Clothes dehydration control method, device and equipment

Abstract

The invention belongs to the technical field of electric appliances, and particularly relates to a clothes dehydration control method, device and equipment, parameter information of clothes treatment equipment in a first dehydration stage is obtained, a simple characteristic value of the first dehydration stage is determined according to the parameter information, and the clothes dehydration control method and device are obtained according to the simple characteristic value. A simple feature decision model is adopted to predict a first operation state of the clothes processing equipment in a second dehydration stage, when the first operation state is used for indicating that a vibration phenomenon exists, the parameter information is sent to a cloud server, and the parameter information is adjusted according to a second operation state fed back by the cloud server, so that the clothes processing equipment is obtained. Obtaining new parameter information, and controlling the clothes treatment equipment to perform dehydration treatment according to the new parameter information in a second dehydration stage; according to the method, the problem that the clothes processing equipment generates vibration in the dewatering stage is avoided, the clothes dewatering efficiency is guaranteed, and the user experience is improved.

Description

Technical Field

[0001] The present application belongs to the field of electrical appliance technology, and specifically relates to a method, device and equipment for controlling clothing dehydration. Background Art

[0002] The dehydration process of the clothing treatment equipment requires the drum to rotate at high speed, using centrifugal force to spin out the water adhering to the clothes to be treated. However, due to uneven distribution of clothes and other reasons, high-speed rotation may cause a large eccentricity in the inner drum of the clothing treatment equipment, resulting in unbalanced dehydration, noise, displacement, or the clothes to be treated cannot be spun dry for a long time.

[0003] In the existing method, when dehydrating the clothes to be processed, the eccentricity threshold is usually set according to manual experience; if the eccentricity value of the inner drum during the dehydration process is not within the range of the eccentricity threshold, the speed of the inner drum cannot be increased; only when the eccentricity value of the inner drum during the dehydration process is within the range of the eccentricity threshold, the speed of the inner drum can be increased to achieve dehydration of the clothes.

[0004] However, although this method avoids some eccentric vibration problems, the inner drum speed will only be increased when the eccentricity value of the inner drum is within the range of the manually set eccentricity threshold value, which will prolong the dehydration time of the clothing processing equipment. In addition, it is difficult to accurately and quickly control the increase in the drum speed using a fixed threshold method. For clothes with a large amount of water absorption, the speed may not be increased for a long time, resulting in poor dehydration effect of the clothes. Summary of the invention

[0005] The present application provides a method, device and equipment for controlling dehydration of clothes, which are used to solve the problem that it is difficult to accurately and quickly control the increase of drum speed by using a fixed threshold method, and the speed may not be increased for a long time for clothes with a large amount of water absorption, resulting in poor dehydration effect of clothes.

[0006] In a first aspect, the present application provides a method for dehydrating clothing, comprising:

[0007] Acquiring parameter information of the clothes processing device in the first dehydration stage, and determining a simple characteristic value of the first dehydration stage according to the parameter information;

[0008] According to the simple feature value, a simple feature decision model is used to predict the first operating state of the clothes processing device in the second dehydration stage, wherein the simple feature decision model is trained according to the eccentricity value of the inner drum of the clothes processing device in the historical data;

[0009] When the first operating state is used to indicate the presence of vibration, the parameter information is sent to the cloud server, and the parameter information is adjusted according to the second operating state fed back by the cloud server to obtain new parameter information, wherein the operating state is used to indicate whether the clothing processing device has vibration in the second dehydration stage;

[0010] The clothes processing device is controlled to perform dehydration processing according to the new parameter information in the second dehydration stage.

[0011] Optionally, predicting the first operating state of the clothes processing device in the second dehydration stage by using a simple feature decision model according to the simple feature value includes:

[0012] Inputting the simple feature value into a simple feature decision model to obtain first prediction state information output by the simple feature decision model;

[0013] A first operating state of the laundry treatment device in a second dehydration stage is determined according to the first predicted state information.

[0014] Optionally, the second operating state includes: vibration operation and normal operation, the parameter information includes: inner drum speed, and the parameter information is adjusted according to the second operating state fed back by the cloud server to obtain new parameter information, including:

[0015] If the second operating state is normal operation, increasing the speed of the inner drum to a first preset speed;

[0016] If the second operating state is vibration operation, the rotation speed of the inner drum is reduced to a second preset rotation speed, wherein the first preset rotation speed is greater than the second preset rotation speed.

[0017] Optionally, the method further includes:

[0018] When the first operating state is used to indicate that there is no vibration phenomenon, the clothes processing device is controlled to perform dehydration processing at the first preset speed in the second dehydration stage, and the first preset speed is the dehydration speed corresponding to the second dehydration stage.

[0019] Optionally, the method further includes:

[0020] Determining whether the first operating state and the second operating state are the same;

[0021] When the first operating state and the second operating state are different, the simple feature decision model is modified according to the second operating state.

[0022] In a second aspect, the present application provides a method for dehydrating clothing, comprising:

[0023] Acquiring parameter information sent by the clothing treatment device, and determining the complex characteristic value of the clothing dehydration treatment device in the first dehydration stage according to the parameter information;

[0024] According to the complex characteristic value, a power simulation model is used to predict a second operating state of the clothing processing device in the second dehydration stage, wherein the power simulation model is trained according to the eccentricity value of the inner drum of the clothing processing device in the historical data, wherein the operating state is used to indicate whether there is a vibration phenomenon in the clothing processing device in the second dehydration stage;

[0025] The complex characteristic value and the second operating state are sent to the laundry processing device.

[0026] Optionally, predicting the second operating state of the clothing processing device in the second dehydration stage using a power simulation model according to the complex characteristic value includes:

[0027] Inputting the complex characteristic value into a power simulation model to obtain second predicted state information output by the power simulation model;

[0028] A second operating state of the laundry treatment device in a second dehydration stage is determined according to the second predicted state information.

[0029] Optionally, before acquiring the parameter information sent by the clothing processing device, the method further includes:

[0030] Acquire a historical eccentricity sample set sent by the clothes processing device, where the historical eccentricity sample set is used to indicate the eccentricity of the inner drum of the clothes processing device during a historical dehydration stage;

[0031] Extracting features from the historical eccentricity sample set to obtain target features, where the target features are features that are correlated with the eccentricity value of the inner cylinder;

[0032] Classify the target features according to the computational complexity to obtain a simple target feature set and a complex target feature set;

[0033] The simple target feature set is used to train the first feature decision model to obtain all simple feature decision models;

[0034] The complex target feature set is used to train the second feature decision model to obtain a power simulation model.

[0035] In a third aspect, the present application provides a clothing dehydration treatment device, comprising:

[0036] An acquisition module, used to acquire parameter information of the clothes processing device in the first dehydration stage;

[0037] A determination module, used to determine a simple characteristic value of the first dehydration stage according to the parameter information;

[0038] a processing module, configured to predict the first operating state of the clothes processing device in the second dehydration stage by using a simple feature decision model according to the simple feature value, wherein the simple feature decision model is trained according to the eccentricity value of the inner drum of the clothes processing device in the historical data;

[0039] a sending module, configured to send the parameter information to a cloud server when the first operating state indicates the presence of a vibration phenomenon, and adjust the parameter information according to a second operating state fed back by the cloud server to obtain new parameter information, wherein the operating state is used to indicate whether the clothing processing device has a vibration phenomenon in the second dehydration stage;

[0040] The control module is used to control the clothes processing device to perform dehydration processing according to the new parameter information in the second dehydration stage.

[0041] Optionally, the processing module is further used to input the simple feature value into a simple feature decision model to obtain first prediction state information output by the simple feature decision model;

[0042] The determination module is further configured to determine a first operating state of the clothing treatment device in a second dehydration stage according to the first predicted state information.

[0043] The control module is specifically configured to increase the speed of the inner drum to a first preset speed if the second operating state is normal operation;

[0044] The control module is specifically used to reduce the inner drum rotation speed to a second preset rotation speed if the second operation state is vibration operation, wherein the first preset rotation speed is greater than the second preset rotation speed.

[0045] Optionally, the control module is specifically used to control the clothing processing equipment to perform dehydration at the first preset speed in the second dehydration stage when the first operating state is used to indicate that there is no vibration phenomenon, and the first preset speed is the dehydration speed corresponding to the second dehydration stage.

[0046] Optionally, the clothing dehydration treatment device further includes: a judgment module;

[0047] The judging module is used to judge whether the first operating state and the second operating state are the same;

[0048] The processing module is further used to correct the simple feature decision model according to the second operating state when the first operating state and the second operating state are different.

[0049] In a fourth aspect, the present application provides a clothing dehydration treatment device, further comprising:

[0050] An acquisition module, used to acquire parameter information sent by the clothing processing device;

[0051] a determination module, used to determine the complex characteristic value of the clothes dehydration treatment device in the first dehydration stage according to the parameter information;

[0052] a processing module, configured to predict, according to the complex characteristic value, a second operating state of the clothing processing device in the second dehydration stage using a power simulation model, wherein the power simulation model is trained based on an eccentricity value of an inner drum of the clothing processing device in historical data, wherein the operating state is used to indicate whether the clothing processing device has a vibration phenomenon in the second dehydration stage;

[0053] A sending module is used to send the complex characteristic value and the second operating state to the clothing processing device.

[0054] Optionally, the processing module is further used to input the complex characteristic value into a power simulation model to obtain second predicted state information output by the power simulation model;

[0055] The determination module is further configured to determine a second operating state of the clothes processing device in a second dehydration stage according to the second predicted state information.

[0056] The acquisition module is further used to acquire a historical eccentricity sample set sent by the clothes processing device, wherein the historical eccentricity sample set is used to indicate the eccentricity of the inner drum of the clothes processing device during a historical dehydration stage;

[0057] The processing module is further used to extract features from the historical eccentricity sample set to obtain target features, where the target features are features that are associated with the eccentricity value of the inner cylinder;

[0058] The processing module is further used to classify the target features according to the computational complexity to obtain a simple target feature set and a complex target feature set;

[0059] The processing module is further used to train the first feature decision model using the simple target feature set to obtain all simple feature decision models;

[0060] The processing module is also used to train the second feature decision model using the complex target feature set to obtain a power simulation model.

[0061] In a fifth aspect, the present application provides a clothing dehydration treatment device, comprising:

[0062] Memory;

[0063] processor;

[0064] Wherein, the memory stores computer-executable instructions;

[0065] The processor executes the computer-executable instructions stored in the memory to implement the clothes dehydration processing method as described in the first aspect and various possible implementations of the first aspect.

[0066] In a sixth aspect, the present application provides a computer storage medium, characterized in that the computer storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the clothing dehydration method as described in the first aspect and various possible implementations of the first aspect.

[0067] The present application provides a method for dehydrating clothes, which obtains parameter information of a clothes processing device in a first dehydration stage, and determines a simple characteristic value of the first dehydration stage based on the parameter information, and uses a simple characteristic decision model to predict a first operating state of the clothes processing device in a second dehydration stage based on the simple characteristic value, and when the first operating state is used to indicate the presence of a vibration phenomenon, the parameter information is sent to a cloud server, and the parameter information is adjusted according to the second operating state fed back by the cloud server to obtain new parameter information, and the clothes processing device is controlled to perform dehydration processing according to the new parameter information in the second dehydration stage; the method avoids the problem of vibration of the clothes processing device in the dehydration stage, ensures the dehydration efficiency of the clothes, and improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0069] Figure 1 It is a schematic diagram of a scene of clothes dehydration processing provided by the present application;

[0070] Figure 2 is a flow chart of the clothing dehydration method provided by the present application;

[0071] Figure 3 It is a flow chart of the clothing dehydration treatment method provided by the present application;

[0072] Figure 4 The structure of the clothes dehydration device provided in this application is shown as follows: Figure 1 ;

[0073] Figure 5 The structure of the clothes dehydration device provided in this application is shown as follows: Figure 2 ;

[0074] Figure 6 This is a schematic diagram of the structure of the clothing dehydration treatment equipment provided in this application.

[0075] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0077] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein, for example.

[0078] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0079] The dehydration process of the clothing treatment equipment requires the drum to rotate at high speed, using centrifugal force to spin out the water adhering to the clothes to be treated. However, due to uneven distribution of clothes and other reasons, high-speed rotation may cause a large eccentricity in the inner drum of the clothing treatment equipment, resulting in unbalanced dehydration, noise, displacement, or the clothes to be treated cannot be spun dry for a long time.

[0080] In the existing method, when dehydrating the clothes to be treated, an eccentricity threshold is usually set according to manual experience; if the eccentricity value of the inner drum during the dehydration process is not within the range of the eccentricity threshold, the speed of the inner drum cannot be increased. At this time, the distribution of the clothes will be redistributed along with the speed of the inner drum, and the eccentricity value of the clothes processing device is repeatedly obtained until the eccentricity value is lower than the threshold, and the drum speed is increased to dehydrate the clothes to be treated; when the eccentricity value of the inner drum during the dehydration process is within the range of the eccentricity threshold, the speed of the inner drum is directly increased to achieve dehydration of the clothes.

[0081] However, although this method avoids some eccentric vibration problems, the inner drum speed will only be increased when the eccentricity value of the inner drum is within the range of the manually set eccentricity threshold value, which will prolong the dehydration time of the clothing processing equipment. In addition, it is difficult to accurately and quickly control the increase in the drum speed using a fixed threshold method. For clothes with a large amount of water absorption, the speed may not be increased for a long time, resulting in poor dehydration effect of the clothes.

[0082] In view of the above problems, the present application provides a method for dehydrating clothes. Figure 1 Schematic diagram of the scene of clothes dehydration provided by this application. Figure 1 As shown, the clothing processing device 1 is in communication connection with the cloud server 2. The clothing processing device 1 can perform simple calculations and analysis on the data in the clothing processing process, and the cloud server 2 can perform complex calculations on the data in the clothing processing process to obtain a more accurate data processing result.

[0083] The present application provides a method for dehydrating clothes, which utilizes the cloud computing power of the Internet of Things, combines artificial intelligence and power simulation, and constructs a method for calculating, updating and modeling control of eccentric timing features of clothing processing equipment; first, training the decision model of timing depth feature extraction and different feature combinations offline in the cloud, and classifying them according to the complexity of feature calculation, writing simple features and simple decision models into the program of the clothing processing equipment, and writing complex features and power simulation models into the cloud server; secondly, using a simple decision model to judge the operating status of the clothing processing equipment, because the accuracy of the simple decision model in judging the operating status of the clothing processing equipment is low, therefore, the power simulation model can be combined to complete the complex timing feature calculation in the cloud, and judge the operating status of the clothing processing equipment; thereby controlling the clothing processing equipment to perform clothing dehydration processing; this method combines artificial intelligence and power simulation to ensure seamless switching of real-time control of the clothing processing equipment, avoids the problem of vibration of the clothing processing equipment during the dehydration stage in advance, ensures the efficiency of clothing dehydration, and improves user experience.

[0084] Figure 2 is a flow chart of the clothing dehydration method provided by the present application. The execution subject of this embodiment can be, for example, Figure 1The laundry processing device shown in the embodiment. Figure 2 As shown, the clothes dehydration method provided in this embodiment includes:

[0085] S101: Acquire parameter information of a clothes treating device in a first dehydration stage, and determine a simple characteristic value of the first dehydration stage according to the parameter information.

[0086] The clothing processing device may be, for example, a washing machine, a care machine, or a drying machine. The first dehydration stage is used to indicate the dehydration stage at the beginning of the dehydration program. It is understandable that the first dehydration stage is mainly used to analyze the degree of eccentricity of the inner drum in the dehydration program, that is, the dehydration speed in the first dehydration stage is relatively low.

[0087] The parameter information is used to indicate the eccentricity value and the drum speed of the clothing processing device in the dehydration stage. The parameter information of the first dehydration stage may be: the eccentricity time series data of the eccentricity value changing with time, the drum speed time series data of the drum speed changing with time, and the corresponding relationship between the eccentricity value and the drum speed. The simple characteristic value is used to indicate the characteristic value of the eccentricity value and the characteristic value of the drum speed in the first dehydration stage. The simple characteristic value may be, for example, the average value, the maximum value, the minimum value, the variance, the inflection point, and the number of inflection points.

[0088] It can be understood that in this step, by obtaining the eccentricity time series of the eccentricity value of the clothes processing device in the first dehydration stage and the drum speed time series of the drum speed changing with time, the corresponding relationship between the eccentricity value and the drum speed of the clothes processing device can also be obtained. The eccentricity time series and the drum speed time series are feature extracted to obtain the simple eigenvalue of eccentricity and the simple eigenvalue of drum speed.

[0089] This step can use tsfresh's built-in feature algorithm to calculate simple feature values. Among them, tsfresh is mainly used to perform system feature engineering from time series and other sequence data. The common point between time series data and other sequence data is that they are sorted by independent variables. The most common independent variable is time (that is, the most common data processed by tsfresh is time series data). tsfresh can automatically calculate a large number of time series features.

[0090] For example, the dehydration time of the clothing processing equipment is 7 minutes, and the first dehydration stage may be the first minute after the dehydration stage begins. Therefore, the eccentricity time series of the eccentricity value changing with time within the first minute of the dehydration stage and the drum speed time series of the drum speed changing with time may be obtained, and the built-in feature algorithm of tsfresh is used to calculate the eccentricity simple eigenvalue and the drum speed simple eigenvalue for the eccentricity time series and the drum speed time series respectively.

[0091] S102: According to the simple feature value, a simple feature decision model is used to predict a first operating state of the clothing processing device in the second dehydration stage, wherein the simple feature decision model is trained according to the eccentricity value of the inner drum of the clothing processing device in historical data.

[0092] Among them, the simple feature decision model is one of the decision models constructed in the cloud by performing feature extraction, feature correlation evaluation and feature complexity analysis on the historical eccentric sample set.

[0093] The first operating state is used to indicate whether the clothing processing device will vibrate in the second dehydration stage. When the clothing processing device vibrates, it indicates that the distribution of the clothes in the drum of the clothing processing device is unreasonable, and the clothes in the drum need to be redistributed; when the clothing processing device does not vibrate, it indicates that the distribution of the clothes in the drum of the clothing processing device is reasonable. At this time, the clothes in the drum do not need to be redistributed, and the clothes to be processed can be efficiently dehydrated.

[0094] The dehydration program includes: a first dehydration stage and a second dehydration stage. The dehydration speed of the second dehydration stage is greater than the dehydration speed of the first dehydration stage. After entering the dehydration program, the clothing processing device will first perform dehydration processing at a smaller dehydration speed in the first dehydration stage. When the first dehydration stage is over, the clothing processing device will perform dehydration processing at a normal or larger dehydration speed in the second dehydration stage. That is: first determine whether the clothing processing device has vibration in the second dehydration stage based on the parameter information of the first dehydration stage. If so, it is necessary to adjust the speed of the second dehydration stage based on the parameter information of the first dehydration stage. If not, the dehydration processing can be performed using the dehydration speed corresponding to the second dehydration stage.

[0095] It can be understood that the clothing processing equipment adopts a simple feature decision model to predict the eccentricity time series of the eccentricity value of the clothing processing equipment changing with time and the drum speed time series of the drum speed changing with time in the second dehydration stage based on the simple characteristic value of eccentricity and the simple characteristic value of drum speed in the first dehydration stage, and determines the operating state of the second dehydration stage based on the correlation between the eccentricity value, the drum speed and the vibration of the clothing processing equipment.

[0096] For example, if the dehydration time of the clothes processing equipment is 7 minutes, the second dehydration stage may be 6 minutes after the dehydration stage, therefore, the eccentricity time series of the eccentricity value changing with time and the drum speed time series of the drum speed changing with time may be obtained 6 minutes after the dehydration stage. The correlation between the eccentricity value, the drum speed and the vibration of the clothes processing equipment is a data result obtained by manual observation and analysis.

[0097] S103: When the first operating state is used to indicate the presence of vibration, the parameter information is sent to the cloud server, and the parameter information is adjusted according to the second operating state fed back by the cloud server to obtain new parameter information, and the operating state is used to indicate whether the clothing processing device has vibration in the second dehydration stage.

[0098] Among them, since the first operating state is the operating state of the clothes processing device in the second dehydration stage determined by analyzing the parameter information with a simple feature decision model, when the first operating state is vibration operation, since the similarity between the determined first operating state and the actual vibration phenomenon of the clothes processing device is relatively low, it is easier to determine the normal operation of the clothes processing device as vibration operation. Therefore, it is necessary to analyze the parameter information again through the power simulation model of the cloud server to re-determine the operating state of the clothes processing device.

[0099] It can be understood that when the second operating state is vibrating operation, it indicates that the clothing treatment device has vibration phenomenon in the second dehydration stage, and the simple feature decision model has a correct judgment result. When the second operating state is normal operation, it indicates that the clothing treatment device does not have vibration phenomenon in the second dehydration stage, and the simple feature decision model has an error in the judgment result. At this time, the simple feature decision model result can be corrected. According to the second operating state, the parameter information can be adjusted so that the clothing treatment device operates according to the adjusted parameter information in the second dehydration stage.

[0100] S104: Control the clothes processing device to perform dehydration processing according to the new parameter information in the second dehydration stage.

[0101] Among them, the new parameter information is used to indicate the eccentricity value and drum speed re-determined by the clothing processing device. The eccentricity value and drum speed can be fixed values, or can vary with the increase in dehydration time, that is, the eccentricity time series and drum speed time series of the second dehydration stage are set. The eccentricity value and drum speed are not set to fixed values ​​to ensure that the clothing processing device can ensure and improve the dehydration effect of the clothes under the condition that the clothing processing device does not vibrate when the clothing processing device dehydrates the clothes to be treated. However, the eccentricity value and the drum speed are values ​​within a range. If the eccentricity value and the drum speed are set too low, it will lead to problems such as too long dehydration time of the clothes or the clothes to be treated cannot be spun dry. If the eccentricity value and the drum speed are set too high, it will easily cause the clothes to be treated to be torn or the clothes to have too many wrinkles after dehydration.

[0102] It can be understood that the operating state includes vibration operation and normal operation. If the clothing processing device generates vibration, it indicates that the distribution of the clothing in the drum of the clothing processing device is unreasonable. At this time, the clothing in the drum needs to be redistributed, that is, the clothing to be processed in the drum of the clothing processing device can be dispersed by adjusting the rotation speed in the drum, and the clothing to be processed can be redistributed in the drum under the rotation of the inner drum. Therefore, the rotation of the inner drum can be controlled according to the new drum speed time sequence, so that the clothing to be processed can be dehydrated; if the clothing processing device does not generate vibration, it indicates that the distribution of the clothing in the drum of the clothing processing device is reasonable. In order to improve the dehydration efficiency of the clothing, a suitable drum speed can be set, and the rotation of the inner drum can be controlled according to the new drum speed time sequence, so that the clothing to be processed can be dehydrated.

[0103] For example: the dehydration time of the clothing processing equipment is 7 minutes, and the second dehydration stage can be 6 minutes after the dehydration stage. The eccentricity time series of the eccentricity value changing with time 6 minutes after the dehydration stage and the drum speed time series of the drum speed changing with time are the current parameter information. The current parameters can be readjusted according to the correlation between the eccentricity value, the drum speed and the vibration of the clothing processing equipment under the operating conditions, and the clothing processing equipment can be controlled to perform dehydration according to the adjusted parameter information.

[0104] Optionally, when the first operating state is used to indicate that there is no vibration phenomenon, the clothing processing device is controlled to perform dehydration processing at the first preset speed in the second dehydration stage, and the first preset speed is the dehydration speed corresponding to the second dehydration stage.

[0105] The first preset speed is used to indicate the drum speed corresponding to the second dehydration stage of the clothes processing device. When the first operating state is used to indicate that there is no vibration phenomenon, the clothes processing device directly uses the parameter information of the second dehydration stage (that is, the first preset speed) predicted by the simple feature decision model according to the first operating state result, and performs dehydration according to the first preset speed.

[0106] The present embodiment provides a method for dehydrating clothes, which obtains parameter information of a clothes processing device in a first dehydration stage, and determines a simple characteristic value of the first dehydration stage based on the parameter information, and uses a simple characteristic decision model to predict a first operating state of the clothes processing device in a second dehydration stage based on the simple characteristic value, and when the first operating state is used to indicate the presence of vibration, sends the parameter information to a cloud server, and adjusts the parameter information according to the second operating state fed back by the cloud server to obtain new parameter information, and controls the clothes processing device to perform dehydration processing according to the new parameter information in the second dehydration stage; the method avoids the problem of vibration of the clothes processing device in the dehydration stage, ensures the dehydration efficiency of the clothes, and improves the user experience.

[0107] Figure 3 : is a flow chart of the clothing dehydration method provided by this application. Figure 3 As shown, in this embodiment Figure 2 Based on the embodiment, the clothes dehydration treatment method is described in detail. The clothes dehydration treatment method shown in this embodiment includes:

[0108] S201: The clothes processing device obtains parameter information of a first dehydration stage, and determines a simple characteristic value of the first dehydration stage according to the parameter information.

[0109] Among them, step S201 is similar to the above-mentioned step S101 and is briefly described here. The executor in this step is the clothing processing device. The clothing processing device obtains the parameter information of the first dehydration stage and can analyze and process the data to obtain the operation result of the clothing processing device, or transmit the data to the cloud server.

[0110] S202: The clothing processing device inputs the simple feature value into a simple feature decision model to obtain first predicted state information output by the simple feature decision model.

[0111] The simple feature decision model inputs the simple feature value of eccentricity and the simple feature value of drum speed in the first dehydration stage into the model, analyzes and processes the simple features, and thus predicts the operating state of the clothing treatment device in the second dehydration stage. The operating state here is used to characterize whether the clothing treatment device vibrates in the second dehydration stage. The operating state includes: vibrating operation and normal operation.

[0112] It is understandable that this step will be executed regardless of whether the clothing processing device receives the complex characteristic value fed back by the cloud server. That is, after the simple characteristic value is determined according to the parameter information, this step can be executed regardless of whether the complex characteristic value fed back by the cloud server is received. This is because the clothing processing device cannot confirm whether it will receive the complex characteristic value fed back by the cloud server in the future. Therefore, in order to avoid not receiving the complex characteristic value and ensure the normal operation of the dehydration program, this step will be executed.

[0113] S203: The clothes processing device determines a first operating state of a second dehydration stage according to the first predicted state information.

[0114] The first predicted state information is used to indicate the eccentricity time series and the drum speed time series of the clothes processing device in the second dehydration stage. Simple features Since the feature value is relatively simple, the clothes processing device can use a simple feature decision model to analyze and process the simple features, predict the eccentricity time series and the drum speed time series of the clothes processing device in the second dehydration stage, and determine the first operating state of the second dehydration stage according to the correlation between the eccentricity value, the drum speed and the vibration of the clothes processing device.

[0115] S204: When the first operating state is used to indicate the presence of a vibration phenomenon, the clothing processing device sends the parameter information to a cloud server.

[0116] Among them, parameter information refers to the parameter data of the clothing processing device itself during its operation. Therefore, the parameter information can be calculated and processed by the processor in the clothing processing device, and can also be calculated and processed by the cloud server. Since the processor in the clothing processing device processes the parameters in a relatively simple way, the accuracy of the processed results is relatively low (this is determined by the computing power of the processor); and the cloud server can perform more complex calculations on the parameter information, and the accuracy of the processed results is relatively high. Therefore, the clothing processing device can send the parameter information to the cloud server so that the cloud server can perform complex calculations based on the parameter information.

[0117] This step is performed after the first operating state indicates that the clothing processing equipment has vibration in the second dehydration stage. It is understandable that, due to the low computing power of the simple feature decision model, the accuracy of the calculation results is relatively low. Therefore, after the simple feature decision model determines that there is a vibration phenomenon, a secondary verification can be performed using a power simulation model set on the cloud server. Since the cloud server has strong computing power and the accuracy of the calculation results is relatively high, a secondary verification using a power simulation model can avoid problems such as inaccurate prediction results of the simple feature decision model, which leads to unreasonable parameter settings of the clothing processing equipment in the second dehydration stage, thereby prolonging the dehydration time of the clothes to be treated, affecting the dehydration efficiency of the clothes to be treated, etc.

[0118] S205: The cloud server determines the complex characteristic value of the first dehydration stage according to the parameter information.

[0119] Among them, the complex eigenvalue is used to indicate the eigenvalue of the eccentricity value and the eigenvalue of the drum speed in the first dehydration stage. Since the executor of this step is the cloud server, and the cloud server has strong computing power, the complex eigenvalue can be obtained by calculating the parameter information of the first dehydration stage.

[0120] It can be understood that this step can obtain the corresponding relationship between the eccentricity value and the drum speed of the clothing processing device by obtaining the eccentricity time series of the eccentricity value of the clothing processing device in the first dehydration stage and the drum speed time series of the drum speed. And extract the features of the eccentricity time series and the drum speed time series to obtain the simple and complex eigenvalues ​​of the eccentricity and the complex eigenvalues ​​of the drum speed. This step can use the power simulation model to calculate the complex eigenvalue, that is, input the parameter information into the power simulation model so that the neural network model outputs the corresponding complex eigenvalue.

[0121] For example, the first dehydration stage is the first minute of dehydration of the clothes to be treated. The eccentricity value and drum speed of the first minute of dehydration of the clothes to be treated can be input into the neural network model, and the neural network model will analyze the data to obtain the complex characteristic values ​​of the data.

[0122] S206: The cloud server inputs the complex characteristic value into the power simulation model to obtain the second predicted state information output by the power simulation model.

[0123] The second predicted state information is used to indicate the eccentricity time series and drum speed time series of the clothes processing device in the second dehydration stage. Since the characteristic values ​​of complex features are relatively complex, the computing power of the cloud server is relatively strong and the results are relatively accurate. Therefore, the power simulation model can be used on the cloud server to analyze and process the complex features and predict the eccentricity time series and drum speed time series of the clothes processing device in the second dehydration stage.

[0124] S207: The cloud server determines a second operating state of a second dehydration stage according to the second predicted state information.

[0125] The second operation state is the vibration state of the clothes processing device, which can be: vibration operation and normal operation. Vibration operation can cause great damage to the clothes processing device. Therefore, this embodiment needs to avoid the clothes processing device being in vibration operation. Normal operation can ensure that the clothes processing device operates at a reasonable drum speed, which can reduce damage to the clothes processing device.

[0126] It can be understood that the cloud server predicts the eccentricity time series and the drum speed time series of the second dehydration stage by analyzing the eccentricity time series and the drum speed time series of the first dehydration stage, and determines the second operating state of the second dehydration stage based on the correlation between the eccentricity value, the drum speed and the vibration of the clothing processing equipment.

[0127] S208: The cloud server sends the complex feature value and the second operating status to the clothing processing device.

[0128] The cloud server sends the complex feature value to the clothing processing device so that the clothing processing device can dynamically update the original complex feature value according to the complex feature value sent by the cloud server. At the same time, sending the complex feature value can also enable the clothing processing device to determine the current connection status with the cloud server.

[0129] Since the program of the clothing processing equipment is directly controlled by the control device in the clothing processing equipment, the cloud server needs to send the operating status of the clothing processing equipment in the second dehydration stage to the clothing processing equipment, so that the control device of the clothing processing equipment can control the clothing processing equipment according to the operating status of the clothing processing equipment, thereby avoiding vibration of the clothing processing equipment during the dehydration stage.

[0130] S209: the clothes processing device adjusts the parameter information according to the second operating state to obtain new parameter information.

[0131] Among them, vibration operation indicates that the clothes processing device will vibrate in the second dehydration stage due to unreasonable distribution of the clothes to be processed, and the clothes to be processed need to be redistributed, so the parameter information needs to be adjusted. Normal operation indicates that the clothes processing device will not vibrate in the second dehydration stage, and the clothes to be processed can be dehydrated according to the estimated parameters of the second stage.

[0132] It is understandable that the operating state can be predicted by the clothing processing device through a simple feature decision model, or can be predicted by the cloud server through a power simulation model. When the clothing processing device is in an offline state, the operating state predicted by the simple feature decision model is used; when the clothing processing device is in a connected state, the operating state predicted by the power simulation model is used.

[0133] Optionally, if the operating state is vibration operation, the inner drum rotation speed is reduced to a first preset rotation speed; if the operating state is normal operation, the inner drum rotation speed is increased to a second preset rotation speed, wherein the first preset rotation speed is less than the second preset rotation speed.

[0134] It is understandable that the clothes treatment equipment is in vibration operation, and at this time, the state of the clothes to be treated in the drum needs to be redistributed, and the clothes to be treated need to be spread out by the rotation of the drum to achieve the effect of redistribution. When the drum speed is high, the clothes to be treated are close to the inner drum wall, and the inner drum speed needs to be reduced to change the distribution of the clothes to be treated, so as to dehydrate the clothes to be treated. The normal operation of the clothes treatment equipment indicates that the clothes are reasonably distributed, and the drum speed can be increased according to the current trend of the drum speed change to ensure that the clothes treatment equipment does not vibrate while efficiently dehydrating the clothes to be treated.

[0135] S211: The clothes processing device controls the second dehydration stage to perform dehydration processing according to the new parameter information.

[0136] Among them, the control module of the clothing processing equipment adjusts the parameters of the clothing processing equipment according to the new parameter information, so as to dehydrate the clothing to be processed, so that the clothing to be processed can achieve the dehydration effect while preventing the clothing processing equipment from vibrating during the dehydration stage.

[0137] Optionally, determine whether the first operating state and the second operating state are the same; when the first operating state and the second operating state are different, modify the simple feature decision model according to the second operating state.

[0138] Among them, the first operating state is the result determined by the simple feature decision model, and the second operating state is the result determined by the power simulation model. The reason for comparing the first operating state and the second operating state in this step is that the accuracy of the data analysis of the simple feature decision model is relatively low, and the first operating state may be misjudged, for example: the normal operation of the clothing processing equipment is judged as the clothing processing equipment is in vibration operation; while the result obtained by the power simulation model through the analysis of the parameter information is highly accurate, and the correctness of the first operating state obtained by the simple feature decision model can be verified based on the second operating state obtained by the power simulation model. When the first operating state and the second operating state are different, the result of the second operating state can be input into the simple feature decision model, and the simple feature decision model can be corrected, thereby improving the accuracy of the data processed by the simple feature decision model.

[0139] Exemplarily, the simple decision model and the power simulation model are trained by the cloud server based on the historical eccentricity sample set, and the specific training process is as follows:

[0140] A historical eccentricity sample set sent by the clothing processing device is obtained, wherein the historical eccentricity sample set is used to indicate the eccentricity of the inner drum of the clothing processing device during a historical dehydration stage; feature extraction is performed on the historical eccentricity sample set to obtain a target feature, wherein the target feature is a feature that is correlated with the eccentricity value of the inner drum; the target features are classified according to computational complexity to obtain a simple target feature set and a complex target feature set; the simple target feature set is used to train a first feature decision model to obtain all simple feature decision models; the complex target feature set is used to train a second feature decision model to obtain all power simulation models.

[0141] Among them, the historical eccentricity sample set is the data information collected during the operation of historical clothing processing equipment. The data information can be used for model training in the cloud. tsfresh is used to extract the features of the eccentricity sample time series data, and LSTM is used to extract implicit features with historical correlation information. The DeepCheck evaluation algorithm is used to evaluate the correlation of the extracted features, and retain features with high correlation with eccentricity control. The features are classified according to the complexity of the calculation of each feature, and are divided into simple feature sets and complex feature sets. Then, two decision models (i.e., simple decision model and power simulation model) are trained based on the simple feature set and the complex feature set (or full feature set).

[0142] The present embodiment provides a method for dehydrating clothes, wherein parameter information of a first dehydration stage is obtained through a clothes processing device, and a simple characteristic value of the first dehydration stage is determined based on the parameter information, and the simple characteristic value is input into a simple characteristic decision model to obtain first predicted state information output by the simple characteristic decision model, and a first operating state of a second dehydration stage is determined based on the first predicted state information. When the first operating state is used to indicate the presence of a vibration phenomenon, the clothes processing device sends the parameter information to a cloud server, and the cloud server determines a complex characteristic value of the first dehydration stage based on the parameter information, and the complex characteristic value is input into a complex characteristic decision model. In the model, the second predicted state information output by the complex feature decision model is obtained, and the second operating state of the second dehydration stage is determined according to the second predicted state information. The cloud server sends the complex feature value and the second operating state to the clothing processing device, and the clothing processing device adjusts the parameter information according to the second operating state to obtain new parameter information, and controls the second dehydration stage to perform dehydration according to the new parameter information. This method combines artificial intelligence and power simulation to ensure seamless switching of real-time control of the clothing processing device, avoids the problem of vibration of the clothing processing device during the dehydration stage in advance, ensures the dehydration efficiency of the clothing, and improves the user experience.

[0143] LSTM (Long Short-Term Memory Network) is a time recurrent neural network specially designed to solve the long-term dependency problem of general RNN (recurrent neural network). All RNNs have a chain form of repeated neural network modules. In standard RNN, this repeated structural module has only a very simple structure, which can extract the more important and difficult to find features of the data. DeepCheck's evaluation algorithm divides the eccentric sample set into a training set, a validation set, and a test set to obtain a simple decision model with low computational complexity and a power simulation model with high computational complexity. The simple decision model is written into the data processor of the clothing processing equipment to perform simple calculations on the parameters generated during the operation of the clothing processing equipment under its own computing power conditions, while the power simulation model will be written into the cloud server, using the strong algorithm capabilities of the cloud server to perform complex calculations on the parameter information to obtain more accurate results.

[0144] The present embodiment provides a method for dehydrating clothes, which obtains parameter information of a clothes processing device in a first dehydration stage, determines a simple characteristic value of the first dehydration stage according to the parameter information, and sends the parameter information to a cloud server, so that the cloud server determines a complex characteristic value of the first dehydration stage according to the parameter information, and determines whether the complex characteristic value fed back by the cloud server is received within a preset time. If the complex characteristic value fed back by the cloud server is received within the preset time, the operating state of the clothes processing device in the second dehydration stage sent by the cloud server is obtained, and if the complex characteristic value fed back by the cloud server is not received within the preset time, the operating state of the clothes processing device in the second dehydration stage sent by the cloud server is obtained. The complex feature value fed back by the server is used, and a simple feature decision model is used according to the simple feature value to predict the operating state of the clothing processing device in the second dehydration stage; if the operating state is vibration operation, the inner drum speed is reduced to a first preset speed; if the operating state is normal operation, the inner drum speed is increased to a second preset speed, and the clothing processing device is controlled to perform dehydration according to the new parameter information in the second dehydration stage; the method adopts an end-cloud collaborative approach to ensure seamless switching of real-time control of the clothing processing device, avoid the problem of vibration of the clothing processing device in the dehydration stage in advance, ensure the dehydration efficiency of the clothing, and improve the user experience.

[0145] Figure 4 The structure of the clothes dehydration device provided in this application is shown as follows: Figure 1 .like Figure 4 As shown, the present application provides a clothing dehydration treatment device, and the clothing dehydration treatment device 400 includes:

[0146] An acquisition module 301 is used to acquire parameter information of a clothes processing device in a first dehydration stage;

[0147] A determination module 302 is used to determine a simple characteristic value of the first dehydration stage according to the parameter information;

[0148] A processing module 303 is used to predict the first operating state of the clothes processing device in the second dehydration stage according to the simple feature value using a simple feature decision model, wherein the simple feature decision model is trained according to the eccentricity value of the inner drum of the clothes processing device in the historical data;

[0149] The sending module 304 is used to send the parameter information to the cloud server when the first operating state is used to indicate the presence of vibration, and adjust the parameter information according to the second operating state fed back by the cloud server to obtain new parameter information, wherein the operating state is used to indicate whether the clothing processing device has vibration in the second dehydration stage;

[0150] The control module 305 is used to control the clothes processing device to perform dehydration processing according to the new parameter information in the second dehydration stage.

[0151] Optionally, the processing module 303 is further used to input the simple feature value into a simple feature decision model to obtain first prediction state information output by the simple feature decision model;

[0152] The determining module 302 is further configured to determine a first operating state of the clothes treating device in a second dehydration stage according to the first predicted state information.

[0153] The control module 305 is specifically configured to increase the speed of the inner drum to a first preset speed if the second operating state is normal operation;

[0154] The control module 305 is specifically configured to reduce the rotation speed of the inner drum to a second preset rotation speed if the second operation state is vibration operation, wherein the first preset rotation speed is greater than the second preset rotation speed.

[0155] Optionally, the control module 305 is specifically used to control the clothing processing equipment to perform dehydration at the first preset speed in the second dehydration stage when the first operating state is used to indicate that there is no vibration phenomenon, and the first preset speed is the dehydration speed corresponding to the second dehydration stage.

[0156] Optionally, the clothing dehydration treatment device further includes: a judgment module 306;

[0157] The judging module 306 is used to judge whether the first operating state and the second operating state are the same;

[0158] The processing module 303 is further configured to modify the simple feature decision model according to the second operating state when the first operating state and the second operating state are different.

[0159] Figure 5 The structure of the clothes dehydration device provided in this application is shown as follows: Figure 2 .like Figure 5 As shown, the present application provides a clothing dehydration treatment device, and the clothing dehydration treatment device 500 includes:

[0160] An acquisition module 401 is used to acquire parameter information sent by a clothing processing device;

[0161] A determination module 402, configured to determine a complex characteristic value of the clothes dehydration treatment device in the first dehydration stage according to the parameter information;

[0162] A processing module 403 is used to predict a second operating state of the clothing processing device in the second dehydration stage using a power simulation model according to the complex characteristic value, wherein the power simulation model is trained according to the eccentricity value of the inner drum of the clothing processing device in the historical data, wherein the operating state is used to indicate whether there is a vibration phenomenon in the clothing processing device in the second dehydration stage;

[0163] The sending module 404 is used to send the complex characteristic value and the second operating state to the clothing processing device.

[0164] Optionally, the processing module 403 is further used to input the complex characteristic value into a power simulation model to obtain second predicted state information output by the power simulation model;

[0165] The determining module 402 is further configured to determine a second operating state of the clothes treating device in a second dehydration stage according to the second predicted state information.

[0166] The acquisition module 401 is further used to acquire a historical eccentricity sample set sent by the clothes processing device, wherein the historical eccentricity sample set is used to indicate the eccentricity of the inner drum of the clothes processing device during a historical dehydration stage;

[0167] The processing module 403 is further used to extract features from the historical eccentricity sample set to obtain target features, where the target features are features that are associated with the eccentricity value of the inner cylinder;

[0168] The processing module 403 is further used to classify the target features according to the computational complexity to obtain a simple target feature set and a complex target feature set;

[0169] The processing module 403 is further used to train the first feature decision model using the simple target feature set to obtain a simple feature decision model;

[0170] The processing module 403 is further used to train the second feature decision model using the complex target feature set to obtain a power simulation model.

[0171] Figure 6 This is a schematic diagram of the structure of the clothing dehydration treatment equipment provided in this application. Figure 6 As shown, the present application provides a clothing dehydration treatment device, and the clothing dehydration treatment device 500 includes: a receiver 501, a transmitter 502, a processor 503 and a memory 504.

[0172] Receiver 501, used for receiving instructions and data;

[0173] A transmitter 502, used for sending instructions and data;

[0174] Memory 504, used to store computer-executable instructions;

[0175] The processor 503 is used to execute the computer-executable instructions stored in the memory 504 to implement the various steps performed by the clothes dehydration treatment method in the above embodiment. For details, please refer to the relevant description in the above clothes dehydration treatment method embodiment.

[0176] Optionally, the memory 504 may be independent or integrated with the processor 503 .

[0177] When the memory 504 is independently provided, the electronic device further includes a bus for connecting the memory 504 and the processor 503 .

[0178] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the clothing dehydration method performed by the above-mentioned clothing dehydration treatment device is implemented.

[0179] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0180] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above embodiments are only used to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling the dehydration of clothes, characterized in that: The method comprises: Acquiring parameter information of the clothes processing device in the first dehydration stage, and determining a simple characteristic value of the first dehydration stage according to the parameter information; According to the simple feature value, a simple feature decision model is used to predict the first operating state of the clothes processing device in the second dehydration stage, wherein the simple feature decision model is trained according to the eccentricity value of the inner drum of the clothes processing device in the historical data; When the first operating state is used to indicate the presence of vibration, the parameter information is sent to the cloud server, and the parameter information is adjusted according to the second operating state fed back by the cloud server to obtain new parameter information, wherein the operating state is used to indicate whether the clothing processing device has vibration in the second dehydration stage; The clothes processing device is controlled to perform dehydration processing according to the new parameter information in the second dehydration stage.

2. The method according to claim 1, characterized in that The predicting the first operating state of the clothes processing device in the second dehydration stage by using a simple feature decision model according to the simple feature value includes: Inputting the simple feature value into a simple feature decision model to obtain first prediction state information output by the simple feature decision model; A first operating state of the laundry treatment device in a second dehydration stage is determined according to the first predicted state information.

3. The method according to claim 1, characterized in that The second operating state includes: vibration operation and normal operation, the parameter information includes: inner drum speed, and the parameter information is adjusted according to the second operating state fed back by the cloud server to obtain new parameter information, including: If the second operating state is normal operation, increasing the speed of the inner drum to a first preset speed; If the second operating state is vibration operation, the rotation speed of the inner drum is reduced to a second preset rotation speed, wherein the first preset rotation speed is greater than the second preset rotation speed.

4. The method according to claim 1, characterized in that: The method further comprises: When the first operating state is used to indicate that there is no vibration phenomenon, the clothes processing device is controlled to perform dehydration processing at the first preset speed in the second dehydration stage, and the first preset speed is the dehydration speed corresponding to the second dehydration stage.

5. The method according to claim 1, characterized in that The method further comprises: Determining whether the first operating state and the second operating state are the same; When the first operating state and the second operating state are different, the simple feature decision model is modified according to the second operating state.

6. A method for dehydrating clothes, characterized in that: The method comprises: Acquiring parameter information sent by the clothing treatment device, and determining the complex characteristic value of the clothing dehydration treatment device in the first dehydration stage according to the parameter information; According to the complex characteristic value, a power simulation model is used to predict a second operating state of the clothing processing device in the second dehydration stage, wherein the power simulation model is trained according to the eccentricity value of the inner drum of the clothing processing device in the historical data, wherein the operating state is used to indicate whether there is a vibration phenomenon in the clothing processing device in the second dehydration stage; The complex characteristic value and the second operating state are sent to the laundry processing device.

7. The method according to claim 6, characterized in that The predicting, according to the complex characteristic value, the second operating state of the clothes processing device in the second dehydration stage by using a power simulation model comprises: Inputting the complex characteristic value into a power simulation model to obtain second predicted state information output by the power simulation model; A second operating state of the laundry treatment device in a second dehydration stage is determined according to the second predicted state information.

8. The method according to claim 5, characterized in that Before acquiring the parameter information sent by the laundry processing device, the method further includes: Acquire a historical eccentricity sample set sent by the clothes processing device, where the historical eccentricity sample set is used to indicate the eccentricity of the inner drum of the clothes processing device during a historical dehydration stage; Extracting features from the historical eccentricity sample set to obtain target features, where the target features are features that are correlated with the eccentricity value of the inner cylinder; Classify the target features according to the computational complexity to obtain a simple target feature set and a complex target feature set; The simple target feature set is used to train the first feature decision model to obtain all simple feature decision models; The complex target feature set is used to train the second feature decision model to obtain a power simulation model.

9. A clothes dehydration device, characterized in that: The device comprises: An acquisition module, used to acquire parameter information of the clothes processing device in the first dehydration stage; A determination module, used to determine a simple characteristic value of the first dehydration stage according to the parameter information; a processing module, configured to predict the first operating state of the clothes processing device in the second dehydration stage by using a simple feature decision model according to the simple feature value, wherein the simple feature decision model is trained according to the eccentricity value of the inner drum of the clothes processing device in the historical data; A sending module, configured to send the parameter information to a cloud server when the first operating state is used to indicate the presence of a vibration phenomenon; The processing module is further used to adjust the parameter information according to the second operating state fed back by the cloud server to obtain new parameter information, and the operating state is used to indicate whether the clothing processing device has vibration in the second dehydration stage; The control module is used to control the clothes processing device to perform dehydration processing according to the new parameter information in the second dehydration stage.

10. A clothing dehydration treatment device, characterized in that: include: Memory; processor; Wherein, the memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the laundry dehydration process according to any one of claims 1 to 8.

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