CN105512893A - Hotel transaction data-based user credit evaluation method and system - Google Patents
Hotel transaction data-based user credit evaluation method and system Download PDFInfo
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Abstract
The invention provides a hotel transaction data-based user credit evaluation method and system. The hotel transaction data-based user credit evaluation method can be realized through using a Zeus system and includes the following steps that: all user name data are imported; user registration information data corresponding to each user name are imported; user financial transaction data corresponding to each user name are imported; user hotel transaction data corresponding to each user name are imported; user malicious complaint data corresponding to each user name are imported; the imported data are pre-processed; pre-processed data are imported into a merge table according to a field order in the merge table; whether registration time, real name authentication, cash account balance, the number of executed orders in a recent period, the number of no show orders in the last year and the number of malicious complaint records in the last year corresponding to the user names satisfy corresponding conditions is judged, if the registration time, real name authentication, cash account balance, the number of executed orders in the recent period, the number of no show orders in the last year and the number of malicious complaint records in the last year corresponding to the user names satisfy the corresponding conditions, the credit ratings of the users are set to be qualified, otherwise, the credit ratings of the users are set to be unqualified. With the hotel transaction data-based user credit evaluation method and system of the invention adopted, the credit ratings of the users can be accurately evaluated more accurately.
Description
Technical field
The present invention relates to communication technical field, particularly a kind of user credit appraisal procedure based on hotel's transaction data and system.
Background technology
Based on the data in the most Shi Yi hotel of user credit appraisal procedure of existing hotel and information, propose the method for customer's credit management.The shortcoming of this method is more unilateral, can only, from the data in hotel one by one, not have the consumption data of user to make reference.
Also having a kind of is that the transaction data buying other consumer goods by user on website carries out user credit assessment, to determine whether user can move in hotel by credit.Although this technology can quantize various influence factor, the consumption concept of user is different.The transaction data of the consumer goods can not reflect other trading activities, and the hotel's trading activity for user cannot provide direct data and analyze, and therefore, the hotel's user credit evaluated by above-mentioned prior art is inaccurate.
Summary of the invention
The technical problem to be solved in the present invention is the inaccurate defect of credit evaluation in order to overcome prior art Zhong Dui hotel user, provides a kind of user credit appraisal procedure based on hotel's transaction data and system.
The present invention solves above-mentioned technical matters by following technical proposals:
The invention provides a kind of user credit appraisal procedure based on hotel's transaction data, its feature is, it utilizes zeus (Zeus) system to realize, and it comprises the following steps:
All username data of registration table are imported from registration database;
From public database, import each user name user's registration information data one to one, these user's registration information data comprise hour of log-on;
From transaction system, import each user name user's data of financial transaction one to one, this user's data of financial transaction comprises whether real-name authentication and cash account balance;
From hotel's database, import each user name user hotel transaction data one to one, this user hotel transaction data to comprise in the nearest time period hotel's conclusion of the business order numbers and NoShow (referring to subscribe the services such as hotel but do not show up) order numbers in nearest a year;
From risk control database, import each user name user's malicious complaint data one to one, these user's malicious complaint data comprise malicious complaint record number in nearest a year;
Pretreatment operation is carried out to those data imported;
Pretreated data are imported this combined statement according to the order of the field in combined statement; Those fields are followed successively by hour of log-on, whether real-name authentication, cash account balance, conclusion of the business order numbers in hotel's in the nearest time period, NoShow order numbers and malicious complaint record number in nearest a year in nearest 1 year;
Judge a user name corresponding whether whether hour of log-on be greater than one first setting value, real-name authentication, whether cash account balance is greater than one second setting value, in the nearest time period, whether conclusion of the business order numbers in hotel's is greater than one the 3rd setting value, in nearest 1 year, whether NoShow order numbers is less than one the 4th setting value and in nearest 1 year, whether malicious complaint record number is less than one the 5th setting value, be that arranging credit grade corresponding to this user name is that credit grade is qualified if be, otherwise to arrange credit grade corresponding to this user name be that credit grade is defective.
Preferably, these user's registration information data also comprise user gradation, whether bind cell-phone number, whether bind mailbox, the Hotel Star that on average strikes a bargain, hotel's consuming capacity and hotel service spending amount.
Preferably, this user's data of financial transaction also comprises user's effective integral, Gift Card remaining sum, wallet balances and credit card and to withhold success ratio.
Preferably, receive the user name of input, show the credit grade that this user name is corresponding.
Preferably, FnvHash algorithm (Fnv hash algorithm) is encapsulated in Java (being a kind of object oriented program language can writing cross-platform program) and is uploaded to this zeus system, those username data are divided into multiple subregion by this FnvHash algorithm by this zeus system, and set up a subscriber's meter for each subregion.
The present invention also provides a kind of user credit evaluating system based on hotel's transaction data, and its feature is, it utilizes zeus system to realize, and this user credit evaluating system comprises:
One first imports module, for importing all username data of registration table from registration database;
One second imports module, and for importing each user name user's registration information data one to one from public database, these user's registration information data comprise hour of log-on;
One the 3rd imports module, and for importing each user name user's data of financial transaction one to one from transaction system, this user's data of financial transaction comprises whether real-name authentication and cash account balance;
One the 4th imports module, and for importing each user name user hotel transaction data one to one from hotel's database, this user hotel transaction data to comprise in the nearest time period hotel's conclusion of the business order numbers and NoShow order numbers in nearest a year;
One the 5th imports module, and for importing each user name user's malicious complaint data one to one from risk control database, these user's malicious complaint data comprise malicious complaint record number in nearest a year;
One pretreatment module, for carrying out pretreatment operation to those data imported;
One the 6th imports module, for pretreated data are imported this combined statement according to the order of the field in combined statement; Those fields are followed successively by hour of log-on, whether real-name authentication, cash account balance, conclusion of the business order numbers in hotel's in the nearest time period, NoShow order numbers and malicious complaint record number in nearest a year in nearest 1 year;
One judge module, for judging whether hour of log-on corresponding to a user name is greater than one first setting value, whether real-name authentication, whether cash account balance is greater than one second setting value, in the nearest time period, whether conclusion of the business order numbers in hotel's is greater than one the 3rd setting value, in nearest 1 year, whether NoShow order numbers is less than one the 4th setting value and in nearest 1 year, whether malicious complaint record number is less than one the 5th setting value, then call one first to arrange credit grade corresponding to this user name of module installation be that credit grade is qualified when judging to be and being, otherwise calling one second, to arrange credit grade corresponding to this user name of module installation be that credit grade is defective.
Preferably, these user's registration information data also comprise user gradation, whether bind cell-phone number, whether bind mailbox, the Hotel Star that on average strikes a bargain, hotel's consuming capacity and hotel service spending amount.
Preferably, this user's data of financial transaction also comprises user's effective integral, Gift Card remaining sum, wallet balances and credit card and to withhold success ratio.
Preferably, this user credit evaluating system also comprises a load module and a display module, and this load module is for receiving the user name of input, and this display module is for showing credit grade corresponding to this user name.
Preferably, this user credit evaluating system also comprises a package module, this package module is used for FnvHash algorithm packaging in Java, be uploaded to this zeus system, those username data are divided into multiple subregion by this FnvHash algorithm by this zeus system, and set up a subscriber's meter for each subregion.
On the basis meeting this area general knowledge, above-mentioned each optimum condition, can combination in any, obtains the preferred embodiments of the invention.
Positive progressive effect of the present invention is:
The user credit evaluating system that the present invention is based on hotel's transaction data is from the data of user at website reserving hotel, the large data platform of zeus is utilized to carry out adding up and calculating, credit evaluation parameter is set, the credit grade of user being moved in hotel is assessed, and can evaluate the credit grade of user more exactly.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the user credit appraisal procedure based on hotel's transaction data of the better embodiment of the present invention.
Embodiment
Mode below by embodiment further illustrates the present invention, but does not therefore limit the present invention among described scope of embodiments.
The present embodiment provides a kind of user credit appraisal procedure based on hotel's transaction data, it utilizes the large data platform of zeus, the data needed for credit evaluation are imported from the true sale database of various dimensions, because data volume is very large, in the process imported, level fractionation is carried out to importing data, gather again after calculating, deposit in the database table of subregion.User credit appraisal procedure is all the mode adopting shell (a kind of programming language) script, and the dispatching method relying on zeus carries out importing and calculating.
Specifically as shown in Figure 1, this user credit appraisal procedure comprises the following steps:
Step 101, from registration database, import all username data of registration table.
Importing username data is first order task, and task below all can take out the data of other scenes according to user name.Because website registered user is very many, data volume is very large, by the computing velocity of influential system, so FnvHash algorithm packaging is uploaded to this zeus system by system in Java, those username data are divided into 40 subregions by this FnvHash algorithm by this zeus system, and each subregion adopts 1-40 to number and is that each subregion sets up a subscriber's meter.Certainly, one skilled in the art will appreciate that the difference along with data volume, the number of partitions marked off is also different.
Step 102, from public database, import each user name user's registration information data one to one, these user's registration information data comprise hour of log-on, user gradation, whether bind cell-phone number, whether bind mailbox, the Hotel Star that on average strikes a bargain, hotel's consuming capacity and hotel service spending amount.
Step 103, from transaction system, import each user name user's data of financial transaction one to one, this user's data of financial transaction comprises whether real-name authentication, cash account balance, user's effective integral, Gift Card remaining sum, wallet balances and credit card and to withhold success ratio.
Step 104, from hotel's database, import each user name user hotel transaction data one to one, this user hotel transaction data comprises a nearest Nian Nei hotel order record, a nearest Nian Nei hotel conclusion of the business order numbers, nearest three Ge Yuenei hotel conclusion of the business order numbers, a nearest Ge Yuenei hotel conclusion of the business order numbers, nearest 1 year NoShow order record, NoShow order numbers and NoShow order numbers in nearest month in nearest 1 year.
Step 105, from risk control database, import each user name user's malicious complaint data one to one, these user's malicious complaint data comprise malicious complaint record number in nearest a year, malicious complaint record number in nearest month, also import each user name nearest 1 year one to one customer complaint record, complains records number and complains records number in nearest month in nearest 1 year from risk control database.
Use the subscriber's meter after subregion to import these four kinds of data of common data, finance data, hotel's data and wind control data, it is all sane level tasks for one-level task, can import the importing that also can walk abreast successively.
Each sane level task inside also has dependence: a nearest Nian Nei hotel conclusion of the business order numbers, and nearest three Ge Yuenei hotel conclusion of the business order numbers and a nearest Ge Yuenei hotel conclusion of the business order numbers all rely on nearest 1 year hotel's order record; Nearest 1 year NoShow quantity on order and nearest one month NoShow quantity on order all rely on nearest 1 year NoShow order record; Within nearest 1 year, complains records number, nearest one month complains records number, nearest 1 year malicious complaint record number and nearest one month malicious complaint record number all rely on nearest 1 year customer complaint record.First can be performed by relying on of task, and then perform follow-up work.
Step 106, to import those data carry out pretreatment operation; After above, Pyatyi task is all finished, those data started importing carry out pretreatment operation, facilitate follow-up data process.
Step 107, pretreated data are imported this combined statement according to the order of the field in combined statement; Those fields are followed successively by hour of log-on, whether real-name authentication, cash account balance, conclusion of the business order numbers in hotel's in the nearest time period, NoShow order numbers and complains records number in nearest a year in nearest 1 year.
Step 108, judge whether hour of log-on corresponding to a user name is greater than one first setting value (such as the first setting value is 365 days), whether real-name authentication, whether cash account balance is greater than one second setting value, in the nearest time period, whether conclusion of the business order numbers in hotel's is greater than one the 3rd setting value, in nearest 1 year, whether NoShow order numbers is less than one the 4th setting value (such as the 4th setting value is zero) and in nearest 1 year, whether complains records number is less than one the 5th setting value (such as the 5th setting value is zero), enter step 109 if be, namely arranging credit grade corresponding to this user name is that credit grade is qualified, process ends, otherwise enter step 110, namely arranging credit grade corresponding to this user name is that credit grade is defective, process ends.
After combined statement has generated, parameter of regularity is imported, carry out rule and calculate, the credit grade of each user is calculated, obtains regular data table.Combined statement, in order to convenience of calculation, continues to use the zoning ordinance of subscriber's meter, is 40 subregions equally, but is according to 4 databases in database, and in each database, 10 tables carry out subregion.Therefore the zoning ordinance according to database is built table by result data, is divided into database name and table name two levels.While combined statement calculates data, result is imported in result table, adopt partition number to obtain database name and table name to 10 deliverys with divided by the mode of 10.Like this, be just evenly distributed in by the data that parameter of regularity calculates with in the table of 4 database name subregions.
Finally use shell script by the data importing database in regular data table, program just obtains the credit information of user and credit grade by accessing database.
For the external reference not having authority to access result database, system provides an interface based on SOA2.0 (Services Oriented Achitecture), the user name of input user, just can obtain this user and whether can use the results such as credit authorization (namely credit grade conforms with the regulations).
Because overseas hotel's audit time is long, the transaction of every month has renewal next month, therefore needs the incremental data to first 3 months to recalculate at the bottom of every month, comprises the user of new registration, the conclusion of the business auditing result of order, the NoShow auditing result etc. of order.According to incremental data, can analyze the development trend of user, be conducive to the exploitation of follow-up machine learning and the optimization of system, have very great significance.
In addition, because customer volume and order data all can upgrade every day, system at the bottom of every month can automatically perform once, recalculates whole user profile, and upgrades incremental data.In essential information can configuration-system scheduling time, the dependence of subtask, type of alarm when failed multiplicity and mission failure.
The present embodiment also provides a kind of user credit evaluating system based on hotel's transaction data, and it utilizes zeus system to realize, and this user credit evaluating system comprises:
One first imports module, for importing all username data of registration table from registration database;
One second imports module, and for importing each user name user's registration information data one to one from public database, these user's registration information data comprise hour of log-on etc.;
One the 3rd imports module, and for importing each user name user's data of financial transaction one to one from transaction system, this user's data of financial transaction comprises whether real-name authentication and cash account balance etc.;
One the 4th imports module, and for importing each user name user hotel transaction data one to one from hotel's database, this user hotel transaction data to comprise in the nearest time period hotel's conclusion of the business order numbers and NoShow order numbers in nearest a year;
One the 5th imports module, and for importing each user name user's malicious complaint data one to one from risk control database, these user's malicious complaint data comprise complains records number in nearest a year;
One pretreatment module, for carrying out pretreatment operation to those data imported;
One the 6th imports module, for pretreated data are imported this combined statement according to the order of the field in combined statement; Those fields are followed successively by hour of log-on, whether real-name authentication, cash account balance, conclusion of the business order numbers in hotel's in the nearest time period, NoShow order numbers and complains records number in nearest a year in nearest 1 year;
One judge module, for judging whether hour of log-on corresponding to a user name is greater than one first setting value, whether real-name authentication, whether cash account balance is greater than one second setting value, in the nearest time period, whether conclusion of the business order numbers in hotel's is greater than one the 3rd setting value, in nearest 1 year, whether NoShow order numbers is less than one the 4th setting value and in nearest 1 year, whether complains records number is less than one the 5th setting value, then call one first to arrange credit grade corresponding to this user name of module installation be that credit grade is qualified when judging to be and being, otherwise calling one second, to arrange credit grade corresponding to this user name of module installation be that credit grade is defective.
This user credit evaluating system also comprises a load module and a display module, and this load module is for receiving the user name of input, and this display module is for showing credit grade corresponding to this user name.
This user credit evaluating system also comprises a package module, this package module is used for FnvHash algorithm packaging in Java, be uploaded to this zeus system, those username data are divided into multiple subregion by this FnvHash algorithm by this zeus system, and set up a subscriber's meter for each subregion.
The present invention is by comparatively succinct mode, hotel's transaction data of website (such as online tourism website) and user data are gathered and analyzed, carry out input parameter comparison and split calculating by the large data platform of zeus, credit evaluation is carried out to registered all users, finally obtains the credit grade of all users.By practical application in ctrip.com station, the registered user more than 500,000,000 at ctrip.com station, wherein has the user more than 50,000 to meet the rule using credit underwriting to move in hotel.Further, present invention achieves the interface for other external systems or user's access.
Although the foregoing describe the specific embodiment of the present invention, it will be understood by those of skill in the art that these only illustrate, protection scope of the present invention is defined by the appended claims.Those skilled in the art, under the prerequisite not deviating from principle of the present invention and essence, can make various changes or modifications to these embodiments, but these change and amendment all falls into protection scope of the present invention.
Claims (10)
1. based on a user credit appraisal procedure for hotel's transaction data, it is characterized in that, it utilizes zeus system to realize, and it comprises the following steps:
All username data of registration table are imported from registration database;
From public database, import each user name user's registration information data one to one, these user's registration information data comprise hour of log-on;
From transaction system, import each user name user's data of financial transaction one to one, this user's data of financial transaction comprises whether real-name authentication and cash account balance;
From hotel's database, import each user name user hotel transaction data one to one, this user hotel transaction data to comprise in the nearest time period hotel's conclusion of the business order numbers and NoShow order numbers in nearest a year;
From risk control database, import each user name user's malicious complaint data one to one, these user's malicious complaint data comprise malicious complaint record number in nearest a year;
Pretreatment operation is carried out to those data imported;
Pretreated data are imported this combined statement according to the order of the field in combined statement; Those fields are followed successively by hour of log-on, whether real-name authentication, cash account balance, conclusion of the business order numbers in hotel's in the nearest time period, NoShow order numbers and malicious complaint record number in nearest a year in nearest 1 year;
Judge a user name corresponding whether whether hour of log-on be greater than one first setting value, real-name authentication, whether cash account balance is greater than one second setting value, in the nearest time period, whether conclusion of the business order numbers in hotel's is greater than one the 3rd setting value, in nearest 1 year, whether NoShow order numbers is less than one the 4th setting value and in nearest 1 year, whether malicious complaint record number is less than one the 5th setting value, be that arranging credit grade corresponding to this user name is that credit grade is qualified if be, otherwise to arrange credit grade corresponding to this user name be that credit grade is defective.
2. user credit appraisal procedure as claimed in claim 1, it is characterized in that, these user's registration information data also comprise user gradation, whether bind cell-phone number, whether bind mailbox, the Hotel Star that on average strikes a bargain, hotel's consuming capacity and hotel service spending amount.
3. user credit appraisal procedure as claimed in claim 1, is characterized in that, this user's data of financial transaction also comprises user's effective integral, Gift Card remaining sum, wallet balances and credit card and to withhold success ratio.
4. user credit appraisal procedure as claimed in claim 1, is characterized in that, receives the user name of input, shows the credit grade that this user name is corresponding.
5. user credit appraisal procedure as claimed in claim 1, it is characterized in that, FnvHash algorithm packaging is uploaded in Java this zeus system, those username data are divided into multiple subregion by this FnvHash algorithm by this zeus system, and set up a subscriber's meter for each subregion.
6. based on a user credit evaluating system for hotel's transaction data, it is characterized in that, it utilizes zeus system to realize, and this user credit evaluating system comprises:
One first imports module, for importing all username data of registration table from registration database;
One second imports module, and for importing each user name user's registration information data one to one from public database, these user's registration information data comprise hour of log-on;
One the 3rd imports module, and for importing each user name user's data of financial transaction one to one from transaction system, this user's data of financial transaction comprises whether real-name authentication and cash account balance;
One the 4th imports module, and for importing each user name user hotel transaction data one to one from hotel's database, this user hotel transaction data to comprise in the nearest time period hotel's conclusion of the business order numbers and NoShow order numbers in nearest a year;
One the 5th imports module, and for importing each user name user's malicious complaint data one to one from risk control database, these user's malicious complaint data comprise malicious complaint record number in nearest a year;
One pretreatment module, for carrying out pretreatment operation to those data imported;
One the 6th imports module, for pretreated data are imported this combined statement according to the order of the field in combined statement; Those fields are followed successively by hour of log-on, whether real-name authentication, cash account balance, conclusion of the business order numbers in hotel's in the nearest time period, NoShow order numbers and malicious complaint record number in nearest a year in nearest 1 year;
One judge module, for judging whether hour of log-on corresponding to a user name is greater than one first setting value, whether real-name authentication, whether cash account balance is greater than one second setting value, in the nearest time period, whether conclusion of the business order numbers in hotel's is greater than one the 3rd setting value, in nearest 1 year, whether NoShow order numbers is less than one the 4th setting value and in nearest 1 year, whether malicious complaint record number is less than one the 5th setting value, then call one first to arrange credit grade corresponding to this user name of module installation be that credit grade is qualified when judging to be and being, otherwise calling one second, to arrange credit grade corresponding to this user name of module installation be that credit grade is defective.
7. user credit evaluating system as claimed in claim 6, it is characterized in that, these user's registration information data also comprise user gradation, whether bind cell-phone number, whether bind mailbox, the Hotel Star that on average strikes a bargain, hotel's consuming capacity and hotel service spending amount.
8. user credit evaluating system as claimed in claim 6, is characterized in that, this user's data of financial transaction also comprises user's effective integral, Gift Card remaining sum, wallet balances and credit card and to withhold success ratio.
9. user credit evaluating system as claimed in claim 6, it is characterized in that, this user credit evaluating system also comprises a load module and a display module, and this load module is for receiving the user name of input, and this display module is for showing credit grade corresponding to this user name.
10. user credit evaluating system as claimed in claim 6, it is characterized in that, this user credit evaluating system also comprises a package module, this package module is used for FnvHash algorithm packaging in Java, be uploaded to this zeus system, those username data are divided into multiple subregion by this FnvHash algorithm by this zeus system, and set up a subscriber's meter for each subregion.
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| CN201510990170.3A CN105512893A (en) | 2015-12-24 | 2015-12-24 | Hotel transaction data-based user credit evaluation method and system |
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| CN201510990170.3A CN105512893A (en) | 2015-12-24 | 2015-12-24 | Hotel transaction data-based user credit evaluation method and system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109558434A (en) * | 2018-10-19 | 2019-04-02 | 深圳点猫科技有限公司 | A kind of consumption data statistical method and device based on education resource platform |
| CN110427858A (en) * | 2019-07-26 | 2019-11-08 | 广州利科科技有限公司 | A kind of personage's Activity recognition track approach of combination image |
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2015
- 2015-12-24 CN CN201510990170.3A patent/CN105512893A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109558434A (en) * | 2018-10-19 | 2019-04-02 | 深圳点猫科技有限公司 | A kind of consumption data statistical method and device based on education resource platform |
| CN110427858A (en) * | 2019-07-26 | 2019-11-08 | 广州利科科技有限公司 | A kind of personage's Activity recognition track approach of combination image |
| CN110427858B (en) * | 2019-07-26 | 2022-07-08 | 广州利科科技有限公司 | Figure behavior track recognition method combined with image |
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