A method and related device for detecting the degree of frosting of a refrigerator evaporator

Abstract

The present application discloses a method and a related device for detecting the degree of frost on a refrigerator evaporator. The method comprises: taking an unfrosted image as a background image, performing differential processing on the image to be detected of the evaporator and the background image to obtain a differential image, and performing brightness correction on the differential image; performing denoising processing on the brightness-corrected differential image to obtain a denoised image, detecting the contour of the denoised image and calculating the contour area, and when the contour area is greater than a preset area threshold, the contour area is determined to be a frosted area; calculating the minimum circumscribed matrix of the frosted area as a framed area, and determining the vertices of the framed area, thereby determining the degree of frost on the frosted area according to the vertices using a double threshold segmentation algorithm; substituting the degree of frost into a frost value calculation formula to obtain a frost value on the image to be detected. The method solves the problem of inconsistent standards for judging the degree of frost in the prior art due to large differences in the light source environment of the cold storage, uneven frost on the evaporator, and changes in the illumination of the cold storage.

Description

Technical Field

[0001] The present application relates to the technical field of refrigerators, and in particular to a method for detecting the degree of frost on an evaporator of a refrigerator and a related device. Background Art

[0002] Frosting is a common problem in the refrigeration and heating industry. Many scholars have proposed different solutions from multiple technical fields. These methods can be roughly divided into three categories: defrosting judgment method based on direct measurement, defrosting judgment method based on indirect monitoring, and defrosting judgment method based on intelligent algorithm. Frosting detection technology based on image recognition belongs to the direct measurement category. Compared with other methods, image recognition technology has the advantages of simplicity, low cost, and automatic operation. Therefore, image recognition can be used as a potential alternative to all existing frosting detection methods.

[0003] In recent years, more and more frost detection algorithms based on digital image processing have been proposed. For example, the original image is converted into a grayscale image, and the grayscale value is used to describe the degree of frost. Another example is the multi-threshold segmentation method to further improve the accuracy of frost detection. At present, many image processing methods for frost degree detection have been proposed, but after engineering practice, it is found that the image methods currently proposed have the following problems: 1. When the light source environment of the cold storage is very different, the frost judgment is affected by the light source; 2. When the evaporator is frosted unevenly, the frost judgment still uses the original judgment threshold, resulting in inaccurate frost judgment; eventually, the evaporator has local frost; 3. The lighting of the cold storage changes. When the light intensity fluctuates, the frost judgment is affected by the light intensity; 4. The standards for judging the degree of frost are inconsistent, and the defrost threshold index needs to be reset for different evaporators. Summary of the invention

[0004] The present application provides a method and related device for detecting the degree of frost on a refrigerator evaporator, which are used to solve the technical problem in the prior art that the standards for judging the degree of frost are inconsistent due to large differences in the light source environment of the cold storage, uneven frost on the evaporator, and changes in the lighting of the cold storage.

[0005] In view of this, the first aspect of the present application provides a method for detecting the degree of frost on an evaporator of a refrigerator, the method comprising:

[0006] Taking the unfrosted image as the background image, performing difference processing between the image to be detected of the evaporator and the background image to obtain a difference image, and performing brightness correction on the difference image;

[0007] Performing denoising processing on the brightness-corrected differential image to obtain a denoised image, detecting the contour of the denoised image and calculating the contour area, and when the contour area is greater than a preset area threshold, determining that the contour area is a frosted area;

[0008] Calculating a minimum circumscribed matrix of the frosted area as a framed area, and determining vertices of the framed area, thereby determining the degree of frosting of the frosted area using a double threshold segmentation algorithm according to the vertices;

[0009] Substitute the frosting degree into the frosting value calculation formula to obtain the frosting value of the image to be detected.

[0010] Optionally, substituting the frosting degree into a frosting value calculation formula to obtain the frosting value of the image to be detected further includes:

[0011] When the frost value is greater than a first preset frost threshold, if so, a defrost command is generated to defrost the evaporator; when the frost value after defrosting is still greater than a second preset frost threshold, an abnormal alarm signal is generated.

[0012] Optionally, the unfrosted image is used as a background image, the image to be detected of the evaporator is subjected to difference processing with the background image to obtain a difference image, and brightness correction is performed on the difference image;

[0013] The unfrosted image is used as a background image, and a difference process is performed between the image to be detected of the evaporator and the background image based on a difference formula to obtain a difference image;

[0014] The image to be detected and the background image are respectively divided into a control area and a frost detection area, and the difference between the average grayscale value of the control area of ​​the image to be detected and the average grayscale value of the control area of ​​the background image is substituted into a brightness correction formula to obtain a difference image after brightness correction;

[0015] Wherein, the difference formula is:

[0016] P(x,y)=I(x,y)-B(x,y)

[0017] Where (x, y) represents the pixel at the xth row and yth column in the image, P represents the difference image, I is the image to be detected, and B is the background image;

[0018] The brightness correction formula is:

[0019] R(x,y)=P(x,y)-C Gap

[0020] C Gap =PC mean -BC mean

[0021] In the formula, C Cap PC is the average gray value of the control area of ​​the image to be detected mean The average gray value BC of the control area with the background image meanThe difference between (x, y) and (x, y) represents the pixel at the xth row and yth column in the image, and R(x, y) is the difference image after brightness correction.

[0022] Optionally, the frosting value calculation formula is:

[0023]

[0024] in,

[0025]

[0026] Wherein, T(x,y) is the degree of frost, 126 is the moderate degree of frost, 254 is the severe degree of frost, Thresh1 and Thresh2 are segmentation thresholds, R(x,y) is the difference image after brightness correction, fv is the frost value, N is the total number of pixels in the image to be detected, and (x,y) represents the pixel in the xth row and yth column of the image.

[0027] A second aspect of the present application provides a system for detecting the degree of frost on a refrigerator evaporator, the system comprising:

[0028] A difference unit, used for taking the unfrosted image as a background image, performing difference processing between the image to be detected of the evaporator and the background image to obtain a difference image, and performing brightness correction on the difference image;

[0029] an analysis unit, configured to perform denoising on the brightness-corrected differential image to obtain a denoised image, detect a contour of the denoised image and calculate a contour area, and when the contour area is greater than a preset area threshold, determine that the contour area is a frosted area;

[0030] A first calculation unit is used to calculate the minimum circumscribed matrix of the frosted area as a framed area, and determine the vertices of the framed area, so as to determine the frosting degree of the frosted area by using a double threshold segmentation algorithm according to the vertices;

[0031] The second calculation unit is used to substitute the frosting degree into a frosting value calculation formula to obtain a frosting value of the image to be detected.

[0032] Optionally, it also includes:

[0033] The defrost processing unit is used to determine whether the frost value is greater than a first preset frost threshold value. If so, a defrost command is generated to defrost the evaporator; when the frost value after defrosting is still greater than a second preset frost threshold value, an abnormal alarm signal is generated.

[0034] Optionally, the differential unit is specifically used to:

[0035] The unfrosted image is used as a background image, and a difference process is performed between the image to be detected of the evaporator and the background image based on a difference formula to obtain a difference image;

[0036] The image to be detected and the background image are respectively divided into a control area and a frost detection area, and the difference between the average grayscale value of the control area of ​​the image to be detected and the average grayscale value of the control area of ​​the background image is substituted into a brightness correction formula to obtain a difference image after brightness correction;

[0037] Wherein, the difference formula is:

[0038] P(x,y)=I(x,y)-B(x,y)

[0039] Where (x, y) represents the pixel at the xth row and yth column in the image, P represents the difference image, I is the image to be detected, and B is the background image;

[0040] The brightness correction formula is:

[0041] R(x,y)=P(x,y)-C Gap

[0042] C Gap =PC mean -BC mean

[0043] In the formula, C Cap PC is the average gray value of the control area of ​​the image to be detected mean The average gray value BC of the control area with the background image mean The difference between (x, y) and (x, y) represents the pixel at the xth row and yth column in the image, and R(x, y) is the difference image after brightness correction.

[0044] Optionally, the frosting value calculation formula is:

[0045]

[0046] in,

[0047]

[0048] Wherein, T(x,y) is the degree of frost, 126 is the moderate degree of frost, 254 is the severe degree of frost, Thresh1 and Thresh2 are segmentation thresholds, R(x,y) is the difference image after brightness correction, fv is the frost value, N is the total number of pixels in the image to be detected, and (x,y) represents the pixel in the xth row and yth column of the image.

[0049] A third aspect of the present application provides a device for detecting the degree of frost on a refrigerator evaporator, the device comprising a processor and a memory:

[0050] The memory is used to store program code and transmit the program code to the processor;

[0051] The processor is used to execute the steps of the method for detecting the degree of frost on the evaporator of a refrigerator as described in the first aspect according to the instructions in the program code.

[0052] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program codes, and the program codes are used to execute the method for detecting the degree of frost on the evaporator of a refrigerator as described in the first aspect.

[0053] It can be seen from the above technical solutions that this application has the following advantages:

[0054] The present application provides a method for detecting the degree of frost on a refrigerator evaporator, comprising: taking an unfrosted image as a background image, performing differential processing on an image of the evaporator to be detected and the background image to obtain a differential image, and performing brightness correction on the differential image; performing denoising processing on the differential image after brightness correction to obtain a denoised image, detecting the contour of the denoised image and calculating the contour area, and when the contour area is greater than a preset area threshold, determining that the contour area is a frosted area; calculating a minimum circumscribed matrix of the frosted area as a framed area, and determining the vertices of the framed area, thereby determining the degree of frost on the frosted area based on the vertices using a dual threshold segmentation algorithm; substituting the degree of frost into a frost value calculation formula to obtain a frost value of the image to be detected.

[0055] The present application is a method for detecting the degree of frost on an evaporator based on image difference. The image difference technology is used to detect the transformation before and after the image to determine the frosting situation, and the frosting detection area and the control area are set to eliminate brightness changes. De-noising, contour detection and other operations are then performed to select the frosted area of ​​the image. The frosted area is segmented by double thresholds, and a frosting value is introduced to measure the degree of frost. The solution of the present invention solves the technical problem of inconsistent standards for judging the degree of frost in the prior art due to large differences in the light source environment of the cold storage, uneven frosting of the evaporator, and changes in the lighting of the cold storage. Compared with other image processing solutions, it has stronger applicability, accuracy, anti-interference, and ease of use. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic flow chart of an embodiment of a method for detecting the degree of frost on a refrigerator evaporator provided in an embodiment of the present application;

[0057] Figure 2 This is a schematic structural diagram of an embodiment of a system for detecting the degree of frost on a refrigerator evaporator provided in an embodiment of the present application;

[0058] Figure 3A schematic diagram of an image difference operation provided in an embodiment of the present application;

[0059] Figure 4a A schematic diagram showing the comparison between the light frosting and the frosting value (29.8%) provided in the examples of the present application;

[0060] Figure 4b A schematic diagram showing the comparison between the moderate frosting and the frosting value (52.5%) provided in the examples of the present application;

[0061] Figure 4c It is a schematic diagram comparing the severe frosting and the frosting value (62.5%) provided in the examples of the present application. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] See also Figure 1 A method for detecting the degree of frost on a refrigerator evaporator provided in an embodiment of the present application includes:

[0064] Step 101: using the unfrosted image as a background image, performing differential processing on the image to be detected of the evaporator and the background image to obtain a differential image, and performing brightness correction on the differential image;

[0065] It should be noted that in the implementation, firstly, an unfrosted image is selected as the image background template, and the camera transmits the image to be detected at a frequency of one image per minute, and all images are processed in grayscale. Then, the image to be detected is subjected to a differential operation with the template image, such as Figure 3 The specific differential formula is as follows:

[0066] P(x,y)=I(x,y)-B(x,y)

[0067] Where (x, y) represents the pixel at the xth row and yth column in the image, P represents the difference image, I is the image to be detected, and B is the background image.

[0068] Since the image obtained after image difference is the result of removing the background image from the image to be detected, the remaining pixel values ​​in the frost detection area are all frost points of the evaporator. The frost points obtained using this method are not affected by the difference in ambient light sources. Although the difference in light sources can be used to remove the influence of differences, there are usually lighting changes such as lighting changes in cold storage. The main purpose of setting the control area is to detect brightness changes. When there is a brightness change, there will be an average grayscale difference between the background image control area and the image to be detected control area.

[0069] Therefore, it is only necessary to calculate the average grayscale difference of the control area to correct the brightness change. The specific operation of brightness correction is: divide the image to be detected and the background image into the control area and the frost detection area respectively, substitute the difference between the average grayscale value of the control area of ​​the image to be detected and the average grayscale value of the control area of ​​the background image into the brightness correction formula, and obtain the difference image after brightness correction;

[0070] The brightness correction formula is:

[0071] P(x,y)=P(x,y)-C Gap

[0072] C Gap =PC mean -BC mean

[0073] In the formula, C Cap PC is the average gray value of the control area of ​​the image to be detected mean The average gray value BC of the control area with the background image mean The difference between (x, y) and (x, y) represents the pixel at the xth row and yth column in the image, and R(x, y) is the difference image after brightness correction.

[0074] Step 102, denoising the brightness-corrected differential image to obtain a denoised image, detecting the contour of the denoised image and calculating the contour area, and when the contour area is greater than a preset area threshold, determining that the contour area is a frosted area;

[0075] It should be noted that in the implementation, the differential image is first denoised using basic denoising operations such as corrosion, median filtering, and dilation. The convolution kernel size of the corrosion, dilation, median filtering and other operations used in this embodiment is 3*3; then the processed image is subjected to contour detection operation, and the areas of all contours are calculated. If the contour area is larger than 1 / 10 of the image, the contour will be considered to be a frosted area.

[0076] Step 103, calculating the minimum circumscribed matrix of the frosted area as the framed area, and determining the vertices of the framed area, thereby determining the frosting degree of the frosted area using a double threshold segmentation algorithm according to the vertices;

[0077] It should be noted that the minimum circumscribed matrix of the frosting area is first calculated as the frame selection area, and the vertices of the frame selection area are determined;

[0078] Then, the vertices are used to segment the frosted area and calculate the degree of frosting, and the image is converted into a binary image using a dual threshold segmentation algorithm based on differential image features. The specific operation steps of the multi-threshold segmentation algorithm based on differential image features are as follows:

[0079] Since image difference removes the difference in ambient light sources, the difference results under different light source environments tend to be uniform. The present invention divides the frost level into two levels: medium and severe by statistically analyzing the grayscale histogram of the differential image. The pixels with medium and severe frost levels are set to 126 and 254 respectively using double threshold segmentation. The specific implementation formula is as follows:

[0080]

[0081] Wherein, T(x, y) is the degree of frost, Thresh1 and Thresh2 are segmentation thresholds, and R(x, y) is the difference image after brightness correction.

[0082] Step 104: Substitute the frosting degree into the frosting value calculation formula to obtain the frosting value of the image to be detected.

[0083] It should be noted that the frost value parameter is introduced at the end to unify the severity of frost, as shown in the figure Figure 4a , 4b and 4c. The formula for calculating the frost value (fv frost value) is:

[0084]

[0085] Wherein, R(x,y) is the difference image after brightness correction, fv is the frosting value, N is the total number of pixels of the image to be detected, and (x,y) represents the pixel in the xth row and yth column of the image.

[0086] Furthermore, the detection method also includes: when the frost value is greater than a first preset frost threshold, if so, generating a defrost command to defrost the evaporator; when the frost value after defrosting is still greater than a second preset frost threshold, generating an abnormal alarm signal.

[0087] It should be noted that during implementation, it is determined whether the frost value exceeds 50% (the first preset frost threshold). If the frost value exceeds 50%, it is determined that defrosting is required, and the frost value is tested again. If the frost value is still higher than 15% (the second preset frost threshold) after defrosting, it is considered that defrosting is abnormal and the system will send an abnormal alarm signal.

[0088] The above is a method for detecting the degree of frost on a refrigerator evaporator provided in an embodiment of the present application, and the following is a system for detecting the degree of frost on a refrigerator evaporator provided in an embodiment of the present application.

[0089] See also Figure 2 , a system for detecting the degree of frost on a refrigerator evaporator provided in an embodiment of the present application comprises:

[0090] The difference unit 201 is used to use the unfrosted image as the background image, perform difference processing on the image to be detected of the evaporator and the background image to obtain a difference image, and perform brightness correction on the difference image;

[0091] The analysis unit 202 is used to perform denoising on the brightness-corrected differential image to obtain a denoised image, detect the contour of the denoised image and calculate the contour area, and when the contour area is greater than a preset area threshold, determine that the contour area is a frosted area;

[0092] The first calculation unit 203 is used to calculate the minimum circumscribed matrix of the frosted area as the framed area, and determine the vertices of the framed area, so as to determine the frosting degree of the frosted area by using a double threshold segmentation algorithm according to the vertices;

[0093] The second calculation unit 204 is used to substitute the frosting degree into the frosting value calculation formula to obtain the frosting value of the image to be detected.

[0094] Furthermore, the detection system also includes: a defrost processing unit, which is used to determine whether the frost value is greater than a first preset frost threshold. If so, a defrost command is generated to defrost the evaporator; when the frost value after defrosting is still greater than a second preset frost threshold, an abnormal alarm signal is generated.

[0095] Furthermore, an embodiment of the present application also provides a device for detecting the degree of frost on a refrigerator evaporator, the device comprising a processor and a memory:

[0096] The memory is used to store program code and transmit the program code to the processor;

[0097] The processor is used to execute the method for detecting the degree of frost on the refrigerator evaporator described in the above method embodiment according to the instructions in the program code.

[0098] Furthermore, a computer-readable storage medium is provided in an embodiment of the present application, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the method for detecting the degree of frost on the evaporator of the refrigerator described in the above method embodiment.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] The terms "first", "second", "third", "fourth", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0101] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0102] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0106] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting the degree of frost on a refrigerator evaporator, characterized in that: include: Taking the unfrosted image as the background image, performing difference processing between the image to be detected of the evaporator and the background image to obtain a difference image, and performing brightness correction on the difference image; Performing denoising processing on the brightness-corrected differential image to obtain a denoised image, detecting the contour of the denoised image and calculating the contour area, and when the contour area is greater than a preset area threshold, determining that the contour area is a frosted area; Calculating a minimum circumscribed matrix of the frosted area as a framed area, and determining vertices of the framed area, thereby determining the degree of frosting of the frosted area using a double threshold segmentation algorithm according to the vertices; Substituting the frosting degree into the frosting value calculation formula to obtain the frosting value of the image to be detected; The method of using the unfrosted image as a background image, performing differential processing on the image to be detected of the evaporator and the background image to obtain a differential image, and performing brightness correction on the differential image includes: The unfrosted image is used as a background image, and a difference process is performed between the image to be detected of the evaporator and the background image based on a difference formula to obtain a difference image; The image to be detected and the background image are respectively divided into a control area and a frost detection area, and the difference between the average grayscale value of the control area of ​​the image to be detected and the average grayscale value of the control area of ​​the background image is substituted into a brightness correction formula to obtain a difference image after brightness correction; Wherein, the difference formula is: ； Where (x, y) represents the pixel at the xth row and yth column in the image, P represents the difference image, I is the image to be detected, and B is the background image; The brightness correction formula is: ； In the formula, is the average gray value of the control area of ​​the image to be detected The average gray value of the contrast area with the background image The difference between (x, y) and (x, y) represents the pixel at the xth row and yth column in the image. is the difference image after brightness correction; The calculation formula of the frosting value is: ； in, ； In the formula, is the frosting degree, 126 is the medium frosting degree, 254 is the severe frosting degree, Thresh1 and Thresh2 are the segmentation thresholds, is the difference image after brightness correction, fv is the frosting value, N is the total number of pixels in the image to be detected, and (x, y) represents the pixel in the xth row and yth column in the image.

2. The method for detecting the degree of frost on the evaporator of a refrigerator according to claim 1, characterized in that: Substituting the frosting degree into the frosting value calculation formula to obtain the frosting value of the image to be detected, further comprising: When the frost value is greater than a first preset frost threshold, if so, a defrost command is generated to defrost the evaporator; when the frost value after defrosting is still greater than a second preset frost threshold, an abnormal alarm signal is generated.

3. A system for detecting the degree of frost on a refrigerator evaporator, characterized in that: include: A difference unit, used for taking the unfrosted image as a background image, performing difference processing between the image to be detected of the evaporator and the background image to obtain a difference image, and performing brightness correction on the difference image; an analysis unit, configured to perform denoising on the brightness-corrected differential image to obtain a denoised image, detect a contour of the denoised image and calculate a contour area, and when the contour area is greater than a preset area threshold, determine that the contour area is a frosted area; A first calculation unit is used to calculate the minimum circumscribed matrix of the frosted area as a framed area, and determine the vertices of the framed area, so as to determine the frosting degree of the frosted area by using a double threshold segmentation algorithm according to the vertices; A second calculation unit, used for substituting the frosting degree into a frosting value calculation formula to obtain a frosting value of the image to be detected; Wherein, the differential unit is specifically used for: The unfrosted image is used as a background image, and a difference process is performed between the image to be detected of the evaporator and the background image based on a difference formula to obtain a difference image; The image to be detected and the background image are respectively divided into a control area and a frost detection area, and the difference between the average grayscale value of the control area of ​​the image to be detected and the average grayscale value of the control area of ​​the background image is substituted into a brightness correction formula to obtain a difference image after brightness correction; Wherein, the difference formula is: ； Where (x, y) represents the pixel at the xth row and yth column in the image, P represents the difference image, I is the image to be detected, and B is the background image; The brightness correction formula is: ； In the formula, is the average gray value of the control area of ​​the image to be detected The average gray value of the contrast area with the background image The difference between (x, y) and (x, y) represents the pixel at the xth row and yth column in the image. is the difference image after brightness correction; The calculation formula of the frosting value is: ； in, ； In the formula, is the frosting degree, 126 is the medium frosting degree, 254 is the severe frosting degree, Thresh1 and Thresh2 are the segmentation thresholds, is the difference image after brightness correction, fv is the frosting value, N is the total number of pixels in the image to be detected, and (x, y) represents the pixel in the xth row and yth column in the image.

4. The system for detecting the degree of frost on the evaporator of a refrigerator according to claim 3, characterized in that: Also includes: A defrost processing unit, used to determine whether the frost value is greater than a first preset frost threshold value, and if so, generate a defrost command to perform defrost processing on the evaporator; When the frost value after defrosting is still greater than the second preset frost threshold, an abnormal alarm signal is generated.

5. A device for detecting the degree of frost on a refrigerator evaporator, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the method for detecting the degree of frost on the evaporator of a refrigerator according to any one of claims 1 to 2 according to the instructions in the program code.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program codes, and the program codes are used to execute the method for detecting the degree of frost on the evaporator of a refrigerator according to any one of claims 1 to 2.

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