CN108255632B - Fall data calculation method based on artificial intelligence and related product - Google Patents

Fall data calculation method based on artificial intelligence and related product Download PDF

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CN108255632B
CN108255632B CN201810059803.2A CN201810059803A CN108255632B CN 108255632 B CN108255632 B CN 108255632B CN 201810059803 A CN201810059803 A CN 201810059803A CN 108255632 B CN108255632 B CN 108255632B
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electronic device
acceleration
value
data
pressure
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CN108255632A (en
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张海平
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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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
    • 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
    • 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

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Abstract

The application provides a fall data calculation method based on artificial intelligence and a related product, comprising the following steps: acquiring acceleration data when the electronic device falls; collecting a pressure value of a shell held by a user when the electronic device falls; according to the acceleration data when the electronic device falls and the pressure value, the falling reason of the electronic device is determined, and the technical scheme provided by the application has the advantage of high user experience.

Description

Fall data calculation method based on artificial intelligence and related product
Technical Field
The application relates to the technical field of terminal equipment, in particular to a fall data calculation method based on artificial intelligence and a related product.
Background
In the prior art, a mobile terminal (such as a mobile phone, a tablet computer, etc.) has become a preferred electronic device for a user and has the highest use frequency, for the mobile terminal, the screen is easy to break, which is a problem that manufacturers or users cannot avoid, and after the screen is broken, the remaining value of the terminal is greatly reduced, because the price for repairing and changing the screen of most manufacturers almost exceeds the remaining value of the terminal. And 2.5D glass is popular in the industry at present as a screen, so that the screen is more easily damaged by falling and broken, and a great amount of research and development cost is spent by each mainstream manufacturer to research and develop the falling resistance of the whole machine.
The existing drop data is complex in calculation mode, effective support cannot be provided for later-stage drop resistance, and user experience is affected.
Content of application
The embodiment of the application provides an electronic device and a related product, which can restore a falling scene, so that a user can visually watch the falling scene, and the user experience is improved.
In a first aspect, an embodiment of the present application provides an electronic device, including: the device comprises an application processor AP gravity sensor and a pressure sensor, wherein the pressure sensor and the gravity sensor are connected with the application processor through at least one circuit;
the gravity sensor is used for acquiring acceleration data when the electronic device falls;
the pressure sensor is used for acquiring the pressure value of a user to the shell when the electronic device falls;
and the AP is used for determining the falling reason of the electronic device according to the acceleration data when the electronic device falls and the pressure value.
In a second aspect, a fall data calculation method based on artificial intelligence is provided, which includes:
acquiring acceleration data when the electronic device falls;
collecting a pressure value of a shell held by a user when the electronic device falls;
and determining the falling reason of the electronic device according to the acceleration data and the pressure value when the electronic device falls.
In a third aspect, an electronic device is provided, which includes: a processing unit, a gravity sensor and a pressure sensor, the gravity sensor and the pressure sensor being connected to the processing unit via at least one circuit,
the gravity sensor is used for acquiring acceleration data of the electronic device when the electronic device falls;
the pressure sensor is used for acquiring the pressure value of a user to the shell when the electronic device falls;
and the processing unit is used for determining the falling reason of the electronic device according to the acceleration data and the pressure value when the electronic device falls.
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:
it can be seen that after the acceleration data is collected through the technical scheme that this application provided, calculate the acceleration value according to the acceleration data, gather the pressure value of casing, when confirming for the state of falling, extract the acceleration value and the pressure value of the state of falling, constitute input data with this acceleration value and pressure value, input this input data and calculate in the artificial neural network model and obtain the output result, just so can obtain electron device's the reason of falling according to this output result.
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 schematic diagram of a parallel plate capacitor provided in an embodiment of the present application.
FIG. 1b is a schematic diagram of another parallel plate capacitor provided in embodiments of the present application.
FIG. 1c is a schematic diagram of yet another parallel plate capacitor provided by an embodiment of the present application.
FIG. 1d is a schematic diagram of acceleration provided by an embodiment of the present application.
Fig. 2 is a schematic view of an electronic device disclosed in an embodiment of the present application.
Fig. 3a is a schematic diagram of a convolution operation disclosed in the embodiment of the present application.
Fig. 3b is a schematic diagram of a convolution operation according to an embodiment of the present application.
Fig. 3c is a schematic diagram of three-dimensional input data without data added according to an embodiment of the present application.
Fig. 3d is a schematic diagram of three-dimensional input data with data added according to an embodiment of the present application.
Fig. 4 is a schematic flowchart of a fall data calculation method based on artificial intelligence according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 6 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.
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: intelligent vehicle-mounted terminal, computer equipment and the like.
In the electronic device of the first aspect, the AP is specifically configured to calculate an acceleration value according to the acceleration data, compose the acceleration value and a pressure value into input data, input the input data into a preset artificial neural network model to calculate an output result, and determine a drop cause of the electronic device according to the output result.
In the electronic device of the first aspect, the AP is specifically configured to extract a number n of acceleration values and a number m of pressure values, and extract a size CI × H × W of preset input data of a preset artificial neural network model; if n + m is larger than or equal to CI H W, the number of acceleration values and the number of pressure values are not added, and if n + m is smaller than CI H W, the number n of the acceleration values and the number m of the pressure values are added according to a preset strategy so that n '+ m' is CI H W;
wherein n, m, CI, H and W are integers which are more than or equal to 2.
In the electronic device of the first aspect, the preset policy includes: a zero-inserted addition strategy or an average-inserted addition strategy.
In the method of the second aspect, the determining a drop cause of the electronic device according to the acceleration data of the electronic device when the electronic device drops and the pressure value includes:
and calculating to obtain an acceleration value according to the acceleration data, forming the acceleration value and the pressure value into input data, inputting the input data into a preset artificial neural network model to calculate to obtain an output result, and determining the falling reason of the electronic device according to the output result.
In the method of the second aspect, the step of combining the acceleration value and the pressure value in the falling state into input data includes:
extracting the number n of acceleration values and the number m of pressure values, and extracting the size CI H W of preset input data of a preset trained artificial neural network model; if n + m is larger than or equal to CI H W, the number of acceleration values and the number of pressure values are not added, and if n + m is smaller than CI H W, the number of the acceleration values and the number of the pressure values are added according to a preset strategy, so that the added n '+ m' is equal to CI H W;
wherein n, m, CI, H and W are integers which are more than or equal to 2.
In the method of the second aspect, the preset policy includes: zero-insertion addition strategy or average-insertion addition strategy
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 touch screen display device comprises a shell 110, a circuit board 120, a battery 130, a cover plate 140, a touch display screen 150 and a Gravity Sensor (G-Sensor for short) 170, wherein the circuit board 120, the battery 130 and the cover plate 140 are arranged on the shell 110, and the circuit board 120 is further provided with a circuit connected with the touch display screen 150; the circuit board 120 may further include: an application processor AP190 and a gravity sensor.
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.
The gravity sensor 170 is used for detecting the direction and magnitude of the acceleration, and is equivalent to detecting the motion state of the electronic device. The function of the G-sensor is simple to understand, and mainly senses the change of the acceleration force, such as various movement changes of shaking, falling, rising, falling and the like, which can be converted into an electric signal by the G-sensor, and then the acceleration value of the electronic device can be determined after the calculation and analysis of the application processor AP 190.
Optionally, the electronic device may further include: the geomagnetic sensor and the gyroscope are respectively connected with the application processor AP 190. On the electronic device, the G-sensor not only works alone, but also works in cooperation with the geomagnetic sensor 171 and the gyroscope 172, providing more accurate and comprehensive motion sensing capability.
Specifically, in the electronic device, the gravity sensor 170 may actually be a parallel plate capacitor, and the capacitance value of the parallel plate capacitor is inversely proportional to the distance between the plates, and the linear acceleration in each direction can be calculated by detecting the capacitance change in the direction X, Y, Z.
Taking the acceleration calculation mode in the X direction as an example, the acceleration value may specifically be:
Figure BDA0001554985380000051
FIG. 1a is a schematic diagram of a parallel plate capacitor.
Referring to FIG. 1a, the acceleration corresponding to FIG. 1a is 0, and as shown in FIG. 1a, since there is no acceleration value, the middle parallel plate is at the initial position, and thus the capacitance C is obtained1＝C0C of the1May be the capacitance between the parallel plate and the lower electrode, C0May be an initial capacitance value. Capacitance value C at this time2＝C0C of the2Can be the capacitance between the parallel plate and the upper electrode, in this case, the capacitance C1Corresponding distance d1＝d0(ii) a Capacitor C2Corresponding distance d2＝d0(ii) a Wherein d is1May be the distance between the parallel plate and the lower electrode, d2May be the distance between the parallel plate and the upper electrode. Since the acceleration value at this time is zero, C1＝C2＝C0(ii) a A can be calculated according to the formulax＝0。
Referring to FIG. 1b, the acceleration corresponding to FIG. 1b is a positive value, since the acceleration is positiveThe parallel plate moves toward the lower electrode, and if the distance is x, the distance between the parallel plate and the upper electrode is increased by x, so that d is the distance1＝d0-x，d2＝d0+ x; the calculation formula according to the plate capacitance is shown as the following formula:
Figure BDA0001554985380000061
where S may be the corresponding area between the two plates of a parallel plate capacitor,. epsilon.is the dielectric constant (which is determined by the material of the plate electrodes), k is the electrostatic constant, and d is the distance between the two plates of the parallel plate capacitor.
The capacitance values shown in FIG. 1b are as follows:
Figure BDA0001554985380000062
Figure BDA0001554985380000063
therefore, C is due to the parallel plate of the parallel plate capacitor moving toward the lower electrode1＞C2I.e. ax＞0。
Referring to FIG. 1c, the acceleration corresponding to FIG. 1c is negative, and the parallel plate moves toward the upper electrode due to the negative acceleration, and if the moving distance is x, the distance between the parallel plate and the upper electrode is increased by x, so that d is the time when the distance between the parallel plate and the upper electrode is increased1＝d0+x，d2＝d0-x; the calculation formula according to the plate capacitance is shown as the following formula:
the capacitance values shown in FIG. 1c are as follows:
Figure BDA0001554985380000064
Figure BDA0001554985380000071
at this time, C is caused by the parallel plate of the parallel plate capacitor moving toward the upper electrode1＜C2I.e. ax＜0。
That is, through the test of the parallel plate capacitor described above, a specific acceleration value can be obtained, and this value can indicate the direction of acceleration.
Specifically, for the electronic device, the tested acceleration value has three directions, as shown in fig. 1d, which is a schematic diagram of the three directions tested by the electronic device, specifically, the acceleration value can be divided into an X-axis direction, a Y-axis direction and a Z-axis direction, and the specific display schematic diagram is shown in fig. 1 d.
Specifically, in an optional drop test, the corresponding acceleration value during the drop process may be:
ax＝0.049m/S2
ay＝—0.026m/S2
az＝9.800m/S2。
the electronic device can be determined to be in a falling state according to the data.
As shown in fig. 2, for a schematic structural diagram of an electronic device provided in the present application, as shown in fig. 2, the electronic device 200 includes: the device comprises a shell, an application processor AP210, a touch display screen 220, a gravity sensor 250, a pressure sensor 260 and a circuit 240, wherein a camera 230 is arranged outside the shell, and the camera and the touch display screen are connected with the application processor AP through at least one circuit. The AP210 is connected to the gravity sensor 250 through another circuit, wherein the circuit 240 specifically includes: a bus, a flexible circuit board, a connection chip, etc., although the circuit 240 may have other expressions in practical applications, and the embodiments of the present invention do not limit the expressions of the circuit 240. The electronic device 200 may further include: a geomagnetic sensor and a gyroscope, which may collect data in conjunction with the gravity sensor 250; the electronic device 200 may further include: an artificial intelligence processor, which may be separately provided or integrated with the application processor AP210, is integrated within the AP210 as in the embodiment shown in fig. 2 for convenience of description.
The gravity sensor 250 is used for acquiring acceleration data when the electronic device falls and transmitting the acceleration data to the application processor AP;
the pressure sensor 260 is used for acquiring a pressure value of a user on the shell when the electronic device falls off and transmitting the pressure value to the application processor AP;
the AP210 is configured to calculate an acceleration value according to the acceleration data, and determine a state of the electronic device according to the acceleration value, where the state includes: a non-falling state and a falling state;
optionally, the acceleration data may be a plurality of capacitance values of the parallel plate capacitor, and specifically, may be C as shown in fig. 1a, 1b, and 1C1And C2The value of (c). In practical application, of course, the acceleration data of X, Y, Z three axes as shown in fig. 1d needs to be collected. Of course, in practical applications, other gravity sensors are used, and the acceleration data may be other types of data, and the embodiments of the present application do not limit the actual expression of the acceleration data.
The number of the acceleration values may be n acceleration values. Specifically, the AP210 traverses n acceleration values in the order of the acquisition points, and determines the AP to be in a falling state if m consecutive acceleration values are greater than a set threshold, or determines the AP to be in a non-falling state. Wherein n and m are integers greater than or equal to 2, and m is less than n.
And the AP210 is also used for determining the falling reason of the electronic device according to the acceleration data and the pressure value when the electronic device falls.
Specifically, the AP210 is configured to extract an acceleration value and a pressure value in a falling state, compose the acceleration value and the pressure value in the falling state into input data, input the input data into a preset trained artificial neural network model to calculate an output result, and determine a falling cause of the electronic device according to the output result.
After the technical scheme that this application provided gathers acceleration data, calculate the acceleration value according to acceleration data, gather the pressure value of casing, when confirming for the state of falling, extract the acceleration value and the pressure value of the state of falling, constitute input data with this acceleration value and pressure value, calculate in inputing this input data to artifical neural network model and obtain the output result, just so can obtain electron device's the reason of falling according to this output result.
Reasons for such falls include, but are not limited to: intentional falls, unintentional falls, accidental falls, and the like.
Optionally, the electronic device further includes: a communication module;
and the AP is also used for controlling the communication module to send the drop reason to network side equipment.
The AP210 is specifically configured to extract the number n of acceleration values and the number m of pressure values, and extract a size CI × H × W of preset input data of a preset trained artificial neural network model; and if the n + m is smaller than the CI H W, adding the number of the acceleration values and the number of the pressure values according to a preset strategy so that the added n '+ m' ═ CI H W.
Specifically, the AP210 is configured to combine the n acceleration values and the m pressure values into three-dimensional input data, and if the size of the three-dimensional input data is CI × H/2 × W, insert an addition value every other line in the H direction so that the size of the three-dimensional input data is the addition value CI × H × W, specifically, insert a zero value or an average value every other line in the H direction, where the average value is an average value of adjacent lines in the H direction.
The following introduces the principle of artificial intelligence, which mostly adopts the calculation of neural network, and the basic operation of neural network is convolution operation although it has multiple layers of operations.
As shown in fig. 3a, a schematic diagram of a convolution operation is shown, as shown in fig. 3a, the input data may be three-dimensional data of CI × H × W, and for the weight of the convolution operation, i.e., the convolution kernel may be convolution data of CO × CI × 3, and the output result may be: as a result of the output of CO (H-2) ((W-2)), each cell is a value, which may be specifically one of the number n of acceleration values or the number m of pressure values, as shown in fig. 3 a.
Referring to fig. 3a, a calculation principle of a neural network is introduced, in an operation of the neural network, that is, artificial intelligence, a trained artificial intelligence model is obtained by a trained operation through preset defined input data, the trained artificial intelligence model is a determined convolution kernel, that is, CO CI 3, and for a kernel, the kernel has a Specification (SIZE) of 3 × 3 and 5 × 5, and of course, in an actual application, the weight may be in other specifications, and the present application does not limit a specific form of the specification.
For training operation, namely, multiple input data CI H W, in actual training, the multiple input data CI, H and W can be different in value, multilayer forward operation of the neural network is executed to obtain an output result, an output result gradient is obtained according to the output result, then multilayer reverse operation is executed on the output result gradient to obtain a weight gradient of each layer, then the weight of each layer is updated through the weight gradient, a final weight is obtained through repeated iterative calculation, and the neural network model at the moment is a trained neural network model. And (3) performing forward operation on the input data which is input and collected again by the trained neural network model to obtain an output result, namely CO (H-2) W-2, analyzing the CO (H-2) W-2 to obtain a corresponding classification, and applying the classification to the application, namely obtaining a final drop reason by analyzing the CO (H-2) W-2.
For convolution operation, a convolution kernel, namely CO CI 3, cannot be directly convolved with input data, namely CI H W, and the operation mode can be that the convolution kernel, namely CO CI 3, is cut into a kernel [ 3 ]; then, convolution operation is performed on the input data CI × H with kernel [ 3 ] as the basic granularity and kernel [ 3 ], that is, kernel [ 3 ] as the basic granularity moves on the input data, and a specific moving schematic diagram of one mode is shown in fig. 3b, where a frame in fig. 3b is cut data after moving. Experiments of the applicant find that for different neural network models, the closer the input data size, namely the CI H W value and the preset input data number in the trained model are to the output result, the more accurate the calculation is, and the better the user experience is.
To illustrate a practical example, the preset input data of the trained neural network model is assumed to be: h50, W50, and CI 64, if too few input data are acquired, assuming that the constituent three-dimensional data is: it is found through experiments that the greater the deviation of the number of convolution cuts from the number of cuts of the preset input data, the lower the accuracy of the obtained drop result, for example, H is 50, W is 50, and the number of cuts of H, W in CI is 64 in one layer is: 48 by 48; the number of cuts H, W in one layer of CI for the input data collected is: 10 × 10, the calculated number of the elements also differs greatly, so to solve this problem, in the technical solution provided by the present application, the element value (i.e. the number of the square blocks) is added through a preset policy, and a specific preset policy may be to add data by adding zero, so that the corresponding value can be reached by adding zero, specifically, as shown in fig. 3c, the original input data is: h is 9, W is 7, CI is 4, H is 18, W is 7, and CI is 4 of the preset input data; it may be added with zeros in such a way that the zeros are inserted into the original input data in an interlaced way, and the specific inserted data is as shown in fig. 3d, and the black interval in fig. 3d is the position of the inserted zero value.
In practical applications, the preset strategy may also be to add the values in a mean value manner, taking fig. 3d as an example, the black section is the position of the inserted mean value, where the mean value is the mean value between two adjacent values in the H direction, for example, the 7 values in the second row in the H direction may be the mean value of the 7 values in the first row in the H direction and the 7 values in the third row, and correspondingly, the value in the last row inserted, that is, the value in the 18 th row in the H direction may be the same value as the value in the 17 th row. Experiments show that the output data obtained by calculation in the mean value mode has higher precision than the output result obtained by calculation in the zero insertion mode.
As shown in table 1, for the comparison of the accuracy between the zero insertion mode and the average mode, 4 dimensions are adopted, the accuracy can reach about 98% by verifying the defensive line, the accuracy can reach about 95% by the zero insertion mode, and the accuracy can reach about 90% by the conventional mode. Therefore, the corresponding recognition precision can be improved through the mode, and the user experience is improved.
Table 1:
Figure BDA0001554985380000111
referring to fig. 4, fig. 4 provides a fall data calculation method based on artificial intelligence, which is applied in an electronic device, and the electronic device includes: the device comprises a shell, an application processor AP, a touch display screen, a gravity sensor, a circuit and a pressure sensor, wherein the pressure sensor, the gravity sensor and the touch display screen are connected with the application processor through at least one circuit; as shown in fig. 4, the method includes:
s401, acquiring acceleration data of the electronic device when the electronic device falls;
s402, collecting a pressure value of a shell held by a user when the electronic device falls;
the fall determination may include: calculating an acceleration value according to the acceleration data, and determining the state of the electronic device according to the acceleration value, wherein the state comprises the following steps: a non-falling state and a falling state;
and S403, determining the falling reason of the electronic device according to the acceleration data when the electronic device falls and the pressure value.
The specific implementation method of step S403 may be: extracting an acceleration value and a pressure value under a falling state, forming input data by the acceleration value and the pressure value under the falling state, inputting the input data into a preset trained artificial neural network model for calculation to obtain an output result, and determining the falling reason of the electronic device according to the output result.
After the technical scheme that this application provided gathers acceleration data, calculate the acceleration value according to acceleration data, gather the pressure value of casing, when confirming for the state of falling, extract the acceleration value and the pressure value of the state of falling, constitute input data with this acceleration value and pressure value, calculate in inputing this input data to artifical neural network model and obtain the output result, just so can obtain electron device's the reason of falling according to this output result.
Referring to fig. 5, fig. 5 provides an electronic device, including: a housing, a circuit board, a battery, a cover plate, a gravity sensor 504, a touch display screen 503, a pressure sensor 501 and a processing unit 502, wherein,
the gravity sensor 504 is configured to collect acceleration data of the electronic device when the electronic device falls, and transmit the acceleration data to the processing unit 502;
the pressure sensor 501 is used for acquiring a pressure value of a shell held by a user when the electronic device falls off and transmitting the pressure value to the processing unit 502;
the processing unit 502 is configured to determine a drop reason of the electronic device according to the acceleration data of the electronic device when the electronic device drops and the pressure value.
Specifically, the processing unit 502 is specifically configured to extract an acceleration value and a pressure value in a falling state, compose the acceleration value and the pressure value in the falling state into input data, input the input data into a preset trained artificial neural network model to perform calculation to obtain an output result, and determine the falling cause of the electronic device according to the output result.
After the technical scheme that this application provided gathers acceleration data, calculate the acceleration value according to acceleration data, gather the pressure value of casing, when confirming for the state of falling, extract the acceleration value and the pressure value of the state of falling, constitute input data with this acceleration value and pressure value, calculate in inputing this input data to artifical neural network model and obtain the output result, just so can obtain electron device's the reason of falling according to this output result.
Fig. 6 is a block diagram illustrating a partial structure of a mobile phone related to a mobile terminal according to an embodiment of the present disclosure. Referring to fig. 6, the handset includes: radio Frequency (RF) circuit 910, memory 920, input unit 930, sensor 950, audio circuit 960, Wireless Fidelity (WiFi) module 970, application processor AP980, and power supply 990. Those skilled in the art will appreciate that the handset configuration shown in fig. 6 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. 6:
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,
and the sensor 950 is used for acquiring acceleration data of the electronic device and a pressure value of the shell and transmitting the acceleration data and the pressure value of the shell to the AP 980.
AP980, which is used for calculating an acceleration value according to the acceleration data, and determining the state of the electronic device according to the acceleration value, wherein the state comprises: a non-falling state and a falling state; extracting an acceleration value and a pressure value under a falling state, forming input data by the acceleration value and the pressure value under the falling state, inputting the input data into a preset trained artificial neural network model for calculation to obtain an output result, and determining the falling reason of the electronic device according to the output result.
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.
RF circuitry 910 may be used for the reception and transmission of information. In general, the RF circuit 910 includes, but is not limited to, an antenna, at least one Amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuit 910 may also communicate with networks and other devices via wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), and the like.
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.
Audio circuitry 960, speaker 961, microphone 962 may provide an audio interface between a user and a cell phone. The audio circuit 960 may transmit the electrical signal converted from the received audio data to the speaker 961, and the audio signal is converted by the speaker 961 to be played; on the other hand, the microphone 962 converts the collected sound signal into an electrical signal, and the electrical signal is received by the audio circuit 960 and converted into audio data, and the audio data is processed by the audio playing AP980, and then sent to another mobile phone via the RF circuit 910, or played to the memory 920 for further processing.
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. 6 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.
It can be seen that, through this application embodiment, after the acceleration data is gathered, the state of electron device is confirmed according to the acceleration data, when confirming for falling the state, gather the first picture on ground through the camera, then obtain the distance on electron device's ground according to acceleration value and acquisition time, extract electron device's second picture (specifically can be the appearance picture), just so can generate and have electron device fall the 3D animation on ground, improved user's experience degree.
Embodiments of the present application also provide 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 fall data calculation methods based on artificial intelligence as described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the artificial intelligence based fall data calculation methods as set forth 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 (6)

1. An electronic device, the electronic device comprising: the system comprises an application processor AP, a gravity sensor and a pressure sensor, wherein the pressure sensor and the gravity sensor are connected with the application processor through at least one circuit;
the gravity sensor is used for acquiring acceleration data when the electronic device falls;
the pressure sensor is used for acquiring the pressure value of a user to the shell when the electronic device falls;
the AP is used for determining the falling reason of the electronic device according to the acceleration data and the pressure value when the electronic device falls;
wherein the content of the first and second substances,
the AP is specifically used for obtaining an acceleration value through calculation according to the acceleration data, forming input data by the acceleration value and a pressure value, inputting the input data into a preset artificial neural network model to obtain an output result through calculation, and determining the falling reason of the electronic device according to the output result;
wherein the content of the first and second substances,
the AP is specifically used for extracting the number n of acceleration values and the number m of pressure values and extracting the size CI H W of preset input data of a preset artificial neural network model; if n + m is larger than or equal to CI H W, the number of acceleration values and the number of pressure values are not added, and if n + m is smaller than CI H W, the number n of the acceleration values and the number m of the pressure values are added according to a preset strategy so that n '+ m' is CI H W;
wherein n, m, CI, H and W are integers which are more than or equal to 2.
2. The electronic device of claim 1,
the preset strategy comprises the following steps: a zero-inserted addition strategy or an average-inserted addition strategy.
3. A fall data calculation method based on artificial intelligence, the method comprising:
acquiring acceleration data when the electronic device falls;
collecting a pressure value of a shell held by a user when the electronic device falls;
determining the falling reason of the electronic device according to the acceleration data and the pressure value when the electronic device falls;
wherein, the determining the falling reason of the electronic device according to the acceleration data and the pressure value when the electronic device falls comprises:
calculating to obtain an acceleration value according to the acceleration data, forming the acceleration value and a pressure value into input data, inputting the input data into a preset artificial neural network model to calculate to obtain an output result, and determining the falling reason of the electronic device according to the output result;
wherein the content of the first and second substances,
the acceleration value and the pressure value under the falling state are combined into input data, and the method comprises the following steps:
extracting the number n of acceleration values and the number m of pressure values, and extracting the size CI H W of preset input data of a preset trained artificial neural network model; if n + m is larger than or equal to CI H W, the number of acceleration values and the number of pressure values are not added, and if n + m is smaller than CI H W, the number of the acceleration values and the number of the pressure values are added according to a preset strategy, so that the added n '+ m' is equal to CI H W;
wherein n, m, CI, H and W are integers which are more than or equal to 2.
4. The method of claim 3,
the preset strategy comprises the following steps: a zero-inserted addition strategy or an average-inserted addition strategy.
5. An electronic device, comprising: a processing unit, a gravity sensor and a pressure sensor, the gravity sensor and the pressure sensor being connected to the processing unit via at least one circuit,
the gravity sensor is used for acquiring acceleration data of the electronic device when the electronic device falls;
the pressure sensor is used for acquiring the pressure value of a user to the shell when the electronic device falls;
the processing unit is used for determining the falling reason of the electronic device according to the acceleration data and the pressure value when the electronic device falls;
wherein, the determining the falling reason of the electronic device according to the acceleration data and the pressure value when the electronic device falls comprises:
calculating to obtain an acceleration value according to the acceleration data, forming the acceleration value and a pressure value into input data, inputting the input data into a preset artificial neural network model to calculate to obtain an output result, and determining the falling reason of the electronic device according to the output result;
wherein the content of the first and second substances,
the acceleration value and the pressure value under the falling state are combined into input data, and the method comprises the following steps:
extracting the number n of acceleration values and the number m of pressure values, and extracting the size CI H W of preset input data of a preset trained artificial neural network model; if n + m is larger than or equal to CI H W, the number of acceleration values and the number of pressure values are not added, and if n + m is smaller than CI H W, the number of the acceleration values and the number of the pressure values are added according to a preset strategy, so that the added n '+ m' is equal to CI H W;
wherein n, m, CI, H and W are integers which are more than or equal to 2.
6. 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 3-4.
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