CN109948422A - A kind of indoor environment adjusting method, device, readable storage medium storing program for executing and terminal device - Google Patents
A kind of indoor environment adjusting method, device, readable storage medium storing program for executing and terminal device Download PDFInfo
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Abstract
The invention belongs to field of computer technology more particularly to a kind of indoor environment adjusting method, device, computer readable storage medium and terminal devices.The method obtains the Indoor Video image acquired by preset camera, and carries out Face datection to the Indoor Video image；Feature extraction is carried out to the facial image detected, and according to the face feature vector for the face characteristic construction indoor user extracted；Gender and the age of the indoor user are determined according to the face feature vector；Every human body physiological data of the indoor user is obtained, and constructs the physiological characteristic vector of the indoor user according to every human body physiological data；Indoor temperature and humidity is adjusted separately according to the gender of the indoor user, age and physiological characteristic vector.Indoor environment can be improved automatically in the case where being not necessarily to any manual intervention gives the feeling of the most comfortable, greatly improves user experience.
Description
Technical field
The invention belongs to field of computer technology more particularly to a kind of indoor environment adjusting methods, device, computer-readable
Storage medium and terminal device.
Background technique
As the improvement of people's living standards, air-conditioning widely enters huge numbers of families.Existing air-conditioned controlling party
Method is usually after user opens air-conditioning, and manually setting is suitble to the parameters such as oneself temperature, humidity, moreover, as air-conditioning is opened
It opens gradually showing for rear effect, the body-sensing of user also can gradually change, at this point, user can also be constantly to the temperature of air-conditioning
The parameters such as degree, humidity are adjusted, that is, user is needed to carry out multiple repetitive operation, can reach and the best of oneself is suitble to relax
Suitable effect, very inconvenient, user experience is poor.
Summary of the invention
In view of this, the embodiment of the invention provides a kind of indoor environment adjusting methods, device, computer-readable storage medium
Matter and terminal device operate comparatively laborious, the poor problem of user experience to solve existing indoor environment adjusting method.
The first aspect of the embodiment of the present invention provides a kind of indoor environment adjusting method, may include:
The Indoor Video image acquired by preset camera is obtained, and face inspection is carried out to the Indoor Video image
It surveys；
Feature extraction is carried out to the facial image detected, and according to the people for the face characteristic construction indoor user extracted
Face feature vector；
Gender and the age of the indoor user are determined according to the face feature vector；
Every human body physiological data of the indoor user is obtained, and according to every human body physiological data construction
The physiological characteristic vector of indoor user；
Indoor temperature and humidity is adjusted respectively according to the gender of the indoor user, age and physiological characteristic vector
It is whole.
The second aspect of the embodiment of the present invention provides a kind of indoor environment adjusting device, may include:
Face detection module, for obtaining the Indoor Video image acquired by preset camera, and to the indoor prison
It controls image and carries out Face datection；
Characteristic extracting module, for carrying out feature extraction to the facial image detected, and it is special according to the face extracted
The face feature vector of sign construction indoor user；
Gender determining module, for determining the gender of the indoor user according to the face feature vector；
Age determining module, for determining the age of the indoor user according to the face feature vector；
Physiological data obtains module, for obtaining every human body physiological data of the indoor user, and according to described each
Item human body physiological data constructs the physiological characteristic vector of the indoor user；
Temperature adjust module, for according to the gender of the indoor user, age and physiological characteristic vector to indoor temperature
Degree is adjusted；
Humidity adjust module, for according to the gender of the indoor user, age and physiological characteristic vector to indoor wet
Degree is adjusted.
The third aspect of the embodiment of the present invention provides a kind of computer readable storage medium, the computer-readable storage
Media storage has computer-readable instruction, and the computer-readable instruction realizes following steps when being executed by processor:
The Indoor Video image acquired by preset camera is obtained, and face inspection is carried out to the Indoor Video image
It surveys；
Feature extraction is carried out to the facial image detected, and according to the people for the face characteristic construction indoor user extracted
Face feature vector；
Gender and the age of the indoor user are determined according to the face feature vector；
Every human body physiological data of the indoor user is obtained, and according to every human body physiological data construction
The physiological characteristic vector of indoor user；
Indoor temperature and humidity is adjusted respectively according to the gender of the indoor user, age and physiological characteristic vector
It is whole.
The fourth aspect of the embodiment of the present invention provides a kind of terminal device, including memory, processor and is stored in
In the memory and the computer-readable instruction that can run on the processor, the processor executes the computer can
Following steps are realized when reading instruction:
The Indoor Video image acquired by preset camera is obtained, and face inspection is carried out to the Indoor Video image
It surveys；
Feature extraction is carried out to the facial image detected, and according to the people for the face characteristic construction indoor user extracted
Face feature vector；
Gender and the age of the indoor user are determined according to the face feature vector；
Every human body physiological data of the indoor user is obtained, and according to every human body physiological data construction
The physiological characteristic vector of indoor user；
Indoor temperature and humidity is adjusted respectively according to the gender of the indoor user, age and physiological characteristic vector
It is whole.
Existing beneficial effect is the embodiment of the present invention compared with prior art: the embodiment of the present invention is obtained by presetting first
Camera acquisition Indoor Video image, Face datection is carried out to the Indoor Video image, and to the face figure detected
As carrying out feature extraction, the face feature vector of indoor user is constructed, the room is then determined according to the face feature vector
The gender of interior user and age construct the indoor user finally, obtaining every human body physiological data of the indoor user
Physiological characteristic vector, and according to the gender of the indoor user, age and physiological characteristic vector to indoor temperature and humidity point
It is not adjusted.Through the embodiment of the present invention, any operation is carried out without user, can be determined automatically by Face datection indoor
The gender of user and age obtain every human body physiological data of user, and on this basis to indoor temperature and humidity point
It is not adjusted, can improve indoor environment automatically in the case where being not necessarily to any manual intervention gives the feeling of the most comfortable,
Greatly improve user experience.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is a kind of one embodiment flow chart of indoor environment adjusting method in the embodiment of the present invention；
Fig. 2 is the schematic flow diagram being adjusted to indoor temperature；
Fig. 3 is the schematic flow diagram being adjusted to indoor humidity；
Fig. 4 is a kind of one embodiment structure chart of indoor environment adjusting device in the embodiment of the present invention；
Fig. 5 is a kind of schematic block diagram of terminal device in the embodiment of the present invention.
Specific embodiment
In order to make the invention's purpose, features and advantages of the invention more obvious and easy to understand, below in conjunction with the present invention
Attached drawing in embodiment, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that disclosed below
Embodiment be only a part of the embodiment of the present invention, and not all embodiment.Based on the embodiments of the present invention, this field
Those of ordinary skill's all other embodiment obtained without making creative work, belongs to protection of the present invention
Range.
Referring to Fig. 1, a kind of one embodiment of indoor environment adjusting method may include: in the embodiment of the present invention
Step S101, obtain the Indoor Video image that is acquired by preset camera, and to the Indoor Video image into
Row Face datection.
Camera in the present embodiment can be any one indoor camera, for example, the camera can be air-conditioning
Camera built in itself is also possible to indoor other equipment, taking the photograph as built in television set, computer, Indoor Monitoring System etc.
As head, the separate camera being specially arranged can also be.
Further, in order to guarantee can more maximum probability collect indoor facial image, can also pass through the above institute
The multiple cameras stated form a camera shooting cluster and carry out monitoring image in collection room.
In the present embodiment, when carrying out Face datection to the Indoor Video image, the same training set can be directed to
The different classifier (Weak Classifier) of training, then gets up these weak classifier sets, constitutes a stronger final classification
Device (strong classifier), according to whether the classification of each sample among each training set correct and the standard of general classification of last time
True rate, to determine the weight of each sample.It gives the new data set for modifying weight to sub-classification device to be trained, finally will
The classifier that training obtains every time finally merges, as last Decision Classfication device, to greatly improve facial image inspection
The accuracy rate of survey.
Step S102, feature extraction is carried out to the facial image detected, and room is constructed according to the face characteristic extracted
The face feature vector of interior user.
Firstly, carrying out the critical point detection based on multiscale.In order to guarantee that the feature extracted becomes scale
Holding stability is changed, the present embodiment carries out the detection of image key points in scale space, using Gaussian kernel to original image
Carry out change of scale, with obtain it is multiple dimensioned under image indicate.
Critical point detection is mainly generated by graphical rule space, and difference of Gaussian pyramid is established, and candidate key point obtains, and is closed
Key point fine positioning and its screening and key point principal direction determine several part compositions.
Graphical rule space, which generates, mainly generates the image sequence under different scale space to given two dimensional image
Figure.
Difference of Gaussian pyramid, which is established, mainly carries out difference of Gaussian (Difference of to scale space images sequence
Gaussian, DOG) operation, i.e., the difference of adjacent gaussian filtering image is mainly to find the pass with scale feature is stablized
Key point.
The acquisition of candidate key point is defined on adjacent scale space mainly in the difference of Gaussian spatial pyramid of foundation
The interior candidate put as image key points with local maximum or local minimum.The middle layer of Gaussian difference scale space
Each pixel and same layer adjacent 8 pixels, upper one layer of adjacent 9 pixels and next layer of adjacent 9 pictures
26 neighbor pixels are compared vegetarian refreshments in total.If pixel is all bigger than the Gauss difference value of 26 adjacent pixels or all small,
Then the point can be used as candidate key point.
The pixel value that key point fine positioning and its screening are primarily due to difference of Gaussian image is more sensitive to noise and edge,
Therefore, it further to be screened in the Local Extremum that difference of Gaussian space detects, and be reoriented to sub-pixel and accuracy rule
Spend position.Also to remove simultaneously low contrast characteristic point and unstable skirt response point, stability and mentioned with enhancing matching
High noise resisting ability.
The determination main purpose of key point principal direction is to guarantee rotational invariance, the gradient side based on characteristic point neighborhood territory pixel
It is each characteristic point assigned direction parameter to distribution character.It is sampled in the neighborhood window centered on characteristic point, and uses gradient
The gradient direction of direction histogram statistics neighborhood territory pixel.
By above step, the extraction of image characteristic point is completed, there are three information for each characteristic point: position, scale
The direction and.It is then possible to the key point feature extraction based on gradient orientation histogram statistics.
In image key points expression, it is not enough to be formed merely with the position of key point, scale and directional information and sentences enough
The certainly feature of property then needs to extract gray-scale statistical characteristics to the region around key point with scale size.Feature extraction it
Before, reference axis is rotated to be to the direction of key point first, to ensure rotational invariance.Then 8 × 8 are taken centered on key point
Window calculates the gradient orientation histogram in 8 directions on every 4 × 4 fritter, draws the accumulated value of each gradient direction.This
The kind united thought of neighborhood directivity information enhances the antimierophonic ability of algorithm, simultaneously for the feature containing position error
With also providing preferable fault-tolerance.To enhance matched robustness in practical calculating process, the region of feature extraction will be enlarged by
Range, to each key point using 4 × 4 totally 16 seed points describe, in this way 128 can be generated for a key point
Data ultimately form the feature vector of 128 dimensions.It, can be by feature vector for the influence for further removing the variation of illumination contrast
Length normalization method.
The face feature vector of the finally obtained indoor user is denoted as herein:
FaceVec=(FaceElm1,FaceElm2,...,FaceElmgn,...,FaceElmGN)
Wherein, gn is the dimension serial number of face feature vector, and 1≤gn≤GN, GN are the dimension sum of face feature vector,
FaceElmgnFor value of the face feature vector in the gn dimension of the indoor user, FaceVec is described indoor
The face feature vector at family.
Step S103, gender and the age of the indoor user are determined according to the face feature vector.
Wherein, the determining process of gender may include:
Firstly, choosing male's sample set and women sample set respectively from preset historical sample library, wherein two samples
Sample size included in this collection should be roughly equal, to keep the harmony of final result.
The face feature vector of each male's sample in male's sample set is denoted as:
MaleVecm=(MaleElmm,1,MaleElmm,2,...,MaleElmm,gn,...,MaleElmm,GN)
M is the serial number of male's sample, and 1≤m≤MaleNum, MaleNum are the sum of male's sample, MaleElmm,gnFor
Value of the face feature vector of m-th of male's sample in the gn dimension, MaleVecmFor the face of m-th of male's sample
Feature vector.
The face feature vector of each women sample in the women sample set is denoted as:
FemVecf=(FemElmf,1,FemElmf,2,...,FemElmf,gn,...,FemElmf,GN)
F is the serial number of women sample, and 1≤f≤FemNum, FemNum are the sum of women sample, FemElmf,gnFor f
Value of the face feature vector of a women sample in the gn dimension, FemVecfFor the face characteristic of f-th of women sample
Vector.
Then, face feature vector and male's sample set and the institute of the indoor user are calculated separately according to the following formula
State the average distance between women sample set:
Wherein, FaceElmgnFor value of the face feature vector in the gn dimension of the indoor user, MaleDis
For the average distance between the face feature vector and male's sample set of the indoor user, FemDis is described indoor
Average distance between the face feature vector at family and the women sample set.
Finally, according to the face feature vector of the indoor user and male's sample set and the women sample set
Between average distance determine the gender of the indoor user.
If MaleDis is greater than FemDis, the gender of the indoor user is determined for male, if MaleDis is less than
FemDis then determines the gender of the indoor user for women.
Further, the process of age determination may include:
Firstly, choosing the sample set of all age group respectively from historical sample library, wherein included in each sample set
Sample size should be roughly equal, to keep the harmony of final result.
It is especially noted that the judgement due to having been completed gender, when choosing the sample of all age group, only
The sample of identical gender is selected, can be further improved the accuracy rate for determining result in this way.
The face feature vector of each sample is denoted as:
AgeVecs,c=(AgeElms,c,1,AgeElms,c,2,...,AgeElms,c,gn,...,AgeElms,c,GN)
S be all age group serial number, 1≤s≤SN, SN be age bracket sum, c be sample serial number, 1≤c≤
CNs, CNsFor the total sample number in the sample set of s-th of age bracket, AgeElms,c,gnFor in the sample set of s-th of age bracket
Value of the face feature vector of c sample in the gn dimension, AgeVecs,cFor in the sample set of s-th of age bracket
The face feature vector of c sample.
Then, calculate separately according to the following formula the indoor user face feature vector and all age group sample set it
Between average distance:
Wherein, AgeDissIt is flat between the face feature vector of the indoor user and the sample set of s-th of age bracket
Equal distance.
Finally, determining the age of the indoor user according to the following formula:
AgeType=argmin (AgeDis1,AgeDis2,...,AgeDiss,...,AgeDisSN)
Wherein, argmin is minimum independent variable function, and AgeType is the serial number of age bracket locating for the indoor user.
Step S104, every human body physiological data of the indoor user is obtained, and according to every Human Physiology number
According to the physiological characteristic vector for constructing the indoor user.
In the present embodiment, the indoor user can be acquired by the wearable smart machine such as Intelligent bracelet/wrist-watch
Human body physiological data, it is full that these human body physiological datas can include but is not limited to heart rate, pulse, shell temperature, blood pressure, blood oxygen
With degree etc..
By these human body physiological datas be configured as shown in physiological characteristic vector:
PhyVec=(PhyElm1,PhyElm2,...,PhyElmpn,...,PhyElmPN)
Wherein, pn is the dimension serial number of physiology feature vector, and 1≤pn≤PN, PN are the dimension sum of physiology feature vector,
PhyElmpnFor value of the physiology feature vector in n dimension of pth, PhyVec is the physiological characteristic vector of the indoor user.
Step S105, according to the gender of the indoor user, age and physiological characteristic vector to indoor temperature and humidity
It is adjusted separately.
As shown in Fig. 2, may include: to the process that temperature is adjusted
Step S201, label is chosen according to the gender of the indoor user and age respectively from historical sample library to be promoted
The first sample set and label of room temperature are the second sample set for reducing room temperature.
Wherein, sample size included in two sample sets should be roughly equal, to keep the harmony of final result.
It is especially noted that, since the people of different sexes, all ages and classes is different the impression of temperature, choosing each sample to being
This when, only selects the sample of identical gender, same age section, can be further improved the accuracy rate for determining result in this way.
The physiological characteristic vector for each sample that the first sample is concentrated is denoted as:
TemUpVectu=(TUElmtu,1,TUElmtu,2,...,TUElmtu,pn,...,TUElmtu,PN)
Tu is the serial number for the sample that the first sample is concentrated, and 1≤tu≤TemUpNum, TemUpNum are first sample
The total sample number of this concentration, TUElmtu,pnPhysiological characteristic vector for the tu sample of first sample concentration is a in pth n
Value in dimension, TemUpVectuFor the physiological characteristic vector for the tu sample that the first sample is concentrated.
The physiological characteristic vector of each sample in second sample set is denoted as:
TemDnVectd=(TDElmtd,1,TDElmtd,2,...,TDElmtd,pn,...,TDElmtd,PN)
Td is the serial number of the sample in second sample set, and 1≤td≤TemDnNum, TemDnNum are second sample
The total sample number of this concentration, TDElmtd,pnIt is a in pth n for the physiological characteristic vector of the td sample in second sample set
Value in dimension, TemDnVectdFor the physiological characteristic vector of the td sample in second sample set.
Step S202, the physiological characteristic vector and the first sample set and described the of the indoor user are calculated separately
Average distance between two sample sets.
For example, can calculate separately according to the following formula the indoor user physiological characteristic vector and the first sample set with
And the average distance between second sample set:
Wherein, TemUpDis is the average departure between the physiological characteristic vector and the first sample set of the indoor user
From TemDnDis is the average distance between the physiological characteristic vector and second sample set of the indoor user.
Step S203, according to the physiological characteristic vector of the indoor user and the first sample set and second sample
Average distance between this collection is adjusted indoor temperature.
If TemUpDis is greater than TemDnDis, room temperature is promoted, if TemUpDis is less than TemDnDis, reduces room
Interior temperature.
As shown in figure 3, may include: to the process that temperature is adjusted
Step S301, label is chosen according to the gender of the indoor user and age respectively from historical sample library to be promoted
The third sample set and label of indoor humidity are the 4th sample set for reducing indoor humidity.
Wherein, sample size included in two sample sets should be roughly equal, to keep the harmony of final result.
It is especially noted that, since the people of different sexes, all ages and classes is different the impression of humidity, choosing each sample to being
This when, only selects the sample of identical gender, same age section, can be further improved the accuracy rate for determining result in this way.
The physiological characteristic vector of each sample in the third sample set is denoted as:
HumUpVechu=(HUElmhu,1,HUElmhu,2,...,HUElmhu,pn,...,HUElmhu,PN)
Hu is the serial number of the sample in the third sample set, and 1≤hu≤HumUpNum, HumUpNum are the third sample
The total sample number of this concentration, HUElmhu,pnIt is a in pth n for the physiological characteristic vector of the hu sample in the third sample set
Value in dimension, HumUpVechuFor the physiological characteristic vector of the hu sample in the third sample set.
The physiological characteristic vector of each sample in 4th sample set is denoted as:
HumDnVechd=(HDElmhd,1,HDElmhd,2,...,HDElmhd,pn,...,HDElmhd,PN)
Hd is the serial number of the sample in the 4th sample set, and 1≤hd≤HumDnNum, HumDnNum are the 4th sample
The total sample number of this concentration, HDElmhd,pnIt is a in pth n for the physiological characteristic vector of the hd sample in the 4th sample set
Value in dimension, HumDnVechdFor the physiological characteristic vector of the hd sample in the 4th sample set.
Step S302, the physiological characteristic vector and the third sample set and described the of the indoor user are calculated separately
Average distance between four sample sets.
For example, can calculate separately according to the following formula the indoor user physiological characteristic vector and the third sample set with
And the average distance between the 4th sample set:
Wherein, HumUpDis is the average departure between the physiological characteristic vector and the third sample set of the indoor user
From HumDnDis is the average distance between the physiological characteristic vector and the 4th sample set of the indoor user.
Step S303, according to the physiological characteristic vector of the indoor user and the third sample set and the 4th sample
Average distance between this collection is adjusted indoor humidity.
If HumUpDis is greater than HumDnDis, room temperature is promoted, if HumUpDis is less than HumDnDis, reduces room
Interior temperature.
In conclusion the embodiment of the present invention obtains the Indoor Video image acquired by preset camera first, to described
Indoor Video image carries out Face datection, and carries out feature extraction to the facial image detected, constructs the face of indoor user
Feature vector, then determines gender and the age of the indoor user according to the face feature vector, finally, obtaining the room
Every human body physiological data of interior user constructs the physiological characteristic vector of the indoor user, and according to the indoor user
Gender, age and physiological characteristic vector are adjusted separately indoor temperature and humidity.Through the embodiment of the present invention, without using
Family carries out any operation, and gender and the age of indoor user can be determined automatically by Face datection, obtains every people of user
Body physiological data, and indoor temperature and humidity is adjusted separately on this basis, in the feelings for being not necessarily to any manual intervention
Indoor environment can be improved under condition automatically gives the feeling of the most comfortable, greatly improves user experience.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limit
It is fixed.
Corresponding to a kind of indoor environment adjusting method described in foregoing embodiments, Fig. 4 shows offer of the embodiment of the present invention
A kind of indoor environment adjusting device one embodiment structure chart.
In the present embodiment, a kind of indoor environment adjusting device may include:
Face detection module 401, for obtaining the Indoor Video image acquired by preset camera, and to the interior
Monitoring image carries out Face datection；
Characteristic extracting module 402, for carrying out feature extraction to the facial image detected, and according to the face extracted
The face feature vector of latent structure indoor user；
Gender determining module 403, for determining the gender of the indoor user according to the face feature vector；
Age determining module 404, for determining the age of the indoor user according to the face feature vector；
Physiological data obtains module 405, for obtaining every human body physiological data of the indoor user, and according to described
Every human body physiological data constructs the physiological characteristic vector of the indoor user；
Temperature adjust module 406, for according to the gender of the indoor user, age and physiological characteristic vector to indoor
Temperature is adjusted；
Humidity adjust module 407, for according to the gender of the indoor user, age and physiological characteristic vector to indoor
Humidity is adjusted.
Further, the gender determining module may include:
First sample selection unit, for choosing male's sample set and women sample respectively from preset historical sample library
This collection, wherein the face feature vector of each male's sample in male's sample set is denoted as:
MaleVecm=(MaleElmm,1,MaleElmm,2,...,MaleElmm,gn,...,MaleElmm,GN)
M is the serial number of male's sample, and 1≤m≤MaleNum, MaleNum are the sum of male's sample, and gn is face characteristic
The dimension serial number of vector, 1≤gn≤GN, GN are the dimension sum of face feature vector, MaleElmm,gnFor m-th of male's sample
Value of the face feature vector in the gn dimension, MaleVecmFor the face feature vector of m-th of male's sample；
The face feature vector of each women sample in the women sample set is denoted as:
FemVecf=(FemElmf,1,FemElmf,2,...,FemElmf,gn,...,FemElmf,GN)
F is the serial number of women sample, and 1≤f≤FemNum, FemNum are the sum of women sample, FemElmf,gnFor f
Value of the face feature vector of a women sample in the gn dimension, FemVecfFor the face characteristic of f-th of women sample
Vector；
First computing unit, for calculate separately according to the following formula the indoor user face feature vector and the male
Average distance between sample set and the women sample set:
Wherein, FaceElmgnFor value of the face feature vector in the gn dimension of the indoor user, MaleDis
For the average distance between the face feature vector and male's sample set of the indoor user, FemDis is described indoor
Average distance between the face feature vector at family and the women sample set；
Gender determination unit, for according to the face feature vector of the indoor user and male's sample set and institute
State the gender that the average distance between women sample set determines the indoor user.
Further, the age determining module may include:
Second sample selection unit, for choosing the sample set of all age group respectively from historical sample library, wherein each
The face feature vector of a sample is denoted as:
AgeVecs,c=(AgeElms,c,1,AgeElms,c,2,...,AgeElms,c,gn,...,AgeElms,c,GN)
S be all age group serial number, 1≤s≤SN, SN be age bracket sum, c be sample serial number, 1≤c≤
CNs, CNsFor the total sample number in the sample set of s-th of age bracket, AgeElms,c,gnFor in the sample set of s-th of age bracket
Value of the face feature vector of c sample in the gn dimension, AgeVecs,cFor in the sample set of s-th of age bracket
The face feature vector of c sample；
Second computing unit, for calculate separately according to the following formula the indoor user face feature vector and each age
Average distance between the sample set of section:
Wherein, AgeDissIt is flat between the face feature vector of the indoor user and the sample set of s-th of age bracket
Equal distance；
Age determination unit, for determining the age of the indoor user according to the following formula:
AgeType=argmin (AgeDis1,AgeDis2,...,AgeDiss,...,AgeDisSN)
Wherein, argmin is minimum independent variable function, and AgeType is the serial number of age bracket locating for the indoor user.
Further, the temperature adjustment module may include:
Third sample selection unit, for being selected respectively from historical sample library according to the gender and age of the indoor user
Take the first sample set that label is promotion room temperature and the second sample set that label is reduction room temperature, wherein described
The physiological characteristic vector for each sample that first sample is concentrated is denoted as:
TemUpVectu=(TUElmtu,1,TUElmtu,2,...,TUElmtu,pn,...,TUElmtu,PN)
Tu is the serial number for the sample that the first sample is concentrated, and 1≤tu≤TemUpNum, TemUpNum are first sample
The total sample number of this concentration, pn are the dimension serial number of physiology feature vector, and 1≤pn≤PN, PN are the dimension of physiology feature vector
Sum, TUElmtu,pnFor physiological characteristic vector the taking in n dimension of pth for the tu sample that the first sample is concentrated
Value, TemUpVectuFor the physiological characteristic vector for the tu sample that the first sample is concentrated；
The physiological characteristic vector of each sample in second sample set is denoted as:
TemDnVectd=(TDElmtd,1,TDElmtd,2,...,TDElmtd,pn,...,TDElmtd,PN)
Td is the serial number of the sample in second sample set, and 1≤td≤TemDnNum, TemDnNum are second sample
The total sample number of this concentration, TDElmtd,pnIt is a in pth n for the physiological characteristic vector of the td sample in second sample set
Value in dimension, TemDnVectdFor the physiological characteristic vector of the td sample in second sample set；
Third computing unit, for calculating separately the physiological characteristic vector and described first of the indoor user according to the following formula
Average distance between sample set and second sample set:
Wherein, PhyElmpnFor value of the physiological characteristic vector in n dimension of pth of the indoor user, TemUpDis
For the average distance between the physiological characteristic vector and the first sample set of the indoor user, TemDnDis is the interior
Average distance between the physiological characteristic vector of user and second sample set；
Temperature adjustment unit, for according to the physiological characteristic vector of the indoor user and the first sample set and institute
The average distance stated between the second sample set is adjusted indoor temperature.
Further, the humidity adjustment module may include:
4th sample selection unit, for being selected respectively from historical sample library according to the gender and age of the indoor user
Take the third sample set that label is promotion indoor humidity and the 4th sample set that label is reduction indoor humidity, wherein described
The physiological characteristic vector of each sample in third sample set is denoted as:
HumUpVechu=(HUElmhu,1,HUElmhu,2,...,HUElmhu,pn,...,HUElmhu,PN)
Hu is the serial number of the sample in the third sample set, and 1≤hu≤HumUpNum, HumUpNum are the third sample
The total sample number of this concentration, HUElmhu,pnIt is a in pth n for the physiological characteristic vector of the hu sample in the third sample set
Value in dimension, HumUpVechuFor the physiological characteristic vector of the hu sample in the third sample set；
The physiological characteristic vector of each sample in 4th sample set is denoted as:
HumDnVechd=(HDElmhd,1,HDElmhd,2,...,HDElmhd,pn,...,HDElmhd,PN)
Hd is the serial number of the sample in the 4th sample set, and 1≤hd≤HumDnNum, HumDnNum are the 4th sample
The total sample number of this concentration, HDElmhd,pnIt is a in pth n for the physiological characteristic vector of the hd sample in the 4th sample set
Value in dimension, HumDnVechdFor the physiological characteristic vector of the hd sample in the 4th sample set；
4th computing unit, for calculate separately according to the following formula the indoor user physiological characteristic vector and the third
Average distance between sample set and the 4th sample set:
Wherein, PhyElmpnFor value of the physiological characteristic vector in n dimension of pth of the indoor user, HumUpDis
For the average distance between the physiological characteristic vector and the third sample set of the indoor user, HumDnDis is the interior
Average distance between the physiological characteristic vector of user and the 4th sample set；
Humidity adjustment unit, for according to the physiological characteristic vector of the indoor user and the third sample set and institute
The average distance stated between the 4th sample set is adjusted indoor humidity.
It is apparent to those skilled in the art that for convenience and simplicity of description, the device of foregoing description,
The specific work process of module and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
The schematic block diagram that Fig. 5 shows a kind of terminal device provided in an embodiment of the present invention is only shown for ease of description
Part related to the embodiment of the present invention.
In the present embodiment, the terminal device 5 can be indoor air conditioner, the terminal device 5 can include: processor
50, memory 51 and it is stored in the computer-readable instruction that can be run in the memory 51 and on the processor 50
52, such as execute the computer-readable instruction of above-mentioned indoor environment adjusting method.The processor 50 executes the computer
The step in above-mentioned each indoor environment adjusting method embodiment, such as step S101 shown in FIG. 1 are realized when readable instruction 52
To S105.Alternatively, the processor 50 realizes each mould in above-mentioned each Installation practice when executing the computer-readable instruction 52
Block/unit function, such as the function of module 401 to 407 shown in Fig. 4.
Illustratively, the computer-readable instruction 52 can be divided into one or more module/units, one
Or multiple module/units are stored in the memory 51, and are executed by the processor 50, to complete the present invention.Institute
Stating one or more module/units can be the series of computation machine readable instruction section that can complete specific function, the instruction segment
For describing implementation procedure of the computer-readable instruction 52 in the terminal device 5.
The processor 50 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
The memory 51 can be the internal storage unit of the terminal device 5, such as the hard disk or interior of terminal device 5
It deposits.The memory 51 is also possible to the External memory equipment of the terminal device 5, such as be equipped on the terminal device 5
Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card dodge
Deposit card (Flash Card) etc..Further, the memory 51 can also both include the storage inside list of the terminal device 5
Member also includes External memory equipment.The memory 51 is for storing the computer-readable instruction and the terminal device 5
Required other instruction and datas.The memory 51 can be also used for temporarily storing the number that has exported or will export
According to.
The functional units in various embodiments of the present invention may be integrated into one processing unit, is also possible to each
Unit physically exists alone, and can also be integrated in one unit with two or more units.Above-mentioned integrated unit both may be used
To use formal implementation of hardware, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention substantially or
Person says that all or part of the part that contributes to existing technology or the technical solution can body in the form of software products
Reveal and, which is stored in a storage medium, including several computer-readable instructions are used so that one
Platform computer equipment (can be personal computer, server or the network equipment etc.) executes described in each embodiment of the present invention
The all or part of the steps of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can be with
Store the medium of computer-readable instruction.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned reality
Applying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of indoor environment adjusting method characterized by comprising
The Indoor Video image acquired by preset camera is obtained, and Face datection is carried out to the Indoor Video image；
Feature extraction is carried out to the facial image detected, and special according to the face for the face characteristic construction indoor user extracted
Levy vector；
Gender and the age of the indoor user are determined according to the face feature vector；
Every human body physiological data of the indoor user is obtained, and the interior is constructed according to every human body physiological data
The physiological characteristic vector of user；
Indoor temperature and humidity is adjusted separately according to the gender of the indoor user, age and physiological characteristic vector.
2. indoor environment adjusting method according to claim 1, which is characterized in that described according to the face feature vector
The gender for determining the indoor user includes:
Choose male's sample set and women sample set respectively from preset historical sample library, wherein male's sample set
In the face feature vector of each male's sample be denoted as:
MaleVecm=(MaleElmm,1,MaleElmm,2,...,MaleElmm,gn,...,MaleElmm,GN)
M is the serial number of male's sample, and 1≤m≤MaleNum, MaleNum are the sum of male's sample, and gn is face feature vector
Dimension serial number, 1≤gn≤GN, GN be face feature vector dimension sum, MaleElmm,gnFor the people of m-th of male's sample
Value of the face feature vector in the gn dimension, MaleVecmFor the face feature vector of m-th of male's sample；
The face feature vector of each women sample in the women sample set is denoted as:
FemVecf=(FemElmf,1,FemElmf,2,...,FemElmf,gn,...,FemElmf,GN)
F is the serial number of women sample, and 1≤f≤FemNum, FemNum are the sum of women sample, FemElmf,gnFor f-th of women
Value of the face feature vector of sample in the gn dimension, FemVecfFor the face feature vector of f-th of women sample；
The face feature vector and male's sample set and the women sample of the indoor user are calculated separately according to the following formula
Average distance between this collection:
Wherein, FaceElmgnFor value of the face feature vector in the gn dimension of the indoor user, MaleDis is institute
The average distance between the face feature vector of indoor user and male's sample set is stated, FemDis is the indoor user
Average distance between face feature vector and the women sample set；
According to flat between the face feature vector of the indoor user and male's sample set and the women sample set
The gender of the determining indoor user of distance.
3. indoor environment adjusting method according to claim 1, which is characterized in that described according to the face feature vector
The age for determining the indoor user includes:
Choose the sample set of all age group respectively from historical sample library, wherein the face feature vector of each sample is denoted as:
AgeVecs,c=(AgeElms,c,1,AgeElms,c,2,...,AgeElms,c,gn,...,AgeElms,c,GN)
S is the serial number of all age group, and 1≤s≤SN, SN are the sum of age bracket, and c is the serial number of sample, 1≤c≤CNs, CNs
For the total sample number in the sample set of s-th of age bracket, AgeElms,c,gnFor c-th of sample in the sample set of s-th of age bracket
Value of this face feature vector in the gn dimension, AgeVecs,cFor c-th of sample in the sample set of s-th of age bracket
This face feature vector；
Being averaged between the face feature vector of the indoor user and the sample set of all age group is calculated separately according to the following formula
Distance:
Wherein, AgeDissFor the average departure between the face feature vector of the indoor user and the sample set of s-th of age bracket
From；
The age of the indoor user is determined according to the following formula:
AgeType=argmin (AgeDis1,AgeDis2,...,AgeDiss,...,AgeDisSN)
Wherein, argmin is minimum independent variable function, and AgeType is the serial number of age bracket locating for the indoor user.
4. indoor environment adjusting method according to any one of claim 1 to 3, which is characterized in that described according to
Gender, age and the physiological characteristic vector of indoor user, which are adjusted indoor temperature, includes:
Choosing label respectively from historical sample library according to the gender of the indoor user and age is promote room temperature the
One sample set and label are the second sample set for reducing room temperature, wherein each sample that the first sample is concentrated
Physiological characteristic vector is denoted as:
TemUpVectu=(TUElmtu,1,TUElmtu,2,...,TUElmtu,pn,...,TUElmtu,PN)
Tu is the serial number for the sample that the first sample is concentrated, and 1≤tu≤TemUpNum, TemUpNum are the first sample set
In total sample number, pn be physiology feature vector dimension serial number, 1≤pn≤PN, PN be physiology feature vector dimension sum,
TUElmtu,pnFor value of the physiological characteristic vector in n dimension of pth for the tu sample that the first sample is concentrated,
TemUpVectuFor the physiological characteristic vector for the tu sample that the first sample is concentrated；
The physiological characteristic vector of each sample in second sample set is denoted as:
TemDnVectd=(TDElmtd,1,TDElmtd,2,...,TDElmtd,pn,...,TDElmtd,PN)
Td is the serial number of the sample in second sample set, and 1≤td≤TemDnNum, TemDnNum are second sample set
In total sample number, TDElmtd,pnFor the td sample in second sample set physiological characteristic vector in n dimension of pth
On value, TemDnVectdFor the physiological characteristic vector of the td sample in second sample set；
The physiological characteristic vector and the first sample set and second sample of the indoor user are calculated separately according to the following formula
Average distance between this collection:
Wherein, PhyElmpnFor value of the physiological characteristic vector in n dimension of pth of the indoor user, TemUpDis is institute
The average distance between the physiological characteristic vector of indoor user and the first sample set is stated, TemDnDis is the indoor user
Physiological characteristic vector and second sample set between average distance；
According to flat between the physiological characteristic vector of the indoor user and the first sample set and second sample set
Equal distance is adjusted indoor temperature.
5. indoor environment adjusting method according to any one of claim 1 to 3, which is characterized in that described according to
Gender, age and the physiological characteristic vector of indoor user, which are adjusted indoor humidity, includes:
Choosing label respectively from historical sample library according to the gender of the indoor user and age is promote indoor humidity the
Three sample sets and label are the 4th sample set for reducing indoor humidity, wherein each sample in the third sample set
Physiological characteristic vector is denoted as:
HumUpVechu=(HUElmhu,1,HUElmhu,2,...,HUElmhu,pn,...,HUElmhu,PN)
Hu is the serial number of the sample in the third sample set, and 1≤hu≤HumUpNum, HumUpNum are the third sample set
In total sample number, HUElmhu,pnFor the hu sample in the third sample set physiological characteristic vector in n dimension of pth
On value, HumUpVechuFor the physiological characteristic vector of the hu sample in the third sample set；
The physiological characteristic vector of each sample in 4th sample set is denoted as:
HumDnVechd=(HDElmhd,1,HDElmhd,2,...,HDElmhd,pn,...,HDElmhd,PN)
Hd is the serial number of the sample in the 4th sample set, and 1≤hd≤HumDnNum, HumDnNum are the 4th sample set
In total sample number, HDElmhd,pnFor the hd sample in the 4th sample set physiological characteristic vector in n dimension of pth
On value, HumDnVechdFor the physiological characteristic vector of the hd sample in the 4th sample set；
The physiological characteristic vector and the third sample set and the 4th sample of the indoor user are calculated separately according to the following formula
Average distance between this collection:
Wherein, PhyElmpnFor value of the physiological characteristic vector in n dimension of pth of the indoor user, HumUpDis is institute
The average distance between the physiological characteristic vector of indoor user and the third sample set is stated, HumDnDis is the indoor user
Physiological characteristic vector and the 4th sample set between average distance；
According to flat between the physiological characteristic vector of the indoor user and the third sample set and the 4th sample set
Equal distance is adjusted indoor humidity.
6. a kind of indoor environment adjusting device characterized by comprising
Face detection module, for obtaining the Indoor Video image acquired by preset camera, and to the Indoor Video figure
As carrying out Face datection；
Characteristic extracting module, for carrying out feature extraction to the facial image detected, and according to the face characteristic structure extracted
Make the face feature vector of indoor user；
Gender determining module, for determining the gender of the indoor user according to the face feature vector；
Age determining module, for determining the age of the indoor user according to the face feature vector；
Physiological data obtains module, for obtaining every human body physiological data of the indoor user, and according to every people
Body physiological data constructs the physiological characteristic vector of the indoor user；
Temperature adjust module, for according to the gender of the indoor user, age and physiological characteristic vector to indoor temperature into
Row adjustment；
Humidity adjust module, for according to the gender of the indoor user, age and physiological characteristic vector to indoor humidity into
Row adjustment.
7. indoor environment adjusting device according to claim 6, which is characterized in that the gender determining module includes:
First sample selection unit, for choosing male's sample set and women sample respectively from preset historical sample library
Collection, wherein the face feature vector of each male's sample in male's sample set is denoted as:
MaleVecm=(MaleElmm,1,MaleElmm,2,...,MaleElmm,gn,...,MaleElmm,GN)
M is the serial number of male's sample, and 1≤m≤MaleNum, MaleNum are the sum of male's sample, and gn is face feature vector
Dimension serial number, 1≤gn≤GN, GN be face feature vector dimension sum, MaleElmm,gnFor the people of m-th of male's sample
Value of the face feature vector in the gn dimension, MaleVecmFor the face feature vector of m-th of male's sample；
The face feature vector of each women sample in the women sample set is denoted as:
FemVecf=(FemElmf,1,FemElmf,2,...,FemElmf,gn,...,FemElmf,GN)
F is the serial number of women sample, and 1≤f≤FemNum, FemNum are the sum of women sample, FemElmf,gnFor f-th of women
Value of the face feature vector of sample in the gn dimension, FemVecfFor the face feature vector of f-th of women sample；
First computing unit, for calculate separately according to the following formula the indoor user face feature vector and male's sample
Average distance between collection and the women sample set:
Wherein, FaceElmgnFor value of the face feature vector in the gn dimension of the indoor user, MaleDis is institute
The average distance between the face feature vector of indoor user and male's sample set is stated, FemDis is the indoor user
Average distance between face feature vector and the women sample set；
Gender determination unit, for according to the face feature vector of the indoor user and male's sample set and the female
Average distance between property sample set determines the gender of the indoor user.
8. indoor environment adjusting device according to claim 6, which is characterized in that the age determining module includes:
Second sample selection unit, for choosing the sample set of all age group respectively from historical sample library, wherein each sample
This face feature vector is denoted as:
AgeVecs,c=(AgeElms,c,1,AgeElms,c,2,...,AgeElms,c,gn,...,AgeElms,c,GN)
S is the serial number of all age group, and 1≤s≤SN, SN are the sum of age bracket, and c is the serial number of sample, 1≤c≤CNs, CNs
For the total sample number in the sample set of s-th of age bracket, AgeElms,c,gnFor c-th of sample in the sample set of s-th of age bracket
Value of this face feature vector in the gn dimension, AgeVecs,cFor c-th of sample in the sample set of s-th of age bracket
This face feature vector；
Second computing unit, for calculating separately the face feature vector and all age group of the indoor user according to the following formula
Average distance between sample set:
Wherein, AgeDissFor the average departure between the face feature vector of the indoor user and the sample set of s-th of age bracket
From；
Age determination unit, for determining the age of the indoor user according to the following formula:
AgeType=argmin (AgeDis1,AgeDis2,...,AgeDiss,...,AgeDisSN)
Wherein, argmin is minimum independent variable function, and AgeType is the serial number of age bracket locating for the indoor user.
9. a kind of computer readable storage medium, the computer-readable recording medium storage has computer-readable instruction, special
Sign is, the indoor ring as described in any one of claims 1 to 5 is realized when the computer-readable instruction is executed by processor
The step of border adjusting method.
10. a kind of terminal device, including memory, processor and storage are in the memory and can be on the processor
The computer-readable instruction of operation, which is characterized in that the processor realizes such as right when executing the computer-readable instruction
It is required that described in any one of 1 to 5 the step of indoor environment adjusting method.
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| CN111444799A (en) * | 2020-03-16 | 2020-07-24 | 深圳市合信达控制系统有限公司 | Output display method and device of temperature control function interface |
| WO2023020026A1 (en) * | 2021-08-19 | 2023-02-23 | 青岛海尔空调器有限总公司 | Air conditioner control method and apparatus, and air conditioner |
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