CN108255632A - Method for computing data and Related product are fallen based on artificial intelligence - Google Patents
Method for computing data and Related product are fallen based on artificial intelligence Download PDFInfo
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- CN108255632A CN108255632A CN201810059803.2A CN201810059803A CN108255632A CN 108255632 A CN108255632 A CN 108255632A CN 201810059803 A CN201810059803 A CN 201810059803A CN 108255632 A CN108255632 A CN 108255632A
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- electronic device
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/079—Root cause analysis, i.e. error or fault diagnosis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M2250/00—Details of telephonic subscriber devices
- H04M2250/12—Details of telephonic subscriber devices including a sensor for measuring a physical value, e.g. temperature or motion
Abstract
Method for computing data and Related product are fallen based on artificial intelligence this application provides a kind of, including：Acquire the acceleration information when electronic device falls；Acquire the pressure value that user when the electronic device falls holds housing；Acceleration information and the pressure value when being fallen according to the electronic device determine the reason of falling of the electronic device, and the technical solution that the application provides has the advantages that user experience is high.
Description
Technical field
This application involves terminal device technical fields, and in particular to a kind of to fall method for computing data based on artificial intelligence
And Related product.
Background technology
In the prior art, mobile terminal (such as mobile phone, tablet computer) has become user's first choice and frequency of use highest
Electronic device, for mobile terminal, fragile screen is producer or the unavoidable problem of user, after screen crushes,
The surplus value of terminal is just had a greatly reduced quality, because the price that most of factory repair changes screen almost alreadys exceed the residue of terminal
Value.And industry prevalence 2.5D glass as screen, is more prone to fall bad and broken screen, each mainstream producer is all spending greatly at present
The R＆D costs research raising complete machine of amount falls drop resistant ability.
The existing calculation for falling data is complicated, can not be that the anti-drop of later stage provides effective support, influence to use
The Experience Degree at family.
Apply for content
The embodiment of the present application provides a kind of electronic device and Related product, can realize and the scene fallen is gone back
Original allows user intuitively to watch, and improves user experience.
In a first aspect, the embodiment of the present application provides a kind of electronic device, the electronic device includes：Application processor AP weights
Force snesor and pressure sensor, the pressure sensor, the gravity sensor pass through at least one circuit and the application
Processor connects；
The gravity sensor, for acquiring the acceleration information when electronic device falls；
The pressure sensor, when falling for acquiring the electronic device, user is to the pressure value of housing；
The AP, acceleration information and the pressure value during for being fallen according to the electronic device determine the electronics
Device falls reason.
Second aspect, provide it is a kind of method for computing data is fallen based on artificial intelligence, including：
Acquire the acceleration information when electronic device falls；
Acquire the pressure value that user when the electronic device falls holds housing；
Acceleration information and the pressure value when being fallen according to the electronic device determine falling for the electronic device
Fall reason.
The third aspect, provides a kind of electronic device, and the electronic device includes：Processing unit, gravity sensor and pressure
Sensor, the gravity sensor, the pressure sensor are connect by least one circuit with the processing unit, wherein,
The gravity sensor, for acquiring acceleration information of the electronic device when falling；
The pressure sensor, for acquire the electronic device when falling user to the pressure value of housing；
The processing unit, acceleration information and the pressure value during for being fallen according to the electronic device determine
The electronic device falls reason.
Fourth aspect, provides a kind of computer readable storage medium, and storage is used for the computer journey of electronic data interchange
Sequence, wherein, the computer program causes computer to perform the method that second aspect provides.
5th aspect, provides a kind of computer program product, and the computer program product includes storing computer journey
The non-transient computer readable storage medium of sequence, the computer program are operable to that computer is made to perform second aspect offer
Method.
Implement the embodiment of the present application, have the advantages that：
As can be seen that after collecting acceleration information by the technical solution that the application provides, according to acceleration information
Acceleration value is calculated, acquires the pressure value of housing, when being determined as falling state, the acceleration figure and pressure of state are fallen in extraction
The acceleration value and pressure value are formed input data by value, by the input data be input in artificial nerve network model into
Output is calculated for row as a result, this makes it possible to obtain electronic device according to the output result to fall reason.
Description of the drawings
In order to illustrate more clearly of the technical solution in the embodiment of the present application, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, the accompanying drawings in the following description is some embodiments of the present application, for ability
For the those of ordinary skill of domain, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is a kind of structure diagram of electronic device provided by the embodiments of the present application.
Fig. 1 a are a kind of schematic diagrames of plane-parallel capacitor provided by the embodiments of the present application.
Fig. 1 b are the schematic diagrames of another plane-parallel capacitor provided by the embodiments of the present application.
Fig. 1 c are the schematic diagrames of another plane-parallel capacitor provided by the embodiments of the present application.
Fig. 1 d are the schematic diagrames of acceleration provided by the embodiments of the present application.
Fig. 2 is a kind of schematic diagram of electronic device disclosed in the embodiment of the present application.
Fig. 3 a are a kind of schematic diagrames of convolution algorithm disclosed in the embodiment of the present application.
Fig. 3 b are a kind of mobile schematic diagrames of convolution algorithm of the embodiment of the present application.
Fig. 3 c are a kind of schematic diagrames of three-dimensional input data for being not added with data of the embodiment of the present application.
Fig. 3 d are a kind of schematic diagrames of the three-dimensional input data of interpolation data of the embodiment of the present application.
Fig. 4 is a kind of flow for falling method for computing data signal based on artificial intelligence provided by the embodiments of the present application
Figure.
Fig. 5 is a kind of structure diagram of electronic device provided by the embodiments of the present application.
Fig. 6 is a kind of structure diagram of mobile phone disclosed in the embodiment of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, the technical solution in the embodiment of the present application is carried out clear, complete
Site preparation describes, it is clear that described embodiment is some embodiments of the present application, instead of all the embodiments.Based on this Shen
Please in embodiment, the every other implementation that those of ordinary skill in the art are obtained without creative efforts
Example, shall fall in the protection scope of this application.
Term " first ", " second ", " third " in the description and claims of this application and the attached drawing and "
Four " etc. be for distinguishing different objects rather than for describing particular order.In addition, term " comprising " and " having " and it
Any deformation, it is intended that cover non-exclusive include.Such as it contains the process of series of steps or unit, method, be
The step of system, product or equipment are not limited to list or unit, but optionally further include the step of not listing or list
Member is optionally further included for the intrinsic other steps of these processes, method, product or equipment or unit.
Referenced herein " embodiment " is it is meant that a particular feature, structure, or characteristic described can wrap in conjunction with the embodiments
It is contained at least one embodiment of the application.Each position in the description occur the phrase might not each mean it is identical
Embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.Those skilled in the art explicitly and
Implicitly understand, embodiment described herein can be combined with other embodiments.
Electronic device in the application can include smart mobile phone (such as Android phone, iOS mobile phones, Windows
Phone mobile phones etc.), tablet computer, palm PC, laptop, mobile internet device (MID, Mobile Internet
Devices) or Wearable etc., above-mentioned electronic device are only citings, and non exhaustive, are filled including but not limited to above-mentioned electronics
It puts, for convenience of description, above-mentioned electronic device is known as user equipment (User equipment, UE) in example below.
Certainly in practical applications, above-mentioned user equipment is also not necessarily limited to above-mentioned realization form, such as can also include：Intelligent vehicle-carried end
End, computer equipment etc..
In the electronic device of first aspect, the AP, specifically for acceleration is calculated according to the acceleration information
The acceleration value and pressure value are formed input data, which are input to preset artificial neural network by angle value
Output is calculated in model as a result, falling reason according to what the output result determined the electronic device.
In the electronic device of first aspect, the AP, specifically for extracting the quantity n and pressure value of acceleration value
Quantity m extracts the size CI*H*W of the default input data of preset artificial nerve network model；Wherein, H is height value, and W is
Width value, CI are depth value, compare whether n+m is more than CI*H*W, such as the n+m >=CI*H*W, do not add the quantity of acceleration value
And the quantity of pressure value, such as n+m ＜ CI*H*W, then by preset strategy to the quantity n of the acceleration value and the number of pressure value
It measures m and adds n '+m '=CI*H*W so that after addition；
Wherein, n, m, CI, H, W are the integer more than or equal to 2.
In the electronic device of first aspect, the preset strategy includes：Zero insertion addition strategy or slotting average value addition plan
Slightly.
In the method for second aspect, acceleration information when being fallen according to the electronic device and the pressure
The determining electronic device of value falls reason, including：
Acceleration value is calculated according to the acceleration information, by the acceleration value and pressure value composition input number
According to the input data being input in preset artificial nerve network model, the output is calculated as a result, according to the output
As a result determine the electronic device falls reason.
In the method for second aspect, the acceleration value and pressure value composition input number this fallen under state
According to, including：
The quantity n of the acceleration value and quantity m of pressure value is extracted, extracts preset trained artificial neural network mould
The size CI*H*W of the default input data of type；Wherein, H is height value, and W is width value, and CI is depth value, whether compares n+m
More than CI*H*W, such as the n+m >=CI*H*W, the quantity of acceleration value and the quantity of pressure value are not added, such as n+m ＜ CI*H*
W then adds n '+m '=CI*H* so that after addition by preset strategy to the quantity of the acceleration value and the quantity of pressure value
W；
Wherein, n, m, CI, H, W are the integer more than or equal to 2.
In the method for second aspect, the preset strategy includes：Zero insertion addition strategy or slotting average value addition strategy
Referring to Fig. 1, Fig. 1, which is the embodiment of the present application, provides a kind of electronic device, referring to Fig. 1, Fig. 1 is of the invention real
It applies example and provides a kind of structure diagram of electronic device 100, above-mentioned electronic device 100 includes：Housing 110, circuit board 120,
Battery 130, cover board 140, touching display screen 150, gravity sensor (English：Gravity Sensor, referred to as：G-sensor)
170, the circuit board 120, the battery 130 and the cover board 140 are set on the housing 110, the circuit board 120 is also set
It is equipped with the circuit for connecting the touching display screen 150；The circuit board 120 can also include：Application processor AP190 and again
Force snesor.
Above-mentioned touching display screen is specifically as follows Thin Film Transistor-LCD (Thin Film Transistor-
Liquid Crystal Display, TFT-LCD), light emitting diode (Light Emitting Diode, LED) display screen, have
Machine light emitting diode (Organic Light-Emitting Diode, OLED) display screen etc..
Gravity sensor 170 for detecting the direction of acceleration and size, is equivalent to the movement shape of detection electronic installation
State.The function of G-sensor understands fairly simple, the mainly variation of perception acceleration of getting up, for example shake, fall, rising,
The various mobile variations such as decline can be converted into electric signal by G-sensor, then pass through the calculating of application processor AP190 point
After analysis, it will be able to determine the acceleration value of the electronic device.
Optionally, above-mentioned electronic device can also include：Geomagnetic sensor and gyroscope, the geomagnetic sensor and gyroscope
It is connect respectively with application processor AP190.On the electronic device, G-sensor not only works independently, and can also be passed with earth magnetism
Sensor 171, gyroscope 172 cooperate together, provide more accurate and comprehensive action induction ability.
Specifically, in electronic device, gravity sensor 170 can be actually a kind of plane-parallel capacitor, for parallel
The capacitance size and distance between plates of plate capacitor are inversely proportional, by detecting X, Y, the capacitance variations in Z-direction, it is possible to calculate
Linear acceleration size on to all directions.
For in a manner of the acceleration calculation of X-direction, acceleration value is specifically as follows：
As shown in Figure 1a, it is a kind of schematic diagram of plane-parallel capacitor.
Refering to Fig. 1 a, the corresponding acceleration of Fig. 1 a is 0, as shown in Figure 1a, due at this time without acceleration value, so intermediate
Parallel-plate be located at initial position, so capacitance C at this time1=C0, the C1Capacitance that can be between parallel-plate and lower electrode
Value, the C0It can be initial capacitance value.Capacitance C at this time2=C0, the C2Capacitance that can be between parallel-plate and top electrode
Value, at this point, capacitance C1Corresponding distance d1=d0；Capacitance C2Corresponding distance d2=d0；Wherein, d1Can be parallel-plate and lower electricity
The distance between pole, d2Can be the distance between parallel-plate and top electrode.Since acceleration value at this time is zero, so C1=
C2=C0；A can be calculated according to above-mentioned formulax=0.
Refering to Fig. 1 b, the corresponding acceleration of Fig. 1 b is positive value, and due to the effect of positive value acceleration, parallel-plate can downward electrode
It is mobile, it is assumed that displacement distance x, then for the distance between parallel-plate and top electrode, i.e., it can increase x, so at this time, d1
=d0- x, d2=d0+x；According to the calculation formula of capacity plate antenna, as shown in following formula：
Wherein, S can corresponding area, ε be that (it is by tablet for dielectric constant between two plates of plane-parallel capacitor
The material of electrode determines), k is dielectric constant, and d is the distance between two plates of plane-parallel capacitor.
Capacitance as shown in Figure 1 b is as follows：
So due to the downward electrode movement of the parallel-plate of plane-parallel capacitor, so C1＞ C2, i.e. ax＞ 0.
Refering to Fig. 1 c, the corresponding acceleration of Fig. 1 c is negative value, and due to the effect of negative value acceleration, parallel-plate can be to top electrode
It is mobile, it is assumed that displacement distance x, then for the distance between parallel-plate and top electrode, i.e., it can increase x, so at this time, d1
=d0+ x, d2=d0-x；According to the calculation formula of capacity plate antenna, as shown in following formula：
Capacitance as illustrated in figure 1 c is as follows：
At this time due to the upward electrode movement of the parallel-plate of plane-parallel capacitor, so C1＜ C2, i.e. ax＜ 0.
I.e. by the test to above-mentioned plane-parallel capacitor, specific acceleration value can be accessed, and the value can
Show the direction of acceleration.
Specifically, for electronic device, there are three directions for the value tool of the acceleration of test, as shown in Figure 1 d, are
The schematic diagram in three directions of testing of electronic devices specifically, can be divided into X-direction, Y direction and Z-direction, has
The display signal of body is as shown in Figure 1 d.
Specifically, in an optional drop test, its corresponding acceleration value can be in falling process：
ax=0.049m/S2
ay=-0.026m/S2
az=9.800m/S2。
State of the electronic device in falling can be determined according to above-mentioned data.
As shown in Fig. 2, for the structure diagram of a kind of electronic device that the application provides, as shown in Fig. 2, the electronic device
200 include：Housing, application processor AP210, touching display screen 220, gravity sensor 250, pressure sensor 260 and circuit
240, the outside of the housing is provided with camera 230, the camera, the touching display screen by least one circuit with
The application processor AP connections.Wherein, the AP210 connects gravity sensor 250 by another circuit, wherein, the circuit
240 can specifically include：Bus, flexible PCB, connection chip etc., certainly in practical applications, foregoing circuit 240 also may be used
To be other forms of expression, the application specific embodiment is not intended to limit the specific manifestation form of foregoing circuit 240.Above-mentioned electricity
Sub-device 200 can also include：Geomagnetic sensor and gyroscope, the geomagnetic sensor and gyroscope can combine gravity sensor
250 gathered datas；The electronic device 200 can also include：Artificial intelligence process device, the artificial intelligent processor can individually be set
It puts, can also be integrated with application processor AP210, for convenience, embodiment as shown in Figure 2, by artificial intelligence
Energy processor is integrated in AP210.
For acquiring acceleration information when electronic device falls, which is transferred to for gravity sensor 250
Application processor AP；
Pressure sensor 260, when falling for acquiring electronic device, which is passed the pressure value of housing by user
It is defeated by application processor AP；
For acceleration value to be calculated according to acceleration information, the electronic device is determined according to the acceleration value by AP210
The state being in, the state include：It is non-to fall state and fall state；
Optionally, above-mentioned acceleration information can be multiple capacitances of plane-parallel capacitor, specifically, can be as schemed
1a, such as Fig. 1 b, C as illustrated in figure 1 c1And C2Value.Certainly in practical applications, due to need to acquire X, Y as shown in Figure 1 d,
The acceleration information of tri- axis of Z.Certainly in practical applications, using other gravity sensors, the acceleration information can also
It is other kinds of data, the application specific embodiment is not intended to limit the practical manifestation form of above-mentioned acceleration information.
The number of above-mentioned acceleration value can be n acceleration value.Specifically, order traversals n of the AP210 by collection point
Acceleration value, such as continuous m acceleration value are determined as falling state more than given threshold, are otherwise determined as non-falling state.Its
In, n, m are integer more than or equal to 2, and m ＜ n.
AP210 is additionally operable to fall reason according to what acceleration information when falling and pressure value determined the electronic device.
Specifically, AP210, for extracting the acceleration value and pressure value that fall under state, this is fallen under state
Acceleration value and pressure value composition input data, preset trained artificial neural network mould is input to by the input data
Carry out being calculated output in type as a result, falling reason according to what the output result determined the electronic device.
After the technical solution that the application provides collects acceleration information, acceleration value is calculated according to acceleration information,
The pressure value of housing is acquired, when being determined as falling state, the acceleration figure and pressure value of state are fallen in extraction, by the acceleration
Value and pressure value composition input data, which is input in artificial nerve network model and carries out that output is calculated
As a result, this makes it possible to obtain electronic device according to the output result to fall reason.
Above-mentioned reason of falling includes but not limited to：Deliberately fall, be not intended to fall, fault is fallen etc..
Optionally, above-mentioned electronic device further includes：Communication module；
The AP is additionally operable to control communication module and the reason of falling is sent to network side equipment.
AP210, specifically for extracting the quantity n of the acceleration value and quantity m of pressure value, extraction is preset trained
Size, that is, CI*H*W of the default input data of artificial nerve network model；Wherein, H is height value, and W is width value, and CI is deep
Angle value, compares whether n+m is more than CI*H*W, if the n+m is more than or equal to CI*H*W, does not add the quantity and pressure of acceleration value
The quantity of value if n+m is less than CI*H*W, then makes the quantity addition of the quantity and pressure value of the acceleration value by preset strategy
N '+m '=CI*H*W after must adding.
Specifically, AP210, for n acceleration value and m pressure value to be formed three-dimensional input data, such as the three-dimensional
The size of input data is CI*H/2*W, then causes the size add value of three-dimensional input data in H directions interlacing insertion add value
CI*H*W, specifically, zero or average value can be inserted into the interlacing of H directions, which is the average value of H directions adjacent rows.
The principle of lower artificial intelligence is described below, for the most of meter using neural network of calculating of artificial intelligence
It calculates, the operation for neural network, although its operation with multilayer, basic operation is convolution algorithm.
As shown in Figure 3a, it is a kind of schematic diagram of convolution algorithm, as shown in Figure 3a, input data can be the three of CI*H*W
Dimension data can be the convolved data of CO*CI*3*3 for weights, that is, convolution kernel of convolution algorithm, and the result of output can be with
For：As a result, as shown in Figure 3a, each grid is a numerical value, which, which is specifically as follows, adds for the output of CO* (H-2) * (W-2)
A value in the quantity n of the velocity amplitude or quantity m of pressure value.
According to Fig. 3 a, the Computing Principle of neural network is introduced, in the operation of neural network, that is, artificial intelligence, instructing
The artificial intelligence model perfected obtains weight data by the default input data defined by trained operation, trains
Artificial intelligence model be determining convolution kernel, i.e. CO*CI*3*3, for core kernel, with 3*3 and 5*5
Specification (SIZE), certainly in practical applications, above-mentioned weights can also be other specifications, and the application be not intended to limit above-mentioned rule
The concrete form of lattice.
For training operation, will more parts of input data CI*H*W, in practical training, more parts of input datas CI, H, W
Value can be different, the multilayer forward operation for performing neural network is exported as a result, being obtained according to output result defeated
Go out result gradient, output result gradient then is performed the reversed operation of multilayer obtains every layer of weights gradient, then passes through weights
Gradient is updated every layer of weights, and the calculating by multiple iteration obtains final weights, neural network at this time
Model is trained neural network model.Input the input data of acquisition again for this trained neural network model
Output result i.e. CO* (H-2) * (W-2) that forward operation obtains is carried out, it can by analyzing CO* (H-2) * (W-2)
Corresponding classification is obtained, is applied in the application, it can be by obtaining final falling original to CO* (H-2) * (W-2) analyses
Cause.
During for convolution algorithm, being for convolution kernel, that is, CO*CI*3*3 can not be direct directly with input data i.e. CI*H*W
Convolution algorithm is carried out, the mode of operation can be that convolution kernel, that is, CO*CI*3*3 is cut into a kernel【3】【3】；
Then with kernel【3】【3】Convolution algorithm is performed for basic granularity and input data CI*H*W, i.e., with kernel【3】【3】For base
This granularity moves on the input data, a kind of mobile schematic diagram of specific mode as shown in Figure 3b, wherein such as the box in 3b
Data for the cutting after movement.It is tested and found by the applicant, for different neural network models, input data
The quantity of the value of size, i.e. CI*H*W and the default input data in trained model exports the calculating of result closer to it and gets over
Accurately, user experience is better.
Illustrated with the example of a reality, it is assumed that the default input data of trained neural network model can be：H
=50, W=50, CI=64, then if to obtain value very little for collected input data, it is assumed that the three-dimensional data of composition is：H=
20, W=20, CI=12, then for the number no matter trained more, weights are more accurate, the output result calculated obtain fall
The precision for falling result is very low, is found through experiments that, i.e., the number of convolution cutting and the cutting times of default input data are inclined
Difference is bigger, then the precision of output result that it is obtained is lower, for example, H, W in H=50, W=50, CI=64 in one layer of CI
Cutting times be：48*48；Cutting times for H, W in one layer of CI of the input data of acquisition are：10*10 is calculated
Quantity is also mutually far short of what is expected, so in order to solve this problem, the technical solution that the application provides passes through preset strategy addition element
It is worth (i.e. the quantity of square), specific preset strategy can be to carry out the addition of data by way of addition zero, so i.e.
Corresponding value can be reached by zero-adding, specifically, as shown in Figure 3c, original input data is：H=9, W=7, CI=
4, the H=18 of preset input data, W=7, CI=4；The mode of so its addition zero can be, in a manner of interlacing zero insertion
It is inserted into original input data, as shown in Figure 3d, the black interval in Fig. 3 d is what is be inserted into the data after specific insertion
The position of zero.
Certainly in practical applications, above-mentioned preset strategy can also be to be added in a manner of average value, black by taking Fig. 3 d as an example
Color section is the position for the average value being inserted into, wherein the average value between the average value two values adjacent for H directions, for example, H
7 numerical value of the second row of direction can be the average value of 7 numerical value of H directions the first row and 7 numerical value of the third line, corresponding
, the value of last column of insertion, the i.e. value of the 18th row of H directions can be the value identical with the 17th row value.It is found through experiments that,
By the way of average value, the output data that is calculated is than the precision higher of output result that zero insertion mode is calculated.
As shown in table 1, it is zero insertion mode and the comparison of the accuracy of average value mode, wherein using 4 dimensions, by testing
Defence line is demonstrate,proved, average value mode is added, precision can reach 98% or so, and by zero insertion mode, accuracy can reach
To 95% or so, by conventional methods, precision can reach 90% or so.So corresponding knowledge can be improved through the above way
Other precision improves the Experience Degree of user.
Table 1：
Refering to Fig. 4, Fig. 4 offers are a kind of to fall method for computing data based on artificial intelligence, and the method is applied to electronics
In device, the electronic device includes：Housing, application processor AP, touching display screen, gravity sensor, circuit and pressure pass
Sensor, the pressure sensor, the gravity sensor, the touching display screen pass through at least one circuit and the application
Manage device connection；As shown in figure 4, the method includes：
Step S401, acceleration information of the electronic device when falling is acquired；
Step S402, the pressure value that electronic device user when falling holds housing is acquired；
It is above-mentioned to fall determining mode and include：Acceleration value is calculated according to acceleration information, according to the acceleration
Angle value determines the state that the electronic device is in, which includes：It is non-to fall state and fall state；
Acceleration information and the pressure value when step S403, being fallen according to the electronic device determine the electronics
Device falls reason.
The concrete methods of realizing of above-mentioned steps S403 can be：Acceleration value and pressure value under state are fallen in extraction,
This is fallen to the acceleration value and pressure value composition input data under state, which is input to preset train
Artificial nerve network model in be calculated output as a result, falling original according to what the output result determined the electronic device
Cause.
After the technical solution that the application provides collects acceleration information, acceleration value is calculated according to acceleration information,
The pressure value of housing is acquired, when being determined as falling state, the acceleration figure and pressure value of state are fallen in extraction, by the acceleration
Value and pressure value composition input data, which is input in artificial nerve network model and carries out that output is calculated
As a result, this makes it possible to obtain electronic device according to the output result to fall reason.
Refering to Fig. 5, Fig. 5 provides a kind of electronic device, and the electronic device includes：Housing, circuit board, battery, cover board, again
Force snesor 504, touching display screen 503, pressure sensor 501 and processing unit 502, wherein,
Gravity sensor 504, for acquiring acceleration information of the electronic device when falling, by the acceleration number of degrees
According to being transferred to processing unit 502；
Pressure sensor 501, for acquiring the pressure value that user when the electronic device falls holds housing, by the pressure
Value is transferred to processing unit 502；
Processing unit 502, acceleration information and the pressure value during for being fallen according to the electronic device determine
The electronic device falls reason.
Specifically, processing unit 502, falls the acceleration value and pressure value under state specifically for extraction, this is fallen
The acceleration value and pressure value composition input data under state are fallen, which is input to preset trained artificial
Carry out being calculated output in neural network model as a result, falling reason according to what the output result determined the electronic device.
After the technical solution that the application provides collects acceleration information, acceleration value is calculated according to acceleration information,
The pressure value of housing is acquired, when being determined as falling state, the acceleration figure and pressure value of state are fallen in extraction, by the acceleration
Value and pressure value composition input data, which is input in artificial nerve network model and carries out that output is calculated
As a result, this makes it possible to obtain electronic device according to the output result to fall reason.
Fig. 6 is illustrated that the block diagram with the part-structure of the relevant mobile phone of mobile terminal provided by the embodiments of the present application.Ginseng
Fig. 6 is examined, mobile phone includes：Radio frequency (Radio Frequency, RF) circuit 910, memory 920, input unit 930, sensor
950th, voicefrequency circuit 960, Wireless Fidelity (Wireless Fidelity, WiFi) module 970, application processor AP980 and
990 grade components of power supply.It will be understood by those skilled in the art that handset structure shown in Fig. 6 does not form the restriction to mobile phone,
It can include either combining certain components or different components arrangement than illustrating more or fewer components.
Each component parts of mobile phone is specifically introduced with reference to Fig. 6：
Input unit 930 can be used for receiving input number or character information and generate with the user setting of mobile phone with
And the key signals input that function control is related.Specifically, input unit 930 may include touching display screen 933, fingerprint identification device
931st, face identification device 936, iris identification device 937 and other input equipments 932.Input unit 930 can also include
Other input equipments 932.Specifically, other input equipments 932 can include but is not limited to physical button, function key (such as sound
Measure control button, switch key etc.), it is trace ball, mouse, one or more in operating lever etc..Wherein,
Sensor 950, for acquiring the pressure value of the acceleration information of the electronic device and housing, by the acceleration
Data and the pressure value of housing are transferred to AP980.
For acceleration value to be calculated according to acceleration information, the electronic device is determined according to the acceleration value by AP980
The state being in, the state include：It is non-to fall state and fall state；Acceleration value and pressure under state are fallen in extraction
This is fallen acceleration value and pressure value composition input data under state, which is input to preset instruction by value
Carry out being calculated output in the artificial nerve network model perfected as a result, determining falling for the electronic device according to the output result
Fall reason.
AP980 is the control centre of mobile phone, using various interfaces and the various pieces of connection whole mobile phone, passes through fortune
Row performs the software program being stored in memory 920 and/or module and calls the data being stored in memory 920,
The various functions of mobile phone and processing data are performed, so as to carry out integral monitoring to mobile phone.Optionally, AP980 may include one or
Multiple processing units；Optionally, AP980 can integrate application processor and modem processor, wherein, application processor is main
Processing operation system, user interface and application program etc., modem processor mainly handle wireless communication.It is appreciated that
It is that above-mentioned modem processor can not also be integrated into AP980.
In addition, memory 920 can include high-speed random access memory, nonvolatile memory, example can also be included
Such as at least one disk memory, flush memory device or other volatile solid-state parts.
RF circuits 910 can be used for sending and receiving for information.In general, RF circuits 910 include but not limited to antenna, at least one
A amplifier, transceiver, coupler, low-noise amplifier (Low Noise Amplifier, LNA), duplexer etc..In addition,
RF circuits 910 can also communicate with network and other equipment by radio communication.Above-mentioned wireless communication can use any communication
Standard or agreement, including but not limited to global system for mobile communications (Global System of Mobile
Communication, GSM), general packet radio service (General Packet Radio Service, GPRS), code it is point more
Location (Code Division Multiple Access, CDMA), wideband code division multiple access (Wideband Code Division
Multiple Access, WCDMA), long term evolution (Long Term Evolution, LTE), Email, short message service
(Short Messaging Service, SMS) etc..
Mobile phone may also include at least one sensor 950, such as optical sensor, motion sensor and other sensors.
Specifically, optical sensor may include ambient light sensor and proximity sensor, wherein, ambient light sensor can be according to ambient light
Light and shade adjust the brightness of touching display screen, proximity sensor can when mobile phone is moved in one's ear, close touching display screen and/
Or backlight.As one kind of motion sensor, accelerometer sensor can detect in all directions (generally three axis) acceleration
Size can detect that size and the direction of gravity when static, can be used to identify mobile phone posture application (such as horizontal/vertical screen switching,
Dependent game, magnetometer pose calibrating), Vibration identification correlation function (such as pedometer, tap) etc.；It can also configure as mobile phone
The other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared ray sensor, details are not described herein.
Voicefrequency circuit 960, loud speaker 961, microphone 962 can provide the audio interface between user and mobile phone.Audio-frequency electric
The transformed electric signal of the audio data received can be transferred to loud speaker 961, sound is converted to by loud speaker 961 by road 960
Signal plays；On the other hand, the voice signal of collection is converted to electric signal by microphone 962, is turned after being received by voicefrequency circuit 960
Be changed to audio data, then after audio data is played AP980 processing, through RF circuits 910 be sent to such as another mobile phone or
Audio data is played to memory 920 to be further processed.
WiFi belongs to short range wireless transmission technology, and mobile phone can help user's transceiver electronics postal by WiFi module 970
Part, browsing webpage and access streaming video etc., it has provided wireless broadband internet to the user and has accessed.Although Fig. 6 is shown
WiFi module 970, but it is understood that, and must be configured into for mobile phone is not belonging to, it can not change as needed completely
Become in the range of the essence of application and omit.
Mobile phone further includes the power supply 990 (such as battery) powered to all parts, and optionally, power supply can pass through power supply pipe
Reason system and AP980 are logically contiguous, so as to realize the work(such as management charging, electric discharge and power managed by power-supply management system
Energy.
Although being not shown, mobile phone can also include camera, bluetooth module, light compensating apparatus, light sensor etc., herein not
It repeats again.
As can be seen that by the embodiment of the present application, after collecting acceleration information, electronics is determined according to acceleration information
The state of device, when being determined as falling state, by camera acquire ground the first picture, then according to acceleration value with
And acquisition time obtains the distance on the ground of electronic device, the second picture for extracting electronic device (is specifically as follows outline drawing
Piece), this makes it possible to generate 3D animations that ground is dropped into electronic device, improve the Experience Degree of user.
The embodiment of the present application also provides a kind of computer storage media, wherein, computer storage media storage is for electricity
The computer program that subdata exchanges, it is any as described in above-mentioned embodiment of the method which so that computer is performed
A kind of part or all of step for falling method for computing data based on artificial intelligence.
The embodiment of the present application also provides a kind of computer program product, and the computer program product includes storing calculating
The non-transient computer readable storage medium of machine program, the computer program are operable to that computer is made to perform such as above-mentioned side
Any part or all of step for falling method for computing data based on artificial intelligence described in method embodiment.
It should be noted that for aforementioned each method embodiment, in order to be briefly described, therefore it is all expressed as a series of
Combination of actions, but those skilled in the art should know, the application is not limited by described sequence of movement because
According to the application, certain steps may be used other sequences or be carried out at the same time.Secondly, those skilled in the art should also know
It knows, embodiment described in this description belongs to alternative embodiment, involved action and module not necessarily the application
It is necessary.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment
Point, it may refer to the associated description of other embodiment.
In several embodiments provided herein, it should be understood that disclosed device, it can be by another way
It realizes.For example, the apparatus embodiments described above are merely exemplary, such as the division of the unit, it is only a kind of
Division of logic function, can there is an other dividing mode in actual implementation, such as multiple units or component can combine or can
To be integrated into another system or some features can be ignored or does not perform.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be by some interfaces, the INDIRECT COUPLING or communication connection of device or unit,
Can be electrical or other forms.
The unit illustrated as separating component may or may not be physically separate, be shown as unit
The component shown may or may not be physical unit, you can be located at a place or can also be distributed to multiple
In network element.Some or all of unit therein can be selected according to the actual needs to realize the mesh of this embodiment scheme
's.
In addition, each functional unit in each embodiment of the application can be integrated in a processing unit, it can also
That each unit is individually physically present, can also two or more units integrate in a unit.Above-mentioned integrated list
The form that hardware had both may be used in member is realized, can also be realized in the form of software program module.
If the integrated unit is realized in the form of software program module and is independent product sale or uses
When, it can be stored in a computer-readable access to memory.Based on such understanding, the technical solution of the application substantially or
Person say the part contribute to the prior art or the technical solution all or part can in the form of software product body
Reveal and, which is stored in a memory, is used including some instructions so that a computer equipment
(can be personal computer, server or network equipment etc.) performs all or part of each embodiment the method for the application
Step.And aforementioned memory includes：USB flash disk, read-only memory (ROM, Read-Only Memory), random access memory
The various media that can store program code such as (RAM, Random Access Memory), mobile hard disk, magnetic disc or CD.
One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is can
It is completed with instructing relevant hardware by program, which can be stored in a computer-readable memory, memory
It can include：Flash disk, read-only memory (English：Read-Only Memory, referred to as：ROM), random access device (English：
Random Access Memory, referred to as：RAM), disk or CD etc..
The embodiment of the present application is described in detail above, specific case used herein to the principle of the application and
Embodiment is expounded, and the explanation of above example is only intended to help to understand the present processes and its core concept；
Meanwhile for those of ordinary skill in the art, according to the thought of the application, can in specific embodiments and applications
There is change part, in conclusion the content of the present specification should not be construed as the limitation to the application.
Claims (11)
1. a kind of electronic device, the electronic device includes：Application processor AP, gravity sensor and pressure sensor, it is described
Pressure sensor, the gravity sensor are connect by least one circuit with the application processor；
The gravity sensor, for acquiring the acceleration information when electronic device falls；
The pressure sensor, when falling for acquiring the electronic device, user is to the pressure value of housing；
The AP, acceleration information and the pressure value during for being fallen according to the electronic device determine the electronic device
Fall reason.
2. electronic device according to claim 1, which is characterized in that
The AP, specifically for acceleration value is calculated according to the acceleration information, by the acceleration value and pressure value
Input data is formed, which is input in preset artificial nerve network model, output is calculated as a result, foundation
What the output result determined the electronic device falls reason.
3. electronic device according to claim 2, which is characterized in that
The AP specifically for extracting the quantity n of the acceleration value and quantity m of pressure value, extracts preset artificial neural network
The size CI*H*W of the default input data of network model；Wherein, H is height value, and W is width value, and CI is depth value, compares n+m
Whether it is more than CI*H*W, such as the n+m >=CI*H*W, the quantity of acceleration value and the quantity of pressure value is not added, such as n+m ＜
CI*H*W then adds the n '+m ' so that after addition by preset strategy to the quantity n of the acceleration value and the quantity m of pressure value
=CI*H*W；
Wherein, n, m, CI, H, W are the integer more than or equal to 2.
4. electronic device according to claim 3, which is characterized in that
The preset strategy includes：Zero insertion addition strategy or slotting average value addition strategy.
5. a kind of fall method for computing data based on artificial intelligence, the method includes：
Acquire the acceleration information when electronic device falls；
Acquire the pressure value that user when the electronic device falls holds housing；
What acceleration information and the pressure value when being fallen according to the electronic device determined the electronic device falls original
Cause.
6. according to the method described in claim 5, it is characterized in that, acceleration number of degrees when being fallen according to the electronic device
According to this and the pressure value determine the electronic device fall reason, including：
Acceleration value is calculated according to the acceleration information, the acceleration value and pressure value are formed into input data, it will
The input data, which is input in preset artificial nerve network model, is calculated the output as a result, true according to the output result
The fixed electronic device falls reason.
7. the according to the method described in claim 5, it is characterized in that, acceleration value and pressure this fallen under state
Value composition input data, including：
The quantity n of the acceleration value and quantity m of pressure value is extracted, extracts preset trained artificial nerve network model
The size CI*H*W of default input data；Wherein, H is height value, and W is width value, and CI is depth value, compares whether n+m is more than
CI*H*W such as the n+m >=CI*H*W, does not add the quantity of acceleration value and the quantity of pressure value, such as n+m ＜ CI*H*W, then
N '+m '=CI*H*W so that after addition is added to the quantity of the acceleration value and the quantity of pressure value by preset strategy；
Wherein, n, m, CI, H, W are the integer more than or equal to 2.
8. the method according to the description of claim 7 is characterized in that
The preset strategy includes：Zero insertion addition strategy or slotting average value addition strategy.
9. a kind of electronic device, which is characterized in that the electronic device includes：Processing unit, gravity sensor and pressure sensing
Device, the gravity sensor, the pressure sensor are connect by least one circuit with the processing unit, wherein,
The gravity sensor, for acquiring acceleration information of the electronic device when falling；
The pressure sensor, for acquire the electronic device when falling user to the pressure value of housing；
The processing unit, acceleration information and the pressure value during for being fallen according to the electronic device determine the electricity
Sub-device falls reason.
10. a kind of computer readable storage medium, which is characterized in that it stores the computer program for electronic data interchange,
Wherein, the computer program causes computer to perform such as claim 5-8 any one of them methods.
11. a kind of computer program product, which is characterized in that the computer program product includes storing computer program
Non-transient computer readable storage medium, the computer program are operable to that computer is made to perform such as claim 5-8
Method described in one.
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