Method, device and storage medium for determining the degree of dirt of a cleaning device

Abstract

The present application discloses a method, device and storage medium for determining the degree of dirt of a cleaning device, belonging to the field of computer technology. It includes: obtaining a target image of a cleaning part on the cleaning device; determining the cleaning part area in the target image; performing area division on the cleaning part area to obtain at least two sub-areas; determining the degree of dirt of the cleaning device based on the pixel values of each sub-area; It can solve the problem that the dirt detection result obtained by the traditional dirt detection method cannot directly reflect the degree of dirt of the cleaning device, and the reliability of the obtained dirt detection result is not high; Since the target image of the cleaning part can be collected, it is possible to directly detect the degree of dirt of the cleaning part and improve the reliability of the dirt degree detection result.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and particularly relates to a method, device, and storage medium for determining the degree of dirt on a cleaning device. Background Art

[0002] As the working time of the cleaning device increases, the degree of dirt on the cleaning parts of the cleaning device gradually increases. To prevent the cleaning parts from causing secondary pollution to the surface to be cleaned, it is usually necessary to detect the degree of dirt on the cleaning device in order to clean the cleaning parts in a timely manner when the degree of dirt is relatively high.

[0003] The traditional method for determining the degree of dirt on a cleaning device includes: comparing the ground environmental parameters of the ground before cleaning and the ground environmental parameters of the ground after cleaning to obtain the absolute value of the difference in ground environmental parameters; comparing this absolute value with a comparison reference value to obtain the detection result of the degree of dirt.

[0004] However, the difference value of the ground environmental parameters can only indirectly reflect the degree of dirt on the cleaning device, and cannot directly detect the degree of dirt on the cleaning device. Summary of the Invention

[0005] The technical problems to be solved by this application include that the dirt detection result obtained by the traditional dirt detection method cannot directly reflect the degree of dirt on the cleaning device, and the reliability of the obtained dirt detection result is not high.

[0006] To solve the above technical problems, this application provides a method for determining the degree of dirt on a cleaning device, including:

[0007] Obtain a target image of the cleaning parts on the cleaning device;

[0008] Determine the cleaning part area in the target image;

[0009] Divide the cleaning part area into at least two sub-areas;

[0010] Determine the degree of dirt on the cleaning device based on the pixel values of each sub-area.

[0011] Optionally, during the working process of the cleaning parts, they rotate around a central axis perpendicular to the surface to be cleaned;

[0012] The step of dividing the cleaning part area into at least two sub-areas includes:

[0013] Taking the central axis of the cleaning parts in the cleaning part area as the center of each sub-area, divide the cleaning part area to obtain the at least two sub-areas.

[0014] Optionally, the cleaning member is circular as a whole. Correspondingly, the cleaning member area is circular as a whole; the central axis passes through the center of the circle.

[0015] Taking the central axis of the cleaning member in the cleaning member area as the center of each sub-area, the cleaning member area is divided into the at least two sub-areas, including:

[0016] Taking the center of the circle as the center of the sub-area, the cleaning member area is divided into n sub-areas according to n preset division coefficients;

[0017] Wherein, the first sub-area is a circle with a radius equal to the product of the first division coefficient and R, and the i-th sub-area is an annular ring obtained by subtracting the inner circle with a radius equal to the product of the (i - 1)-th division coefficient and R from the outer circle with a radius equal to the product of the i-th division coefficient and R; the R is the radius of the cleaning member area; the i sequentially takes integers from 2 to n, the n is an integer greater than 1, and the n-th division coefficient is 1.

[0018] Optionally, after determining the degree of dirt of the cleaning device based on the pixel values of each sub-area, it further includes:

[0019] Generating a data description value of the degree of dirt; wherein, the data description value includes a high-order bit and a low-order bit, both the high-order bit and the low-order bit are positively correlated with the degree of dirt, the high-order bit is used to describe the number of integer bits of the degree of dirt, the low-order bit is the first m digits of the integer bit when the integer bit is not zero, and the low-order bit is the decimal part of the degree of dirt when the integer bit is zero; the m is a positive integer;

[0020] Using the data description value of the degree of dirt to compare different degrees of dirt to determine the cleaning effect of the cleaning device.

[0021] Optionally, the using the data description value to compare different degrees of dirt includes:

[0022] Comparing the high-order bits corresponding to different degrees of dirt;

[0023] When the high-order bits corresponding to different degrees of dirt are the same, comparing the low-order bits corresponding to different degrees of dirt;

[0024] When the high-order bits corresponding to different degrees of dirt are different, determining that the degree of dirt with a larger high-order bit is larger.

[0025] Optionally, the degree of dirt includes the local degrees of dirt of different sub-areas; the using the data description value to compare different degrees of dirt includes:

[0026] Use the described data description values to compare the local soiling degrees of different sub-regions;

[0027] Among them, the different sub-regions include the same sub-region corresponding to different cleaning parts on the cleaning device; and / or, different sub-regions corresponding to the same cleaning part on the cleaning device.

[0028] Optionally, determining the soiling degree of the cleaning device based on the pixel values of each sub-region includes:

[0029] Determine the sum of pixel values of target pixels in the sub-region, where the target pixels are pixels in the sub-region that meet the preset soiling conditions;

[0030] Determine the local soiling degree of each sub-region based on the sum of pixel values to obtain the soiling degree of the cleaning device; there is a negative correlation between the sum of pixel values and the local soiling degree.

[0031] Optionally, the method further includes:

[0032] Based on the pixel values of the cleaning part region, determine the global soiling degree of the cleaning part region, and the soiling degree of the cleaning device includes the global soiling degree and the local soiling degree of each sub-region;

[0033] Based on the local soiling degree and the global soiling degree, determine the cleaning effect of the cleaning device.

[0034] Optionally, determining the cleaning part region in the target image includes:

[0035] Perform foreground extraction on the target image to obtain the cleaning part region.

[0036] On the other hand, the present application also provides a cleaning device, which includes: a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement the method for determining the soiling degree of the cleaning device provided in the above aspect.

[0037] In another aspect, the present application also provides a computer-readable storage medium, characterized in that a program is stored in the storage medium, and when the program is executed by a processor, it implements the method for determining the soiling degree of the cleaning device provided in the above aspect.

[0038] The technical solution provided by this application has at least the following advantages: By obtaining the target image of the cleaning part on the cleaning device; determining the cleaning part area in the target image; dividing the cleaning part area into at least two sub-areas; determining the degree of dirt on the cleaning device based on the pixel values of each sub-area; it can solve the problem that the dirt detection result obtained by the traditional dirt detection method cannot directly reflect the degree of dirt on the cleaning device, and the reliability of the obtained dirt detection result is not high; Since the target image of the cleaning part can be collected, the degree of dirt on the cleaning part can be directly detected, improving the reliability of the dirt degree detection result.

[0039] In addition, for a cleaning part that rotates around the central axis perpendicular to the surface to be cleaned during the working process, since the dirt degree distribution in the same concentric circle area is the same, therefore, taking the central axis of the cleaning part in the cleaning part area as the center of each sub-area and dividing the cleaning part area can make the dirt distribution in each obtained sub-area basically the same; It can avoid the problem that when the dirt distribution in the same sub-area is inconsistent, the local dirt degree obtained cannot accurately reflect the dirt degree at each position in the sub-area; It can improve the accuracy of the determined local dirt degree.

[0040] In addition, by calculating the local dirt degree of the sub-area based on the sum of the pixel values of the target pixels in each sub-area, since the target pixels are the pixels in the sub-area that meet the preset dirt conditions; Therefore, it can avoid the influence of other pixels in the sub-area except the target pixels on the local dirt degree, and further improve the accuracy of determining the local dirt degree.

[0041] In addition, since the target image may include other interference areas in addition to the cleaning part area, in this embodiment, by determining the cleaning part area in the target image, other interference areas can be excluded. On the one hand, it can save the computing resources consumed when analyzing other interference areas, and on the other hand, it can also avoid the problem that other interference areas affect the detection result of the dirt degree of the cleaning part, thereby improving the reliability of the dirt degree detection.

[0042] In addition, by converting different dirt degrees into data description values for comparison, it may not be necessary to compare all the numerical values of the dirt degree, which can improve the comparison efficiency of the dirt degree.

[0043] In addition, by combining the local dirt degree and the global dirt degree to determine the cleaning efficiency of the cleaning device, when the electronic device judges the cleaning effect of the cleaning device, it can analyze from both local and global perspectives, thereby improving the reliability of judging the cleaning effect. Description of the Drawings

[0044] To more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0045] Figure 1 is a schematic structural diagram of a cleaning device provided by an embodiment of the present application;

[0046] Figure 2 is a schematic structural diagram of a dirt level determination system of a cleaning device provided by an embodiment of the present application;

[0047] Figure 3 is a flowchart of a method for determining the dirt level of a cleaning device provided by an embodiment of the present application;

[0048] Figure 4 is a schematic diagram of a cleaning part area obtained by using a foreground extraction algorithm provided by an embodiment of the present application;

[0049] Figure 5 is a schematic diagram of a cleaning part area obtained through a segmentation operation provided by an embodiment of the present application;

[0050] Figure 6 is a schematic diagram of a sub - area provided by an embodiment of the present application;

[0051] Figure 7 is a schematic diagram of the dirt level of each cleaning part provided by an embodiment of the present application;

[0052] Figure 8 is a schematic diagram of the dirt level and corresponding data description values provided by an embodiment of the present application;

[0053] Figure 9 is a block diagram of a dirt level determination device of a cleaning device provided by an embodiment of the present application;

[0054] Figure 10 is a block diagram of an electronic device provided by an embodiment of the present application. Specific Embodiments

[0055] The following will clearly and completely describe the technical solutions of the present application in conjunction with the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0056] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

[0057] In this application, unless otherwise stated, the orientation terms such as "upper", "lower", "top", "bottom" are usually in reference to the direction shown in the drawings, or in reference to the vertical, perpendicular or gravitational direction of the component itself; similarly, for the sake of understanding and description, "inner" and "outer" refer to the inner and outer of the contour of each component itself, but the above orientation terms do not limit this application.

[0058] Figure 1 FIG. is a schematic structural diagram of a cleaning device provided by an embodiment of this application. The cleaning device can be an electronic device with a cleaning function such as a floor sweeper, a vacuum cleaner, a mopping machine, etc. This embodiment does not limit the implementation manner of the cleaning device. As Figure 1 shown, the cleaning device at least includes: a cleaning member 110 and a controller (not shown in the figure).

[0059] The cleaning member 110 refers to a component that contacts the surface to be cleaned during the cleaning process of the cleaning device to clean the surface to be cleaned. Among them, the surface to be cleaned can be the ground, the tabletop, the wall surface, the surface of a solar cell, etc. This embodiment does not limit the type of the surface to be cleaned.

[0060] Optionally, the cleaning member 110 can be a rag, a brush, and / or a roller brush, etc. This embodiment does not limit the implementation manner of the cleaning member 110. In addition, the number of the cleaning members 110 can be one or at least two. When there are at least two cleaning members, the types of different cleaning members can be the same or different. This embodiment does not limit the number of the cleaning members 110.

[0061] Without loss of generality, the cleaning member 110 is connected to the cleaning device through a driving member, and the driving member is connected to the controller to drive the cleaning member 110 to move under the control of the controller.

[0062] In one example, the cleaning member 110 rotates around a central axis perpendicular to the surface to be cleaned during operation. For example: referring to Figure 1 the cleaning device shown, at this time, the cleaning member 110 is located at the bottom of the cleaning device and rotates around the rotation axis with the central axis perpendicular to the surface to be cleaned as the rotation axis.

[0063] When the cleaning member 110 rotates around a central axis perpendicular to the surface to be cleaned, the degree of dirt at the positions on the edge of the same concentric circle is usually the same.

[0064] Figure 1Taking the center of the cleaning member passing through the central axis (i.e., the center coincides with the central axis) as an example for illustration, in actual implementation, it can also be one end of the cleaning member passing through the central axis. For example, the rotation mode of the brush on the floor sweeper. In this embodiment, the rotation mode of the cleaning member is not limited.

[0065] In addition, Figure 1 Taking the cleaning member 110 as a whole being circular as an example for illustration, in actual implementation, the cleaning member 110 can also be rectangular or irregular in shape. In this embodiment, the shape of the cleaning member 110 is not limited.

[0066] Among them, "being circular as a whole" means that when the cleaning device remains stationary, the cleaning range formed after the cleaning member 110 operates is circular. Based on this, there may be uneven burrs at the edge of the cleaning member 110, however, this does not affect the result that the cleaning range is circular.

[0067] In other examples, the cleaning member 110 can also rotate around a central axis parallel to the surface to be cleaned during the working process. For example, the rotation mode of the roller brush on the floor washer. Similarly, when the cleaning member 110 rotates around a central axis parallel to the surface to be cleaned, the degree of dirt at the edge positions of the same concentric circles is usually the same.

[0068] The structure of the above cleaning device is only schematic. In actual implementation, the cleaning device may further include other components required for performing the cleaning work, such as: a power supply component, a communication component, etc. This embodiment will not list them one by one here.

[0069] Optionally, based on the above embodiments, since the distribution of the dirt situation is regular during the working process of the cleaning member. Based on this, this embodiment provides a dirt degree determination system, which can collect the target image of the cleaning member on the cleaning device and analyze the target image to detect the dirt degree of the cleaning device.

[0070] Refer to Figure 2 The shown dirt degree determination system, which at least includes a placement rack 210, an image acquisition component 220 located on the placement rack 210, and an electronic device 230 connected to the image acquisition component 220.

[0071] The placement rack 210 is used to place one or at least two cleaning devices so that the cleaning member on the cleaning device is within the acquisition range of the image acquisition component 220.

[0072] The image acquisition component 220 can be an electronic device with an image acquisition function such as a camera, a camera, a mobile phone, etc. In this embodiment, the implementation manner of the image acquisition component 220 is not limited.

[0073] In this embodiment, the image acquisition component 220 is used to acquire the target image including the cleaning member.

[0074] The electronic device 230 can be a desktop computer, a tablet computer, a laptop computer, a mobile phone, etc. The implementation manner of the electronic device 230 is not limited in this embodiment.

[0075] In this embodiment, the electronic device 230 is used to obtain a target image of the cleaning part on the cleaning device; determine the cleaning part area in the target image; divide the cleaning part area to obtain at least two sub-areas; and determine the degree of dirt of the cleaning device based on the pixel values of each sub-area.

[0076] The at least two sub-areas are divided based on the dirt distribution on the cleaning part.

[0077] In this embodiment, by dividing the cleaning part area and determining the degree of dirt of the cleaning device based on the pixel values of each sub-area, it is possible to directly detect the degree of dirt of the cleaning part and improve the reliability of the dirt degree detection result.

[0078] The method for determining the degree of dirt of the cleaning device will be introduced below. In the following embodiments, the execution subject of this method is Figure 2 illustrated by taking the electronic device 230 in the dirt degree determination system as an example.

[0079] It should be added that in actual implementation, the dirt degree determination method is not limited to being applied in Figure 2 the illustrated dirt degree determination system, and can also be applied to a system without a placement rack 210, or, applied to a system in which the image acquisition component 220 and the electronic device 230 are implemented in the same device. The application scenario of the dirt degree determination method is not limited in this embodiment.

[0080] Figure 3 is a flowchart of the method for determining the degree of dirt of the cleaning device provided by an embodiment of the present application. The method at least includes the following steps:

[0081] Step 301, obtain a target image of the cleaning part on the cleaning device.

[0082] Specifically, the target image is obtained by image acquisition of the side of the cleaning part that contacts the surface to be cleaned. In this way, by analyzing the target image, the degree of dirt of the cleaning part can be directly obtained.

[0083] Step 302, determine the cleaning part area in the target image.

[0084] Since the target image may include other interference regions in addition to the cleaning member region, based on this, by determining the cleaning member region in the target image, other interference regions can be excluded. On the one hand, it can save the computing resources consumed when analyzing other interference regions, and on the other hand, it can also avoid the problem that other interference regions affect the detection result of the dirtiness degree of the cleaning member, thereby improving the reliability of the dirtiness degree detection.

[0085] In one example, determining the cleaning member region in the target image includes: performing foreground extraction on the target image to obtain the cleaning member region.

[0086] The electronic device performs foreground extraction on the target image using a foreground extraction algorithm. Among them, the foreground extraction algorithm includes but is not limited to: an algorithm based on image segmentation, that is, dividing pixels into background or foreground based on the discontinuity and correlation of pixels, such as: edge detection algorithm, or clustering algorithm, etc.; or, an algorithm based on image matting, which emphasizes details more than the algorithm based on image segmentation, such as: Knockout matting algorithm, or image matting algorithm based on probability statistics, etc. The implementation manner of the foreground extraction algorithm is not limited in this embodiment.

[0087] For example: The cleaning member region obtained using the foreground extraction algorithm is as Figure 4 shown.

[0088] In another example, determining the cleaning member region in the target image includes: receiving a segmentation operation on the cleaning member region in the target image to obtain the cleaning member region. The segmentation operation can be an operation of outlining the edge of the cleaning member in the target image, or an operation of clicking on the edge position of the cleaning member. The implementation manner of the segmentation operation is not limited in this embodiment.

[0089] For example: The cleaning member region segmented by the segmentation operation is as Figure 5 shown.

[0090] Step 303, perform regional division on the cleaning member region to obtain at least two sub-regions.

[0091] In this embodiment, the cleaning member region is regionally divided based on the distribution law of the dirtiness degree during the working process of the cleaning member.

[0092] For example: As the cleaning member shown in Figure 1 rotates around the central axis perpendicular to the surface to be cleaned during the working process. At this time, the distribution of the dirtiness degree in the same concentric circle region is the same. Based on this, the cleaning member region is regionally divided to obtain at least two sub-regions, including: taking the central axis of the cleaning member in the cleaning member region as the center of each sub-region, and regionally dividing the cleaning member region to obtain at least two sub-regions.

[0093] Assume that the cleaning member is circular as a whole. Correspondingly, the cleaning member area is circular as a whole; the central axis passes through the center of the circle. Taking the central axis of the cleaning member in the cleaning member area as the center of each sub-area, the cleaning member area is divided into at least two sub-areas, including: taking the center of the circle as the center of the sub-area, and dividing the cleaning member area into n sub-areas according to n preset division coefficients.

[0094] Among them, the first sub-area is a circle with a radius equal to the product of the first division coefficient and R. The i-th sub-area is an annular ring obtained by subtracting the inner circle with a radius equal to the product of the (i - 1)-th division coefficient and R from the outer circle with a radius equal to the product of the i-th division coefficient and R; R is the radius of the cleaning member area; i takes integers from 2 to n in sequence, n is an integer greater than 1, and the n-th division coefficient is 1. The division coefficient is a value greater than 0 and less than or equal to 1, and the i-th division coefficient has a positive correlation with the value of i.

[0095] Taking n = 3, the first division coefficient = 0.3, and the second division coefficient = 0.7 as an example for illustration, the at least two sub-areas obtained are referred to Figure 6 . According to Figure 6 It can be known that the first sub-area 61 is a circle with the central axis of the cleaning member as the center and a radius of 0.3R; the second sub-area 62 is an annular ring obtained by subtracting the first sub-area 61 from the circle with the central axis of the cleaning member as the center and a radius of 0.7R; the third sub-area 63 is an annular ring obtained by subtracting the circle with a radius of 0.7R from the circle with the central axis of the cleaning member as the center and a radius of R.

[0096] Figure 6 Taking n = 3, the first division coefficient = 0.3, and the second division coefficient = 0.7 as an example for illustration in

[0097] In actual implementation, the value of n and the value of the i-th division coefficient can both be adjusted based on the detection requirements. This embodiment does not limit the values of n and the division coefficient.

[0097] When the cleaning member is not circular, it can also be divided based on the above regional division principle. The difference is that R is the minimum distance between the central axis of the cleaning member and the edge of the cleaning member; the n-th sub-area is no longer an annular ring, but an area obtained by subtracting the inner circle with a radius equal to the product of the (n - 1)-th division coefficient and R from the figure formed by the edge of the cleaning member. This embodiment does not limit the shape of the cleaning member.

[0098] Similarly, when the cleaning member rotates about a central axis parallel to the surface to be cleaned, the distribution of the degree of soiling in the same concentric circle area is consistent. Based on this, the cleaning member area is divided into at least two sub-areas, including: in the direction of the central axis of the cleaning member in the cleaning member area, the cleaning member area is sequentially divided into at least two sub-areas. The at least two sub-areas are divided according to a preset division coefficient, and the present embodiment does not limit the value of the division coefficient.

[0099] Step 304, determine the degree of soiling of the cleaning device based on the pixel values of each sub-area.

[0100] In one example, the degree of soiling of the cleaning device includes the local degree of soiling of each sub-area. At this time, determining the degree of soiling of the cleaning device based on the pixel values of each sub-area includes: determining the sum of the pixel values of the target pixels within the sub-area; determining the local degree of soiling of each sub-area based on the sum of the pixel values to obtain the degree of soiling of the cleaning device. Among them, the sum of the pixel values and the local degree of soiling are negatively correlated.

[0101] The target pixel is a pixel within the sub-area that satisfies the preset soiling condition. For a certain pixel position, the pixel value at this pixel position is negatively correlated with the degree of soiling, that is, the smaller the pixel value, the higher the degree of soiling. For example, the black pixel value is 0, and at this time, the degree of soiling at the corresponding pixel position is higher. Among them, the pixel value can be the pixel value of a grayscale image or the pixel value of a color image. The present embodiment does not limit the value type of the pixel value. Based on this, a pixel that satisfies the preset soiling condition is a pixel whose pixel value is less than the pixel threshold. The pixel threshold is determined based on the detection requirements of the degree of soiling. For example, the pixel threshold can be 255, that is, pixels that are not white are all pixels that satisfy the preset soiling condition. Of course, the value of the pixel threshold can also be other values, and the present embodiment does not limit the value of the pixel threshold.

[0102] Schematically, determining the local degree of soiling of each sub-area based on the sum of the pixel values to obtain the degree of soiling of the cleaning device includes: taking the reciprocal of the sum of the pixel values and then performing a normalization process to obtain the degree of soiling. Specifically, the above process can be expressed by the following formula:

[0103] Dv = Norm(Sum(255 / sum(r + g + b)) / Area)

[0104] Where r refers to the R pixel value in the color image pixel, g refers to the G pixel value in the color image pixel, b refers to the G pixel value in the color image pixel; sum represents the summation function; Area is the total number of pixels within the sub-area; Norm represents the normalization function.

[0105] In actual implementation, the method of determining the local dirt degree based on the accumulated sum of pixel values can also be to take the reciprocal of the accumulated sum of pixel values. This embodiment does not limit the calculation method of the local dirt degree.

[0106] In another example, the dirt degree of the cleaning device further includes the global dirt degree of the cleaning member area. At this time, determining the dirt degree of the cleaning device based on the pixel values of each sub-region includes: determining the global dirt degree of the cleaning member area based on the pixel values of the cleaning member area.

[0107] Among them, determining the global dirt degree of the cleaning member area based on the pixel values of the cleaning member area includes: determining the accumulated sum of the pixel values of the target pixels in each sub-region, and determining the global dirt degree based on the accumulated sum of the pixel values to obtain the dirt degree of the cleaning device. At this time, the calculation principle of the global dirt degree is the same as that of the local dirt degree, and this embodiment will not elaborate here.

[0108] For example: The cleaning device includes 4 cleaning members. After calculating the dirt degree of each sub-region on each cleaning member respectively, the dirt degree of the cleaning device obtained is for reference Figure 7 . According to Figure 7 It can be seen that the dirt degree includes the local dirt degree of each sub-region on each cleaning member. Specifically, refer to Figure 7 the OutsideScore of the sub-region Outside, the MiddleScore of the sub-region Middle, and the CoreScore of the sub-region Core, and the global dirt degree of the overall cleaning member. Specifically, refer to Figure 7 the GlobalScore in

[0109] In summary, the method for determining the dirt degree of the cleaning device provided in this embodiment can solve the problem that the dirt detection result obtained by the traditional dirt detection method cannot directly reflect the dirt degree of the cleaning device, and the reliability of the obtained dirt detection result is not high. By obtaining the target image of the cleaning member on the cleaning device; determining the cleaning member area in the target image; dividing the cleaning member area into at least two sub-regions; and determining the dirt degree of the cleaning device based on the pixel values of each sub-region. Since the target image of the cleaning member can be collected, the dirt degree of the cleaning member can be directly detected, improving the reliability of the dirt degree detection result.

[0110] In addition, for a cleaning member that rotates around a central axis perpendicular to the surface to be cleaned during operation, since the degree of soiling distribution in the same concentric circle area is consistent, therefore, taking the central axis of the cleaning member in the cleaning member area as the center of each sub-area and dividing the cleaning member area can make the soiling distribution within each obtained sub-area basically consistent; it can avoid the problem that when the soiling distribution within the same sub-area is inconsistent, the determined local soiling degree cannot accurately reflect the soiling degree at each position within the sub-area; and it can improve the accuracy of the determined local soiling degree.

[0111] In addition, by calculating the local soiling degree of each sub-area based on the sum of pixel values of target pixels within the sub-area, since the target pixels are the pixels within the sub-area that meet the preset soiling conditions; therefore, it can avoid the influence of other pixels except the target pixels within the sub-area on the local soiling degree, and further improve the accuracy of determining the local soiling degree.

[0112] In addition, since the target image may include other interference areas in addition to the cleaning member area, in this embodiment, by determining the cleaning member area in the target image, other interference areas can be excluded. On the one hand, it can save the computing resources consumed when analyzing other interference areas, and on the other hand, it can also avoid the problem that other interference areas affect the detection result of the soiling degree of the cleaning member, thereby improving the reliability of soiling degree detection.

[0113] Optionally, based on the above embodiment, after determining the soiling degree, that is, after step 304, the electronic device can also compare different soiling degrees to determine the cleaning effect of the cleaning device.

[0114] Since the higher the soiling degree, it means that the cleaning member absorbs most of the dust on the surface to be cleaned, and the cleaning effect of the cleaning member is better. Based on this, there is a positive correlation between the soiling degree and the cleaning effect. That is, the higher the soiling degree, the better the cleaning effect.

[0115] In one example, comparing different soiling degrees includes: comparing the local soiling degrees of different sub-areas.

[0116] Optionally, comparing the local soiling degrees of different sub-areas includes: for the same sub-area corresponding to different cleaning members on the cleaning device, comparing the local soiling degrees of the sub-areas on different cleaning members.

[0117] For example: For Figure 7For the four cleaning parts shown, the local dirtiness degrees OutsideScore of the same sub-region Outside of the four cleaning parts are compared, and the sorting result obtained in the order from the largest to the smallest local dirtiness degree is: {(3Outside, 4420.9926), (4Outside, 1582.896), (2Outside, 0.0382), (1Outside, 0.0008)}.

[0118] For the MiddleScore of the same sub-region Middle of the four cleaning parts are compared, and the sorting result obtained in the order from the largest to the smallest local dirtiness degree is: {(3Middle, 670.7639), (4Middle, 69.1194), (2Middle, 0.0026), (1Middle, 0.0006)}.

[0119] For the CoreScore of the same sub-region Core of the four cleaning parts are compared, and the sorting result obtained in the order from the largest to the smallest local dirtiness degree is: {(3Core, 0.0935), (4Core, 0.0002), (2Core, 0.0001), (1Core, 0.0)}.

[0120] And / or, compare the local dirtiness degrees of different sub-regions, including: for different sub-regions corresponding to the same cleaning part on the cleaning device, compare the local dirtiness degrees of different sub-regions.

[0121] For example: for Figure 7 the third cleaning part shown, compare the local dirtiness degrees of the sub-regions Core, Middle and Outside of this cleaning part, and the sorting result obtained in the order from the largest to the smallest local dirtiness degree is: {(3Outside, 4420.9926), (3Middle, 670.7639), (3Core, 0.0935)}.

[0122] In another example, compare different dirtiness degrees, including: compare the global dirtiness degrees of different cleaning parts.

[0123] For example: for Figure 7 the four cleaning parts shown, compare the global dirtiness degrees GlobalScore of the four cleaning parts, and the sorting result obtained in the order from the largest to the smallest global dirtiness degree is: {(3Global, 2523.0202), (4Global, 834.9248), (2Global, 0.0205), (1Global, 0.0006)}.

[0124] In the above embodiments, when the number of digits of different dirt levels is relatively large, the electronic device needs to compare a relatively large number of digits, which may lead to a problem of low comparison efficiency of the dirt level.

[0125] Based on the above technical problems, in this embodiment, after determining the dirt level of the cleaning device based on the pixel values of each sub-region, that is, after step 304, it further includes: generating a data description value of the dirt level; using the data description value of the dirt level to compare different dirt levels to determine the cleaning effect of the cleaning device.

[0126] Among them, the data description value includes a high-order bit and a low-order bit. Both the high-order bit and the low-order bit are positively correlated with the dirt level. The high-order bit is used to describe the number of integer bits of the dirt level. When the integer bit is not zero, the low-order bit is the first m digits of the integer bit. When the integer bit is zero, the low-order bit is the decimal part of the dirt level; m is a positive integer. For example: m can be 2, or all integer bits, etc. This embodiment does not limit the value of m. In this way, the electronic device can describe the dirt level from two dimensions. Specifically, the high-order bit level describes the dirt level from the dimension of the number of integer bits. The more integer bits, the larger the value. The low-order bit degree describes the dirt level from the dimension of the specific value of the decimal part. The larger the decimal part, the larger the value.

[0127] For example: After converting the dirt level into a data description value, as shown in Figure 8 According to Figure 8 it can be known that the local dirt level OutsideScore of the sub-region Outside can be described by the data description values oLevel and oDegree; the local dirt level MiddleScore of the sub-region Middle can be described by the data description values mLevel and mDegree; the local dirt level CoreScoree of the sub-region Core can be described by the data description values cLevel and cDegree.

[0128] Based on the above principle, when comparing the dirt levels, the high-order bit level can be compared first. When the high-order bit level is the same, the low-order bit degree is then compared. Specifically, using the data description value to compare different dirt levels includes: comparing the high-order bits corresponding to different dirt levels; when the high-order bits corresponding to different dirt levels are the same, comparing the low-order bits corresponding to different dirt levels; when the high-order bits corresponding to different dirt levels are different, determining that the dirt level with the larger high-order bit is larger.

[0129] When the low-order bits corresponding to different dirt levels are the same, the original values of the dirt levels can be compared.

[0130] In this way, for the degrees of dirtiness with different integer digits, the sorting result can be obtained by comparing the higher-order bits; for the degrees of dirtiness with the same higher-order bits, the sorting result can be obtained by comparing the lower-order bits, which can improve the comparison efficiency of the degrees of dirtiness.

[0131] In one example, the degree of dirtiness includes the local degrees of dirtiness of different sub-regions; correspondingly, comparing different degrees of dirtiness using the data description values includes: comparing the local degrees of dirtiness of different sub-regions using the data description values; wherein, the different sub-regions include the same sub-region corresponding to different cleaning parts on the cleaning device; and / or, different sub-regions corresponding to the same cleaning part on the cleaning device.

[0132] In another example, the degree of dirtiness includes the global degrees of dirtiness of different cleaning parts; correspondingly, comparing different degrees of dirtiness using the data description values includes: comparing the global degrees of dirtiness of different cleaning parts using the data description values.

[0133] In this embodiment, by converting different degrees of dirtiness into data description values for comparison, it may not be necessary to compare all the digit values of the degree of dirtiness, which can improve the comparison efficiency of the degree of dirtiness.

[0134] Optionally, based on the above embodiment, when the degree of dirtiness includes the local degrees of dirtiness of each sub-region and the global degree of dirtiness of each cleaning part, the electronic device can also determine the cleaning effect of the cleaning device based on the local degree of dirtiness and the global degree of dirtiness.

[0135] Specifically, when each local degree of dirtiness is greater than the first threshold and each global degree of dirtiness is greater than the second threshold, it is determined that the cleaning device achieves the desired cleaning effect; when at least one local degree of dirtiness is less than or equal to the first threshold or at least one global degree of dirtiness is less than or equal to the second threshold, it is determined that the cleaning device does not achieve the desired cleaning effect.

[0136] In this embodiment, taking the division of the cleaning effect into achieving the desired cleaning effect and not achieving the desired cleaning effect as an example, in actual implementation, the cleaning effect can also be divided into more levels. Correspondingly, the local degree of dirtiness and the global degree of dirtiness can also adaptively correspond to multiple division thresholds. This embodiment does not limit the method of determining the cleaning effect based on the local degree of dirtiness and the global degree of dirtiness.

[0137] This embodiment determines the cleaning efficiency of the cleaning device by combining the local degree of dirtiness and the global degree of dirtiness, enabling the electronic device to analyze from both local and global perspectives when judging the cleaning effect of the cleaning device, thereby improving the reliability of judging the cleaning effect.

[0138] Figure 9It is a block diagram of a device for determining the degree of dirt of a cleaning device provided in an embodiment of the present application. The device at least includes the following several modules: an image acquisition module 910, a region determination module 920, a region division module 930, and a dirt determination module 940.

[0139] The image acquisition module 910 is configured to acquire a target image of a cleaning member on the cleaning device;

[0140] The region determination module 920 is configured to determine a cleaning member region in the target image;

[0141] The region division module 930 is configured to divide the cleaning member region to obtain at least two sub-regions;

[0142] The dirt determination module 940 is configured to determine the degree of dirt of the cleaning device based on the pixel values of each sub-region.

[0143] For related details, refer to the above embodiment.

[0144] It should be noted that: when determining the degree of dirt of the cleaning device by the device for determining the degree of dirt of the cleaning device provided in the above embodiment, only the division of the above functional modules is used for illustration. In practical applications, the above functions can be assigned to different functional modules according to needs, that is, the internal structure of the device for determining the degree of dirt of the cleaning device is divided into different functional modules to complete all or part of the functions described above. In addition, the device for determining the degree of dirt of the cleaning device provided in the above embodiment and the embodiment of the method for determining the degree of dirt of the cleaning device belong to the same concept, and the specific implementation process is detailed in the method embodiment and will not be repeated here.

[0145] Figure 10 It is a block diagram of an electronic device provided in an embodiment of the present application. The device may be Figure 1 the above-mentioned electronic device, and the device at least includes a processor 1001 and a memory 1002.

[0146] The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 1001 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1001 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1001 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0147] The memory 1002 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 1002 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1002 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 1001 to implement the method for determining the dirtiness degree of the cleaning device provided in the method embodiments of the present application.

[0148] In some embodiments, the external parameter calibration device may optionally further include a peripheral device interface and at least one peripheral device. The processor 1001, the memory 1002, and the peripheral device interface may be connected through a bus or signal lines. Each peripheral device may be connected to the peripheral device interface through a bus, signal lines, or a circuit board. Schematically, the peripheral devices include but are not limited to: a radio frequency circuit, a touch display screen, an audio circuit, and a power supply, etc.

[0149] Of course, the external parameter calibration device may also include fewer or more components, and this embodiment does not limit this.

[0150] Optionally, the present application further provides a computer-readable storage medium, and a program is stored in the computer-readable storage medium, and the program is loaded and executed by a processor to implement the method for determining the dirtiness degree of the cleaning device in the above method embodiments.

[0151] Optionally, the present application further provides a computer product, which includes a computer-readable storage medium storing a program, and the program is loaded and executed by a processor to implement the method for determining the degree of dirt of the cleaning device in the above method embodiment.

[0152] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0153] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

[0154] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, those of ordinary skill in the art can make other different forms of changes or modifications without creative efforts, and all should belong to the protection scope of the present application.

Claims

1. A method for determining the degree of dirt of a cleaning device, characterized in that, The method includes: Obtaining a target image of a cleaning part on the cleaning device; Determining a cleaning part area in the target image; Performing area division on the cleaning part area to obtain at least two sub-areas; Determining the degree of dirtiness of the cleaning device based on the pixel values of each sub-area; Wherein, during the working process, the cleaning part rotates around a central axis perpendicular to the surface to be cleaned; The performing area division on the cleaning part area to obtain at least two sub-areas includes: Taking the central axis of the cleaning part in the cleaning part area as the center of each sub-area, and performing area division on the cleaning part area to obtain the at least two sub-areas; After determining the degree of dirtiness of the cleaning device based on the pixel values of each sub-area, it further includes: Generating a data description value of the degree of dirtiness; wherein, the data description value includes a high-order bit and a low-order bit, both the high-order bit and the low-order bit are positively correlated with the degree of dirtiness, the high-order bit is used to describe the number of integer bits of the degree of dirtiness, the low-order bit is the first m digits of the integer bit when the integer bit is not zero, and the low-order bit is the decimal part of the degree of dirtiness when the integer bit is zero; the m is a positive integer; Using the data description value of the degree of dirtiness to compare different degrees of dirtiness to determine the cleaning effect of the cleaning device.

2. The method according to claim 1, characterized in that, The cleaning part is integrally circular, correspondingly, the cleaning part area is integrally circular; the central axis passes through the center of the circle; The taking the central axis of the cleaning part in the cleaning part area as the center of each sub-area, and performing area division on the cleaning part area to obtain the at least two sub-areas includes: Taking the center of the circle as the center of the sub-area, and dividing the cleaning part area into n sub-areas according to n preset division coefficients; Wherein, the first sub-area is a circle with a radius equal to the product of the first division coefficient and R, and the i-th sub-area is an annular ring obtained by subtracting an inner circle with a radius equal to the product of the (i - 1)-th division coefficient and R from an outer circle with a radius equal to the product of the i-th division coefficient and R; the R is the radius of the cleaning part area; the i sequentially takes integers from 2 to n, the n is an integer greater than 1, and the n-th division coefficient is 1.

3. The method according to claim 1, characterized in that The using the data description value to compare different degrees of dirtiness includes: Comparing the high-order bits corresponding to different degrees of dirtiness; When the high-order bits corresponding to different degrees of dirtiness are the same, comparing the low-order bits corresponding to different degrees of dirtiness; When the high-order bits corresponding to different degrees of dirtiness are different, determining that the degree of dirtiness with a larger high-order bit is larger.

4. The method according to claim 1, wherein The degree of dirtiness includes the local dirtiness degrees of different sub-areas; the using the data description value to compare different degrees of dirtiness includes: Using the data description value to compare the local dirtiness degrees of different sub-areas; Wherein, different sub-areas include the same sub-area corresponding to different cleaning parts on the cleaning device; and / or, different sub-areas corresponding to the same cleaning part on the cleaning device.

5. The method according to claim 1, characterized in that The determining the degree of dirtiness of the cleaning device based on the pixel values of each sub-area includes: Determine the sum of pixel values of target pixels within the sub-region, where the target pixels are pixels within the sub-region that meet a preset soiling condition; Based on the sum of pixel values, determine the local soiling degree of each sub-region to obtain the soiling degree of the cleaning device; there is a negative correlation between the sum of pixel values and the local soiling degree.

6. The method according to claim 1, characterized in that, The method further includes: Based on the pixel values of the cleaning member region, determine the global soiling degree of the cleaning member region, where the soiling degree of the cleaning device includes the global soiling degree and the local soiling degree of each sub-region; Based on the local soiling degree and the global soiling degree, determine the cleaning effect of the cleaning device.

7. The method according to claim 1, characterized in that, The determining the cleaning member region in the target image includes: Perform foreground extraction on the target image to obtain the cleaning member region.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory connected to the processor. A program is stored in the memory, and when the processor executes the program, it is used to implement the method for determining the soiling degree of the cleaning device according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, A program is stored in the storage medium, and when the program is executed by a processor, it is used to implement the method for determining the soiling degree of the cleaning device according to any one of claims 1 to 7.

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