CN108361927A - A kind of air-conditioner control method, device and air conditioner based on machine learning - Google Patents
A kind of air-conditioner control method, device and air conditioner based on machine learning Download PDFInfo
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- CN108361927A CN108361927A CN201810130399.3A CN201810130399A CN108361927A CN 108361927 A CN108361927 A CN 108361927A CN 201810130399 A CN201810130399 A CN 201810130399A CN 108361927 A CN108361927 A CN 108361927A
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- air conditioner
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/65—Electronic processing for selecting an operating mode
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/20—Feedback from users
Abstract
The invention discloses a kind of air-conditioner control method based on machine learning, device and air conditioner.Air-conditioner control method includes：Obtain air conditioner operating parameter and setting time set by user；When obtaining user's setting air conditioner operating parameter, the environmental parameter corresponding to the setting time；Using the air conditioner operating parameter repeatedly recorded as output, corresponding environmental parameter is trained neural network control models, the neural network control models after being trained as input；And air conditioner is controlled according to the neural network control models after training.The self study of user's control air conditioner behavior can be achieved in the present invention, improves the intelligence of airconditioning control.Make user booting when need not input control parameter, can realize a key be switched on, exempt complex operations.Even realize the fully automated control of air conditioner.
Description
Technical field
The present invention relates to air-conditioning technical fields, more particularly to based on a kind of air conditioner controlling party based on machine learning
Method, control device and air conditioner.
Background technology
To realize the convenience and ease for use of air conditioner control, data are generally realized by the gateway of data converter class
Conversion, achieve the purpose that remote control air-conditioning.It is to control in recent years in particular with the APP of mobile terminal as control terminal
The more popular trend in device field.Mobile terminal APP is communicated by gateway with air-conditioning, realizes remote control, the operation log of air-conditioning
The functions such as record, schedule reservation.
By mobile terminal come remote control air-conditioning, need to rely on user setting relevant parameter or adding schedule plan every time
Realize the control of air-conditioning.In order to keep the control of air-conditioning more intelligent, there are some trials in the related technology, but can not carry
For flexible, the control mode of various environmental parameters variations is adapted to.For example, the Chinese patent of Publication No. CN107504656A
Application proposes a kind of air conditioner Learning Control Method, by the switching on and shutting down time data of automatic collection APP, and passes through function
The switching on and shutting down time of air conditioner is predicted in operation, to automatically controlled to the switching on and shutting down of air conditioner progress.But the program is only
It is to be predicted the split unused time, there is still a need for users to be voluntarily arranged for the various operating parameters after booting, and intelligence degree is remote
Far from meeting the needs of users.
Invention content
The present invention is directed to solve at least to a certain extent it is above-mentioned in the related technology the technical issues of one of, a kind of energy is provided
The means for enough automatically providing air conditioner control parameter make the control parameter of offer utmostly be accustomed to close to user's practicality.
For this purpose, first aspect present invention is designed to provide a kind of air-conditioner control method based on machine learning；The
Two aspects are designed to provide a kind of computer readable storage medium；A kind of calculate that be designed to provide of the third aspect sets
It is standby；Fourth aspect is to provide a kind of air conditioner controlling device based on machine learning；5th aspect is designed to provide
A kind of air conditioner.
In order to achieve the above object, embodiment according to a first aspect of the present invention proposes a kind of sky based on machine learning
Adjust device control method.This method includes：Obtain air conditioner operating parameter and setting time set by user；It obtains user and sets sky
When adjusting device operating parameter, the environmental parameter corresponding to the setting time；Using the air conditioner operating parameter repeatedly recorded as defeated
Go out, corresponding environmental parameter is trained neural network control models, the ANN Control after being trained as input
Model；Air conditioner is controlled according to the neural network control models after training.
In some embodiments, the air conditioner operating parameter includes：The operational mode of air conditioner, set temperature, wind speed,
Wave one or more of control.
In some embodiments, the corresponding environmental parameter includes：Indoor environment temperature, outdoor environment temperature, user
Operating time, the operation duration of air conditioner, air conditioner position one or more of longitude and latitude.
In some embodiments, the corresponding environmental parameter further includes indoor air humidity and outside air humidity.
In some embodiments, the neural network control models are multilayer depth belief network model.
In some embodiments, the neural network control models according to after training, which to air conditioner control, includes：
Obtain the current ambient parameter information of air conditioner；By the neural network control after the current ambient parameter information input training of air conditioner
Simulation；Air conditioner control parameter is obtained according to the output of neural network control models；And control air conditioner is according to obtained sky
Adjust the operation of device control parameter.
In some embodiments, the neural network control models according to after training control in air-conditioning air conditioner
Device executes when being switched on, and specifically includes：Receive a key power-on instruction of air conditioner；Obtain the current ambient parameter information of air conditioner；
By the neural network control models after the current ambient parameter information input training of air conditioner；According to neural network control models
Output obtains air conditioner control parameter；And it is run according to obtained air conditioner control parameter after control air conditioner booting.
Embodiment according to a second aspect of the present invention proposes a kind of non-transitorycomputer readable storage medium, deposits thereon
Executable instruction is contained, when the executable instruction is executed by processor, is realized described in embodiment according to a first aspect of the present invention
The air-conditioner control method based on machine learning.
Embodiment according to a third aspect of the present invention proposes a kind of computing device, which includes memory, place
The application program managed device and storage on a memory and can run on a processor, it is real when processor executes the application program
The now air-conditioner control method based on machine learning described in embodiment according to a first aspect of the present invention.
The invention also provides a kind of air-conditioner control methods based on machine learning, for including server, client's control
The air conditioner remote control system at end processed and air conditioner, including：Server obtains the subscriber identity information of client's control terminal acquisition
With air conditioner operating parameter set by user and setting time；When server obtains user's setting air conditioner operating parameter, institute
State the environmental parameter corresponding to setting time；Server is using the air conditioner operating parameter repeatedly recorded as output, corresponding environment
Parameter is trained neural network control models, the neural network control models after being trained as input；And service
Device controls air conditioner according to the neural network control models after training.
In some embodiments, server includes according to the neural network control models control air conditioner after training：It receives
A key power-on instruction from client；Obtain the current ambient parameter information of air conditioner；By the current environmental parameter of air conditioner
Neural network control models after information input training；Sky is obtained according to the output of the neural network control models after the training
Adjust device control parameter；And it is run according to the control parameter after control air conditioner booting.
Using the present invention the air-conditioner control method based on machine learning, it can be achieved that user's control air conditioner behavior from
Study, improves the intelligence of airconditioning control.Can make user booting when need not input control parameter, can realize a key be switched on,
Exempt the complex operations of user.Even on this basis, the fully automated control of air conditioner is realized in combination with multiple sensors.
When storage medium and computing device according to the present invention are implemented, the method that the present invention may be implemented, to obtain and side of the present invention
The similar effect of method.
Embodiment according to a fourth aspect of the present invention proposes a kind of air conditioner controlling device based on machine learning, the dress
Set including：Operating parameter acquisition module, for obtaining air conditioner operating parameter and setting time set by user；Environmental parameter obtains
Modulus block, when obtaining user's setting air conditioner operating parameter, the environmental parameter corresponding to the setting time；Controlling model is trained
Module, for using the air conditioner operating parameter repeatedly recorded as output, corresponding environmental parameter to be as input, to neural network control
Simulation is trained, the neural network control models after being trained；And control parameter output module, for according to training
Neural network control models afterwards obtain air conditioner control parameter.
In some embodiments, the air conditioner operating parameter includes：The operational mode of air-conditioning, wind speed, is shaken at set temperature
One or more of pendulum control.
In some embodiments, the corresponding environmental parameter includes：Indoor environment temperature, outdoor environment temperature, user
Operating time, the operation duration of air-conditioning, air-conditioning position one or more of longitude and latitude.
In some embodiments, the corresponding environmental parameter further includes indoor air humidity and outside air humidity.
In some embodiments, the neural network control models are multilayer depth belief network model.
In some embodiments, control parameter output module obtains air conditioner according to the neural network control models after training
Control parameter includes：Obtain the current ambient parameter information of air-conditioning；After the current ambient parameter information input training of air-conditioning
Neural network control models；And air conditioner control parameter is obtained according to the output of neural network control models.
In some embodiments, described device further includes：One-key start module, for receiving air-conditioning power-on instruction and determination
Air-conditioning booting operating parameter, the one-key start module further comprise：Power-on instruction receiving unit, for receiving user's input
Power-on instruction；Be switched on operating parameter acquiring unit, and for being communicated with control parameter output module, it is defeated to obtain control parameter
Go out the air conditioner control parameter of module output, and as booting operating parameter；And operation control unit, for controlling sky
Device is adjusted to be brought into operation according to the booting operating parameter.
Embodiment according to a fifth aspect of the present invention provides a kind of air conditioner, which includes according to the present invention the 4th
The air conditioner controlling device based on machine learning described in the embodiment of aspect.
Using the present invention the air conditioner controlling device based on machine learning, it can be achieved that user's control air conditioner behavior from
Study, improves the intelligence of airconditioning control.Can make user booting when need not input control parameter, can realize a key be switched on,
Exempt the complex operations of user.Even on this basis, the fully automated control of air conditioner is realized in combination with multiple sensors.
Air conditioner according to the present invention includes the air conditioner controlling device based on machine learning, is obtained and present invention control to implement the moment
The similar effect of device processed.
Description of the drawings
Above-mentioned and/or additional aspect and advantage of the invention will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, wherein：
Fig. 1 is the flow chart of the air-conditioner control method according to an embodiment of the invention based on machine learning；
Fig. 2 is neural network control models structural schematic diagram according to an embodiment of the invention；
Fig. 3 is that the neural network control models according to an embodiment of the invention according to after training control air conditioner
The flow diagram of system；
Fig. 4 is the flow chart according to an embodiment of the invention that one-key start control is carried out to air conditioner；
Fig. 5 is air conditioner remote control system structural schematic diagram according to an embodiment of the invention；
Fig. 6 is the flow chart of the air-conditioner control method in accordance with another embodiment of the present invention based on machine learning；
Fig. 7 is one key control method flow diagram of the ends APP according to an embodiment of the invention；
Fig. 8 is the structure diagram of the air conditioner controlling device according to an embodiment of the invention based on machine learning；
Fig. 9 is the structure diagram of the air conditioner controlling device in accordance with another embodiment of the present invention based on machine learning；
Figure 10 is the structure diagram of one-key start module according to an embodiment of the invention.
Specific implementation mode
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to for explaining the present invention, and is not considered as limiting the invention.
Currently, the control strategy in related air conditioner controlling technology is greatly both for single control parameter, and according to user
Mode that the control information once inputted is searched carries out " study ", record of its essence of this mode or machinery,
It is not really to learn.Inventor notices that the actual working environment of air-conditioning is continually changing, the side based on record-lookup
Formula can not possibly enumerate all working environment parameters.It is therefore contemplated that a kind of intelligent mode of learning is designed, to make to user
More reasonably mathematical model is established with custom, so as to cope with different working environments.And it further provides based on nerve net
The machine learning method of network controls the operating parameter of air conditioner, makes automatically controlling for air-conditioning that can meet user's use habit
Used general idea.
The embodiment of the present invention is described in detail below with reference to attached drawing.
Fig. 1 is the flow chart of the air-conditioner control method according to an embodiment of the invention based on machine learning.This reality
The control method for applying example mainly includes the following steps that S110 to S140.
In step S110, air conditioner operating parameter and setting time set by user are obtained.In general, air conditioner is being opened
It when machine, needs to carry out initial setting to operating parameter, in air conditioner operational process, it is also possible to according to the variation of environment, change
The operating parameter of air conditioner, each initial setting or change can be viewed as user's setting, occur together with setting
Time is recorded together.The purpose of setting time, primarily to obtaining environmental parameter at that time.Wherein, air conditioner operation ginseng
Number includes：The operational mode of air conditioner, wind speed, waves control etc. at set temperature, every time setting can be directed to one of those or
Multiple parameters carry out.
In step S120, when obtaining user's setting air conditioner operating parameter, the environmental parameter corresponding to the setting time.
The corresponding environmental parameter includes：Indoor environment temperature, outdoor environment temperature, the operation of the operating time of user, air conditioner
Duration, air conditioner position longitude and latitude etc..Since the sendible temperature of people also has direct relation with air humidity, certain
In a little embodiments, the corresponding environmental parameter further includes indoor air humidity and outside air humidity.
Wherein, indoor environment temperature and air humidity can be surveyed by the temperature sensor and humidity sensor of air conditioner
, outdoor temperature and air humidity can be measured by the temperature sensor and humidity sensor being arranged on air-conditioner outdoor unit.When
So, above-mentioned environmental parameter can not also be measured by air conditioner itself, but be obtained from other channels by communication mode.Example
Such as, for being equipped with other smart home devices or monitoring the operative scenario of system, the temperature and humidity etc. of indoor and outdoor air can
It is obtained according to the record of other smart machines, outdoor temperature, humidity etc. can also be from online server according to weather forecast
Equal information acquisitions.And the operating time of user as environmental parameter, mainly consider 1 year in Various Seasonal, in one day
Sooner or later different, it can all influence the setting that empty user exchanges device.
In step S130, using the air conditioner operating parameter repeatedly recorded as output, corresponding environmental parameter is used as input,
Neural network control models are trained, the neural network control models after being trained.
Here, what the training of neural network control models was taken is the mode of supervised learning, and trained purpose is to make mould
Behavior pattern of the decision-making mechanism of type closer to user.Under identical environment, be neural network output control parameter with
The control parameter of actual user is more close.Trained sample size will have direct influence, sample to the precision of model after training
This is more, and training effect is better, and therefore, according to the method for the present invention, the use that the Controlling model provided will be with user is got over
Come more exactly and ideally.
The network structure of various known suitable supervised learnings may be used in the neural network control models, such as feels
Know device model, sorter model, the basic neural network structure such as Hopfield networks, various corresponding mainstream training methods
It may be used to the determination of model parameter.One typical neural network control models structure is as shown in Figure 2.Wherein neural network
500 include an input layer 510, an output layer 530 and several mid-level net network layers 520.
In one embodiment of the present of invention, Controlling model is established based on multilayer depth belief network model (DBN).Depth
Belief network is deep-neural-network (DNN) one kind, can be regarded as a kind of generative probabilistic model, it both can be used for non-supervisory
Study is similar to a self-editing ink recorder；Supervised learning is can be used for, is used as grader.With traditional discrimination model
Neural network it is opposite, generate model be establish one observation data and label between joint probability distribution, to prior probability
It is all assessed with posterior probability, and discrimination model only and has evaluated the latter, therefore, such network is more suitable for institute of the present invention
For application scenarios.
DBN networks can be considered by multiple limitation Boltzmann machines (restricted Boltzmann machines, RBM)
Layer composition, RBM are a kind of neural perceptrons, are made of an aobvious layer and a hidden layer, aobvious to be between layer and the neuron of hidden layer
Two-way full connection.Several RBM " series connection " are got up, a DBN is constituted, wherein the hidden layer of a upper RBM is as next
The aobvious layer of a RBM, the output of a upper RBM are the input of next RBM.In training process, need to train up last layer
RBM after could train the RBM of current layer, until last layer.About the structure of RBM and the training of DBN, in the related art
There are many ripe method, the present invention is not particularly limited training method, and details are not described herein.
During model training, by environmental parameter, such as the operation of indoor environment temperature, outdoor environment temperature, user
Time, the operation duration of air conditioner, air conditioner position longitude and latitude, air humidity, the part or complete in air themperature etc.
Portion is as input.By air conditioner operating parameter, such as the operational mode of air conditioner, set temperature, wind speed, wave control etc.
Partly or entirely as output.According to output vector come with having supervision training entity relationship grader, each layer is constantly adjusted
RBM networks weights, achieve the training that model is completed by the Feature Mapping of input vector to output vector.
It outputs and inputs and specifically includes which parameter can voluntarily be selected with application scenarios according to demand by user.According to
The each setting in family is constantly trained model.The accurate training of model needs to use a large amount of user data, therefore preceding
During phase use, a period of time is needed to be familiar with the operating habit of user, constantly Optimized model.Thus pushing away with the time
It moves, model optimization degree is higher, and the precision of self study can be also improved.
In step S140, air conditioner is controlled according to the neural network control models after training.After the completion of training, when
When operation of air conditioner, you can the environment being presently according to air-conditioning is joined according to the operation of corresponding environmental parameter adjust automatically air-conditioning
Number.A kind of specific control method is the neural network control according to an embodiment of the invention according to after training referring to 3, Fig. 3
The flow diagram that simulation controls air conditioner.In the method for the present embodiment, according to the ANN Control after training
It includes sub-step S141 to S144 that model carries out control to air conditioner.
In sub-step S141, the current ambient parameter information of air conditioner is obtained.The content and acquisition modes of environmental parameter can
Referring to the description for combining Fig. 1 and step S120, details are not described herein.
In sub-step S142, by the neural network control models after the current ambient parameter information input training of air conditioner.
In sub-step S143, air conditioner control parameter is obtained according to the output of neural network control models.Neural network mould
The output of type is under current environment parameter the one of the possible control decision of user (that is, control parameter that user may set)
Kind prediction, therefore, the operation that the output of neural network control models is used as air conditioner control parameter that can make air conditioner is joined
The possibility of number closer to user are set.
In sub-step S144, control air conditioner is run according to obtained air conditioner control parameter.
In view of the setting to air conditioner operating parameter, the scene occurred most frequently is exactly when air conditioner is switched on.Substantially, existing
User can be all required to carry out initial setting when all air conditioners are in booting, and this is very inconvenient for users.
Enable user in booting from numerous one-key start control method of setting the present invention provides a kind of.Fig. 4 is according to this hair
The flow chart that one-key start control is carried out to air conditioner of bright one embodiment.
Referring to Fig. 4 and Fig. 3, one-key start control can be by the neural network control models according to after training to sky
It adjusts device to be controlled to execute to realize when air conditioner is switched on.One-key start control method specifically includes step S141 ', and arrives
S141 to S144.Wherein, in step S141 ', a key power-on instruction of air conditioner is received.This instruction can be by being arranged in air-conditioning
Power on button on device/air conditioner remote controler or the triggering of individual key starting key；Or when air conditioner is using long-range control
When processed, a key power-on operation at origin remote automatic control end triggers.
According to the difference of air conditioner, method of the invention can have different ways of realization.When air conditioner is controlled using local
When processed, step S110 to step S140 can be realized by the controller of air-conditioning itself.Since the study of neural network is to be based on
User's actual setting operation every time, and the Mean Time Between Replacement of the operation of user is usually quite length, therefore, Neural Network Science
The operation of habit also need not be real-time, and computational burden is little, excessive extra cost will not be brought to pay to air conditioner.
And when air conditioner uses the remote controlled manner based on server, another embodiment of the invention is will be refreshing
Study through network is realized in server end.Above-mentioned steps S110 to step S140 can be realized in server end.
It is air conditioner remote control system structural schematic diagram according to an embodiment of the invention referring to Fig. 5, Fig. 5.Wherein,
Air conditioner 100 is communicated by gateway 400 and Cloud Server 200, can carry out remote control by server.And client's control terminal is logical
Cross cordless communication network and server communication.For example, a kind of currently more mode is, with answering on smart mobile phone
It uses program (APP) as client's control terminal, air conditioner is controlled.
Fig. 6 is the flow chart of the air-conditioner control method in accordance with another embodiment of the present invention based on machine learning,
In when showing the control method as server end, correspond to the more specifically embodiment of step S110 to S140.This
The air-conditioner control method of embodiment includes step S210 to step S240.
In step S210, server obtains the subscriber identity information and air conditioner set by user fortune of client's control terminal acquisition
Row parameter and setting time.Can one user journal module be set in the APP of client's control terminal, it is each to login user
Setup parameter is recorded, and record is uploaded onto the server.
In step S220, when server obtains user's setting air conditioner operating parameter, the ring corresponding to the setting time
Border parameter.This step concrete details can be found in the description as described in step S120, and only executive agent is by being located at air conditioner local
It has changed into and has been located at server end, which is not described herein again.
In step S230, server is using the air conditioner operating parameter repeatedly recorded as output, corresponding environmental parameter conduct
Input, is trained neural network control models, the neural network control models after being trained.About neural network model
With its training, reference can be made to the description in conjunction with Fig. 2 to step S130, only executive agent has locally been changed by being located at air conditioner and has been located at
Server end, which is not described herein again.
In step S240, server controls air conditioner according to the neural network control models after training.About specific control
Mode processed, reference can be made to the description in conjunction with Fig. 3-Fig. 4 to step S140, only executive agent has locally changed position by being located at air conditioner
In server end, which is not described herein again.For the control method of server end, the control parameter of generation is sent to air-conditioning
Device realizes the control to air conditioner.
Equally, server end can also realize key booting work(in response to the key power-on instruction from client's control terminal
Energy.Specific steps can be found in the description to step S140 in conjunction with Fig. 3-Fig. 4, repeat no more.
There is wider application prospect in remote control framework to the one-key start control method of air conditioner.Based on service
The one-key start of device is more advantageous to and obtains more accurate environmental parameter, and using the powerful operational capability of server, structure is more
Profound network structure, and neural network is preferably trained.Because of usually, the mould of multilayer neural network
Type precision and its number of plies and every node layer number are proportionate, also that is, model accuracy and structure and the meter needed for training pattern
Calculation amount positive correlation.
Fig. 7 is one key control method flow diagram of the ends APP according to an embodiment of the invention.First, learning
Stage, the ends APP execute step S301 and step S302.
The user's operation data of each user are recorded in step S301, APP, such as can be carried out in the form of user journal
Record.User's operation data include：User name, air conditioner operating parameter and setting time set by user.
User's operation data are uploaded onto the server in step S302, APP.
Wherein, the operation data of each user is recorded respectively, to distinguish for the use habit of each user
Study.It is even directed to the local area control of the same room with them of same air conditioner or central air-conditioning, different user may
Different demands for control is had, recording mode is distinguished using the user based on APP, for being more accurate to user's individual can be provided
The control of property.Here, user refers to the air-conditioning user using APP Account Logons, and a user corresponds to stepping on using APP
ID is recorded, a user may be a natural person, it is also possible to a group.
Next, in server end, step S310 to step S333 is executed.
In step S310, the user's operation data uploaded by the ends APP are obtained.
In step S320, corresponding environmental parameter is obtained.
In step S331, by user's operation data and environmental parameter input control model.
It is with having carried out supervision trained to Controlling model in step S332.
In step S333, the Controlling model after the study for each user is obtained.
By the continuous study to user's operation data, model constantly can be trained to and update.In the incipient stage, by
Few in data volume, model training does not complete, and can select the manual setup parameter of user, starts a key again after training a period of time
Startup function.But it makes troubles in this way to user.A kind of better way is, according to condition of similarity (such as close geographical position
Set in range) the trained model of other users result of calculation, as the initial value of a key control, if user is unsatisfied with this
A initial value, then voluntarily adjust again；If user is lazy in being configured, one at least can be also provided the user in booting relatively
Acceptable pattern.
Also, it can be divided for single user in structure according to the operation data of the different user of acquisition in server end
Except other neural network control models.Using the big data of these users, establish for the god in central air-conditioning adjustable range
Through network synthesis Controlling model, while considering energy consumption, air-conditioner operation time, each Central air-conditioning unit of the foundation such as power load
Controlling model not only provides customer satisfaction system air conditioning, also plans more efficient energy-efficient air-conditioner host operating scheme simultaneously.
Further, it is also possible to establish the neural network Comprehensive Control model for all users in a certain territorial scope.By
It is to have time delay and the process of overshoot to the adjusting of air themperature in air conditioner, general user is only to set control by body-sensing
Parameter processed, user, which can't go to calculate, will reach an optimum control process needed for its desired control effect and adjust accordingly
Air conditioner operation parameters are saved, so the actual set of single user is not often the optimal solution under current environment parameter.By right
The modeling of mass data, and by the output of collective model, the training of user model is added to using certain proportion as training sample
Among, it can be by the experience of other users under identical natural environment and individual users to share.By the training of all users, most
The control strategy that each model can be provided eventually may reach the parameter that initially sets up than user oneself and more meet itself
The air conditioning effect of demand.
When client's control terminal executes one-key start operation, the ends execution step S340, APP acquisition active user ID, and on
Pass to server.
Server end executes step S341, searches the corresponding Controlling model of User ID.And step S342, according to User ID
The control parameter of corresponding Controlling model operation output air-conditioning, and it is sent to air conditioner.
Using the present invention the air-conditioner control method based on machine learning, it can be achieved that user's control air conditioner behavior from
Study, improves the intelligence of airconditioning control.Can make user booting when need not input control parameter, can realize a key be switched on,
Exempt the complex operations of user.Even on this basis, the fully automated control of air conditioner is realized in combination with multiple sensors.
In order to realize the above method, embodiment according to a second aspect of the present invention provides a kind of sky based on machine learning
Adjust device control device.
Fig. 8 is the structure diagram of the air conditioner controlling device according to an embodiment of the invention based on machine learning.Its
In, air conditioner controlling device 100 includes operating parameter acquisition module 110, environmental parameter acquisition module 120, Controlling model training
Module 130 and control parameter output module 140.
Operating parameter acquisition module 110 is for obtaining air conditioner operating parameter and setting time set by user.
When environmental parameter acquisition module 120 obtains user's setting air conditioner operating parameter, corresponding to the setting time
Environmental parameter.
The air conditioner operating parameter that Controlling model training module 130 is used to repeatedly to record is as output, corresponding environment ginseng
Number is trained neural network control models, the neural network control models after being trained as input.
Control parameter output module 140 is used to obtain air conditioner control ginseng according to the neural network control models after training
Number.
The air conditioner controlling device 100 can realize that memory is deposited by the computing device containing processor and memory
Contain executable program.Wherein modules can be program function module, when being executed by processor, realize corresponding work(
Energy.For the embodiment of local control mode, the air conditioner controlling device 100 can be one of local air-conditioner controller
Point, it is realized by the center control of air conditioner.For remote control framework, the air conditioner controlling device 100, which can be arranged, to be taken
It is engaged on device, is realized by the processor and memory of server.
The function of modules and realization may refer to the description carried out in above method embodiment, for identical work(
Can, embodiment of the method is similar with the specific implementation of device embodiment, repeats no more.
Fig. 9 is the structure diagram of the air conditioner controlling device in accordance with another embodiment of the present invention based on machine learning.
Wherein, in order to realize a key control function, facilitate the power-on operation of user, relative to the embodiment of Fig. 8, be additionally arranged one-key start
Module 150, for receiving air-conditioning power-on instruction and determining air-conditioning booting operating parameter.
Figure 10 is the structure diagram of one-key start module according to an embodiment of the invention.The one-key start module
150 further comprise：Power-on instruction receiving unit 151, booting operating parameter acquiring unit 152 and operation control unit 153.
Power-on instruction receiving unit 151 is for receiving power-on instruction input by user.
It is defeated to obtain control parameter for being communicated with control parameter output module for booting operating parameter acquiring unit 152
Go out the air conditioner control parameter of module output, and as booting operating parameter.
Operation control unit 153 brings into operation for controlling air conditioner according to the booting operating parameter.
Using the present invention the air-conditioner control method based on machine learning, it can be achieved that user's control air conditioner behavior from
Study, improves the intelligence of airconditioning control.Can make user booting when need not input control parameter, can realize a key be switched on,
Exempt the complex operations of user.Even on this basis, the fully automated control of air conditioner is realized in combination with multiple sensors.
The embodiment of third aspect present invention provides a kind of air conditioner comprising containing real according to a second aspect of the present invention
Apply the air conditioner controlling device based on machine learning of example.The corresponding air conditioner is that machine learning is locally realized in air conditioner
Situation, and for the embodiment in server end progress machine learning, it does not need to particularly change air conditioner, it can
To be realized on the basis of existing air conditioner.
A kind of non-transitorycomputer readable storage medium is also proposed in some embodiments of the invention, is stored thereon with
Computer program realizes the air conditioner controlling party based on machine learning such as above example when the program is executed by processor
Method.The storage medium can be arranged as a part for air conditioner on air conditioner；Or when air conditioner can be remote by server
When process control, which can be arranged on the remote server controlled air conditioner.
A kind of computing device is also proposed in some embodiments of the invention, which includes memory, processor
And the application program that can be run on a memory and on a processor is stored, when processor executes the application program, realize such as
The air-conditioner control method based on machine learning of above example.The computing device can be by the central control unit of air conditioner
It realizes, as the part in the function of air conditioner central control unit.It can also be realized by individual computing device, with air-conditioning
The central control unit of device communicates to connect.It can also be provided on the server that can be carried out telecommunication with air conditioner.The meter
The realization for calculating equipment may include but be not limited to, computer, special neural network chip, various programmable logic controller (PLC) parts etc..
The above-mentioned air conditioner using the air conditioner controlling device based on machine learning according to a second aspect of the present invention, storage
The specific implementation mode of medium and computing device, relevant portion can be from the air-conditioning based on machine learning of the corresponding present invention
It is obtained in device control method or the embodiment of device, and there is the air conditioner based on machine learning with the corresponding present invention to control
The similar advantageous effect of method or apparatus, details are not described herein.
It should be noted that in the description of this specification, any mistake described otherwise above in flow chart or herein
Journey or method description are construed as, and expression includes the steps that one or more for realizing specific logical function or process
Executable instruction code module, segment or part, and the range of the preferred embodiment of the present invention includes other
Realize, wherein sequence that is shown or discussing can not be pressed, include according to involved function by it is basic simultaneously in the way of or press
Opposite sequence, to execute function, this should be understood by the embodiment of the present invention person of ordinary skill in the field.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (system of such as computer based system including processor or other can be held from instruction
The instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium " can any can be included, store, communicating, propagating or passing
Defeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium includes following：Electricity with one or more wiring
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other are suitable
Medium, because can be for example by carrying out optical scanner to paper or other media, then into edlin, interpretation or when necessary with it
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the present invention can be realized with hardware, software, firmware or combination thereof.Above-mentioned
In embodiment, software that multiple steps or method can in memory and by suitable instruction execution system be executed with storage
Or firmware is realized.It, and in another embodiment, can be with well known in the art for example, if realized with hardware
Any one of following technology or their combination are realized：With the logic gate electricity for realizing logic function to data-signal
The discrete logic on road, the application-specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA) are existing
Field programmable gate array (FPGA) etc..
Those skilled in the art are appreciated that all or part that the method for realizing above-described embodiment carries
Step is that relevant hardware can be instructed to complete by program, and the program can be stored in a kind of computer-readable storage
In medium, which includes the steps that one or a combination set of embodiment of the method when being executed.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiments or example.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
In addition, term " first ", " second " are used for description purposes only, it is not understood to indicate or imply relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include at least one this feature.In the description of the present invention, the meaning of " plurality " is two or more, such as two
It is a, three etc., unless otherwise specifically defined.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is not considered as limiting the invention, those skilled in the art within the scope of the invention can be to above-mentioned
Embodiment is changed, changes, replacing and modification.
Claims (19)
1. a kind of air-conditioner control method based on machine learning, which is characterized in that including：
Obtain air conditioner operating parameter and setting time set by user；
When obtaining user's setting air conditioner operating parameter, the environmental parameter corresponding to the setting time；
Using the air conditioner operating parameter repeatedly recorded as output, corresponding environmental parameter is as input, to ANN Control
Model is trained, the neural network control models after being trained；And
Air conditioner is controlled according to the neural network control models after training.
2. the air-conditioner control method according to claim 1 based on machine learning, which is characterized in that the air conditioner fortune
Row parameter includes：
The operational mode of air conditioner, wind speed, one or more of waves control at set temperature.
3. the air-conditioner control method according to claim 1 based on machine learning, which is characterized in that the corresponding ring
Border parameter includes：
Indoor environment temperature, outdoor environment temperature, the operating time of user, the operation duration of air conditioner, air conditioner position
One or more of longitude and latitude.
4. the air-conditioner control method according to claim 3 based on machine learning, which is characterized in that the corresponding ring
Border parameter further includes indoor air humidity and outside air humidity.
5. the air-conditioner control method according to claim 1 based on machine learning, which is characterized in that the neural network
Controlling model is multilayer depth belief network model.
6. the air-conditioner control method according to claim 1 based on machine learning, which is characterized in that described according to training
Neural network control models afterwards carry out control to air conditioner：
Obtain the current ambient parameter information of air conditioner；
By the neural network control models after the current ambient parameter information input training of air conditioner；
Air conditioner control parameter is obtained according to the output of neural network control models；And
Control air conditioner is run according to obtained air conditioner control parameter.
7. the air-conditioner control method according to claim 1 based on machine learning, which is characterized in that described according to training
Neural network control models afterwards control air conditioner and are executed when air conditioner is switched on, and specifically include：
Receive a key power-on instruction of air conditioner；
Obtain the current ambient parameter information of air conditioner；
By the neural network control models after the current ambient parameter information input training of air conditioner；
Air conditioner control parameter is obtained according to the output of neural network control models；And
It is run according to obtained air conditioner control parameter after control air conditioner booting.
8. a kind of non-transitorycomputer readable storage medium, is stored thereon with executable instruction, which is characterized in that described to hold
When row instruction is executed by processor, the air conditioner according to any one of claims 1-7 based on machine learning is realized
Control method.
9. a kind of computing device, the computing device include memory, processor and storage on a memory and can be on a processor
The application program of operation, which is characterized in that when processor executes the application program, realize according to arbitrary in claim 1-7
The air-conditioner control method based on machine learning described in one.
10. a kind of air-conditioner control method based on machine learning, for including server, the sky of client's control terminal and air conditioner
Adjust device tele-control system, which is characterized in that including：
Server obtains the subscriber identity information that client's control terminal acquires and air conditioner operating parameter set by user and setting
Time；
When server obtains user's setting air conditioner operating parameter, the environmental parameter corresponding to the setting time；
Server is using the air conditioner operating parameter repeatedly recorded as output, and corresponding environmental parameter is as input, to neural network
Controlling model is trained, the neural network control models after being trained；And
Server controls air conditioner according to the neural network control models after training.
11. the air-conditioner control method according to claim 10 based on machine learning, which is characterized in that server according to
Neural network control models after training control air conditioner：
Receive the key power-on instruction from client；
Obtain the current ambient parameter information of air conditioner；
By the neural network control models after the current ambient parameter information input training of air conditioner；
Air conditioner control parameter is obtained according to the output of the neural network control models after the training；And
It is run according to the control parameter after controlling air conditioner booting.
12. a kind of air conditioner controlling device based on machine learning, which is characterized in that including：
Operating parameter acquisition module, for obtaining air conditioner operating parameter and setting time set by user；
Environmental parameter acquisition module, when obtaining user's setting air conditioner operating parameter, the environment ginseng corresponding to the setting time
Number；
Controlling model training module, for using the air conditioner operating parameter repeatedly recorded as output, corresponding environmental parameter conduct
Input, is trained neural network control models, the neural network control models after being trained；And
Control parameter output module, for obtaining air conditioner control parameter according to the neural network control models after training.
13. the air conditioner controlling device according to claim 12 based on machine learning, which is characterized in that the air conditioner
Operating parameter includes：
The operational mode of air-conditioning, wind speed, one or more of waves control at set temperature.
14. the air conditioner controlling device according to claim 12 based on machine learning, which is characterized in that described corresponding
Environmental parameter includes：
Indoor environment temperature, outdoor environment temperature, the operating time of user, the operation duration of air-conditioning, air-conditioning position warp
One or more of latitude.
15. the air conditioner controlling device according to claim 14 based on machine learning, which is characterized in that described corresponding
Environmental parameter further includes indoor air humidity and outside air humidity.
16. the air conditioner controlling device according to claim 12 based on machine learning, which is characterized in that the nerve net
Network Controlling model is multilayer depth belief network model.
17. the air conditioner controlling device according to claim 12 based on machine learning, which is characterized in that control parameter is defeated
Depanning root tuber obtains air conditioner control parameter according to the neural network control models after training：
Obtain the current ambient parameter information of air-conditioning；
By the neural network control models after the current ambient parameter information input training of air-conditioning；And
Air conditioner control parameter is obtained according to the output of neural network control models.
18. the air conditioner controlling device according to claim 12 based on machine learning, which is characterized in that further include：One
Key starting module, for receiving air-conditioning power-on instruction and determining that air-conditioning booting operating parameter, the one-key start module are further
Including：
Power-on instruction receiving unit, for receiving power-on instruction input by user；
Be switched on operating parameter acquiring unit, for being communicated with control parameter output module, obtains control parameter output module
The air conditioner control parameter of output, and as booting operating parameter；And
Control unit is run, is brought into operation according to the booting operating parameter for controlling air conditioner.
19. a kind of air conditioner, which is characterized in that include according to any one of claim 12-18 based on engineering
The air conditioner controlling device of habit.
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