CN114263018A - Clothes processing equipment control method and device, storage medium and electronic equipment - Google Patents
Clothes processing equipment control method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application relates to the technical field of household appliance control, in particular to a control method and device of clothes treatment equipment and electronic equipment, and solves the problems that in the prior art, the clothes treatment equipment generates large-amplitude vibration and noise. The method comprises the steps of inputting clothes parameters of clothes to be processed in the clothes processing equipment, current operation parameters of the clothes processing equipment in a dehydration process and a current noise value into a pre-acquired rotating speed calculation neural network model, and accurately controlling the rotating speed of the clothes processing equipment by utilizing a maximum rotating speed value finally output by a hidden layer of the neural network model, so that the clothes processing equipment can increase the dehydration rotating speed to the maximum within a noise, vibration and motor load capacity range, namely the current dehydration rotating speed of the clothes processing equipment reaches the maximum and the vibration and the noise are minimum.
Description
Technical Field
The present application relates to the field of home appliance control technologies, and in particular, to a method and an apparatus for controlling a laundry processing device, a storage medium, and an electronic device.
Background
Generally, functions that can be realized by a laundry treating apparatus in an existing home appliance include a function of washing laundry, a function of dehydrating the washed laundry, or a function of drying the washed laundry. One laundry treating apparatus may implement any one or more than two of the above functions. The laundry treating apparatus dehydrates washed laundry by rotating a dehydration tub at a high speed to remove water from the laundry, accelerating the dehydration tub to a high rotation speed, and discharging the water attached to the laundry out of the tub through dehydration holes on a surface of the dehydration tub by centrifugal force to complete dehydration.
The clothes treatment equipment adopting the above mode to dewater has the advantages that when the dewatering rotating speed is accelerated to a high rotating speed, the clothes inside the dewatering barrel are unevenly placed, so that the clothes treatment equipment generates large-amplitude vibration and noise in the using process.
Disclosure of Invention
The application provides a clothes treatment equipment control method, a clothes treatment equipment control device, a storage medium and electronic equipment, aiming at the problems of large amplitude vibration and noise generated by the clothes treatment equipment in the prior art.
In a first aspect, the present application provides a laundry treating apparatus control method, the method including:
acquiring clothes parameters of clothes to be processed in the clothes processing equipment;
monitoring a current operating parameter and a current noise value of the laundry treating apparatus in a dehydration process;
inputting the clothes parameter, the current operation parameter and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value;
controlling the dehydration rotation speed of the clothes treatment equipment according to the maximum rotation speed value.
In the above embodiment, the clothes parameters of the clothes to be processed in the clothes processing device, the current operation parameters of the clothes processing device in the dehydration process, and the current noise value are input into the pre-acquired rotation speed calculation neural network model, and the rotation speed of the clothes processing device is accurately controlled by using the maximum rotation speed value finally output by the hidden layer of the neural network model, so that the dehydration rotation speed of the clothes processing device can be increased to the maximum within the allowable range of noise, vibration, and motor load capacity, that is, the current dehydration rotation speed of the clothes processing device reaches the highest and vibration and noise are the smallest.
According to an embodiment of the application, optionally, in the above-mentioned method for controlling a laundry treatment apparatus, after the step of controlling the spin-drying speed of the laundry treatment apparatus according to the maximum rotation speed value, the method further includes:
judging whether the laundry treating apparatus finishes a dehydrating process;
if not, the step of monitoring the current operation parameters and the current noise value of the clothes processing equipment in the dehydration process is carried out.
In the above embodiment, if the laundry processing apparatus has not finished the dehydration process, the current operation parameters and the current noise value of the laundry processing apparatus in the dehydration process are continuously detected, and then the maximum rotation speed value is calculated by using the rotation speed calculation neural network model obtained in advance, so as to realize real-time adjustment of the rotation speed of the laundry processing apparatus, thereby ensuring the accuracy of control.
According to an embodiment of the application, optionally, in the above-mentioned method for controlling a laundry treatment apparatus, after the step of controlling the spin-drying speed of the laundry treatment apparatus according to the maximum rotation speed value, the method further includes:
judging whether the laundry treating apparatus finishes a dehydrating process;
if yes, acquiring operation parameters of the clothes processing equipment in a dehydration process;
and optimizing a pre-acquired rotating speed calculation neural network model according to the operating parameters.
In the above embodiment, after the clothes treatment device finishes the dehydration process, the pre-obtained rotation speed calculation neural network model may be optimized according to the operation parameters of the clothes treatment device in the dehydration process at this time, so that a more accurate maximum rotation speed value may be obtained when the rotation speed calculation neural network model is used for calculation next time.
According to an embodiment of the application, optionally, in the above method for controlling a laundry processing apparatus, before the step of inputting the laundry parameter, the current operation parameter, and the current noise value into a pre-obtained rotation speed calculation neural network model to obtain an output maximum rotation speed value, the method further includes:
judging whether the clothes processing equipment is connected with a cloud server or not;
if not, acquiring historical data stored in the clothes processing equipment;
and acquiring the rotating speed calculation neural network model stored in the historical data last time.
According to an embodiment of the application, optionally, in the method for controlling a laundry processing apparatus, after the step of determining whether the laundry processing apparatus is connected to the cloud server, the method further includes:
and if the clothes processing equipment is connected with the cloud server, acquiring a rotating speed calculation neural network model arranged in the cloud server.
Due to the fact that the rotating speed calculation neural network model can be rapidly processed by means of the strong calculation capacity of the cloud server, the rotating speed calculation neural network model can be rapidly and accurately obtained to obtain the maximum rotating speed value according to the rotating speed calculation neural network model, and then the clothes processing equipment can be controlled accurately and rapidly.
According to an embodiment of the application, optionally, in the method for controlling a laundry processing apparatus, before the step of obtaining the rotation speed calculation neural network model provided in the cloud server, the method includes:
judging whether the rotating 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 application, optionally, in the above-mentioned method for controlling a laundry treatment apparatus, after the step of controlling the spin-drying speed of the laundry treatment apparatus according to the maximum rotation speed value, the method further includes:
judging whether the laundry treating apparatus finishes a dehydrating process;
if yes, acquiring operation parameters of the clothes processing equipment in a dehydration process;
and optimizing a rotating speed calculation neural network model arranged in the cloud server according to the operating parameters.
In a second aspect, the present application also provides a laundry treating apparatus control device, the device including:
the clothes parameter acquiring module is used for acquiring clothes parameters of clothes to be processed in the clothes processing equipment;
an operation data module for monitoring a current operation parameter and a current noise value of the laundry treating apparatus in a dehydration process;
the rotating speed calculation module is used for inputting the clothes parameters, the current operation parameters and the current noise values into a rotating speed calculation 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 dehydration rotating speed of the clothes treatment equipment according to the maximum rotating speed value.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
a dehydration state judgment module for judging whether the clothes treatment device finishes the 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 the current operation parameters and the current noise value are not in the dehydration process.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
a first dehydration state judgment module for judging whether the clothes treatment device finishes the dehydration process;
the first operation parameter acquisition module is used for acquiring operation parameters of the clothes processing equipment in the dehydration process if the operation parameters are the same as the first operation parameters;
and the first optimization module is used for optimizing the pre-acquired rotating speed calculation neural network model according to the operating parameters.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
the connection judging module is used for judging whether the clothes processing equipment is connected with a cloud server;
a historical data acquisition module, configured to, if not, acquire historical data stored in the laundry processing apparatus;
and the first rotating speed calculation neural network model acquisition module is used for acquiring the rotating speed calculation neural network model which is stored in the historical data last time.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
and the second rotating speed calculation neural network model acquisition module is used for acquiring the rotating 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 application, optionally, in the above clothes treatment apparatus control device, the device includes:
the updating judgment 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 an updating parameter if the rotation speed calculation neural network model is the same as the rotation speed calculation neural network model, and updating the rotation speed calculation neural network model according to the updating parameter.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
a second dehydration state judgment module for judging whether the clothes treatment device finishes the dehydration process;
the second operation parameter acquisition module is used for acquiring the operation parameters of the clothes processing equipment in the dehydration process if the operation parameters are the same as the operation parameters of the clothes processing equipment in the dehydration process;
and the second optimization module is used for optimizing a rotating speed calculation neural network model arranged in the cloud server according to the operating parameters.
In a third aspect, the present application provides a storage medium storing a computer program executable by one or more processors and operable to implement 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, wherein the memory stores a computer program, and the computer program is executed by the processor to execute the control method of the clothes treatment device.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
the application provides a clothes treatment equipment control method, a clothes treatment equipment control device, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring clothes parameters of clothes to be processed in the clothes processing equipment; monitoring a current operating parameter and a current noise value of the laundry treating apparatus in a dehydration process; inputting the clothes parameter, the current operation parameter and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value; controlling the dehydration rotation speed of the clothes treatment equipment according to the maximum rotation speed value. In the above embodiment, the clothes parameters of the clothes to be processed in the clothes processing device, the current operation parameters of the clothes processing device in the dehydration process, and the current noise value are input into the pre-acquired rotation speed calculation neural network model, and the rotation speed of the clothes processing device is accurately controlled by using the maximum rotation speed value finally output by the hidden layer of the neural network model, so that the dehydration rotation speed of the clothes processing device can be increased to the maximum within the allowable range of noise, vibration, and motor load capacity, that is, the current dehydration rotation speed of the clothes processing device reaches the highest and vibration and noise are the smallest.
Drawings
The present application will be described in more detail below on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a control method of a clothes treatment apparatus according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a rotation speed calculation neural network model according to an embodiment of the present disclosure.
Fig. 3 is a block diagram schematically illustrating a structure of a control device of a clothes 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 designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
The following detailed description will be provided with reference to the accompanying drawings and embodiments, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and various features in the embodiments of the present application can be combined with each other without conflict, and the formed technical solutions are all within the scope of protection of the present application.
Example one
The present invention provides a laundry treating apparatus control method, referring to fig. 1, the method comprising the steps of:
step S110: laundry parameters of laundry to be treated in a laundry treatment apparatus are acquired.
The acquired laundry parameters may include other related parameters such as laundry type and laundry weight. When acquiring the clothes parameters, the clothes parameters may be acquired through manual input by a user, or may be acquired through a corresponding parameter acquiring device, for example, when acquiring the clothes type, the clothes parameters may be acquired through image recognition. When the weight of the laundry is obtained, the weight of the laundry may be obtained by measuring through the weight sensor.
Step S120: monitoring a current operating parameter of the laundry treating apparatus in a dehydration process and a current noise value.
The current operation parameter may include a rotation speed of a 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 through equipment for monitoring the operation state of the clothes treatment equipment. When the current noise value is obtained, it can be obtained by a sound detection device, for example, a decibel meter. It is understood that the current operation parameters may also include other parameters, for example, the laundry treating apparatus is in a high voltage or low voltage state or the like,
step S130: and inputting the clothes parameters, the current operation 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 rotation speed calculation neural network model is a pre-trained neural network model. Before the rotation speed calculation neural network model is obtained, a large amount of sample data such as historical data and experimental data can be collected as a data set, and the initial rotation speed calculation neural network model is trained by using the data set to obtain the trained rotation speed calculation neural network model.
A neural network is a complex network system formed by a large number of simple processing units (called neurons) widely connected to each other, reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. The neural network has the capabilities of large-scale parallel, distributed storage and processing, self-organization, self-adaptation and self-learning, and is particularly suitable for processing inaccurate and fuzzy information processing problems which need to consider many factors and conditions simultaneously. The rotation speed calculation neural network model may be a convolutional neural network model, a long-short term memory application network model, a BP neural network, or the like, and the specific type of the rotation speed calculation neural network model may be determined according to actual requirements, which is not limited herein.
For example, the pre-acquired rotation 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 laundry parameters include the laundry type Q and the laundry weight W, the current operation parameters include the current motor rotation speed V, the current eccentricity value K, the motor current I, and the motor power P. And the current noise decibel value X, the input layer of the BP neural network comprises the clothes type Q, the clothes weight W, the current motor rotating speed V, the current eccentricity value K, the motor current I, the motor power P and the current noise decibel value X. The number of nodes of the hidden layer is generally determined according to the following empirical formula:
Wherein N represents the number of hidden layer nodes, XNRepresenting the number of input level nodes, YNRepresenting the number of output layer nodes.
The node number N ═ 7+1)/2 ═ 4 of the hidden layer can be determined from the above parameters and an empirical formula.
The number of the hidden layers can be determined through practical application, for example, if the rotation speed calculation neural network model is a pure numerical neural network, complex iteration is not needed, and the number of the hidden layers is 1-3.
As shown in fig. 2, the parameters of the input layer are iterated through the hidden layer, and then transmitted to the output layer through the hidden layer. And (4) obtaining an output layer, namely the current dehydration highest rotating speed Vmax, by the input parameters through iterative calculation of 2 hidden layers.
Step S140: controlling the dehydration rotation speed of the clothes treatment equipment according to the maximum rotation speed value.
The dehydration rotation speed of the laundry treating apparatus may be adjusted to the calculated maximum rotation speed value so that the laundry treating apparatus may reach the maximum rotation speed within an allowable range of vibration, noise and motor load capacity during the dehydration process, even if the current dehydration rotation speed of the laundry treating apparatus reaches the maximum and the generated vibration and noise are minimized.
According to an embodiment of the present application, in the above-mentioned laundry treating apparatus control method, after the step S140 of controlling the dehydration rotation speed of the laundry treating apparatus according to the maximum rotation speed value, the method further includes the steps of:
step S141: it is judged whether the laundry treating apparatus ends the dehydrating process.
Step S142: if not, the step of monitoring the current operation parameters and the current noise value of the clothes processing equipment in the dehydration process is carried out.
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, the current operation parameters and the current noise value of the clothes treatment equipment in the dehydration process are continuously detected, and then the maximum rotating speed value is calculated by utilizing the pre-acquired rotating speed calculation neural network model, so that the rotating speed of the clothes treatment equipment is adjusted in real time, and the accuracy of control is ensured.
According to an embodiment of the present application, in the above laundry treating apparatus control method, after the step of controlling the dehydration rotation speed of the laundry treating apparatus according to the maximum rotation speed value, the method further includes the following processes.
Firstly, judging whether the clothes treatment equipment finishes the dehydration process, if so, acquiring 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 rotation speed calculation neural network model can be optimized according to the operation parameters of the clothes treatment equipment in the dehydration process at this time, so that a more accurate maximum rotation speed value can be obtained when the rotation speed calculation neural network model is used for calculation next time.
In summary, the present application provides a laundry treating apparatus control method, including: acquiring clothes parameters of clothes to be processed in the clothes processing equipment; monitoring a current operating parameter and a current noise value of the laundry treating apparatus in a dehydration process; inputting the clothes parameter, the current operation parameter and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value; controlling the dehydration rotation speed of the clothes treatment equipment according to the maximum rotation speed value. The method comprises the steps of inputting clothes parameters of clothes to be processed in the clothes processing equipment, current operation parameters of the clothes processing equipment in a dehydration process and a current noise value into a pre-acquired rotating speed calculation neural network model, and accurately controlling the rotating speed of the clothes processing equipment by utilizing a maximum rotating speed value finally output by a hidden layer of the neural network model, so that the clothes processing equipment can increase the dehydration rotating speed to the maximum within an allowable range of noise, vibration and motor load capacity, namely the current dehydration rotating speed of the clothes processing equipment reaches the maximum and the vibration and the noise are minimum, and further the dehydration time is reduced.
Example two
On the basis of the first embodiment, the present embodiment explains the method in the first embodiment through a specific implementation case.
Before the step of inputting the clothes parameter, the current operation parameter and the current noise value into a pre-obtained rotating speed calculation neural network model to obtain an output maximum rotating speed value, the rotating speed calculation neural network model needs to be obtained. The following are several ways of obtaining a rotation speed calculation neural network model provided in the embodiments of the present application.
First oneImplementation methodThe formula may be acquired through history data stored in the laundry treating apparatus.
In a second embodiment, the rotation speed calculation neural network model set in the cloud server can be used for obtaining the rotation speed.
As an embodiment, before the step of inputting the laundry parameter, the current operation parameter and the current noise value into the pre-acquired rotation speed calculation neural network model to obtain the output maximum rotation speed value, the method further comprises the following steps:
and judging whether the clothes processing equipment is connected with a cloud server.
If not, acquiring historical data stored in the clothes processing equipment.
And acquiring the rotating speed calculation neural network model stored in the historical data last time.
Firstly, whether the clothes processing equipment is connected with the cloud server or not is judged, and if the clothes processing equipment is not connected with the cloud server, the clothes processing equipment can directly obtain a rotating speed calculation neural network model which is stored in historical data stored in the clothes processing equipment at the last time. For example, the rotation speed calculation neural network model used in the first dehydration process is stored in the history data, and when the clothes treatment device is not connected to the cloud server in the second dehydration process, the rotation speed calculation neural network model used in the last time can be directly obtained from the history data, and the rotation speed can be calculated.
As another embodiment, after the step of determining whether the laundry processing device is connected to the cloud server, the method further includes:
and if the clothes processing equipment is connected with the cloud server, acquiring a rotating speed calculation neural network model arranged in the cloud server.
Due to the fact that the rotating speed calculation neural network model can be rapidly processed by means of the strong calculation capacity of the cloud server, the rotating speed calculation neural network model can be rapidly and accurately obtained to obtain the maximum rotating speed value according to the rotating speed calculation neural network model, and then the clothes processing equipment can be controlled accurately and rapidly.
In the above embodiment, before the step of obtaining the rotation speed calculation neural network model provided 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 or not, if so, determining an updating parameter, and updating the rotating speed calculation neural network model according to the updating parameter.
When judging whether the rotating speed calculation neural network model arranged in the cloud server needs to be updated or not, the judgment can be realized by judging the operating environment of the clothes treatment equipment. For example, the judgment of whether the operation environment is changed may be determined by the actual condition of the laundry treating apparatus. Whether the rotating 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 by negative feedback according to a threshold value and an offset value in the rotating speed calculation neural network model, and for example, forward calculation is carried out on the rotating speed calculation neural network model according to the output maximum rotating speed value. In addition, the rotating 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-mentioned laundry treating apparatus control method, after the step of controlling the spinning speed of the laundry treating apparatus according to the maximum rotation speed value, the method further includes the following steps.
Firstly, judging whether the clothes processing equipment finishes the dehydration process, if so, acquiring operation parameters of the clothes processing equipment in the dehydration process, and then 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 controlling device 300, which includes:
a laundry parameter acquiring module 310 for acquiring laundry parameters of laundry to be processed in the laundry processing apparatus;
an operation data module 320 for monitoring a current operation parameter and a current noise value of the laundry treating apparatus in the dehydration process;
a rotation speed calculation module 330, configured to input the laundry parameter, the current operation parameter, and the current noise value into a rotation speed calculation neural network model obtained in advance, so as to obtain an output maximum rotation speed value;
a rotation speed control module 340 for controlling the dehydration rotation speed of the laundry treating apparatus according to the maximum rotation speed value.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
a dehydration state judgment module for judging whether the clothes treatment device finishes the 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 the current operation parameters and the current noise value are not in the dehydration process.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
a first dehydration state judgment module for judging whether the clothes treatment device finishes the dehydration process;
the first operation parameter acquisition module is used for acquiring operation parameters of the clothes processing equipment in the dehydration process if the operation parameters are the same as the first operation parameters;
and the first optimization module is used for optimizing the pre-acquired rotating speed calculation neural network model according to the operating parameters.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
the connection judging module is used for judging whether the clothes processing equipment is connected with a cloud server;
a historical data acquisition module, configured to, if not, acquire historical data stored in the laundry processing apparatus;
and the first rotating speed calculation neural network model acquisition module is used for acquiring the rotating speed calculation neural network model which is stored in the historical data last time.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
and the second rotating speed calculation neural network model acquisition module is used for acquiring the rotating 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 application, optionally, in the above clothes treatment apparatus control device, the device includes:
the updating judgment 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 an updating parameter if the rotation speed calculation neural network model is the same as the rotation speed calculation neural network model, and updating the rotation speed calculation neural network model according to the updating parameter.
According to an embodiment of the application, optionally, in the above clothes treatment apparatus control device, the device further includes:
a second dehydration state judgment module for judging whether the clothes treatment device finishes the dehydration process;
the second operation parameter acquisition module is used for acquiring the operation parameters of the clothes processing equipment in the dehydration process if the operation parameters are the same as the operation parameters of the clothes processing equipment in the dehydration process;
and the second optimization module is used for optimizing a rotating speed calculation neural network model arranged in the cloud server according to the operating parameters.
In summary, the present application provides a laundry treating apparatus controlling device 300, comprising: a laundry parameter acquiring module 310 for acquiring laundry parameters of laundry to be processed in the laundry processing apparatus; an operation data module 320 for monitoring a current operation parameter and a current noise value of the laundry treating apparatus in the dehydration process; a rotation speed calculation module 330, configured to input the laundry parameter, the current operation parameter, and the current noise value into a rotation speed calculation neural network model obtained in advance, so as to obtain an output maximum rotation speed value; a rotation speed control module 340 for controlling the dehydration rotation speed of the laundry treating apparatus according to the maximum rotation speed value. Inputting clothes parameters of clothes to be processed in the clothes processing equipment, current operation parameters of the clothes processing equipment in a dehydration process and a current noise value into a pre-acquired rotating speed calculation neural network model, and accurately controlling the rotating speed of the clothes processing equipment by utilizing a maximum rotating speed value finally output by a hidden layer of the neural network model, so that the clothes processing equipment can increase the dehydration rotating speed to the maximum within a noise, vibration and motor load capacity allowable range, namely the current dehydration rotating speed of the clothes processing equipment reaches the maximum and the vibration and noise are the minimum.
Example four
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an 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 mall, etc., where a computer program is stored, and the computer program may implement the method steps in the foregoing embodiments when executed by a processor.
EXAMPLE five
The embodiment of the application provides an electronic device, which can be a mobile phone, a computer, a tablet computer or the like, and comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to realize the control method of the clothes treatment device as described in the first embodiment. It is understood 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 used for executing all or part of the steps in the control method of the clothes treatment device according to 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 implemented by an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is configured to execute the method for controlling the clothes Processing Device in the first 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 (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), 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 further be stored in a memory or transmitted through a communication component. The audio assembly also includes at least one speaker for outputting audio signals.
The I/O interface 404 provides an interface between the processor 401 and other interface modules, such as 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 (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding Communication component 405 may include: Wi-Fi module, bluetooth module, NFC module.
In summary, the present application provides a method, an apparatus, a storage medium and an electronic device for controlling a clothes processing device, wherein the method comprises: acquiring clothes parameters of clothes to be processed in the clothes processing equipment; monitoring a current operating parameter and a current noise value of the laundry treating apparatus in a dehydration process; inputting the clothes parameter, the current operation parameter and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value; controlling the dehydration rotation speed of the clothes treatment equipment according to the maximum rotation speed value. Inputting clothes parameters of clothes to be processed in the clothes processing equipment, current operation parameters of the clothes processing equipment in a dehydration process and a current noise value into a pre-acquired rotating speed calculation neural network model, and accurately controlling the rotating speed of the clothes processing equipment by utilizing a maximum rotating speed value finally output by a hidden layer of the neural network model, so that the clothes processing equipment can increase the dehydration rotating speed to the maximum within a noise, vibration and motor load capacity allowable range, namely the current dehydration rotating speed of the clothes processing equipment reaches the maximum and the vibration and noise are the 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 ways. 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although the embodiments disclosed in the present application are described above, the descriptions are only for the convenience of understanding the present application, and are not intended to limit the present application. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims.
Claims (10)
1. A laundry treating apparatus controlling method, characterized in that the method comprises:
acquiring clothes parameters of clothes to be processed in the clothes processing equipment;
monitoring a current operating parameter and a current noise value of the laundry treating apparatus in a dehydration process;
inputting the clothes parameter, the current operation parameter and the current noise value into a pre-acquired rotating speed calculation neural network model to obtain an output maximum rotating speed value;
controlling the dehydration rotation speed of the clothes treatment equipment according to the maximum rotation speed value.
2. The method according to claim 1, wherein after the step of controlling the dehydration rotation speed of the laundry treating apparatus according to the maximum rotation speed value, the method further comprises:
judging whether the laundry treating apparatus finishes a dehydrating process;
if not, the step of monitoring the current operation parameters and the current noise value of the clothes processing equipment in the dehydration process is carried out.
3. The method according to claim 1, wherein after the step of controlling the dehydration rotation speed of the laundry treating apparatus according to the maximum rotation speed value, the method further comprises:
judging whether the laundry treating apparatus finishes a dehydrating process;
if yes, acquiring operation parameters of the clothes processing equipment in a dehydration process;
and optimizing a pre-acquired rotating speed calculation neural network model according to the operating parameters.
4. The method of claim 1, wherein prior to the step of inputting the laundry parameter, the current operating 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 processing equipment is connected with a cloud server or not;
if not, acquiring historical data stored in the clothes processing equipment;
and acquiring the rotating speed calculation neural network model stored in the historical data last time.
5. The method according to claim 4, wherein after the step of determining whether the laundry processing device is connected to a cloud server, the method further comprises:
and if the clothes processing 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 the step of obtaining the rpm computational neural network model provided in the cloud server is preceded by:
judging whether the rotating 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 according to claim 5, wherein after the step of controlling the dehydration rotation speed of the laundry treating apparatus according to the maximum rotation speed value, the method further comprises:
judging whether the laundry treating apparatus finishes a dehydrating process;
if yes, acquiring operation parameters of the clothes processing equipment in a dehydration process;
and optimizing a rotating speed calculation neural network model arranged in the cloud server according to the operating parameters.
8. A laundry treating apparatus controlling device, characterized in that the device comprises:
the clothes parameter acquiring module is used for acquiring clothes parameters of clothes to be processed in the clothes processing equipment;
an operation data module for monitoring a current operation parameter and a current noise value of the laundry treating apparatus in a dehydration process;
the rotating speed calculation module is used for inputting the clothes parameters, the current operation parameters and the current noise values into a rotating speed calculation 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 dehydration rotating speed of the clothes treatment equipment according to the maximum rotating speed value.
9. A storage medium characterized in that the storage medium stores a computer program which, when executed by one or more processors, is adapted to implement the laundry treatment apparatus control method according to any one of claims 1-7.
10. An electronic device, characterized by comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs a laundry treatment device control method according to any one of claims 1-7.
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