CN107656443A - A kind of intelligent home control system and method based on deep learning - Google Patents
A kind of intelligent home control system and method based on deep learning Download PDFInfo
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- G05B15/02—Systems controlled by a computer electric
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- 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
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Abstract
The embodiment of the present invention proposes a kind of intelligent home control system and method based on deep learning, it is related to Smart Home technical field, the system includes control device, the intelligent domestic appliance controller and intelligent home device, and control device is connected with the intelligent domestic appliance controller by wireless communication mode.Have the advantages that intelligence degree is high, convenience is high and remote control.
Description
Technical field
The present invention relates to Smart Home technical field, in particular to a kind of smart home based on mobile radio communication
Control system and method.
Background technology
Smart home be using house as platform, using comprehensive wiring technology, the network communications technology, security precautions technology, from
Dynamic control technology, audio frequency and video technology integrate the relevant facility of life staying idle at home, build efficient housing facilities and family's schedule thing
The management system of business, house security, convenience, comfortableness, artistry can be lifted, and realize the inhabitation ring of environmental protection and energy saving
Border.
Smart home is that the Internet of Things under Internet of Things influence embodies.Smart home will be each in family by technology of Internet of things
Kind equipment (such as audio ＆ video equipment, illuminator, curtain control, airconditioning control, safety-protection system, Digital Theater System, network home appliance
And three table make a copy for) connect together, there is provided home wiring control, Lighting control, curtain control, remote control using telephone, indoor and outdoor
The multiple functions such as remote control, burglar alarm, environmental monitoring, HVAC control, infrared forwarding and programmable Timer control and means.Intelligence
Energy household is the subsystems related to life staying idle at home, including home wiring control, security alarm, remote control, environmental monitoring, society
Multiple subsystems such as area's service, network service combine, and can be that user creates a safety, comfortable, convenient, height
The living environment of effect.Whole system can typically merge security protection control system, family's automatic control system, multimedia entertainment system,
Tele-control system, intelligent terminal can use touch big screen LCD, visual in image graphical operation interface,
The experience of operational facility and fashion can be brought for user.
In current intelligent domestic system, man-machine interaction is mainly realized, it is necessary to user and equipment by touch-screen
Close contact, operation could be realized, if user has certain distance with equipment, touch-screen, operation side can not be operated
Formula has certain limitation.
Also there is the Intelligent housing scheme based on Gesture Recognition at present.However, gesture identification is used at present
Intelligent housing scheme all rely on controller, user necessarily be in the region that controller front end camera is specified
Effective gesture operation can be carried out, this obviously causes very big inconvenience.
The content of the invention
It is an object of the invention to provide a kind of intelligent home control system based on deep learning, has intelligence degree
The advantages that high, convenience height and remote control.
Another object of the present invention is to provide a kind of intelligent home furnishing control method based on deep learning, have corresponding
Effect.
To achieve these goals, the technical scheme that the embodiment of the present invention uses is as follows：
In a first aspect, the embodiments of the invention provide a kind of intelligent home control system based on deep learning, the system
System includes control device, the intelligent domestic appliance controller and intelligent home device, and control device passes through wireless with the intelligent domestic appliance controller
Communication modes connect；Wherein：
Control device, the movement of user's limbs space is captured using camera, it is determined that being moved corresponding to user's limbs space
Intelligent housing order, and the Intelligent housing order is sent to smart home by the wireless communication mode
Controller；The wireless communication mode includes bluetooth communication, infrared communication, wifi communications, 2G communications, 3G communications or 4G communications；
Connection between the intelligent domestic appliance controller and intelligent home device connects for bus；
The intelligent domestic appliance controller, ordered for receiving the Intelligent housing order, and based on the Intelligent housing
Order is controlled to intelligent home device；
Intelligent home device, for receiving the control of the intelligent domestic appliance controller, perform corresponding operation；
Control device, for determining smart home based on the first space movement in the movement combination of user's limbs space
Device type；Intelligent home device numbering is determined based on the second space movement in the movement combination of user's limbs space；
The intelligent domestic appliance controller, for the intelligent home device numbering in corresponding to the intelligent home device type
Intelligent home device, send operational order set in advance.
Further, the control device includes：Data input layer, the first convolutional layer, sub-sampling layer, the second convolutional layer and
Data output layer；The data input layer signal is connected to the first convolutional layer；The first convolutional layer signal is connected to sub-sampling
Layer；The sub-sampling layer signal is connected to the second convolutional layer；The second convolutional layer signal is connected to data output layer.
Further, the data input layer is used to obtain original view data, and the view data of acquisition is carried out in advance
Processing, the first convolutional layer is sent to by pretreated view data.
Further, first convolutional layer is used to the image zooming-out of input go out several feature extraction figures, will extract
Several feature extraction figures be sent to sub-sampling layer.
Further, the sub-sampling layer is used to several feature extraction figures of acquisition carrying out feature extraction and sampling again
Compression, the second convolutional layer is sent to by the image after processing；Second convolutional layer is used to characteristic pattern being extracted into local detection
Scheme, and the scoring of these local detection figures is obtained by hidden layer.
Further, the system also blocks wave filter including several, and the folding is divided into different brackets when rate ripple device, high
The component of one-level is made up of the component of low one-level, and advanced component is also just complete, and five-star component may also be located
In the state of being blocked, and then obtain local detection figure.
Second aspect, the embodiment of the present invention are additionally provided for a kind of intelligent home furnishing control method based on deep learning, institute
The method of stating comprises the following steps：
Using the space movement of mobile terminal camera capture user's limbs, and determine that corresponding to user's limbs space moves
Intelligent housing order；
The Intelligent housing order is sent to the intelligent domestic appliance controller by wireless communication mode；The channel radio
News mode includes bluetooth communication, infrared communication, wifi communications, 2G communications, 3G communications or 4G communications；
The intelligent domestic appliance controller is controlled based on the Intelligent housing order to intelligent home device；The intelligence
Connection between home controller and intelligent home device connects for bus；User's limbs space movement is to include the first sky
Between the movement of mobile and second space the movement combination of user's limbs space；
The Intelligent housing order for determining to correspond to user's limbs space movement includes：
Intelligent home device type is determined based on the first space movement in the movement combination of user's limbs space；It is based on
Second space movement in user's limbs space movement combination determines intelligent home device numbering；
The intelligent domestic appliance controller based on the Intelligent housing order intelligent home device is controlled including：
The intelligent domestic appliance controller intelligence that the intelligent home device is numbered in corresponding to the intelligent home device type
Home equipment, send operational order set in advance.
Further, it is described using the space movement of mobile terminal camera capture user's limbs, and determine to correspond to the use
The method of the Intelligent housing order of family limbs space movement comprises the following steps：
Step 1：The global feature of pedestrian is extracted first, then builds deep learning model, obtains image accumulation
Gradient information, find out pedestrian target area-of-interest that may be present；
Step 2：The local feature of pedestrian is further extracted again：Human trunk model is built, utilizes texture feature extraction
Algorithm obtains pedestrian's textural characteristics；
Step 3：Using the first convolutional layer and sub-sampling layer collective effect, the feature extraction layer of model is formed, utilizes
Two convolutional layers obtain local detection figure after handling feature extraction figure.
Further, the method for the Texture Segmentation Algorithm is：The gradient of pixel is calculated first, according to
The region that result of calculation is found and occurs to change to a certain degree using pixel carries out emphasis pass as area-of-interest to these regions
Note, reduce the Search Area of feature extraction, the real-time that Enhanced feature calculates；The average brightness value of pixel in region is taken to be simultaneously
Threshold smoothing feature texture information.
A kind of intelligent home control system and method based on deep learning provided in an embodiment of the present invention, using movement eventually
The space movement of camera capture user's limbs is held, and determines the Intelligent housing life for corresponding to user's limbs space movement
Order；The Intelligent housing order is sent to the intelligent domestic appliance controller by wireless communication mode；The intelligent domestic appliance controller
Intelligent home device is controlled based on the Intelligent housing order.As can be seen here, in embodiment of the present invention, moving
In the case that dynamic terminal is connected with the effective wireless network of smart home control device holding, as long as user holds mobile terminal and set
It is standby, and without being confined to the designated area of controller camera, you can to realize the remote operation to smart home control device,
The man-machine interaction mode of smart home control device is extended, improves the ease for use of smart home operation.
Meanwhile deformation process is incorporated into convolutional neural networks, network is mutually related using multilayer to simulate the mankind's
The multilayer abstract mechanism of brain and the processing procedure of visual information, abstract processing is successively carried out to input picture, extracts image
Notable feature, the abstractdesription to input picture and classification are realized, the recognizer that should be extracted based on deep learning and trunk is had
Effect reduces the fault rate of limbs space movement detection, has very strong practical value.
To enable the above objects, features and advantages of the present invention to become apparent, preferred embodiment cited below particularly, make detailed
It is described as follows.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below by embodiment it is required use it is attached
Figure is briefly described, it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, therefore be not construed as pair
The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, can also be according to this
A little accompanying drawings obtain other related accompanying drawings.
Fig. 1 shows a kind of system knot of intelligent home control system based on deep learning provided in an embodiment of the present invention
Structure schematic diagram.
Fig. 2 shows a kind of control dress of intelligent home control system based on deep learning provided in an embodiment of the present invention
Put structural representation.
Embodiment
Below in conjunction with accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Ground describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.Generally exist
The component of the embodiment of the present invention described and illustrated in accompanying drawing can be configured to arrange and design with a variety of herein.Cause
This, the detailed description of the embodiments of the invention to providing in the accompanying drawings is not intended to limit claimed invention below
Scope, but it is merely representative of the selected embodiment of the present invention.Based on embodiments of the invention, those skilled in the art are not doing
The every other embodiment obtained on the premise of going out creative work, belongs to the scope of protection of the invention.
It should be noted that：Similar label and letter represents similar terms in following accompanying drawing, therefore, once a certain Xiang Yi
It is defined, then it further need not be defined and explained in subsequent accompanying drawing in individual accompanying drawing.Meanwhile the present invention's
In description, term " first ", " second " etc. are only used for distinguishing description, and it is not intended that instruction or hint relative importance.
First embodiment
A kind of intelligent home control system based on deep learning, the system include control device, Intelligent housing
Device and intelligent home device, control device are connected with the intelligent domestic appliance controller by wireless communication mode；Wherein：
Control device, the movement of user's limbs space is captured using camera, it is determined that being moved corresponding to user's limbs space
Intelligent housing order, and the Intelligent housing order is sent to smart home by the wireless communication mode
Controller；The wireless communication mode includes bluetooth communication, infrared communication, wifi communications, 2G communications, 3G communications or 4G communications；
Connection between the intelligent domestic appliance controller and intelligent home device connects for bus；
The intelligent domestic appliance controller, ordered for receiving the Intelligent housing order, and based on the Intelligent housing
Order is controlled to intelligent home device；
Intelligent home device, for receiving the control of the intelligent domestic appliance controller, perform corresponding operation；
Control device, for determining smart home based on the first space movement in the movement combination of user's limbs space
Device type；Intelligent home device numbering is determined based on the second space movement in the movement combination of user's limbs space；
The intelligent domestic appliance controller, for the intelligent home device numbering in corresponding to the intelligent home device type
Intelligent home device, send operational order set in advance.
Further, the control device includes：Data input layer, the first convolutional layer, sub-sampling layer, the second convolutional layer and
Data output layer；The data input layer signal is connected to the first convolutional layer；The first convolutional layer signal is connected to sub-sampling
Layer；The sub-sampling layer signal is connected to the second convolutional layer；The second convolutional layer signal is connected to data output layer.
Further, the data input layer is used to obtain original view data, and the view data of acquisition is carried out in advance
Processing, the first convolutional layer is sent to by pretreated view data.
Further, first convolutional layer is used to the image zooming-out of input go out several feature extraction figures, will extract
Several feature extraction figures be sent to sub-sampling layer.
Further, the sub-sampling layer is used to several feature extraction figures of acquisition carrying out feature extraction and sampling again
Compression, the second convolutional layer is sent to by the image after processing；Second convolutional layer is used to characteristic pattern being extracted into local detection
Scheme, and the scoring of these local detection figures is obtained by hidden layer.
Further, the system also blocks wave filter including several, and the folding is divided into different brackets when rate ripple device, high
The component of one-level is made up of the component of low one-level, and advanced component is also just complete, and five-star component may also be located
In the state of being blocked, and then obtain local detection figure.
Second embodiment
A kind of intelligent home furnishing control method based on deep learning, methods described comprise the following steps：
Using the space movement of mobile terminal camera capture user's limbs, and determine that corresponding to user's limbs space moves
Intelligent housing order；
The Intelligent housing order is sent to the intelligent domestic appliance controller by wireless communication mode；The channel radio
News mode includes bluetooth communication, infrared communication, wifi communications, 2G communications, 3G communications or 4G communications；
The intelligent domestic appliance controller is controlled based on the Intelligent housing order to intelligent home device；The intelligence
Connection between home controller and intelligent home device connects for bus；User's limbs space movement is to include the first sky
Between the movement of mobile and second space the movement combination of user's limbs space；
The Intelligent housing order for determining to correspond to user's limbs space movement includes：
Intelligent home device type is determined based on the first space movement in the movement combination of user's limbs space；It is based on
Second space movement in user's limbs space movement combination determines intelligent home device numbering；
The intelligent domestic appliance controller based on the Intelligent housing order intelligent home device is controlled including：
The intelligent domestic appliance controller intelligence that the intelligent home device is numbered in corresponding to the intelligent home device type
Home equipment, send operational order set in advance.
Further, it is described using the space movement of mobile terminal camera capture user's limbs, and determine to correspond to the use
The method of the Intelligent housing order of family limbs space movement comprises the following steps：
Step 1：The global feature of pedestrian is extracted first, then builds deep learning model, obtains image accumulation
Gradient information, find out pedestrian target area-of-interest that may be present；
Step 2：The local feature of pedestrian is further extracted again：Human trunk model is built, utilizes texture feature extraction
Algorithm obtains pedestrian's textural characteristics；
Step 3：Using the first convolutional layer and sub-sampling layer collective effect, the feature extraction layer of model is formed, utilizes
Two convolutional layers obtain local detection figure after handling feature extraction figure.
Further, the method for the Texture Segmentation Algorithm is：The gradient of pixel is calculated first, according to
The region that result of calculation is found and occurs to change to a certain degree using pixel carries out emphasis pass as area-of-interest to these regions
Note, reduce the Search Area of feature extraction, the real-time that Enhanced feature calculates；The average brightness value of pixel in region is taken to be simultaneously
Threshold smoothing feature texture information.
In several embodiments provided herein, it should be understood that disclosed apparatus and method, can also pass through
Other modes are realized.Device embodiment described above is only schematical, for example, flow chart and block diagram in accompanying drawing
Show the device of multiple embodiments according to the present invention, method and computer program product architectural framework in the cards,
Function and operation.At this point, each square frame in flow chart or block diagram can represent the one of a unit, program segment or code
Part, a part for the unit, program segment or code include one or more and are used to realize holding for defined logic function
Row instruction.It should also be noted that at some as in the implementation replaced, the function that is marked in square frame can also with different from
The order marked in accompanying drawing occurs.For example, two continuous square frames can essentially perform substantially in parallel, they are sometimes
It can perform in the opposite order, this is depending on involved function.It is it is also noted that every in block diagram and/or flow chart
The combination of individual square frame and block diagram and/or the square frame in flow chart, function or the special base of action as defined in performing can be used
Realize, or can be realized with the combination of specialized hardware and computer instruction in the system of hardware.
In addition, each functional unit in each embodiment of the present invention can integrate to form an independent portion
Point or unit individualism, can also two or more units be integrated to form an independent part.
If the function is realized in the form of SFU software functional unit and is used as independent production marketing or in use, can be with
It is stored in a computer read/write memory medium.Based on such understanding, technical scheme is substantially in other words
The part to be contributed to prior art or the part of the technical scheme can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, including some instructions are causing a computer equipment (can be
People's computer, server, or network equipment etc.) perform all or part of step of each embodiment methods described of the present invention.
And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Onl8Memor8), arbitrary access are deposited
Reservoir (RAM, RandomAccess Memor8), magnetic disc or CD etc. are various can be with the medium of store program codes.
It should be noted that herein, such as first and second or the like relational terms are used merely to a reality
Body or operation make a distinction with another entity or operation, and not necessarily require or imply and deposited between these entities or operation
In any this actual relation or order.Moreover, term " comprising ", "comprising" or its any other variant are intended to
Nonexcludability includes, so that process, method, article or equipment including a series of elements not only will including those
Element, but also the other element including being not expressly set out, or it is this process, method, article or equipment also to include
Intrinsic key element.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that
Other identical element also be present in process, method, article or equipment including the key element.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.It should be noted that：Similar label and letter exists
Similar terms is represented in following accompanying drawing, therefore, once being defined in a certain Xiang Yi accompanying drawing, is then not required in subsequent accompanying drawing
It is further defined and explained.
Claims (9)
1. a kind of intelligent home control system based on deep learning, it is characterised in that the system includes control device, intelligence
Home controller and intelligent home device, control device are connected with the intelligent domestic appliance controller by wireless communication mode；Wherein：
Control device, the movement of user's limbs space is captured using camera, it is determined that the intelligence corresponding to user's limbs space movement
Energy home control order, and the Intelligent housing order is sent to Intelligent housing by the wireless communication mode
Device；The wireless communication mode includes bluetooth communication, infrared communication, wifi communications, 2G communications, 3G communications or 4G communications；It is described
Connection between the intelligent domestic appliance controller and intelligent home device connects for bus；
The intelligent domestic appliance controller, for receiving the Intelligent housing order, and it is based on the Intelligent housing order pair
Intelligent home device is controlled；
Intelligent home device, for receiving the control of the intelligent domestic appliance controller, perform corresponding operation；
Control device, for determining intelligent home device based on the first space movement in the movement combination of user's limbs space
Type；Intelligent home device numbering is determined based on the second space movement in the movement combination of user's limbs space；
The intelligent domestic appliance controller, for the intelligence that the intelligent home device is numbered in corresponding to the intelligent home device type
Home equipment, send operational order set in advance.
2. the intelligent home control system based on deep learning as claimed in claim 1, it is characterised in that the control device
Including：Data input layer, the first convolutional layer, sub-sampling layer, the second convolutional layer and data output layer；The data input layer signal
It is connected to the first convolutional layer；The first convolutional layer signal is connected to sub-sampling layer；The sub-sampling layer signal is connected to second
Convolutional layer；The second convolutional layer signal is connected to data output layer.
3. the intelligent home control system based on deep learning as claimed in claim 2, it is characterised in that the data input
Layer is used to obtain original view data, and the view data of acquisition is pre-processed, pretreated view data is sent
To the first convolutional layer.
4. the intelligent home control system based on deep learning as claimed in claim 3, it is characterised in that first convolution
Layer is used to the image zooming-out of input go out several feature extraction figures, and several feature extraction figures extracted are sent into sub-sampling
Layer.
5. the intelligent home control system based on deep learning as claimed in claim 4, it is characterised in that the sub-sampling layer
For several feature extraction figures of acquisition to be carried out into feature extraction and sampling compression again, the image after processing is sent to second
Convolutional layer；Second convolutional layer is used to characteristic pattern being extracted into local detection figure, and obtains these by hidden layer and locally examine
The scoring of mapping.
6. the intelligent home control system based on deep learning as claimed in claim 5, it is characterised in that the system is also wrapped
Including several and block wave filter, the folding is divided into different brackets when rate ripple device, and higher leveled component is made up of the component of low one-level,
Advanced component is also just complete, and five-star component may also be in the state that is blocked, and then obtain local detection
Figure.
7. a kind of method of the intelligent home control system based on deep learning based on described in one of claim 1 to 6, it is special
Sign is that methods described includes：
Using the space movement of mobile terminal camera capture user's limbs, and determine the intelligence for corresponding to user's limbs space movement
Can home control order；
The Intelligent housing order is sent to the intelligent domestic appliance controller by wireless communication mode；The wireless telecommunications side
Formula includes bluetooth communication, infrared communication, wifi communications, 2G communications, 3G communications or 4G communications；
The intelligent domestic appliance controller is controlled based on the Intelligent housing order to intelligent home device；The smart home
Connection between controller and intelligent home device connects for bus；User's limbs space movement is to include the shifting of the first space
User's limbs space movement combination of dynamic and second space movement；
The Intelligent housing order for determining to correspond to user's limbs space movement includes：
Intelligent home device type is determined based on the first space movement in the movement combination of user's limbs space；Based on described
Second space movement in the movement combination of user's limbs space determines intelligent home device numbering；
The intelligent domestic appliance controller based on the Intelligent housing order intelligent home device is controlled including：
The intelligent domestic appliance controller smart home that the intelligent home device is numbered in corresponding to the intelligent home device type
Equipment, send operational order set in advance.
8. the intelligent home furnishing control method based on deep learning as claimed in claim 7, it is characterised in that described to utilize movement
The space movement of terminal camera capture user's limbs, and determine the Intelligent housing life for corresponding to user's limbs space movement
The method of order comprises the following steps：
Step 1：The global feature of pedestrian is extracted first, then builds deep learning model, obtains image accumulation gradient
Information, find out pedestrian target area-of-interest that may be present；
Step 2：The local feature of pedestrian is further extracted again：Human trunk model is built, utilizes Texture Segmentation Algorithm
Obtain pedestrian's textural characteristics；
Step 3：Using the first convolutional layer and sub-sampling layer collective effect, the feature extraction layer of model is formed, utilizes volume Two
Lamination obtains local detection figure after handling feature extraction figure.
9. the intelligent home furnishing control method based on deep learning as claimed in claim 8, it is characterised in that the textural characteristics
The method of extraction algorithm is：The gradient of pixel is calculated first, is found and pixel occurred certain according to result of calculation
These regions are paid close attention to, reduce the Search Area of feature extraction, are increased as area-of-interest in the region of degree change
The real-time of strong feature calculation；The average brightness value for taking pixel in region simultaneously is threshold smoothing feature texture information.
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