Cleaning control method and system based on image processing

Abstract

The invention relates to the field of image processing, in particular to a cleaning control method and system based on image processing, and the method comprises the steps: obtaining a grayscale image of filtrate filtration, carrying out the image enhancement of the grayscale image, and obtaining a preprocessed filtered image; determining a flowing path area of filtrate in an analysis area corresponding to each hole in the filtering image, analyzing the flowing path area, acquiring the flowing condition of the filtrate in each hole at different moments in the filtering image, and determining the total blocking degree of the holes; the gray value difference of pixel points at the same position between the images at any two moments in the filtered image is analyzed, and the overall blockage degree is determined; and obtaining the total blockage degree of the image according to the hole blockage degree and the overall blockage degree. The overall blocking degree of the image can be reflected through the hole blocking degree and the overall blocking degree, whether the filter needs to be cleaned or not is judged according to the size, and the accuracy of filter blocking degree recognition is improved.

Description

Technical Field

[0001] The present invention generally relates to the field of image processing and more particularly to a cleaning control method and system based on image processing. Background Art

[0002] In the catering industry, food cleanliness and hygiene have always been an important part of ensuring food quality and consumer health. In the prior art, a filter is installed at each hole in the food filter where filtrate flows out, to filter fat and other substances in the filtrate. However, the filter is inside the filter, and it is impossible to directly analyze the filtrate outflow from each hole in the filter to obtain the clogging status of the filter, making it difficult to clean the filter in time.

[0003] At present, a patent application document with publication number CN115690099A discloses an air purifier cleaning warning method based on data recognition, including collecting a filter surface image, obtaining a weight according to the distance from a pixel point to the center point of the filter surface image, and using the weight to grayscale the filter surface image to obtain a filter grayscale image; obtaining the degree of grayscale change according to the grayscale change of the pixel points in the surface image, obtaining the degree of flocculent accumulation according to the gradient change of the pixel points in the surface image, and obtaining the degree of filter dirtiness based on the degree of grayscale change and the degree of flocculent accumulation; obtaining the degree of filter dirtiness when the filter sample reaches saturation as reference data, comparing the degree of filter dirtiness with the reference data, and judging whether the filter needs to be cleaned or replaced.

[0004] The above patent application document uses image processing technology to identify the degree of dirt on the filter surface and judges the dirt status of the filter by comparing it with reference data. However, since the filter often temporarily adheres to some impurities that are easy to wash away, simply identifying the dirt on the filter surface cannot accurately identify the blockage of the filter holes, and there may even be a problem of frequent cleaning. Summary of the invention

[0005] In order to solve the problem that the clogging state of the filter is not accurately obtained by only identifying the dirty state of the filter surface when the filter is filtering the filtrate, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a cleaning control method based on image processing includes: acquiring a grayscale image of filtrate filtration, performing image enhancement on the grayscale image, and obtaining a preprocessed filtration image; Determine and analyze the flow path area of ​​the filtrate in the analysis area corresponding to each hole in the filter image, obtain the filtrate flow conditions of each hole in the filter image at different times, and determine the overall blockage degree of the hole; Analyze the gray value difference of the pixel points at the same position between the images at any two moments in the filtered image to determine the overall blockage degree; The total blockage degree of the image is obtained according to the total blockage degree of the holes and the overall blockage degree; Determine whether to perform cleaning operation based on the overall blockage degree of the image.

[0007] In one embodiment, determining the flow path area of ​​the filtrate in the analysis area corresponding to each hole in the filter image includes: Divide the analysis area corresponding to each hole in the filtered image; The pixel with the largest gray value is selected in the analysis area, and the flow path area of ​​the filtrate in the analysis area is obtained using the region growing algorithm.

[0008] In one embodiment, the filtrate flow condition includes the thickness of the filtrate flow path and the consistency of the edge direction of the filtrate flow path.

[0009] In one embodiment, the expression for the coarseness of the filtrate flow path is:

[0010]

[0011]

[0012]

[0013] in, , , …, Respectively represent the 1st, 2nd, ..., The corresponding hole number in the row, , , …, In the At this moment, the first row The coarseness of the pore filtrate flow path, the second row The coarseness of the pore filtrate flow path, ..., No. The coarseness of the pore filtrate flow path, , , …, are respectively the normal flow path width of the filtrate in the first row of holes at the first moment, the normal flow path width of the filtrate in the second row of holes, ..., the normal flow path width of the filtrate in the first row of holes at the first moment. The normal flow path width of the pore filtrate, , , …, In the At this moment, the first row The flow path width of the pore filtrate, the The flow path width of the pore filtrate, ..., No. The width of the pore filtrate flow path.

[0014] In one embodiment, the expression for the degree of consistency of the edge direction of the filtrate flow path is:

[0015]

[0016]

[0017]

[0018] in, , , …, In the At this moment, the first row The degree of consistency of the edge direction of the pore filtrate flow path, the The degree of consistency of the edge direction of the pore filtrate flow path, ..., No. The degree of consistency of the edge direction of the pore filtrate flow path, Indicates the first row The variance of the direction range of all edge pixels of the hole, Indicates the second row The variance of the direction range of all edge pixels of the hole, ..., Indicates No. The variance of the direction range of all edge pixels of the hole, It means that the edge direction consistency of the filtrate flow path of each hole in each row is linearly normalized, and the direction range represents the direction range to which the gradient direction of each edge pixel point belongs.

[0019] In one embodiment, the expression for the overall pore blockage degree is:

[0020]

[0021]

[0022]

[0023]

[0024] In the formula, , , …, Respectively represent the 1st, 2nd, ..., The number of holes in the row, is an exponential function with the natural constant e as the base, which represents , , …, Perform normalization processing, , , …, Respectively represent the 1st, 2nd, ..., Line The moment , , …, The degree of pore blockage of the pores, Indicates the overall degree of pore blockage.

[0025] In one embodiment, the expression of the overall blockage degree is:

[0026]

[0027] In the formula, is the total number of pixels in the filtered image, Indicated in Within a moment, filter the image Pixel point Gray value at the moment With the filtered image Pixel point Gray value at the moment The difference Indicates that weight is given to the difference The average difference after summing, Indicates that the average value is normalized to obtain the first Pixel point There is always a possibility of hole clogging , is the overall blockage degree, where .

[0028] In one embodiment, the expression of the total image blockage degree is:

[0029] In the formula, is the total blockage degree of the image, The degree of hole blockage The weight of The overall blockage degree The weight of , satisfy .

[0030] According to a second aspect of the present invention, there is also provided a cleaning control system based on image processing, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a cleaning control method based on image processing according to the first aspect of the present invention is implemented.

[0031] The present invention has the following effects: The present invention analyzes the flow path area of ​​filtrate in each hole in the filter image, calculates the thickness of the flow path of the filtrate in each hole and the consistency of the edge direction of the flow path, and jointly judges the overall blockage degree of the hole according to the thickness of the flow path and the consistency of the edge direction of the flow path. Secondly, the difference in grayscale values ​​of pixels at the same position in the filter image at different times is analyzed to obtain the overall blockage degree. The overall blockage degree of the hole and the overall blockage degree are combined to reflect the overall blockage degree of the image. According to the size of the overall blockage degree of the image, it is judged whether the filter needs to be cleaned to avoid the filter being blocked and unable to work normally, thereby affecting the filtration of the filtrate. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] 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 invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a method flow chart of steps S1-S5 in a cleaning control method based on image processing in an embodiment of the present invention.

[0033] Figure 2 It is a schematic diagram of a template image in a cleaning control method based on image processing according to an embodiment of the present invention.

[0034] Figure 3 It is a schematic diagram of the analysis area of ​​a filtered image in a cleaning control method based on image processing according to an embodiment of the present invention.

[0035] Figure 4 It is a structural block diagram of a cleaning control system based on image processing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0037] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0038] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] Reference Figure 1 , a cleaning control method based on image processing includes steps S1-S5, which are as follows: S1: Obtain a grayscale image of filtrate filtration, and perform image enhancement on the grayscale image to obtain a preprocessed filtered image.

[0040] Since the filtrate needs to be continuously heated and filtered during the processing, the camera used to collect the filtrate filtration image will generate noise and motion blur when working for a long time, resulting in blurred edges of the outflow path of the filtrate in each hole in the collected filtration image. By using adaptive filtering and Canny operator to enhance the grayscale image of the filtrate, the clarity of the edge of the flow path of the filtrate in each hole in the grayscale image is improved.

[0041] S2: Determine and analyze the flow path area of ​​the filtrate in the analysis area corresponding to each hole in the filter image, obtain the filtrate flow conditions of each hole in the filter image at different times, and determine the overall blockage degree of the hole.

[0042] The process of determining the flow path area of ​​the filtrate is as follows: Divide the analysis area corresponding to each hole in the filtered image; The pixel with the largest gray value is selected in the analysis area, and the flow path area of ​​the filtrate in the analysis area is obtained using the region growing algorithm.

[0043] Exemplarily, the filter surface is set to have OK A row of holes with the same size evenly distributed; Through the Hough circle detection algorithm, all holes on the filtered image are identified, and the holes with the same vertical coordinate of the circle center are divided into a row and used as the template image; Divide the template image to obtain the analysis area corresponding to each hole, and each analysis area corresponds to one hole; Since the filtrate is milky white, the grayscale value of the pixel corresponding to the filtrate is larger than that of the metal filter. Therefore, the pixel with the largest grayscale value is selected as the seed point in the analysis area, and the flow path area of ​​the filtrate in the analysis area is obtained using the regional growing algorithm.

[0044] In this embodiment, there may be blockage when the amount of filtrate flowing out of the filter gradually decreases, but it is not ruled out that it may be caused by the filtration of the filtrate is about to end. Therefore, by analyzing the flow path of the filtrate, when the holes are not blocked, the flow path width of the filtrate flowing out is a normal width and the direction consistency of the path edge is the highest; if the holes are blocked, the flow path width of the filtrate flowing out is a smaller width and the direction consistency of the path edge is medium; if the holes are severely blocked, the flow path width of the filtrate flowing out is the minimum width and the direction consistency of the path edge is the lowest.

[0045] Therefore, the filtrate flow conditions of the holes can be obtained from two aspects: the thickness of the filtrate flow path and the consistency of the edge direction of the filtrate flow path.

[0046] Specifically, based on the filtered image at the first moment, the analysis area of ​​each row of holes is studied, and the two boundary pixels with the same vertical coordinates in the boundary pixels of the filtrate flow path area corresponding to each hole in each row are divided into a group, and the Euclidean distance is used to calculate the distance between each group of two boundary pixels in each hole. The Euclidean distance of each group of two boundary pixels is summed and averaged, so as to obtain the filtrate flow path width corresponding to each hole in each row at the first moment, and the average value is taken, which is recorded as the normal flow path width of the filtrate in each row of holes.

[0047] It should be noted that when filtering the filtrate at the first moment, the filtrate flows out of each hole normally, but the filtrate flowing out of the first row will affect the filtrate path of the second row, and thus the normal flow path of the filtrate flowing out of each row is different, so it is necessary to calculate the normal flow path width of the filtrate in each row of holes.

[0048] Similarly, based on the filter image at the current moment, the filtrate flow path width corresponding to each hole in each row at the current moment is obtained respectively.

[0049] According to the width of the filtrate flow path corresponding to each row of holes, the thickness of the corresponding filtrate flow path at the current moment is obtained. The specific calculation formula is as follows: The expression for the coarseness of the filtrate flow path is:

[0050]

[0051]

[0052]

[0053] in, , , …, Respectively represent the 1st, 2nd, ..., The corresponding hole number in the row, , , …, In the At this moment, the first row The coarseness of the pore filtrate flow path, the second row The coarseness of the pore filtrate flow path, ..., No. The coarseness of the pore filtrate flow path, , , …, are respectively the normal flow path width of the filtrate in the first row of holes at the first moment, the normal flow path width of the filtrate in the second row of holes, ..., the normal flow path width of the filtrate in the first row of holes at the first moment. The normal flow path width of the pore filtrate, , , …, In the At this moment, the first row The flow path width of the pore filtrate, the The flow path width of the pore filtrate, ..., No. The width of the pore filtrate flow path.

[0054] The larger the width of the filtrate flow path in each row of holes, the greater the coarseness of the filtrate flow path; otherwise, it is smaller.

[0055] Each boundary pixel point of the flow path area corresponding to each hole is obtained, and the gradient direction of each edge pixel point is calculated using the Sobel operator. The gradient direction is quantified and divided into several discrete direction ranges. For each edge pixel point, the direction range to which the gradient direction of each edge pixel point belongs is used as the direction range of each edge pixel point, and the variance of the direction ranges of all edge pixels is calculated to measure the consistency of the edge direction of the filtrate flow path.

[0056] The calculation formula for the consistency of the edge direction of the above filtrate flow path is:

[0057]

[0058]

[0059]

[0060] in, , , …, In the At this moment, the first row The degree of consistency of the edge direction of the pore filtrate flow path, the The degree of consistency of the edge direction of the pore filtrate flow path, ..., No. The degree of consistency of the edge direction of the pore filtrate flow path, Indicates the first row The variance of the direction range of all edge pixels of the hole, Indicates the second row The variance of the direction range of all edge pixels of the hole, ..., Indicates No. The variance of the direction range of all edge pixels of the hole, It means that the edge direction consistency of the filtrate flow path of each hole in each row is linearly normalized, and the direction range represents the direction range to which the gradient direction of each edge pixel point belongs.

[0061] The overall blockage degree of the pores is calculated based on the coarseness of the filtrate flow path and the consistency of the edge direction of the filtrate flow path. The specific calculation formula is as follows:

[0062]

[0063]

[0064]

[0065]

[0066] In the formula, , , …, Respectively represent the 1st, 2nd, ..., The number of holes in the row, is an exponential function with the natural constant e as the base, which represents , , …, Perform normalization processing, , , …, Respectively represent the 1st, 2nd, ..., Line The moment , , …, The degree of pore blockage of the pores, Indicates the overall degree of pore blockage.

[0067] For ease of understanding, in this embodiment, Figure 2 As shown, it is assumed that a filter surface has 3 rows and 3 columns, with 9 holes of the same size evenly distributed, and the filtered image at the first moment is used as the template image, where the holes in each row are numbered 1, 2, and 3, respectively. In other embodiments, 1-9 can also be set as the hole numbers arranged from left to right and from top to bottom on the template image.

[0068] like Figure 3 As shown in the figure, take the analysis area of ​​the first hole as an example: take 1 / 2 of the distance between the center of the first hole and the center of the second hole as the right boundary of the analysis area, and symmetrically take the left boundary of the analysis area; take the straight line where the vertex of the first hole is located as the upper boundary of the analysis area, and the straight line where the vertex of the fourth hole is located as the lower boundary of the analysis area of ​​the first hole. The closed area surrounded by the left and right boundaries and the upper and lower boundaries is the analysis area of ​​the first hole. By analogy, the analysis area corresponding to each hole in the template image is determined.

[0069] It should be added that, since the filtrate is excluded from flowing above the holes when the filtrate flows out in the first row, only the holes and the area below the holes are analyzed.

[0070] Exemplarily, in the above formula for calculating the coarseness of the filtrate flow path and the formula for calculating the consistency of the edge direction of the filtrate flow path, , You can choose 1, 2, or 3. You can choose 1, 2, or 3. You can choose 1, 2, 3, and the overall hole blockage degree calculation formula: , , In other embodiments, in the above formula for calculating the thickness of the filtrate flow path and the formula for calculating the consistency of the edge direction of the filtrate flow path, , You can choose 1, 2, or 3. The options are 4, 5, 6, 7, 8, 9 can be taken, as well as the calculation formula for the overall blockage degree of the holes. , , Take 3, 6, and 9 respectively. 1. 4. 7 is preferred.

[0071] It should be noted that, in the present invention, considering that If the time is close to the first time, the filtrate may not have flowed out yet. If the time difference is too large, the possibility of blockage is greater. Therefore, exemplary time is selected: =10 and start recording.

[0072] S3: Analyze the gray value difference of the pixel points at the same position between the images at any two moments in the filtered image to determine the overall blockage degree.

[0073] As time goes by, the pores become more and more clogged during the filtration process. The filtered image at the time of the filtering may be significantly different from the image at the beginning of the filtering due to blockage and other reasons, that is, the grayscale values ​​of the pixels at the same position in the images at different times may be different. The greater the difference between the grayscale values ​​of all pixels in the image at the moment and the grayscale values ​​of all pixels in the image at the beginning of filtering, the The more you filter the image, the greater the possibility that there will be holes that are blocked.

[0074] In the embodiment of the present invention, it is set that the total number of filtered images is pixels, the first pixel in the filter image Pixel point The gray value at the moment is recorded as , the first Pixel point The gray value at the moment is recorded as When judging the overall blockage degree, filter the image Pixel point Gray value at the moment With the filtered image Pixel point Gray value at the moment The difference in the impact of each moment is different, so it is necessary to and The difference of is given the corresponding weight. Pixel point There is always a possibility of hole clogging And sum them up to get the overall blockage degree , the overall degree of blockage The expression is as follows:

[0075]

[0076] In the formula, Indicated in Within a moment, filter the image Pixel point Gray value at the moment With Gray value at the moment The difference Indicates that weight is given to the difference And sum the average values, we get Filter the image within a certain time period Pixel point The gray value at the moment The average difference in grayscale values ​​at time instant, Indicates that the average difference is normalized to obtain the first Pixel point There is a possibility of hole clogging at this time ,in, Indicates the filtering image and The weight given to the difference .

[0077] It is necessary to add that =10.

[0078] When Pixel point There is always a possibility of hole clogging The larger the overall blockage The larger the value.

[0079] S4: Obtain the total blockage degree of the image according to the hole blockage degree and the overall blockage degree.

[0080] Overall pore blockage Can be reflected in The degree of blockage of each hole at the moment, and the overall blockage Can be reflected in The overall blockage degree of the filtered image at the time, so the overall blockage degree of the hole The overall blockage Together they provide a more precise picture of the overall degree of clogging, facilitating subsequent filter cleaning.

[0081] Specifically, the calculation formula for the total image blockage degree is as follows:

[0082] In the formula, Indicates the total degree of image blockage. The degree of hole blockage The weight of The overall blockage degree The weight of , satisfy .

[0083] For example, is 0.6, is 0.4.

[0084] when and When the corresponding values ​​are large, the total degree of image blockage The value of is also large, and vice versa.

[0085] S5: Determine whether to perform a cleaning operation based on the overall blockage degree of the image.

[0086] For example, when the total blockage level of the image Greater than or equal to 0.5, it is considered that The blockage of the moment filter image is serious and needs to be cleaned.

[0087] The present invention also provides a cleaning control system based on image processing. Figure 4 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a cleaning control method based on image processing according to the first aspect of the present invention is implemented.

[0088] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0089] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0090] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0091] Although this specification has shown and described a number of embodiments of the present invention, 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 conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A cleaning control method based on image processing, characterized in that: include: Acquire a grayscale image of filtrate filtration, and perform image enhancement on the grayscale image to obtain a preprocessed filtered image; Determine and analyze the flow path area of ​​the filtrate in the analysis area corresponding to each hole in the filter image, obtain the filtrate flow conditions of each hole in the filter image at different times, and determine the overall blockage degree of the hole; Analyze the gray value difference of the pixel points at the same position between the images at any two moments in the filtered image to determine the overall blockage degree; The total blockage degree of the image is obtained according to the total blockage degree of the holes and the overall blockage degree; Determine whether to perform cleaning operation based on the overall blockage degree of the image.

2. A cleaning control method based on image processing according to claim 1, characterized in that: Determining the flow path area of ​​the filtrate in the analysis area corresponding to each hole in the filtration image includes: Divide the analysis area corresponding to each hole in the filtered image; The pixel with the largest gray value is selected in the analysis area, and the flow path area of ​​the filtrate in the analysis area is obtained using the region growing algorithm.

3. A cleaning control method based on image processing according to claim 2, characterized in that: The filtrate flow condition includes the thickness of the filtrate flow path and the consistency of the edge direction of the filtrate flow path.

4. A cleaning control method based on image processing according to claim 3, characterized in that: The expression for the coarseness of the filtrate flow path is: in, , , …, Respectively represent the 1st, 2nd, ..., The corresponding hole number in the row, , , …, In the At this moment, the first row The coarseness of the pore filtrate flow path, the second row The coarseness of the pore filtrate flow path, ..., No. The coarseness of the pore filtrate flow path, , , …, are respectively the normal flow path width of the filtrate in the first row of holes at the first moment, the normal flow path width of the filtrate in the second row of holes, ..., the normal flow path width of the filtrate in the first row of holes at the first moment. The normal flow path width of the pore filtrate, , , …, In the At this moment, the first row The flow path width of the pore filtrate, the The flow path width of the pore filtrate, ..., No. The width of the pore path for the filtrate to flow.

5. A cleaning control method based on image processing according to claim 4, characterized in that: The expression for the degree of consistency of the edge direction of the filtrate flow path is: in, , , …, In the At this moment, the first row The degree of consistency of the edge direction of the pore filtrate flow path, the The degree of consistency of the edge direction of the pore filtrate flow path, ..., No. The degree of consistency of the edge direction of the pore filtrate flow path, Indicates the first row The variance of the direction range of all edge pixels of the hole, Indicates the second row The variance of the direction range of all edge pixels of the hole, ..., Indicates No. The variance of the direction range of all edge pixels of the hole, It means that the edge direction consistency of the filtrate flow path of each hole in each row is linearly normalized, and the direction range represents the direction range to which the gradient direction of each edge pixel point belongs.

6. A cleaning control method based on image processing according to claim 5, characterized in that: The expression of the overall pore blockage degree is: In the formula, , , …, Respectively represent the 1st, 2nd, ..., The number of holes in the row, is an exponential function with the natural constant e as the base, which represents , , …, Perform normalization processing, , , …, Respectively represent the 1st, 2nd, ..., Line The moment , , …, The degree of pore blockage of the pores, Indicates the overall degree of pore blockage.

7. A cleaning control method based on image processing according to claim 6, characterized in that: The expression of overall blockage degree is: In the formula, is the total number of pixels in the filtered image, Indicated in Within a moment, filter the image Pixel point Gray value at the moment With the filtered image Pixel point Gray value at the moment The difference Indicates that weight is given to the difference The average difference after summing, Indicates that the average value is normalized to obtain the first Pixel point There is always a possibility of hole clogging , is the overall blockage degree, where .

8. A cleaning control method based on image processing according to claim 7, characterized in that: The expression of the total blocking degree of the image is: In the formula, is the total blockage degree of the image, The degree of hole blockage The weight of The overall blockage degree The weight of , satisfy .

9. A cleaning control system based on image processing, 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 cleaning control method based on image processing according to any one of claims 1 to 8 is implemented.

Images