CN115100456B - Intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide - Google Patents

Intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide Download PDF

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CN115100456B
CN115100456B CN202210587131.9A CN202210587131A CN115100456B CN 115100456 B CN115100456 B CN 115100456B CN 202210587131 A CN202210587131 A CN 202210587131A CN 115100456 B CN115100456 B CN 115100456B
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黄斌斌
罗霜
林金华
丘贵龙
袁瑞明
赖志林
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Abstract

The application relates to the field of intelligent detection of electronic grade gas, and particularly discloses an intelligent toxic and harmful gas alarm system and an alarm method for preparing electronic grade hydrogen peroxide.

Description

Intelligent toxic and harmful gas alarm system for preparing electronic grade hydrogen peroxide
Technical Field
The invention relates to the field of intelligent detection of electronic-grade gas, in particular to an intelligent toxic and harmful gas alarm system and an alarm method for preparing electronic-grade hydrogen peroxide.
Background
The hydrogen peroxide (hydrogen peroxide, H2O 2) production is performed by an electrolysis method, an isopropanol method and an anthraquinone method, and the anthraquinone method has general development in recent years due to the fact that raw materials are simple and easy to obtain and a large amount of energy consumption is saved. However, various dangerous and harmful factors exist in the production process of the anthraquinone dioxide method, and once an accident happens, extremely serious consequences can be caused.
Hydrogen peroxide has a certain toxicity, which is mainly caused by its active oxidation, such as chemical burns to eyes, mucous membranes and skin, and fires of ordinary clothes. The hydrogen peroxide can be inhaled through respiratory tract, absorbed by skin contact and swallowed to cause poisoning. However, the vapor pressure of the device is small, and the volatility is low, so that whether the content of hydrogen peroxide gas in the space exceeds a preset standard or not is difficult to accurately monitor by a conventional monitoring means to generate intelligent early warning, and the safety of related personnel is ensured.
Therefore, an intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide is expected.
In recent years, deep learning and neural networks have been widely used in the fields of computer vision, natural language processing, text signal processing, and the like.
In addition, deep learning and neural networks also exhibit a level close to or even exceeding that of humans in the fields of image classification, object detection, semantic segmentation, text translation and the like.
In recent years, deep learning and development of a neural network provide a new idea and scheme for warning toxic and harmful gases in the preparation process of electronic-grade hydrogen peroxide.
Disclosure of Invention
The present application is proposed to solve the above-mentioned technical problems.
The embodiment of the application provides an intelligent toxic and harmful gas alarm system and an alarm method thereof for preparing electronic-grade hydrogen peroxide, wherein a convolutional neural network model is used for extracting high-dimensional implicit correlation characteristics among hydrogen peroxide concentrations at various positions in a place to be monitored and topological characteristics of a plurality of gas chromatographs to generate a characteristic vector and a characteristic matrix, in order to fuse characteristic information of the high-dimensional implicit correlation characteristics and the topological characteristics to strengthen spatial correlation of hydrogen peroxide distribution based on the topological characteristics, component characteristic vectors are further generated through class characteristics of spatial mapping from the characteristic vectors to the characteristic matrix, so that simultaneous projection of the characteristic vectors to a high-dimensional characteristic space of the characteristic matrix under specific label class probability is realized, the alignment of the characteristic vectors in the dimension of the high-dimensional characteristic space is improved, and the sensitivity and the accuracy of early warning classification are further improved.
According to one aspect of the application, an intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide is provided, which comprises: an air data acquisition unit for acquiring a plurality of gas chromatograms acquired by a plurality of gas chromatographs disposed at a plurality of positions of a site to be monitored; a spatial encoding unit, configured to pass each of the plurality of gas chromatograms through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps; the channel correlation coding unit is used for cascading the plurality of first feature maps along the channel dimension and then obtaining a first feature vector through a second convolution neural network using a three-dimensional convolution kernel; a topology information extraction unit, configured to pass a topology matrix of the plurality of gas chromatographs through a third convolutional neural network to obtain a first feature matrix, where a feature value of each position at a non-diagonal position in the topology matrix is a distance between two corresponding gas chromatographs, and a feature value of each position at a diagonal position in the topology matrix is zero; a feature fusion unit configured to fuse the first feature vector and the first feature matrix to obtain a classified feature vector based on a spatially mapped class characterization of the first feature vector to the first feature matrix, wherein the spatially mapped class characterization of the first feature vector to the first feature matrix is generated based on a two-norm of a feature vector obtained by multiplying the first feature matrix by the first feature vector and dividing the feature vector obtained by multiplying the first feature matrix by the first feature vector; and the alarm result generating unit is used for enabling the classification characteristic vectors to pass through a classifier to obtain a classification result, and the classification result is used for representing whether an alarm prompt is generated or not.
In the above intelligent toxic and harmful gas alarm system for preparing electronic grade hydrogen peroxide, the spatial coding unit is further configured to: each layer of the first convolutional neural network respectively performs the following operations on input data in the forward transmission of the layer: performing convolution processing on the input data based on a two-dimensional convolution kernel to generate a convolution feature map; pooling the convolved feature map to generate a pooled feature map; performing activation processing on the pooled feature map to generate an activated feature map; calculating a quotient of the feature value mean value of the feature matrix corresponding to each channel in the activation feature map and the sum of the feature value mean values of the feature matrices corresponding to all channels as a weighting coefficient of the feature matrix corresponding to each channel; and weighting the feature matrix of each channel by using the weighting coefficient of each channel in the activation feature map to generate the first feature map.
In the above intelligent toxic and harmful gas alarm system for preparing electronic grade hydrogen peroxide, the channel association coding unit is further configured to: processing the feature maps after the plurality of first feature maps are cascaded along the channel dimension by using a second convolutional neural network of the three-dimensional convolutional kernel according to the following formula to generate the first feature vector; wherein the formula is:
Figure 180292DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 875716DEST_PATH_IMAGE002
、
Figure 320603DEST_PATH_IMAGE003
and
Figure 985940DEST_PATH_IMAGE004
respectively representing the length, width and height of the three-dimensional convolution kernel,mis shown as
Figure 160569DEST_PATH_IMAGE005
The number of the layer feature maps is,
Figure 698998DEST_PATH_IMAGE007
is and
Figure 818133DEST_PATH_IMAGE005
first of a layermA convolution kernel connected with the feature map,
Figure 428106DEST_PATH_IMAGE008
in order to be offset,
Figure 332608DEST_PATH_IMAGE009
representing an activation function.
In the above intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide, the topology information extraction unit is further configured to: performing convolution processing, mean pooling along channel dimensions, and activation processing on input data in forward pass of layers using layers of the third convolutional neural network to generate the first feature matrix from a last layer of the third convolutional neural network, wherein an input of the first layer of the third convolutional neural network is a topology matrix of the plurality of gas chromatographs.
In the above-mentioned intelligent poisonous and harmful gas alarm system for preparation of electronic grade hydrogen peroxide, the feature fusion unit is further used for: based on the class characterization of the spatial mapping of the first feature vector to the first feature matrix, fusing the first feature vector and the first feature matrix to obtain the classified feature vector in the following formula; wherein the formula is:
Figure 571433DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 787651DEST_PATH_IMAGE011
representing the first feature vector in a first set of features,
Figure 873418DEST_PATH_IMAGE012
representing the first feature matrix in a first order,
Figure 881694DEST_PATH_IMAGE013
representing the two-norm of the feature vector.
In the above-mentioned intelligent poisonous and harmful gas alarm system for preparation of electronic grade hydrogen peroxide, the alarm result generating unit is further configured to: processing the classification feature vector using the classifier to obtain the classification result with the following formula:
Figure 355401DEST_PATH_IMAGE014
wherein, in the step (A),
Figure 934281DEST_PATH_IMAGE015
to
Figure 807428DEST_PATH_IMAGE016
In order to be a weight matrix, the weight matrix,
Figure 811156DEST_PATH_IMAGE017
to
Figure 331130DEST_PATH_IMAGE018
In order to be a vector of the offset,
Figure 521940DEST_PATH_IMAGE020
is the classification feature vector.
According to another aspect of the application, the alarm method of the intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide comprises the following steps: acquiring a plurality of gas chromatograms acquired by a plurality of gas chromatographs deployed at a plurality of locations of a site to be monitored; passing each of the plurality of gas chromatograms through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps; cascading the plurality of first feature maps along a channel dimension and then obtaining a first feature vector through a second convolutional neural network using a three-dimensional convolutional kernel; passing the topological matrices of the plurality of gas chromatographs through a third convolutional neural network to obtain a first characteristic matrix, wherein the characteristic value of each position on a non-diagonal position in the topological matrices is the distance between the two corresponding gas chromatographs, and the characteristic value of each position on a diagonal position in the topological matrices is zero; based on a spatially mapped class representation of the first feature vector to the first feature matrix, fusing the first feature vector and the first feature matrix to obtain a classified feature vector, wherein the spatially mapped class representation of the first feature vector to the first feature matrix is generated based on a second norm of a feature vector obtained by multiplying the first feature matrix by the first feature vector divided by a feature vector obtained by multiplying the first feature matrix by the first feature vector; and enabling the classified characteristic vector to pass through a classifier to obtain a classification result, wherein the classification result is used for representing whether an alarm prompt is generated or not.
In the above alarm method for an intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation, each of the plurality of gas chromatograms is passed through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first characteristic maps, which includes: each layer of the first convolutional neural network performs input data in forward transmission of the layer respectively: performing convolution processing on the input data based on a two-dimensional convolution kernel to generate a convolution feature map; pooling the convolution feature map to generate a pooled feature map; performing activation processing on the pooled feature map to generate an activated feature map; calculating the quotient of the eigenvalue mean of the eigenvalue matrix corresponding to each channel in the activation characteristic diagram and the sum of the eigenvalue mean of the eigenvalue matrix corresponding to all channels as the weighting coefficient of the eigenvalue matrix corresponding to each channel; and weighting the feature matrix of each channel by using the weighting coefficient of each channel in the activation feature map to generate the first feature map.
In the above alarm method for an intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation, after cascading the plurality of first feature maps along a channel dimension, a first feature vector is obtained by using a second convolutional neural network of a three-dimensional convolutional kernel, which includes: processing the feature maps after the plurality of first feature maps are cascaded along the channel dimension by using a second convolutional neural network of the three-dimensional convolutional kernel according to the following formula to generate the first feature vector; wherein the formula is:
Figure 402040DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 994696DEST_PATH_IMAGE022
、
Figure 685571DEST_PATH_IMAGE023
and
Figure 287978DEST_PATH_IMAGE024
respectively representing the length, width and height of the three-dimensional convolution kernel,mis shown as
Figure 847136DEST_PATH_IMAGE025
The number of the layer characteristic maps is,
Figure 435243DEST_PATH_IMAGE026
is and is
Figure 156074DEST_PATH_IMAGE025
First of a layermA convolution kernel connected to each of the feature maps,
Figure 711690DEST_PATH_IMAGE027
in order to be offset,
Figure 808959DEST_PATH_IMAGE028
representing an activation function.
In the above alarm method for an intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide, the step of passing the topology matrices of the plurality of gas chromatographs through a third convolutional neural network to obtain a first feature matrix includes: performing convolution processing, mean pooling along channel dimensions, and activation processing on input data in forward pass of layers using layers of the third convolutional neural network to generate the first feature matrix from a last layer of the third convolutional neural network, wherein an input of the first layer of the third convolutional neural network is a topology matrix of the plurality of gas chromatographs.
In the above alarm method for an intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide, based on the class characterization of the spatial mapping from the first eigenvector to the first characteristic matrix, fusing the first eigenvector and the first characteristic matrix to obtain a classified eigenvector, including: based on the class characterization of the spatial mapping of the first feature vector to the first feature matrix, fusing the first feature vector and the first feature matrix to obtain the classified feature vector in the following formula; wherein the formula is:
Figure 313889DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 330256DEST_PATH_IMAGE011
representing the first feature vector in a first set of features,
Figure 248533DEST_PATH_IMAGE012
representing the first feature matrix in a first order,
Figure 24859DEST_PATH_IMAGE029
representing the two-norm of the feature vector.
In the above alarm method for an intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide, the step of passing the classification feature vector through a classifier to obtain a classification result includes: processing the classification feature vector using the classifier to obtain the classification result with a formula:
Figure 181034DEST_PATH_IMAGE030
wherein, in the step (A),
Figure 633881DEST_PATH_IMAGE031
to
Figure 773875DEST_PATH_IMAGE032
In order to be a weight matrix, the weight matrix,
Figure 150630DEST_PATH_IMAGE033
to
Figure 820033DEST_PATH_IMAGE034
In order to be a vector of the offset,
Figure 319148DEST_PATH_IMAGE035
the classified feature vector is obtained.
Compared with the prior art, the intelligent toxic and harmful gas alarm system and the alarm method for preparing the electronic-grade hydrogen peroxide utilize a convolutional neural network model to extract high-dimensional implicit correlation characteristics between hydrogen peroxide concentrations at various positions in a place to be monitored and topological characteristics of a plurality of gas chromatographs to generate a characteristic vector and a characteristic matrix, in order to fuse characteristic information of the high-dimensional implicit correlation characteristics and the topological characteristics to strengthen spatial correlation of hydrogen peroxide distribution based on the topological characteristics, component characteristic vectors are further generated through class characteristics of spatial mapping from the characteristic vectors to the characteristic matrix, simultaneous projection of the characteristic vectors to a high-dimensional characteristic space of the characteristic matrix under specific label class probability is achieved, alignment of dimensions of the characteristic vectors in the high-dimensional characteristic space is improved, and sensitivity and accuracy of early warning classification are further improved.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing in more detail embodiments of the present application with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings, like reference numbers generally indicate like parts or steps.
Fig. 1 is an application scene diagram of an intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide according to an embodiment of the application.
Fig. 2 is a block diagram of an intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to an embodiment of the application.
Fig. 3 is a flowchart of an alarm method of the intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to the embodiment of the application.
Fig. 4 is a schematic configuration diagram of an alarm method of an intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to an embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.
Overview of a scene
As mentioned above, hydrogen peroxide (H2O 2) production includes electrolytic process, isopropyl alcohol process and anthraquinone process, and anthraquinone process has been developed in recent years due to its simple and easy availability of raw materials and its large energy saving. However, various dangerous and harmful factors exist in the production process of the anthraquinone dioxide method, and very serious consequences can be caused once an accident occurs.
Hydrogen peroxide has a certain toxicity, which is mainly caused by its active oxidation, such as chemical burns to eyes, mucous membranes and skin, and fires to ordinary clothes. The hydrogen peroxide can be inhaled through respiratory tract, absorbed by skin contact and swallowed to cause poisoning. However, the vapor pressure of the device is small, and the volatility is low, so that whether the content of hydrogen peroxide gas in the space exceeds a preset standard or not is difficult to accurately monitor by a conventional monitoring means to generate intelligent early warning, and the safety of related personnel is ensured.
Therefore, an intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide is expected.
In recent years, deep learning and neural networks have been widely used in the fields of computer vision, natural language processing, text signal processing, and the like. In addition, deep learning and neural networks also exhibit a level close to or even exceeding that of humans in the fields of image classification, object detection, semantic segmentation, text translation, and the like.
In recent years, deep learning and development of a neural network provide a new idea and scheme for warning toxic and harmful gases in the preparation process of electronic-grade hydrogen peroxide.
The convolutional neural network has excellent performance in the aspect of extracting high-dimensional features of images, so that the convolutional neural network can be used for extracting high-dimensional hidden features in a gas chromatogram of air of a place to be monitored, and a classifier is used for classifying to determine whether an alarm signal needs to be generated.
However, the vapor pressure of hydrogen peroxide is small, the volatility is low, and the distribution of the volatilized hydrogen peroxide at each position in the to-be-monitored place is uneven, so that in order to improve the sensitivity and accuracy of alarming, in the technical scheme of the application, gas chromatographs are deployed at a plurality of positions in the to-be-monitored place so as to judge whether to carry out intelligent early warning or not based on the global air analysis of the to-be-monitored place.
Specifically, in the technical solution of the present application, a plurality of gas chromatograms acquired by a plurality of gas chromatographs deployed at a plurality of locations of a site to be monitored are first acquired.
It should be understood that the gas chromatogram can show the air components and the ratio of each component in the air, but due to the characteristics of small vapor pressure and low volatility of hydrogen peroxide, the hydrogen peroxide existing in the air is weak in image representation of the gas chromatogram.
Therefore, in the embodiment of the present application, a convolutional neural network model using a channel attention mechanism is adopted to focus on capturing a high-dimensional feature representation of hydrogen peroxide in the gas chromatogram to obtain first feature maps corresponding to the respective positions.
The hydrogen peroxide gas has the characteristic of spatial distribution uniformity in the place to be monitored, that is, the hydrogen peroxide concentration in each position is correlated, for example, the hydrogen peroxide concentration is high in the place close to the hydrogen peroxide preparation equipment, and the hydrogen peroxide concentration is low in the place far away from the hydrogen peroxide preparation equipment.
Therefore, the first feature map of each location is further encoded using a convolutional neural network model of a three-dimensional convolutional kernel to extract high-dimensional implicit associations between hydrogen peroxide of each location in the site to be monitored to obtain a first feature vector. However, the spatial correlation of the hydrogen peroxide distribution is not fully utilized by only using the three-dimensional convolutional neural network to extract the high-dimensional implicit correlation. Therefore, the topology information of the plurality of gas chromatographs is further taken into consideration.
Specifically, a convolutional neural network model is used for encoding topological matrices of the plurality of gas chromatographs so as to extract spatial high-dimensional implicit association features among the plurality of gas chromatographs to obtain a first feature matrix, wherein feature values of positions on non-diagonal positions in the topological matrices are distances between two corresponding gas chromatographs, and feature values of positions on diagonal positions in the topological matrices are zero.
Furthermore, early warning classification can be performed by fusing the first feature vector and the first feature matrix to obtain a classification feature vector comprising spatial topology information of the hydrogen peroxide at each position and composition related information of the hydrogen peroxide at each position.
However, in the process of mapping the first feature vector into the feature space where the first feature matrix is located to obtain the classified feature vector, since the first feature vector is subjected to channel dimension cascade of the feature map based on the channel attention mechanism, in the process of performing cross-channel feature extraction by using the three-dimensional convolution kernel, joint correlation in channel dimension is generated, and the first feature matrix is subjected to global pooling in channel dimension, so that when mapping is performed, it is necessary to ensure dimension alignment as much as possible.
Specifically, the calculation process of the classification feature vector is as follows:
Figure 556225DEST_PATH_IMAGE010
Figure 798988DEST_PATH_IMAGE036
representing the two-norm of the feature vector.
That is, the classification feature vector realizes simultaneous projection of the feature vector to a high-dimensional feature space of the feature matrix under a specific label class probability through class characterization of spatial mapping of the feature vector to the feature matrix, so as to be adapted to joint correlation of the feature vector in a channel dimension, and improve alignment of the feature distribution of the feature vector in the high-dimensional feature space. Therefore, the sensitivity and the accuracy of early warning classification are improved.
Based on this, this application has proposed an intelligent poisonous and harmful gas alarm system for preparation of electronic grade hydrogen peroxide, it includes: an air data acquisition unit for acquiring a plurality of gas chromatograms acquired by a plurality of gas chromatographs disposed at a plurality of positions of a site to be monitored; a spatial encoding unit, configured to pass each of the plurality of gas chromatograms through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps; the channel correlation coding unit is used for cascading the plurality of first feature maps along the channel dimension and then obtaining a first feature vector through a second convolutional neural network using a three-dimensional convolutional kernel; a topology information extraction unit, configured to pass a topology matrix of the plurality of gas chromatographs through a third convolutional neural network to obtain a first feature matrix, where a feature value of each position at a non-diagonal position in the topology matrix is a distance between two corresponding gas chromatographs, and a feature value of each position at a diagonal position in the topology matrix is zero; a feature fusion unit configured to fuse the first feature vector and the first feature matrix to obtain a classified feature vector based on a spatially mapped class characterization of the first feature vector to the first feature matrix, wherein the spatially mapped class characterization of the first feature vector to the first feature matrix is generated based on a two-norm of a feature vector obtained by multiplying the first feature matrix by the first feature vector and dividing the feature vector obtained by multiplying the first feature matrix by the first feature vector; and the alarm result generating unit is used for enabling the classified characteristic vectors to pass through a classifier to obtain a classification result, and the classification result is used for representing whether an alarm prompt is generated or not.
Fig. 1 is a diagram illustrating an application scenario of an intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to an embodiment of the present application. As shown in fig. 1, in this application scenario, first, a plurality of gas chromatograms are acquired by a plurality of gas chromatographs (e.g., T as illustrated in fig. 1) deployed at a plurality of locations of a site to be monitored (e.g., P as illustrated in fig. 1).
Then, the obtained plurality of gas chromatograms are input into a server (for example, a server S as illustrated in fig. 1) deployed with an intelligent toxic and harmful gas alarm algorithm for electronic-grade hydrogen peroxide preparation, wherein the server can process the plurality of gas chromatograms by using the intelligent toxic and harmful gas alarm algorithm for electronic-grade hydrogen peroxide preparation to obtain a classification result for representing whether an alarm prompt is generated.
Having described the general principles of the present application, various non-limiting embodiments of the present application will now be described with reference to the accompanying drawings.
Exemplary System
Fig. 2 illustrates a block diagram of an intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide production according to an embodiment of the present application.
As shown in fig. 2, an intelligent toxic and harmful gas alarm system 200 for electronic-grade hydrogen peroxide preparation according to an embodiment of the present application includes: an air data acquisition unit 210 for acquiring a plurality of gas chromatograms acquired by a plurality of gas chromatographs disposed at a plurality of positions of a site to be monitored; a spatial encoding unit 220, configured to pass each of the plurality of gas chromatograms through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps; a channel correlation encoding unit 230, configured to cascade the plurality of first feature maps along a channel dimension, and obtain a first feature vector through a second convolutional neural network using a three-dimensional convolutional kernel; a topology information extraction unit 240, configured to pass a topology matrix of the plurality of gas chromatographs through a third convolutional neural network to obtain a first feature matrix, where a feature value of each position at a non-diagonal position in the topology matrix is a distance between two corresponding gas chromatographs, and a feature value of each position at a diagonal position in the topology matrix is zero; a feature fusion unit 250 configured to fuse the first feature vector and the first feature matrix to obtain a classified feature vector based on a spatially mapped class characterization of the first feature vector to the first feature matrix, wherein the spatially mapped class characterization of the first feature vector to the first feature matrix is generated based on a two-norm of a feature vector obtained by multiplying the first feature matrix by the first feature vector and dividing the feature vector obtained by multiplying the first feature matrix by the first feature vector; and an alarm result generating unit 260, configured to pass the classification feature vector through a classifier to obtain a classification result, where the classification result is used to characterize whether an alarm prompt is generated.
Specifically, in the embodiment of the present application, the air data acquiring unit 210 and the spatial encoding unit 220 are configured to acquire a plurality of gas chromatograms acquired by a plurality of gas chromatographs deployed at a plurality of locations of a site to be monitored, and pass each of the plurality of gas chromatograms through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps.
As mentioned above, since the convolutional neural network has excellent performance in extracting high-dimensional features of an image, the convolutional neural network can be used to extract high-dimensional implicit features in a gas chromatogram of air of the site to be monitored, and a classifier is used to perform classification to determine whether an alarm signal needs to be generated. However, considering that the vapor pressure of hydrogen peroxide is small, the volatility is low, and the distribution of the volatilized hydrogen peroxide at each position in the to-be-monitored place is uneven, in order to improve the sensitivity and accuracy of the alarm, in the technical scheme of the application, gas chromatographs are deployed at a plurality of positions in the to-be-monitored place so as to judge whether to perform intelligent early warning based on the global air analysis of the to-be-monitored place.
That is, specifically, in the technical solution of the present application, first, a plurality of gas chromatograms are acquired by a plurality of gas chromatographs disposed at a plurality of positions of the site to be monitored.
It should be understood that the gas chromatogram can show the air components and the ratio of each component in the air, but due to the characteristics of small vapor pressure and low volatility of hydrogen peroxide, the hydrogen peroxide existing in the air is weak in image representation of the gas chromatogram.
Therefore, in the technical solution of the present application, a convolutional neural network model of a channel attention mechanism is further adopted to focus on capturing a high-dimensional feature representation of hydrogen peroxide in the gas chromatogram to obtain first feature maps corresponding to respective positions.
More specifically, in an embodiment of the present application, the spatial encoding unit is further configured to: each layer of the first convolutional neural network respectively performs the following operations on input data in the forward transmission of the layer: firstly, performing convolution processing on the input data based on a two-dimensional convolution kernel to generate a convolution characteristic diagram; then, performing pooling processing on the convolution feature map to generate a pooled feature map; then, activating the pooled feature map to generate an activated feature map; then, calculating the quotient of the eigenvalue mean value of the eigenvalue matrix corresponding to each channel in the activation characteristic diagram and the sum of the eigenvalue mean values of the eigenvalue matrices corresponding to all channels as the weighting coefficient of the eigenvalue matrix corresponding to each channel; finally, the feature matrix of each channel is weighted by the weighting coefficient of each channel in the activation feature map to generate the first feature map.
Specifically, in this embodiment of the present application, the channel correlation encoding unit 230 is configured to obtain the first feature vector by using a second convolutional neural network of a three-dimensional convolutional kernel after cascading the plurality of first feature maps along the channel dimension.
It should be understood that, because the hydrogen peroxide gas has a characteristic of spatial distribution uniformity in the site to be monitored, that is, the hydrogen peroxide gas concentrations at various positions are correlated, for example, the hydrogen peroxide gas concentration at a place close to the hydrogen peroxide preparation equipment is high, and the hydrogen peroxide gas concentration at a place far away from the hydrogen peroxide preparation equipment is low. Therefore, in the technical scheme of the application, the first feature maps of the positions are further encoded by using a second convolutional neural network model of the three-dimensional convolutional kernel to extract high-dimensional implicit associations between hydrogen peroxide solutions of the positions in the to-be-monitored place so as to obtain first feature vectors.
More specifically, in an embodiment of the present application, the channel-associated encoding unit is further configured to: processing the feature maps after the plurality of first feature maps are cascaded along the channel dimension by using a second convolutional neural network of the three-dimensional convolutional kernel according to the following formula to generate the first feature vector;
wherein the formula is:
Figure 319968DEST_PATH_IMAGE037
wherein the content of the first and second substances,
Figure 989983DEST_PATH_IMAGE038
、
Figure 776674DEST_PATH_IMAGE039
and
Figure 682182DEST_PATH_IMAGE040
respectively representing the length, width and height of the three-dimensional convolution kernel,mis shown as
Figure 198614DEST_PATH_IMAGE041
The number of the layer feature maps is,
Figure 649318DEST_PATH_IMAGE042
is and
Figure 251200DEST_PATH_IMAGE041
first of a layermConvolution with connected feature mapsThe core is a core of a plurality of types of fibers,
Figure 960399DEST_PATH_IMAGE043
in order to be biased,
Figure 3442DEST_PATH_IMAGE044
representing an activation function.
Specifically, in this embodiment of the present application, the topology information extraction unit 240 is configured to pass topology matrices of the plurality of gas chromatographs through a third convolutional neural network to obtain a first feature matrix, where a feature value of each position at an off-diagonal position in the topology matrices is a distance between two corresponding gas chromatographs, and a feature value of each position at a diagonal position in the topology matrices is zero.
It should be appreciated that the spatial correlation of the hydrogen peroxide distribution is not fully exploited in view of the fact that only the three-dimensional convolutional neural network is used to extract the high-dimensional implicit associations between hydrogen peroxide at various locations in the site to be monitored. Therefore, in the present invention, the topology information of the plurality of gas chromatographs is further taken into consideration.
That is, specifically, the third convolutional neural network model is used to encode the topological matrix of the plurality of gas chromatographs to extract spatial high-dimensional implicit correlation features between the plurality of gas chromatographs to obtain a first feature matrix, wherein the feature value of each position at an off-diagonal position in the topological matrix is the distance between the corresponding two gas chromatographs, and the feature value of each position at a diagonal position in the topological matrix is zero.
Accordingly, in one particular example, the layers of the third convolutional neural network are used to convolve input data in a forward pass of layers, to average pooling along a channel dimension, and to activate to generate the first feature matrix from a last layer of the third convolutional neural network, wherein an input to the first layer of the third convolutional neural network is a topology matrix of the plurality of gas chromatographs.
Specifically, in this embodiment of the present application, the feature fusion unit 250 is configured to fuse the first feature vector and the first feature matrix to obtain a classified feature vector based on a class characterization of a spatial mapping of the first feature vector to the first feature matrix, where the class characterization of the spatial mapping of the first feature vector to the first feature matrix is generated based on a two-norm of a feature vector obtained by multiplying the first feature matrix by the first feature vector and dividing the feature vector obtained by multiplying the first feature matrix by the first feature vector.
It should be understood that, in the technical solution of the present application, further, the early warning classification may be performed by fusing the first feature vector and the first feature matrix to obtain a classification feature vector including spatial topology information of the hydrogen peroxide solution at each position and composition related information of the hydrogen peroxide solution at each position.
However, in the process of mapping the first feature vector into the feature space where the first feature matrix is located to obtain the classification feature vector, since the first feature vector passes through the channel dimension cascade of the feature map based on the channel attention mechanism, in the process of performing cross-channel feature extraction by using the three-dimensional convolution kernel, a joint correlation in the channel dimension is generated, and the first feature matrix is subjected to global pooling in the channel dimension, so that it is necessary to ensure the dimension alignment as much as possible when performing mapping. Therefore, in the technical solution of the present application, the first feature vector and the first feature matrix are fused to obtain a classified feature vector further based on a class characterization of the spatial mapping of the first feature vector to the first feature matrix.
More specifically, in an embodiment of the present application, the feature fusion unit is further configured to: based on the class characterization of the spatial mapping of the first feature vector to the first feature matrix, fusing the first feature vector and the first feature matrix to obtain the classified feature vector in the following formula; wherein the formula is:
Figure 15260DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 966423DEST_PATH_IMAGE011
representing the first feature vector in a first set of features,
Figure 89100DEST_PATH_IMAGE012
a first feature matrix representing a first set of features,
Figure 189911DEST_PATH_IMAGE045
representing the two-norm of the feature vector. It should be understood that the classified feature vector realizes the simultaneous projection of the feature vector to the high-dimensional feature space of the feature matrix under a specific label class probability through the class characterization of the spatial mapping of the feature vector to the feature matrix, so as to be adapted to the joint correlation of the feature vector in the channel dimension, improve the alignment of the dimension of the feature distribution of the feature vector in the high-dimensional feature space, and further improve the sensitivity and accuracy of the early warning classification.
Specifically, in this embodiment of the present application, the alarm result generating unit 260 is configured to pass the classification feature vector through a classifier to obtain a classification result, where the classification result is used to characterize whether to generate an alarm prompt.
That is, in the technical solution of the present application, after obtaining the classification feature vector, the classification feature vector is further passed through a classifier to obtain a classification result for characterizing whether an alarm prompt is generated. Accordingly, in one specific example, the classification feature vector is processed using the classifier to obtain the classification result with the following formula:
Figure 372631DEST_PATH_IMAGE046
wherein, in the step (A),
Figure 73739DEST_PATH_IMAGE047
to
Figure 672211DEST_PATH_IMAGE048
In order to be a weight matrix, the weight matrix,
Figure 752162DEST_PATH_IMAGE049
to
Figure 495996DEST_PATH_IMAGE050
In order to be a vector of the offset,
Figure 28609DEST_PATH_IMAGE051
is the classification feature vector.
In summary, the intelligent toxic and harmful gas alarm system 200 for preparing electronic-grade hydrogen peroxide according to the embodiment of the present application is stated, and a convolutional neural network model is used to extract high-dimensional implicit correlation features between hydrogen peroxide concentrations at various positions in a to-be-monitored site and topological features of a plurality of gas chromatographs to generate a feature vector and a feature matrix, in order to fuse feature information of the two, and strengthen spatial correlation of hydrogen peroxide distribution based on the topological features, so as to improve classification accuracy, a component feature vector is further generated through class characterization of spatial mapping of the feature vector to the feature matrix, so as to realize simultaneous projection of the feature vector to a high-dimensional feature space of the feature matrix under a specific label class probability, improve alignment of dimensions of feature distribution of the feature vector in the high-dimensional feature space, and further improve sensitivity and accuracy of early warning classification.
As described above, the intelligent toxic and harmful gas alarm system 200 for electronic-grade hydrogen peroxide preparation according to the embodiment of the present application can be implemented in various terminal devices, such as a server for an intelligent toxic and harmful gas alarm algorithm for electronic-grade hydrogen peroxide preparation.
In one example, the intelligent toxic and harmful gas alarm system 200 for electronic-grade hydrogen peroxide preparation according to the embodiment of the present application can be integrated into a terminal device as a software module and/or a hardware module.
For example, the intelligent toxic and harmful gas alarm system 200 for preparing electronic-grade hydrogen peroxide can be a software module in an operating system of the terminal device, or can be an application program developed for the terminal device; of course, the intelligent toxic and harmful gas alarm system 200 for preparing electronic-grade hydrogen peroxide can also be one of numerous hardware modules of the terminal device.
Alternatively, in another example, the intelligent toxic and harmful gas alarm system 200 for electronic-grade hydrogen peroxide preparation and the terminal device can also be separate devices, and the intelligent toxic and harmful gas alarm system 200 for electronic-grade hydrogen peroxide preparation can be connected to the terminal device through a wired and/or wireless network and transmit interactive information according to an agreed data format.
Exemplary method
Fig. 3 illustrates a flow chart of an alarm method of the intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation.
As shown in fig. 3, the alarm method of the intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide according to the embodiment of the application includes the following steps: s110, acquiring a plurality of gas chromatograms acquired by a plurality of gas chromatographs deployed at a plurality of positions of a place to be monitored; s120, enabling each gas chromatogram in the plurality of gas chromatograms to pass through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps; s130, cascading the plurality of first feature maps along the channel dimension, and then obtaining a first feature vector through a second convolution neural network using a three-dimensional convolution kernel; s140, passing the topological matrices of the plurality of gas chromatographs through a third convolutional neural network to obtain a first characteristic matrix, wherein the characteristic value of each position at a non-diagonal position in the topological matrix is the distance between the two corresponding gas chromatographs, and the characteristic value of each position at a diagonal position in the topological matrix is zero; s150, fusing the first feature vector and the first feature matrix to obtain a classified feature vector based on class characterization of spatial mapping of the first feature vector to the first feature matrix, wherein the class characterization of the spatial mapping of the first feature vector to the first feature matrix is generated based on a two-norm of a feature vector obtained by multiplying the first feature matrix by the first feature vector and dividing the feature vector obtained by multiplying the first feature matrix by the first feature vector; and S160, enabling the classified feature vectors to pass through a classifier to obtain a classification result, wherein the classification result is used for representing whether an alarm prompt is generated or not.
Fig. 4 is a schematic diagram illustrating an architecture of an alarm method of an intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to an embodiment of the present application.
As shown in fig. 4, in the network architecture of the alarm method for the intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation, first, each gas chromatogram (for example, P1 as illustrated in fig. 4) in the plurality of gas chromatograms is passed through a first convolutional neural network (for example, CNN1 as illustrated in fig. 4) using a channel attention mechanism to obtain a plurality of first characteristic maps (for example, F1 as illustrated in fig. 4); then, after cascading the plurality of first feature maps along the channel dimension, obtaining a first feature vector (e.g., VF1 as illustrated in fig. 4) through a second convolutional neural network (e.g., CNN2 as illustrated in fig. 4) using a three-dimensional convolutional kernel; then, passing the topological matrices of the plurality of gas chromatographs (e.g., M as illustrated in fig. 4) through a third convolutional neural network (e.g., CNN3 as illustrated in fig. 4) to obtain a first feature matrix (e.g., MF1 as illustrated in fig. 4); then, based on the spatially mapped class representation of the first feature vector to the first feature matrix, fusing the first feature vector and the first feature matrix to obtain a classified feature vector (e.g., MF as illustrated in fig. 4); and, finally, passing the classified feature vector through a classifier (e.g., circle S as illustrated in fig. 4) to obtain a classification result, which is used to characterize whether an alarm prompt is generated.
More specifically, in steps S110 and S120, a plurality of gas chromatograms acquired by a plurality of gas chromatographs disposed at a plurality of locations of a site to be monitored are acquired, and each of the plurality of gas chromatograms is passed through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps.
It will be appreciated that since convolutional neural networks have excellent performance in extracting high-dimensional features of images, they can be used to extract high-dimensional implicit features in the gas chromatogram of the air of the site to be monitored, and a classifier is used to perform classification to determine whether an alarm signal needs to be generated.
However, considering that the vapor pressure of hydrogen peroxide is small, the volatility is low, and the distribution of the volatilized hydrogen peroxide at each position in the to-be-monitored place is uneven, in order to improve the sensitivity and accuracy of the alarm, in the technical scheme of the application, gas chromatographs are deployed at a plurality of positions in the to-be-monitored place so as to judge whether to carry out intelligent early warning based on the global air analysis of the to-be-monitored place.
That is, specifically, in the technical solution of the present application, first, a plurality of gas chromatograms are acquired by a plurality of gas chromatographs disposed at a plurality of positions of the site to be monitored.
It should be understood that the gas chromatogram can show the air components and the ratio of each component in the air, but due to the characteristics of small vapor pressure and low volatility of hydrogen peroxide, the hydrogen peroxide existing in the air is weak in image representation of the gas chromatogram.
Therefore, in the technical solution of the present application, a convolutional neural network model of a channel attention mechanism is further adopted to focus on capturing a high-dimensional feature representation of hydrogen peroxide in the gas chromatogram to obtain first feature maps corresponding to respective positions.
More specifically, in step S130, the plurality of first feature maps are cascaded along the channel dimension and then passed through a second convolutional neural network using a three-dimensional convolutional kernel to obtain a first feature vector.
It should be understood that, because the hydrogen peroxide gas has a characteristic of spatial distribution uniformity in the site to be monitored, that is, the hydrogen peroxide gas concentrations at various positions are correlated, for example, the hydrogen peroxide gas concentration at a place close to the hydrogen peroxide preparation equipment is high, and the hydrogen peroxide gas concentration at a place far away from the hydrogen peroxide preparation equipment is low.
Therefore, in the technical scheme of the application, the first feature maps of the positions are further encoded by using a second convolutional neural network model of the three-dimensional convolutional kernel to extract high-dimensional implicit associations between hydrogen peroxide solutions of the positions in the to-be-monitored place so as to obtain first feature vectors.
More specifically, in S140, the topology matrices of the plurality of gas chromatographs are passed through a third convolutional neural network to obtain a first feature matrix, in which the feature value of each position at an off-diagonal position in the topology matrices is the distance between the corresponding two gas chromatographs, and the feature value of each position at a diagonal position in the topology matrices is zero.
It should be appreciated that the spatial correlation of the hydrogen peroxide distribution is not fully exploited in view of the fact that only the three-dimensional convolutional neural network is used to extract the high-dimensional implicit associations between hydrogen peroxide at various locations in the site to be monitored. Therefore, in the present invention, the topology information of the plurality of gas chromatographs is further taken into consideration.
That is, specifically, the third convolutional neural network model is used to encode the topological matrix of the plurality of gas chromatographs to extract spatial high-dimensional implicit correlation features between the plurality of gas chromatographs to obtain a first feature matrix, wherein the feature value of each position at an off-diagonal position in the topological matrix is the distance between the corresponding two gas chromatographs, and the feature value of each position at a diagonal position in the topological matrix is zero.
Accordingly, in one particular example, the layers of the third convolutional neural network are used to convolve input data in a forward pass of layers, a mean pooling along channel dimensions, and an activation process to generate the first feature matrix from a last layer of the third convolutional neural network, wherein an input to the first layer of the third convolutional neural network is a topology matrix of the plurality of gas chromatographs.
More specifically, in step S150, based on the class characterization of the spatial mapping of the first feature vector to the first feature matrix, the first feature vector and the first feature matrix are fused to obtain a classified feature vector, wherein the class characterization of the spatial mapping of the first feature vector to the first feature matrix is generated based on the second norm of the feature vector obtained by multiplying the first feature matrix by the first feature vector and dividing the feature vector obtained by multiplying the first feature matrix by the first feature vector.
It should be understood that, in the technical solution of the present application, further, the early warning classification may be performed by fusing the first feature vector and the first feature matrix to obtain a classification feature vector including spatial topology information of hydrogen peroxide at each position and component related information of hydrogen peroxide at each position.
However, in the process of mapping the first feature vector into the feature space where the first feature matrix is located to obtain the classification feature vector, since the first feature vector passes through the channel dimension cascade of the feature map based on the channel attention mechanism, in the process of performing cross-channel feature extraction by using the three-dimensional convolution kernel, a joint correlation in the channel dimension is generated, and the first feature matrix is subjected to global pooling in the channel dimension, so that it is necessary to ensure the dimension alignment as much as possible when performing mapping.
Therefore, in the technical solution of the present application, the first feature vector and the first feature matrix are fused to obtain a classified feature vector further based on a class characterization of the spatial mapping of the first feature vector to the first feature matrix.
More specifically, in step S160, the classified feature vector is passed through a classifier to obtain a classification result, and the classification result is used for characterizing whether an alarm prompt is generated or not.
That is, in the technical solution of the present application, after the classification feature vector is obtained, the classification feature vector is further passed through a classifier to obtain a classification result for characterizing whether an alarm prompt is generated. Accordingly, in one specific example, the classification feature vector is processed using the classifier to obtain the classification result with the following formula:
Figure 634034DEST_PATH_IMAGE052
wherein, in the process,
Figure 568492DEST_PATH_IMAGE053
to
Figure DEST_PATH_IMAGE055
In the form of a matrix of weights,
Figure DEST_PATH_IMAGE056
to
Figure 217648DEST_PATH_IMAGE050
In order to be a vector of the offset,
Figure 618980DEST_PATH_IMAGE035
is the classification feature vector.
In summary, the alarm method for the intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide according to the embodiment of the present application is clarified, a convolutional neural network model is used to extract high-dimensional implicit correlation features between hydrogen peroxide concentrations at various positions in a place to be monitored and topological features of a plurality of gas chromatographs to generate a feature vector and a feature matrix, in order to fuse feature information of the two, the spatial correlation of hydrogen peroxide distribution is strengthened based on the topological features, so that the accuracy of classification is improved, a classification feature vector is further generated through class representation of spatial mapping of the feature vector to the feature matrix, so that simultaneous projection of the feature vector to a high-dimensional feature space of the feature matrix under a specific label class probability is realized, the alignment of the feature distribution of the feature vector in the high-dimensional feature space is improved, and the sensitivity and accuracy of early warning classification are further improved.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application.
Furthermore, the foregoing disclosure of specific details is provided for purposes of illustration and understanding only, and is not intended to limit the application to the details which are set forth in order to provide a thorough understanding of the present application.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams.
These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by one skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably herein. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application.
Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims (9)

1. An intelligent toxic and harmful gas alarm system for preparing electronic-grade hydrogen peroxide is characterized by comprising:
an air data acquisition unit for acquiring a plurality of gas chromatograms acquired by a plurality of gas chromatographs disposed at a plurality of positions of a site to be monitored;
a spatial encoding unit, configured to pass each of the plurality of gas chromatograms through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps;
the channel correlation coding unit is used for cascading the plurality of first feature maps along the channel dimension and then obtaining a first feature vector through a second convolution neural network using a three-dimensional convolution kernel;
a topology information extraction unit, configured to pass a topology matrix of the plurality of gas chromatographs through a third convolutional neural network to obtain a first feature matrix, where a feature value of each position at a non-diagonal position in the topology matrix is a distance between two corresponding gas chromatographs, and a feature value of each position at a diagonal position in the topology matrix is zero;
a feature fusion unit for fusing the first feature vector and the first feature matrix to obtain a classified feature vector based on a spatially mapped class characterization of the first feature vector to the first feature matrix;
the alarm result generating unit is used for enabling the classified characteristic vectors to pass through a classifier to obtain a classification result, and the classification result is used for representing whether an alarm prompt is generated or not;
wherein the feature fusion unit is further configured to: based on the class characterization of the spatial mapping of the first feature vector to the first feature matrix, fusing the first feature vector and the first feature matrix to obtain the classified feature vector in the following formula;
wherein the formula is:
Figure FDA0003952438260000011
wherein, V 1 Representing said first feature vector, M 1 Representing the first feature matrix, | · |. Non-woven phosphor 2 Representing the two-norm of the feature vector.
2. The intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to claim 1, wherein the spatial coding unit is further configured to: each layer of the first convolutional neural network performs input data in forward transmission of the layer respectively:
performing convolution processing on the input data based on a two-dimensional convolution kernel to generate a convolution feature map;
pooling the convolution feature map to generate a pooled feature map;
performing activation processing on the pooled feature map to generate an activated feature map;
calculating a quotient of the feature value mean value of the feature matrix corresponding to each channel in the activation feature map and the sum of the feature value mean values of the feature matrices corresponding to all channels as a weighting coefficient of the feature matrix corresponding to each channel;
and weighting the feature matrix of each channel by using the weighting coefficient of each channel in the activation feature map to generate the first feature map.
3. The intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to claim 2, wherein the channel association coding unit is further configured to: processing the feature maps after the plurality of first feature maps are cascaded along the channel dimension by using a second convolutional neural network of the three-dimensional convolutional kernel according to the following formula to generate the first feature vector;
wherein the formula is:
Figure FDA0003952438260000021
wherein H j 、W j And R j Respectively represents the length, width and height of the three-dimensional convolution kernel, m represents the number of the (l-1) th layer characteristic diagram,
Figure FDA0003952438260000022
is a convolution kernel connected to the mth feature map of the (l-1) layer, b lj For biasing, f (-) represents the activation function.
4. The intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to claim 3, wherein the topology information extraction unit is further configured to: performing convolution processing, mean pooling along channel dimensions, and activation processing on input data in forward pass of layers using layers of the third convolutional neural network to generate the first feature matrix from a last layer of the third convolutional neural network, wherein an input of the first layer of the third convolutional neural network is a topology matrix of the plurality of gas chromatographs.
5. The intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to claim 4, wherein the alarm result generation unit is further configured to: processing the classification feature vector using the classifier to obtain the classification result with the following formula: softmax { (W) n ,B n ):…:(W 1 ,B 1 ) I X }, wherein W 1 To W n As a weight matrix, B 1 To B n Is a bias vector and X is the classification feature vector.
6. An alarm method of an intelligent toxic and harmful gas alarm system for preparing electronic grade hydrogen peroxide is characterized by comprising the following steps:
acquiring a plurality of gas chromatograms acquired by a plurality of gas chromatographs deployed at a plurality of locations of a site to be monitored;
passing each gas chromatogram of the plurality of gas chromatograms through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first feature maps;
after the plurality of first feature maps are cascaded along the channel dimension, a first feature vector is obtained through a second convolution neural network using a three-dimensional convolution kernel;
passing the topological matrices of the plurality of gas chromatographs through a third convolutional neural network to obtain a first characteristic matrix, wherein the characteristic value of each position on a non-diagonal position in the topological matrices is the distance between the two corresponding gas chromatographs, and the characteristic value of each position on a diagonal position in the topological matrices is zero;
based on a spatially mapped class representation of the first feature vector to the first feature matrix, fusing the first feature vector and the first feature matrix to obtain a classified feature vector; and
enabling the classified feature vectors to pass through a classifier to obtain a classification result, wherein the classification result is used for representing whether an alarm prompt is generated or not;
wherein said fusing the first feature vector with the first feature matrix to obtain a classified feature vector based on spatially mapped class characterization of the first feature vector to the first feature matrix comprises: based on the class characterization of the spatial mapping of the first feature vector to the first feature matrix, fusing the first feature vector and the first feature matrix to obtain the classified feature vector in the following formula;
wherein the formula is:
Figure FDA0003952438260000031
wherein, V 1 Represents the first feature vector, M 1 Represents the aboveFirst feature matrix, | · | | non-conducting phosphor 2 Representing the two-norm of the feature vector.
7. The alarm method of the intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to claim 6, wherein the step of passing each of the plurality of gas chromatograms through a first convolutional neural network using a channel attention mechanism to obtain a plurality of first characteristic maps comprises the steps of:
each layer of the first convolutional neural network respectively performs the following operations on input data in the forward transmission of the layer:
performing convolution processing on the input data based on a two-dimensional convolution kernel to generate a convolution feature map;
pooling the convolution feature map to generate a pooled feature map;
performing activation processing on the pooled feature map to generate an activated feature map;
calculating a quotient of the feature value mean value of the feature matrix corresponding to each channel in the activation feature map and the sum of the feature value mean values of the feature matrices corresponding to all channels as a weighting coefficient of the feature matrix corresponding to each channel;
and weighting the feature matrix of each channel by using the weighting coefficient of each channel in the activation feature map to generate the first feature map.
8. The alarm method of the intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to claim 7, wherein the step of obtaining the first feature vector by using the second convolutional neural network of the three-dimensional convolutional kernel after cascading the plurality of first feature maps along the channel dimension comprises the steps of:
processing the feature maps after the plurality of first feature maps are cascaded along the channel dimension by using a second convolutional neural network of the three-dimensional convolutional kernel according to the following formula to generate the first feature vector;
wherein the formula is:
Figure FDA0003952438260000041
wherein H j 、W j And R j Respectively represents the length, width and height of the three-dimensional convolution kernel, m represents the number of the (l-1) th layer characteristic diagram,
Figure FDA0003952438260000042
is a convolution kernel connected to the mth feature map of the (l-1) layer, b lj For biasing, f (-) represents the activation function.
9. The alarm method of the intelligent toxic and harmful gas alarm system for electronic-grade hydrogen peroxide preparation according to claim 8, wherein passing the topological matrices of the plurality of gas chromatographs through a third convolutional neural network to obtain a first signature matrix comprises:
performing convolution processing, mean pooling along channel dimensions, and activation processing on input data in forward pass of layers using layers of the third convolutional neural network to generate the first feature matrix from a last layer of the third convolutional neural network, wherein an input of the first layer of the third convolutional neural network is a topology matrix of the plurality of gas chromatographs.
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