Clothes processing equipment, control method and device thereof and storage medium

Abstract

The invention provides clothes processing equipment, a control method and device thereof and a computer readable storage medium, and the control method of the clothes processing equipment comprises the steps: obtaining a feature parameter set during the operation of the clothes processing equipment; acquiring the current working condition of the clothes processing equipment; determining a control rule base corresponding to the current working condition according to the current working condition; according to the characteristic parameter set and the control rule base, determining an adjustment strategy of the clothes processing equipment; and controlling the clothes processing equipment to operate according to the adjustment strategy. The technical problem that in the prior art, clothes processing equipment adopts the same adjusting strategy, and working conditions are not matched with the adjusting strategy is solved.

Description

Technical Field

[0001] The present application relates to the technical field of clothing processing, and in particular to a clothing processing device and a control method, device and storage medium thereof. Background Art

[0002] Clothes handling equipment is a common household appliance. Due to its unique operating mode, it is prone to tilting and sideways accidents. Severe accidents can cause serious personal injury and property damage. Clothes handling equipment has multiple operating conditions. Under each operating condition, the equipment uses the same adjustment strategy. A mismatch between the operating conditions and the adjustment strategy can lead to untimely adjustments in dangerous conditions, resulting in harm, and incorrect adjustments in safe conditions. Summary of the Invention

[0003] The present application provides a clothing processing device and a control method, device and storage medium thereof, aiming to solve the technical problem in the prior art that clothing processing devices adopt the same adjustment strategy but the working conditions do not match the adjustment strategy.

[0004] In a first aspect, the present application provides a control method for a clothes processing device, comprising:

[0005] Acquire a set of characteristic parameters of the clothes processing device during operation;

[0006] Obtaining the current operating condition of the laundry processing device;

[0007] Determining a control rule library corresponding to the current operating condition according to the current operating condition;

[0008] determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library;

[0009] The clothes treating device is controlled to operate according to the adjustment strategy.

[0010] Optionally, the feature parameter set includes sub-parameters;

[0011] Determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library includes:

[0012] Obtaining a threshold value set corresponding to the sub-parameter in the control rule library;

[0013] An adjustment strategy is determined according to the sub-parameters and the threshold set.

[0014] Optionally, the characteristic parameter set includes at least two different sub-parameters;

[0015] The acquiring of the threshold value set corresponding to the sub-parameter in the control rule library includes:

[0016] Acquire different threshold sets corresponding to the at least two different sub-parameters in the control rule library;

[0017] Determining an adjustment strategy according to the sub-parameters and the threshold set includes:

[0018] An adjustment strategy is determined according to the at least two different sub-parameters and their corresponding threshold sets.

[0019] Optionally, determining a control rule library corresponding to the current operating condition according to the current operating condition includes:

[0020] If the current working condition is a washing working condition, determining that the control rule base is a first control rule base;

[0021] If the current working condition is a dehydration working condition, determining that the control rule base is a second control rule base;

[0022] In which, the first control rule base includes a first threshold set, and the second control rule base includes a second threshold set; the first threshold set includes multiple first threshold points for characterizing different risk levels of different sub-parameters, and the second threshold set includes multiple second threshold points for characterizing different risk levels of different sub-parameters; wherein, the first threshold point of the same risk level of the same sub-parameter is greater than the second threshold point of the same risk level of the same sub-parameter.

[0023] Optionally, obtaining a set of characteristic parameters of the laundry processing device during operation includes:

[0024] Acquiring raw data from different sensors at the same position on the laundry processing device; the different sensors include at least two of an acceleration sensor, a speed sensor, a displacement sensor, an angle sensor, and a gyroscope sensor;

[0025] Performing data fusion on the original data to obtain position dynamic parameters of the position;

[0026] The characteristic parameter set is extracted from the position dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

[0027] Optionally, obtaining a set of characteristic parameters of the laundry processing device during operation includes:

[0028] Acquiring first raw data of a same sensor at different positions on the laundry processing device; the sensor includes at least one of an acceleration sensor, a speed sensor, a displacement sensor, an angle sensor, and a gyroscope sensor;

[0029] acquiring second raw data of a gyroscope sensor at a center of gravity position on the clothes processing device;

[0030] fusing the first original data and the second original data respectively to obtain overall dynamic parameters of the clothing processing device;

[0031] The characteristic parameter set is extracted from the overall dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

[0032] Optionally, the acceleration sensor is used to monitor the acceleration raw data of the laundry processing device in the X, Y and Z directions;

[0033] The speed sensor is used to monitor the original speed data of the clothes processing device in the X direction, Y direction and Z direction;

[0034] The displacement sensor is used to monitor the original displacement data of the clothing processing device in the X, Y and Z directions;

[0035] The angle sensor is used to monitor raw data of the tilt angle of the laundry processing device relative to the horizontal plane;

[0036] The gyro sensor is used to monitor the rotational angular velocity of the clothing processing device around the Z axis; wherein the Z axis is parallel to the Z direction, the Z direction is perpendicular to the horizontal plane; the X direction and the Y direction constitute the horizontal plane.

[0037] In a second aspect, the present application further provides a control device for a clothes processing device, the control device comprising:

[0038] An acquisition module, configured to acquire a set of characteristic parameters of the clothes processing device during operation and to acquire a current operating condition of the clothes processing device;

[0039] a determination module, configured to determine a control rule library corresponding to the current operating condition according to the current operating condition; and determine an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library; and

[0040] A control module is used to control the clothing processing device to operate according to the adjustment strategy.

[0041] In a third aspect, the present application further proposes a clothing processing device, comprising a controller, wherein the controller is configured to execute the steps in the control method of the clothing processing device as described above.

[0042] In a fourth aspect, the present application further proposes a computer-readable storage medium on which a computer program is stored. The computer program is loaded by a processor to execute the steps in the control method of the clothing processing device as described above.

[0043] In the technical solution of the embodiment of the present application, during the operation of the clothing processing equipment, a set of characteristic parameters during operation is obtained; the current working condition is obtained, and a control rule library corresponding to the current working condition is determined based on the current working condition; then, an adjustment strategy of the clothing processing equipment is determined based on the characteristic parameter set and the control rule library; based on the characteristic parameter set and the control rule library, the adjustment strategy of the clothing processing equipment is determined so that the clothing processing equipment adopts an adjustment strategy that matches the working condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is a flow chart of an embodiment of a method for controlling a clothes processing device provided by an embodiment of the present application;

[0046] Figure 2 yes Figure 1 Schematic diagram of sub-steps of step S400;

[0047] Figure 3 yes Figure 2 Schematic diagram of sub-steps of step S410;

[0048] Figure 4 yes Figure 1 Schematic diagram of sub-steps of step S300;

[0049] Figure 5 yes Figure 1 Schematic diagram of another sub-step of step S100;

[0050] Figure 6 yes Figure 1 Schematic diagram of another sub-step of step S100;

[0051] Figure 7 It is a structural schematic diagram of the control device of the clothing processing equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following is a collection of drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of the present invention.

[0053] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0054] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to make and use the invention. In the following description, details are listed for the purpose of explanation. It should be understood that one of ordinary skill in the art will recognize that the invention can be practiced without these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0055] Clothes handling equipment is a common household appliance, including washing machines, washer-dryers, and dryers. The operation of clothing handling equipment relies on the rotation of the inner drum to physically act on the load. Under different operating conditions, the speed of the inner drum varies, and the probability and severity of accidents that may cause damage to the clothing handling equipment also vary. However, existing technologies use the same adjustment measures for clothing handling equipment under different operating conditions, resulting in a mismatch between the operating conditions and the adjustment measures. For example, untimely adjustments in dangerous conditions can cause harm, while incorrect adjustments can occur under safe conditions.

[0056] To this end, embodiments of the present application provide a clothing processing device and a control method, device, and storage medium thereof, which are described in detail below.

[0057] like Figure 1 FIG. 1 is a flow chart of an embodiment of a method for controlling a clothes processing device according to an embodiment of the present application. The method for controlling a clothes processing device includes:

[0058] S100, obtaining a set of characteristic parameters of the laundry processing device during operation;

[0059] S200, obtaining the current working condition of the clothes processing device;

[0060] S300, determining a control rule library corresponding to the current operating condition according to the current operating condition;

[0061] S400, determining an adjustment strategy for the clothes processing device according to the characteristic parameter set and the control rule library;

[0062] S500: Control the clothes processing device to operate according to the adjustment strategy.

[0063] In the technical solution of the embodiment of the present application, during the operation of the clothing processing equipment, a set of characteristic parameters during operation is obtained; the current working condition is obtained, and a control rule library corresponding to the current working condition is determined based on the current working condition; then, an adjustment strategy of the clothing processing equipment is determined based on the characteristic parameter set and the control rule library; based on the characteristic parameter set and the control rule library, the adjustment strategy of the clothing processing equipment is determined so that the clothing processing equipment adopts an adjustment strategy that matches the working condition.

[0064] In an embodiment, the characteristic parameter set includes at least one of a set of motion parameters of the laundry treatment device during operation, a set of parameters determined based on the motion parameters, and the remaining operation time of the laundry treatment device. For example, the motion parameters may be displacement, velocity, acceleration, or tilt angle. The set of parameters determined based on the motion parameters may be a rate of change of the tilt angle, a frequency, etc.

[0065] In some embodiments, the operating conditions of the laundry processing device are determined by the model. For example, for a washing machine, the operating conditions include dehydration and washing. The operating condition can be the current operating mode (such as washing, rinsing, or dehydration) determined by the laundry processing device based on operating parameters such as the current motor speed, water level, and wash time. In some embodiments, the load condition (clothes weight, uniformity of distribution) can also be combined to determine whether the operating condition is high-risk or low-risk.

[0066] As an optional implementation of the above embodiment, Figure 2As shown, the characteristic parameter set includes sub-parameters;

[0067] Determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library includes:

[0068] S410, obtaining a threshold value set corresponding to the sub-parameter in the control rule library;

[0069] S420: Determine an adjustment strategy according to the sub-parameters and the threshold set.

[0070] In an embodiment, the characteristic parameters include sub-parameters. Adjustment strategies corresponding to different thresholds are configured in a control rule library. Based on the sub-parameters and threshold sets, an adjustment strategy is determined to determine whether adjustment is necessary and the adjustment strategy when adjustment is necessary based on the current operating conditions and motion.

[0071] For example, in some embodiments, the sub-parameter is the tilt angle. The corresponding threshold value set for the tilt angle in the washing condition is (0, 5°), [5°, 15°), [15°, +∞). In the control rule library for the washing condition, if the tilt angle is (0, 5°), no adjustment is made; if the tilt angle is [5°, 15°), gentle braking is performed; and if the tilt angle is [15°, +∞), forced braking is performed.

[0072] In the specific adjustment strategy, "adaptive adjustment" of braking force is achieved. For example: when the tilt angle is <5°, the braking force does not exceed 30% of the rated value to avoid damage to clothing caused by sudden braking; when the tilt angle is >15°, 100% braking force is applied within 0.2s, and the braking response time is shortened by 40% compared with traditional solutions.

[0073] As an optional implementation of the above embodiment, Figure 3 As shown, the characteristic parameter set includes at least two different sub-parameters;

[0074] The acquiring of the threshold value set corresponding to the sub-parameter in the control rule library includes:

[0075] S411, obtaining different threshold sets corresponding to the at least two different sub-parameters in the control rule library;

[0076] Determining an adjustment strategy according to the sub-parameters and the threshold set includes:

[0077] S421: Determine an adjustment strategy according to the at least two different sub-parameters and their corresponding threshold sets.

[0078] In this embodiment, there are multiple sub-parameters, such as the sub-parameters of tilt angle and acceleration. The tilt angle and acceleration each correspond to a threshold set. For example, in some embodiments, the sub-parameter is tilt angle. The threshold set corresponding to the tilt angle under the washing condition is (0, 5°), [5°, 15°), [15°, +∞). The threshold set corresponding to the acceleration under the washing condition is (0, 0.5g), [0.5g, 1.0g), [1.0g, +∞).

[0079] In this embodiment, the sub-parameters are fuzzified. For example, the threshold value set corresponding to the tilt angle is (0, 5°), [5°, 15°), [15°, +∞), which are small, medium and large after fuzzification; for example, the threshold value set corresponding to acceleration is (0, 0.5g), [0.5g, 1.0g), [1.0g, +∞) (where g is the acceleration of gravity), which are low, medium and high after fuzzification; the sub-parameters are input into the fuzzy model to obtain a fuzzy evaluation result; the fuzzy evaluation result is set corresponding to the adjustment strategy. For example, if "big" or "high" appears in the fuzzy evaluation results, forced braking measures need to be taken, and a severe warning message can also be issued; if "big" or "high" does not appear in the fuzzy evaluation results, but "medium" appears, soft braking measures need to be taken, and a moderate warning message can also be issued, such as enhanced prompts (volume increased, indicator light color changed) + mobile phone APP push warnings; if only "small" and "low" appear in the fuzzy evaluation results, there is no need to brake, and the car can run at the current operating speed, and a mild warning message can be issued, such as a flashing indicator light + a soft prompt sound, to remind the user to check the placement of clothes or the flatness of the ground.

[0080] In the embodiment, the control method uses a fuzzy control algorithm to precisely control the braking force and duration of the electromagnetic brake based on the real-time tilt angle, acceleration, displacement speed, and remaining run time. When the tilt angle is small and the displacement speed is low, a gentle braking strategy is adopted to avoid damage to clothing and equipment caused by sudden braking. When the tilt angle is large and the displacement speed is high, maximum braking force is quickly applied to shut down the machine in the shortest possible time, and the water inlet and drain valves are simultaneously closed to prevent water overflow caused by displacement.

[0081] In this embodiment, the correspondence between fuzzy evaluation results and adjustment strategies varies across different models. For example, in some models, if only "small" and "low" appear in the fuzzy evaluation, a single soft braking strategy is employed. If the fuzzy evaluation results do not include "large" or "high" but "medium" appear, soft braking is employed while iterative optimization is performed based on operational data.

[0082] In some embodiments, there can be more sub-parameters. In more cases, a fuzzy control algorithm can also be used to fuzzify them into small, medium, large or low, medium, high, etc. according to the threshold set; then the evaluation results of the individual sub-parameters are fused to obtain the final fuzzy evaluation result. The fusion may be different in different models. For example, if at least one degree is "large or high", the fuzzy evaluation result is severe; for example, when there is no large or high, and at least one degree is "medium", the fuzzy evaluation result is medium; in other cases, the fuzzy evaluation result is mild, etc.

[0083] In an embodiment, the number of threshold sets may be two, three or more; the greater the number, the finer the division, the more refined the adjustment of the clothing processing device, and the better the performance of the controller, actuator, etc. of the clothing processing device required.

[0084] For example, in a specific embodiment, the sub-parameters include tilt angle, acceleration, speed, and remaining time. The parameters are fuzzy as follows:

[0085] The tilt angle θ is divided into “small” (0°–5°), “medium” (5°–15°), and “large” (>15°);

[0086] The tilt angle a is classified as “low” (<0.5g), “medium” (0.5g–1.0g), and “high” (>1.0g);

[0087] The speed v is divided into “slow” (<10 mm / s), “medium” (10 mm / s to 30 mm / s), and “fast” (>30 mm / s);

[0088] The remaining time t is divided into “short” (<10s), “medium” (10s-30s), and “long” (>30s).

[0089] Its control rule base is:

[0090] If θ = "large" and v = "fast" and t = "short", use the maximum braking force and the shortest braking time (prioritize stopping to prevent tipping);

[0091] If θ = "small" and a = "low" and t = "long", use soft braking (gradual deceleration to avoid clothing entanglement);

[0092] If θ = "medium" and v = "medium" and t = "medium", then the medium braking force configuration is used for dynamic adjustment (iterative optimization based on real-time data);

[0093] In other cases, if θ = "large" and v = "fast" do not occur at the same time, the medium braking force configuration is used for dynamic adjustment (iterative optimization based on real-time data); if they occur at the same time, the maximum braking force is used.

[0094] In some embodiments, when the working condition switches, the system can adjust the threshold point through a support vector machine (SVM). Furthermore, the threshold point can also be adjusted dynamically, such as by establishing a "working condition-threshold" mapping model through a support vector machine (SVM), inputting the detected motor speed, water level, sensor characteristic parameters and other data, and outputting the optimal threshold value under the current working condition. For example, high-risk working conditions (dehydration, full load): the threshold is lowered by 20%, and the sensitivity is improved; low-risk working conditions (washing, no-load): the threshold is increased by 15%, and a 5s anti-shake delay is set (the warning is triggered only when the threshold is exceeded for 5 consecutive seconds) to reduce false alarms.

[0095] As an optional implementation of the above embodiment, Figure 4 As shown, the determining of the control rule library corresponding to the current operating condition according to the current operating condition includes:

[0096] S310, if the current working condition is a washing working condition, determining that the control rule base is a first control rule base;

[0097] S320, if the current working condition is a dehydration working condition, determining that the control rule base is a second control rule base;

[0098] In which, the first control rule base includes a first threshold set, and the second control rule base includes a second threshold set; the first threshold set includes multiple first threshold points for characterizing different risk levels of different sub-parameters, and the second threshold set includes multiple second threshold points for characterizing different risk levels of different sub-parameters; wherein, the first threshold point of the same risk level of the same sub-parameter is greater than the second threshold point of the same risk level of the same sub-parameter.

[0099] For washing machines, the speed is low during washing and high during spin operation. Therefore, for the same sub-parameter and the same risk level, the threshold value is smaller for washing and higher for spin operation. This speeds up risk identification in spin operation and avoids false alarms and adjustments during washing. In other words, increasing the threshold sensitivity for spin operation allows for early identification of potential risks, while reducing the sensitivity for washing operation to avoid false alarms and adjustments caused by normal vibrations.

[0100] For example, for washing conditions, the tilt angle θ (sub-parameter) is divided into "small" (0°-5°), "medium" (5°-15°), and "large" (>15°); for dehydration conditions, the tilt angle θ is divided into "small" (0°-4°), "medium" (4°-12°), and "large" (>12°); large, medium, and small represent risk levels. In washing conditions, the first threshold points of the tilt angle corresponding to the large, medium, and small risk levels are 15° and 5° respectively; in dehydration conditions, the first threshold points of the tilt angle corresponding to the large, medium, and small risk levels are 12° and 4° respectively.

[0101] For example, in some embodiments, under washing conditions, the threshold point corresponding to the acceleration is 1.2g, which avoids false alarms due to normal stirring vibrations; while under dehydration conditions, the threshold point corresponding to the acceleration is 0.8g, which triggers adjustments earlier.

[0102] As an optional implementation of the embodiment, Figure 5 As shown, the step of obtaining a characteristic parameter set of the clothes processing device during operation includes:

[0103] S111, acquiring raw data from different sensors at the same position on the laundry processing device; the different sensors include at least two of an acceleration sensor, a velocity sensor, a displacement sensor, an angle sensor, and a gyroscope sensor;

[0104] S112, performing data fusion on the original data to obtain position dynamic parameters of the position;

[0105] S113, extracting the characteristic parameter set from the position dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

[0106] In an embodiment, the acceleration, tilt angle, and gyroscope raw data of the same position (such as a corner or center of gravity) are fused in time series using a Kalman filter algorithm to eliminate the noise of a single sensor (high-frequency vibration interference of the acceleration sensor and temperature drift error of the tilt angle sensor) to obtain a dynamic state estimate of the position; then, Fourier transform and wavelet analysis are used to extract at least one of the vibration frequency, acceleration peak value, and tilt angle change rate from the fused position dynamic parameters.

[0107] In some embodiments, acceleration sensors and tilt angle sensors are installed at each of the four corners of the washing machine, and a gyroscope sensor is installed at the center of gravity of the washing machine. The Kalman filter algorithm is used to perform time series fusion of the acceleration sensors and tilt angles at each corner to obtain a dynamic state estimate for that location. Fourier transform and wavelet analysis are then used to extract at least one of the following from the fused position dynamic parameters: vibration frequency, peak acceleration, and tilt angle change rate. This can reflect the motion status of each corner.

[0108] In an embodiment, a Kalman filter estimates and corrects sensor errors in real time, improving its response speed to sudden displacements by over 50% compared to traditional sliding average filters. The Kalman filter algorithm is a highly efficient recursive filter capable of estimating the state of a dynamic system from a series of incomplete and noisy measurements. Its core lies in its recursive nature and the principle of optimal estimation. By comprehensively considering the estimated and measured values, it iteratively produces the value with the least uncertainty, thereby achieving accurate state estimation. Kalman filters are widely used in fields such as navigation, control systems, and signal processing. They effectively handle uncertainty and noise, fusing raw data from different sensors to effectively remove noise interference and improve data stability and accuracy. Based on the fused data, methods such as Fourier transform and wavelet analysis are used to extract key characteristic parameters of the operating state of the clothing handling equipment, such as vibration frequency, peak acceleration, displacement velocity, and rate of change of tilt angle. These characteristic parameters serve as important basis for subsequent warning and braking decisions.

[0109] As an optional implementation of the embodiment, Figure 6 As shown, the step of obtaining a characteristic parameter set of the clothes processing device during operation includes:

[0110] S121, acquiring first raw data of a same sensor at different positions on the laundry processing device; the sensor includes at least one of an acceleration sensor, a velocity sensor, a displacement sensor, an angle sensor, and a gyroscope sensor;

[0111] S122, obtaining second raw data of a gyroscope sensor at a center of gravity position of the laundry processing device;

[0112] S123, fusing the first original data and the second original data to obtain overall dynamic parameters of the clothing processing device;

[0113] S124, extracting the characteristic parameter set from the overall dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

[0114] For example, in an embodiment, the tilt angles at different locations (such as a corner or center of gravity) are fused using a weighted averaging algorithm. The raw data from the gyroscope sensors is combined to obtain the overall tilt angle and rotational angular velocity (if the difference between the two diagonal tilt angles exceeds a threshold, the fuselage is determined to have rolled over). The raw data from the acceleration sensors (or displacement sensors, or velocity sensors) at different locations (such as a corner or center of gravity) are fused using a spatial filtering algorithm to extract the mean and variance of the fuselage translational acceleration, distinguishing between normal vibration (high frequency, low amplitude) and dangerous displacement (low frequency, high amplitude). Then, through Fourier transform and wavelet analysis, characteristic parameters such as vibration frequency, acceleration peak value, and tilt angle change rate are extracted from the fused time series data, along with a defined control rule library, to determine whether adjustments are necessary.

[0115] In the technical solution of the embodiment of the present application, the layered fusion strategy in the technical solution of the present application realizes: spatiotemporal multi-dimensional data association: through the spatial distribution of corner and center of gravity sensors and the collection of time series filtering, it is possible to accurately distinguish "normal vibration" (high-frequency small displacement during washing) and "dangerous displacement" (low-frequency large tilt during dehydration), and thus realize different adjustment strategies.

[0116] In some embodiments, acceleration sensors and tilt angle sensors are respectively provided at the four corners of the washing machine, and a gyroscope sensor is provided at the center of gravity of the washing machine.

[0117] As an optional implementation manner of the embodiment, the acceleration sensor is used to monitor the acceleration raw data of the clothing processing in the X direction, Y direction and Z direction;

[0118] The speed sensor is used to monitor the original speed data of the clothes processing device in the X direction, Y direction and Z direction;

[0119] The displacement sensor is used to monitor the original displacement data of the clothing processing device in the X, Y and Z directions;

[0120] The angle sensor is used to monitor raw data of the tilt angle of the laundry processing device relative to the horizontal plane;

[0121] The gyro sensor is used to monitor the rotational angular velocity of the clothing processing device around the Z axis; wherein the Z axis is parallel to the Z direction, the Z direction is perpendicular to the horizontal plane; the X direction and the Y direction constitute the horizontal plane.

[0122] In this embodiment, the acceleration sensors are: one triaxial acceleration sensor is installed at each of the four corners (bottom surface corners) and the center of gravity of the washing machine (a total of five), to collect acceleration data in the X, Y, and Z axes. The corner sensors can monitor the vibration and displacement trends of each corner of the washing machine, while the center of gravity sensor reflects the motion state of the entire center of mass.

[0123] Tilt angle sensor: A dual-axis tilt angle sensor is set at the bottom of each of the four corners (a total of four), which monitors the tilt angle of each corner relative to the horizontal plane (tilt in the X-axis and Y-axis directions) in real time, and determines the overall tilt state of the fuselage through the fusion of multi-position angle data.

[0124] Gyroscope sensor: A single-axis or three-axis gyroscope sensor is set at the center of gravity to collect the angular velocity of the fuselage around the vertical axis (Z axis) to assist in determining whether the fuselage has shifted sideways or rotated.

[0125] Through the distributed sensor layout of "corners + center of gravity", all-round monitoring of the three-dimensional movement of the fuselage (translation, tilt, rotation) is achieved. Compared with the data obtained by a single position sensor, the displacement type can be identified more accurately (for example, the tilt of the corners indicates the risk of rollover, and the sudden change of the center of gravity acceleration indicates severe vibration).

[0126] In order to better implement the control method of the clothes processing device in the embodiment of the present application, based on the control method of the clothes processing device, the embodiment of the present application also provides a control device for the clothes processing device, such as Figure 7 As shown, the control device of the clothes processing equipment includes:

[0127] An acquisition module 10 is configured to acquire a set of characteristic parameters of the laundry processing device during operation and to acquire a current operating condition of the laundry processing device;

[0128] a determination module 20 for determining a control rule library corresponding to the current operating condition according to the current operating condition; and determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library; and

[0129] The control module 30 is used to control the clothing processing device to operate according to the adjustment strategy.

[0130] Optionally, the feature parameter set includes sub-parameters;

[0131] Determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library includes:

[0132] An acquisition module acquires a threshold value set corresponding to the sub-parameter in the control rule library;

[0133] The determination module determines an adjustment strategy according to the sub-parameters and the threshold set.

[0134] Optionally, the characteristic parameter set includes at least two different sub-parameters;

[0135] The acquiring of the threshold value set corresponding to the sub-parameter in the control rule library includes:

[0136] The acquisition module acquires different threshold sets corresponding to the at least two different sub-parameters in the control rule library;

[0137] Determining an adjustment strategy according to the sub-parameters and the threshold set includes:

[0138] The determination module determines an adjustment strategy according to the at least two different sub-parameters and their corresponding threshold sets.

[0139] Optionally, determining a control rule library corresponding to the current operating condition according to the current operating condition includes:

[0140] If the current working condition is a washing working condition, the determining module determines that the control rule base is a first control rule base;

[0141] If the current working condition is a dehydration working condition, the determining module determines that the control rule base is a second control rule base;

[0142] In which, the first control rule base includes a first threshold set, and the second control rule base includes a second threshold set; the first threshold set includes multiple first threshold points for characterizing different risk levels of different sub-parameters, and the second threshold set includes multiple second threshold points for characterizing different risk levels of different sub-parameters; wherein, the first threshold point of the same risk level of the same sub-parameter is greater than the second threshold point of the same risk level of the same sub-parameter.

[0143] Optionally, obtaining a set of characteristic parameters of the laundry processing device during operation includes:

[0144] The acquisition module acquires raw data from different sensors at the same position on the clothing processing device; the different sensors include at least two of an acceleration sensor, a speed sensor, a displacement sensor, an angle sensor, and a gyroscope sensor;

[0145] The fusion module performs data fusion on the original data to obtain the position dynamic parameters of the position;

[0146] The extraction module extracts the characteristic parameter set from the position dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

[0147] Optionally, obtaining a set of characteristic parameters of the laundry processing device during operation includes:

[0148] The acquisition module acquires first raw data of the same sensor at different positions on the clothing processing device; the sensor includes at least one of an acceleration sensor, a speed sensor, a displacement sensor, an angle sensor, and a gyroscope sensor;

[0149] The acquisition module acquires second original data of the gyroscope sensor at the center of gravity of the clothes processing device;

[0150] A fusion module fuses the first original data and the second original data respectively to obtain the overall dynamic parameters of the clothing processing device;

[0151] The extraction module extracts the characteristic parameter set from the overall dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

[0152] An embodiment of the present application also proposes a control system for a middle-sized clothing processing device, comprising: one or more processors; a memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the control method of the middle-sized clothing processing device as described above.

[0153] Generally speaking, the control system of the clothing processing device usually includes: at least one processor, at least one memory, and a control program of the control system of the clothing processing device stored in the memory and executable on the processor to implement the steps of the above control method.

[0154] The processor may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor may be implemented in at least one of the following hardware forms: a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), or a PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. The processor may also include an AI (Artificial Intelligence) processor, which is used to process control method operations related to the control system of the clothing processing device, so that the control method model of the control system of the clothing processing device can be autonomously trained and learned to improve efficiency and accuracy.

[0155] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory is used to store at least one instruction, which is used to be executed by the processor to implement the control method of the clothing processing device in the control system of the clothing processing device provided in the method embodiment of the present application.

[0156] Acquire a set of characteristic parameters of the clothes processing device during operation;

[0157] Obtaining the current operating condition of the laundry processing device;

[0158] Determining a control rule library corresponding to the current operating condition according to the current operating condition;

[0159] determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library;

[0160] The clothes treating device is controlled to operate according to the adjustment strategy.

[0161] Optionally, the feature parameter set includes sub-parameters;

[0162] Determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library includes:

[0163] Obtaining a threshold value set corresponding to the sub-parameter in the control rule library;

[0164] An adjustment strategy is determined according to the sub-parameters and the threshold set.

[0165] Optionally, the characteristic parameter set includes at least two different sub-parameters;

[0166] The acquiring of the threshold value set corresponding to the sub-parameter in the control rule library includes:

[0167] Acquire different threshold sets corresponding to the at least two different sub-parameters in the control rule library;

[0168] Determining an adjustment strategy according to the sub-parameters and the threshold set includes:

[0169] An adjustment strategy is determined according to the at least two different sub-parameters and their corresponding threshold sets.

[0170] Optionally, determining a control rule library corresponding to the current operating condition according to the current operating condition includes:

[0171] If the current working condition is a washing working condition, determining that the control rule base is a first control rule base;

[0172] If the current working condition is a dehydration working condition, determining that the control rule base is a second control rule base;

[0173] In which, the first control rule base includes a first threshold set, and the second control rule base includes a second threshold set; the first threshold set includes multiple first threshold points for characterizing different risk levels of different sub-parameters, and the second threshold set includes multiple second threshold points for characterizing different risk levels of different sub-parameters; wherein, the first threshold point of the same risk level of the same sub-parameter is greater than the second threshold point of the same risk level of the same sub-parameter.

[0174] Optionally, obtaining a set of characteristic parameters of the laundry processing device during operation includes:

[0175] Acquiring raw data from different sensors at the same position on the laundry processing device; the different sensors include at least two of an acceleration sensor, a speed sensor, a displacement sensor, an angle sensor, and a gyroscope sensor;

[0176] Performing data fusion on the original data to obtain position dynamic parameters of the position;

[0177] The characteristic parameter set is extracted from the position dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

[0178] Optionally, obtaining a set of characteristic parameters of the laundry processing device during operation includes:

[0179] Acquiring first raw data of a same sensor at different positions on the laundry processing device; the sensor includes at least one of an acceleration sensor, a speed sensor, a displacement sensor, an angle sensor, and a gyroscope sensor;

[0180] acquiring second raw data of a gyroscope sensor at a center of gravity position on the clothes processing device;

[0181] fusing the first original data and the second original data respectively to obtain overall dynamic parameters of the clothing processing device;

[0182] The characteristic parameter set is extracted from the overall dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

[0183] Optionally, the acceleration sensor is used to monitor the acceleration raw data of the laundry processing device in the X, Y and Z directions;

[0184] The speed sensor is used to monitor the original speed data of the clothes processing device in the X direction, Y direction and Z direction;

[0185] The displacement sensor is used to monitor the original displacement data of the clothing processing device in the X, Y and Z directions;

[0186] The angle sensor is used to monitor raw data of the tilt angle of the laundry processing device relative to the horizontal plane;

[0187] The gyro sensor is used to monitor the rotational angular velocity of the clothing processing device around the Z axis; wherein the Z axis is parallel to the Z direction, the Z direction is perpendicular to the horizontal plane; the X direction and the Y direction constitute the horizontal plane.

[0188] The above is a detailed introduction to a clothing processing device and its control method, device and computer-readable storage medium provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for controlling a clothes processing device, characterized in that: include: Acquire a set of characteristic parameters of the clothes processing device during operation; Obtaining the current operating condition of the laundry processing device; Determining a control rule library corresponding to the current operating condition according to the current operating condition; determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library; The clothes treating device is controlled to operate according to the adjustment strategy.

2. The control method according to claim 1, wherein: The characteristic parameter set includes sub-parameters; Determining an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library includes: Obtaining a threshold value set corresponding to the sub-parameter in the control rule library; An adjustment strategy is determined according to the sub-parameters and the threshold set.

3. The control method according to claim 2, wherein: The characteristic parameter set includes at least two different sub-parameters; The acquiring of the threshold value set corresponding to the sub-parameter in the control rule library includes: Acquire different threshold sets corresponding to the at least two different sub-parameters in the control rule library; Determining an adjustment strategy according to the sub-parameters and the threshold set includes: An adjustment strategy is determined according to the at least two different sub-parameters and their corresponding threshold sets.

4. The control method according to claim 1, wherein: The determining, based on the current operating condition, a control rule library corresponding to the current operating condition includes: If the current working condition is a washing working condition, determining that the control rule base is a first control rule base; If the current working condition is a dehydration working condition, determining that the control rule base is a second control rule base; In which, the first control rule base includes a first threshold set, and the second control rule base includes a second threshold set; the first threshold set includes multiple first threshold points for characterizing different risk levels of different sub-parameters, and the second threshold set includes multiple second threshold points for characterizing different risk levels of different sub-parameters; wherein, the first threshold point of the same risk level of the same sub-parameter is greater than the second threshold point of the same risk level of the same sub-parameter.

5. The control method according to claim 1, wherein: The step of obtaining a set of characteristic parameters of the laundry processing device during operation includes: Acquiring raw data from different sensors at the same position on the laundry processing device; the different sensors include at least two of an acceleration sensor, a speed sensor, a displacement sensor, an angle sensor, and a gyroscope sensor; Performing data fusion on the original data to obtain position dynamic parameters of the position; The characteristic parameter set is extracted from the position dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

6. The control method according to claim 1, wherein: The step of obtaining a set of characteristic parameters of the laundry processing device during operation includes: Acquiring first raw data of a same sensor at different positions on the laundry processing device; the sensor includes at least one of an acceleration sensor, a velocity sensor, a displacement sensor, an angle sensor, and a gyroscope sensor; acquiring second raw data of a gyroscope sensor at a center of gravity position on the clothes processing device; fusing the first original data and the second original data respectively to obtain overall dynamic parameters of the clothing processing device; The characteristic parameter set is extracted from the overall dynamic parameters; the characteristic parameter set includes at least one of vibration frequency, acceleration peak value, and tilt angle change rate.

7. The control method according to claim 5 or 6, characterized in that: The acceleration sensor is used to monitor the acceleration raw data of the laundry processing in the X, Y and Z directions; The speed sensor is used to monitor the original speed data of the clothes processing device in the X direction, Y direction and Z direction; The displacement sensor is used to monitor the original displacement data of the clothing processing device in the X, Y and Z directions; The angle sensor is used to monitor raw data of the tilt angle of the laundry processing device relative to the horizontal plane; The gyro sensor is used to monitor the rotational angular velocity of the clothing processing device around the Z axis; wherein the Z axis is parallel to the Z direction, the Z direction is perpendicular to the horizontal plane; the X direction and the Y direction constitute the horizontal plane.

8. A control device for a clothes processing device, characterized in that: The control device comprises: An acquisition module, configured to acquire a set of characteristic parameters of the clothes processing device during operation and to acquire a current operating condition of the clothes processing device; a determination module, configured to determine a control rule library corresponding to the current operating condition according to the current operating condition; and determine an adjustment strategy for the laundry processing device according to the characteristic parameter set and the control rule library; and A control module is used to control the clothing processing device to operate according to the adjustment strategy.

9. A clothes processing device, characterized in that: The device comprises a controller configured to execute the steps of the control method of the laundry treating apparatus according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in the control method of a clothes processing device according to any one of claims 1 to 7.

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