CN111681115B - Payday identification method, system, equipment and storage medium - Google Patents
Payday identification method, system, equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2134—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/12—Accounting
- G06Q40/125—Finance or payroll
Abstract
The embodiment of the invention discloses a payday identification method, a payday identification system, payday identification equipment and a storage medium. The payday identification method comprises the following steps: acquiring first account entering information of a target user in a preset month number; generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information; and unmixing the sparse matrix by using a preset algorithm to confirm payday of the target user. The embodiment of the invention realizes accurate identification of the user payday.
Description
Technical Field
The embodiment of the invention relates to a financial technology, in particular to a payday identification method, a payday identification system, payday identification equipment and a storage medium.
Background
In the personal financial management field, it is necessary to identify the income level and payday of the user and provide the user with the expense planning advice and cash flow management conforming to the income level thereof; in the fields of data analysis and user operation, the income and balance level of the user also need to be estimated according to the operation requirement of the actual service field, and an operation strategy conforming to the characteristics of the user is formulated. For example, investment financing products are pushed appropriately for users with a stable balance, and loan products are pushed for users with stable income but occasional unaddressed. Therefore, identifying the payroll level and payday of the user is a necessary premise for personal financial management and accurate operation of the user.
In identifying user paydays and revenue levels, basic statistical methods are typically used. When the payroll level of the user is estimated, statistics is carried out on all income amounts of the user in a natural month to be regarded as payroll income, or SQL is utilized to carry out preliminary screening on the running water of the user by using keywords, such as payroll, payroll and the like, which are strongly related to payroll income, so that simple addition statistics is carried out. When the user payday is estimated, the date with more income flowing is directly counted, or the payday is identified by using a statistical method such as moving average, and the day with the highest money is taken as the estimate of the payday, which is a relatively visual solution. The conventional method is intuitive in that simple statistical methods such as summation, moving average method, setting of moving window median, etc. are utilized, and easy to understand and simple to operate.
Although the traditional scheme is visual and easy to understand, the disadvantages are quite obvious: (1) The simple summing of the amounts charged in estimating the payroll of the user is susceptible to extrema and cannot distinguish which are frequent revenues for the user and which are sporadic revenues for the user. (2) When the wage level and the payday of a user are estimated, the moving average method is easy to be influenced by extremely high fluctuation and easy to be influenced by the account amount, and is particularly sensitive to large-scale flow records, but has insufficient stable flow recognition capability to small scales. (3) The user records the event that the own income flows at a relatively low frequency, and when the number of samples is small, the recognition result of setting the median of the moving window is easy to generate larger deviation.
Disclosure of Invention
The embodiment of the invention provides a payday identification method, a system, equipment and a storage medium, which are used for accurately identifying the payday of a user.
To achieve the object, an embodiment of the present invention provides a payday identification method, including:
acquiring first account entering information of a target user in a preset month number;
generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information;
and unmixing the sparse matrix by using a preset algorithm to confirm payday of the target user.
Further, the unmixing the sparse matrix using a preset algorithm to confirm payday of the target user includes:
unmixing the sparse matrix by using a preset algorithm to obtain an analytic matrix;
confirming a variance summation value corresponding to each date in the analysis matrix, and taking the date corresponding to the variance summation value larger than a first threshold value as the payroll of the target user.
Further, the confirming the sum of variances corresponding to each date in the analysis matrix, taking the date corresponding to the sum of variances larger than the first threshold as the payroll of the target user includes:
confirming a variance summation value corresponding to each date in the analysis matrix, and sequencing the variance summation values larger than a first threshold value from large to small;
and taking the date corresponding to the sum of the variances of the preset digits before ranking as the payroll of the target user.
Further, the unmixing the sparse matrix using a preset algorithm to confirm the payday of the target user includes:
acquiring a first account amount corresponding to the payday;
confirming the change rate of the first account amount based on the preset month number to obtain a second account amount when the change rate is minimum;
and taking the second account amount as the salary level of the target user.
Further, the confirming the change rate of the first account amount based on the preset month number to obtain a second account amount when the change rate is minimum includes:
confirming the change rate of the first account amount based on the preset month number to obtain a third account amount when the change rate is smaller than a second threshold value;
sorting the third account amount from big to small;
and taking the third account amount ranked in the first position as the second account amount.
Further, the step of taking the second account amount as the salary level of the target user includes:
judging whether the salary level of the target user is higher than a third threshold value;
and pushing investment financial products to the target user if the salary level of the target user is higher than a third threshold.
Further, the step of taking the second account amount as the salary level of the target user includes:
judging whether the salary level of the target user is lower than a fourth threshold value;
and pushing loan products to the target user if the salary level of the target user is lower than a fourth threshold.
In one aspect, an embodiment of the present invention further provides a payday recognition system, where the system includes:
the information acquisition module is used for acquiring first account entering information of a target user in a preset month number;
the matrix generation module is used for generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information;
and the matrix unmixing module is used for unmixing the sparse matrix by using a preset algorithm to confirm the payday of the target user.
On the other hand, the embodiment of the invention also provides a payday identification device, which comprises: one or more processors; and a storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement a method as provided by any of the embodiments of the present invention.
In yet another aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as provided by any of the embodiments of the present invention.
The embodiment of the invention obtains the first account information in the preset month number of the target user; generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information; the sparse matrix is unmixed by using a preset algorithm to confirm the payday of the target user, so that the problem that the payday of the user cannot be accurately identified due to sporadic large fluctuation and small sample number of the user account entering amount is solved, and the effect of accurately identifying the payday of the user is realized.
Drawings
Fig. 1 is a flow chart of a payday identification method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a sparse matrix according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of an analytical matrix according to a first embodiment of the present invention;
fig. 4 is a flow chart of a payday identification method according to a second embodiment of the present invention;
fig. 5 is a flow chart of a payday identification method according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of a payday recognition system according to a fourth embodiment of the present invention;
fig. 7 is a schematic structural diagram of a payday identification device according to a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are for purposes of illustration and not of limitation. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Furthermore, the terms "first," "second," and the like, may be used herein to describe various directions, acts, steps, or elements, etc., but these directions, acts, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, a first module may be referred to as a second module, and similarly, a second module may be referred to as a first module, without departing from the scope of the present application. Both the first module and the second module are modules, but they are not the same module. The terms "first," "second," and the like, are 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 one or more of the described features. In the description of the embodiments of the present invention, the meaning of "plurality" is at least two, for example, two, three, etc., unless explicitly defined otherwise.
Example 1
As shown in fig. 1, a first embodiment of the present invention provides a payday identification method, which includes:
s110, acquiring first account information of a target user in a preset month.
In this embodiment, if a payday of a target user is to be identified, first accounting information within a preset month number of the target user needs to be acquired, where the preset month number may be 3 months, 12 months or any other month number, the preset month number may be regarded as a sample size, and the larger the sample size, the more accurate the identification is. The first account information can be acquired through the flow information of the bank card of the target user, or the first account information can be acquired through the flow information recorded on a preset platform by the user. The specific first posting information includes posting amount, transaction time, transaction type and remark information of the target user transaction, and the transaction type and remark information can explicitly display the calendar of the amount, for example, the first posting information includes: 1. the account entry amount is 1 element, and the remark information is fund financial income; 2. an account entry amount of 5000 yuan, a transaction type of payroll, and the like. Illustratively, first billing information for the target user is obtained within 6 months.
Preferably, the aggregate information that does not have a practical meaning in the first accounting information needs to be removed, for example, aggregate information such as 0 or 99999999 of the accounting amount, or aggregate information such as negative of the accounting amount used by the user for paying out or transferring.
S120, generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information.
S130, unmixing the sparse matrix by using a preset algorithm to obtain an analysis matrix.
In this embodiment, after the first account entry information is acquired, a sparse matrix based on the preset month number and the corresponding date is generated according to the first account entry information, referring to fig. 2, in the sparse matrix 10, each horizontal row represents each month of the preset month number, each vertical row represents the corresponding specific date in each month, the sparse matrix 10 is a 31×7 matrix, it is easy to see that about 10 of the user has one income per month, about 25 of the user has one income per month, but the specific payroll day needs to be further determined by an algorithm. Preferably, the sparse matrix 10 may also be obtained to generate a histogram or a line graph with dates on the abscissa and sum values on the ordinate based on sum values of the amounts to be charged for each date in a preset month, so as to facilitate further observation and analysis.
Further, the sparse matrix is unmixed by using a preset algorithm to obtain an analysis matrix, and payroll days of the target user can be confirmed according to the analysis matrix. The preset algorithm may be ICA (Independent Component Correlation Algorithm, independent component analysis) algorithm. Since there are some companies with double payroll and the target users have additional subsidies each month in addition to the fixed payroll in actual practice, if the whole family balance is recorded in one account book, there will be two or more revenue sources, which can be regarded as revenue source signals, and preferably, several revenue source signals are determined according to remark information and transaction type of the first account information, and then the number of revenue source signals is set accordingly. It should be noted that if the number of revenue source signal settings is less than the actual number of revenue sources, the ICA algorithm can still identify a date with the highest revenue and the most regular even if a missed decision occurs.
For example, when the ICA algorithm is used to unmixe the income matrix of the user, 3 income signal sources are set, referring to fig. 3, the sparse matrix 10 may be used as input of the ICA algorithm, and 3 income signal sources are designated to obtain a 31×3 analysis matrix 20, in the analysis matrix 20, each row represents each income signal source, each column represents a specific date in each month, and the number in the analysis matrix 20 is not practical, that is, the dimension, the size, or even the sign of the income matrix is not practical, but at this time, we can obviously judge that the payday of the target user is 10 # and 25 # in each month through the analysis matrix. Further, we can determine the sum of variances corresponding to each date in the analysis matrix, and then take the date corresponding to the sum of variances larger than the first threshold as the payday of the target user, where the first threshold can be any value. Preferably, a histogram or line graph with date on the abscissa and sum of variance on the ordinate may also be generated for further observation and analysis.
And S140, confirming the variance summation value corresponding to each date in the analysis matrix, and sequencing the variance summation values larger than the first threshold value from large to small.
S150, taking the date corresponding to the sum of variances of the preset digits before ranking as the payroll of the target user.
In this embodiment, for various reasons, there may be many variance summation values greater than the first threshold, and it is preferable that the obtained variance summation values greater than the first threshold are ranked from large to small, and then only the date corresponding to the variance summation value with the preset number of digits before ranking is used as the payday of the target user. The preset digits can be 3 digits, 4 digits or any digits, and the variance summation value of the first digits can be judged according to remark information and transaction type of the first account entering information.
The embodiment of the invention obtains the first account information in the preset month number of the target user; generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information; the sparse matrix is unmixed by using a preset algorithm to confirm the payday of the target user, so that the problem that the payday of the user cannot be accurately identified due to sporadic large fluctuation and small sample number of the user account entering amount is solved, and the effect of accurately identifying the payday of the user is realized.
Example two
As shown in fig. 4, a second embodiment of the present invention provides a payday recognition method, and the second embodiment of the present invention is further explained based on the first embodiment of the present invention, where the method includes:
s210, acquiring first account information of a target user in a preset month.
S220, generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information.
S230, unmixing the sparse matrix by using a preset algorithm to confirm payroll days of the target user.
S240, acquiring a first account amount corresponding to the payday.
In this embodiment, in order to determine the payroll level of the user, after confirming the payroll of the target user, the first account amount corresponding to the payroll may be further obtained, and in order to eliminate sporadic unstable incomes, it is further required to calculate the change rate of the first account amount based on the preset month number, that is, calculate the change rate of the first account amount in 6 months, and finally directly use the second account amount with the minimum change rate as the payroll level of the user.
S250, confirming the change rate of the first account amount based on the preset month number to obtain a third account amount when the change rate is smaller than a second threshold value.
And S260, sorting the third account amount from big to small.
S270, taking the third account amount ranked at the first position as the second account amount.
S280, taking the second account amount as the salary level of the target user.
In this embodiment, since there may be investment benefits with small but very stable amounts in the first account amount, in order to exclude such investment benefits from payroll levels, when calculating the rate of change of the first account amount based on the preset month number, a plurality of third account amounts with the rate of change smaller than the second threshold are taken, and the second threshold may be taken, but it is necessary to ensure that at least one third account amount can be obtained, then the third account amounts are ordered from large to small, the third account amount ranked in the first position is taken as the second account amount, and the second account amount is taken as the payroll level of the target user. Preferably, the second posting amount may be further determined based on the third posting amount by the transaction type and the remark information, so as to determine the payroll level of the user.
Example III
As shown in fig. 5, a third embodiment of the present invention provides a payday recognition method, which is further explained based on the second embodiment of the present invention, and after step S280, the method further includes:
and S310, judging whether the salary level of the target user is higher than a third threshold.
S320, pushing investment financial products to the target user if the salary level of the target user is higher than a third threshold.
S330, judging whether the salary level of the target user is lower than a fourth threshold.
And S340, pushing loan products to the target user if the salary level of the target user is lower than a fourth threshold.
In this embodiment, after the salary level of the target user is obtained, an operation policy conforming to the user characteristics may be formulated. If the salary level of the target user is higher than a third threshold, pushing investment financial products to the target user, wherein the third threshold can be obtained by analysis after obtaining the expenditure level of the target user, the expenditure level of the user can be directly used as the third threshold, if the expenditure level of the user cannot be obtained, identity information and family information of the target user can be obtained, and the estimated standard of the expenditure level of the target is determined. If the identity information and the family information of the target user cannot be obtained, the average expenditure level of the city where the target user is located is directly used as a third threshold value. Correspondingly, if the salary level of the target user is lower than the fourth threshold, pushing loan-type products to the target user. The fourth threshold and the third threshold are determined in the same manner, optionally the fourth threshold is determined to be lower than the user payout level.
Example IV
As shown in fig. 6, a fourth embodiment of the present invention provides a payday recognition system 100, where the payday recognition system 100 provided in the third embodiment of the present invention can execute the payday recognition method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. The payday identification system 100 includes an information acquisition module 200, a matrix generation module 300, and a matrix unmixing module 400.
Specifically, the information obtaining module 200 is configured to obtain first accounting information within a preset month of the target user; the matrix generation module 300 is configured to generate a sparse matrix based on the preset month number and the corresponding date according to the first posting information; the matrix unmixing module 400 is configured to unmixe the sparse matrix using a preset algorithm to confirm payday of the target user.
In this embodiment, the matrix unmixing module 400 is specifically configured to unmixe the sparse matrix by using a preset algorithm to obtain an analytic matrix; confirming a variance summation value corresponding to each date in the analysis matrix, and taking the date corresponding to the variance summation value larger than a first threshold value as the payroll of the target user. The matrix unmixing module 400 is specifically further configured to confirm a variance summation value corresponding to each date in the parsing matrix, and order the variance summation values greater than the first threshold from large to small; and taking the date corresponding to the sum of the variances of the preset digits before ranking as the payroll of the target user.
Further, the payday recognition system 100 further includes a payday confirmation module 500 and a payday judgment module 600.
Specifically, the payroll confirming module 500 is configured to obtain a first account amount corresponding to the payroll day; confirming the change rate of the first account amount based on the preset month number to obtain a second account amount when the change rate is minimum; and taking the second account amount as the salary level of the target user. The payroll confirming module 500 is specifically configured to confirm a rate of change of the first account amount based on the preset month number to obtain a third account amount when the rate of change is less than a second threshold; sorting the third account amount from big to small; and taking the third account amount ranked in the first position as the second account amount. The salary judging module 600 is configured to judge whether the salary level of the target user is higher than a third threshold; and pushing investment financial products to the target user if the salary level of the target user is higher than a third threshold. The salary judging module 600 is further configured to judge whether the salary level of the target user is lower than a fourth threshold; and pushing loan products to the target user if the salary level of the target user is lower than a fourth threshold.
Example five
Fig. 7 is a schematic structural diagram of a payday identification computer device according to a fifth embodiment of the present invention. Fig. 7 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in fig. 7 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the invention.
As shown in fig. 7, the computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 7, commonly referred to as a "hard disk drive"). Although not shown in fig. 7, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the methods provided by embodiments of the present invention:
acquiring first account entering information of a target user in a preset month number;
generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information;
and unmixing the sparse matrix by using a preset algorithm to confirm payday of the target user.
Example six
The sixth embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as provided by all the inventive embodiments of the present application:
acquiring first account entering information of a target user in a preset month number;
generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information;
and unmixing the sparse matrix by using a preset algorithm to confirm payday of the target user.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the invention, the scope of which is determined by the scope of the appended claims.
Claims (9)
1. A payday identification method, comprising:
acquiring first account entering information of a target user in a preset month number;
generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information;
unmixing the sparse matrix using a preset algorithm to confirm payday of the target user, including:
unmixing the sparse matrix by using a preset algorithm to obtain an analytic matrix; wherein the preset algorithm is an ICA independent component analysis algorithm;
confirming a variance summation value corresponding to each date in the analysis matrix, and taking the date corresponding to the variance summation value larger than a first threshold value as the payroll of the target user.
2. The method of claim 1, wherein the validating the variance summation value for each date in the parsing matrix, the date corresponding to the variance summation value greater than a first threshold being the payday of the target user comprises:
confirming a variance summation value corresponding to each date in the analysis matrix, and sequencing the variance summation values larger than a first threshold value from large to small;
and taking the date corresponding to the sum of the variances of the preset digits before ranking as the payroll of the target user.
3. The method of claim 1, wherein said unmixing the sparse matrix using a preset algorithm to confirm payday of the target user comprises:
acquiring a first account amount corresponding to the payday;
confirming the change rate of the first account amount based on the preset month number to obtain a second account amount when the change rate is minimum;
and taking the second account amount as the salary level of the target user.
4. The method of claim 3, wherein the validating the rate of change of the first amount based on the preset number of months to obtain a second amount of the first amount at which the rate of change is minimum comprises:
confirming the change rate of the first account amount based on the preset month number to obtain a third account amount when the change rate is smaller than a second threshold value;
sorting the third account amount from big to small;
and taking the third account amount ranked in the first position as the second account amount.
5. A method according to claim 3, wherein said taking said second amount as the payroll level of the target user is followed by:
judging whether the salary level of the target user is higher than a third threshold value;
and pushing investment financial products to the target user if the salary level of the target user is higher than the third threshold.
6. A method according to claim 3, wherein said taking said second amount as the payroll level of the target user is followed by:
judging whether the salary level of the target user is lower than a fourth threshold value;
and pushing loan products to the target user if the salary level of the target user is lower than the fourth threshold.
7. A payday identification system, comprising:
the information acquisition module is used for acquiring first account entering information of a target user in a preset month number;
the matrix generation module is used for generating a sparse matrix based on the preset month number and the corresponding date according to the first account entering information;
the matrix unmixing module is used for unmixing the sparse matrix by using a preset algorithm to confirm the payday of the target user;
the matrix unmixing module is specifically configured to unmixe the sparse matrix by using a preset algorithm to obtain an analytic matrix; wherein the preset algorithm is an ICA independent component analysis algorithm; confirming a variance summation value corresponding to each date in the analysis matrix, and taking the date corresponding to the variance summation value larger than a first threshold value as the payroll of the target user.
8. A payday identification device, comprising:
one or more processors;
storage means for storing one or more programs,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.
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