CN110824930A - Control method, device and system of household appliance - Google Patents
Control method, device and system of household appliance Download PDFInfo
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- CN110824930A CN110824930A CN201810898331.XA CN201810898331A CN110824930A CN 110824930 A CN110824930 A CN 110824930A CN 201810898331 A CN201810898331 A CN 201810898331A CN 110824930 A CN110824930 A CN 110824930A
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- 238000004378 air conditioning Methods 0.000 description 7
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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
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2642—Domotique, domestic, home control, automation, smart house
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Abstract
The application discloses a control method, a device and a system of household electrical appliance equipment. Wherein, the method comprises the following steps: acquiring a characteristic information set of a target object; inputting the characteristic information in the characteristic information set into a decision tree prediction model for analysis to obtain a prediction result of the running state of the household appliance, wherein the decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set; and controlling the running state of the household appliance according to the running state indicated by the prediction result. The method and the device solve the technical problems that the household appliance cannot set the corresponding running state according to different objects, cannot provide personalized services for the objects, and are poor in object experience.
Description
Technical Field
The application relates to the field of intelligent household appliances, in particular to a control method, a device and a system of household appliances.
Background
Along with the improvement of the intelligent service requirement of the object and the continuous development of artificial intelligence, the automation control technology of the household appliance is more and more mature. However, at present, home appliances generally operate according to an operation state of the home appliance preset by an object or controlled in real time, and cannot perform personalized service on the object according to the acquired object information in real time, so that the object 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 control method, a control device and a control system of household appliances, and aims to at least solve the technical problems that the household appliances cannot set corresponding running states according to different objects, cannot provide personalized services for the objects, and are poor in object experience.
According to an aspect of an embodiment of the present application, there is provided a method for controlling a home appliance, including: acquiring a characteristic information set of a target object; inputting the characteristic information in the characteristic information set into a decision tree prediction model for analysis to obtain a prediction result of the running state of the household appliance, wherein the decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set; and controlling the running state of the household appliance according to the running state indicated by the prediction result.
Optionally, obtaining a feature information set of the target object includes: acquiring character description information corresponding to a target object; determining a keyword from the text description information, wherein the keyword is used for reflecting the characteristics of the target object; and determining a characteristic information set according to the keywords.
Optionally, the feature of the target object in the feature information set is determined by: determining the size of a context window in a Word2Vector model; determining other keywords related to the keywords according to the size of the context window, wherein the number of the other keywords is equal to the size of the context window; and determining the characteristics of the target object in the characteristic information set according to the keywords and other keywords.
Optionally, obtaining a feature information set of the target object includes: acquiring image information at least comprising a target object; extracting feature information of the target object from the image information; and determining a characteristic information set of the target object according to the characteristic information.
Optionally, the image information includes: a plurality of objects; before determining the feature information set of the target object according to the image information, the method comprises the following steps: determining priorities of a plurality of objects; and determining the object with the highest priority as the target object from the plurality of objects.
Optionally, the image information includes a plurality of target objects; before controlling the operation state of the household appliance according to the operation state indicated by the prediction result, the method comprises the following steps: acquiring weights of a plurality of target objects; and determining the target operation state of the household appliance according to the weight and the operation states corresponding to the target objects.
According to another aspect of the embodiments of the present application, there is provided another method for controlling a home appliance, including: acquiring characteristic information of a target object; determining a target running state of the household appliance according to the characteristic information; and controlling the running state of the household appliance according to the target running state.
According to another aspect of the embodiments of the present application, there is provided a control system for a home appliance, including: the acquisition device is used for acquiring a characteristic information set of the target object; and the processor is used for inputting the characteristic information in the characteristic information set into the decision tree prediction model for analysis to obtain the prediction result of the running state of the household appliance, wherein the decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set; and the controller is used for controlling the running state of the household appliance according to the running state indicated by the prediction result.
According to another aspect of the embodiments of the present application, there is provided a control apparatus for a home appliance, including: the acquisition module is used for acquiring a characteristic information set of a target object; the processing module is used for inputting the characteristic information in the characteristic information set into the decision tree prediction model for analysis to obtain the prediction result of the running state of the household appliance, wherein the decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set; and the control module is used for controlling the running state of the household appliance according to the running state indicated by the prediction result.
According to still another aspect of embodiments of the present application, there is provided a storage medium including a stored program, wherein the program controls a device on which the storage medium is located to perform the above control method of the home appliance when the program is executed.
According to still another aspect of the embodiments of the present application, there is provided a processor for executing a program, wherein the program executes the above control method for a home appliance.
In the embodiment of the application, a characteristic information set of a target object is obtained; inputting the characteristic information in the characteristic information set into a decision tree prediction model for analysis to obtain a prediction result of the running state of the household appliance, wherein the decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set; and controlling the running state of the household appliance according to the running state indicated by the prediction result. The home appliance determines the running state of the home appliance corresponding to the target object according to the analysis of the target object characteristic information set, can serve the user in a personalized manner, and improves the technical effect of user experience. And then the technical problems that the household appliance cannot set the corresponding running state according to different objects, cannot provide personalized service for the objects and has poor object experience 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 flowchart of a method for controlling a home appliance according to an embodiment of the present application;
fig. 2 is a flowchart of another home appliance control method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a control system of a home appliance according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a control device of 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.
Fig. 1 is a flowchart of a method for controlling a home appliance according to an embodiment of the present application.
As shown in fig. 1, a method for controlling a home device according to an embodiment of the present application at least includes steps S102 to S106:
step S102, acquiring a characteristic information set of the target object.
In some embodiments of the present application, this step may be implemented by, but is not limited to: acquiring character description information corresponding to a target object; determining a keyword from the text description information, wherein the keyword is used for reflecting the characteristics of the target object; and determining a characteristic information set according to the keywords. The target object may be a human or an animal.
In an alternative embodiment, the text description information may be text information in the electronic archive or image information corresponding to the target object, so as to determine the keyword from the text description information. Specifically, the keywords may be dressing information, and/or height information, and/or body state information of the target object. The image acquisition device can be installed indoors, the scene of the room is shot through the image acquisition device, the keywords are determined according to the shot image information, specifically, the RGB values of the image can be analyzed first, and the dressing information and/or the height information of the object in the image information can be determined, so that the keywords can be determined.
The characteristics of the target object in the characteristic information set are determined by the following modes: determining the size of a context window in a Word2Vector model; determining other keywords related to the keywords according to the size of the context window, wherein the number of the other keywords is equal to the size of the context window; and determining the characteristics of the target object in the characteristic information set according to the keywords and other keywords.
Word2Vector is a tool for converting words into Vector form, and can simplify the processing of text content into Vector operation in K-dimensional Vector space, and the similarity in the Vector space can be used for expressing the similarity in text semantics. If the window size of Word2Vector is 5, the association relationship between the first five words and the last five words is considered.
In an alternative embodiment, the feature information set may also be determined by: acquiring image information at least comprising a target object; and extracting the characteristic information of the target object from the image information, and acquiring a characteristic information set of the target object according to the characteristic information. Specifically, image information at least including a target object can be acquired through a camera installed indoors, feature information is extracted from the image information, specifically, a target object area can be determined through edge extraction on the target object in the image, and the feature information can be determined according to RGB values in the area, wherein the feature information can be dressing color and/or height. The obtaining of the feature information set of the target object according to the feature information may be: and determining a characteristic information set containing the dress color and/or the height according to the dress color and/or the height. Wherein the characteristic information set also contains other information, such as occupation, physical condition.
In addition, a plurality of objects may be included in the image information; before determining the feature information set of the target object according to the image information, the method comprises the following steps: determining priorities of a plurality of objects; and determining the object with the highest priority as the target object from the plurality of objects. For example, the image acquired by the camera includes a plurality of objects, and the objects in the image can be analyzed, specifically, areas of different objects of the plurality of objects can be determined by edge extraction for the plurality of objects in the image, the characteristic information can be determined according to RGB values in the areas of the different objects, the characteristic information can be a dressing color and/or a height, and then an object with the highest priority among the plurality of objects is selected to be determined as a target object according to a preset priority related to the different dressing colors and/or the heights. For example: only the young children wear yellow clothes at home, the preset priority is the highest of the young children, and the object with the yellow clothes can be set as the target object.
And step S104, inputting the characteristic information in the characteristic information set into a decision tree prediction model for analysis to obtain a prediction result of the running state of the household appliance.
The decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set.
For example: the 1 st group of data in the multiple groups of data is: { girl, age 8, yellow clothing, 140}, the labels of the operating states of the corresponding home devices are: air-conditioning temperature: and 27 degrees.
The 2 nd group of data in the multiple groups of data is: { adult, 28 years old, black clothing, 180}, the labels of the operating states of the corresponding home devices are: air-conditioning temperature: 26 degrees.
The household electrical appliance can be an air conditioner, an air purifier, a fan and a bedroom ceiling lamp.
The Decision Tree may be a GBDT (Gradient Boost Decision Tree). GBDT is an iterative regression decision tree algorithm consisting of multiple regression trees with all the tree conclusions summed up to complete the prediction. In GBDT, each model is built in the gradient descent direction of the previously built model loss function, i.e. each new model is built in order to reduce the residual error of the previous model. The classification tree measures the maximum entropy and the information gain, and the regression tree measures the minimum mean square error. From the bias-variance decomposition point of view, GBDT focuses mainly on reducing bias and can build strong integration based on a learner whose generalization performance is rather weak. GBDT starts from a weak learning algorithm, and learns repeatedly to obtain a series of basic classifiers, which are then combined to form a strong classifier. The lifting method actually adopts an addition model and a forward distribution algorithm, the lifting method taking the decision tree as a basis function is called a lifting tree, the gradient lifting algorithm is a function for minimizing loss by using a steepest descent method, and the method has a good effect on classification.
In an alternative embodiment, the feature information set associated with the feature may be determined according to the obtained or determined feature information, for example, a feature that can identify the user identity may be determined first, and then the feature information set corresponding to the user identity may be determined based on the user identity. For example: according to the characteristic information in the shot image information: age and height, determining a characteristic information set as follows: age, height, and physical condition, for example, if the feature information set estimated from the feature information indicates that the current target user has eye disease, the brightness of the indoor lighting can be adjusted to control the brightness of the indoor lighting to the brightness suitable for the eye disease patient.
And step S106, controlling the running state of the household appliance according to the running state indicated by the prediction result.
And if the running state indicated by the target object is 26 degrees of air conditioner, controlling the running state of the air conditioner to be 26 degrees.
In another alternative embodiment, a plurality of target objects are included in the image information; before controlling the running state of the household appliance according to the running state indicated by the prediction result, weights of a plurality of target objects need to be acquired; and determining the target operation state of the household appliance according to the weight and the operation states corresponding to the target objects.
For example: when the weight of the target object a is 0.5, the corresponding prediction result is that the target set temperature is 26 degrees, the weight of the target object B is 0.3, the corresponding prediction result is that the air-conditioning operation state is 28 degrees, the weight of the target object C is 0.2, the corresponding prediction result is that the air-conditioning operation state is 25 degrees, and the air-conditioning equipment can be 26 degrees according to the condition that 0.5 +0.3 + 28+0.2 + 25 is 26.3.
In the embodiment of the application, a characteristic information set of a target object is obtained; inputting the characteristic information in the characteristic information set into a decision tree prediction model for analysis to obtain a prediction result of the running state of the household appliance, wherein the decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set; and controlling the running state of the household appliance according to the running state indicated by the prediction result. The home appliance determines the running state of the home appliance corresponding to the target object according to the analysis of the target object characteristic information set, can serve the object in a personalized manner, and improves the technical effect of object experience. And then the technical problems that the household appliance cannot set the corresponding running state according to different objects, cannot provide personalized service for the objects and has poor object experience are solved.
Fig. 2 is a flowchart of another method for controlling a home device according to an embodiment of the present application.
As shown in fig. 2, a method for controlling a home device according to another embodiment of the present application at least includes steps S202-S206:
step S202, acquiring characteristic information of a target object;
in some embodiments of the present application, this step may be implemented by, but is not limited to: acquiring character description information corresponding to a target object; determining a keyword from the text description information, wherein the keyword is used for reflecting the characteristics of the target object; determining feature information according to the keywords, wherein the feature information can be a set. The target object may be a human or an animal.
In an alternative embodiment, the text description information may be text information in the electronic archive or image information corresponding to the target object, so as to determine the keyword from the text description information. Specifically, the keywords may be dressing information, and/or height information, and/or body state information of the target object. The image acquisition device can be installed indoors, the scene of the room is shot through the image acquisition device, the keywords are determined according to the shot image information, specifically, the RGB values of the image can be analyzed first, and the dressing information and/or the height information of the object in the image information can be determined, so that the keywords can be determined.
The feature of the target object in the feature information may be determined by: determining the size of a context window in a Word2Vector model; determining other keywords related to the keywords according to the size of the context window, wherein the number of the other keywords is equal to the size of the context window; and determining the characteristics of the target object in the characteristic information according to the keywords and other keywords.
Word2Vector is a tool for converting words into Vector form, and can simplify the processing of text content into Vector operation in K-dimensional Vector space, and the similarity in the Vector space can be used for expressing the similarity in text semantics. If the window size of Word2Vector is 5, the association relationship between the first five words and the last five words is considered.
Step S204, determining the target running state of the household appliance according to the characteristic information;
in an optional embodiment, the characteristic information is input into the decision tree prediction model for analysis, so as to obtain a prediction result of the running state of the household appliance.
The decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the characteristic information and a mark used for marking the running state of the household appliance corresponding to the characteristic information.
The Decision Tree may be a GBDT (Gradient Boost Decision Tree). GBDT is an iterative regression decision tree algorithm consisting of multiple regression trees with all the tree conclusions summed up to complete the prediction. In GBDT, each model is built in the gradient descent direction of the previously built model loss function, i.e. each new model is built in order to reduce the residual error of the previous model. The classification tree measures the maximum entropy and the information gain, and the regression tree measures the minimum mean square error. From the bias-variance decomposition point of view, GBDT focuses mainly on reducing bias and can build strong integration based on a learner whose generalization performance is rather weak. GBDT starts from a weak learning algorithm, and learns repeatedly to obtain a series of basic classifiers, which are then combined to form a strong classifier. The lifting method actually adopts an addition model and a forward distribution algorithm, the lifting method taking the decision tree as a basis function is called a lifting tree, the gradient lifting algorithm is a function for minimizing loss by using a steepest descent method, and the method has a good effect on classification.
For example: the 1 st group of data in the multiple groups of data is: { girl, age 8, yellow clothing, 140}, the labels of the operating states of the corresponding home devices are: air-conditioning temperature: and 27 degrees.
The 2 nd group of data in the multiple groups of data is: { adult, 28 years old, black clothing, 170}, the labels of the operating states of the corresponding home devices are: air-conditioning temperature: 26 degrees.
The household electrical appliance can be an air conditioner, an air purifier, a fan and a bedroom ceiling lamp.
In an alternative embodiment, the brightness of the indoor lighting may be adjusted if the target object determined from the characteristic information is an eye disease patient.
And step S206, controlling the running state of the household appliance according to the target running state.
And if the operation state indicated by the target operation state is 26 degrees of the air conditioner, controlling the operation state of the air conditioner to be 26 degrees.
In the embodiment of the application, the characteristic information of the target object is obtained; determining a target running state of the household appliance according to the characteristic information; and controlling the running state of the household appliance according to the target running state. The home appliance determines the running state of the home appliance corresponding to the target object according to the analysis of the target object characteristic information set, can serve the object in a personalized manner, and improves the technical effect of object experience. And then the technical problems that the household appliance cannot set the corresponding running state according to different objects, cannot provide personalized service for the objects and has poor object experience are solved.
Fig. 3 is a schematic structural diagram of a control system of a home appliance according to an embodiment of the present application. As shown in fig. 3, the system includes: a collection device 32; a processor 34; a controller 36. Wherein:
the acquisition device 32 is used for acquiring a characteristic information set of the target object;
the acquisition device 32 may be an image acquisition device, such as: a camera is provided.
And the processor 34 is configured to input the feature information in the feature information set into a decision tree prediction model for analysis, so as to obtain a prediction result of the operating state of the household appliance, where the decision tree prediction model is obtained by training multiple sets of data, and each set of data in the multiple sets of data includes: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set;
and a controller 36 for controlling the operation state of the household appliance according to the operation state indicated by the prediction result.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 and fig. 2 for a preferred implementation of the embodiment shown in fig. 3, and details are not repeated here.
Fig. 4 is a schematic structural diagram of a control device of a home appliance according to an embodiment of the present application. As shown in fig. 4, the system includes: an acquisition module 42; a processing module 44; a control module 46. Wherein:
the acquisition module 42 is used for acquiring a characteristic information set of the target object;
the acquisition module 42 may be an image acquisition module, for example: a camera module.
The processing module 44 is configured to input the feature information in the feature information set into a decision tree prediction model for analysis, so as to obtain a prediction result of the operation state of the household appliance, where the decision tree prediction model is obtained by training multiple sets of data, and each set of data in the multiple sets of data includes: the characteristic information set and a mark used for marking the running state of the household appliance corresponding to the characteristic information set;
and the control module 46 is configured to control the operation state of the home appliance according to the operation state indicated by the prediction result.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 and fig. 2 for a preferred implementation of the embodiment shown in fig. 4, and details are not repeated here.
According to still another aspect of embodiments of the present application, there is provided a storage medium including a stored program, wherein the program controls a device on which the storage medium is located to perform the above control method of the home appliance when the program is executed.
According to still another aspect of the embodiments of the present application, there is provided a processor for executing a program, wherein the program executes the above control method for a home appliance.
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, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not 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 according to 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 (11)
1. A method for controlling a home appliance, comprising:
acquiring a characteristic information set of a target object;
inputting the characteristic information in the characteristic information set into a decision tree prediction model for analysis to obtain a prediction result of the running state of the household appliance, wherein the decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the home appliance information set comprises a characteristic information set and a mark for marking the running state of the home appliance corresponding to the characteristic information set;
and controlling the running state of the household appliance according to the running state indicated by the prediction result.
2. The method of claim 1, wherein the obtaining the feature information set of the target object comprises:
acquiring character description information corresponding to the target object; determining a keyword from the text description information, wherein the keyword is used for reflecting the characteristics of the target object; and determining the characteristic information set according to the key words.
3. The method of claim 2, wherein the feature of the target object in the feature information set is determined by:
determining the size of a context window in a Word2Vector model; determining other keywords related to the keywords according to the size of the contextual window, wherein the number of the other keywords is equal to the size of the window; and determining the characteristics of the target object in the characteristic information set according to the keywords and the other keywords.
4. The method of claim 1, wherein the obtaining the feature information set of the target object comprises:
acquiring image information at least comprising the target object; extracting feature information of the target object from the image information; and determining a characteristic information set of the target object according to the characteristic information.
5. The method of claim 4, wherein the image information comprises: a plurality of objects; before determining the feature information set of the target object according to the image information, the method includes:
determining priorities of the plurality of objects; determining an object with the highest priority from the plurality of objects as the target object.
6. The method according to claim 4, wherein a plurality of target objects are included in the image information; before controlling the operation state of the household appliance according to the operation state indicated by the prediction result, the method comprises the following steps:
acquiring weights of the target objects; and determining the target operation state of the household appliance according to the weight and the operation states corresponding to the target objects.
7. A method for controlling a home appliance, comprising:
acquiring characteristic information of a target object;
determining the target running state of the household appliance according to the characteristic information;
and controlling the running state of the household appliance according to the target running state.
8. A control system for an electrical household appliance, comprising:
the acquisition device is used for acquiring a characteristic information set of the target object;
the processor is configured to input the feature information in the feature information set into a decision tree prediction model for analysis, so as to obtain a prediction result of an operating state of the household appliance, where the decision tree prediction model is obtained by training multiple sets of data, and each set of data in the multiple sets of data includes: the home appliance information set comprises a characteristic information set and a mark for marking the running state of the home appliance corresponding to the characteristic information set;
and the controller is used for controlling the running state of the household appliance according to the running state indicated by the prediction result.
9. A control device for a home appliance, comprising:
the acquisition module is used for acquiring a characteristic information set of a target object;
the processing module is used for inputting the characteristic information in the characteristic information set into a decision tree prediction model for analysis to obtain a prediction result of the running state of the household appliance, wherein the decision tree prediction model is obtained by training a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the home appliance information set comprises a characteristic information set and a mark for marking the running state of the home appliance corresponding to the characteristic information set;
and the control module is used for controlling the running state of the household appliance according to the running state indicated by the prediction result.
10. A storage medium, comprising a stored program, wherein the program, when executed, controls a device on which the storage medium is located to perform the method for controlling a home appliance according to any one of claims 1 to 6 or claim 7.
11. A processor configured to execute a program, wherein the program executes the method for controlling a home device according to any one of claims 1 to 6 or the method for controlling a home device according to claim 7.
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