CN113191766A - Method, device and equipment for verifying payment behavior safety based on cloud computing - Google Patents
Method, device and equipment for verifying payment behavior safety based on cloud computing Download PDFInfo
- Publication number
- CN113191766A CN113191766A CN202110498360.9A CN202110498360A CN113191766A CN 113191766 A CN113191766 A CN 113191766A CN 202110498360 A CN202110498360 A CN 202110498360A CN 113191766 A CN113191766 A CN 113191766A
- Authority
- CN
- China
- Prior art keywords
- payment
- behavior data
- key
- strategy
- verification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
- G06Q20/382—Payment protocols; Details thereof insuring higher security of transaction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Biophysics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Accounting & Taxation (AREA)
- Health & Medical Sciences (AREA)
- Computer Security & Cryptography (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
- Time Recorders, Dirve Recorders, Access Control (AREA)
Abstract
The application discloses a method, a device and equipment for verifying payment behavior safety based on cloud computing, when payment records of payment behavior data are received, interactive safety factor verification is carried out on key payment strategies by utilizing a correlation time mechanism, behavior data resetting processing and behavior data difference lists are verified, and the starting time sequence of the behavior data resetting processing time sequence and the final time sequence of the behavior data difference lists are respectively recorded, so that the payment safety of the behavior data can be accurately and efficiently calculated.
Description
Technical Field
The disclosure relates to the technical field of cloud computing and data verification, and in particular relates to a method, a device and equipment for verifying payment behavior security based on cloud computing.
Background
With the rapid development of internet technology, online payment, that is, a payment method through a payment interface with a bank provided by a third party, is used by a large number of users due to the great convenience of online payment.
In the existing payment implementation technology, a payment link is generally generated at a client of an application platform, and a user opens the link through the client to jump to a payment page for payment.
However, the information of the application provider is directly exposed in the payment skip link by means of link skip, so that the information security of the application provider is threatened.
Disclosure of Invention
In order to solve the technical problems in the related art, the present disclosure provides a method, an apparatus, and a device for verifying payment behavior security based on cloud computing.
The application provides a method for verifying payment behavior safety based on cloud computing, which comprises the following steps:
based on the payment record of the received payment behavior data, performing interactive safety factor verification on the key payment strategy by using an associated training model;
when the behavior data resetting processing list is checked, acquiring a target candidate time sequence of the behavior data resetting processing list;
when the behavior data difference list is verified, acquiring a target final time sequence of the behavior data difference list;
determining behavioral data payment security based on the target candidate timing sequence and the target final timing sequence.
Further, the interactive safety factor verification of the key payment strategy by using the association training model comprises:
and when the key payment strategy is reset, constructing an interactive safety coefficient verification derivative type for the key payment type, wherein the interactive safety coefficient verification derivative type extracts the model relevance of the key payment type.
Further, the method further comprises:
determining whether the key payment strategies of the interactive safety factor verification derivative types have corresponding relevance;
when the key payment strategy of the interactive safety coefficient verification derivative type is determined to have corresponding relevance, extracting the key payment strategy of the interactive safety coefficient verification derivative type to perform behavior data resetting processing;
when determining that the key payment strategy of the interactive safety factor verification derivative type has no corresponding relevance, extracting the key payment strategy of the key payment type to perform behavior data resetting processing; wherein the behavior data reset processing list is determined to be verified when the verification key payment policy is extracted.
Further, the method further comprises:
when a preset behavior data difference state is reached, determining whether the key difference strategy of the interaction safety coefficient check derivative type has corresponding relevance;
when the fact that the key difference strategies of the interactive safety coefficient verification derivative types do not have corresponding relevance is determined, extracting the key difference strategies of the interactive safety coefficient verification derivative types;
and when determining that the key difference strategy of the interactive safety coefficient verification derivative type has no corresponding relevance, extracting the key difference strategy of the key payment type, wherein when the key difference strategy is extracted, determining that a behavior data difference list is verified.
Further, the method further comprises:
acquiring a behavior data tag of the payment behavior data;
establishing an incidence relation between the behavior data label and the behavior data payment security, and storing the incidence relation;
and determining the average payment security corresponding to the behavior data label based on the incidence relation between the behavior data label and the behavior data payment security.
Further, the method further comprises:
after the behavior data resetting processing list is verified, acquiring key strategy data of a historical payment track of behavior data payment according to a preset time sequence time length;
when the payment of the behavior data is finished, determining the data calculation frequency of each neural network subjected to data calculation based on the acquired key strategy data;
calculating frequency and preset time sequence time length based on data of each neural network, and determining the matching time sequence of each neural network;
and sequencing the matching time sequence of each neural network, and determining a target neural network based on the sequencing result.
Further, the method further comprises:
and when the behavior data payment safety and/or the average payment safety exceed the standard value of a safety parameter, transmitting the behavior data payment safety and the network tag of the target neural network to the terminal equipment, so that the terminal equipment carries out safety identification processing based on the neural network tag of the target neural network.
The application provides a device based on cloud computing verification payment action safety, including action data payment equipment and terminal equipment, action data payment equipment with end equipment communication connection, terminal equipment includes:
the payment record receiving module is used for carrying out interactive safety factor verification on the key payment strategy by utilizing the associated training model based on the received payment record of the payment behavior data;
the data resetting processing module is used for acquiring a target candidate time sequence of the behavior data resetting processing list when the behavior data resetting processing list is checked;
the data difference checking module is used for acquiring a target final time sequence of the behavior data difference list when the behavior data difference list is checked;
and the payment security determination module is used for determining the payment security of the behavior data based on the target candidate time sequence and the target final time sequence.
The application provides a terminal device, including:
a memory for storing a computer program;
a processor coupled to the memory for executing the computer program stored by the memory to implement the method of any of the above.
The present application provides a computer-readable storage medium having stored thereon a computer program which, when executed, performs the method of any one of the above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
According to the method, the device and the equipment for verifying the payment behavior safety based on the cloud computing, when a payment record of the payment behavior data is received, an association time mechanism is utilized to carry out interactive safety factor verification on a key payment strategy so as to verify behavior data resetting processing and a behavior data difference list, and a starting time sequence of the behavior data resetting processing time sequence and a final time sequence of the behavior data difference list are respectively recorded, so that the payment safety of the behavior data can be accurately and efficiently calculated.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic architecture diagram of a system for verifying payment behavior security based on cloud computing according to an embodiment of the present application;
fig. 2 is a flowchart of a method for verifying payment behavior security based on cloud computing according to an embodiment of the present application;
fig. 3 is a functional block diagram of an apparatus for verifying payment behavior security based on cloud computing according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In order to facilitate the description of the method, the apparatus, and the device for verifying the security of the payment behavior based on the cloud computing, please refer to fig. 1, which provides a schematic view of a communication architecture of a system 100 for verifying the security of the payment behavior based on the cloud computing according to an embodiment of the present disclosure. The system 100 for verifying payment behavior security based on cloud computing can comprise a behavior data payment device 200 and a terminal device 300, wherein the behavior data payment device 200 is in communication connection with the terminal device 300.
In a specific embodiment, the terminal device 300 may be a desktop computer, a tablet computer, a notebook computer, a mobile phone, or other terminal devices capable of associating data processing and data communication, which is not limited herein.
On the basis, please refer to fig. 2 in combination, which is a schematic flow chart of a method for verifying security of payment behavior based on cloud computing according to an embodiment of the present application, where the method for verifying security of payment behavior based on cloud computing may be applied to the terminal device 300 in fig. 1, and further, the method for verifying security of payment behavior based on cloud computing may specifically include the contents described in the following steps S21 to S24.
And step S21, based on the payment record of the received payment behavior data, performing interactive safety factor verification on the key payment strategy by using the associated training model.
For example, the payment record represents transaction credentials left over during the transaction, such as: payment transaction records, WeChat transaction records, bank card transaction records, and the like.
In step S22, when the behavior data reset processing list is checked, a target candidate timing of the behavior data reset processing list is acquired.
For example, the target candidate timing sequence represents a sequence corresponding to a list formed after the related data is recombined.
In step S23, when the behavior data difference list is verified, the target final timing of the behavior data difference list is obtained.
For example, the target final timing represents a sequence corresponding to a list formed after the difference of the related data.
And step S24, determining the payment security of the behavior data based on the target candidate time sequence and the target final time sequence.
It can be understood that, in executing the contents described in the above steps S21-S24, when a payment record of payment behavior data is received, an interactive security coefficient check is performed on the key payment policy by using an association-time mechanism to check the behavior data resetting process and the behavior data difference list, and the starting timing of the behavior data resetting process timing and the final timing of the behavior data difference list are respectively recorded, so that the security of the behavior data payment can be accurately and efficiently calculated.
In an alternative embodiment, the interactive safety factor verification is performed on the key payment policy by using the association training model, and in order to improve the above technical problem, the step of performing the interactive safety factor verification on the key payment policy by using the association training model described in step S21 may specifically include the following step S211.
And S211, when the key payment strategy is reset, establishing an interactive safety coefficient verification derivative type for the key payment type.
Illustratively, the interaction security factor verifies the model relevance of the derivative type by extracting the key payment type.
It can be understood that, when the content described in the above step S211 is executed, in the critical payment policy resetting process, the resetting accuracy is improved by building the interactive security coefficient check derivative type through the critical payment type.
Based on the above basis, the method also comprises the following steps A1-A3.
Step A1, determining whether the interactive safety factor verification derivative type key payment strategy has corresponding relevance.
Step A2, when the key payment strategy of the interactive safety coefficient verification derivative type is determined to have corresponding relevance, extracting the key payment strategy of the interactive safety coefficient verification derivative type to perform behavior data resetting processing.
Step A3, when determining that the key payment strategy of the interactive safety factor verification derivative type has no corresponding relevance, extracting the key payment strategy of the key payment type to perform behavior data resetting processing; wherein the behavior data reset processing list is determined to be verified when the verification key payment policy is extracted.
It can be understood that when the content described in the above steps a 1-A3 is executed, whether the derived key payment policy has corresponding relevance is checked through the interactive safety factor, which effectively improves the accuracy of the action data resetting process.
Based on the above basis, the following descriptions of step Q1-step Q3 can also be included.
And step Q1, when the preset behavior data difference state is reached, determining whether the key difference strategy of the interaction safety coefficient check derivative type has corresponding relevance.
And step Q2, when determining that the key difference strategy of the interactive safety coefficient verification derivative type has no corresponding relevance, extracting the key difference strategy of the interactive safety coefficient verification derivative type.
And step Q3, when the key difference strategies of the interactive safety factor verification derivative types are determined not to have corresponding relevance, extracting the key difference strategies of the key payment types, wherein when the key difference strategies are extracted, a behavior data difference list is determined to be verified.
It can be understood that when the contents described in the above steps Q1-Q3 are executed, the behavior data difference list can be accurately determined by checking whether the derived key difference policy has corresponding relevance according to the interaction safety factor.
Based on the above basis, the method also comprises the following contents described in the steps E1-E3.
And E1, acquiring the behavior data label of the payment behavior data.
And E2, establishing an association relation between the behavior data label and the behavior data payment security, and storing the association relation.
And E3, determining the average payment security corresponding to the behavior data label based on the incidence relation between the behavior data label and the behavior data payment security.
It can be understood that, when the contents described in the above steps E1-E3 are executed, the average payment security corresponding to the behavior data tag can be accurately determined by the behavior data tag of the payment behavior data.
Based on the above basis, the method also comprises the following steps of r 1-r 4.
And step r1, after the behavior data resetting processing list is checked, acquiring key strategy data of the historical payment track of the behavior data payment according to the preset time sequence time length.
And step r2, when the behavior data payment is finished, determining the data calculation frequency of each neural network calculated by the data based on the acquired key strategy data.
And r3, calculating frequency and presetting time sequence time length based on the data of each neural network, and determining the matching time sequence of each neural network.
And r4, sequencing the matching time sequence of each neural network, and determining a target neural network based on the sequencing result.
It can be understood that when the contents described in the above steps r 1-r 4 are executed, the accuracy of the target neural network is effectively improved through the key strategy data of the historical payment track paid by the behavior data.
Based on the above basis, what is described in the following step t1 is also included.
And t1, when the behavior data payment security and/or the average payment security exceed the standard value of the security parameter, transmitting the behavior data payment security and the network label of the target neural network to the terminal device, so that the terminal device performs security identification processing based on the neural network label of the target neural network.
It is understood that when the above-mentioned description of step t1 is performed, the behavior data payment security and the network tag of the target neural network can be accurately determined to be transmitted to the terminal device.
In an alternative embodiment, there is a problem that the sequence cannot be matched accurately based on the target candidate timing and the target final timing, so that it is difficult to determine the payment security of the behavior data accurately, and in order to improve the above technical problem, the step of determining the payment security of the behavior data based on the target candidate timing and the target final timing described in step S24 may specifically include the following steps S241 to S245.
Step S241, acquiring the target candidate timing and the target final timing.
Step S242, extract the first key sequence in the target candidate timing sequence.
Step S243, extracting a second key sequence in the target final time sequence.
Step S244, merging the first key sequence and the second key sequence to obtain a key sequence intersection.
Step S245, inputting the intersection of the key sequences into a neural network training model, and determining the payment security of the behavior data.
It is understood that, when the contents described in the above steps S241 to S245 are executed, based on the target candidate timing and the target final timing, the problem that the sequences cannot be matched exactly is avoided, so that the payment security of the behavior data can be determined exactly.
Based on the same inventive concept, a system for verifying payment behavior security based on cloud computing is also provided, the system comprises behavior data payment equipment and terminal equipment, the behavior data payment equipment is in communication connection with the terminal equipment, and the terminal equipment is specifically used for:
based on the payment record of the received payment behavior data, performing interactive safety factor verification on the key payment strategy by using an associated training model;
when the behavior data resetting processing list is checked, acquiring a target candidate time sequence of the behavior data resetting processing list;
when the behavior data difference list is verified, acquiring a target final time sequence of the behavior data difference list;
determining behavioral data payment security based on the target candidate timing sequence and the target final timing sequence.
Further, the terminal device is specifically configured to:
and when the key payment strategy is reset, constructing an interactive safety coefficient verification derivative type for the key payment type, wherein the interactive safety coefficient verification derivative type extracts the model relevance of the key payment type.
Further, the terminal device is specifically configured to:
determining whether the key payment strategies of the interactive safety factor verification derivative types have corresponding relevance;
when the key payment strategy of the interactive safety coefficient verification derivative type is determined to have corresponding relevance, extracting the key payment strategy of the interactive safety coefficient verification derivative type to perform behavior data resetting processing;
when determining that the key payment strategy of the interactive safety factor verification derivative type has no corresponding relevance, extracting the key payment strategy of the key payment type to perform behavior data resetting processing; wherein the behavior data reset processing list is determined to be verified when the verification key payment policy is extracted.
Further, the terminal device is specifically configured to:
when a preset behavior data difference state is reached, determining whether the key difference strategy of the interaction safety coefficient check derivative type has corresponding relevance;
when the fact that the key difference strategies of the interactive safety coefficient verification derivative types do not have corresponding relevance is determined, extracting the key difference strategies of the interactive safety coefficient verification derivative types;
and when determining that the key difference strategy of the interactive safety coefficient verification derivative type has no corresponding relevance, extracting the key difference strategy of the key payment type, wherein when the key difference strategy is extracted, determining that a behavior data difference list is verified.
Further, the terminal device is specifically configured to:
acquiring a behavior data tag of the payment behavior data;
establishing an incidence relation between the behavior data label and the behavior data payment security, and storing the incidence relation;
and determining the average payment security corresponding to the behavior data label based on the incidence relation between the behavior data label and the behavior data payment security.
Further, the terminal device is specifically configured to:
key policy data for historical payment trajectories for data payments;
when the payment of the behavior data is finished, determining the data calculation frequency of each neural network subjected to data calculation based on the acquired key strategy data;
calculating frequency and preset time sequence time length based on data of each neural network, and determining the matching time sequence of each neural network;
and sequencing the matching time sequence of each neural network, and determining a target neural network based on the sequencing result.
Further, the terminal device is specifically configured to:
and when the behavior data payment safety and/or the average payment safety exceed the standard value of a safety parameter, transmitting the behavior data payment safety and the network tag of the target neural network to the terminal equipment, so that the terminal equipment carries out safety identification processing based on the neural network tag of the target neural network.
Further, the terminal device is specifically configured to:
acquiring the target candidate time sequence and the target final time sequence;
extracting a first key sequence in the target candidate time sequence;
extracting a second key sequence in the target final time sequence;
merging the first key sequence and the second key sequence to obtain a key sequence intersection;
and inputting the intersection of the key sequences into a neural network training model, and determining the payment security of the behavior data.
Based on the same inventive concept, please refer to fig. 3, a functional block diagram of an apparatus 500 for verifying security of payment behavior based on cloud computing is also provided, and the following describes details of the apparatus 500 for verifying security of payment behavior based on cloud computing.
the payment record receiving module 510 is configured to perform interactive safety factor verification on the key payment policy by using the association training model based on the received payment record of the payment behavior data;
a data resetting processing module 520, configured to, when the behavior data resetting processing list is checked, obtain a target candidate timing sequence of the behavior data resetting processing list;
a data difference checking module 530, configured to, when the behavior data difference list is checked, obtain a target final time sequence of the behavior data difference list;
a payment security determination module 540, configured to determine the behavior data payment security based on the target candidate timing sequence and the target final timing sequence.
A terminal device, comprising: a memory for storing a computer program; a processor coupled to the memory for executing the computer program stored by the memory to implement the method of any of fig. 2.
A computer-readable storage medium, in which a computer program is stored which, when executed, performs the method of any of fig. 2.
In summary, according to the method, the device and the equipment for verifying payment behavior security based on cloud computing, when a payment record of payment behavior data is received, an association timing mechanism is utilized to perform interactive security factor verification on a key payment strategy so as to verify behavior data resetting processing and a behavior data difference list, and a starting time sequence of a behavior data resetting processing time sequence and a final time sequence of the behavior data difference list are respectively recorded, so that the security of payment of the behavior data can be accurately and efficiently calculated.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for verifying payment behavior security based on cloud computing is characterized by comprising the following steps:
based on the payment record of the received payment behavior data, performing interactive safety factor verification on the key payment strategy by using an associated training model;
when the behavior data resetting processing list is checked, acquiring a target candidate time sequence of the behavior data resetting processing list;
when the behavior data difference list is verified, acquiring a target final time sequence of the behavior data difference list;
determining behavioral data payment security based on the target candidate timing sequence and the target final timing sequence.
2. The method of claim 1, wherein the interactive security factor verification of the key payment strategy using the association training model comprises:
and when the key payment strategy is reset, constructing an interactive safety coefficient verification derivative type for the key payment type, wherein the interactive safety coefficient verification derivative type extracts the model relevance of the key payment type.
3. The method of claim 2, further comprising:
determining whether the key payment strategies of the interactive safety factor verification derivative types have corresponding relevance;
when the key payment strategy of the interactive safety coefficient verification derivative type is determined to have corresponding relevance, extracting the key payment strategy of the interactive safety coefficient verification derivative type to perform behavior data resetting processing;
when determining that the key payment strategy of the interactive safety factor verification derivative type has no corresponding relevance, extracting the key payment strategy of the key payment type to perform behavior data resetting processing; wherein the behavior data reset processing list is determined to be verified when the verification key payment policy is extracted.
4. The method of claim 2, further comprising:
when a preset behavior data difference state is reached, determining whether the key difference strategy of the interaction safety coefficient check derivative type has corresponding relevance;
when the fact that the key difference strategies of the interactive safety coefficient verification derivative types do not have corresponding relevance is determined, extracting the key difference strategies of the interactive safety coefficient verification derivative types;
and when determining that the key difference strategy of the interactive safety coefficient verification derivative type has no corresponding relevance, extracting the key difference strategy of the key payment type, wherein when the key difference strategy is extracted, determining that a behavior data difference list is verified.
5. The method according to any one of claims 1 to 4, further comprising:
acquiring a behavior data tag of the payment behavior data;
establishing an incidence relation between the behavior data label and the behavior data payment security, and storing the incidence relation;
and determining the average payment security corresponding to the behavior data label based on the incidence relation between the behavior data label and the behavior data payment security.
6. The method of claim 5, further comprising:
after the behavior data resetting processing list is verified, acquiring key strategy data of a historical payment track of behavior data payment according to a preset time sequence time length;
when the payment of the behavior data is finished, determining the data calculation frequency of each neural network subjected to data calculation based on the acquired key strategy data;
calculating frequency and preset time sequence time length based on data of each neural network, and determining the matching time sequence of each neural network;
and sequencing the matching time sequence of each neural network, and determining a target neural network based on the sequencing result.
7. The method of claim 6, further comprising:
and when the behavior data payment safety and/or the average payment safety exceed the standard value of a safety parameter, transmitting the behavior data payment safety and the network tag of the target neural network to the terminal equipment, so that the terminal equipment carries out safety identification processing based on the neural network tag of the target neural network.
8. The device for verifying payment behavior security based on cloud computing is characterized by comprising behavior data payment equipment and terminal equipment, wherein the behavior data payment equipment is in communication connection with the terminal equipment, and the terminal equipment comprises:
the payment record receiving module is used for carrying out interactive safety factor verification on the key payment strategy by utilizing the associated training model based on the received payment record of the payment behavior data;
the data resetting processing module is used for acquiring a target candidate time sequence of the behavior data resetting processing list when the behavior data resetting processing list is checked;
the data difference checking module is used for acquiring a target final time sequence of the behavior data difference list when the behavior data difference list is checked;
and the payment security determination module is used for determining the payment security of the behavior data based on the target candidate time sequence and the target final time sequence.
9. A terminal device, comprising:
a memory for storing a computer program;
a processor coupled to the memory for executing the computer program stored by the memory to implement the method of any of claims 1-7.
10. A computer-readable storage medium, in which a computer program is stored which, when running, performs the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110498360.9A CN113191766B (en) | 2021-05-08 | 2021-05-08 | Method, device and equipment for verifying payment behavior safety based on cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110498360.9A CN113191766B (en) | 2021-05-08 | 2021-05-08 | Method, device and equipment for verifying payment behavior safety based on cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113191766A true CN113191766A (en) | 2021-07-30 |
| CN113191766B CN113191766B (en) | 2022-09-13 |
Family
ID=76984225
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110498360.9A Active CN113191766B (en) | 2021-05-08 | 2021-05-08 | Method, device and equipment for verifying payment behavior safety based on cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113191766B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976960A (en) * | 2023-09-22 | 2023-10-31 | 广州扬盛计算机软件有限公司 | Data processing method and system for two-dimensional code payment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050049963A1 (en) * | 2001-06-01 | 2005-03-03 | Barry Gerard J. | Secure on-line payment system |
| US20130138563A1 (en) * | 2011-05-26 | 2013-05-30 | Global Standard Financial, Inc. | Systems and methods for prepaid merchant payment services |
| CN105809045A (en) * | 2016-03-15 | 2016-07-27 | 青岛海信移动通信技术股份有限公司 | Method and device for processing equipment systems during data reset |
| US9940123B1 (en) * | 2015-12-29 | 2018-04-10 | Amazon Technologies, Inc. | Updating device code through a bus |
| CN111582875A (en) * | 2020-05-08 | 2020-08-25 | 国网吉林省电力有限公司 | Payment settlement pool and time sequence optimized payment strategy construction method |
-
2021
- 2021-05-08 CN CN202110498360.9A patent/CN113191766B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050049963A1 (en) * | 2001-06-01 | 2005-03-03 | Barry Gerard J. | Secure on-line payment system |
| US20130138563A1 (en) * | 2011-05-26 | 2013-05-30 | Global Standard Financial, Inc. | Systems and methods for prepaid merchant payment services |
| US9940123B1 (en) * | 2015-12-29 | 2018-04-10 | Amazon Technologies, Inc. | Updating device code through a bus |
| CN105809045A (en) * | 2016-03-15 | 2016-07-27 | 青岛海信移动通信技术股份有限公司 | Method and device for processing equipment systems during data reset |
| CN111582875A (en) * | 2020-05-08 | 2020-08-25 | 国网吉林省电力有限公司 | Payment settlement pool and time sequence optimized payment strategy construction method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976960A (en) * | 2023-09-22 | 2023-10-31 | 广州扬盛计算机软件有限公司 | Data processing method and system for two-dimensional code payment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113191766B (en) | 2022-09-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2017028630A1 (en) | Verification method and server | |
| CN107451816B (en) | Method and device for realizing offline transaction | |
| CN112651841B (en) | Online business handling method, online business handling device, server and computer readable storage medium | |
| CN108846676A (en) | Biological characteristic assistant payment method, device, computer equipment and storage medium | |
| CN107730385A (en) | Simulation process method, apparatus, storage medium and the computer equipment of sequence information | |
| CN109871445A (en) | Fraudulent user recognition methods, device, computer equipment and storage medium | |
| CN106485261A (en) | A kind of method and apparatus of image recognition | |
| CN108133373A (en) | Seek the method and device for the adventure account for relating to machine behavior | |
| CN107182042A (en) | Short message channel method for evaluating quality, device, medium and system | |
| CN111461223B (en) | Training method of abnormal transaction identification model and abnormal transaction identification method | |
| CN110351672A (en) | Information-pushing method, device and electronic equipment | |
| CN109670931A (en) | Behavioral value method, apparatus, equipment and the storage medium of loan user | |
| CN113191766B (en) | Method, device and equipment for verifying payment behavior safety based on cloud computing | |
| CN109801169A (en) | Financing lease application method, device, computer equipment and storage medium | |
| CN110414197B (en) | Bank user identity verification method and device based on behavior characteristics | |
| CN109840852B (en) | Automatic policy processing method and related products | |
| CN111882425A (en) | Service data processing method and device and server | |
| KR102121938B1 (en) | Apparatus and method for providing a simple settlement service of a corporation account | |
| CN112732676B (en) | Block chain-based data migration method, device, equipment and storage medium | |
| WO2022271652A1 (en) | Prioritizing holds when selecting transactions for transaction-based knowledge-based authentication | |
| CN110807630B (en) | Payment method and device based on face recognition, computer equipment and storage medium | |
| CN113887214A (en) | Artificial intelligence based wish presumption method and related equipment thereof | |
| CN109345215B (en) | Digital currency processing method, device, computer equipment and storage medium | |
| CN113159788A (en) | Transaction data processing method and device, storage medium and electronic equipment | |
| CN111786991A (en) | Block chain-based platform authentication login method and related device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |