CN116681447A - Business big data processing method and system based on big data and artificial intelligence - Google Patents
Business big data processing method and system based on big data and artificial intelligence Download PDFInfo
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Abstract
The embodiment of the application relates to the technical field of payment and discloses a business big data processing method and a business big data processing system based on big data and artificial intelligence, wherein the business big data processing method comprises the steps of firstly acquiring a face image of a payment request user and payment request information, then determining the user type of the payment request user based on the face image, and then acquiring historical payment business data of a target terminal; generating a payment virtual tag of the historical payment requester based on the historical payment service data; and then matching the payment service request information, the user type and the payment virtual tag to obtain a matching result, and finally outputting a payment service processing result based on the matching result. That is, the payment is restricted when it is determined that there is at least one mismatch. Thus, payment management can be effectively performed on underage students, and economic losses are reduced.
Description
Technical Field
The application relates to the technical field of payment, in particular to a business big data processing method and system based on big data and artificial intelligence.
Background
With the development of electronic technology, electronic products such as mobile phones gradually enter thousands of households, and the electronic products bring great convenience to the life of people and also bring a lot of problems to teenagers such as students, such as loving games.
In the related art, when a minor teenager (especially a pupil) uses a mobile phone APP requiring a payment service (e.g., a hand tour requiring a game coin recharge), since the teenager has no related consciousness and the payment system cannot limit and manage an erroneous payment behavior, money of a bank card account is often unintentionally paid out, resulting in economic loss.
Disclosure of Invention
The application mainly aims to provide a business big data processing method and system based on big data and artificial intelligence, and aims to solve the technical problem that payment management cannot be effectively carried out on underage students in the prior art.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides a service big data processing method based on big data and artificial intelligence, which is applied to a payment management system, including:
responding to a payment request instruction of a target terminal, and acquiring a face image of a payment request user and payment request information, wherein the payment request information comprises payment time and payment amount;
determining a user type of a payment request user based on the face image, wherein the user type comprises a student group and a non-student group;
acquiring historical payment service data of the target terminal;
generating a payment virtual tag of a historical payment requester based on the historical payment service data, wherein the payment virtual tag comprises a historical payment user type sub-tag, a historical payment timeline sub-tag and a historical payment limit range sub-tag;
matching the payment service request information, the user type and the payment virtual tag to obtain a matching result;
and outputting a payment service processing result based on the matching result.
Preferably, the historical payment service data includes a historical payment time and a historical single payment amount, and the generating a payment virtual tag of a historical payment requester based on the historical payment service data includes:
when the historical payment time of the preset specific gravity is in a non-learning time period and the historical single payment amount of the preset specific gravity is smaller than a preset amount value, determining that the user type of the historical payment requester is a student group sub-tag, and otherwise, determining that the user type of the historical payment requester is a non-student group sub-tag.
Preferably, determining the user type of the payment request user based on the face image includes:
inputting the face image into a convolutional neural network to extract face characteristic values;
inquiring a mapping table based on the face characteristic value to obtain the age of the user;
and determining the user type of the payment request user according to the user age.
Preferably, the matching the payment service request information, the user type and the payment virtual tag to obtain a matching result includes:
matching the user type of the payment request user with the sub-tag of the historical payment user type to obtain a first matching result;
matching the payment time of the payment request user with the historical payment time line sub-tag to obtain a second matching result;
and matching the payment amount of the payment request user with the sub-label of the historical payment amount range to obtain a third matching result.
Preferably, the outputting the payment service processing result based on the matching result includes:
outputting a first processing result for limiting payment when at least one of the first matching result, the second matching result and the third matching result is unmatched;
and outputting a second processing result allowing payment when the first matching result, the second matching result and the third matching result are matched.
Preferably, the outputting the first processing result of the limited payment further includes:
sending prompt information to the target terminal, wherein the prompt information is used for prompting a payment request user to input a correct payment request instruction again;
responding to a payment request instruction re-input by a payment request user, and judging whether the payment request instruction is abnormal or not;
and determining that the payment request instruction is abnormal, and outputting a third processing result of prohibiting payment within a preset duration.
Preferably, the determining whether the payment request instruction has an abnormality includes:
when the payment request instruction input is continuously performed for preset times and each time the payment is successful, judging that the payment request instruction is abnormal.
Preferably, the target terminal includes an image capturing device, and the acquiring the face image of the payment request user includes: the camera is activated and the focal length is automatically adjusted based on the distance between the camera and the user.
In a second aspect, in an embodiment of the present application, a processor and a memory are further provided; wherein the memory is for storing program code and the processor is for invoking the program code to perform the method according to the first aspect.
In a third aspect, there is also provided in an embodiment of the application a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method according to the first aspect.
Compared with the prior art, the business big data processing method based on big data and artificial intelligence provided by the embodiment of the application is characterized in that when a user requests payment, a face image of the payment request user and payment request information are firstly obtained, then the user type of the payment request user is determined based on the face image, and then the historical payment business data of a target terminal are obtained; generating a payment virtual tag of the historical payment requester based on the historical payment service data; and then matching the payment service request information, the user type and the payment virtual tag to obtain a matching result, and finally outputting a payment service processing result based on the matching result. That is, the system allows payment when it is determined that the type of user requesting payment, the time of payment, and the amount of payment all match the historical data, and when it is determined that there is at least one mismatch, it indicates that there is an anomaly in the payment request, at which point the system limits the payment. Thus, payment management can be effectively performed on underage students, and economic losses are reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a payment service big data processing method according to an embodiment of the application;
FIG. 2 is a flow chart of a payment service big data processing method according to another embodiment of the present application;
FIG. 3 is a flow chart of a payment service big data processing method according to another embodiment of the present application;
fig. 4 is a schematic hardware structure of a payment management system according to an embodiment of the application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present application are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, "and/or" throughout this document includes three schemes, taking a and/or B as an example, including a technical scheme, a technical scheme B, and a technical scheme that both a and B satisfy; in addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
Along with the development of electronic technology, electronic products such as mobile phones gradually enter thousands of households, various APP in the electronic products bring great convenience to the life of people, but the electronic products also bring a lot of problems to teenagers such as students, such as Internet addiction, and the like.
In the related art, underage teenagers (especially pupils) often pay unintentionally for money of a bank card account for various reasons when playing a mobile phone APP requiring a payment service (e.g., a hand tour requiring a game chip recharge), resulting in economic loss of parents. In view of this situation, many parents in rural areas cannot perform related setting operations in the mobile phone to limit the child to pay due to limited cultural level.
The mobile phone user group comprises a student group and a non-student group, wherein the student group in the embodiment of the application refers to a user who takes a mobile phone to learn normal use and an abnormal user who takes a parent mobile phone to play on weekends, the non-student group (such as parents) generally has better self-management consciousness and management level, and the student group has heavy curiosity and is easy to addict to games and lacks effective self-management consciousness. The current payment system has no solution for effectively managing the payment of the student groups and the non-student groups, so that the normal payment operation of the student groups and the non-student groups can be ensured, and the abnormal payment operation of the student groups can be managed.
In view of the above problems, the embodiments of the present application provide a business big data processing method based on big data and artificial intelligence, which is applied to a payment management system, and the method limits the payment by determining that at least one mismatch exists between the type of the user requesting the payment, the payment time and the payment amount and the corresponding payment history data. Thus, payment management can be effectively performed on underage students, and economic losses are reduced.
The specific steps of the big data, artificial intelligence based business big data processing method will be described mainly below, it being noted that although a logical order is shown in the flow chart, in some cases the steps shown or described may be performed in an order different from that here. Referring to fig. 1, the service big data processing method based on big data and artificial intelligence includes the following steps:
s100, responding to a payment request instruction of a target terminal, and acquiring a face image of a payment request user and payment request information, wherein the payment request information comprises payment time and payment amount;
the payment request instruction may be a payment password input by the user, an input payment fingerprint, or recognized face information, and when the user inputs a request instruction to request payment, the system needs to acquire a face image of the user and payment request information to determine which payment management policy is to be adopted, and the payment request information may include one or both of payment time and payment amount, and in other embodiments, other payment request information may also be introduced.
For example, when a user inputs a payment password to request payment, the system acquires a face image of the user, a payment amount and the current time of requesting payment, and before the payment is successful, the system sets a waiting progress bar to prompt the user to wait for successful payment.
In an embodiment, the target terminal includes a camera device, and the acquiring the face image of the payment request user includes: and starting the image pickup device and automatically adjusting the focal length based on the distance between the image pickup device and the user so as to clearly shoot and obtain the face image of the user, thereby improving the judgment accuracy of the user type in the next step.
S200, determining a user type of a payment request user based on the face image, wherein the user type comprises a student group and a non-student group;
in the embodiment of the application, after the face image is acquired, image recognition is carried out according to the face image of the user to determine whether the user is a student or a non-student, namely whether the payment operation is that the student is carrying out operation payment or that the parent is carrying out payment operation, and the type of the user is judged so as to output a corresponding payment management strategy.
S300, acquiring historical payment service data of the target terminal;
in the embodiment of the application, the historical payment record in the terminal payment system of the equipment is read to obtain the historical payment service data, wherein the historical payment service data can comprise the historical payment time of each payment and the corresponding payment amount;
in an embodiment, the system may determine whether the device is normally used by a student or a parent according to the historical payment time of each payment and the corresponding payment amount, for example, when the historical payment time of the preset specific gravity is in a non-learning period and the historical single payment amount of the preset specific gravity is smaller than the preset amount value, it is determined that the user type of the historical payment requester is a student group sub-tag, and otherwise is a non-student group sub-tag.
By way of example, when 80% of the payment time is within a breakfast, middling or supper period and each payment amount is within 20 yuan, it may be determined that the terminal device is in use by a student; when 80% of the payment time is in the school time period of the student or 80% of the payment amount paid by a single pen is in 20 yuan, the terminal device can be judged to be used by parents of the student.
It should be noted that, the preset proportion and the historical single payment amount may be set according to practical situations, for example, the preset proportion is set to 60%, and the single payment amount is set to 15 yuan.
The terminal equipment is normally used by students, and the parents can pay normally only when the terminal equipment is used within a reasonable range by indicating that the parents trust the students and configuring the terminal equipment for the students.
S400, generating a payment virtual tag of a historical payment requester based on the historical payment business data, wherein the payment virtual tag comprises a historical payment user type sub-tag, a historical payment time line sub-tag and a historical payment limit range sub-tag;
the payment virtual tag (user type sub-tag) can be used for representing the payment user type of the terminal of the device, namely whether the terminal device is used frequently by students or parents, and can also represent that the terminal device is a student common machine or a parent common machine.
The payment virtual tag (the historical payment timeline sub-tag) may also characterize a payment timeline for historical payments, e.g., the historical payment timeline is 10 am to 8 pm, a small portion of the deviation values may be culled when generating the sub-tag for the historical payment timeline, e.g., in the historical payment data, only one payment is made at 10 pm, and the other payments are made at 10 am to 8 pm, at which time the payment timeline may be culled at 10 pm.
The payment virtual tag (sub-tag of the historical payment amount range) may also be used to characterize the historical payment amount range, for example, the historical payment amount range is between 5-50 yuan, and similarly, when generating the sub-tag of the historical payment amount, a small part of the deviation value may be removed, and the specific principle of removing is the same as above, which is not repeated here.
S500, matching the payment service request information, the user type and the payment virtual tag to obtain a matching result;
it can be understood that the payment time and payment amount of student payment and parent payment are different, the general student payment time is mostly in a leisure time period without lessons, and the parent payment time is generally in any daytime period, namely, the student and parent have great difference in payment data, so that the embodiment of the application obtains a matching result by matching the current payment service request information and the user type with the payment virtual tag corresponding to the payment equipment terminal.
In one embodiment, the matching process and the corresponding matching result include the following three cases: (1) Matching the user type of the payment request user with the sub-tag of the historical payment user type to obtain a first matching result; (2) Matching the payment time of the payment request user with the historical payment time line sub-tag to obtain a second matching result; (3) And matching the payment amount of the payment request user with the sub-label of the historical payment amount range to obtain a third matching result.
And S600, outputting a payment service processing result based on the matching result.
Outputting different payment management strategies according to different matching results, and in an embodiment, outputting a first processing result for limiting payment when at least one of the first matching result, the second matching result and the third matching result is unmatched; and outputting a second processing result allowing payment when the first matching result, the second matching result and the third matching result are matched. If the type of the currently paid user is inconsistent with the common user of the terminal equipment, or the current payment amount is inconsistent with the historical payment amount range, or the current payment time is inconsistent with the historical payment time line, indicating or highly probable indicating that the student performs wrong operation payment by using the terminal equipment (mobile phone) of the parent, for example, the student performs payment by using the mobile phone of the parent or performs payment by using the abnormal use time at night or performs large abnormal use payment, and the like, at the moment, the system limits the current payment to prevent economic loss caused by wrong payment operation; when the three types are matched and consistent, the student or the parent is informed to pay at the normal use terminal equipment, and the system allows the current payment, for example, the student can buy the meal to pay normally.
In other embodiments, if the parent needs to pay with a student common machine, the payment needs to be released from the setting.
Therefore, the payment business processing method obtains different payment management strategies by comparing the current payment information with the historical payment data, can ensure normal payment operation in daily life, and can accurately identify wrong payment operation so as to effectively manage payment of underage students, thereby reducing economic loss.
Referring to fig. 2, in an embodiment, determining a user type of the payment-requesting user based on the face image includes:
s210, inputting the face image into a convolutional neural network to extract face characteristic values;
the features of the face image comprise eye features, wrinkle features and the like, and when the feature values are extracted, the face image can be respectively input into different convolutional neural networks to extract the feature values of the different face features.
S220, inquiring a mapping table based on the face characteristic value to obtain the age of the user;
it can be understood that the more the eye is focused, the less the user's age is, as each functional part (organ) of the human body is mature; the tighter the skin of the user, the fewer wrinkles, indicating that the user is younger;
according to the embodiment of the application, the mapping table of the characteristic value and the age of the user is obtained through a plurality of groups of tests, so that the query can be directly read when needed.
S230, determining the user type of the payment request user according to the user age.
It can be understood that the popularization of the 9-year obligation education at the present stage is that children can go to school generally by the age of the proper age, so that the application judges whether the user is a student or a non-student according to the age of the user. Since students under 14 years old lack self-management consciousness in payment and are prone to error payment operations, in one embodiment, they can be managed differently with 14 years old as a demarcation point, managed as students under 14 years old, and managed as non-students over or equal to 14 years old.
As known from the above, the service processing method of the present application obtains two payment management results, namely a first payment limiting result and a second payment allowing result, according to the current user payment information (user type, payment amount and payment time) in combination with the historical payment data, and when the payment limiting occurs, the user can input a correct request instruction to request the payment again, thereby improving the user's payment experience. In an embodiment, as shown in fig. 3, the outputting the first processing result of the limited payment further includes:
s610, sending prompt information to the target terminal, wherein the prompt information is used for prompting a payment request user to input a correct payment request instruction again;
if the current user payment information is not matched with the historical payment data to obtain a management result of limiting payment, the system can timely send out prompt information to enable the user to pay again, and the prompt information can be voice prompt or text prompt.
Aiming at the behavior that students pay by using a parent mobile phone, parents can be requested to pay if necessary; aiming at the abnormal use time or the large abnormal use payment behavior of students at night, the system generally cannot input the correct payment request instruction again in real time to pay. However, for such non-immediate payment, some escape restriction methods are adopted, for example, when the current payment amount of the user is too large and the result of restricting the payment appears, the user divides the large payment amount into a plurality of small payments to escape the payment restriction management;
s620, responding to the payment request instruction re-input by the payment request user, and judging whether the payment request instruction is abnormal or not;
after receiving the prompt information, the user can re-input a payment request instruction, wherein the payment request instruction accompanies the acquisition of the human face, namely the acquisition of the human face is immediately carried out after the payment request instruction is input, and the system judges whether the abnormality exists after receiving the payment request instruction; the instruction abnormality of the present application refers to payment restriction management that occurs when a user uses a payment policy for evasion restriction management, but not normal payment.
In one embodiment, when the current payment amount of the user is too large and the result of limiting the payment appears, the user divides the large payment amount into a plurality of small payments to pay so as to avoid payment limiting management; therefore, when the payment request instruction is input continuously for a preset number of times and each time the payment is successful, it is judged that the payment request instruction is abnormal.
In other embodiments, the user may also make evasions of payment restrictions on the time of payment, e.g., make successive micropayments on a pay-enabled timeline, and when such a situation exists, may also determine that there is an anomaly in the payment request instructions.
S630, determining that the payment request instruction is abnormal, and outputting a third processing result of prohibiting payment within a preset duration.
When the system finds that the payment is abnormal, the system can set limit payment within 3 hours or longer, for example, permanent limit payment, and only manual release of the limit can continue payment, so as to further manage the payment of underage students, thereby reducing economic loss.
The embodiment of the application further provides a payment management system 100, referring to fig. 4, fig. 4 is a schematic hardware structure diagram of the payment management system according to the embodiment of the application.
Wherein the processor 101 is configured to provide computing and control capabilities for causing the payment management system to perform corresponding tasks, for example, to control the payment management system to perform the business big data processing method in any of the method embodiments described above, the method comprising: responding to a payment request instruction of a target terminal, and acquiring a face image of a payment request user and payment request information, wherein the payment request information comprises payment time and payment amount; determining a user type of a payment request user based on the face image, wherein the user type comprises a student group and a non-student group; acquiring historical payment service data of the target terminal; generating a payment virtual tag of a historical payment requester based on the historical payment service data, wherein the payment virtual tag comprises a historical payment user type sub-tag, a historical payment timeline sub-tag and a historical payment limit range sub-tag; matching the payment service request information, the user type and the payment virtual tag to obtain a matching result; and outputting a payment service processing result based on the matching result.
The processor 101 may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (Digital Signal Processing, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), programmable logic device (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (field-programmable gate array, FPGA), general-purpose array logic (generic array logic, GAL), or any combination thereof.
The memory 102 serves as a non-transitory computer readable storage medium, and may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the methods of determining operating parameters in embodiments of the present application. The processor 101 may implement the payment service big data processing method in any of the method embodiments described above by running non-transitory software programs, instructions and modules stored in the memory 102.
In particular, the memory 102 may include Volatile Memory (VM), such as random access memory (random access memory, RAM); the memory 102 may also include a non-volatile memory (NVM), such as read-only memory (ROM), flash memory (flash memory), hard disk (HDD) or Solid State Drive (SSD), or other non-transitory solid state storage devices; the memory 102 may also include a combination of the types of memory described above.
In summary, the payment management system of the present application adopts the technical scheme of any one of the embodiments of the payment service big data processing method, so at least the beneficial effects brought by the technical scheme of the embodiments are not described in detail herein.
The embodiment of the present application also provides a computer readable storage medium, for example, a memory including program code executable by a processor to perform the payment service big data processing method in the above embodiment. For example, the computer readable storage medium may be Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), compact disc Read-Only Memory (CDROM), magnetic tape, floppy disk, optical data storage device, etc.
Embodiments of the present application also provide a computer program product comprising one or more program codes stored in a computer-readable storage medium. The processor of the electronic device reads the program code from the computer-readable storage medium, and the processor executes the program code to complete the steps of the payment service big data processing method provided in the above-described embodiment.
It will be appreciated by those of ordinary skill in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by program code related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a read only memory, a magnetic disk or optical disk, etc.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Those skilled in the art will appreciate that all or part of the processes implementing the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and where the program may include processes implementing the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the application, and all equivalent structural changes made by the description of the present application and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the application.
Claims (9)
1. The business big data processing method based on big data and artificial intelligence is applied to a payment management system and is characterized by comprising the following steps:
responding to a payment request instruction of a target terminal, and acquiring a face image of a payment request user and payment request information, wherein the payment request information comprises payment time and payment amount;
determining a user type of a payment request user based on the face image, wherein the user type comprises a student group and a non-student group;
acquiring historical payment service data of the target terminal;
generating a payment virtual tag of a historical payment requester based on the historical payment service data, wherein the payment virtual tag comprises a historical payment user type sub-tag, a historical payment timeline sub-tag and a historical payment limit range sub-tag;
matching the payment service request information, the user type and the payment virtual tag to obtain a matching result;
and outputting a payment service processing result based on the matching result.
2. The big data, artificial intelligence based business big data processing method of claim 1, wherein the historical payment business data includes a historical payment time and a historical single payment amount, the generating a payment virtual tag of a historical payment requester based on the historical payment business data comprises:
when the historical payment time of the preset specific gravity is in a non-learning time period and the historical single payment amount of the preset specific gravity is smaller than a preset amount value, determining that the user type of the historical payment requester is a student group sub-tag, and otherwise, determining that the user type of the historical payment requester is a non-student group sub-tag.
3. The big data, artificial intelligence based business big data processing method of claim 1, wherein determining the user type of the payment requesting user based on the face image comprises:
inputting the face image into a convolutional neural network to extract face characteristic values;
inquiring a mapping table based on the face characteristic value to obtain the age of the user;
and determining the user type of the payment request user according to the user age.
4. The big data and artificial intelligence based business big data processing method according to claim 1, wherein the matching the payment business request information, the user type and the payment virtual tag to obtain a matching result comprises:
matching the user type of the payment request user with the sub-tag of the historical payment user type to obtain a first matching result;
matching the payment time of the payment request user with the historical payment time line sub-tag to obtain a second matching result;
and matching the payment amount of the payment request user with the sub-label of the historical payment amount range to obtain a third matching result.
5. The big data, artificial intelligence based business big data processing method of claim 4, wherein the outputting payment business processing results based on the matching results comprises:
outputting a first processing result for limiting payment when at least one of the first matching result, the second matching result and the third matching result is unmatched;
and outputting a second processing result allowing payment when the first matching result, the second matching result and the third matching result are matched.
6. The big data, artificial intelligence based business big data processing method of claim 5, wherein outputting the first processing result of the limited payment further comprises:
sending prompt information to the target terminal, wherein the prompt information is used for prompting a payment request user to input a correct payment request instruction again;
responding to a payment request instruction re-input by a payment request user, and judging whether the payment request instruction is abnormal or not;
and determining that the payment request instruction is abnormal, and outputting a third processing result of prohibiting payment within a preset duration.
7. The big data, artificial intelligence based business big data processing method of claim 6, wherein the determining whether the payment request instruction is abnormal comprises:
when the payment request instruction input is continuously performed for preset times and each time the payment is successful, judging that the payment request instruction is abnormal.
8. The big data and artificial intelligence based business big data processing method according to claim 1, wherein the target terminal comprises a camera device, and the acquiring the face image of the payment request user comprises: the camera is activated and the focal length is automatically adjusted based on the distance between the camera and the user.
9. A payment management system, comprising: a processor and a memory; wherein the memory is for storing program code, the processor is for invoking the program code to perform the method of any of claims 1 to 8.
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