Clothes care device, in-drum clothes hanging care method and equipment and storage medium

Abstract

According to the clothes care device and the in-barrel clothes hanging care method, the depth distribution data of the in-barrel space of the clothes care device is obtained, the hanging execution result of the clothes in the barrel is determined through the depth distribution data, clothes care is executed according to the hanging execution result, intelligent and accurate sensing of a clothes clamping result can be achieved, and the clothes care efficiency is improved. And measures are taken in time when the clothes are not successfully clamped, so that the user experience is improved. The clothes contour is identified through the depth data, the state of the clamped clothes is sensed, the clothes clamping effect can be accurately judged, measures are taken when the clothes are clamped in a cluster, the clothes are prevented from being clamped in the cluster, particularly clamped and hung in the cluster, and the clothes washing, drying and nursing efficiency and the user experience are improved. The clothes clamping device is controlled on the basis of the depth data, so that the execution effectiveness of the clothes clamping action of the clothes clamping device can be ensured, and the situation that the user experience is affected due to false triggering of a user, clothes adhering to the wall after dehydration and the like can be avoided.

Description

Technical Field

[0001] This invention relates to the field of garment care device control technology, and particularly to garment care devices, methods, equipment and storage media for hanging and caring for garments inside a garment basket. Background Technology

[0002] Currently, most washing machines have functions such as washing, spin-drying, drying, and garment care. Depending on user needs, washing machines will dry and care for clothes after the washing and spin-drying cycles. Since clothes are piled up inside the washing machine drum, in order to improve the efficiency of washing, drying, and garment care, and to maintain the washing, drying, and garment care effects, it is necessary to clamp and hang the clothes inside the washing machine.

[0003] To address this, Chinese Patent Application No. CN202223339070.9 discloses a washing machine clothes clamping device based on light detection results. This device has a light emitting device and a light receiving device respectively installed at the free ends of its two mechanical claws to determine whether the clothes have been effectively clamped through light detection. However, this washing machine clothes clamping device has a complex structural design. Furthermore, because the light emitting and receiving devices are located at the free ends of the mechanical claws, they come into direct contact with the clothes and the environment inside the drum. The condition of the clothes and the environment inside the drum affects the normal operation of the light emitting and receiving devices, thus affecting the accuracy of the clothes clamping result judgment. Therefore, this solution is difficult to implement in practice. Summary of the Invention

[0004] In order to achieve the above-mentioned objectives and other advantages of the present invention, a first objective of the present invention is to provide a method for hanging and caring for clothes inside a tub, comprising the following steps:

[0005] Acquire depth distribution data of the space inside the garment care device's cylinder;

[0006] The suspension performance of the tube-shaped garment is determined using the depth distribution data;

[0007] Clothing care is performed based on the results of the suspension process.

[0008] Furthermore, the acquisition of the depth distribution data is configured to be performed after the clothing gripping device has completed the clothing gripping action.

[0009] Furthermore, prior to the step of determining the suspension performance result of the tube-shaped underwear using the depth distribution data, the method further includes:

[0010] The depth distribution data is compared with the baseline depth distribution data to determine whether there is clothing in the clothing clamping area;

[0011] If there is clothing in the clothing clamping area, the clothing clamping device on the inner cylinder lifting rib is controlled to perform a clothing clamping action.

[0012] If there is no clothing in the clothing clamping area, the inner drum is controlled to operate and the depth distribution data of the inner drum space is continuously monitored and / or a prompt message is sent.

[0013] Furthermore, after the garment gripping device completes the garment gripping action, the step of determining the suspension result of the tubular underwear garment through the depth distribution data includes:

[0014] The depth distribution data is compared with the baseline depth distribution data to determine whether there is clothing in the clothing hanging area;

[0015] If there are no clothes in the clothing hanging area, the clothing hanging is deemed to have failed.

[0016] If there is clothing in the clothing hanging area, the clothing is considered to have been successfully hung.

[0017] Furthermore, it also includes the following steps:

[0018] If the garment fails to hang, the garment clamping device is controlled to perform the garment clamping action again.

[0019] Further, the step of comparing the depth distribution data with the reference depth distribution data includes:

[0020] Compare the current depth data of the pixels in the cylinder with the reference depth data of the pixels;

[0021] If the depth data of the pixel is less than the reference depth data, then determine whether the number of pixels whose depth data has changed is greater than the threshold.

[0022] If yes, then it is determined that there are clothes inside the tube;

[0023] No, then it is determined that there are no clothes inside the tube;

[0024] If the depth data of the pixel is not less than the reference depth data, it is determined that there is no clothing inside the tube.

[0025] Furthermore, the depth distribution data is acquired through a data acquisition device.

[0026] Furthermore, it also includes the following steps:

[0027] Obtain the current depth data of all pixels within the cylinder to obtain a depth image;

[0028] Identify the target region containing clothing in the depth image;

[0029] The outline of clothing is identified by using the reference depth data and current depth data of the pixels in the target area.

[0030] Furthermore, the step of identifying the target region containing clothing in the depth image includes:

[0031] The target region containing clothing is identified using a target detection algorithm in the depth image.

[0032] Furthermore, after the clothing is successfully retrieved, the following steps are also included:

[0033] Calculate the shape matching degree between the identified clothing outline and the preset clothing hanging outline;

[0034] Compare the calculated shape matching degree with the preset difference value;

[0035] If the shape matching degree is not greater than the preset difference value, the current posture of the clothing is a spread-out clamping state;

[0036] If the shape matching degree is greater than the preset difference value, the current posture of the clothing is a clumped clamping state. After the clothing clamping device on the inner cylinder lifting rib releases the clothing, it will clamp the clothing again.

[0037] Furthermore, the step of calculating the shape matching degree between the identified clothing outline and the preset clothing hanging outline includes:

[0038] The shape matching degree of the garment outline and the garment hanging outline is detected by calculating Hu moment matching.

[0039] Furthermore, it also includes the following steps:

[0040] Calculate the relative distance between the garment outline and the garment gripping device on the inner cylinder lifting rib;

[0041] Compare the calculated relative distance with the preset distance range;

[0042] If the relative distance is within a preset distance range, the clothing clamping device is controlled to perform a clothing clamping action;

[0043] If the relative distance is not within the preset distance range, the operating parameters of the inner drum are controlled according to the relative distance, and the relative distance between the outline of the garment and the garment clamping device is continuously monitored during the operation of the inner drum until the relative distance is adjusted to the preset distance range.

[0044] Further, the step of calculating the relative distance between the garment outline and the garment gripping device on the inner drum lifting rib includes:

[0045] Obtain the clothing gripping point information on the clothing gripping device;

[0046] Calculate the shortest distance from the garment gripping point to the garment outline.

[0047] A second objective of the present invention is to provide a garment care device that applies the above-described method.

[0048] A third object of the present invention is to provide an electronic device comprising: a memory having program code stored thereon; and a processor connected to the memory, wherein the above-described method is implemented when the program code is executed by the processor.

[0049] A fourth objective of this invention is to provide a computer-readable storage medium having program instructions stored thereon, which, when executed, implement the method described above.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] This invention provides a garment care device, a method, equipment, and storage medium for hanging and caring for garments inside a garment care tube. It acquires depth distribution data of the space inside the tube, determines the hanging result of the garments based on this data, and performs garment care according to the hanging result. By achieving depth distribution data detection covering the entire space inside the tube, the detection range for hanging garments is expanded, resulting in more accurate and intelligent perception of garment handling and hanging results, thus improving the user experience. The invention features a simple structural design; the depth distribution data acquisition sensor does not directly contact the garments or the environment inside the tube. The state of the garments and the environment inside the tube does not affect the normal operation of the depth distribution data acquisition sensor, thereby ensuring the accuracy of the garment handling result judgment. This solution is convenient for practical application.

[0052] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0053] 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:

[0054] Figure 1 This is a flowchart of the hanging and care method for clothes inside a tube in Example 1;

[0055] Figure 2 This is a flowchart of Example 1 showing the process of identifying clothing before it is picked up based on depth distribution data;

[0056] Figure 3 This is a flowchart illustrating the execution result of determining the suspension of tubular clothing using depth distribution data in Example 1.

[0057] Figure 4 This is a flowchart comparing the depth distribution data of Example 1 with the baseline depth distribution data;

[0058] Figure 5 Here is a flowchart of clothing outline recognition in Example 1;

[0059] Figure 6 Here is a flowchart of the clothing current posture recognition process in Example 1;

[0060] Figure 7 This is a flowchart of the clothing clamping control in Example 1;

[0061] Figure 8 This is a flowchart illustrating the calculation of the relative distance between the garment outline and the garment gripping device in Example 1.

[0062] Figure 9 This is a diagram showing the internal structure of the garment care device in Example 2;

[0063] Figure 10 This is a schematic diagram of the electronic device in Example 3;

[0064] Figure 11 This is a schematic diagram of the storage medium in Example 4.

[0065] In the diagram: 1. Door; 2. Inner cylinder; 3. Depth distribution data acquisition sensor; 4. Lifting rib; 41. Air outlet; 5. Door glass; 6. Clothing. Detailed Implementation

[0066] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0067] 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.

[0068] The drawing numbers in this application are only used to distinguish the steps in the scheme and are not used to limit the execution order of the steps. The specific execution order is as described in the specification.

[0069] 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.

[0070] Figure 1 This is a flowchart of a method for hanging and caring for clothes inside a drum, as provided in Embodiment 1 of the present invention. Embodiment 1 is applicable to the use of a clothes care device to hang clothes, enabling accurate judgment of the result of the clothes clamping device on the lifting ribs of the inner drum of the clothes care device, thus achieving intelligent and accurate perception of the clothes hanging. The clothes hanging here includes, but is not limited to, hanging clothes after drying, washing, or care, as well as hanging clothes before and / or during drying and care. It should be noted that Embodiment 1 is also applicable to the use of a clothes care device to wash clothes (e.g., detecting the number of clothes before washing, after draining, before water intake, and other situations where the inner drum is dry, requiring clothes clamping, etc.), enabling accurate judgment of the result of the clothes clamping device, thus achieving intelligent and accurate perception of the clothes clamping result.

[0071] This method can be executed by the main control unit of the garment care device. The 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, PC, or server.

[0072] Example 1

[0073] A method for hanging and caring for clothes inside a tube, such as Figure 1 As shown, it includes the following steps:

[0074] S1. Obtain depth distribution data of the space inside the garment care device cylinder;

[0075] Depth distribution data needs to be collected by a depth distribution data acquisition sensor. This sensor is installed inside the garment care device, facing the interior of the drum, and its installation position is fixed. Before the depth distribution data acquisition sensor operates, its detection range needs to be calibrated. Specifically, with no clothing inside the drum, the depth distribution data acquisition sensor pre-collects a depth image of the drum's interior space. The depth data of the pixels in the collected depth image is the baseline depth distribution data. During the actual operation of the garment care device, the spatial position of the depth distribution data acquisition sensor can remain fixed or rotate with the inner drum, but the spatial position when collecting depth distribution data must be consistent with the position during calibration. Based on this, the interior space of the garment care device is the interior space determined during the calibration of the depth distribution data acquisition sensor. It can be the entire interior space or a portion of the interior space. This interior space can be determined based on the characteristics of the depth distribution data acquisition sensor itself, its placement position, and orientation.

[0076] In some alternative embodiments, such as Figure 9 As shown, the depth distribution data acquisition sensor 3 is fixedly installed inside the door glass 5. Before the depth distribution data acquisition sensor operates, its detection range is calibrated to obtain the space inside the cylinder. Since the depth distribution data acquisition sensor is fixed inside the door glass, its spatial position is fixed (once a certain acquisition direction is determined), and the space inside the cylinder is also fixed, ensuring the validity of the data acquired by the sensor.

[0077] In some alternative embodiments, the depth distribution data acquisition sensor can also be fixedly mounted on the garment gripping device of the inner drum lifting rib. Before the depth distribution data acquisition sensor operates, its detection range is calibrated to obtain the space inside the drum. During calibration, both the garment gripping device and the sensor are in specific positions. Although the depth distribution data acquisition sensor is fixedly mounted on the garment gripping device, its spatial position is not fixed because it moves with the inner drum. To ensure the validity of the data collected by the sensor, it is necessary to control the sensor to stop at the calibrated position via a motor before collecting depth distribution data. At this time, the space inside the drum is also fixed.

[0078] This embodiment does not limit the specific installation location of the depth distribution data acquisition sensor. In addition to the installation locations described in the above embodiment, the depth distribution data acquisition sensor can also be installed outside the door glass, on the rear wall of the cylinder, or on the lifting rib, as long as it can achieve depth distribution data acquisition of the space inside the garment care device cylinder.

[0079] The aforementioned depth distribution data acquisition sensor uses 3D vision technology to acquire complete geometric information of real three-dimensional scenes. It uses images with depth information to achieve accurate digitization of the scene, thereby realizing key machine vision functions such as high-precision recognition, positioning, reconstruction, and scene understanding.

[0080] Optionally, various 3D technologies include Time-of-Flight (ToF), binocular vision, and structured light. Binocular vision works similarly to the human eye. Its 3D principle involves using two 2D cameras to capture synchronized images from different perspectives to obtain depth information. To calculate 3D data, the relative positions (external parameters) between the two cameras must be known. Furthermore, the internal parameters of each camera, such as the optical center and focal length of the lens, are also required.

[0081] To calculate depth information, the images captured by two 2D cameras are first calibrated. Then, an adaptation algorithm is used to search for corresponding pixels in the left and right images. Finally, using the calibration values, the depth image of the scene or object can be generated as a point cloud. The optimal working distance in this process depends on the distance between the two cameras and their angles, and therefore will vary.

[0082] Unlike binocular vision technology, structured light technology requires replacing one of the cameras with a stripe light projector. The deformation of the stripes projected onto the surface can be used to calculate 3D information and obtain more accurate measurement results.

[0083] Chips with structured light capabilities can achieve high accuracy at close range. However, this generates a high computational load, requiring the acquisition and analysis of multiple images one by one. It is not suitable for moving objects, so it is only suitable for real-time applications within a limited range; otherwise, higher costs are required.

[0084] Time-of-Flight (ToF) can be used to measure distance to obtain depth data. A light source integrated into the camera emits light pulses that illuminate the object. The object reflects the light pulses back to the camera. Based on the time required for the light pulses to travel, the distance between the object and the camera is determined, thus yielding the depth value.

[0085] The 3D value of the detected object is output as a spatial image in the form of a range map or point cloud, and a 2D intensity image and confidence map are also provided for each pixel in the form of grayscale values.

[0086] When using Time-of-Flight (ToF) cameras for 3D data acquisition, they are less affected by the intensity and color of objects, and require no edge calibration, corner adjustments, or other functional settings. Therefore, image processing techniques can be used to easily separate objects from the background. This acquisition process is also suitable for moving objects, performing up to 9 million distance measurements per second with millimeter-level accuracy. Compared to other 3D cameras, ToF cameras are more economical, compact, and simple, allowing for easy installation and integration.

[0087] Preferably, this embodiment uses a ToF sensor to collect depth distribution data of the space inside the tube. The depth distribution data can be presented as a ToF image. Each pixel in the ToF image is considered a point, or a pixel point. The pixels in the ToF image record the distance information from the camera in the ToF sensor to the corresponding position in the space inside the tube. That is, each pixel point represents a specific position in the space inside the tube and its corresponding depth data. The ToF sensor transmits the collected depth distribution data of the space inside the tube to the main control unit of the garment care device so that the main control unit can determine the garment clamping result.

[0088] In practical applications of garment care devices, depth distribution data of the space inside the tube can be collected using one or more ToF sensors.

[0089] In some alternative embodiments, the acquisition of the depth distribution data is performed after the garment gripping device configured on the inner cylinder lifting ribs completes the garment gripping action. To achieve intelligent garment gripping in the garment care device and minimize the power consumption of the ToF sensor, a driver program can be designed to effectively configure the ToF sensor as an energy-efficient detector. Once the garment gripping device has completed its gripping action, the driver program immediately switches the ToF sensor back to standard ranging mode. That is, after controlling the garment gripping device to complete its gripping action, the ToF sensor is then controlled to collect depth distribution data within the cylinder space. This fully leverages the advantages of Time-of-Flight (ToF) technology and significantly reduces power consumption.

[0090] It should be noted that the clamping in this invention can be interpreted as grasping, picking up, hooking, holding, etc. Any method of keeping clothing out of contact with the bottom of the tube for a long time is within the protection scope of this application.

[0091] In some alternative embodiments, prior to the step of determining the suspension performance result of the tube-shaped garment using the depth distribution data, such as Figure 2 As shown, it also includes:

[0092] S11. Compare the depth distribution data with the baseline depth distribution data to determine whether there is clothing in the clothing clamping area;

[0093] Among them, the reference depth distribution data is the depth data of all pixels in the cylinder space recorded during the calibration of the ToF sensor, which is the distance between the pixels and the ToF sensor. This reference depth distribution data is stored in the ToF sensor.

[0094] After the ToF sensor is calibrated, during continuous monitoring of the space inside the tube, when clothing is present, it will obscure the pixels in the tube, reducing the depth data of the obscured pixels. Based on this, the collected depth distribution data is compared with baseline depth distribution data, and the presence of clothing inside the tube of the garment care device is determined by whether the detected depth distribution data changes.

[0095] S12. If the depth distribution data is less than the reference depth distribution data, it is determined that there is clothing in the clothing clamping area, and the clothing clamping device on the inner cylinder lifting rib is controlled to perform a clothing clamping action. This ensures the effectiveness of the clothing clamping action of the clothing clamping device.

[0096] It should be noted that the internal space of the cylinder can be different in different optional embodiments, and the internal space can be adaptively adjusted according to the technical solutions of different embodiments. For example, the collection area of ​​the depth distribution data acquisition sensor can be pre-set to obtain the internal space of the cylinder. At this time, the internal space of the cylinder may only include at least a part of the clothing clamping area, or at least a part of the clothing hanging area, or it may include at least a part of the clothing clamping area and at least a part of the clothing hanging area at the same time. In this case, the internal space of the cylinder can be divided into spaces corresponding to the clothing clamping area and the clothing hanging area according to the pre-calibrated space. It should also be noted that the collection area of ​​the depth distribution data acquisition sensor can be dynamically adjusted according to the objectives of different processes to obtain a dynamic internal space of the cylinder. For example, when it is necessary to detect whether there is clothing in the clothing clamping area, the collection area of ​​the depth distribution data acquisition sensor is adjusted to the clothing clamping area; when it is necessary to detect whether there is clothing in the clothing hanging area, the collection area of ​​the depth distribution data acquisition sensor is adjusted to the clothing hanging area, etc. In the embodiment to which step S12 belongs, the internal space of the cylinder is an inner cylinder space that includes at least a part of the clothing clamping area. The clothing clamping area is usually the bottom area of ​​the inner cylinder, the lower middle area of ​​the inner cylinder, etc.

[0097] S13. If the depth distribution data is not less than the reference depth distribution data, it is determined that there is no clothing in the clothing clamping area. The inner drum is controlled to operate, and the depth distribution data of the inner drum space is continuously monitored and / or a prompt message is sent. When it is determined that there is no clothing in the clothing clamping area, a prompt message can be given to inform the user of the situation and allow them to check the situation inside the drum. Alternatively, the inner drum can be controlled to operate first, and the depth distribution data of the inner drum space can be continuously monitored. Then, the presence or absence of clothing in the clothing clamping area can be determined based on the monitored depth distribution data. If it is still determined that there is no clothing, a prompt message can be given to inform the user of the situation and allow them to check the situation inside the drum. If it is determined that there is clothing, the clothing clamping device is controlled to perform the clothing clamping action. This step can avoid situations such as accidental triggering by the user or clothing sticking to the wall after spin-drying, which may affect the user experience.

[0098] It is understood that the technical solutions of the above optional embodiments can be used individually or in combination, depending on the actual needs. For example, if the garment care device is accidentally triggered, there is only one garment in the drum, there are many garments in the drum, or the garments are large / long, multiple technical solutions may need to be used simultaneously to achieve better detection results.

[0099] S2. Determine the hanging result of the tubular clothing using the depth distribution data. This step detects whether the clothing gripping and hanging were successful, enabling the perception of the clothing gripping and hanging results and achieving intelligent clothing gripping and hanging.

[0100] Figure 3 The flowchart for determining the hanging result of clothes inside the tube using the depth distribution data is based on step S2 and further optimized. It can be combined with one or more of the above optional embodiments.

[0101] In some alternative embodiments, such as Figure 3 As shown, after the garment gripping device completes the garment gripping action, the step of determining the suspension result of the tubular underwear using the depth distribution data includes:

[0102] S21. Compare the depth distribution data with the baseline depth distribution data to determine whether there is clothing in the clothing hanging area;

[0103] After the garment clamping device completes the garment clamping action, the garment care device controls the inner drum to rotate so that the garment clamping device can move the garment to the hanging area or other preset areas corresponding to drying, care, or washing needs.

[0104] It should be noted that, in the embodiment to which step S21 belongs, the inner tub space refers to the inner tub space that includes at least a portion of the clothing hanging area. This clothing hanging area is typically the top area of ​​the inner tub, the upper middle area of ​​the inner tub, etc., for example... Figure 9 The space inside the cylinder indicated by the middle arrow. Figure 9 The direction and space indicated by the middle arrow are merely examples and do not limit the space inside the cylinder to only such a direction. Figure 9 As shown.

[0105] After the garment gripping device completes the garment gripping action, it continuously monitors the space inside the drum. When garments are present, they occlude pixels within the drum, reducing the depth data of these occluded pixels. When no garments are present, the pixels remain unoccluded, and their depth data remains largely unchanged. Therefore, after the garment gripping device completes the gripping action, the collected depth distribution data is compared with baseline depth distribution data. The success of garment gripping and suspending is determined by whether changes in the detected depth distribution data are observed.

[0106] S22. If the depth distribution data is not less than the reference depth distribution data, it is determined that there is no clothing in the clothing hanging area, and the clothing hanging has failed. This step can detect when clothing has not been successfully grabbed, allowing for timely remedial measures and improving the user experience.

[0107] S23. If the depth distribution data is less than the reference depth distribution data, it is determined that clothing exists in the clothing hanging area, and the clothing is successfully hung. This enables the clothing gripping device to accurately perceive the clothing gripping and hanging results.

[0108] In some embodiments, after determining that the clothing clamping / hanging has failed, the following steps are also included:

[0109] If the garment clamping / hanging fails, the garment clamping device on the inner drum's lifting ribs is controlled to perform the clamping action again. Specifically, the inner drum can be controlled to move the garment clamping device to the garment clamping area, and then the device can be controlled to perform the clamping action again. The process then returns to step S1 to determine if the garment has been successfully clamped. This step allows for timely remedial action when garments are not successfully clamped / hanged, improving the user experience.

[0110] Figure 4 The flowchart for comparing the depth distribution data with the reference depth distribution data is further optimized based on steps S11 / S21, and can be combined with one or more of the above optional embodiments.

[0111] In some alternative embodiments, such as Figure 4 As shown, the step of comparing the depth distribution data with the reference depth distribution data includes:

[0112] The current depth data of the pixel in the cylinder space is compared with the reference depth data of the pixel; specifically, S211, it is determined whether the current depth data of the pixel in the cylinder space is less than the reference depth data of the pixel;

[0113] During the continuous detection of the internal space using a ToF sensor, the ToF sensor detects the depth data of each pixel in each frame of the detected image and compares it with the reference depth data of each pixel stored during calibration.

[0114] S212. If the depth data of a pixel is less than the reference depth data, it indicates that the depth data of that pixel has changed, possibly due to clothing obscuring it. To avoid misjudgment, it is determined whether the number of pixels with changed depth data exceeds a threshold. This threshold can be set according to clothing parameter information and specific requirements. The threshold can be a specific value or a range of values.

[0115] S213, if yes, then it is determined that there is clothing in the space inside the cylinder; that is, when the number of pixels that have changed reaches a preset threshold, it can be determined that there is clothing in the space inside the cylinder.

[0116] S214. If not, it is determined that there is no clothing in the space inside the cylinder; that is, when the number of pixels that have changed does not reach the preset threshold, it can be determined that there is no clothing in the space inside the cylinder.

[0117] If the depth data of the pixel is not less than the reference depth data, it is determined that there is no clothing in the space inside the cylinder, indicating that the depth data of the pixel has not changed significantly and there is no clothing obstructing it.

[0118] To prevent clothing from being clumped up and hung up, and to perceive the state of clothing after it has been clumped up, the depth data mentioned above can be used to form the outline of the clothing to determine the clumping and hanging effect.

[0119] Figure 5 The flowchart for recognizing clothing outlines adds a clothing outline recognition process to the above steps, and can be combined with one or more of the above optional embodiments.

[0120] S3. Perform garment care based on the hanging result. Specifically, when the garment is successfully hung, perform garment care; when the garment fails to hang, control the garment clamping device to perform the garment clamping action again.

[0121] In some alternative embodiments, such as Figure 5 As shown, it also includes the following steps:

[0122] S4. Obtain the current depth data of all pixels in the cylinder space to obtain a depth image;

[0123] Optionally, the ToF sensor acquires a depth image of the space inside the tube. This depth image can be displayed using depth data from multiple pixels arranged in an array. The depth data of each pixel represents the distance between the ToF sensor and a specific location within the tube. The ToF sensor transmits the acquired depth image of the space inside the tube to the main control unit of the garment care device, allowing the main control unit to identify the outline of the garment.

[0124] S5. Identify the target region containing clothing in the depth image;

[0125] Optionally, an object detection algorithm is used to identify target regions containing clothing in the depth image. An object detection model can be generated using this algorithm and trained on a training dataset to obtain a model that meets the detection requirements. The training dataset can be depth images containing clothing, with labels indicating whether the depth image contains clothing. To optimize the object detection model's recognition performance, a training dataset can be generated using depth data of clothing in different poses. This object detection model is then used to identify whether clothing exists in the depth image, and if clothing is present, to determine the region where the clothing is located.

[0126] Among them, the target detection algorithm can adopt traditional target detection methods, and combine Haar features, HOG features, LBP features, and machine learning methods, such as AdaBoost, SVM, DPM, etc., to achieve the recognition of target regions containing clothing in depth images.

[0127] Object detection algorithms can also employ deep learning (CNN) based algorithms, including the RCNN series, SSD, YOLO series, etc. The RCNN series algorithms can include R-CNN, Fast R-CNN, etc.

[0128] Taking the Fast R-CNN algorithm as an example, the depth image is processed by a feature extractor to extract features and obtain a feature map. At the same time, a selective search algorithm is run on the original image to map the region of interest onto the feature map. Then, region pooling is performed on each region of interest to obtain feature vectors of equal length. These feature vectors are sorted into positive and negative samples (maintaining a certain ratio of positive and negative samples) and fed into the parallel R-CNN sub-network in batches for classification and regression. The loss of the two is unified and trained to obtain an object detection model. The actual depth image is input into the object detection model to identify the target region containing clothing, which can achieve high-speed and accurate object detection.

[0129] The Region of Interest (ROI) pooling operation is a crucial operation for preparing the data input to the R-CNN subnetwork. Since the resulting ROIs typically have varying sizes, they will produce feature tensors of different sizes after being mapped onto the feature map. The ROI pooling operation first divides the ROI into a grid of the target number of elements, and then performs max pooling on each grid to obtain ROI feature vectors of equal length.

[0130] S6. Identify the outline of clothing using the reference depth data and current depth data of pixels in the target area.

[0131] Step S4 identifies the target region containing clothing, and step S5 identifies the clothing outline from the target region. Specifically, the current depth data of pixels in the target region is compared with reference depth data, and the clothing outline is determined based on the difference in pixel depth values.

[0132] Time-of-Flight (ToF) sensors emit pulsed lasers and calculate the time it takes for the laser pulses to travel from the target to the detector, i.e., the time of flight. The time of flight equals the product of the number of pulses (n) and the pulse interval (t), and the distance is equal to the product of the time of flight and the velocity. This method is simple and direct, and the accuracy of ranging does not deteriorate with increasing distance.

[0133] ToF sensors can also employ more precise ranging methods, specifically measuring the phase difference between the modulated signal and the initial signal. The light emitted from the laser is amplitude modulated, and the modulated signal is detected after reflection. The phase difference between the reflected signal and the original phase is then measured, and the distance can be obtained based on the known angular frequency of the modulated signal.

[0134] The distance between the ToF sensor and each specific location within the tube can be obtained using the methods described above. The pre-stored reference depth data in this step represents the distance between the ToF sensor and each specific location within the tube when there is no clothing inside. If there is clothing inside, the pulsed light emitted by the ToF sensor will reach the reflective surface (i.e., the clothing surface) earlier, meaning the pulsed light has a shorter flight time and is reflected back to the ToF sensor more quickly.

[0135] By comparing the baseline depth data at the same location in the target area with the current depth data, it can be determined whether clothing exists at that location, thereby identifying the pixels where the clothing is located and obtaining the outline of the clothing.

[0136] Figure 6 The flowchart for clothing current posture recognition is provided. Based on the above steps, a process for clothing current posture recognition is added, which can be combined with one or more of the above optional embodiments.

[0137] In some alternative embodiments, such as Figure 6As shown, after the clothing is successfully retrieved, the following steps are also included:

[0138] S7. Calculate the shape matching degree between the identified clothing outline and the preset clothing hanging outline; it should be noted that when this method is applied to the judgment of clothing clamping effect in other scenarios, the preset clothing hanging outline here can be understood as the preset clothing clamping outline in other scenarios. Any scheme that matches the preset clothing clamping outline with the identified clothing outline is within the protection scope of this application.

[0139] It should be noted that in the embodiment to which step S6 belongs, the inner tub space is the inner tub space that includes at least part of the clothing hanging area. This clothing hanging area is usually the top area of ​​the inner tub, the upper middle area of ​​the inner tub, etc.

[0140] Optionally, the shape matching degree of the clothing outline and the clothing after clamping is detected by calculating Hu moment matching.

[0141] Hu moments are an image feature description method. These moments are invariant features used to describe the shape and geometric features of an image, exhibiting translation, rotation, and scale invariance. Hu moments can be used to describe shapes in images, thereby enabling shape recognition. They are invariant to image scale, rotation, and translation transformations, thus allowing shape matching under different poses and sizes.

[0142] In practical applications, shape recognition can be achieved using the Hu moment function. Specifically, the syntax of the function cv2.HuMoments() is as follows:

[0143] hu = cv2.HuMoments(m)

[0144] In the formula, the return value hu represents the returned Hu moment value; the parameter m is the moment eigenvalue calculated by the function cv2.moments().

[0145] Hu moments are linear combinations of normalized central moments, and each moment is obtained through the combination operation of normalized central moments.

[0146] S8. Compare the calculated shape matching degree with the preset difference value;

[0147] Specifically, even after translation, rotation, and scaling, similar images will still have relatively close return values ​​from the function cv2.HuMoments(m), with small differences between the return values.

[0148] The function cv2.HuMoments(m) returns significantly different values ​​for dissimilar images.

[0149] Step S6 calculates the Hu moment between the garment outline and the preset garment clamping outline. If the difference between the two is 0 or close to 0, it indicates that the garment outline and the preset garment clamping outline are essentially the same. It should be noted that the preset difference value is not limited to the specific value mentioned above and can be set according to the garment parameter information and specific requirements. This preset difference value can be a specific value or a range of values.

[0150] S9. If the shape matching degree is not greater than the preset difference value, it means that the outline of the clothing is basically consistent with the preset clothing hanging outline, and the current posture of the clothing is the spread-out clamping state.

[0151] S10. If the shape matching degree is greater than the preset difference value, it means that the clothing outline is significantly different from the preset clothing hanging outline, and the current posture of the clothing is a clump-like clamping state. At this time, the clothing clamping device on the inner drum lifting rib can be controlled to release the clothing first, and then clamp the clothing again. During this process, the inner drum running parameters can be controlled to shake the clothing, and then the clothing clamping device can be controlled to perform the clothing clamping action.

[0152] Figure 7 This is a flowchart of clothing clamping control. Based on the above steps, a clothing clamping control process is added, which can be combined with one or more of the above optional embodiments.

[0153] In some alternative embodiments, such as Figure 7 As shown, it also includes the following steps:

[0154] S100. Calculate the relative distance between the outline of the garment and the garment clamping device on the inner cylinder lifting rib;

[0155] In practical use, the lifting ribs in the inner drum of the garment care device generate friction between the garment and the ribs. The part of the garment near the lifting ribs rubs against the relatively moving parts, creating a kneading effect. Simultaneously, the lifting ribs cause the garment to rotate, lifting it to a certain height before it falls back into the inner drum due to gravity. Therefore, there may be a certain distance between the lifting ribs and the garment. To make garment gripping more efficient and precise, the relative distance between the garment's outline and the garment gripping device can be calculated to control the garment gripping process.

[0156] It should be noted that in the embodiment to which step S100 belongs, the inner tube space is an inner tube space that includes at least a portion of the clothing clamping area, which is usually the bottom area of ​​the inner tube, the lower middle area of ​​the inner tube, etc.

[0157] S110. Compare the calculated relative distance with a preset distance range; the preset distance range can be set according to the space range that the clothing clamping device can reach.

[0158] S120. If the relative distance is within the preset distance range, it means that the clothes are within the space range that the clothes clamping device can clamp. At this time, controlling the clothes clamping device on the inner cylinder lifting rib to perform the clothes clamping action can improve the efficiency and accuracy of the clothes clamping device in clamping clothes.

[0159] S130. If the relative distance is not within the preset distance range, it means that the clothing is outside the space range that the clothing clamping device can clamp. At this time, the operating parameters of the inner drum are controlled according to the relative distance, and the relative distance between the outline of the clothing and the clothing clamping device is continuously monitored during the operation of the inner drum until the relative distance is adjusted to the preset distance range. Then, the clothing clamping device on the inner drum lifting rib is controlled to perform the clothing clamping action.

[0160] In practical use, when the relative distance is outside the preset range, the operating parameters of the inner drum can be controlled based on the relative distance. This includes, but is not limited to, controlling the inner drum to rotate forward, reverse, or alternately in both directions, as well as controlling the inner drum to perform forward and reverse rotation to create a shaking motion. During the operation of the inner drum, the relative distance between the garment outline and the garment gripping device can be continuously monitored according to a set monitoring time unit. When the relative distance is detected to be within the preset range, the garment gripping device on the inner drum's lifting ribs is then controlled to perform a garment gripping action, which can improve the efficiency and accuracy of the garment gripping device in gripping garments.

[0161] Figure 8 This is a flowchart for calculating the relative distance between the garment outline and the garment gripping device. Based on the above steps, the process of calculating the relative distance between the garment outline and the garment gripping device is further optimized and can be combined with one or more of the above optional embodiments.

[0162] In some alternative embodiments, such as Figure 8 As shown, the step of calculating the relative distance between the garment outline and the garment gripping device on the inner drum lifting rib includes:

[0163] S101. Obtain clothing gripping point information on the clothing gripping device; wherein, the clothing gripping points can be set according to the number and position of the clothing gripping devices on the lifting ribs of the inner cylinder. In the practical application of the clothing care device, the relative distance between the clothing gripping device and the outline of the clothing can be calculated through one or more clothing gripping points.

[0164] The garment gripping point information can be the coordinates of the garment gripping point. Before using the garment care device, the inner drum coordinate system, including the world coordinate system and polar coordinate system, is pre-set. The coordinates of the garment gripping point can be obtained by the current position of the garment gripping point on the garment gripping device or the rotation angle of the inner drum drive motor. The polar coordinate information can be converted to world coordinate information as needed. Taking the polar coordinate system as an example, the position of the garment gripping point on the garment gripping device relative to the inner drum drive motor in the garment care device can be pre-calibrated, and then the polar coordinates of the garment gripping point can be obtained by the rotation angle of the inner drum drive motor.

[0165] S102. Calculate the shortest distance from the clothing gripping point to the clothing outline.

[0166] In practical use, the function `cv2.pointPolygonTest()` can be used to calculate the shortest distance (i.e., the perpendicular distance) from the garment's gripping point to its outline. Specifically, the syntax of this function is:

[0167] retva l=cv2.po i ntPo l ygonTest(contour,pt,measureD i st)

[0168] In the formula, the return value is retval, which is related to the value of the parameter measureDist, contour is the outline of the clothing, pt is the gripping point of the clothing to be judged, and measureDist is a Boolean value that indicates the distance judgment method.

[0169] When measureDisk is True, it calculates the distance from the garment gripping point to the garment outline. If the garment gripping point is outside the garment outline, the return value is negative; if the garment gripping point is on the garment outline, the return value is 0; if the garment gripping point is inside the garment outline, the return value is positive.

[0170] This embodiment provides a method for hanging and caring for clothes inside a drum. It acquires depth distribution data of the space inside the drum of a clothes care device, determines the hanging result of the clothes based on this data, and performs clothes care according to the result. By achieving depth distribution data detection covering the entire drum space, the detection range for hanging clothes inside the drum is expanded, resulting in more accurate and intelligent perception of the clothes clamping and hanging results. This allows for timely intervention when clothes are not successfully hung, improving the user experience. Furthermore, depth data can be used to identify the outline of the clothes, enabling perception of the state after clamping. This allows for accurate judgment of the clamping effect and timely intervention when clothes are clumped, preventing them from being clumped and improving the efficiency of washing, drying, and care, as well as the user experience. In addition, controlling the clothes clamping device based on depth data ensures the effectiveness of the clamping action and avoids issues such as accidental triggering by the user or clothes sticking to the drum walls after spin-drying, which could negatively impact the user experience.

[0171] The present invention has a simple structural design. The depth distribution data acquisition sensor does not come into direct contact with the clothing and the environment inside the tube. The state of the clothing and the environment inside the tube will not affect the normal use of the depth distribution data acquisition sensor, thereby ensuring the accuracy of the clothing clamping result judgment. This solution is convenient for practical implementation and application.

[0172] Example 2

[0173] A garment care device is provided, employing the method described above. A detailed description of the method can be found in the corresponding descriptions of the above method embodiments, and will not be repeated here. In some embodiments, the garment care device can be applied as a washing machine; in other embodiments, it can be applied as a washer-dryer combo; and it can also be applied as a clothes dryer. Figure 9 As shown, the garment care device includes an inner cylinder 2, lifting ribs 4, a depth distribution data acquisition sensor 3, and a main control board. At least one lifting rib is provided inside the inner cylinder, and at least one garment clamping device is mounted on the lifting rib. The garment clamping device is used to clamp garments 6. The main control board supplies power to the garment clamping device through the inner cylinder power supply module. The depth distribution data acquisition sensor is communicatively connected to the main control board and is used to collect depth distribution data of the space inside the cylinder of the garment care device. The main control board determines the hanging execution result of the garment inside the cylinder based on the depth distribution data collected by the depth distribution data acquisition sensor, and performs garment care according to the hanging execution result.

[0174] The depth distribution data acquisition sensor is installed inside the garment care device, facing the interior of the drum, and its installation position is fixed. Before the depth distribution data acquisition sensor operates, its detection range needs to be calibrated. Specifically, with no clothing inside the drum, the depth distribution data acquisition sensor pre-captures a depth image of the drum's interior space. The depth data of the pixels in the acquired depth image serves as the baseline depth distribution data. During actual operation of the garment care device, the spatial position of the depth distribution data acquisition sensor can remain fixed or rotate with the inner drum, but the spatial position when acquiring depth distribution data must be consistent with the position during calibration. Based on this, the interior space of the garment care device is the interior space determined during the calibration of the depth distribution data acquisition sensor; it can be the entire inner drum space or a portion of the inner drum. This interior space can be determined based on the characteristics of the depth distribution data acquisition sensor itself, its installation position, and orientation.

[0175] In some alternative embodiments, such as Figure 9 As shown, the depth distribution data acquisition sensor 3 is fixedly installed inside the door glass 5, and the door glass 5 is installed on the door 1. Before the depth distribution data acquisition sensor operates, its detection range is calibrated to obtain the space inside the cylinder. Since the depth distribution data acquisition sensor is fixedly installed inside the door glass, its spatial position is fixed, and the space inside the cylinder is also fixed (after determining a certain acquisition direction), which ensures the validity of the data acquired by the sensor.

[0176] In some alternative embodiments, the depth distribution data acquisition sensor can also be fixedly mounted on the garment gripping device of the inner drum lifting rib. Before the depth distribution data acquisition sensor operates, its detection range is calibrated to obtain the space inside the drum. During calibration, both the garment gripping device and the sensor are in specific positions. Although the depth distribution data acquisition sensor is fixedly mounted on the garment gripping device, its spatial position is not fixed because it moves with the inner drum. To ensure the validity of the data collected by the sensor, it is necessary to control the sensor to stop at the calibrated position via a motor before collecting depth distribution data. At this time, the space inside the drum is also fixed.

[0177] The aforementioned depth distribution data acquisition sensor uses 3D vision technology to acquire complete geometric information of real three-dimensional scenes. It uses images with depth information to achieve accurate digitization of the scene, thereby realizing key machine vision functions such as high-precision recognition, positioning, reconstruction, and scene understanding.

[0178] Optionally, various 3D technologies include Time-of-Flight (ToF), binocular vision, and structured light. Binocular vision works similarly to the human eye. Its 3D principle involves using two 2D cameras to capture synchronized images from different perspectives to obtain depth information. To calculate 3D data, the relative positions (external parameters) between the two cameras must be known. Furthermore, the internal parameters of each camera, such as the optical center and focal length of the lens, are also required.

[0179] To calculate depth information, the images captured by two 2D cameras are first calibrated. Then, an adaptation algorithm is used to search for corresponding pixels in the left and right images. Finally, using the calibration values, the depth image of the scene or object can be generated as a point cloud. The optimal working distance in this process depends on the distance between the two cameras and their angles, and therefore will vary.

[0180] Unlike binocular vision technology, structured light technology requires replacing one of the cameras with a stripe light projector. The deformation of the stripes projected onto the surface can be used to calculate 3D information and obtain more accurate measurement results.

[0181] Chips with structured light capabilities can achieve high accuracy at close range. However, this generates a high computational load, requiring the acquisition and analysis of multiple images one by one. It is not suitable for moving objects, so it is only suitable for real-time applications within a limited range; otherwise, higher costs are required.

[0182] Time-of-Flight (ToF) can be used to measure distance to obtain depth data. A light source integrated into the camera emits light pulses that illuminate the object. The object reflects the light pulses back to the camera. Based on the time required for the light pulses to travel, the distance between the object and the camera is determined, thus yielding the depth value.

[0183] The 3D value of the detected object is output as a spatial image in the form of a range map or point cloud, and a 2D intensity image and confidence map are also provided for each pixel in the form of grayscale values.

[0184] When using Time-of-Flight (ToF) cameras for 3D data acquisition, they are less affected by the intensity and color of objects, and require no edge calibration, corner adjustments, or other functional settings. Therefore, image processing techniques can be used to easily separate objects from the background. This acquisition process is also suitable for moving objects, performing up to 9 million distance measurements per second with millimeter-level accuracy. Compared to other 3D cameras, ToF cameras are more economical, compact, and simple, allowing for easy installation and integration.

[0185] Preferably, this embodiment uses a ToF sensor to collect depth distribution data of the space inside the tube. The depth distribution data can be presented as a ToF image. Each pixel in the ToF image is considered a point, or a pixel point. The pixels in the ToF image record the distance information from the camera in the ToF sensor to the corresponding position in the space inside the tube. That is, each pixel point represents a specific position in the space inside the tube and its corresponding depth data. The ToF sensor transmits the collected depth distribution data of the space inside the tube to the main control unit of the garment care device so that the main control unit can determine the garment clamping result.

[0186] In practical applications of garment care devices, depth distribution data of the space inside the tube can be collected using one or more ToF sensors.

[0187] It should be noted that this embodiment does not limit the specific implementation of the clothing clamping device; all solutions that can achieve clothing clamping are included in this application. For example, the clothing clamping device for a washing machine based on light judgment results described in patent application number CN202223339070.9, entitled "A Clothing Clamping Device for a Washing Machine Based on Light Judgment Results"; and the clamping device described in patent application number CN202211607521.4, entitled "A Washing Control Method and Washing Device for Automatically Hanging Clothes," etc.

[0188] It should be noted that this embodiment does not limit the specific implementation method of the inner drum power supply module; all solutions that can achieve inner drum power supply are included in this application. For example, the invention described in patent application number CN202211607494.0, entitled "A Device for Powering the Inside of a Drum Washing Machine and a Washing Machine," and the invention described in patent application number CN2023116816837, entitled "A Device for Powering the Inside of a Drum Washing Machine and a Washing Machine," etc.

[0189] This embodiment provides a garment care device that acquires depth distribution data of the space inside the device's drum. The depth distribution data is used to determine the hanging result of the garments inside the drum, and garment care is performed based on this result. By achieving depth distribution data detection covering the entire drum space, the range of garment hanging detection is expanded, resulting in more accurate and intelligent perception of garment clamping and hanging results. This allows for timely intervention when garments are not successfully hung, improving the user experience. Furthermore, depth data can be used to identify the garment outline, enabling perception of the garment's post-clamping state. This allows for accurate judgment of the clamping effect and timely intervention when garments are clumped, preventing them from being clumped and improving washing, drying, and care efficiency, as well as the user experience. In addition, controlling the garment clamping device based on depth data ensures the effectiveness of the clamping action and prevents issues such as accidental user triggering or garments sticking to the drum walls after spin-drying, which could negatively impact the user experience.

[0190] The present invention has a simple structural design. The depth distribution data acquisition sensor does not come into direct contact with the clothing and the environment inside the tube. The state of the clothing and the environment inside the tube will not affect the normal use of the depth distribution data acquisition sensor, thereby ensuring the accuracy of the clothing clamping result judgment. This solution is convenient for practical implementation and application.

[0191] Example 3

[0192] An electronic device, such as Figure 10 As shown, it includes: a memory storing program code; and a processor connected to the memory, which, when executed by the processor, implements a method for hanging and caring for clothes inside a tub. For a detailed description of the method, please refer to the corresponding description in the above method embodiments, which will not be repeated here.

[0193] Example 4

[0194] A computer-readable storage medium, such as Figure 11 As shown, it stores program instructions, which, when executed, implement a method for hanging and caring for clothing inside a tube. For a detailed description of the method, please refer to the corresponding description in the above method embodiments; it will not be repeated here.

[0195] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0196] Although 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. For those skilled in the art, other modifications can be easily made. 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 illustrations shown and described herein.

[0197] 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.

[0198] 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 units implementing the method and structures within a hardware component.

[0199] The systems, apparatuses, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above apparatuses are described separately as various units based on their functions. 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.

[0200] 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 a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0201] 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, create a machine 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] This specification may be described in the general context of computer-executable instructions, such as program units, that are executed by a computer. Generally, program units include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification may 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 units may reside in local and remote computer storage media, including storage devices.

[0206] 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.

[0207] 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 hanging and caring for clothing inside a tube, characterized in that, Includes the following steps: Acquire depth distribution data of the space inside the garment care device's cylinder; The suspension performance of the tube-shaped garment is determined using the depth distribution data; Clothing care is performed based on the results of the suspension process.

2. The method for hanging and caring for clothing inside a tube as described in claim 1, characterized in that: The acquisition of the depth distribution data is configured to take place after the clothing gripping device has completed the clothing gripping action.

3. The method for hanging and caring for clothing inside a tube as described in claim 1, characterized in that: Before the step of determining the suspension performance result of the tube-shaped underwear using the depth distribution data, the method further includes: The depth distribution data is compared with the baseline depth distribution data to determine whether there is clothing in the clothing clamping area; If there is clothing in the clothing clamping area, the clothing clamping device on the inner cylinder lifting rib is controlled to perform a clothing clamping action. If there is no clothing in the clothing clamping area, the inner drum is controlled to operate and the depth distribution data of the inner drum space is continuously monitored and / or a prompt message is sent.

4. A method for hanging and caring for clothing inside a tube as described in any one of claims 1 to 3, characterized in that: After the garment clamping device completes the garment clamping action, the step of determining the hanging result of the tubular underwear using the depth distribution data includes: The depth distribution data is compared with the baseline depth distribution data to determine whether there is clothing in the clothing hanging area; If there are no clothes in the clothing hanging area, the clothing hanging is deemed to have failed. If there is clothing in the clothing hanging area, the clothing is considered to have been successfully hung.

5. The method for hanging and caring for clothing inside a tube as described in claim 4, characterized in that: It also includes the following steps: If the garment fails to hang, the garment clamping device is controlled to perform the garment clamping action again.

6. The method for hanging and caring for clothing inside a tube as described in claim 3, characterized in that: The step of comparing the depth distribution data with the reference depth distribution data includes: Compare the current depth data of the pixels in the cylinder with the reference depth data of the pixels; If the depth data of the pixel is less than the reference depth data, then determine whether the number of pixels whose depth data has changed is greater than the threshold. If yes, then it is determined that there are clothes inside the tube; No, then it is determined that there are no clothes inside the tube; If the depth data of the pixel is not less than the reference depth data, it is determined that there is no clothing inside the tube.

7. The method for hanging and caring for clothing inside a tube as described in claim 1, characterized in that: The depth distribution data is acquired by a data acquisition device.

8. The method for hanging and caring for clothing inside a tube as described in claim 1, characterized in that: It also includes the following steps: Obtain the current depth data of all pixels within the cylinder to obtain a depth image; Identify the target region containing clothing in the depth image; The outline of clothing is identified by using the reference depth data and current depth data of pixels in the target area.

9. A method for hanging and caring for clothing inside a tube as described in claim 8, characterized in that: The step of identifying the target region containing clothing in the depth image includes: The target region containing clothing in the depth image is identified using an object detection algorithm.

10. A method for hanging and caring for clothing inside a tube as described in claim 8, characterized in that: After the clothing is successfully retrieved, the following steps are also included: Calculate the shape matching degree between the identified clothing outline and the preset clothing hanging outline; Compare the calculated shape matching degree with the preset difference value; If the shape matching degree is not greater than the preset difference value, the current posture of the clothing is the open and clamped state; If the shape matching degree is greater than the preset difference value, the current posture of the clothing is a clumped clamping state. After the clothing clamping device on the inner cylinder lifting rib releases the clothing, it will clamp the clothing again.

11. The method for hanging and caring for clothing inside a tube as described in claim 10, characterized in that: The step of calculating the shape matching degree between the identified clothing outline and the preset clothing hanging outline includes: The shape matching degree of the garment outline and the garment hanging outline is detected by calculating Hu moment matching.

12. The method for hanging and caring for clothing inside a tube as described in claim 8, characterized in that: It also includes the following steps: Calculate the relative distance between the garment outline and the garment gripping device on the inner cylinder lifting rib; Compare the calculated relative distance with the preset distance range; If the relative distance is within a preset distance range, the clothing clamping device is controlled to perform a clothing clamping action; If the relative distance is not within the preset distance range, the operating parameters of the inner drum are controlled according to the relative distance, and the relative distance between the outline of the garment and the garment clamping device is continuously monitored during the operation of the inner drum until the relative distance is adjusted to the preset distance range.

13. The method for hanging and caring for clothing inside a tube as described in claim 12, characterized in that: The step of calculating the relative distance between the garment outline and the garment gripping device on the inner drum lifting ribs includes: Obtain the clothing gripping point information on the clothing gripping device; Calculate the shortest distance from the garment gripping point to the garment outline.

14. A garment care device, characterized in that: The method described in any one of claims 1 to 13 is applied.

15. An electronic device, characterized in that, include: A memory that stores program code; A processor connected to the memory, which, when the program code is executed by the processor, implements the method as described in any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that, It stores program instructions that, when executed, implement the method as described in any one of claims 1 to 13.

Images