Quantitative judgment method and device for surface smudginess degree of cleaning tool

Abstract

The invention relates to the technical field of intelligent electric appliances, and provides a quantitative judgment method and device for the surface smudginess degree of a cleaning tool, and the method comprises the steps: determining an average saturation value corresponding to a multi-view image of the cleaning tool, and determining the smudginess degree of the cleaning tool based on the average saturation value. According to the quantitative judgment method for the smudginess degree of the surface of the cleaning tool, the smudginess degree of the surface of the cleaning tool can be quantitatively judged, the accuracy and efficiency of quantitative judgment of the smudginess degree of the surface of the cleaning tool are effectively improved, and therefore the use experience of a user is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent electrical appliance technology, and in particular to a method and device for quantitatively determining the degree of dirtiness on the surface of a cleaning tool, and also to an electronic device and a non-transitory computer-readable storage medium. Background Art

[0002] With the rapid development of the economy and society, various intelligent electrical appliances are being used more and more widely in people's daily lives. In particular, with the improvement of people's living standards, intelligent household cleaning equipment has gradually entered thousands of households, among which semi-automatic handheld cleaning tool surfaces have been favored by consumers in the market. Due to the semi-automatic design, this type of household intelligent cleaning equipment usually has the function of automatically cleaning the core cleaning components. However, for existing semi-automatic household intelligent cleaning equipment, it is difficult to effectively judge the current degree of dirtiness. Therefore, how to provide a faster and more accurate quantitative judgment scheme for the degree of dirtiness on the surface of cleaning tools has become a problem that needs to be solved urgently. Summary of the invention

[0003] The present invention provides a method for quantitatively judging the degree of dirt on the surface of a cleaning tool, which is used to solve the defect that the scheme for quantitatively judging the degree of dirt on the surface of a cleaning tool in the prior art has high limitations.

[0004] The present invention provides a method for quantitatively judging the degree of dirtiness on the surface of a cleaning tool, comprising:

[0005] Determine an average saturation value corresponding to the multi-view images of the cleaning tool;

[0006] A degree of soiling of the cleaning tool is determined based on the average saturation value.

[0007] Furthermore, before determining the average saturation value corresponding to the multi-perspective image of the cleaning tool, it also includes: based on a preset standardized image shooting environment and equipment, obtaining a multi-perspective image of the cleaning tool, and selecting pixel blocks of the target component from the multi-perspective image; wherein the multi-perspective image contains the target component of the cleaning tool.

[0008] Furthermore, before determining the average saturation value corresponding to the multi-view image of the cleaning tool, it also includes: obtaining binary images corresponding to the multi-view images of the target component in the cleaning tool, determining multiple area contours corresponding to the dirty areas in the binary image, determining the target component in the multi-view image based on the multiple area contours, and selecting pixel blocks of the target component.

[0009] Furthermore, determining the average saturation value corresponding to the multi-perspective image of the cleaning tool specifically includes: calculating the average saturation value corresponding to the pixel block, and determining the average saturation value corresponding to the multi-perspective image of the cleaning tool based on the average saturation value corresponding to the pixel block.

[0010] Furthermore, the step of acquiring binary images corresponding to the multi-view images of the target component in the cleaning tool specifically includes:

[0011] Acquire multi-view images of target parts in the cleaning tool according to preset angles;

[0012] The multi-view images are processed using a preset multi-level edge detection algorithm model to obtain binary images corresponding to the multi-view images.

[0013] Further, determining a plurality of area contours corresponding to the dirty areas in the binary image specifically includes:

[0014] Acquire contour information corresponding to the dirty area in the binary image;

[0015] A corresponding target connected domain is constructed based on the contour information to obtain a plurality of region contours contained in the binary image.

[0016] Furthermore, the determining the target component in the multi-view image based on the multiple region contours specifically includes:

[0017] Acquire multiple minimum enclosing areas corresponding to the multiple area contours;

[0018] The multiple minimum enclosing areas are screened, and a target area contour corresponding to a target minimum enclosing area whose center point position and size parameters meet preset conditions are selected from the multiple minimum enclosing areas, and the target area contour is used as the target component of the multi-view image; wherein the size parameters include a length parameter and a width parameter of the minimum enclosing area.

[0019] The present invention also provides a device for quantitatively determining the degree of dirt on the surface of a cleaning tool, comprising:

[0020] A saturation value calculation unit, used to determine an average saturation value corresponding to the multi-view images of the cleaning tool;

[0021] A dirtiness determination unit is used to determine the dirtiness of the cleaning tool based on the average saturation value.

[0022] Furthermore, before determining the average saturation value corresponding to the multi-perspective image of the cleaning tool, it also includes: a cleaning tool surface image acquisition unit, which is used to acquire the multi-perspective image of the cleaning tool based on a preset standardized image shooting environment and equipment, and select pixel blocks of the target component from the multi-perspective image; wherein the multi-perspective image contains the target component of the cleaning tool.

[0023] Furthermore, before determining the average saturation value corresponding to the multi-perspective image of the cleaning tool, it also includes: a cleaning tool surface main image capture unit, which is used to obtain binary images corresponding to the multi-perspective images of the target component in the cleaning tool, determine multiple area contours corresponding to the dirty area in the binary image, determine the target component in the multi-perspective image based on the multiple area contours, and select the pixel block of the target component.

[0024] Furthermore, the saturation value calculation unit is specifically used to calculate an average saturation value corresponding to the pixel block, and determine an average saturation value corresponding to the multi-view image of the cleaning tool based on the average saturation value corresponding to the pixel block.

[0025] Furthermore, the step of acquiring binary images corresponding to the multi-view images of the target component in the cleaning tool specifically includes:

[0026] Acquire multi-view images of target parts in the cleaning tool according to preset angles;

[0027] The multi-view images are processed using a preset multi-level edge detection algorithm model to obtain binary images corresponding to the multi-view images.

[0028] Further, determining a plurality of area contours corresponding to the dirty areas in the binary image specifically includes:

[0029] Acquire contour information corresponding to the dirty area in the binary image;

[0030] A corresponding target connected domain is constructed based on the contour information to obtain a plurality of region contours contained in the binary image.

[0031] Furthermore, the determining the target component in the multi-view image based on the multiple region contours specifically includes:

[0032] Acquire multiple minimum enclosing areas corresponding to the multiple area contours;

[0033] The multiple minimum enclosing areas are screened, and a target area contour corresponding to a target minimum enclosing area whose center point position and size parameters meet preset conditions are selected from the multiple minimum enclosing areas, and the target area contour is used as the target component of the multi-view image; wherein the size parameters include a length parameter and a width parameter of the minimum enclosing area.

[0034] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for quantitatively determining the degree of dirtiness on the surface of a cleaning tool as described in any one of the above are implemented.

[0035] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for quantitatively determining the degree of dirtiness on the surface of a cleaning tool.

[0036] The method for quantitatively judging the degree of dirt on the surface of a cleaning tool provided by the present invention determines the average saturation value corresponding to the multi-view images of the cleaning tool, and determines the degree of dirt on the cleaning tool based on the average saturation value. The method can quantitatively judge the degree of dirt on the surface of the cleaning tool, and effectively improves the accuracy and efficiency of quantitative judgment of the degree of dirt on the surface of the cleaning tool, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a flow chart of a method for quantitatively judging the degree of dirtiness on the surface of a cleaning tool provided by the present invention;

[0039] Figure 2 It is a schematic diagram of the structure of the device for quantitatively judging the degree of dirtiness on the surface of a cleaning tool provided by the present invention;

[0040] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] It should be noted that the terms "first", "second", etc. in the specification of the present invention and the above-mentioned drawings are used to distinguish similar users, 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 interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0043] The following is a detailed description of an embodiment of the method for quantitatively determining the degree of dirt on the surface of a cleaning tool according to the present invention. Figure 1 As shown, it is a flow chart of the method for quantitatively judging the degree of dirt on the surface of a cleaning tool provided by the present invention, and the specific implementation process includes the following steps:

[0044] Step 101: Determine an average saturation value corresponding to the multi-view images of the cleaning tool.

[0045] The cleaning tool may be a handheld floor scrubber, a washing and drying integrated floor sweeping robot, etc., which are not specifically limited here. The multi-view image may be an image of a roller brush or other components of the cleaning tool, and the components include various types of parts on the cleaning tool for cleaning.

[0046] In an embodiment of the present invention, a standard experimental box and a shooting context can be used to obtain binary images corresponding to the multi-view images of the target component in the cleaning tool, determine multiple area contours corresponding to the dirty area in the binary image, determine the target component in the multi-view image based on the multiple area contours, select the pixel block of the target component, and calculate the average saturation value corresponding to the pixel block. Based on the average saturation value corresponding to the pixel block, determine the average saturation value corresponding to the multi-view image of the cleaning tool

[0047] For example, standardized image acquisition is performed for the state of the roller brush. Specifically, multi-view images of the target component in the cleaning tool are acquired at preset angles, such as acquiring images at three viewing angles, each 120°, with consistent positions, lighting conditions, and camera parameters. The multi-view images are processed using a preset multi-level edge detection algorithm model to obtain binary images corresponding to the multi-view images. Then, multiple area contours corresponding to the dirty areas in the binary image are determined; and the target components in the multi-view images are determined based on the multiple area contours. Among them, the multiple area contours corresponding to the dirty areas in the binary image are determined, and the corresponding specific implementation process may include acquiring contour information corresponding to the dirty areas in the binary image, and constructing a corresponding target connected domain based on the contour information to obtain multiple area contours contained in the binary image. The determination of the target component in the multi-view image based on the multiple region outlines may include obtaining multiple minimum enclosing regions (such as minimum enclosing rectangles) corresponding to the multiple region outlines, and screening the multiple minimum enclosing regions to select a target region outline corresponding to the target minimum enclosing region whose center point position and size parameters meet preset conditions from the multiple minimum enclosing regions, and using the target region outline as the target component in the multi-view image. The size parameters include but are not limited to the length parameter and width parameter of the minimum enclosing region.

[0048] For example, the multi-view images may be three images with a viewing angle of 120° respectively. Specifically, the multi-view images include a first viewing angle image, a second viewing angle image and a third viewing angle image. Among them, the first viewing angle image, the second viewing angle image and the third viewing angle image are images with a viewing angle of 120° respectively, and together constitute the global viewing angle image corresponding to the target component, that is, a 360° global viewing angle image. Of course, other multiple groups of pictures can also be collected, as long as the collected pictures can describe the overall component, which is not specifically limited here. The target component in the multi-view image is determined based on the multiple regional contours, the pixel block of the target component is selected, and the average saturation value corresponding to the pixel block is calculated. The corresponding specific implementation process may include: determining the first target component in the first multi-view image, selecting the first pixel block of the first target component, and calculating the first average saturation value corresponding to the first pixel block; and determining the second target component in the second multi-view image, selecting the second pixel block of the second target component, and calculating the second average saturation value corresponding to the second pixel block; and determining the third target component in the third multi-view image, selecting the third pixel block of the third target component, and calculating the third average saturation value corresponding to the third pixel block.

[0049] Step 102: Determine the dirtiness of the roller brush based on the average saturation value.

[0050] Specifically, an average value analysis may be performed based on the first average saturation value, the second average saturation value, and the third average saturation value to obtain a target average saturation value, and then the degree of dirtiness of the cleaning tool may be determined based on the target average saturation value. The specific implementation process of determining the degree of dirtiness of the cleaning tool based on the target average saturation value may include: comparing the target average saturation values ​​with each other to obtain a comparison result, and determining the degree of dirtiness of the cleaning tool according to the comparison result.

[0051] In a complete embodiment, a group of multi-view images that can be collected can be input into the image processing system of the present invention for processing, and the specific processing process is as follows: focus acquisition of multi-view images; extraction of the roller brush (i.e., the target component); due to the use of a standard shooting context, image blocks of a fixed pixel range can be extracted or the following methods can be used for extraction and optimization or other commonly used target component recognition algorithms. In the extraction and optimization process, it specifically includes using a preset multi-level edge detection algorithm model (i.e., the Canny algorithm) to process the multi-view images and output corresponding binary images. Among them, the Canny algorithm may include but is not limited to graying, Gaussian filtering, calculating gradients, non-maximum suppression, dual threshold determination and other processing processes, which are not described in detail here. Further, the binary image is corroded and expanded to achieve the purpose of denoising the edge, and then multiple regional contours existing in the binary image are found; each minimum enclosing area containing each regional contour is obtained; the regional contour corresponding to the minimum enclosing area that does not meet the preset conditions (such as the area is too small or the contour center is not near the center of the image, etc.) is removed; specifically, the regional contour corresponding to the minimum preset condition rectangle whose center point position and length, width and other size parameters meet the expected conditions can be selected as the target regional contour, and then the target regional contour is used as the position of the target component. The multi-view image is converted into saturation-related information, and the pixel block of the target component is selected according to the above method, and its average saturation value is calculated. Each group of multi-view images (such as three images with a 120-degree viewing angle) is processed in the same way, and then the average value is calculated, thereby obtaining a quantitative value of the degree of dirtiness of the roller brush on the surface of the cleaning tool. It should be noted that the present invention includes but is not limited to the method of calculating the average value. Of course, some numerical optimization methods can also be used to remove obvious deviation points, which are not specifically limited here.

[0052] The method for quantitatively judging the degree of dirt on the surface of a cleaning tool provided by the present invention determines the average saturation value corresponding to the multi-view images of the cleaning tool, and determines the degree of dirt on the cleaning tool based on the average saturation value. The method can quantitatively judge the degree of dirt on the surface of the cleaning tool, and effectively improves the accuracy and efficiency of the quantitative judgment of the degree of dirt on the surface of the cleaning tool, thereby improving the user experience.

[0053] The device for quantitatively judging the degree of dirtiness on the surface of a cleaning tool provided by the present invention is described below. The device for quantitatively judging the degree of dirtiness on the surface of a cleaning tool described below and the method for quantitatively judging the degree of dirtiness on the surface of a cleaning tool described above can be referred to each other.

[0054] refer to Figure 2 As shown, it is a schematic diagram of the structure of the device for quantitatively judging the degree of dirt on the surface of a cleaning tool provided by the present invention. The device for quantitatively judging the degree of dirt on the surface of a cleaning tool provided by the present invention specifically includes the following parts:

[0055] A saturation value calculation unit 201 is used to determine an average saturation value corresponding to the multi-view images of the cleaning tool;

[0056] The dirtiness determination unit 202 is configured to determine the dirtiness of the cleaning tool based on the average saturation value.

[0057] Furthermore, before determining the average saturation value corresponding to the multi-perspective image of the cleaning tool, it also includes: a cleaning tool surface image acquisition unit, which is used to acquire the multi-perspective image of the cleaning tool based on a preset standardized image shooting environment and equipment, and select pixel blocks of the target component from the multi-perspective image; wherein the multi-perspective image contains the target component of the cleaning tool.

[0058] Furthermore, before determining the average saturation value corresponding to the multi-perspective image of the cleaning tool, it also includes: a cleaning tool surface main image capture unit, which is used to obtain binary images corresponding to the multi-perspective images of the target component in the cleaning tool, determine multiple area contours corresponding to the dirty area in the binary image, determine the target component in the multi-perspective image based on the multiple area contours, and select the pixel block of the target component.

[0059] Furthermore, the saturation value calculation unit is specifically used to calculate an average saturation value corresponding to the pixel block, and determine an average saturation value corresponding to the multi-view image of the cleaning tool based on the average saturation value corresponding to the pixel block.

[0060] Furthermore, the step of acquiring binary images corresponding to the multi-view images of the target component in the cleaning tool specifically includes:

[0061] Acquire multi-view images of target parts in the cleaning tool according to preset angles;

[0062] The multi-view images are processed using a preset multi-level edge detection algorithm model to obtain binary images corresponding to the multi-view images.

[0063] Further, determining a plurality of area contours corresponding to the dirty areas in the binary image specifically includes:

[0064] Acquire contour information corresponding to the dirty area in the binary image;

[0065] A corresponding target connected domain is constructed based on the contour information to obtain a plurality of region contours contained in the binary image.

[0066] Furthermore, the determining the target component in the multi-view image based on the multiple region contours specifically includes:

[0067] Acquire multiple minimum enclosing areas corresponding to the multiple area contours;

[0068] The multiple minimum enclosing areas are screened, and a target area contour corresponding to a target minimum enclosing area whose center point position and size parameters meet preset conditions are selected from the multiple minimum enclosing areas, and the target area contour is used as the target component of the multi-view image; wherein the size parameters include a length parameter and a width parameter of the minimum enclosing area.

[0069] The device for quantitatively judging the degree of dirt on the surface of a cleaning tool provided by the present invention determines the average saturation value corresponding to the multi-view images of the cleaning tool, and determines the degree of dirt on the cleaning tool based on the average saturation value. This method can quantitatively judge the degree of dirt on the surface of the cleaning tool, and effectively improves the accuracy and efficiency of quantitative judgment of the degree of dirt on the surface of the cleaning tool, thereby improving the user experience.

[0070] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3As shown, the electronic device may include: a processor 301, a communication interface 304, a memory 302 and a communication bus 303, wherein the processor 301, the communication interface 304 and the memory 302 communicate with each other through the communication bus 303. The processor 301 may call the logic instructions in the memory 302 to execute a quantitative determination method of the degree of dirt on the surface of a cleaning tool, the method comprising: determining an average saturation value corresponding to the multi-view images of the cleaning tool; and determining the degree of dirt on the cleaning tool based on the average saturation value.

[0071] In addition, the logic instructions in the above-mentioned memory 302 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the 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 for 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 invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0072] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the quantitative judgment method of the degree of dirtiness on the surface of a cleaning tool provided by the above-mentioned methods, and the method includes: determining the average saturation value corresponding to the multi-perspective images of the cleaning tool; determining the degree of dirtiness of the cleaning tool based on the average saturation value.

[0073] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to perform the above-mentioned methods for quantitatively judging the degree of dirtiness of the surface of the cleaning tool. The method includes: determining the average saturation value corresponding to the multi-perspective images of the cleaning tool; determining the degree of dirtiness of the cleaning tool based on the average saturation value.

[0074] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0075] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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 invention.

Claims

1. A method for quantitatively determining the degree of dirtiness on the surface of a cleaning tool, characterized in that: include: Determine an average saturation value corresponding to the multi-view images of the cleaning tool; A degree of soiling of the cleaning tool is determined based on the average saturation value.

2. The method for quantitatively determining the degree of dirtiness on the surface of a cleaning tool according to claim 1, characterized in that: Before determining the average saturation value corresponding to the multi-view image of the cleaning tool, it also includes: based on a preset standardized image shooting environment and equipment, obtaining the multi-view image of the cleaning tool, and selecting pixel blocks of the target component from the multi-view image; wherein the multi-view image contains the target component of the cleaning tool.

3. The method for quantitatively determining the degree of dirtiness on the surface of a cleaning tool according to claim 1, characterized in that: Before determining the average saturation value corresponding to the multi-view images of the cleaning tool, the method also includes: obtaining binary images corresponding to the multi-view images of the target parts in the cleaning tool, determining multiple area contours corresponding to the dirty areas in the binary images, determining the target parts in the multi-view images based on the multiple area contours, and selecting pixel blocks of the target parts.

4. The method for quantitatively determining the degree of dirtiness on the surface of a cleaning tool according to claim 2, characterized in that: Determining the average saturation value corresponding to the multi-view image of the cleaning tool specifically includes: calculating the average saturation value corresponding to the pixel block, and determining the average saturation value corresponding to the multi-view image of the cleaning tool based on the average saturation value corresponding to the pixel block.

5. The method for quantitatively determining the degree of dirtiness on the surface of a cleaning tool according to claim 3, characterized in that: The determining of the target component in the multi-view image based on the multiple area contours specifically includes: Acquire multiple minimum enclosing areas corresponding to the multiple area contours; The multiple minimum enclosing areas are screened, and a target area contour corresponding to a target minimum enclosing area whose center point position and size parameters meet preset conditions are selected from the multiple minimum enclosing areas, and the target area contour is used as the target component of the multi-view image; wherein the size parameters include a length parameter and a width parameter of the minimum enclosing area.

6. The method for quantitatively determining the degree of dirtiness on the surface of a cleaning tool according to claim 3, characterized in that: The step of acquiring binary images corresponding to the multi-view images of the target component in the cleaning tool specifically includes: Acquire multi-view images of target parts in the cleaning tool according to preset angles; The multi-view images are processed using a preset multi-level edge detection algorithm model to obtain binary images corresponding to the multi-view images.

7. A device for quantitatively judging the degree of dirt on the surface of a cleaning tool, characterized in that: include: A saturation value calculation unit, used to determine an average saturation value corresponding to the multi-view images of the cleaning tool; A dirtiness determination unit is used to determine the dirtiness of the cleaning tool based on the average saturation value.

8. The device for quantitatively judging the degree of dirt on the surface of a cleaning tool according to claim 7, characterized in that: Before determining the average saturation value corresponding to the multi-view image of the cleaning tool, it also includes: a cleaning tool surface image acquisition unit, which is used to acquire the multi-view image of the cleaning tool based on a preset standardized image shooting environment and equipment, and select pixel blocks of the target component from the multi-view image; wherein the multi-view image contains the target component of the cleaning tool.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for quantitatively determining the degree of dirtiness on the surface of the cleaning tool as claimed in any one of claims 1 to 6 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for quantitatively determining the degree of dirtiness on the surface of a cleaning tool as claimed in any one of claims 1 to 6 are implemented.

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