A method for monitoring the anti-blocking alarm of a washing machine filter screen

Abstract

This invention relates to the field of washing machine technology, specifically to a method for monitoring and alarming the clogging of a washing machine filter. The method includes: uploading washing machine filter specifications; constructing a two-dimensional model of the washing machine filter based on these specifications; acquiring image data of the washing machine filter; performing image enhancement on the acquired image data; and further performing pixel-level feature recognition on the enhanced image data. This invention can acquire images of the washing machine filter in real time and monitor the filter in conjunction with the filter's drainage flow rate. Based on the acquired data, it analyzes and calculates the filter clogging rate and the frizz rate of the clothes to be washed. Furthermore, it combines the filter clogging rate and the frizz rate to analyze the cleaning effect of the washing machine's washing task, and then monitors and alarms the clogging of the washing machine filter based on the cleaning effect of the washing machine's washing task.

Description

Technical Field

[0001] This invention relates to the field of washing machine technology, and specifically to a method for monitoring and alarming the anti-clogging of washing machine filters. Background Technology

[0002] Washing machines are cleaning appliances that use electrical energy to generate mechanical action to wash clothes. They are divided into two categories according to their rated washing capacity: household and commercial.

[0003] A water inlet judgment method is disclosed in invention patent application number 202111088816.0, characterized by the following steps: opening the water inlet valve and drain valve of the washing machine to form a water flow channel that does not pass through the inner drum of the washing machine; detecting whether water flows through the water flow channel; if not, determining that the water inlet timeout has occurred; when the washing machine starts, running the program of the water inlet judgment method; the program of the water inlet judgment method ends after running for a preset time; after determining that the water inlet timeout has occurred, an alarm prompts the user. 5. The water inlet judgment method as described in claim 1 is characterized in that, if yes, it is determined that the water inlet is normal; when the water inlet is determined to be normal, the program of the water inlet judgment method is exited, and the user-set washing program is run.

[0004] The application aims to address the following problem: "In washing machine usage, people may forget to turn on the tap, or the water pressure in the pipes may be too low, or the inlet valve filter may be clogged, resulting in insufficient water supply. Current washing machine control technology requires the washing machine to complete weighing before entering the water intake process after pressing the start button. It takes a considerable amount of time during this process to detect any abnormalities and trigger a water intake timeout alarm. During this time, people may have already left home and cannot promptly detect the water intake timeout alarm. Consequently, when people return to retrieve their washed clothes, they find the washing machine malfunctioning, wasting time and resulting in a poor user experience."

[0005] However, to meet user needs, washing machines have become increasingly diversified in terms of cleaning capabilities and functions. Yet, current development and improvements haven't focused on the washing machine's filter, which directly impacts its cleaning performance.

[0006] To address these shortcomings, we propose a method for monitoring and alarming the anti-clogging function of washing machine filters. Summary of the Invention

[0007] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method for monitoring and alarming the anti-clogging of washing machine filter, which solves the technical problems mentioned in the background.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A method for monitoring and clogging alarms of a washing machine filter includes:

[0010] Upload the washing machine filter specifications, construct a two-dimensional model of the washing machine filter based on the specifications, collect washing machine filter image data, perform image enhancement on the collected washing machine filter image data, further perform pixel-level feature recognition on the enhanced washing machine filter image data, and then feed the pixel-level feature recognition results back into the two-dimensional model of the washing machine filter. Using the two-dimensional model of the washing machine filter as a background, the recognized pixel-level feature recognition results are represented.

[0011] The status is represented by pixel-level feature recognition results on the two-dimensional model of the washing machine filter. The clogging rate of the washing machine filter is evaluated. Real-time image data of clothes received by the washing machine is collected. The frizz rate of clothes is analyzed based on the image data. The cleaning effect of the current washing machine washing task is analyzed based on the frizz rate of clothes and the clogging rate of the washing machine filter. The washing machine filter clogging alarm is issued based on the cleaning effect of the washing machine washing task.

[0012] The logic for calculating the frizz rate of clothing is expressed as follows:

[0013]

[0014] Where: m is the set of continuously acquired clothing image data; H1 is the homogeneity of the first group of clothing image data; D1 is the nonlocal similarity of the first group of clothing image data; e1 is the entropy of the first group of clothing image data; A1 is the second moment of the angle of the first group of clothing image data; The total number of pixels contained in the texture feature recognition region of the first group of clothing image data; The texture feature recognition region for all clothing image data contains the total number of pixels; m0 is the total amount of clothing image data.

[0015] Based on the above formula, Then for the formula The value of is discarded in the formula, and vice versa. Further... or Then for The value of is discarded in the formula, and vice versa, and so on.

[0016] Furthermore, a pinhole camera is deployed at the installation location of the washing machine filter. The image data of the washing machine filter is collected based on the pinhole camera. The pinhole camera runs synchronously with the washing machine and performs the image data collection operation of the washing machine filter during the washing machine's drainage stage.

[0017] The pinhole camera collects image data of the washing machine filter during the washing machine drainage stage, monitors the washing machine drainage flow in real time, and collects image data of the washing machine filter at the peak of the drainage flow and after the drainage stage ends. The two sets of collected washing machine filter image data are then used as image enhancement targets for image enhancement processing.

[0018] Furthermore, a flow sensor is deployed on the outside of the washing machine filter. The flow sensor operates synchronously with the washing machine to monitor the flow rate of the drainage passing through the filter. The washing machine drainage flow rate is calculated using the following formula:

[0019]

[0020] Where: g is the acceleration due to gravity; J is the drainage drop of the washing machine filter; d is the total pore size of the washing machine filter surface; ε is the surface roughness of the washing machine filter; υ is the kinematic viscosity coefficient of drainage; S is the peak rotational speed of the washing machine during the drainage stage; F c ω represents the centrifugal force corresponding to S; ω⁻¹ is the weight.

[0021] in, The value is directly substituted into the flow sensor measurement value through user-side decision-making. Table correction, ω takes the value of The integer part of the value.

[0022] Furthermore, the image data of the washing machine filter after image enhancement processing is output using the following formula:

[0023]

[0024] In the formula: R(x,y) is the washing machine filter image obtained after image enhancement processing; N is the set of color feature vector terms in the image; Q i Let S(x,y) be the color feature vector corresponding to the i-th image; S(x,y) is the original image; λ i σ is the enhancement coefficient; i For kernel parameters;

[0025] The image enhancement processing operation of the washing machine filter image data is executed continuously for the number of times determined by the user terminal, and the washing machine filter image data is continuously enhanced based on the above formula.

[0026] Furthermore, the enhancement coefficient λ i satisfy:

[0027]

[0028] In the formula: x is the abscissa of the image; y is the ordinate of the image; d(x,y) is the diagonal distance of the image range; δi This refers to the image scale parameter.

[0029] Furthermore, after the two-dimensional model of the washing machine filter is constructed, the color of the washing machine filter body is applied simultaneously for rendering, and pixel-level feature recognition of the washing machine filter image data is performed, that is, color feature vector recognition is performed on each pixel block of the washing machine filter image in the washing machine filter image data.

[0030] The pixel block color feature vector recognition result is obtained by the following formula:

[0031]

[0032] In the formula: F(a,b) is the color feature vector of image patch (a,b); mean(R(a,b)) is the average pixel value of image patch (a,b) based on the global image of the washing machine filter; R(a,b) is the color component of image patch (a,b) in R space; G(a,b) is the color component in G space; B(a,b) is the color component in B space.

[0033] Furthermore, after color feature vector recognition is completed, each pixel block of the washing machine filter image in the washing machine filter image data is compared with the corresponding color feature vector of the rendered two-dimensional model of the washing machine filter. Pixel blocks in the washing machine filter image that are inconsistent with the corresponding color feature vector of the rendered two-dimensional model of the washing machine filter are obtained. The obtained pixel blocks are then replaced at their corresponding positions in the washing machine filter image. The resulting washing machine filter image is the result of representing the pixel-level feature recognition results with the two-dimensional model of the washing machine filter as the background.

[0034] In the washing machine filter image data, the ratio of the washing machine filter image to the two-dimensional model of the washing machine filter is 1:1.

[0035] Furthermore, the logic for calculating the washing machine filter clogging rate is as follows:

[0036] I: Obtain the color feature vector of the two-dimensional model of the washing machine filter, and obtain the color feature vector of each pixel block in the image of the washing machine filter;

[0037] II: Measure the number of pixel blocks in the color feature vector of each pixel block in the washing machine filter image that are the same as the color feature vector of the two-dimensional model of the washing machine filter.

[0038] III: Calculate the difference between the number of pixel blocks measured in II and the number of pixel blocks contained in the washing machine filter image;

[0039] IV: Compare the difference obtained in III with the number of pixel blocks contained in the washing machine filter image, and calculate the ratio, which is recorded as the washing machine filter clogging rate evaluation result f.

[0040] Furthermore, the cleaning effect of the washing machine's washing task is represented by the product of γ and f. The larger the product of γ and f, the worse the cleaning effect of the washing machine's washing task, and vice versa. The user terminal sets a safety threshold and compares the safety threshold with the cleaning effect of the washing machine's washing task to decide whether to issue an alarm.

[0041] Furthermore, a speaker is installed inside, which performs audio playback operations based on the decision results. The audio content played by the speaker is manually set by the user on the system side.

[0042] Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects:

[0043] 1. This invention provides a method for monitoring and alarming the anti-clogging of a washing machine filter. During operation, this method can collect images of the washing machine filter in real time and monitor the filter in conjunction with the drainage flow rate. Based on the collected data, the filter clogging rate and the frizz rate of the clothes to be washed are analyzed and calculated. The cleaning effect of the washing machine's washing task is further analyzed by combining the filter clogging rate and the frizz rate of the clothes. Finally, the clogging of the washing machine filter is monitored and alarmed based on the cleaning effect of the washing machine's washing task.

[0044] 2. In the process of executing the method of the present invention, the washing machine filter clogging rate is analyzed by identifying the features of the washing machine filter at the pixel level and constructing a two-dimensional model of the washing machine filter. This effectively improves the accuracy of the washing machine filter clogging rate calculation result and provides accurate data support for the method to monitor and determine the clogging of the washing machine filter. In addition, the washing machine filter clogging monitoring and early warning in this method can be combined with the frizz rate of the clothes to be washed for analysis, which effectively improves the adaptability of the washing machine filter alarm monitoring operation cycle.

[0045] 3. In the stage of collecting washing machine filter image data, the method of the present invention analyzes the drainage flow rate under the running state of the washing machine to ensure that the collected image data is clearer and more stable. With the addition of image enhancement processing, the collected washing machine filter image data is more clearly applied to the determination of the washing machine filter clogging rate, thus ensuring the accuracy of the determination result. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0047] Figure 1 This is a schematic diagram of the execution logic of a washing machine filter anti-clogging alarm monitoring method;

[0048] Figure 2 This is a schematic diagram showing the deployment position relationship of the flow sensor, pinhole camera, and washing machine filter in this invention;

[0049] The labels in the diagram represent: 1. Washing machine filter; 2. Flow sensor; 3. Pinhole camera; 4. Drainage. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0051] The present invention will be further described below with reference to embodiments.

[0052] Example 1

[0053] This embodiment provides a method for monitoring and alarming the anti-clogging function of a washing machine filter, such as... Figure 1 As shown, it includes:

[0054] Upload the washing machine filter specifications, construct a two-dimensional model of the washing machine filter based on the specifications, collect washing machine filter image data, perform image enhancement on the collected washing machine filter image data, further perform pixel-level feature recognition on the enhanced washing machine filter image data, and then feed the pixel-level feature recognition results back into the two-dimensional model of the washing machine filter. Using the two-dimensional model of the washing machine filter as a background, the recognized pixel-level feature recognition results are represented.

[0055] The status is represented by pixel-level feature recognition results on the two-dimensional model of the washing machine filter. The clogging rate of the washing machine filter is evaluated. Real-time image data of clothes received by the washing machine is collected. The frizz rate of clothes is analyzed based on the image data. The cleaning effect of the current washing machine washing task is analyzed based on the frizz rate of clothes and the clogging rate of the washing machine filter. The washing machine filter clogging alarm is issued based on the cleaning effect of the washing machine washing task.

[0056] The logic for calculating the frizz rate of clothing is expressed as follows:

[0057]

[0058] Where: m is the set of continuously acquired clothing image data; H1 is the homogeneity of the first group of clothing image data; D1 is the nonlocal similarity of the first group of clothing image data; e1 is the entropy of the first group of clothing image data; A1 is the second moment of the angle of the first group of clothing image data; The total number of pixels contained in the texture feature recognition region of the first group of clothing image data; The texture feature recognition region for all clothing image data contains the total number of pixels; m0 is the total amount of clothing image data.

[0059] Based on the above formula, Then for the formula The value of is discarded in the formula, and vice versa. Further... or Then for The value of is discarded in the formula, and vice versa; and so on.

[0060] After the two-dimensional model of the washing machine filter is constructed, the color of the washing machine filter body is applied simultaneously for rendering. Pixel-level feature recognition of the washing machine filter image data, that is, color feature vector recognition of each pixel block of the washing machine filter image in the washing machine filter image data.

[0061] The pixel block color feature vector recognition result is obtained by the following formula:

[0062]

[0063] In the formula: F(a,b) is the color feature vector of image patch (a,b); mean(R(a,b)) is the average pixel value of image patch (a,b) based on the global image of the washing machine filter; R(a,b) is the color component of image patch (a,b) in R space; G(a,b) is the color component in G space; B(a,b) is the color component in B space.

[0064] In the washing machine filter image data, after each pixel block of the washing machine filter image is identified by color feature vector, it is compared with the corresponding color feature vector of the rendered two-dimensional model of the washing machine filter. Pixel blocks in the washing machine filter image that are inconsistent with the corresponding color feature vector of the rendered two-dimensional model of the washing machine filter are obtained. The obtained pixel blocks are then replaced at their corresponding positions in the washing machine filter image. The resulting washing machine filter image is the result of representing the identified pixel-level feature recognition results with the two-dimensional model of the washing machine filter as the background.

[0065] In the washing machine filter image data, the ratio of the washing machine filter image to the two-dimensional model of the washing machine filter is 1:1.

[0066] The logic for calculating the washing machine filter clogging rate is as follows:

[0067] I: Obtain the color feature vector of the two-dimensional model of the washing machine filter, and obtain the color feature vector of each pixel block in the image of the washing machine filter;

[0068] II: Measure the number of pixel blocks in the color feature vector of each pixel block in the washing machine filter image that are the same as the color feature vector of the two-dimensional model of the washing machine filter.

[0069] III: Calculate the difference between the number of pixel blocks measured in II and the number of pixel blocks contained in the washing machine filter image;

[0070] IV: Compare the difference obtained in III with the number of pixel blocks contained in the washing machine filter image, and calculate the ratio, which is recorded as the washing machine filter clogging rate evaluation result f;

[0071] The cleaning effect of the washing machine's washing task is represented by the product of γ and f. The larger the product of γ and f, the worse the cleaning effect of the washing machine's washing task, and vice versa. The user terminal sets a safety threshold and compares the safety threshold with the cleaning effect of the washing machine's washing task to decide whether to issue an alarm.

[0072] It is equipped with speakers, which perform audio playback operations based on the decision results. The audio content played by the speakers is manually set by the user on the system side.

[0073] In this embodiment, by executing the above method, the pixel-level calculation of the washing machine filter clogging rate is combined with the frizz rate of the clothes to be washed by the washing machine to issue a clogging alarm for the washing machine filter, thereby ensuring the stable operation of the washing machine and ensuring that each washing task of the washing machine can bring good cleaning effect to the clothes.

[0074] The above formula is used to perform color feature vector recognition on the collected washing machine filter image data to provide an accurate calculation of the washing machine filter clogging rate. Furthermore, the washing machine filter clogging rate is combined with the frizz rate of the washed clothes to trigger a clogging alarm for the washing machine filter, bringing better maintenance results to the washing machine.

[0075] See Figure 2 As shown in the figure, based on the markings of washing machine filter 1, flow sensor 2, pinhole camera 3, drain 4 and the direction of the arrow, it can be seen that the washing machine drain 4 passes through the washing machine filter and collects data through the flow sensor and pinhole camera, providing stable data support for the execution of this method.

[0076] Example 2

[0077] At the implementation level, based on Example 1, this example refers to... Figure 1 A further detailed explanation of the anti-clogging alarm monitoring method for a washing machine filter in Example 1 is provided below:

[0078] A pinhole camera is installed at the location of the washing machine filter. The image data of the washing machine filter is collected based on the pinhole camera. The pinhole camera runs synchronously with the washing machine and performs the image data collection operation of the washing machine filter during the washing machine's drainage stage.

[0079] Among them, when the pinhole camera collects image data of the washing machine filter during the washing machine drainage stage, it monitors the washing machine drainage flow in real time. At the peak moment of the drainage flow during the washing machine drainage stage and after the drainage stage ends, it collects image data of the washing machine filter. Then, it uses the two sets of collected washing machine filter image data as image enhancement targets to perform image enhancement processing.

[0080] The image data of the washing machine filter after image enhancement processing is output using the following formula:

[0081]

[0082] In the formula: R(x,y) is the washing machine filter image obtained after image enhancement processing; N is the set of color feature vector terms in the image; Q i Let S(x,y) be the color feature vector corresponding to the i-th image; S(x,y) is the original image; λ i σ is the enhancement coefficient; i For kernel parameters;

[0083] The image enhancement processing operation of the washing machine filter image data is executed continuously for the number of times determined by the user terminal, and the washing machine filter image data is continuously enhanced based on the above formula.

[0084] By setting up and recording the above formula, image enhancement processing is performed on the washing machine filter image data collected by the pinhole camera 3, so that when calculating the washing machine filter clogging rate, clearer washing machine filter image data is used as supporting data for calculation, ensuring the accuracy of the washing machine filter clogging rate calculation result.

[0085] like Figure 1 As shown, a flow sensor is deployed on the outside of the washing machine filter. The flow sensor operates synchronously with the washing machine to monitor the flow rate of the drainage passing through the filter. The washing machine drainage flow rate is calculated using the following formula:

[0086]

[0087] Where: g is the acceleration due to gravity; J is the drainage drop of the washing machine filter; d is the total pore size of the washing machine filter surface; ε is the surface roughness of the washing machine filter; υ is the kinematic viscosity coefficient of drainage; S is the peak rotational speed of the washing machine during the drainage stage; F c ω represents the centrifugal force corresponding to S; ω⁻¹ is the weight.

[0088] in, The value is directly substituted into the flow sensor measurement value through user-side decision-making. Table correction, ω takes the value of The integer part of the value.

[0089] The above formula is used to calculate the drainage flow rate of the washing machine. Based on this, the method can achieve clear image acquisition when collecting washing machine filter image data through a pinhole camera, and avoid the operation of collecting a large amount of washing machine filter image data in order to improve image acquisition accuracy.

[0090] like Figure 1 As shown, the enhancement coefficient λ i satisfy:

[0091]

[0092] In the formula: x is the abscissa of the image; y is the ordinate of the image; d(x,y) is the diagonal distance of the image range; δ i This refers to the image scale parameter.

[0093] The above formula provides the necessary data support for calculating the output formula of the washing machine filter image data after image enhancement processing, ensuring the stable output of the washing machine filter image data after image enhancement processing.

[0094] In summary, the method described in the above embodiments can acquire images of the washing machine filter in real time and monitor the filter by combining the drainage flow rate. Based on the acquired data, it analyzes and calculates the filter clogging rate and the frizz rate of the clothes to be washed. Furthermore, it combines the filter clogging rate and the frizz rate to analyze the cleaning effect of the washing machine's washing task, and then monitors and alarms the filter clogging based on the cleaning effect. Moreover, during the execution of this method, pixel-level washing machine filter feature recognition and the constructed two-dimensional model of the washing machine filter are used to analyze the filter clogging rate, effectively improving the accuracy of the calculated filter clogging rate, and further enhancing the accuracy of the washing machine filter cleaning process. This method provides accurate data support for monitoring and determining washing machine filter clogging. Furthermore, it combines the analysis of the frizz rate of the clothes to be washed, effectively improving the adaptability of the washing machine filter alarm monitoring cycle. Simultaneously, during the washing machine filter image data acquisition stage, the method analyzes the drainage flow rate under the washing machine's operating conditions, ensuring clearer and more stable image data. Image enhancement processing further enhances the clarity of the acquired image data, making it more suitable for calculating the washing machine filter clogging rate and ensuring accurate results.

[0095] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the anti-clogging alarm of a filter screen of a washing machine, characterized in that, The method comprises the following steps: uploading the filter screen specification parameters of the washing machine, constructing a two-dimensional model of the filter screen of the washing machine based on the filter screen specification parameters of the washing machine, collecting washing machine filter screen image data, performing image enhancement on the collected washing machine filter screen image data, performing pixel-level feature recognition on the washing machine filter screen image data after image enhancement processing, and feeding back the pixel-level feature recognition result to the two-dimensional model of the washing machine filter screen, and representing the recognized pixel-level feature recognition result with the two-dimensional model of the washing machine filter screen as the background. Based on the pixel-level feature recognition result representation state on the two-dimensional model of the washing machine filter screen, the washing machine filter screen clogging rate is evaluated, the washing machine receives the clothes image data in real time, the clothes roughness rate is analyzed based on the clothes image data, the current washing machine washing task cleaning effect is analyzed according to the clothes roughness rate and the washing machine filter screen clogging rate, and the washing machine filter screen clogging alarm is sent out through the washing machine washing task cleaning effect. Wherein, the clothes roughness rate calculation logic is represented as: ； wherein: is a set of continuously collected laundry image data; is homogeneity of the first set of laundry image data; is non-local similarity of the first set of laundry image data; is entropy of the first set of laundry image data; is angular second moment of the first set of laundry image data; is total amount of pixels in the texture feature recognition area of the first set of laundry image data; is total amount of pixels in the texture feature recognition area of all laundry image data; is total amount of laundry image data; wherein, based on the above formula, then the value of in the formula is discarded, and otherwise is retained, or then the value of in the formula is discarded, and otherwise is retained, and so on.

2. The anti-clogging alarm monitoring method for the filter screen of a washing machine according to claim 1, characterized in that, A pinhole camera is arranged at the installation position of the washing machine filter screen, and the washing machine filter screen image data is collected based on the pinhole camera. The pinhole camera runs synchronously with the washing machine. During the washing machine drainage stage, the washing machine filter screen image data collection operation is performed. Wherein, when the pinhole camera collects the washing machine filter screen image data during the washing machine drainage stage, the washing machine drainage flow is monitored in real time. The washing machine filter screen image data is collected at the peak moment of the washing machine drainage flow and after the end of the washing machine drainage stage. Then, the two groups of collected washing machine filter screen image data are taken as the image enhancement target for image enhancement processing.

3. The anti-clogging alarm monitoring method for the filter screen of a washing machine according to claim 2, characterized in that, A flow sensor is arranged outside the washing machine filter screen. The flow sensor runs synchronously with the washing machine to monitor the flow of the drainage through the washing machine filter screen. The washing machine drainage flow is calculated by the following formula: ； In the formula: It is the acceleration due to gravity; For washing machine filter drainage ratio reduction; This represents the total pore size of the washing machine filter surface. The roughness of the washing machine filter surface; The viscosity coefficient of the drainage kinematics; This represents the peak rotation speed during the washing machine's drainage phase. For the corresponding Centrifugal force; As weight; wherein, the value of the flow rate is determined by the user terminal decision using the value measured by the flow sensor, table correction, the value of the flow rate is determined by the user terminal decision using the value measured by the flow sensor, the integer part of the value.

4. The anti-clogging alarm monitoring method for the filter screen of a washing machine according to claim 2, characterized in that, The washing machine filter screen image data after image enhancement processing is output by the following formula: ； In the formula: is a washing machine filter screen image obtained after image enhancement processing; is a set of color feature vector items in the image; is a corresponding value of the color feature vector in the i-th image; is an original image; is an enhancement coefficient; is a kernel parameter; Wherein, the image enhancement processing operation of the washing machine filter screen image data is continuously executed by the user terminal decision for a certain number of times. The washing machine filter screen image data is continuously subjected to image enhancement processing based on the above formula.

5. The anti-clogging alarm monitoring method for the filter screen of a washing machine according to claim 4, characterized in that, The enhancement coefficient satisfies: ； where: is the horizontal coordinate of the image; is the vertical coordinate of the image; is the diagonal distance of the image range; is the image scale parameter.

6. The anti-clogging alarm monitoring method for the filter screen of a washing machine according to claim 1, characterized in that, After the completion of the construction of the washing machine filter screen two-dimensional model, the washing machine filter screen body color is rendered synchronously. The pixel-level feature recognition of the washing machine filter screen image data is to identify the color feature vector of each pixel block of the washing machine filter screen image in the washing machine filter screen image data. Wherein, the pixel block color feature vector recognition result is calculated by the following formula: ； wherein: is a color feature vector of the image block ; is a color feature vector of the image block based on the global pixel average of the washing machine filter screen image; is a color component in R space of the image block ; is a color component in G space of the image block ; is a color component in B space of the image block .

7. The anti-clogging alarm monitoring method for the filter screen of a washing machine according to claim 6, characterized in that, After the completion of the color feature vector recognition of each pixel block of the washing machine filter screen image in the washing machine filter screen image data, the corresponding color feature vector of the rendered washing machine filter screen two-dimensional model is compared, and the pixel block with the corresponding color feature vector inconsistent with the rendered washing machine filter screen two-dimensional model in the washing machine filter screen image is obtained. The corresponding position of the obtained pixel block in the washing machine filter screen image is replaced. The replaced washing machine filter screen image is the result of representing the recognized pixel-level feature recognition result with the washing machine filter screen two-dimensional model as the background. Wherein, the ratio of the washing machine filter screen image in the washing machine filter screen image data to the washing machine filter screen two-dimensional model is 1:

1.

8. The anti-clogging alarm monitoring method for the filter screen of a washing machine according to claim 7, characterized in that, The laundry machine filter screen clogging rate obtaining logic is: I: Obtain the color feature vector of the two-dimensional model of the laundry machine filter screen, and obtain the color feature vector of each pixel block in the laundry machine filter screen image; II: Measure the number of pixel blocks in the color feature vector of each pixel block in the laundry machine filter screen image that are the same as the color feature vector of the two-dimensional model of the laundry machine filter screen; III: Obtain the difference between the number of pixel blocks measured in II and the number of pixel blocks contained in the laundry machine filter screen image; IV: the ratio of the difference obtained in III to the number of pixel blocks contained in the washing machine filter screen image, denoted as the washing machine filter screen clogging rate evaluation result .

9. The anti-clogging alarm monitoring method for the filter screen of a washing machine according to claim 8, characterized in that, The cleaning effect of the washing machine laundry task is represented by the product of and , and The greater the product, the worse the cleaning effect of the washing machine laundry task, and vice versa. The user end sets a safety threshold to compare the safety threshold with the cleaning effect of the washing machine laundry task to decide whether to issue an alarm.

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