Clothes airing machine height adjusting method and equipment based on clothes types and medium

Abstract

The invention discloses a clothes airing machine height adjusting method and device based on clothes types and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: obtaining the multi-mode associated information of to-be-aired clothes; determining a target clothes type corresponding to the to-be-aired clothes based on the multi-modal association information; and determining a target airing height based on the target clothes type, and controlling the clothes airing machine to run to the target airing height. By acquiring the multi-modal associated information, the accuracy and robustness of clothes type identification are remarkably improved, and the identification limitation of a single information source in a complex scene is effectively overcome. Based on the recognition result, the system can automatically determine the optimal airing height according to the characteristics of different clothes, and a personalized airing strategy is achieved. By controlling the clothes airing machine to accurately run to the target height, the manual adjustment requirement is thoroughly eliminated, the operation convenience is improved, meanwhile, the optimization of the airing effect is ensured, and finally the intelligent airing system integrating sensing, decision making and execution is constructed.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a method, device and medium for adjusting the height of a clothes drying rack based on clothing type. Background Technology

[0002] Currently, electric lifting functions are common in household clothes drying racks on the market, but the setting of their drying height mostly depends on manual operation by the user or is fixed in a certain position.

[0003] In practice, users need to rely on experience to determine the appropriate height for different types of clothing and adjust it manually. However, clothing types vary greatly, with significant differences in material, shape, and drying requirements. This reliance on manual judgment and operation not only increases the burden on users but also makes it difficult to ensure that all types of clothing are in the optimal drying environment. Furthermore, a fixed height mode cannot adapt to these diverse needs, resulting in poor drying efficiency and impacting the user experience.

[0004] Therefore, the existing technology has a core flaw: it cannot automatically identify the type of clothing and adaptively adjust the drying height accordingly, which limits the optimization of drying effect and the intelligence of operation. Summary of the Invention

[0005] This invention provides a method, device, and medium for adjusting the height of a clothes drying rack based on clothing type, aiming to solve the technical problem in the prior art that it cannot automatically identify clothing type and adaptively adjust the drying height accordingly, thus limiting the optimization of drying effect and the intelligence of operation.

[0006] In a first aspect, embodiments of the present invention provide a method for adjusting the height of a clothes drying rack based on clothing type, comprising: Obtain multimodal association information of clothes to be dried; The target clothing type corresponding to the clothes to be dried is determined based on the multimodal association information; The target drying height is determined based on the target clothing type, and the clothes drying machine is controlled to operate at the target drying height.

[0007] Optionally, obtaining the multimodal association information of the clothes to be dried includes: Obtain preset washing mode information from smart washing devices; Obtain image information of the clothes to be dried; The load information of the clothes drying rack is obtained, wherein the multimodal association information includes the washing mode information, the image information, and the load information.

[0008] Optionally, determining the target clothing type corresponding to the clothes to be dried based on the multimodal association information includes: The input feature vector is determined based on the multimodal association information; The input feature vector is input into a pre-trained clothing type recognition model, so that the clothing type recognition model outputs the target clothing type corresponding to the clothing to be dried.

[0009] Optionally, determining the input feature vector based on the multimodal association information includes: The washing mode information is encoded into a washing mode vector; Obtain the image feature vector corresponding to the image information; The load information is encoded into a load feature vector; The input feature vector is obtained by fusing the washing mode vector, image feature vector, and load feature vector.

[0010] Optionally, determining the target drying height based on the target clothing type includes: Determine the corresponding basic drying height based on the target clothing type; Real-time environmental status information is acquired, and the basic drying height is dynamically corrected based on the environmental status information to obtain the target drying height.

[0011] Optionally, the step of dynamically correcting the basic drying height based on the environmental state information to obtain the target drying height includes: Based on the environmental state information, the height correction amount is determined using a pre-trained environmental correction model. The target drying height is obtained by adding the base drying height to the height correction amount.

[0012] Optionally, determining the height correction amount based on the environmental state information using a pre-trained environmental correction model includes: Based on the environmental state information, obtain the environmental feature vector; The environmental feature vector is input into a pre-trained environment correction model, which then outputs the height correction amount.

[0013] Optionally, determining the corresponding basic drying height based on the target clothing type includes: Based on the target clothing type, a preset clothing type-basic drying height mapping relationship is queried to determine the basic drying height corresponding to the target clothing type.

[0014] Secondly, embodiments of the present invention also provide a clothes drying rack height adjustment device based on clothing type, comprising: The acquisition unit is used to acquire multimodal association information of the clothes to be dried. The determining unit is used to determine the target clothing type corresponding to the clothing to be dried based on the multimodal association information; The adjustment unit is used to determine the target drying height based on the target clothing type and control the clothes dryer to operate at the target drying height.

[0015] Thirdly, embodiments of the present invention also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0017] This invention provides a method, device, and medium for adjusting the height of a clothes drying rack based on clothing type. The method includes: acquiring multimodal association information of the clothing to be dried; determining the target clothing type based on the multimodal association information; determining the target drying height based on the target clothing type; and controlling the clothes drying rack to operate at the target drying height. By acquiring multimodal association information, the accuracy and robustness of clothing type identification are significantly improved, effectively overcoming the limitations of identification from a single information source in complex scenarios. Based on the identification results, the system can automatically determine the optimal drying height for different clothing characteristics, realizing a personalized drying strategy. By controlling the clothes drying rack to precisely operate at the target height, the need for manual adjustment is completely eliminated, improving operational convenience while ensuring optimized drying results, ultimately constructing an intelligent drying system integrating perception, decision-making, and execution. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for adjusting the height of a clothes drying rack based on clothing type, provided in an embodiment of the present invention; Figure 2 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0025] Please see Figure 1 This invention provides a method for adjusting the height of a clothes drying rack based on the type of clothing. The method includes the following steps: S1, obtain the multimodal association information of the clothes to be dried.

[0026] In practice, the system acquires multimodal association information of the clothes to be dried. By collecting data features from different dimensions, it constructs a comprehensive data foundation reflecting the attributes of the clothes. Compared with solutions relying on a single information source, multimodal association information can improve recognition accuracy. For example, when visual information is difficult to accurately capture the outline due to occlusion, combining weight distribution features and possible device linkage information can form an effective supplement. This cross-validation mechanism of multi-source data significantly improves the system's perception capability and robustness in complex scenarios, providing rich and reliable data input for subsequent type judgment.

[0027] In some preferred embodiments, the above step "obtaining multimodal association information of clothes to be dried" specifically includes the following steps: obtaining the washing mode information of a preset smart washing device; obtaining the image information of the clothes to be dried; obtaining the load information of the clothes dryer, wherein the multimodal association information includes the washing mode information, the image information, and the load information.

[0028] In practice, by specifically defining the composition of multimodal association information, the comprehensiveness and reliability of clothing type recognition are further enhanced.

[0029] Washing mode information can directly reflect the washing requirements and material characteristics of clothing. For example, large item washing corresponds to large items such as bed sheets and duvet covers. This type of information provides important prior knowledge for clothing type identification.

[0030] Image information, through visual feature extraction, can capture the shape, texture, and structural details of clothing, thereby helping to determine its specific category.

[0031] Load information provides data on the quality of clothing from the perspective of physical properties. For example, the weight of clothing in a wet state can indirectly reflect information such as its water absorption, thickness, and length.

[0032] By combining washing mode information, image information, and load information, the system can achieve more comprehensive feature coverage in different scenarios. For example, when image information is difficult to accurately identify due to insufficient light or occlusion, load information and washing mode information can serve as effective supplementary information, avoiding misjudgments caused by the failure of a single information source. This multi-source data fusion strategy significantly improves the system's adaptability and fault tolerance in practical applications, making the clothing type identification results more stable and reliable, and laying a solid foundation for subsequent height adjustments.

[0033] S2, determine the target clothing type corresponding to the clothes to be dried based on the multimodal association information.

[0034] In specific implementation, the target clothing type corresponding to the clothes to be dried is determined based on the multimodal association information. The system transforms heterogeneous data into a unified type judgment criterion through feature fusion and pattern recognition technology. This process fully utilizes the complementarity and correlation between various types of information. For example, the washing mode can indicate the type of clothing, image features can identify its shape and structure, and load data indirectly reflects information such as its thickness, absorbency, and length. By comprehensively evaluating the inherent relationship between these features, the system can accurately distinguish clothing types. This invention can transform low-level data into clothing type identifiers with clear semantics, thereby establishing an operable judgment basis for subsequent high-level decision-making, while also avoiding overall control failure caused by misjudgment due to a single piece of information.

[0035] In some preferred embodiments, the above step "determining the target clothing type corresponding to the clothes to be dried based on the multimodal association information" specifically includes the following steps: determining an input feature vector based on the multimodal association information; inputting the input feature vector into a pre-trained clothing type recognition model, so that the clothing type recognition model outputs the target clothing type corresponding to the clothes to be dried.

[0036] In practice, a pre-trained clothing type recognition model was introduced to achieve efficient and intelligent processing of multimodal association information. This model can automatically learn the complex mapping relationship between different modal features and clothing types, thereby significantly improving the accuracy and generalization ability of classification.

[0037] Specifically, the system first constructs an input feature vector based on multimodal association information, transforming heterogeneous data into a unified format that the model can process. Then, it performs deep feature extraction and pattern matching using a clothing type recognition model. This machine learning-based approach effectively captures the non-linear relationships between features. For example, laundry mode information may have an implicit correlation with the image features of certain clothing items, while load information may be closely related to the material properties of the clothing. Compared to traditional rule-based judgment methods, this model can adaptively optimize feature weights through training data, reducing the subjectivity and limitations of manually set thresholds.

[0038] In addition, the model has the potential for continuous optimization. Through feedback from subsequent data and retraining, it can continuously adapt to new clothing types or user habits, further improving the long-term applicability of the system.

[0039] It should be noted that the clothing type recognition model is a trained machine learning or deep learning model. Its core function is to comprehensively analyze various input information and finally output the specific type of clothing.

[0040] The input to this clothing type model is a feature vector formed by fusing multimodal correlation information (such as laundry pattern information, image information, and weight information). This feature vector transforms different types of data (such as categorical data, image pixel data, and numerical data) into a unified mathematical representation that the model can process. For example, the laundry pattern is encoded as a vector representing program characteristics, images are processed through a convolutional neural network to extract feature vectors describing texture and shape, and weight data is normalized into numerical features.

[0041] This clothing type model is trained on a large amount of labeled clothing data, learning the complex mapping relationship between these feature vectors and the final clothing type (such as "shirt," "sheet," and "towel"). Its technical value lies in its ability to automatically learn and weigh the importance of different features. For example, when identifying a "sweater," the model learns that the "gentle mode" information from the washing machine has high confidence, the "knitted texture" identified in the image is a strong feature, and "medium weight" is also an auxiliary feature. Even if a certain information source is inaccurate (such as the folds in the clothing in the image being difficult to identify), the model can still make robust judgments based on other high-confidence information sources, thereby significantly improving the recognition accuracy and the robustness of the system.

[0042] In some preferred embodiments, the above step "determining the input feature vector based on the multimodal association information" specifically includes the following steps: encoding the laundry mode information into a laundry mode vector; obtaining the image feature vector corresponding to the image information; encoding the load information into a load feature vector; and fusing the laundry mode vector, the image feature vector, and the load feature vector to obtain the input feature vector.

[0043] In practice, the representation and fusion process of multimodal information was further optimized by specifically limiting the construction method of the input feature vector.

[0044] Encoding laundry pattern information into laundry pattern vectors transforms discrete categorical data into continuous numerical representations, thus enabling better integration into model computation. For example, different laundry patterns can be mapped to different laundry pattern vectors.

[0045] Image feature vector extraction is achieved through deep learning or other visual processing methods to capture key visual features of clothing, such as color distribution, texture, and shape contour. These features play an important role in distinguishing clothing types that look similar.

[0046] The load feature vector transforms the physical weight information of clothing into a form that the model can process through numerical encoding, enabling it to be analyzed collaboratively with other modal features on the same dimension.

[0047] Finally, by fusing the laundry pattern vector, image feature vector, and load feature vector into a unified input feature vector, the system can effectively integrate and complement multi-source information. For example, when identifying a certain type of clothing, if the image features are incomplete due to occlusion, the laundry pattern vector and load feature vector can provide auxiliary judgment criteria, avoiding recognition failure caused by the lack of single-modal information. This feature-level fusion strategy not only improves the efficiency of data utilization but also enhances the model's adaptability to complex situations, providing an important guarantee for the stable operation of the clothing type recognition model.

[0048] S3, determine the target drying height based on the target clothing type, and control the clothes drying machine to run to the target drying height.

[0049] In practice, the target drying height is determined based on the target clothing type, and the clothes dryer is controlled to operate at the target drying height. The system combines the type identification result with the preset drying strategy to achieve a complete closed loop from perception to execution. This step establishes a mapping relationship between clothing type and physical height, enabling the system to provide customized drying solutions for different clothing characteristics. Furthermore, by controlling the clothes dryer to automatically operate at the target height, the system completely eliminates the need for manual adjustment by the user, significantly improving operational convenience and ensuring the consistency and accuracy of height execution. The overall effect of this series of steps is to construct an intelligent drying system capable of autonomous perception, judgment, and execution. This system improves drying efficiency while optimizing clothing care quality, transforming the traditional experience- and manual drying process into a reliable, efficient, and user-friendly automated service.

[0050] In some preferred embodiments, the above step "determine the target drying height based on the target clothing type" specifically includes the following steps: determining the corresponding basic drying height according to the target clothing type; acquiring environmental status information in real time, and dynamically correcting the basic drying height based on the environmental status information to obtain the target drying height.

[0051] In practice, the system dynamically adjusts the basic drying height by incorporating environmental status information, allowing the drying height setting to adapt to real-time environmental changes and significantly improving the drying effect. For example, the system first determines the basic drying height based on the type of clothing being dried. This height is preset based on the general characteristics of the clothing and can meet the basic needs in most scenarios.

[0052] However, the actual drying effect is significantly affected by environmental factors, such as sunlight intensity, air humidity, wind speed, and temperature, all of which directly affect the drying speed and quality of clothes. By acquiring real-time environmental information, the system can dynamically adjust the drying height to adapt to current conditions. For example, in environments with strong sunlight, the system can appropriately lower the drying height to allow clothes to receive more sunlight and accelerate moisture evaporation; while in humid or rainy conditions, the system can raise the drying height to avoid the effects of ground moisture or to take advantage of better ventilation at a higher elevation.

[0053] This dynamic correction mechanism frees the drying process from fixed preset values, allowing it to automatically optimize based on environmental changes and maintain high drying performance under various weather and seasonal conditions. Furthermore, this method can cope with sudden environmental changes, such as sudden rainfall or increased wind speeds, by adjusting the height in a timely manner to protect clothing from damage.

[0054] In some preferred embodiments, the above step "dynamically correcting the basic drying height based on the environmental state information to obtain the target drying height" specifically includes the following steps: determining the height correction amount through a pre-trained environmental correction model based on the environmental state information; adding the basic drying height to the height correction amount to obtain the target drying height.

[0055] In practice, a pre-trained environmental correction model is used to determine the height correction amount, providing a scientific and adaptive calculation basis for dynamic height adjustment. This environmental correction model can learn the complex relationship between environmental parameters and the ideal drying height, thereby achieving accurate correction of the base height. For example, the environmental correction model can comprehensively consider the interactive effects of multiple environmental factors. For instance, high temperature combined with strong sunlight may require a larger negative correction to reduce the height, while high humidity combined with weak wind speed may require a positive correction to improve ventilation efficiency.

[0056] By calculating height corrections using an environmental correction model, the system avoids the limitations of simple rule-based corrections. For example, traditional methods may only make linear adjustments based on a single environmental factor, failing to handle the nonlinear effects of multiple coupled factors. The environmental correction model automatically learns the optimal correction strategy by using historical environmental and drying effect records from the training data, and generalizes to unseen environmental scenarios in practical applications. Furthermore, this environmental correction model can be continuously optimized based on long-term operational data, gradually adapting to personalized conditions such as the user's geographical location and seasonal changes.

[0057] Ultimately, by adding the base drying height to the height correction amount output by the model, the system can obtain a more accurate and applicable target drying height, thus maintaining optimal drying results under varying environmental conditions. This method not only improves the accuracy of height adjustment but also enhances the system's adaptability to different environmental scenarios.

[0058] It's important to note that the environmental correction model is a trained machine learning model. Instead of directly identifying clothing, it analyzes real-time environmental data and quantifies the impact of these environmental factors on the ideal drying height.

[0059] The input to the environmental correction model is an environmental feature vector, which contains real-time environmental state information obtained from sensors or external interfaces, such as temperature, humidity, light intensity, UV index, and wind speed. During training, the environmental correction model learns the intrinsic relationship between massive amounts of environmental data and optimal drying results, thereby understanding the potential impact of various environmental combinations.

[0060] The environmental correction model outputs a height correction, which is a specific numerical value (positive or negative). This height correction is an intelligent adjustment suggestion based on the current environment and the baseline drying height. For example, when the model detects a combination of "high light intensity" and "low humidity," it determines, based on its learned knowledge, that these are conditions conducive to rapid drying and may output a negative correction, suggesting that the drying height be appropriately lowered to make full use of sunlight and heat near the ground. Conversely, when the input features are "high humidity" and "no wind," the model will determine that drying conditions are unfavorable and thus output a positive correction, suggesting that the clothes be raised to seek better airflow.

[0061] The technological value of this environmental correction model lies in its leap from static, fixed height settings to dynamic, adaptive optimization. It enables the drying system to move beyond simply relying on the initial type of clothing, actively responding to changing environments and dynamically seeking the most suitable microclimate conditions, thereby proactively optimizing drying efficiency and effectiveness under various weather conditions.

[0062] In some preferred embodiments, the above step "determine the corresponding basic drying height according to the target clothing type" specifically includes the following steps: querying a preset clothing type-basic drying height mapping relationship based on the target clothing type to determine the basic drying height corresponding to the target clothing type.

[0063] In practice, the basic drying height is determined by querying a preset mapping relationship between clothing type and basic drying height, providing a stable and configurable benchmark for height adjustment. This clothing type-basic drying height mapping relationship is built based on in-depth research and experimental data on the drying needs of different clothing types, ensuring that all types of clothing receive a suitable initial drying height under standard conditions. For example, this mapping relationship can set a lower basic height for highly absorbent, heavy cotton clothing to promote moisture evaporation, and a higher basic height for large items such as sheets and duvet covers to prevent them from dragging on the floor. Through this preset mapping method, the system can quickly determine the corresponding basic height after identifying the target clothing type, without the need for complex real-time calculations, thereby improving response speed and operational efficiency.

[0064] Furthermore, the configurability of the mapping relationship allows the system to flexibly adapt to the needs of different users or product updates. For example, by updating the mapping table, new clothing drying strategies can be added or the recommended height for existing clothing can be adjusted. This method also ensures the consistency and reliability of the basic height decision, avoiding the impact of environmental interference or data noise on the initial height setting. Ultimately, by associating clothing type with the basic drying height, an accurate benchmark value is provided for subsequent steps in achieving height adaptive control. Combined with dynamic environmental correction, this constitutes a complete and efficient drying height optimization scheme.

[0065] In some preferred embodiments, the above step "determine the height correction amount based on the environmental state information using a pre-trained environmental correction model" specifically includes the following steps: obtaining an environmental feature vector based on the environmental state information; inputting the environmental feature vector into the pre-trained environmental correction model so that the environmental correction model outputs the height correction amount.

[0066] In practice, by transforming environmental state information into environmental feature vectors and using a pre-trained environmental correction model to output height correction values, an end-to-end intelligent mapping from environmental information to height decision-making is achieved. The construction of environmental feature vectors integrates various environmental parameters into a unified numerical representation. For example, data such as temperature, humidity, light intensity, and wind speed can be encoded into different dimensions of the vector, thus preserving their original distribution and correlation characteristics. This feature representation method enables the environmental correction model to more effectively capture the complex relationships between environmental factors. For instance, the combination of high humidity and low wind speed may mean a significant decrease in drying rate, requiring a larger positive height correction to improve ventilation. Through deep learning or other machine learning methods, the environmental correction model can learn the mapping pattern between environmental features and ideal height correction values ​​from a large amount of historical data and make reasonable predictions when facing new environmental conditions.

[0067] For example, during training, the model can learn that certain combinations of environmental features typically correspond to specific height adjustment requirements, thus enabling rapid response to similar scenarios in practical applications. This model-based approach not only improves the accuracy of height correction but also endows the system with the ability to generalize to unknown environmental conditions, allowing it to exhibit stable performance in diverse real-world usage scenarios. Ultimately, by combining environmental feature vectors with the pre-trained model, the system achieves automated and optimized calculation of height correction, providing reliable technical support for the dynamic adjustment of drying height.

[0068] This invention proposes a method for adjusting the height of a clothes drying rack based on clothing type. The method includes: acquiring multimodal association information of the clothes to be dried; determining the target clothing type based on the multimodal association information; determining the target drying height based on the target clothing type; and controlling the clothes drying rack to operate at the target drying height. By acquiring multimodal association information, the accuracy and robustness of clothing type recognition are significantly improved, effectively overcoming the limitations of single information sources in complex scenarios. Based on the recognition results, the system can automatically determine the optimal drying height for different clothing characteristics, realizing a personalized drying strategy. By controlling the clothes drying rack to precisely operate at the target height, the need for manual adjustment is completely eliminated. This improves operational convenience while ensuring optimized drying results, ultimately constructing an intelligent drying system integrating perception, decision-making, and execution.

[0069] Corresponding to the above-described method for adjusting the height of a clothes drying rack based on clothing type, the present invention also provides a device for adjusting the height of a clothes drying rack based on clothing type. This device includes a unit for executing the above-described method for adjusting the height of a clothes drying rack based on clothing type, and can be configured in a desktop computer, tablet computer, laptop computer, or other terminal. Specifically, the device includes: The acquisition unit is used to acquire multimodal association information of the clothes to be dried. The determining unit is used to determine the target clothing type corresponding to the clothing to be dried based on the multimodal association information; The adjustment unit is used to determine the target drying height based on the target clothing type and control the clothes dryer to operate at the target drying height.

[0070] In some preferred embodiments, obtaining the multimodal association information of the clothes to be dried includes: Obtain preset washing mode information from smart washing devices; Obtain image information of the clothes to be dried; The load information of the clothes drying rack is obtained, wherein the multimodal association information includes the washing mode information, the image information, and the load information.

[0071] In some preferred embodiments, determining the target clothing type corresponding to the clothing to be dried based on the multimodal association information includes: The input feature vector is determined based on the multimodal association information; The input feature vector is input into a pre-trained clothing type recognition model, so that the clothing type recognition model outputs the target clothing type corresponding to the clothing to be dried.

[0072] In some preferred embodiments, determining the input feature vector based on the multimodal association information includes: The washing mode information is encoded into a washing mode vector; Obtain the image feature vector corresponding to the image information; The load information is encoded into a load feature vector; The input feature vector is obtained by fusing the washing mode vector, image feature vector, and load feature vector.

[0073] In some preferred embodiments, determining the target drying height based on the target clothing type includes: Determine the corresponding basic drying height based on the target clothing type; Real-time environmental status information is acquired, and the basic drying height is dynamically corrected based on the environmental status information to obtain the target drying height.

[0074] In some preferred embodiments, the step of dynamically correcting the baseline drying height based on the environmental state information to obtain the target drying height includes: Based on the environmental state information, the height correction amount is determined using a pre-trained environmental correction model. The target drying height is obtained by adding the base drying height to the height correction amount.

[0075] In some preferred embodiments, determining the height correction amount based on the environmental state information using a pre-trained environmental correction model includes: Based on the environmental state information, obtain the environmental feature vector; The environmental feature vector is input into a pre-trained environment correction model, which then outputs the height correction amount.

[0076] In some preferred embodiments, determining the corresponding basic drying height based on the target clothing type includes: Based on the target clothing type, a preset clothing type-basic drying height mapping relationship is queried to determine the basic drying height corresponding to the target clothing type.

[0077] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned clothes drying rack height adjustment device based on clothing type and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0078] The aforementioned clothes drying rack height adjustment device based on clothing type can be implemented as a computer program, which can, for example... Figure 2 It runs on the computer device shown.

[0079] Please see Figure 2 , Figure 2 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a smartphone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server or a server cluster composed of multiple servers.

[0080] The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0081] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it causes the processor 502 to execute a method for adjusting the height of a clothes drying rack based on the type of clothing.

[0082] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0083] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for adjusting the height of a clothes drying rack based on the type of clothing.

[0084] The network interface 505 is used for network communication with other devices. Those skilled in the art will understand that the above structure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. A specific computer device 500 may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.

[0085] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps: Obtain multimodal association information of clothes to be dried; The target clothing type corresponding to the clothes to be dried is determined based on the multimodal association information; The target drying height is determined based on the target clothing type, and the clothes drying machine is controlled to operate at the target drying height.

[0086] In some preferred embodiments, obtaining the multimodal association information of the clothes to be dried includes: Obtain preset washing mode information from smart washing devices; Obtain image information of the clothes to be dried; The load information of the clothes drying rack is obtained, wherein the multimodal association information includes the washing mode information, the image information, and the load information.

[0087] In some preferred embodiments, determining the target clothing type corresponding to the clothing to be dried based on the multimodal association information includes: The input feature vector is determined based on the multimodal association information; The input feature vector is input into a pre-trained clothing type recognition model, so that the clothing type recognition model outputs the target clothing type corresponding to the clothing to be dried.

[0088] In some preferred embodiments, determining the input feature vector based on the multimodal association information includes: The washing mode information is encoded into a washing mode vector; Obtain the image feature vector corresponding to the image information; The load information is encoded into a load feature vector; The input feature vector is obtained by fusing the washing mode vector, image feature vector, and load feature vector.

[0089] In some preferred embodiments, determining the target drying height based on the target clothing type includes: Determine the corresponding basic drying height based on the target clothing type; Real-time environmental status information is acquired, and the basic drying height is dynamically corrected based on the environmental status information to obtain the target drying height.

[0090] In some preferred embodiments, the step of dynamically correcting the baseline drying height based on the environmental state information to obtain the target drying height includes: Based on the environmental state information, the height correction amount is determined using a pre-trained environmental correction model. The target drying height is obtained by adding the base drying height to the height correction amount.

[0091] In some preferred embodiments, determining the height correction amount based on the environmental state information using a pre-trained environmental correction model includes: Based on the environmental state information, obtain the environmental feature vector; The environmental feature vector is input into a pre-trained environment correction model, which then outputs the height correction amount.

[0092] In some preferred embodiments, determining the corresponding basic drying height based on the target clothing type includes: Based on the target clothing type, a preset clothing type-basic drying height mapping relationship is queried to determine the basic drying height corresponding to the target clothing type.

[0093] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0094] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0095] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program. When executed by a processor, the computer program causes the processor to perform the following steps: Obtain multimodal association information of clothes to be dried; The target clothing type corresponding to the clothes to be dried is determined based on the multimodal association information; The target drying height is determined based on the target clothing type, and the clothes drying machine is controlled to operate at the target drying height.

[0096] In some preferred embodiments, obtaining the multimodal association information of the clothes to be dried includes: Obtain preset washing mode information from smart washing devices; Obtain image information of the clothes to be dried; The load information of the clothes drying rack is obtained, wherein the multimodal association information includes the washing mode information, the image information, and the load information.

[0097] In some preferred embodiments, determining the target clothing type corresponding to the clothing to be dried based on the multimodal association information includes: The input feature vector is determined based on the multimodal association information; The input feature vector is input into a pre-trained clothing type recognition model, so that the clothing type recognition model outputs the target clothing type corresponding to the clothing to be dried.

[0098] In some preferred embodiments, determining the input feature vector based on the multimodal association information includes: The washing mode information is encoded into a washing mode vector; Obtain the image feature vector corresponding to the image information; The load information is encoded into a load feature vector; The input feature vector is obtained by fusing the washing mode vector, image feature vector, and load feature vector.

[0099] In some preferred embodiments, determining the target drying height based on the target clothing type includes: Determine the corresponding basic drying height based on the target clothing type; Real-time environmental status information is acquired, and the basic drying height is dynamically corrected based on the environmental status information to obtain the target drying height.

[0100] In some preferred embodiments, the step of dynamically correcting the baseline drying height based on the environmental state information to obtain the target drying height includes: Based on the environmental state information, the height correction amount is determined using a pre-trained environmental correction model. The target drying height is obtained by adding the base drying height to the height correction amount.

[0101] In some preferred embodiments, determining the height correction amount based on the environmental state information using a pre-trained environmental correction model includes: Based on the environmental state information, obtain the environmental feature vector; The environmental feature vector is input into a pre-trained environment correction model, which then outputs the height correction amount.

[0102] In some preferred embodiments, determining the corresponding basic drying height based on the target clothing type includes: Based on the target clothing type, a preset clothing type-basic drying height mapping relationship is queried to determine the basic drying height corresponding to the target clothing type.

[0103] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code. The computer-readable storage medium can be non-volatile or volatile.

[0104] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0105] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0106] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0107] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0108] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0109] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0110] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for adjusting the height of a clothes drying rack based on clothing type, characterized in that, include: Obtain multimodal association information of clothes to be dried; The target clothing type corresponding to the clothes to be dried is determined based on the multimodal association information; The target drying height is determined based on the target clothing type, and the clothes drying machine is controlled to operate at the target drying height.

2. The method for adjusting the height of a clothes drying rack based on clothing type according to claim 1, characterized in that, The acquisition of multimodal association information of the clothes to be dried includes: Obtain preset washing mode information from smart washing devices; Obtain image information of the clothes to be dried; The load information of the clothes drying rack is obtained, wherein the multimodal association information includes the washing mode information, the image information, and the load information.

3. The method for adjusting the height of a clothes drying rack based on clothing type according to claim 2, characterized in that, Determining the target clothing type corresponding to the clothes to be dried based on the multimodal association information includes: The input feature vector is determined based on the multimodal association information; The input feature vector is input into a pre-trained clothing type recognition model, so that the clothing type recognition model outputs the target clothing type corresponding to the clothing to be dried.

4. The method for adjusting the height of a clothes drying rack based on clothing type according to claim 3, characterized in that, Determining the input feature vector based on the multimodal association information includes: The washing mode information is encoded into a washing mode vector; Obtain the image feature vector corresponding to the image information; The load information is encoded into a load feature vector; The input feature vector is obtained by fusing the washing mode vector, image feature vector, and load feature vector.

5. The method for adjusting the height of a clothes drying rack based on clothing type according to claim 1, characterized in that, Determining the target drying height based on the target clothing type includes: Determine the corresponding basic drying height based on the target clothing type; Real-time environmental status information is acquired, and the basic drying height is dynamically corrected based on the environmental status information to obtain the target drying height.

6. The method for adjusting the height of a clothes drying rack based on clothing type according to claim 5, characterized in that, The step of dynamically correcting the base drying height based on the environmental state information to obtain the target drying height includes: Based on the environmental state information, the height correction amount is determined using a pre-trained environmental correction model. The target drying height is obtained by adding the base drying height to the height correction amount.

7. The method for adjusting the height of a clothes drying rack based on clothing type according to claim 6, characterized in that, The step of determining the height correction amount based on the environmental state information using a pre-trained environmental correction model includes: Based on the environmental state information, obtain the environmental feature vector; The environmental feature vector is input into a pre-trained environment correction model, which then outputs the height correction amount.

8. The method for adjusting the height of a clothes drying rack based on clothing type according to claim 5, characterized in that, Determining the corresponding basic drying height based on the target clothing type includes: Based on the target clothing type, a preset clothing type-basic drying height mapping relationship is queried to determine the basic drying height corresponding to the target clothing type.

9. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1-8.

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