CN111103805A - Method, system and device for controlling household appliance and household appliance - Google Patents
Method, system and device for controlling household appliance and household appliance Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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Abstract
The application discloses a method, a system and a device for controlling a household appliance and the household appliance. Wherein, the method comprises the following steps: collecting at least one working data of the household appliance; processing at least one working data of the household appliance based on a prediction model to generate a prediction result, wherein the prediction result comprises: at least one control instruction for the household appliance, wherein the predictive model is a model generated by training sample data of the household appliance; and controlling the household appliance to work based on the prediction result. The application solves the technical problems that the user controls the running state of the household appliance through controlling the remote control equipment, the operation is complex, and the user experience is poor.
Description
Technical Field
The application relates to the field of intelligent household appliances, in particular to a method, a system and a device for controlling a household appliance and the household appliance.
Background
At present, when a user controls each household appliance, the remote control equipment is required to control the running state of the opening and closing machine of the household appliance, the operation is more complicated, the time and the energy of people are wasted, if the user cannot find the remote control equipment when needed, the corresponding household appliance cannot be opened, and the user experience is poor.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a method, a system and a device for controlling a household appliance and the household appliance, and aims to at least solve the technical problems that a user controls the running state of the household appliance by controlling a remote control device, the operation is complex, and the user experience is poor.
According to an aspect of an embodiment of the present application, there is provided a method of controlling a home appliance, including: collecting at least one working data of the household appliance; processing at least one working data of the household appliance based on the prediction model to generate a prediction result, wherein the prediction result comprises: at least one control instruction for the household appliance, wherein the prediction model is a model generated by training sample data of the household appliance; and controlling the household appliance to work based on the prediction result.
Optionally, before processing at least one working data of the household appliance based on the prediction model to generate a prediction result, the method further includes: acquiring a historical database of the household appliance, wherein the historical database records working data generated when the household appliance works in a historical time period; the method comprises the steps of constructing a prediction model by training a historical database of the household appliance, wherein the prediction model comprises a deep learning model and an ARIMA model combined with wavelet transformation denoising.
Optionally, in a case that a plurality of household appliances need to be controlled, historical operating data of different household appliances in different time periods is acquired, and the historical operating data is saved in the historical database.
Optionally, under the condition that a plurality of household appliances need to be controlled, acquiring working data of different household appliances, processing the working data of the different household appliances by using a deep learning model and an ARIMA model, and predicting to obtain control instructions of the different household appliances, wherein the working data includes at least one of the following data: running time and working state.
Optionally, target operation data to which the household appliance needs to be adjusted is predicted.
Optionally, after acquiring at least one operating data of the household appliance, the method further comprises: detecting the running power of the household appliance; and if the running power is smaller than the corresponding threshold value, the household appliance is forbidden to work according to the prediction result.
According to an aspect of an embodiment of the present application, there is provided a system for controlling a home appliance, including: at least one home appliance for transmitting at least one operation data of the home appliance; the server is communicated with at least one household appliance and used for processing at least one working data of the household appliance based on a prediction model, generating a prediction result and feeding back the prediction result to the corresponding household appliance, wherein the prediction model is a model generated by training sample data of the household appliance; wherein the household appliance operates based on the prediction result.
Optionally, the prediction result comprises: at least one control instruction for the household appliance.
According to an aspect of an embodiment of the present application, there is provided a home appliance including: the collector is used for collecting at least one working data of the household appliance; a processor, configured to process at least one working data of the household appliance based on the prediction model, and generate a prediction result, where the prediction result includes: at least one control instruction for the household appliance, wherein the prediction model is a model generated by training sample data of the household appliance; and the controller is used for controlling the household appliance to work based on the prediction result.
According to an aspect of an embodiment of the present application, there is provided an apparatus for controlling a home appliance, including: the acquisition module is used for acquiring at least one working data of the household appliance; the prediction module is used for processing at least one working data of the household appliance based on the prediction model to generate a prediction result, wherein the prediction result comprises: at least one control instruction for the household appliance, wherein the prediction model is a model generated by training sample data of the household appliance; and the control module is used for controlling the household appliance to work based on the prediction result.
According to an aspect of the embodiments of the present application, there is provided a storage medium, wherein the storage medium includes a stored program, and wherein when the program runs, the apparatus where the storage medium is controlled executes the method for controlling the household appliance.
According to an aspect of the embodiments of the present application, there is provided a processor, wherein the processor is configured to execute a program, and wherein the program executes a method of controlling a home appliance.
In the embodiment of the application, at least one piece of work data of the household appliance is acquired; processing at least one working data of the household appliance based on the prediction model to generate a prediction result, wherein the prediction result comprises: at least one control instruction for the household appliance, wherein the prediction model is a model generated by training sample data of the household appliance; and controlling the household appliance to work based on the prediction result. The effect of automatically controlling the household appliance to work by acquiring the working data of the household appliance and processing the working data according to the current working data is achieved, so that the technical problems that a user controls the running state of the household appliance by controlling a remote control device, the operation is complex and the user experience is poor are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart diagram of an alternative method of controlling a home appliance according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative system for controlling a home appliance according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an alternative household appliance according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an alternative apparatus for controlling a home appliance according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present application, there is provided a method embodiment of an appliance device for controlling a home appliance, it should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that here.
Fig. 1 is a schematic flow chart of a method of controlling a home appliance according to an embodiment of the present application, as shown in fig. 1, the method at least including the steps of:
step S102, collecting at least one working data of the household appliance;
in some optional embodiments of the present application, the working data may be current working time information and/or working state;
step S104, processing at least one working data of the household appliance based on the prediction model to generate a prediction result, wherein the prediction result comprises: at least one control instruction for the household appliance, wherein the prediction model is a model generated by training sample data of the household appliance;
in an alternative embodiment of the present application, before the step S104, the following steps are further performed:
step S1022, acquiring a historical database of the household appliance, wherein the historical database records working data generated when the household appliance works in a historical time period;
in some optional embodiments of the present application, the history repository is configured to store historical data of each household appliance in a past historical time period; the historical working time period refers to a specific working time period of each household appliance in a day, and the historical working data refers to the running state of each household appliance in different historical working time periods, such as: the working state of the air conditioner at 7 o 'clock-8 o' clock at night is refrigeration 26 degrees. The past time period may refer to the past month or to a season of the last year.
And the historical time period information and the working data in the time period are used for training a prediction model.
In an alternative embodiment of the present application, the current running time of the household appliance is input into the prediction model, and then the target running state corresponding to the current running time of the household appliance is output.
In another alternative embodiment of the present application, the current operation time and the operation state of the home appliance are input to the prediction model, and the target operation state to which the current operation time and the operation state of the home appliance are adjusted is output.
In an optional embodiment of the present application, before collecting at least one working data of the home appliance, the smart home system may obtain a voice instruction or a gesture instruction of the user; after the intelligent home system obtains a voice instruction or a gesture instruction of a user, the collection of at least one working data of the household appliance is triggered.
And step S1024, constructing a prediction model by training a historical database of the household appliance, wherein the prediction model comprises a deep learning model and an ARIMA model combined with wavelet transformation denoising.
In some optional embodiments of the present application, the deep learning model is an LSTM model, and both the deep learning model and the ARIMA model for wavelet transform denoising are referred to as a hybrid model.
The hybrid model predicts the running state of the household appliance and synthesizes prediction results from different models to obtain a prediction result with higher accuracy;
in some optional embodiments of the present application, after the corresponding prediction result may be obtained by the LSTM model, the prediction result is verified by using the ARIMA model of wavelet transform denoising, and if the two results are the same, the prediction result is determined to be the final prediction result. If not, the correctness of the prediction result of the ARIMA model for LSTM or wavelet transformation denoising can be improved by retraining the ARIMA model for LSTM or wavelet transformation denoising.
And step S106, controlling the household appliance to work based on the prediction result.
In an optional embodiment of the present application, when the collected working data of the household appliance is the current working state, the current working state and the time information are input into the prediction model;
in some optional embodiments of the present application, in a case that a plurality of home appliances need to be controlled, historical operating data of different home appliances in different time periods is obtained, and the historical operating data is saved to a historical database.
Under the condition that a plurality of household appliances need to be controlled, working data of different household appliances are collected, the working data of the different household appliances are processed by adopting a deep learning model and an ARIMA model, and control instructions of the different household appliances are obtained through prediction, wherein the working data can be at least one of the following data: running time and working state.
The historical database can divide the area of the data storage module according to the type of the household appliance, so that the intelligent home system can conveniently obtain the key-on state of the household appliance when the household appliance is started, and after the current working state of the household appliance is read, the target running states of different household appliances are determined by inputting the current time information into the prediction model according to the current time information, namely, the prediction result is determined.
The predicted data is target working data to which the household appliance needs to be adjusted.
After the above steps are performed to collect at least one working data of the household appliance, the following steps are also performed:
detecting the running power of the household appliance; and if the running power is smaller than the corresponding threshold value, the household appliance is forbidden to work according to the prediction result.
In some optional embodiments of the application, after a voice instruction or a gesture instruction sent by a user is acquired, if it is determined that the household appliance is not currently turned on or the corresponding household appliance is not turned on, that is, the operating power is smaller than the corresponding threshold value, the household appliance is prohibited from operating according to the prediction result.
In the embodiment of the application, at least one piece of work data of the household appliance is acquired; processing at least one working data of the household appliance based on the prediction model to generate a prediction result, wherein the prediction result comprises: at least one control instruction for the household appliance, wherein the prediction model is a model generated by training sample data of the household appliance; and controlling the household appliance to work based on the prediction result. The effect of automatically controlling the household appliance to work by acquiring the working data of the household appliance and processing the working data according to the current working data is achieved, so that the technical problems that a user controls the running state of the household appliance by controlling a remote control device, the operation is complex and the user experience is poor are solved.
FIG. 2 is a schematic diagram of an alternative system for controlling a home appliance according to an embodiment of the present application; as shown in fig. 2, the system comprises at least: at least one household appliance 22, a server 24; wherein:
at least one home appliance 22 for transmitting at least one operation data of the home appliance;
the server 24 is communicated with at least one household appliance and is used for processing at least one working data of the household appliance based on a prediction model, generating a prediction result and feeding back the prediction result to the corresponding household appliance, wherein the prediction model is a model generated by training sample data of the household appliance; wherein the household appliance operates based on the prediction result.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 2, and details are not described here again.
Fig. 3 is a schematic structural diagram of a household appliance according to an embodiment of the present application, and as shown in fig. 3, the household appliance at least includes: a collector 32, a processor 34, a controller 36; wherein:
a collector 32 for collecting at least one working data of the household appliance;
a processor 34, configured to process at least one working data of the household appliance based on the prediction model, and generate a prediction result, where the prediction result includes: at least one control instruction for the household appliance, wherein the prediction model is a model generated by training sample data of the household appliance;
and a controller 36 for controlling the home appliance to operate based on the prediction result.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 3, and details are not described here again.
Fig. 4 is a schematic structural diagram of an apparatus for controlling a home appliance according to an embodiment of the present application, the apparatus at least including: an acquisition module 42, a prediction module 44, a control module 46; wherein:
an acquisition module 42 for acquiring at least one working data of the household appliance;
a prediction module 44, configured to process at least one working data of the household appliance based on the prediction model, and generate a prediction result, where the prediction result includes: at least one control instruction for the household appliance, wherein the prediction model is a model generated by training sample data of the household appliance;
and the control module 46 is used for controlling the household appliance to work based on the prediction result.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 4, and details are not described here again.
According to another aspect of the embodiments of the present application, there is also provided a storage medium, wherein the storage medium includes a stored program, and wherein when the program is executed, the apparatus on which the storage medium is controlled performs the method for controlling a household appliance.
According to another aspect of the embodiments of the present application, there is also provided a processor, wherein the processor is configured to execute a program, and the program executes the method for controlling a home appliance.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (12)
1. A method of controlling a home appliance, comprising:
collecting at least one working data of the household appliance;
processing at least one working data of the household appliance based on a prediction model to generate a prediction result, wherein the prediction result comprises: at least one control instruction for the household appliance, wherein the predictive model is a model generated by training sample data of the household appliance;
and controlling the household appliance to work based on the prediction result.
2. The method of claim 1, wherein before processing at least one operational data of the home appliance based on a predictive model to generate a predicted result, the method further comprises:
acquiring a historical database of the household appliance, wherein the historical database records working data generated when the household appliance works in a historical time period;
and constructing the prediction model by training a historical database of the household appliance, wherein the prediction model comprises a deep learning model and an ARIMA model combined with wavelet transformation denoising.
3. The method according to claim 2, wherein, in the case that a plurality of household appliances need to be controlled, historical operating data of different household appliances in different time periods is obtained and saved to the historical database.
4. The method according to claim 2, wherein in a case that a plurality of household appliances need to be controlled, working data of different household appliances are collected, the deep learning model and the ARIMA model are used for processing the working data of the different household appliances, and control instructions of the different household appliances are predicted, wherein the working data comprise at least one of the following data: running time and working state.
5. The method according to claim 1, characterized in that target operating data to which the household appliance needs to be adjusted is predicted.
6. The method according to any one of claims 1 to 5, wherein after collecting at least one operational data of the household appliance, the method further comprises:
detecting the running power of the household appliance;
and if the running power is smaller than the corresponding threshold value, the household appliance is prohibited from working according to the prediction result.
7. A system for controlling a home appliance, comprising:
at least one home appliance for transmitting at least one operation data of the home appliance;
the server is communicated with the at least one household appliance and used for processing at least one piece of working data of the household appliance based on a prediction model, generating a prediction result and feeding back the prediction result to the corresponding household appliance, wherein the prediction model is a model generated by training sample data of the household appliance;
wherein the home appliance operates based on the prediction result.
8. The system of claim 7, wherein the predicted outcome comprises: at least one control instruction for the household appliance.
9. A household appliance, characterized in that it comprises:
the collector is used for collecting at least one working data of the household appliance;
a processor, configured to process at least one working data of the household appliance based on a prediction model, and generate a prediction result, where the prediction result includes: at least one control instruction for the household appliance, wherein the predictive model is a model generated by training sample data of the household appliance;
and the controller is used for controlling the household appliance to work based on the prediction result.
10. An apparatus for controlling a home appliance, comprising:
the acquisition module is used for acquiring at least one working data of the household appliance;
the prediction module is used for processing at least one working data of the household appliance based on the prediction model to generate a prediction result, wherein the prediction result comprises: at least one control instruction for the household appliance, wherein the predictive model is a model generated by training sample data of the household appliance;
and the control module is used for controlling the household appliance to work based on the prediction result.
11. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, the device where the storage medium is located is controlled to execute the method for controlling the household appliance according to any one of claims 1 to 6.
12. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the method of controlling a home appliance according to any one of claims 1 to 6 when running.
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| CN201811253147.6A CN111103805A (en) | 2018-10-25 | 2018-10-25 | Method, system and device for controlling household appliance and household appliance |
| PCT/CN2019/099377 WO2020082852A1 (en) | 2018-10-25 | 2019-08-06 | Method, system and apparatus for controlling home appliance, and home appliance |
Applications Claiming Priority (1)
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| CN201811253147.6A CN111103805A (en) | 2018-10-25 | 2018-10-25 | Method, system and device for controlling household appliance and household appliance |
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| CN113518023A (en) * | 2021-09-13 | 2021-10-19 | 深圳小小小科技有限公司 | Control method and device for household appliance |
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| 包晓安等: "基于LSTM 的智能家居机器学习系统预测模型研究基于LSTM 的智能家居机器学习系统预测模型研究", 《浙江理工大学学报(自然科学版)》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113518023A (en) * | 2021-09-13 | 2021-10-19 | 深圳小小小科技有限公司 | Control method and device for household appliance |
| CN113518023B (en) * | 2021-09-13 | 2021-11-30 | 深圳小小小科技有限公司 | Control method and device for household appliance |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020082852A1 (en) | 2020-04-30 |
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