CN114263018B - Clothes treatment equipment control method and device, storage medium and electronic equipment - Google Patents
Clothes treatment equipment control method and device, storage medium and electronic equipment Download PDFInfo
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- CN114263018B CN114263018B CN202111435714.1A CN202111435714A CN114263018B CN 114263018 B CN114263018 B CN 114263018B CN 202111435714 A CN202111435714 A CN 202111435714A CN 114263018 B CN114263018 B CN 114263018B
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- 238000000034 method Methods 0.000 title claims abstract description 132
- 238000003062 neural network model Methods 0.000 claims abstract description 104
- 238000004364 calculation method Methods 0.000 claims abstract description 99
- 230000018044 dehydration Effects 0.000 claims abstract description 86
- 238000006297 dehydration reaction Methods 0.000 claims abstract description 86
- 238000012544 monitoring process Methods 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 abstract description 30
- 238000004891 communication Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
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- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000003925 brain function Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
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- 210000002569 neuron Anatomy 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B40/00—Technologies aiming at improving the efficiency of home appliances, e.g. induction cooking or efficient technologies for refrigerators, freezers or dish washers
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Abstract
The application relates to the technical field of household appliance control, in particular to a clothes treatment equipment control method and device and electronic equipment, and solves the problem that clothes treatment equipment in the prior art generates large-amplitude vibration and noise. The method inputs the clothes parameters of the clothes to be processed in the clothes processing equipment, the current running parameters of the clothes processing equipment in the dehydration process and the current noise value into a pre-acquired rotating speed calculation neural network model, and accurately controls the rotating speed of the clothes processing equipment by utilizing the maximum rotating speed value finally output by the hidden layer of the neural network model, so that the dehydrating rotating speed of the clothes processing equipment can be increased to the maximum within the allowable range in terms of noise, vibration and motor load capacity, namely, the current dehydrating rotating speed of the clothes processing equipment reaches the maximum and the vibration and noise are minimum.
Description
Technical Field
The present application relates to the field of home appliance control technology, and in particular, to a method and apparatus for controlling a laundry device, a storage medium, and an electronic device.
Background
In general, functions that can be achieved by the laundry treating apparatus in the existing home appliance include a function of washing laundry, a function of dehydrating washed laundry, or a function of drying washed laundry. One laundry treating apparatus may implement any one or two or more of the above functions. The laundry treating apparatus dehydrates the washed laundry by rotating a dehydration tub at a high speed to remove water from the laundry, and the dehydration tub is accelerated to a high rotation speed to discharge water attached to the laundry to the outside of the tub through dehydration holes on the surface of the dehydration tub by centrifugal force, thereby completing dehydration.
The clothes treatment equipment for dewatering by adopting the mode can generate large-amplitude vibration and noise in the use process due to the uneven arrangement of clothes in the dewatering barrel when the dewatering rotating speed is accelerated to a high rotating speed.
Disclosure of Invention
Aiming at the problems of large vibration and noise generated by clothes treatment equipment in the prior art, the application provides a clothes treatment equipment control method, a clothes treatment equipment control device, a storage medium and electronic equipment.
In a first aspect, the present application provides a laundry treatment apparatus control method, the method comprising:
acquiring clothes parameters of clothes to be treated in clothes treatment equipment;
monitoring a current operating parameter of the laundry treatment apparatus in a dehydration process and a current noise value;
inputting the clothes parameters, the current running parameters and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value;
and controlling the dehydration rotating speed of the clothes treatment equipment according to the maximum rotating speed value.
In the above embodiment, the parameters of the laundry in the laundry treatment apparatus, the current operation parameters of the laundry treatment apparatus during the dehydration process, and the current noise value are input into the pre-acquired rotational speed calculation neural network model, and the rotational speed of the laundry treatment apparatus is accurately controlled by using the maximum rotational speed value finally output by the hidden layer of the neural network model, so that the rotational speed of the laundry treatment apparatus can be increased to the maximum in the allowable range of noise, vibration, and motor load capacity, i.e., the current rotational speed of the laundry treatment apparatus reaches the maximum and vibration and noise are minimum.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control method, after the step of controlling the dehydration rotational speed of the laundry treatment apparatus according to the maximum rotational speed value, the method further includes:
judging whether the laundry treating apparatus ends a dehydration process;
if not, turning to the step of monitoring the current operation parameters and the current noise values of the clothes treating apparatus in the dehydration process.
In the above embodiment, if the laundry treatment apparatus has not finished the dehydration process, the current operation parameter and the current noise value of the laundry treatment apparatus in the dehydration process are continuously detected, and then the maximum rotation speed value is calculated by using the previously obtained rotation speed calculation neural network model, so as to realize real-time adjustment of the rotation speed of the laundry treatment apparatus, thereby ensuring the accuracy of control.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control method, after the step of controlling the dehydration rotational speed of the laundry treatment apparatus according to the maximum rotational speed value, the method further includes:
judging whether the laundry treating apparatus ends a dehydration process;
if yes, acquiring the operation parameters of the clothes treatment equipment in the dehydration process;
and optimizing the pre-acquired rotating speed calculation neural network model according to the operation parameters.
In the above embodiment, after the clothes treatment apparatus finishes the dehydration process, the pre-acquired rotational speed calculation neural network model may be optimized according to the operation parameters of the clothes treatment apparatus in the dehydration process, so that a more accurate maximum rotational speed value may be obtained when the rotational speed calculation neural network model is used for calculation next time.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control method, before the step of inputting the laundry parameter, the current operation parameter, and the current noise value into a pre-acquired rotational speed calculation neural network model to obtain an output maximum rotational speed value, the method further includes:
judging whether the clothes treatment equipment is connected with a cloud server or not;
if not, acquiring the historical data stored in the clothes treatment equipment;
and acquiring a rotating speed calculation neural network model stored in the historical data last time.
According to an embodiment of the present application, optionally, in the method for controlling a laundry treatment apparatus, after the step of determining whether the laundry treatment apparatus is connected to a cloud server, the method further includes:
and if the clothes treatment equipment is connected with the cloud server, acquiring a rotating speed calculation neural network model arranged in the cloud server.
The rotational speed calculation neural network model can be rapidly processed by utilizing the strong calculation capacity of the cloud server, so that the maximum rotational speed value can be rapidly and accurately obtained according to the rotational speed calculation neural network model, and then the clothes treatment equipment can be accurately and rapidly controlled.
According to an embodiment of the present application, optionally, in the method for controlling a laundry device, before the step of obtaining the rotational speed calculation neural network model set in the cloud server, the method includes:
judging whether the rotational speed calculation neural network model arranged in the cloud server needs to be updated or not;
if yes, determining an updating parameter, and updating the rotating speed calculation neural network model according to the updating parameter.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control method, after the step of controlling the dehydration rotational speed of the laundry treatment apparatus according to the maximum rotational speed value, the method further includes:
judging whether the laundry treating apparatus ends a dehydration process;
if yes, acquiring the operation parameters of the clothes treatment equipment in the dehydration process;
and optimizing the rotating speed calculation neural network model arranged in the cloud server according to the operation parameters.
In a second aspect, the present application also provides a laundry treatment apparatus control device comprising:
the clothes parameter acquisition module is used for acquiring clothes parameters of clothes to be treated in the clothes treatment equipment;
an operation data module for monitoring a current operation parameter and a current noise value of the laundry treatment apparatus in a dehydration process;
the rotating speed calculating module is used for inputting the clothes parameters, the current running parameters and the current noise value into a rotating speed calculating neural network model which is acquired in advance so as to obtain an output maximum rotating speed value;
and the rotating speed control module is used for controlling the dewatering rotating speed of the clothes treatment equipment according to the maximum rotating speed value.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
a dehydration state judging module for judging whether the laundry treating apparatus ends a dehydration process;
and the operation data module is also used for monitoring the current operation parameters and the current noise value of the clothes treatment equipment in the dehydration process if not.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
a first dehydrating state judging module for judging whether the laundry treating apparatus ends a dehydrating process;
the first operation parameter acquisition module is used for acquiring the operation parameters of the clothes treatment equipment in the dehydration process if yes;
and the first optimization module is used for optimizing the pre-acquired rotating speed calculation neural network model according to the operation parameters.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
the connection judging module is used for judging whether the clothes treatment equipment is connected with the cloud server or not;
a history data obtaining module, configured to obtain history data stored in the laundry processing apparatus if not;
the first rotational speed calculation neural network model acquisition module is used for acquiring the rotational speed calculation neural network model stored in the historical data last time.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
the second rotational speed calculation neural network model obtaining module is used for obtaining the rotational speed calculation neural network model arranged in the cloud server if the clothes processing equipment is connected with the cloud server.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device includes:
the updating judging module is used for judging whether the rotating speed calculation neural network model arranged in the cloud server needs to be updated or not;
and the updating module is used for determining updating parameters if yes, and updating the rotating speed calculation neural network model according to the updating parameters.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
a second dehydrating state judging module for judging whether the laundry treating apparatus ends the dehydrating process;
the second operation parameter acquisition module is used for acquiring the operation parameters of the clothes treatment equipment in the dehydration process if yes;
and the second optimization module is used for optimizing the rotating speed calculation neural network model arranged in the cloud server according to the operation parameters.
In a third aspect, the present application provides a storage medium storing a computer program executable by one or more processors for implementing a laundry treatment apparatus control method as described above.
In a fourth aspect, the present application provides an electronic device comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the laundry treatment apparatus control method described above.
One or more embodiments of the above-described solution may have the following advantages or benefits compared to the prior art:
the application provides a control method and device of clothes treatment equipment, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring clothes parameters of clothes to be treated in clothes treatment equipment; monitoring a current operating parameter of the laundry treatment apparatus in a dehydration process and a current noise value; inputting the clothes parameters, the current running parameters and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value; and controlling the dehydration rotating speed of the clothes treatment equipment according to the maximum rotating speed value. In the above embodiment, the parameters of the laundry in the laundry treatment apparatus, the current operation parameters of the laundry treatment apparatus during the dehydration process, and the current noise value are input into the pre-acquired rotational speed calculation neural network model, and the rotational speed of the laundry treatment apparatus is accurately controlled by using the maximum rotational speed value finally output by the hidden layer of the neural network model, so that the rotational speed of the laundry treatment apparatus can be increased to the maximum in the allowable range of noise, vibration, and motor load capacity, i.e., the current rotational speed of the laundry treatment apparatus reaches the maximum and vibration and noise are minimum.
Drawings
The application will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a control method of a laundry device according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a neural network model for calculating a rotational speed according to an embodiment of the present application.
Fig. 3 is a schematic block diagram showing the construction of a control apparatus for a laundry treating apparatus according to a third embodiment of the present application.
Fig. 4 is a connection block diagram of an electronic device according to a fifth embodiment of the present application.
In the drawings, like parts are given like reference numerals, and the drawings are not drawn to scale.
Detailed Description
The following will describe embodiments of the present application in detail with reference to the drawings and examples, thereby solving the technical problems by applying technical means to the present application, and realizing the corresponding technical effects can be fully understood and implemented accordingly. The embodiment of the application and the characteristics in the embodiment can be mutually combined on the premise of no conflict, and the formed technical scheme is within the protection scope of the application.
Example 1
The present application provides a laundry treatment apparatus control method, referring to fig. 1, comprising the steps of:
step S110: the method comprises the steps of obtaining clothes parameters of clothes to be treated in clothes treatment equipment.
The obtained laundry parameters may include laundry type, laundry weight, and other related parameters. The parameters of the clothes can be acquired by manual input of a user, and the parameters can also be acquired by a corresponding parameter acquisition device, for example, the parameters can be acquired by image recognition when the type of the clothes is acquired. When the laundry weight is acquired, the weight sensor may measure the laundry weight.
Step S120: current operating parameters of the laundry treating apparatus in a dehydration process and current noise values are monitored.
The current operation parameters may include a rotation speed of the motor in the laundry treating apparatus, a current eccentricity value, a motor current, a motor power, and the like. When the current operation parameters are acquired, the current operation parameters can be acquired by equipment for monitoring the operation state of the clothes treatment equipment. When the current noise value is acquired, it may be acquired by a sound detection device, for example, a decibel meter. It will be appreciated that the current operating parameters may also include other parameters, such as, for example, the laundry treatment device being in a high voltage or low voltage state or the like,
step S130: and inputting the clothes parameters, the current running parameters and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value.
The pre-acquired rotational speed calculation neural network model is a pre-trained neural network model. Before the rotational speed calculation neural network model is obtained, a large amount of sample data, such as historical data and experimental data, can be collected first and used as a data set, and the initial rotational speed calculation neural network model is trained by using the data set so as to obtain a trained rotational speed calculation neural network model.
Neural networks are complex network systems formed by a large number of simple processing units (called neurons) widely interconnected, reflecting many of the fundamental features of human brain function, and are highly complex nonlinear dynamic learning systems. Neural networks have massively parallel, distributed storage and processing, self-organizing, adaptive, and self-learning capabilities, and are particularly suited to address imprecise and ambiguous information processing issues that require consideration of many factors and conditions simultaneously. The rotating speed calculation neural network model can be a convolution neural network model, a long-short-term memory application network model, a BP neural network model or the like, and the specific type of the rotating speed calculation neural network model can be determined according to actual requirements without limitation.
For example, the pre-acquired rotational speed calculation neural network model is a BP neural network model, please refer to fig. 2. The BP neural network model is mainly divided into an input layer, a hidden layer and an output layer. If the clothes parameters comprise clothes type Q and clothes weight W, the current operation parameters comprise current motor rotation speed V, current eccentric value K, motor current I and motor power P. The input layer of the BP neural network comprises a clothes type Q, a clothes weight W, a current motor rotating speed V, a current eccentric value K, a motor current I, a motor power P and a current noise decibel value X. The number of nodes of the hidden layer is generally determined according to the following empirical formula:
or->
Wherein N represents the number of hidden layer nodes, X N Representing the number of nodes at the input layer, Y N Representing the number of output layer nodes.
The node number n= (7+1)/2=4 of the hidden layer can be determined according to the above parameters and the empirical formula.
The number of hidden layers can be determined by practical application, for example, if the rotational speed calculation neural network model is a pure numerical neural network, no complex iteration is needed, and the number of hidden layers is 1-3.
As shown in fig. 2, the parameters of the input layer are transmitted to the output layer through the hidden layer after the iteration of the hidden layer. The input parameters are subjected to iterative calculation of 2 hidden layers to obtain an output layer, namely the current highest dehydration rotation speed Vmax.
Step S140: and controlling the dehydration rotating speed of the clothes treatment equipment according to the maximum rotating speed value.
The dehydration rotational speed of the laundry treatment apparatus may be adjusted to the calculated maximum rotational speed value such that the laundry treatment apparatus can reach the maximum rotational speed within the allowable range of vibration, noise and motor load capacity during the dehydration process, even though the current dehydration rotational speed of the laundry treatment apparatus reaches the maximum and the generated vibration and noise are minimum.
According to an embodiment of the present application, in the above-described laundry treatment apparatus control method, after the step S140 of controlling the dehydration rotational speed of the laundry treatment apparatus according to the maximum rotational speed value, the method further includes the steps of:
step S141: judging whether the laundry treating apparatus ends the dehydration process.
Step S142: if not, turning to the step of monitoring the current operation parameters and the current noise values of the clothes treating apparatus in the dehydration process.
If the laundry treating apparatus has finished the dehydration process, the adjustment of the rotation speed of the laundry treating apparatus may be stopped. If the clothes treatment equipment does not finish the dehydration process, continuously detecting the current operation parameters and the current noise value of the clothes treatment equipment in the dehydration process, and calculating the maximum rotation speed value by utilizing a pre-obtained rotation speed calculation neural network model so as to realize real-time adjustment of the rotation speed of the clothes treatment equipment, thereby ensuring the control accuracy.
According to an embodiment of the present application, in the above-described laundry treatment apparatus control method, after the step of controlling the dehydration rotational speed of the laundry treatment apparatus according to the maximum rotational speed value, the method further includes the following process.
Firstly judging whether the clothes treatment equipment finishes the dehydration process, if so, acquiring the operation parameters of the clothes treatment equipment in the dehydration process, and then optimizing a pre-acquired rotating speed calculation neural network model according to the operation parameters. After the clothes treatment equipment finishes the dehydration process, the pre-acquired rotating speed calculation neural network model can be optimized according to the operation parameters of the clothes treatment equipment in the dehydration process, so that a more accurate maximum rotating speed value can be obtained when the rotating speed calculation neural network model is used for calculating next time.
In summary, the present application provides a laundry treatment apparatus control method, comprising: acquiring clothes parameters of clothes to be treated in clothes treatment equipment; monitoring a current operating parameter of the laundry treatment apparatus in a dehydration process and a current noise value; inputting the clothes parameters, the current running parameters and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value; and controlling the dehydration rotating speed of the clothes treatment equipment according to the maximum rotating speed value. The method comprises the steps of inputting clothes parameters of clothes to be processed in clothes processing equipment, current operation parameters of the clothes processing equipment in a dehydration process and current noise values into a pre-acquired rotating speed calculation neural network model, accurately controlling the rotating speed of the clothes processing equipment by utilizing the maximum rotating speed value finally output by a hidden layer of the neural network model, enabling the clothes processing equipment to increase the dehydration rotating speed to the maximum within the allowable range of noise, vibration and motor load capacity, namely, enabling the current dehydration rotating speed of the clothes processing equipment to be the highest and the vibration and noise to be the minimum, and then reducing dehydration time.
Example two
On the basis of the first embodiment, the present embodiment describes the method in the first embodiment by way of specific embodiments.
Before the step of inputting the laundry parameter, the current operation parameter and the current noise value into a pre-obtained rotational speed calculation neural network model to obtain an output maximum rotational speed value, the rotational speed calculation neural network model needs to be obtained. The following are several ways of obtaining the rotational speed calculation neural network model provided in the embodiments of the present application.
First kindIn an embodiment, the acquisition may be performed by historical data stored by the laundry treatment apparatus.
In the second embodiment, the rotation speed calculation neural network model set in the cloud server can be obtained.
As an embodiment, before the step of inputting the laundry parameter, the current operation parameter, and the current noise value into a pre-acquired rotational speed calculation neural network model to obtain an output maximum rotational speed value, the method further includes the steps of:
and judging whether the clothes treatment equipment is connected with a cloud server or not.
If not, acquiring the historical data stored in the clothes treatment equipment.
And acquiring a rotating speed calculation neural network model stored in the historical data last time.
Firstly judging whether the clothes treatment equipment is connected with a cloud server or not, and if the clothes treatment equipment is not connected with the cloud server, directly acquiring a rotating speed calculation neural network model stored in the stored historical data by the clothes treatment equipment. For example, the rotational speed calculation neural network model used in the first dehydration process is stored in the history data, and when the dehydration is performed for the second time, the laundry treatment device is not connected to the cloud server, the rotational speed calculation neural network model used last time may be directly obtained from the history data, and the rotational speed may be calculated.
As another embodiment, after the step of determining whether the laundry processing apparatus is connected to the cloud server, the method further includes the following steps:
and if the clothes treatment equipment is connected with the cloud server, acquiring a rotating speed calculation neural network model arranged in the cloud server.
The rotational speed calculation neural network model can be rapidly processed by utilizing the strong calculation capacity of the cloud server, so that the maximum rotational speed value can be rapidly and accurately obtained according to the rotational speed calculation neural network model, and then the clothes treatment equipment can be accurately and rapidly controlled.
In the above embodiment, before the step of obtaining the rotational speed calculation neural network model set in the cloud server, the method includes the following steps: firstly, judging whether the rotating speed calculation neural network model arranged in the cloud server needs to be updated, if so, determining updating parameters, and updating the rotating speed calculation neural network model according to the updating parameters.
When judging whether the rotational speed calculation neural network model arranged in the cloud server needs to be updated, the judgment can be realized by judging the running environment of the clothes treatment equipment. For example, determining whether the operating environment has changed may be determined by the actual condition of the laundry treating apparatus. Whether the rotational speed calculation neural network model arranged in the cloud server needs to be updated or not can be judged according to the calculated value, for example, whether correction is needed or not is determined through negative feedback of a threshold value and an offset value in the rotational speed calculation neural network model, and for example, forward calculation is performed on the rotational speed calculation neural network model through the output maximum rotational speed value. In addition, the rotational speed calculation neural network model can be updated according to the cleaning effect.
Further, according to an embodiment of the present application, in the above-described laundry treatment apparatus control method, after the step of controlling the dehydration rotational speed of the laundry treatment apparatus according to the maximum rotational speed value, the method further includes the following steps.
Firstly, judging whether the clothes treatment equipment finishes a dehydration process, if so, acquiring operation parameters of the clothes treatment equipment in the dehydration process, and optimizing a rotating speed calculation neural network model arranged in the cloud server according to the operation parameters.
Example III
Referring to fig. 3, the present application provides a laundry treating apparatus control device 300, comprising:
a laundry parameter acquiring module 310 for acquiring laundry parameters of laundry to be treated in the laundry treating apparatus;
an operation data module 320 for monitoring a current operation parameter of the laundry treating apparatus in a dehydration process and a current noise value;
the rotational speed calculation module 330 is configured to input the laundry parameter, the current operation parameter, and the current noise value into a rotational speed calculation neural network model acquired in advance, so as to obtain an output maximum rotational speed value;
the rotation speed control module 340 is configured to control the dehydration rotation speed of the laundry treatment apparatus according to the maximum rotation speed value.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
a dehydration state judging module for judging whether the laundry treating apparatus ends a dehydration process;
and the operation data module is also used for monitoring the current operation parameters and the current noise value of the clothes treatment equipment in the dehydration process if not.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
a first dehydrating state judging module for judging whether the laundry treating apparatus ends a dehydrating process;
the first operation parameter acquisition module is used for acquiring the operation parameters of the clothes treatment equipment in the dehydration process if yes;
and the first optimization module is used for optimizing the pre-acquired rotating speed calculation neural network model according to the operation parameters.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
the connection judging module is used for judging whether the clothes treatment equipment is connected with the cloud server or not;
a history data obtaining module, configured to obtain history data stored in the laundry processing apparatus if not;
the first rotational speed calculation neural network model acquisition module is used for acquiring the rotational speed calculation neural network model stored in the historical data last time.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
the second rotational speed calculation neural network model obtaining module is used for obtaining the rotational speed calculation neural network model arranged in the cloud server if the clothes processing equipment is connected with the cloud server.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device includes:
the updating judging module is used for judging whether the rotating speed calculation neural network model arranged in the cloud server needs to be updated or not;
and the updating module is used for determining updating parameters if yes, and updating the rotating speed calculation neural network model according to the updating parameters.
According to an embodiment of the present application, optionally, in the above-mentioned laundry treatment apparatus control device, the device further includes:
a second dehydrating state judging module for judging whether the laundry treating apparatus ends the dehydrating process;
the second operation parameter acquisition module is used for acquiring the operation parameters of the clothes treatment equipment in the dehydration process if yes;
and the second optimization module is used for optimizing the rotating speed calculation neural network model arranged in the cloud server according to the operation parameters.
In summary, the present application provides a laundry treating appliance control device 300, comprising: a laundry parameter acquiring module 310 for acquiring laundry parameters of laundry to be treated in the laundry treating apparatus; an operation data module 320 for monitoring a current operation parameter of the laundry treating apparatus in a dehydration process and a current noise value; the rotational speed calculation module 330 is configured to input the laundry parameter, the current operation parameter, and the current noise value into a rotational speed calculation neural network model acquired in advance, so as to obtain an output maximum rotational speed value; the rotation speed control module 340 is configured to control the dehydration rotation speed of the laundry treatment apparatus according to the maximum rotation speed value. The method comprises the steps of inputting clothes parameters of clothes to be processed in clothes processing equipment, current operation parameters of the clothes processing equipment in a dehydration process and current noise values into a pre-acquired rotating speed calculation neural network model, and accurately controlling the rotating speed of the clothes processing equipment by utilizing the maximum rotating speed value finally output by a hidden layer of the neural network model, so that the dehydrating rotating speed of the clothes processing equipment can be increased to the maximum in the allowable range of noise, vibration and motor load capacity, namely, the current dehydrating rotating speed of the clothes processing equipment reaches the maximum and the vibration and noise are minimum.
Example IV
The present embodiment also provides a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, where the computer program when executed by a processor can implement the steps of the method in the above embodiment, and the specific embodiment procedure can be referred to the above embodiment, and the detailed description of the embodiment is not repeated herein.
Example five
The embodiment of the application provides electronic equipment which can be a mobile phone, a computer or a tablet personal computer and the like, and comprises a memory and a processor, wherein the memory is stored with a computer program, and the computer program realizes the control method of the clothes treatment equipment in the first embodiment when being executed by the processor. It will be appreciated that as shown in fig. 4, the electronic device 400 may further include: a processor 401, a memory 402, a multimedia component 403, an input/output (I/O) interface 404, and a communication component 405.
Wherein the processor 401 is for performing all or part of the steps in the laundry treating apparatus control method as in the first embodiment. The memory 402 is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The processor 401 may be an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), a digital signal processor (Digital Signal Processor, abbreviated as DSP), a digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), a programmable logic device (Programmable Logic Device, abbreviated as PLD), a field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), a controller, a microcontroller, a microprocessor, or other electronic component implementation for executing the laundry processing device control method in the above embodiment.
The Memory 402 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The multimedia component 403 may include a screen, which may be a touch screen, and an audio component for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in a memory or transmitted through a communication component. The audio assembly further comprises at least one speaker for outputting audio signals.
The I/O interface 404 provides an interface between the processor 401 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.
The communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the corresponding communication component 405 may thus comprise: wi-Fi module, bluetooth module, NFC module.
In summary, the application provides a method, a device, a storage medium and an electronic device for controlling a clothes treatment device, wherein the method comprises the following steps: acquiring clothes parameters of clothes to be treated in clothes treatment equipment; monitoring a current operating parameter of the laundry treatment apparatus in a dehydration process and a current noise value; inputting the clothes parameters, the current running parameters and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value; and controlling the dehydration rotating speed of the clothes treatment equipment according to the maximum rotating speed value. The method comprises the steps of inputting clothes parameters of clothes to be processed in clothes processing equipment, current operation parameters of the clothes processing equipment in a dehydration process and current noise values into a pre-acquired rotating speed calculation neural network model, and accurately controlling the rotating speed of the clothes processing equipment by utilizing the maximum rotating speed value finally output by a hidden layer of the neural network model, so that the dehydrating rotating speed of the clothes processing equipment can be increased to the maximum in the allowable range of noise, vibration and motor load capacity, namely, the current dehydrating rotating speed of the clothes processing equipment reaches the maximum and the vibration and noise are minimum.
In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed system and method may be implemented in other manners. The system and method embodiments described above are merely illustrative.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although the embodiments of the present application are described above, the embodiments are only used for facilitating understanding of the present application, and are not intended to limit the present application. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the appended claims.
Claims (10)
1. A laundry treatment apparatus control method, characterized by comprising:
a rotational speed calculation neural network model is obtained in advance, and the rotational speed calculation neural network model is updated according to the running environment of the clothes treatment equipment; the hidden layer node number in the rotating speed calculation neural network model is determined according to the input layer node number and the output layer node number;
acquiring clothes parameters of clothes to be treated in the clothes treatment equipment;
monitoring a current operating parameter of the laundry treatment apparatus in a dehydration process and a current noise value;
inputting the clothes parameters, the current running parameters and the current noise value into the rotating speed calculation neural network model to obtain an output maximum rotating speed value; the maximum rotation speed value is used for representing the maximum rotation speed which can be reached by the clothes treatment equipment in the allowable range of vibration, noise and motor load capacity in the current dehydration process;
and controlling the dehydration rotating speed of the clothes treatment equipment according to the maximum rotating speed value.
2. The method according to claim 1, wherein after the step of controlling the dehydration rotation speed of the laundry treatment apparatus according to the maximum rotation speed value, the method further comprises:
judging whether the laundry treating apparatus ends a dehydration process;
if not, turning to the step of monitoring the current operation parameters and the current noise values of the clothes treating apparatus in the dehydration process.
3. The method according to claim 1, wherein after the step of controlling the dehydration rotation speed of the laundry treatment apparatus according to the maximum rotation speed value, the method further comprises:
judging whether the laundry treating apparatus ends a dehydration process;
if yes, acquiring the operation parameters of the clothes treatment equipment in the dehydration process;
and optimizing the pre-acquired rotating speed calculation neural network model according to the operation parameters.
4. The method according to claim 1, wherein before the step of inputting the laundry parameter, the current operation parameter, and the current noise value into a pre-acquired rotational speed calculation neural network model to obtain an output maximum rotational speed value, the method further comprises:
judging whether the clothes treatment equipment is connected with a cloud server or not;
if not, acquiring the historical data stored in the clothes treatment equipment;
and acquiring a rotating speed calculation neural network model stored in the historical data last time.
5. The method of claim 4, further comprising, after the step of determining whether the laundry treatment apparatus is connected to a cloud server:
and if the clothes treatment equipment is connected with the cloud server, acquiring a rotating speed calculation neural network model arranged in the cloud server.
6. The method of claim 5, wherein prior to the step of obtaining the rotational speed calculation neural network model disposed in the cloud server, the method further comprises:
judging whether the rotational speed calculation neural network model arranged in the cloud server needs to be updated or not;
if yes, determining an updating parameter, and updating the rotating speed calculation neural network model according to the updating parameter.
7. The method of claim 5, wherein after the step of controlling the dehydration rotational speed of the laundry treatment apparatus according to the maximum rotational speed value, the method further comprises:
judging whether the laundry treating apparatus ends a dehydration process;
if yes, acquiring the operation parameters of the clothes treatment equipment in the dehydration process;
and optimizing the rotating speed calculation neural network model arranged in the cloud server according to the operation parameters.
8. A laundry treating appliance control device, the device comprising:
the clothes parameter acquisition module is used for acquiring clothes parameters of clothes to be treated in the clothes treatment equipment;
an operation data module for monitoring a current operation parameter and a current noise value of the laundry treatment apparatus in a dehydration process;
the rotating speed calculation module is used for obtaining a rotating speed calculation neural network model in advance, updating the rotating speed calculation neural network model according to the operating environment of the clothes treatment equipment, and determining the hidden layer node number in the rotating speed calculation neural network model according to the input layer node number and the output layer node number; inputting the clothes parameters, the current running parameters and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value; the maximum rotation speed value is used for representing the maximum rotation speed which can be reached by the clothes treatment equipment in the allowable range of vibration, noise and motor load capacity in the current dehydration process;
and the rotating speed control module is used for controlling the dewatering rotating speed of the clothes treatment equipment according to the maximum rotating speed value.
9. A storage medium storing a computer program which, when executed by one or more processors, is adapted to carry out the laundry treatment apparatus control method according to any one of claims 1 to 7.
10. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the laundry treatment apparatus control method according to any one of claims 1-7.
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