Intelligent clothes hanger automatic control method and system based on machine vision

Abstract

The invention relates to the field of control of clothes hangers, in particular to an intelligent clothes hanger automatic control method and system based on machine vision, and the method comprises the steps: in the airing process, controlling a hanger body of a clothes hanger to rotate so as to adjust the airing position and angle of hung clothes; the position, corresponding to the maximum value of the overall airing efficiency, of the clothes hanger is taken as the optimal position, and the hanger body is controlled to rotate to the optimal position. The clothes airing device has the effect of automatically adjusting the position of the clothes according to the clothes airing condition.

Description

Technical Field

[0001] The present application relates to the field of control of clothes drying racks, and in particular to an automatic control method and system for intelligent clothes drying racks based on machine vision. 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] Users can use remote controllers, smartphone apps, or voice control and other smart devices to achieve functions such as lifting and lowering the clothes drying rack. For example, the patent application document with patent publication number CN118584846A discloses a control method, device, equipment, and medium for a clothes drying rack. The method 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; obtaining the target descent height of the clothes drying rack according to the user's height and the clothes drying scene information, and controlling the clothes drying rack to perform a descent action according to the target descent height.

[0004] The existing technology can also control the movement position of the clothes drying rack, such as the patent document with the authorization announcement number CN110297434B discloses a clothes drying rack control method, device and storage medium. After receiving the message notification that the clothes have been washed, the clothes drying rack moves to a position close to the clothes. When the clothes drying rack receives a return signal, the clothes drying rack moves to the initial position.

[0005] When users use smart clothes drying racks to dry clothes, the clothes dry at different speeds when hung in different positions, and the drying position on the smart clothes drying rack needs to be manually adjusted according to the drying conditions. Therefore, how to automatically adjust the position of the clothes according to the drying conditions has become an urgent problem to be solved. Summary of the invention

[0006] In order to solve the technical problem of how to automatically adjust the position of clothes according to the clothes drying situation, the present application provides an automatic control method and system of an intelligent clothes drying rack based on machine vision.

[0007] In the first aspect, the present application provides an automatic control method of an intelligent clothes drying rack based on machine vision, which adopts the following technical solution: The method for automatically controlling an intelligent clothes drying rack based on machine vision comprises the following steps: during the drying process, controlling the rotation of the clothes drying rack to adjust the drying position and angle of the hung clothes; wherein the clothes drying rack comprises a rack and petals circumferentially distributed on the rack, the rack can rotate around its own axis, and the petals are rotatably connected to the rack to adjust the opening angle of the petals; The method for controlling the rotation of the clothes drying rack is as follows: in response to an initial drying state continuing for a preset cycle, the drying efficiency of the clothes hung under each petal within the cycle is calculated, and the drying efficiency is the ratio of the change value of the clothes weight within the cycle to the cycle; the area of ​​the vertical projection of the petals is taken as a position, and any petal is taken as the target petal, and the target petal is moved from the initial position to any other position, and the cumulative sum of the drying efficiency differences of each petal at different positions is taken as the overall drying efficiency; the target petal traverses all positions, and the position of the target petal corresponding to the maximum value of the overall drying efficiency is taken as the optimal position, and the rack body is controlled to rotate so that the target petal moves to the optimal position.

[0008] The beneficial effect is that after the clothes are hung to dry for a period of time, the clothes drying rack is rotated to move the clothes with lower drying efficiency to the position with higher drying efficiency, so as to achieve the global optimal solution. The overall drying efficiency is obtained by comparing the drying efficiency difference of each petal at different positions, that is, the drying efficiency difference of all petals at different positions is accumulated to obtain an overall drying efficiency index. It ensures that the clothes can be dried evenly and efficiently during the drying process, especially when the environmental factors such as light and wind speed change, it can automatically adjust to adapt to the best drying conditions.

[0009] Optionally, in response to an instruction to stop rotating the frame, the petals are adjusted from a minimum opening angle to a maximum opening angle; in this process, the light intensity of the clothes hanging under the petals is calculated, and the opening angle of the petals corresponding to the maximum light intensity is taken as the optimal opening angle, and the opening angle of the petals is adjusted to the optimal opening angle.

[0010] The beneficial effect is that in order to optimize the drying scheme on each petal, the light intensity of the clothes hung on the petals is maximized by adjusting the opening angle of the petals. By optimizing the opening angle of the petals, it can be ensured that the clothes receive more uniform and sufficient sunlight during the drying process, thereby accelerating the drying speed of the clothes and improving the drying efficiency.

[0011] Optionally, the light intensity is calculated as: , where For the The light intensity of hanging clothes on each petal; For the The total drying area for drying clothes under the petals; express The gray value of the pixel.

[0012] The beneficial effect is that the grayscale value can be regarded as a quantitative representation of the light intensity received by the pixel. By adding up the grayscale values ​​of all pixels in the clothing image, the total light intensity received by the entire clothing can be obtained. This sum is the accumulation of the light intensity of all parts of the clothing. The introduction of mean calculation is to ensure that clothing of different sizes is consistent when comparing light intensity, because clothing with a larger area naturally receives more total light, but the average light intensity may be lower.

[0013] Optionally, the method for calculating the total drying area includes the steps of: inputting the captured image of the clothing into a trained target detection model, the input of the target detection model is the captured image, and the output is the range of the clothing in the image; taking the total number of pixels within the range of the clothing as the total drying area.

[0014] The beneficial effect is that the target detection model is used to identify the boundaries of clothes, so that the actual drying area of ​​clothes can be measured more accurately.

[0015] Optionally, the training method of the target detection model is: annotate the clothing image set, determine the main area of ​​the clothing, and use the minimum outer rectangle of the main area of ​​the clothing as a candidate box, and label the candidate box with a category; use the annotated data to train a preset neural network model until the target detection model is obtained after the training is completed.

[0016] Optionally, an initial height of the clothes rack is set, and in response to a clothes rack lowering instruction, an image of the user is obtained, the user's identity is identified based on the user image to obtain height information, an optimal clothes drying height is calculated based on the height information, and the clothes rack is controlled to descend to the optimal clothes drying height; in response to a clothes rack raising instruction, the clothes rack is controlled to rise to the initial height.

[0017] Optionally, the optimal clothes drying height is calculated as: , where For users Optimal height for drying clothes; is the initial height; For users height; Adjustment parameters for suspension height.

[0018] The beneficial effect is that the clothes drying rack automatically adjusts to a preset optimal height according to the recognized height information, ensuring that the user can use it comfortably. If the user is short, the clothes drying rack may be lowered to a lower position so that the user can easily hang and take clothes.

[0019] In the second aspect, the present application provides an intelligent clothes drying rack automatic control system based on machine vision, which adopts the following technical solutions: A machine vision-based intelligent clothes drying rack automatic control system 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 machine vision-based intelligent clothes drying rack automatic control method described above is implemented.

[0020] The beneficial effect is: the above-mentioned machine vision-based intelligent clothes drying rack automatic control method 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.

[0021] This application has the following technical effects: 1. After the clothes have been hung to dry for a period of time, turn the clothes rack to move the clothes with low drying efficiency to the position with high drying efficiency, so as to achieve the best overall solution. The overall drying efficiency is obtained by comparing the difference in drying efficiency of each petal at different positions, that is, the difference in drying efficiency of all petals at different positions is accumulated to obtain an overall drying efficiency index. Ensure that the clothes can be dried evenly and efficiently during the drying process, especially when environmental factors such as light and wind speed change, and can automatically adjust to adapt to the best drying conditions.

[0022] 2. In order to optimize the drying plan on each petal, the light intensity of the clothes hanging on the petals is maximized by adjusting the opening angle of the petals. By optimizing the opening angle of the petals, it can ensure that the clothes receive more uniform and sufficient sunlight during the drying process, thereby speeding up the drying speed of the clothes and improving the drying efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 This is a method flow chart of the automatic control method of the intelligent clothes drying rack based on machine vision in an embodiment of the present application.

[0025] Figure 2 It is a schematic diagram of the structure of the clothes drying rack in the automatic control method of the intelligent clothes drying rack based on machine vision in an embodiment of the present application.

[0026] Figure 3 This is a flow chart of a method for controlling the rotation of a clothes drying rack according to an automatic control method of an intelligent clothes drying rack based on machine vision in an embodiment of the present application.

[0027] Figure numerals: 1. clothes drying part; 2. storage slot; 3. hook; 4. second motor. DETAILED DESCRIPTION

[0028] 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 those skilled in the art without creative work are within the scope of protection of the present application.

[0029] 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.

[0030] The present application embodiment discloses an automatic control method of an intelligent clothes drying rack based on machine vision, such as Figure 1 As shown, the method includes steps S1 to S3: S1: Set the initial height of the clothes rack, respond to the clothes rack lowering instruction, obtain a user image, identify the user identity based on the user image to obtain height information, calculate the optimal clothes drying height based on the height information, and control the clothes rack to descend to the optimal clothes drying height; respond to the clothes rack raising instruction, control the clothes rack to rise to the initial height.

[0031] In one embodiment, a lifting assembly is provided on the clothes drying rack, and the lifting assembly can be a telescopic frame or a hanging rope on the clothes drying rack in the prior art to control the lifting of the clothes drying rack. The clothes drying rack can receive voice commands or remote control commands of a remote controller to lift and lower.

[0032] Reference Figure 2 The clothes drying rack includes a disc-shaped frame and multiple clothes drying components 1. The multiple clothes drying components 1 are distributed along the circumference of the frame and can be unfolded in the shape of petals. The clothes drying components 1 can be a plate-like structure or a rod-like structure. For the convenience of description, the clothes drying components 1 are hereinafter referred to as petals. One end of the petals is rotatably connected to the frame through a hinge shaft. A storage slot 2 is opened on the frame. A first motor is fixedly placed in the storage slot 2. The output shaft of the first motor is coaxially fixed with the hinge shaft. The first motor can drive the petals to rotate to adjust the opening angle of the petals.

[0033] A plurality of hooks 3 are suspended below the petals, the hooks 3 are hinged to the petals, and the plurality of hooks 3 are spaced and evenly arranged along the length direction of the petals. A shell is fixedly arranged on the disc, and a second motor 4 is arranged on the upper surface of the shell, and an output shaft of the second motor 4 is fixedly connected to the frame, and the output shaft of the second motor 4 is coaxial with the output shaft of the frame. The lifting component is connected to the second motor 4. Exemplarily, in the case where the lifting component is a hanging rope, a hanging ring can be welded on the casing of the second motor 4 to connect the hanging rope. In the case where the lifting component is a telescopic frame, one end of the telescopic frame that moves and extends is welded to the casing of the second motor 4.

[0034] In order to identify the height information of a person, the user needs to manually set the initial height and enter the face. When the user uses the clothes drying rack for the first time, he needs to manually enter his height information and enter the face data. This can be done through an application or control panel connected to the clothes drying rack. A camera is set on the clothes drying rack. When the user issues a descending command (possibly through a voice command, a remote control, or an application button), the clothes drying rack starts to descend, shoots a video of the user, identifies the user's identity through face recognition, and retrieves the height information associated with the user from the database.

[0035] The formula for calculating the optimal height of clothes drying is: , where For users Optimal height for drying clothes; is the initial height; For users height; is the suspension height adjustment parameter. For example, , which can be adjusted by the user according to the actual application scenario.

[0036] The clothes drying rack automatically adjusts to the preset optimal height based on the recognized height information to ensure that the user can use it comfortably. If the user is shorter, the clothes drying rack may be lowered to a lower position so that the user can hang and take clothes easily.

[0037] In other embodiments, the lifting assembly may not be provided, and the second motor 4 of the clothes drying rack may be fixed on the storage rack, and the storage rack may be placed on the ground.

[0038] S2: During the drying process, the frame of the clothes drying rack is controlled to rotate to adjust the drying position and angle of the hanging clothes.

[0039] Reference Figure 3 The method for controlling the rotation of the clothes drying rack includes steps S20 to S22, which are specifically as follows: S20: In response to the initial drying state continuing for a preset period, the drying efficiency of the clothes hung under each petal within the period is calculated, where the drying efficiency is the ratio of the change in the weight of the clothes within the period to the period.

[0040] For example, after the clothes are hung, the user can issue a command to indicate that the clothes are hung by voice command or by using a button on the remote control. At this time, the first cycle starts to be calculated. One cycle can be 1 hour. 1 hour is a hyperparameter that can be adjusted by the user according to the actual application scenario. The clothes drying state of the first cycle is the initial drying state.

[0041] A gravity sensor is installed on each petal. During the drying process of clothes, the weight change of clothes is collected by the gravity sensor. After a cycle, the drying efficiency of the clothes hung under each petal in the cycle is calculated. The calculation formula of the drying efficiency is: , where For the Drying clothes under the petals in the cycle The rate of change of the weight inside, the greater the rate of change, the more conducive the location of the petal is to drying clothes; For the The initial weight of the clothes drying under the petals; For the cycle After The weight of laundry drying under the petals.

[0042] S21: Taking the area of ​​the vertical projection of the petals as a position and any petal as the target petal, the target petal is moved from the initial position to any other position, and the cumulative sum of the differences in the drying efficiencies of the petals at different positions is taken as the overall drying efficiency.

[0043] The rotation here is not an actual rotation, but is to calculate the overall drying efficiency under each rotation scheme, so as to find the scheme with the highest drying efficiency. The angle that the turntable can rotate is determined by the number of petals. When the number of petals is 4, the turntable can rotate to 90°, 180°, 270° and 360°.

[0044] S22: Make the target petals traverse all positions, take the position of the target petals corresponding to the maximum value of the overall drying efficiency as the optimal position, and control the frame to rotate to move the target petals to the optimal position.

[0045] Calculate the overall drying efficiency. This efficiency is obtained by comparing the difference in drying efficiency of each petal at different positions, that is, adding up the difference in drying efficiency of all petals at different positions to get an overall drying efficiency index. Ensure that the clothes can be dried evenly and efficiently during the drying process, especially when environmental factors such as light and wind speed change, it can automatically adjust to adapt to the best drying conditions.

[0046] S3: In response to the instruction to stop rotating the frame, the petals are adjusted from the minimum opening angle to the maximum opening angle; in this process, the light intensity of the clothes hanging under the petals is calculated, and the petal opening angle corresponding to the maximum light intensity is taken as the optimal opening angle, and the opening angle of the petals is adjusted to the optimal opening angle.

[0047] When the system receives the command to stop the rack, it will start the process of adjusting the petal opening angle. This step is the trigger point of the entire optimization process, ensuring that the clothes drying rack is in a stationary state for subsequent adjustment operations.

[0048] In one embodiment, the endpoint values ​​of the angle range of the petals' opening are the maximum angle and the minimum angle at which the petals can open. For example, with the horizontal plane as zero degrees, the petals can open to a position of 60° below the horizontal plane and 120° above the horizontal plane. For the convenience of description, the angles below the horizontal plane are recorded as negative numbers, and the angles above the horizontal plane are recorded as positive numbers. When the frame stops rotating, the petals will start from 60° below the horizontal plane and gradually adjust to 120° above the horizontal plane. The light intensities corresponding to -60°, -50°, ...0°, ...120° are recorded at intervals of 10°.

[0049] Calculate the total drying area and then based on the light intensity of the total drying area.

[0050] In one embodiment, the total drying area is calculated as follows: the captured image of the clothing is input into a trained target detection model, the input of the target detection model is the captured image, and the output is the range of clothing in the image; the total number of pixels within the range of clothing is taken as the total drying area.

[0051] The camera for collecting images here can be the same as the camera for identifying the user's identity mentioned above, or it can be set separately. The camera can be set on one side of the petals to capture the collected images of the clothes hanging on the vector petals. This setting method can reduce the occlusion between the clothes, because the camera shoots from the side and can capture the outline and details of each piece of clothing, but can only capture the side of the clothes. It can also be set at the outer end of the petals to obtain the collected images of the clothes hanging on the petals, but under the collection angle of this scheme, there are more occlusions between the clothes, but the front of the clothes can be photographed, especially the front of the clothes on the outside, and the light-receiving area of ​​this part of the clothes is larger. The shadow and occlusion of clothes are an inevitable part of the drying process, so these factors are taken into account when designing the shooting angle to obtain the actual area of ​​light relatively accurately.

[0052] The training method of the target detection model is as follows: annotate the clothing image set, determine the main area of ​​the clothing, and use the minimum outer rectangle of the main area of ​​the clothing as the candidate box, and label the candidate box with a category; use the annotated data to train the preset neural network model until the target detection model is obtained after the training is completed.

[0053] The model can be a CNN (Convolutional Neural Networks) model or a neural network model such as YOLO that can be applied to target recognition. The model and the model training process are existing technologies and will not be described in detail here.

[0054] In one embodiment, the calculation method of light intensity is: , where For the The light intensity of hanging clothes on each petal; For the The total drying area for drying clothes under the petals; express The gray value of the pixel.

[0055] In image processing, grayscale values ​​are usually used to represent the brightness of each pixel in an image. Grayscale values ​​usually range from 0 to 255, where 0 represents black (no light) and 255 represents white (brightest). Therefore, the grayscale value can be regarded as a quantitative representation of the light intensity received by the pixel. By adding up the grayscale values ​​of all pixels in the clothing image, the total light intensity received by the entire clothing can be obtained. This sum is the accumulation of the light intensity of all parts of the clothing. The mean calculation is introduced to ensure that clothing of different sizes is consistent when comparing light intensity, because clothing with a larger area naturally receives more total light, but the average light intensity may be lower.

[0056] In one embodiment, the drying angles of the petals may be the same. If they are the same, it will affect the overall ventilation efficiency of drying clothes. Therefore, when the drying angles of the petals are the same, the drying angle of one of the petals is fixed, and the drying angle of the other petal is adjusted to the angle other than the fixed angle at which the light intensity of the clothes is the highest.

[0057] An embodiment of the present application also discloses an intelligent clothes drying rack automatic control system based on machine vision, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the intelligent clothes drying rack automatic control method based on machine vision according to the present application is implemented.

[0058] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0059] 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.

[0060] Although this specification has shown and described a plurality of embodiments of the present application, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those 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.

[0061] 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 automatic control method for an intelligent clothes drying rack based on machine vision, characterized in that: Includes steps: During the drying process, the frame of the clothes drying rack is controlled to rotate to adjust the drying position and angle of the hung clothes; wherein the clothes drying rack comprises a frame and petals circumferentially distributed on the frame, the frame can rotate around its own axis, and the petals are rotatably connected to the frame to adjust the opening angle of the petals; The method to control the rotation of the clothes drying rack is: In response to the initial drying state continuing for a preset period, the drying efficiency of the clothes hung under each petal in the period is calculated, where the drying efficiency is the ratio of the change value of the clothes weight in the period to the period; The vertical projection area of ​​the petals is taken as a position, and any petal is taken as the target petal. The target petal is moved from the initial position to any other position, and the cumulative sum of the differences in the drying efficiencies of the petals at different positions is taken as the overall drying efficiency; The target petals are made to traverse all positions, and the position of the target petals corresponding to the maximum overall drying efficiency is taken as the optimal position, and the frame is controlled to rotate to move the target petals to the optimal position.

2. The automatic control method of the intelligent clothes drying rack based on machine vision according to claim 1 is characterized in that: include: In response to the instruction of the frame body to stop rotating, the petals are adjusted from the minimum opening angle to the maximum opening angle; In this process, the light intensity of the clothes hanging under the petals is calculated, and the petal opening angle corresponding to the maximum light intensity is taken as the optimal opening angle, and the petal opening angle is adjusted to the optimal opening angle.

3. The automatic control method of the intelligent clothes drying rack based on machine vision according to claim 2 is characterized in that: The calculation method of light intensity is: , where For the The light intensity of hanging clothes on each petal; For the The total drying area for drying clothes under the petals; express The gray value of the pixel.

4. The automatic control method of the intelligent clothes drying rack based on machine vision according to claim 3 is characterized in that: The method for calculating the total drying area includes the following steps: The collected image of the clothes is input into the trained target detection model. The input of the target detection model is the collected image, and the output is the range of the clothes in the image. The total number of pixels in the range of the clothes is taken as the total drying area.

5. The method for automatic control of an intelligent clothes drying rack based on machine vision according to claim 4 is characterized in that: The training method of the target detection model is as follows: annotate the clothing image set, determine the main area of ​​the clothing, and use the minimum outer rectangle of the main area of ​​the clothing as the candidate box, and label the candidate box with a category; use the annotated data to train the preset neural network model until the target detection model is obtained after the training is completed.

6. The method for automatically controlling an intelligent clothes drying rack based on machine vision according to any one of claims 1 to 5, characterized in that: Also includes the steps: Set the initial height of the clothes rack, respond to the clothes rack lowering instruction, obtain the user image, identify the user identity according to the user image to obtain the height information, calculate the optimal clothes drying height according to the height information, and control the clothes rack to descend to the optimal clothes drying height; respond to the clothes rack raising instruction, control the clothes rack to rise to the initial height.

7. The automatic control method of the intelligent clothes drying rack based on machine vision according to claim 6 is characterized in that: The formula for calculating the optimal height of clothes drying is: , where For users The optimal height for drying clothes; is the initial height; For users height; Adjustment parameters for suspension height.

8. An intelligent clothes drying rack automatic control system based on machine vision, 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 automatic control method of the intelligent clothes drying rack based on machine vision according to any one of claims 1 to 7 is implemented.

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