Self-adaptive control method and system for movable intelligent clothes hanger

Abstract

The invention relates to the field of intelligent clothes hanger control, in particular to a self-adaptive control method and system for a movable intelligent clothes hanger, and the method comprises the steps: collecting an image containing a user and a clothes pole, recognizing skeleton points of the user through OpenPose, and screening the skeleton points to obtain skeleton key points which comprise a head top point, a shoulder point, a knee point and a foot point; using a YOLO target detection algorithm to identify the position and the shape of the clothes airing rod; establishing a three-dimensional coordinate system; taking the absolute difference value of the coordinate values of the head top point and the foot point on the Z axis in the three-dimensional coordinate system as the height of the user; taking the absolute difference value of the coordinate values of the shoulder point and any point on the clothes airing rod on the X axis as the horizontal distance of the shoulder point; calculating the elevation angle of the user; inputting the height of the user, the horizontal distance of the shoulder point and the elevation angle into a preset matching model to obtain the dynamic optimal height of the clothes airing rod, and adjusting the real-time height of the clothes airing rod according to the dynamic optimal height. The clothes airing device has the effect of improving the clothes airing comfort.

Description

Technical Field

[0001] The present application relates to the field of intelligent clothes drying rack control, and in particular to an adaptive control method and system for a movable intelligent clothes drying rack. Background Art

[0002] The smart clothes rack is a modern household appliance that integrates multiple functions. It not only solves the problems of traditional clothes racks with single functions and inconvenient operation, but also improves the efficiency and experience of drying clothes.

[0003] The intelligent lifting function is one of the core functions of the smart clothes drying rack. It uses a motor to realize the automatic lifting of the clothes drying rod on the clothes drying rack, making it more convenient and easy for users to dry and collect clothes. Users can use smart devices such as remote controls, smartphone apps or voice control to achieve one-button lifting and lowering operations.

[0004] In the prior art, smart clothes drying racks usually allow users to freely adjust the height of the clothes drying rack according to their personal height and clothes drying needs. For example, the application document with publication number CN118584846A discloses a control method, device, equipment and medium for clothes drying racks, which includes: obtaining the user's height according to the clothes drying instruction issued by the user, the clothes drying instruction is used to instruct the user to perform clothes drying operations on the clothes drying rack and to obtain the user's height; obtaining clothes drying scene information, the clothes drying scene information includes at least one length of clothes to be dried; according to the user's height and clothes drying scene information, obtaining the target descent height of the clothes drying rack, and controlling the clothes drying rack to perform the descent action according to the target descent height. This method provides convenience in scenarios where different family members have different heights, and intelligent lifting improves the user experience. At the same time, the descent height takes into account the clothes length information and avoids the problem of the longest clothes touching the ground when drying and taking clothes.

[0005] The comfort level of drying clothes is not only related to the user's height, but also to the distance between the user and the clothes drying rack and the degree to which the user's arms are raised. How to adaptively adjust the clothes drying rod according to the user's height and standing position has become a problem that needs to be solved. Summary of the invention

[0006] In order to solve the problem of how to adaptively adjust the clothes drying rod according to the user's height and the user's standing position, the present application provides an adaptive control method and system for a movable intelligent clothes drying rack.

[0007] In a first aspect, the present application provides an adaptive control method for a movable intelligent clothes drying rack, which adopts the following technical solution: A method for adaptively controlling a movable intelligent clothes drying rack comprises the following steps: collecting an image including a user and a clothes drying rack, using OpenPose to identify the skeleton points of the user, screening the skeleton points to obtain skeleton key points, wherein the skeleton key points include the head vertex, shoulder points, knee points and foot points; using a YOLO target detection algorithm to identify the position and shape of the clothes drying rack; establishing a three-dimensional coordinate system, wherein the three-dimensional coordinate system includes an X-axis, a Y-axis and a Z-axis; taking the absolute difference between the coordinate values ​​of the head vertex and the foot points on the Z-axis in the three-dimensional coordinate system as the height of the user; taking the absolute difference between the coordinate values ​​of the shoulder point and any point on the clothes drying rack on the X-axis as the horizontal distance of the shoulder point; and calculating the elevation angle of the user, wherein the calculation formula of the elevation angle is: , Indicates the elevation angle from the user's head to the clothesline. Indicates the coordinate value of the clothes drying rod on the Z axis. Indicates the coordinate value of the foot point on the Z axis. Indicates the horizontal distance of the shoulder point, is an inverse tangent function; the user's height, the horizontal distance of the shoulder point and the elevation angle are input into the preset matching model to obtain the dynamic optimal height of the clothesline, and the real-time height of the clothesline is adjusted according to the dynamic optimal height.

[0008] The beneficial effects are: taller users may need a higher clothesline, while shorter users may need a lower clothesline. The horizontal distance of the shoulder point indicates the horizontal distance between the user's standing position and the clothesline. The elevation angle reflects the degree to which the user needs to raise or lower his head. If the elevation angle is large, it means that the user needs to raise his head more, which may not be comfortable when hanging clothes. The real-time user height, shoulder point distance and elevation angle are input into the matching model, and the optimal height of the clothesline is matched according to the matching model and the height of the clothesline is dynamically controlled to adjust the height of the clothesline according to the user's height and the user's standing position, so as to ensure that the user can easily hang clothes without bending or overstretching, thereby improving the user experience.

[0009] Optionally, the shoulder point includes a left shoulder point and a right shoulder point. Before using the clothes drying rack, the user sets the habitual mode to left hand or right hand. When the habitual mode is left hand, the left shoulder point is used as the shoulder point, otherwise the right shoulder point is used as the shoulder point.

[0010] The beneficial effect is that it is more in line with the actual behavior habits of users. When hanging clothes, users tend to use their dominant hand. By selecting the shoulder point corresponding to the user's dominant hand, the position of the clothes drying rod can be more in line with the user's comfort range, further improving the user's experience.

[0011] Optionally, constructing a matching model includes the steps of: constructing a data set, the data set includes multiple sets of data and labels, the multiple sets of data include: height, elevation angle and horizontal distance of shoulder points, and the label is the optimal height of the clothesline; inputting the data set into a preset neural network model, training the neural network model until the training is completed when the number of training reaches a preset number or the loss function is less than a preset value, and obtaining a matching model.

[0012] Optionally, cameras are arranged around the clothes drying rack, and the positions of the cameras are used as the origins of the three-dimensional coordinate system.

[0013] Optionally, the distance from the shoulder point to the knee point is calculated, and images at moments when the distance is less than a preset distance threshold are deleted.

[0014] The beneficial effect is that in the scene of hanging clothes, users are more likely to squat to take clothes from a container (such as a basin, etc.). At this time, the user's height, the horizontal distance of the shoulder point, and the elevation angle are not of reference value, and the image corresponding to the squatting moment is deleted. Specifically, when the user is standing and walking, the distance between the knee point and the shoulder point is large, and when the user squats, the distance between the knee point and the shoulder point is close, so whether the user squats or squats similarly is determined based on the distance between the knee point and the shoulder point.

[0015] Optionally, the Euclidean distances of the shoulder point and the knee point on the same side of the human body are calculated respectively, and the minimum value of the Euclidean distance is taken as the distance from the shoulder point to the knee point.

[0016] The beneficial effect is that when the user leans sideways to take clothes, the distance between the shoulder point and the knee point on one side is relatively small, while the distance between the shoulder point and the knee point on the other side is relatively large. By calculating the Euclidean distance on both sides and taking the minimum value, it is possible to more accurately identify whether the user is taking the action of leaning sideways to take clothes.

[0017] Optionally, the Euclidean distance between the knee point and the shoulder point on either side of the human body is the distance from the shoulder point to the knee point.

[0018] Optionally, in response to receiving an instruction to lower the clothesline, the image is captured and the clothesline is lowered, and in response to receiving an instruction to stop lowering, the image capture and the lowering of the clothesline are stopped.

[0019] The beneficial effect is: in order to avoid the clothes drying rod from being adjusted incorrectly when the user passes by the clothes drying rod but does not dry clothes, the setting is to collect images and lower the clothes drying rod after receiving the instruction to lower the clothes drying rod.

[0020] In the second aspect, the present application provides an adaptive control system for a movable intelligent clothes drying rack, which adopts the following technical solutions: An adaptive control system for a movable intelligent clothes drying rack comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the adaptive control method for the movable intelligent clothes drying rack is implemented.

[0021] The beneficial effect is: the adaptive control method of the movable intelligent clothes drying rack is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0022] This application has the following technical effects: 1. Use OpenPose to identify the user's skeleton points, use the YOLO target detection algorithm to identify the position and shape of the clothesline, and determine the user's height, horizontal distance and elevation angle of the shoulder points based on the skeleton points. The horizontal distance and elevation angle of the shoulder points can reflect the user's standing position. According to the matching model, the optimal height of the clothesline is matched and the height of the clothesline is dynamically controlled to adjust the height of the clothesline according to the user's height and standing position, so as to ensure that the user can easily hang clothes without bending or overstretching, thereby improving the user experience.

[0023] 2. In the scene of hanging clothes, users are more likely to squat to take clothes from containers (such as basins, etc.). At this time, the user's height, horizontal distance of the shoulder point, and elevation angle are not of reference value. The image at the corresponding moment of squatting is deleted. The distance between the knee point and the shoulder point is used to determine whether the user is squatting or squatting-like behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers represent the same or corresponding parts.

[0025] Figure 1 This is a method flow chart of an adaptive control method of a movable intelligent clothes drying rack according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by technical users in this field without creative work are within the scope of protection of this application.

[0027] It should be understood that when the terms "first", "second", etc. are used in the claims, specification and drawings of the present application, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the specification and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections.

[0028] The present application embodiment discloses an adaptive control method for a movable intelligent clothes drying rack, referring to Figure 1 , including steps S1 to S6, which are specifically as follows: S1: Collect images containing the user and the clothesline, use OpenPose to identify the user's skeleton points, filter the skeleton points to obtain bone key points, which include the head vertex, shoulder points, knee points and foot points; use the YOLO target detection algorithm to identify the position and shape of the clothesline.

[0029] In one embodiment, a camera is arranged around the clothes drying rack, and a depth camera is selected as the camera. The camera is arranged on the ceiling where the clothes drying rack is installed, and the camera can shoot the user and the clothes drying rod from above. The camera collects real-time video of the user and the clothes drying rod, and extracts frames from the real-time video to obtain an image containing the user and the clothes drying rod. The present application uses a fixed time interval for extracting frames, and for example, one frame is extracted every second.

[0030] OpenPose is an open source library for human key point detection and full body posture estimation. It uses a deep learning model for real-time human key point detection. From the detected human key points, the head vertex, shoulder points, knee points and foot points are filtered out. These points have corresponding indexes in the output of OpenPose. For example, the head vertex is index 0, the shoulder points are indexes 2 and 3, the knee points are indexes 8 and 9, and the foot points are indexes 10 and 11. Using OpenPose to identify the user's skeletal points and filter key points are both existing technologies and will not be elaborated here.

[0031] The YOLO (You Only Look Once) target detection algorithm is a real-time target detection system that can identify and locate objects in an image. The YOLO target detection algorithm is an existing technology and will not be described in detail here.

[0032] S2: Establish a three-dimensional coordinate system, which includes an X-axis, a Y-axis, and a Z-axis.

[0033] In one embodiment, a three-dimensional coordinate system is established with the position of the camera as the coordinate origin, the extension direction of the Y axis is parallel to the length direction of the clothes drying rod, the plane where the X axis and Y axis are located is parallel to the ground, and the plane where the X axis and Z axis are located is perpendicular to the ground.

[0034] S3: In the three-dimensional coordinate system, the absolute difference between the coordinate values ​​of the head vertex and the foot vertex on the Z axis is used as the user's height.

[0035] Using the skeleton points identified by OpenPose, the 2D coordinates of the head vertex and foot points are converted into 3D coordinates. The key skeleton points of the user identified by OpenPose include the head vertex, shoulder points, knee points and foot points. The positions of these points in the 2D image can be obtained through the output of OpenPose, that is, each point has its corresponding x-coordinate and y-coordinate. The depth information (z-coordinate) of each key point can be obtained through the depth camera.

[0036] The absolute difference refers to the absolute value of the difference. The absolute difference between the coordinates of the head vertex and the foot vertex on the Z axis is taken as the user's height. Taller users need a higher clothesline, while shorter users need a lower clothesline.

[0037] S4: The absolute difference between the coordinate values ​​of the shoulder point and any point on the clothesline on the X-axis is taken as the horizontal distance of the shoulder point.

[0038] From the output of the YOLO algorithm, determine multiple key points on the clothesline, which can be the end points of the clothesline or any other feature points. Convert the 2D coordinates of the clothesline points into points in the 3D coordinate system. Since the plane where the Y-axis and X-axis are located is parallel to the ground, the coordinate value of the X-axis can be directly used to calculate the horizontal distance.

[0039] In one embodiment, there are multiple key points on the clothes drying rod, the horizontal distance from the shoulder point to each key point is calculated, and the minimum distance is selected as the final horizontal distance of the shoulder point.

[0040] The horizontal distance from the shoulder point indicates the horizontal distance between the user's standing position and the clothesline. If the user stands closer to the clothesline, this distance will be smaller; if the user stands farther away, the distance will be larger. According to the horizontal distance between the user and the clothesline, adjust the height of the clothesline to ensure that the user can easily hang the clothes without bending or overstretching.

[0041] S5: Calculate the elevation angle of the user.

[0042] The formula for calculating the elevation angle is: , Indicates the elevation angle from the user's head to the clothesline. Indicates the coordinate value of the clothes drying rod on the Z axis. Indicates the coordinate value of the foot point on the Z axis. Indicates the horizontal distance from the shoulder point. Represents the inverse tangent function.

[0043] Since the coordinate values ​​of the key points on the clothesline are the same or approximately the same on the Z axis, any point on the clothesline is used as the key point of the clothesline to calculate the elevation angle from the top of the head to the clothesline.

[0044] The elevation angle is the angle from the top of the user's head to the clothes-drying rod, which reflects the degree to which the user needs to raise or lower his head. If the elevation angle is large, it means that the user needs to raise his head more, which may not be comfortable when hanging clothes.

[0045] S6: Calculate the distance from the shoulder point to the knee point, and delete the images at the moment when the distance is less than a preset distance threshold.

[0046] In the scene of hanging clothes, it is easy for users to squat and take clothes from containers (such as basins, etc.). At this time, the user's height, horizontal distance of the shoulder point and elevation angle are not of reference value. If people squat to hang clothes, the probability of clothes contacting the ground is high. Therefore, this application does not consider the scene of squatting to hang clothes. In order to avoid the influence of squatting to take clothes on the height control of the clothesline, the image corresponding to the squatting moment is deleted.

[0047] In one embodiment, the Euclidean distances of the shoulder point and the knee point on the same side of the human body are calculated respectively, and the minimum value of the Euclidean distance is taken as the distance from the shoulder point to the knee point.

[0048] In one embodiment, the Euclidean distance between the knee point and the shoulder point on either side of the human body is calculated as the distance from the shoulder point to the knee point.

[0049] When the user is standing or walking, the distance between the knee point and the shoulder point is larger. When the user is squatting, the distance between the knee point and the shoulder point is closer. Therefore, whether the user is squatting or performing squatting-like behavior is determined based on the distance between the knee point and the shoulder point.

[0050] The preset threshold may be 0.5 m, and the preset threshold may be determined through experiments and adjustments.

[0051] S6: Input the user's height, the horizontal distance of the shoulder point and the elevation angle into a preset matching model to obtain the dynamic optimal height of the clothes drying rod, and adjust the real-time height of the clothes drying rod according to the dynamic optimal height.

[0052] The construction of the matching model includes the following steps: constructing a data set, the data set includes multiple sets of data and labels, the multiple sets of data include: height, elevation angle and horizontal distance of shoulder points, and the label is the optimal height of the clothesline.

[0053] The data set is put into a preset neural network model, and the neural network model is trained until the training times reach the preset times or the loss function is less than the preset value, and the training is completed to obtain a matching model. Exemplarily, the preset times are 1000 times and the preset value is 0.01.

[0054] In one embodiment, the neural network model may be a CNN (Convolutional Neural Networks) model, and the loss function may be a mean square error loss function. The CNN model, the mean square error loss function, and the training of the model are all prior arts and will not be described in detail here.

[0055] The height, elevation angle and horizontal distance of the shoulder point can be a specific value or a range. For example, the height range is 1.7m-1.73m, the elevation angle is 20°-25°, and the horizontal distance of the shoulder point is 30cm-35cm. The interval range of the above data includes the endpoint values.

[0056] In one embodiment, the shoulder point includes a left shoulder point and a right shoulder point. In order to further improve the user experience comfort, before using the clothes rack, the user sets the habitual mode to left hand or right hand. When the habitual mode is left hand, the left shoulder point is used as the shoulder point, otherwise the right shoulder point is used as the shoulder point.

[0057] In one embodiment, a real-time height detection method for a clothesline during its descent can be achieved by setting a distance detection sensor on the clothesline to detect the distance from the clothesline to the ceiling, and obtaining the height of the clothesline from the ground by the difference between the distance from the ceiling to the floor and the distance from the clothesline to the ceiling.

[0058] In response to receiving an instruction to lower the clothes-drying rod, the image is captured and the clothes-drying rod is lowered. In response to receiving an instruction to stop lowering, the image capture and the lowering action of the clothes-drying rod are stopped.

[0059] In order to avoid the clothes drying rod from being adjusted incorrectly when the user passes by the clothes drying rod but does not hang clothes, the setting is to collect images and lower the clothes drying rod after receiving the instruction to lower the clothes drying rod.

[0060] In one embodiment, the command to lower the clothes drying rod can be a voice command, such as receiving a voice command of "lower the clothes drying rod" from the user, the camera starts to collect images, and the clothes drying rod starts to lower. The user can also press or click the lowering button by controlling the remote control of the clothes drying rod or the mobile phone APP, so that the clothes drying rack starts to lower and the camera starts to collect images.

[0061] During the descent process, the optimal height corresponding to each moment is obtained. For example, the optimal height of the clothesline corresponding to the height and standing position of the user in the previous second is 1m. The user moves one end of the clothesline, and the optimal height in the next second is 1.2m. It should be noted that the goal of the clothesline in the previous second is to drop to a position 1m above the ground. The descent process of the clothesline can be regarded as a uniform descent. It is possible that when it has not dropped to a position 1m above the ground in the previous second, the goal of the next second is to drop to a position 1.2m above the ground, and the goal is changed to drop to a position 1.2m above the ground. If a user of the same height stands closer to the clothesline, the height of the clothesline is set slightly higher. Otherwise, the height of the clothesline is set slightly lower to improve the user's comfort in drying clothes.

[0062] In one embodiment, during any process of descent, if the user issues a voice command of "stop descent" or operates the stop button on the remote control, the clothes drying rack stops descending and the camera stops capturing. When the clothes are finished, the user can issue a voice command of "raise the clothes drying rack" or operate the up button on the remote control, and the clothes drying rack rises to the highest position and the camera stops capturing.

[0063] An embodiment of the present application also discloses an adaptive control system for a movable intelligent clothes rack, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an adaptive control method for the movable intelligent clothes rack according to the present application is implemented.

[0064] The above system also includes other components such as a communication bus and a communication interface that are well known to technical users in the field. Their settings and functions are known in the art and will not be described in detail here.

[0065] In the present application, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0066] Although this specification has shown and described a plurality of embodiments of the present application, it is obvious to a user skilled in the art that such embodiments are provided by way of example only. A user skilled in the art will think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein may be adopted.

[0067] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An adaptive control method for a movable intelligent clothes drying rack, characterized in that: Includes steps: Collect images containing the user and the clothesline, use OpenPose to identify the user's skeleton points, filter the skeleton points to obtain the bone key points, which include the head vertex, shoulder points, knee points and foot points; use the YOLO target detection algorithm to identify the position and shape of the clothesline; Establish a three-dimensional coordinate system, which includes an X-axis, a Y-axis, and a Z-axis; In the three-dimensional coordinate system, the absolute difference between the coordinate values ​​of the head vertex and the foot vertex on the Z axis is taken as the user's height; The absolute difference between the coordinates of the shoulder point and any point on the clothesline on the X-axis is taken as the horizontal distance of the shoulder point; Calculate the user's elevation angle. The calculation formula for the elevation angle is: , Indicates the elevation angle from the user's head to the clothesline. Indicates the coordinate value of the clothes drying rod on the Z axis. Indicates the coordinate value of the foot point on the Z axis. Indicates the horizontal distance of the shoulder point, is the inverse tangent function; The user's height, horizontal distance from the shoulder point and elevation angle are input into the preset matching model to obtain the dynamic optimal height of the clothesline, and the real-time height of the clothesline is adjusted according to the dynamic optimal height.

2. The adaptive control method of the movable intelligent clothes drying rack according to claim 1, characterized in that: The shoulder points include a left shoulder point and a right shoulder point. Before using the clothes drying rack, the user sets the preferred mode to be left-handed or right-handed. When the preferred mode is left-handed, the left shoulder point is used as the shoulder point, otherwise the right shoulder point is used as the shoulder point.

3. The adaptive control method of the movable intelligent clothes drying rack according to claim 1, characterized in that: The construction of the matching model includes the following steps: constructing a data set, the data set includes multiple sets of data and labels, the multiple sets of data include: height, elevation angle and horizontal distance of shoulder points, and the label is the optimal height of the clothesline; putting the data set into a preset neural network model, training the neural network model until the training is completed when the number of training times reaches the preset number or the loss function is less than the preset value, and obtaining a matching model.

4. The adaptive control method of the movable intelligent clothes drying rack according to claim 1, characterized in that: Cameras are set around the clothes drying rack, and the positions of the cameras are used as the origins of the three-dimensional coordinate system.

5. The adaptive control method of the movable intelligent clothes drying rack according to claim 1, characterized in that: Calculate the distance from the shoulder point to the knee point, and delete the images at the moment when the distance is less than the preset distance threshold.

6. The adaptive control method of the movable intelligent clothes drying rack according to claim 5, characterized in that: Calculate the Euclidean distances of the shoulder point and the knee point on the same side of the human body respectively, and take the minimum value of the Euclidean distance as the distance from the shoulder point to the knee point.

7. The adaptive control method of the movable intelligent clothes drying rack according to claim 5, characterized in that: The European distance between the knee point and the shoulder point on either side of the human body is the distance from the shoulder point to the knee point.

8. The adaptive control method of a movable intelligent clothes drying rack according to any one of claims 1 to 7, characterized in that: In response to receiving an instruction to lower the clothes-drying rod, the image is captured and the clothes-drying rod is lowered. In response to receiving an instruction to stop lowering, the image capture and the lowering action of the clothes-drying rod are stopped.

9. An adaptive control system for a movable intelligent clothes drying rack, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the adaptive control method of the movable intelligent clothes drying rack according to any one of claims 1 to 8 is implemented.

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