CN105894273A - Method of judging payment behavior according to action - Google Patents
Method of judging payment behavior according to action Download PDFInfo
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- CN105894273A CN105894273A CN201610203101.8A CN201610203101A CN105894273A CN 105894273 A CN105894273 A CN 105894273A CN 201610203101 A CN201610203101 A CN 201610203101A CN 105894273 A CN105894273 A CN 105894273A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/08—Payment architectures
- G06Q20/20—Point-of-sale [POS] network systems
- G06Q20/204—Point-of-sale [POS] network systems comprising interface for record bearing medium or carrier for electronic funds transfer or payment credit
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
- G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
- G06Q20/327—Short range or proximity payments by means of M-devices
- G06Q20/3278—RFID or NFC payments by means of M-devices
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4014—Identity check for transactions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4014—Identity check for transactions
- G06Q20/40145—Biometric identity checks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G3/00—Alarm indicators, e.g. bells
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- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Finance (AREA)
- Computer Security & Cryptography (AREA)
- Computer Networks & Wireless Communication (AREA)
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- Economics (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
To reduce misjudgment of payment events, habitual judgment of payment actions is introduced. The rotation degree, linear acceleration and other motion values of a payment action are acquired through an action sensor deployed on a payment device, whether an action is the payment action of a specific user is judged based on the motion values, and payment is performed if an action is the payment action of a specific user. Further, a payment action is automatically learned and recognized through machine learning and pattern recognition, the possibility that an action conforms to a payment action is judged through pattern recognition, and an action is judged as a legal payment action when the possibility that the action conforms to a payment action is greater than a certain threshold.
Description
Technical field
The present invention relates to E-Payment field, particularly relate to use the E-Payment field of intelligent apparatus.
Background technology
1. Patents
The present invention is, the relevant of number of patent application " a kind of charge and the system and method for payment data transmission/coupling " is sent out
Bright.
2. background
The motion sensor disposed in number of patent application 201610127081.0 uses charging device and payment mechanism is detectd
Measure crash, using crash as the establishment about of business's deed of purchase, and utilize moment that contract sets up as charge request and
Pay the matching condition of request, complete payment processes.
Same, it is loaded with the means of payment of NFC+SE, with the payment form (Card of smart mobile phone simulation non-contact card
Emulation Mode), the method allows payment mechanism to complete to pay in static state, and therefore malicious user utilizes and can move
Dynamic charging device, it is possible to steal the electronic money in payment mechanism easily.
3. problem
Number of patent application 201610127081.0 uses motion sensor detect shock and trigger payment with crash
Flow process, however when using motion sensor detecting problematically, the action that obtains from motion sensor, very difficult judgement is to shake
Dynamic, or clash into, when movement range is bigger before impact for user when, False Rate is higher.Use simple maximum
Algorithm, or Android, the Simple rocking one that the development platform such as iOS provides shakes (Shake Event) or upset (flip
Over) payment action cannot be made accurate judgement.
Motion sensor detecting crash is used to trigger another of payment flow problematically, hitting in payment process
If dynamics of hitting is excessive, what consumer cannot be graceful shows business's deed of purchase atmosphere about, and it is possible to make because clashing into energetically
Become the damage of device.
Same, utilize NFC, the magnetic force induction device (magnetic field sensor) loaded in payment mechanism, also same
The problem of wrong diagnosis faced by sample, and owing to current method of payment allows payment mechanism to complete the side paid when static
Formula, therefore these methods all suffer from charging device and are individually moved, and carry out the security risk stolen for payment mechanism.
4. subject invention
Based on above problem, the present invention proposes the payment action by holding user, to judge whether the most accurately
It it is payment behavior.
1. payment mechanism, uses the action that more complicated algorithm detecting user uses payment mechanism to complete contract of payment, more
Accurate detection payment action, such as the payment events such as NFC, shock.
2. further, for processing various means of payment, do not define standard payment action, and use rote learning and pattern to know
Method for distinguishing, it is judged that payment action.
Summary of the invention
The motion sensor carried by payment mechanism, it is thus achieved that the motion value after quantization, such as linear acceleration, rotary speed
Deng, obtain rigidity particle motion characteristic value by calculating.
Further, process user-defined various action, use the side of rote learning (Machine Learning)
Method, it is achieved the identification (Pattern Recognition) of payment action.
When described motion value meets the payment action of consumer, and signify as terminating using contract of payment, it is judged that for paying
Event occurs.Described contract symbol it may be that described in number of patent application 201610127081.0 crash, NFC props up
NFC communication foundation etc. in the mode of paying.
The progressive of the present invention is:
1. detecting crash compared to motion sensor, the method for the present invention can judge payment action more accurately,
Improve the convenience of use.
2. compared to methods of payment such as NFC, the method for present invention definition, it is desirable to payment mechanism must have mobile just can completing to pay
Program, it is to avoid charging device is individually moved and carries out, close to payment mechanism, the behavior stolen.
3., compared to other means of payment, the present invention records the habitual movement that consumer is unique, extracts feature from action
Value, if the anglec of rotation and dynamics are as soft raw body characteristics (Soft biometrics), carries out pattern recognition thus further carries
The high safety paid.
4. in whole payment process, the method that the present invention provides, it is not necessary to any operation outside being accustomed to is done by consumer.
In sum, the present invention, while improve convenience, enhances safety.
Accompanying drawing explanation
Fig. 1 payment mechanism structural map
Fig. 2 payment mechanism software construction figure
Fig. 3 payment program structure chart
Fig. 4 utilizes action to judge method of payment flow chart
Fig. 5 NFC pays movable model figure
Fig. 6 NFC payment action numeric value analysis figure
Fig. 7 free payment action model figure
Fig. 8 pays spinning movement analysis diagram in action
Fig. 9 pays power thrusts analysis diagram in action
The method that Figure 10 automatically learns and identifies payment action
Figure 11 rote learning flow chart
Figure 12 pattern recognition flow chart
Detailed description of the invention
In this specification, by structure drawing of device, process block diagram, being embodied as and the side of realization of detailed notebook invention
Formula, the circuit structure for general common known method, step and city's dealer's module is not been described by, avoiding expository writing tediously long
Obscure emphasis.
The structure of payment mechanism
Payment mechanism structure used in the present invention is as follows:
Fig. 1 payment mechanism structural map
As it can be seen, payment mechanism (1) is made up of following formant
The most one or more processors (11), are used for calculating process,
2. control storage (12), for controlling the read/write of storage,
3. storage (121), is used for preserving OS (124), payment program (123), and the number that payment program (123) is managed/generates
According to (122).Storage state be high speed with
Machine internal memory (RAM), disk stores, flash memory (flash memory) or other non-volatile state memorization bodies, further
Include stored by the Dropbox of network,
7. motion sensor (143), city's dealer's motion sensor, including acceleration induction device (Accelerometer), and sense of rotation
Answer device (gyroscope) etc.,
The most preferable, NFC+SE, fingerprint recognition etc. is used for the module (145) paid,
9. power supply (190), it is provided that electric current needed for circuit and device,
10. other (199), other auxiliary units.
Above-mentioned module, by connecting circuit (110), completes communication and data exchanges.Connecting circuit (110) is data/address bus
, or simple signal of communication line (bus).
Payment mechanism (1) outward appearance and form can be but not limited to, portable electronic devices, as smart mobile phone, wrist-watch, flat board,
PDA, portable game machine etc..
Fig. 2 payment mechanism software construction figure
The software configuration of described payment mechanism (1), is saved in storage (121), constitutes and comprises with lower unit；
1.OS (124), be used for controlling described hardware cell, it is provided that the access method of storage, the various resources of management hardware cell.
The form of OS is including but not limited to android, iOS, Windows, Linux；
2. payment program (123), computer-executable code, or it is embedded in the executable code of readable storage body, it is used for
Analyze payment action, the business such as further payment program can expand to member and identify, ticket management, reward voucher, exchange ticket
Field.
3. application data (122), preserve the motion characteristic data in the present invention, and described data pass through summary database, or
Person's document form preserves, and is provided access method by OS (124).
The resource that the interface function (125) that payment program (123) is provided by OS (124) utilizes OS (124) to be managed, and utilize
The interface function (125) management data (122) that OS (124) provides.
Described payment program (123), described in program structure below figure.
Fig. 3 payment program structure chart
Described payment program (123), comprises with lower module:
1. payment module (1230)；In order to payment processes.
2.UI module (1231)；Mutual in order to user, UI includes the method for the man-machine interactions such as portrait, speech, vibrations,
3. action control module (1232), the processing method of the present invention, it is included in this module.-
4. other functional modules (1233)；Other are not belonging to the present invention such as member management, payment function module, other functional modules
Interest field.
Utilize the method that action judges to pay
Fig. 4 utilizes action to judge method of payment flow chart
As it can be seen, judge method of payment according to following steps,
S1. payment mechanism (1) is from motion sensor (143), it is thus achieved that motion value,
S2. judge whether motion value meets the payment feature of user,
If S3. met, then contract is set up, and starts and pays,
Meeting if S4. do not met, carrying out Exception handling, described Exception handling is not in interest field of the present invention
Further, described step 2, it is judged that whether motion value meets the method paying feature, comprises the steps of
S21. motion characteristic value is calculated,
S22. the eigenvalue of standard of comparison action, the anglec of rotation and Impact direction, as being just then judged as in allowed band,
Described in described step S21, the computational methods of eigenvalue, illustrate according to following instance, first pay dynamic with NFC
As example, the feature of single action is described, then explains the feature of composite move with shock payment action.
Fig. 5 NFC pays movable model figure
As it can be seen, hand-held payment mechanism, rigidity particle movement (Rigid Patical in three-dimensional rectangular coordinate system can be constructed
Motion) model.Owing to NFC pays, the position needing the NFC antenna of payment mechanism or NFC module is light with NFC reader
Touching, therefore pay in the model of action, payment mechanism is the action along the rotation of x-axis of the particle.
Setting up right hand solid rectangular coordinate system with payment mechanism particle for core, wherein horizontal direction is x-axis, hangs down with the earth's core
Nogata is to for y-axis, and simultaneously and x-axis, y-axis vertical plane intersecting straight lines is z-axis.Pass through motion sensor, it is possible to obtain around x,
Y, the radian rotating speed of z-axis can obtain following parameter:
Described NFC payment action can set up rigid objects, the action model rotated along x-axis.In the t time, particle is along x-axis
The angle rotated(0, n), wherein t is the time to t ∈, and 0 starts for payment action, and n is that payment action ends, f
T () is i.e. according to motion sensor, it is thus achieved that the function of rotary speed according to time change.
Fig. 6 NFC payment action numeric value analysis figure
As it can be seen, transverse axis is the time, the unit second.Vertical pivot is rotary speed, and unit rad/s can obtain x from motion sensor,
Y, the speed (rad/s) that z-axis rotates, curve C1, C2, C3, respectively x, y, the speed-time curve that z-axis rotates.NFC pays
Action mainly along x-axis rotate, therefore swing θ can be obtained by following computational methods.
Wherein λ=max{ Δ x1,Δx2,…,Δxn, Δ xiBetween minute
Every, it is 0.005 second in instances.f(ξi) it is the rotary speed of the x-axis in i moment.From the definition of definite integral, swing θ is
Figure is enclosed by along x-axis rotary speed-time graph between payment action start time (C4) and payment release time (C5)
The area (C6) that the shadow part become divides.
Be computed, it is illustrated that the eigenvalue of NFC payment action be that radian value is-1.1178, be converted into-64 degree.At the right hand
In three-dimensional cartesian coordinate system, reversely rotate 64 degree along x-axis.The eigenvalue of described action, is not only size and also comprises direction, counting
Reason is referred to as vector, cheap for describing, use matrix to describe.In actual treatment, the structures such as numerical value can be used.
Equally, use the payment mechanism of other portability forms such as wrist-watch, also have similar spinning movement, phase can be passed through
Judge whether it is payment action with method.
In this example, the scope along x-axis reverse rotation 90 ° ± 30 ° is judged as the payment action of NFC, is compared to " shake one
Shake " action identification method such as (a kind of direct use development platform provide shake event method), improve judge whether be
The precision of payment action.
Fig. 7 free payment action model figure
Different with NFC payment action, utilize and clash into, or closely induction apparatus acquisition business's deed of purchase action about need not
NFC antenna alignment card reader, therefore payment action is freer.Action pattern change is more.
Motion sensor is considered as by several separate as a particle, payment mechanism motion before shock
The synthesis of motion, the motion shown in figure is flat fortune and the superposition rotated.
In example as depicted, consumer's right hand holds payment mechanism, after being confirmed by UI (1231), comes into effect payment
Action, the feature after decomposition of movement is:
1. rotate along x-axis, rotate-40 ° ± 20 °
2. rotate along y-axis, as figure rotates-135 ° ± 20 °
3. rotate along x-axis, half-twist ± 20 °
4. (y-axis negative direction) is moved in action from top to bottom
5. (z-axis negative direction) is moved in action from inside to outside
6. additionally the most with good grounds subscriber station stands position random motion along the x-axis direction
Identical with NFC payment action, payment mechanism has respectively along x, y, the action that z-axis rotates, wherein x, rotating to be of y-axis
Anti-right-handed coordinate system direction, z-axis rotates to be Cartesian coordinate system direction.Can obtain according to same method,
Fig. 8 pays spinning movement analysis diagram in action
As it can be seen, C1, C2, C3 are f (t), g (t), h (t) time graph, θ,X between ρ, respectively time 0～n, y, z-axis
The anglec of rotation.The computational methods identical by NFC action can calculate the anglec of rotation.Wherein θ,ρ is respectively ,-20 ° ,-
91 ° ,-98 °.
The most also having when paying, along x, the vector shift in y, z direction, owing to motion sensor cannot sense the most straight
Line moves, and employing acceleration induction device so using, passing through motion sensor, it is possible to obtain the vector in x, y, z direction accelerates
Degree.According to physical equation weWhereinFor by force vector, m is quality fixed number,For vector acceleration.At this
In example, payment mechanism weight is 130g, and the elapsed time paying start to finish is 1 second.
According to Particle Mechanics, vector acceleration can be obtained,WhereinFor vector acceleration；
It is respectively x, the unit vector in y, z direction.,
Setting up right hand solid rectangular coordinate system with payment mechanism particle for core, wherein horizontal direction is x-axis, hangs down with the earth's core
Nogata is to for y-axis, and simultaneously and x-axis, y-axis vertical plane intersecting straight lines is z-axis.Pass through motion sensor, it is possible to obtain x, y, z side
To vector acceleration, as under android development environment, it is possible to obtain following parameter:
Fig. 9 pays power thrusts analysis diagram in action
As it can be seen, by acceleration, we obtain x, the transition diagram of the vector acceleration-time in y, z direction, wherein x, y, z
Acceleration-the time graph of axle is C1, C2, C3, and transverse axis is the elapsed time, and vertical pivot is accekeration, and unit is m/s2, by obtaining
The motion value obtained, available Impact direction and dynamics.
Moving the most from top to bottom, i.e. y-axis negative direction, average dynamics is 4.8 newton.
Moving the most from inside to outside, i.e. z-axis negative direction, average dynamics is 3.5 newton.
Moving the most from left to right, i.e. x-axis positive direction, average dynamics is 6.2 newton.
Standard operation tolerance band according to motion value and definition, in example, action meets definition, it is judged that for payment action.
Automatically the method that study identifies payment action
The method judging payment action above by rotary speed and linear acceleration, is merely able to judge that general payment is moved
Make.Owing to individual is poor, such as biological characteristics such as high height and long arm, dynamics, different motion characteristics can be produced.On the other hand owing to producing
Business and the individual variation of motion sensor itself, also determine a fixing standard operation definition, it is impossible to realize more accurate
Judgement, therefore, further, import rote learning method, record the pattern of payment action, and pass through pattern recognition
(pattern recognition) identifies that user pays the biological characteristic of custom.
The method that Figure 10 automatically learns and identifies payment action
As it can be seen, the method automatically learning and identifying payment action, comprise the steps of
A1. rote learning,
A2. pattern recognition,
Characteristic results is stored in the action database in described storage (121) by described step rote learning, for described step
Pattern recognition reference.
The method of described step rote learning, as shown below comprises the steps of
Figure 11 rote learning flow chart
As it can be seen, learning by rote comprises the steps of
M1. the motion value of customer acceptance action is obtained,
M2. extract/calculate eigenvalue,
M3. eigenvalue is preserved,
Described step M1, by described UI module (1231), it is desirable to what user confirmed that it inputs is correct payment action,
Before the process confirmed can be with user action, it is also possible to be after user pays.
Described step M1, the method for described acquisition motion value and as it was noted above, by OS obtain motion sensor capture
Motion value, x as previously described, the rotation velocity vector in y, z direction and x, the linear acceleration vector in y, z direction.
Described step M2, the method for described extraction/calculating eigenvalue is as it was noted above, by x, the rotation in y, z direction is fast
Degree vectormeter calculates x, the anglec of rotation in y, z direction；By x, the linear acceleration vector in y, z direction, calculate x, y, z direction
Impact direction and size.
Described step M3, described eigenvalue is stored in the storage (121) of payment mechanism.Described eigenvalue is hereinafter referred to as trained
Instruction data (training Data) T, wherein comprises motion characteristic value { T1,T2…Tn, described motion characteristic value is vector.
Described pattern recognition, comprises the steps of as shown in the figure
Figure 12 pattern recognition flow chart
As it can be seen, movement recognition flow process comprises the steps of
P1. tested motion characteristic value is extracted,
P2. the probability that coupling is possible is calculated,
P3. judge to mate whether possible probability is more than threshold values,
If P4. more than threshold values, then return is
The most otherwise, return no
Preferable P6. preserves the motion characteristic value of step P1 extraction.
Described step P1, described tested motion characteristic value, hereinafter referred to as test data test Data, abstracting method is the most front
Literary composition is described, and by x, the rotation velocity vector in y, z direction calculates x, the anglec of rotation in y, z direction；By x, the line in y, z direction
Property acceleration, calculates x, the Impact direction in y, z direction and size.Described test data save as payment action X.
Described step P2, the computational methods of coupling probability probability, as described below.
Owing to each payment action can not fit like a glove standard operation pattern feature vector, it is also not possible to completely disengage from mark
Quasi-action pattern, obtains through learning us, and the probability density of coupling probability p (x) of payment action X meets Gauss distribution
(Gaussian distribution).In matching judgment, use following methods implementation pattern identification (pattern
Recognitions):
According to Gauss distribution, we obtainWherein, averagelyPoint
DissipateP is the number of training data set, and p (x) is probability density function.For its p (x) action of action x
The when of mating possible probability less than threshold values of pattern, it is judged that be no, be otherwise considered as payment action.
Threshold values described in described step P3, requires to set according to safety of payment.As with reference to the practice in the present invention
In, initial value is set as 0.6, after have accumulated enough training datas, gradually steps up threshold values.
Preferable, preserve and pay motion characteristic data, after payment processes terminates, for propping up that acquisition user recognizes
Pay, paid feature and be stored in training data (training Data) T, as the reference object paying characteristic matching later.
The rotary speed of heretofore described payment action, angle, firmly size, direction, be the specific custom of user
And feature, although cannot identify as biological characteristic (biometric) as finger print identifying, but the same with height, body weight,
Identification object can be reduced as soft biological characteristic (soft biometic).
The present invention is by above implementation, it is achieved that a kind of method judging payment behavior according to action
The specific embodiment of the present invention is described by specific case used above, and the explanation of this embodiment is simply used
In helping to understand method and the core concept of the present invention；Simultaneously for one of ordinary skill in the art, according to the think of of the present invention
Think, the most all have change and change part, such as
The most micro-amendment present configuration, increases/reduces detailed elements, it is intensive or independent of correlation unit；
2. implement the inching of sequence of steps, as the process not having sequencing exchanged；
3. equivalent replacement, as used the knowledge in general motion mechanics, uses rectangular coordinate, under natural coordinates, or polar coordinate
The equation of motion calculate displacement, speed, acceleration；
4. increase other preferable unit do not innovated or steps；
The most do not change the structure of device, only revise device name, as by payment mechanism, referred to as payment device, or be used in
Integration, member card, honored guest's certificate etc. pay derivative service.
Therefore, this specification content should not be construed as limitation of the present invention, all is made within the spirit and principles in the present invention
Any amendment, replace, delete the improvement of additional step on an equal basis, in the range of being all contained in the comprising of the present invention.
Claims (8)
1. the method judging payment behavior according to action, it is characterised in that described method comprises the steps of
1. payment mechanism is from motion sensor, it is thus achieved that motion value,
2. judge whether motion value meets the payment action of user,
If 3. met, then contract is set up, and starts payment processes.
2. payment mechanism described in step 1 as claimed in claim 1, it is characterised in that comprise in described device with lower module:
The most one or more processors,
2. control storage,
3. storage, the state of described storage is high speed random memory RAM, disk store, flash memory (flash memory) or
Other non-volatile state memorization bodies, further include being stored by the Dropbox of network,
4. motion sensor, city's dealer's motion sensor, including acceleration induction device, and rotary inductor etc.,
The most preferable, NFC+SE, fingerprint recognition etc. is used for the module paid,
6. power supply, it is provided that electric current needed for circuit and device,
7. other auxiliary units,
Above-mentioned module, by connecting circuit, completes communication and data exchanges.Connection circuit is data/address bus, or simple communication
Holding wire.Payment mechanism can be but not limited to hand-hold electronic device, such as smart mobile phone, wrist-watch, flat board, PDA, portable type game
Machine etc..
3. memory element as claimed in claim 2, it is characterised in that preserve OS, payment program and payment in described memory element
The data that program is managed, described payment program is including but not limited to following modules:
1. payment module,
2.UI module,
3. action control module,
4. other functional modules.
4. judge whether motion value meets the payment action of user, described determination methods described in step 2 as claimed in claim 1
It is characterized in that, use the method that standard operation eigenvalue is judged, described method to comprise step；
1. calculate motion characteristic value,
2. the eigenvalue of standard of comparison action, as being just then judged as in allowed band.
5. motion characteristic value described in step 1 as claimed in claim 4, it is characterised in that described motion characteristic value comprises rotation
Degree, and stress, described eigenvalue is vector.
6. judge whether motion value meets the payment action of user, described determination methods described in step 2 as claimed in claim 1
It is further characterized in that, preferable, a kind of use automatic study identification payment action method.Described automatic study identification pays
The method of action is characterized in that comprising step
1. rote learning,
2. pattern recognition,
Described step rote learning obtains eigenvalue and is stored in action database, for described step pattern identification reference.
7. step rote learning as claimed in claim 6, it is characterised in that comprise step
1. obtain customer acceptance motion value,
2. extraction/calculate eigenvalue,
3. preserve eigenvalue.
8. step pattern identification as claimed in claim 6, it is characterised in that comprise step
1. extract tested motion characteristic value,
2. calculate the probability that coupling is possible,
3. judge to mate whether possible probability is more than threshold values,
If 4. more than threshold values, then return is
The most otherwise, return no.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610203101.8A CN105894273A (en) | 2016-04-01 | 2016-04-01 | Method of judging payment behavior according to action |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610203101.8A CN105894273A (en) | 2016-04-01 | 2016-04-01 | Method of judging payment behavior according to action |
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| Publication Number | Publication Date |
|---|---|
| CN105894273A true CN105894273A (en) | 2016-08-24 |
Family
ID=57011945
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| Application Number | Title | Priority Date | Filing Date |
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| CN201610203101.8A Pending CN105894273A (en) | 2016-04-01 | 2016-04-01 | Method of judging payment behavior according to action |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106873997A (en) * | 2017-02-14 | 2017-06-20 | 北京奇虎科技有限公司 | Puzzle type task-cycle control method and device |
| CN108364422A (en) * | 2018-02-24 | 2018-08-03 | 广州逗号智能零售有限公司 | Self-service method and device |
| WO2020019985A1 (en) * | 2018-07-27 | 2020-01-30 | 阿里巴巴集团控股有限公司 | Payment processing method and apparatus, and server and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102057386A (en) * | 2008-06-06 | 2011-05-11 | 电子湾有限公司 | Trusted service manager (TSM) architectures and methods |
| CN102811276A (en) * | 2011-05-30 | 2012-12-05 | 罗魏熙 | Mobile payment method and device with acceleration measurement function |
| CN102810144A (en) * | 2011-05-30 | 2012-12-05 | 罗魏熙 | Method and device for distance-controllable mobile payment |
| CN103778374A (en) * | 2014-02-19 | 2014-05-07 | 邹候文 | Trusted terminal, double-channel card, anti-cloning chip, chip fingerprint and channel attack resistance method |
| CN105224081A (en) * | 2015-09-22 | 2016-01-06 | 东南大学 | There is the Intelligent bracelet of fingerprint identification module and carry out the method for mobile payment with it |
-
2016
- 2016-04-01 CN CN201610203101.8A patent/CN105894273A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102057386A (en) * | 2008-06-06 | 2011-05-11 | 电子湾有限公司 | Trusted service manager (TSM) architectures and methods |
| CN102811276A (en) * | 2011-05-30 | 2012-12-05 | 罗魏熙 | Mobile payment method and device with acceleration measurement function |
| CN102810144A (en) * | 2011-05-30 | 2012-12-05 | 罗魏熙 | Method and device for distance-controllable mobile payment |
| CN103778374A (en) * | 2014-02-19 | 2014-05-07 | 邹候文 | Trusted terminal, double-channel card, anti-cloning chip, chip fingerprint and channel attack resistance method |
| CN105224081A (en) * | 2015-09-22 | 2016-01-06 | 东南大学 | There is the Intelligent bracelet of fingerprint identification module and carry out the method for mobile payment with it |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106873997A (en) * | 2017-02-14 | 2017-06-20 | 北京奇虎科技有限公司 | Puzzle type task-cycle control method and device |
| CN108364422A (en) * | 2018-02-24 | 2018-08-03 | 广州逗号智能零售有限公司 | Self-service method and device |
| WO2020019985A1 (en) * | 2018-07-27 | 2020-01-30 | 阿里巴巴集团控股有限公司 | Payment processing method and apparatus, and server and device |
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