CN108205261B - Intelligent household control method based on brain waves and electronic device - Google Patents
Intelligent household control method based on brain waves and electronic device Download PDFInfo
- Publication number
- CN108205261B CN108205261B CN201810236057.XA CN201810236057A CN108205261B CN 108205261 B CN108205261 B CN 108205261B CN 201810236057 A CN201810236057 A CN 201810236057A CN 108205261 B CN108205261 B CN 108205261B
- Authority
- CN
- China
- Prior art keywords
- parameters
- state
- brain wave
- conflict
- control command
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 210000004556 brain Anatomy 0.000 title claims abstract description 115
- 238000000034 method Methods 0.000 title claims abstract description 33
- 239000011159 matrix material Substances 0.000 claims description 30
- 238000012545 processing Methods 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 11
- 238000003062 neural network model Methods 0.000 claims description 8
- 238000013507 mapping Methods 0.000 claims description 7
- 239000000047 product Substances 0.000 description 34
- 230000015654 memory Effects 0.000 description 14
- 238000010586 diagram Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000033764 rhythmic process Effects 0.000 description 4
- 230000007177 brain activity Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000033001 locomotion Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 210000000554 iris Anatomy 0.000 description 2
- 238000010295 mobile communication Methods 0.000 description 2
- 210000003205 muscle Anatomy 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000005236 sound signal Effects 0.000 description 2
- 230000000638 stimulation Effects 0.000 description 2
- 206010002091 Anaesthesia Diseases 0.000 description 1
- 208000036993 Frustration Diseases 0.000 description 1
- 206010024264 Lethargy Diseases 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 238000013019 agitation Methods 0.000 description 1
- 230000037005 anaesthesia Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000004397 blinking Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 210000003710 cerebral cortex Anatomy 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000002567 electromyography Methods 0.000 description 1
- 230000002996 emotional effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 208000028327 extreme fatigue Diseases 0.000 description 1
- 210000000887 face Anatomy 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000036630 mental development Effects 0.000 description 1
- 230000003340 mental effect Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000004118 muscle contraction Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000001242 postsynaptic effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000001020 rhythmical effect Effects 0.000 description 1
- 238000010079 rubber tapping Methods 0.000 description 1
- 210000004761 scalp Anatomy 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 239000010409 thin film Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2642—Domotique, domestic, home control, automation, smart house
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Telephone Function (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The application provides an intelligent home control method based on brain waves and an electronic device, wherein the electronic device comprises: the device comprises an application processor AP, a transceiver and a brain wave component, wherein the AP is used for controlling the brain wave component to acquire brain wave data when the AP is determined to be located in an indoor environment, and analyzing the brain wave data to determine a first state corresponding to the brain wave data; the AP is further configured to obtain N parameters of an intelligent household product in an indoor environment, determine whether the N parameters conflict with the first state, obtain a conflict result if the N parameters conflict with the first state, and generate a control command according to the conflict result, where the control command is used to adjust the N parameters; n is an integer greater than or equal to 1; the transceiver is used for sending the control command to the intelligent household product. The technical scheme provided by the application has the advantage of high user experience.
Description
Technical Field
The application relates to the field of terminal equipment and the Internet of things, in particular to an intelligent home control method based on brain waves and an electronic device.
Background
In the prior art, mobile terminals (such as mobile phones, tablet computers, etc.) have become electronic devices preferred and most frequently used by users. Along with the popularization of smart phones, the interaction between people and the smart phones is more and more diversified, such as voice, fingerprints, irises, human faces, images and the like, but the information sent by the engine brain of a human body is not related at present. Along with the improvement of living standard of people, the smart home is more and more applied to ordinary families, but the control of current smart home is based on contact control, for example, the control to smart home is realized through equipment such as remote controller or shift knob, can't control smart home through the brain wave, influences user experience.
Content of application
The embodiment of the application provides an intelligent home control method and an electronic device based on brain waves, which can realize control over intelligent home through the brain waves, realize control over non-contact intelligent home and improve user experience.
In a first aspect, an embodiment of the present application provides an electronic device, including: an application processor AP, a transceiver; the electronic device further includes: a brain wave part connected with the AP through at least one circuit;
the AP is used for controlling the brain wave component to acquire brain wave data when the AP is determined to be located in an indoor environment, and analyzing the brain wave data to determine a first state corresponding to the brain wave data;
the AP is further configured to obtain N parameters of an intelligent household product in an indoor environment, determine whether the N parameters conflict with the first state, obtain a conflict result if the N parameters conflict with the first state, and generate a control command according to the conflict result, where the control command is used to adjust the N parameters so that the adjusted N parameters do not conflict with the first state; n is an integer greater than or equal to 1;
the transceiver is used for sending the control command to the intelligent household product
In a second aspect, a smart home control method based on brain waves is provided, where the method is applied to an electronic device, and the electronic device includes: an application processor AP, a transceiver, the electronic device further comprising: a brain wave part connected with the AP through at least one circuit;
when the indoor environment is determined to be located, controlling the brain wave component to acquire brain wave data, and analyzing the brain wave data to determine a first state corresponding to the brain wave data;
acquiring N parameters of an intelligent household product in an indoor environment, determining whether the N parameters conflict with the first state, if so, acquiring a conflict result, and generating a control command according to the conflict result, wherein the control command is used for adjusting the N parameters so that the adjusted N parameters do not conflict with the first state; n is an integer greater than or equal to 1;
and sending the control command to the intelligent household product.
In a third aspect, an electronic device is provided, which includes: processing unit, transceiver, the electron device still includes: a brain wave component connected with the processing unit;
the processing unit is used for controlling the brain wave component to acquire brain wave data when the brain wave component is determined to be located in an indoor environment, and analyzing the brain wave data to determine a first state corresponding to the brain wave data;
the processing unit is further configured to obtain N parameters of an intelligent home product in an indoor environment, determine whether the N parameters conflict with the first state, obtain a conflict result if the N parameters conflict with the first state, and generate a control command according to the conflict result, where the control command is used to adjust the N parameters so that the adjusted N parameters do not conflict with the first state; n is an integer greater than or equal to 1;
the transceiver is used for sending the control command to the intelligent household product.
In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method provided in the second aspect.
In a fifth aspect, there is provided a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform the method provided by the second aspect.
The embodiment of the application has the following beneficial effects:
according to the technical scheme, when the intelligent home product is determined to be in an indoor environment, electroencephalogram data are obtained, the electroencephalogram data are analyzed to obtain a first state, then N parameters of the intelligent home product are obtained, whether the N parameters conflict with the first state or not is analyzed, if yes, a control command is generated according to the first state, the control command is sent to the intelligent home product, and therefore the intelligent home can be controlled through the electroencephalogram data of a user, and user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Fig. 1a is a waveform diagram of a delta wave.
Fig. 1b is a waveform diagram of a θ wave.
Fig. 1c is a waveform diagram of an α -wave.
Fig. 1d is a waveform diagram of a beta wave.
Fig. 2 is a schematic view of an electronic device disclosed in an embodiment of the present application.
Fig. 3 is a schematic flow chart of an intelligent home control method based on brain waves according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of a mobile phone disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In the electronic device provided in the first aspect, the AP is specifically configured to analyze the electroencephalogram data to determine whether a delta wave exists, if so, determine that the first state is a sleep state, and if not, determine that the first state is a non-sleep state.
In the electronic device according to the first aspect, the AP is specifically configured to extract p values of the brain wave data, form an input data matrix from the p values, input the input data matrix into a preset neural network model, perform a multi-layer forward operation to obtain a forward operation result, extract Z elements that are greater than a set threshold from among a plurality of elements in the forward operation result matrix, extract Z positions of the forward operation result matrix corresponding to the Z elements, extract Z states corresponding to the Z positions, determine that the first state is a sleep state if more than half of the Z states are sleep states, and determine that the first state is a non-sleep state if more than half of the Z states are non-sleep states.
In the electronic device provided in the first aspect, the AP is specifically configured to determine, according to the first state, a range of M parameters corresponding to the first state from a preset state-parameter mapping relationship, extract x parameters from the N parameters, where the x parameters are overlapped parameters of the N parameters and the M parameters, determine whether the x parameters belong to the range, and if the x parameters exceed the range, determine that the N parameters conflict with the first state, and extract y parameters exceeding the range from the x parameters as a conflict result, where x and y are both integers greater than or equal to 1 and less than or equal to N, M.
In the electronic device provided in the first aspect, the AP is specifically configured to generate a first control command if the y parameters are greater than the range, where the first control command is used to decrease the values of the y parameters, and generate a second control command if the y parameters are smaller than the set range, where the second control command is used to increase the values of the y parameters.
In a method provided by a second aspect, the analyzing the brain wave data to determine a first state corresponding to the brain wave data includes:
analyzing the brain wave data to determine whether delta waves exist or not, if yes, determining that the first state is a sleep state, and if not, determining that the first state is a non-sleep state.
In a method provided by a second aspect, the analyzing the brain wave data to determine a first state corresponding to the brain wave data includes:
extracting p values of the electroencephalogram data, forming an input data matrix by the p values, inputting the input data matrix into a preset neural network model, executing multilayer forward operation to obtain a forward operation result, extracting Z elements which are larger than a set threshold value from a plurality of elements in the forward operation result matrix, extracting Z positions of the forward operation result matrix corresponding to the Z elements, extracting Z states corresponding to the Z positions, determining that the first state is a sleep state if more than half of the Z states are sleep states, and determining that the first state is a non-sleep state if more than half of the Z states are non-sleep states.
In a method provided in a second aspect, a range of M parameters corresponding to a first state is determined from a preset state-parameter mapping relationship according to the first state, x parameters are extracted from the N parameters, the x parameters are overlapped parameters of the N parameters and the M parameters, whether the x parameters belong to the range is determined, if the x parameters exceed the range, it is determined that the N parameters conflict with the first state, y parameters exceeding the range are extracted from the x parameters as a conflict result, and x and y are both integers greater than or equal to 1 and less than or equal to N, M.
The electronic device in the present application may include a smart Phone (e.g., an Android Phone, an iOS Phone, a Windows Phone, etc.), a tablet computer, a palm computer, a notebook computer, a Mobile Internet device (MID, Mobile Internet Devices), or a wearable device, and the electronic Devices are merely examples, but not exhaustive, and include but are not limited to the electronic Devices, and for convenience of description, the electronic Devices are referred to as User Equipment (UE) in the following embodiments. Of course, in practical applications, the user equipment is not limited to the above presentation form, and may also include: wireless routers, home robots, computer devices, and the like.
Referring to fig. 1, fig. 1 is a schematic view of an electronic device according to an embodiment of the present disclosure, fig. 1 is a schematic view of an electronic device 100 according to an embodiment of the present disclosure, where the electronic device 100 includes: the brain wave touch screen comprises a shell body 110, a circuit board 120, a battery 130, a cover plate 140, a touch control display screen 150, a brain wave part 170 and a transceiver 180, wherein the circuit board 120, the battery 130 and the cover plate 140 are arranged on the shell body 110, and a circuit connected with the touch control display screen 150 is further arranged on the circuit board 120; the circuit board 120 may further include: the application processor AP190, the brain wave section 170. The above-mentioned brain wave part 170 may be different devices according to different apparatuses for collecting brain waves, for example, if brain waves are collected by electronic devices, the brain wave part 170 may be a brain wave sensor or a brain wave collector. The brain wave part 170 may be a brain wave transceiver if brain waves are collected through peripheral devices. Of course, in practical applications, other brain wave devices may be used, and the embodiments of the present invention are not limited to the specific expression of the brain wave components.
The transceiver 180 may be different transceivers according to different types of electronic devices, for example, if the electronic device is a mobile phone, the transceiver may be a wireless transceiver, if the electronic device is a smart router, and the transceiver may be a network port.
The touch Display screen may be a Thin Film Transistor-Liquid Crystal Display (TFT-LCD), a Light Emitting Diode (LED) Display screen, an Organic Light Emitting Diode (OLED) Display screen, or the like.
Different neural activity produces different brain wave patterns and thus presents different brain states. Different brain wave patterns emit brain waves with different amplitudes and frequencies, and besides the brain waves, contraction of muscles also generates different patterns of fluctuation, which is called electromyography. The intelligent device can detect muscle movement such as blinking and the like, so that electric waves generated by the muscles can be filtered out when electroencephalogram is measured.
Brain wave (Brain wave) is data obtained by recording Brain activity using electrophysiological indicators, and is formed by summing the postsynaptic potentials generated synchronously by a large number of neurons during Brain activity. It records the electrical wave changes during brain activity, which is a general reflection of the electrophysiological activity of brain neurons on the surface of the cerebral cortex or scalp.
The brain waves are spontaneous rhythmic nerve electrical activities with frequency ranging from 1 to 30 times per second, and are generally divided into four bands by frequency, namely, delta (1-3 Hz), theta (4-7 Hz), alpha (8-13 Hz), and beta (14-30 Hz). In addition, when a certain event is focused, a gamma wave with a frequency higher than that of a beta wave is often seen, the frequency is 30-80 Hz, and the amplitude range is indefinite; other normal brain waves with special waveforms, such as hump wave, sigma wave, lambda wave, kappa-complex wave, mu wave, etc., can also appear during sleep.
FIG. 1a shows a waveform of a delta wave, with a frequency of 1 to 3Hz and an amplitude of 20 to 200 μ V. This band is recorded in the temporal and apical lobes when a person is immature during infancy or mental development, and an adult is under extreme fatigue, lethargy or anesthesia.
FIG. 1b shows a waveform of a θ wave, with a frequency of 4 to 7Hz and an amplitude of 5 to 20 μ V. This wave is extremely pronounced in adults who are willing to suffer from frustration or depression, as well as in psychiatric patients.
FIG. 1c shows the waveform of the alpha wave, with a frequency of 8 to 13Hz (average 10Hz) and an amplitude of 20 to 100 μ V. It is the basic rhythm of the normal human brain waves, whose frequency is fairly constant if there is no applied stimulus. This rhythm is most pronounced when a person is awake, quiet, and closed, and the alpha wave disappears immediately when the eyes are opened (subject to light stimulation) or subject to other stimulation.
FIG. 1d shows a waveform of beta wave, with a frequency of 14 to 30Hz and an amplitude of 100 to 150 μ V. This wave occurs when mental stress and emotional agitation or excitement, and when a person wakes up from shocking sleep, the original slow wave rhythm is immediately replaced by the rhythm.
Referring to fig. 2, fig. 2 is an electronic device provided in the present application, and as shown in fig. 2, the electronic device may include: a touch display screen, an application processor AP202, a transceiver 201, and a brain wave component 203; the touch display screen, the brain wave component 203 and the transceiver 201 are connected with the AP202 through at least one circuit 204; optionally, other sensors may be disposed within the electronic device, including but not limited to: cameras, gravity sensors, distance sensors, speakers, etc.
The AP202 is used for controlling the brain wave component 203 to acquire brain wave data when the indoor environment is determined to be located, and analyzing the brain wave data to determine a first state corresponding to the brain wave data;
for example, in an alternative embodiment of the present application, a name 1 of a network access point of the electronic device may be extracted, and whether the name 1 is a company access point name or a home access point name may be determined, for example, if the name 1 is determined to be the company access point name or the home access point name, it is determined that the electronic device is located in an indoor environment.
For another example, in another optional technical solution of the present application, when the position coordinate of the electronic device is extracted and determined to belong to the position coordinate of the company address or the position coordinate of the home address, it is determined that the electronic device is in the indoor environment. Of course, in practical applications, the indoor environment may be determined in other manners, and the specific implementation manner of determining the indoor environment is not limited in the specific embodiments of the present application.
The brain wave component 203 may also acquire brain wave data in various ways, for example, in an alternative technical solution, the brain wave data may be acquired through brain wave electrodes, and in another alternative technical solution, the brain wave data may be acquired through a sensor in a contactless manner. The present embodiment is not limited to the above-described electroencephalogram data acquisition method.
The AP202 is further configured to obtain N parameters of an intelligent home product in an indoor environment, determine whether the N parameters conflict with the first state, obtain the conflict result if it is determined that the N parameters conflict with the first state, and generate a control command according to the conflict result, where the control command is used to adjust the N parameters so that the adjusted N parameters do not conflict with the first state; n is an integer of 1 or more.
The N parameters may be specifically temperature, humidity, on, off, brightness, volume, and the like. Specifically, if this intelligent household product includes: during intelligence lamp and intelligent stereo set, this N parameter can include: brightness and volume. If this intelligent household product includes: in the case of intelligent doors and windows and air conditioners, the N parameters can include opening or closing and temperature. Of course, the N parameters are only for illustration, and the embodiments of the present application are not limited to the specific expression of the N parameters.
The transceiver 201 is configured to send the control command to the smart home product.
The sending of the control command to the smart home product may specifically be sending the control command in a unicast manner, and certainly in practical application, the control command may also be sent in a broadcast manner. The transmission mode of the command sending may also be a wired transmission mode or a wireless transmission mode, for example, when the command sending is a wireless transmission mode, the control command may be transmitted in a short-distance communication mode, and of course, in practical applications, the control command may also be transmitted in a mobile communication mode.
The electronic device acquires electroencephalogram data when the electronic device is determined to be in an indoor environment, analyzes the electroencephalogram data to obtain a first state, acquires N parameters of an intelligent household product, analyzes whether the N parameters conflict with the first state or not, and generates a control command according to the first state if the N parameters conflict with the first state, and sends the control command to the intelligent household product, so that the intelligent household can be controlled through the electroencephalogram data of a user.
Optionally, the first state may specifically include: sleep state, non-sleep state.
Optionally, the implementation scheme for determining whether the N parameters conflict with the first state may specifically be:
determining a range of M parameters corresponding to a first state from a preset state and parameter mapping relation according to the first state, extracting x parameters from the N parameters, wherein the x parameters are parameters of which the N parameters and the M parameters are overlapped, determining whether the x parameters belong to the range, if the x parameters exceed the range, determining that the N parameters conflict with the first state, and extracting y parameters exceeding the range from the x parameters as a conflict result, otherwise, determining that the N parameters do not conflict with the first state if the x parameters belong to the range, wherein x and y are integers which are more than or equal to 1 and are less than or equal to N, M.
The following describes an implementation of whether to conflict with an actual example, which may specifically be:
for example, the first state is a sleep state, and the corresponding M parameters may be temperature and volume, which may range from a temperature range [ 20 ℃, 28 ℃) to a volume range [ 0,30dB ], where x parameters are extracted from the N parameters, specifically, the temperature is 25 ℃ and the volume is 35 dB; since the parameter volume exceeds this range, it is determined that the N parameters conflict with the first state, resulting in a volume of 35 dB.
Optionally, the generating the control command according to the collision result may specifically be determining y parameters in the collision result, and if the y parameters are greater than the range, generating a decrease command for the y parameters, and if the y parameters are smaller than the set range, generating an increase command for the y parameters.
Optionally, the AP202 is specifically configured to analyze the electroencephalogram data to determine whether there is a delta wave, if so, determine that the first state is a sleep state, and if not, determine that the first state is a non-sleep state.
Optionally, the AP202 is specifically configured to extract p values of the brain wave data, form the p values into an input data matrix, input the input data matrix into a preset neural network model, perform a multi-layer forward operation to obtain a forward operation result, and determine the first state according to the forward operation result. The value range of p may be an integer greater than or equal to 2, specifically, the value range of p may be larger, for example, greater than or equal to 1000, and the value of p may also be determined according to the size of the input matrix in the preset neural network model, for example, the input matrix of the preset time-domain neural network model is H × W, where H is a height value of the input matrix, and W is a width value of the input matrix, and then p ═ H × W.
Specifically, the determining the first state according to the forward operation result may specifically include:
performing multi-layer forward operation on the input data matrix to obtain a forward operation result matrix, extracting Z elements which are larger than a set threshold value from a plurality of elements in the forward operation result matrix, extracting Z positions (namely H, W values) of the forward operation result matrix corresponding to the Z elements, and extracting Z states corresponding to the Z positions, wherein if more than half of the Z states are sleep states, the first state is determined to be a sleep state, otherwise, if more than half of the Z states are non-sleep states, the first state is determined to be a non-sleep state.
Referring to fig. 3, fig. 3 provides an information response method based on brain waves, which is applied to an electronic device having a structure as shown in fig. 1 or 2, and includes the steps of:
step S301, when the indoor environment is determined to be located, controlling the brain wave component to acquire brain wave data, and analyzing the brain wave data to determine a first state corresponding to the brain wave data;
for example, in an alternative embodiment of the present application, a name 1 of a network access point of the electronic device may be extracted, and whether the name 1 is a company access point name or a home access point name may be determined, for example, if the name 1 is determined to be the company access point name or the home access point name, it is determined that the electronic device is located in an indoor environment.
For another example, in another optional technical solution of the present application, when the position coordinate of the electronic device is extracted and determined to belong to the position coordinate of the company address or the position coordinate of the home address, it is determined that the electronic device is in the indoor environment. Of course, in practical applications, the indoor environment may be determined in other manners, and the specific implementation manner of determining the indoor environment is not limited in the specific embodiments of the present application.
Step S302, obtaining N parameters of an intelligent household product in an indoor environment, determining whether the N parameters conflict with the first state, if so, obtaining a conflict result, and generating a control command according to the conflict result, wherein the control command is used for adjusting the N parameters so that the adjusted N parameters do not conflict with the first state; n is an integer greater than or equal to 1;
the N parameters may be specifically temperature, humidity, on, off, brightness, volume, and the like. Specifically, if this intelligent household product includes: during intelligence lamp and intelligent stereo set, this N parameter can include: brightness and volume. If this intelligent household product includes: in the case of intelligent doors and windows and air conditioners, the N parameters can include opening or closing and temperature. Of course, the N parameters are only for illustration, and the embodiments of the present application are not limited to the specific expression of the N parameters.
And S303, sending the control command to the intelligent household product.
According to the technical scheme, when the intelligent home product is determined to be in an indoor environment, electroencephalogram data are obtained, the electroencephalogram data are analyzed to obtain a first state, then N parameters of the intelligent home product are obtained, whether the N parameters conflict with the first state or not is analyzed, if so, a control command is generated according to the first state, the control command is sent to the intelligent home product, and therefore the intelligent home can be controlled through the electroencephalogram data of a user, and user experience is improved.
Referring to fig. 4, fig. 4 provides an electronic device including: the processing unit 401, the touch display screen, the brain wave component 403, and the transceiver 404 may be connected to the processing unit 401 through a bus, but in practical applications, other connection manners may also be adopted, and the present application does not limit the specific representation of the connection.
The processing unit 401 is configured to, when it is determined that the indoor environment is located, control the brain wave component 403 to acquire brain wave data, analyze the brain wave data, and determine a first state corresponding to the brain wave data;
the processing unit 401 is further configured to obtain N parameters of an intelligent home product in an indoor environment, determine whether the N parameters conflict with the first state, obtain a conflict result if the N parameters conflict with the first state, and generate a control command according to the conflict result, where the control command is used to adjust the N parameters so that the adjusted N parameters do not conflict with the first state; n is an integer greater than or equal to 1;
a transceiver 404, configured to send the control command to the smart home product.
According to the technical scheme, when the intelligent home product is determined to be in an indoor environment, electroencephalogram data are obtained, the electroencephalogram data are analyzed to obtain a first state, then N parameters of the intelligent home product are obtained, whether the N parameters conflict with the first state or not is analyzed, if so, a control command is generated according to the first state, the control command is sent to the intelligent home product, and therefore the intelligent home can be controlled through the electroencephalogram data of a user, and user experience is improved.
Fig. 5 is a block diagram illustrating a partial structure of a mobile phone related to a mobile terminal provided in an embodiment of the present application. Referring to fig. 5, the handset includes: radio Frequency (RF) circuit 910, memory 920, input unit 930, sensor 950, audio collector 960, Wireless Fidelity (WiFi) module 970, application processor AP980, and power supply 990, brain wave unit 999, etc. Those skilled in the art will appreciate that the handset configuration shown in fig. 5 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the mobile phone in detail with reference to fig. 5:
the input unit 930 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the cellular phone. Specifically, the input unit 930 may include a touch display screen 933, a fingerprint recognition apparatus 931, a face recognition apparatus 936, an iris recognition apparatus 937, and other input devices 932. The input unit 930 may also include other input devices 932. In particular, other input devices 932 may include, but are not limited to, one or more of physical keys, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. Wherein the content of the first and second substances,
the AP980 is used for controlling the brain wave component 999 to acquire brain wave data when the indoor environment is determined to be located, and analyzing the brain wave data to determine a first state corresponding to the brain wave data;
the AP980 is further configured to obtain N parameters of the smart home product in the indoor environment, determine whether the N parameters conflict with the first state, obtain the conflict result if it is determined that the N parameters conflict with the first state, and generate a control command according to the conflict result, where the control command is used to adjust the N parameters so that the adjusted N parameters do not conflict with the first state; n is an integer greater than or equal to 1;
and the wireless fidelity module 970 is configured to send the control command to the smart home product.
Optionally, the AP980 is specifically configured to analyze the electroencephalogram data to determine whether a delta wave exists, if so, determine that the first state is a sleep state, and if not, determine that the first state is a non-sleep state.
Optionally, the AP980 is specifically configured to extract p values of the brain wave data, form an input data matrix from the p values, input the input data matrix into a preset neural network model, perform multi-layer forward operation to obtain a forward operation result, extract Z elements greater than a set threshold from among multiple elements in the forward operation result matrix, extract Z positions of the forward operation result matrix corresponding to the Z elements, extract Z states corresponding to the Z positions, determine that the first state is a sleep state if more than half of the Z states are sleep states, and determine that the first state is a non-sleep state if more than half of the Z states are non-sleep states.
Optionally, the AP980 is specifically configured to determine, according to the first state, a range of M parameters corresponding to the first state from a preset state-parameter mapping relationship, extract x parameters from the N parameters, where the x parameters are overlapped parameters of the N parameters and the M parameters, determine whether the x parameters belong to the range, determine that the N parameters conflict with the first state if the x parameters exceed the range, and extract y parameters exceeding the range from the x parameters as a conflict result, where x and y are both integers greater than or equal to 1 and less than or equal to N, M.
Optionally, the AP980 is specifically configured to generate a first control command if the y parameters are greater than the range, where the first control command is used to decrease the values of the y parameters, and generate a second control command if the y parameters are smaller than the set range, where the second control command is used to increase the values of the y parameters.
The AP980 is a control center of the mobile phone, connects various parts of the entire mobile phone by using various interfaces and lines, and performs various functions and processes of the mobile phone by operating or executing software programs and/or modules stored in the memory 920 and calling data stored in the memory 920, thereby integrally monitoring the mobile phone. Optionally, AP980 may include one or more processing units; alternatively, the AP980 may integrate an application processor that handles primarily the operating system, user interface, and applications, etc., and a modem processor that handles primarily wireless communications. It will be appreciated that the modem processor described above may not be integrated into the AP 980.
Further, the memory 920 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The handset may also include at least one sensor 950, such as a light sensor, motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the touch display screen according to the brightness of ambient light, and the proximity sensor may turn off the touch display screen and/or the backlight when the mobile phone moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.
WiFi belongs to short-distance wireless transmission technology, and the mobile phone can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 970, and provides wireless broadband Internet access for the user. Although fig. 5 shows the WiFi module 970, it is understood that it does not belong to the essential constitution of the handset, and can be omitted entirely as needed within the scope of not changing the essence of the application.
The handset also includes a power supply 990 (e.g., a battery) for supplying power to various components, and optionally, the power supply may be logically connected to the AP980 via a power management system, so that functions of managing charging, discharging, and power consumption are implemented via the power management system.
Although not shown, the mobile phone may further include a camera, a bluetooth module, a light supplement device, a light sensor, and the like, which are not described herein again.
According to the method and the device, when the intelligent home product is determined to be in an indoor environment, electroencephalogram data are obtained, the electroencephalogram data are analyzed to obtain the first state, then N parameters of the intelligent home product are obtained, whether the N parameters conflict with the first state or not is analyzed, if yes, a control command is generated according to the first state, the control command is sent to the intelligent home product, and therefore the intelligent home can be controlled through the electroencephalogram data of a user, and user experience is improved.
The present embodiment also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the brain wave-based smart home control methods as set forth in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any one of the brain wave-based smart home control methods as described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (9)
1. An electronic device, the electronic device comprising: an application processor, a transceiver; characterized in that, the electronic device further comprises: a brain wave component connected with the application processor through at least one circuit;
the application processor is used for controlling the brain wave component to acquire brain wave data when the application processor is determined to be located in an indoor environment, and analyzing the brain wave data to determine a first state corresponding to the brain wave data; the first state comprises a sleep state and a non-sleep state;
the application processor is further configured to obtain N parameters of an intelligent home product in an indoor environment, determine whether the N parameters conflict with the first state, obtain a conflict result if the N parameters conflict with the first state, and generate a control command according to the conflict result, where the control command is used to adjust the N parameters; n is an integer greater than or equal to 1;
the transceiver is used for sending the control command to the intelligent household product;
the application processor is specifically configured to determine, according to the first state, a range of M parameters corresponding to the first state from a preset mapping relationship between a state and a parameter, extract x parameters from the N parameters, where the x parameters are overlapped parameters of the N parameters and the M parameters, determine whether the x parameters belong to the range, determine that the N parameters conflict with the first state if the x parameters exceed the range, and extract y parameters exceeding the range from the x parameters as a conflict result, where x and y are both integers greater than or equal to 1 and less than or equal to N, M.
2. The electronic device of claim 1,
the application processor is specifically configured to analyze the electroencephalogram data to determine whether a delta wave exists, determine that the first state is a sleep state if the delta wave exists, and determine that the first state is a non-sleep state if the delta wave does not exist.
3. The electronic device of claim 1,
the application processor is specifically configured to extract p values of the brain wave data, form an input data matrix from the p values, input the input data matrix into a preset neural network model, perform multi-layer forward operation to obtain a forward operation result, extract Z elements greater than a set threshold from among a plurality of elements in the forward operation result matrix, extract Z positions of the forward operation result matrix corresponding to the Z elements, extract Z states corresponding to the Z positions, determine that the first state is a sleep state if more than half of the Z states are sleep states, and determine that the first state is a non-sleep state if more than half of the Z states are non-sleep states.
4. The electronic device of claim 1,
the application processor is specifically configured to generate a first control command if the y parameters are greater than the range, where the first control command is used to decrease the values of the y parameters, and generate a second control command if the y parameters are less than the range, where the second control command is used to increase the values of the y parameters.
5. A smart home control method based on brain waves is applied to an electronic device, and the electronic device comprises the following steps: an application processor, a transceiver, the electronic device further comprising: a brain wave component connected with the application processor through at least one circuit;
when the indoor environment is determined to be located, controlling the brain wave component to acquire brain wave data, and analyzing the brain wave data to determine a first state corresponding to the brain wave data; the first state comprises a sleep state and a non-sleep state;
acquiring N parameters of an intelligent household product in an indoor environment, determining whether the N parameters conflict with the first state, if so, acquiring a conflict result, and generating a control command according to the conflict result, wherein the control command is used for adjusting the N parameters; n is an integer greater than or equal to 1;
sending the control command to the intelligent household product;
determining a range of M parameters corresponding to the first state from a preset mapping relation between the state and the parameters according to the first state, extracting x parameters from the N parameters, wherein the x parameters are overlapped parameters of the N parameters and the M parameters, determining whether the x parameters belong to the range, determining that the N parameters conflict with the first state if the x parameters exceed the range, extracting y parameters exceeding the range from the x parameters as a conflict result, and both x and y are integers greater than or equal to 1 and less than or equal to N, M.
6. The method of claim 5, wherein analyzing the brain wave data to determine a first state to which the brain wave data corresponds comprises:
analyzing the brain wave data to determine whether delta waves exist or not, if yes, determining that the first state is a sleep state, and if not, determining that the first state is a non-sleep state.
7. The method of claim 5, wherein analyzing the brain wave data to determine a first state to which the brain wave data corresponds comprises:
extracting p values of the electroencephalogram data, forming an input data matrix by the p values, inputting the input data matrix into a preset neural network model, executing multilayer forward operation to obtain a forward operation result, extracting Z elements which are larger than a set threshold value from a plurality of elements in the forward operation result matrix, extracting Z positions of the forward operation result matrix corresponding to the Z elements, extracting Z states corresponding to the Z positions, determining that the first state is a sleep state if more than half of the Z states are sleep states, and determining that the first state is a non-sleep state if more than half of the Z states are non-sleep states.
8. An electronic device, the electronic device comprising: processing unit, transceiver characterized in that, electronic device still includes: a brain wave component connected with the processing unit;
the processing unit is used for controlling the brain wave component to acquire brain wave data when the brain wave component is determined to be located in an indoor environment, and analyzing the brain wave data to determine a first state corresponding to the brain wave data; the first state comprises a sleep state and a non-sleep state;
the processing unit is further configured to obtain N parameters of an intelligent home product in an indoor environment, determine whether the N parameters conflict with the first state, obtain a conflict result if the N parameters conflict with the first state, and generate a control command according to the conflict result, where the control command is used to adjust the N parameters so that the adjusted N parameters do not conflict with the first state; n is an integer greater than or equal to 1;
the transceiver is used for sending the control command to the intelligent household product;
determining a range of M parameters corresponding to the first state from a preset mapping relation between the state and the parameters according to the first state, extracting x parameters from the N parameters, wherein the x parameters are overlapped parameters of the N parameters and the M parameters, determining whether the x parameters belong to the range, determining that the N parameters conflict with the first state if the x parameters exceed the range, extracting y parameters exceeding the range from the x parameters as a conflict result, and both x and y are integers greater than or equal to 1 and less than or equal to N, M.
9. A computer-readable storage medium, characterized in that it stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method according to any one of claims 5-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810236057.XA CN108205261B (en) | 2018-03-21 | 2018-03-21 | Intelligent household control method based on brain waves and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810236057.XA CN108205261B (en) | 2018-03-21 | 2018-03-21 | Intelligent household control method based on brain waves and electronic device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108205261A CN108205261A (en) | 2018-06-26 |
| CN108205261B true CN108205261B (en) | 2021-09-14 |
Family
ID=62606652
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810236057.XA Expired - Fee Related CN108205261B (en) | 2018-03-21 | 2018-03-21 | Intelligent household control method based on brain waves and electronic device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108205261B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112256534A (en) * | 2020-10-13 | 2021-01-22 | 武汉朋鼎科技有限公司 | Method for transmitting information in building system |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101359220A (en) * | 2007-07-31 | 2009-02-04 | 株式会社日立制作所 | External condition control device based on measurement of brain functions |
| CN203101953U (en) * | 2013-01-07 | 2013-07-31 | 广东美的制冷设备有限公司 | Household appliance |
| CN104914727A (en) * | 2015-02-28 | 2015-09-16 | 阮江海 | Household-electric-appliance intelligence control system based on human brain wave signal detection |
| CN105045234A (en) * | 2015-07-10 | 2015-11-11 | 西安交通大学 | Intelligent household energy management method based on intelligent wearable equipment behavior perception |
| CN105182765A (en) * | 2015-08-12 | 2015-12-23 | 小米科技有限责任公司 | Household equipment control method and household equipment control device |
| CN205375373U (en) * | 2016-01-19 | 2016-07-06 | 郑州轻工业学院 | Intelligence house based on brain wave and gesture control |
| CN106200400A (en) * | 2016-08-18 | 2016-12-07 | 南昌大学 | A kind of house control system based on brain electricity APP |
| CN106388813A (en) * | 2016-09-21 | 2017-02-15 | 广州视源电子科技股份有限公司 | A sleep state identification model training method and system based on electroencephalogram signals |
| CN106963369A (en) * | 2017-03-27 | 2017-07-21 | 广州视源电子科技股份有限公司 | A kind of electric allowance recognition methods of the brain based on neural network model and device |
-
2018
- 2018-03-21 CN CN201810236057.XA patent/CN108205261B/en not_active Expired - Fee Related
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101359220A (en) * | 2007-07-31 | 2009-02-04 | 株式会社日立制作所 | External condition control device based on measurement of brain functions |
| CN203101953U (en) * | 2013-01-07 | 2013-07-31 | 广东美的制冷设备有限公司 | Household appliance |
| CN104914727A (en) * | 2015-02-28 | 2015-09-16 | 阮江海 | Household-electric-appliance intelligence control system based on human brain wave signal detection |
| CN105045234A (en) * | 2015-07-10 | 2015-11-11 | 西安交通大学 | Intelligent household energy management method based on intelligent wearable equipment behavior perception |
| CN105182765A (en) * | 2015-08-12 | 2015-12-23 | 小米科技有限责任公司 | Household equipment control method and household equipment control device |
| CN205375373U (en) * | 2016-01-19 | 2016-07-06 | 郑州轻工业学院 | Intelligence house based on brain wave and gesture control |
| CN106200400A (en) * | 2016-08-18 | 2016-12-07 | 南昌大学 | A kind of house control system based on brain electricity APP |
| CN106388813A (en) * | 2016-09-21 | 2017-02-15 | 广州视源电子科技股份有限公司 | A sleep state identification model training method and system based on electroencephalogram signals |
| CN106963369A (en) * | 2017-03-27 | 2017-07-21 | 广州视源电子科技股份有限公司 | A kind of electric allowance recognition methods of the brain based on neural network model and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108205261A (en) | 2018-06-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108874130B (en) | Play control method and related product | |
| CN106648058B (en) | Song switching method and device | |
| CN108418962B (en) | Information response method based on brain wave and related product | |
| CN109656511A (en) | A kind of audio frequency playing method, terminal and computer readable storage medium | |
| CN108512995B (en) | Electronic device, brain wave control method and related product | |
| CN108491076B (en) | Display control method and related product | |
| CN108319329A (en) | A kind of display control method, flexible screen terminal and computer readable storage medium | |
| CN110022401A (en) | A kind of control parameter setting method, terminal and computer readable storage medium | |
| CN103889040A (en) | Method, device and system for controlling transmission | |
| CN108196777A (en) | A kind of flexible screen application process, equipment and computer readable storage medium | |
| CN108924352A (en) | Sound quality method for improving, terminal and computer readable storage medium | |
| CN112060080A (en) | Robot control method and device, terminal equipment and storage medium | |
| CN109276881A (en) | A kind of game control method, equipment | |
| CN108541116A (en) | Lamp light control method, terminal and computer readable storage medium | |
| CN108205261B (en) | Intelligent household control method based on brain waves and electronic device | |
| CN108429956B (en) | Wireless earphone, control operation method and related product | |
| CN108427296B (en) | Intelligent household control method and related product | |
| CN108375912B (en) | Intelligent household control method and related product | |
| CN112084483A (en) | Application locking and unlocking method, device, equipment and storage medium | |
| CN108144291A (en) | Game control method and Related product based on brain wave | |
| CN109011561A (en) | The quick control method of game, mobile terminal and computer readable storage medium | |
| CN108339267B (en) | Game menu control method based on brain wave and related product | |
| CN108446025B (en) | Shooting control method and related product | |
| CN108399004B (en) | Brain wave analysis method and related product | |
| CN109766211A (en) | Method, terminal and the storage medium of shuangping san false-touch prevention |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: Changan town in Guangdong province Dongguan 523860 usha Beach Road No. 18 Applicant after: GUANGDONG OPPO MOBILE TELECOMMUNICATIONS Corp.,Ltd. Address before: No.18, Wusha Haibin Road, Chang'an Town, Dongguan City, Guangdong Province Applicant before: GUANGDONG OPPO MOBILE TELECOMMUNICATIONS Corp.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210914 |