CN108288164A - Mobile payment control method - Google Patents

Abstract

The invention discloses a mobile payment control method, which comprises the following steps: the intelligent wearable device detects whether the intelligent wearable device is worn on a user; if the wearable intelligent equipment is worn on a user, the intelligent wearable equipment starts a built-in multi-axis accelerometer; the intelligent wearable device detects the motion of the user through the multi-axis accelerometer; when the intelligent wearable device learns that the motion amplitude of the user is smaller than a preset value within a continuous period of time, starting a camera built in the intelligent wearable device, identifying the face area of the user, judging whether the user is in a closed-eye state within a period of time, and if so, determining that the user is in a sleep state by the intelligent wearable device; receiving a verification request associated with the payment request; outputting an instruction related to rejecting the payment request based on a determination that the user is in a sleep state. The invention can improve the payment safety.

Description

A mobile payment control method

technical field

The invention relates to the field of mobile payment, in particular to a mobile payment control method.

Background technique

With the continuous development of smart wearable devices in recent years, users' requirements for the functions of smart wearable devices have also been continuously improved.

With the rapid development and popularization of mobile payment, users have higher and higher requirements for the convenience of mobile payment. Combining smart wearable devices with mobile payments has become a trend.

In the payment verification process of mobile payment, the existing methods generally use passwords, fingerprints, face recognition, etc. For the situation where face recognition is adopted, a malicious situation of "swiping the face" in the user's sleeping state may occur, thereby causing property damage.

Therefore, how to determine that the user is in a sleep state to avoid property damage is practically at hand.

Contents of the invention

The technical problem to be solved by the present invention is to provide a mobile payment control method to reduce the risk of malicious payment when the user is in a sleeping state.

In order to solve the above technical problems, the present invention provides a mobile payment control method, the method comprising:

The smart wearable device detects whether it is worn by the user;

If worn by the user, the smart wearable device activates the built-in multi-axis accelerometer;

The smart wearable device detects the user's motion through the multi-axis accelerometer;

When the smart wearable device learns that the user's motion range is less than a preset value for a continuous period of time, it starts the built-in camera of the smart wearable device to identify the user's face area and judge the Whether the user is in a closed-eye state for a period of time, if so, the smart wearable device determines that the user is in a sleep state;

Receive verification requests related to payment requests;

Based on the judgment that the user is in a sleeping state, output an instruction related to rejecting the payment request.

Wherein, the built-in camera is a two-axis rotatable camera.

Wherein, said starting the built-in camera of said smart wearable device, and identifying said user's face area includes:

Start the built-in camera of the smart wearable device to scan around to determine the user's face area;

The smart wearable device determines its own location parameters, and then adjusts the shooting area according to the location parameters, so that the shooting area can continuously cover the user's face area.

Wherein, said adjusting the shooting area according to said location parameters includes:

The smart wearable device obtains a pitch angle through the position parameter, and the pitch angle is used to indicate a downward or upward offset angle of the smart wearable device relative to the user's face area;

The smart wearable device uses the following formula to calculate the adjustment angle of the camera according to the pitch angle, and the smart wearable device adjusts the shooting angle of the camera according to the adjustment angle, so that the shooting area can continuously cover the user facial area;

or,

Wherein, (X _world , Z _world ) is the coordinates of the user's face area, (X _camera , Z _camera ) is the coordinates of the shooting area, is the pitch angle, φ is the adjustment angle, θ is the viewing angle of the shooting area, m is the length of the camera, d _x is the vertical distance between the camera and the user’s face area, d _z is the horizontal distance between the camera and the user's face area.

Wherein, the multi-axis accelerometer is a three-axis accelerometer.

Wherein, before the smart wearable device detects the user's movement through the multi-axis accelerometer, it includes: collecting data in advance to construct a feature database.

Wherein, it also includes: judging whether the currently detected sleep state data matches at least one set of data in the feature database, and taking the result of judging as matching as a necessary condition for determining being in a sleep state.

Wherein, the sleep state data includes the sleep state characteristics of the current moment, and the sleep state characteristics are obtained in the following manner:

Obtain the signal (v _x , v _y , v _z ) fed back by the three-axis accelerometer at each unit time before the current time period A, after the B period, and at the current time;

Calculating the modulus v of the signal of each unit moment in the A period and the B period is the square of the square of v _x plus the square of v _y plus the square of v _z , and then open the square root of the sum value;

Calculate the volatility statistical results SA and SB of the modulus of the signal at each unit time in the A period and the B period;

The feature of the sleep state at the current moment is C(t)=(v _x (t), v _y (t), v _z (t), SA(t), SB(t)), where t is the current moment.

Wherein, the construction feature database includes:

Obtain the determined sleep start time;

collecting a triaxial acceleration signal detected by the triaxial accelerometer after the determined sleep onset time;

determining an arbitrary time t after the sleep onset time;

Obtain the signal (v _x , v _y , v _z ) fed back by the three-axis accelerometer at each unit moment in the period a, period b and t; wherein, the period a is the period before the moment t, The period b is a period after the time t;

Calculating the modulus v of the signal of each unit moment in the a period and the b period is the square of v _x plus the square of v _y plus the square of v _z , and then the square root of the sum value;

Calculate the volatility statistical results sa and sb of the modulus value of the signal at each unit moment in the a period and b period;

Constructing the sleep state feature C(t) at the time t: C(t)=(v _x (t), v _y (t), v _z (t), sa (t), sb (t));

The foregoing steps are repeated until feature extraction at all moments during sleep is completed to form the feature database.

In the present invention, when the smart wearable device is worn by the user, the smart wearable device detects the user's movement through the built-in multi-axis accelerometer. When the smart wearable device learns that the user's movement range is less than the preset value, and further use the camera to identify that the user is in a state of closed eyes, it is determined that the user is in a sleep state, thereby indicating that the user has no payment ability, and then rejects the relevant payment operation. In the present invention, a multi-axis accelerometer is used to detect the user's movement. No matter whether the user is in deep sleep or light sleep, the change of the movement amplitude is relatively uniform, and combined with the recognition of the user's closed eyes by the camera to further judge whether the user is in Sleep state, so smart wearable devices can more accurately determine the user's sleep state, thereby providing a basis for ensuring payment security.

Description of drawings

Fig. 1 is a flowchart of an embodiment of a mobile payment control method of the present invention.

Detailed ways

First, the principle of the present invention is briefly explained. The present invention recognizes the sleep state of the user, and when it is determined to be in the sleep state, it is presumed by default that the user does not have the ability to pay. Then, at this time, the mobile payment request initiated by the outside world will be rejected. The verification of the mobile payment request here can be the verification of biometric features such as fingerprints and face recognition. No matter what kind of verification, the payment request will be rejected when the user is determined to be in a sleep state.

Among them, a scenario based on A’s bracelet can be associated with A’s mobile phone through a Bluetooth device (short-range wireless communication) or a mobile network (long-distance wireless communication). interface, the interface prompts you to enter your fingerprint or face. Then this malicious other person may aim the camera of the mobile phone at the face of A in sleep for identification, or place A's finger on the fingerprint recognition sensor. Faced with this scenario, when the mobile phone enters the mobile payment interface, it will send a message to the bracelet in order to get a reply to eliminate malicious scenarios. The information related to this information is sent to the mobile phone, and the mobile phone will reject the payment request after receiving the information. So as to protect the safety of users' property.

The present invention will be described in detail below in conjunction with FIG. 1 .

With reference to Fig. 1, it illustrates a kind of mobile payment control method of the present invention, and this method can be applied in above-mentioned scenario, and described method comprises the following steps:

S11: The smart wearable device detects whether it is worn by the user; for example, the user can manually set the status as "worn" after using the smart wearable device, or it can also be detected through heart rate detection, distance detection, temperature detection, communication Connection detection, switch detection and other methods to comprehensively judge.

S12: If worn by the user, the smart wearable device activates the built-in multi-axis accelerometer;

S13: The smart wearable device detects the motion of the user through the multi-axis accelerometer;

S14: When the smart wearable device learns that the user's motion range is less than a preset value for a sustained period of time, start the built-in camera of the smart wearable device to identify the user's face area, Judging whether the user is in a closed-eye state for a period of time, if so, the smart wearable device determines that the user is in a sleeping state;

For starting the built-in camera of the smart wearable device to identify the user's face area, specifically, the built-in camera of the smart wearable device can be started to scan around to determine the user's face area;

The smart wearable device determines its own location parameters, and then adjusts the shooting area according to the location parameters, so that the shooting area can continuously cover the user's face area.

Wherein, to determine the user's face area, in one embodiment of the present invention, an area may only be determined from an approximate orientation, and the user's face is included in this area. That is to say, the area of the user's face is larger than or equal to the real face of the user.

In an embodiment of the present invention, the adjusting the shooting area according to the location parameters includes:

The smart wearable device obtains a pitch angle through the position parameter, and the pitch angle is used to indicate a downward or upward offset angle of the smart wearable device relative to the user's face area;

The smart wearable device uses the following formula to calculate the adjustment angle of the camera according to the pitch angle, and the smart wearable device adjusts the shooting angle of the camera according to the adjustment angle, so that the shooting area can continuously cover the user facial area;

or,

Wherein, (X _world , Z _world ) is the coordinates of the user's face area, (X _camera , Z _camera ) is the coordinates of the shooting area, is the pitch angle, φ is the adjustment angle, θ is the viewing angle of the shooting area, m is the length of the camera, d _x is the vertical distance between the camera and the user’s face area, d _z is the horizontal distance between the camera and the user's face area.

S15: Receive a verification request related to the payment request; the verification request here can be sent to the bracelet by the mobile phone as mentioned in the above scenario description, to request to confirm whether the payment is legal. And the wristband can be done in step S16 for the judgment of legitimacy.

S16: Based on the judgment that the user is in a sleeping state, output an instruction related to rejecting the payment request.

In this step, the smart wearable device (bracelet) judges that the current user is sleeping, so there is no need for payment, so the feedback is sent to the mobile phone. The feedback from the wristband can directly be an instruction to refuse payment, or it can just inform the user that it is currently in an unpayable state (such as a sleep state), and the mobile phone rejects the payment request according to this unpayable state. Of course, the judgment work at this time is placed on the mobile phone.

In order to make the recognition of the user's face area more accurate and fast, in one embodiment of the present invention, the built-in camera is a two-axis rotatable camera. In this way, the shooting area of the camera will be wider and the shooting angles will be more, so that more effective image information can be obtained.

In an embodiment of the present invention, the multi-axis accelerometer is a three-axis accelerometer.

In another embodiment of a mobile payment control method of the present invention, on the basis of the embodiment shown in Figure 1, the following necessary processes and conditions are added for the judgment that the user is in a sleep state:

Before the smart wearable device detects the user's motion through the multi-axis accelerometer, data collection is performed in advance to construct a feature database.

When judging whether the currently detected sleep state data matches at least one set of data in the feature database, it is considered that the necessary process and condition are met.

Wherein, the sleep state data includes the sleep state characteristics of the current moment, and the sleep state characteristics are obtained in the following manner:

Obtain the signal (v _x , v _y , v _z ) fed back by the three-axis accelerometer at each unit time before the current time period A, after the B period, and at the current time;

Calculating the modulus v of the signal of each unit moment in the A period and the B period is the square of the square of v _x plus the square of v _y plus the square of v _z , and then open the square root of the sum value;

Calculate the volatility statistical results SA and SB of the modulus of the signal at each unit time in the A period and B period; the volatility statistical results here can be understood as the rate of change.

Then the feature of the sleep state at the current moment is C(t)=(v _x (t), v _y (t), v _z (t), SA(t), SB(t)), where t is the current moment.

In the embodiment of the present invention, the construction of the characteristic database can be realized in the following manner:

Acquire the determined sleep start time; the specific acquisition method can be a manual input by the user, or a confirmed preset time.

collecting a triaxial acceleration signal detected by the triaxial accelerometer after the determined sleep onset time;

determining an arbitrary time t after the sleep onset time;

Obtain the signal (v _x , v _y , v _z ) fed back by the three-axis accelerometer at each unit moment in the period a, period b and t; wherein, the period a is the period before the moment t, The period b is a period after the time t;

Calculating the modulus v of the signal of each unit moment in the a period and the b period is the square of v _x plus the square of v _y plus the square of v _z , and then the square root of the sum value;

Calculate the volatility statistical results sa and sb of the modulus value of the signal at each unit moment in the a period and b period;

Constructing the sleep state feature C(t) at the time t: C(t)=(v _x (t), v _y (t), v _z (t), sa (t), sb (t));

Repeat the foregoing steps until the feature extraction of all moments during sleep is completed to form the feature database, which is denoted as D(C)=(C(1), C(2),...,C(Y)), where Y is the unit time total. For unit time, it can be in seconds. The unit can also be divided into minutes, and in this case, the arithmetic mean value of the data corresponding to 60 seconds in one minute is used as the data of the minute.

For those skilled in the art, it is obvious that the embodiments of the present invention are not limited to the details of the above-mentioned exemplary embodiments, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential features of the embodiments of the present invention. example. Therefore, no matter from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the embodiments of the present invention is defined by the appended claims rather than the above description, so it is intended that the All changes within the meaning and range of equivalents to the claims are included in the embodiments of the present invention. Any reference sign in a claim should not be construed as limiting the claim concerned. In addition, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Multiple units, modules or devices stated in system, device or terminal claims may also be realized by the same unit, module or device through software or hardware. The words first, second, etc. are used to denote names and do not imply any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and not to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those of ordinary skill in the art should understand that they can Modifications or equivalent replacements to the technical solutions of the embodiments of the present invention should not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (9)

1. a kind of mobile payment control method, which is characterized in that the method includes：

Intelligent wearable equipment detects whether itself is worn on user；

If being worn on user, the intelligent wearable equipment starts built-in multiaxis accelerograph；

The intelligent wearable equipment detects the movement of the user by the multiaxis accelerograph；

When the motion amplitude that the intelligent wearable equipment is informed in the user in lasting a period of time is less than default value When, start the camera built in the intelligent wearable equipment, the face area of the user is identified, judges the use Whether family is in closed-eye state whithin a period of time, if so, the intelligent wearable equipment determines that user is in sleep state；

It receives and the relevant checking request of payment request；

It is in dormant judgement based on the user, export and refuses the described and relevant instruction of payment request.

2. according to the method described in claim 1, it is characterized in that, the built-in camera is the rotatable camera shooting of two axis Head.

3. according to the method described in claim 2, it is characterized in that, the camera shooting started built in the intelligent wearable equipment Head, to the face area of the user be identified including：

Start the camera built in the intelligent wearable equipment and carry out surrounding scanning, determines user's face area；

The intelligent wearable equipment determines the location parameter of itself, then adjusts shooting area according to the location parameter, So that the shooting area can continue to cover user's face area.

4. according to the method described in claim 3, it is characterized in that, described adjust shooting area packet according to the location parameter It includes：

The intelligent wearable equipment obtains pitch angle by the location parameter, and the pitch angle is used to indicate the intelligence and wears Wear the angle that downward or upward deviates of the formula equipment relative to user's face area；

The intelligent wearable equipment calculates the adjustment angle of the camera according to the pitch angle using following formula, described Intelligent wearable device is according to the shooting angle for adjusting the angle the adjustment camera, so that shooting area can persistently cover Cover user's face area；

Or,

Wherein, (X_world, Z_world) be user's face area coordinate, (X_camera, Z_camera) be the shooting area seat Mark,For the pitch angle, φ is the adjustment angle, and θ is the angle of the shooting area, and m is the length of the camera Degree, d_xFor the vertical range of the camera and user's face area, d_zFor the camera and user's face area Horizontal distance.

5. according to the method described in claim 4, it is characterized in that, the multiaxis accelerograph is 3-axis acceleration instrument.

6. according to the method described in claim 5, it is characterized in that, the intelligent wearable equipment passes through the multiaxis acceleration Include before the movement of instrument detection user：Data are carried out in advance acquires construction feature database.

7. according to the method described in claim 6, it is characterized in that, further including：Judge currently detected sleep state data Whether at least one set of Data Matching in the property data base, and will be deemed as matched result as determining in sleep The necessary condition of state.

8. the method according to the description of claim 7 is characterized in that the sleep state data include the sleep shape at current time State feature, the sleep state feature obtain in the following manner:

Each per time instance 3-axis acceleration instrument is anti-in A periods, later B periods and current time before obtaining current time It is fed back to the signal (v come_x,v_y,v_z)；

The modulus value v for calculating the signal of each per time instance in the A periods, B periods is v_xSquare plus v_ySquare plus v_z's Square, then pair and value open radical sign；

Calculate fluctuation the statistical result SA and SB of the modulus value of the signal of each per time instance in the A periods, B periods；

Current time sleep state feature is C (t)=(v_x(t),v_y(t),v_z(t), SA (t), SB (t)), when wherein t is current It carves.

9. according to the method described in claim 8, it is characterized in that, the construction feature database includes：

Obtain determining sleep onset time；

Acquire the 3-axis acceleration signal that the 3-axis acceleration instrument after the sleep onset time of the determination detects；

Determine any time t after the sleep onset time；

Obtain the signal (v that 3-axis acceleration instrument is fed back described in each interior per time instance of a periods, b periods and t moment_x, v_y,v_z)；Wherein, a periods are the period before the t moment, and the b periods are the period after the t moment；

The modulus value v for calculating the signal of each per time instance in a periods, b periods is v_xSquare plus v_ySquare plus v_z's Square, then pair and value open radical sign；

Calculate fluctuation the statistical result sa and sb of the modulus value of the signal of each per time instance in a periods, b periods；

Build the t moment sleep state feature C (t)：C (t)=(v_x(t),v_y(t),v_z(t),sa(t),sb(t))；

Abovementioned steps are repeated, until the feature extraction at whole moment constitutes the property data base during completing sleep.