CN113050439A - Self-learning intelligent household control method, control equipment and computer readable storage medium - Google Patents
Self-learning intelligent household control method, control equipment and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses a self-learning intelligent home control method, control equipment and a computer readable storage medium. Wherein, the method comprises the following steps: counting sleep data corresponding to sleep requirements through operation information of the networking equipment, wherein the sleep data comprises one or more of sleep position data, sleep time data and sleep auxiliary data; analyzing the sleep data to obtain sleep conditions related to the sleep requirements of the target user; and adjusting the working state of the networking equipment according to the current state of the target user and the sleep condition so as to meet the sleep requirement of the target user. The self-learning intelligent home control scheme is realized, so that the intelligent home can dynamically adjust the corresponding working state according to the sleep habit of the user, manual adjustment or complex association setting of the user is not needed, the intelligent degree of the intelligent home is improved, and the user experience is enhanced.
Description
Technical Field
The invention relates to the technical field of internet of things, in particular to a self-learning intelligent home control method, control equipment and a computer readable storage medium.
Background
In the prior art, along with the continuous development of smart homes based on the internet of things technology, the control requirements of users on smart homes are also continuously improved. For example, for the sleeping requirement of the home environment, the user can specifically send a control instruction to a curtain, a light, an air conditioner and other devices, so that the user can sleep more easily or get up better. However, in a home environment, a family generally has a plurality of rooms, each room belongs to different family members, and the work and rest time and the living habits of each family member are different, for example, some family members have a habit of afternoon nap, some family members have a habit of listening to music for a period of time before sleeping, some family members have a habit of early falling asleep, and some family members have a habit of late falling asleep. In the existing intelligent home control scheme, the server cannot perform self-learning control on corresponding intelligent equipment according to the sleep habits of all users, the control scheme of all the intelligent equipment needs to be manually set and adjusted by the users, and the experience of the users is poor.
Disclosure of Invention
In order to solve the technical defects in the prior art, the invention provides a self-learning intelligent home control method which is applied to an intelligent control panel and comprises the following steps:
determining a networked device that is relevant to a sleep need of a target user, wherein the sleep need comprises one or more of a sleep area need, a sleep time need, and a sleep assistance need;
counting sleep data corresponding to the sleep requirement through the operation information of the networking equipment, wherein the sleep data comprises one or more of sleep position data, sleep time data and sleep auxiliary data;
analyzing the sleep data to obtain sleep conditions related to the sleep requirements of the target user;
and adjusting the working state of the networking equipment according to the current state of the target user and the sleep condition so as to meet the sleep requirement of the target user.
Optionally, the determining a networking device related to sleep need of a target user, wherein the sleep need includes one or more of a sleep area need, a sleep time need, and a sleep assistance need, and comprises:
determining identity information of the target user;
and determining control equipment for acquiring the sleep requirement, and inputting the identity information into the control equipment.
Optionally, the determining a networking device related to sleep need of a target user, wherein the sleep need includes one or more of a sleep area need, a sleep time need, and a sleep assistance need, comprises:
determining a first networking device corresponding to the sleep area requirement, the first networking device comprising a device for detecting the sleep position of the target user; and/or the presence of a gas in the gas,
determining a second networking device corresponding to the sleep time requirement, wherein the second networking device comprises a device for detecting, identifying or calculating the sleep time of the target user; and/or the presence of a gas in the gas,
determining a third networking device corresponding to the sleep assistance requirement, the third networking device comprising a device used by the target user in the sleep position or the sleep time.
Optionally, the counting sleep data corresponding to the sleep requirement by the operation information of the networking device includes:
monitoring the sleeping position of the target user through the first networking equipment to obtain the sleeping position data; and/or the presence of a gas in the gas,
monitoring the sleep time of the target user through the second networking equipment to obtain the sleep time data; and/or the presence of a gas in the gas,
and monitoring the device functions used by the target user in the pre-sleep stage, the middle sleep stage and the post-sleep stage through the third networking device to obtain the sleep auxiliary data.
Optionally, the analyzing the sleep data to obtain a sleep condition related to the sleep requirement of the target user includes:
analyzing one or more data of the sleep position data, the sleep time data and the sleep assisting data to obtain one or more of sleep position habits, sleep time habits and assisted sleep habits;
and determining the association relationship between the sleep position habit, the sleep time habit and the auxiliary sleep habit and the networking equipment, and setting the sleep condition for triggering the networking equipment to enter the working state according to the association relationship.
Optionally, the adjusting the working state of the networking device according to the current state of the target user and the sleep condition to meet the sleep requirement of the target user includes:
monitoring the current state of the target user through the intelligent control panel;
and when the current state is in accordance with the sleep condition, adjusting the working state of the networking equipment in real time according to the sleep condition and the current state so as to meet the sleep requirement of the target user.
Optionally, the adjusting the working state of the networking device according to the current state of the target user and the sleep condition to meet the sleep requirement of the target user includes:
monitoring a sleep state of the target user through the intelligent control panel arranged at a sleep position;
and when the target user is in a pre-sleep state, a sleeping state or a post-sleep state, adjusting the working parameters corresponding to the networking equipment through the intelligent control panel according to the sleep condition.
Optionally, the sleep condition further includes at least one of additional data such as season information and schedule information, the sleep condition further includes at least one of environmental data such as ambient temperature information, ambient humidity information, air quality information and ambient noise information, and the operating state of the networking device is adjusted according to the current state of the target user and the sleep condition to meet the sleep requirement of the target user, further including:
according to at least one of the sleep data, the additional data and the environment data of the target user, when the target user is in the current state, the working parameters of the corresponding networking equipment are adjusted;
and sending the updated working parameters to the networking equipment in real time through the intelligent control panel so as to meet the sleep requirement of the user in the current state.
The invention also provides a control device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the self-learning intelligent household control method according to any one of the above items when being executed by the processor.
The invention further provides a computer readable storage medium, wherein a self-learning intelligent home control program is stored on the computer readable storage medium, and when being executed by a processor, the self-learning intelligent home control program realizes the steps of the self-learning intelligent home control method.
Implementing the self-learning smart home control method, control device and computer readable storage medium of the present invention, by determining a networked device associated with a sleep need of a target user, wherein the sleep need comprises one or more of a sleep area need, a sleep time need and a sleep assistance need; counting sleep data corresponding to the sleep requirement through the operation information of the networking equipment, wherein the sleep data comprises one or more of sleep position data, sleep time data and sleep auxiliary data; analyzing the sleep data to obtain sleep conditions related to the sleep requirements of the target user; and adjusting the working state of the networking equipment according to the current state of the target user and the sleep condition so as to meet the sleep requirement of the target user. The self-learning intelligent home control scheme is realized, so that the intelligent home can dynamically adjust the corresponding working state according to the sleep habit of the user, manual adjustment or complex association setting of the user is not needed, the intelligent degree of the intelligent home is improved, and the user experience is enhanced.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic flow chart diagram illustrating a self-learning smart home control method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram illustrating a self-learning smart home control method according to another embodiment of the present application;
FIG. 3 is a schematic flow chart diagram illustrating a self-learning smart home control method according to another embodiment of the present application;
FIG. 4 is a schematic flow chart diagram illustrating a self-learning smart home control method according to another embodiment of the present application;
FIG. 5 is a schematic flow chart diagram illustrating a self-learning smart home control method according to another embodiment of the present application;
fig. 6 is a block diagram of a control device according to another embodiment of the present application;
fig. 7 is a block diagram of a computer-readable storage medium according to another embodiment of the present application.
Detailed Description
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 a part of the embodiments of the present application, and not all of the 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.
With the development of communication technology and smart home, the number and functions of smart home devices are increasing, and convenience is brought to daily life of people. The intelligent home control system is characterized in that the intelligent home control system comprises a plurality of intelligent devices, a plurality of intelligent devices and a control program, wherein the intelligent devices are connected with the intelligent devices through a network, the intelligent devices are connected with the control program through a network, the control program comprises a plurality of intelligent devices, the intelligent devices are connected with the intelligent devices through a network, the intelligent devices are connected with the intelligent devices through the network, the intelligent devices.
Therefore, the inventor provides a self-learning smart home control method, a control device and a computer readable storage medium provided in the embodiment of the present application, so that a smart home control system can timely meet sleep requirements of users at different stages, and this embodiment provides a self-learning smart home control scheme, in which networking devices related to the sleep requirements of users are determined, then dynamic operation information of each networking device and sleep data corresponding to the sleep requirements of the users are obtained, and the recorded sleep data are analyzed, so as to obtain sleep conditions corresponding to the sleep requirements of the users, and control of the networking devices is performed in real time according to the current state and the sleep conditions of the users, so that the environment in the current state can meet the sleep requirements of the users, and the intelligence degree of the smart home control system is improved, the user experience is improved.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a self-learning smart home control method according to an embodiment of the present application. The intelligent home control scheme provided by the embodiment is not only suitable for the situation of a single user, but also suitable for multiple users in a home environment, and can determine the self-learning control schemes corresponding to the users according to different users, so that the sleep requirements of the users in the home environment can be met.
S1, determining networking equipment relevant to the sleep requirement of the target user, wherein the sleep requirement comprises one or more of a sleep area requirement, a sleep time requirement and a sleep auxiliary requirement.
In this embodiment, the sleep area requirements include the area locations where the target user frequently sleeps and the area locations where the target user occasionally sleeps, specifically, the sleep areas include but are not limited to various sleep areas such as a guest room, a living room, a study room, a bedroom, etc., the smart home control system may include a smart home control panel, and a plurality of networking devices networked with the smart home control panel, where the networking devices of this embodiment are home devices in the sleep areas, and the networking devices include but are not limited to door and window sensors, smart switches, lamps, air conditioners, curtains, televisions, refrigerators, fans, and smart beds. The sleep time requirement can be obtained according to various analyses of different sleep stages, sleep habit data, schedule information and the like, specifically, the sleep time requirement comprises the sleep time of different stages of a target user such as afternoon nap, late nap and rest, and the sleep time comprises the time before sleep starts until the target user falls asleep, the time in the sleeping process and the time of getting up after waking up; the sleep assisting requirements include the use requirements of the target user on some auxiliary sleep devices in the different sleep areas or different sleep times, and the sleep assisting requirements include but are not limited to factors such as ambient temperature, humidity, light conditions, ambient noise conditions, sleep assisting music and the like capable of assisting the user in sleeping.
And S2, counting sleep data corresponding to the sleep requirement through the operation information of the networking equipment, wherein the sleep data comprises one or more of sleep position data, sleep time data and sleep auxiliary data.
In this embodiment, the operation information is current setting information of each piece of networking equipment, such as a playing track, a track style, a track category, a volume and the like of the music playing equipment, and further such as an overall opening and closing degree of the electric curtain, an opening and closing degree of the inner light shielding layer, an opening and closing degree of the outer light transmitting layer and the like; the sleep position data, the sleep time data and the sleep auxiliary data are respectively statistical data corresponding to the sleep region requirements, the sleep time requirements and the sleep auxiliary requirements, for example, the playing tracks of the music playing device have twenty tracks, the track styles have four tracks, the track categories have three tracks, the volume has five levels and the like, and for example, the integral opening and closing degree of the electric curtain is fully closed, the opening and closing degree of the inner shading layer is fully closed and semi-closed, and the opening and closing degree of the outer light-transmitting layer is fully opened and three-quarters opened and the like.
S3, analyzing the sleep data to obtain the sleep condition related to the sleep requirement of the target user.
In this embodiment, the sleep data is analyzed to obtain a sleep condition related to the sleep requirement of the target user, where the sleep condition is a statistical analysis result of the sleep data. Specifically, in this embodiment, in the process of statistical analysis of the sleep data, it is necessary to determine a user habit with a high probability according to a distribution state of the data, and determine the usage states of the networking devices and the networking devices used under the habits of each user, so as to determine self-learned model data based on the working parameters of the networking devices and the networking devices, and perform subsequent sleep requirement determination of the target user according to the self-learned model data.
S4, adjusting the working state of the networking device according to the current state of the target user and the sleep condition so as to meet the sleep requirement of the target user.
In this embodiment, the working state of the networking device is adjusted according to the current state of the target user and the sleep condition to meet the sleep requirement of the target user, wherein, as described in the above example, after the self-learning model data determines the sleep requirement of the subsequent target user, the working parameters of the networking device are adjusted according to the current state of the target user and the sleep condition, so that each networking device is in the working state according with the current sleep habit of the target user. For example, the operating parameters of the networking devices are adjusted according to whether the target user is in a sleep area, whether the current time period is sleep time, whether the current sleep assistance condition meets historical data, and the like, so that each networking device is in an operating state that meets the current sleep habit for the target.
The method has the advantages that the networking equipment related to the sleep requirement of the target user is determined, wherein the sleep requirement comprises one or more of a sleep area requirement, a sleep time requirement and a sleep auxiliary requirement; counting sleep data corresponding to the sleep requirement through the operation information of the networking equipment, wherein the sleep data comprises one or more of sleep position data, sleep time data and sleep auxiliary data; analyzing the sleep data to obtain sleep conditions related to the sleep requirements of the target user; and adjusting the working state of the networking equipment according to the current state of the target user and the sleep condition so as to meet the sleep requirement of the target user. The self-learning intelligent home control scheme is realized, so that the intelligent home can dynamically adjust the corresponding working state according to the sleep habit of the user, manual adjustment or complex association setting of the user is not needed, the intelligent degree of the intelligent home is improved, and the user experience is enhanced.
Example two
Referring to fig. 2, fig. 2 is a schematic flow chart illustrating a self-learning smart home control method according to another embodiment of the present application. Based on the foregoing embodiment, in order to further accurately determine a networked device related to a sleep need of a target user, where the sleep need includes one or more of a sleep area need, a sleep time need, and a sleep assistance need, the present embodiment further includes:
and S01, determining the identity information of the target user.
In this embodiment, the identity information of the target user may be identified by the control panel in the networked device through a plurality of biometric authentication methods, such as face identification, voiceprint identification, fingerprint identification, and gait identification, to the user, so as to determine the identity information corresponding to the preset biometric information.
And S02, determining the control equipment for acquiring the sleep requirement, and recording the identity information into the control equipment.
In this embodiment, the control device for acquiring the sleep requirement includes various control devices which are consistent with the sleep habit of the target user and may be used. For example, devices such as motorized curtains, air conditioners, televisions, humidifiers, background music players, lights, motorized beds, etc. may be included within the scope of the control device of the present embodiment.
Optionally, based on the foregoing embodiment, in order to further determine a networking device related to a sleep requirement of a target user, where the sleep requirement includes one or more of a sleep area requirement, a sleep time requirement, and a sleep assistance requirement, this embodiment further includes: determining a first networking device corresponding to the sleep area requirement, the first networking device comprising a device for detecting the sleep position of the target user; and/or determining a second networking device corresponding to the sleep time requirement, wherein the second networking device comprises a device for detecting, identifying or calculating the sleep time of the target user; and/or determining a third networking device corresponding to the sleep assistance requirement, wherein the third networking device comprises a device used by the target user in the sleep position or the sleep time.
The sleep area requirements include the area positions where the target user frequently sleeps and the area positions where the target user occasionally takes a nap, so that the first networking device of the embodiment can be a device which supports biological feature recognition, such as face recognition, voiceprint recognition, fingerprint recognition and gait recognition, in each area, for example, an intelligent control panel arranged on the entrance wall surface of each area; similarly, as described in the above example, the sleep time requirements include sleep times of different stages of the target user such as nap, and the like, and the sleep time includes a time before the target user starts sleeping to fall asleep, a time during the sleep process, and a time of getting up when the target user wakes up to get up, so the second network connection device of this embodiment may be an intelligent control panel disposed at a bedside or a sofa side; similarly, as described in the above example, the sleep assisting requirement includes a requirement for the target user to use some auxiliary sleep devices in different sleep areas or different sleep times, for example, a requirement for the target user to use a music playing device in a time before sleep, a requirement for opening and closing control of a motorized window curtain, a requirement for setting a temperature of an air conditioner, and the like, and therefore, the third networking device in this embodiment may be a device such as a motorized window curtain, an air conditioner, a television, a humidifier, a background music player, a light, and a motorized bed.
Optionally, based on the foregoing embodiment, in order to further count sleep data corresponding to the sleep requirement through the operation information of the networking device, this embodiment further includes: monitoring the sleep position of the target user through one or more identification ways of biological feature identification such as face identification, voiceprint identification, fingerprint identification and gait identification of the first networking equipment to obtain the sleep position data; and/or monitoring the sleep time of the target user through one or more identification ways of biological feature identification such as face identification, voiceprint identification, fingerprint identification and gait identification of the second networking equipment to obtain the sleep time data; and/or monitoring the device functions of the target user in the pre-sleep stage, the middle sleep stage and the post-sleep stage through one or more identification ways of the biological feature identification such as face identification, voiceprint identification, fingerprint identification and gait identification of the third networking device to obtain the sleep auxiliary data. Similarly, as described in the above example, the sleep position data of the target user is acquired through one or more identification ways of face identification, voiceprint identification, fingerprint identification, gait identification and other biological feature identification of the intelligent control panel arranged on the entrance wall surface of each area; acquiring sleep time data of a target user through intelligent control panels arranged at the bedside and the sofa side; the sleep assisting data of the target user is obtained through devices such as an electric curtain, an air conditioner, a television, a humidifier, a background music player, light and an electric bed.
Specifically, based on the above embodiment, in order to further perform identification through one or more ways of biometric identification such as face identification, voiceprint identification, fingerprint identification, and gait identification through the three networked devices, so as to perform linkage control on each device, in this embodiment, various embodiments are proposed. For example, first, through a face recognition approach of the first networked device, when a target user enters a sleep area, or when the target user is located in a location area with face recognition in a room, an obtained sleep location is recognized, and then the sleep location data is obtained; then, through a voiceprint recognition path of the second networking equipment, recognizing normal voice sound of a target user and uniform breath sound of the target user to obtain sleep time of the target user, and further obtaining the sleep time data; finally, through a gait recognition approach, the device functions used by the target user in the pre-sleep stage, the middle sleep stage and the post-sleep stage are monitored, for example, gait recognition of fitness exercise in the pre-sleep stage, side turning recognition in the middle sleep stage and getting-up action recognition in the post-sleep stage, so as to obtain the sleep assistance data, that is, the exercise intensity, exercise type and exercise time of the fitness exercise in the pre-sleep stage, the length of falling asleep time of side turning in the middle sleep stage, the sleep stability degree in the sleep process and the flexibility degree of getting-up action in the post-sleep stage are respectively obtained, and then various sleep assistance data related to the sleep quality are further speculated. For another example, through the face recognition path and the gait recognition path of the first networking device, a position area with face recognition when the target user enters a sleep area and a gait feature recognition path to be laid down when the target user is in the position area are recognized, and the obtained sleep position is recognized, so that the sleep position data is obtained; then, through a voiceprint recognition path and a gait recognition path of the second networking equipment, normal voice sound of the target user is recognized, lying limb actions of the target user are recognized, and finally uniform breathing sound after falling asleep is recognized, so that the sleeping time of the target user is recognized and obtained, and the sleeping time data is obtained; finally, the device functions of the target user in the pre-sleep stage, the middle sleep stage and the post-sleep stage are monitored through a gait recognition path and a fingerprint recognition path, for example, gait recognition of fitness exercise in the pre-sleep stage, fingerprint recognition of mobile phone devices in the pre-sleep stage, turning-over recognition of the side in the middle sleep stage, and getting-up recognition in the post-sleep stage, thereby obtaining the sleep assisting data, namely respectively obtaining the exercise intensity, the exercise type and the exercise time of the body-building exercise in the early stage of sleep, the content browsed by the mobile phone or the used function is used in the early sleep stage, the length of the falling asleep time of turning over the side during the sleep stage and the sleep stability degree during the sleep process, and the flexibility of the getting-up action in the post-sleep stage, and further speculating to obtain various sleep auxiliary data related to the sleep quality.
Referring to fig. 3, fig. 3 is a schematic flow chart illustrating a self-learning smart home control method according to another embodiment of the present application. Based on the foregoing embodiment, in order to further analyze the sleep data to obtain a sleep condition related to the sleep requirement of the target user, this embodiment further includes:
and S31, analyzing one or more data of the sleep position data, the sleep time data and the sleep assisting data to obtain one or more of sleep position habits, sleep time habits and assisted sleep habits.
S32, determining the association relationship between the sleep position habit, the sleep time habit and the auxiliary sleep habit and the networking equipment, and setting the sleep condition for triggering the networking equipment to enter the working state according to the association relationship.
The specific implementation manner of this embodiment is to perform probability analysis on the sleep position data, the sleep time data, and the sleep assistance data, respectively, and determine that the sleep position data, the sleep time data, and the sleep assistance data with a large distribution probability are respectively used as the sleep position habit, the sleep time habit, and the sleep assistance habit, that is, the sleep position habit is obtained by the distribution probability of the sleep position data, the sleep time habit is obtained by the distribution probability of the sleep time data, and the sleep assistance habit is obtained by the distribution probability of the sleep assistance data.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating a self-learning smart home control method according to another embodiment of the present application. Based on the foregoing embodiment, in order to further adjust the working state of the networking device according to the current state of the target user and the sleep condition, so as to meet the sleep requirement of the target user, this embodiment further includes:
and S41, monitoring the current state of the target user through the intelligent control panel.
And S42, when the current state is in accordance with the sleep condition, adjusting the working state of the networking equipment in real time according to the sleep condition and the current state so as to meet the sleep requirement of the target user.
In this embodiment, the intelligent control panel for acquiring the current state of the target user may be an intelligent control panel arranged on an entrance wall surface of each area for acquiring sleep position data of the target user, or an intelligent control panel arranged on a bedside or a sofa side for acquiring sleep time data of the target user; the current state can be a regional state in which the target user is about to start sleeping, a time state in which the target user is about to start sleeping, and the like; adjusting the working state of the networked device according to the determined sleep condition includes identifying the current state of the target user, optionally, the identified device may be one or more intelligent control panels, for example, two intelligent control panels respectively identify the state of an area where the target user is about to start sleeping and the state of time when the target user is about to start sleeping, so as to determine the working parameters about to be configured of the corresponding networked device, and send the working parameters to the networked devices through the intelligent control panel or a server for controlling the intelligent control panel, so that each networked device works in the working state according with the sleep habit of the target user.
Referring to fig. 5, fig. 5 is a schematic flow chart illustrating a self-learning smart home control method according to another embodiment of the present application. Based on the foregoing embodiment, in order to further adjust the working state of the networking device according to the current state of the target user and the sleep condition, so as to meet the sleep requirement of the target user, this embodiment further includes:
s43, monitoring the sleeping state of the target user through the intelligent control panel arranged in the sleeping position.
And S44, when the target user is in a pre-sleep state, a sleeping state or a post-sleep state, adjusting the respective corresponding working parameters of the networking equipment according to the sleeping conditions through the intelligent control panel.
In this embodiment, in order to further perform refined learning and adaptation on the sleeping habits of the target user, in this embodiment, the pre-sleep state, the in-sleep state, or the post-sleep state may be further divided into a plurality of pre-sleep time periods, in-sleep time periods, or post-sleep time periods, and in each time period, the respective corresponding operating parameters of the networking devices are progressively adjusted through the intelligent control panel according to the sleeping conditions, for example, the music playing device has twenty music tracks, four music styles, three music categories, five levels of volume, and the like, so that in a plurality of time periods before sleep, progressively mild music and progressively reduced volume are respectively played, and for example, the whole opening and closing degree of the electric curtain is fully closed, the opening and closing degree of the inner light shielding layer is fully closed and semi-closed, the opening and closing degree of the outer light-transmitting layer is fully opened and three-quarters opened, and the like, therefore, in a plurality of time periods after waking up, the inner shading layer curtain is gradually opened, and the outer transparent layer curtain is gradually closed.
Optionally, based on the above embodiment, in order to further include at least one of additional data such as season information and schedule information, the sleep condition further includes at least one of environmental data such as ambient temperature information, ambient humidity information, air quality information and ambient noise information, and the adjusting the operating state of the networked device according to the current state of the target user and the sleep condition to meet the sleep requirement of the target user further includes: firstly, according to at least one of sleep data, the additional data and the environment data of the target user, when the target user is in the current state, the working parameters of the corresponding networking equipment are adjusted; and then, sending the updated working parameters to the networking equipment in real time through the intelligent control panel so as to meet the sleep requirement of the user in the current state. In order to further perform refined learning and adaptation on the sleep habits of the target user, additional data such as seasonal information and schedule information may be introduced, for example, the sleep habits of the target user such as different sleep durations, getting-up time and falling-asleep time are determined according to different seasons, and temporary regulation and control parameters of the target user based on the existing sleep habits are actively adjusted according to the schedule arrangement in a certain time period, for example, when a schedule item is set before the getting-up time of the target user, the opening of the electric curtain is correspondingly controlled in advance to a preset degree, so that the target user is naturally awakened.
Optionally, based on the foregoing embodiment, in this embodiment, at least one of environmental data such as ambient temperature information, ambient humidity information, air quality information, and ambient noise information may also be introduced as the sleep condition of this embodiment, that is, at least one of the environmental data such as the ambient temperature information, the ambient humidity information, the air quality information, and the ambient noise information is used as the operating parameter of the networking device with the corresponding function, so that the corresponding networking device can operate in the operating state consistent with the sleep condition.
Optionally, based on the above embodiment, taking a control flow of the server or the intelligent control panel as an example, first, by recording the time and the motion of one or more networked devices being controlled, and performing a summary analysis on all recorded sleep data, for example, a curtain in room a is closed at about 13:00 every day, opened at about 13:30, closed at about 22:30, opened at about 07:00 the next day, music playing devices in room a at about 22:30 play music, and stop playing at about 23: 00; then, according to the data, the sleeping habits of the user in the room A, namely 13: 00-13: 30 afternoon nap every day, the user in the evening is habitually prepared to sleep at 22:30, the user listens to music for half an hour, starts to sleep at 23:00 and gets up at 07:00 the next day, can be analyzed; finally, after the sleeping habits of the user are analyzed, the server side or the intelligent control panel side automatically controls each networking device without manually controlling the devices by the user, namely, the server side or the intelligent control panel side automatically controls the curtain in the room A to automatically and fully close at 12:55 every noon, the curtain in the room A is automatically and fully closed at 13:30 at 15% of the time, the curtain in the room A is automatically and slowly opened to 100% at 13:35, the curtain is automatically and fully closed at 22:25, the music favored by the user is played from 22:30, the music is automatically closed at 23:00, the curtain is automatically opened at 07:00 at the next day, and the curtain is automatically opened at 07:15 to 100% at 07: 00. Therefore, the automatic control scheme of the electric curtain and other equipment can be matched with the sleep habit of the user, the user is prevented from manually setting various items, and the use burden of the user is reduced.
Optionally, in this embodiment, in the self-learning stage, the recorded time, action, and the like of each networked device actively controlled by the target, and the command of each networked device once, generate a piece of data, which is stored at the server side or the intelligent control panel side, where, for example, the networked devices in room a:
| room name | Device name | Season | Date | Time | Device action |
| A | Window curtain | Summer (summer) | 6 months and 1 day | 07:00:01 | Open |
| A | Window curtain | Summer (summer) | 6 months and 1 day | 13:00:05 | Close off |
| A | Window curtain | Summer (summer) | 6 months and 1 day | 13:30:08 | Open |
| A | Window curtain | Summer (summer) | 6 months and 1 day | 22:30:00 | Close off |
| A | Background music | Summer (summer) | 6 months and 1 day | 22:30:01 | Playing music |
| A | Background music | Summer (summer) | 6 months and 1 day | 23:00:01 | Stop playing music |
| A | Window curtain | Summer (summer) | 6 months and 2 days | 07:00:30 | Open |
| ... | ... | ... | ... | ... | ... |
| A | Window curtain | Winter season | 12 month and 5 days | 07:30:01 | Open |
| A | Window curtain | Winter season | 12 month and 5 days | 22:30:00 | Close off |
| A | Background music | Winter season | 12 month and 5 days | 22:30:01 | Playing music |
| A | Background music | Winter season | 12 month and 5 days | 23:00:01 | Stop playing music |
| ... | ... | ... | ... | ... | ... |
It can be seen that, in this embodiment, the server or the intelligent control panel end classifies, summarizes, and cleans up the dirty data, so as to obtain the rule that one or more networked devices in the room a are controlled, and thus analyze and obtain the sleep habit of the target user in the room a:
a1: a summer habit of getting up at 07:00, afternoon nap at 13: 00-13: 00, sleeping at 23:00, and listening to music for half an hour before sleeping;
a2: the habit in winter is 07:30 to get up, not to have a nap, 23:00 to sleep, and listening to music half an hour before sleep;
a3: opening the curtain when getting up in summer habit 07:00, keeping the curtain closed during the afternoon nap in 13: 00-13: 00 period, and closing the curtain when sleeping at 23:00 period;
a4: the curtain is opened when getting up at a ratio of 07:30 in winter and closed when sleeping at a ratio of 23: 00.
Likewise, with the example of networked devices in room B:
| room name | Device name | Season | Date | Time | Device action |
| B | Window curtain | Summer (summer) | 6 months and 1 day | 08:00:01 | Open |
| B | Window curtain | Summer (summer) | 6 months and 1 day | 23:30:00 | Close off |
| B | Window curtain | Summer (summer) | 6 months and 2 days | 08:00:30 | Open |
| B | Window curtain | Summer (summer) | 6 months and 2 days | 23:30:00 | Close off |
| B | Window curtain | Summer (summer) | 6 months and 3 days | 08:00:10 | Open |
| ... | ... | ... | ... | ... | ... |
| B | Window curtain | Winter season | 12 month and 5 days | 08:00:01 | Open |
| B | Window curtain | Winter season | 12 month and 5 days | 23:30:00 | Close off |
| B | Window curtain | Winter season | 12 month and 5 days | 08:00:30 | Open |
| B | Window curtain | Winter season | 12 month and 5 days | 23:30:00 | Close off |
| B | Window curtain | Winter season | 12 month and 5 days | 08:00:10 | Open |
Similarly, in this embodiment, the server or the intelligent control panel end classifies, summarizes, and cleans the sleep data, that is, a rule that one or more networked devices in the room B are controlled can be obtained, so as to infer a sleep habit of the user in the room B:
b1: the habit of getting up at 08:00 and sleeping at 23:30 all the year round;
b2: there is no habit of listening to music before afternoon nap and sleep;
b3: the window curtain is opened when getting up at 08:00 habitually all the year round;
b4: the curtain is closed when people sleep at the habit of 23:30 all the year round.
The method and the device have the advantages that the adjustment of the working state of the networked equipment according to the sleep condition is more suitable for the sleep requirement of the target user by further subdividing one or more of the requirements of the sleep area, the sleep time and the sleep assistance and further expanding the sleep condition related to the sleep requirement. The self-learning intelligent home control scheme is realized, so that the intelligent home can dynamically adjust the corresponding working state according to the sleep habit of the user, manual adjustment or complex association setting of the user is not needed, the intelligent degree of the intelligent home is improved, and the user experience is enhanced.
EXAMPLE III
Referring to fig. 6, fig. 6 is a block diagram illustrating a control device according to another embodiment of the present application. The invention also proposes a control device 600 comprising a memory 601, a processor 602 and a computer program stored on said memory and executable on said processor, said computer program, when executed by said processor, implementing the steps of the self-learning smart home control method as defined in any of the above.
It should be noted that the device embodiment and the method embodiment belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment, and technical features in the method embodiment are correspondingly applicable in the device embodiment, which is not described herein again.
Example four
Referring to fig. 7, fig. 7 is a block diagram illustrating a computer-readable storage medium according to another embodiment of the present application. The invention further provides a computer-readable storage medium 700, on which a self-learning smart home control program 701 is stored, and when being executed by a processor, the self-learning smart home control program realizes the steps of the self-learning smart home control method according to any one of the above items.
It should be noted that the media embodiment and the method embodiment belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment, and technical features in the method embodiment are correspondingly applicable in the media embodiment, which is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A self-learning intelligent home control method is applied to an intelligent control panel, and comprises the following steps:
determining a networked device that is relevant to a sleep need of a target user, wherein the sleep need comprises one or more of a sleep area need, a sleep time need, and a sleep assistance need;
counting sleep data corresponding to the sleep requirement through the operation information of the networking equipment, wherein the sleep data comprises one or more of sleep position data, sleep time data and sleep auxiliary data;
analyzing the sleep data to obtain sleep conditions related to the sleep requirements of the target user;
and adjusting the working state of the networking equipment according to the current state of the target user and the sleep condition so as to meet the sleep requirement of the target user.
2. The self-learning smart home control method of claim 1, wherein the determining networked devices relevant to sleep needs of a target user, wherein the sleep needs including one or more of sleep zone needs, sleep time needs, and sleep assistance needs is preceded by:
determining identity information of the target user;
and determining control equipment for acquiring the sleep requirement, and inputting the identity information into the control equipment.
3. The self-learning smart home control method of claim 1, wherein the determining networked devices relevant to sleep needs of a target user, wherein the sleep needs include one or more of sleep zone needs, sleep time needs, and sleep assistance needs, comprises:
determining a first networking device corresponding to the sleep area requirement, the first networking device comprising a device for detecting the sleep position of the target user; and/or the presence of a gas in the gas,
determining a second networking device corresponding to the sleep time requirement, wherein the second networking device comprises a device for detecting, identifying or calculating the sleep time of the target user; and/or the presence of a gas in the gas,
determining a third networking device corresponding to the sleep assistance requirement, the third networking device comprising a device used by the target user in the sleep position or the sleep time.
4. The self-learning smart home control method according to claim 3, wherein the counting sleep data corresponding to the sleep demand by the operation information of the networking device comprises:
monitoring the sleeping position of the target user through the first networking equipment to obtain the sleeping position data; and/or the presence of a gas in the gas,
monitoring the sleep time of the target user through the second networking equipment to obtain the sleep time data; and/or the presence of a gas in the gas,
and monitoring the device functions used by the target user in the pre-sleep stage, the middle sleep stage and the post-sleep stage through the third networking device to obtain the sleep auxiliary data.
5. The self-learning smart home control method of claim 1, wherein the analyzing the sleep data to obtain sleep conditions associated with the sleep needs of the target user comprises:
analyzing one or more data of the sleep position data, the sleep time data and the sleep assisting data to obtain one or more of sleep position habits, sleep time habits and assisted sleep habits;
and determining the association relationship between the sleep position habit, the sleep time habit and the auxiliary sleep habit and the networking equipment, and setting the sleep condition for triggering the networking equipment to enter the working state according to the association relationship.
6. The self-learning smart home control method of claim 1, wherein the adjusting the working state of the networked device according to the current state of the target user and the sleep condition to meet the sleep requirement of the target user comprises:
monitoring the current state of the target user through the intelligent control panel;
and when the current state is in accordance with the sleep condition, adjusting the working state of the networking equipment in real time according to the sleep condition and the current state so as to meet the sleep requirement of the target user.
7. The self-learning smart home control method of claim 1, wherein the adjusting the working state of the networked device according to the current state of the target user and the sleep condition to meet the sleep requirement of the target user comprises:
monitoring a sleep state of the target user through the intelligent control panel arranged at a sleep position;
and when the target user is in a pre-sleep state, a sleeping state or a post-sleep state, adjusting the working parameters corresponding to the networking equipment through the intelligent control panel according to the sleep condition.
8. The self-learning smart home control method according to any one of claims 1-7, wherein the sleep condition further includes at least one of additional data such as season information and schedule information, the sleep condition further includes at least one of environmental data such as ambient temperature information, ambient humidity information, air quality information and ambient noise information, and the adjusting the operating state of the networked device according to the current state of the target user and the sleep condition to meet the sleep requirement of the target user further comprises:
according to at least one of the sleep data, the additional data and the environment data of the target user, when the target user is in the current state, the working parameters of the corresponding networking equipment are adjusted;
and sending the updated working parameters to the networking equipment in real time through the intelligent control panel so as to meet the sleep requirement of the user in the current state.
9. A control device, characterized in that the control device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the self-learning smart home control method according to any one of claims 1 to 8.
10. A computer-readable storage medium, wherein a self-learning smart home control program is stored on the computer-readable storage medium, and when executed by a processor, the steps of the self-learning smart home control method according to any one of claims 1 to 8 are implemented.
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