Clothes dehydration control method and device and washing machine

Abstract

The invention provides a clothes dehydration control method and device and electronic equipment. Comprising the steps that posture data of the washing machine are obtained, and the posture data at least comprise acceleration and angular speed; analyzing the attitude data through a target neural network model to obtain an eccentricity value, the eccentricity value representing an off-center degree, the target neural network model being obtained through training of multiple groups of data, and each group of data in the multiple groups of data including historical attitude data and a historical eccentricity value; under the condition that the eccentric value is larger than or equal to a preset eccentric value, the washing machine is controlled to enter an unwrapping process, and the unwrapping process shows that the winding state of the clothes in the washing machine is released. Through the method and the device, the problems of more clothes unwrapping times and poor user experience caused by inaccurate measurement of the eccentric value of the washing machine are solved.

Description

Technical Field

[0001] The present application relates to the technical field of clothes dehydration control of a washing machine, and in particular to a clothes dehydration control method, device, computer-readable storage medium and washing machine. Background Art

[0002] At present, the drum washing machine often has uneven distribution of laundry during the washing, drying and dehydration process due to the different materials, sizes and water absorption of the clothes. The uneven distribution of clothes will not only reduce the washing effect of the clothes, but also cause excessive vibration of the washing machine during high-speed dehydration due to excessive eccentricity, increase noise and reduce the service life of the washing machine. Generally, during the dehydration process, if there are too many clothes or too many entanglements, the eccentricity value will be too large. At this time, the unwinding operation is required. The traditional unwinding process is to perform a cycle of forward and reverse rotation, and then restart the speed increase eccentricity. When the eccentricity value is too large, the unwinding operation starts again, and it is repeated until the eccentricity value is reduced to the point where the speed can be increased.

[0003] In order to reduce the vibration during washing and increase the service life of the washing machine, the current processing method is to measure the eccentricity of the clothes in advance and then determine the maximum speed at which the clothes can rise. Measuring eccentricity is particularly important during the dehydration process of the washing machine. The accuracy of eccentricity detection often depends on the dehydration performance and vibration of the washing machine.

[0004] The prior art discloses a dehydration processing method, device, equipment and storage medium based on load distribution. After obtaining the parameter information of the clothing processing device in the low-speed dehydration stage, the parameter information is input into the load distribution identification network model, and when it is determined that the output result of the load distribution identification network model is a uniform distribution state, the clothing processing device is controlled to enter the high-speed dehydration stage, but these methods may not be able to successfully enter the high-speed dehydration stage because the clothing cannot reach a uniform distribution state for a long time. Another prior art discloses a clothing processing device and its control method, and the dehydration stage of the washing machine is provided with a dehydration speed-up stage. In the dehydration speed-up stage, the motor adjustment parameters are determined based on the vibration value and eccentricity value of the clothing processing device; and whether the operation of the motor needs to be adjusted is determined according to the motor adjustment parameters. However, the accuracy of clothing eccentricity detection in the above-mentioned prior art is low, the number of times the clothing is untangled is large, and the user experience is poor. Summary of the invention

[0005] The main purpose of the present application is to provide a control method, device, computer-readable storage medium and washing machine for dehydrating clothes, so as to at least solve the problem in the prior art that the eccentricity value of the washing machine is inaccurately measured, resulting in a large number of untangling times of clothes and poor user experience.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for controlling dehydration of clothes is provided, comprising: obtaining posture data of a washing machine, wherein the posture data at least includes the acceleration and angular velocity of a washing tub; analyzing the posture data through a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training with multiple sets of data, each set of the multiple sets of data includes: historical posture data and historical eccentricity values; when the eccentricity value is greater than or equal to a preset eccentricity value, controlling the washing machine to enter an untangling process, wherein the untangling process indicates untangling the clothes in the washing machine.

[0007] Optionally, before analyzing the posture data through the target neural network model, the method also includes: obtaining the historical posture data corresponding to the eccentricity detection modules of multiple preset weights, and obtaining the historical eccentricity values ​​corresponding to the eccentricity detection modules of each preset weight; establishing an initial target neural network model, and training the initial target neural network model through the historical posture data and the historical eccentricity values ​​until the loss function of the initial target neural network model is an optimal value, thereby obtaining the target neural network model.

[0008] Optionally, the historical posture data corresponding to the eccentricity detection modules of multiple preset weights are obtained, including: when the washing machine starts to dehydrate, obtaining the current speed; when the current speed is greater than a speed threshold, obtaining the historical posture data corresponding to the eccentricity detection modules of multiple preset weights, wherein the speed threshold is the minimum speed that makes the clothes stick to the barrel wall.

[0009] Optionally, controlling the washing machine to enter the untangling process includes: controlling the washing machine tub to rotate forward by a preset angle; and controlling the washing machine tub to rotate reversely by the preset angle.

[0010] Optionally, after controlling the washing machine to enter the untangling process, the method further includes one of the following: controlling the washing machine to enter the dehydration process when the untangling time is greater than or equal to a preset time, wherein the untangling time represents the running time of the untangling process; obtaining the current eccentricity value, and controlling the washing machine to enter the dehydration process when the current eccentricity value is less than the preset eccentricity value.

[0011] Optionally, controlling the washing machine to enter the dehydration process includes: determining an optimal rotation speed corresponding to the current eccentricity value, and controlling the washing machine to enter the dehydration process at the optimal rotation speed.

[0012] Optionally, determining the optimal speed corresponding to the current eccentricity value includes: obtaining a one-to-one mapping relationship between the eccentricity value and the optimal speed; and determining the optimal speed corresponding to the current eccentricity value according to the one-to-one mapping relationship between the eccentricity value and the optimal speed.

[0013] According to another aspect of the present application, a control device for dehydrating clothes is provided, comprising: a first acquisition unit, used to acquire posture data of a washing machine, wherein the posture data at least includes the acceleration and angular velocity of a washing tub; an analysis unit, used to analyze the posture data through a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, each of the multiple sets of data includes: historical posture data and historical eccentricity values; a first control unit, used to control the washing machine to enter an untangling process when the eccentricity value is greater than or equal to a preset eccentricity value, wherein the untangling process indicates untangling the clothes in the washing machine.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for controlling dehydration of clothes.

[0015] According to another aspect of the present application, a washing machine is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a control method for executing any one of the laundry dehydration methods.

[0016] The technical solution of the present application is applied to obtain the posture data of the washing machine, wherein the posture data includes at least acceleration and angular velocity; the posture data is analyzed by the target neural network model to obtain the eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, and each set of data in the multiple sets of data includes: historical posture data and historical eccentricity value; when the eccentricity value is greater than or equal to the preset eccentricity value, the washing machine is controlled to enter the untangling process, wherein the untangling process indicates the untangling state of the clothes in the washing machine. Compared with the prior art, the eccentricity value of the washing machine is inaccurately measured, resulting in a large number of untangling times of clothes and poor user experience, the present application predicts the eccentricity value through the posture data of the washing machine, and controls the washing machine to untangle according to the predicted eccentricity value, avoiding the problem of too many untangling times, and improving the user experience. Therefore, it can solve the problem of poor user experience caused by too many untangling times of clothes in the prior art, and improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic flow chart of a method for controlling dehydration of clothes provided in an embodiment of the present application is shown;

[0019] Figure 2 A schematic diagram showing the installation position of a posture sensor provided in an embodiment of the present application in a drum washing machine is shown;

[0020] Figure 3 A schematic diagram of data collection and training process of a posture sensor provided in an embodiment of the present application is shown;

[0021] Figure 4 A specific method for controlling dehydration of clothes provided by an embodiment of the present application is shown;

[0022] Figure 5 A structural block diagram of a clothes dehydration control device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] As introduced in the background technology, the eccentricity value of the washing machine in the prior art is not accurately measured, resulting in a large number of untangling times of clothes and a poor user experience. To solve the problem of poor user experience, the embodiments of the present application provide a control method, device, computer-readable storage medium and washing machine for dehydrating clothes.

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0028] In this embodiment, a method for controlling the dehydration of clothes running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 1 FIG. 1 is a flow chart of a method for controlling the dehydration of clothes according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0030] Step S201, acquiring posture data of the washing machine, wherein the posture data at least includes acceleration and angular velocity of the washing tub;

[0031] Specifically, the present invention uses a posture sensor to obtain the posture data of the clothes during the dehydration process. The posture reflects the vibration of the clothes in the washing tub. The posture data, such as acceleration (a x ,a y ,a z ) and angular velocity (r x ,r y ,r z ). Figure 2 This is a schematic diagram of the installation position of the attitude sensor in the drum washing machine. In order to better extract the attitude of the washing machine during the washing process, it is installed on the outer drum and kept in the middle of the top of the inner drum. The outer drum will shake to varying degrees during the washing process. The intensity of the shaking is related to the rotation speed and the eccentricity of the clothes. The attitude sensor installed on the outer drum can reflect the vibration of the drum during the washing process.

[0032] Step S202, analyzing the posture data through a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, each set of data in the multiple sets of data includes: historical posture data and historical eccentricity values;

[0033] Specifically, the above-mentioned posture data is input into the neural network prediction model to accurately predict the eccentricity value of the clothes, and the clothes are untangled according to the eccentricity value and the speed of the motor is adjusted according to the eccentricity value, thereby improving the accuracy of clothing eccentricity detection, reducing the number of times the clothes are untangled, and saving dehydration time.

[0034] Step S203, when the eccentricity value is greater than or equal to a preset eccentricity value, controlling the washing machine to enter an untangling process, wherein the untangling process represents untangling the clothes in the washing machine.

[0035] Specifically, the clothes are untangled according to the eccentricity value and the rotation speed of the motor is adjusted according to the eccentricity value, that is, when the eccentricity value is greater than or equal to the preset eccentricity value, the clothes are untangled.

[0036] Through this embodiment, the posture data of the washing machine is obtained, wherein the posture data includes at least acceleration and angular velocity; the posture data is analyzed by the target neural network model to obtain the eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, and each set of data in the multiple sets of data includes: historical posture data and historical eccentricity values; when the eccentricity value is greater than or equal to the preset eccentricity value, the washing machine is controlled to enter the untangling process, wherein the untangling process indicates the untangling state of the clothes in the washing machine. Compared with the prior art, the eccentricity value of the washing machine is inaccurately measured, resulting in a large number of untangling times of clothes and poor user experience, the present application predicts the eccentricity value through the posture data of the washing machine, and controls the washing machine to untangle according to the predicted eccentricity value, avoiding the problem of too many untangling times, and improving the user experience. Therefore, it can solve the problem of poor user experience caused by too many untangling times of clothes in the prior art, and improve the user experience.

[0037] In the specific implementation process, before analyzing the posture data through the target neural network model, the method further includes the following steps: step S204: obtaining the historical posture data corresponding to the eccentricity detection modules of multiple preset weights, and obtaining the historical eccentricity values ​​corresponding to the eccentricity detection modules of each preset weight; step S205: establishing an initial target neural network model, training the initial target neural network model through the historical posture data and the historical eccentricity values, until the loss function of the initial target neural network model is the optimal value, and obtaining the target neural network model. The method trains the neural network model through the above steps to obtain a target neural network model suitable for eccentricity value prediction and accurately predict the eccentricity value.

[0038] Specifically, the input layer of the initial target neural network model contains 6 neurons, corresponding to the normalization of 3 linear accelerations and angular accelerations, respectively, which contains 2 hidden layers, the first hidden layer contains 64 neurons, the second layer contains 32 neurons, and the output layer is 1 neuron for outputting the predicted value. Each neuron is fully connected to the neurons in the next layer. When determining whether to hit the barrel, the neural network model needs to be trained. Due to the differences in the installation positions of the posture sensors, they are more sensitive to the current position and posture. In order to obtain more consistent data, the posture sensors are calibrated before training to eliminate the errors of each installation. The historical posture data of the inner drum at the rotation speed of the eccentricity detection modules (100g, 200g, 500g, 700g, 1000g) with different preset weights are collected and input into the neural network; and when the rotation speed reaches the point where the clothes are close to the drum wall, the eccentricity value is measured. The eccentricity detection module is generally an eccentric block.

[0039] The collected acceleration sensor is input into the neural network for training. The output of each neuron is calculated from the input layer to the output layer. For the i-th neuron in the l-th layer, its output is a i (l) for

[0040]

[0041] Where n(l-1) represents the number of neurons in the l-1th layer; w ij (l) is the weight connecting the jth neuron in the l-1th layer and the ith neuron in the lth layer; a j (l-1) is the output of the jth neuron in the l-1th layer; f is the activation function, f(x) = max(0, x); b i (l) is the bias of the ith neuron in the lth layer.

[0042] For this neural network, the input vector of the input layer is X, the weight matrix from the input layer to the hidden layer is W1, and the bias vector is b1. The output of the first hidden layer is:

[0043] z1=XW1+b1

[0044] a1=f(z1)

[0045] Similarly, the output of the second hidden layer is:

[0046] z2=XW2+b2

[0047] a2=f(z2)

[0048] For the last layer, the node computes the value Z directly.

[0049] However, for an untrained neural network, the output prediction value Z is inaccurate, which requires continuous iteration of the neural network, calculating the loss function MSE (mean square error) on the training set, and calculating the gradient of the loss function for each parameter, where N is the number of samples and yi is the actual value. is the predicted value.

[0050]

[0051] Each fall must reduce the gradient of the damage function, so each iteration must be updated to make the gradient decrease:

[0052]

[0053] Where α is the learning rate, and the training is iterated to adjust the values ​​of W and b until The set value is met or the maximum number of iterations is reached. The collected eccentricity value and the posture data at the current maximum speed are trained, and the neural network is continuously updated until the damage function MSE reaches a relatively ideal value. After the training is completed, the neural network can predict the eccentricity value.

[0054] In some optional implementations, step S204 obtains the historical posture data corresponding to the eccentricity detection modules of multiple preset weights, which can be implemented by the following steps: step S2041: when the washing machine starts to dehydrate, obtain the current rotation speed; step S2042: when the current rotation speed is greater than the rotation speed threshold, obtain the historical posture data corresponding to the eccentricity detection modules of multiple preset weights, wherein the rotation speed threshold is the minimum rotation speed that makes the clothes close to the barrel wall. The method obtains the historical posture data through the above steps, which can ensure that the historical posture data is obtained when the conditions are met, so as to further ensure the accurate prediction of the eccentricity value.

[0055] In the specific implementation process, Figure 3 For the data collection and training process of the attitude sensor, the eccentric block pre-weight is set as shown above. When the washing machine is dehydrating, the speed starts to increase. When the speed reaches the point where the clothes are close to the drum wall, that is, the speed threshold is the critical value of the speed that makes the clothes close to the drum wall, the eccentricity value is measured to obtain the eccentricity value under the current eccentric block weight.

[0056] In some other optional embodiments, the above step S203 controls the washing machine to enter the untangling process, which can be achieved by the following steps: step S2031: controlling the washing machine tub to rotate forward by a preset angle; step S2032: controlling the washing machine tub to rotate reversely by the preset angle. The method performs untangling through the above steps, which can avoid tangling of clothes.

[0057] Specifically, the preset angle is 180 degrees, and the untangling adopts a continuous 180-degree forward and reverse untangling operation. During the untangling process, the neural network will be continuously called to judge the eccentricity value. When the eccentricity value is less than D or the maximum untangling time is reached, the speed will be increased to complete a dehydration, and the cycle will be repeated until the dehydration is completed.

[0058] In a specific implementation process, after controlling the washing machine to enter the untangling process, the method further includes one of the following: controlling the washing machine to enter the dehydration process when the untangling time is greater than or equal to the preset time, wherein the untangling time represents the running time of the untangling process; obtaining the current eccentricity value, and controlling the washing machine to enter the dehydration process when the current eccentricity value is less than the preset eccentricity value. The method exits the untangling process and enters the dehydration process through the above steps, so that the clothes can be washed more evenly and cleanly.

[0059] Specifically, after entering the untangling process, when the set untangling time is reached or the eccentricity value is less than the maximum eccentricity value at the set speed, dehydration is performed at the optimal speed n, and it is further determined whether n is greater than the set speed or the maximum untangling times K is reached. If yes, the process ends; if not, the dehydration process continues.

[0060] In some optional embodiments, controlling the washing machine to enter the dehydration process comprises the following steps: determining the optimal speed corresponding to the current eccentricity value, and controlling the washing machine to enter the dehydration process at the optimal speed. The method determines the optimal speed corresponding to the current eccentricity value through the above steps, so that the clothes can be dehydrated at the most suitable speed to achieve the optimal dehydration effect.

[0061] In the specific implementation process, the eccentricity value predicted by the neural network needs to be used to determine the optimal rotation speed during the dehydration process, so that the dehydration effect can be optimized and the noise is controlled within an acceptable range.

[0062] In some optional implementations, the above step of determining the optimal speed corresponding to the current eccentricity value can be achieved by the following steps: obtaining a one-to-one mapping relationship between the eccentricity value and the optimal speed; and determining the optimal speed corresponding to the current eccentricity value according to the one-to-one mapping relationship between the eccentricity value and the optimal speed. The method determines the optimal speed by a one-to-one mapping relationship, which can ensure that the clothes are dehydrated at the optimal speed and the dehydration effect is the best.

[0063] Specifically, Table 1-1 shows the optimal rotation speeds under different eccentricity values ​​obtained from experiments.

[0064] Table 1-1 Optimal speed under different eccentricity values

[0065] Eccentric mass Maximum speed 100g 1320rad / min 200g 1320rad / min 500g 837rad / min 700g 525rad / min 1000g 249rad / min 1500g 126rad / min

[0066] The binomial is used to fit this curve to obtain the relationship between the eccentricity value and the speed as shown in the following formula: y = 0.000623x 2 -1.925x+1593.213, x represents the eccentricity value, and y represents the optimal speed.

[0067] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the method for controlling the dehydration of clothes of the present application will be described in detail below in combination with specific embodiments.

[0068] This embodiment relates to a specific method for controlling the dehydration of clothes. Figure 4 As shown, the following steps are included:

[0069] Step S1: dehydration starts;

[0070] Step S2: posture sensor calibration;

[0071] Step S3: Start to increase the speed. When the speed reaches the point where the clothes can fit tightly against the wall of the tub, the neural network predicts the eccentricity value.

[0072] Step S4: the eccentricity value is greater than the maximum eccentricity value at the set speed;

[0073] Step S5: unwinding by rotating 180 degrees forward and 180 degrees reversely, and predicting the eccentricity value;

[0074] Step S6: Determine whether the set unwinding time has been reached or the eccentricity value is less than the eccentricity value at the set speed;

[0075] Step S7: if yes, dehydration is performed at the optimal speed n;

[0076] Step S8: Determine whether n is greater than the set rotation speed or reaches the maximum unwinding times K. If not, continue to execute the above step SS. If yes, end.

[0077] The embodiment of the present application also provides a control device for dehydrating clothes. It should be noted that the control device for dehydrating clothes in the embodiment of the present application can be used to execute the control method for dehydrating clothes provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions have been made and will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0078] The following is an introduction to the control device for dehydrating clothes provided in an embodiment of the present application.

[0079] Figure 5Schematic diagram of a control device for dehydrating clothes according to an embodiment of the present application. Figure 5 As shown, the device comprises:

[0080] A first acquisition unit 10 is used to acquire posture data of the washing machine, wherein the posture data at least includes acceleration and angular velocity of the washing tub;

[0081] Specifically, the present invention uses a posture sensor to obtain the posture data of the clothes during the dehydration process. The posture reflects the vibration of the clothes in the washing tub. The posture data, such as acceleration (a x ,a y ,a z ) and angular velocity (r x ,r y ,r z ). Figure 2 The installation position of the attitude sensor in the drum washing machine is to better extract the attitude of the washing machine during the washing process. It is installed on the outer drum and kept in the middle of the top of the inner drum. The outer drum will shake to varying degrees during the washing process. The intensity of the shaking is related to the rotation speed and the eccentricity of the clothes. The attitude sensor installed on the outer drum can reflect the vibration of the drum during the washing process.

[0082] An analysis unit 20 is used to analyze the posture data through a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates a degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, each set of data in the multiple sets of data includes: historical posture data and historical eccentricity values;

[0083] Specifically, the above-mentioned posture data is input into the neural network prediction model to accurately predict the eccentricity value of the clothes, and the clothes are untangled according to the eccentricity value and the speed of the motor is adjusted according to the eccentricity value, thereby improving the accuracy of clothing eccentricity detection, reducing the number of times the clothes are untangled, and saving dehydration time.

[0084] The first control unit 30 is used to control the washing machine to enter an untangling process when the eccentricity value is greater than or equal to a preset eccentricity value, wherein the untangling process represents untangling the clothes in the washing machine.

[0085] Specifically, the clothes are untangled according to the eccentricity value and the rotation speed of the motor is adjusted according to the eccentricity value, that is, when the eccentricity value is greater than or equal to the preset eccentricity value, the clothes are untangled.

[0086] Through this embodiment, the posture data of the washing machine is obtained, wherein the posture data includes at least acceleration and angular velocity; the posture data is analyzed by the target neural network model to obtain the eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, and each set of data in the multiple sets of data includes: historical posture data and historical eccentricity values; when the eccentricity value is greater than or equal to the preset eccentricity value, the washing machine is controlled to enter the untangling process, wherein the untangling process indicates the untangling state of the clothes in the washing machine. Compared with the prior art, the eccentricity value of the washing machine is inaccurately measured, resulting in a large number of untangling times of clothes and poor user experience, the present application predicts the eccentricity value through the posture data of the washing machine, and controls the washing machine to untangle according to the predicted eccentricity value, avoiding the problem of too many untangling times, and improving the user experience. Therefore, it can solve the problem of poor user experience caused by too many untangling times of clothes in the prior art, and improve the user experience.

[0087] In the specific implementation process, before analyzing the posture data through the target neural network model, the device also includes a second acquisition unit and a training unit, the second acquisition unit is used to obtain the historical posture data corresponding to the eccentricity detection modules of multiple preset weights, and obtain the historical eccentricity values ​​corresponding to the eccentricity detection modules of each preset weight; the training unit is used to establish an initial target neural network model, and train the initial target neural network model through the historical posture data and the historical eccentricity values ​​until the loss function of the initial target neural network model is the optimal value, thereby obtaining the target neural network model. The device trains the neural network model through the above steps to obtain a target neural network model suitable for eccentricity value prediction and accurately predict the eccentricity value.

[0088] Specifically, the input layer of the initial target neural network model contains 6 neurons, corresponding to the normalization of 3 linear accelerations and angular accelerations, respectively, which contains 2 hidden layers, the first hidden layer contains 64 neurons, the second layer contains 32 neurons, and the output layer is 1 neuron for outputting the predicted value. Each neuron is fully connected to the neurons in the next layer. When determining whether to hit the barrel, the neural network model needs to be trained. Due to the differences in the installation positions of the posture sensors, they are more sensitive to the current position and posture. In order to obtain more consistent data, the posture sensors are calibrated before training to eliminate the errors of each installation. The historical posture data of the inner drum at the rotation speed of the eccentricity detection modules (100g, 200g, 500g, 700g, 1000g) with different preset weights are collected and input into the neural network; and when the rotation speed reaches the point where the clothes are close to the drum wall, the eccentricity value is measured. The eccentricity detection module is generally an eccentric block.

[0089] The collected acceleration sensor is input into the neural network for training. The output of each neuron is calculated from the input layer to the output layer. For the i-th neuron in the l-th layer, its output is a i (l) for

[0090]

[0091] Where n(l-1) represents the number of neurons in the l-1th layer; w ij (l) is the weight connecting the jth neuron in the l-1th layer and the ith neuron in the lth layer; a j (l-1) is the output of the jth neuron in the l-1th layer; f is the activation function, f(x) = max(0, x); b i (l) is the bias of the ith neuron in the lth layer.

[0092] For this neural network, the input vector of the input layer is X, the weight matrix from the input layer to the hidden layer is W1, and the bias vector is b1. The output of the first hidden layer is:

[0093] z1=XW1+b1

[0094] a1=f(z1)

[0095] Similarly, the output of the second hidden layer is:

[0096] z2=XW2+b2

[0097] a2=f(z2)

[0098] For the last layer, the node computes the value Z directly.

[0099] However, for an untrained neural network, the output prediction value Z is inaccurate, which requires continuous iteration of the neural network, calculating the loss function MSE (mean square error) on the training set, and calculating the gradient of the loss function for each parameter, where N is the number of samples and yi is the actual value. is the predicted value.

[0100]

[0101] Each fall must reduce the gradient of the damage function, so each iteration must be updated to make the gradient decrease:

[0102]

[0103]

[0104] Where α is the learning rate, and the training is iterated to adjust the values ​​of W and b until The set value is met or the maximum number of iterations is reached. The collected eccentricity value and the posture data at the current maximum speed are trained, and the neural network is continuously updated until the damage function MSE reaches a relatively ideal value. After the training is completed, the neural network can predict the eccentricity value.

[0105] In some optional embodiments, the second acquisition unit includes a first acquisition module and a second acquisition module, the first acquisition module is used to acquire the current rotation speed when the washing machine starts to dehydrate; the second acquisition module is used to acquire the historical posture data corresponding to the eccentricity detection modules of multiple preset weights when the current rotation speed is greater than the rotation speed threshold, wherein the rotation speed threshold is the minimum rotation speed that makes the clothes close to the barrel wall. The device acquires the historical posture data through the above steps, so that the historical posture data can be obtained when the conditions are met, so as to further ensure the accurate prediction of the eccentricity value.

[0106] In the specific implementation process, Figure 3 For the data collection and training process of the attitude sensor, when the washing machine is dehydrating, the speed starts to increase. When the speed reaches the point where the clothes stick to the drum wall, that is, the speed threshold is the critical value of the speed that makes the clothes stick to the drum wall, the eccentricity value is measured to obtain the eccentricity value under the current eccentric block weight.

[0107] In some other optional embodiments, the first control unit includes a first control module and a second control module, the first control module is used to control the washing machine tub to rotate forward by a preset angle; the second control module is used to control the washing machine tub to rotate reversely by the preset angle. The device performs untangling through the above steps, which can prevent clothes from being entangled.

[0108] Specifically, the preset angle is 180 degrees, and the untangling adopts a continuous 180-degree forward and reverse untangling operation. During the untangling process, the neural network will be continuously called to judge the eccentricity value. When the eccentricity value is less than D or the maximum untangling time is reached, the speed will be increased to complete a dehydration, and the cycle will be repeated until the dehydration is completed.

[0109] In a specific implementation process, after controlling the washing machine to enter the untangling process, the device further includes a second control unit and a third control unit, wherein the second control unit is used to control the washing machine to enter the dehydration process when the untangling time is greater than or equal to the preset time, wherein the untangling time represents the running time of the untangling process; and the third control unit is used to obtain the current eccentricity value, and when the current eccentricity value is less than the preset eccentricity value, control the washing machine to enter the dehydration process. The device exits the untangling process and enters the dehydration process through the above steps, so that the clothes can be washed more evenly and cleanly.

[0110] Specifically, after entering the untangling process, when the set untangling time is reached or the eccentricity value is less than the maximum eccentricity value at the set speed, dehydration is performed at the optimal speed n, and it is further determined whether n is greater than the set speed or the maximum untangling times K is reached. If yes, the process ends; if not, the dehydration process continues.

[0111] In some optional embodiments, the third control unit includes a third control module, which determines the optimal speed corresponding to the current eccentricity value, and controls the washing machine to enter the dehydration process according to the optimal speed. The device determines the optimal speed corresponding to the current eccentricity value through the above steps, so that the clothes can be dehydrated at the most suitable speed to achieve the optimal dehydration effect.

[0112] In the specific implementation process, the eccentricity value predicted by the neural network needs to be used to determine the optimal rotation speed during the dehydration process, so that the dehydration effect can be optimized and the noise is controlled within an acceptable range.

[0113] In some optional embodiments, the third control module includes an acquisition submodule and a determination submodule, wherein the acquisition submodule is used to acquire a one-to-one mapping relationship between the eccentricity value and the optimal speed; and the determination submodule is used to determine the optimal speed corresponding to the current eccentricity value according to the one-to-one mapping relationship between the eccentricity value and the optimal speed. The device determines the optimal speed by the one-to-one mapping relationship, so that the clothes can be dehydrated at the optimal speed and the dehydration effect is the best.

[0114] Specifically, Table 1-1 shows the optimal rotation speeds under different eccentricity values ​​obtained from experiments.

[0115] Table 1-1 Optimal speed under different eccentricity values

[0116] Eccentric mass Maximum speed 100g 1320rad / min 200g 1320rad / min 500g 837rad / min 700g 525rad / min 1000g 249rad / min 1500g 126rad / min

[0117] The binomial is used to fit this curve to obtain the relationship between the eccentricity value and the speed as shown in the following formula: y = 0.000623x 2 -1.925x+1593.213, x represents the eccentricity value, and y represents the optimal speed.

[0118] The control device for clothes dehydration includes a processor and a memory, wherein the first acquisition unit, the analysis unit and the first control unit are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0119] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to solve the problem of inaccurate measurement of the eccentricity value of the washing machine, resulting in a large number of untangling of clothes and poor user experience.

[0120] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0121] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the control method for dehydrating clothes.

[0122] Specifically, the control method for dehydrating clothes includes:

[0123] Step S201, acquiring posture data of the washing machine, wherein the posture data at least includes acceleration and angular velocity of the washing tub;

[0124] Specifically, the present invention uses a posture sensor to obtain the posture data of the clothes during the dehydration process. The posture reflects the vibration of the clothes in the washing tub. The posture data, such as acceleration (a x ,a y ,a z ) and angular velocity (r x ,r y ,r z ). Figure 2 The installation position of the attitude sensor in the drum washing machine is to better extract the attitude of the washing machine during the washing process. It is installed on the outer drum and kept in the middle of the top of the inner drum. The outer drum will shake to varying degrees during the washing process. The intensity of the shaking is related to the rotation speed and the eccentricity of the clothes. The attitude sensor installed on the outer drum can reflect the vibration of the drum during the washing process.

[0125] Step S202, analyzing the posture data through a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, each set of data in the multiple sets of data includes: historical posture data and historical eccentricity values;

[0126] Specifically, the above-mentioned posture data is input into the neural network prediction model to accurately predict the eccentricity value of the clothes, and the clothes are untangled according to the eccentricity value and the speed of the motor is adjusted according to the eccentricity value, thereby improving the accuracy of clothing eccentricity detection, reducing the number of times the clothes are untangled, and saving dehydration time.

[0127] Step S203, when the eccentricity value is greater than or equal to a preset eccentricity value, controlling the washing machine to enter an untangling process, wherein the untangling process represents untangling the clothes in the washing machine.

[0128] Specifically, the clothes are untangled according to the eccentricity value and the rotation speed of the motor is adjusted according to the eccentricity value, that is, when the eccentricity value is greater than or equal to the preset eccentricity value, the clothes are untangled.

[0129] An embodiment of the present invention provides a washing machine, including a processor, a memory, and a program stored in the memory and executable on the processor, wherein the processor implements at least the following steps when executing the program:

[0130] Step S201, acquiring posture data of the washing machine, wherein the posture data at least includes acceleration and angular velocity of the washing tub;

[0131] Specifically, the present invention uses a posture sensor to obtain the posture data of the clothes during the dehydration process. The posture reflects the vibration of the clothes in the washing tub. The posture data, such as acceleration (a x ,a y ,a z ) and angular velocity (r x ,r y ,r z ). Figure 2 The installation position of the attitude sensor in the drum washing machine is to better extract the attitude of the washing machine during the washing process. It is installed on the outer drum and kept in the middle of the top of the inner drum. The outer drum will shake to varying degrees during the washing process. The intensity of the shaking is related to the rotation speed and the eccentricity of the clothes. The attitude sensor installed on the outer drum can reflect the vibration of the drum during the washing process.

[0132] Step S202, analyzing the posture data through a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, each set of data in the multiple sets of data includes: historical posture data and historical eccentricity values;

[0133] Specifically, the above-mentioned posture data is input into the neural network prediction model to accurately predict the eccentricity value of the clothes, and the clothes are untangled according to the eccentricity value and the speed of the motor is adjusted according to the eccentricity value, thereby improving the accuracy of clothing eccentricity detection, reducing the number of times the clothes are untangled, and saving dehydration time.

[0134] Step S203, when the eccentricity value is greater than or equal to a preset eccentricity value, controlling the washing machine to enter an untangling process, wherein the untangling process represents untangling the clothes in the washing machine.

[0135] Specifically, the clothes are untangled according to the eccentricity value and the rotation speed of the motor is adjusted according to the eccentricity value, that is, when the eccentricity value is greater than or equal to the preset eccentricity value, the clothes are untangled.

[0136] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0137] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method described in each embodiment of the present application:

[0138] Step S201, acquiring posture data of the washing machine, wherein the posture data at least includes acceleration and angular velocity of the washing tub;

[0139] Specifically, the present invention uses a posture sensor to obtain posture data of clothes during the dehydration process. The posture reflects the vibration of the clothes in the washing tub. The posture data includes acceleration (ax, ay, az) and angular velocity (rx, ry, rz). Figure 2 The installation position of the attitude sensor in the drum washing machine is to better extract the attitude of the washing machine during the washing process. It is installed on the outer drum and kept in the middle of the top of the inner drum. The outer drum will shake to varying degrees during the washing process. The intensity of the shaking is related to the rotation speed and the eccentricity of the clothes. The attitude sensor installed on the outer drum can reflect the vibration of the drum during the washing process.

[0140] Step S202, analyzing the posture data through a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, each set of data in the multiple sets of data includes: historical posture data and historical eccentricity values;

[0141] Specifically, the above-mentioned posture data is input into the neural network prediction model to accurately predict the eccentricity value of the clothes, and the clothes are untangled according to the eccentricity value and the speed of the motor is adjusted according to the eccentricity value, thereby improving the accuracy of clothing eccentricity detection, reducing the number of times the clothes are untangled, and saving dehydration time.

[0142] Step S203, when the eccentricity value is greater than or equal to a preset eccentricity value, controlling the washing machine to enter an untangling process, wherein the untangling process represents untangling the clothes in the washing machine.

[0143] Specifically, the clothes are untangled according to the eccentricity value and the rotation speed of the motor is adjusted according to the eccentricity value, that is, when the eccentricity value is greater than or equal to the preset eccentricity value, the clothes are untangled.

[0144] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0145] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0146] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0149] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0150] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0151] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0152] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0153] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0154] 1) In the control method for dehydrating clothes of the present application, the posture data of the washing machine is obtained, wherein the posture data includes at least acceleration and angular velocity; the posture data is analyzed by the target neural network model to obtain the eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, and each set of data in the multiple sets of data includes: historical posture data and historical eccentricity value; when the eccentricity value is greater than or equal to the preset eccentricity value, the washing machine is controlled to enter the untangling process, wherein the untangling process indicates the untangling state of the clothes in the washing machine. Compared with the prior art, the eccentricity value of the washing machine is inaccurately measured, resulting in a large number of untangling times of clothes and poor user experience, the present application predicts the eccentricity value through the posture data of the washing machine, and controls the washing machine to untangle according to the predicted eccentricity value, so as to avoid the problem of too many untangling times and improve the user experience. Therefore, it can solve the problem of poor user experience caused by too many untangling times of clothes in the prior art and improve the user experience.

[0155] 2) In the control device for dehydrating clothes of the present application, the posture data of the washing machine is obtained, wherein the posture data includes at least acceleration and angular velocity; the posture data is analyzed by the target neural network model to obtain the eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, and each set of data in the multiple sets of data includes: historical posture data and historical eccentricity value; when the eccentricity value is greater than or equal to the preset eccentricity value, the washing machine is controlled to enter the untangling process, wherein the untangling process indicates the untangling state of the clothes in the washing machine. Compared with the prior art, the eccentricity value of the washing machine is measured inaccurately, resulting in a large number of untangling times of clothes and poor user experience, the present application predicts the eccentricity value through the posture data of the washing machine, and controls the washing machine to untangle according to the predicted eccentricity value, so as to avoid the problem of too many untangling times and improve the user experience. Therefore, it can solve the problem of poor user experience caused by too many untangling times of clothes in the prior art and improve the user experience.

[0156] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling dehydration of clothes, characterized in that: include: Acquiring posture data of the washing machine, wherein the posture data at least includes acceleration and angular velocity of the washing tub; The posture data is analyzed by a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates the degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, each set of data in the multiple sets of data includes: historical posture data and historical eccentricity values; When the eccentricity value is greater than or equal to a preset eccentricity value, the washing machine is controlled to enter an untangling process, wherein the untangling process represents untangling the clothes in the washing machine.

2. The method for controlling the dehydration of clothes according to claim 1, characterized in that: Before analyzing the posture data by the target neural network model, the method further includes: Obtain the historical posture data corresponding to the eccentricity detection modules of multiple preset weights, and obtain the historical eccentricity value corresponding to each eccentricity detection module of the preset weight; An initial target neural network model is established, and the initial target neural network model is trained using the historical posture data and the historical eccentricity values ​​until the loss function of the initial target neural network model is an optimal value, thereby obtaining the target neural network model.

3. The method for controlling the dehydration of clothes according to claim 2, characterized in that: Acquiring the historical posture data corresponding to the eccentricity detection modules of multiple preset weights, including: When the washing machine starts to spin, obtaining a current rotation speed; When the current rotation speed is greater than a rotation speed threshold, the historical posture data corresponding to the eccentricity detection modules of multiple preset weights are obtained, wherein the rotation speed threshold is a minimum rotation speed that makes the clothes stick to the barrel wall.

4. The method for controlling the dehydration of clothes according to claim 1, characterized in that: Controlling the washing machine to enter the untangling process, comprising: Control the washing machine drum to rotate forward to a preset angle; The washing machine drum is controlled to rotate in the opposite direction to the preset angle.

5. The method for controlling the dehydration of clothes according to claim 1, characterized in that: After controlling the washing machine to enter the untangling process, the method further includes one of the following: When the untangling time is greater than or equal to a preset time, controlling the washing machine to enter a dehydration process, wherein the untangling time represents the running time of the untangling process; A current eccentricity value is obtained, and when the current eccentricity value is less than the preset eccentricity value, the washing machine is controlled to enter the dehydration process.

6. The method for controlling the dehydration of clothes according to claim 5, characterized in that: Controlling the washing machine to enter the dehydration process includes: The optimal rotation speed corresponding to the current eccentricity value is determined, and the washing machine is controlled to enter the dehydration process according to the optimal rotation speed.

7. The method for controlling the dehydration of clothes according to claim 6, characterized in that: Determining the optimal rotation speed corresponding to the current eccentricity value includes: Obtain a one-to-one mapping relationship between the eccentricity value and the optimal speed; The optimal rotational speed corresponding to the current eccentricity value is determined according to a one-to-one mapping relationship between the eccentricity value and the optimal rotational speed.

8. A control device for dehydrating clothes, characterized in that: include: A first acquisition unit, configured to acquire posture data of the washing machine, wherein the posture data at least includes acceleration and angular velocity of the washing tub; An analysis unit is used to analyze the posture data through a target neural network model to obtain an eccentricity value, wherein the eccentricity value indicates a degree of deviation from the center, and the target neural network model is obtained by training multiple sets of data, each of the multiple sets of data includes: historical posture data and historical eccentricity values; The first control unit is used to control the washing machine to enter an untangling process when the eccentricity value is greater than or equal to a preset eccentricity value, wherein the untangling process represents untangling the clothes in the washing machine.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for controlling dehydration of clothes according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a control method for executing the clothes dehydration according to any one of claims 1 to 7.

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