CN114037648A - Intelligent rate parameter control method based on similar Softmax function information entropy - Google Patents
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Abstract
The application relates to intelligent sealing operation parameter control in the field of intelligent manufacturing, and particularly discloses an intelligent rate parameter control method based on Softmax-like function information entropy, which adopts a convolutional neural network to extract spatial features of a gluing position in an image, obtains the relevance of the local spatial features of the gluing position and the distribution characteristics of the whole spatial features of a windshield in a high-dimensional space through the information entropy expression of the Softmax-like function, and fuses the information entropy matrix of the Softmax-like function and the local spatial high-dimensional features of the gluing position to construct a feature matrix so as to obtain desired parameters, namely glue outlet rate and moving rate, from the feature matrix through an encoder.
Description
Technical Field
The invention relates to intelligent sealing operation parameter control in the field of intelligent manufacturing, in particular to an intelligent rate parameter control method based on a Softmax-like function information entropy, an intelligent rate parameter control system based on the Softmax-like function information entropy and electronic equipment.
Background
When installing or replacing the windshield of the automobile, the sealing operation is required to effectively bond the windshield and the frame. When the current glue sealing operation is carried out, an automatic glue discharging device replaces manual glue extruding, so that the glue discharging operation of the windshield is greatly improved.
The automatic glue dispensing devices of the prior art dispense glue at a constant rate at each glue application location and move at a constant speed along the frame of the windscreen to ensure uniform glue application at each glue application location. However, this gluing method sometimes results in uneven gluing, since the windshield is not laid flat in the frame during the windshield gluing operation, and the glue itself may move due to its own fluid properties.
Therefore, it is desirable to provide a solution for intelligently controlling the glue dispensing rate and the moving rate of an automatic glue dispensing apparatus.
At present, deep learning and neural networks have been widely applied 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.
The deep learning and the development of the neural network provide a new solution for intelligently controlling the glue discharging speed and the moving speed of the automatic glue discharging device.
Disclosure of Invention
The present application is proposed to solve the above-mentioned technical problems. The embodiment of the application provides an intelligent rate parameter control method based on information entropy of a Softmax-like function, an intelligent rate parameter control system based on information entropy of the Softmax-like function and electronic equipment, wherein a convolutional neural network is adopted to extract spatial features of a gluing position in an image, the relevance of the local spatial features of the gluing position and the distribution characteristics of the whole spatial features of a windshield in a high-dimensional space is obtained through the information entropy expression of the Softmax-like function, and a feature matrix is constructed by fusing the information entropy matrix of the Softmax-like function and the local spatial high-dimensional features of the gluing position, so that the required parameters, namely glue outlet rate and moving rate, are obtained from the feature matrix through an encoder.
According to one aspect of the application, an intelligent rate parameter control method based on the information entropy of a Softmax-like function is provided, and comprises the following steps:
acquiring a first image of the whole of a windshield on which a gluing operation is being performed and a second image of a gluing position on which the gluing is being performed;
respectively passing the first image and the second image through a depth convolution neural network to obtain a first feature map corresponding to the first image and a second feature map corresponding to the second image, wherein the last layer of the depth convolution neural network is activated by a Sigmoid activation function so that the feature value of each position in the first feature map and the second feature map is in the interval of 0 to 1;
for the eigenvalue of each position in the second feature map, calculating the information entropy of the eigenvalue of each position relative to the Softmax-like function of the first feature map by the following formula to obtain an information entropy matrix of the second feature map relative to the Softmax-like function of the first feature map, wherein the formula is as follows:
wherein P represents the information entropy of the Softmax-like function, yi represents the characteristic value of each position in the second characteristic diagram, and xj represents the characteristic value of each position in the first characteristic diagram;
calculating a position-weighted sum of the information entropy matrix of the Softmax-like function and the second feature map to obtain a fused feature map; and
and enabling the fused characteristic diagram to pass through an encoder, wherein the output digit of the last layer of the encoder is two, so as to obtain two encoding values, and the two encoding values are used for respectively representing the glue discharging speed and the moving speed of the automatic glue discharging device.
In the above intelligent rate parameter control method based on the information entropy of the Softmax-like function, acquiring a first image of the whole windshield undergoing a gluing operation and a second image of a gluing position undergoing gluing, including: acquiring an original second image of a gluing position where gluing is performed; and amplifying the original second image by a preset magnification to obtain the second image.
In the above intelligent rate parameter control method based on the information entropy of the Softmax-like function, the resolution of the first image is smaller than the resolution of the original second image.
In the above intelligent rate parameter control method based on the information entropy of the Softmax-like function, the encoder is a deep full-connection network.
In the above intelligent rate parameter control method based on the information entropy of the Softmax-like function, the deep fully-connected network includes a fully-connected layer having a preset depth, and the preset depth is greater than or equal to ten.
In the above intelligent rate parameter control method based on the information entropy of the Softmax-like function, the deep convolutional neural network is a deep residual error network.
According to another aspect of the application, an intelligent rate parameter control system based on the information entropy of a Softmax-like function comprises:
an image acquisition unit for acquiring a first image of the entirety of the windshield on which the glue application operation is being performed and a second image of the glue application position on which the glue application is being performed;
a feature map generation unit, configured to pass the first image and the second image obtained by the image acquisition unit through a depth convolution neural network respectively to obtain a first feature map corresponding to the first image and a second feature map corresponding to the second image, where a last layer of the depth convolution neural network is activated by a Sigmoid activation function so that a feature value of each position in the first feature map and the second feature map is within an interval of 0 to 1;
an information entropy matrix generating unit, configured to calculate, for the feature value of each position in the second feature map obtained by the feature map generating unit, information entropy of the feature value of each position with respect to the Softmax-like function of the first feature map obtained by the feature map generating unit, to obtain an information entropy matrix of the second feature map with respect to the Softmax-like function of the first feature map, according to a formula:
wherein P represents the information entropy of the Softmax-like function, yi represents the characteristic value of each position in the second characteristic diagram, and xj represents the characteristic value of each position in the first characteristic diagram;
a fused feature map generation unit, configured to calculate a position-wise weighted sum of the information entropy matrix of the Softmax-like function obtained by the information entropy matrix generation unit and the second feature map obtained by the feature map generation unit, so as to obtain a fused feature map; and
and the encoding value generating unit is used for enabling the fused characteristic diagram obtained by the fused characteristic diagram generating unit to pass through an encoder, the output digit of the last layer of the encoder is two, so as to obtain two encoding values, and the two encoding values are used for respectively representing the glue discharging rate and the moving rate of the automatic glue discharging device.
In the above intelligent rate parameter control system based on information entropy of a Softmax-like function, the image obtaining unit includes: the original second image acquisition subunit is used for acquiring an original second image of the gluing position where gluing is carried out; and a second image generation subunit, configured to enlarge the original second image obtained by the original second image obtaining subunit by a preset magnification to obtain the second image.
In the above-mentioned intelligent rate parameter control system based on the information entropy of the Softmax-like function, the resolution of the first image is smaller than the resolution of the original second image.
In the above intelligent rate parameter control system based on the information entropy of the Softmax-like function, the encoder is a deep full-connection network.
In the above intelligent rate parameter control system based on the information entropy of the Softmax-like function, the deep fully-connected network includes a fully-connected layer having a preset depth, and the preset depth is greater than or equal to ten.
In the above intelligent rate parameter control system based on the information entropy of the Softmax-like function, the deep convolutional neural network is a deep residual error network.
According to still another aspect of the present application, there is provided an electronic apparatus including: a processor; and a memory having stored therein computer program instructions which, when executed by the processor, cause the processor to perform the method of intelligent rate parameter control based on information entropy of a Softmax-like function as described above.
According to yet another aspect of the present application, there is provided a computer readable medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the method of intelligent rate parameter control based on information entropy of a Softmax-like function as described above.
Compared with the prior art, the intelligent rate parameter control method based on the information entropy of the Softmax-like function, the intelligent rate parameter control system based on the information entropy of the Softmax-like function and the electronic equipment adopt the convolutional neural network to extract the spatial feature of the gluing position in the image, obtain the relevance of the local spatial feature of the gluing position and the distribution characteristic of the whole spatial feature of the windshield in a high-dimensional space through the information entropy expression of the Softmax-like function, and fuse the information entropy matrix of the Softmax-like function and the local spatial high-dimensional feature of the gluing position to construct the feature matrix so as to obtain the required parameters, namely the glue outlet rate and the moving rate, from the feature matrix through the encoder.
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 represent like parts or steps.
Fig. 1 illustrates an application scenario of an intelligent rate parameter control method based on information entropy of a Softmax-like function according to an embodiment of the present application;
FIG. 2 illustrates a flow chart of a method for intelligent rate parameter control based on information entropy of a Softmax-like function according to an embodiment of the application;
FIG. 3 illustrates a system architecture diagram of an intelligent rate parameter control method based on information entropy of a Softmax-like function according to an embodiment of the present application;
fig. 4 illustrates a flowchart of acquiring a first image of the whole of a windshield undergoing a gluing operation and a second image of a gluing position undergoing gluing in the intelligent rate parameter control method based on the information entropy of the Softmax-like function according to the embodiment of the application;
fig. 5 illustrates a block diagram of an intelligent rate parameter control system based on information entropy of a Softmax-like function according to an embodiment of the present application.
Fig. 6 illustrates a block diagram of an image acquisition unit in an intelligent rate parameter control system based on information entropy of a Softmax-like function according to an embodiment of the present application.
FIG. 7 illustrates a block diagram of an electronic device in accordance with 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, the current automatic glue dispensing device dispenses glue at a constant rate at each glue dispensing position and moves at a constant speed along the frame of the windshield, but this glue dispensing method sometimes results in uneven glue dispensing because the windshield is not laid flat in the frame during the windshield glue dispensing operation and the glue itself may move due to its own fluid properties.
Based on this, the inventor of the present application expects to be able to control the glue discharging speed and the moving speed of the automatic glue discharging device, so as to adjust the current glue discharging speed and the moving speed according to the specific gluing position, thereby improving the uniformity of gluing.
The inventors of the present application consider that the glue application position represents the spatial characteristics of the position itself with respect to the whole of the windshield to be mounted, and are therefore adapted to employ a convolutional neural network to extract the spatial features of a predetermined region in the image and construct a feature vector by its correlation with the spatial features of the whole of the image to obtain the desired parameters, i.e., the glue discharge rate and the movement rate, from the feature vector by the encoder.
In further consideration of the high-dimensional property of the spatial features extracted by the convolutional neural network, in the scheme of the application, the relevance of the local spatial features and the distribution characteristics of the overall spatial features in the high-dimensional space is obtained based on the information entropy expression of the Softmax-like function. In addition, due to the fact that the scale difference between the local spatial feature and the overall spatial feature is large in the application, when the source image space is sampled, the source image with the local spatial feature is obtained at a high resolution to compensate the large scale difference.
Based on this, the application provides an intelligent rate parameter control method based on the information entropy of the Softmax-like function, which comprises the following steps: acquiring a first image of the whole of a windshield on which a gluing operation is being performed and a second image of a gluing position on which the gluing is being performed; respectively passing the first image and the second image through a depth convolution neural network to obtain a first feature map corresponding to the first image and a second feature map corresponding to the second image, wherein the last layer of the depth convolution neural network is activated by a Sigmoid activation function so that the feature value of each position in the first feature map and the second feature map is in the interval of 0 to 1; for the eigenvalue of each position in the second feature map, calculating the information entropy of the eigenvalue of each position relative to the Softmax-like function of the first feature map by the following formula to obtain an information entropy matrix of the second feature map relative to the Softmax-like function of the first feature map, wherein the formula is as follows:
wherein P represents the information entropy of the Softmax-like function, yi represents the characteristic value of each position in the second characteristic diagram, and xj represents the characteristic value of each position in the first characteristic diagram; calculating a position-weighted sum of the information entropy matrix of the Softmax-like function and the second feature map to obtain a fused feature map; and enabling the fused characteristic diagram to pass through an encoder, wherein the output bit number of the last layer of the encoder is two, so as to obtain two encoding values, and the two encoding values are used for respectively representing the glue discharging speed and the moving speed of the automatic glue discharging device.
Fig. 1 illustrates an application scenario of the intelligent rate parameter control method based on the information entropy of the Softmax-like function according to the embodiment of the application.
As shown in fig. 1, in this application scenario, a first image of the whole of the windshield undergoing the gluing operation and a second image of the gluing position undergoing the gluing are acquired by a camera (for example, as indicated by C in fig. 1); the first and second images are then input into a server (e.g., S as illustrated in fig. 1) that is deployed with a smart rate parameter control algorithm based on the Softmax-like function entropy information, wherein the server is capable of processing the first and second images based on the Softmax-like function entropy information-based smart rate parameter control algorithm to generate encoded values representing the dispensing rate and the movement rate of the automated dispensing device.
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 method
Fig. 2 illustrates a flow chart of a method of intelligent rate parameter control based on information entropy of a Softmax-like function. As shown in fig. 2, the method for controlling an intelligent rate parameter based on information entropy of a Softmax-like function according to the embodiment of the present application includes: s110, acquiring a first image of the whole windshield on which the gluing operation is carried out and a second image of the gluing position on which the gluing is carried out; s120, respectively passing the first image and the second image through a depth convolution neural network to obtain a first feature map corresponding to the first image and a second feature map corresponding to the second image, wherein the last layer of the depth convolution neural network is activated by a Sigmoid activation function so that the feature value of each position in the first feature map and the second feature map is in an interval of 0 to 1; s130, for the eigenvalue of each position in the second feature map, calculating the information entropy of the eigenvalue of each position relative to the Softmax-like function of the first feature map by using the following formula, so as to obtain an information entropy matrix of the second feature map relative to the Softmax-like function of the first feature map, where the formula is:
wherein P represents the information entropy of the Softmax-like function, yi represents the characteristic value of each position in the second characteristic diagram, and xj represents the characteristic value of each position in the first characteristic diagram; s140, calculating a position-weighted sum of the information entropy matrix of the Softmax-like function and the second feature map to obtain a fused feature map; and S150, enabling the fused characteristic diagram to pass through an encoder, wherein the output digit of the last layer of the encoder is two, so as to obtain two encoding values, and the two encoding values are used for respectively representing the glue discharging speed and the moving speed of the automatic glue discharging device.
Fig. 3 illustrates an architecture diagram of an intelligent rate parameter control method based on information entropy of a Softmax-like function according to an embodiment of the present application. As shown IN fig. 3, IN the network architecture of the intelligent rate parameter control method based on the information entropy of the Softmax-like function, firstly, a first image (for example, IN1 as illustrated IN fig. 3) of the whole windshield undergoing a gluing operation and a second image (for example, IN2 as illustrated IN fig. 3) of a gluing position undergoing gluing are respectively acquired through a deep convolutional neural network (for example, CNN1 as illustrated IN fig. 3) to obtain a first characteristic diagram (for example, F1 as illustrated IN fig. 3) corresponding to the first image and a second characteristic diagram (for example, F2 as illustrated IN fig. 3) corresponding to the second image; then, for the eigenvalue of each position in the second feature map, calculating the information entropy of the eigenvalue of each position relative to the Softmax-like function of the first feature map to obtain an information entropy matrix (e.g., as illustrated in fig. 3, M1) of the Softmax-like function of the second feature map relative to the first feature map; then, calculating a position-weighted sum of the information entropy matrix of the Softmax-like function and the second feature map to obtain a fused feature map (e.g., Fr as illustrated in FIG. 3); the fused signature graph is then passed through an encoder (e.g., an encoder as illustrated in fig. 3) having a final layer with an output bit number of two to obtain two encoded values representing a glue dispensing rate (e.g., K1 as illustrated in fig. 3) and a moving rate (e.g., K2 as illustrated in fig. 3) of the automatic glue dispensing apparatus, respectively.
In step S110, a first image of the entirety of the windshield on which the glue application operation is being performed and a second image of the glue application position on which the glue application is being performed are acquired. It will be appreciated that the glue application location represents the spatial characteristics of the location itself relative to the entirety of the windscreen to be mounted, and is therefore suitable for using a convolutional neural network to extract spatial features of a predetermined region in the image and process it by correlation with the spatial features of the image as a whole to obtain the desired parameters.
Specifically, in the embodiment of the present application, the process of acquiring a first image of the entirety of a windshield on which a glue application operation is being performed and a second image of a glue application position on which the glue application is being performed includes: firstly, an original second image of a gluing position where gluing is performed is obtained, wherein the resolution of the first image is smaller than that of the original second image, that is, the original second image of the gluing position where gluing is performed is obtained at a higher resolution, and it can be understood that, since the scale difference between the local spatial feature of the gluing position and the overall spatial feature of the windshield in the present application is larger, when a source image space is sampled, a large scale difference is compensated by obtaining a source image of the local spatial feature at a higher resolution. Then, the original second image is enlarged by a preset magnification to obtain the second image. It will be appreciated that the original second image is smaller in size than the first image of the windscreen as a whole, and for ease of subsequent calculations, the original second image is enlarged at a preset magnification so that the size of the second image coincides with the size of the first image.
Fig. 4 illustrates a flowchart for acquiring a first image of the whole of a windshield undergoing a gluing operation and a second image of a gluing position undergoing gluing in the intelligent rate parameter control method based on the information entropy of the Softmax-like function according to the embodiment of the application. As shown in fig. 4, acquiring a first image of the whole of the windshield undergoing the gluing operation and a second image of the gluing position undergoing the gluing operation comprises: s210, acquiring an original second image of a gluing position where gluing is performed; and S220, magnifying the original second image by a preset magnification to obtain the second image.
In step S120, the first image and the second image are respectively passed through a deep convolutional neural network to obtain a first feature map corresponding to the first image and a second feature map corresponding to the second image, wherein the last layer of the deep convolutional neural network is activated by a Sigmoid activation function so that a feature value of each position in the first feature map and the second feature map is within an interval of 0 to 1. Namely, extracting high-dimensional features in the first image and the second image by using a deep convolutional neural network, mapping the high-dimensional features in the first image and the second image to a high-dimensional nonlinear interval by using a Sigmoid activation function for explanation, solving the problem which cannot be solved by a linear model, and mapping a feature value of each position in the first feature map and the second feature map in an interval from 0 to 1 by using Sigmoid activation function activation.
In particular, in embodiments of the present application, the deep convolutional neural network may employ a deep residual neural network, e.g., ResNet 50. It should be known to those skilled in the art that, compared to the conventional convolutional neural network, the deep residual network is an optimized network structure proposed on the basis of the conventional convolutional neural network, which mainly solves the problem of gradient disappearance during the training process. The depth residual error network introduces a residual error network structure, the network layer can be made deeper through the residual error network structure, and the problem of gradient disappearance can not occur. The residual error network uses the cross-layer link thought of a high-speed network for reference, breaks through the convention that the traditional neural network only can provide N layers as input from the input layer of the N-1 layer, enables the output of a certain layer to directly cross several layers as the input of the later layer, and has the significance of providing a new direction for the difficult problem that the error rate of the whole learning model is not reduced and inversely increased by superposing multiple layers of networks.
In step S130, for the feature value of each position in the second feature map, calculating the information entropy of the feature value of each position relative to the Softmax-like function of the first feature map by the following formula to obtain the information entropy matrix of the second feature map relative to the Softmax-like function of the first feature map, where the formula is:
wherein P represents the information entropy of the Softmax-like function, and yi represents the characteristic value of each position in the second characteristic diagram; xj represents a feature value of each position in the first feature map. That is, in the scheme of the present application, the correlation of the local spatial feature and the distribution characteristic of the global spatial feature in the high-dimensional space is obtained based on the information entropy expression of the Softmax-like function.
In step S140, a position-weighted sum of the information entropy matrix of the Softmax-like function and the second feature map is calculated to obtain a fused feature map. That is, a position-weighted sum of the information entropy matrix of the Softmax-like function and the feature matrix of the second feature map is calculated, the information entropy matrix of the Softmax-like function being equivalent to masking weights that characterize different regions of the second feature map that should be noticed.
In step S150, the fused feature map is passed through an encoder, where the number of output bits of the last layer of the encoder is two, so as to obtain two encoded values, where the two encoded values are used to respectively represent the glue discharging rate and the moving rate of the automatic glue discharging device.
Specifically, in the embodiment of the present application, the encoder is a deep fully-connected network, and the deep fully-connected network includes a fully-connected layer having a preset depth, where the preset depth is greater than or equal to ten. It should be understood that the higher the depth of the neural network is, the more abstract the extracted features are, and the greater the attention to details, here, the deeper the depth of the full connection layer is, and the relevance of the local spatial features and the overall spatial features in the fused feature map can be fully utilized to ensure the coding effect.
In summary, the intelligent rate parameter control method based on the Softmax-like function information entropy is stated, the convolutional neural network is adopted to extract the spatial feature of the gluing position in the image, the relevance of the local spatial feature of the gluing position and the distribution characteristic of the whole spatial feature of the windshield in the high-dimensional space is obtained through the information entropy expression of the Softmax-like function, and the information entropy matrix of the Softmax-like function is fused with the local spatial high-dimensional feature of the gluing position to construct the feature matrix, so that the encoder is used for obtaining the desired parameters, namely the glue discharging rate and the moving rate, from the feature matrix.
Exemplary System
Fig. 5 illustrates a block diagram of an intelligent rate parameter control system based on information entropy of a Softmax-like function according to an embodiment of the present application.
As shown in fig. 5, an intelligent rate parameter control system 500 based on information entropy of a Softmax-like function according to an embodiment of the present application includes: an image acquisition unit 510 for acquiring a first image of the entirety of the windshield on which the glue application operation is being performed and a second image of the glue application position on which the glue application is being performed; a feature map generation unit 520, configured to pass the first image and the second image obtained by the image obtaining unit 510 through a deep convolutional neural network respectively to obtain a first feature map corresponding to the first image and a second feature map corresponding to the second image, where a last layer of the deep convolutional neural network is activated by a Sigmoid activation function so that a feature value of each position in the first feature map and the second feature map is within an interval of 0 to 1; an information entropy matrix generating unit 530, configured to calculate, for the feature value of each position in the second feature map obtained by the feature map generating unit 520, information entropy of the feature value of each position relative to the Softmax-like function of the first feature map obtained by the feature map generating unit, so as to obtain an information entropy matrix of the second feature map relative to the Softmax-like function of the first feature map, where:
wherein P represents the information entropy of the Softmax-like function, yi represents the characteristic value of each position in the second characteristic diagram, and xj represents the characteristic value of each position in the first characteristic diagram; a fused feature map generating unit 540, configured to calculate a weighted sum, according to location, of the information entropy matrix of the Softmax-like function obtained by the information entropy matrix generating unit 530 and the second feature map obtained by the feature map generating unit 520, so as to obtain a fused feature map; and a parameter generating unit 550, configured to pass the fused feature map obtained by the fused feature map generating unit 540 through an encoder, where the number of output bits of the last layer of the encoder is two, so as to obtain two encoded values, where the two encoded values are used to respectively indicate a glue discharging rate and a moving rate of the automatic glue discharging apparatus.
In one example, in the above-mentioned intelligent rate parameter control system 500, as shown in fig. 6, the image obtaining unit 510 includes: an original second image obtaining subunit 511, configured to obtain an original second image of a gluing position where gluing is being performed; and a second image generation sub-unit 512 configured to enlarge the original second image obtained by the original second image obtaining sub-unit 511 at a preset magnification to obtain the second image.
In one example, in the above-described intelligent rate parameter control system 500, the resolution of the first image is less than the resolution of the original second image.
In one example, in the intelligent rate parameter control system 500 described above, the encoder is a deep fully-connected network.
In one example, in the above-described intelligent rate parameter control system 500, the deep fully-connected network includes a fully-connected layer having a preset depth, the preset depth being equal to or greater than ten.
In one example, in the intelligent rate parameter control system 500 described above, the deep convolutional neural network is a deep residual network.
Here, it will be understood by those skilled in the art that the specific functions and operations of the respective units and modules in the above-described intelligent rate parameter control system 500 have been described in detail in the above description of the intelligent rate parameter control method based on the information entropy of the Softmax-like function with reference to fig. 1 to 4, and thus, a repetitive description thereof will be omitted.
As described above, the intelligent rate parameter control system 500 according to the embodiment of the present application may be implemented in various terminal devices, such as a server for controlling the glue discharging rate and the moving rate of the automatic glue discharging device. In one example, the intelligent rate parameter control system 500 according to the embodiments of the present application may be integrated into a terminal device as one software module and/or hardware module. For example, the intelligent rate parameter control system 500 may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the intelligent rate parameter control system 500 may also be one of many hardware modules of the terminal device.
Alternatively, in another example, the intelligent rate parameter control system 500 and the terminal device may be separate devices, and the intelligent rate parameter control system 500 may be connected to the terminal device through a wired and/or wireless network and transmit the interaction information according to the agreed data format.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 7.
FIG. 7 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 7, the electronic device 10 includes one or more processors 11 and a memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
In one example, the electronic device 10 may further include: an input system 13 and an output system 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input system 13 may comprise, for example, a keyboard, a mouse, etc.
The output system 14 can output various information including coded values and the like to the outside. The output system 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 7, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the functions of the intelligent rate parameter control method based on Softmax-like function information entropy, according to various embodiments of the present application, as described in the "exemplary methods" section above of this specification.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, an embodiment of the present application may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the intelligent rate parameter control method based on information entropy of a Softmax-like function described in the above-mentioned "exemplary methods" section of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
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 for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
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 those 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 therewith. 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 (10)
1. An intelligent rate parameter control method based on the information entropy of a Softmax-like function is characterized by comprising the following steps:
acquiring a first image of the whole of a windshield on which a gluing operation is being performed and a second image of a gluing position on which the gluing is being performed;
respectively passing the first image and the second image through a depth convolution neural network to obtain a first feature map corresponding to the first image and a second feature map corresponding to the second image, wherein the last layer of the depth convolution neural network is activated by a Sigmoid activation function so that the feature value of each position in the first feature map and the second feature map is in the interval of 0 to 1;
for the eigenvalue of each position in the second feature map, calculating the information entropy of the eigenvalue of each position relative to the Softmax-like function of the first feature map by the following formula to obtain an information entropy matrix of the second feature map relative to the Softmax-like function of the first feature map, wherein the formula is as follows:
calculating a position-weighted sum of the information entropy matrix of the Softmax-like function and the second feature map to obtain a fused feature map; and
and enabling the fused characteristic diagram to pass through an encoder, wherein the output digit of the last layer of the encoder is two, so as to obtain two encoding values, and the two encoding values are used for respectively representing the glue discharging speed and the moving speed of the automatic glue discharging device.
2. The intelligent speed parameter control method based on the information entropy of the Softmax-like function according to claim 1, wherein obtaining a first image of the entirety of the windshield on which the glue is being applied and a second image of the glue application position on which the glue is being applied comprises:
acquiring an original second image of a gluing position where gluing is performed; and
and amplifying the original second image at a preset magnification to obtain the second image.
3. The intelligent rate parameter control method based on information entropy of a Softmax-like function of claim 1, wherein the resolution of the first image is less than the resolution of the original second image.
4. The intelligent rate parameter control method based on Softmax-like function information entropy of claim 1, wherein the encoder is a deep fully connected network.
5. The intelligent rate parameter control method based on Softmax-like function information entropy of claim 4, wherein the deep fully-connected network comprises a fully-connected layer having a preset depth, the preset depth being greater than or equal to ten.
6. The intelligent rate parameter control method based on Softmax-like function information entropy according to claim 1, wherein the deep convolutional neural network is a deep residual network.
7. An intelligent rate parameter control system based on information entropy of a Softmax-like function is characterized by comprising the following components:
an image acquisition unit for acquiring a first image of the entirety of the windshield on which the glue application operation is being performed and a second image of the glue application position on which the glue application is being performed;
a feature map generation unit, configured to pass the first image and the second image obtained by the image acquisition unit through a depth convolution neural network respectively to obtain a first feature map corresponding to the first image and a second feature map corresponding to the second image, where a last layer of the depth convolution neural network is activated by a Sigmoid activation function so that a feature value of each position in the first feature map and the second feature map is within an interval of 0 to 1;
an information entropy matrix generating unit, configured to calculate, for the feature value of each position in the second feature map obtained by the feature map generating unit, information entropy of the feature value of each position with respect to the Softmax-like function of the first feature map obtained by the feature map generating unit, to obtain an information entropy matrix of the second feature map with respect to the Softmax-like function of the first feature map, according to a formula:
wherein P represents the information entropy of the Softmax-like function, yi represents the characteristic value of each position in the second characteristic diagram, and xj represents the characteristic value of each position in the first characteristic diagram;
a fused feature map generation unit, configured to calculate a position-wise weighted sum of the information entropy matrix of the Softmax-like function obtained by the information entropy matrix generation unit and the second feature map obtained by the feature map generation unit, so as to obtain a fused feature map; and
and the encoding value generating unit is used for enabling the fused characteristic diagram obtained by the fused characteristic diagram generating unit to pass through an encoder, the output digit of the last layer of the encoder is two, so as to obtain two encoding values, and the two encoding values are used for respectively representing the glue discharging rate and the moving rate of the automatic glue discharging device.
8. The intelligent rate parameter control system based on the information entropy of the Softmax-like function of claim 7, wherein the image acquisition unit comprises:
the original second image acquisition subunit is used for acquiring an original second image of the gluing position where gluing is carried out; and
and the second image generation subunit is configured to enlarge the original second image obtained by the original second image obtaining subunit by a preset magnification to obtain the second image.
9. The smart rate parameter control system based on information entropy of a Softmax-like function of claim 7, wherein a resolution of the first image is less than a resolution of the original second image.
10. An electronic device, comprising:
a processor; and
memory having stored therein computer program instructions which, when executed by the processor, cause the processor to perform the method of intelligent rate parameter control based on Softmax-like function entropy information according to any of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114662615A (en) * | 2022-05-07 | 2022-06-24 | 浙江加力仓储设备股份有限公司 | Electric lifting forklift with induction handle and control method thereof |
| CN114693562A (en) * | 2022-04-15 | 2022-07-01 | 黄淮学院 | Image enhancement method based on artificial intelligence |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108982546A (en) * | 2018-08-29 | 2018-12-11 | 燕山大学 | A kind of intelligent robot gluing quality detecting system and method |
| CN109926275A (en) * | 2018-11-08 | 2019-06-25 | 蔚来汽车有限公司 | The monitoring system of automatic double surface gluer and control method for automatic double surface gluer |
| US10482603B1 (en) * | 2019-06-25 | 2019-11-19 | Artificial Intelligence, Ltd. | Medical image segmentation using an integrated edge guidance module and object segmentation network |
| CN111461110A (en) * | 2020-03-02 | 2020-07-28 | 华南理工大学 | Small target detection method based on multi-scale image and weighted fusion loss |
| CN111804520A (en) * | 2020-09-08 | 2020-10-23 | 歌尔光学科技有限公司 | Gluing system and gluing method thereof |
| CN211726359U (en) * | 2020-01-20 | 2020-10-23 | 宁德时代新能源科技股份有限公司 | Gluing equipment |
-
2021
- 2021-02-04 CN CN202110155181.5A patent/CN114037648A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108982546A (en) * | 2018-08-29 | 2018-12-11 | 燕山大学 | A kind of intelligent robot gluing quality detecting system and method |
| CN109926275A (en) * | 2018-11-08 | 2019-06-25 | 蔚来汽车有限公司 | The monitoring system of automatic double surface gluer and control method for automatic double surface gluer |
| US10482603B1 (en) * | 2019-06-25 | 2019-11-19 | Artificial Intelligence, Ltd. | Medical image segmentation using an integrated edge guidance module and object segmentation network |
| CN211726359U (en) * | 2020-01-20 | 2020-10-23 | 宁德时代新能源科技股份有限公司 | Gluing equipment |
| CN111461110A (en) * | 2020-03-02 | 2020-07-28 | 华南理工大学 | Small target detection method based on multi-scale image and weighted fusion loss |
| CN111804520A (en) * | 2020-09-08 | 2020-10-23 | 歌尔光学科技有限公司 | Gluing system and gluing method thereof |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114693562A (en) * | 2022-04-15 | 2022-07-01 | 黄淮学院 | Image enhancement method based on artificial intelligence |
| CN114693562B (en) * | 2022-04-15 | 2022-11-25 | 黄淮学院 | Image enhancement method based on artificial intelligence |
| CN114662615A (en) * | 2022-05-07 | 2022-06-24 | 浙江加力仓储设备股份有限公司 | Electric lifting forklift with induction handle and control method thereof |
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