CN109146686A - Transaction data cross-matched method, credit method and its system - Google Patents
Transaction data cross-matched method, credit method and its system Download PDFInfo
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Abstract
The present invention relates to financial field, a kind of transaction data cross-matched method, credit method and its system are disclosed.In the present invention, the transaction data cross-matched method is the following steps are included: configure the data item of cross-matched model with cross validation needed for the model；According to the data item of cross validation needed for the model, data item is extracted from the first data system and the second data system, wherein the first data system is the data system of enterprise, and the second data system is the upstream of the enterprise or the data system of downstream trading partner；Transaction data cross-matched is carried out to extracted data item according to the cross-matched model；Export the result of the cross-matched.By being deployed in the data model of enterprises end, acquires enterprise itself transaction data and upstream and downstream trading partner's corresponding data carries out multi-layer transaction data cross validation, from different levels objective quantification enterprise trade authenticity.
Description
Technical field
The present invention relates to financial field, in particular to a kind of transaction data cross-matched technology.
Background technique
To veritify business transaction data validity, mostly financial institution is directed to each checking request and sends people in the prior art
Work from document rank or summarizes rank progress data verification, needs many places manpower intervention in the process, artificially collect detail as used
Or summarize data, artificial treatment data (such as desensitizing), artificial veritification data.
Current art be easy to produce enterprise's sensitive data leak, can not be completed in a manner of automatic standardizing data veritify,
Financial institution largely manually veritifies the problems that data generate.
Therefore, a kind of transaction data cross-matched method that can effectively solve the problem that the above problem is needed at present.
Summary of the invention
The purpose of the present invention is to provide a kind of transaction data cross-matched method, credit method and its systems, pass through portion
The data model in enterprises end is affixed one's name to, enterprise itself transaction data is acquired and upstream and downstream trading partner's corresponding data carries out multi-layer friendship
Easy data cross verifying, from different levels objective quantification enterprise trade authenticity.
To solve the above-mentioned problems, this application discloses a kind of transaction data cross-matched methods, comprising the following steps:
The data item of cross validation needed for configuring cross-matched model and the model；
According to the data item of cross validation needed for the model, data are extracted from the first data system and the second data system
, wherein the first data system is the data system of enterprise, and the second data system is upstream or the downstream trade partner of the enterprise
The data system of companion；
Transaction data cross-matched is carried out to extracted data item according to the cross-matched model；
Export the result of the cross-matched.
Disclosed herein as well is a kind of credit methods, comprising the following steps:
The data of cross validation needed for the system of financial institution sends cross-matched model and the model to third party system
?；
Third party system executes above-mentioned transaction data cross-matched method, and cross-matched result is sent to financial institution
System, the transaction data original to the system mask of financial institution；
The system of financial institution carries out credit according to cross-matched result.
Disclosed herein as well is a kind of transaction data cross-matched systems, comprising:
Configuration unit, the data item for cross validation needed for configuring cross-matched model and the model；
Data extracting unit, for the data item of the cross validation according to needed for the model, from the first data system and
Two data systems extract data item, wherein the first data system is the data system of enterprise, and the second data system is the enterprise
Upstream or downstream trading partner data system；
Cross-matched unit, for carrying out transaction data intersection to extracted data item according to the cross-matched model
Matching；
As a result output unit, for exporting the result of the cross-matched.
Compared with prior art, the main distinction and its effect are embodiment of the present invention:
By being deployed in the data model of enterprises end, enterprise itself transaction data and upstream and downstream trading partner's respective counts are acquired
According to progress cross validation, objective quantification enterprise trade authenticity.
Further, by multi-layer transaction data cross-matched, business transaction data validity is veritified from different levels.
Further, it by completing transaction data cross-matched in enterprises end, can effectively reduce outside enterprise's sensitive data
Possibility is let out, business data safety is enhanced.
Further, it by configuring the matching way pre-value of different each trade partners of enterprise's upstream and downstream, can effectively adapt to
The data open policy that different trade partners are taken is reduced because the matching caused by data open policy difference hinders, and is made
With range more extensively and more practical operation.
Further, it is acquired in a manner of automatic standardizing and veritifies data, guarded against because artificial treatment data are issuable
Human error or artificial modification.
Further, data are acquired and veritified in a manner of automatic standardizing, are solving to need largely manually to veritify data
While drawback, improves data and veritify efficiency and accuracy.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of transaction data cross-matched method in first embodiment of the invention；
Fig. 2 is a kind of flow diagram of credit method in second embodiment of the invention；
Fig. 3 is a kind of structural schematic diagram of transaction data cross-matched system in third embodiment of the invention；
Fig. 4 is a kind of flow diagram of transaction data cross-matched method of a specific embodiment of the invention.
Specific embodiment
In the following description, in order to make the reader understand this application better, many technical details are proposed.But this
The those of ordinary skill in field is appreciated that even if without these technical details and many variations based on the following respective embodiments
And modification, each claim of the application technical solution claimed can also be realized.
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to implementation of the invention
Mode is described in further detail.
First embodiment of the invention is related to a kind of transaction data cross-matched method.Fig. 1 is transaction data intersection
The flow diagram of method of completing the square.
Specifically, as shown in Figure 1, the transaction data cross-matched method the following steps are included:
In a step 101, the data item of configuration cross-matched model and cross validation needed for the model.
Then into step 102, the data item of the cross validation according to needed for model, from the first data system and the second data
System extracts data item, wherein the first data system is the data system of enterprise, and the second data system is the upstream of the enterprise
Or the data system of downstream trading partner.
It should be noted that the cross-matched model is disposed on client, acquire enterprise itself transaction data and on
The intersection data of downstream trading partner are also to carry out in client, can reduce enterprise's sensitive data in this way and leak possibility,
Enhance business data safety.
Then into step 103, transaction data cross-matched is carried out to extracted data item according to cross-matched model.
Then into step 104, the matched result of output cross.
Hereafter terminate this process.
Further, specifically, in step 103, further include following sub-step:
Dispose the matching way pre-value pre of every trading partner of the enterprisei:
Wherein, i is trading partner's identification number,Or 4,It is right respectively
" stringent document matching ", " fuzzy document matching ", " monthly summarizing matching ", " summarizing digests match " this four layers of matching ways are answered,
" 0 " value indicates that trading partner i is not suitable for the j layers of matching way, and " 1 " value indicates that trading partner i is applicable in the j layers of matching way；
According to the matching way pre-value pre of every trading partneri, determine the matching way that every trading partner is applicable in；With
“preiFor=0001 ", indicate that the trading partner i is only applicable in " summarizing digests match " mode；
According to the matching way that every trading partner is applicable in, respective counts are extracted according to the field of demand corresponding to matching way
According to item, when matching way is document rank, extracts data item and be accurate to document rank, when matching way is to summarize rank,
It extracts data item and is accurate to and summarize rank, to the data item extracted from every trading partner and the corresponding data extracted from the enterprise
Item carries out cross-matched.
Further, it is preferable that corresponding being extracted to the data item extracted from every trading partner and from the enterprise
Data item carried out before the step of cross-matched, it is also necessary to carry out initialization process, the initialization process to every trading partner
The following steps are included:
Each parameter and index value are initialized, following values is related generally to:
M0=0, --- matching accumulated amount；
T0=E, --- total volume of trade, wherein the enterprise itself ERP data statistics is resulting with family trade partner according to E
The total value of companion；
--- trade matching degree；
A0=E*D0=0, --- trade matches the amount of money.
Next, above-mentioned four layers of matching way is discussed in detail:
First layer: stringent document matching, stringent document matching refer to date, odd numbers and the trade of enterprise itself trade order
Partner corresponds to date of trade order, odd numbers while matching；
Under stringent document matching way, the document amount of money adds up into matching accumulated amount M1In, total volume of trade T1It maintains not
Become, then calculates lower document；
If date or odd numbers mismatch, lower document is directly calculated.
The second layer: fuzzy document matching, fuzzy document matching refer to date and the trading partner of enterprise itself trade order
The date intervals of corresponding trade order are in the first preset value；
In the present embodiment, it is preferable that the first preset value is 3 days.
In other embodiments, the first preset value can adjust as the case may be.
Under fuzzy document matching way, the document amount of money adds up into matching accumulated amount M2In, total volume of trade T2It maintains not
Become, then calculates lower document；
If the date intervals not in the preset value, directly calculate lower document.
Third layer: monthly summarizing matching, monthly summarizes matching and refers to that enterprise monthly summarizes data and monthly converges with trading partner
Total data point moon matching；
It is described monthly summarize matching way under, summarize the amount of money and add up into matching accumulated amount M3In, total volume of trade T3It maintains
It is constant, then calculate next month；
If monthly summarizing data mismatch, both sides' amount of money size is determined:
If the enterprise monthly summarizes, the amount of money is larger, and trading partner monthly summarizes the amount of money and adds up into matching accumulated amount M3
In, total volume of trade T3It remains unchanged, then calculates next month；
If the enterprise monthly summarizes, the amount of money is smaller, which monthly summarizes the amount of money and add up into matching accumulated amount M3
In, total volume of trade T3The difference that the amount of money (enterprise monthly summarizes the amount of money and trading partner monthly summarizes the amount of money) must be summarized plus two,
Then next month is calculated.
4th layer: summarizing digests match, summarize digests match and refer to that enterprise summarizes summary data and summarizes with trading partner and pluck
Take data point moon matching；
It is described summarize digests match mode under, which summarizes the amount of money and adds up into matching accumulated amount M4In, trade is total
Volume T4It remains unchanged, then calculates next month；
It is mismatched if summarizing abstract, directly calculates next month.
Do you so how to judge that summarizing abstract matches? summarize the judgment method of digests match the following steps are included:
Monthly calculate separately enterprise itself, trading partner summarizes abstractInitial value is 0；
For enterprise, calculated according to data relevant to trading partner i in its own document
For trading partner i, calculated according to data relevant to corresponding enterprise in its goods entry, stock and sales data
In above-mentioned formula, document is this month institute's documentary；The document amount of money is the amount of money of above-mentioned document；Time interval is document
It is spaced the date, is i.e. on the interval date of the upper immediate document of the document and a upper time, was spaced the calculation principle on date
On refine to day；
IfThen summarize digests match, otherwise to mismatch, wherein e is the second preset value；
In the present embodiment, it is preferable that e takes 7.8.
In other embodiments, e can also take other values, be not limited thereto.
It should be noted that every trading partner can be applicable in a variety of matching ways, and above-mentioned four layers of matching way simultaneously
Priority orders are as follows:
Stringent document matching > fuzzy document matching > monthly summarizing matching > summarizes digests match
In the step of carrying out transaction data cross-matched to extracted data item according to the cross-matched model also
Including following sub-step:
According to the matched matching accumulated amount of each layer and the matched total volume of trade of each layer, the matched trade matching of each layer is calculated
Degree and the matched trade of each layer match total value, specifically:
1. generating each layer trade matching degree (D) of single trading partner and the trade matching amount of money (A) according to following equation:
First layer: stringent document matching,
The second layer: fuzzy document matching,
Third layer: monthly summarizing matching,
4th layer: summarize digests match,A4=E*D4；
AH=max { A1,A2,A3,A4,Wherein, AHIndicate the very big trade matching gold of family trading partner
Volume, DHIndicate the very big trade matching degree of family trading partner.
ATTake A1,A2,A3,A4In first value being not zero,Wherein, ATIndicate the pole of family trading partner
Excellent trade matches the amount of money, DTIndicate the extremely excellent trade matching degree of family trading partner.
Embodied in priority:
The stringent fuzzy document matching > of document matching > monthly summarizes matching > and summarizes digests match
2. complete it is above-mentioned matched after numerical value calculates for single trading partner, by A1,A2,A3,A4,AH,AT, E is respectively worth difference
It adds up into total parameterETIn, each initial parameter value that amounts to is zero；
Respectively:
--- total stringent matching amount of money；
--- total fuzzy matching amount of money；
--- always summarize the matching amount of money；
--- total digests match amount of money；
--- total maximal matching amount of money；
--- total extremely excellent matching amount of money；
Matching degree is generated according to the following formula:
--- total stringent matching degree；
--- total fuzzy matching degree；
--- always summarize matching degree；
--- total digests match degree；
--- total maximal matching degree；
--- total extremely excellent matching degree.
Second embodiment of the invention is related to a kind of credit method.Fig. 2 is the flow diagram of the credit method.
Specifically, as shown in Fig. 2, the credit method the following steps are included:
In step 201, the system of financial institution sends to third party system and hands over needed for cross-matched model and the model
Pitch the data item of verifying.
Then into step 202, third party system executes above-mentioned transaction data cross-matched method, by cross-matched result
The system for being sent to financial institution, the transaction data original to the system mask of financial institution.
Then into step 203, the system of financial institution carries out credit according to cross-matched result.
Hereafter terminate this process.
Present embodiment is method implementation corresponding with first embodiment, and present embodiment can be implemented with first
Mode is worked in coordination implementation.The relevant technical details mentioned in first embodiment are still effective in the present embodiment, in order to
It reduces and repeats, which is not described herein again.Correspondingly, the relevant technical details mentioned in present embodiment are also applicable in the first implementation
In mode.
Third embodiment of the invention is related to a kind of transaction data cross-matched system.Fig. 3 is transaction data intersection
The structural schematic diagram of match system.
Specifically, as shown in figure 3, the transaction data cross-matched system includes:
Configuration unit, the data item for cross validation needed for configuring cross-matched model and the model；
Data extracting unit, for the data item of the cross validation according to needed for the model, from the first data system and
Two data systems extract data item, wherein the first data system is the data system of enterprise, and the second data system is the enterprise
Upstream or downstream trading partner data system；
Cross-matched unit, for carrying out transaction data intersection to extracted data item according to the cross-matched model
Matching；
As a result output unit, for exporting the result of the cross-matched.
Present embodiment is system embodiment corresponding with first embodiment, and present embodiment can be implemented with first
Mode is worked in coordination implementation.The relevant technical details mentioned in first embodiment are still effective in the present embodiment, in order to
It reduces and repeats, which is not described herein again.Correspondingly, the relevant technical details mentioned in present embodiment are also applicable in the first implementation
In mode.
Illustrate the specific embodiment of the application below.
To veritify business transaction data validity, current art has enterprise's sensitive data to leak, can not be with automatic standardizing
Mode completes data veritification, financial institution and largely manually veritifies the problems that data generate.It is preposition in enterprises end by configuring
Data model, trade matching degree, the trade matching amount of money exports after enterprises end calculating to financial institution, financial institution is with this
A little data auxiliary complete credit.Effectively solve the above problems.
It is preposition by model, it can be avoided enterprise's sensitive data and leak；Meanwhile, it is capable to which reference needed for rapid configuration model refers to
The data item being related to is marked, quickly extracts corresponding data items from enterprise's ERP/ JXC System.
Trade matching degree is exported, financial institution can be assisted to judge the trade authenticity of enterprise with quantized data；Assist gold
Melt the automatic reference of mechanism and credit.
It exports trade and matches the amount of money, financial institution can be assisted to judge the trade authenticity of enterprise with quantized data；It assists
The automatic reference of financial institution and credit.
Transaction data cross-matched refers to enterprise's upstream and downstream trading partner corresponding data and enterprise itself buying, sale
Data carry out cross validation, objective quantification enterprise trade authenticity.
The preposition business transaction data cross matching process of model and system, detailed process is as shown in figure 4, include following
Step:
Step 1. determines the label and version for the ERP/ JXC System that enterprise uses；
The data item of cross validation needed for step 2. allocation models；
Step 3. configures enterprise's upstream and downstream trading partner's data-interface；
Step 4. extracts corresponding data items in enterprise ERP/ JXC System；
Step 5. extracts enterprise's upstream and downstream trading partner transaction data corresponding data items；
Step 6. transaction data cross-matched；
Step 7. generates different levels trade matching degree, trade matches the amount of money；
Step 8. exports related cross-matched result to financial institution；
Step 9. financial institution is based on cross-matched result and completes credit.
Trade matching degree and the trade matching amount of money are according to upstream and downstream separate computations:
One, the label and version (above-mentioned step of the ERP/ JXC System that enterprise uses manually or automatically are determined
It is rapid 1), in above-mentioned steps 2 data item configuration support is provided.
Two, the system label and version confirmed according to above-mentioned steps 1, the address of data item in systems needed for mapping
(position) provides support for follow-up data extraction.
Three, enterprise's upstream and downstream trading partner data-interface (above-mentioned steps 3) are configured, including but not limited to following content:
I, determines trading partner family's number, and be numbered and (start counting from 1, upstream and downstream by enterprise itself transaction data
Separate computations are divided into n, m)；
II, disposes the matching way pre-value (pre of every trading partner (i)i):
Wherein i is trading partner's identification number, and identification number definition rule is as follows:
I is made of upstream and downstream identifier with ordinal number, and u indicates that the trading partner is upstream, under d indicates that the trading partner is
Trip, ordinal number r are upstream and downstream trading partner serial number, indicate r family trading partner, are nonnegative integer.0≤r for upstream
≤ n, i=u0 under default scenario indicate the 0th upstream trading partner；0≤r≤m for downstream, i=under default scenario
D0 indicates the 0th downstream trading partner.By taking upstream as an example, it is assumed that there are 1500 upstream trading partners in enterprise, then on the 100th
The ordinal number r for swimming trading partner is 100, and " i " value is u100, wherein the ordinal number r of the 1500th upstream trading partner is 1500, " i "
Value is u1500.
WhereinRespectively correspond " stringent document matching " (a), " fuzzy document
Matching " (b), " monthly summarizing matching " (c), " summarizing digests match " (d) four layers of matching way.Every trade partner under default situation
With the pre of iiFor " 0000 ", the expression of " 0 " value is not suitable for the matching way, and the expression of " 1 " value is applicable in the matching way.With " prei=
For 0001 ", indicate that the trading partner i is only applicable in " summarizing digests match " mode.
Multi-layer transaction data cross-matched is completed commercial data matching in a manner of multi-layer, more fully shows trade
Matching；From the goodness of fit for showing corresponding data in various degree, agree with the different situations and demand of enterprise.
Four, corresponding data item in enterprise itself ERP/ JXC System is extracted according to above-mentioned steps 2 and above-mentioned steps 3, is
Subsequent cross-matched provides the original detailed data of enterprises end, summarizes data or summary data and its parameter.
Five, enterprise's upstream and downstream trading partner is extracted according to above-mentioned steps 2 and above-mentioned steps 3 and accordingly intersect data, be subsequent
Cross-matched provides the original detailed data of enterprise trade partner-side, summarizes data or summary data and its parameter.
Six, wherein transaction data cross-matched (above-mentioned steps 6) process is as follows:
Enabling the ordinal number r in trading partner's identification number is numerical value 0, successively executes following below scheme to every trading partner:
I, initialization process (A)；
II, is according to the matching way pre-value (pre of every trading partneri), it carries out multi-layer Matched (B).
A. initialization process includes but is not limited to following content:
By taking upstream as an example (downstream processing mode is similar):
Each parameter and index value are initialized, following values are related generally to:
·M0=0, --- matching accumulated amount；
·T0=E, --- total volume of trade, wherein the enterprise itself ERP data statistics is resulting with family's trade according to E
The total value of partner；
·--- trade matching degree；
·A0=E*D0=0, --- trade matches the amount of money.
B. multi-layer Matched process is as follows:
According to enterprise's upstream and downstream trading partner's data-interface configure (above-mentioned steps 3), for every trading partner successively into
Row is following to be judged and enters respective streams journey:
For i-th trading partner, according toDetermine its applicable match-type.
A. it is applicable in stringent document matching? (Stringent document matching is adapted to, is otherwise not suitable with)
B. whether it is applicable in fuzzy document matching? (Fuzzy document matching is adapted to, is otherwise not suitable with)
C. whether it is applicable in and monthly summarizes matching? (Adaptation monthly summarizes matching, is otherwise not suitable with)
D. whether it is applicable in and summarizes digests match? (Adaptation summarizes digests match, is otherwise not suitable with)
A, b, c, d parameter once show to adapt to the match-type, then successively call specific module to be calculated.
Matching in following modules refers both to the Inspection between enterprise's data and trading partner's corresponding data.
Ms--- matching accumulated amount takes 1,2,3,4 according to disparate modules s, initial value is M respectively0；
Ts--- total volume of trade takes 1,2,3,4 according to different s respectively, and initial value is T0；
Module a (stringent document matching):
Definite odd numbers is compared according to the trade order date:
If the date matches simultaneously with odd numbers, the document amount of money adds up into matching accumulated amount M1In, total volume of trade T1Dimension
Hold constant, lower document of calculating；
If date or odd numbers mismatch, lower document is calculated.
Module b (fuzzy document matching):
Definite odd numbers is compared according to the trade order date:
If odd numbers date intervals (can adjust) as the case may be within 3 days, the document amount of money adds up into matching
Accumulated amount M2In, total volume of trade T2It remains unchanged, calculates lower document；
If date or odd numbers mismatch, lower document is calculated.
Module c (monthly summarizes matching):
It is matched according to data point moon are monthly summarized:
If monthly summarizing Data Matching, summarizes the amount of money and add up into matching accumulated amount M3In, total volume of trade T3It maintains
It is constant；(summarizing data monthly for monthly amount of money accumulated value, be considered as matching when equal)
If monthly summarizing data mismatch, both sides' amount of money size is determined:
If the enterprise monthly summarizes, the amount of money is larger, and trading partner monthly summarizes the amount of money and adds up into matching accumulated amount
M3In, total volume of trade T3It remains unchanged, calculates next month；
If the enterprise monthly summarizes, the amount of money is smaller, which monthly summarizes the amount of money and add up into matching accumulated amount M3
In, total volume of trade T3The difference that the amount of money must be summarized plus two, calculates next month；
Calculate next month.
Module d (summarizes digests match):
It is matched according to summary data point moon is summarized:
If summarizing digests match, which summarizes the amount of money and adds up into matching accumulated amount M4In, total volume of trade T4Dimension
It holds constant；
If summarizing abstract to mismatch, next month is calculated；
Calculate next month.
Integration module:
1. generating each layer trade matching degree (D) and the trade matching amount of money (A) according to following equation:
·A1=E*D1；
·A2=E*D2；
·A3=E*D3；
·A4=E*D4；
·AH=max { A1,A2,A3,A4,
·ATTake A1,A2,A3,A4In first value being not zero,Embodied in priority:
Stringent document matching > fuzzy document matching > monthly summarizing matching > summarizes digests match
2. complete it is above-mentioned matched after numerical value calculates for single trading partner, by A1,A2,A3,A4,AH,AT, E is respectively worth difference
It adds up into total parameterETIn, each initial parameter value that amounts to is zero；
·Respectively:
--- total stringent matching amount of money；
--- total fuzzy matching amount of money；
--- always summarize the matching amount of money；
--- total digests match amount of money；
--- total maximal matching amount of money；
--- total extremely excellent matching amount of money；
Matching degree is generated according to the following formula:
--- total stringent matching degree；
--- total fuzzy matching degree；
--- always summarize matching degree；
--- total digests match degree；
--- total maximal matching degree；
--- total extremely excellent matching degree；
The ordinal number r=r+1 in trading partner's identification number is enabled, player whose turn comes next trading partner's Data Matching is carried out.
Summarizing digests match can calculate as follows:
1. monthly calculating separately enterprise itself, trading partner summarizes abstractInitial value is 0.
For enterprise, calculated according to data relevant to trading partner (i) in its own document
For trading partner (i), calculated according to data relevant to corresponding enterprise in its goods entry, stock and sales data
In above-mentioned formula:
I. document is this month all related document；
II. the document amount of money is the corresponding amount of money of above-mentioned document；
III. time interval is document interval date, i.e. the interval date of certain document and upper one nearest time document, meter
Calculation mode is refine to daily in principle.
A. classification processing is not carried out to interior document on the same day, i.e., is divided into 0 between document on the same day.At this time will on the same day in document
The amount of money is cumulative to be used as a document amount of money, then calculates the interval date of itself and upper one nearest time document.For example, there is elder generation in certain day
Two documents afterwards, then between two documents between be divided into 0.
B. first day document gold is uniformly taken due to a supreme comparison date for the processing of the document of the earliest date
The corresponding time interval of volume takes 1.
For following table:
Assuming that calculating since in September, 17 summarize abstract, and September has 5 documents altogether, then September summarizes abstract are as follows:
5000*1+ (4000+3000+2500) * 10+1000*3=103000
If 2.Then summarize digests match, is otherwise mismatch.E takes 7.8 under default situation.
By summarizing digests match, desensitization process generation is carried out to commercial data and summarizes abstract for matching, prevents sensitivity
Data leak；Desensitization Data Matching mode can promote trading partner's fitness.
In conclusion acquiring enterprise by the metadata acquisition tool (data gateway) and preposition model that are deployed in enterprises end
Itself transaction data and corresponding upstream and downstream intersect data, remove and automatically generate (including the upstream trade of trade matching degree after sensitive information
Easy matching degree and downstream trade matching degree), the trade matching amount of money (including the upstream trade matching amount of money and downstream trade matching gold
Volume) and it is filed in financial institution, as collage-credit data auxiliary, financial institution completes credit process.
Mainly solve following problems:
1. veritifying business transaction data validity from different levels；
The possibility 2. reduction enterprise's sensitive data leaks, enhances business data safety；
3. being acquired in a manner of automatic standardizing and veritifying data, guard against because of the issuable human error of artificial treatment data
Or artificial modification；
4. promoting reference and credit flow path efficiency.While solving to need a large amount of artificial drawbacks for veritifying data, improve
Data veritify efficiency and accuracy.
It should be noted that each method embodiment of the invention can be realized in a manner of software, hardware, firmware etc..
Regardless of the present invention is realized in a manner of software, hardware or firmware, instruction code may be stored in any kind of computer
In addressable memory (such as permanent perhaps revisable volatibility is perhaps non-volatile solid or non-solid
State, fix or replaceable medium etc.).Equally, memory may, for example, be programmable logic array
(Programmable Array Logic, referred to as " PAL "), random access memory (Random Access Memory, referred to as
" RAM "), programmable read only memory (Programmable Read Only Memory, referred to as " PROM "), read-only memory
(Read-Only Memory, referred to as " ROM "), electrically erasable programmable read-only memory (Electrically Erasable
Programmable ROM, referred to as " EEPROM "), disk, CD, digital versatile disc (Digital Versatile Disc,
Referred to as " DVD ") etc..
The each unit or module mentioned in each system embodiment of the present invention are all logic unit or module, physically,
One logic unit or module can be a physical unit or module, be also possible to one of a physical unit or module
Point, it can also be realized with the combination of multiple physical units or module, the Physical realization of these logic units or module itself
Be not it is most important, the combination for the function that these logic units or module are realized, which is only, solves technology proposed by the invention
The key of problem.In addition, the above-mentioned each equipment embodiment of the present invention will not be with solution in order to protrude innovative part of the invention
The less close unit of technical problem relationship certainly proposed by the invention or module introduce, this does not indicate above equipment embodiment party
Simultaneously other units or module is not present in formula.
It should be noted that in the claim and specification of this patent, such as first and second or the like relationship
Term is only used to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying
There are any actual relationship or orders between these entities or operation.Moreover, the terms "include", "comprise" or its
Any other variant is intended to non-exclusive inclusion so that include the process, methods of a series of elements, article or
Equipment not only includes those elements, but also including other elements that are not explicitly listed, or further include for this process,
Method, article or the intrinsic element of equipment.In the absence of more restrictions, being wanted by what sentence " including one " limited
Element, it is not excluded that there is also other identical elements in the process, method, article or apparatus that includes the element.
Although being shown and described to the present invention by referring to some of the preferred embodiment of the invention,
It will be understood by those skilled in the art that can to it, various changes can be made in the form and details, without departing from this hair
Bright spirit and scope.
Claims (10)
1. a kind of transaction data cross-matched method, which comprises the following steps:
The data item of cross validation needed for configuring cross-matched model and the model；
According to the data item of cross validation needed for the model, data item is extracted from the first data system and the second data system,
Wherein, the first data system is the data system of enterprise, and the second data system is upstream or the downstream trading partner of the enterprise
Data system；
Transaction data cross-matched is carried out to extracted data item according to the cross-matched model；
Export the result of the cross-matched.
2. transaction data cross-matched method according to claim 1, which is characterized in that described " according to the intersection
With model to extracted data item carry out transaction data cross-matched " the step of in include following sub-step:
Dispose the matching way pre-value pre of every trading partner of the enterprisei:
Wherein, i is trading partner's identification number,Or 4,It respectively corresponds
" stringent document matching ", " fuzzy document matching ", " monthly summarizing matching ", " summarizing digests match " this four layers of matching ways, " 0 "
Value indicates that trading partner i is not suitable for the j layers of matching way, and " 1 " value indicates that trading partner i is applicable in the j layers of matching way；
According to the matching way pre-value pre of every trading partneri, determine the matching way that every trading partner is applicable in；
According to the matching way that every trading partner is applicable in, corresponding data is extracted according to the field of demand corresponding to matching way
, it when matching way is document rank, extracts data item and is accurate to document rank, when matching way is to summarize rank, mention
It takes data item to be accurate to and summarizes rank, to the data item extracted from every trading partner and the corresponding data extracted from the enterprise
Item carries out cross-matched.
3. transaction data cross-matched method according to claim 2, which is characterized in that the stringent document matching refers to
The date of enterprise itself trade order, the date of odd numbers trade order corresponding with trading partner, odd numbers match simultaneously；
Under the stringent document matching way, the document amount of money adds up into matching accumulated amount M1In, total volume of trade T1It maintains not
Become, then calculates lower document；
If date or odd numbers mismatch, lower document is directly calculated.
4. transaction data cross-matched method according to claim 2, which is characterized in that the fuzzy document matching refers to
The date intervals of the date of enterprise itself trade order trade order corresponding with trading partner are in the first preset value；
Under the fuzzy document matching way, the document amount of money adds up into matching accumulated amount M2In, total volume of trade T2It maintains not
Become, then calculates lower document；
If the date intervals not in the preset value, directly calculate lower document.
5. transaction data cross-matched method according to claim 2, which is characterized in that described monthly to summarize matching and refer to
Enterprise, which monthly summarizes data and monthly summarizes data point moon with trading partner, to be matched；
It is described monthly summarize matching way under, summarize the amount of money and add up into matching accumulated amount M3In, total volume of trade T3It maintains not
Become, then calculates next month；
If monthly summarizing data mismatch, both sides' amount of money size is determined:
If the enterprise monthly summarizes, the amount of money is larger, and trading partner monthly summarizes the amount of money and adds up into matching accumulated amount M3In, trade
Easy total value T3It remains unchanged, then calculates next month；
If the enterprise monthly summarizes, the amount of money is smaller, which monthly summarizes the amount of money and add up into matching accumulated amount M3In, trade
Total value T3The difference that the amount of money must be summarized plus two, then calculates next month.
6. transaction data cross-matched method according to claim 2, which is characterized in that the digests match that summarizes refers to
Enterprise, which summarizes summary data and trading partner and summarizes summary data point moon, to be matched；
It is described summarize digests match mode under, which summarizes the amount of money and adds up into matching accumulated amount M4In, total volume of trade T4Dimension
It holds constant, then calculates next month；
It is mismatched if summarizing abstract, directly calculates next month.
7. transaction data cross-matched method according to claim 6, which is characterized in that described to summarize sentencing for digests match
Disconnected method the following steps are included:
Monthly calculate separately enterprise itself, trading partner summarizes abstractInitial value is 0；
For enterprise, calculated according to data relevant to trading partner i in its own document
For trading partner i, calculated according to data relevant to corresponding enterprise in its goods entry, stock and sales data
In above-mentioned formula, document is this month institute's documentary；The document amount of money is the amount of money of above-mentioned document；Time interval is document interval
The interval date on date, i.e. document and upper immediate document of the upper time；
IfThen summarize digests match, otherwise to mismatch, wherein e is the second preset value.
8. the transaction data cross-matched method according to any one of claim 3-7, which is characterized in that described " according to
The cross-matched model to extracted data item carry out transaction data cross-matched " the step of in further include following sub-step
It is rapid:
According to the matched matching accumulated amount of each layer and the matched total volume of trade of each layer, calculate the matched trade matching degree of each layer and
Each matched trade of layer matches total value.
9. a kind of credit method, which comprises the following steps:
The data item of cross validation needed for the system of financial institution sends cross-matched model and the model to third party system；
Third party system perform claim requires method described in any one of 1-8, and cross-matched result is sent to financial institution
System, the transaction data original to the system mask of financial institution；
The system of financial institution carries out credit according to cross-matched result.
10. a kind of transaction data cross-matched system characterized by comprising
Configuration unit, the data item for cross validation needed for configuring cross-matched model and the model；
Data extracting unit, for the data item of the cross validation according to needed for the model, from the first data system and the second number
Data item is extracted according to system, wherein the first data system is the data system of enterprise, and the second data system is the upper of the enterprise
The data system of trip or downstream trading partner；
Cross-matched unit, for carrying out transaction data intersection to extracted data item according to the cross-matched model
Match；
As a result output unit, for exporting the result of the cross-matched.
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