CN116109879B - Control system and control method of leveling machine - Google Patents

Control system and control method of leveling machine Download PDF

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CN116109879B
CN116109879B CN202310389735.7A CN202310389735A CN116109879B CN 116109879 B CN116109879 B CN 116109879B CN 202310389735 A CN202310389735 A CN 202310389735A CN 116109879 B CN116109879 B CN 116109879B
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邱杰
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Dongguan Jieda Machinery Co ltd
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Abstract

A control system of a leveling machine and a control method thereof are disclosed, which adaptively select a roll shaft of an appropriate diameter to improve leveling efficiency and effect based on the surface condition of a material to be leveled. Specifically, in the technical scheme of the application, an artificial intelligence algorithm based on deep learning is adopted to extract long-distance context semantic association information and medium-distance context semantic association information from the surface image of the material to be leveled, and the type label classification of the triaxial roller is carried out by fusing the characteristic information. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling efficiency and effect.

Description

Control system and control method of leveling machine
Technical Field
The present application relates to the field of automation equipment, and more particularly to a control system for a leveling machine and a control method thereof.
Background
The leveling machine extrudes the uneven metal plate through the upper and lower rollers to achieve the leveling effect. The general leveling machine is provided with a plurality of roll shafts, for example, a three-shaft roll leveling machine, the construction efficiency is higher by adopting a larger shaft diameter, the flatness is better, but the surface slurry is easier to separate, and the slurry is thinner. The smaller shaft diameter is adopted, so that the pulp lifting effect is better, but the shaft is easy to deform, and correction should be noted.
Accordingly, a control system of a leveling machine is desired that can select a roller shaft having an appropriate diameter based on the surface condition of an object to be operated to improve the leveling effect.
Disclosure of Invention
The present application has been made in order to solve the above technical problems. Embodiments of the present application provide a control system of a leveling machine and a control method thereof, which adaptively select a roll shaft with an appropriate diameter to improve the leveling effect based on the surface condition of a material to be leveled. Specifically, in the technical solution of the present application, considering that this is essentially a classification problem, that is, for example, for a three-axis roll leveler, an artificial intelligence algorithm based on deep learning is adopted to extract long-distance context semantic association information and mid-distance context semantic association information from a surface image of a material to be leveled, and the feature information is fused to perform type tag classification of the three-axis roll. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling effect.
According to one aspect of the present application, there is provided a control system for a trowel, comprising: the surface image acquisition unit is used for acquiring a surface image of the material to be leveled; an image dividing unit for dividing the surface image of the material to be leveled into a two-dimensional image block sequence; the image block vectorization unit is used for enabling each two-dimensional image block in the two-dimensional image block sequence to pass through a linear embedding layer respectively so as to obtain a sequence of image block feature vectors; a first scale context coding unit, configured to pass the sequence of image block feature vectors through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors; the cascade unit is used for cascading the plurality of image block semantic feature vectors to obtain long-distance dependent image feature vectors; the second scale context coding unit is used for enabling the sequence of the image block feature vectors to pass through a two-way long-short-term memory neural network model to obtain a middle-distance dependent image feature vector; the multi-scale fusion unit is used for fusing the long-distance dependent image feature vector and the middle-distance dependent image feature vector to obtain a classification feature vector; the vector correction unit is used for carrying out geometric constraint re-parameterization of a positive sizing space on the classification feature vector so as to obtain a corrected classification feature vector; and a leveling control result generating unit for passing the corrected classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for representing the type label of the roll shaft.
In the control system of the leveling machine, the image dividing unit is further configured to uniformly divide the surface image of the material to be leveled into the two-dimensional image block sequence.
In the control system of the leveling machine, the image block vectorization unit is further configured to perform linear projection on each two-dimensional image block in the two-dimensional image block sequence with a learning embedding matrix by using the linear embedding layer to obtain the sequence of image block feature vectors.
In the control system of the above leveling machine, the first scale context encoding unit is further configured to: arranging the sequence of the image block feature vectors into an input vector; converting the input vector into a query vector and a key vector through a learning embedding matrix respectively; calculating the product between the query vector and the transpose vector of the key vector to obtain a self-attention correlation matrix; carrying out standardization processing on the self-attention association matrix to obtain a standardized self-attention association matrix; inputting the standardized self-attention association matrix into a Softmax activation function to activate so as to obtain a self-attention feature matrix; and multiplying the self-attention feature matrix with each image block feature vector in the sequence of image block feature vectors to obtain the plurality of image block semantic feature vectors.
In the control system of the above leveling machine, the multi-scale fusion unit is further configured to: fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain the classification feature vector by the following formula; wherein, the formula is:
Figure SMS_1
wherein (1)>
Figure SMS_2
Representing the long-range dependent image feature vector,
Figure SMS_3
representing the mid-range dependent image feature vector, < >>
Figure SMS_4
Representing a cascading function.
In the control system of the leveling machine, the vector correction unit is further configured to: performing geometric constraint re-parameterization of positive definite excipient space on the classification feature vector by the following formula to obtain a corrected classification feature vector; wherein the correction is commonThe formula is:
Figure SMS_7
wherein->
Figure SMS_10
Is the classification feature vector,/->
Figure SMS_13
And->
Figure SMS_6
Is feature set +.>
Figure SMS_12
Mean and variance of>
Figure SMS_15
Is a transpose of the classification feature vector,
Figure SMS_17
representing the square of the two norms of the vector, +.>
Figure SMS_5
Frobenius norms of the matrix are represented, < >>
Figure SMS_9
Is the classification feature vector->
Figure SMS_14
Is>
Figure SMS_16
Characteristic value of individual position->
Figure SMS_8
Is the +.f of the corrected classification feature vector>
Figure SMS_11
Characteristic values of the individual positions.
In the control system of the above leveling machine, the leveling control result generating unit is further configured to: using the classifier to classify the data according to the following formula Processing the corrected classification feature vector to generate the classification result; wherein, the formula is:
Figure SMS_18
wherein->
Figure SMS_19
For outputting result vector, ++>
Figure SMS_20
And->
Figure SMS_21
Respectively +.>
Figure SMS_22
Weights and bias vectors corresponding to the respective classifications, +.>
Figure SMS_23
An exponential operation representing a vector that represents a natural exponential function value that is a power of a eigenvalue of each position in the vector.
The control system of the leveling machine further comprises a training module for training the linear embedded layer, the context encoder based on the converter, the two-way long-short-term memory neural network model and the classifier.
In the control system of the leveling machine, the training module includes: the training device comprises a training surface image acquisition unit for training a material to be leveled, and a training data acquisition unit for acquiring training data, wherein the training data comprise training surface images of the material to be leveled and the true value of the type label of the roll shaft; the training image dividing unit is used for dividing the training surface image of the material to be leveled into a training two-dimensional image block sequence; the training image block vectorization unit is used for enabling each training two-dimensional image block in the training two-dimensional image block sequence to pass through the linear embedding layer respectively so as to obtain a sequence of training image block feature vectors; a training first scale context coding unit, configured to pass the sequence of training image block feature vectors through the converter-based context encoder to obtain a plurality of training image block semantic feature vectors; the training cascade unit is used for cascading the plurality of training image block semantic feature vectors to obtain training long-distance dependent image feature vectors; the training second scale context coding unit is used for enabling the sequence of the training image block feature vectors to pass through the two-way long-short-term memory neural network model to obtain the training middle-distance dependent image feature vectors; the training multi-scale fusion unit is used for fusing the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector to obtain a training classification feature vector; a classification loss function value calculation unit, configured to pass the training classification feature vector through the classifier to obtain a classification loss function value; the classification mode digestion inhibition loss function value calculation unit is used for calculating the classification mode digestion inhibition loss function value of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector; and a training unit configured to perform training on the linear embedding layer, the converter-based context encoder, the two-way long-short-term memory neural network model, and the classifier as a loss function value by using a weighted sum of the classification loss function value and the classification loss function value.
In the control system of the above leveling machine, the classification mode digestion suppression loss function value calculation unit is further configured to: calculating the classification mode digestion inhibition loss function values of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector according to the following formula; wherein, the formula is:
Figure SMS_25
wherein->
Figure SMS_27
And->
Figure SMS_30
The training long-distance dependent image feature vector and the training medium-distance dependent image feature vector are respectively +.>
Figure SMS_26
And->
Figure SMS_28
The classifier is a weight matrix of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector respectively, < >>
Figure SMS_31
Representing the square of the two norms of the vector, +.>
Figure SMS_32
Frobenius norms of the matrix are represented, < >>
Figure SMS_24
Representing subtraction by position +.>
Figure SMS_29
Representing an exponential operation.
According to another aspect of the present application, there is also provided a control method of a leveling machine, including: acquiring a surface image of a material to be leveled; dividing the surface image of the material to be leveled into a two-dimensional image block sequence; respectively passing each two-dimensional image block in the two-dimensional image block sequence through a linear embedding layer to obtain a sequence of image block feature vectors; passing the sequence of image block feature vectors through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors; cascading the plurality of image block semantic feature vectors to obtain a long-distance dependent image feature vector; the sequence of the image block feature vectors is processed through a two-way long-short-term memory neural network model to obtain a medium-distance dependent image feature vector; fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain a classification feature vector; performing geometric constraint re-parameterization of the positive assignment norm space on the classification feature vector to obtain a corrected classification feature vector; and passing the corrected classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for representing the type label of the roll shaft.
Compared with the prior art, the control system and the control method of the leveling machine, provided by the application, are used for adaptively selecting the roll shaft with the proper diameter to improve the leveling effect based on the surface condition of the material to be leveled. Specifically, in the technical solution of the present application, considering that this is essentially a classification problem, that is, for example, for a three-axis roll leveler, an artificial intelligence algorithm based on deep learning is adopted to extract long-distance context semantic association information and mid-distance context semantic association information from a surface image of a material to be leveled, and the feature information is fused to perform type tag classification of the three-axis roll. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling effect.
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The foregoing and other objects, features and advantages of the present application will become more apparent from the following more particular description of embodiments of the present application, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
FIG. 1 illustrates a block diagram of a control system of a screed according to an embodiment of the present application.
FIG. 2 illustrates a system architecture diagram of a control system of a screed according to an embodiment of the present application.
FIG. 3 illustrates a block diagram of a training module in a control system of a screed according to an embodiment of the present application.
Fig. 4 illustrates a flowchart of a control method of the screed according to an embodiment of the present application.
Fig. 5 illustrates a flowchart of a training phase in a control method of a screed 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 apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Summary of the application: as described above, the leveling machine is a machine for leveling an uneven metal plate by pressing a strip or plate having a certain thickness by upper and lower rolls. The general leveling machine is provided with a plurality of roll shafts, for example, a three-shaft roll leveling machine, the construction efficiency is higher by adopting a larger shaft diameter, the flatness is better, but the surface slurry is easier to separate, and the slurry is thinner. The smaller shaft diameter is adopted, so that the pulp lifting effect is better, but the shaft is easy to deform, and correction should be noted. Accordingly, a control system of a leveling machine is desired that can select a roller shaft having an appropriate diameter based on the surface condition of an object to be operated to improve the leveling effect.
At present, deep learning and neural networks have been widely used in the fields of computer vision, natural language processing, speech signal processing, and the like. In addition, deep learning and neural networks have also shown levels approaching and even exceeding 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 new solutions and schemes for intelligent roll shaft selection of a leveling machine.
Accordingly, in the technical solution of the present application, it is considered that the selection of the roll shafts for different types needs to be performed according to the surface condition of the operated object, that is, the roll shaft with a suitable diameter is adaptively selected based on the surface condition of the material to be leveled to improve the leveling effect. Specifically, in the technical solution of the present application, considering that this is essentially a classification problem, that is, for example, for a three-axis roll leveler, an artificial intelligence algorithm based on deep learning is adopted to extract long-distance context semantic association information and mid-distance context semantic association information from a surface image of a material to be leveled, and the feature information is fused to perform type tag classification of the three-axis roll. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling effect.
Specifically, in the technical scheme of the application, first, a surface image of a material to be leveled is acquired. The surface image of the material to be leveled is then divided into a two-dimensional sequence of image blocks. That is, the image of the surface of the material to be leveled is diced to obtain non-overlapping and fixed-size image blocks, wherein each image block represents the surface state of each local area of the material to be leveled.
And then, respectively passing each two-dimensional image block in the two-dimensional image block sequence through a linear embedding layer to convert each two-dimensional image block into a one-dimensional embedding vector so as to obtain a sequence of image block characteristic vectors, wherein the linear embedding layer linearly projects each two-dimensional image block in the two-dimensional image block sequence into a one-dimensional embedding vector through a learnable embedding matrix, and the one-dimensional embedding vector represents an embedding vector representation of the surface state of each local area of the material to be leveled. In other words, in the technical scheme of the application, the linear embedded layer is located at the front end of the network, the blocking operation is performed on the defect image, so that the non-overlapped image blocks with fixed sizes can be obtained, mapped into the embedded vectors, and then the category vectors and the position codes are added.
Further, the sequence of image block feature vectors may be passed through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors, and the plurality of image block semantic feature vectors may be concatenated to obtain a long-range dependent image feature vector. That is, based on the transformer concept, the converter is used to capture the long-range context dependent characteristic, and the global context-based semantic coding is performed on each image block feature vector in the sequence of image block feature vectors to obtain a context semantic association feature representation with the overall semantic association of the sequence of image block feature vectors as a context, i.e. the plurality of image block semantic feature vectors. It should be appreciated that in the context of the present application, a long-range contextual semantic association feature representation of the surface state features of the respective localized areas of the material to be leveled relative to the overall surface state features of the material to be leveled may be captured by the transducer-based context encoder.
And then, the sequence of the image block feature vectors passes through a two-way long-short-term memory neural network model to obtain the medium-distance dependent image feature vectors, namely, the two-way long-term memory neural network model is utilized to capture the medium-distance context dependent characteristics. It should be understood that in the technical solution of the present application, the mid-distance context semantic association feature representation of the surface state features of each local area of the material to be leveled relative to the surface state features of other local areas of the material to be leveled may be captured by the two-way long-short term memory neural network model.
And then fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain the overall feature distribution information of the surface state of the material to be leveled, thereby obtaining the classification feature vector. Further, the corrected classification feature vector is passed through a classifier to obtain a classification result, and the classification result is used for representing the type label of the roll shaft. That is, the classification processing is performed with the classification feature vector, and a classification result for representing the type tag of the roll shaft can be obtained.
In particular, in the technical solution of the present application, since the classification feature vector is obtained by fusing the long-distance dependent image feature vector and the intermediate-distance dependent image feature vector, when the gradient of the loss function is calculated and counter-propagated from the classifier to the model in the model training, the gradient may pass through the context encoder based on the converter and the two-way long-short-term memory neural network model, respectively, and at this time, the resolution of the feature pattern extracted by the context encoder based on the converter and the two-way long-short-term memory neural network model may be caused due to abnormal gradient branching.
Thus, in addition to the classification loss function, a classification mode digestion inhibition loss function is further introduced to address digestion of the extracted feature mode, specifically expressed as:
Figure SMS_34
，/>
Figure SMS_37
and->
Figure SMS_39
The long-distance dependent image feature vector and the medium-distance dependent image feature vector, respectively, and +.>
Figure SMS_35
And->
Figure SMS_36
Respectively for the classifier
Figure SMS_38
And->
Figure SMS_40
Weight matrix of>
Figure SMS_33
Representing the square of the two norms of the vector.
That is, by introducing a classification mode digestion suppression loss function, the pseudo-difference of classifier weights is pushed to the true feature distribution difference of the long-distance dependent image feature vector and the medium-distance dependent image feature vector, so that the directional derivative in the process of gradient back propagation is guaranteed to be regularized near a gradient branching point, that is, the gradient is subjected to over-weighting between the context encoder based on the converter and the two-way long-short-term memory neural network model, so that the classification mode digestion of the features is suppressed, and the extraction capacity of classification features of the context encoder based on the converter and the two-way long-short-term memory neural network model is improved, so that the accuracy of classification results of the classification feature vectors is correspondingly improved.
In particular, in the inference stage, when the classification feature vector is obtained by fusing the long-distance-dependent image feature vector and the intermediate-distance-dependent image feature vector, in order to make full use of the long-distance-dependent information and the intermediate-distance-dependent information of the image feature vector, the classification feature vector is preferably obtained by directly concatenating the long-distance-dependent image feature vector and the intermediate-distance-dependent image feature vector, but in the case of direct concatenation, the explicit difference of the feature distributions of each of the long-distance-dependent image feature vector and the intermediate-distance-dependent image feature vector still causes a problem of discretization of the overall feature distribution of the classification feature vector, so that the classification feature vector has poor convergence with respect to the probability of a predetermined target class when classification regression is performed by the classifier, which may affect the training speed of the classifier and the accuracy of the classification result.
Thus, in the solution of the present application, the classification feature vector is, for example, denoted as
Figure SMS_42
Performing geometric constraint re-parameterization of a positive-localization space, wherein the geometric constraint re-parameterization specifically comprises the following steps: />
Figure SMS_46
，/>
Figure SMS_49
And->
Figure SMS_43
Is feature set +.>
Figure SMS_45
Mean and variance of>
Figure SMS_50
Representing the square of the two norms of the vector, +. >
Figure SMS_51
Is a transpose of the classification feature vector, < >>
Figure SMS_44
Frobenius norms of the matrix are represented, < >>
Figure SMS_48
And->
Figure SMS_52
Said classification feature vector +.>
Figure SMS_53
Is>
Figure SMS_41
Characteristic value of individual position, and->
Figure SMS_47
Is in the form of a row vector.
Here, the classification feature vector
Figure SMS_54
The geometric constrained repartitioning of the forward-defined excipient space of (2) may be based on a projection modulo length relation of the Bessel inequality by projecting the square of the vector norm expressed in the form of an inner product within the associated set space of vectors themselves such that the set of distributions of vectors has modulo length constraints within the geometric metric subspace of the forward-defined excipient space to repartitionize the distribution space to a bounded forward-defined excipient space having a closed subspace based on the geometric constraints of the feature distribution. Thus, the classification feature vector +.>
Figure SMS_55
The overall characteristic distribution has convergence relative to the target class probability value in the classification domain, so that the training speed of the classifier and the accuracy of the classification result are improved. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling effect.
Based on this, the present application proposes a control system of a screed comprising: the surface image acquisition unit is used for acquiring a surface image of the material to be leveled; an image dividing unit for dividing the surface image of the material to be leveled into a two-dimensional image block sequence; the image block vectorization unit is used for enabling each two-dimensional image block in the two-dimensional image block sequence to pass through a linear embedding layer respectively so as to obtain a sequence of image block feature vectors; a first scale context coding unit, configured to pass the sequence of image block feature vectors through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors; the cascade unit is used for cascading the plurality of image block semantic feature vectors to obtain long-distance dependent image feature vectors; the second scale context coding unit is used for enabling the sequence of the image block feature vectors to pass through a two-way long-short-term memory neural network model to obtain a middle-distance dependent image feature vector; the multi-scale fusion unit is used for fusing the long-distance dependent image feature vector and the middle-distance dependent image feature vector to obtain a classification feature vector; the vector correction unit is used for carrying out geometric constraint re-parameterization of a positive sizing space on the classification feature vector so as to obtain a corrected classification feature vector; and a leveling control result generating unit for passing the corrected classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for representing the type label of the roll shaft.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Exemplary System: FIG. 1 illustrates a block diagram of a control system of a screed according to an embodiment of the present application. As shown in fig. 1, a control system 100 of a trowel according to an embodiment of the present application includes: a material surface image acquisition unit 110 for acquiring a surface image of the material to be leveled; an image dividing unit 120 for dividing the surface image of the material to be leveled into a two-dimensional image block sequence; an image block vectorization unit 130, configured to pass each two-dimensional image block in the two-dimensional image block sequence through a linear embedding layer to obtain a sequence of image block feature vectors; a first scale context encoding unit 140 for passing the sequence of image block feature vectors through a converter-based context encoder to obtain a plurality of image block semantic feature vectors; a concatenation unit 150, configured to concatenate the plurality of image block semantic feature vectors to obtain a long-distance dependent image feature vector; a second scale context encoding unit 160, configured to pass the sequence of image block feature vectors through a two-way long-short term memory neural network model to obtain a medium-distance dependent image feature vector; a multi-scale fusion unit 170 for fusing the long-distance dependent image feature vector and the middle-distance dependent image feature vector to obtain a classification feature vector; a vector correction unit 180, configured to perform geometric constraint re-parameterization of the normal vector space on the classification feature vector to obtain a corrected classification feature vector; and a leveling control result generating unit 190 for passing the corrected classification feature vector through a classifier to obtain a classification result for representing a type tag of the roll shaft.
FIG. 2 illustrates a system architecture diagram of a control system of a screed according to an embodiment of the present application. In this system architecture, as shown in fig. 2, a surface image of a material to be leveled is first acquired and divided into a two-dimensional image block sequence. And then, respectively passing each two-dimensional image block in the two-dimensional image block sequence through a linear embedding layer to obtain a sequence of image block feature vectors. Then, the sequence of image block feature vectors is passed through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors, and the plurality of image block semantic feature vectors are concatenated to obtain a long-range dependent image feature vector. And then, the sequence of the image block feature vectors is passed through a two-way long-short-term memory neural network model to obtain the medium-distance dependent image feature vectors. Then, fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain a classification feature vector, and performing geometric constraint re-parameterization of a positive excipient space on the classification feature vector to obtain a corrected classification feature vector; and then, the corrected classification feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for representing the type label of the roll shaft.
In the control system 100 of the leveling machine, the surface image acquisition unit 110 is configured to acquire a surface image of the material to be leveled. The leveling machine is used for extruding the uneven metal plate through the upper roller and the lower roller to achieve the leveling effect. For example, the special three-in-one servo feeding leveling machine for the thin plate in the LMC2 can process materials to be leveled with the thickness of 0.3-3.2 mm, can be matched with hydraulic shearing to cut the materials to be leveled, and can be used in industries such as household appliances, automobiles, air conditioner platinum and the like.
The general leveling machine is provided with a plurality of roller shafts, for example, a three-roller leveling machine, the construction efficiency is higher by adopting a larger shaft diameter, the flatness is better, but the surface slurry is easier to separate, and the slurry is thinner. The smaller shaft diameter is adopted, so that the pulp lifting effect is better, but the shaft is easy to deform, and correction should be noted. Accordingly, a control system of a leveling machine is desired that can select a roller shaft having an appropriate diameter based on the surface condition of an object to be operated to improve the leveling effect. At present, deep learning and neural networks have been widely used in the fields of computer vision, natural language processing, speech signal processing, and the like. In addition, deep learning and neural networks have also shown levels approaching and even exceeding 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 new solutions and schemes for intelligent roll shaft selection of a leveling machine.
Accordingly, in the technical solution of the present application, it is considered that the selection of the roll shafts for different types needs to be performed according to the surface condition of the operated object, that is, the roll shaft with a suitable diameter is adaptively selected based on the surface condition of the material to be leveled to improve the leveling effect. Specifically, in the technical solution of the present application, considering that this is essentially a classification problem, that is, for example, for a control system of a three-axis roller leveling machine, an artificial intelligence algorithm based on deep learning is adopted to extract long-distance context semantic association information and mid-distance context semantic association information from a surface image of a material to be leveled, and the feature information is fused to perform type tag classification of the three-axis roller. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling effect. Specifically, in the technical scheme of the application, first, a surface image of a material to be leveled is acquired. Wherein the surface image of the material to be leveled may be acquired by a camera disposed on a control system of the screed.
In the control system 100 of the leveling machine, the image dividing unit 120 is configured to divide the surface image of the material to be leveled into a two-dimensional image block sequence. That is, the image of the surface of the material to be leveled is diced to obtain non-overlapping and fixed-size image blocks, wherein each image block represents the surface state of each local area of the material to be leveled. In this way, provision is made for feature extraction of the medium-long distance dependent image of the material image to be flattened.
Specifically, in the embodiment of the present application, the image dividing unit 120 is further configured to uniformly divide the surface image of the material to be leveled into the two-dimensional image block sequence.
In the control system 100 of the above-mentioned leveling machine, the image block vectorization unit 130 is configured to pass each two-dimensional image block in the two-dimensional image block sequence through a linear embedding layer to obtain a sequence of image block feature vectors. That is, each two-dimensional image block in the two-dimensional image block sequence is respectively passed through a linear embedding layer to convert each two-dimensional image block into a one-dimensional embedding vector to obtain a sequence of image block feature vectors, wherein the linear embedding layer linearly projects each two-dimensional image block in the two-dimensional image block sequence into a one-dimensional embedding vector through a learning embedding matrix, and the one-dimensional embedding vector represents an embedding vector representation of the surface state of each local area of the material to be leveled. In other words, in the technical scheme of the application, the linear embedded layer is located at the front end of the network, the blocking operation is performed on the defect image, so that the non-overlapped image blocks with fixed sizes can be obtained, mapped into the embedded vectors, and then the category vectors and the position codes are added.
Specifically, in the embodiment of the present application, the image block vectorization unit 130 is further configured to use the linear embedding layer to perform linear projection on each two-dimensional image block in the two-dimensional image block sequence with a learning embedding matrix to obtain the sequence of image block feature vectors.
In the control system 100 of the above-mentioned leveling machine, the first scale context encoding unit 140 is configured to pass the sequence of image block feature vectors through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors. That is, based on the transformer concept, the converter is used to capture the long-range context dependent characteristic, and the global context-based semantic coding is performed on each image block feature vector in the sequence of image block feature vectors to obtain a context semantic association feature representation with the overall semantic association of the sequence of image block feature vectors as a context, i.e. the plurality of image block semantic feature vectors. It should be appreciated that in the context of the present application, a long-range contextual semantic association feature representation of the surface state features of the respective localized areas of the material to be leveled relative to the overall surface state features of the material to be leveled may be captured by the transducer-based context encoder.
Specifically, in the embodiment of the present application, the first scale context encoding unit 140 is further configured to: arranging the sequence of the image block feature vectors into an input vector; converting the input vector into a query vector and a key vector through a learning embedding matrix respectively; calculating the product between the query vector and the transpose vector of the key vector to obtain a self-attention correlation matrix; carrying out standardization processing on the self-attention association matrix to obtain a standardized self-attention association matrix; inputting the standardized self-attention association matrix into a Softmax activation function to activate so as to obtain a self-attention feature matrix; and multiplying the self-attention feature matrix with each image block feature vector in the sequence of image block feature vectors to obtain the plurality of image block semantic feature vectors.
In the control system 100 of the above-mentioned leveling machine, the cascade unit 150 is configured to cascade the plurality of image block semantic feature vectors to obtain a long-distance dependent image feature vector. That is, the plurality of image semantic feature vectors are concatenated to integrate long-range contextual semantic-related feature representations of the surface state features of the respective localized regions relative to the overall surface state features of the material to be leveled.
In the control system 100 of the above-mentioned leveling machine, the second scale context encoding unit 160 is configured to pass the sequence of image block feature vectors through a two-way long-short term memory neural network model to obtain the medium-distance dependent image feature vector. That is, the two-way long and short term memory neural network model is utilized to capture mid-range context dependent characteristics. It should be understood that in the technical solution of the present application, the mid-distance context semantic association feature representation of the surface state features of each local area of the material to be leveled relative to the surface state features of other local areas of the material to be leveled may be captured by the two-way long-short term memory neural network model. The two-way long-short-term memory neural network (BiLSTM) is formed by adopting a two-way connection mode for cell structures of the long-short-term memory neural network (LSTM), so that the gradient disappearance problem of the traditional circulating neural network (RNN) is solved, and the integrity of the extracted information is ensured.
In the control system 100 of the above-mentioned leveling machine, the multi-scale fusion unit 170 is configured to fuse the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain a classification feature vector. In this way, the long-distance dependent image feature vector and the middle-distance dependent image feature vector are fused to obtain the overall feature distribution information of the surface state of the material to be leveled, so as to obtain the classification feature vector.
Specifically, in the present embodiment, the multi-scale fusion unit 170 is further configured to: fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain the classification feature vector by the following formula; wherein, the formula is:
Figure SMS_56
wherein->
Figure SMS_57
Representing the long-range dependent image feature vector, < >>
Figure SMS_58
Representing the mid-range dependent image feature vector, < >>
Figure SMS_59
Representing a cascading function.
When the long-distance-dependent image feature vector and the middle-distance-dependent image feature vector are fused to obtain the classification feature vector, in order to make full use of the long-distance-dependent information and the middle-distance-dependent information of the image feature vector, the classification feature vector is preferably obtained by directly concatenating the long-distance-dependent image feature vector and the middle-distance-dependent image feature vector, but in the case of direct concatenation, the explicit difference of the feature distributions of the long-distance-dependent image feature vector and the middle-distance-dependent image feature vector still causes the problem of discretization of the overall feature distribution of the classification feature vector, so that the classification feature vector has poor convergence with respect to a predetermined target class probability when classified regression is performed through a classifier, and thus the training speed of the classifier and the accuracy of the classification result are affected.
In the control system 100 of the above-mentioned leveling machine, the vector correction unit 180 is configured to perform geometric constraint re-parameterization of the normal-localization space on the classification feature vector to obtain a corrected classification feature vector.
Specifically, in the embodiment of the present application, the vector correction unit 180 is further configured to: performing geometric constraint re-parameterization of positive definite excipient space on the classification feature vector by the following formula to obtain a corrected classification feature vector; wherein, the correction formula is:
Figure SMS_61
wherein->
Figure SMS_67
Is the classification feature vector,/->
Figure SMS_70
And->
Figure SMS_63
Is feature set +.>
Figure SMS_66
Mean and variance of>
Figure SMS_69
Is a transpose of the classification feature vector,
Figure SMS_71
representing the square of the two norms of the vector, +.>
Figure SMS_60
Frobenius norms of the matrix are represented, < >>
Figure SMS_64
Is the classification feature vector->
Figure SMS_68
Is>
Figure SMS_72
Characteristic value of individual position->
Figure SMS_62
Is the +.f of the corrected classification feature vector>
Figure SMS_65
Characteristic values of the individual positions.
Here, the classification feature vector
Figure SMS_73
The geometric constrained repartitioning of the forward-defined excipient space of (2) may be based on a projection modulo length relation of the Bessel inequality by projecting the square of the vector norm expressed in the form of an inner product within the associated set space of vectors themselves such that the set of distributions of vectors has modulo length constraints within the geometric metric subspace of the forward-defined excipient space to repartitionize the distribution space to a bounded forward-defined excipient space having a closed subspace based on the geometric constraints of the feature distribution. Thus, the classification feature vector +. >
Figure SMS_74
The overall characteristic distribution has convergence relative to the target class probability value in the classification domain, so that the training speed of the classifier and the accuracy of the classification result are improved.
In the control system 100 of the above-mentioned leveling machine, the leveling control result generating unit 190 is configured to pass the corrected classification feature vector through a classifier to obtain a classification result, where the classification result is used to represent a type tag of the roll shaft. That is, the classification processing is performed with the classification feature vector, and a classification result for representing the type tag of the roll shaft can be obtained.
Specifically, in the embodiment of the present application, the leveling control result generating unit 190 is further configured to: processing the corrected classification feature vector using the classifier in the following formula to generate the classification result; wherein, the formula is:
Figure SMS_75
wherein->
Figure SMS_76
For outputting result vector, ++>
Figure SMS_77
And->
Figure SMS_78
Respectively +.>
Figure SMS_79
Weights and bias vectors corresponding to the respective classifications, +.>
Figure SMS_80
An exponential operation representing a vector that represents a natural exponential function value that is a power of a eigenvalue of each position in the vector.
In the control system 100 of the above-described screed, a training module 200 for training the linear embedded layer, the converter-based context encoder, the two-way long and short term memory neural network model, and the classifier is further included.
FIG. 3 illustrates a block diagram of a training module in a control system of a screed according to an embodiment of the present application. As shown in fig. 3, the training module 200 includes: a training surface image acquisition unit 210 for acquiring training data including a training surface image of the material to be leveled and a true value of a type tag of the roller shaft; a training image dividing unit 220 for dividing the training surface image of the material to be leveled into a training two-dimensional image block sequence; a training image block vectorization unit 230, configured to pass each training two-dimensional image block in the training two-dimensional image block sequence through the linear embedding layer to obtain a sequence of training image block feature vectors; a training first scale context encoding unit 240, configured to pass the sequence of training image block feature vectors through the converter-based context encoder to obtain a plurality of training image block semantic feature vectors; a training concatenation unit 250, configured to concatenate the plurality of training image block semantic feature vectors to obtain a training long-distance dependent image feature vector; a training second scale context encoding unit 260, configured to pass the sequence of training image block feature vectors through the two-way long-short-term memory neural network model to obtain a training mid-distance dependent image feature vector; a training multi-scale fusion unit 270, configured to fuse the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector to obtain a training classification feature vector; a classification loss function value calculation unit 280, configured to pass the training classification feature vector through the classifier to obtain a classification loss function value; a classification mode cancellation suppression loss function value calculation unit 290 configured to calculate a classification mode cancellation suppression loss function value of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector; and a training unit 300 for training the linear embedding layer, the converter-based context encoder, the two-way long and short term memory neural network model, and the classifier as a weighted sum of the classification loss function value and the suppression loss function value in the classification mode.
In particular, in the technical solution of the present application, since the classification feature vector is obtained by fusing the long-distance dependent image feature vector and the intermediate-distance dependent image feature vector, when the gradient of the loss function is calculated and counter-propagated from the classifier to the model in the model training, the gradient may pass through the context encoder based on the converter and the two-way long-short-term memory neural network model, respectively, and at this time, the resolution of the feature pattern extracted by the context encoder based on the converter and the two-way long-short-term memory neural network model may be caused due to abnormal gradient branching. Thus, in addition to the classification loss function, a classification mode resolution suppression loss function is further introduced to address resolution of the extracted feature mode.
Specifically, in the embodiment of the present application, the classification mode resolution suppression loss function value calculation unit 290 is further configured to: calculating the classification mode digestion inhibition loss function values of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector according to the following formula; wherein, the formula is:
Figure SMS_83
Wherein->
Figure SMS_86
And->
Figure SMS_88
The training long-distance dependent image feature vector and the training medium-distance dependent image feature vector are respectively +.>
Figure SMS_82
And->
Figure SMS_85
The classifier is a weight matrix of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector respectively, < >>
Figure SMS_87
Representation ofSquare of two norms of vector, +.>
Figure SMS_89
Frobenius norms of the matrix are represented, < >>
Figure SMS_81
Representing subtraction by position +.>
Figure SMS_84
Representing an exponential operation.
That is, by introducing a classification mode digestion suppression loss function, the pseudo-difference of classifier weights is pushed to the true feature distribution difference of the long-distance dependent image feature vector and the medium-distance dependent image feature vector, so that the directional derivative in the process of gradient back propagation is guaranteed to be regularized near a gradient branching point, that is, the gradient is subjected to over-weighting between the context encoder based on the converter and the two-way long-short-term memory neural network model, so that the classification mode digestion of the features is suppressed, and the extraction capacity of classification features of the context encoder based on the converter and the two-way long-short-term memory neural network model is improved, so that the accuracy of classification results of the classification feature vectors is correspondingly improved. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling effect.
In summary, the control system 100 of the trowel according to embodiments of the present application is illustrated that adaptively selects a roller of an appropriate diameter to enhance the flattening effect based on the topography of the material to be flattened. Specifically, in the technical solution of the present application, considering that this is essentially a classification problem, that is, for example, for a three-axis roll leveler, an artificial intelligence algorithm based on deep learning is adopted to extract long-distance context semantic association information and mid-distance context semantic association information from a surface image of a material to be leveled, and the feature information is fused to perform type tag classification of the three-axis roll. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling effect.
An exemplary method is: fig. 4 illustrates a flowchart of a control method of the screed according to an embodiment of the present application. As shown in fig. 4, the control method of the leveling machine according to the embodiment of the application includes the steps of: s110, acquiring a surface image of a material to be leveled; s120, dividing the surface image of the material to be leveled into a two-dimensional image block sequence; s130, respectively passing each two-dimensional image block in the two-dimensional image block sequence through a linear embedding layer to obtain a sequence of image block feature vectors; s140, passing the sequence of image block feature vectors through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors; s150, cascading the plurality of image block semantic feature vectors to obtain a long-distance dependent image feature vector; s160, passing the sequence of the image block feature vectors through a two-way long-short-term memory neural network model to obtain a medium-distance dependent image feature vector; s170, fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain a classification feature vector; s180, performing geometric constraint re-parameterization of a positive definite excipient space on the classification feature vector to obtain a corrected classification feature vector; and S190, passing the corrected classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for representing the type label of the roll shaft.
In one example, in the control method of the leveling machine, the dividing the surface image of the material to be leveled into a two-dimensional image block sequence includes: and uniformly dividing the surface image of the material to be leveled into the two-dimensional image block sequence.
In an example, in the control method of the leveling machine, the step of passing each two-dimensional image block in the two-dimensional image block sequence through a linear embedding layer to obtain a sequence of image block feature vectors includes: and linearly projecting each two-dimensional image block in the two-dimensional image block sequence by using the linear embedding layer in a learning embedding matrix to obtain the sequence of the image block feature vectors.
In one example, in the control method of the above leveling machine, the step of passing the sequence of image block feature vectors through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors includes: arranging the sequence of the image block feature vectors into an input vector; converting the input vector into a query vector and a key vector through a learning embedding matrix respectively; calculating the product between the query vector and the transpose vector of the key vector to obtain a self-attention correlation matrix; carrying out standardization processing on the self-attention association matrix to obtain a standardized self-attention association matrix; inputting the standardized self-attention association matrix into a Softmax activation function to activate so as to obtain a self-attention feature matrix; and multiplying the self-attention feature matrix with each image block feature vector in the sequence of image block feature vectors to obtain the plurality of image block semantic feature vectors.
In one example, in the control method of the above leveling machine, the fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain the classification feature vector includes: fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain the classification feature vector by the following formula; wherein, the formula is:
Figure SMS_90
wherein->
Figure SMS_91
Representing the long-range dependent image feature vector, < >>
Figure SMS_92
Representing the mid-range dependent image feature vector, < >>
Figure SMS_93
Representing a cascading function.
In one example, in the control method of the above leveling machine, performing geometric constraint re-parameterization of the classification feature vector in a positive excipient space to obtain a corrected classification feature vector includes: the classification characteristic is oriented by the following formulaPerforming geometric constraint re-parameterization of the normal vector space to obtain corrected classification feature vectors; wherein, the correction formula is:
Figure SMS_95
wherein->
Figure SMS_100
Is the classification feature vector,/->
Figure SMS_103
And
Figure SMS_96
is feature set +.>
Figure SMS_99
Mean and variance of>
Figure SMS_102
Is a transpose of the classification feature vector, < >>
Figure SMS_105
Representing the square of the two norms of the vector, +. >
Figure SMS_97
Frobenius norms of the matrix are represented, < >>
Figure SMS_101
Is the classification feature vector->
Figure SMS_104
Is>
Figure SMS_106
Characteristic value of individual position->
Figure SMS_94
Is the +.f of the corrected classification feature vector>
Figure SMS_98
Characteristic values of the individual positions.
In one ofIn an example, in the control method of the above leveling machine, the step of passing the corrected classification feature vector through a classifier to obtain a classification result includes: processing the corrected classification feature vector using the classifier in the following formula to generate the classification result; wherein, the formula is:
Figure SMS_107
wherein->
Figure SMS_108
For outputting result vector, ++>
Figure SMS_109
And->
Figure SMS_110
Respectively +.>
Figure SMS_111
Weights and bias vectors corresponding to the respective classifications, +.>
Figure SMS_112
An exponential operation representing a vector that represents a natural exponential function value that is a power of a eigenvalue of each position in the vector.
In one example, the control method of the leveling machine further includes a training phase for training the linear embedded layer, the context encoder based on the converter, the two-way long-short term memory neural network model and the classifier.
Fig. 5 illustrates a flowchart of a training phase in a control method of a screed according to an embodiment of the present application. As shown in fig. 5, the training phase includes the steps of: s210, acquiring training data, wherein the training data comprises training surface images of materials to be leveled and true values of type labels of the roll shafts; s220, dividing the training surface image of the material to be leveled into a training two-dimensional image block sequence; s230, respectively passing each training two-dimensional image block in the training two-dimensional image block sequence through the linear embedding layer to obtain a sequence of training image block feature vectors; s240, passing the sequence of training image block feature vectors through the context encoder based on the converter to obtain a plurality of training image block semantic feature vectors; s250, cascading the plurality of training image block semantic feature vectors to obtain training long-distance dependent image feature vectors; s260, passing the sequence of the training image block feature vectors through the two-way long-short-term memory neural network model to obtain a distance dependent image feature vector in training; s270, fusing the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector to obtain a training classification feature vector; s280, passing the training classification feature vector through the classifier to obtain a classification loss function value; s290, calculating a classification mode digestion inhibition loss function value of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector; and S300, training the linear embedded layer, the context encoder based on the converter, the two-way long-short-term memory neural network model and the classifier by taking the weighted sum of the classification loss function value and the suppression loss function value as the loss function value.
In summary, a control method of the trowel according to embodiments of the present application is illustrated that adaptively selects a roll shaft of an appropriate diameter to enhance a flattening effect based on a surface condition of a material to be flattened. Specifically, in the technical solution of the present application, considering that this is essentially a classification problem, that is, for example, for a three-axis roll leveler, an artificial intelligence algorithm based on deep learning is adopted to extract long-distance context semantic association information and mid-distance context semantic association information from a surface image of a material to be leveled, and the feature information is fused to perform type tag classification of the three-axis roll. In this way, the roller shaft having an appropriate diameter can be adaptively selected based on the actual condition of the surface of the operated object to improve the leveling effect.

Claims (9)

1. A control system for a troweling machine, comprising:
the surface image acquisition unit is used for acquiring a surface image of the material to be leveled;
an image dividing unit for dividing the surface image of the material to be leveled into a two-dimensional image block sequence;
the image block vectorization unit is used for enabling each two-dimensional image block in the two-dimensional image block sequence to pass through a linear embedding layer respectively so as to obtain a sequence of image block feature vectors;
A first scale context coding unit, configured to pass the sequence of image block feature vectors through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors;
the cascade unit is used for cascading the plurality of image block semantic feature vectors to obtain long-distance dependent image feature vectors;
the second scale context coding unit is used for enabling the sequence of the image block feature vectors to pass through a two-way long-short-term memory neural network model to obtain a middle-distance dependent image feature vector;
the multi-scale fusion unit is used for fusing the long-distance dependent image feature vector and the middle-distance dependent image feature vector to obtain a classification feature vector;
the vector correction unit is used for carrying out geometric constraint re-parameterization of a positive sizing space on the classification feature vector so as to obtain a corrected classification feature vector; and
the leveling control result generation unit is used for enabling the corrected classification feature vector to pass through a classifier to obtain a classification result, wherein the classification result is used for representing the type label of the roll shaft;
wherein the vector correction unit is further configured to: performing geometric constraint re-parameterization of positive definite excipient space on the classification feature vector by using the following correction formula to obtain a corrected classification feature vector;
Wherein, the correction formula is:
Figure QLYQS_1
，
wherein,,
Figure QLYQS_5
is the classification feature vector,/->
Figure QLYQS_8
And->
Figure QLYQS_10
Is feature set +.>
Figure QLYQS_3
Mean and variance of>
Figure QLYQS_11
Is a transpose of the classification feature vector, < >>
Figure QLYQS_12
Representing the square of the two norms of the vector, +.>
Figure QLYQS_13
Frobenius norms of the matrix are represented, < >>
Figure QLYQS_2
Is the classification feature vector->
Figure QLYQS_6
Is>
Figure QLYQS_7
Characteristic value of individual position->
Figure QLYQS_9
Is the +.f of the corrected classification feature vector>
Figure QLYQS_4
Characteristic values of the individual positions.
2. The control system of a screed according to claim 1, wherein the image dividing unit is further configured to uniformly divide the surface image of the material to be screed into the two-dimensional image block sequence.
3. The control system of claim 2, wherein the image block vectorization unit is further configured to linearly project each two-dimensional image block in the sequence of two-dimensional image blocks with a learnable embedding matrix using the linear embedding layer to obtain the sequence of image block feature vectors.
4. The control system of a trowel according to claim 3, wherein the first scale context encoding unit is further configured to:
arranging the sequence of the image block feature vectors into an input vector;
Converting the input vector into a query vector and a key vector through a learning embedding matrix respectively;
calculating the product between the query vector and the transpose vector of the key vector to obtain a self-attention correlation matrix;
carrying out standardization processing on the self-attention association matrix to obtain a standardized self-attention association matrix;
inputting the standardized self-attention association matrix into a Softmax activation function to activate so as to obtain a self-attention feature matrix; and
and multiplying the self-attention feature matrix with each image block feature vector in the sequence of image block feature vectors respectively to obtain a plurality of image block semantic feature vectors.
5. The control system of a trowel as in claim 4, wherein the trowel control result generation unit is further configured to: processing the corrected classification feature vector using the classifier in the following formula to generate the classification result;
wherein, the formula is:
Figure QLYQS_14
，
wherein the method comprises the steps of
Figure QLYQS_15
For outputting result vector, ++>
Figure QLYQS_16
And->
Figure QLYQS_17
Respectively +.>
Figure QLYQS_18
The weight and bias vector corresponding to each category,
Figure QLYQS_19
an exponential operation representing a vector that represents a natural exponential function value that is a power of a eigenvalue of each position in the vector.
6. The control system of a trowel according to claim 1, further comprising a training module for training the linear embedded layer, the transducer-based context encoder, the two-way long and short term memory neural network model, and the classifier.
7. The control system of a trowel as in claim 6, wherein said training module comprises:
the training device comprises a training surface image acquisition unit for training a material to be leveled, and a training data acquisition unit for acquiring training data, wherein the training data comprise training surface images of the material to be leveled and the true value of the type label of the roll shaft;
the training image dividing unit is used for dividing the training surface image of the material to be leveled into a training two-dimensional image block sequence;
the training image block vectorization unit is used for enabling each training two-dimensional image block in the training two-dimensional image block sequence to pass through the linear embedding layer respectively so as to obtain a sequence of training image block feature vectors;
a training first scale context coding unit, configured to pass the sequence of training image block feature vectors through the converter-based context encoder to obtain a plurality of training image block semantic feature vectors;
The training cascade unit is used for cascading the plurality of training image block semantic feature vectors to obtain training long-distance dependent image feature vectors;
the training second scale context coding unit is used for enabling the sequence of the training image block feature vectors to pass through the two-way long-short-term memory neural network model to obtain the training middle-distance dependent image feature vectors;
the training multi-scale fusion unit is used for fusing the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector to obtain a training classification feature vector;
a classification loss function value calculation unit, configured to pass the training classification feature vector through the classifier to obtain a classification loss function value;
the classification mode digestion inhibition loss function value calculation unit is used for calculating the classification mode digestion inhibition loss function value of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector; and
and the training unit is used for training the linear embedded layer, the context encoder based on the converter, the two-way long-short-term memory neural network model and the classifier by taking the weighted sum of the classification loss function value and the suppression loss function value as the loss function value.
8. The control system of a trowel according to claim 7, wherein the classification mode resolution suppression loss function value calculation unit is further configured to:
calculating the classification mode digestion inhibition loss function values of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector according to the following formula;
wherein, the formula is:
Figure QLYQS_20
，
wherein the method comprises the steps of
Figure QLYQS_23
And->
Figure QLYQS_25
The training long-distance dependent image feature vector and the training medium-distance dependent image feature vector are respectively +.>
Figure QLYQS_26
And->
Figure QLYQS_22
The classifier is a weight matrix of the training long-distance dependent image feature vector and the training medium-distance dependent image feature vector respectively, < >>
Figure QLYQS_24
Representing the square of the two norms of the vector, +.>
Figure QLYQS_27
Frobenius norms of the matrix are represented, < >>
Figure QLYQS_28
Representing subtraction by position +.>
Figure QLYQS_21
Representing an exponential operation.
9. A control method of a leveling machine, comprising:
acquiring a surface image of a material to be leveled;
dividing the surface image of the material to be leveled into a two-dimensional image block sequence;
respectively passing each two-dimensional image block in the two-dimensional image block sequence through a linear embedding layer to obtain a sequence of image block feature vectors;
Passing the sequence of image block feature vectors through a context encoder based on a converter to obtain a plurality of image block semantic feature vectors;
cascading the plurality of image block semantic feature vectors to obtain a long-distance dependent image feature vector;
the sequence of the image block feature vectors is processed through a two-way long-short-term memory neural network model to obtain a medium-distance dependent image feature vector;
fusing the long-distance dependent image feature vector and the medium-distance dependent image feature vector to obtain a classification feature vector;
performing geometric constraint re-parameterization of the positive assignment norm space on the classification feature vector to obtain a corrected classification feature vector; and
the corrected classification feature vector passes through a classifier to obtain a classification result, wherein the classification result is used for representing the type label of the roll shaft;
wherein, carry on the geometric constraint repartitioning of the normal definite form space to the said classification characteristic vector, in order to get the classification characteristic vector after correcting, include: performing geometric constraint re-parameterization of positive definite excipient space on the classification feature vector by using the following correction formula to obtain a corrected classification feature vector;
wherein, the correction formula is:
Figure QLYQS_29
，
Wherein,,
Figure QLYQS_31
is the classification feature vector,/->
Figure QLYQS_34
And->
Figure QLYQS_37
Is feature set +.>
Figure QLYQS_32
Mean and variance of>
Figure QLYQS_35
Is a transpose of the classification feature vector, < >>
Figure QLYQS_38
Representing the square of the two norms of the vector, +.>
Figure QLYQS_40
Frobenius norms of the matrix are represented, < >>
Figure QLYQS_30
Is the classification feature vector->
Figure QLYQS_36
Is>
Figure QLYQS_39
Characteristic value of individual position->
Figure QLYQS_41
Is the +.f of the corrected classification feature vector>
Figure QLYQS_33
Characteristic values of the individual positions.
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