Laundry treating apparatus, laundry weight determining method, and electronic device

Abstract

This invention relates to the field of clothing processing, and more particularly to a clothing processing device, a method for determining clothing weight, and an electronic device. The method includes: collecting motor current data during a phased movement of a clothing processing drum containing clothing; the phased movement includes a first movement causing the clothing to tumble and a second movement causing the clothing to rotate synchronously with the clothing processing drum; extracting feature data based on the motor current data; and determining the weight of the clothing based on the feature data and the correspondence between the feature data and the clothing weight. Using this embodiment of the application allows for obtaining a more accurate clothing weight.

Description

Technical Field

[0001] This invention relates to the field of clothing processing, and more particularly to a clothing processing device, a method for determining clothing weight, and an electronic device. Background Technology

[0002] Traditional washing machine weighing technology mainly relies on pressure sensors or motor current integration, which has the following technical defects: First, static weighing methods cannot eliminate torque errors caused by the eccentric distribution of clothes, resulting in large fluctuations in measurement accuracy; Second, existing dynamic weighing schemes use a single rotation speed mode, and the detection results deviate significantly from the actual values.

[0003] Therefore, how to provide an accurate method for determining the weight of clothing is a topic of concern in the industry. Summary of the Invention

[0004] To address the problem of accurately determining the weight of clothing in related technologies, embodiments of the present invention provide a clothing processing device, a method for determining the weight of clothing, and an electronic device.

[0005] According to a first aspect of the embodiments of this application, a method for determining the weight of clothing applied in a clothing processing device is proposed, the method comprising:

[0006] Motor current data is collected during the phased movement process of the clothing processing drum, which contains clothing. The phased movement process includes a first movement process that causes the clothing to tumble and a second movement process that causes the clothing to rotate synchronously with the clothing processing drum.

[0007] Feature data is obtained by extracting features from the motor current data;

[0008] The weight of the clothing is determined based on the feature data and the correspondence between the feature data and the weight of the clothing.

[0009] Optionally, in one implementation of the first aspect of this embodiment, the phased motion process further includes a deceleration rotation process following the second motion process.

[0010] Optionally, in one implementation of the first aspect of this embodiment, the method includes:

[0011] The garment processing drum is controlled to accelerate from an initial rotational speed to a first rotational speed with a first acceleration to achieve the first motion process;

[0012] The garment processing drum is controlled to accelerate from the first rotational speed to the second rotational speed with a second acceleration in order to achieve the second motion process;

[0013] Alternatively, the method may further include: after the second motion process, controlling the garment processing drum to decelerate from the second rotational speed to the third rotational speed with a third acceleration.

[0014] Optionally, in one implementation of the first aspect of this embodiment, the garment processing drum rotates at least one revolution from the first rotational speed to the second rotational speed; the first rotational speed is a critical speed at which the garments and the garment processing drum rotate synchronously; the second rotational speed is a critical speed at which the garment processing drum does not collide with the drum.

[0015] Optionally, in one implementation of the first aspect of this embodiment, the step of extracting feature data based on the motor current data includes:

[0016] The motor current data is standardized to obtain standardized current data.

[0017] Based on the standardized current data, at least one of the following feature data dimensions is obtained: time domain features, frequency domain features, power features, and friction compensation features.

[0018] Optionally, in one implementation of the first aspect of this embodiment, the method further includes:

[0019] Before standardizing the motor current data to obtain standardized current data, it is determined whether the motor current data meets the threshold requirement. If it does, the standardization process is performed; otherwise, the motor current data is re-collected after the clothing is shaken out.

[0020] Optionally, in one implementation of the first aspect of this embodiment, the standardization processing of the motor current data includes:

[0021] The motor current data is standardized using reference friction parameters; or,

[0022] The motor current data is standardized using the mean value of the steady current phase in the motor current data.

[0023] Optionally, in one implementation of the first aspect of this embodiment, determining the weight of the clothing based on the feature data and the correspondence between the feature data and the weight of the clothing includes:

[0024] The feature data is input into the weight prediction model to obtain the weight of the clothing output by the weight prediction model;

[0025] The weight prediction model is used to reflect the correspondence between the feature data and the weight of the clothing, and the weight prediction model is trained based on historical feature data and historical clothing weights with a definite correspondence.

[0026] A second aspect of this application provides an electronic device, including a memory for storing computer instructions and a processor for calling and executing the computer instructions to implement the method provided in the first aspect of this application.

[0027] A third aspect of this application provides a garment processing device that employs the method of the first aspect of this application, or the electronic device of the first aspect of this application.

[0028] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit the present invention. By employing the embodiments of this application, through analyzing and processing motor current data in at least two states—clothes tumbling and clothes rotating synchronously with the clothes processing drum—the method provided by the embodiments of this application can comprehensively determine the weight of the clothes by integrating the current data of the clothes in different motion states, thereby improving accuracy. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0030] Figure 1 This is a schematic flowchart of a method for determining the weight of clothing applied to a clothing processing device according to an embodiment of this application;

[0031] Figure 2 This is a flowchart illustrating a standardization process according to an embodiment of this application;

[0032] Figure 3 This is a flowchart illustrating another method for determining the weight of clothing applied to a clothing processing device according to an embodiment of this application. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0034] It should be noted that the terms "first," "second," etc., 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 orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0036] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0037] According to the inventors' research, washing machine weighing technology mainly relies on pressure sensors or motor current integration, which has three main technical shortcomings. First, static weighing methods cannot eliminate torque errors caused by eccentric distribution of clothing, resulting in significant fluctuations in measurement accuracy. Second, existing dynamic weighing schemes use a single rotation speed mode and do not consider the nonlinear effects of different machines on the coefficient of friction. Third, mainstream algorithms use empirical formulas for fitting, failing to address the generalization problem caused by differences in the mechanical structures of different machines.

[0038] Furthermore, tests revealed that the technology is inaccurate in judging small loads and large eccentricities; the potential differences caused by different clothes under the same load can also lead to inaccurate weight prediction, affecting subsequent washing programs and user experience.

[0039] Machine learning, a branch of artificial intelligence, enables computers to learn from data and improve their performance without explicit programming. By building mathematical models, machine learning algorithms can extract patterns and regularities from large amounts of data and use these patterns to make predictions or decisions. Common machine learning tasks include classification, regression, clustering, and dimensionality reduction.

[0040] To address at least some of the deficiencies in related technologies, embodiments of this application provide a method for determining the weight of clothing applied to a clothing processing device. For example... Figure 1 As shown, the method for determining the weight of clothing applied to clothing processing equipment includes the following processing steps.

[0041] 100: Collect motor current data during the phased movement of the clothing processing bin, which contains clothing.

[0042] Using the method provided in this embodiment, the clothing processing tub is controlled to move in stages, so that the collected current data can more fully reflect the influence of clothing weight on current under various movement states.

[0043] The phased motion process includes at least a first motion process that causes the clothes to tumble and a second motion process that causes the clothes to rotate synchronously with the clothes processing drum. In this way, the collected current data can comprehensively reflect the states of the clothes tumbling and the synchronous rotation of the clothes with the clothes processing drum.

[0044] 102: Feature data is obtained by feature extraction based on motor current data.

[0045] In this embodiment, feature data can be obtained using feature engineering. Since motor current data can be related to various motion states of the clothing, the extracted feature data also includes features related to the weight of the clothing under different motion states.

[0046] 104: Determine the weight of the clothing based on feature data and the correspondence between feature data and clothing weight. For example, the correspondence can be a mapping relationship, a functional relationship, or a correspondence reflected by a mathematical model (including a neural network model).

[0047] The method provided in this embodiment collects motor current data during the phased movement of the garment processing drum, extracts features based on the motor current data, and obtains the garment weight by using the correspondence between the feature data and the garment weight. This method integrates the influence of garment weight on motor current under various movement states (e.g., garment tumbling state, garment and garment rotating synchronously with the garment processing drum), making the obtained garment weight more accurate.

[0048] Optionally, in one implementation of this embodiment, the phased motion process further includes a deceleration rotation process following the second motion process. This way, the collected motor current data covers motor current data from more stages, making the subsequently determined weight of the clothing more accurate.

[0049] Optionally, in one implementation of this embodiment, a first motion process is achieved by controlling the garment processing tub to accelerate from an initial rotational speed to a first rotational speed with a first acceleration, and a second motion process is achieved by controlling the garment processing tub to accelerate from the first rotational speed to a second rotational speed with a second acceleration. Furthermore, after the second motion process, the garment processing tub can be controlled to decelerate from the second rotational speed to a third rotational speed with a third acceleration.

[0050] This implementation method ensures that the acquired current data covers three states: speed v1 acceleration feature acquisition → speed v2 acceleration (or including acceleration and stabilization) feature acquisition → speed v3 deceleration feature acquisition. Furthermore, friction features are decoupled through dual-gradient acceleration using acceleration a1 and acceleration a2.

[0051] For example, the first rotational speed is a critical rotational speed at which the clothes rotate synchronously with the clothes processing drum (e.g., the rotational speed at which the clothes just begin to rotate synchronously with the clothes processing drum during gradual acceleration). The second rotational speed is a critical rotational speed at which the clothes processing drum does not collide with the drum (e.g., the rotational speed at which the clothes processing drum approaches collision without colliding during gradual acceleration).

[0052] For example, neither the first acceleration nor the second acceleration causes the clothing processing drum to collide. The difference between the first and second accelerations helps to decouple friction characteristics and reduce their impact on the final result.

[0053] For example, the garment processing drum rotates at least one revolution during the speed increase from the first rotational speed to the second rotational speed. This helps to avoid periodic errors interfering with the accuracy of the final determined weight.

[0054] For example, under the second acceleration, the garment processing drum rotates at least one revolution, so that a stable current curve of the complete cycle can be used to determine the reference friction parameters, and the periodic error can be eliminated based on the reference friction parameters.

[0055] Optionally, in one implementation of this embodiment, such as Figure 2 As shown, processing 102 can be implemented in the following way.

[0056] 1020: Standardize the motor current data to obtain standardized current data.

[0057] For example, the motor current data can be standardized using reference friction parameters; or, the motor current data can be standardized using the average value of the steady current phase in the motor current data. This standardized current data can effectively reduce the impact of parameter differences between different washing machines of the same model on the weight detection results, thus improving the versatility of the method provided in this application.

[0058] 1022: Based on standardized current data, obtain at least one of the following dimensions of feature data: time domain features, frequency domain features, power features, and friction compensation features.

[0059] For example, the time-domain feature could be a uniform downsampling feature. The frequency-domain feature could be a Fourier transform waveform feature. The power feature could be the average power. The friction compensation feature could be the friction deviation feature between different machines.

[0060] The method provided in this embodiment enables the feature data to fully reflect the weight of clothing while reducing the influence of other factors on the weight of clothing, improving the accuracy of clothing weight detection, and having versatility.

[0061] Optionally, in this implementation, before step 1020, it is determined whether the motor current data meets the threshold requirement. If it does, standardization is performed; otherwise, the data is re-acquired after the clothing is shaken out. This implementation, by limiting the maximum value of the acquired current data to no more than a certain threshold and the minimum value to no less than a certain threshold, aims to control the stability of the data input to the prediction model. For example, excessive load leading to excessive current might affect the model's judgment. In such cases, the threshold can be used to directly identify it as an overload, avoiding its inclusion in subsequent model predictions.

[0062] Optionally, in one implementation of this embodiment, processing 104 can be implemented as follows: inputting feature data into a weight prediction model to obtain the weight of clothing output by the weight prediction model. The weight prediction model reflects the correspondence between feature data and clothing weight, and is trained based on historical feature data and historical clothing weights with a defined correspondence.

[0063] For example, the weight prediction model can be a machine learning model, such as a lightweight model like LightGBM, linear regression, etc.

[0064] Figure 3 This is a schematic flowchart illustrating a method for determining the weight of clothing applied to a clothing processing device according to an embodiment of this application. (Refer to...) Figure 3 The method includes the following processing steps.

[0065] First, the washing program begins, and the user opens the washing machine door and puts in the clothes.

[0066] After that, the washing machine performs variable initialization.

[0067] Next, a shaking process is performed to improve the uniformity of clothing distribution. For example, an alternating forward and reverse rotation strategy (5 times / s direction switching) is used to eliminate residual torque error.

[0068] Afterwards, the control bucket accelerates to v1 at an acceleration of a1 (for example, from 0 rpm to v1). The entire process can include all program initialization processes. The requirement for selecting v1 is to ensure that the clothes inside the bucket do not fall out of the bucket as much as possible, and to reduce random errors that occur during the process.

[0069] Next, the bucket is accelerated from v1 to v2 with an acceleration of a2. The requirement for choosing v2 is to minimize the impact of clothes inside the bucket on the clothes, reducing random errors during the process. The design of a2 ensures that the entire acceleration process lasts at least one cycle, calculated using the following formula (where t represents the acceleration time):

[0070]

[0071]

[0072] Assuming v1 is 70 rpm and v2 is 93 rpm, then the maximum acceleration that satisfies the requirement of the acceleration process lasting at least one revolution is 31.2 rpm / s.

[0073] Next, the bucket is decelerated from v2 to v3 with an acceleration of a3 to complete the full current acquisition. Here, the acquired current is evaluated to see if it meets the threshold condition; if not, the process needs to return to the jittering stage and re-acquire the current. Here, a2 and a3 can be the same or different.

[0074] Afterwards, the current is standardized, and features such as downsampling, waveform, and eccentricity are extracted.

[0075] After obtaining standardized current data, feature engineering is used for processing to facilitate feature extraction for subsequent machine learning models. This application can adopt the following feature processing methods, including: (1) Uniform speed downsampling features. The principle is to reduce the data dimension while retaining key information. By reducing the sampling rate (such as taking the mean, variance, etc.), high-dimensional time series data is compressed into low-dimensional features to capture the dynamic change patterns of these stages; (2) Fourier transform waveform features. The principle is to perform Fourier transform on the signal in the uniform speed stage, extract frequency domain features, decompose the time domain signal into sinusoidal wave components of different frequencies, and identify periodic vibrations or abnormal frequency components; (3) Average power features. The principle is to calculate the signal energy. The power difference in the acceleration / deceleration stages will reflect the load change; (4) Friction deviation features of different machines. The principle is to describe the inherent characteristics of the equipment through static parameters. The eccentricity value characterizes the degree of imbalance of rotating parts and directly affects the vibration amplitude. Friction deviation reflects the friction difference caused by equipment assembly or wear.

[0076] Next, the feature data is input into a machine learning model to predict clothing weight. Lightweight models such as LightGBM and linear regression can be selected. The model is trained through extensive experimentation and the collection of a large number of features. The model is deployed by directly embedding it into the driver program.

[0077] This application also provides an electronic device, including a memory and a processor. The memory stores computer instructions, and the processor calls and executes the computer instructions to implement the clothing weight determination method provided in this application.

[0078] This application also provides a clothing processing device, which adopts the clothing weight determination method provided in the previous embodiments of this application, or has the electronic equipment provided in the previous embodiments of this application.

[0079] Any process or method description in the flowcharts of the various embodiments of this application or otherwise described herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which the embodiments of this application pertain.

[0080] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0081] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0082] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0083] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0084] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for determining the weight of clothing used in a clothing processing device, the method comprising: Motor current data is collected during the phased movement process of the clothing processing drum, which contains clothing. The phased movement process includes a first movement process that causes the clothing to tumble and a second movement process that causes the clothing to rotate synchronously with the clothing processing drum. Feature data is obtained by extracting features from the motor current data; The weight of the clothing is determined based on the feature data and the correspondence between the feature data and the weight of the clothing. The step of extracting feature data based on the motor current data includes: standardizing the motor current data to obtain standardized current data; and obtaining at least one of the following feature data dimensions based on the standardized current data: time domain features, frequency domain features, power features, and friction compensation features. The standardization process for the motor current data includes: standardizing the motor current data using reference friction parameters; or, standardizing the motor current data using the average value of the steady current phase in the motor current data. The method further includes: Before standardizing the motor current data to obtain standardized current data, it is determined whether the motor current data meets the threshold requirement. If it does, the standardization process is performed; otherwise, the motor current data is re-collected after the clothing is shaken out.

2. The method according to claim 1, characterized in that, The phased motion process also includes a deceleration rotation process following the second motion process.

3. The method according to claim 1, characterized in that, The method includes: The garment processing drum is controlled to accelerate from an initial rotational speed to a first rotational speed with a first acceleration to achieve the first motion process; The garment processing drum is controlled to accelerate from the first rotational speed to the second rotational speed with a second acceleration in order to achieve the second motion process; Alternatively, the method may further include: after the second motion process, controlling the garment processing drum to decelerate from the second rotational speed to the third rotational speed with a third acceleration.

4. The method according to claim 3, characterized in that, As the speed increases from the first rotational speed to the second rotational speed, the garment processing drum rotates at least one revolution. The first rotational speed is the critical rotational speed that makes the clothes rotate synchronously with the clothes processing drum; The second rotational speed is the critical rotational speed at which the clothing processing drum does not collide with the drum.

5. The method according to claim 1, characterized in that, Determining the weight of the clothing based on the feature data and the correspondence between the feature data and the weight of the clothing includes: The feature data is input into the weight prediction model to obtain the weight of the clothing output by the weight prediction model; The weight prediction model is used to reflect the correspondence between the feature data and the weight of the clothing, and the weight prediction model is trained based on historical feature data and historical clothing weights with a definite correspondence.

6. An electronic device, characterized in that, The electronic device includes: Memory, used to store computer instructions; A processor for invoking and executing the computer instructions to implement the method as described in any one of claims 1-5.

7. A garment processing device, characterized in that, The garment processing device includes the method as described in any one of claims 1-5, or includes the electronic device as described in claim 6.

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