CN114265320A - Smart home control method and system for analyzing user habits based on deep learning - Google Patents
Smart home control method and system for analyzing user habits based on deep learning Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent home, in particular to an intelligent home control method and system for analyzing user habits based on deep learning. According to the intelligent home control method based on deep learning and user habit analysis, intelligent home data information is received through a deep learning system; calling a prediction model to obtain prediction data of the smart home according to the smart home data information; the intelligent home control system has the advantages that the intelligent home prediction data are output to the intelligent home control platform, the intelligent home control platform generates corresponding intelligent home control instructions according to the intelligent home prediction data, and the whole intelligent home is automatically and intensively controlled based on user habits, so that a user is liberated from complicated equipment operation, service non-sensitivity is realized, user habits are fully developed, user experience is optimized, and service intellectualization is realized.
Description
Technical Field
The invention relates to the technical field of intelligent home, in particular to an intelligent home control method and system for analyzing user habits based on deep learning.
Background
The intelligent home is characterized in that a home is used as a platform, facilities related to home life are integrated by utilizing a comprehensive wiring technology, a network communication technology, a safety precaution technology, an automatic control technology and an audio and video technology, an efficient management system of home facilities and family schedule affairs is constructed, home safety, convenience, comfortableness and artistry can be improved, and an environment-friendly and energy-saving living environment is realized.
In recent years, the intelligent home system is used as a user instruction entry through a smart phone APP, a touch screen, a voice assistant and other modes, scene linkage of home equipment is realized, but the automatic management and control technology of the current intelligent home platform still stays in a stage of control based on preset rules of a user, the use complexity and the learning cost of the equipment are increased through complex rule input, and rigid logic control is more difficult to adapt to changeable home environments.
In the field of artificial intelligence, machine learning, particularly deep learning methods, have made remarkable achievements in recent years compared with the traditional technology, and the deep learning methods greatly improve the performances of speech recognition, image recognition and natural language processing, and have good development and application prospects. Therefore, the deep neural network model training is carried out based on the intelligent home environment data, the behavior habits of the intelligent home users are subjected to perception analysis, and automatic centralized control of the intelligent home environment is realized.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent home control method for analyzing user habits based on deep learning, which can perform perception analysis on intelligent home user behavior habits and realize automatic centralized control of an intelligent home environment.
The invention relates to an intelligent home control method for analyzing user habits based on deep learning, which comprises the following steps:
receiving intelligent family data information;
calling a prediction model to obtain prediction data of the smart home according to the smart home data information;
and outputting the smart home prediction data to the smart home management and control platform.
As a further optimization of the invention, the prediction model comprises one or more of a reference prediction model and a family prediction model.
As a further optimization of the invention, a benchmark prediction model is formulated.
As further optimization of the method, before the prediction data of the smart home is obtained by calling the prediction model, the smart home data information is analyzed according to the reference prediction model, and the attribute of the smart home data information is judged.
As a further optimization of the invention, when the smart family data information is new smart family data information, the smart family data information is updated and stored as an individualized family prediction model; otherwise, calling the family prediction model to obtain the prediction data of the smart family.
As a further optimization of the present invention, the smart home data information attribute includes environmental data, and device status information corresponding to the environmental data.
The intelligent home control method for analyzing the user habits based on deep learning comprises the following steps:
the method comprises the steps of obtaining intelligent family data information and sending the intelligent family data information to a deep learning system;
receiving intelligent family prediction data;
and generating a corresponding instruction for controlling the smart home according to the smart home prediction data.
An intelligent household control system based on deep learning analysis of user habits comprises
The receiving module is used for receiving the intelligent family data information;
the prediction execution module is used for calling a prediction model to obtain prediction data of the smart home according to the data information of the smart home;
and the output module is used for outputting the intelligent family prediction data to the intelligent home management and control platform.
As a further optimization of the present invention, the method further comprises a formulation module, wherein the formulation module is used for formulating a benchmark prediction model.
The intelligent household data information analysis method is further optimized by the aid of an analysis module, and the analysis module analyzes the intelligent household data information according to a reference prediction model and judges the attribute of the intelligent household data information.
As a further optimization of the invention, the system further comprises an updating module which is used for updating and saving the family prediction model.
An intelligent household control system based on deep learning analysis of user habits comprises
The acquisition module is used for acquiring intelligent family data information and/or intelligent family prediction data;
the sending module is used for sending the intelligent family data information to a deep learning system;
and the generation module is used for generating a corresponding instruction for controlling the smart home according to the smart home prediction data.
Compared with the prior art, the invention has the beneficial effects that:
the intelligent home control method based on deep learning analysis of user habits receives intelligent home data information through a deep learning system; calling a prediction model to obtain prediction data of the smart home according to the smart home data information; the intelligent home control system has the advantages that the intelligent home prediction data are output to the intelligent home control platform, the intelligent home control platform generates corresponding intelligent home control instructions according to the intelligent home prediction data, and the whole intelligent home is automatically and intensively controlled based on user habits, so that a user is liberated from complicated equipment operation, service non-sensitivity is realized, user habits are fully developed, user experience is optimized, and service intellectualization is realized.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings.
FIG. 1 is a schematic structural diagram of an intelligent home control system for analyzing user habits based on deep learning according to the present invention;
FIG. 2 is a flowchart of an intelligent home control method for analyzing user habits based on deep learning according to the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, the smart home control system for analyzing user habits based on deep learning of the present invention mainly comprises a smart home control platform 1 and a deep learning system 2, wherein the smart home control platform 1 is responsible for centralized control of smart homes and sending data to the deep learning system 2, the deep learning system 2 receives data of the smart home control platform 1, outputs predicted data to the smart home control platform 1 through learning and reasoning, and the smart home control platform 2 sends a command to control smart homes 3.
Wherein the deep learning system 2 comprises
The system comprises a receiving module 201, a processing module and a processing module, wherein the receiving module is used for receiving intelligent family data information, and the attribute of the intelligent family data information comprises environmental data and equipment state information corresponding to the environmental data;
the prediction execution module 202 is configured to call a prediction model to obtain prediction data of the smart home according to the smart home data information, that is, predict states of each smart device in the current home, such as on/off states of a fan, a lamp, and a socket; then, the intelligent home management and control platform can generate a control instruction corresponding to the equipment according to the prediction data of the equipment state, and intelligent control of the equipment is completed;
the output module 203 is configured to output the smart home prediction data to the smart home management and control platform, that is, the smart home management and control platform generates a control instruction corresponding to the device according to the prediction data of the device state, completes intelligent control of the device, and implements seamless switching of the smart scene.
As another embodiment of the present invention, different from the above embodiments, the present embodiment further includes a formulation module 204, which is configured to formulate a reference prediction model, and since the environmental characteristics have uniformity, that is, people have sensory uniformity with respect to the environmental characteristics (light and dark, cold and hot, drying dampness, etc.), the control of the devices in the home is also substantially consistent. Therefore, this model can be used as a reference for each family prediction model. And the family prediction model is established at the beginning, the reference prediction model is taken as the basis, and then the machine learning system updates the prediction model of the corresponding family according to the new sample data, so that the prediction model adapts to the environmental characteristics of the family, and the prediction accuracy of the equipment state is improved.
As another embodiment of the present invention, different from the above embodiments, the present embodiment further includes an analysis module 205, where the analysis module analyzes the smart home data information according to a reference prediction model, and determines the attribute of the smart home data information, because there is always a small difference in the sense of the environmental characteristics of the individual people, and there is a certain difference in the home environment due to geography, season, and climate, the machine learning system needs to perform personalized customization for each home based on the reference prediction model, that is, the reference prediction model is updated according to the existing home environment data, so as to become a prediction model specific to the home.
As another embodiment of the present invention, different from the above embodiment, the present embodiment further includes an updating module 206, where the updating module is configured to update and store the family prediction model, that is, when the attribute of the smart family data information is new smart family data information, the family prediction model is updated according to new smart family sample data, so that the prediction model learns new family behavior characteristics, and the new sample data is new environment data and device state information acquired when the user actively controls the device, and represents the new family behavior characteristics, thereby implementing a technique of analyzing user habits to implement smart home automation centralized control.
Intelligent house management and control platform 1 includes
The acquiring module 101 is used for acquiring intelligent family data information and/or intelligent family prediction data;
the sending module 102 is configured to send the smart family data information to a deep learning system;
and the generating module 103 is used for generating a corresponding instruction for controlling the smart home according to the smart home prediction data, so that the intelligent control of the equipment is completed, and the seamless switching of the intelligent scene is realized.
As shown in fig. 2, the intelligent home control method for analyzing user habits based on deep learning performs data mutual transmission through an intelligent home control platform and a deep learning system, so as to realize automatic centralized control of the whole intelligent home based on user habits, and includes the following steps:
s1, the smart home management and control platform acquires smart home data information and sends the smart home data information to the deep learning system;
s2, the deep learning system receives the data information of the smart home, and calls a prediction model to obtain prediction data of the smart home according to the data information of the smart home;
s3, outputting the intelligent household prediction data to an intelligent household control platform;
s4, the intelligent home management and control platform generates a corresponding instruction for controlling the intelligent home according to the intelligent home prediction data.
Therefore, the perception analysis of the behavior habits of the intelligent home users is realized by introducing a deep learning network on the basis of the current intelligent home management and control platform and related systems, the intelligent home equipment management capacity is provided, and finally the automatic centralized management and control of the whole intelligent home based on the user habits is realized.
In this embodiment, the prediction model includes a reference prediction model and a family prediction model. The reference prediction model is generated by training based on a large amount of sample data, which is environment data inside and outside a home and a device state corresponding to the environment data, and is called as a reference prediction model because the environment characteristics have uniformity, that is, people have sensory consistency with respect to the environment characteristics (light and dark, cold and hot, drying dampness, and the like), the control of devices in the home is also substantially consistent, and the model can be used as a reference of each home prediction model.
However, since there are small differences in the individual senses of the environmental characteristics of the human, and there are also certain differences in the household environment due to geography, season, and climate, the machine learning system needs to be personalized for each household based on the reference model, that is, the reference model is updated according to the existing household environmental data, so as to become a household-specific prediction model. Therefore, the deep learning system of the design establishes a prediction model for each family of the access system, the prediction model of the family is based on the reference prediction model at the beginning of establishment, and then the machine learning system updates the prediction model of the corresponding family according to new sample data, so that the prediction model adapts to the environmental characteristics of the family, and the prediction accuracy of the equipment state is improved.
As another embodiment of the present invention, unlike the above embodiments, the present embodiment further includes a reference prediction model, and since the environmental characteristics are uniform, that is, the environmental characteristics (light and dark, cold and hot, drying, etc.) are consistent in sense, the control of the devices in the home is also substantially consistent. Therefore, this model can be used as a reference for each family prediction model. And the family prediction model is established at the beginning, the reference prediction model is taken as the basis, and then the machine learning system updates the prediction model of the corresponding family according to the new sample data, so that the prediction model adapts to the environmental characteristics of the family, and the prediction accuracy of the equipment state is improved.
As another embodiment of the present invention, different from the above embodiments, before the prediction data of the smart home is obtained by calling the prediction model, the smart home data information is analyzed according to the reference prediction model to determine the attribute of the smart home data information; when the intelligent family data information is new intelligent family data information, updating and storing the intelligent family data information as a family prediction model; on the contrary, the family prediction model is called to obtain the prediction data of the smart family, because when the user actively controls the equipment, the smart family data information changes, and at the moment, the collected new environment data and the equipment state information represent new family behavior characteristics, the prediction model needs to be updated, and the exclusive prediction model of the family is stored, so that the technology of automatic centralized control of the smart home is realized by analyzing the habits of the user.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "bottom", "top", "front", "rear", "inner", "outer", "left", "right", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the present invention.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no structural conflict. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (12)
1. The intelligent home control method for analyzing the user habits based on deep learning is characterized by comprising the following steps:
receiving intelligent family data information;
calling a prediction model to obtain prediction data of the smart home according to the smart home data information;
and outputting the smart home prediction data to the smart home management and control platform.
2. The smart home control method based on deep learning and user habit analysis according to claim 1, wherein the method comprises the following steps: the prediction model comprises one or more of a reference prediction model and a family prediction model.
3. The smart home control method based on deep learning and user habit analysis according to claim 2, wherein the method comprises the following steps: the method also comprises the step of establishing a reference prediction model.
4. The smart home control method based on deep learning and user habit analysis according to claim 3, wherein the method comprises the following steps: before the prediction data of the smart home is obtained by calling the prediction model, the smart home data information is analyzed according to the reference prediction model, and the smart home data information attribute is judged.
5. The smart home control method based on deep learning and user habit analysis according to claim 4, wherein the method comprises the following steps: when the intelligent family data information is new intelligent family data information, updating and storing the intelligent family data information as an individualized family prediction model; otherwise, calling the family prediction model to obtain the prediction data of the smart family.
6. The smart home control method based on deep learning and user habit analysis according to claim 1, wherein the method comprises the following steps: the smart home data information attribute includes environmental data and device state information corresponding to the environmental data.
7. The intelligent home control method for analyzing the user habits based on deep learning is characterized by comprising the following steps:
the method comprises the steps of obtaining intelligent family data information and sending the intelligent family data information to a deep learning system;
receiving intelligent family prediction data;
and generating a corresponding instruction for controlling the smart home according to the smart home prediction data.
8. Intelligent house control system based on degree of deep learning analysis user habit, its characterized in that: comprises that
The receiving module is used for receiving the intelligent family data information;
the prediction execution module is used for calling a prediction model to obtain prediction data of the smart home according to the data information of the smart home;
and the output module is used for outputting the intelligent family prediction data to the intelligent home management and control platform.
9. The smart home control method based on deep learning and user habit analysis according to claim 8, wherein the method comprises the following steps: the system further comprises a formulation module, and the formulation module is used for formulating a reference prediction model.
10. The smart home control method based on deep learning and user habit analysis according to claim 9, wherein: the intelligent household data information analysis system further comprises an analysis module, wherein the analysis module analyzes the intelligent household data information according to a reference prediction model and judges the attribute of the intelligent household data information.
11. The smart home control method based on deep learning and user habit analysis according to claim 10, wherein: the family prediction model updating system further comprises an updating module, and the updating module is used for updating and storing the family prediction model.
12. Intelligent house control system based on degree of deep learning analysis user habit, its characterized in that: comprises that
The acquisition module is used for acquiring intelligent family data information and/or intelligent family prediction data;
the sending module is used for sending the intelligent family data information to a deep learning system;
and the generation module is used for generating a corresponding instruction for controlling the smart home according to the smart home prediction data.
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