Clothes processing device, clothes fading detection method and equipment thereof and storage medium

Abstract

The invention relates to a clothes processing device, a clothes fading detection method and equipment thereof and a storage medium. The clothes fading detection method specifically comprises the steps of obtaining running state information of a clothes processing device, and determining a target observation time period meeting a preset immersion observation condition according to the running state information; acquiring a monitoring image which comes from the image acquisition device and corresponds to the target observation time period; performing image feature analysis on the monitoring image to extract color feature information representing the color of the washing liquid; and determining the fading condition of the clothes according to the color feature information. Compared with the prior art, the image acquisition device is arranged on the lifting rib of the inner cylinder, and the immersion observation time period is locked by using the running state information, so that the precise in-situ acquisition of the washing liquid is realized; dynamic clothes interference is eliminated by combining time domain image feature analysis, and color features of the washing liquid are accurately extracted. According to the invention, foam shielding and environmental interference are effectively overcome, and the accuracy of fading detection is improved.

Description

Technical Field

[0001] This invention relates to the field of clothing care, and in particular to a clothing treatment device and its method, equipment, and storage medium for detecting color fading in clothing. Background Technology

[0002] With the improvement of living standards, washing machines have become an essential cleaning appliance in households. During daily washing, dark-colored or colorfast clothing is prone to dye leaching, which, if not detected in time, can easily cause color bleeding onto other light-colored clothing. Therefore, the detection function for color fading and color bleeding has become an important research direction in washing machine technology.

[0003] Several solutions for identifying color bleeding in clothing exist in the prior art. For example, Chinese patent application publication number CN114277542A discloses an intelligent method for identifying color bleeding in clothing and a washing machine using this method. This method acquires images of the clothes to be washed and takes pictures of the wet clothes and wash water at regular intervals during the washing process, then compares the color difference changes between the images to obtain the color bleeding risk level. This solution attempts to provide early warning by monitoring the color changes of clothes and water during the washing process. However, in practical applications, image acquisition is severely affected by field-of-view obstruction and environmental interference. Existing image acquisition devices are usually installed on the inner side wall of the outer drum or near the observation window. During the washing process, a large amount of foam generated in the inner drum easily floats on the liquid surface or adheres to the lens, and the tumbling clothes easily accumulate near the observation window or side wall, causing the camera's field of view to be obstructed, making it difficult to directly and clearly capture images of the washing liquid that can characterize color bleeding. Summary of the Invention

[0004] To achieve the above-mentioned objectives and other advantages of the present invention, according to a first aspect of the present invention, a method for detecting color fading of clothing is provided, applied to a clothing processing device having an image acquisition device disposed on the inner drum lifting ribs, the method comprising: Obtain the operating status information of the clothing processing device, and determine the target observation period that meets the preset immersion observation conditions based on the operating status information; Acquire monitoring images originating from the image acquisition device and corresponding to the target observation period; Image feature analysis was performed on the monitoring images to extract color feature information that characterizes the color of the detergent. Determine the color fading status of clothing based on color characteristic information.

[0005] Optionally, the step of "acquiring the operating status information of the clothing processing device and determining the target observation period that meets the preset immersion observation conditions based on the operating status information" includes: Real-time water level data and the real-time rotation phase of the inner cylinder are obtained as operating status information; Based on real-time water level data and the geometric parameters of the inner cylinder, the effective phase interval corresponding to when the lifting ribs are completely submerged below the liquid surface is calculated. The duration during which the real-time rotation phase falls within the effective phase interval is defined as the target observation period.

[0006] Optionally, the step of "acquiring the operating status information of the clothing processing device and determining the target observation period that meets the preset immersion observation conditions based on the operating status information" includes: The image statistical indicators of the preview video stream output by the image acquisition device are used as running status information. The image statistical indicators include the average brightness or texture complexity of the current frame. When a step change in the average brightness is detected, or when the texture complexity is lower than the preset complexity threshold, the current time is marked as the start time of the target observation period.

[0007] Optionally, the monitoring images include multiple time-series images corresponding to the target observation period; The steps of “performing image feature analysis on the monitoring images to extract color feature information characterizing the color of the detergent” include: Map the monitored images to a unified pixel coordinate system; For each pixel in the pixel coordinate system, we statistically analyze its numerical dispersion characteristics in the time dimension. Based on the numerical discreteness characteristics, the dynamic interference region and the steady-state background region are segmented from the pixel coordinate system; Color feature information is calculated based on the pixel values ​​of the stable background region.

[0008] Optionally, the step of "segmenting the dynamic interference region and the steady-state background region from the pixel coordinate system based on numerical discreteness features" includes: Calculate the variance of pixel values ​​in a multi-frame time-series image; If the variance of a pixel value is greater than a preset stability threshold, the pixel is classified into the dynamic interference region. If the variance of a pixel value is less than or equal to the stability threshold, the pixel is classified into the stable background region.

[0009] Optionally, the step of "calculating color feature information based on pixel values ​​of the stable background region" includes: Median filtering is applied to the pixel value sequence of each pixel point that is classified into the steady-state background region in the time dimension to obtain the synthetic background image; Extract color feature information from the synthesized background image.

[0010] Optionally, the operating status information also includes the real-time rotational speed of the inner cylinder; The step of “acquiring monitoring images originating from the image acquisition device and corresponding to the target observation period” includes: Determine whether the real-time rotation speed of the inner cylinder is greater than the preset imaging critical rotation speed. If it is, generate a rotation speed adjustment command to control the inner cylinder to decelerate. When the inner cylinder is in a low-speed state in response to the speed adjustment command and the time falls within the target observation period, a monitoring image is acquired.

[0011] Optionally, the step of "performing image feature analysis on the monitoring image to extract color feature information characterizing the color of the detergent" includes: Calculate the sharpness evaluation index for each connected region in the monitoring image; Based on the depth-of-field characteristics of the image acquisition device, areas with sharpness evaluation indicators below the preset focus threshold are identified as near-field occlusion areas and removed. The region where the sharpness evaluation index is within the preset transparency range is retained, and color feature information is extracted from it.

[0012] Optionally, the step of "determining the color fading status of clothing based on color characteristic information" includes: Acquire the baseline washing liquid color characteristics of the garment processing device during the water intake stage or the initial washing stage; Calculate the color difference between the color feature information and the color feature of the reference detergent; If the color difference value exceeds the preset color fading threshold, it is determined that the clothing has faded.

[0013] Optionally, the step of "calculating the color difference value between the color feature information and the reference detergent color feature" specifically includes: Map the color features of the reference washing liquid to the reference zero point in the pixel coordinate system; Obtain the vector offset magnitude of the color feature information relative to the reference zero point in the pixel coordinate system, and determine it as the color difference value.

[0014] Optionally, the method further includes: after determining that there is color fading in the clothing, performing the following steps in sequence: Control the clothing handling device to perform drainage until the water level drops to the preset empty water level; The inner cylinder is controlled to perform centrifugal dehydration at a low centrifugal speed, which is lower than the preset standard dehydration speed. The garment handling device is controlled to perform the water intake action, and the heating element of the garment handling device is kept off during the water intake action and the subsequent rinsing action.

[0015] Optionally, the above method further includes: after determining that there is color fading in the clothing, performing the following steps in sequence: Control the garment handling device to perform the drainage action; The dispensing component of the garment handling device is controlled to perform a dispensing action, dispensing the pre-set color-protecting additive into the inner drum; Control the garment handling unit to run the color-protecting washing program; During the color-protecting washing program, the washing water temperature is controlled to not exceed the preset color-protecting temperature threshold, and the inner drum is controlled to rotate according to the preset gentle care rhythm.

[0016] According to a second aspect of the present invention, a garment processing apparatus is provided, configured to perform the above-described method steps, specifically including: The inner cylinder has raised lifting ribs on its inner wall; The image acquisition device is located in the receiving cavity formed inside the lifting rib, with the lens facing the inside of the inner cylinder; The controller, electrically connected to the image acquisition device, is used to acquire monitoring images and perform image feature analysis.

[0017] According to a third aspect of the present invention, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the above-described method steps.

[0018] According to a fourth aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the above-described method steps.

[0019] The technical solution provided in this application has at least the following advantages compared with the prior art: (1) This application achieves in-situ immersion observation of the washing liquid by setting the image acquisition device in the receiving cavity of the inner cylinder lifting rib and utilizing the rotation of the inner cylinder. This setting effectively avoids the obstruction of the foam layer floating on the liquid surface and the accumulation of clothing at the observation window in the prior art, and uses the flushing effect of the water flow at the moment of immersion to keep the lens surface clean, thus solving the problem of not being able to obtain effective washing liquid images due to field of view obstruction and environmental interference.

[0020] (2) This application accurately determines the target observation period by acquiring operational status information, thereby realizing on-demand sampling of image data. This method ensures that the acquisition device only operates during the period when the preset immersion conditions are met, avoiding the acquisition of invalid areas such as air and foam accumulation areas, and significantly improving the efficiency of image data and the accuracy of color fading detection.

[0021] (3) This application adopts an image feature extraction method based on time domain analysis, which effectively solves the problem of underwater clothing background interference. By statistically analyzing the numerical dispersion features of multiple frames of images, it is possible to accurately distinguish between dynamically changing clothing textures and relatively stable detergent backgrounds, thereby accurately restoring the essential color of the detergent and reducing the misjudgment rate.

[0022] (4) This application constructs a complete closed loop from detection to intervention. After color fading is determined, active measures such as controlling drainage, low-speed centrifugation or applying color-protecting agents are taken to cut off the dye diffusion path or inhibit dye activity in a timely manner, effectively reducing the risk of contact color bleeding in clothing. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 A flowchart of a method for detecting color fading of clothing applied to a clothing processing device provided in this application; Figure 2 A flowchart illustrating the first method of "acquiring the operating status information of the clothing processing device and determining the target observation period that meets the preset immersion observation conditions" provided in this application embodiment; Figure 3 A flowchart illustrating the second method of "acquiring the operating status information of the clothing processing device and determining the target observation period that meets the preset immersion observation conditions" provided in this application embodiment; Figure 4 A flowchart for "acquiring monitoring images originating from an image acquisition device and corresponding to a target observation period" provided in an embodiment of this application; Figure 5 A flowchart illustrating the first method of "performing image feature analysis on a monitoring image to extract color feature information characterizing the color of the detergent" provided in this application embodiment; Figure 6 A flowchart illustrating the second method of "performing image feature analysis on a monitoring image to extract color feature information characterizing the color of the detergent" provided in this application embodiment; Figure 7 A flowchart for "determining the color fading status of clothing based on color feature information" is provided in an embodiment of this application; Figure 8 A flowchart illustrating the steps performed after determining that clothing has faded, as provided in the embodiments of this application; Figure 9 A flowchart illustrating the steps performed after determining that clothing has faded, as provided in the embodiments of this application; Figure 10 A schematic diagram of the garment processing apparatus provided in this application; Figure 11 Schematic diagram of the electronic device provided in this application; Figure 12 A schematic diagram of the computer-readable storage medium provided in this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish different objects, rather than to limit a specific order.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0028] It should be noted that the clothing handling device in this invention can be a drum washing machine, a washer-dryer combo, a mini washing machine, or other clothing handling devices with clothing care functions, and the inner drum of the clothing handling device has a clothing hanging function. For ease of description, this invention uses a drum washing machine as an example for illustration, and should not be construed as a limitation on the type of clothing handling device.

[0029] The methods described in this specification can be executed by the main control unit of the garment processing device. This main control unit can be implemented in the form of software and / or hardware, and is generally integrated into any electronic device with network communication capabilities, such as a mobile terminal, a PC, or a server.

[0030] According to one aspect of the embodiments of this application, a method for detecting color fading of clothing applied to a clothing processing device is provided. For example... Figure 1 As shown, the method includes: S1. Obtain the operating status information of the clothing processing device, and determine the target observation period that meets the preset immersion observation conditions based on the operating status information; Specifically, the processing device first needs to determine a suitable time window for image acquisition based on the current equipment status. Optionally, the operating status information may include, but is not limited to, real-time water level data of the garment processing device, real-time rotation phase of the inner drum, motor speed, or image statistical indicators of the preview video stream output by the image acquisition device. Based on this information, the system comprehensively determines whether the image acquisition device mounted on the lifting rib is below the liquid level or meets the imaging conditions.

[0031] It should be noted that the “target observation period” here should be understood broadly as a time frame, which can be either an instantaneous triggering moment or a continuous period of time.

[0032] Optionally, "preset immersion observation conditions" refers to a state in which the lens optical path of the image acquisition device is mainly covered by the washing liquid, or a state in which there are no air bubbles blocking the front of the lens. This does not exclude the possibility of a temporary unimmersed state when the liquid surface fluctuates, as long as the state meets the basic optical requirements for imaging.

[0033] S2. Acquire monitoring images originating from the image acquisition device and corresponding to the target observation period; Specifically, after determining a suitable target observation period, the system acquires image data generated within that period. Optionally, this step can be implemented using a trigger mode, whereby an acquisition command is sent to the image acquisition device to generate an image upon detecting the entry into the target observation period; alternatively, this step can also be implemented using a filtering mode, where the image acquisition device is continuously operating and outputting a video stream, and the system extracts image frames from its output data stream that correspond temporally to the target observation period.

[0034] It should be noted that the term "derived from image acquisition device" used in this step is intended to cover the entire process of image data from generation to acquisition by the processor. Whether the image data is directly transmitted to the processor in real time by the image acquisition device or stored in a cache or memory before being read by the processor, it falls within the protection scope of this application.

[0035] S3. Perform image feature analysis on the monitoring images to extract color feature information that characterizes the color of the washing liquid; Specifically, the system processes the acquired monitoring images using algorithms to extract information characterizing the essential color of the detergent from the complex inner drum environment. Optionally, image feature analysis may include spatial domain analysis, such as calculating sharpness evaluation indicators to eliminate near-field occlusion; it may also include temporal domain analysis, such as performing discrete statistics on multiple frames of images to eliminate dynamic interference. Through these analyses, the system can identify and eliminate interfering regions in the monitoring images, such as areas obscured by clothing, reflective areas from air bubbles, or areas obscured by lifting rib structures, thereby retaining the effective area.

[0036] It should be noted that the "color feature information" here should not be limited to a specific color space. It can be the component value in RGB space, the hue value in HSV space, the color difference value in Lab space, or the spectral feature vector after normalization. As long as it can characterize the optical color properties of the liquid, it is within the scope of the embodiments of this application.

[0037] S4. Determine the color fading status of clothing based on color characteristic information.

[0038] Specifically, the system makes a final business judgment based on the extracted color feature information. Optionally, this can be achieved by comparing the current color feature information with the color feature of a reference washing liquid and calculating the color difference value; or by analyzing the rate of change of the color feature information over time. It should be noted that "determining the color fading status of clothing" in this embodiment includes outputting a qualitative judgment result, such as determining whether there is a risk of color fading; it also includes outputting a quantitative risk level; or directly generating a corresponding control signal to trigger subsequent alarm or washing control processes.

[0039] In one alternative implementation, such as Figure 2 As shown, the aforementioned step S1, "obtaining the operating status information of the clothing processing device and determining the target observation period that meets the preset immersion observation conditions based on the operating status information," specifically includes the following steps: S111. Obtain real-time water level data and the real-time rotation phase of the inner cylinder as operating status information; S112. Based on real-time water level data and the geometric parameters of the inner cylinder, calculate the effective phase interval corresponding to when the lifting ribs are completely submerged below the liquid surface. S113. The duration during which the real-time rotation phase falls within the effective phase interval is determined as the target observation period.

[0040] Specifically, regarding step S111 above, the garment handling device collects physical environment data in real time using built-in sensor components. Optionally, real-time water level data (denoted as...) The water level can be obtained through a frequency-based or pressure-based water level sensor located at the bottom of the outer tub. This data characterizes the vertical height of the washing liquid level relative to the lowest point of the inner tub. Optionally, the phase rotation (denoted as...) can be used in real time. This data can be obtained through a Hall sensor or photoelectric encoder of the drive motor, and it characterizes the inner cylinder at the current moment. The rotation angle relative to the preset zero point position. In a specific embodiment, for ease of geometric calculation, the preset zero point position is defined as the lowest point of the inner cylinder cross-section (i.e., the 6 o'clock direction).

[0041] Further, regarding step S112 above, a geometric model of the inner cylinder's cross-section is established to determine the critical angles at which the image acquisition device mounted on the lifting ribs enters and exits the liquid surface. Specifically, it is assumed that the radius of the inner cylinder is... And with the center of the inner cylinder cross-section as the origin, the system calculates based on real-time water level data. With inner cylinder radius Geometric relationships, calculating the critical half-angle when the lifting rib is below the liquid surface. .

[0042] Optionally, the critical half-angle It can be calculated using the following formula: in, The range of values ​​is Based on this critical half-angle, the effective phase interval for the lifting rib to be completely submerged below the liquid surface can be determined. In one specific embodiment, when the phase zero point is defined as the lowest point of the inner cylinder, the effective phase interval is... It can be represented as: Or in In the coordinate system, it is represented as: It should be noted that in the above formula These are preset correction parameters. Optionally, introduce... This is a preset safety margin to compensate for the structural height of the lifting rib itself, the installation position deviation of the image acquisition device on the lifting rib, or to avoid interference from liquid surface turbulence at the moment of water entry and exit. This is achieved by introducing correction parameters. This ensures that the image acquisition device is indeed in a stable submerged environment within the calculated range.

[0043] Specifically, regarding step S113 above, the system monitors and compares the motion state of the inner cylinder in real time, determining the current rotation phase in real time. Does it meet the following conditions: Specifically, when monitored First entry into the effective phase interval The time is recorded as the start time. ,when Leaving the effective phase interval The time is recorded as the end time. The system will allocate time intervals. The target observation period has been determined.

[0044] Optionally, considering the response latency of the underlying hardware or the fluctuation of motor speed, S113 may also include secondary verification logic for the validity of the time period. Specifically, if the calculated duration... If the time is less than the preset minimum imaging time threshold, the system can ignore the time period or trigger active deceleration control logic to ensure that the image acquisition device has enough time to complete exposure and data transmission within that time period.

[0045] In another alternative implementation, such as Figure 3 As shown, the aforementioned step S1, "obtaining the operating status information of the clothing processing device and determining the target observation period that meets the preset immersion observation conditions based on the operating status information," specifically includes the following steps: S121. Obtain the image statistics of the preview video stream output by the image acquisition device as running status information. The image statistics include the average brightness or texture complexity of the current frame. S122. Monitor the image statistical indicators in real time. When a step change is detected in the average brightness or the texture complexity is lower than the preset complexity threshold, mark the current time as the start time of the target observation period.

[0046] Specifically, in step S121 above, the system utilizes the low-power preview mode of the image acquisition device for environmental perception. Optionally, to reduce the computational load on the processor, the resolution of the preview video stream output by the image acquisition device can be lower than the resolution used when subsequently acquiring monitoring images. In this step, the processor does not perform complex full-pixel reconstruction of the preview video stream, but instead extracts lightweight image statistical indicators.

[0047] Optionally, the average brightness (denoted as) This characterizes the overall brightness of the current frame image. In specific calculations, the current frame image can be converted to a grayscale image, and the average grayscale value of all pixels can be calculated. It should be understood that due to the significant differences in the refractive index and absorption rate of light between air and washing liquid, and the diffuse reflection caused by air bubbles upon entering the water, the overall brightness of the image will change significantly.

[0048] Optionally, texture complexity (denoted as) This characterizes the richness of high-frequency details in the current frame image. In specific calculations, the grayscale variance of the current frame image can be calculated, or the mean edge density can be calculated after extracting edge information using the Laplacian operator. Typically, when the image acquisition device is in the air or facing a pile of clothing, the image contains rich texture details and has high texture complexity; however, when the image acquisition device is completely submerged in washing liquid, due to the uniformity of the water and changes in depth of field (near-field blurring), the image exhibits smoother characteristics, and the texture complexity is significantly reduced.

[0049] Specifically, regarding step S122 above, the system determines the observation timing based on changes in statistical indicators between consecutive frames. The processor performs real-time time series analysis on the indicators acquired in S121.

[0050] In one specific decision-making logic, the processor monitors the rate of change of the average brightness. When it detects a difference in the average brightness between adjacent frames or within a short time window... When the following conditions are met: in, The average brightness at the current moment. The average brightness value at the previous moment. The system determines that a medium switch has occurred (i.e., from air to liquid) based on a preset step threshold, thus identifying the water entry action.

[0051] In another specific decision-making logic, the processor monitors the absolute value of the texture complexity. When the texture complexity of the current frame is detected... When the following conditions are met: in, The system determines that the medium in front of the lens is uniform (i.e., washing liquid) rather than clothing or foam with complex textures, based on a preset complexity threshold (or smoothness threshold).

[0052] Furthermore, when any one of the above conditions is met, or both conditions are met simultaneously, the processor will... The start time of the target observation period is marked. Optionally, in order to determine the end time of the target observation period, the system can continuously monitor the above indicators until the indicators return to the characteristic range of the air medium, or directly set a preset fixed duration (e.g., 200ms) from the start time as the target observation period, thereby completing the adaptive identification of the "immersion observation conditions".

[0053] In one alternative implementation, such as Figure 4 As shown, step S2, "acquiring monitoring images originating from the image acquisition device and corresponding to the target observation period," specifically includes the following steps: S201. Determine whether the real-time rotation speed of the inner cylinder, which serves as the operating status information, is greater than the preset imaging critical rotation speed. S202. If the judgment result is yes, then generate a speed adjustment command to control the inner cylinder to decelerate. S203. When the inner cylinder is in a low-speed state in response to the speed adjustment command and is within the target observation period, acquire a monitoring image.

[0054] In a specific embodiment, the logic of determining the observation period based on geometric calculation in the aforementioned steps S111 to S113 is used for coordinated control, and S201 to S203 are described in detail.

[0055] Specifically, for step S201, it is determined whether the real-time rotation speed of the inner cylinder, which serves as the operating status information, is greater than the preset critical rotation speed for imaging.

[0056] In this step, the system utilizes the "predictive properties" of the geometric model established in S112 to perform velocity verification. Optionally, since S112 can be based on real-time water level data... Calculate in advance the starting moment when the lifting rib enters the liquid surface. (corresponding to entering the effective phase interval) (boundary), a preset safety buffer angle (e.g., before entering water) can be used before the inner cylinder rotates to this effective phase range. When reading the real-time rotation speed of the inner cylinder, the following steps are taken: .

[0057] Specifically, the system will measure the real-time rotational speed. Compared with the preset critical imaging rotation speed Comparison is performed. Optionally, the imaging critical rotation speed is... The calculation depends on the exposure parameters of the image acquisition device. If the following conditions are met: If the current rotation speed is too fast, directly capturing images within the subsequent effective phase interval will result in unacceptable motion blur, thus proceeding to step S202. If the condition is not met, it indicates that the rotation speed meets the requirements, and the system waits for the inner cylinder to rotate to the target observation period determined in S113 before directly acquiring images.

[0058] Specifically, for S202, if the judgment result is yes, a speed adjustment command is generated to control the inner cylinder to decelerate.

[0059] In this step, to ensure that the inner cylinder is at a low speed when reaching the effective phase range determined in S113, the processor generates an active deceleration command. Optionally, this speed adjustment command is configured to control the motor to perform instantaneous braking or high-torque deceleration, with the goal of reducing the inner cylinder speed to a safe shooting speed. .

[0060] It should be understood that, combined with the geometric calculation logic of S112 described above, this step has significant timing advantages. Due to the effective phase interval... The system uses a fixed physical position determined by a mathematical model to accurately calculate the remaining time required to rotate from the current position to the water entry point. Specifically, the processor can determine the remaining time. The deceleration is dynamically adjusted to ensure that the lifting rib just touches the liquid surface (i.e., reaches the desired depth). At that instant, the rotational speed dropped precisely to This minimizes disruption to the washing cycle.

[0061] Specifically, for S203, when the inner cylinder is in a low-speed state in response to the speed adjustment command and the time falls within the target observation period, a monitoring image is acquired.

[0062] In this step, the system performs a phase-velocity AND logic trigger. The processor monitors two conditions in real time: Condition 1: the real-time rotation phase of the inner cylinder. Falling into the effective phase range calculated in S112 above (i.e., within the target observation period); Condition 2: Real-time rotational speed of the inner cylinder It has been reduced to the following.

[0063] Specifically, when both of the above conditions are met simultaneously, the processor sends an acquisition command to the image acquisition device. Optionally, thanks to the fact that the target observation period determined in S113 is calculated based on the "complete immersion" condition, and that this step eliminates motion blur by decelerating, the system is able to acquire clear, bubble-free, and motion-free high-quality monitoring images during this period.

[0064] Furthermore, if the inner cylinder misses the effective phase interval of the current cycle due to the deceleration action (for example, the deceleration distance exceeds the remaining phase distance), the system can maintain the low-speed state and predict the water entry time of the next cycle according to the geometric model of S112 mentioned above, and perform data acquisition during the target observation period of the next cycle.

[0065] In a specific embodiment, S201 to S203 are described in detail by combining the logic of determining the observation period based on geometric calculation in the aforementioned steps S121 to S122 with coordinated control.

[0066] Specifically, for S201, it is determined whether the real-time rotation speed of the inner cylinder, which serves as the operating status information, is greater than the preset critical rotation speed for imaging.

[0067] In this step, the system uses the visual trigger signal from S122 as a "start trigger" for speed verification. Optionally, when the processor detects a step change in the average brightness of the preview video stream or a texture complexity below a preset threshold in S122, it indicates that the image acquisition device has just entered the washing liquid (i.e., marking the start time of the target observation period). Specifically, at this time, the processor does not immediately trigger high-definition shooting, but instead prioritizes reading the real-time rotation speed of the inner drum.

[0068] Optionally, the processor compares the real-time rotation speed with a preset imaging critical rotation speed. If the real-time rotation speed is greater than the imaging critical rotation speed, it indicates that although the current environment is "underwater," the movement speed is too fast, and direct shooting would result in severe motion blur, failing to meet the requirements for subsequent feature extraction. Therefore, the process proceeds to step S202. If the real-time rotation speed is less than or equal to the imaging critical rotation speed, the monitoring image is directly acquired within the current target observation period.

[0069] Specifically, for S202, if the judgment result is yes, a speed adjustment command is generated to control the inner cylinder to decelerate.

[0070] In this step, the processor generates a deceleration control signal for the drive motor. Optionally, considering that S122 is triggered in real time based on visual perception, this means that the lifter rib is already below the liquid level. To capture the current observation window as much as possible, the speed adjustment command can be configured to "rapidly decelerate" or "maintain low speed for the next revolution".

[0071] In one specific embodiment, if the current rotational speed is slightly higher than the critical imaging speed, the system controls the motor to perform instantaneous braking, attempting to reduce the speed before the lifting rib leaves the liquid surface. In another specific embodiment, if the current rotational speed is significantly higher than the critical imaging speed, forced deceleration may cause severe vibration. The processor then controls the motor to smoothly decelerate to a safe imaging speed and maintains this low speed, waiting for the inner cylinder to rotate to its next revolution before re-entering the water.

[0072] Specifically, for S203, when the inner cylinder is in a low-speed state in response to the speed adjustment command and the time falls within the target observation period, a monitoring image is acquired.

[0073] In this step, the system executes dual confirmation logic based on "visual feedback" and "speed status." The processor continuously monitors the preview stream statistics output by the image acquisition device, while simultaneously monitoring the motor speed.

[0074] Specifically, when the processor confirms that the inner cylinder rotation speed has stabilized below the preset safe shooting speed, and at the same time detects that the texture complexity of the preview stream is still below the preset complexity threshold (indicating that the lens is still in an underwater environment), it sends a capture command to the image acquisition device to obtain the monitoring image.

[0075] Furthermore, if the texture complexity of the preview flow suddenly increases during the deceleration of the inner cylinder (indicating that the lifting ribs have rotated out of the water), the processor will pause the acquisition and keep the inner cylinder at a low speed. When the inner cylinder continues to rotate until the texture complexity of the preview flow falls below the preset threshold again (indicating that it has re-entered the water), the processor will immediately perform image acquisition, thereby ensuring that the monitoring image is acquired under low-speed and fully submerged conditions.

[0076] In one alternative implementation, such as Figure 5 As shown, the aforementioned step S3, "performing image feature analysis on the monitoring image to extract color feature information characterizing the color of the washing liquid," specifically includes the following steps: S311. Map the monitoring image to a unified pixel coordinate system; S312. For pixels in the pixel coordinate system, statistically analyze their numerical dispersion characteristics in the time dimension. S313. Based on the numerical discreteness characteristics, the dynamic interference region and the steady-state background region are segmented from the pixel coordinate system. S314. Calculate color feature information based on the pixel values ​​of the stable background region.

[0077] Specifically, regarding step S311 above, a monitoring image is first acquired, which comprises multiple time-series images continuously acquired during the target observation period. Optionally, the number of acquired image frames is denoted as... (in ), No. Frame image denoted as To eliminate the minute mechanical vibrations or displacement deviations that may occur during the low-speed rotation of the inner cylinder, image registration was first performed on the multi-frame images.

[0078] Specifically, with the first frame image Using the baseline frame, calculate the values ​​for subsequent frames using optical flow or feature point matching algorithms. The homography matrix relative to the reference frame is used to transform all frames to the same pixel coordinate system. Alternatively, if the inner cylinder rotates at extremely low speeds and has good mechanical stability, it can be assumed that the images in each frame are pixel-aligned, and a three-dimensional pixel matrix can be directly constructed.

[0079] Specifically, for step S312 above, the positions in the pixel coordinate system are traversed one by one. For any pixel Extract its in The sequence of pixel values ​​in a frame image. Specifically, the variance of pixel values ​​at a given location across multiple frames of time-series images is calculated as a numerical dispersion feature (denoted as ). ).

[0080] Optionally, the formula for calculating the variance of this pixel value is as follows: in, Representing coordinates The variance of pixel values ​​at a given location is a physical quantity that characterizes the degree of fluctuation of pixel values ​​at that location over time. This represents the total number of monitoring image frames involved in the calculation, i.e., the length of the time series; Represents the temporal index of an image frame, with a value range of 100. to ; Indicates the first Frame image in coordinates The pixel value at that location (depending on the image format, it can be a grayscale value or a component value of any RGB channel); Representing coordinates The pixel value at that location The arithmetic mean of a frame image is calculated using the following formula: .

[0081] Specifically, regarding step S313 above, based on the calculated... Generate a binarized mask. Specifically, a stability threshold is preset. For each pixel, perform the following judgment logic: If the pixel value variance is greater than the preset stability threshold (i.e.) The location is determined to have undergone drastic changes in the time series, characterized by moving clothing, bubbles, or floating objects, and the pixel is classified into the dynamic interference region (denoted as...). ); If the pixel value variance is less than or equal to the stability threshold (i.e.) The location was determined to be relatively stable in the time series, characterized as a transparent washing liquid background, and the pixel was classified into the steady-state background region (denoted as...). ).

[0082] Optionally, to eliminate the influence of isolated noise points, after generating the mask, the steady-state background area can also be targeted. Perform morphological opening operations to smooth region boundaries.

[0083] Specifically, regarding step S314 above, only using The information within the background is used to reconstruct the true color of the detergent. Specifically, the pixel value sequence of each pixel point belonging to the steady-state background region is subjected to median filtering in the time dimension to obtain the synthesized background image (denoted as...). ).

[0084] Optionally, each pixel in the synthesized background image The calculation formula is as follows: in: Indicates the composite background image in coordinates The synthesized pixel value at that location; This represents the median operator, which sorts the numerical sequence within the parentheses and takes the value at the middle position. Representing coordinates exist A sequence of pixel values ​​in a frame image.

[0085] Further, color feature information is extracted from the synthesized background image. Optionally, the statistical mean of the colors of all valid pixels in the synthesized background image is calculated. This assumes a stable background region. The total number of pixels contained is Then the color feature information (in terms of hue mean) The calculation formula (for example) is as follows: in: This represents the color feature information extracted to characterize the color of the washing liquid. Represents the steady-state background region The number of pixels contained within, that is, satisfying The total number of pixels; Represents the composite background image In coordinates The pixel value at that location (specifically, the H component value after conversion to the HSV color space).

[0086] In one alternative implementation, such as Figure 6 As shown, the aforementioned step S3, "performing image feature analysis on the monitoring image to extract color feature information characterizing the color of the washing liquid," specifically includes the following steps: S321. Calculate the clarity evaluation index of each connected region in the monitoring image; S322. Based on the depth-of-field characteristics of the image acquisition device, areas with sharpness evaluation indicators below the preset focus threshold are identified as near-field occlusion areas and removed. S323. Retain areas where the sharpness evaluation index is within the preset transparency range, and extract color feature information from them.

[0087] Specifically, regarding S321 above, a single frame of monitoring image is first acquired. Optionally, to achieve refined analysis of local features, the monitoring image is first segmented into several connected regions. Specifically, this segmentation can be achieved through simple grid partitioning (e.g., It can be implemented using pixel blocks, or it can be implemented using superpixel segmentation algorithms (such as the SLIC algorithm) to ensure that pixels in each region have similar color or texture features.

[0088] Furthermore, the sharpness evaluation index for each connected region is calculated (denoted as ). Optionally, this metric is constructed based on the high-frequency component energy of the image. In one specific embodiment, the Laplacian operator is used to extract edge information, and the variance of the gradient within the region is calculated as a sharpness evaluation metric.

[0089] Optionally, for the first A connected region, its sharpness evaluation index The calculation formula is as follows: in, Indicates the first A sharpness evaluation index for connected regions; the larger the value, the sharper the texture and the sharper the edges of the region. Indicates the first A set of pixel coordinates of connected regions; Indicates the first The total number of pixels contained within a connected region; Indicates the image in coordinates The Laplacian convolution response value (second derivative) at that point is used to characterize the edge strength at that point; Indicates the first The arithmetic mean of the Laplace response values ​​of all pixels within a connected region.

[0090] Specifically, regarding S322 above, region selection is performed using the physical characteristics of optical imaging. It should be understood that the image acquisition device is located inside the lifting rib, and its lens design typically has a fixed depth of field range. When clothing is close to or extremely close to the lens (located in the near field, less than the closest focusing distance), severe optical blurring occurs, leading to loss of high-frequency information and a significant reduction in sharpness evaluation indicators. In contrast, the washing liquid, as a light-transmitting medium filling the depth of field range, or as a background located near the focal plane, results in relatively clear images or maintains smooth gradient characteristics.

[0091] Specifically, the calculated With preset focus threshold Compare: If the conditions are met If the area is determined to be a near-field obstruction in a state of out of focus (such as clothing fibers close to the lens), it is marked as an invalid area and removed. If the conditions are met The region is determined to be within the depth of field or has effective optical features, and is marked as a candidate region.

[0092] Optionally, to prevent misjudgment of blurry areas due to the washing liquid being too clear (lacking texture), this step can also incorporate the average brightness information of the area for auxiliary judgment. If an area has low clarity but extremely high brightness (such as bubble reflection), it is also classified as an interference area.

[0093] Specifically, regarding S323 above, color statistics are performed only on the retained candidate regions. Specifically, all identified candidate regions that meet the criteria... The connected regions are merged into the effective observation region.

[0094] Further, the color characteristics of the effective observation area are calculated. Optionally, to improve calculation accuracy, a weighted average method can be used, i.e., using sharpness evaluation indicators. As a weight, the color mean of each effective region is weighted and merged.

[0095] Optionally, the final color feature information (in RGB channel vectors) The calculation formula (for example) is as follows: in, This represents the final extracted color feature vector characterizing the color of the washing liquid; This represents the set of indices for all valid connected regions that are preserved. Indicates the first The average color vector of each effective connected region (e.g.) ); For the first The clarity evaluation index of each connected region is used here as a confidence weight, meaning that the clearer the region (the less obscured), the greater its contribution to the final water color.

[0096] In one alternative implementation, such as Figure 7 As shown, the aforementioned step S4, "determining the color fading status of clothing based on color feature information," specifically includes the following steps: S401. Obtain the reference washing liquid color characteristics of the garment treatment device during the water intake stage or the initial washing stage. S402. Calculate the color difference value between the color feature information and the color feature of the reference detergent; S403. Determine whether the color difference value exceeds the preset color fading threshold; if it does, determine that the clothing is fading.

[0097] Specifically, regarding step S401 above, the system aims to establish a "zero-state" color reference baseline. Optionally, the processor continuously monitors the water inlet process of the garment processing device. When the water level reaches the preset washing water level and the inner drum completes the detergent dissolution process, the processor triggers an image acquisition and feature extraction operation.

[0098] Specifically, the logic of this feature extraction action is consistent with the logic of extracting monitoring image features in S3 mentioned above. The processor marks the color feature information extracted at this time as the baseline washing liquid color feature (denoted as...). It should be understood that by obtaining a baseline at the beginning of the wash cycle, the system can automatically offset the influence of the detergent's own color and the raw water's turbidity on the final judgment, ensuring that the color difference calculated subsequently is solely due to the precipitation of dye from the clothing.

[0099] In an optional implementation, regarding the aforementioned step S402 of calculating the color difference value between the color feature information and the reference washing liquid color feature, the aforementioned step S402 of "calculating the color difference value between the color feature information and the reference washing liquid color feature" specifically includes: S4021. Map the color features of the reference washing liquid to the reference zero point in the pixel coordinate system; S4022. Obtain the vector offset magnitude of the color feature information relative to the reference zero point in the pixel coordinate system, and determine it as the color difference value.

[0100] Specifically, for step S4021 above, the processor defines the water quality characteristics at the initial stage of washing as the calculation origin of color feature information, thereby achieving zero-point mapping of the pixel coordinate system.

[0101] Optionally, the processor first obtains the reference detergent color features extracted in step S401. Further, in order to eliminate inherent interference caused by different regional tap water turbidity, detergent background color, and ambient light color temperature, the processor maps the reference detergent color features to a reference zero point in the pixel coordinate system.

[0102] It should be noted that in the mapping logic here, the system sets the background color of the initial washing stage as the origin of the coordinate system, thereby decoupling the amount of color change in subsequent monitoring from the complex background color of the environment.

[0103] Specifically, the formula for the reference zero point in the RGB pixel space is: in, This represents the reference zero-point vector established in the pixel coordinate system to represent the state without color fading interference; , , These represent the pixel component values ​​of the red, green, and blue channels extracted from the initial water quality image, respectively.

[0104] Specifically, in step S4022 above, the processor obtains the color feature information extracted from the monitored image at the current moment and defines it as a real-time color vector. To quantify the degree of color fading, the processor calculates the vector offset magnitude of this real-time color vector relative to the reference zero point in the pixel coordinate system and determines this magnitude as the color difference value. Specifically, the formula for calculating this color difference value is as follows: in, This represents the calculated real-time color difference value, which reflects the intensity of the color shift of the washing liquid relative to the reference zero point state at the current moment. , , These represent the real-time component values ​​of the current monitored image in each channel of the pixel coordinate system; , , These represent the pixel reference values ​​of the aforementioned reference zero point in the corresponding channel.

[0105] Preferably, by using the above-described offset calculation method based on the zero point of the pixel coordinate system, it can be ensured that the color difference value is determined only by the increase in the amount of dye released from the clothing, thus maintaining the accuracy of the determination under different water qualities and detergent formulations. It should be noted that the above calculation process is not limited to the RGB space; a similar zero-point offset calculation can also be performed after converting the color vector to the CIELAB uniform color space.

[0106] Furthermore, in step S402, when performing the comparison, it is also possible to convert the color vector to the CIELAB uniform color space and then perform similar zero-point offset calculations.

[0107] Optionally, for step S402 above, the processor extracts the color feature information from the currently monitored image (denoted as...). Color characteristics of the reference washing liquid Perform a vector space comparison. Optionally, to better align with the characteristics of human visual perception, the processor first... and Convert from RGB color space to CIELAB ( Uniform color space.

[0108] Specifically, the processor calculates the Euclidean distance between two feature vectors as the color difference value (denoted as ). Optionally, the formula for calculating this color difference value is as follows: in: This represents the calculated color difference value, which reflects the degree to which the color of the washing liquid deviates from its initial state at the current moment; This represents the luminance component value of the currently monitored image features in the CIELAB color space; This represents the red and green hue components of the currently monitored image features in the CIELAB color space; This represents the yellow and blue hue components of the currently monitored image features in the CIELAB color space. This represents the luminance component value of the reference detergent color characteristics in the CIELAB color space; This represents the red and green hue components of the reference detergent color characteristics in the CIELAB color space. This represents the yellow-blue hue component of the reference detergent color characteristics in the CIELAB color space.

[0109] It should be understood that by using the above-mentioned offset calculation method based on the zero point of the pixel coordinate system, the color difference value can be guaranteed. The determination is solely based on the increase in the amount of dye released from the clothing, thus maintaining extremely high consistency in judgment under different water qualities and detergent formulations, avoiding misjudgments of color fading caused by detergents that are too dark in color themselves.

[0110] Alternatively, if computational power requirements are more sensitive, the processor can directly calculate the weighted Euclidean distance in the RGB space, or simply calculate the difference between the hue channels as a simplified version of the color difference value.

[0111] Specifically, regarding step S403 above, the system is based on quantization. A binary judgment is performed. Specifically, the system has a preset color fading threshold. Real-time processor comparison: If the conditions are met This indicates that the color of the current washing liquid has deviated significantly and perceptibly from its initial state, indicating that the clothes are fading. If the conditions are met This indicates that the color of the detergent remains stable, and it is determined that there is no color fading of the clothes.

[0112] Optionally, to further improve the robustness of the judgment and prevent misjudgment due to light fluctuations at a single moment, S403 may also include time-dimensional stability verification logic. Specifically, the processor can maintain a sliding time window, only checking for consecutive... Sub-sampling (or during continuous sampling) Color difference value calculated within seconds All consistently greater than Only then will the system finally output a "color fading exists" result, thereby triggering subsequent alarm or recovery processes.

[0113] In one alternative implementation, such as Figure 8 As shown, after determining in step S403 that there is color fading in the clothing, in order to physically prevent the secondary adhesion of free dye, the method can also implement a physical emergency strategy, which specifically includes the following steps: S511. Control the clothing handling device to perform a drainage action until the water level drops to the preset empty water level; S512. Control the inner cylinder to perform centrifugal dehydration at a low centrifugal speed, wherein the low centrifugal speed is lower than the preset standard dehydration speed. S513. Control the garment handling device to perform the water intake action, and control the heating element of the garment handling device to remain off during the water intake action and the subsequent rinsing action.

[0114] The following section provides a detailed explanation of S511 to S513, using specific physical control logic and mathematical formulas.

[0115] Specifically, in step S511, the system aims to remove the high-concentration dye solution from the inner cylinder as quickly as possible. Specifically, the processor sends an activation command to the drain pump and continuously monitors real-time water level data. Optionally, a preset empty water level (denoted as...) is established. It is usually set as the detection zero point of the water level sensor or the upper limit of the physical drainage dead zone.

[0116] It should be understood that rapid drainage can cut off the liquid medium path for dye molecules to diffuse to clothes that have not faded. Optionally, during the drainage process, the processor can also control the inner drum to perform intermittent shaking or low-speed reverse rotation to disrupt the layered structure between clothes and release residual detergent trapped in the folds of the clothes.

[0117] Specifically, in step S512, the system uses centrifugal force to separate the dye-containing liquid adsorbed inside the clothing fibers, but simultaneously limits the rotation speed to avoid contact color bleeding. Specifically, the processor controls the motor to drive the inner drum to accelerate to a low centrifugal speed (denoted as...). ).

[0118] Optionally, low centrifugal speed Compared with the preset standard dehydration speed (denoted as ), (Typically between 800 RPM and 1400 RPM) must satisfy the following constraints: in, This indicates the target value for the low-speed centrifugation rotation speed performed in this step; This indicates the standard spin speed of the garment processing device during the final spin-drying stage of a regular washing program; This is a preset limiting coefficient, and its value range is... to .

[0119] It should be understood that using low-speed centrifugation instead of high-speed centrifugation is the key technical method in this step. If the rotation speed is too high, the clothes will adhere tightly to the drum wall under strong centrifugal pressure, and the contact pressure between the color-fading clothes and adjacent clothes will increase sharply, which can easily lead to "imprint-like" color bleeding; while using a speed that satisfies the above formula It can effectively shake out the dirty water between the fibers, while keeping the clothes relatively loose to prevent secondary pollution caused by excessive compression.

[0120] Specifically, in step S513, the system uses low-temperature clean water to dilute and rinse the clothes. Specifically, the processor controls the water inlet valve to open, introducing tap water and monitoring the temperature sensor. Optionally, regardless of the temperature parameters set by the user in the original washing program, in S513 and subsequent rinsing stages, the processor forcibly cuts off the power to the heating element or heating plate, or resets the target heating temperature to "cold water" mode (e.g., the set target temperature). ).

[0121] It should be understood that the activity and diffusion rate of dye molecules are positively correlated with temperature. By forcibly maintaining a cold water environment, the Brownian motion of dye molecules and their adsorption capacity on the fiber surface can be significantly reduced, thereby achieving the technical effect of "inhibiting diffusion". Optionally, after the water intake is completed, the processor controls the inner drum to perform a high water ratio rinsing action, further diluting the concentration of residual dye by increasing the water volume.

[0122] In one alternative implementation, such as Figure 9 As shown, after determining in step S403 that there is color fading in the clothing, in order to inhibit dye diffusion and repair the color of the clothing through chemical means, the method can also implement a chemical emergency strategy, which specifically includes S521 to S523: S521. Control the garment handling device to perform a drainage action; S522, Control the dispensing component of the garment handling device to perform a dispensing action, dispensing the pre-set color-protecting additive into the inner drum; S523. Control the garment handling device to run the color-protecting washing program; Specifically, during the color-protecting washing program, the washing water temperature is controlled to not exceed the preset color-protecting temperature threshold, and the inner drum is controlled to rotate according to the preset gentle care rhythm.

[0123] Optionally, in step S521, the washing liquid in the inner drum where the dye has dissolved is first removed to prevent it from continuing to soak the clothes. Specifically, the drain pump is turned on to empty the liquid in the inner drum. Optionally, to ensure that the liquid containing free dye is completely drained, the drain pump can be kept running for a preset redundancy time (e.g., 10 to 20 seconds) after the water level is detected to have reached the empty water level.

[0124] Optionally, in step S522, an automatic dispensing technique is used to introduce chemical reagents. Specifically, the dispensing assembly includes a separate chamber for storing color-protecting additives (e.g., a color-fixing agent containing a cationic surfactant) and a metering pump. The inlet valve is controlled to open to introduce fresh water, and the metering pump is simultaneously controlled to operate, using the water flow to flush the color-protecting additives into the inner cylinder.

[0125] Optionally, the amount of clothing distributed can be controlled based on the current clothing load. Assume the load is... The standard additive dosage required per unit load is The total amount of additives used in this delivery action satisfy: in, This indicates the total mention or total mass of color-protecting additives to be delivered this time; This indicates the current weight of the clothing load detected by the clothing handling device (which can be obtained through a weighing sensor or motor inertia detection). This represents the standard additive dosage constant required per unit weight of clothing to achieve the preset color-fixing effect. This is the concentration adjustment coefficient. Optionally, this coefficient can be based on the color difference value calculated in step S402 above. Perform adaptive adjustments (e.g., when) When the value is large, select the larger value. (to increase concentration).

[0126] Optionally, in step S523, a "low-temperature, low-mechanical-force" washing environment can be created to help the color-fixing agent function and reduce color fading caused by physical friction.

[0127] Regarding temperature control: Specifically, the washing water temperature is monitored in real time (denoted as...). And control the working status of the heating element to ensure that the water temperature does not exceed the preset color protection temperature threshold (denoted as ). Optionally, Usually set to or If the current inlet water temperature is already higher than... (For example, if the user has connected a hot water pipe), the inlet valve can be controlled to perform intermittent water intake or mix cold water to reduce the temperature inside the tank.

[0128] Regarding mechanical rhythm control: Specifically, the inner drum is controlled to rotate according to a preset gentle rhythm. It should be understood that "gentle rhythm" refers to a rotation mode with a small duty cycle, designed to reduce the impact of tumbling on clothing.

[0129] Optionally, the mechanical strength coefficient of the inner cylinder can be defined. Assume that the inner drum rotates for a period of time during one washing cycle. The rest time is ,but It can be represented as: Specifically, in S523, the motor is controlled to... Less than the preset soft protection threshold (For example That is, the rotation time is much shorter than the rest time. Optionally, the rotation speed of the inner cylinder... It is also limited to a lower range (e.g.) This ensures that the clothes are mainly soaked and gently tumbled in the drum, rather than violently beaten, thereby minimizing dye loss caused by mechanical friction while using chemical agents to fix the color.

[0130] According to another aspect of the present invention, a garment handling apparatus 80 is also provided. For example... Figure 10 As shown, the clothing processing device 80 is configured to perform the clothing color fading detection method described in any of the foregoing embodiments.

[0131] The following section provides a detailed description of the clothing processing device 80, taking into account its specific hardware structure.

[0132] Specifically, the garment processing device 80 includes an inner drum 88, an image acquisition device 889, and a controller.

[0133] Specifically, the inner tub 88 is rotatably disposed inside the outer tub of the garment handling device 80. A plurality of raised lifting ribs 881 are spaced apart on the circumference of the inner wall of the inner tub 88. Optionally, the lifting ribs 881 are hollow, with independent receiving cavities formed inside. To ensure that the image acquisition device 889 can acquire images of the interior of the inner tub 88, a light-transmitting window is provided on the side of the lifting rib 881 facing the center of the inner tub, or the lifting rib 881 may be entirely or partially made of a material with high light transmittance.

[0134] Regarding the image acquisition device 889, specifically, the device is encapsulated and fixedly disposed within the receiving cavity of the lifting rib 881. Optionally, as... Figure 10As shown, the image acquisition device 889 employs a sealed chamber structure design to prevent washing moisture from entering and corroding electronic components. The optical axis of the lens of the image acquisition device 889 is pointed towards the internal space of the inner drum 88, so as to provide a comprehensive observation of the washing medium and clothes inside the drum when the lifting ribs 881 rotate with the inner drum 88.

[0135] Furthermore, to address the power supply and signal transmission issues within the rotating components, the garment handling device 80 may optionally include a spindle assembly connected to the inner drum 88. Specifically, a conductive path is provided inside the spindle assembly. Optionally, this conductive path can be a wiring configuration within a hollow spindle or a conductive slip ring structure. The image acquisition device 889 is electrically connected to an external power supply and controller via the conductive path. Compared to wireless battery power, this wired connection method enables the image acquisition device 889 to perform long-term, high-frame-rate video stream acquisition, meeting the hardware requirements for multi-frame time-series image acquisition in the aforementioned method embodiments.

[0136] Specifically, the controller is electrically connected to the image acquisition device 889. The controller may include one or more processors for executing computer program instructions stored in memory.

[0137] Specifically, the controller is configured to perform the following operations: The operating status information of the clothing processing device 80 is acquired, and the target observation period that meets the preset immersion observation conditions is determined based on the operating status information. The image acquisition device 889 is controlled to operate during the target observation period and to acquire monitoring images originating from the image acquisition device 889; Image feature analysis was performed on the monitoring images to extract color feature information that characterizes the color of the detergent. Determine the color fading status of clothing based on color characteristic information.

[0138] Optionally, the garment handling device 80 also includes an auxiliary lighting unit. Specifically, the auxiliary lighting unit is disposed within the receiving cavity of the lifting rib 881 and located on one side of the light-transmitting window. The controller is also configured to activate the auxiliary lighting unit during the target observation period to provide a constant illumination source in the low-light underwater environment, thereby improving the accuracy of color feature extraction.

[0139] Optionally, the garment treatment device 80 also includes a dispensing component and a heating component. The controller is configured to generate corresponding control commands after determining that there is color fading in the garments, to drive the dispensing component to deliver the color-protecting agent, or to control the heating component to shut off to maintain a cold water environment, thereby achieving physical or chemical emergency treatment for the color-faded garments.

[0140] It should be noted that although the illustration shows the structure of the clothes handling device 80 using a drum washing machine as an example, the scope of protection of this embodiment is not limited thereto. Any pulsator washing machine, washer-dryer combo, or dryer that has an inner drum and lifting rib structure and can be tested using the method described in this application falls within the scope of protection of this application.

[0141] According to another aspect of the present invention, an electronic device 50 is also provided. For example... Figure 11 As shown, the electronic device 50 includes a processor 51 and a memory 52.

[0142] The following section provides a detailed description of the electronic device 50, taking into account its specific hardware architecture.

[0143] Specifically, the processor 51 and the memory 52 are connected via a communication bus (not shown) or other means to achieve data transmission and interaction.

[0144] Specifically, processor 51 is the computational and control core of electronic device 50. Optionally, processor 51 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits used to control the execution of the present invention. Optionally, processor 51 may also include digital signal processors (DSPs), field-programmable gate arrays (FPGAs), or other programmable logic devices. In one possible implementation, given that this application involves image feature analysis and matrix operations (such as time-domain variance calculation and median filtering in the foregoing embodiments), processor 51 may integrate a dedicated graphics processing unit (GPU) or neural network processing unit (NPU) to improve the efficiency of floating-point operations and parallel processing.

[0145] Specifically, memory 52 is used to store computer programs, instructions, and temporary data. Optionally, memory 52 may include high-speed random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device, flash memory, universal flash storage (UFS) device, or other solid-state storage device.

[0146] Specifically, the memory 52 stores a computer program, which, when executed by the processor 51, implements the clothing color fading detection method described in any of the aforementioned method embodiments.

[0147] Specifically, when processor 51 executes the computer program, it is configured to perform the following steps: Obtain the operating status information of the clothing processing device, and determine the target observation period that meets the preset immersion observation conditions based on the operating status information; Acquire monitoring images originating from the image acquisition device and corresponding to the target observation period; Image feature analysis was performed on the monitoring images to extract color feature information that characterizes the color of the detergent. Determine the color fading status of clothing based on color characteristic information.

[0148] Optionally, the electronic device 50 may also include a communication interface (not shown). Specifically, the communication interface is used for wired or wireless communication between the electronic device 50 and other devices (e.g., an image acquisition device, a motor driver, a water level sensor, or a remote cloud server). Optionally, the processor 51 receives video stream data from the image acquisition device through the communication interface and sends speed adjustment commands to the motor through the communication interface.

[0149] It should be noted that the electronic device 50 described in this embodiment can have various physical forms. In one optional implementation, the electronic device 50 is a main control board integrated inside the garment processing device. In another optional implementation, the electronic device 50 can be a remote server or mobile terminal that is communicatively connected to the garment processing device; in this scenario, the garment processing device is responsible for acquiring and uploading images, while the electronic device 50 performs complex image feature analysis and color fading determination logic in the cloud or remotely, and returns the determination result to the garment processing device.

[0150] According to another aspect of the present invention, a computer-readable storage medium 60 is also provided. For example... Figure 12 As shown, the storage medium 60 includes a stored computer program.

[0151] The storage medium 60 will be described in detail below, taking into account its specific storage content and execution logic.

[0152] Specifically, the computer-readable storage medium 60 may be a tangible device capable of holding and storing instructions used by an instruction execution device. When the computer program stored on the storage medium 60 is run on a computer, processor, or similar computing device, it causes the computer, processor, or computing device to perform all or part of the steps in the method provided in the above-described method embodiments.

[0153] Specifically, when this computer program is executed, the following operational logic is implemented: Obtain the operating status information of the clothing processing device, and determine the target observation period that meets the preset immersion observation conditions based on the operating status information; Acquire monitoring images originating from the image acquisition device and corresponding to the target observation period; Image feature analysis was performed on the monitoring images to extract color feature information that characterizes the color of the detergent. Determine the color fading status of clothing based on color characteristic information.

[0154] Optionally, when the computer program is executed, it is also used to implement the detailed steps regarding how to determine the target observation period in the aforementioned method embodiments. Specifically, this includes logic for calculating the effective phase interval based on real-time water level data and inner cylinder geometric parameters; or logic for locking the start time based on image statistical indicators of the preview video stream.

[0155] Optionally, when the computer program is executed, it is also used to implement the detailed steps of image feature analysis in the aforementioned method embodiments. Specifically, it includes the algorithm logic of mapping pixel coordinates to multiple frames of time-series images, calculating the numerical dispersion features (such as variance) of the time dimension, segmenting dynamic interference regions and steady-state background regions based on the dispersion features, and performing median filtering on the steady-state background regions to extract color.

[0156] Optionally, when the computer program is executed, it is also used to implement the control logic regarding the post-fading treatment in the aforementioned method embodiments. Specifically, this includes generating control instructions to control the garment processing device to perform a physical emergency procedure of draining, low-speed centrifugal dehydration, and cold water rinsing; or generating control instructions to drive the dispensing component to deliver color-protecting additives and run a gentle care procedure, which is a chemical emergency procedure.

[0157] Optionally, the computer-readable storage medium 60 may include, but is not limited to, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, and any suitable combination thereof.

[0158] It should be noted that the computer-readable storage medium 60 described in the embodiments of this application may be an internal storage unit existing in the aforementioned electronic device or clothing processing device, such as a hard disk or memory; or it may be an external storage device of the aforementioned electronic device or clothing processing device, such as an equipped plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc.

[0159] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention, and other modifications can be easily made by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

[0160] The apparatus, electronic device, and non-volatile computer storage medium and method provided in the embodiments of this specification are corresponding. Therefore, the apparatus, electronic device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, electronic device, and non-volatile computer storage medium will not be repeated here.

[0161] Those skilled in the art will also know that, besides implementing the controller in the form of purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller take the form of logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices included within it for implementing various functions can also be considered structures within that hardware component. Alternatively, the devices for implementing various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0162] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0163] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0164] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects.

[0165] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0166] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0167] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0168] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0169] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0170] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0171] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside on local and remote computer storage media, including storage devices.

[0172] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0173] The above description is merely an embodiment of this specification and is not intended to limit the scope of one or more embodiments of this specification. Various modifications and variations can be made to one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of one or more embodiments of this specification.

Claims

1. A method for detecting color fading in clothing, characterized in that, A garment processing device applied to an image acquisition device mounted on the inner drum lifting ribs, the method comprising: Obtain the operating status information of the clothing processing device, and determine the target observation period that meets the preset immersion observation conditions based on the operating status information; Acquire monitoring images originating from the image acquisition device and corresponding to the target observation period; Image feature analysis is performed on the monitored images to extract color feature information that characterizes the color of the washing liquid; The color fading status of the clothing is determined based on the color feature information.

2. The method according to claim 1, characterized in that, The step of "determining the color fading status of clothing based on the color feature information" includes: Acquire the baseline washing liquid color characteristics of the garment processing device during the water intake stage or the initial washing stage; Calculate the color difference value between the color feature information and the reference detergent color feature; If the color difference value exceeds the preset color fading threshold, it is determined that the clothing has faded.

3. The method according to claim 2, characterized in that, The step of "calculating the color difference value between the color feature information and the reference detergent color feature" specifically includes: The color features of the reference washing liquid are mapped to the reference zero point in the pixel coordinate system; The vector offset magnitude of the color feature information relative to the reference zero point in the pixel coordinate system is obtained and determined as the color difference value.

4. The method according to claim 1, characterized in that, The step of "obtaining the operating status information of the clothing processing device and determining the target observation period that meets the preset immersion observation conditions based on the operating status information" includes: The real-time water level data and the real-time rotation phase of the inner cylinder are obtained as the operating status information; Based on the real-time water level data and the geometric parameters of the inner cylinder, the effective phase interval corresponding to when the lifting rib is completely submerged below the liquid surface is calculated; The duration during which the real-time rotation phase falls within the effective phase interval is defined as the target observation period.

5. The method according to claim 1, characterized in that, The step of "obtaining the operating status information of the clothing processing device and determining the target observation period that meets the preset immersion observation conditions based on the operating status information" includes: The image statistical indicators of the preview video stream output by the image acquisition device are used as the running status information. The image statistical indicators include the average brightness or texture complexity of the current frame. When a step change is detected in the average brightness, or when the texture complexity is lower than a preset complexity threshold, the current time is marked as the start time of the target observation period.

6. The method according to claim 1, characterized in that, The monitoring images include multiple time-series images corresponding to the target observation period; The step of "performing image feature analysis on the monitoring image to extract color feature information characterizing the color of the detergent" includes: The monitored images are mapped to a unified pixel coordinate system; For each pixel in the pixel coordinate system, its numerical dispersion characteristics in the time dimension are statistically analyzed. Based on the numerical dispersion characteristics, the dynamic interference region and the steady-state background region are segmented from the pixel coordinate system; The color feature information is calculated based on the pixel values ​​of the stable background region.

7. The method according to claim 6, characterized in that, The step of "segmenting the dynamic interference region and the steady-state background region from the pixel coordinate system based on the numerical dispersion feature" includes: Calculate the variance of the pixel values ​​of the pixel in the multi-frame time series image; If the variance of the pixel value is greater than a preset stability threshold, the pixel is classified into the dynamic interference region. If the variance of the pixel value is less than or equal to the stability threshold, the pixel is classified into the stable background region.

8. The method according to claim 7, characterized in that, The step of "calculating the color feature information based on the pixel values ​​of the stable background region" includes: The pixel value sequence of each pixel point belonging to the steady-state background region in the time dimension is subjected to median filtering to obtain a synthetic background image; The color feature information is extracted from the synthesized background image.

9. The method according to claim 1, characterized in that, The operating status information also includes the real-time rotational speed of the inner cylinder; The step of "acquiring monitoring images originating from the image acquisition device and corresponding to the target observation period" includes: Determine whether the real-time rotation speed of the inner cylinder is greater than the preset imaging critical rotation speed. If it is greater, generate a rotation speed adjustment command to control the inner cylinder to decelerate. The monitoring image is acquired when the inner cylinder is in a low-speed state in response to the speed adjustment command and the time falls within the target observation period.

10. The method according to claim 1, characterized in that, The step of "performing image feature analysis on the monitoring image to extract color feature information characterizing the color of the detergent" includes: Calculate the sharpness evaluation index for each connected region in the monitored image; Based on the depth-of-field characteristics of the image acquisition device, areas with sharpness evaluation indicators below a preset focus threshold are identified as near-field occlusion areas and removed. The region where the clarity evaluation index is within the preset transparency range is retained, and the color feature information is extracted from it.

11. The method according to claim 2, characterized in that, The method further includes: after determining that there is color fading in the clothing, performing the following steps in sequence: Control the clothing handling device to perform a drainage action until the water level drops to a preset empty water level; The inner cylinder is controlled to perform centrifugal dehydration at a low centrifugal speed, wherein the low centrifugal speed is lower than the preset standard dehydration speed. The garment processing device is controlled to perform a water intake action, and during the water intake action and the subsequent rinsing action, the heating component of the garment processing device is controlled to remain in a closed state.

12. The method according to claim 2, characterized in that, The method further includes: after determining that there is color fading in the clothing, performing the following steps in sequence: Control the garment handling device to perform the drainage action; The dispensing component of the garment processing device is controlled to perform a dispensing action, dispensing the pre-set color-protecting additive into the inner drum; Control the garment processing device to run the color-protecting washing program; During the color-protecting washing program, the washing water temperature is controlled to be no higher than a preset color-protecting temperature threshold, and the inner drum is controlled to rotate according to a preset gentle care rhythm.

13. A garment handling apparatus configured to perform the method according to any one of claims 1 to 12, characterized in that, include: The inner cylinder has raised lifting ribs on its inner wall; An image acquisition device is disposed in a receiving cavity formed inside the lifting rib, with the lens facing the inside of the inner cylinder; The controller is electrically connected to the image acquisition device and is used to acquire the monitored image and perform the image feature analysis.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 12.

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