classification matrix obtained after Gaussian discretization is further modified.
  • step S190 the corrected classification feature map is passed through a classifier to obtain a classification result, which is used to indicate that the inflow rate of anhydrous hydrofluoric acid in the metering tank should be increased or decreased.
  • the corrected classification feature map is further passed through a classifier to obtain a classification result indicating that the inflow rate of anhydrous hydrofluoric acid in the metering tank should be increased or decreased.
  • the classifier processes the corrected classification feature map with the following formula to generate a classification result, where the formula is: softmax ⁇ (W n ,B n ):..
  • the batching method of the automatic batching system for the preparation of electronic grade hydrofluoric acid has been clarified, which uses the intelligent control method of artificial intelligence technology to control the system from the control end based on the global dynamic perspective. Dynamically adjust the inflow rate of anhydrous hydrofluoric acid in the metering tank, thereby optimizing the preparation efficiency and purification accuracy of the intelligent manufacturing line for electronic grade hydrofluoric acid.
  • each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations shall be considered equivalent versions of this application.

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Abstract

本申请涉及智能制造的领域，其具体地公开了一种用于电子级氢氟酸制备的自动配料系统及其配料方法，其采用人工智能技术的智能控制方法来从控制端，以基于全局的动态角度来动态地调整计量槽中的无水氢氟酸的流入速率，进而对所述电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。

Description

用于电子级氢氟酸制备的自动配料系统及其配料方法 技术领域
本发明涉及智能制造的领域，且更为具体地，涉及一种用于电子级氢氟酸制备的自动配料系统及其配料方法。
背景技术
电子级氢氟酸为强酸性清洗剂、腐蚀剂，主要用于超大规模集成电路生产。随着半导体芯片制造业和LCD制造业向我国转移，国内电子级氢氟酸的用量将随之猛增，电子级氢氟酸市场无疑将具有广阔的发展前景。
目前，电子级氢氟酸的制备一般可采用精馏、亚沸蒸馏、气体吸收等工艺来制备，但是这些制备工艺复杂、生产成本很高。现有的对于电子级氢氟酸的改进主要是对用于制备电子级氢氟酸的设备进行改进，例如，采用多级精馏塔来替代一级精馏塔，但这种物理结构层面的改进成本高且周期长。因此，期望一种优化的用于电子级氢氟酸制备的自动配料系统，以对电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。
近年来，深度学习以及神经网络已经广泛应用于计算机视觉、自然语言处理、语音信号处理等领域。此外，深度学习以及神经网络在图像分类、物体检测、语义分割、文本翻译等领域，也展现出了接近甚至超越人类的水平。
深度学习以及神经网络的发展为电子级氢氟酸的智能制造产线的搭建提供了技术支持，也就是，深度学习以及神经网络的发展为电子级氢氟酸的自动配料与生产提供了新的解决思路和方案。
发明内容
为了解决上述技术问题，提出了本申请。本申请的实施例提供了一种用于电子级氢氟酸制备的自动配料系统及其配料方法，其采用人工智能技术的智能控制方法来从控制端，以基于全局的动态角度来动态地调整计量槽中的无水氢氟酸的流入速率，进而对所述电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。
根据本申请的一个方面，提供了一种用于电子级氢氟酸制备的自动配料 系统，其包括：
控制参数数据获取单元，用于获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图；
第一卷积编码单元，用于将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量；
第二卷积编码单元，用于将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量；
第一高斯密度图构造单元，用于构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差；
第二高斯密度图构造单元，用于构造所述第二特征向量的第二高斯密度图，其中，所述第二高斯密度图的均值向量为所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差；
响应性估计单元，用于计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵；
高斯离散单元，用于对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵；
特征矩阵校正单元，用于对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数值与以所述分类特征图中所有位置的特 征值的均值为幂的自然指数函数值之间的差值来进行；以及
控制结果生成单元，用于将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。
在上述用于电子级氢氟酸制备的自动配料系统中，所述第一卷积编码单元，进一步用于：使用所述三维卷积核的第一卷积神经网络对所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图进行处理，以获得所述第一特征向量；其中，所述公式为：
Figure PCTCN2022116222-appb-000001
其中，H j、W j和R j分别表示三维卷积核的长度、宽度和高度，m表示第(l-1)层特征图的个数，
Figure PCTCN2022116222-appb-000002
是与(l-1)层的第m个特征图相连的卷积核，b lj为偏置，f(·)表示激活函数。
在上述用于电子级氢氟酸制备的自动配料系统中，所述第二卷积编码单元，进一步用于：将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵；使用所述第二卷积神经网络的各层在层的正向传递中对输入数据进行卷积处理、沿特征矩阵的池化处理和激活处理以由所述第二卷积神经网络的最后一层生成所述第二特征向量，其中，所述第二卷积神经网络的第一层的输入为所述参数矩阵。
在上述用于电子级氢氟酸制备的自动配料系统中，所述第一高斯密度图构造单元，进一步用于：以如下公式构造所述第一特征向量的所述第一高斯密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000003
其中x 1表示合成后的高斯向量，μ 1为所述第一特征向量，且∑ 1中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差。
在上述用于电子级氢氟酸制备的自动配料系统中，所述第二高斯密度图构造单元，进一步用于：以如下公式构造所述第二特征向量的所述第二高斯 密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000004
其中x 2表示合成后的高斯向量，μ 2为所述第二特征向量，且∑ 2中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差。
在上述用于电子级氢氟酸制备的自动配料系统中，所述响应性估计单元，进一步用于：以如下公式计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得所述响应性高斯密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000005
其中⊙表示向量点乘，⊙-1表示对向量的每个位置的值取倒数，且
Figure PCTCN2022116222-appb-000006
表示矩阵乘法。
在上述用于电子级氢氟酸制备的自动配料系统中，所述特征矩阵校正单元，进一步用于：以如下公式对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成所述校正后分类特征图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000007
其中m i,j为所述分类矩阵的第i行和第j列对应位置的特征值，且
Figure PCTCN2022116222-appb-000008
是所述分类矩阵的各个位置的特征值的全局均值。
在上述用于电子级氢氟酸制备的自动配料系统中，所述控制结果生成单元，进一步用于：所述分类器以如下公式对所述校正后分类特征图进行处理以生成分类结果，其中，所述公式为：softmax{(W n,B n):...:(W 1,B 1)|Project(F)}，其中Project(F)表示将所述校正后分类特征图投影为向量，W 1至W n为各层全连接层的权重矩阵，B 1至B n表示各层全连接层的偏置矩阵。
根据本申请的另一方面，一种用于电子级氢氟酸制备的自动配料系统的配料方法，其包括：
获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图；
将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通 过使用三维卷积核的第一卷积神经网络以获得第一特征向量；
将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量；
构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差；
构造所述第二特征向量的第二高斯密度图，其中，所述第二高斯密度图的均值向量为所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差；
计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵；
对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵；
对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数值与以所述分类特征图中所有位置的特征值的均值为幂的自然指数函数值之间的差值来进行；以及
将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。
在上述用于电子级氢氟酸制备的自动配料系统的配料方法中，将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量，包括：使用所述三维卷积核的第一卷积神经网络对所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图进行处理，以获得所述第一特征向量；
其中，所述公式为：
Figure PCTCN2022116222-appb-000009
其中，H j、W j和R j分别表示三维卷积核的长度、宽度和高度，m表示第(l-1)层特征图的个数，
Figure PCTCN2022116222-appb-000010
是与(l-1)层的第m个特征图相连的卷积核，b lj为偏置，f(·)表示激活函数。
在上述用于电子级氢氟酸制备的自动配料系统的配料方法中，将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量，包括：将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵；使用所述第二卷积神经网络的各层在层的正向传递中对输入数据进行卷积处理、沿特征矩阵的池化处理和激活处理以由所述第二卷积神经网络的最后一层生成所述第二特征向量，其中，所述第二卷积神经网络的第一层的输入为所述参数矩阵。
在上述用于电子级氢氟酸制备的自动配料系统的配料方法中，构造所述第一特征向量的第一高斯密度图，包括：以如下公式构造所述第一特征向量的所述第一高斯密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000011
其中x 1表示合成后的高斯向量，μ 1为所述第一特征向量，且∑ 1中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差。
在上述用于电子级氢氟酸制备的自动配料系统的配料方法中，构造所述第二特征向量的第二高斯密度图，包括：以如下公式构造所述第二特征向量的所述第二高斯密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000012
其中x 2表示合成后的高斯向量，μ 2为所述第二特征向量，且∑ 2中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差。
在上述用于电子级氢氟酸制备的自动配料系统的配料方法中，计算所述 第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，包括：以如下公式计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得所述响应性高斯密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000013
其中⊙表示向量点乘，⊙-1表示对向量的每个位置的值取倒数，且
Figure PCTCN2022116222-appb-000014
表示矩阵乘法。
在上述用于电子级氢氟酸制备的自动配料系统的配料方法中，对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，包括：以如下公式对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成所述校正后分类特征图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000015
其中m i,j为所述分类矩阵的第i行和第j列对应位置的特征值，且
Figure PCTCN2022116222-appb-000016
是所述分类矩阵的各个位置的特征值的全局均值。
在上述用于电子级氢氟酸制备的自动配料系统的配料方法中，将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小，包括：所述分类器以如下公式对所述校正后分类特征图进行处理以生成分类结果，其中，所述公式为：softmax{(W n,B n):...:(W 1,B 1)|Project(F)}，其中Project(F)表示将所述校正后分类特征图投影为向量，W 1至W n为各层全连接层的权重矩阵，B 1至B n表示各层全连接层的偏置矩阵。
与现有技术相比，本申请提供的用于电子级氢氟酸制备的自动配料系统及其配料方法，其采用人工智能技术的智能控制方法来从控制端，以基于全局的动态角度来动态地调整计量槽中的无水氢氟酸的流入速率，进而对所述电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。
附图说明
通过结合附图对本申请实施例进行更详细的描述，本申请的上述以及其他目的、特征和优势将变得更加明显。附图用来提供对本申请实施例的进一步理解，并且构成说明书的一部分，与本申请实施例一起用于解释本申请， 并不构成对本申请的限制。在附图中，相同的参考标号通常代表相同部件或步骤。
图1为根据本申请实施例的用于电子级氢氟酸制备的自动配料系统的应用场景图。
图2为根据本申请实施例的用于电子级氢氟酸制备的自动配料系统的框图。
图3为根据本申请实施例的用于电子级氢氟酸制备的自动配料系统的配料方法的流程图。
图4为根据本申请实施例的用于电子级氢氟酸制备的自动配料系统的配料方法的架构示意图。
具体实施方式
下面，将参考附图详细地描述根据本申请的示例实施例。显然，所描述的实施例仅仅是本申请的一部分实施例，而不是本申请的全部实施例，应理解，本申请不受这里描述的示例实施例的限制。
场景概述
如前所述，电子级氢氟酸为强酸性清洗剂、腐蚀剂，主要用于超大规模集成电路生产。随着半导体芯片制造业和LCD制造业向我国转移，国内电子级氢氟酸的用量将随之猛增，电子级氢氟酸市场无疑将具有广阔的发展前景。
目前，电子级氢氟酸的制备一般可采用精馏、亚沸蒸馏、气体吸收等工艺来制备，但是这些制备工艺复杂、生产成本很高。现有的对于电子级氢氟酸的改进主要是对用于制备电子级氢氟酸的设备进行改进，例如，采用多级精馏塔来替代一级精馏塔，但这种物理结构层面的改进成本高且周期长。
近年来，深度学习以及神经网络已经广泛应用于计算机视觉、自然语言处理、语音信号处理等领域。此外，深度学习以及神经网络在图像分类、物体检测、语义分割、文本翻译等领域，也展现出了接近甚至超越人类的水平。
深度学习以及神经网络的发展为电子级氢氟酸的智能制造产线的搭建提供了技术支持，也就是，深度学习以及神经网络的发展为电子级氢氟酸的自动配料与生产提供了新的解决思路和方案。
例如，在现行的一种电子级氢氟酸的制备工艺中，其包括步骤：
S1、将无水氢氟酸输送到缓冲槽中待用；
S2、从所述缓冲槽中输送一定量的无水氢氟酸到投料槽中，再通过重力作用使所述无水氢氟酸流经缓冲塔进入到计量槽中准确计量；
S3、将所述计量槽中的无水氢氟酸以预定速率流经初级过滤器后进入塔釜；
S4、在塔釜中通入50～60℃的热水，采用低温蒸发，得到高纯度无水氢氟酸气体；
S5、将所述高纯度无水氢氟酸气体通入纯化塔中，并通入氧化气体与所述高纯度无水氢氟酸气体反应得到超纯无水氢氟酸气体，从所述纯化塔顶部排出，其中所述氧化气体为氟气和氮气的混合物；
S6、将所述超纯无水氢氟酸气体经冷凝器冷凝后，进入调配槽中调整浓度。
经研究本申请发明人发现：在现有的配料方案中，计量槽中的无水氢氟酸以预定速率流经初级过滤器后进入塔釜，这种配料方式由于没有充分考虑计量槽中的无水氢氟酸的流入速率与后续的控制参数息息相关而导致现有的用于电子级氢氟酸制备的制备效率和提纯精度难以提高。也就是，如果能够基于全局的动态角度来动态地调整计量槽中的无水氢氟酸的流入速率，可对电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。这可以通过基于深度神经网络的人工智能控制技术来实现。
具体地，所述电子级氢氟酸的智慧产线的多个控制参数和一个实时结果参数，所述多个控制参数包括缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、在塔釜中通入的热水的温度和冷凝器的温度，另外在本申请实施例中，以超纯无水氢氟酸气体经冷凝器冷凝后的产物的液相色谱图作为所述实时结果参数。
考虑到所述电子级氢氟酸的智慧产线中的所述多个控制参数之间存在关联，因此，使用卷积神经网络模型对所述电子级氢氟酸的智慧产线中的所述多个控制参数进行编码。具体地，将获取的多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵。通过构造所述参数矩阵建立起同一预定时间点的各项控制参数之间的关联，不同预定时间点的不同控制参数之间的关联，以及，同一控制参数在不同预 定时间点之间的关联。卷积神经网络在提取局部特征方面具有优异表现，因此，使用所述卷积神经网络模型能够提取出所述参数矩阵中同一预定时间点的各项控制参数之间的高维隐含关联，不同预定时间点的不同控制参数之间的高维隐含关联，以及，同一控制参数在不同预定时间点之间的高维隐含关联，以得到所述第二特征向量。
应可以理解，在本申请实施例中，对所述电子级氢氟酸的智慧产线的控制参数进行调控的目的是为了获得满足预设要求的产物，因此，可利用结果数据从实时响应的角度来对当前的控制效果进行评估以此来提高参数控制的准度和精度。
具体地，以使用三维卷积核的卷积神经网络模型对所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图进行编码以捕捉所述产物在时序维度上的高维绝对特征和高维绝对特征，也就是，氢氟酸的纯度的绝对值和相对变化值的高维隐含特征表示。
考虑到所述多个控制参数和实时结果参数之间的数据模态、数据尺度和数据量都不同，即，所述多个控制参数为离散数据而所述实时结果参数为图像数据，因此，在本申请实施例中，使用基于高斯密度图来对所述第一特征向量和所述第二特征向量进行数据增强以使得两者在高维特征空间中的数据流形的位置和形状能够更为邻近。
具体地，构造所述第一特征图的第一高斯密度图和构造所述第二特征图的第二密度图，其中，所述第一高斯密度图用公式可表示为
Figure PCTCN2022116222-appb-000017
其中，μ 1为所述第一特征向量，且∑ 1的各个位置的值为所述第一特征向量中相应两个位置的特征值的方差，所述第二高斯密度图用公式可表示为
Figure PCTCN2022116222-appb-000018
其中，μ 2为所述第二特征向量，且∑ 2的各个位置的值为所述第二特征向量中相应两个位置的特征值的方差。
考虑到所述多个控制参数和所述实时结果参数之间存在响应性关系，因此，进一步计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图。接着，对所述响应性高斯密度图进行高斯离散化以生成分类特征矩阵，并将所述分类特征矩阵通过分类器后就可以获得相应的控制结果。应可以理解，所述分类特征矩阵包含多个控制参数之间的高维隐含关联，所述多个控制参数在时序维度上的高维关联信息、所述产物在时序维度上的高维变化特征，以及，所述产物相对于所述多个控制参数的高 维关联信息，因此，以所述分类特征矩阵进行分类可提高控制的精度和准度。
进一步地，由于卷积网络的卷积核作为过滤器，对于源数据进行了小尺度的像素级别的关联特征提取，因此特征向量作为高斯密度图下的概率分布会存在扰动。而在构造特征向量的高斯密度图时，由于协方差矩阵是每两个位置的特征值之间的方差，这又会放大个别概率分布的扰动性，并且，这种扰动性可能在计算响应性密度图和高斯离散化的过程中进一步放大，从而影响分类矩阵的整体概率分布的均衡性。
基于此，对高斯离散化后得到的分类矩阵进行修正，表示为：
Figure PCTCN2022116222-appb-000019
m i,j为分类矩阵的第i行和第j列对应位置的特征值，且
Figure PCTCN2022116222-appb-000020
是分类矩阵的各个位置的特征值的全局均值。
该修正方式是通过以高斯离散化后得到的分类矩阵的单个位置的特征值作为单变量，计算其归属于类别概率下的差分的柯西损失形式负对数，从而将单个特征值分布的特殊性进行泛化，从而对整体分布内的个别概率分布的扰动性进行掩蔽，以改进分类矩阵的整体概率分布的均衡性。这样，进一步地提高参数控制的精度和准度。
基于此，本申请提出了一种用于电子级氢氟酸制备的自动配料系统，其包括：控制参数数据获取单元，用于获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图；第一卷积编码单元，用于将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量；第二卷积编码单元，用于将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量；第一高斯密度图构造单元，用于构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差；第二高斯密度图构造单元，用于构造所述第二特征向量的第二高斯密度图，其中，所述第二高斯密度图的均值向量为 所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差；响应性估计单元，用于计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵；高斯离散单元，用于对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵；特征矩阵校正单元，用于对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数值与以所述分类特征图中所有位置的特征值的均值为幂的自然指数函数值之间的差值来进行；以及，控制结果生成单元，用于将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。
图1图示了根据本申请实施例的用于电子级氢氟酸制备的自动配料系统的应用场景图。如图1所示，在该应用场景中，首先，通过部署于所述自动配料系统中的各个传感器(例如，如图1中所示意的T1-Tn)获取多个预定时间点的缓冲槽(例如，如图1中所示意的B)中的液位、计量槽(例如，如图1中所示意的M)中无水氢氟酸的流入速率、塔釜(例如，如图1中所示意的K)中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及通过部署于所述自动配料系统中的摄像头(例如，如图1中所示意的C)所述多个预定时间点的经所述冷凝器(例如，如图1中所示意的N)冷凝后的产物的液相色谱图。然后，将获取的所述多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图输入至部署有用于电子级氢氟酸制备的自动配料算法的服务器中(例如，如图1中所示意的云服务器S)，其中，所述服务器能够以用于电子级氢氟酸制备的自动配料算法对所述多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷 凝后的产物的液相色谱图进行处理，以生成用于表示计量槽中无水氢氟酸的流入速率应增大或应减小的分类结果。
在介绍了本申请的基本原理之后，下面将参考附图来具体介绍本申请的各种非限制性实施例。
示例性系统
图2图示了根据本申请实施例的用于电子级氢氟酸制备的自动配料系统的框图。如图2所示，根据本申请实施例的用于电子级氢氟酸制备的自动配料系统200，包括：控制参数数据获取单元210，用于获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图；第一卷积编码单元220，用于将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量；第二卷积编码单元230，用于将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量；第一高斯密度图构造单元240，用于构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差；第二高斯密度图构造单元250，用于构造所述第二特征向量的第二高斯密度图，其中，所述第二高斯密度图的均值向量为所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差；响应性估计单元260，用于计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵；高斯离散单元270，用于对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵；特征矩阵校正单元280，用于对所述分类特 征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数值与以所述分类特征图中所有位置的特征值的均值为幂的自然指数函数值之间的差值来进行；以及，控制结果生成单元290，用于将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。
具体地，在本申请实施例中，所述控制参数数据获取单元210和所述第一卷积编码单元220，用于获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图，并将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量。如前所述，在现有的配料方案中，计量槽中的无水氢氟酸以预定速率流经初级过滤器后进入塔釜，这种配料方式由于没有充分考虑所述计量槽中的无水氢氟酸的流入速率与后续的控制参数息息相关而导致现有的用于电子级氢氟酸制备的制备效率和提纯精度难以提高。也就是，如果能够基于全局的动态角度来动态地调整所述计量槽中的无水氢氟酸的流入速率，可对所述电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。这可以通过基于深度神经网络的人工智能控制技术来实现。
具体地，通过对于所述电子级氢氟酸的智慧产线的多个控制参数和一个实时结果参数的分析来进行动态控制，所述多个控制参数包括缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、在塔釜中通入的热水的温度和冷凝器的温度，另外在本申请实施例中，以超纯无水氢氟酸气体经冷凝器冷凝后的产物的液相色谱图作为所述实时结果参数。
然后，应可以理解，在本申请的技术方案中，对所述电子级氢氟酸的智慧产线的控制参数进行调控的目的是为了获得满足预设要求的产物，因此，可利用结果数据从实时响应的角度来对当前的控制效果进行评估以此来提高参数控制的准度和精度。具体地，接着，进一步使用三维卷积核的卷积神经网络模型对所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色 谱图进行编码以捕捉所述产物在时序维度上的高维绝对特征和高维绝对特征，也就是，氢氟酸的纯度的绝对值和相对变化值的高维隐含特征表示。
更具体地，在本申请实施例中，所述第一卷积编码单元，进一步用于：使用所述三维卷积核的第一卷积神经网络对所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图进行处理，以获得所述第一特征向量；
其中，所述公式为：
Figure PCTCN2022116222-appb-000021
其中，H j、W j和R j分别表示三维卷积核的长度、宽度和高度，m表示第(l-1)层特征图的个数，
Figure PCTCN2022116222-appb-000022
是与(l-1)层的第m个特征图相连的卷积核，b lj为偏置，f(·)表示激活函数。
具体地，在本申请实施例中，所述第二卷积编码单元230，用于将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量。应可以理解，考虑到所述电子级氢氟酸的智慧产线中的所述多个控制参数之间存在关联，因此，使用卷积神经网络模型对所述电子级氢氟酸的智慧产线中的所述多个控制参数进行编码。具体地，将获取的多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵，进而通过构造所述参数矩阵建立起同一预定时间点的各项控制参数之间的关联，不同预定时间点的不同控制参数之间的关联，以及，同一控制参数在不同预定时间点之间的关联。然后，由于卷积神经网络在提取局部特征方面具有优异表现，因此，使用所述卷积神经网络模型能够提取出所述参数矩阵中同一预定时间点的各项控制参数之间的高维隐含关联，不同预定时间点的不同控制参数之间的高维隐含关联，以及，同一控制参数在不同预定时间点之间的高维隐含关联，以得到所述第二特征向量。
更具体地，在本申请实施例中，所述第二卷积编码单元，进一步用于：将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的 流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵；使用所述第二卷积神经网络的各层在层的正向传递中对输入数据进行卷积处理、沿特征矩阵的池化处理和激活处理以由所述第二卷积神经网络的最后一层生成所述第二特征向量，其中，所述第二卷积神经网络的第一层的输入为所述参数矩阵。
具体地，在本申请实施例中，所述第一高斯密度图构造单元240和所述第二高斯密度图构造单元250，用于构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差，并构造所述第二特征向量的第二高斯密度图，其中，所述第二高斯密度图的均值向量为所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差。应可以理解，考虑到所述多个控制参数和实时结果参数之间的数据模态、数据尺度和数据量都不同，即，所述多个控制参数为离散数据而所述实时结果参数为图像数据，因此，在本申请实施例中，使用基于高斯密度图来对所述第一特征向量和所述第二特征向量进行数据增强以使得两者在高维特征空间中的数据流形的位置和形状能够更为邻近。
具体地，在本申请的技术方案方案中，进一步构造所述第一特征图的第一高斯密度图和构造所述第二特征图的第二密度图，其中，所述第一高斯密度图用公式可表示为
Figure PCTCN2022116222-appb-000023
其中，μ 1为所述第一特征向量，且∑ 1的各个位置的值为所述第一特征向量中相应两个位置的特征值的方差，所述第二高斯密度图用公式可表示为
Figure PCTCN2022116222-appb-000024
其中，μ 2为所述第二特征向量，且∑ 2的各个位置的值为所述第二特征向量中相应两个位置的特征值的方差。
更具体地，在本申请实施例中，所述第一高斯密度图构造单元，进一步用于：以如下公式构造所述第一特征向量的所述第一高斯密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000025
其中x 1表示合成后的高斯向量，μ 1为所述第一特征向量，且∑ 1中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差。
更具体地，在本申请实施例中，所述第二高斯密度图构造单元，进一步用于：以如下公式构造所述第二特征向量的所述第二高斯密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000026
其中x 2表示合成后的高斯向量，μ 2为所述第二特征向量，且∑ 2中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差。
具体地，在本申请实施例中，所述响应性估计单元260和所述高斯离散单元270，用于计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵，并对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵。应可以理解，考虑到所述多个控制参数和所述实时结果参数之间存在响应性关系，因此，在本申请的技术方案中，进一步计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图。接着，对所述响应性高斯密度图进行高斯离散化以生成分类特征矩阵，并将所述分类特征矩阵通过分类器后就可以获得相应的控制结果。应可以理解，这样，所述分类特征矩阵包含多个控制参数之间的高维隐含关联，所述多个控制参数在时序维度上的高维关联信息、所述产物在时序维度上的高维变化特征，以及，所述产物相对于所述多个控制参数的高维关联信息，因此，以所述分类特征矩阵进行分类可提高控制的精度和准度。
更具体地，在本申请实施例中，所述响应性估计单元，进一步用于：以如下公式计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得所述响应性高斯密度图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000027
其中⊙表示向量点乘，⊙-1表示对向量的每个位置的值取倒数，且
Figure PCTCN2022116222-appb-000028
表示矩阵乘法。
具体地，在本申请实施例中，所述特征矩阵校正单元280，用于对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数 值与以所述分类特征图中所有位置的特征值的均值为幂的自然指数函数值之间的差值来进行。应可以理解，由于所述卷积网络的卷积核作为过滤器，对于源数据进行了小尺度的像素级别的关联特征提取，因此所述特征向量作为高斯密度图下的概率分布会存在扰动。而在构造所述特征向量的高斯密度图时，由于所述协方差矩阵是每两个位置的特征值之间的方差，这又会放大个别概率分布的扰动性，并且，这种扰动性可能在计算所述响应性密度图和所述高斯离散化的过程中进一步放大，从而影响所述分类矩阵的整体概率分布的均衡性。因此，在本申请的技术方案中，进一步对高斯离散化后得到的所述分类矩阵进行修正。
更具体地，在本申请实施例中所述特征矩阵校正单元，进一步用于：以如下公式对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成所述校正后分类特征图；
其中，所述公式为：
Figure PCTCN2022116222-appb-000029
其中m i,j为所述分类矩阵的第i行和第j列对应位置的特征值，且
Figure PCTCN2022116222-appb-000030
是所述分类矩阵的各个位置的特征值的全局均值。应可以理解，该所述修正方式是通过以高斯离散化后得到的所述分类矩阵的单个位置的特征值作为单变量，计算其归属于类别概率下的差分的柯西损失形式负对数，从而将单个特征值分布的特殊性进行泛化，从而对整体分布内的个别概率分布的扰动性进行掩蔽，以改进所述分类矩阵的整体概率分布的均衡性。这样，进一步地提高参数控制的精度和准度。
具体地，在本申请实施例中，所述控制结果生成单元290，用于将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。也就是，在本申请的技术方案中，进一步将所述校正后分类特征图通过分类器以获得用于表示计量槽中无水氢氟酸的流入速率应增大或应减小的分类结果。相应地，在一个具体示例中，所述分类器以如下公式对所述校正后分类特征图进行处理以生成分类结果，其中，所述公式为：softmax{(W n,B n):...:(W 1,B 1)|Project(F)}，其中Project(F)表示将所述校正后分类特征图投影为向量，W 1至W n为各层全连接层的权重矩阵，B 1至B n表示各层全连接层的偏置矩阵。
综上，基于本申请实施例的所述用于电子级氢氟酸制备的自动配料系统 200被阐明，其采用人工智能技术的智能控制方法来从控制端，以基于全局的动态角度来动态地调整计量槽中的无水氢氟酸的流入速率，进而对所述电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。
如上所述，根据本申请实施例的用于电子级氢氟酸制备的自动配料系统200可以实现在各种终端设备中，例如用于电子级氢氟酸制备的自动配料算法的服务器等。在一个示例中，根据本申请实施例的用于电子级氢氟酸制备的自动配料系统200可以作为一个软件模块和/或硬件模块而集成到终端设备中。例如，该用于电子级氢氟酸制备的自动配料系统200可以是该终端设备的操作系统中的一个软件模块，或者可以是针对于该终端设备所开发的一个应用程序；当然，该用于电子级氢氟酸制备的自动配料系统200同样可以是该终端设备的众多硬件模块之一。
替换地，在另一示例中，该用于电子级氢氟酸制备的自动配料系统200与该终端设备也可以是分立的设备，并且该用于电子级氢氟酸制备的自动配料系统200可以通过有线和/或无线网络连接到该终端设备，并且按照约定的数据格式来传输交互信息。
示例性方法
图3图示了用于电子级氢氟酸制备的自动配料系统的配料方法的流程图。如图3所示，根据本申请实施例的用于电子级氢氟酸制备的自动配料系统的配料方法，包括步骤：S110，获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图；S120，将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量；S130，将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量；S140，构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差；S150，构造所述第二特征向量的第 二高斯密度图，其中，所述第二高斯密度图的均值向量为所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差；S160，计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵；S170，对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵；S180，对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数值与以所述分类特征图中所有位置的特征值的均值为幂的自然指数函数值之间的差值来进行；以及，S190，将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。
图4图示了根据本申请实施例的用于电子级氢氟酸制备的自动配料系统的配料方法的架构示意图。如图4所示，在所述用于电子级氢氟酸制备的自动配料系统的配料方法的网络架构中，首先，将获得的所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图(例如，如图4中所示意的P1)通过使用三维卷积核的第一卷积神经网络(例如，如图4中所示意的CNN1)以获得第一特征向量(例如，如图4中所示意的VF1)；接着，将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度(例如，如图4中所示意的P2)按照样本维度和时间维度排列为二维的参数矩阵(例如，如图4中所示意的M)后通过第二卷积神经网络(例如，如图4中所示意的CNN2)以获得第二特征向量(例如，如图4中所示意的VF2)；然后，构造所述第一特征向量的第一高斯密度图(例如，如图4中所示意的GD1)；接着，构造所述第二特征向量的第二高斯密度图(例如，如图4中所示意的GD2)；然后，计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图(例如，如图4中所示意的GD)；接着，对所述响应性高斯密度图进行高斯离散化处理以得到分类 特征矩阵(例如，如图4中所示意的MF)；然后，对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图(例如，如图4中所示意的FC)；以及，最后，将所述校正后分类特征图通过分类器(例如，如图4中所示意的分类器)以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。
更具体地，在步骤S110和步骤S120中，获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图，并将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量。应可以理解，在现有的配料方案中，计量槽中的无水氢氟酸以预定速率流经初级过滤器后进入塔釜，这种配料方式由于没有充分考虑所述计量槽中的无水氢氟酸的流入速率与后续的控制参数息息相关而导致现有的用于电子级氢氟酸制备的制备效率和提纯精度难以提高。也就是，如果能够基于全局的动态角度来动态地调整所述计量槽中的无水氢氟酸的流入速率，可对所述电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。这可以通过基于深度神经网络的人工智能控制技术来实现。
具体地，通过对于所述电子级氢氟酸的智慧产线的多个控制参数和一个实时结果参数的分析来进行动态控制，所述多个控制参数包括缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、在塔釜中通入的热水的温度和冷凝器的温度，另外在本申请实施例中，以超纯无水氢氟酸气体经冷凝器冷凝后的产物的液相色谱图作为所述实时结果参数。
然后，应可以理解，在本申请的技术方案中，对所述电子级氢氟酸的智慧产线的控制参数进行调控的目的是为了获得满足预设要求的产物，因此，可利用结果数据从实时响应的角度来对当前的控制效果进行评估以此来提高参数控制的准度和精度。具体地，接着，进一步使用三维卷积核的卷积神经网络模型对所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图进行编码以捕捉所述产物在时序维度上的高维绝对特征和高维绝对特征，也就是，氢氟酸的纯度的绝对值和相对变化值的高维隐含特征表示。
更具体地，在步骤S130中，将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入 所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量。应可以理解，考虑到所述电子级氢氟酸的智慧产线中的所述多个控制参数之间存在关联，因此，使用卷积神经网络模型对所述电子级氢氟酸的智慧产线中的所述多个控制参数进行编码。具体地，将获取的多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵，进而通过构造所述参数矩阵建立起同一预定时间点的各项控制参数之间的关联，不同预定时间点的不同控制参数之间的关联，以及，同一控制参数在不同预定时间点之间的关联。然后，由于卷积神经网络在提取局部特征方面具有优异表现，因此，使用所述卷积神经网络模型能够提取出所述参数矩阵中同一预定时间点的各项控制参数之间的高维隐含关联，不同预定时间点的不同控制参数之间的高维隐含关联，以及，同一控制参数在不同预定时间点之间的高维隐含关联，以得到所述第二特征向量。
更具体地，在不在S140和步骤S150中，构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差，并构造所述第二特征向量的第二高斯密度图，其中，所述第二高斯密度图的均值向量为所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差应可以理解，考虑到所述多个控制参数和实时结果参数之间的数据模态、数据尺度和数据量都不同，即，所述多个控制参数为离散数据而所述实时结果参数为图像数据，因此，在本申请实施例中，使用基于高斯密度图来对所述第一特征向量和所述第二特征向量进行数据增强以使得两者在高维特征空间中的数据流形的位置和形状能够更为邻近。
具体地，在本申请的技术方案方案中，进一步构造所述第一特征图的第一高斯密度图和构造所述第二特征图的第二密度图，其中，所述第一高斯密度图用公式可表示为
Figure PCTCN2022116222-appb-000031
其中，μ 1为所述第一特征向量，且∑ 1的各个位置的值为所述第一特征向量中相应两个位置的特征值的方差，所述第二高斯密度图用公式可表示为
Figure PCTCN2022116222-appb-000032
其中，μ 2为所述第二特征向量， 且∑ 2的各个位置的值为所述第二特征向量中相应两个位置的特征值的方差。
更具体地，在步骤S160和步骤S170中，计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵，并对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵。应可以理解，考虑到所述多个控制参数和所述实时结果参数之间存在响应性关系，因此，在本申请的技术方案中，进一步计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图。接着，对所述响应性高斯密度图进行高斯离散化以生成分类特征矩阵，并将所述分类特征矩阵通过分类器后就可以获得相应的控制结果。应可以理解，这样，所述分类特征矩阵包含多个控制参数之间的高维隐含关联，所述多个控制参数在时序维度上的高维关联信息、所述产物在时序维度上的高维变化特征，以及，所述产物相对于所述多个控制参数的高维关联信息，因此，以所述分类特征矩阵进行分类可提高控制的精度和准度。
更具体地，在步骤S180中，对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数值与以所述分类特征图中所有位置的特征值的均值为幂的自然指数函数值之间的差值来进行。应可以理解，由于所述卷积网络的卷积核作为过滤器，对于源数据进行了小尺度的像素级别的关联特征提取，因此所述特征向量作为高斯密度图下的概率分布会存在扰动。而在构造所述特征向量的高斯密度图时，由于所述协方差矩阵是每两个位置的特征值之间的方差，这又会放大个别概率分布的扰动性，并且，这种扰动性可能在计算所述响应性密度图和所述高斯离散化的过程中进一步放大，从而影响所述分类矩阵的整体概率分布的均衡性。因此，在本申请的技术方案中，进一步对高斯离散化后得到的所述分类矩阵进行修正。
更具体地，在步骤S190中，将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。也就是，在本申请的技术方案中，进一步将所述校正后分类特征 图通过分类器以获得用于表示计量槽中无水氢氟酸的流入速率应增大或应减小的分类结果。相应地，在一个具体示例中，所述分类器以如下公式对所述校正后分类特征图进行处理以生成分类结果，其中，所述公式为：softmax{(W n,B n):...:(W 1,B 1)|Project(F)}，其中Project(F)表示将所述校正后分类特征图投影为向量，W 1至W n为各层全连接层的权重矩阵，B 1至B n表示各层全连接层的偏置矩阵。
综上，基于本申请实施例的所述用于电子级氢氟酸制备的自动配料系统的配料方法被阐明，其采用人工智能技术的智能控制方法来从控制端，以基于全局的动态角度来动态地调整计量槽中的无水氢氟酸的流入速率，进而对所述电子级氢氟酸的智能制造产线的制备效率和提纯精度进行优化。
以上结合具体实施例描述了本申请的基本原理，但是，需要指出的是，在本申请中提及的优点、优势、效果等仅是示例而非限制，不能认为这些优点、优势、效果等是本申请的各个实施例必须具备的。另外，上述公开的具体细节仅是为了示例的作用和便于理解的作用，而非限制，上述细节并不限制本申请为必须采用上述具体的细节来实现。
本申请中涉及的器件、装置、设备、系统的方框图仅作为例示性的例子并且不意图要求或暗示必须按照方框图示出的方式进行连接、布置、配置。如本领域技术人员将认识到的，可以按任意方式连接、布置、配置这些器件、装置、设备、系统。诸如“包括”、“包含”、“具有”等等的词语是开放性词汇，指“包括但不限于”，且可与其互换使用。这里所使用的词汇“或”和“和”指词汇“和/或”，且可与其互换使用，除非上下文明确指示不是如此。这里所使用的词汇“诸如”指词组“诸如但不限于”，且可与其互换使用。
还需要指出的是，在本申请的装置、设备和方法中，各部件或各步骤是可以分解和/或重新组合的。这些分解和/或重新组合应视为本申请的等效方案。
提供所公开的方面的以上描述以使本领域的任何技术人员能够做出或者使用本申请。对这些方面的各种修改对于本领域技术人员而言是非常显而易见的，并且在此定义的一般原理可以应用于其他方面而不脱离本申请的范围。因此，本申请不意图被限制到在此示出的方面，而是按照与在此公开的原理和新颖的特征一致的最宽范围。
为了例示和描述的目的已经给出了以上描述。此外，此描述不意图将本申请的实施例限制到在此公开的形式。尽管以上已经讨论了多个示例方面和实施例，但是本领域技术人员将认识到其某些变型、修改、改变、添加和子组合。

Claims (10)

  1. 一种用于电子级氢氟酸制备的自动配料系统，其特征在于，包括：控制参数数据获取单元，用于获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图；第一卷积编码单元，用于将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量；第二卷积编码单元，用于将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量；第一高斯密度图构造单元，用于构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差；第二高斯密度图构造单元，用于构造所述第二特征向量的第二高斯密度图，其中，所述第二高斯密度图的均值向量为所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差；响应性估计单元，用于计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵；高斯离散单元，用于对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵；特征矩阵校正单元，用于对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数值与以所述分类特征图中所有位置的特征值的均值为幂的自然指数函数值之间的差值来进行；以及控制结果生成单元，用于将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。
  2. 根据权利要求1所述的用于电子级氢氟酸制备的自动配料系统，其 中，所述第一卷积编码单元，进一步用于：使用所述三维卷积核的第一卷积神经网络对所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图进行处理，以获得所述第一特征向量；其中，所述公式为：
    Figure PCTCN2022116222-appb-100001
    其中，H j、W j和R j分别表示三维卷积核的长度、宽度和高度，m表示第(l-1)层特征图的个数，
    Figure PCTCN2022116222-appb-100002
    是与(l-1)层的第m个特征图相连的卷积核，b lj为偏置，f(·)表示激活函数。
  3. 根据权利要求2所述的用于电子级氢氟酸制备的自动配料系统，其中，所述第二卷积编码单元，进一步用于：将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵；使用所述第二卷积神经网络的各层在层的正向传递中对输入数据进行卷积处理、沿特征矩阵的池化处理和激活处理以由所述第二卷积神经网络的最后一层生成所述第二特征向量，其中，所述第二卷积神经网络的第一层的输入为所述参数矩阵。
  4. 根据权利要求3所述的用于电子级氢氟酸制备的自动配料系统，其中，所述第一高斯密度图构造单元，进一步用于：以如下公式构造所述第一特征向量的所述第一高斯密度图；其中，所述公式为：
    Figure PCTCN2022116222-appb-100003
    其中x 1表示合成后的高斯向量，μ 1为所述第一特征向量，且∑ 1中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差。
  5. 根据权利要求4所述的用于电子级氢氟酸制备的自动配料系统，其中，所述第二高斯密度图构造单元，进一步用于：以如下公式构造所述第二特征向量的所述第二高斯密度图；其中，所述公式为：
    Figure PCTCN2022116222-appb-100004
    其中x 2表示合成后的高斯向量，μ 2为所述第二特征向量，且∑ 2中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差。
  6. 根据权利要求5所述的用于电子级氢氟酸制备的自动配料系统，其中，所述响应性估计单元，进一步用于：以如下公式计算所述第一高斯密度 图相对于所述第二高斯密度图的响应性估计以获得所述响应性高斯密度图；
    其中，所述公式为：
    Figure PCTCN2022116222-appb-100005
    其中⊙表示向量点乘，⊙-1表示对向量的每个位置的值取倒数，且
    Figure PCTCN2022116222-appb-100006
    表示矩阵乘法。
  7. 根据权利要求6所述的用于电子级氢氟酸制备的自动配料系统，其中，所述特征矩阵校正单元，进一步用于：以如下公式对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成所述校正后分类特征图；其中，所述公式为：
    Figure PCTCN2022116222-appb-100007
    其中m i,j为所述分类矩阵的第i行和第j列对应位置的特征值，且
    Figure PCTCN2022116222-appb-100008
    是所述分类矩阵的各个位置的特征值的全局均值。
  8. 根据权利要求7所述的用于电子级氢氟酸制备的自动配料系统，其中，所述控制结果生成单元，进一步用于：所述分类器以如下公式对所述校正后分类特征图进行处理以生成分类结果，其中，所述公式为：softmax{(W n,B n):…:(W 1,B 1)|Project(F)}，其中Project(F)表示将所述校正后分类特征图投影为向量，W 1至W n为各层全连接层的权重矩阵，B 1至B n表示各层全连接层的偏置矩阵。
  9. 一种用于电子级氢氟酸制备的自动配料系统的配料方法，其特征在于，包括：获取多个预定时间点的缓冲槽中的液位、计量槽中无水氢氟酸的流入速率、塔釜中的液位、通入所述塔釜的热水的温度和冷凝器的温度，以及，所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图；将所述多个预定时间点的经所述冷凝器冷凝后的产物的液相色谱图通过使用三维卷积核的第一卷积神经网络以获得第一特征向量；将所述多个预定时间点的所述缓冲槽中的液位、所述计量槽中无水氢氟酸的流入速率、所述塔釜中的液位、所述通入所述塔釜的热水的温度和所述冷凝器的温度按照样本维度和时间维度排列为二维的参数矩阵后通过第二卷积神经网络以获得第二特征向量；构造所述第一特征向量的第一高斯密度图，其中，所述第一高斯密度图的均值向量为所述第一特征向量，所述第一高斯密度图的协方差矩阵中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差；构造所述第二特征向量的第二高斯密度图，其中，所述第二高斯密度图 的均值向量为所述第二特征向量，所述第二高斯密度图的协方差矩阵中各个位置的值对应于所述第二特征向量中相应两个位置的特征值之间的方差；计算所述第一高斯密度图相对于所述第二高斯密度图的响应性估计以获得响应性高斯密度图，所述响应性高斯密度图的均值向量为所述第一高斯密度图的均值向量与所述第二高斯密度图的向量的逐点做除法所得的向量，所述响应性密度图的协方差矩阵为所述第一高斯密度图的协方差矩阵乘以所述第二高斯密度图的协方差矩阵的逆矩阵；对所述响应性高斯密度图进行高斯离散化处理以得到分类特征矩阵；对所述分类特征矩阵进行基于所述分类特征矩阵的整体概率分布的均衡修正以生成校正后分类特征图，其中，所述基于所述分类特征矩阵的整体概率分布的均衡修正基于以所述分类特征图中各个位置的特征值为幂的自然指数函数值与以所述分类特征图中所有位置的特征值的均值为幂的自然指数函数值之间的差值来进行；以及将所述校正后分类特征图通过分类器以获得分类结果，所述分类结果用于表示计量槽中无水氢氟酸的流入速率应增大或应减小。
  10. 根据权利要求8所述的用于电子级氢氟酸制备的自动配料系统的配料方法，其中，构造所述第一特征向量的第一高斯密度图，包括：以如下公式构造所述第一特征向量的所述第一高斯密度图；其中，所述公式为：
    Figure PCTCN2022116222-appb-100009
    其中x 1表示合成后的高斯向量，μ 1为所述第一特征向量，且∑ 1中各个位置的值对应于所述第一特征向量中相应两个位置的特征值之间的方差。
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