CN111981644A - Air conditioner control method and device and electronic equipment - Google Patents
Air conditioner control method and device and electronic equipment Download PDFInfo
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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
- F24F11/52—Indication arrangements, e.g. displays
- F24F11/526—Indication arrangements, e.g. displays giving audible indications
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/79—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling the direction of the supplied air
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
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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
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
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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
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
- F24F2120/12—Position of occupants
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
- G10L2015/223—Execution procedure of a spoken command
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Abstract
The invention provides an air conditioner control method, an air conditioner control device and electronic equipment, and relates to the technical field of smart home, wherein the method comprises the following steps: receiving voice information of a user; identifying control parameters corresponding to the voice information; acquiring position information of the position of the user according to the voice information; and controlling the air conditioner based on the control parameters and the position information. The embodiment of the invention can improve the intelligent degree of controlling the air conditioner.
Description
Technical Field
The invention relates to the technical field of smart home, in particular to an air conditioner control method and device and electronic equipment.
Background
Along with the development of science and technology, smart home is more and more favored by people. The intelligent home can improve home safety, convenience, comfort and artistry, and can realize environment-friendly and energy-saving living environment.
The intelligent control to the air conditioner is the important link of realizing intelligent house, and at present, the intelligent control to the air conditioner is usually to realize the automation of air conditioner and open and close, and specific implementation scheme does: the air conditioner is turned on when there is a person, and turned off when there is no person. And the adjustment of the operating parameters of the air conditioner still needs to be controlled by a user through an air conditioner remote controller, so that the intelligent degree is low.
Disclosure of Invention
The embodiment of the invention provides an air conditioner control method, an air conditioner control device and electronic equipment, and aims to solve the problems that in the prior art, the adjustment of the operating parameters of an air conditioner still needs to be controlled by a user through an air conditioner remote controller, and the intelligent degree is low.
In order to solve the technical problem, the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides an air conditioner control method, where the method includes:
receiving voice information of a user;
identifying control parameters corresponding to the voice information;
acquiring position information of the position of the user according to the voice information;
and controlling the air conditioner based on the control parameters and the position information.
In a second aspect, an embodiment of the present invention provides an air conditioning control apparatus, including:
the receiving module is used for receiving voice information of a user;
the recognition module is used for recognizing the control parameters corresponding to the voice information;
the first acquisition module is used for acquiring the position information of the position where the user is located according to the voice information;
and the first control module is used for controlling the air conditioner based on the control parameters and the position information.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and when executed by the processor, the electronic device implements the steps of the air conditioner control method according to the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the air conditioner control method according to the first aspect.
In the embodiment of the invention, voice information of a user is received; identifying control parameters corresponding to the voice information; acquiring position information of the position of the user according to the voice information; and controlling the air conditioner based on the control parameters and the position information. Therefore, the air conditioner is automatically controlled through the voice information of the user and the position information of the user, the control through an air conditioner remote controller is not needed, and the intelligent degree of the air conditioner control can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of an air conditioner control method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an air conditioning control device according to an embodiment of the present invention;
fig. 3 is a second schematic structural diagram of an air conditioning control device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the embodiment of the present invention, the electronic device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted mobile terminal, a wearable device, a sound box, an air conditioner, and the like.
Referring to fig. 1, fig. 1 is a flowchart of an air conditioner control method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
The voice information of the user can be received by the sound sensor, for example, the voice information of the user can be received by a microphone array. After the sound sensor receives the sound signal, whether the sound signal is voice information or noise can be judged through the short-time energy and the zero crossing rate of the sound signal, if the sound signal is judged to be noise, the voice information is not identified, and the calculated amount is saved.
And 102, identifying a control parameter corresponding to the voice information.
The control parameter may be a parameter for controlling the air conditioner, may be a parameter for controlling the temperature of the air conditioner, may be a parameter for controlling the humidity of the air conditioner, or may be a parameter for controlling the wind direction of the air conditioner, and the like, which is not limited in this embodiment of the present invention.
In addition, the control parameter corresponding to the voice information can be identified through a voice identification technology. The speech information may be recognized using a Hidden Markov Model (HMM) based on a mixture gaussian model; or, a DNN (deep neural network) -HMM algorithm can be adopted to establish a model to recognize the voice information; alternatively, the speech information may be recognized using an end-to-end technique. The control parameter corresponding to the voice information may be identified by identifying a command word in the voice information and using a short sentence containing the command word as a control parameter of the air conditioner. For example, if the voice message includes "prohibit direct blowing", the command word "prohibit" may be recognized; the voice message includes "start blow-through", the command word "start" can be recognized, and "start blow-through" and "inhibit blow-through" are used as control parameters.
Further, identifying a control parameter corresponding to the voice information, or matching the identified voice information with stored preset information, and if the identified voice information is matched with the stored preset information, using the matched information as the control parameter. For example, if the stored preset information includes "start direct blowing", and the recognized voice information is "start direct blowing", the "start direct blowing" may be used as the control parameter. The identifying of the control parameter corresponding to the voice information may also be identifying the control parameter corresponding to the voice information by adopting a semantic identification method, and the like.
And 103, acquiring the position information of the position of the user according to the voice information.
And acquiring the position information of the position of the user according to the voice information by using sound source positioning. The location information may include a distance between the location of the user and the air conditioner, and/or a direction of the location of the user with respect to the air conditioner. The probability of the voice information can be respectively calculated by using the pre-trained probability distribution functions of multiple types of sound source positions so as to identify the first type of sound source position corresponding to the voice information, the probability of the voice information calculated by the probability distribution function of the first type of sound source position is the highest, and the position of a user at the first type of sound source position can be predicted; or, other sound source positioning methods may be adopted to obtain the position information of the position where the user is located.
And 104, controlling the air conditioner based on the control parameters and the position information.
The wind direction of the air conditioner can be controlled to face the position or avoid the position based on the control parameter and the position information, or the temperature or the humidity of the air conditioner can be controlled based on the control parameter and the position information, or the wind speed of the air conditioner can be controlled based on the control parameter and the position information, or the wind direction of the air conditioner can be controlled to face the position or avoid the position based on the control parameter and the position information, and the wind speed, the temperature or the humidity of the air conditioner and the like can be controlled at the same time. For example, if the voice message of the user is "start direct blowing and control the wind speed to be at the middle gear", the wind direction of the air conditioner may be controlled to face the position of the user and the wind speed of the air conditioner may be controlled to be at the middle gear.
In the embodiment of the invention, voice information of a user is received; identifying control parameters corresponding to the voice information; acquiring position information of the position of the user according to the voice information; and controlling the air conditioner based on the control parameters and the position information. Therefore, the air conditioner is automatically controlled through the voice information of the user and the position information of the user, the control through an air conditioner remote controller is not needed, and the intelligent degree of the air conditioner control can be improved.
Optionally, the controlling the air conditioner based on the control parameter and the location information includes:
and controlling the wind direction of the air conditioner to face the position or avoid the position based on the control parameter and the position information.
The control parameter is used for indicating the wind direction of the air conditioner to face the position where the user is located, the wind direction of the air conditioner can be controlled to face the position where the user is located, and the control parameter is used for indicating the wind direction of the air conditioner to avoid the position where the user is located, and the wind direction of the air conditioner can be controlled to be far away from the position where the user is located.
In this embodiment, the direction of the wind of the air conditioner is controlled to the position or to avoid the position based on the control parameter and the position information, so that the direction of the wind of the air conditioner can be automatically controlled based on the control parameter and the position, and the degree of intellectualization of air conditioning control can be further improved.
Optionally, the obtaining the location information of the location where the user is located according to the voice information includes:
and determining the position information of the position of the user sending the voice information through sound source positioning.
Wherein the air conditioner may be provided with one or more sound sensors, e.g. microphones. Sound source localization may be performed by one or more sound sensors, determining location information of a location where a user who uttered the voice information is located.
In the embodiment, the position information of the position of the user sending the voice information is determined through sound source positioning, and the position information of the user can be acquired more accurately.
Optionally, the controlling the wind direction of the air conditioner to face the location or avoid the location based on the control parameter and the location information includes:
and under the condition that the control parameter indicates that the wind direction of the air conditioner faces the user, controlling the wind direction of the air conditioner to face the first type sound source position.
Wherein the first type sound source position may be a position where a user is predicted to be located. The location of the user may be in one of a plurality of types of source locations. The maximum rotation angle of the air deflector at the air outlet of the air conditioner is 180 degrees, and the wind direction of the air conditioner can be controlled through the rotation angle of the air deflector. The first type of sound source position can be one of the sound source positions.
Further, the control parameter is an indication that the wind direction of the air conditioner is avoided under the condition of the user, the wind direction of the air conditioner can be controlled to face any type of sound source position except the first type of sound source position, or the wind direction of the air conditioner can be controlled to face the second type of sound source position, and the second type of sound source position is a distance from the first type of sound source position to the farthest type of sound source position.
In this embodiment, in a case where the control parameter indicates that the wind direction of the air conditioner is directed toward the user, the wind direction of the air conditioner is controlled toward the first type sound source position. Therefore, under the condition that the user indicates that the wind direction of the air conditioner faces the user, the wind direction of the air conditioner can face the user to the maximum extent, and the user experience is good.
Optionally, the position information includes a distance between a position where the user is located and the air conditioner, and the controlling the wind direction of the air conditioner to face the first type sound source position includes:
controlling the wind direction of the air conditioner to face the first type sound source position, controlling the wind speed of the air conditioner to be a target wind speed, and enabling the target wind speed to be positively correlated with the distance between the position where the user is located and the air conditioner.
When the distance between the position of the user and the air conditioner is large, namely the user is far away from the air conditioner, the wind speed of the air conditioner can be controlled to be large; when the distance between the position of the user and the air conditioner is small, namely the user is close to the air conditioner, the wind speed of the air conditioner can be controlled to be small.
In the embodiment, the air speed of the air conditioner is controlled to be the target air speed, the target air speed is positively correlated with the distance between the position of the user and the air conditioner, the air speed can be intelligently controlled, and the user experience is better.
Optionally, the controlling the wind direction of the air conditioner to face the location or avoid the location based on the control parameter and the location information includes:
and controlling the wind direction of the air conditioner to face the second type sound source position under the condition that the control parameter indicates that the wind direction of the air conditioner avoids the user, wherein the second type sound source position is the type of sound source position which is farthest away from the first type sound source position.
The second type sound source position may be a type of sound source position having the largest angle deviation from the first type sound source position, for example, if the first type sound source position is divided every 10 °, 18 type sound source positions are obtained, and if the first type sound source position is 60 °, the second type sound source position may be 180 °.
In this embodiment, the control parameter is for instructing the wind direction of air conditioner is avoided under the condition of user, control the wind direction orientation of air conditioner the second class sound source position, second class sound source position is the distance the first class sound source position of the farthest, like this, instructs at the user the wind direction of air conditioner is avoided under the condition of user, can be the at utmost make the wind direction of air conditioner avoid the user, user experience is better.
Optionally, the obtaining the location information of the location where the user is located according to the voice information includes:
and respectively calculating the probability of the voice information by using the pre-trained probability distribution functions of the sound source positions of multiple classes to identify the sound source position of the first class corresponding to the voice information, wherein the probability distribution function of the sound source position of the first class calculates the highest probability of the voice information.
Wherein, use the probability distribution function of multiclass sound source position of training in advance to calculate respectively voice information's probability can be, calculate voice information's eigenvector, based on voice information's eigenvector uses the probability distribution function of multiclass sound source position of training in advance to calculate respectively voice information's probability can calculate voice information's eigenvector is at the posterior probability of all kinds of sound source positions, and first class sound source position can be the one class sound source position that posterior probability is the biggest, can predict user's position most probably is at first class sound source position.
In this embodiment, the probability of the speech information is calculated by using the pre-trained probability distribution functions of the positions of the multiple sound sources, so as to identify the position of the first sound source corresponding to the speech information, and the position of the user can be predicted more accurately.
Optionally, the probability distribution function of the positions of the multiple sound sources is obtained by training in the following manner:
classifying according to the angle of the sound source position relative to the air outlet of the air conditioner to obtain multiple types of sound source positions;
acquiring a plurality of voice signals corresponding to each type of sound source position in the plurality of types of sound source positions, and taking the plurality of voice signals corresponding to each type of sound source position as training samples;
and acquiring a probability distribution function corresponding to the position of each type of sound source based on the training sample.
The rotation angle of the air deflector at the air outlet of the air conditioner is 180 degrees at most, and the wind direction of the air conditioner can be controlled through the rotation angle of the air deflector. The sound source positions can be divided into one class every 180/N degrees to obtain N classes of sound source positions, wherein N is a positive integer. For example, the sound source positions can be divided into 18 types every 10 degrees, so that 18 types of sound source positions are obtained; or, the sound source positions can be divided into one type of sound source positions every 20 degrees to obtain 9 types of sound source positions; alternatively, the sound source positions may be divided into one type every 30 °, and 6 types of sound source positions may be obtained. A plurality of voice signals can be obtained in the area where the position of each type of sound source is located, the voice signals are used as training samples of the sound source position, and the probability distribution function corresponding to the sound source position is obtained based on the training samples of the sound source position.
Further, the probability distribution function corresponding to each type of sound source position is obtained based on the training sample, which may be that a generalized cross-correlation-phase transformation method GCC-PHAT is used to obtain a feature vector of the training sample, linear discriminant analysis LDA training is performed based on the feature vector of the training sample, the feature vector of the training sample is used to project the trained LDA, a maximum likelihood estimation method is used to obtain a mean value and a variance of a gaussian model for the projected feature vector, and a probability distribution function corresponding to each type of sound source position is obtained based on the mean value and the variance of the gaussian model; or, a Linear Prediction Coefficient (LPC) method may be used to obtain a feature vector of the training sample, Linear Discriminant Analysis (LDA) training is performed based on the feature vector of the training sample, the feature vector of the training sample is used to project the trained LDA, a maximum likelihood estimation method is used to obtain a mean and a variance of a gaussian model for the projected feature vector, and a probability distribution function corresponding to each type of sound source position is obtained based on the mean and the variance of the gaussian model; or, a Discrete Wavelet Transform (DWT) method may be used to obtain the feature vector of the training sample, a maximum likelihood estimation method may be used to obtain a mean and a variance of a gaussian model for the obtained feature vector, and a probability distribution function corresponding to the position of each type of sound source may be obtained based on the mean and the variance of the gaussian model. The embodiment of the present invention does not limit this.
In the embodiment, the sound source positions are classified according to the angles of the sound source positions relative to the air outlet of the air conditioner, so that multiple types of sound source positions are obtained; acquiring a plurality of voice signals corresponding to each type of sound source position in the plurality of types of sound source positions, and taking the plurality of voice signals corresponding to each type of sound source position as training samples; and acquiring a probability distribution function corresponding to the position of each type of sound source based on the training sample. Therefore, the probability distribution function corresponding to each type of sound source position is obtained based on the training sample, and the accuracy of obtaining the position of the user by sound source positioning can be improved.
Optionally, the obtaining, based on the training sample, a probability distribution function corresponding to the position of each type of sound source includes:
acquiring a feature vector of the training sample by adopting a generalized cross-correlation-phase transformation method GCC-PHAT;
performing Linear Discriminant Analysis (LDA) training based on the feature vectors of the training samples;
projecting the trained LDA by adopting the characteristic vector of the training sample;
obtaining the mean value and the variance of the Gaussian model by adopting a maximum likelihood estimation method for the projected feature vector;
and acquiring a probability distribution function corresponding to each type of sound source position based on the mean value and the variance of the Gaussian model.
The Generalized Cross-Correlation-PHAse Transformation method GCC-PHAT (Generalized Cross Correlation PHAse Transformation) may be used to extract the features of the training samples, and in the case of collecting speech information through two microphones, the following method may be used:
the channel signals of two microphones can be collected, and the weighted generalized cross-correlation function is calculated by adopting the following formula
Wherein τ represents time, τmaxω is the time difference between the arrival of the speech signal at the two microphones and ω is the angular frequency.
Representing the phase weighting function:
X1(ω) is the result of a windowed fourier transform of the speech signal picked up by the first microphone,
the conjugate of the windowed fourier transform of the speech signal collected by the second microphone.
Has a total length of 2 taumax+1, i.e. 2 τ is obtainedmax+1 features, assuming N command words, N × (2 τ) can be obtainedmax+1) dimensional features.
Further, performing Linear Discriminant Analysis (LDA) training based on the feature vectors of the training samples, which may be implemented as follows:
the overall divergence matrix can be calculated using the following formula:
wherein, X represents the whole training sample set, X represents the feature vector extracted by each training sample, and μ represents the feature mean vector of all the training samples. The relationship between the overall divergence matrix and the intra-class divergence matrix and the inter-class divergence matrix is as follows: sT＝SB+Sw，SwRepresenting intra-class divergence matrices, S, due to the existence of multiple classes of sound source position classeswCan be obtained by using the following formula:
wherein S isiAn intra-class divergence matrix, S, representing the position of the ith class sound sourceiCan be obtained by using the following formula:
where x represents the extracted feature vector, μ, of each training sample belonging to the i-th class sound source positioniAnd representing the feature mean vector of all training samples of the ith type sound source position.
The following formula can be adopted to obtain the inter-class divergence matrix SB：
SB＝ST-Sw
According to SwAnd STThrough derivation simplification, the following can be obtained:
wherein N isiAnd the total number of training samples representing the position of the ith type sound source.
The loss function can be set as follows:
the optimal projection matrix W can be obtained by maximizing the loss function J by using the lagrange multiplier method.
The feature vectors of all training samples can be projected by using the trained LDA according to the position of each type of sound source, and the mean value and the variance of a Gaussian model can be obtained by using a maximum likelihood estimation algorithm after the projection, so that the probability distribution function of each type of sound source position can be obtained.
In the embodiment, a generalized cross-correlation-phase transformation method GCC-PHAT is adopted to obtain the feature vector of the training sample; performing Linear Discriminant Analysis (LDA) training based on the feature vectors of the training samples; projecting the trained LDA by adopting the characteristic vector of the training sample; obtaining the mean value and the variance of the Gaussian model by adopting a maximum likelihood estimation method for the projected feature vector; and acquiring a probability distribution function corresponding to each type of sound source position based on the mean value and the variance of the Gaussian model. Therefore, the positions of the various sound sources can be better classified, and the accuracy of sound source positioning is improved.
Optionally, the method further includes:
acquiring current environmental parameters;
matching the current environment parameters with preset environment parameters;
if the current environmental parameter is matched with the preset environmental parameter, adopting a control parameter corresponding to the preset environmental parameter to control the air conditioner;
wherein the current environmental parameter includes at least one of an ambient temperature, an ambient humidity, an ambient light intensity, and a time.
The control system comprises a control unit, a control unit and a control unit, wherein preset environment parameters and control parameters corresponding to the preset environment parameters can be stored in advance, and the preset environment parameters can comprise at least one of preset environment temperature, preset environment humidity, preset environment light intensity and preset time. The current environmental parameter may be greater than or equal to the preset environmental parameter, the preset environmental parameter may include a preset environmental temperature, and the control parameter corresponding to the preset environmental parameter may be a control parameter corresponding to the preset environmental temperature. For example, the preset ambient temperature may be 38 ℃, and the control parameters corresponding to the preset ambient temperature are: and opening cold air, controlling the target temperature of the air conditioner to be 25 ℃, and controlling the air conditioner to open the cold air and controlling the target temperature of the air conditioner to be 25 ℃ when the ambient temperature reaches 38 ℃. The current environmental parameter is matched with the preset environmental parameter, or the current environmental parameter is smaller than the preset environmental parameter, for example, the preset environmental temperature may be 20 ℃, and the control parameter corresponding to the preset environmental temperature is: and starting hot air, controlling the target temperature of the air conditioner to be 28 ℃, and when the ambient temperature is lower than 20 ℃, controlling the air conditioner to start hot air and controlling the target temperature of the air conditioner to be 28 ℃.
Further, the preset environment parameters may further include a preset environment temperature and a preset environment light intensity, for example, the preset environment temperature may be 30 ℃, the preset environment light intensity is 10Lux, and the control parameters corresponding to the preset environment parameters are: and (3) starting cold air, controlling the target temperature of the air conditioner to be 25 ℃, and when the ambient temperature is higher than 30 ℃ and the ambient light intensity is higher than 10Lux, controlling the air conditioner to start the cold air and controlling the target temperature of the air conditioner to be 25 ℃. The temperature sensor can be adopted to acquire the ambient temperature, the optical sensor is adopted to acquire the ambient light intensity, and the judgment is carried out at night according to the ambient light intensity, so that the air conditioner is not started, the air conditioner is controlled according to the ambient temperature and the ambient light intensity, the automatic opening of the air conditioner at night is avoided, and the sleeping environment of a user is interfered.
It should be noted that the air conditioner can be controlled by the intelligent sound box, a temperature sensor and an optical sensor can be installed inside the intelligent sound box, and the intelligent sound box obtains the temperature and the light intensity of the environment where the sound box is located through the temperature sensor and the optical sensor. Can gather various environment parameters that the people of letting such as high temperature high light intensity, high temperature low light intensity, low temperature low light intensity and low temperature high light intensity felt discomfort, regard this environment parameter that the people felt discomfort as presetting the environment parameter storage at smart audio amplifier, and with the control parameter that the environmental parameter that presets corresponds is saved at smart audio amplifier with the form of audio amplifier broadcast information. For example, the indoor temperature reaches 38 ℃, and the smart speaker can play: and opening an air conditioner, opening cold air and adjusting the target temperature to be 25 ℃. The intelligent sound box can also judge that the current time is day time or night time through ambient temperature and ambient light intensity, thereby adopting different control strategies at day time and night time. The air conditioner receives the voice played by the intelligent sound box, performs voice recognition on the voice, and can control the air conditioner according to the content of the voice recognition so as to execute the command transmitted by the voice; or, in order to improve the control effect of the intelligent sound box for controlling the air conditioner, the content of voice recognition can be judged, if the content of voice recognition is stored in the air conditioner, the air conditioner is controlled according to the content of voice recognition, and if the content of voice recognition is not stored in the air conditioner, the content of voice recognition is discarded.
In this embodiment, current environmental parameters are obtained; matching the current environment parameters with preset environment parameters; if the current environmental parameters are matched with the preset environmental parameters, the control parameters corresponding to the preset environmental parameters are adopted to control the air conditioner, the air conditioner can be automatically controlled according to the current environmental parameters, and the intelligent degree of air conditioner control is further improved.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an air conditioning control device according to an embodiment of the present invention, and as shown in fig. 2, the air conditioning control device 200 includes:
a receiving module 201, configured to receive voice information of a user;
the recognition module 202 is configured to recognize a control parameter corresponding to the voice information;
a first obtaining module 203, configured to obtain location information of a location where the user is located according to the voice information;
and the first control module 204 is configured to control the air conditioner based on the control parameter and the location information.
Optionally, the first control module 204 is specifically configured to:
and controlling the wind direction of the air conditioner to face the position or avoid the position based on the control parameter and the position information.
Optionally, the first obtaining module 203 is specifically configured to:
and determining the position information of the position of the user sending the voice information through sound source positioning.
Optionally, the first control module 204 is specifically configured to:
and under the condition that the control parameter indicates that the wind direction of the air conditioner faces the user, controlling the wind direction of the air conditioner to face a first type sound source position.
Optionally, the location information includes a distance between the location of the user and the air conditioner, and the first control module 204 is further configured to:
controlling the wind direction of the air conditioner to face the first type sound source position, controlling the wind speed of the air conditioner to be a target wind speed, and enabling the target wind speed to be positively correlated with the distance between the position where the user is located and the air conditioner.
Optionally, the first control module 204 is specifically configured to:
and controlling the wind direction of the air conditioner to face a second type sound source position under the condition that the control parameter indicates that the wind direction of the air conditioner avoids the user, wherein the second type sound source position is a type of sound source position which is farthest away from the first type sound source position.
Optionally, the first obtaining module 203 is specifically configured to:
and respectively calculating the probability of the voice information by using the pre-trained probability distribution functions of the sound source positions of multiple classes to identify the sound source position of the first class corresponding to the voice information, wherein the probability distribution function of the sound source position of the first class calculates the highest probability of the voice information.
Optionally, the probability distribution function of the positions of the multiple sound sources is obtained by training in the following manner:
classifying according to the angle of the sound source position relative to the air outlet of the air conditioner to obtain multiple types of sound source positions;
acquiring a plurality of voice signals corresponding to each type of sound source position in the plurality of types of sound source positions, and taking the plurality of voice signals corresponding to each type of sound source position as training samples;
and acquiring a probability distribution function corresponding to the position of each type of sound source based on the training sample.
Optionally, the first obtaining module 203 is further configured to:
acquiring a feature vector of the training sample by adopting a generalized cross-correlation-phase transformation method GCC-PHAT;
performing Linear Discriminant Analysis (LDA) training based on the feature vectors of the training samples;
projecting the trained LDA by adopting the characteristic vector of the training sample;
obtaining the mean value and the variance of the Gaussian model by adopting a maximum likelihood estimation method for the projected feature vector;
and acquiring a probability distribution function corresponding to each type of sound source position based on the mean value and the variance of the Gaussian model.
Optionally, as shown in fig. 3, the air conditioning control apparatus 200 further includes:
a second obtaining module 205, configured to obtain a current environment parameter;
a matching module 206, configured to match the current environment parameter with a preset environment parameter;
the second control module 207 is configured to control the air conditioner by using a control parameter corresponding to the preset environment parameter if the current environment parameter matches the preset environment parameter;
wherein the current environmental parameter includes at least one of an ambient temperature, an ambient humidity, an ambient light intensity, and a time.
The air conditioner control device can implement each process implemented in the method embodiment of fig. 1, and is not described herein again to avoid repetition.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device 300 includes: a memory 302, a processor 301, and a program stored on the memory 302 and executable on the processor 301, wherein:
the processor 301 reads the program in the memory 302 for executing:
receiving voice information of a user;
identifying control parameters corresponding to the voice information;
acquiring position information of the position of the user according to the voice information;
and controlling the air conditioner based on the control parameters and the position information.
In the embodiment of the invention, voice information of a user is received; identifying control parameters corresponding to the voice information; acquiring position information of the position of the user according to the voice information; and controlling the air conditioner based on the control parameters and the position information. Therefore, the air conditioner is automatically controlled through the voice information of the user and the position information of the user, the control through an air conditioner remote controller is not needed, and the intelligent degree of the air conditioner control can be improved.
Optionally, the controlling the air conditioner based on the control parameter and the location information performed by the processor 301 includes:
and controlling the wind direction of the air conditioner to face the position or avoid the position based on the control parameter and the position information.
Optionally, the obtaining, by the processor 301, the location information of the location where the user is located according to the voice information includes:
and determining the position information of the position of the user sending the voice information through sound source positioning.
Optionally, the controlling, by the processor 301, the wind direction of the air conditioner towards the location or avoiding the location based on the control parameter and the location information includes:
and under the condition that the control parameter indicates that the wind direction of the air conditioner faces the user, controlling the wind direction of the air conditioner to face the first type sound source position.
Optionally, the position information includes a distance between the position of the user and the air conditioner, and the controlling, performed by the processor 301, the wind direction of the air conditioner towards the first type sound source position includes:
controlling the wind direction of the air conditioner to face the first type sound source position, controlling the wind speed of the air conditioner to be a target wind speed, and enabling the target wind speed to be positively correlated with the distance between the position where the user is located and the air conditioner.
Optionally, the controlling, by the processor 301, the wind direction of the air conditioner towards the location or avoiding the location based on the control parameter and the location information includes:
and controlling the wind direction of the air conditioner to face a second type sound source position under the condition that the control parameter indicates that the wind direction of the air conditioner avoids the user, wherein the second type sound source position is a type of sound source position which is farthest away from the first type sound source position.
Optionally, the obtaining, by the processor 301, the location information of the location where the user is located according to the voice information includes:
and respectively calculating the probability of the voice information by using the pre-trained probability distribution functions of the sound source positions of multiple classes to identify the sound source position of the first class corresponding to the voice information, wherein the probability distribution function of the sound source position of the first class calculates the highest probability of the voice information.
Optionally, the processor 301 is further configured to perform:
classifying according to the angle of the sound source position relative to the air outlet of the air conditioner to obtain multiple types of sound source positions;
acquiring a plurality of voice signals corresponding to each type of sound source position in the plurality of types of sound source positions, and taking the plurality of voice signals corresponding to each type of sound source position as training samples;
and acquiring a probability distribution function corresponding to the position of each type of sound source based on the training sample.
Optionally, the obtaining, by the processor 301, a probability distribution function corresponding to the position of each type of sound source based on the training sample includes:
acquiring a feature vector of the training sample by adopting a generalized cross-correlation-phase transformation method GCC-PHAT;
performing Linear Discriminant Analysis (LDA) training based on the feature vectors of the training samples;
projecting the trained LDA by adopting the characteristic vector of the training sample;
obtaining the mean value and the variance of the Gaussian model by adopting a maximum likelihood estimation method for the projected feature vector;
and acquiring a probability distribution function corresponding to each type of sound source position based on the mean value and the variance of the Gaussian model.
Optionally, the processor 301 is further configured to perform:
acquiring current environmental parameters;
matching the current environment parameters with preset environment parameters;
if the current environmental parameter is matched with the preset environmental parameter, adopting a control parameter corresponding to the preset environmental parameter to control the air conditioner;
wherein the current environmental parameter includes at least one of an ambient temperature, an ambient humidity, an ambient light intensity, and a time.
In fig. 4, the bus architecture may include any number of interconnected buses and bridges, with one or more processors represented by processor 301 and various circuits of memory represented by memory 302 being linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface.
The processor 301 is responsible for managing the bus architecture and general processing, and the memory 302 may store data used by the processor 301 in performing operations.
It should be noted that any implementation manner in the method embodiment of the present invention may be implemented by the electronic device in this embodiment, and achieve the same beneficial effects, and details are not described here.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the above-mentioned air conditioner control method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (12)
1. An air conditioner control method, characterized in that the method comprises:
receiving voice information of a user;
identifying control parameters corresponding to the voice information;
acquiring position information of the position of the user according to the voice information;
and controlling the air conditioner based on the control parameters and the position information.
2. The method of claim 1, wherein the controlling the air conditioner based on the control parameter and the location information comprises:
and controlling the wind direction of the air conditioner to face the position or avoid the position based on the control parameter and the position information.
3. The method according to claim 2, wherein the obtaining the location information of the location where the user is located according to the voice information comprises:
and determining the position information of the position of the user sending the voice information through sound source positioning.
4. The method of claim 2, wherein the controlling the wind direction of the air conditioner toward the location or away from the location based on the control parameter and the location information comprises:
and under the condition that the control parameter indicates that the wind direction of the air conditioner faces the user, controlling the wind direction of the air conditioner to face a first type sound source position.
5. The method of claim 4, wherein the location information comprises a distance between a location of the user and the air conditioner, and the controlling the wind direction of the air conditioner toward the first type sound source location comprises:
controlling the wind direction of the air conditioner to face the first type sound source position, controlling the wind speed of the air conditioner to be a target wind speed, and enabling the target wind speed to be positively correlated with the distance between the position where the user is located and the air conditioner.
6. The method of claim 4, wherein the controlling the wind direction of the air conditioner towards the location or away from the location based on the control parameter and the location information comprises:
and controlling the wind direction of the air conditioner to face a second type sound source position under the condition that the control parameter indicates that the wind direction of the air conditioner avoids the user, wherein the second type sound source position is a type of sound source position which is farthest away from the first type sound source position.
7. The method according to any one of claims 4 to 6, wherein the obtaining the location information of the location where the user is located according to the voice information comprises:
and respectively calculating the probability of the voice information by using the pre-trained probability distribution functions of the sound source positions of multiple classes to identify the sound source position of the first class corresponding to the voice information, wherein the probability distribution function of the sound source position of the first class calculates the highest probability of the voice information.
8. The method of claim 7, wherein the probability distribution function of the multi-class sound source positions is trained by:
classifying according to the angle of the sound source position relative to the air outlet of the air conditioner to obtain multiple types of sound source positions;
acquiring a plurality of voice signals corresponding to each type of sound source position in the plurality of types of sound source positions, and taking the plurality of voice signals corresponding to each type of sound source position as training samples;
and acquiring a probability distribution function corresponding to the position of each type of sound source based on the training sample.
9. The method according to claim 8, wherein the obtaining the probability distribution function corresponding to each type of sound source position based on the training sample comprises:
acquiring a feature vector of the training sample by adopting a generalized cross-correlation-phase transformation method GCC-PHAT;
performing Linear Discriminant Analysis (LDA) training based on the feature vectors of the training samples;
projecting the trained LDA by adopting the characteristic vector of the training sample;
obtaining the mean value and the variance of the Gaussian model by adopting a maximum likelihood estimation method for the projected feature vector;
and acquiring a probability distribution function corresponding to each type of sound source position based on the mean value and the variance of the Gaussian model.
10. The method of claim 1, further comprising:
acquiring current environmental parameters;
matching the current environment parameters with preset environment parameters;
if the current environmental parameter is matched with the preset environmental parameter, adopting a control parameter corresponding to the preset environmental parameter to control the air conditioner;
wherein the current environmental parameter includes at least one of an ambient temperature, an ambient humidity, an ambient light intensity, and a time.
11. An air conditioning control device characterized by comprising:
the receiving module is used for receiving voice information of a user;
the recognition module is used for recognizing the control parameters corresponding to the voice information;
the first acquisition module is used for acquiring the position information of the position where the user is located according to the voice information;
and the first control module is used for controlling the air conditioner based on the control parameters and the position information.
12. An electronic device, comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the air conditioning control method according to any one of claims 1 to 10.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015108464A (en) * | 2013-12-04 | 2015-06-11 | 日立アプライアンス株式会社 | Operation control system |
| CN104728990A (en) * | 2013-12-19 | 2015-06-24 | 海尔集团公司 | Air-conditioner air supply speed control device and method and air conditioner |
| CN107246714A (en) * | 2017-06-28 | 2017-10-13 | 广东美的制冷设备有限公司 | Air conditioning control method and its equipment |
| CN110488887A (en) * | 2019-08-30 | 2019-11-22 | 百度在线网络技术（北京）有限公司 | Temprature control method, device, electronic equipment and the storage medium of smart home |
| CN111239687A (en) * | 2020-01-17 | 2020-06-05 | 浙江理工大学 | Sound source positioning method and system based on deep neural network |
| CN111261163A (en) * | 2020-03-27 | 2020-06-09 | 四川虹美智能科技有限公司 | Voice control method and system and intelligent air conditioner |
-
2020
- 2020-08-26 CN CN202010870383.3A patent/CN111981644B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015108464A (en) * | 2013-12-04 | 2015-06-11 | 日立アプライアンス株式会社 | Operation control system |
| CN104728990A (en) * | 2013-12-19 | 2015-06-24 | 海尔集团公司 | Air-conditioner air supply speed control device and method and air conditioner |
| CN107246714A (en) * | 2017-06-28 | 2017-10-13 | 广东美的制冷设备有限公司 | Air conditioning control method and its equipment |
| CN110488887A (en) * | 2019-08-30 | 2019-11-22 | 百度在线网络技术（北京）有限公司 | Temprature control method, device, electronic equipment and the storage medium of smart home |
| CN111239687A (en) * | 2020-01-17 | 2020-06-05 | 浙江理工大学 | Sound source positioning method and system based on deep neural network |
| CN111261163A (en) * | 2020-03-27 | 2020-06-09 | 四川虹美智能科技有限公司 | Voice control method and system and intelligent air conditioner |
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