CN111754325A - Service data processing method and system - Google Patents
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Abstract
The application discloses a method and a system for processing service data, wherein the method comprises the following steps: acquiring service data of a service to be handled; the business data comprises business stroke number, money taking amount interval and transactor; obtaining a first probability that a target object handles the service to be handled in a public self-service terminal and a second probability that the target object handles the service to be handled in a counter according to the service data; when the first probability is greater than the second probability, increasing a quota provided for the pair of public self-service terminals; increasing a quota provided for the counter when the first probability is less than the second probability. According to the technical scheme, the first probability that the target object handles the business at the public self-service terminal and the second probability that the target object handles the business at the counter are obtained through the business data of the business to be handled, and then the quota of the public self-service terminal or the quota of the counter is adjusted by comparing the first probability with the second probability.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and a system for processing service data.
Background
The counter and the public self-service terminal both support services such as withdrawal, however, when the quota of the counter or the public self-service terminal is insufficient, the withdrawal services cannot be handled for the client.
However, in the prior art, it is unknown whether the customer in the next day is handling the withdrawal service at a public self-service terminal or at a counter. Therefore, an accurate quota cannot be provided for public self-service terminals or counters. When the quota or counter of the public self-service terminal is inaccurate, the use value of the quota can be reduced.
Disclosure of Invention
In order to solve the technical problem, the application provides a method and a system for processing service data, which can provide an accurate quota for a quota or a counter of a public self-service terminal.
The embodiment of the application discloses the following technical scheme:
in a first aspect, the present application provides a method for processing service data, including:
acquiring service data of a service to be handled; the business data comprises business stroke number, money taking amount interval and transactor;
obtaining a first probability that a target object handles the service to be handled in a public self-service terminal and a second probability that the target object handles the service to be handled in a counter according to the service data;
when the first probability is greater than the second probability, increasing a quota provided for the pair of public self-service terminals;
increasing a quota provided for the counter when the first probability is less than the second probability.
Optionally, the obtaining, according to the service data, a first probability that the target object handles the service to be handled in the public self-service terminal includes:
determining a first sub-probability corresponding to the historical number consistent with the business number through a pre-obtained database of the target object when the public self-service terminal handles the historical business; determining a second sub-probability corresponding to a historical withdrawal volume interval that is consistent with the withdrawal volume interval; determining a third sub-probability corresponding to a historical transactant consistent with the transactant;
and obtaining the first probability according to the first sub-probability, the second sub-probability and the third sub-probability.
Optionally, the obtaining, according to the service data, a second probability that the target object handles the service to be handled over the counter includes:
determining a fourth sub-probability corresponding to the historical stroke number consistent with the service stroke number through a database obtained in advance when the target object transacts the historical service at the counter; determining a fifth sub-probability corresponding to a historical withdrawal amount interval consistent with the withdrawal amount interval; determining a sixth sub-probability corresponding to a historical transactant consistent with the transactant;
and obtaining the second probability according to the fourth sub-probability, the fifth sub-probability and the sixth sub-probability.
Optionally, the first probability is obtained according to the first sub-probability, the second sub-probability and the third sub-probability, and is specifically obtained by the following formula:
P(A)＝k·P(A1)·P(A2)·P(A3)
wherein P (A) is the first probability, P (A)1) Is the first sub-probability, P (A)2) Is the second sub-probability, P (A)3) Is the third sub-probability, k is the adjustment coefficient, 0<k≤1。
Optionally, the second probability is obtained according to the fourth sub-probability, the fifth sub-probability and the sixth sub-probability, and is specifically obtained by the following formula:
P(B)＝k·P(B1)·P(B2)·P(B3)
wherein P (B) is the second probability, P (B) is the fourth sub-probability, P (B)2) Is the fifth sub-probability, P (B)3) Is the sixth sub-probability, k is the adjustment coefficient, 0<k≤1。
In a second aspect, the present application provides a system for processing service data, including: an acquisition unit and a processing unit;
the acquiring unit is used for acquiring service data of the service to be handled; the business data comprises business stroke number, money taking amount interval and transactor; obtaining a first probability that a target object handles the service to be handled in a public self-service terminal and a second probability that the target object handles the service to be handled in a counter according to the service data;
the processing unit is used for increasing the quota provided for the public self-service terminal when the first probability is greater than the second probability; increasing a quota provided for the counter when the first probability is less than the second probability.
Optionally, the obtaining module is specifically configured to determine, through a database obtained in advance when the target object handles the historical service for the public self-service terminal, a first sub-probability corresponding to a historical number of times that is consistent with the number of times of the service; determining a second sub-probability corresponding to a historical withdrawal volume interval that is consistent with the withdrawal volume interval; determining a third sub-probability corresponding to a historical transactant consistent with the transactant; and obtaining the first probability according to the first sub-probability, the second sub-probability and the third sub-probability.
Optionally, the obtaining module is specifically configured to determine, through a database obtained in advance when the target object handles the historical service over the counter, a fourth sub-probability corresponding to a historical number that is consistent with the historical number of the service; determining a fifth sub-probability corresponding to a historical withdrawal amount interval consistent with the withdrawal amount interval; determining a sixth sub-probability corresponding to a historical transactant consistent with the transactant; and obtaining the second probability according to the fourth sub-probability, the fifth sub-probability and the sixth sub-probability.
Optionally, the obtaining module is specifically configured to obtain the first probability by using the following formula:
P(A)＝k·P(A1)·P(A2)·P(A3)
wherein P (A) is the first probability, P (A)1) Is the first sub-probability, P (A)2) Is the second sub-probability, P (A)3) Is the third sub-probability, k is the adjustment coefficient, 0<k≤1。
Optionally, the obtaining module is specifically configured to obtain the second probability through the following formula:
P(B)＝k·P(B1)·P(B2)·P(B3)
wherein P (B) is the second probability, P (B) is the fourth sub-probability, P (B)2) Is the fifth sub-probability, P (B)3) Is the sixth sub-probability, k is the adjustment coefficient, 0<k≤1。
According to the technical scheme, the method has the following advantages:
the application provides a method and a system for processing service data, wherein the method comprises the following steps: acquiring service data of a service to be handled; the business data comprises business stroke number, money taking amount interval and transactor; obtaining a first probability that a target object handles the service to be handled in a public self-service terminal and a second probability that the target object handles the service to be handled in a counter according to the service data; when the first probability is greater than the second probability, increasing a quota provided for the pair of public self-service terminals; increasing a quota provided for the counter when the first probability is less than the second probability. According to the technical scheme, the first probability that the target object handles the business at the public self-service terminal and the second probability that the target object handles the business at the counter are obtained through the business data of the business to be handled, and then the quota of the public self-service terminal or the quota of the counter is adjusted by comparing the first probability with the second probability.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for processing service data according to an embodiment of the present application;
fig. 2 is a schematic view of a service data processing system according to an embodiment of the present application.
Detailed Description
When handling the service of withdrawing money from a public, a customer may handle the service of withdrawing money from a public at a counter or at a public self-service terminal. When the position of a client for handling the public withdrawal service cannot be accurately judged, an accurate quota cannot be provided for a counter or a public self-service terminal, so that the quota of the counter is idle, but the quota of the public self-service terminal is insufficient; or the quota of the counter is insufficient, but the quota of the public self-service terminal is idle, so that the counter with insufficient quota or the public self-service terminal cannot provide service for the client, and when the quota of the counter or the self-service terminal is idle, the quota cannot be used, and the use value of the quota is reduced.
In order to solve the problems, the application provides a service data processing method and a service data processing system, and the method can obtain a first probability that a client handles a to-be-handled service to a public self-service terminal and a second probability that the client handles the to-be-handled service to a counter through service data including service number, a money-drawing amount interval and the to-be-handled service of a transactor. And then adjusting the quota provided by the counter and the public self-service terminal according to the first probability and the second probability. Therefore, by adopting the technical scheme of the application, the fact that the client handles the public withdrawal service on the counter or the public self-service terminal can be accurately predicted, and the quota of the client handling the counter corresponding to the public withdrawal service or the public self-service terminal is increased.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The first embodiment is as follows:
an embodiment of the present application provides a method for processing service data, which is described in detail below with reference to the accompanying drawings.
Referring to fig. 1, this figure is a flowchart of a method for processing service data according to an embodiment of the present application.
The service data processing method comprises the following steps:
step 101: acquiring service data of a service to be handled; the business data comprises business stroke number, money taking amount interval and transactor.
The service data of the service to be handled is service data corresponding to the target object; the target object is a company which withdraws money from a public self-service terminal, and the identification of the target object can be the name of the company or a public account.
It should be noted that the target object is an object whose number of times of handling the public withdrawal service by the bank corresponding to the self-service terminal and/or the counter in a preset time period is greater than a preset threshold. That is, only data of companies that can be affected by a bank is processed, and a company that cannot be affected by the bank does not handle a cash withdrawal transaction at a self-service terminal or a counter corresponding to the bank.
As one possible implementation, determining whether a company is affected by a bank may be accomplished by:
step 1011: the position of the bank is determined.
Step 1012: companies within a preset threshold distance from the location are obtained.
The preset threshold may be 5 km.
Step 1013: companies within a preset threshold are treated as companies affected by the bank.
After determining the companies affected by the bank, whether the companies transact the public account in the bank can be determined, if the companies do not transact the public account, statistics are not carried out, and the processing efficiency is further improved.
The business data comprises business stroke number, money taking amount interval and transactor.
The service data of the service to be handled can be obtained through a pre-established network model.
Because different companies handle different business data in banks, a network model needs to be established for each company, and then the business to be handled needs to be handled in the banks of each company is predicted.
The network model is obtained by training a training sample and a training result, wherein the training sample is a first historical time period, a second historical time period and a first service numerical control corresponding to the first historical time period; the training result is second service data corresponding to the second historical period; the second history period is continuous with the first history date and is later than the first history period.
The specific establishment process of the network model specifically comprises the following steps:
and collecting business data corresponding to business handling in the bank by the target company within one year for the public self-service terminal. And then training the network model by using the service data of two continuous time intervals. For example: and training by using the 7 th week service data and the 8 th week service data, specifically, training the network model by using the 7 th week service data, the 7 th week service data and the 8 th week service data as training samples and using the 8 th week service data as training results. After the network model is trained for all the withdrawal businesses in two continuous time periods, the network model capable of accurately predicting business data can be obtained.
Further, the accurate service stroke number, the money-drawing amount interval and the transactor are obtained, and the judgment accuracy is further improved.
Step 102: and acquiring a first probability that the target object handles the to-be-handled service for a public self-service terminal and a second probability that the target object handles the to-be-handled service at a counter according to the service data.
First, a first probability that the target object handles the to-be-handled service for the self-service terminal is obtained according to the service data is introduced below.
The obtaining of the first probability that the target object handles the service to be handled in the public self-service terminal according to the service data includes: determining a first sub-probability corresponding to the historical number consistent with the business number through a pre-obtained database of the target object when the public self-service terminal handles the historical business; determining a second sub-probability corresponding to a historical withdrawal volume interval that is consistent with the withdrawal volume interval; determining a third sub-probability corresponding to a historical transactant that is consistent with the transactant.
For example, when the number of service strokes of the obtained service data of the service to be handled is 3, the money-taking interval is 2999 of 2000, and the transactor is a user a, historical service data corresponding to the service data is searched in the database, the historical service data is recorded in the database, the number of service strokes of the historical service data is 3, the probability of handling the public self-service terminal is 30%, the probability of handling the public self-service terminal is 20% when the money-taking interval is 2999 of 2000, and the probability of handling the public self-service terminal is 40% when the transactor is the user a.
And obtaining the first probability according to the first sub-probability, namely 30%, the second sub-probability 20% and the third sub-probability 40%.
Specifically, the first probability is obtained according to the first sub-probability, the second sub-probability and the third sub-probability, and is specifically obtained by the following formula:
P(A)＝k·P(A1)·P(A2)·P(A3)
wherein P is: (A) Is the first probability, P (A)1) Is the first sub-probability, P (A)2) Is the second sub-probability, P (A)3) Is the third sub-probability, k is the adjustment coefficient, 0<k≤1。
As can be seen from the above equation, the first probability P (a) k · P (a)1)·P(A2)·P(A3)＝
k×30％×20％×40％＝0.024k。
A second probability that the target object handles the to-be-handled service over the counter according to the service data is obtained.
The obtaining of the second probability that the target object handles the to-be-handled service over the counter according to the service data includes:
determining a fourth sub-probability corresponding to the historical stroke number consistent with the service stroke number through a database obtained in advance when the target object transacts the historical service at the counter; determining a fifth sub-probability corresponding to a historical withdrawal amount interval consistent with the withdrawal amount interval; determining a sixth sub-probability corresponding to a historical transactant consistent with the transactant;
and obtaining the second probability according to the fourth sub-probability, the fifth sub-probability and the sixth sub-probability.
Similarly, the first probability is obtained according to the first sub-probability, the second sub-probability and the third sub-probability, and is specifically obtained by the following formula:
P(A)＝k·P(A1)·P(A2)·P(A3)
wherein P (A) is the first probability, P (A)1) Is the first sub-probability, P (A)2) Is the second sub-probability, P (A)3) Is the third sub-probability, k is the adjustment coefficient, 0<k≤1。
It should be noted that there may be pending business that does not handle business prediction for the target object, that is, the target object does not go to the counter or the self-service terminal, so there is a case that the sum of the first probability p (a) and the second probability p (b) is not equal to 1.
Step 103: determining whether the first probability is greater than the second probability. If yes, go to step 104; if not, go to step 105.
Step 104: and increasing the quota provided for the public self-service terminal.
When the first probability is greater than the second probability, it indicates that the target object is more likely to handle the to-do service for the self-service terminal, thereby increasing the quota provided for the self-service terminal.
Step 105: increasing a quota provided for the counter.
When the first probability is less than the second probability, it indicates that the target object is more likely to handle the pending traffic at the counter, thus increasing the quota provided for the counter.
The method for processing the service data provided by the embodiment of the application comprises the following steps: acquiring service data of a service to be handled; the business data comprises business stroke number, money taking amount interval and transactor; obtaining a first probability that a target object handles the service to be handled in a public self-service terminal and a second probability that the target object handles the service to be handled in a counter according to the service data; when the first probability is greater than the second probability, increasing a quota provided for the pair of public self-service terminals; increasing a quota provided for the counter when the first probability is less than the second probability. According to the technical scheme, the first probability that the target object handles the business at the public self-service terminal and the second probability that the target object handles the business at the counter are obtained through the business data of the business to be handled, and then the quota of the public self-service terminal or the quota of the counter is adjusted by comparing the first probability with the second probability.
Example two:
the second embodiment of the present application provides a system for processing service data, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 2, the figure is a schematic view of a service data processing system according to an embodiment of the present application.
The processing system of the business data comprises: an acquisition unit 201 and a processing unit 202; the acquiring unit 201 is configured to acquire service data of a service to be handled; the business data comprises business stroke number, money taking amount interval and transactor; and acquiring a first probability that the target object handles the to-be-handled service for a public self-service terminal and a second probability that the target object handles the to-be-handled service at a counter according to the service data.
The processing unit 202 is configured to increase the quota provided for the pair of public self-service terminals when the first probability is greater than the second probability; increasing a quota provided for the counter when the first probability is less than the second probability.
Optionally, the obtaining module 201 is specifically configured to determine, through a database obtained in advance when the target object handles the historical service for the public self-service terminal, a first sub-probability corresponding to a historical number of times that is consistent with the number of times of the service; determining a second sub-probability corresponding to a historical withdrawal volume interval that is consistent with the withdrawal volume interval; determining a third sub-probability corresponding to a historical transactant consistent with the transactant; and obtaining the first probability according to the first sub-probability, the second sub-probability and the third sub-probability.
Optionally, the obtaining module 201 is specifically configured to determine, through a database obtained in advance when the target object transacts the historical service over the counter, a fourth sub-probability corresponding to a historical number consistent with the historical number of the service; determining a fifth sub-probability corresponding to a historical withdrawal amount interval consistent with the withdrawal amount interval; determining a sixth sub-probability corresponding to a historical transactant consistent with the transactant; and obtaining the second probability according to the fourth sub-probability, the fifth sub-probability and the sixth sub-probability.
Optionally, the obtaining module 201 is specifically configured to obtain the first probability by using the following formula:
P(A)＝k·P(A1)·P(A2)·P(A3)
wherein P (A) is the first probability, P (A)1) Is the first sub-probability, P (A)2) Is the second sub-probability, P (A)3) Is the third sub-probability, k is the adjustment coefficient, 0<k≤1。
Optionally, the obtaining module 201 is specifically configured to obtain the second probability through the following formula:
P(B)＝k·P(B1)·P(B2)·P(B3)
wherein P (B) is the second probability, P (B) is the fourth sub-probability, P (B)2) Is the fifth sub-probability, P (B)3) Is the sixth sub-probability, k is the adjustment coefficient, 0<k≤1。
The application provides a service data processing system, which comprises an acquisition unit and a processing unit; the acquiring unit is used for acquiring service data of the service to be handled; the business data comprises business stroke number, money taking amount interval and transactor; obtaining a first probability that a target object handles the service to be handled in a public self-service terminal and a second probability that the target object handles the service to be handled in a counter according to the service data; the processing unit is used for increasing the quota provided for the public self-service terminal when the first probability is greater than the second probability; increasing a quota provided for the counter when the first probability is less than the second probability. According to the technical scheme, the first probability that the target object handles the business at the public self-service terminal and the second probability that the target object handles the business at the counter are obtained through the business data of the business to be handled, and then the quota of the public self-service terminal or the quota of the counter is adjusted by comparing the first probability with the second probability.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, they are described in a relatively simple manner, and reference may be made to some descriptions of method embodiments for relevant points. The above-described system embodiments are merely illustrative, and the units and modules described as separate components may or may not be physically separate. In addition, some or all of the units and modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application in any way. Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application. Those skilled in the art can now make numerous possible variations and modifications to the disclosed embodiments, or modify equivalent embodiments, using the methods and techniques disclosed above, without departing from the scope of the claimed embodiments. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application still fall within the protection scope of the technical solution of the present application without departing from the content of the technical solution of the present application.
Claims (10)
1. A method for processing service data is characterized by comprising the following steps:
acquiring service data of a service to be handled; the business data comprises business stroke number, money taking amount interval and transactor;
obtaining a first probability that a target object handles the service to be handled in a public self-service terminal and a second probability that the target object handles the service to be handled in a counter according to the service data;
when the first probability is greater than the second probability, increasing a quota provided for the pair of public self-service terminals;
increasing a quota provided for the counter when the first probability is less than the second probability.
2. The method of claim 1, wherein the obtaining a first probability that the target object handles the to-be-handled service on a self-service terminal according to the service data comprises:
determining a first sub-probability corresponding to the historical number consistent with the business number through a pre-obtained database of the target object when the public self-service terminal handles the historical business; determining a second sub-probability corresponding to a historical withdrawal volume interval that is consistent with the withdrawal volume interval; determining a third sub-probability corresponding to a historical transactant consistent with the transactant;
and obtaining the first probability according to the first sub-probability, the second sub-probability and the third sub-probability.
3. The method of claim 1, wherein obtaining the second probability that the target object transacts the pending business over the counter according to the business data comprises:
determining a fourth sub-probability corresponding to the historical stroke number consistent with the service stroke number through a database obtained in advance when the target object transacts the historical service at the counter; determining a fifth sub-probability corresponding to a historical withdrawal amount interval consistent with the withdrawal amount interval; determining a sixth sub-probability corresponding to a historical transactant consistent with the transactant;
and obtaining the second probability according to the fourth sub-probability, the fifth sub-probability and the sixth sub-probability.
4. The method according to claim 2, wherein the first probability is obtained from the first, second and third sub-probabilities by:
P(A)＝k·P(A1)·P(A2)·P(A3)
wherein P (A) is the first probability, P (A)1) Is the first sub-probability, P (A)2) Is the second sub-probability, P (A)3) Is the third sub-probability, k is the adjustment coefficient, 0<k≤1。
5. The method according to claim 3, wherein the second probability is obtained according to the fourth, fifth and sixth sub-probabilities, in particular by the following formula:
P(B)＝k·P(B1)·P(B2)·P(B3)
wherein P (B) is the second probability, P (B) is the fourth sub-probability, P (B)2) Is the fifth sub-probability, P (B)3) Is the sixth sub-probability, k is the adjustment coefficient, 0<k≤1。
6. A system for processing traffic data, comprising: an acquisition unit and a processing unit;
the acquiring unit is used for acquiring service data of the service to be handled; the business data comprises business stroke number, money taking amount interval and transactor; obtaining a first probability that a target object handles the service to be handled in a public self-service terminal and a second probability that the target object handles the service to be handled in a counter according to the service data;
the processing unit is used for increasing the quota provided for the public self-service terminal when the first probability is greater than the second probability; increasing a quota provided for the counter when the first probability is less than the second probability.
7. The system according to claim 6, wherein the obtaining module is specifically configured to determine, through a database obtained in advance when the target object handles the historical business to the public self-service terminal, a first sub-probability corresponding to a historical number of the business; determining a second sub-probability corresponding to a historical withdrawal volume interval that is consistent with the withdrawal volume interval; determining a third sub-probability corresponding to a historical transactant consistent with the transactant; and obtaining the first probability according to the first sub-probability, the second sub-probability and the third sub-probability.
8. The system according to claim 6, wherein the obtaining module is specifically configured to determine, through a database obtained in advance when the target object transacts historical business over the counter, a fourth sub-probability corresponding to a historical number of times that is consistent with the business number of times; determining a fifth sub-probability corresponding to a historical withdrawal amount interval consistent with the withdrawal amount interval; determining a sixth sub-probability corresponding to a historical transactant consistent with the transactant; and obtaining the second probability according to the fourth sub-probability, the fifth sub-probability and the sixth sub-probability.
9. The system according to claim 7, characterized in that said acquisition module is particularly configured to obtain said first probability by means of the following formula:
P(A)＝k·P(A1)·P(A2)·P(A3)
wherein P (A) is the first probability, P (A)1) Is the first sub-probability, P (A)2) Is the second sub-probability, P (A)3) Is the third sub-probability, k is the adjustment coefficient, 0<k≤1。
10. The system according to claim 8, wherein the obtaining module is specifically configured to obtain the second probability by using the following formula:
P(B)＝k·P(B1)·P(B2)·P(B3)
wherein P (B) is the second probability, P (B) is the fourth sub-probability, P (B)2) Is the fifth sub-probability, P (B)3) Is the sixth sub-probability, k is the adjustment coefficient, 0<k≤1。
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