Adjusting method and device of clothes airing machine, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a clothes airing machine adjusting method and device, electronic equipment and a storage medium, and relates to the field of Internet of Things. The method comprises the following steps: acquiring a sun position angle and weather parameters of a place where the clothes airing machine is located within a preset time period, and clothes information and current posture parameters of clothes aired by the clothes airing machine; inputting the weather parameter, the sun position angle, the clothes information and the current posture parameter into a pre-trained first model, and obtaining a sunlight radiation area, output by the first model, of the aired clothes when the clothes airing machine is switched to each reference posture parameter; obtaining airing information input by a user in advance, and determining a target posture parameter from the reference posture parameters based on the airing information and the sunlight radiation area corresponding to each reference posture parameter; the airing information is information related to the airing demand of the user; and adjusting the posture of the clothes airing machine based on the target posture parameter. According to the invention, under the condition that the airing efficiency is ensured, the airing requirements of users are met at the same time.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and more specifically, to a method, apparatus, electronic device, and storage medium for adjusting a clothes drying rack. Background Technology

[0002] With the acceleration of urbanization and increasingly compact living spaces, outdoor drying conditions are limited, and traditional drying methods face severe challenges: they rely on manual judgment of weather and sunlight, resulting in large fluctuations in drying time, especially during the rainy season or in high-latitude regions, where the problem of clothing mildew is prominent, and they are highly dependent on the environment; in addition, global household dryers consume more than 120 billion kilowatt-hours of electricity and emit 700 million tons of carbon, resulting in serious waste of resources. Therefore, the relevant technologies suffer from problems such as strong dependence on the drying environment, low drying efficiency, and difficulty in meeting users' drying needs. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and storage medium for adjusting a clothes drying rack, which solves the technical problems of low drying efficiency and difficulty in meeting users' drying needs.

[0004] According to a first aspect of the embodiments of this application, a method for adjusting a clothes drying rack is provided, the method comprising: obtaining the solar position angle and weather parameters of the location of the clothes drying rack within a preset time period, the clothing information of the clothes currently drying on the clothes drying rack, and the current posture parameters; The clothing information, current posture parameters, weather parameters within a preset time period, and solar position angle are input into the pre-trained first model to obtain the solar radiation area of ​​the clothes when the clothes are switched to various reference posture parameters on the clothes drying machine. The system obtains the drying information pre-input by the user, and determines the target posture parameter based on the drying information and the solar radiation area corresponding to each reference posture parameter; the drying information is information related to the user's drying needs. The posture of the clothes drying rack is adjusted based on the target posture parameters.

[0005] According to a second aspect of the embodiments of this application, an adjustment device for a clothes drying rack is provided, the device comprising: The acquisition module is used to acquire the current sun position angle and weather parameters at the location of the clothes drying rack, the clothing information of the clothes currently drying on the clothes drying rack, and the current posture parameters of the drying rod; The input module is used to input weather parameters, solar position angle, clothing information and current posture parameters into the pre-trained first model to obtain the solar radiation area of ​​the clothes drying on the clothes drying machine when switching to each reference posture parameter. The determination module is used to obtain the drying information pre-input by the user, and determine the target posture parameters based on the drying information and the solar radiation area corresponding to each reference posture parameter; the drying information is information related to the user's drying needs; The adjustment module is used to adjust the posture of the clothes dryer based on the target posture parameters.

[0006] According to a third aspect of the embodiments of this application, an electronic device is provided, the electronic device including a memory, a processor and a computer program stored in the memory, wherein the processor executes the program to implement the steps of the method provided in the first aspect.

[0007] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method provided in the first aspect.

[0008] According to a fifth aspect of the present application, a computer program product is provided, the computer program product including computer instructions stored in a computer-readable storage medium, wherein when a processor of a computer device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the computer device to perform steps implementing the method provided in the first aspect.

[0009] The beneficial effects of the technical solutions provided in this application are: The clothes drying rack adjustment method provided in this application provides strong data support for adjusting the posture of the clothes drying rack based on the current actual weather conditions and drying conditions by acquiring the sun position angle and weather parameters of the location of the clothes drying rack within a preset time period, the clothing information of the clothes currently being dried on the clothes drying rack, and the current posture parameters.

[0010] By inputting clothing information, current posture parameters, weather parameters within a preset time period, and the sun's position angle into a pre-trained first model, the model outputs the solar radiation area when the clothes drying rack switches to various reference posture parameters. This enables the analysis of the current drying situation from multiple dimensions, including sun position, weather conditions, drying rack position, and clothing information, to obtain the solar radiation area when the clothes drying rack switches to various reference posture parameters. This not only comprehensively considers the actual drying situation from multiple aspects but also provides multiple options for selecting target posture parameters according to user needs, thus meeting various drying needs of users.

[0011] By acquiring the drying information pre-input by the user, and based on the drying information and the solar radiation area corresponding to each reference posture parameter, the target posture parameter is determined from each reference posture parameter. That is, the solar radiation area that meets the user's drying needs is determined according to the drying information, thereby determining the target posture parameter. This ensures that when confirming the posture of the clothes drying machine to be adjusted, the user's drying needs are fully considered, thereby improving user satisfaction.

[0012] The posture of the clothes drying rack is adjusted based on the target posture parameters, which ensures drying efficiency while meeting the user's drying needs and avoiding damage to the clothes. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0014] Figure 1 A schematic diagram of the system architecture for implementing the clothes drying rack adjustment method provided in this application embodiment; Figure 2 A flowchart illustrating an adjustment method for a clothes drying rack provided in an embodiment of this application; Figure 3 A flowchart illustrating a method for determining solar radiation area provided in an embodiment of this application; Figure 4 A schematic diagram illustrating another method for adjusting a clothes drying rack provided in an embodiment of this application; Figure 5 A flowchart illustrating the method for determining target posture parameters in an adjustment method for a clothes drying rack provided in this application embodiment; Figure 6 A flowchart illustrating another method for adjusting a clothes drying rack provided in this application embodiment; Figure 7 A flowchart illustrating the updating method of the first model in an adjustment method for a clothes drying rack provided in an embodiment of this application; Figure 8 A schematic diagram of the structure of an adjustment device for a clothes drying rack provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0015] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0016] Those skilled in the art will understand that, unless otherwise stated, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.”

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0018] The following is an explanation of YY technology: Traditional sun-drying techniques have the following problems: Highly dependent on the environment: Clothing is easily damaged by sudden weather events (such as rain showers or strong winds); Drying efficiency is low: it takes an average of 3-6 hours to dry, and is significantly affected by temperature and light. Serious waste of resources: Over-reliance on dryers leads to high energy consumption, with high electricity consumption per drying cycle; Traditional smart clothes drying racks have a standard deviation of 2.8 hours in drying time during the rainy season; false alarms of sudden rainfall lead to a laundry rewash rate as high as 17%; and users still intervene an average of 1.5 times per day (the expected "zero intervention" goal has not been achieved).

[0019] In view of at least one of the above-mentioned technical problems or areas that need improvement in the related technologies, this application proposes a method for adjusting a clothes drying rack. This method provides strong data support for adjusting the posture of the clothes drying rack based on the current actual weather conditions and drying conditions by obtaining the solar position angle and weather parameters of the location of the clothes drying rack within a preset time period, the clothing information of the clothes currently being dried on the clothes drying rack, and the current posture parameters.

[0020] By inputting clothing information, current posture parameters, weather parameters within a preset time period, and the sun's position angle into a pre-trained first model, the model outputs the solar radiation area when the clothes drying rack switches to various reference posture parameters. This enables the analysis of the current drying situation from multiple dimensions, including sun position, weather conditions, drying rack position, and clothing information, to obtain the solar radiation area when the clothes drying rack switches to various reference posture parameters. This not only comprehensively considers the actual drying situation from multiple aspects but also provides multiple options for selecting target posture parameters according to user needs, thus meeting various drying needs of users.

[0021] By acquiring the drying information pre-input by the user, and based on the drying information and the solar radiation area corresponding to each reference posture parameter, the target posture parameter is determined from each reference posture parameter. That is, the solar radiation area that meets the user's drying needs is determined according to the drying information, thereby determining the target posture parameter. This ensures that when confirming the posture of the clothes drying machine to be adjusted, the user's drying needs are fully considered, thereby improving user satisfaction.

[0022] The posture of the clothes drying rack is adjusted based on the target posture parameters, which ensures drying efficiency while meeting the user's drying needs and avoiding damage to the clothes.

[0023] The technical solutions of this application and their effects are described below through several exemplary embodiments. It should be noted that the following embodiments can be referenced, borrowed from, or combined with each other. Identical terms, similar features, and similar implementation steps in different embodiments will not be repeated.

[0024] Figure 1 This is a schematic diagram of the system architecture for implementing the clothes drying rack adjustment method provided in an embodiment of this application, wherein the system architecture includes: a terminal 120 and a server 140.

[0025] Terminal 120 installs and runs an application with a method for adjusting the clothes drying rack. Terminal 120 is used to adjust the posture of the clothes drying rack based on the acquired target posture package number.

[0026] Terminal 120 is connected to server 140 via a wireless network or a wired network.

[0027] Server 140 includes at least one of a single server, multiple servers, a cloud computing platform, and a virtualization center. Illustratively, server 140 includes a processor 144 and a memory 142, the memory 142 including a display module 1421, a control module 1422, and a receiving module 1423. Server 140 is used to provide background services for the application of the method. Optionally, server 140 undertakes the primary computing work, and terminal 120 undertakes secondary computing work; or, server 140 undertakes secondary computing work, and terminal 120 undertakes primary computing work; or, server 140 and terminal 120 collaborate on computing using a distributed computing architecture.

[0028] Those skilled in the art will understand that the number of terminals described above can be more or less. For example, there may be only one terminal, or there may be dozens or hundreds of terminals, or even more. This application does not limit the number of terminals or the type of device.

[0029] This application provides a method for adjusting a clothes drying rack, such as... Figure 2 As shown, the method includes: S101, obtain the sun's position angle and weather parameters for the location of the clothes drying rack within a preset time period, as well as the clothing information and current posture parameters of the clothes currently drying on the clothes drying rack.

[0030] In this embodiment of the application, the solar position angle and weather parameters within the preset time period refer to the historical solar position angle and historical weather parameters within the preset time period. This refers to the trajectory of all changes in solar position angle and weather parameters from the current time to a specific historical time. For example, if the current time is 10:00 AM and the preset time period is two hours, then the solar position angle from 8:00 AM to 10:00 AM is taken as the solar position angle within the preset time period.

[0031] In the embodiments of this application, the solar position angle includes the solar altitude angle and the solar azimuth angle. The solar altitude angle refers to the height of the sun relative to the horizon, which determines the incident angle and intensity of sunlight. The larger the altitude angle, the closer the sunlight is to vertical illumination, and the higher the energy utilization rate. The solar azimuth angle is the horizontal angle of the sun relative to due south or due north, which determines the horizontal position of the sun in the sky.

[0032] In this embodiment, the current solar altitude angle and solar azimuth angle can be calculated using the following formulas: Sun azimuth ( ):

[0033] Solar altitude angle (α):

[0034] in, The latitude of the clothes drying rack is represented by δ; the solar declination angle is represented by ω; and the hour angle is represented by ω.

[0035] In this embodiment, weather parameters refer to the current weather parameters at the location of the clothes drying rack. These parameters can be determined using the location information uploaded by the clothes drying rack, the current time, and the date. Weather parameters include ambient temperature, wind speed, relative humidity, cloud cover, and light intensity—all weather-related parameters that affect the drying effect. Ambient temperature affects the saturated vapor pressure on the surface of clothing; higher temperatures lead to more vigorous water molecule movement and faster evaporation. Wind can carry away saturated, humid air near the surface of clothing, accelerating air circulation and reducing relative humidity, thus increasing the evaporation rate. Wind speed, besides sunlight, is another important factor promoting clothing drying. Relative humidity is the ratio of actual water vapor pressure in the air to saturated water vapor pressure at the same temperature. Higher relative humidity means the air is closer to saturation, has a lower capacity to hold more water vapor, and a slower evaporation rate. Cloud cover directly affects the amount of solar radiation reaching the ground and the predictability of the sun's position. Light intensity refers to the solar radiation energy received per unit area, directly affecting the heat absorbed by clothing and the number of photons received, and is one of the main energy sources driving moisture evaporation.

[0036] In this embodiment of the application, clothing information refers to information related to the clothing that needs to be dried on the clothes drying rack, such as the size, material, color, and thickness of the clothing.

[0037] In this embodiment of the application, the current posture parameter refers to the posture parameter of the clothes drying rack. The posture parameter of the drying rack can be composed of the height of the drying rack, or it can be composed of the height of the drying rack and the angle of the drying rack. The specific posture parameter depends on the adjustable parameters of the drying rack of the smart clothes drying rack.

[0038] In this embodiment, the user's geographical location (latitude and longitude) and current time are obtained as the basis for calculating the sun's position angle. A weather API is accessed to obtain real-time weather conditions, including cloud thickness, humidity, and wind speed, factors that may affect sunlight intensity. Users set clothing information and drying needs (such as whether direct sunlight should be avoided) through a mobile app or the control panel on the clothes dryer.

[0039] In this embodiment, astronomical formulas can be used to calculate the current solar altitude angle and azimuth angle in real time using mathematical models based on geographical location, time, and date. Based on historical data and weather forecasts, the changing trend of the sun's trajectory position over the next few hours can be predicted.

[0040] S102, input the clothing information, current posture parameters, weather parameters within a preset time period and the sun position angle into the pre-trained first model, and obtain the solar radiation area when the clothes are switched to various reference posture parameters on the clothes drying machine.

[0041] In this application embodiment, the solar radiation area generally refers to the area on the surface of clothing where solar radiation (also called solar radiation intensity or solar irradiance) is projected. It refers to the area covered by sunlight.

[0042] In this embodiment of the application, the output of the first model is the area of ​​sunlight radiation that the clothes drying rack can receive under different posture parameters, such as the area of ​​sunlight radiation of clothes drying at different heights of the drying rack. In this embodiment, the solar radiation area determines the drying rate of clothes. The solar radiation area and the water vapor pressure difference between the clothes and the environment jointly determine the drying rate of clothes. The larger the solar radiation area and the larger the water vapor pressure difference between the clothes and the environment, the faster the clothes will dry.

[0043] In this embodiment of the application, the first model can be a trained model. When the first model is a trained model, the first model is trained using the sample solar angle position and sample weather parameters, sample clothing information, and sample clothes dryer posture parameters within a preset time period as training samples, and the sample solar angle position and sample weather parameters, sample clothing information, and sample clothes dryer posture parameters within a preset time period as training samples, and the solar radiation area when the sample clothes dryer switches to each sample posture parameter under the above conditions as training labels.

[0044] In one example, the sample clothes drying rack has the following preset time period: solar altitude angle changes from 30° to 60°, azimuth angle changes from 90° to 180°, wind speed is 5 m / s, cloud cover is 0, clothing material is cotton, and current posture parameters are: height is 1 meter. The above content constitutes a training sample. The training labels corresponding to the above training samples are as follows: Sample posture parameter 1 is a height of 1.2 meters, corresponding to a solar radiation area of ​​1.5 square meters; Sample posture parameter 2 is a height of 1.4 meters, corresponding to a solar radiation area of ​​1.8 square meters; Sample posture parameter 3 is a height of 1.6 meters, corresponding to a solar radiation area of ​​2.1 square meters.

[0045] In this embodiment of the application, the first model can also be a large language model, such as DeepSeek. When the first model is a large language model, the output of the first model is obtained by using the historical solar position angle and historical weather parameters, historical clothing information, historical posture parameters, and the corresponding solar radiation area of ​​the clothes dryer when switching to each historical posture parameter within a historical preset time period as auxiliary information.

[0046] In this embodiment, the first model captures the complex and nonlinear trajectory of the sun in the sky by inputting the sun's position angle and weather parameters within a preset time period. This enables the first model to make high-precision predictions of the sun's altitude angle and position angle in the near future. As a result, the first model combines clothing information and attitude parameters to predict the solar radiation area of ​​the clothes when the clothes dryer switches to various reference attitude parameters.

[0047] In this embodiment of the application, the first model extracts key features from the input data, analyzes the historical trends of the sun's position and weather parameters, predicts the solar radiation intensity and weather conditions in the near future, and determines the solar radiation area corresponding to each reference attitude parameter by combining solar radiation intensity, weather conditions, clothing information and attitude parameters.

[0048] S103: Obtain the drying information pre-input by the user, and determine the target attitude parameter from the reference attitude parameters based on the drying information and the solar radiation area corresponding to each reference attitude parameter.

[0049] In this embodiment of the application, the drying information is information related to the user's drying needs, that is, the user's drying preferences for the current clothes, such as whether direct sunlight needs to be avoided, whether even drying is required, and whether excessive exposure to sunlight should be avoided.

[0050] In this embodiment, the first model can also extract key features from the input data to obtain other information related to drying clothes. For example, it can extract key features from the input data such as solar altitude angle, azimuth angle, weather conditions, and clothing type. Based on the extracted key features, it can analyze the historical trend of the sun's position, identify the peak period of solar intensity, and finally formulate a strategy for adjusting the drying height based on trend analysis and user needs. For example, it can increase the drying height during peak solar intensity to increase the area exposed to sunlight, and decrease the height when the cloud cover is thick or the sunlight is weak to avoid the clothes getting damp.

[0051] In this embodiment of the application, since the solar radiation area output by the first model is different under each reference attitude parameter, the required solar radiation area can be determined based on the drying information. Then, the reference attitude parameter corresponding to the required solar radiation area is used as the target drying parameter. For example, if the drying requirement is to be exposed to direct sunlight, the reference attitude parameter corresponding to the largest solar radiation area is selected as the target drying parameter. If the drying requirement is to avoid direct sunlight, the reference attitude parameter corresponding to the smallest solar radiation area is selected as the target drying parameter.

[0052] S104, Adjust the attitude of the clothes drying rack based on the target attitude parameters.

[0053] In this embodiment of the application, after determining the target drying posture parameters, the posture parameters of the clothes drying rack are adjusted to the target posture parameters. For example, the clothes drying rack is adjusted to the target posture parameters by adjusting its height, or by adjusting its height and angle.

[0054] In this embodiment of the application, after determining the target posture parameters, the motor drive system, electric push rod, or hydraulic lifting system of the clothes drying rack adjusts the height and angle of the drying rod according to the target posture parameters.

[0055] In the above scheme, by obtaining the sun's position angle and weather parameters at the location of the clothes drying rack within a preset time period, as well as the clothing information and current posture parameters of the clothes drying rack, it provides strong data support for adjusting the posture of the clothes drying rack based on the current actual weather conditions and drying conditions.

[0056] By inputting clothing information, current posture parameters, weather parameters within a preset time period, and the sun's position angle into a pre-trained first model, the model outputs the solar radiation area when the clothes drying rack switches to various reference posture parameters. This enables the analysis of the current drying situation from multiple dimensions, including sun position, weather conditions, drying rack position, and clothing information, to obtain the solar radiation area when the clothes drying rack switches to various reference posture parameters. This not only comprehensively considers the actual drying situation from multiple aspects but also provides multiple options for selecting target posture parameters according to user needs, thus meeting various drying needs of users.

[0057] By acquiring the drying information pre-input by the user, and based on the drying information and the solar radiation area corresponding to each reference posture parameter, the target posture parameter is determined from each reference posture parameter. That is, the solar radiation area that meets the user's drying needs is determined according to the drying information, thereby determining the target posture parameter. This ensures that when confirming the posture of the clothes drying machine to be adjusted, the user's drying needs are fully considered, thereby improving user satisfaction.

[0058] The posture of the clothes drying rack is adjusted based on the target posture parameters, which ensures drying efficiency while meeting the user's drying needs and avoiding damage to the clothes.

[0059] Based on the above embodiments, as an optional embodiment, the method for determining the solar radiation area corresponding to each reference attitude parameter is as follows: Figure 3 As shown, the specific content is as follows: S201: After projecting clothing information, current posture parameters, weather parameters within a preset time period, and solar position angle onto the same high-dimensional space, the data are stitched together to obtain comprehensive features. S202 captures the relationship between any two time steps in the comprehensive features to obtain a high-order feature sequence; S203, prediction is performed based on high-order feature sequences to obtain prediction information; S204. Select parameters from the initial attitude parameters that have a drying time not greater than the drying time threshold and any probability value in the risk probability values ​​not greater than the corresponding probability threshold, and use them as reference attitude parameters. S205, obtain the solar radiation area of ​​clothes when the clothes dryer switches to each reference attitude parameter as output by the first model.

[0060] In this embodiment of the application, the output of the first model further includes: the estimated drying time and risk probability value of the clothes drying when the clothes drying machine switches to each reference posture parameter, and the risk probability includes the probability value of the clothes drying being covered by shadow within a preset time period and the probability value of the clothes drying slipping off within a preset time period.

[0061] In S201 of this application embodiment, since the input solar position angle and weather parameters are within a preset time period, the features corresponding to the solar position angle and weather parameters at each time step can be obtained, the various input features can be mapped to a high-dimensional subspace, and the mapped features can be spliced ​​and compressed to obtain comprehensive features.

[0062] In the embodiments of this application, the higher-order features include vector features of multiple time steps. The vector feature of each time step is a unified representation that integrates all input features. The vector features of different time steps correspond to the unified representation of all input features in different periods. For example, the feature vector of the early time step is a unified representation of the long-term trend of all input features, such as the seasonal change trajectory of the sun's position angle. The feature vector of the recent time step is a unified representation of the instantaneous features of all input features, such as the impact of the current cloud cover rate on the solar radiation area.

[0063] In S202 of this application embodiment, the comprehensive features are projected into three sets of vectors, and then split into multiple attention heads. For each attention head, the attention weight of each time step to all time steps is calculated. After normalization by softmax, the weighted aggregate value vector is obtained to obtain the output of the attention head. The outputs of multiple attention heads are concatenated along the feature dimension and the multi-head information is fused to obtain the attention output. The attention output is added to the original input data to retain the basic features. The nonlinear transformation of each time step is performed independently through the feedforward network to enhance the nonlinear features. Finally, the high-order features are output. In the above process, the self-attention mechanism allows each time step to dynamically focus on the historical moment that is most valuable to its prediction result.

[0064] In S203 of this application embodiment, the high-order feature sequence is input into multiple task heads, including: a solar radiation area prediction head, a drying time prediction head, and a risk prediction head. The solar radiation area prediction head predicts the solar radiation area of ​​the clothes under each initial posture adoption number based on the input high-order feature sequence. The drying time prediction head predicts the estimated drying time required for the clothes to dry based on the input high-order feature sequence. The risk prediction head obtains the risk probability value based on the high-order feature sequence.

[0065] In S204 of this application embodiment, since it is necessary to ensure the drying efficiency of clothes and reduce the drying risk, parameters with a drying estimated time not greater than the drying estimated time threshold and any probability value of the risk probability value not greater than the corresponding probability threshold are selected from each initial attitude parameter and used as reference attitude parameters.

[0066] In S205 of this application embodiment, after obtaining each reference posture parameter based on the drying prediction time and risk probability threshold, the solar radiation area corresponding to each reference posture parameter is output so that when the drying rack is adjusted in the future, the posture parameter in the reference posture parameter is adjusted, ensuring that the adjusted drying rack has low risk and high drying efficiency.

[0067] In the above scheme, the nonlinear changes in the sun's trajectory are captured through the Transformer architecture; a unified multi-task framework is adopted to simultaneously handle related tasks such as weather forecasting, clothes drying modeling, and risk assessment, improving computational efficiency while greatly enhancing the comprehensiveness and real-time nature of decision-making. The model no longer simply adjusts the clothesline based on the sun's position, but comprehensively considers multiple factors.

[0068] Based on the above embodiments, as an optional embodiment, a method for adjusting a clothes drying rack is also provided, such as... Figure 4 As shown, the specific content is as follows: S301, determine the first angle of incidence of the sun on the drying rack based on the sun's position angle and current attitude parameters; S302, Based on the first incident angle and the pre-established correspondence between the incident angle and the range of attitude parameters, determine the range of the first attitude values ​​corresponding to the clothes drying machine; S303, input the range of values ​​for the first attitude parameters, along with clothing information, current attitude parameters, weather parameters within a preset time period, and the sun's position angle, into the first model.

[0069] In this embodiment, the first incident angle refers to the angle between sunlight and the normal of the drying rod. The size of the sunlight radiation area is related to the first incident angle. When the first incident angle is 0°, the sunlight radiation area reaches its maximum value. The sunlight radiation area decreases as the degree of the first incident angle increases.

[0070] In S301 of this application embodiment, the current solar azimuth angle and solar altitude angle are determined, the azimuth angle and tilt angle of the drying rod are determined according to the current height and angle of the drying rod, and the first angle of incidence of the sun on the drying rod is determined according to the solar azimuth angle, altitude angle, azimuth angle and tilt angle of the drying rod.

[0071] In S302 of this application embodiment, since the first incident angle affects the drying efficiency of clothes, the range of values ​​of an attitude parameter can be initially determined based on the current first incident angle during the subsequent adjustment of the drying rack. By pre-determining which range of values ​​the attitude parameter can be adjusted under different incident angles to ensure that the clothes being dried are not covered by shadows, a correspondence between the incident angle and the range of values ​​of the attitude parameter is established. When the clothes drying rack needs to be adjusted, the range of values ​​of the first attitude parameter that can ensure that the clothes being dried are exposed to sunlight is determined by the first incident angle and the pre-established correspondence.

[0072] In this embodiment, the range of first attitude parameters is input into the first model along with other input features, so that the first model determines each reference attitude parameter from the range of values ​​of the first attitude parameters. When the first model takes values ​​for the reference attitude parameters, it takes values ​​from the range of the first attitude parameters, ensuring that the reference attitude parameters involved in calculating the solar radiation area are all parameters that can ensure that clothes can be exposed to sunlight when drying. This avoids selecting reference attitude parameters with low drying efficiency for calculating the solar radiation area, thus avoiding wasting computing resources.

[0073] In this embodiment, the dynamic range of the clothes drying rack is typically 0.8m (minimum) to 2.5m (maximum). The specific height range can be set according to the actual application scenario and user needs.

[0074] In the above scheme, the first incident angle of the sun on the clothesline is determined in advance based on the sun's position angle and the current attitude parameters. In order to ensure that the clothes are within the sunlight range, a preliminary first attitude parameter range corresponding to the clothesline is determined based on the first incident angle. Then, when the first model predicts the solar radiation area corresponding to each attitude parameter, it only considers the solar radiation area of ​​the reference attitude parameters within the first attitude parameter range. This reduces the processing burden of the first model while ensuring the referenceability of the output results.

[0075] Based on the above embodiments, as an optional embodiment, the method for determining the target attitude parameters is as follows: Figure 5 As shown, the specific content is as follows: S401, determine at least one first solar radiation area that satisfies the drying information from the solar radiation areas corresponding to each reference attitude parameter; S402, Select the largest first solar radiation area from at least one first solar radiation area as the target solar radiation area; S403, obtain the reference attitude parameters corresponding to the target's solar radiation area as the target attitude parameters.

[0076] In S401 of this application embodiment, the drying information is used to indicate the minimum requirement for the solar radiation area. When the drying information requires rapid drying, it is necessary to ensure that the clothes can be evenly exposed to sunlight. Therefore, the first solar radiation area that meets the drying information can be the maximum solar radiation area. For clothes that need to avoid direct sunlight but still need a certain amount of sunlight, the solar radiation area in the middle range can be selected as the first radiation area.

[0077] In this embodiment of the application, the first solar radiation area can also be selected by taking into account the influence of the posture parameters corresponding to the solar radiation area on the clothes drying.

[0078] In embodiment S402 of this application, in order to maximize the drying efficiency of clothes, the largest solar radiation area is selected from the first radiation area as the target solar radiation area.

[0079] In S403 of this application embodiment, the reference attitude parameters corresponding to the target solar radiation area are obtained as target reference attitude parameters so that the drying rod is adjusted based on the target attitude parameters, thereby maximizing the drying efficiency of clothes in combination with the actual environmental conditions and user needs.

[0080] In the above scheme, at least one first solar radiation area that meets the drying needs of clothes is selected from the solar radiation area, and the largest first solar radiation area is selected as the target solar radiation area. This ensures that the drying needs are met while maximizing the drying efficiency.

[0081] Based on the above embodiments, as an optional embodiment, the number of adjustments required before the clothes are completely dried and the interval between two adjacent adjustments are determined according to the clothing information and drying information. The posture of the clothes drying machine is then adjusted according to the number of adjustments and the interval.

[0082] In the application embodiments, the clothing information for drying clothes includes at least one of the following: Clothing material; Clothing thickness; Clothing weight; Clothing color.

[0083] In the embodiments of this application, the material of the clothes to be dried will affect the moisture absorption of the clothes. Different materials (such as cotton, linen, and chemical fibers) have different moisture absorption and drying rates. For example, cotton clothes have strong moisture absorption and may require a longer time or higher temperature to dry, while chemical fiber clothes dry faster.

[0084] In this embodiment of the application, the weight and thickness of the clothes to be dried also affect the drying efficiency. Heavy clothes (such as jeans) require a long time to dry, which means they need a larger area exposed to sunlight and a longer drying time.

[0085] In this embodiment of the application, clothes with different light resistance will react differently to sunlight. For example, dark clothes or clothes that are easy to fade need to be protected from direct sunlight for a long time. The posture of the clothes drying rack needs to be adjusted multiple times to avoid localized direct sunlight on the clothes.

[0086] In the embodiments of this application, the drying information includes rapid drying, uniform drying, or avoiding direct sunlight. If the drying information is rapid drying, that is, maximizing drying efficiency is prioritized. The posture of the drying rod is adjusted to ensure that the clothes are exposed to the maximum area of ​​sunlight for a long time, increasing the time and area of ​​the clothes exposed to sunlight. If the drying information is uniform drying, the posture of the drying rod is dynamically adjusted to ensure that all parts of the clothes are evenly exposed to sunlight, avoiding local over-drying or over-wetting. If the drying information is avoiding direct sunlight, the parameters of the drying rod are adjusted frequently to reduce the area and duration of the clothes directly exposed to sunlight, avoiding fading or damage.

[0087] In this embodiment of the application, by combining clothing information and drying information, the number of adjustments required before the clothes are completely dry and the interval between two adjacent adjustments are determined. For example, for dark-colored clothes that need to be dried evenly, the number of adjustments is increased as much as possible and the interval between two adjacent adjustments is reduced. For thick clothes that need to be exposed to high temperatures, the number of adjustments is determined according to the rate of change of the incident angle. While ensuring that the area exposed to sunlight is large enough, the clothes are adjusted as infrequently as possible, and the interval is adjusted accordingly.

[0088] In the above scheme, after adjusting the posture of the drying rod based on the target posture parameters output by the first model, the number of adjustments and the interval time of the drying rod are determined based on the clothing information and drying information. This ensures that the drying is carried out in a targeted manner according to the actual situation of the clothes being dried and the user's drying needs, so as to avoid damage to the clothes, meet the user's drying needs, and improve drying efficiency.

[0089] In a preferred embodiment of this application, the posture of the drying rack can be adjusted every preset minutes, taking into account the slow changes in the sun's position and the need for clothes to dry, in order to maintain the best drying effect. For example, the adjustment frequency is once every 15 minutes.

[0090] Based on the above embodiments, as an optional embodiment, the clothes drying rack can be adjusted as follows: Figure 6 As shown, the specific content is as follows: S501, obtain real-time cloud cover, current wind direction and current sun position angle; S502-1, If ​​the cloud cover is not greater than the first coverage threshold, then the first attitude parameters of the clothes drying rack are determined based on the current solar position angle; S502-2, If the cloud cover is greater than the first coverage threshold but not greater than the second coverage threshold, then the first attitude parameters of the clothes drying rack are determined based on the current wind direction and the current solar position angle. S502-3, If the cloud cover is greater than the second coverage threshold, the first attitude parameters of the clothes drying rack are determined based on the current wind direction; S503, adjusts the posture of the clothes drying rack based on the first posture parameters.

[0091] In S501 of this application embodiment, real-time cloud cover rate is obtained by calling a professional meteorological API. The cloud cover rate will affect the sunlight radiating onto the clothes being dried, thereby affecting the drying efficiency.

[0092] In S502-1 of this application embodiment, if it is determined that when the posture of the clothes drying rack needs to be adjusted, the real-time cloud cover rate is not greater than the first coverage rate threshold, it means that the current cloud cover has little impact on the sunlight radiating onto the clothes being dried, and the first posture parameters of the clothes drying rack can still be determined with reference to the current solar position angle.

[0093] In S502-2 of this application embodiment, if the real-time cloud cover rate is greater than the first coverage rate threshold but not greater than the second coverage rate threshold, it indicates that the current cloud cover has a certain degree of influence on the sunlight radiating onto the clothes being dried. Since wind is another factor affecting the drying efficiency of clothes, the first attitude parameters of the clothes drying machine are determined by comprehensively considering the influence of the current wind direction and the sun position angle on drying, with the sun position angle and wind direction as references.

[0094] In S502-3 of this application embodiment, if the real-time cloud cover rate is greater than the second coverage rate threshold, it indicates that the current cloud cover has a great influence on the sunlight radiating onto the clothes being dried. For example, if the cloud layer is thick and completely blocks the sunlight, the light intensity radiating onto the clothes being dried is extremely low. Therefore, the first attitude parameters of the clothes drying machine are determined with reference to the wind direction.

[0095] In S503 of this application embodiment, for any adjustment, the information to be used as a reference for determining the first attitude parameter is determined based on the current cloud cover rate, thereby determining the first attitude parameter, and finally adjusting the attitude of the clothes drying rack according to the first attitude parameter.

[0096] In the above scheme, when the drying rack needs to be adjusted, the current real-time cloud cover, wind direction, and sun position angle are obtained. When the cloud cover is low, it means that the sun is not obstructed. Determining the attitude parameters of the drying rack based on the sun position angle is more conducive to speeding up the drying rate. When the cloud cover is moderate, the drying rack is adjusted by comprehensively considering the sun position angle and wind direction to maximize the drying rate. When the cloud cover is high, it means that the sun is heavily obstructed. Therefore, the drying attitude is adjusted based on the wind direction to maximize the drying rate.

[0097] Based on the above embodiments, as an optional embodiment, the update method of the first model is as follows: Figure 7 As shown, the specific content is as follows: S601 determines the light intensity on the surface of clothes being dried using a brightness sensor; S602 determines the first humidity change rate of the surface of clothes during a preset time period using a humidity sensor, and determines the corresponding target humidity change rate based on clothing information, drying needs and light intensity. S603, if it is determined that the first humidity change rate is less than the target humidity change rate threshold, then the attitude of the clothes dryer is adjusted according to the difference between the target humidity change rate and the first humidity change rate threshold, the change trend between the target attitude parameter and the solar radiation area corresponding to each reference attitude parameter, until it is determined that the first humidity change rate of the surface of the clothes to be dried is not less than the target humidity change rate, and the current attitude parameter of the clothes dryer is used as the second attitude parameter. S604, obtain the current first solar position angle and first weather parameters, and determine the second solar radiation area corresponding to the second attitude parameters based on the first solar position angle and second attitude parameters; S605 uses the current weather parameters, the first sun position angle, clothing information, and target attitude parameters as new training samples, and uses the second solar radiation area corresponding to the second attitude parameters as the training label of the new training samples. S606 updates the parameters of the first model based on the new training samples and training labels.

[0098] In S601 of this application embodiment, the light intensity on the surface of the clothes being dried is determined by a brightness sensor installed on the clothes drying machine.

[0099] In S602 of this application embodiment, a first humidity change rate of the surface of the clothes being dried is determined by a humidity sensor installed on the clothes drying rack over a preset time period. That is, the humidity of the surface of the clothes being dried is recorded from the moment the clothes drying rack is adjusted. After the preset time period, the humidity of the surface of the clothes is recorded again. Based on the difference in humidity change before and after the preset time period and the preset time period, the first humidity change rate is determined. The material and thickness of the clothes, the user's drying needs, and the current light intensity will have a certain impact on the humidity change. Therefore, the corresponding target humidity change rate is determined according to the clothes information, drying needs, and light intensity. That is, the expected humidity change rate of the clothes is determined based on the current actual drying situation, and the target humidity change rate is obtained.

[0100] In S603 of this application embodiment, if it is determined that the first humidity change rate is less than the target humidity change rate, it indicates that the drying efficiency under the current posture has not achieved the expected effect. Therefore, based on the difference between the target humidity change rate and the first humidity change rate, and the changing trend between the target posture parameter and the solar radiation area corresponding to each reference posture parameter, the posture of the clothes drying machine is adjusted. By determining the difference between the target humidity change rate and the first humidity change rate, a difference between the pre-determined drying effect and the actual drying effect can be roughly determined. Then, referring to the changing trend between the current target posture parameter and the solar radiation area corresponding to each reference posture parameter, a suitable reference posture parameter is selected to start posture adjustment. If it is determined that the first humidity change rate of the drying clothes surface is still less than the target humidity change rate, then the adjustment is made based on the solar radiation area corresponding to the current posture parameter, which is larger than that of the current posture parameter, until it is determined that the first humidity change rate of the drying clothes surface is not less than the target humidity change rate. Then, the adjustment of the posture parameters of the clothes drying machine is stopped, and the current posture parameter of the clothes drying machine is used as the second posture parameter.

[0101] In S604 of this application embodiment, the first solar position angle and first weather parameters of the current location of the clothes drying rack are obtained. Based on the first solar position angle and the second attitude parameters, the second solar radiation area corresponding to the second attitude parameters is determined. For example, the azimuth angle and tilt angle of the drying rod are determined by the height and angle in the second attitude parameters. The angle of incidence of the sun on the drying rod is determined based on the azimuth angle, altitude angle, and the azimuth angle and tilt angle of the drying rod. The projected area of ​​the drying rod is determined based on the geometric dimensions of the drying rod. Finally, the solar radiation area is determined based on the projected area and the angle of incidence. The specific formula is as follows: A eff =A0·cos θ inc Among them, A eff Let A0 be the area of ​​solar radiation, θ be the projected area, and θ be the area of ​​solar radiation. inc The angle of incidence is denoted as .

[0102] In S605 of this application embodiment, the current first weather parameters, first solar position angle, clothing information and target posture parameters are used as new training samples, and the second solar radiation area corresponding to the second posture parameters is used as the training label of the new training samples. That is, the actual drying effect is detected by the sensor, and the drying rod is adjusted according to the detection results. At the same time, the first model can be further trained based on the acquired data so that the first model is more adaptable and has higher accuracy.

[0103] In S606 of this application embodiment, after collecting a preset number of training samples and corresponding training labels, the parameters of the first model are updated based on the new training samples and training labels.

[0104] In the above scheme, after adjusting the drying rack, the target humidity change rate corresponding to the current drying clothes is determined based on the acquired light intensity, clothing information, and drying requirements. Then, the actual first humidity change rate of the drying clothes surface is determined by the humidity sensor. If the first humidity change rate is less than the target humidity change rate, it means that the drying has not met the expected drying requirements. Therefore, the attitude of the drying rack is adjusted according to the difference between the target humidity change rate and the first humidity change rate and the changing trend between the solar radiation area corresponding to each reference attitude parameter, until the first humidity change rate of the drying clothes surface meets the target humidity change rate. The model is then updated based on the second attitude parameter and solar radiation area that meet the target humidity change rate.

[0105] In this embodiment, the actual light intensity and drying status of the clothes are monitored by sensors and used as feedback information to continuously optimize the first model. Users can also view the drying progress, adjust the drying parameters, or receive a reminder when the drying is complete through a mobile APP or control panel.

[0106] In this embodiment, when adjusting the clothes drying rack, it is necessary to maximize the solar radiation area while ensuring user needs are met. Therefore, adjustments can be made using two strategies: height adjustment and angle adjustment. When adjusting the height, the goal is to bring the angle of incidence as close to 0° as possible to improve drying efficiency. The angle adjustment strategy aims to minimize θinc by precisely controlling the azimuth and elevation angles to ensure the clothes drying rack always faces the sun, reducing the angle of incidence and improving drying efficiency. The dynamic range is: azimuth: 0-360° (step accuracy 0.1°); elevation angle: 0-90° (step accuracy 0.05°). This high-precision angle adjustment ensures the clothes drying rack accurately tracks the sun's position.

[0107] In one example, the following adjustment methods apply to various situations: when the solar altitude angle increases, raise the clothesline; when the solar altitude angle decreases, lower the clothesline. On sunny days, with a solar altitude angle of 15°-75°, the clothesline adjustment strategy is: height 1.2-2.0m (increasing with solar altitude), angle: real-time tracking of the sun's position. On cloudy days, with a solar altitude angle of 20°-60°, the clothesline adjustment strategy is: height 1.5m (fixed), angle: dynamically adjusted based on cloud gaps. On rainy days, with no solar altitude angle, the clothesline adjustment strategy is: height 0.8m (lowest position), angle: 0° (horizontally folded up). In this embodiment, processing using a large language model enables spatiotemporal modeling capabilities. The Transformer architecture captures the nonlinear changes in the sun's trajectory (azimuth prediction error <0.5°). A unified multi-task framework is employed to simultaneously process related tasks such as weather forecasting, clothes drying modeling, and risk assessment, improving computational efficiency by 5 times. Rapid adaptation using small samples allows for fine-tuning of the model with only a small amount of local data, resolving projection calculation issues caused by differences in balcony structures. Leveraging the powerful analytical and predictive capabilities of the large language model, combined with mobile phone positioning, weather information, and sun position angle data, intelligent sunlight tracking for clothes drying is achieved, ensuring clothes receive full sunlight while avoiding overexposure, thus improving drying efficiency and quality.

[0108] In this embodiment, the system architecture for adjusting the clothes drying rack includes a perception layer, a decision-making layer, an execution layer, and a user interaction layer. The perception layer includes sensors such as light sensors and angle sensors to monitor real-time information such as sun position, light intensity, and wind direction and speed. The decision-making layer, based on a DeepSeek large-scale model, is an intelligent decision-making system responsible for analyzing the data from the perception layer and formulating adjustment strategies. The execution layer consists of a motor drive system, an electric push rod, or a hydraulic lifting system, responsible for adjusting the height and angle of the clothes drying rack according to the instructions from the decision-making layer. The user interaction layer is a mobile app or control panel for users to set parameters, view the drying status, and receive notifications.

[0109] In this embodiment, the predictive capabilities of the model are utilized, combined with real-time collected data such as sun position, light intensity, ambient temperature, and wind speed, to accurately predict future drying conditions. A motor-driven system or a hydraulic lifting system is used to automatically adjust the height and angle of the clothesline. This solves the problem of users potentially needing to manually adjust the height and angle of the clothesline, resulting in a higher level of intelligence and reduced human intervention. By integrating multiple sensors and a large model, environmental information and sun position can be collected in real time, and precise decisions can be made based on this data. This solves the problem of insufficient drying efficiency due to a lack of precise decision-making mechanisms. By adjusting the height and angle of the clothesline, the solar radiation area is brought as close to its maximum as possible, significantly improving the drying efficiency of clothes. The large model can continuously learn and optimize adjustment strategies to adapt to the drying needs of different regions and seasons.

[0110] In this embodiment, a large model is used in conjunction with real-time collected data on solar position, light intensity, ambient temperature, and wind speed to accurately predict future drying conditions. This predictive capability allows the clothesline to be adjusted to its optimal position in advance, thereby maximizing drying efficiency. Based on the model's predictions, this invention can intelligently formulate adjustment strategies for the clothesline, including adjustments to height and angle, to ensure that clothes can be dried under optimal conditions. This invention allows for dynamic adjustment of the clothesline to adapt to different drying needs and environmental conditions. To ensure that the drying effect remains optimal at all times, this invention specifies that the height of the clothesline is adjusted at a predetermined frequency. This high-frequency adjustment can respond promptly to environmental changes. The azimuth angle in this invention can be adjusted within the range of 0-360° with a step accuracy of 0.1°; the elevation angle can be adjusted within the range of 0-90° with a step accuracy of 0.05°. This high-precision adjustment capability allows the clothesline to precisely face the sun, further reducing the angle of sunlight incidence and improving drying efficiency. By precisely controlling the azimuth and elevation angles, this invention aims to minimize the angle (angle of incidence) between sunlight and the normal of the clothesline, thereby increasing the effective radiation receiving area and improving the drying effect.

[0111] This application provides an adjustment device for a clothes drying rack, such as... Figure 8 As shown, the adjustment device 80 of the clothes drying rack may include: an acquisition module 801, an input module 802, a determination module 803, and an adjustment module 804.

[0112] Specifically, the acquisition module 801 is used to acquire the sun position angle and weather parameters of the location of the clothes drying rack within a preset time period, the clothing information of the clothes currently drying on the clothes drying rack, and the current posture parameters. Input module 802 is used to input clothing information, current posture parameters, weather parameters within a preset time period and solar position angle into a pre-trained first model to obtain the solar radiation area output by the first model when the clothes are switched to various reference posture parameters on the clothes drying machine. The determination module 803 is used to obtain the drying information pre-input by the user, and determine the target posture parameter from each reference posture parameter based on the drying information and the solar radiation area corresponding to each reference posture parameter; the drying information is information related to the user's drying needs; The adjustment module 804 is used to adjust the posture of the clothes drying rack based on the target posture parameters.

[0113] The clothes drying rack adjustment device provided in this application provides strong data support for adjusting the posture of the clothes drying rack based on the current actual weather conditions and drying conditions by acquiring the sun position angle and weather parameters of the location of the clothes drying rack within a preset time period, the clothing information of the clothes currently being dried on the clothes drying rack, and the current posture parameters.

[0114] By inputting clothing information, current posture parameters, weather parameters within a preset time period, and the sun's position angle into a pre-trained first model, the model outputs the solar radiation area when the clothes drying rack switches to various reference posture parameters. This enables the analysis of the current drying situation from multiple dimensions, including sun position, weather conditions, drying rack position, and clothing information, to obtain the solar radiation area when the clothes drying rack switches to various reference posture parameters. This not only comprehensively considers the actual drying situation from multiple aspects but also provides multiple options for selecting target posture parameters according to user needs, thus meeting various drying needs of users.

[0115] By acquiring the drying information pre-input by the user, and based on the drying information and the solar radiation area corresponding to each reference posture parameter, the target posture parameter is determined from each reference posture parameter. That is, the solar radiation area that meets the user's drying needs is determined according to the drying information, thereby determining the target posture parameter. This ensures that when confirming the posture of the clothes drying machine to be adjusted, the user's drying needs are fully considered, thereby improving user satisfaction.

[0116] The posture of the clothes drying rack is adjusted based on the target posture parameters, which ensures drying efficiency while meeting the user's drying needs and avoiding damage to the clothes.

[0117] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0118] This application provides an electronic device (computer device / equipment / system) including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps of a clothes drying rack adjustment method. Compared with related technologies, this method can achieve the following: by acquiring the solar position angle and weather parameters of the clothes drying rack location within a preset time period, the clothing information of the clothes currently being dried, and the current posture parameters, it provides strong data support for adjusting the posture of the clothes drying rack based on the current actual weather and drying conditions. By inputting the clothing information, current posture parameters, weather parameters within the preset time period, and solar position angle into a pre-trained first model, the model outputs the solar radiation area when the clothes drying rack switches to various reference posture parameters. This realizes the analysis of the current drying situation from multiple dimensions, including solar position, weather conditions, drying rod position, and clothing information, to obtain the solar radiation area when the clothes drying rack switches to various reference posture parameters. This not only comprehensively considers the current actual drying situation from multiple aspects but also provides multiple options for selecting target posture parameters according to user needs, so as to meet various drying needs of users. By acquiring the drying information pre-input by the user, and based on the drying information and the solar radiation area corresponding to each reference posture parameter, the target posture parameter is determined from each reference posture parameter. That is, the solar radiation area that meets the user's drying needs is determined according to the drying information, thereby determining the target posture parameter. This ensures that when confirming the posture of the clothes drying machine to be adjusted, the user's drying needs are fully considered, thereby improving user satisfaction.

[0119] The posture of the clothes drying rack is adjusted based on the target posture parameters, which ensures drying efficiency while meeting the user's drying needs and avoiding damage to the clothes.

[0120] In one alternative embodiment, an electronic device is provided, such as Figure 9 As shown, Figure 9 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0121] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0122] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0123] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium capable of carrying or storing computer programs and capable of being read by a computer, without limitation herein.

[0124] The memory 4003 is used to store computer programs that execute the embodiments of this application, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer programs stored in the memory 4003 to implement the steps shown in the foregoing method embodiments.

[0125] The electronic device package may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0126] This application provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program can implement the steps and corresponding content of the aforementioned method embodiments. Compared with the prior art, it can achieve the following: by acquiring the solar position angle and weather parameters of the clothes drying rack location within a preset time period, the clothing information of the clothes currently being dried on the clothes drying rack, and the current posture parameters, it provides strong data support for adjusting the posture of the clothes drying rack based on the current actual weather conditions and drying conditions. By inputting the clothing information, current posture parameters, weather parameters within the preset time period, and solar position angle into a pre-trained first model, the solar radiation area of ​​the clothes drying rack when switching to various reference posture parameters is obtained from the output of the first model. This realizes the analysis of the current drying conditions from multiple dimensions, including solar position, weather conditions, drying rack position, and clothing information, to obtain the solar radiation area of ​​the clothes drying rack when switching to various reference posture parameters. This not only comprehensively considers the current actual drying conditions from multiple aspects, but also provides multiple options for selecting target posture parameters according to user needs, so as to meet various drying needs of users. By acquiring pre-input drying information from the user, and based on this information and the corresponding solar radiation area for each reference posture parameter, the target posture parameter is determined from among these reference parameters. In other words, the solar radiation area that best meets the user's drying needs is determined based on the drying information, thus establishing the target posture parameter. This ensures that the user's drying needs are fully considered when confirming the posture of the clothes dryer to be adjusted, thereby improving user satisfaction. Adjusting the clothes dryer's posture based on the target posture parameter ensures both drying efficiency and meets the user's drying needs, preventing damage to clothes.

[0127] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium, a computer-readable medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0128] This application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments. Compared with the prior art, it can achieve: By acquiring the solar position angle and weather parameters of the clothes drying rack's location within a preset time period, along with information on the clothes currently being dried and their current posture parameters, this provides strong data support for adjusting the clothes drying rack's posture based on the actual weather and drying conditions. By inputting the clothing information, current posture parameters, weather parameters within the preset time period, and solar position angle into a pre-trained first model, the model outputs the solar radiation area when the clothes drying rack switches to various reference posture parameters. This allows for analysis of the current drying situation from multiple dimensions, including solar position, weather conditions, drying rod position, and clothing information, to determine the solar radiation area when the clothes drying rack switches to different reference posture parameters. This not only comprehensively considers the actual drying situation from multiple perspectives but also provides multiple options for selecting target posture parameters based on user needs, thus meeting various drying requirements. By acquiring pre-input drying information from the user, and based on this information and the corresponding solar radiation area for each reference posture parameter, the target posture parameter is determined from among these reference parameters. In other words, the solar radiation area that best meets the user's drying needs is determined based on the drying information, thus establishing the target posture parameter. This ensures that the user's drying needs are fully considered when confirming the posture of the clothes dryer to be adjusted, thereby improving user satisfaction. Adjusting the clothes dryer's posture based on the target posture parameter ensures both drying efficiency and meets the user's drying needs, preventing damage to clothes.

[0129] The terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the illustrations or text descriptions.

[0130] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0131] The above are only optional implementation methods for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application, without departing from the technical concept of this application, also fall within the protection scope of the embodiments of this application.

Claims

1. A method for adjusting a clothes drying rack, characterized in that, include: Obtain the sun's position angle and weather parameters at the location of the clothes drying rack within a preset time period, as well as the clothing information and current posture parameters of the clothes currently drying on the clothes drying rack; The clothing information, the current posture parameters, the weather parameters within the preset time period, and the solar position angle are input into a pre-trained first model to obtain the solar radiation area of ​​the clothes when the clothes are switched to each reference posture parameter on the clothes drying machine. The system obtains the drying information pre-input by the user, and determines the target posture parameter from the reference posture parameters based on the drying information and the solar radiation area corresponding to each reference posture parameter; the drying information is information related to the user's drying needs. The posture of the clothes drying rack is adjusted based on the target posture parameters.

2. The method according to claim 1, characterized in that, The output of the first model also includes: the estimated drying time and risk probability value of the clothes when the clothes drying machine switches to each reference posture parameter, wherein the risk probability includes the probability value of the clothes being covered by shadow within a preset time period and the probability value of the clothes slipping off within a preset time period. The clothing information, the current posture parameters, the weather parameters within the preset time period, and the solar position angle are input into a pre-trained first model to obtain the solar radiation area output by the first model when the clothes being dried are switched to various reference posture parameters on the clothes drying machine, including: The clothing information, current posture parameters, weather parameters within the preset time period, and solar position angle are projected onto the same high-dimensional space and then stitched together to obtain comprehensive features. Capture the relationship between any two time steps in the comprehensive features to obtain a high-order feature sequence; the high-order feature sequence includes vector features of multiple time steps, and the vector feature of each time step is a unified representation that integrates all input features. The vector features of different time steps correspond to the unified representation of all input features at different times. Based on the high-order feature sequence, prediction information is obtained, including the solar radiation area, estimated drying time, and risk probability value of the clothes when the clothes drying machine switches to each initial posture parameter. Parameters that are selected from the initial attitude parameters, where the estimated drying time is no greater than the estimated drying time threshold and any probability value among the risk probability values ​​is no greater than the corresponding probability threshold, are used as reference attitude parameters. Obtain the solar radiation area of ​​the clothes being dried when the clothes dryer switches to each reference posture parameter, as output by the first model.

3. The method according to claim 1, characterized in that, The step of inputting the weather parameters, the sun position angle, the clothing information, and the current posture parameters into a pre-trained first model includes: The first angle of incidence of the sun on the drying rack is determined based on the sun's position angle and the current attitude parameters; Based on the first incident angle and the pre-established correspondence between the incident angle and the range of attitude parameters, the range of the first attitude values ​​corresponding to the clothes drying machine is determined. The first attitude parameter value range, along with the clothing information, the current attitude parameter, the weather parameters within the preset time period, and the solar position angle, are input into the first model so that the first model can determine each reference attitude parameter from the first attitude parameter value range.

4. The method according to claim 1, characterized in that, The drying information is used to indicate the minimum required area for solar radiation. The step of determining the target posture parameter from the reference posture parameters based on the drying information and the solar radiation area corresponding to each reference posture parameter includes: From the solar radiation areas corresponding to each reference attitude parameter, determine at least one first solar radiation area that satisfies the drying information; From the at least one first solar radiation area, select the largest first solar radiation area as the target solar radiation area; The reference attitude parameters corresponding to the target solar radiation area are obtained as the target attitude parameters.

5. The method according to claim 1, characterized in that, After adjusting the posture of the clothes drying rack based on the target posture parameters, the method further includes: Based on the clothing information and the drying information, determine the number of adjustments required for the drying clothing before it is completely dried and the interval between two adjacent adjustments; The posture of the clothes drying rack is adjusted according to the number of adjustments and the interval duration. The clothing information includes at least one of the following: Clothing material; Clothing thickness; Clothing weight; Clothing color; The drying information includes quick drying, even drying, or avoiding direct sunlight.

6. The method according to claim 5, characterized in that, For any given adjustment, the clothes drying rack is adjusted as follows: Obtain real-time cloud cover, current wind direction, and current sun position angle; If the cloud cover is not greater than the first coverage threshold, then the first attitude parameters of the clothes drying rack are determined based on the current solar position angle. If the cloud cover is greater than the first coverage threshold but not greater than the second coverage threshold, then the first attitude parameters of the clothes drying rack are determined based on the current wind direction and the current sun position angle. If the cloud cover is greater than the second coverage threshold, then the first attitude parameter of the clothes drying rack is determined based on the current wind direction; The posture of the clothes drying rack is adjusted based on the first posture parameter.

7. The method according to claim 1, characterized in that, The process of adjusting the posture of the clothes drying rack based on the target posture parameters further includes: The light intensity on the surface of the clothes being dried is determined by a brightness sensor; The first humidity change rate of the surface of the clothes to be dried is determined by a humidity sensor over a preset time period, and the corresponding target humidity change rate is determined based on the clothing information, drying requirements and light intensity. If it is determined that the first humidity change rate is less than the target humidity change rate threshold, then the attitude of the clothes dryer is adjusted according to the difference between the target humidity change rate and the first humidity change rate threshold, the change trend between the target attitude parameter and the solar radiation area corresponding to each reference attitude parameter, until it is determined that the first humidity change rate of the surface of the clothes being dried is not less than the target humidity change rate, and the current attitude parameter of the clothes dryer is used as the second attitude parameter. Obtain the current first solar position angle and first weather parameters, and determine the second solar radiation area corresponding to the second attitude parameters based on the first solar position angle and the second attitude parameters; The current first weather parameters, first solar position angle, clothing information and target attitude parameters are used as new training samples, and the second solar radiation area corresponding to the second attitude parameters is used as the training label of the new training samples. The parameters of the first model are updated based on the new training samples and training labels.

8. An adjustment device for a clothes drying rack, characterized in that, include: The acquisition module is used to acquire the sun's position angle and weather parameters at the location of the clothes drying rack within a preset time period, as well as the clothing information and current posture parameters of the clothes drying rack currently drying. The input module is used to input the clothing information, the current posture parameters, the weather parameters within the preset time period, and the solar position angle into a pre-trained first model to obtain the solar radiation area output by the first model when the clothes being dried are switched to various reference posture parameters on the clothes drying machine. The determination module is used to obtain the drying information pre-input by the user, and determine the target posture parameter from the reference posture parameters based on the drying information and the solar radiation area corresponding to each reference posture parameter; the drying information is information related to the user's drying needs; An adjustment module is used to adjust the posture of the clothes drying rack based on the target posture parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-7.

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