Intelligent control method and system for an egg spraying liquid machine

Abstract

This application relates to the field of image processing, and particularly to an intelligent control method and system for an egg spraying machine. The method includes: dividing a monitored image into dirty areas; calculating a cleaning evaluation of the dirty areas; in response to the cleaning evaluation being less than an evaluation threshold, controlling a preset cleaning device to clean the dirty areas; the calculation method of the cleaning evaluation includes: taking the ratio of the number of pixel points in the same dirty area in adjacent frame monitored images as the quantity ratio; calculating the gradient value of each pixel point in the dirty area, taking the ratio of the maximum value to the minimum value of the gradient values as the gradient value ratio, and taking the difference between the gradient value ratios in adjacent frame monitored images as the ratio difference; multiplying the absolute values of the difference between the quantity ratio and 1 and the ratio difference respectively to obtain a product, and taking the sum of the normalized result of the product and a preset bias factor as the dirt evaluation. This application has the technical effect of accurately identifying the stain area for secondary cleaning.

Description

Technical Field

[0001] This application relates to the field of image processing, and particularly to an intelligent control method and system for an egg spraying machine. Background Art

[0002] In the process of food production, sometimes it is necessary to smear or spray egg liquid on the surface of food. For example, a layer of egg liquid is set on the surface of bread to make the baked bread golden in color.

[0003] In the prior art, a Chinese patent with the publication number CN108522567A discloses an egg spraying machine controlled by a numerical control program, including: a main frame, a variable-speed motor, a recovery box, a transmission roller, a girder, an outer cover, an exhaust motor, a mounting frame, an exhaust pipe and a spray head. The food is placed on a tray and conveyed to below the spray head through the transmission roller. The food is sprayed with egg liquid through the spray head. After completion, the spray head can spray clean water or cleaning liquid to clean the tray on which the food is placed.

[0004] However, during the process of cleaning the tray, after one cleaning, the situation of incomplete cleaning may occur, and there are still stain areas with residual egg liquid on the tray. The prior art cannot accurately identify the stain areas for secondary cleaning. Summary of the Invention

[0005] In order to solve the technical problem of being unable to accurately identify the stain areas for secondary cleaning, this application provides an intelligent control method and system for an egg spraying machine.

[0006] In a first aspect, this application provides an intelligent control method for an egg spraying machine, adopting the following technical solution:

[0007] An intelligent control method for an egg spraying machine includes the steps of: after processing the monitoring image, dividing the dirty area; calculating the cleaning evaluation of the dirty area; in response to the cleaning evaluation being less than the evaluation threshold, controlling a preset cleaning device to clean the dirty area; the calculation method of the cleaning evaluation includes the steps of: taking the ratio of the number of pixel points in the same dirty area in adjacent frame monitoring images as the quantity ratio; calculating the gradient value of each pixel point in the dirty area, taking the ratio of the maximum value to the minimum value of the gradient value as the gradient value ratio, and taking the difference between the gradient value ratios in adjacent frame monitoring images as the ratio difference; multiplying the absolute value of the difference between the quantity ratio and 1 and the ratio difference respectively to obtain a product, and taking the sum of the normalized result of the product and a preset bias factor as the dirt evaluation.

[0008] The beneficial effects are as follows: By quantifying the cleaning evaluation, it is determined whether secondary cleaning is required and the location of cleaning, and the dirty areas can be monitored in real time. Through intelligent control, precise secondary cleaning is performed on the remaining stained areas after primary cleaning, and secondary cleaning is only performed when necessary, reducing unnecessary cleaning times, improving the cleaning efficiency, ensuring that the expected cleaning effect can be achieved, and enhancing the cleaning quality.

[0009] Optionally, the method for dividing the dirty areas includes the steps of: calculating the dirt coefficient of each pixel point, replacing the pixel value of the pixel point with the corresponding dirt coefficient of the pixel point to obtain a dirt coefficient map; obtaining the maximum value in each row and each column of the dirt coefficient map, taking the pixel point corresponding to the maximum value as the starting point, performing continuous gradient descent at a preset angle until the gradient descent is less than or equal to zero to obtain boundary points, and connecting all the boundary points to form a closed interval as the dirty area.

[0010] The beneficial effects are as follows: During the cleaning process, there will be a trailing area for the egg liquid residue. The trailing area is the area left where the egg liquid is not completely cleaned, and its characteristic is the existence of a yellow gradient area, that is, the yellow gradually changes from deep to shallow until it completely disappears. During the gradual change process, that is, the gradient continuously decreases from the central position point of the dirty area to the surrounding area, so the area of continuous gradient descent is used as the dirty area.

[0011] Optionally, the calculation formula for the dirt coefficient is: , where is the dirt coefficient of the th pixel point, is the color channel vector of the th pixel point, is the color channel vector of the egg liquid pixel value.

[0012] Optionally, the method for obtaining the color channel vector of the egg liquid pixel value is: obtaining an egg liquid pool image; calculating the Euclidean distance between the pixel value of the pixel point in the egg liquid pool image and the standard yellow pixel value; taking the pixel values of the three channels of the pixel point with the smallest Euclidean distance as the three elements in the color channel vector of the egg liquid pixel value.

[0013] The beneficial effects are as follows: Calculate the Euclidean distance between the color of the egg liquid in the egg liquid pool and the standard yellow pixel value. The standard yellow is not directly used because there will also be color differences between different batches of egg liquid. Using the standard yellow will reduce the accuracy of subsequent results. And it avoids the influence of the bubbles on the surface of the egg liquid in the egg liquid pool to accurately collect the color characteristics of the egg liquid in the egg liquid pool, which is convenient for subsequent analysis of the egg liquid residue through the color characteristics.

[0014] Optionally, the method for calculating the gradient value of each pixel point in the dirty area is: calculating the ratio of the dirt coefficient of the pixel point to the pixel points in its eight-neighborhood, and retaining the smallest ratio as the gradient value of the pixel point.

[0015] The beneficial effects are as follows: By retaining the smallest ratio as the gradient value, the contrast between the dirty area and the clean area in the image can be enhanced, making the dirty area more obvious and facilitating observation and processing.

[0016] Optionally, the expression of the bias factor is: , where is the bias factor, is the set of gradient values of the dirty area in the subsequent frame of the adjacent-frame monitoring image, is the maximum value function, is the minimum value function, represents a preset constant.

[0017] Optionally, the evaluation threshold has the same value as the bias factor.

[0018] In a second aspect, the present application provides an intelligent control system for an egg liquid spraying machine, adopting the following technical solution:

[0019] An intelligent control system for an egg liquid spraying machine includes: a processor and a memory, and the memory stores computer program instructions, which, when executed by the processor, implement the intelligent control method for the egg liquid spraying machine according to the above.

[0020] The beneficial effects are as follows: The intelligent control method for the egg liquid spraying machine is generated into a computer program and stored in the memory to be loaded and executed by the processor. Thus, according to the memory and the processor, the system is convenient to use.

[0021] The present application has the following technical effects:

[0022] 1. By quantifying the cleaning evaluation, it is determined whether secondary cleaning is required and the cleaning position, enabling real-time monitoring of the dirty area. Through intelligent control, precise secondary cleaning is performed on the remaining stain areas after primary cleaning, and secondary cleaning is only performed when necessary, reducing unnecessary cleaning times, improving the cleaning efficiency, ensuring that the expected cleaning effect can be achieved, and enhancing the cleaning quality.

[0023] 2. During the cleaning process, there will be a trailing area for the remaining egg liquid. The trailing area is the area where the egg liquid is not completely cleaned, characterized by the presence of a yellow gradient area, that is, the yellow gradually fades from deep to shallow until it completely disappears. During the fading process, that is, from the central position point of the dirty area to the surrounding, the gradient continuously decreases. Therefore, the area with continuous gradient descent is regarded as the dirty area. Description of the Drawings

[0024] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present application will become readily understood. In the drawings, several embodiments of the present application are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0025] Figure 1 is a flowchart of a method for intelligent control of an egg liquid spraying machine according to an embodiment of the present application.

[0026] Figure 2 is a flowchart of a method for dividing a dirty area in the intelligent control method of an egg liquid spraying machine according to an embodiment of the present application.

[0027] Figure 3 is a flowchart of a method for calculating a cleaning evaluation in the intelligent control method of an egg liquid spraying machine according to an embodiment of the present application. Detailed Embodiments

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present application.

[0029] It should be understood that when the claims, specifications, and drawings of the present application use terms such as "first" and "second", they are only used to distinguish different objects, rather than to describe a specific order. The terms "including" and "comprising" used in the specifications and claims of the present application indicate the existence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0030] An embodiment of the present application discloses an intelligent control method for an egg liquid spraying machine. The application scenario is: after the egg liquid spraying machine cleans a tray once, identify the residual egg liquid area on the tray and adaptively change the cleaning position and frequency to accurately clean the egg liquid residual area. Refer to Figure 1 , which includes steps S1 - S3, specifically as follows:

[0031] S1: After processing the monitoring image, divide the dirty area.

[0032] Cameras are set above the tray and above the egg liquid pool respectively to obtain the monitoring image of the tray and the egg liquid pool image. The method for obtaining the monitoring image is: by extracting frames from the monitoring video, multiple frames of monitoring images are obtained. The frame extraction frequency can be set according to the actual application scenario. Exemplarily, 60 frames are extracted per second.

[0033] Obtain the color channel vector of the egg liquid pixel values from the egg liquid pool image. The obtaining method is as follows:

[0034] Calculate the Euclidean distance between the pixel value of the pixel point in the egg liquid pool image and the standard yellow pixel value. Exemplarily, set the pixel value of the pixel point in the egg liquid pool image as , represents the pixel value of the red channel, represents the pixel value of the green channel, represents the pixel value of the blue channel, and the standard yellow pixel value is (255, 255, 0).

[0035] The calculation of the Euclidean distance is a prior art and will not be elaborated here. Take the pixel values of the three channels of the pixel point with the minimum Euclidean distance as the three elements in the color channel vector of the egg liquid pixel values. Exemplarily, the color channel vector can be (255, 255, 0).

[0036] The above steps are to avoid the influence of the bubbles on the surface of the egg liquid in the egg liquid pool, so as to accurately collect the color characteristics of the egg liquid in the egg liquid pool, which is convenient for subsequent analysis of the egg liquid residue through the color characteristics.

[0037] Refer to Figure 2 , the method for dividing the dirty area includes steps S10 - step S11:

[0038] S10: Calculate the dirt coefficient of each pixel point, and replace the pixel value of the pixel point with the corresponding dirt coefficient to obtain the dirt coefficient map.

[0039] Among them, the calculation formula of the dirt coefficient is: , where is the dirt coefficient of the th pixel point, is the color channel vector of the th pixel point, is the color channel vector of the egg liquid pixel values. represents and 's cosine similarity.

[0040] S11: Obtain the maximum value in each row and each column of the dirt coefficient map. Starting from the pixel point corresponding to the maximum value, perform continuous gradient descent at a preset angle until the gradient descent is less than or equal to zero to obtain the boundary points, and connect all the boundary points to form a closed interval as the dirty area.

[0041] In one embodiment, the preset angle is , , and , the polygonal area formed by the four angular directions is the soiled area.

[0042] S2: Calculate the cleaning evaluation of the soiled area. Refer to Figure 3 , including step S20 - step S22:

[0043] S20: Take the ratio of the number of pixel points in the same soiled area in adjacent frame monitoring images as the quantity ratio.

[0044] S21: Calculate the gradient value of each pixel point in the soiled area, take the ratio of the maximum value to the minimum value of the gradient values as the gradient value ratio, and take the difference between the gradient value ratios in adjacent frame monitoring images as the ratio difference.

[0045] In one embodiment, the method for calculating the gradient value of each pixel point in the soiled area is: calculate the ratio of the soiling coefficient corresponding to the pixel point and the pixel points in its eight-neighborhood, and retain the smallest ratio as the gradient value of the pixel point. Among them, the eight-neighborhood refers to the neighborhood composed of 8 pixel points around a pixel point. Specifically, it includes the adjacent pixel points in the up, down, left, right, and four diagonal directions of the pixel point.

[0046] During the cleaning process, there will be a trailing area for egg liquid residue. The trailing area is the area left when the egg liquid is not completely cleaned, and its characteristic is the existence of a yellow gradient area, that is, the yellow fades from deep to shallow until it completely disappears. During the fading process, that is, the gradient continuously decreases from the central position point of the soiled area to the surrounding, calculate the gradual change of the gradient as the soiling evaluation of the soiled area.

[0047] S22: Multiply the absolute values of the difference between the quantity ratio and 1 and the ratio difference respectively to obtain a product, and take the sum of the normalized result of the product and the preset bias factor as the soiling evaluation.

[0048] In one embodiment, the calculation formula of the bias factor is:

[0049] , where is the bias factor, is the set of gradient values of the soiled area in the subsequent frame image of adjacent frame monitoring images, is the maximum value function, is the minimum value function, represents a preset constant. Exemplarily, takes 0.5. When the ratio of the maximum gradient value to the minimum gradient value in the soiled area is less than 0.95, it indicates that the soiled area has been cleaned, and the bias factor is 0.5. Conversely, it is determined that further cleaning is required, and the bias factor is 0.

[0050] In one embodiment, the expression of the soiling evaluation is:

[0051] , where is the dirt evaluation of any dirt area, is the number of pixel points of the dirt area in the previous frame of monitoring image, is the number of pixel points of the dirt area in the subsequent frame image, is the gradient value set of the dirt area in the previous frame image, is the gradient value set of the dirt area in the subsequent frame image, is the maximum value function, is the minimum value function, is the bias factor, is the hyperbolic tangent function.

[0052] S3: In response to the cleaning evaluation being less than the evaluation threshold, control the preset cleaning device to clean the dirt area.

[0053] In one embodiment, make the evaluation threshold the same as the value of the bias factor, that is, set the dirt evaluation threshold to 0.5. When the cleaning evaluation threshold is greater than 0.5, control the cleaning device to clean the dirt area again; otherwise, do not clean. In one embodiment, the dirt area on the tray to be cleaned can be moved to below the nozzle of the cleaning device through a transfer device for cleaning, or the position of the spray head of the cleaning device can be adjusted to move it above the dirt area of the tray to be cleaned.

[0054] The embodiment of the present application also discloses an intelligent control system for an egg liquid spraying machine, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent control method of the egg liquid spraying machine according to the present application is implemented.

[0055] The above system also includes other components well known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be elaborated here.

[0056] In this application, the foregoing memory can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application program, module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0057] Although this specification has shown and described multiple embodiments of the present application, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and scope of the present application. It should be understood that various alternatives to the embodiments of the present application described herein can be employed in the practice of the present application.

[0058] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application shall be covered within the protection scope of the present application.

Claims

1. An intelligent control method for an egg liquid spraying machine, characterized in that: Includes steps: After processing the monitoring image, the dirty area is divided; a cleaning evaluation of the dirty area is calculated; in response to the cleaning evaluation being less than an evaluation threshold, a preset cleaning device is controlled to clean the dirty area; The method of demarcating the dirty area comprises the steps of: Calculate the dirtiness coefficient of each pixel point, and replace the pixel value of the pixel point with the dirtiness coefficient corresponding to each pixel point to obtain a dirtiness coefficient map; Get the maximum value in each row and column of the dirt coefficient map, take the pixel point corresponding to the maximum value as the starting point, perform continuous gradient descent at a preset angle until the gradient drops to less than or equal to zero, and obtain the boundary point. The closed interval formed by connecting all the boundary points is the dirty area; The calculation formula of the dirtiness coefficient is: ,in, For the The dirtiness coefficient of each pixel, For the The color channel vector of pixels, is the color channel vector of the egg liquid pixel value; The calculation method of the cleaning evaluation includes the following steps: The ratio of the number of pixels in the same dirty area in adjacent frame monitoring images is used as the quantity ratio; Calculate the gradient value of each pixel in the dirty area, take the ratio of the maximum value to the minimum value of the gradient value as the gradient value ratio, and take the difference of the gradient value ratios in adjacent frame monitoring images as the ratio difference; The absolute value of the difference between the quantity ratio and 1 and the ratio difference are taken and multiplied to obtain the product, and the normalized result of the product and the sum of the preset bias factor are used as the dirtiness evaluation. The method for calculating the gradient value of each pixel in the dirty area is: calculating the ratio of the dirtiness coefficients of the pixel and the pixels in its eight neighborhoods, and retaining the smallest ratio as the gradient value of the pixel.

2. The intelligent control method of the egg liquid spraying machine according to claim 1, characterized in that: The method for obtaining the color channel vector of the egg liquid pixel value is: Acquire an image of the egg pool; Calculate the Euclidean distance between the pixel value of the pixel point in the egg pool image and the standard yellow pixel value; The pixel values ​​of the three channels of the pixel point with the smallest Euclidean distance are used as the three elements in the color channel vector of the egg liquid pixel value.

3. The intelligent control method of the egg liquid spraying machine according to claim 1 or 2, characterized in that: The expression of the bias factor is: ,in, is the bias factor, is the set of gradient values ​​of the dirty area in the next frame of the adjacent frame monitoring image, is the maximum value function, is the minimum function, Indicates a preset constant.

4. The intelligent control method of the egg liquid spraying machine according to claim 3, characterized in that: The evaluation threshold has the same value as the bias factor.

5. An intelligent control system for an egg liquid spraying machine, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent control method of the egg liquid spraying machine according to any one of claims 1-4 is implemented.

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