Laundry clumping detection method, washing machine, device and system

Abstract

The present disclosure relates to a clothes clumping detection method, a washing machine, an apparatus and a system, and relates to the field of intelligent household appliance control. A real-time vibration signal of a clothes processing barrel is obtained, and a multi-dimensional vibration feature corresponding to the real-time vibration signal is determined. The multi-dimensional vibration feature is compared with a respective corresponding dimensional baseline feature, and a dimensional comparison result corresponding to each dimensional vibration feature is obtained. The dimensional baseline feature is a vibration feature baseline value corresponding to the clothes processing barrel at a normal working speed. According to the dimensional comparison result corresponding to each dimensional vibration feature, it is determined that the clothes state in the clothes processing barrel is clothes clumping, and the quality of subsequent clothes drying can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of smart home appliance control, and in particular to a method for detecting clumping of clothing, a washing machine, equipment, and system. Background Technology

[0002] During the drying stage, washer-dryer combos typically control the clothes handling drum to rotate at low speed according to a preset program and periodically reverse direction to complete the tumbling of clothes and hot air drying.

[0003] In related technologies, when a user loads clothes into a clothes processing drum and starts the drying program, the clothes may clump together during the drying process due to differences in the material of the clothes, uneven load, or rotational dynamics. This can cause some clothes to be damaged due to local overheating (such as shrinkage or deformation) or not to be completely dried due to insufficient heating, resulting in lower drying quality. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a method, washing machine, device and system for detecting clumping of clothing.

[0005] According to a first aspect of the present disclosure, a method for detecting clothing clumping is provided, comprising:

[0006] Acquire the real-time vibration signal of the clothing processing drum and determine the multi-dimensional vibration characteristics corresponding to the real-time vibration signal;

[0007] The multi-dimensional vibration features are compared with their respective corresponding dimensional baseline features to obtain the dimensional comparison results corresponding to each dimensional vibration feature; wherein, the dimensional baseline features are the baseline values ​​of each dimensional vibration feature corresponding to the normal operating speed of the clothing processing drum.

[0008] Based on the dimensional comparison results corresponding to the vibration characteristics in each dimension, the state of the clothing in the clothing processing bucket is determined.

[0009] According to a second aspect of the present disclosure, a device for detecting agglomeration abnormalities is provided, comprising a monitoring module, a determining module, a comparison module, and a judgment module, wherein...

[0010] The acquisition module is used to acquire the real-time vibration signal of the clothing processing drum;

[0011] The determination module is used to determine the multi-dimensional vibration characteristics corresponding to the real-time vibration signal;

[0012] The comparison module is used to compare the multi-dimensional vibration features with their respective corresponding dimensional baseline features to obtain the dimensional comparison results corresponding to each dimensional vibration feature; wherein, the dimensional baseline features are the baseline values ​​of each dimensional vibration feature corresponding to the normal operating speed of the clothing processing drum.

[0013] The determination module is used to determine the state of the clothes in the clothes processing bucket based on the dimensional comparison results corresponding to the vibration characteristics of each dimension.

[0014] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0015] processor;

[0016] Memory used to store processor-executable instructions;

[0017] The processor is configured to execute the executable instructions to implement the method of any of the first aspects.

[0018] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, comprising:

[0019] When the instructions in the storage medium are executed by the processor, they enable the processor to perform any of the methods described in the first aspect above.

[0020] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising:

[0021] A computer program, and a method by which a computer program, when executed by a processor, implements any of the first aspects described above.

[0022] According to a sixth aspect of the present disclosure, a chip system is provided, comprising:

[0023] The chip system includes a processing unit and an interface circuit. The processing unit obtains program instructions through the interface circuit, and the program instructions are executed by the processing unit. The processing unit is used to execute the method of any of the first aspects mentioned above.

[0024] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: acquiring real-time vibration signals of the clothing processing drum and determining the multi-dimensional vibration characteristics corresponding to the real-time vibration signals; comparing the multi-dimensional vibration characteristics with their respective corresponding dimensional baseline characteristics to obtain dimensional comparison results corresponding to each dimensional vibration characteristic; wherein, the dimensional baseline characteristics are the vibration characteristic baseline values ​​corresponding to the clothing processing drum at normal operating speed; and determining the state of the clothing in the clothing processing drum based on the dimensional comparison results corresponding to each dimensional vibration characteristic. In this application, by continuously acquiring, extracting features, comparing baselines, and controlling the vibration state of the clothing processing drum during the drying stage, clumping can be identified based on the actual movement state of the clothing, transforming the drying control from fixed-rhythm drive to feedback control based on real-time state, thereby improving the quality of clothing drying.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0027] Figure 1A This is a schematic diagram of a scenario shown according to some embodiments of the present disclosure;

[0028] Figure 1B These are schematic diagrams of scenarios illustrated according to some embodiments of this disclosure. Figure 2 ;

[0029] Figure 2 This is a flowchart illustrating a method for detecting clothing clumping according to some embodiments of the present disclosure;

[0030] Figure 3 This is a flowchart illustrating a method for detecting clothing clumping according to some embodiments of the present disclosure. Figure 2 ;

[0031] Figure 4 This is an axial schematic diagram shown according to some embodiments of the present disclosure;

[0032] Figure 5 This is a flowchart illustrating a method for detecting clothing clumping according to some embodiments of the present disclosure. Figure 3 ;

[0033] Figure 6 This is a block diagram of a clothing clumping detection device according to some embodiments of the present disclosure;

[0034] Figure 7 This is a block diagram illustrating an electronic device according to some embodiments of the present disclosure;

[0035] Figure 8 This is a block diagram illustrating another electronic device according to some embodiments disclosed in a book;

[0036] Figure 9 This is a block diagram of a chip system according to some embodiments of the present disclosure. Detailed Implementation

[0037] Some embodiments of this disclosure will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted.

[0038] The embodiments described in the following examples of this disclosure are not representative of all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0039] Clothing drying control technology involves identifying and adjusting the state of clothes in the clothes handling drum during the drying stage of a washer-dryer combo. It is applicable to scenarios where household or commercial laundry equipment continues the drying process after washing. In this scenario, the clothes handling drum, drive motor, heating and air supply components, and detection unit for collecting movement information of the clothes handling drum work together to achieve clothes tumbling and hot air penetration.

[0040] Existing washer-dryer combos typically drive the clothes handling drum at low speed according to a preset program during the drying stage, and reverse direction at fixed intervals to ensure even heating of the clothes through continuous tumbling. This type of solution relies primarily on pre-set speed, duration, and reversal rhythm, rather than dynamically adjusting based on the actual movement of the clothes within the drum.

[0041] In practical use, large garments tend to stick to the inner wall of the garment processing drum under the influence of centrifugal force and gravity, while small garments may clump together due to static electricity or entanglement, thus disrupting normal tumbling. Because existing solutions lack the ability to sense the real-time status of the drying stage, the system struggles to promptly identify whether the garments have clumped and continues to heat and rotate according to the predetermined program.

[0042] Figure 1A This is a schematic diagram of a scenario shown according to some embodiments of the present disclosure, such as... Figure 1AAs shown, this illustrates a common scenario during the drying process where large garments tend to clump together and adhere to the drum wall. A hot air circulation zone forms inside the garment processing drum. As the drum rotates, the garments adhere to the drum wall and rotate synchronously with it, making it difficult for them to effectively fall or impact. In this situation, the vibration signals collected by the vibration sensor will exhibit characteristics such as a decrease in the Impact Pulse Count (IPC), changes in the Unbalance Index (UBI) vibration, and a decrease in the variance of the three-axis combined vector. These characteristics can be used to identify abnormal clumping, adhesion to the drum wall, or jamming. The garment processing drum can be a roller drum or a pulsator drum, etc.

[0043] Figure 1B These are schematic diagrams of scenarios illustrated according to some embodiments of this disclosure. Figure 2 ,like Figure 1B As shown, during the drying stage, multiple garments become entangled and clump together, typically small items such as socks and underwear forming dense, abnormally clumped clumps. Besides the clumps located off-center in the center of the drying drum, a small number of loose garments that can tumble normally are scattered throughout, causing a significant shift in the overall center of gravity along with the clumps. Vibration sensors at the bottom collect vibration data from the entire machine. Under this condition, three types of vibration characteristics are observed: a decrease in the impact pulse count (IPC), indicating a significant reduction in the frequency of drum wall impacts; a substantial increase in the shaft imbalance index (UBI), highlighting the eccentric imbalance; and a significant decrease in the variance of the three-axis combined vector. Simultaneously, airflow is difficult within the clumps, resulting in a lower temperature in the core area compared to other areas of the drum, leading to incomplete drying in certain areas. These multi-dimensional vibration characteristics can be used to accurately identify this type of abnormal clumping state.

[0044] In related technologies, when a user loads clothes into a clothes processing drum and starts the drying program, the clothes may clump together during the drying process due to differences in the material of the clothes, uneven load, or rotational dynamics. This can cause some clothes to be damaged due to local overheating (such as shrinkage or deformation) or not to be completely dried due to insufficient heating, resulting in lower drying quality.

[0045] To address the aforementioned technical problems, this disclosure proposes the following concept: by integrating a vibration sensor into the washer-dryer to collect vibration signals from the clothes processing drum in real time, and combining multi-dimensional feature analysis with an active control strategy, the automatic identification and untangling of clothes during the drying stage can be achieved, thereby improving the quality of clothes drying.

[0046] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0047] Figure 2 This is a flowchart illustrating a method for detecting clothing clumping according to some embodiments of this disclosure, as shown in Figure 1. Figure 2 As shown, the method for detecting clothes tangling is used in the main control board, microprocessor, or control unit that communicates with the motor controller in the washing machine control system, and includes the following steps.

[0048] S201. Obtain the real-time vibration signal of the clothing processing drum.

[0049] The vibration sensor can monitor the real-time vibration signal of the clothes processing drum during the drying stage. It is a sensing component that obtains dynamic state information such as clothes tumbling, impacting, and sticking to the wall. The vibration sensor is installed at a position that can reflect the mechanical vibration of the clothes processing drum.

[0050] The sampling rate can be set to 20Hz to continuously acquire real-time vibration signals. The sampling frequency is lower than the sampling rate during the baseline establishment phase to reduce hardware sampling power consumption. A vibration detection window can be set every 10 seconds.

[0051] To ensure that the sampling data accurately reflects the dynamic changes of clothes during the drying stage, the vibration sensor is triggered to start sampling after the drying program starts and the clothes processing drum is running stably. The sampling object is the coupled mechanical vibration signal caused by the clothes rolling, falling, hitting the drum wall, partially sticking to the wall, and shifting the center of gravity during the operation of the clothes processing drum.

[0052] The clothes handling drum is used to hold clothes to be dried. During the drying stage, it is driven to rotate. It is not only the carrier of the excitation to generate vibration signals, but also the controlled object to perform abnormal clumping state detection and de-clumping control.

[0053] Real-time vibration signal refers to the raw triaxial vibration data continuously collected by the vibration sensor under drying conditions, which is used to quantitatively characterize the real-time movement state of clothes inside the clothes processing drum.

[0054] S202. Determine the multi-dimensional vibration characteristics corresponding to the real-time vibration signal.

[0055] After receiving the real-time vibration signal, the signal can be processed, and multi-dimensional vibration features can be extracted based on the processed vibration data.

[0056] Multidimensional vibration features are a set of multiple features used to characterize the dynamic state of clothes in the clothes processing drum. They serve as the basis for subsequent comparison with multidimensional baseline features and for determining abnormal clumping conditions.

[0057] Multidimensional vibration characteristics can be at least two of the following to characterize clothing: impact pulse count, axial imbalance index, and triaxial summative vector variance.

[0058] The impact pulse count reflects the frequency of clothing impacting the drum wall. Under normal tumbling conditions, the clothing will inevitably impact the drum wall every revolution, and the impact pulse count is stable and significantly greater than zero. When there is an abnormal clumping state, if the clothing rotates against the wall as a whole (such as large garments rolling around), the impact pulse count will decrease significantly. If the clothing is partially entangled (such as small garments getting tangled), some impact behavior may still be retained, and the decrease in the impact pulse count will be smaller.

[0059] The variance of the three-axis composite vector reflects the fluctuation of the garment processing drum vibration. Under normal tumbling conditions, the garments alternately experience impact and weightlessness, resulting in drastic fluctuations in the amplitude of the three-axis composite vector and a large variance. When abnormal clumping occurs, the garments rotate synchronously with the garment processing drum, the three-axis composite vector tends to stabilize, and the variance decreases significantly.

[0060] When the clothes are in a normal tumbling state, the vibration of the clothes processing drum exhibits a relatively stable dynamic change; when the clothes gradually clump together and adhere to the inner wall, the vibration state of the clothes processing drum will change accordingly. Therefore, the normal tumbling and clumping trend can be distinguished through multi-dimensional vibration characteristics.

[0061] In one possible embodiment, feature information corresponding to the current moment is generated during the sampling process and used as input data for subsequent comparison processing. It should be understood that the installation location, sensor type, sampling method, feature extraction method, and related parameters of the vibration sensor described above are merely illustrative and not limiting. As long as a real-time vibration signal reflecting the movement state of the clothes in the clothing processing drum can be obtained and the corresponding multi-dimensional vibration characteristics can be calculated, it can be applied to this application.

[0062] S203. Compare the multi-dimensional vibration features with their respective corresponding dimensional baseline features to obtain the dimensional comparison results corresponding to each dimensional vibration feature.

[0063] The dimensional baseline feature is the vibration characteristic of the garment processing drum under normal tumbling conditions, which is used for comparison with its corresponding dimensional vibration feature.

[0064] The dimensional comparison result represents the judgment result after comparing the dimensional vibration characteristics with the dimensional baseline characteristics. It is an intermediate result for determining whether to enter the agglomeration abnormal state.

[0065] The dimensional baseline features are pre-established under the normal tumbling state during the drying stage. Specifically, vibration data can be collected while the garment is in a normal tumbling state, and the dimensional baseline features can be determined using methods corresponding to steps S101 and S102. By pre-establishing multi-dimensional baseline features, a reference can be provided for subsequent real-time detection, making subsequent comparison results more stable.

[0066] When comparing dimensional vibration features with dimensional baseline features, the comparison method can be difference comparison, ratio comparison, or other comparison methods that can reflect the degree of deviation.

[0067] Under normal tumbling conditions, clothes continuously tumble within the garment processing drum, and the drum's vibration is typically within the baseline range. When clothes clump together, the clump may shift to one side of the drum or rotate synchronously along the inner wall, causing the vibration to deviate from the normal tumbling state. Therefore, a comparison can be used to determine whether the clothing's movement deviates from the normal tumbling state.

[0068] To reduce misjudgments caused by occasional fluctuations, the dimensional comparison results in this application can be formed by combining the comparison results over a continuous period of time.

[0069] S204. Based on the dimensional comparison results corresponding to the vibration characteristics of each dimension, determine the state of the clothes in the clothes processing bucket.

[0070] The dimensional comparison results are used to characterize whether there is a significant deviation between the dimensional vibration characteristics and the dimensional baseline characteristics. When it is determined that the clothes in the clothes processing bucket are in an abnormal clumping state, the clothes are redispersed by changing the operating state of the clothes processing bucket.

[0071] The controller can generate a clumping control command and send it to the drive motor. After receiving the clumping control command, the drive motor adjusts the current operating state of the clothing processing drum to dissolve the clumped clothing, allowing the clothing to be dispersed and return to a normal tumbling state.

[0072] The drive motor is an actuator used to drive the clothing processing drum to perform a de-clumping action when it is determined that the clothing in the clothing processing drum is in an abnormal clumping state.

[0073] In some possible embodiments, the method further includes: in response to the condition of the clothes in the clothes processing drum being in an abnormal clumping state, controlling the clothes processing drum to stop rotating within a second preset time period; controlling the clothes processing drum to rotate a first preset number of times at a first preset speed in the opposite direction of the preset rotation direction, and then rotating a second preset number of times at a second preset speed in the preset rotation direction.

[0074] The preset rotation direction is the direction that the garment processing drum is pre-set to rotate, which can be clockwise or counterclockwise. When the preset rotation direction is clockwise, the opposite direction is counterclockwise.

[0075] The first preset speed is greater than the second preset speed, and the first preset number of revolutions is less than or equal to the second preset number of revolutions.

[0076] The second preset duration for which the garment processing drum stops rotating can be set to several seconds, allowing the garments to slip relative to each other under the influence of gravity and loosen any localized adhesion. Subsequently, the drive motor switches to the reverse output state, driving the garment processing drum to rotate a first preset number of revolutions in the opposite direction of the preset rotation direction at a first preset speed. The inertial impact generated by the higher speed disperses the entangled areas. Then, it switches back to the normal rotation state in the preset rotation direction, rotating a second preset number of revolutions in the forward direction at a lower second preset speed, causing the disturbed garments to redistribute within the garment processing drum.

[0077] For example, the second preset duration is 0.5 seconds, the first preset rotation speed is 1.2 times the normal rotation speed, the first preset number of revolutions is 5, the second preset rotation speed is 20 rpm, and the second preset number of revolutions is 10. Then, the garment processing drum brakes and stops within 0.5 seconds, using inertia to cause the clumped garments to slide relative to each other; it then rotates counterclockwise 5 times at 1.2 times the normal rotation speed, using the inertial impact force generated by the sudden change in direction to break up the tangled garments; finally, it rotates clockwise 10 times at a low speed of 20 rpm, using gravity to allow the untangled garments to naturally scatter and distribute themselves during the slow tumbling.

[0078] The garment processing drum is controlled to reverse at a high speed at a first preset speed (e.g., 1.2 times the normal speed) to impact and disassemble the tangled garments using the inertial force generated by the sudden change in direction; then it rotates at a low speed in the forward direction at a second preset speed (e.g., 20 rpm) so that the disassembled garments fall naturally under the action of gravity.

[0079] In this embodiment, after adopting the above control method, the stop, reverse impact and low-speed forward stretching can act on the clumped clothes in sequence, making it easier for the clothes in the clothes processing drum to return to a dispersed state, thereby improving the subsequent hot air penetration conditions and making the drying control more stable.

[0080] After the de-clumping process is completed, the monitoring and comparison process is restarted. The real-time vibration signal after de-clumping is re-determined for multi-dimensional vibration characteristics and compared with multi-dimensional baseline characteristics. If the comparison result returns to the normal tumbling range, the abnormal clumping state marker is removed, and the garment processing drum returns to normal drying operation; if the comparison result still shows that the garments are in an abnormal clumping state, the corresponding de-clumping control can continue to be executed.

[0081] By associating vibration recognition results with drive motor control actions, the equipment can make targeted adjustments to the movement of the clothing processing drum after determining an abnormal clumping state, instead of continuing to operate according to a fixed program. This allows subsequent hot air to re-penetrate the dispersed clothing layers.

[0082] It should be understood that the above-mentioned unclogging control method is only an example and not a limitation. As long as it can control the drive motor to change the operating state of the clothing processing tank and cause the clothing to redisperse after determining that the clothing in the clothing processing tank is in an abnormal clumping state, it can be applied to this application.

[0083] The clothing clumping detection method provided in this application can monitor the real-time vibration signal of the clothing processing drum during the drying stage using a vibration sensor, and determine the multi-dimensional vibration characteristics corresponding to the real-time vibration signal. The multi-dimensional vibration characteristics are compared with multi-dimensional baseline characteristics to obtain a dimensional comparison result. Based on the dimensional comparison result, when it is determined that the clothing in the clothing processing drum is in an abnormal clumping state, the drive motor of the clothing processing drum is controlled to unclump the clothes. In this application, by continuously collecting, extracting features, comparing baselines, and controlling the vibration state of the clothing processing drum during the drying stage, clumping can be identified and unclumping actions triggered based on the actual movement state of the clothing. This transforms the drying control from a fixed-rhythm drive to a feedback control based on real-time status, enabling real-time perception of the clothing status, avoiding localized overheating damage or under-drying caused by clumping, ensuring uniform penetration of hot air into the clothing, and improving the quality of clothing drying.

[0084] In one possible implementation, the multidimensional vibration characteristics include at least two of the following: shock pulse count, shaft imbalance index, and triaxial summation vector variance.

[0085] After acquiring real-time vibration signals, the signals are filtered and amplitude normalized. At least two of the following are extracted as multi-dimensional vibration features within a preset sampling window: impact pulse count, shaft imbalance index, and three-axis summative vector variance.

[0086] The calculation of the impact pulse count can be performed using an acceleration threshold triggering method. The threshold can be set between 0.8g (gravity) and 2.5g, taking into account the equipment model, cylinder mass, and rated rotation speed of the clothing processing drum.

[0087] The shaft imbalance index can be calculated based on the time-series root mean square difference or ratio of the transverse and longitudinal vibration components.

[0088] The variance of the triaxial sum vector can be calculated based on the triaxial sum vector acceleration sequence of continuous sampling points.

[0089] The vibration sensor can be a MEMS (Micro-Electro-Mechanical Systems) triaxial accelerometer, which is small in size, fast in response and easy to install. In practical applications, other models of this component can also be selected, and this application does not limit them.

[0090] During operation, the dimensional vibration characteristics are compared with the dimensional baseline characteristics formed under normal tumbling conditions. When the impact pulse count increases significantly, the axial imbalance index rises, or the variance of the triaxial summation vector changes abnormally, it indicates that the distribution state of the clothing deviates from the normal tumbling state. Based on this feature set, a more stable state characterization of whether the clothing is clumped can be formed, providing a basis for subsequent unclogging control.

[0091] In this embodiment, the multi-dimensional vibration characteristics can characterize the state of the clothing processing drum from three dimensions: impact frequency, balance, and fluctuation. This makes the identification of clumping abnormalities no longer dependent on a single vibration value, thus improving the adaptability to different clothing types and different loading scenarios, and improving the stability of clumping determination.

[0092] During the comparison process, the difference, ratio, or grade determination result between the vibration characteristics of each dimension and the baseline characteristics of the corresponding dimension can be calculated, and the difference or grade determination result can be mapped to the corresponding dimension comparison result.

[0093] For example, when the current feature value of a certain dimension exceeds the baseline feature range of the dimension and continues to meet the preset judgment conditions, the comparison result of that dimension can be determined as an abnormal clumping state; when it does not exceed the baseline feature range of the dimension, the comparison result of that dimension is determined as a normal state.

[0094] Since multidimensional vibration characteristics can represent at least two of the impact pulse counts, axial imbalance index, and triaxial vector variance of clothing, the multidimensional baseline characteristics of the response can also represent at least two of the impact pulse count baseline, axial imbalance index baseline, and triaxial vector variance baseline. The following section combines these characteristics... Figure 3 The execution process for determining abnormal clustering states based on three dimensions is explained.

[0095] Figure 3 This is a flowchart illustrating a method for detecting clothing clumping according to some embodiments of the present disclosure. Figure 2 ,like Figure 3 As shown, the method for detecting clothes tangling is used in the main control board, microprocessor, or control unit that communicates with the motor controller in the washing machine control system, and includes the following steps.

[0096] S301. If the impact pulse count is less than the impact pulse determination threshold and the duration is greater than the first preset duration, then the dimension comparison result corresponding to the impact pulse count is determined to be an abnormal clustering state.

[0097] The impact pulse determination threshold is determined by multiplying the impact pulse count baseline by the first preset ratio; if the impact pulse count is less than and the duration is greater than the first preset duration, the dimensional comparison result is determined to be an abnormal clumping state.

[0098] After acquiring the real-time vibration signal after synchronization and alignment, noise reduction filtering, feature amplification, and pulse recognition processing are sequentially performed on the original acceleration waveform. Valid peak events with amplitudes exceeding the pulse determination threshold are included in the impact pulse count, and the count results are iteratively updated within a frame-by-frame continuous sampling window.

[0099] The impact pulse count is compared with the impact pulse count baseline multiplied by a first preset ratio to obtain the impact pulse judgment threshold; when the impact pulse count is continuously lower than the impact pulse judgment threshold and the duration exceeds the first preset duration, the output dimension comparison result is a clumping abnormal state.

[0100] The first preset duration can be set to the duration corresponding to one or more reversing cycles of the clothing processing drum. Based on the inherent operating cycle constraint criteria of the clothing processing drum, misjudgments caused by instantaneous vibration disturbances are avoided. If the impact pulse count recovers to above the impact pulse judgment threshold, the dimensional comparison result is updated synchronously to show the normal tumbling state of the clothing.

[0101] For example, assuming the first preset ratio is 40% and the first preset duration is 20 seconds; if the impact pulse count is lower than 40% of the impact pulse count baseline and the duration exceeds 20 seconds, it is judged as "suspected clumping / stuck", which indicates that the clothes in the drum lose the throwing and impact action, no longer generate effective drum wall impact, and rotate uniformly with the clothes processing drum on the same axis, losing normal tumbling behavior.

[0102] In this embodiment, the impact pulse count corresponding to normal tumbling of clothes under stable drying conditions is selected as a reference baseline. A first preset ratio is introduced to convert the count into a quantitative judgment threshold. A secondary verification is performed by superimposing a continuous duration constraint. This can accurately distinguish between short-term vibration fluctuations and continuous low-impact abnormal conditions, ensuring that the clumping judgment result closely matches the actual operating conditions of the clothes processing drum. When the impact pulse count is consistently lower than the impact pulse judgment threshold, the clumping judgment result can be stably output, providing a reliable judgment basis for subsequent closed-loop de-clumping control.

[0103] S302. If the axis imbalance index is greater than the index judgment threshold, then the dimension comparison result corresponding to the axis imbalance index is determined to be an abnormal clumping state.

[0104] The index determination threshold is the product of the axis imbalance index baseline and a preset multiple.

[0105] The three-axis detection directions are matched with the vibrations of the first, second, and third directions of the clothing processing drum, and the eccentric vibration detection plane is consistent with the radial eccentric vibration direction of the main axis of the clothing processing drum, in order to collect the radial unbalanced vibration signal caused by the eccentric distribution of clothing during the rotation of the clothing processing drum.

[0106] Figure 4 This is an axial schematic diagram illustrating some embodiments of the present disclosure. Please refer to [link / reference]. Figure 4Taking the garment processing drum as an example, the drum rotates clockwise during normal rotation. The first direction is used to indicate the horizontal radial positive vibration acquisition direction, the second direction is used to indicate the horizontal radial oblique vibration acquisition direction of the drum, and the third direction is used to indicate the vertical upward radial vibration acquisition direction of the drum. The first direction, the second direction, and the third direction are perpendicular to each other.

[0107] The real-time vibration signal after synchronous preprocessing is denoised in the time domain and frequency domain, and then the lateral and second-direction acceleration features are extracted and the shaft imbalance index is calculated. The pre-calibrated and stored shaft imbalance index baseline is retrieved and the index judgment threshold is calculated by combining the preset multiple.

[0108] For example, the preset multiple corresponding to the imbalance determination can be configured to be 3 times.

[0109] If the axis imbalance index continues to exceed the index judgment threshold for a set period of time, the current dimension comparison result will be judged as an abnormal clumping state, and this result will be used as the triggering basis for subsequent unclogging control.

[0110] When the clothes in the garment processing drum are tumbling evenly, the vibration amplitudes in the first and second directions are balanced, and the vibration amplitude in the third direction remains within the baseline range. The axial imbalance index will not continuously exceed the index judgment threshold. When the clothes are radially offset and aggregated, or locally compacted and clumped, the rotational eccentric load of the garment processing drum intensifies, the difference in radial vibration amplitude of the drum increases, the axial imbalance index rises and exceeds the judgment threshold, and the working condition is judged to be an abnormal clumping state.

[0111] In this embodiment, the abnormal conditions of eccentric aggregation and clumping of clothes inside the clothes processing drum are identified based on the axial imbalance index quantification. The impact pulse dimension mentioned above is linked to complete the multi-dimensional joint judgment, and the judgment results are used to empower the closed-loop control of the drying stage to improve the stability of the drying operation.

[0112] S303. If the variance of the three-axis sum vector is less than the vector judgment threshold, then the dimension comparison result corresponding to the variance of the three-axis sum vector is determined to be an abnormal clumping state.

[0113] The product of the three-axis combined vector variance baseline and the second preset ratio can be used as the vector judgment threshold; if the three-axis combined vector variance is less than the vector judgment threshold, the dimension comparison result is a clumping abnormal state.

[0114] The three-axis vibration components in the first, second, and third directions can be retrieved from the vibration sensor through synchronous sampling. First, multi-channel time alignment and waveform noise reduction preprocessing are completed. Then, the three-axis resultant vector acceleration is solved at each sampling time. Next, the resultant vector acceleration time sequence within the preset sampling time window is extracted, and the time variance is calculated to obtain the three-axis resultant vector variance.

[0115] Retrieve the pre-calibrated stored three-axis summation vector variance baseline and calculate the vector judgment threshold by combining it with the second preset ratio; compare the three-axis summation vector variance with the vector judgment threshold, and when the three-axis summation vector variance is continuously lower than the threshold, output the clustering abnormality status signal of this dimension and push it to the back-end unclustering control logic.

[0116] The second preset ratio can be pre-fixed into the machine parameter calibration table, and the parameter values ​​can be adapted to the vibration attenuation characteristics corresponding to different drum diameters, rated loads, and drying speed ranges. During the mass production stage of the machine, iterative optimization can be carried out based on the machine model calibration data, and this application does not limit this. For example, the second preset ratio can be 30%.

[0117] By integrating three-dimensional raw vibration data to solve for the variance feature of a single triaxial sum vector, and generating a quantitative judgment threshold based on a pre-calibrated baseline coupled with a second preset ratio, the intensity of discrete fluctuations in the drum vibration under the condition of clothing tumbling can be accurately characterized. When the clothing is compacted and clumped together, and rotates synchronously against the drum wall, the impact vibration of the drum disappears, the acceleration fluctuation amplitude narrows, and the variance of the triaxial sum vector decreases synchronously, thereby accurately identifying the abnormal clumping state.

[0118] In this embodiment, a threshold determination can be made for the vibration weakening phenomenon caused by clothing agglomeration during the drying process. The output result is stable and easy to call directly, thereby making the agglomeration identification closer to the actual movement state of the clothing processing drum and providing a clear basis for subsequent de-agglomeration control.

[0119] The results of comparisons across multiple dimensions are summarized and the dimensional comparison results are determined based on the number, weight, or consistency of abnormal clustering states presented in the multiple dimensions.

[0120] In one possible implementation, if the comparison result in only one dimension is an abnormal clumping state, the state of the clothes in the clothes processing bucket is determined to be an abnormal clumping state.

[0121] In one possible implementation, if the comparison results of at least two dimensions indicate an abnormal clumping state, then the state of the clothes in the clothes processing bin is determined to be an abnormal clumping state; otherwise, it is determined to be a normal state.

[0122] The comparison results of each dimension are mapped to either a normal state or a clustering abnormal state when output. These results are statistically analyzed, and when at least two dimensions are in a clustering abnormal state, the dimension comparison result is determined to be in a clustering abnormal state.

[0123] The vibration characteristics of each dimension (such as impact pulse count, axial imbalance index, and three-axis sum vector variance) are compared with the baseline characteristics of their corresponding dimensions. If the comparison results of at least two dimensions show that the vibration characteristics deviate significantly from the baseline (such as a decrease in impact pulse count, an increase in axial imbalance index, and a decrease in three-axis sum vector variance), then the clothes in the clothes processing bucket are determined to be in an abnormal clumping state.

[0124] The comparison results are not based on a single vibration feature, but rather cross-verify the results of multiple dimensions. When clothes in the garment processing drum are indeed entangled, clinging to the walls, or clustered, different vibration dimensions often deviate from normal tumbling characteristics simultaneously. Therefore, at least two dimensions simultaneously exhibiting abnormal clumping can be used as a criterion for determining that the dimensional comparison results indicate an abnormal clumping state.

[0125] In this embodiment, the common direction of comparison results from multiple dimensions can be used to complete the clumping confirmation, reduce the impact of false alarms from a single dimension on the overall judgment, and make the triggering conditions for subsequent unclogging control more stable, thereby improving the reliability of identifying abnormal clumping states in the clothing processing bin.

[0126] In one possible implementation, if the comparison results of at least two dimensions indicate an abnormal clumping state, the rotation speed of the clothing processing drum is reduced to a third preset rotation speed and maintained for a third preset duration. During this period, it is detected whether the acceleration signal exhibits free fall characteristics. If so, it indicates that some clothing can still move freely, indicating mild clumping; if not, it indicates that the clothing is completely fixed, indicating severe clumping.

[0127] For example, the third preset speed is 10 rpm and the third preset duration is 3 seconds.

[0128] The clothing clumping detection method provided in this application can match vibration information of different dimensions with the baseline features of the corresponding dimensions, and form a dimensional comparison result under the common constraint of multiple dimensional results. This allows single-dimensional anomalies to be mutually verified with the overall state judgment, thereby improving the stability and accuracy of clothing state identification in the clothing processing bin and providing a reliable basis for subsequent unclogging control.

[0129] In one possible implementation, the triaxial resultant vector acceleration of each acquisition point is determined based on the first directional acceleration, the second directional acceleration, and the third directional acceleration corresponding to each acquisition point; the number of times the triaxial resultant vector acceleration is greater than the acceleration threshold is determined as the impact pulse count.

[0130] After receiving the raw acceleration signal output from the vibration sensor, the signal is filtered, denoised, and synchronized, and data from each acquisition point is extracted according to the preset sampling window.

[0131] Acceleration information from multiple data collection points in the garment processing bin is uniformly converted into comparable vibration metrics, enabling the identification and quantification of impact, eccentricity, and overall vibration fluctuations, thereby providing a stable data foundation for determining the condition of the garments.

[0132] For impact pulse counting, an instantaneous triaxial composite acceleration can be constructed based on the first-direction acceleration, second-direction acceleration, and third-direction acceleration at each collection point. When the triaxial composite acceleration produces a pulse exceeding the threshold at the moment when the clothing falls freely or collides with the clothing processing drum, it is counted.

[0133] After synchronizing and aligning the triaxial accelerations at each acquisition point, the corresponding triaxial resultant vector acceleration is calculated according to the vector composition relationship. The magnitude of the triaxial resultant vector acceleration and the acceleration threshold are compared point by point within the preset sampling window. When the instantaneous triaxial resultant acceleration at any acquisition point at a certain acquisition moment is higher than the threshold, it is recorded as a valid impact pulse. Finally, the total number of valid impact pulses within a single sampling window is counted as the impact pulse count corresponding to that window.

[0134] The resultant vector acceleration along the three axes can be found in the following formula: ,in, For acceleration in the first direction, Second-direction acceleration, Third-direction acceleration, It is the resultant vector acceleration along the three axes.

[0135] During normal tumbling, the clothes inevitably hit the wall every time they roll, and the baseline value of the impact pulse count is stable and significantly greater than zero; after clumping, the clothes no longer hit the wall as they rotate with the drum, and the impact pulse count decreases significantly.

[0136] In a specific implementation, the acceleration threshold can be obtained from calibration data under normal tumbling conditions. During calibration, multiple sets of triaxial acceleration samples are collected under non-clumping abnormal conditions, and the statistical values ​​of the samples are used as the basis for the threshold so that the impact pulse count can reflect the true frequency of clothing hitting the drum wall. For example, the acceleration threshold can be twice the weight acceleration.

[0137] In this embodiment, the impact pulse count can directly characterize the impact activity of the clothes in the garment processing drum and provide a reliable basis for subsequent judgment of abnormal clumping states, thereby improving the consistency of garment movement state identification and the accuracy of control response.

[0138] In one possible implementation, the axis imbalance index is determined based on the time-mean-square root of acceleration in the first direction and the time-mean-square root of acceleration in the second direction corresponding to each acquisition point.

[0139] The shaft imbalance index can be generated by taking the root mean square of the acceleration in the first direction and the acceleration in the second direction, and then generating the shaft imbalance index based on the combination of the two results.

[0140] Please refer to the following formula: ,in, Let be the time-mean square root of acceleration in the first direction. The root mean square of the time sequence for the acceleration in the second direction is given. is the axis imbalance index, with a theoretical range of [0,1].

[0141] Under normal tumbling conditions, the clothes in the garment processing drum are evenly distributed, the vibration amplitudes in the X and Y axes are symmetrical, and the baseline of the axial imbalance index is close to 0. When the clothes are eccentrically clumped together, the vibration amplitude in a single axis increases significantly, and the axial imbalance index rises synchronously.

[0142] In one possible implementation, the variance of the triaxial sum vector acceleration at each acquisition point is determined as the triaxial sum vector variance.

[0143] For the variance of the three-axis composite vector, the acceleration of the three-axis composite vector after time alignment of each acquisition point can be calculated synchronously first, and then the variance of all composite vector data in the sequence can be solved to obtain the vibration discrete quantity that characterizes the degree of vibration fluctuation.

[0144] Select a 60-second continuous sampling window, and calculate the three-axis resultant vector at each synchronous sampling moment within the window. The formula for calculating time series variance is as follows: Under normal tumbling conditions, the clothes alternately experience impacts from the drum wall ( ) and weightlessness during flight ( When the sum of vector amplitudes alternates between high and low and fluctuates violently, the variance of the three-axis sum of vectors is relatively large; when large garments are close to the drum wall or are clumped together and rotate at a constant speed with the garment processing drum, the garments have no relative motion to the drum, the sum of vector amplitudes tends to be constant, and the variance of the three-axis sum of vectors decreases significantly.

[0145] The multi-dimensional vibration features all originate from the original real-time vibration signals within the same synchronous time window. They can reflect the operating status of the clothing processing drum from three dimensions: impact frequency, axial eccentricity, and vibration fluctuation, respectively. They complement each other to characterize the equipment's working condition, avoid the risk of misjudgment by single feature discrimination, and enhance the consistency of abnormal tumbling state identification.

[0146] After completing the above multi-dimensional vibration feature calculation and processing, the system synchronously outputs three types of feature quantities corresponding to the current dynamic of the clothing processing drum: impact pulse count, shaft imbalance index, and three-axis sum vector variance. These are then compared item by item with the pre-calibrated multi-dimensional baseline features to complete the working condition determination.

[0147] The clothing clumping detection method provided in this application can extract the vibration state of the clothing processing drum from multiple directions and statistical perspectives, so that the impact changes, axial imbalance changes and vibration discrete changes caused by abnormal clumping can be effectively characterized, thereby improving the feature sufficiency and judgment reliability of subsequent state comparison, and providing accurate input for real-time control of the drying stage.

[0148] Based on the above embodiments, before monitoring the vibration signal of the clothes processing drum in real time using a vibration sensor, multi-dimensional baseline features can also be obtained based on the current drying of the clothes. The following will combine... Figure 5 The execution process will be explained in detail.

[0149] Figure 5 This is a flowchart illustrating a method for detecting clothing clumping according to some embodiments of the present disclosure. Figure 3 ,like Figure 5 As shown, the method for detecting clothes tangling is used in the main control board, microprocessor, or control unit that communicates with the motor controller in the washing machine control system, and includes the following steps.

[0150] S501, Start the clothing processing tank at the initial rotation speed.

[0151] First, a drive signal corresponding to the initial speed is output to the drive motor, so that the clothing processing drum rotates at a preset low speed and uniform speed, and collects third-direction acceleration data at a fixed sampling frequency in each running circle.

[0152] After the sampled signals are filtered and de-DC biased, they are grouped and stored according to the running cycles, and the peak value, mean value, zero-crossing characteristics or free fall determination quantity of each cycle are calculated.

[0153] For example, the initial rotation speed can be 15 rpm, the number of revolutions can be 10, and the sampling frequency can be 100 Hz.

[0154] S502. Initialize and verify the status of the clothes in the clothes processing bin, and determine the initialization verification result.

[0155] If the third-direction acceleration signal of each running circle meets the preset dispersion range and material drop characteristic threshold, the initialization verification result is passed. If the third-direction acceleration signal of any running circle continuously deviates from the preset threshold, it indicates that the clothes are clumped together in the drum and inhibit normal tumbling, and the initialization verification result is directly judged as failed.

[0156] Furthermore, the third-direction acceleration signal of each running circle of the clothing processing drum during initialization is collected; based on the third-direction acceleration signal of each running circle, the number of free fall states is determined; if the number of states is greater than or equal to the number threshold, the initialization verification result is determined to be passed; if the number of states is less than the number threshold, the initialization verification result is determined to be failed.

[0157] After the clothing processing drum starts at the initial rotation speed, it acquires third-direction acceleration data within each running cycle according to the preset sampling frequency, and identifies the free fall state based on the acceleration change amplitude, duration, and peak fall characteristics.

[0158] Under low-speed tumbling conditions, if the clothes are in a normal loose state, there will inevitably be a process of the clothes falling freely from the height of the clothes processing drum in each rotation. The corresponding third-axis acceleration signal (Z-axis) will periodically show a free fall characteristic close to 0g for a short period of time (duration of about 50~150ms, amplitude less than 0.3g). If the clothes are clumped together or large clothes are rolled up and stuck to the wall, they will not fall with the drum rotation, and the third-axis acceleration signal will not show the above free fall characteristic.

[0159] When the free fall criterion is met within a certain running circle, that running circle is counted in the state count. Then, the counted state count is compared with the count threshold. If the comparison result is not less than the threshold, the initialization verification pass signal is output, and the subsequent multi-dimensional baseline feature acquisition continues.

[0160] If the comparison result is less than the threshold, an initialization verification failure signal will be output, and the clothes in the clothes processing bin will be judged as being in an abnormal clumping state.

[0161] The quantity threshold can be preset based on the number of low-speed running cycles of the clothing processing drum, the sampling frequency, and the type of clothing, and stored in the parameter table of the control unit. In practical applications, this parameter can also be adjusted according to the equipment specifications, but this application does not limit it.

[0162] By counting the number of times the free fall state occurs in each running circle, it can be determined whether the clothing is in a loose and tumbling state. This enables the identification of abnormal clumping during the initialization phase and provides an effective basis for the subsequent establishment of multi-dimensional baseline features.

[0163] For example, during 10 laps at low speed, if the Z-axis free fall feature occurs ≥5 times, the initial state is considered normal and passes; if it occurs <5 times, the initial state is considered clumped, and the untangling control sequence is directly called to perform untangling once. After untangling, the S401 check is re-executed, with a maximum of 2 retries; if the check still fails after 2 untangling attempts, a "Clothes tangled, please open the door to tidy up" prompt is sent to the user, and the program is paused, waiting for the user to manually handle the situation before restarting.

[0164] In this embodiment, the initialization verification can quickly determine the movement state of the clothes in the clothes processing drum at the beginning of the drying stage. When the number of free fall states meets the threshold, it indicates that the clothes have normal falling characteristics and the initialization verification result is reliable. When the number of occurrences is insufficient, it can promptly identify abnormal states and block erroneous initialization, thereby improving the accuracy of clumping detection and the effectiveness of subsequent control.

[0165] S503. If the initialization verification result is passed, the rotation speed of the clothing processing drum is increased from the initial rotation speed to the normal operating speed, and the baseline values ​​of the vibration characteristics of each dimension of the clothing processing drum under the normal operating speed are determined.

[0166] If the initial verification result is passed, continue to increase to the normal operating speed and count the impact pulses, shaft imbalance index or three-axis summative vector variance of the clothing processing drum under normal tumbling state, thereby forming multi-dimensional baseline characteristics.

[0167] The initial rotational speed is increased to the normal operating speed, and the baseline vibration signal within the second preset time period is collected using a vibration sensor; based on the baseline vibration signal, multi-dimensional baseline characteristics are determined.

[0168] First, increase the speed of the drive motor from the initial speed to the normal operating speed, and then open the baseline sampling window after the speed reaches the set stable threshold.

[0169] The acquired baseline vibration signal may include at least one of the first directional acceleration, the second directional acceleration, and the third directional acceleration, or it may be vibration data synthesized from multi-axis acceleration.

[0170] Statistical summarization of multiple sampling results within the second preset time period is performed to form a multi-dimensional baseline feature that can characterize the normal tumbling state. The multi-dimensional baseline feature may include at least one of the following: impact pulse count baseline, axis imbalance index baseline, or triaxial sum vector variance baseline.

[0171] For example, the normal operating speed can be 50 rpm, the second preset time period can be 60 seconds, and the sampling rate can be 100Hz, that is, the triaxial acceleration signal is continuously acquired at a sampling rate of 100Hz for 60 seconds.

[0172] During operation, continuous vibration data is collected after the clothing processing drum reaches its normal operating speed. The vibration response of the clothing processing drum during stable tumbling within this period is used as a reference to construct the multi-dimensional baseline features required for subsequent comparison.

[0173] Multidimensional baseline characteristics can reflect the inertial impact, rotational balance, and acceleration fluctuation levels of clothing when it is not clumped, thus providing a unified reference for the subsequent determination of multidimensional vibration characteristics.

[0174] In this embodiment, the multi-dimensional baseline features are formed based on the actual vibration state under normal operating speed, and are more stable after multiple sampling and summarization within the second preset time period. This provides a reliable comparison basis for subsequent cluster identification and improves the consistency and accuracy of multi-dimensional vibration feature comparison.

[0175] It is worth noting that the execution process of determining multi-dimensional baseline characteristics based on baseline vibration signals is similar to S102 and... Figure 3 The principle for determining the multi-dimensional vibration characteristics corresponding to the real-time vibration signal is the same in the corresponding embodiments, and will not be repeated here.

[0176] S504. If the initial verification result is not passed, the clothing in the clothing processing bucket is determined to be in an abnormal clumping state.

[0177] Output the result of determining that the clothes in the clothing processing bin are in an abnormal clumping state.

[0178] The garment clumping detection method provided in this application completes initial state screening before real-time monitoring during the initialization and verification process. This ensures that the subsequently established multi-dimensional baseline features correspond to normal loose tumbling conditions, and that abnormal clumping states can be directly identified even when initial vibration characteristics are clearly abnormal. Therefore, the reference features used for subsequent baseline comparisons are more stable, the starting point for clumping determination is clearer, the possibility of mistaking abnormal initial states for normal baselines is reduced, and the accuracy of garment state identification and consistency of control response during the drying stage are improved.

[0179] Figure 6 This is a block diagram of a clothing clumping detection device according to some embodiments of the present disclosure. (Refer to...) Figure 6 The agglomeration abnormality detection device 600 includes an acquisition module 601, a determination module 602, a comparison module 603, and a judgment module 604, wherein...

[0180] The acquisition module 601 is used to acquire the real-time vibration signal of the clothing processing drum;

[0181] The determination module 602 is used to determine the multi-dimensional vibration characteristics corresponding to the real-time vibration signal;

[0182] The comparison module 603 is used to compare the multi-dimensional vibration features with their respective corresponding dimensional baseline features to obtain the dimensional comparison results corresponding to each dimensional vibration feature; wherein, the dimensional baseline features are the baseline values ​​of each dimensional vibration feature corresponding to the normal operating speed of the clothing processing drum.

[0183] The determination module 604 is used to determine the state of the clothes in the clothes processing bucket based on the dimensional comparison results corresponding to the vibration characteristics of each dimension.

[0184] As an alternative implementation, the multidimensional vibration characteristics include at least two of the following: shock pulse count, shaft imbalance index, and triaxial summation vector variance.

[0185] As an optional implementation, the comparison module 603 is specifically used for:

[0186] If the impact pulse count is less than the impact pulse determination threshold and the duration is greater than the first preset duration, then the dimension comparison result corresponding to the impact pulse count is determined to be an abnormal clustering state; wherein, the impact pulse determination threshold is the product of the impact pulse count baseline and the first preset ratio;

[0187] And / or, if the axis imbalance index is greater than the index judgment threshold, then the dimension comparison result corresponding to the axis imbalance index is determined to be an abnormal clumping state; wherein, the index judgment threshold is the product of the axis imbalance index baseline and a preset multiple.

[0188] And / or, if the variance of the three-axis combined vector is less than the vector determination threshold, then the dimension comparison result corresponding to the variance of the three-axis combined vector is determined to be an abnormal clumping state; wherein, the vector determination threshold is the product of the baseline of the variance of the three-axis combined vector and the second preset ratio.

[0189] As an optional implementation, the comparison module 603 is specifically used for:

[0190] If the comparison results of at least two dimensions show an abnormal clumping state, then the state of the clothes in the clothes processing bin is determined to be an abnormal clumping state.

[0191] As an optional implementation, the real-time vibration signal includes the first direction acceleration, the second direction acceleration, and the third direction acceleration of the clothing processing drum at each collection point, wherein the first direction, the second direction, and the third direction are perpendicular to each other; the determining module 602 is specifically used for:

[0192] Based on the first-direction acceleration, second-direction acceleration, and third-direction acceleration corresponding to each acquisition point, determine the triaxial resultant vector acceleration of each acquisition point;

[0193] The number of times the triaxial resultant vector acceleration exceeds the acceleration threshold is defined as the impact pulse count.

[0194] As an optional implementation, the determining module 602 is further configured to:

[0195] The variance of the triaxial sum vector acceleration at each acquisition point is defined as the triaxial sum vector variance.

[0196] And / or, the time root mean square of the first directional acceleration and the time root mean square of the second directional acceleration corresponding to each acquisition point are determined as the axis imbalance index.

[0197] As an optional implementation, the determination module 604 is specifically used for:

[0198] In response to the abnormal clumping state of the clothes in the clothes processing bin, the clothes processing bin is controlled to stop rotating within a second preset time period;

[0199] The garment processing drum is controlled to rotate in the opposite direction of the preset rotation direction at a first preset speed for a first preset number of revolutions, and then rotates in the preset rotation direction at a second preset speed for a second preset number of revolutions. The first preset speed is greater than the second preset speed, and the first preset number of revolutions is less than or equal to the second preset number of revolutions.

[0200] As an optional implementation, the device further includes an initial verification module, which is used for:

[0201] The clothing processing tank is started at the initial rotation speed;

[0202] Initialize and verify the status of the clothes in the clothing processing bin;

[0203] If the initialization verification result is passed, the rotation speed of the clothing processing drum is increased from the initial rotation speed to the normal operating rotation speed, and the baseline values ​​of the vibration characteristics of each dimension of the clothing processing drum under the normal operating rotation speed are determined; if the initialization verification result is failed, the state of the clothing in the clothing processing drum is determined to be an abnormal clumping state.

[0204] As an optional implementation, the initial verification module is specifically used for:

[0205] Collect the third-axis acceleration signal of each running cycle of the clothing processing drum during initial rotation speed operation;

[0206] The number of states of free fall is determined based on the third-direction acceleration signal of each orbit.

[0207] If the number of states is greater than or equal to the number threshold, the initialization verification result is determined to be passed; if the number of states is less than the number threshold, the initialization verification result is determined to be failed.

[0208] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0209] Figure 7 This is a block diagram illustrating an electronic device according to some embodiments of the present disclosure. For example, the electronic device 700 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0210] Reference Figure 7 The electronic device 700 may include one or more of the following components: processing component 702, memory 704, power component 707, multimedia component 708, audio component 710, input / output (I / O) interface 712, sensor component 714, and communication component 717.

[0211] Processing component 702 typically controls the overall operation of electronic device 700, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 702 may include one or more modules to facilitate interaction between processing component 702 and other components. For example, processing component 702 may include a multimedia module to facilitate interaction between multimedia component 708 and processing component 702.

[0212] Memory 704 is configured to store various types of data to support the operation of electronic device 700. Examples of this data include instructions for any application or method operating on electronic device 700, contact data, phonebook data, messages, pictures, videos, etc. Memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0213] Power component 706 provides power to various components of electronic device 700. Power component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 700.

[0214] Multimedia component 708 includes a screen that provides an output interface between electronic device 700 and user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 708 includes a front-facing camera and / or a rear-facing camera. When electronic device 700 is in an operating mode, such as a shooting mode or video mode, the front-facing camera and / or rear-facing camera may receive multimedia data from outside. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0215] Audio component 710 is configured to output and / or input audio signals. For example, audio component 710 includes a microphone (MIC) configured to receive outdoor audio signals when electronic device 700 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 704 or transmitted via communication component 717. In some embodiments, audio component 710 also includes a speaker for outputting audio signals.

[0216] I / O interface 712 provides an interface between processing component 702 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0217] Sensor assembly 714 includes one or more sensors for providing state assessments of various aspects of electronic device 700. For example, sensor assembly 714 may detect the on / off state of electronic device 700, the relative positioning of components such as the display and keypad of electronic device 700, changes in position of electronic device 700 or a component of electronic device 700, the presence or absence of user contact with electronic device 700, orientation or acceleration / deceleration of electronic device 700, and temperature changes of electronic device 700. Sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 714 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0218] Communication component 716 is configured to facilitate wired or wireless communication between electronic device 700 and other devices. Electronic device 700 can access wireless networks based on communication standards, such as WiFi, 3G, 4G, 5G, other communication standards, or combinations thereof. In some embodiments of this disclosure, communication component 716 receives broadcast signals or broadcast-related information from an outdoor broadcast management system via a broadcast channel. In some embodiments of this disclosure, communication component 716 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0219] In some embodiments of this disclosure, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0220] In some embodiments of this disclosure, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 704 including instructions, which can be executed by a processor 720 of an electronic device 700 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0221] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to perform a method for detecting clothing clumping.

[0222] Figure 8 This is a block diagram illustrating another electronic device according to some embodiments disclosed in a publication. For example, electronic device 800 may be provided as an air conditioner. (See also...) Figure 8 The electronic device 800 includes a processing component 822, which further includes one or more processors, and memory resources represented by a memory 832 for storing instructions, such as application programs, that can be executed by the processing component 822. The application programs stored in the memory 832 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 822 is configured to execute instructions to perform the aforementioned clothing clumping detection method.

[0223] The electronic device 800 may also include a power supply component 826 configured to perform power management of the device 800, a wired or wireless network interface 850 configured to connect the electronic device 800 to a network, and an input / output (I / O) interface 858. The device 800 may operate on an operating system stored in memory 832, such as Windows Server™, MacOS X™, Unix™, Linux™, FreeBSD™, or similar.

[0224] Some embodiments of this disclosure also provide a chip system, such as Figure 9As shown, the chip system includes at least one processor 901 and at least one interface circuit 902. The processor 901 and the interface circuit 902 are interconnected via lines. For example, the interface circuit 902 can be used to receive signals from other devices (e.g., the memory of an electronic device). As another example, the interface circuit 902 can be used to send signals to other devices (e.g., the processor 901). Exemplarily, the interface circuit 902 can read instructions stored in memory and send those instructions to the processor 901. When the instructions are executed by the processor 901, the torque command compensation device can perform the steps in the above embodiments. Of course, the chip system may also include other discrete devices, and some embodiments of this disclosure do not specifically limit this.

[0225] In some embodiments of this disclosure, the interface circuit 902 can acquire data, program instructions, and / or information from the internal storage area of ​​the chip system; it can also acquire data, program instructions, and / or information from outside the chip system.

[0226] Optionally, the chip system also includes a memory 903 for storing necessary computer programs and data.

[0227] In some embodiments of this disclosure, an air conditioner is also provided for performing the clothing clumping detection method provided in any of the above embodiments.

[0228] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented through hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this application.

[0229] In the above detailed description, reference has been made to the accompanying drawings, which illustrate specific aspects of this disclosure by way of illustration. In this regard, terms indicating direction or positional relationship, such as “center,” “longitudinal,” “lateral,” “length,” “width,” “thickness,” “upper,” “lower,” “front,” “rear,” “left,” “right,” “vertical,” “horizontal,” “top,” “bottom,” “inner,” “outer,” “clockwise,” “counterclockwise,” “axial,” “radial,” and “circumferential,” are used with reference to the orientation of the described figures. Since components of the described device can be positioned in multiple different orientations, directional terms are used for illustrative purposes and not for limitation. It should be understood that other aspects can be utilized and structural or logical changes can be made without departing from the concept of this disclosure. Therefore, the following detailed description should not be considered limiting.

[0230] It should be understood that, unless otherwise specifically indicated, features of various embodiments of this disclosure described herein can be combined with each other. As used herein, the term “and / or” includes any one of the relevant listed items and any combination of any two or more; similarly, “at least one of…” includes any one of the relevant listed items and any combination of any two or more.

[0231] Although terms such as “first,” “second,” and “third” may be used herein to describe various components, parts, regions, layers, or sections, these components, parts, regions, layers, or sections are not limited to these terms. Rather, these terms are used only to distinguish one component, part, region, layer, or section from another. Therefore, without departing from the teachings of the examples described herein, the first component, part, region, layer, or section mentioned in the examples may also be referred to as the second component, part, region, layer, or section. 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 indicated technical features. Thus, a feature defined as “first” or “second” may explicitly or implicitly include at least one of that feature. In the description herein, “a plurality” means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0232] Furthermore, the term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous compared to other aspects or designs. Rather, the use of the term “exemplary” is intended to present the concept in a concrete manner. As used herein, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from the context, “X applies A or B” is intended to mean any of the natural inclusive arrangements. That is, “X applies A or B” satisfies any of the foregoing instances if X applies A; X applies B; or both X applies A and B. Additionally, unless otherwise specified or clear from the context to refer to the singular form, the articles “a” and “an” as used in this application and the appended claims are generally understood to mean “one or more.”

[0233] Similarly, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, the terminology used to describe such components is intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if structurally not equivalent to the disclosed structure. Furthermore, although specific features of this disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations, as may be desired and advantageous to any given or particular application. Moreover, with regard to the terms “comprising,” “owning,” “having,” “having,” or variations thereof as used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term “including.”

[0234] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0235] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for detecting clumping of clothing, characterized in that, include: Acquire the real-time vibration signal of the clothing processing drum and determine the multi-dimensional vibration characteristics corresponding to the real-time vibration signal; The multi-dimensional vibration features are compared with their respective corresponding dimensional baseline features to obtain the dimensional comparison results corresponding to each dimensional vibration feature; wherein, the dimensional baseline features are the baseline values ​​of each dimensional vibration feature corresponding to the normal operating speed of the clothing processing drum. Based on the dimensional comparison results corresponding to the vibration characteristics in each dimension, the state of the clothing in the clothing processing bucket is determined.

2. The method according to claim 1, characterized in that, The multidimensional vibration characteristics include at least two of the following: shock pulse count, shaft imbalance index, and triaxial summation vector variance.

3. The method according to claim 2, characterized in that, The step of comparing the multi-dimensional vibration features with their respective corresponding dimensional baseline features to obtain the dimensional comparison results for each dimensional vibration feature includes: If the impact pulse count is less than the impact pulse determination threshold and the duration is greater than the first preset duration, then the dimension comparison result corresponding to the impact pulse count is determined to be an abnormal clustering state; wherein, the impact pulse determination threshold is the product of the impact pulse count baseline and the first preset ratio; And / or, if the axis imbalance index is greater than the index determination threshold, then the dimension comparison result corresponding to the axis imbalance index is determined to be an abnormal clumping state; wherein, the index determination threshold is the product of the axis imbalance index baseline and a preset multiple; And / or, if the variance of the three-axis combined vector is less than the vector determination threshold, then the dimension comparison result corresponding to the variance of the three-axis combined vector is determined to be an abnormal clumping state; wherein, the vector determination threshold is the product of the baseline of the variance of the three-axis combined vector and the second preset ratio.

4. The method according to claim 3, characterized in that, The step of determining the state of the clothing in the clothing processing bin based on the dimensional comparison results corresponding to the vibration characteristics in each dimension includes: If the comparison results of at least two of the dimensions indicate an abnormal clumping state, then the state of the clothing in the clothing processing bin is determined to be an abnormal clumping state.

5. The method according to claim 2, characterized in that, The real-time vibration signal includes the first direction acceleration, the second direction acceleration, and the third direction acceleration of the clothing processing drum at each collection point, wherein the first direction, the second direction, and the third direction are perpendicular to each other; Determining the multi-dimensional vibration characteristics corresponding to the real-time vibration signal includes: Based on the first directional acceleration, second directional acceleration, and third directional acceleration corresponding to each acquisition point, determine the triaxial resultant vector acceleration of each acquisition point; The number of times the triaxial resultant vector acceleration is greater than the acceleration threshold is determined as the impact pulse count.

6. The method according to claim 5, characterized in that, The determination of the multi-dimensional vibration characteristics corresponding to the real-time vibration signal further includes: The variance of the triaxial sum vector acceleration at each acquisition point is determined as the triaxial sum vector variance; And / or, the time-series root mean square of the first directional acceleration and the time-series root mean square of the second directional acceleration corresponding to each acquisition point are determined as the axis imbalance index.

7. The method according to any one of claims 1-6, characterized in that, The method for detecting clumping of clothing also includes: In response to the abnormal clumping of clothes in the clothes processing drum, the clothes processing drum is controlled to stop rotating for a second preset time period. The garment processing drum is controlled to rotate in the opposite direction of the preset rotation direction at a first preset speed for a first preset number of revolutions, and then at a second preset speed for a second preset number of revolutions in the preset rotation direction. The first preset speed is greater than the second preset speed, and the first preset number of revolutions is less than or equal to the second preset number of revolutions.

8. The method according to any one of claims 1-6, characterized in that, Before acquiring the real-time vibration signal of the garment processing drum, the following steps are also included: The clothing processing drum is started at the initial rotation speed; The status of the clothes in the clothing processing bin is initialized and verified, and the initialization verification result is determined. If the initialization verification result is passed, the rotation speed of the clothing processing drum is increased from the initial rotation speed to the normal operating rotation speed, and the baseline values ​​of the vibration characteristics of each dimension of the clothing processing drum at the normal operating rotation speed are determined; if the initialization verification result is failed, the state of the clothing in the clothing processing drum is determined to be an abnormal clumping state.

9. The method according to claim 8, characterized in that, The initialization verification of the clothing status in the clothing processing bin includes: Collect the third-direction acceleration signal of each running cycle of the clothing processing drum when it runs at the initial rotation speed; The number of free fall states is determined based on the third-direction acceleration signal of each running circle. If the number of states is greater than or equal to the number threshold, the initialization verification result is determined to be passed; if the number of states is less than the number threshold, the initialization verification result is determined to be failed.

10. A device for detecting clumping of clothing, characterized in that, Used to implement the method according to any one of claims 1-9.

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