CN112686760B

CN112686760B - Financial business processing method and platform based on big data

Info

Publication number: CN112686760B
Application number: CN202110077533.XA
Authority: CN
Inventors: 何青波
Original assignee: Shenzhen Panoramic Network Co ltd
Current assignee: Shenzhen Panoramic Network Co.,Ltd.
Priority date: 2021-01-20
Filing date: 2021-01-20
Publication date: 2021-09-14
Anticipated expiration: 2041-01-20
Also published as: CN112686760A; CN113486090A; CN113609182A

Abstract

The application provides a financial business processing method and platform based on big data, and relates to the technical field of financial business processing. In the application, first, financial service inquiry request information for a target user, which is sent by a target device, is received, wherein the financial service inquiry request information carries identity information of the target user. Secondly, whether the financial service inquiry request information belongs to legal inquiry request information is determined. And then, if the financial service inquiry request information belongs to legal inquiry request information, inquiring the financial service information corresponding to the target user based on the identity information. And finally, sending the financial service information to the target equipment. Based on the method, the problem of low business safety in the existing financial business processing technology can be solved.

Description

Financial business processing method and platform based on big data

Technical Field

The application relates to the technical field of financial business processing, in particular to a financial business processing method and platform based on big data.

Background

The application range of random computer technology and internet technology is continuously expanded due to the continuous development of the random computer technology and the internet technology. For example, the application in financial business greatly facilitates the business operation of users. However, the inventor researches and discovers that the existing financial business processing technology has the problem of low business safety particularly for the inquiry of some financial businesses.

Disclosure of Invention

In view of the above, an object of the present application is to provide a financial transaction processing method and platform based on big data, so as to solve the problem of low security of the existing financial transaction processing technology.

In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:

a financial business processing method based on big data comprises the following steps:

receiving financial service query request information aiming at a target user and sent by target equipment, wherein the financial service query request information carries identity information of the target user;

determining whether the financial service inquiry request information belongs to legal inquiry request information;

if the financial service inquiry request information belongs to legal inquiry request information, inquiring financial service information corresponding to the target user based on the identity information;

and sending the financial service information to the target equipment.

In a possible embodiment, in the big data based financial transaction processing method, the step of determining whether the financial transaction inquiry request information belongs to legal inquiry request information includes:

acquiring a historical service query request information set, wherein the historical service query request information set comprises a plurality of pieces of historical financial service query request information, and the plurality of pieces of historical financial service query request information are generated by performing multiple query request operations on a plurality of different user objects historically based on the target equipment;

screening the historical service query request information sets to obtain a first historical financial service query request information set, wherein the first historical financial service query request information set comprises a plurality of first sub-historical financial service query request information sets, each first sub-historical financial service query request information set comprises a first query time center, each first query time center is a query time center of a plurality of pieces of historical financial service query request information in the first sub-historical financial service query request information set, the time interval length between any two first query time centers meets a first preset condition, and the maximum time length of the first historical financial service query request information set meets a second preset condition;

when the set number of a first sub-historical financial service query request information set included in the first historical financial service query request information set is not consistent with the preset set number, according to the time sequence of the first historical financial service query request information set, selecting a union of historical financial service query request information of a plurality of first sub-historical financial service query request information sets in the first historical financial service query request information set before normalization as the historical financial service query request information of one first sub-historical financial service query request information set in the first historical financial service query request information set after normalization;

or, selecting historical financial service query request information of one first sub-historical financial service query request information set in the first historical financial service query request information set before normalization as historical financial service query request information of a plurality of first sub-historical financial service query request information sets in the first historical financial service query request information set after normalization, wherein the set number of the first historical financial service query request information set before normalization is expanded or compressed to the set preset number, and the set number of the first historical financial service query request information set after normalization is the set preset number;

clustering the first historical financial service query request information set to generate a second historical financial service query request information set, wherein the second historical financial service query request information set comprises a plurality of second sub-historical financial service query request information sets, the second sub-historical financial service query request information set is a subclass with the most clustered historical financial service query request information in the corresponding first sub-historical financial service query request information set, the second sub-historical financial service query request information set comprises a second query time center, and the time lengths of the plurality of second query time centers are less than or equal to the time lengths of the plurality of first query time centers;

determining a target user object according to the corresponding user object of each piece of historical financial service query request information in the second sub-historical financial service query request information set, wherein the target user object is a user object of which the information quantity of the corresponding historical financial service query request information meets a third preset condition;

according to the target user object, filtering historical financial service query request information corresponding to the target user object in the second sub-historical financial service query request information set to obtain a corresponding third sub-historical financial service query request information set, wherein the user object corresponding to each piece of historical financial service query request information in the third sub-historical financial service query request information set does not belong to the target user object;

and determining whether the financial service query request information belongs to legal query request information or not according to each piece of historical financial service query request information included in each third sub-historical financial service query request information set.

In a possible embodiment, in the financial service processing method based on big data, the step of determining whether the financial service query request information belongs to legal query request information according to each piece of historical financial service query request information included in each of the third sub-historical financial service query request information sets includes:

for each third sub-historical financial service query request information set, determining query frequency information of each piece of historical financial service query request information included in the third sub-historical financial service query request information set;

and determining whether the financial service inquiry request information belongs to legal inquiry request information or not based on the inquiry frequency information.

In a possible embodiment, in the financial service processing method based on big data, the step of determining whether the financial service inquiry request information belongs to legal inquiry request information based on the inquiry frequency information includes:

calculating frequency mean value information of a plurality of query frequency information corresponding to a plurality of third sub-historical financial service query request information sets;

and determining whether the financial service inquiry request information belongs to legal inquiry request information or not based on the frequency mean value information and preset first frequency threshold information.

counting the number of frequency mean value information larger than preset second frequency threshold value information in the frequency mean value information of a plurality of query frequency information corresponding to a plurality of third sub-historical financial service query request information sets to obtain a first number;

and determining whether the financial service inquiry request information belongs to legal inquiry request information or not based on the first quantity.

In a possible embodiment, in the financial transaction processing method based on big data, the step of determining whether the financial transaction inquiry request information belongs to legal inquiry request information based on the first quantity includes:

counting the number of frequency mean value information of a plurality of query frequency information corresponding to the plurality of third sub-historical financial service query request information sets to obtain a second number;

calculating the ratio of the first quantity to the second quantity to obtain a first ratio;

and determining whether the financial service inquiry request information belongs to legal inquiry request information or not based on the first ratio and a preset ratio threshold.

calculating the query frequency information of each piece of historical financial service query request information included in each third sub-historical financial service query request information set;

acquiring a plurality of pieces of historical financial service query request information in a target period, wherein the plurality of pieces of historical financial service query request information are generated by performing multiple query request operations on a plurality of different user objects historically on the basis of the target equipment;

sequencing the plurality of pieces of historical financial service query request information based on the generation time information of the historical financial service query request information to obtain a historical query request information sequence;

acquiring the request information quantity of the plurality of pieces of historical financial service query request information;

generating a frequency of query request operation performed by the target device based on the quantity of the request information and the duration of the target time interval, wherein the frequency is used as a target history request frequency corresponding to the target device;

if the request frequency quantity in the history request frequency set where the target history request frequency is located is smaller than or equal to a frequency statistical threshold, determining an average value of each history request frequency in the history request frequency set as a frequency average value corresponding to the target history request frequency, wherein each history request frequency in the history request frequency set is generated based on a plurality of pieces of history financial service query request information in each different target time period;

if the number of the request frequencies in the historical request frequency set is greater than the frequency statistical threshold, sequentially acquiring the request frequencies in the historical request frequency set according to the time sequence of the recording time to obtain statistical historical request frequencies, wherein the number of the statistical historical request frequencies is the frequency statistical threshold;

deleting the historical request frequencies except the statistical historical request frequency in the historical request frequency set, and determining the average value of the historical request frequencies included in the current historical request frequency set as the frequency average value corresponding to the target historical request frequency;

determining the request information screening duration of the target equipment based on the frequency mean value;

determining a starting position identifier of first target historical financial service query request information in the historical query request information sequence, wherein a user object of the first target historical financial service query request information is the target user or has an association relation with the target user;

acquiring the maximum value of the sequence position of the historical query request information sequence;

if the sum of the initial position identification and the request information screening duration is greater than or equal to the maximum sequence position, determining the maximum sequence position as a target position identification, and acquiring historical financial service query request information corresponding to the target position identification from the historical query request information sequence to obtain second target historical financial service query request information;

if the sum of the initial position identification and the request information screening duration is smaller than the maximum value of the sequence position, determining the sum of the initial position identification and the request information screening duration as the target position identification;

if the target position identification is an integer, acquiring historical financial service query request information at the target position identification in the historical query request information sequence to obtain second target historical financial service query request information;

if the target position identification is a decimal, determining a first target position identification and a second target position identification based on the target position identification, wherein historical financial service query request information corresponding to the first target position identification and historical financial service query request information corresponding to the second target position identification are adjacent in position in the historical query request information sequence, and the target position identification is larger than the first target position identification and smaller than the second target position identification;

obtaining historical financial service query request information at the first target position identifier from the historical query request information sequence, and obtaining historical financial service query request information at the second target position identifier;

acquiring historical financial service query request information at the first target position identifier or historical financial service query request information at the second target position identifier based on a user object of the historical financial service query request information at the first target position identifier and a user object of the historical financial service query request information at the second target position identifier to obtain second target historical financial service query request information;

and in the historical query request information sequence, determining whether the financial service query request information belongs to legal query request information or not based on each piece of historical financial service query request information starting from the first target historical financial service query request information and ending at the second target historical financial service query request information.

In a possible embodiment, in the financial service processing method based on big data, if the financial service inquiry request information belongs to legal inquiry request information, the step of inquiring the financial service information corresponding to the target user based on the identity information includes:

inquiring at least one service identification information in a target database based on the identity information and a pre-established identity-service identification corresponding relation;

and inquiring corresponding financial service information in the target database aiming at each piece of service identification information, wherein when each piece of financial service information is stored through the target database, the financial service information is subjected to feature extraction to obtain unique service identification information, and the service identification information and the identity information of a target user corresponding to the financial service information establish a corresponding relation.

The application also provides a financial service processing platform based on big data, which is used for:

and sending the financial service information to the target equipment.

According to the financial service processing method and platform based on big data, after financial service query request information of target equipment is received, whether the financial service query request information is legal or not is determined before corresponding financial service information is queried based on the financial service query request information, so that when the financial service query request information is legal, corresponding financial service information is queried based on identity information in the financial service query request information and is sent to the target equipment. Therefore, the safety of the financial service information can be improved, the problem of low service safety in the existing financial service processing technology is solved, and the method has high practical value.

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

Fig. 1 is a block diagram of a financial transaction processing platform based on big data according to an embodiment of the present disclosure.

Fig. 2 is a schematic flowchart illustrating steps included in a financial transaction processing method based on big data according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

As shown in fig. 1, an embodiment of the present application provides a financial transaction processing platform based on big data. The network data monitoring system may include a memory and a processor, among other things.

In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the big data-based financial transaction processing method provided by the embodiment of the present application (described later).

The big data based financial transaction processing platform may be configured to:

and sending the financial service information to the target equipment.

Alternatively, the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.

The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.

Moreover, the structure shown in fig. 1 is only an illustration, and the big data-based financial transaction processing platform may further include more or less components than those shown in fig. 1, or have a different configuration from that shown in fig. 1, for example, may include a communication unit for information interaction with other devices.

In an alternative example, the big data-based financial service processing platform may be a server with data processing capability.

With reference to fig. 2, an embodiment of the present application further provides a financial service processing method based on big data, which is applicable to the financial service processing platform based on big data. The method steps defined by the flow related to the financial business processing method based on big data can be realized by the financial business processing platform based on big data.

The specific process shown in FIG. 2 will be described in detail below.

Step S110, receiving financial service inquiry request information for a target user.

In this embodiment, the financial service processing platform based on big data may receive financial service query request information for a target user sent by a target device.

The financial service inquiry request information carries the identity information of the target user.

Step S120, determining whether the financial service inquiry request information belongs to legal inquiry request information.

In this embodiment, after receiving the financial service inquiry request information based on step S110, the financial service processing platform based on big data may determine whether the financial service inquiry request information belongs to legal inquiry request information.

When the financial service inquiry request information belongs to legal inquiry request information, step S130 may be executed; when the financial service inquiry request information does not belong to legal inquiry request information, the corresponding financial service information is not inquired, or the inquired financial service information is not sent to the target equipment, so that the safety of the financial service information is ensured.

Step S130, querying financial service information corresponding to the target user based on the identity information.

In this embodiment, when it is determined that the financial service query request information belongs to legal query request information based on step S120, the financial service processing platform based on big data may query financial service information corresponding to the target user based on the identity information of the target user carried in the financial service query request information (where the financial service information may include credit investigation report information, payment bill information, receipt bill information, and the like of the user).

Step S140, sending the financial service information to the target device.

In this embodiment, after querying the financial service information based on step S130, the financial service processing platform based on big data may send the financial service information to the target device, so that the target device completes querying the financial service information.

Based on the method, after the financial service inquiry request information of the target equipment is received, whether the financial service inquiry request information is legal or not is determined before the corresponding financial service information is inquired based on the financial service inquiry request information, so that when the financial service inquiry request information is legal, the corresponding financial service information is inquired based on the identity information in the financial service inquiry request information and is sent to the target equipment. Therefore, the safety of the financial business information can be improved, and the problem of low business safety in the existing financial business processing technology is solved.

In the first aspect, it should be noted that, in step S120, a specific manner for determining whether the financial service inquiry request information belongs to the legal inquiry request information is not limited, for example, the manner for determining may be different based on different requirements. In the present embodiment, the following three examples are provided, respectively.

In a first example, whether the financial service query request information belongs to legal query request information may be determined based on the following steps:

a first step of obtaining a historical service query request information set, where the historical service query request information set includes a plurality of pieces of historical financial service query request information, and the plurality of pieces of historical financial service query request information are generated based on multiple query request operations performed by the target device on a plurality of different user objects historically (for example, historical financial service query request information corresponding to all the historical query request operations performed by the target device on the big data-based financial service processing platform may be obtained, and thus, redundant historical financial service query request information is obtained);

and a second step of filtering the historical financial service query request information set to obtain a first historical financial service query request information set, where the first historical financial service query request information set includes a plurality of first sub-historical financial service query request information sets (that is, the historical financial service query request information set may be filtered and divided into a plurality of first sub-historical financial service query request information sets according to a first preset condition and a second preset condition described later, so that a first historical financial service query request information set including the plurality of first sub-historical financial service query request information sets may be obtained), the first sub-historical financial service query request information set includes a first query time center, and the first query time center is a query time center of the plurality of historical financial service query request information in the first sub-historical financial service query request information set (e.g., corresponds to the plurality of historical financial service query request information in the first sub-historical financial service query request information set) A median of multiple generation times), a length of a time interval between any two of the first query time centers meets a first preset condition (for example, a difference between the two first query time centers should be greater than a preset value, which may be generated based on a configuration operation performed by a user according to actual needs and is not specifically limited herein), and a maximum length of time of the first historical financial service query request information set meets a second preset condition (for example, a difference between an earliest generation time and a latest generation time of the historical financial service query request information in the first historical financial service query request information set should be less than a time threshold, which may be generated based on a configuration operation performed by a user according to actual needs and is not specifically limited herein);

third, when the set number of the first sub-historical financial service query request information sets included in the first historical financial service query request information set is not consistent with a set preset number (the set preset number may be generated based on configuration operation performed by a user according to actual needs, and is not specifically limited herein), according to a time sequence of the first historical financial service query request information set, selecting a plurality of the first historical financial service query request information sets before normalization (for example, selecting a certain number of first sub-historical financial service query request information sets corresponding to an earliest first query time center, such as a difference between the set number and the set preset number) as a union of the historical financial service query request information of the first sub-historical financial service query request information sets after normalization, as one of the first sub-historical financial service query request information sets after normalization Requesting historical financial service inquiry request information of the information set;

or, selecting one of the first historical financial service query request information sets before normalization (for example, a first sub-historical financial service query request information set with the earliest corresponding first query time center may be selected) as the historical financial service query request information of the first sub-historical financial service query request information set, and taking the historical financial service query request information of a plurality of the first sub-historical financial service query request information sets in the first historical financial service query request information set after normalization (i.e. splitting a selected first sub-historical financial service query request information set), wherein the number of sets of the first historical financial service query request information set before normalization is expanded (corresponding to splitting the set in the above) or compressed (corresponding to merging the set in the above) to the preset number, the set number of the first historical financial service query request information sets after the normalization is the set preset number;

a fourth step of clustering the first historical financial service query request information sets to generate a second historical financial service query request information set, wherein the second historical financial service query request information set includes a plurality of second sub-historical financial service query request information sets (that is, the historical financial service query request information in each of the first sub-historical financial service query request information sets can be clustered based on the generation time, such as clustering based on a proximity algorithm or other clustering algorithm in a time dimension to obtain at least one subclass), the second sub-historical financial service query request information set is a subclass of the clustered historical financial service query request information in the corresponding first sub-historical financial service query request information sets, and the second sub-historical financial service query request information set includes a second query time center (as described above, may be a median of the generation times), the length of time of the plurality of second query time centers (i.e., the difference between the largest second query time center and the smallest second query time center) is less than or equal to the length of time of the plurality of first query time centers (i.e., the difference between the largest first query time center and the smallest first query time center);

fifthly, determining a target user object according to a user object corresponding to each piece of historical financial service query request information in the second sub-historical financial service query request information set, wherein the target user object is a user object of which the information quantity of the corresponding historical financial service query request information meets a third preset condition (for example, in an alternative example, the target user object may be a user object of which the corresponding information quantity is the largest; for example, in another alternative example, the target user object may be a user object of which the corresponding information quantity is the smallest; and can be determined according to the query characteristics of equipment which generally performs illegal queries in actual application);

sixthly, according to the target user object, filtering out historical financial service query request information corresponding to the target user object in the second sub-historical financial service query request information set to obtain a corresponding third sub-historical financial service query request information set, wherein the user object corresponding to each piece of historical financial service query request information in the third sub-historical financial service query request information set does not belong to the target user object;

and seventhly, determining whether the financial service inquiry request information belongs to legal inquiry request information or not according to each piece of historical financial service inquiry request information included in each third sub-historical financial service inquiry request information set.

In a second example, whether the financial service inquiry request information belongs to legal inquiry request information may be determined based on the following steps:

the method comprises the steps of firstly, acquiring a plurality of pieces of historical financial service query request information in a target time period (for example, the time period may be a latest time period, and the time length may be generated based on configuration operation performed by a user according to actual application requirements), wherein the plurality of pieces of historical financial service query request information are generated based on multiple query request operations performed on a plurality of different user objects historically by the target device;

secondly, sorting the plurality of pieces of historical financial service query request information based on the generation time information of the historical financial service query request information to obtain a historical query request information sequence (for example, the historical financial service query request information with earlier generation time is sorted in the front, and the historical financial service query request information with later generation time is sorted in the back);

thirdly, acquiring the request information quantity of the plurality of pieces of historical financial service query request information;

fourthly, based on the quantity of the request information and the duration of the target time interval, generating a frequency of query request operations performed by the target device (for example, the frequency may be obtained by dividing the quantity of the request information by the duration) as a target history request frequency corresponding to the target device;

fifthly, if the number of the request frequencies in the history request frequency set where the target history request frequency is located is less than or equal to a frequency statistical threshold, determining an average value of each history request frequency in the history request frequency set as a frequency average value corresponding to the target history request frequency, where each history request frequency in the history request frequency set is generated based on a plurality of pieces of historical financial service query request information in each different target time period (for example, if the current time is 2021/1/2021, the target time period in the first step may be 10/1/2020 to 12/31/2020, and thus, historical financial service query request information from 7/1/2020 to 9/31/2020 may also be obtained to generate a corresponding history request frequency, and historical financial service query request information from 4/1/2020 to 6/31/2020 may also be obtained, generating a corresponding historical request frequency; as such, the set of historical request frequencies may be formed);

a sixth step of, if the number of request frequencies in the history request frequency set is greater than the frequency statistical threshold, performing sequential acquisition operations in the history request frequency set according to a time sequence of recording time (corresponding time period) to obtain a statistical history request frequency, where the number of statistical history request frequencies is the frequency statistical threshold (for example, acquiring the most recent history request frequencies of the frequency statistical threshold, or performing sampling based on the frequency statistical threshold);

seventhly, deleting the history request frequencies except the statistic history request frequency in the history request frequency set, and determining the average value of the history request frequencies included in the current history request frequency set (namely calculating the average value of the statistic history request frequency) as the frequency average value corresponding to the target history request frequency;

eighthly, determining the request information screening time length of the target device based on the frequency average (for example, the reciprocal of the frequency average may be used as the request information screening time length, or the reciprocal is multiplied by a certain proportionality coefficient as the request information screening time length, where the proportionality coefficient may be greater than 1, and a specific numerical value may be generated based on configuration operation performed by a user according to actual needs);

ninth, determining a starting position identifier of a first target historical financial service query request message in the historical query request message sequence, wherein a user object of the first target historical financial service query request message is the target user or has an association relationship with the target user (when the first target historical financial service query request message is a plurality of pieces, the position of the first target historical financial service query request message with the earliest generation time can be determined as the starting position identifier, and the association relationship with the target user can mean that a service transaction, such as a loan, exists between the target user and the target user);

step ten, acquiring a maximum value of a sequence position of the historical query request information sequence (the maximum value of the sequence position is the length of the historical query request information sequence and is the number of the included historical financial service query request information);

a tenth step, if the sum of the starting position identifier and the request information screening duration is greater than or equal to the maximum sequence position, determining the maximum sequence position as a target position identifier, and obtaining historical financial service query request information corresponding to the target position identifier from the historical query request information sequence to obtain second target historical financial service query request information (that is, taking the last piece of historical financial service query request information in the historical query request information sequence as the second target historical financial service query request information);

a twelfth step of determining the sum of the initial position identifier and the request information screening duration as the target position identifier if the sum of the initial position identifier and the request information screening duration is smaller than the maximum value of the sequence position;

step three, if the target position mark is an integer, acquiring historical financial service inquiry request information at the target position mark in the historical inquiry request information sequence to obtain second target historical financial service inquiry request information;

fourteenth, if the target location identifier is a decimal, determining a first target location identifier and a second target location identifier based on the target location identifier, where historical financial service query request information corresponding to the first target location identifier and historical financial service query request information corresponding to the second target location identifier are adjacent in position in the historical query request information sequence, and the target location identifier is larger than the first target location identifier and smaller than the second target location identifier;

fifteenth, acquiring historical financial service query request information at the first target position identifier and acquiring historical financial service query request information at the second target position identifier from the historical query request information sequence;

sixthly, acquiring historical financial service query request information at the first target position identifier or historical financial service query request information at the second target position identifier based on the user object of the historical financial service query request information at the first target position identifier and the user object of the historical financial service query request information at the second target position identifier to obtain second target historical financial service query request information (for example, if the user object corresponding to the historical financial service query request information at the first target position identifier is the target user or the associated user of the target user, the historical financial service query request information at the first target position identifier is used as the second target historical financial service query request information, and if the user object corresponding to the historical financial service query request information at the second target position identifier is the target user or the associated user of the target user If so, using the historical financial service query request information at the second target position identifier as the historical financial service query request information of the second target);

seventeenth, in the historical query request information sequence, based on each piece of historical financial service query request information (which may include the first target historical financial service query request information and the second target historical financial service query request information) starting from the first target historical financial service query request information and ending at the second target historical financial service query request information, determining whether the financial service query request information belongs to a legal query request message.

In a third example, it may be determined whether the financial service query request information belongs to legitimate query request information based on, among other things, the following steps:

a first step of obtaining a plurality of pieces of historical financial service query request information, where the plurality of pieces of historical financial service query request information are generated by performing multiple query request operations on a plurality of different user objects historically based on the target device (as described above, details are not repeated here);

a second step, based on whether the user objects corresponding to each piece of historical financial service inquiry request information are the same or are associated, dividing a plurality of pieces of historical financial service inquiry request information, which are the same or associated with the user objects, into the same request information set to obtain a plurality of request information sets (that is, the user objects corresponding to the historical financial service inquiry request information in different request information sets are different or do not have an association relationship, wherein the association relationship can refer to the above description);

thirdly, determining a request information set with the largest quantity of historical financial service query request information in the plurality of request information sets as a target request information set;

a fourth step of determining a time duration (i.e. a difference between a time when the query is started and a time when the query is ended, such as a generation time of the historical financial service query request information and a time when the corresponding financial service information is sent to the target device or a time when the corresponding financial service information is determined not to belong to legal query request information) corresponding to each piece of the historical financial service query request information in the target request information set, and a request start time (i.e. a time when the query is started) and a request end time (i.e. a time when the query is ended) of each time duration;

step five, determining a plurality of pieces of historical financial service query request information in the target request information set, a time duration corresponding to each piece of historical financial service query request information, and a request start time and a request end time of the time duration as a data set to be processed, and determining a current position (which may be one or more) of the historical financial service query request information corresponding to a user object which is the same as or associated with the target user in the data set to be processed;

sixthly, obtaining a target screening time period according to the time duration of the historical financial service query request information of the (each) current position (the target screening time period can be the time for starting to perform query and the time for finishing query based on the historical financial service query request information);

seventhly, determining historical financial service query request information intersected with the target screening time period based on the corresponding request starting time and request ending time in each piece of historical financial service query request information to obtain to-be-processed historical financial service query request information;

eighthly, determining at least one piece of historical financial service query request information to be processed, and taking the position of the at least one piece of historical financial service query request information to be processed in the target request information set as a target position;

ninthly, obtaining a new target screening time period according to the time duration of the historical financial service inquiry request information of each target position;

tenth, determining historical financial service query request information intersected with the new target screening time period based on the corresponding request start time and request end time in each piece of historical financial service query request information to obtain to-be-processed historical financial service query request information;

eleventh, determining whether the financial service inquiry request information belongs to legal inquiry request information based on each piece of the historical financial service inquiry request information to be processed.

Optionally, in the above example, a specific manner for determining whether the financial service query request information belongs to legal query request information based on the historical financial service query request information is not limited, and may be selected according to actual application requirements (in this embodiment, the first example is taken as an example for description, and the other two examples are not repeated one by one, and are referred to for reference).

For example, in an alternative example, whether the financial service inquiry request information belongs to legal inquiry request information may be determined based on the following steps:

first, the query frequency information of each piece of historical financial service query request information included in each of the third sub-sets of historical financial service query request information may be calculated (e.g., the number of information in all sets is divided by the difference between the earliest generation time and the latest generation time); secondly, it may be determined whether the financial service inquiry request information belongs to legal inquiry request information based on the inquiry frequency information (for example, it may be determined whether the inquiry frequency information is greater than a threshold value, and if so, it may be determined that the financial service inquiry request information belongs to legal inquiry request information).

For another example, in another alternative example, whether the financial service inquiry request information belongs to legal inquiry request information may be determined based on the following steps:

firstly, for each of the third sub-historical financial service query request information sets, determining query frequency information of each piece of historical financial service query request information included in the third sub-historical financial service query request information set; then, it may be determined whether the financial service inquiry request information belongs to legal inquiry request information based on the inquiry frequency information.

It is to be understood that, in the above example, the specific manner of determining whether the financial service inquiry request information belongs to the legal inquiry request information based on the inquiry frequency information is not limited.

For example, in one alternative example:

firstly, frequency mean value information of a plurality of query frequency information corresponding to a plurality of third sub-historical financial service query request information sets can be calculated; then, it may be determined whether the financial service inquiry request information belongs to legal inquiry request information based on the frequency mean information and preset first frequency threshold information (the first frequency threshold information may be generated based on configuration operations performed by a user according to actual application requirements, and specific numerical values are not specifically limited herein).

For another example, in another alternative example:

first, the number of frequency mean value information larger than preset second frequency threshold information (the second frequency threshold information may be generated based on configuration operation performed by a user according to actual application requirements, and specific numerical values are not specifically limited herein) may be counted in the frequency mean value information of the plurality of query frequency information corresponding to the plurality of third sub-historical financial service query request information sets, so as to obtain a first number; secondly, it may be determined whether the financial service inquiry request information belongs to legal inquiry request information based on the first number.

In the above example, the specific manner for determining whether the financial service inquiry request information belongs to the legal inquiry request information based on the first number is not limited, and for example, the specific manner may be:

firstly, the number of frequency mean value information of a plurality of query frequency information corresponding to the plurality of third sub-historical financial service query request information sets is counted by ky to obtain a second number; secondly, the ratio of the first quantity to the second quantity can be calculated to obtain a first ratio; then, it may be determined whether the financial service inquiry request information belongs to the legal inquiry request information based on the first ratio and a preset ratio threshold (the ratio threshold may be generated based on a configuration operation performed by a user according to an actual application requirement, and a specific numerical value is not specifically limited herein).

In the second aspect, it should be noted that, in step S130, a specific manner for querying the financial service information corresponding to the target user based on the identity information is not limited.

For example, in an alternative example, the financial transaction information corresponding to the target user may be queried based on the following steps:

firstly, at least one service identification information can be inquired in a target database based on the identity information and a pre-established identity-service identification corresponding relation; secondly, for each piece of the service identification information, corresponding financial service information can be inquired in the target database, wherein when each piece of financial service information is stored through the target database, feature extraction (such as conversion into a hash value) is performed on the financial service information to obtain unique service identification information, and a corresponding relation is established between the service identification information and the identity information of a target user corresponding to the financial service information.

In summary, according to the financial service processing method and platform based on big data provided by the application, after the financial service query request information of the target device is received, whether the financial service query request information is legal is determined before the corresponding financial service information is queried based on the financial service query request information, so that when the financial service query request information is legal, the corresponding financial service information is queried based on the identity information in the financial service query request information, and is sent to the target device. Therefore, the safety of the financial service information can be improved, the problem of low service safety in the existing financial service processing technology is solved, and the method has high practical value.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A financial business processing method based on big data is characterized by comprising the following steps:

sending the financial service information to the target device;

wherein, the step of determining whether the financial service inquiry request information belongs to legal inquiry request information comprises:

clustering the first historical financial service query request information set based on the generation time to generate a second historical financial service query request information set, wherein the second historical financial service query request information set comprises a plurality of second sub-historical financial service query request information sets, the second sub-historical financial service query request information set is a subclass of the clustered historical financial service query request information in the corresponding first sub-historical financial service query request information set, the second sub-historical financial service query request information set comprises a second query time center, and the time lengths of the plurality of second query time centers are less than or equal to the time lengths of the plurality of first query time centers;

2. The big data-based financial transaction processing method according to claim 1, wherein the step of determining whether the financial transaction query request information belongs to legal query request information according to each piece of historical financial transaction query request information included in each of the third sub-historical financial transaction query request information sets comprises:

3. The big data-based financial transaction processing method according to claim 2, wherein said step of determining whether the financial transaction inquiry request information belongs to legal inquiry request information based on the inquiry frequency information comprises:

4. The big data-based financial transaction processing method according to claim 2, wherein said step of determining whether the financial transaction inquiry request information belongs to legal inquiry request information based on the inquiry frequency information comprises:

5. The big data-based financial transaction processing method according to claim 4, wherein said step of determining whether said financial transaction inquiry request information belongs to legal inquiry request information based on said first amount comprises:

6. The big data-based financial transaction processing method according to claim 1, wherein the step of determining whether the financial transaction query request information belongs to legal query request information according to each piece of historical financial transaction query request information included in each of the third sub-historical financial transaction query request information sets comprises:

7. The big data-based financial transaction processing method according to any one of claims 1 to 6, wherein the step of querying the financial transaction information corresponding to the target user based on the identity information if the financial transaction query request information belongs to legal query request information comprises:

8. A big data based financial transaction processing platform, configured to:

sending the financial service information to the target device;