CN117217749A - Channel fee settlement method and device - Google Patents
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Abstract
The application relates to the field of computers and provides a channel expense settlement method and device. The method comprises the following steps: establishing information of each order in the service system as a user portrait; encoding weights for channel types, business types, and products in multiple user portraits; correlating the channel fee business code with the weight of at least one corresponding channel type, the weight of at least one business type and the weight of at least one product code to obtain at least one business sample; determining the distance between a single user representation and all business samples; updating the channel fee business codes corresponding to the single user pictures according to the distances; and distributing the user portrait data corresponding to the updated business codes of different channel fees to different Redis for channel fee settlement. The channel expense settlement method and device provided by the embodiment of the application can solve the problems of association operation and cross-instance access data in the settlement process, and improve the settlement efficiency.
Description
Technical Field
The application relates to the technical field of computers, in particular to a channel expense settlement method and device.
Background
At present, the channel expense settlement system mainly adopts remote dictionary service Redis (Remote Dictionary Server, redis), namely, metadata collected by a service system is preprocessed to form structural data required by channel expense settlement. The existing scheme is that metadata of each data acquisition interface is respectively stored in different Hive data tables (Hive is a data warehouse tool based on Hadoop), and then data of each table is uniformly loaded into a Redis cluster for service distributed processing according to a Hash algorithm (i.e. a Hash algorithm).
Because the prior art scheme does not classify the data according to the service characteristics, a large amount of data inquiry and calculation of association rules can occur in the actual channel cost settlement service processing process, so that the settlement efficiency is affected.
Disclosure of Invention
The embodiment of the application provides a channel expense settlement method and device, which are used for solving the technical problem of low settlement efficiency caused by association operation and cross-instance access data in the prior art.
In a first aspect, an embodiment of the present application provides a channel fee settlement method, including:
establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits; the information of each order includes: channel type, service type, product code, and channel fee service code;
encoding weights for said channel types, said business types, and said products in said plurality of user portraits;
correlating the channel fee service code with the corresponding weight of at least one channel type, the corresponding weight of at least one service type and the corresponding weight of at least one product code to obtain at least one service sample;
determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples;
updating the channel fee business codes corresponding to the single user portrait according to the distance;
And distributing the user portrait data corresponding to the updated channel fee business codes to different Redis for channel fee settlement.
In one embodiment, said encoding weights for said channel type, said business type, and said product in said plurality of user portraits comprises:
determining the duty ratio of the type business volume of the target channel according to the ratio of the total business volume of the type business volume of the target channel to the total business volume; the target channel type is any channel type in the plurality of user portraits;
calculating the ratio of the successful total amount to the total amount of the target channel type service according to the target channel type service charge, and determining the conversion ratio of the target channel type service charge;
and weighting the target channel type according to the product of the target channel type service volume duty ratio and the target channel type service cost conversion ratio.
In one embodiment, said encoding weights for said channel type, said business type, and said product in said plurality of user portraits comprises:
determining the duty ratio of the target business type business volume according to the ratio of the total business volume of the target business type to the total business volume; the target service type is any service type in the plurality of user portraits;
Calculating the ratio of the successful total amount to the total amount of the target service type service according to the target service type service cost, and determining the conversion ratio of the target service type service cost;
and weighting the target service type according to the product of the service volume duty ratio of the target service type and the service cost conversion ratio of the target service type.
In one embodiment, said encoding weights for said channel type, said business type, and said product in said plurality of user portraits comprises:
determining the code traffic volume duty ratio of the target product according to the ratio of the code traffic volume of the target product to the total traffic volume; the target product code is any one of the product codes in the plurality of user portraits;
calculating the ratio of the successful total amount to the total amount of the target product coding service according to the target product coding service cost, and determining the conversion ratio of the target product coding service cost;
and weighting the target product code according to the product of the target product code service volume duty ratio and the target product code service cost conversion ratio.
In one embodiment, the determining the distance between the single user portrait and all service samples according to the weights of the channel types, the weights of the service types, and the weights of the product codes, and the weights of the channel types, the weights of the service types, and the weights of the product codes corresponding to the user portraits includes:
Determining the distance between a single user portrait and all service samples according to an Euler algorithm, wherein the Euler algorithm is as follows:
wherein D is ij X is the distance between user representation i and business sample j i Weighting the channel type corresponding to user portrait i, X j For the weight of the channel type corresponding to the service sample j, Y i Portrayal for a useri corresponds to the weight of the service type, Y j Z is the weight of the service type corresponding to the service sample j i Weights coded for the product corresponding to user representation i, Z j And (5) coding the weight of the product corresponding to the service sample j.
In one embodiment, said updating said channel fee service code corresponding to a single said user representation based on said distance comprises:
sorting the distances between a single user portrait and all service samples from small to large, and selecting channel cost service codes of the first N sorted distance corresponding samples as candidate channel cost service codes, wherein N is an integer greater than or equal to 5;
and updating the channel fee business codes corresponding to the single user portrait by using the channel fee business codes candidate with the largest quantity in the N channel fee business codes candidate.
In one embodiment, the distributing the updated user portrait data corresponding to different channel fee service codes to different Redis for channel fee settlement includes:
obtaining channel fee settlement rules according to the updated different channel fee business codes;
and carrying out channel fee settlement on the user portrait data received by different Redis in parallel according to the channel fee settlement rule.
In a second aspect, an embodiment of the present application provides a channel fee settlement apparatus, including:
the user portrait generation module is used for: establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits; the information of each order includes: channel type, service type, product code, and channel fee service code;
a weighting module, configured to: encoding weights for said channel types, said business types, and said products in said plurality of user portraits;
a service sample generation module, configured to: correlating the channel fee service code with the corresponding weight of at least one channel type, the corresponding weight of at least one service type and the corresponding weight of at least one product code to obtain at least one service sample;
A distance calculation module for: determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples;
the channel fee business code updating module is used for: updating the channel fee business codes corresponding to the single user portrait according to the distance;
a channel fee settlement module for: and distributing the user portrait data corresponding to the updated business codes of different channel fees to different Redis for channel fee settlement.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory storing a computer program, where the processor implements the steps of the channel fee settlement method according to the first aspect when executing the program.
In a fourth aspect, embodiments of the present application provide a computer program product comprising a computer program which, when executed by a processor, implements the steps of the channel fee settlement method of the first aspect.
According to the channel expense settlement method and device provided by the embodiment of the application, discrete data of each order in a business system are structured into user images, data association inquiry is reduced, at least one business sample is obtained according to the association of channel expense business codes with weights of at least one channel type corresponding to the channel expense business codes, weights of at least one business type and weights of at least one product code, the distance between a single user image and all business samples is determined, channel expense business codes corresponding to the single user image are updated according to the distance, finally user image data corresponding to different channel expense business codes after updating are distributed to different Redis for channel expense settlement, which is equivalent to classifying each user image according to a certain rule, and user image data of different categories are distributed to different Redis uniformly, so that all data in one user image only exist in the same Redis, bandwidth resource consumption caused by Redis cross-instance access data is reduced in the process of channel expense settlement, and the problem of association operation and cross-instance access data in the process can be effectively solved, and the settlement efficiency is improved.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a channel fee settlement method according to an embodiment of the present application;
FIG. 2 is a second flow chart of a channel fee settlement method according to the embodiment of the application;
FIG. 3 is a third flow chart of a channel fee settlement method according to the embodiment of the application;
FIG. 4 is a flow chart of a channel fee settlement method according to an embodiment of the present application;
FIG. 5 is a fifth flow chart of a channel fee settlement method according to the embodiment of the present application;
FIG. 6 is a flowchart of a channel fee settlement method according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a channel fee settlement apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Fig. 1 is a schematic flow chart of a channel fee settlement method according to an embodiment of the present application. Referring to fig. 1, an embodiment of the present application provides a channel fee settlement method, which may include:
101. establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits;
the information for each order includes: channel type, service type, product code, and channel fee service code.
102. Encoding weights for channel types, business types, and products in multiple user portraits;
103. correlating the channel fee business code with the weight of at least one corresponding channel type, the weight of at least one business type and the weight of at least one product code to obtain at least one business sample;
It should be noted that, the same channel fee service code may correspond to a plurality of different channel types or a plurality of different service types or a plurality of different product codes, and then the same channel fee service code may correspond to a plurality of weights of different channel types or a plurality of weights of different service types or a plurality of weights of different product codes, so the same channel fee service code may generate a plurality of service samples.
The associating of the channel fare business code with the weight of the corresponding at least one channel type, the weight of the at least one business type and the weight of the at least one product code may be performed as follows:
1. obtaining configuration data and channel type weight in a channel cost service-channel type corresponding relation configuration factor ($channel type) in a channel cost policy calculation rule, and obtaining a list: channel fee business code |agent type|weight value;
2. acquiring configuration data and service type weight in a channel cost service-service type corresponding relation configuration factor ($service type) configuration factor in a channel cost policy calculation rule, and acquiring a list: channel fee business code |business type|weight value;
3. Acquiring configuration data and product coding weights in a service-product configuration table to obtain a list: channel fee business code |product id|weight value;
4. acquiring service codes and product corresponding relation data in a channel cost calculation result table (data which does not depend on a service-product configuration table to replace a cost settlement product exists in the cost calculation process): channel fee business code |product id|weight value;
5. and obtaining a final service sample by associating the four results through service coding: channel cost business code |channel type weight|business type weight|product code weight.
Each traffic sample may be exemplified as follows:
| channel type weighting | Service type weight | Product coding weights | Channel fee service coding |
| 0.2416 | 0.5600 | 2.677 | SH1010001 |
| 0.1521 | 0.5600 | 2.677 | SH1010001 |
| 0.1088 | 0.5600 | 2.677 | SH1010001 |
| 0.2416 | 0.5600 | 3.1667 | SH1010001 |
| 0.1521 | 0.5600 | 3.1667 | SH1010001 |
| 0.1088 | 0.5600 | 3.1667 | SH1010001 |
| 0.2416 | 13.6087 | 10.4503 | SH1780001 |
| 0.1521 | 13.6087 | 10.4503 | SH1780001 |
| 0.1088 | 13.6087 | 10.4503 | SH1780001 |
| 0.0877 | 13.6087 | 10.4503 | SH1780001 |
| 0.2416 | 13.6087 | 6.4809 | SH1780001 |
| 0.1521 | 13.6087 | 6.4809 | SH1780001 |
| 0.1088 | 13.6087 | 6.4809 | SH1780001 |
| 0.0877 | 13.6087 | 6.4809 | SH1780001 |
| 0.2416 | 0.7312 | 5.3239 | SH2000001 |
| 0.1521 | 0.7312 | 4.7398 | SH2000001 |
| 0.1088 | 0.7312 | 4.336 | SH2000001 |
| 0.0877 | 0.7312 | 3.1667 | SH2000001 |
| …… | …… | …… | …… |
TABLE 1
104. Determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples, which correspond to the user portraits;
105. updating the channel fee business codes corresponding to the single user pictures according to the distance;
that is, the single user image is reclassified and the updated channel fee service codes are used as classification.
106. And distributing the user portrait data corresponding to the updated business codes of different channel fees to different Redis for channel fee settlement.
Namely, the category and the Redis are distributed in a one-to-one correspondence manner, so that all data of each user portrait can be ensured to be in the same Redis.
According to the channel expense settlement method provided by the embodiment, discrete data of each order in a business system are structured into user images, data association inquiry is reduced, at least one business sample is obtained according to the association of channel expense business codes with weights of at least one channel type corresponding to the channel expense business codes, weights of at least one business type and weights of at least one product code, the distance between a single user image and all business samples is determined, channel expense business codes corresponding to the single user image are updated according to the distance, finally user image data corresponding to different updated channel expense business codes are distributed to different Redis for channel expense settlement, the user image data of different categories are equally distributed to different Redis according to a certain rule, all data in one user image only exist in the same Redis, bandwidth resource consumption caused by access of the Redis instance to data is reduced in the channel expense settlement process, the problem of association operation and instance data cross-instance settlement process can be effectively solved, and settlement efficiency is improved.
FIG. 2 is a second flow chart of a channel fee settlement method according to an embodiment of the present application. Referring to FIG. 2, in one embodiment, a method for weighting channel types in multiple user portraits is provided and may include:
201. determining the duty ratio of the business volume of the target channel type according to the ratio of the business volume of the target channel type to the total business volume;
the target channel type is any channel type in the plurality of user portraits.
202. Calculating the ratio of the successful total amount to the total amount of the target channel type service according to the target channel type service cost, and determining the conversion ratio of the target channel type service cost;
203. the target channel type is weighted according to the product of the target channel type traffic volume ratio and the target channel type traffic cost conversion ratio.
The channel type weights may be exemplified as follows:
| channel type | Channel type | Weighting of |
| AG | Social channel | 0.2416 |
| DD | Alliance with store | 0.1521 |
| SQ | Package agent | 0.1088 |
| HZ | Introduction of a partner | 0.0877 |
| FQ | Universal channel | 0.0013 |
| EC | Electronic channel | 0.2660 |
| HL | Hot wire channel | 0.1423 |
| …… | …… | …… |
TABLE 2
In the embodiment, the target channel type is weighted according to the product of the target channel type traffic volume ratio and the target channel type traffic cost conversion ratio, and because in the channel cost settlement, the index which is critical for the channel type is the traffic volume of the channel and the traffic cost conversion ratio of the channel, the importance degree of each channel type can be reasonably represented by the way of calculating the weight.
FIG. 3 is a third flow chart of a channel fee settlement method according to the embodiment of the application. Referring to fig. 3, in one embodiment, a method for weighting traffic types in multiple user portraits is provided, which may include:
301. determining the duty ratio of the business volume of the target business type according to the ratio of the business volume of the target business type to the total business volume;
the target traffic type is any traffic type in the plurality of user portraits.
302. Calculating the ratio of the successful total amount to the total amount of the target service type service according to the target service type service cost, and determining the conversion ratio of the target service type service cost;
303. the target traffic type is weighted according to the product of the target traffic type traffic volume duty cycle and the target traffic type traffic cost conversion ratio.
The respective traffic type weights may be exemplified as follows:
| service type | Service name | Weighting of |
| ChangeProduct | Product change | 13.6087 |
| UPSSCharge | Nationwide unified payment system collection business | 3.2723 |
| ChangeCustInfo | Customer profile modification | 6.1244 |
| OpenSubsQF | Arrearage starting machine | 4.8767 |
| ChangeSubsInfo | User profile modification | 1.9514 |
| Install | Account opening | 0.7312 |
| Charge | Charging method | 0.5600 |
| UserFirstActiveNotify | Prepaid card activation notification | 0.3819 |
| RegCustInfoConfirm | Identity verification | 0.3740 |
| UserFirstActive | Prepaid card activation | 0.3374 |
| FamilyMemAdd | Family member addition | 0.3259 |
| FamilyInstallAddMem | Family opening master number member addition | 0.1786 |
| FamilyInstall | Household main body product account opening | 0.1786 |
| …… | …… | …… |
TABLE 3 Table 3
In this embodiment, the target service type is weighted according to the product of the target service type service volume ratio and the target service type service cost conversion ratio, and because in the channel cost settlement, the index which is critical for the service type is the service volume of the service type and the service cost conversion ratio of the service type, the importance degree of each service type can be reasonably represented by the way of calculating the weight.
Fig. 4 is a flowchart of a channel fee settlement method according to an embodiment of the present application. Referring to FIG. 4, in one embodiment, a method of encoding weights for products in a plurality of user representations is provided and may include:
401. determining the code traffic volume duty ratio of the target product according to the ratio of the code traffic volume of the target product to the total traffic volume;
the target product code is any product code in the plurality of user representations.
402. Calculating the ratio of the successful total amount to the total amount of the target product coding service according to the target product coding service cost, and determining the conversion ratio of the target product coding service cost;
403. and weighting the target product code according to the product of the target product code traffic volume duty ratio and the target product code traffic cost conversion ratio.
The individual product coding weights may be exemplified as follows:
| product coding | Weighting of |
| prod.10000000066663 | 10.4503 |
| prod.10086000032181 | 6.4809 |
| prod.10086000030184 | 5.7923 |
| prod.10086000027962 | 5.3239 |
| prod.10086000025192 | 4.7398 |
| prod.10086000019869 | 4.336 |
| prod.10086000027957 | 3.1667 |
| prod.10086000034120 | 2.677 |
| prod.10086000027971 | 2.3379 |
| …… | …… |
TABLE 4 Table 4
In this embodiment, the target product code is weighted according to the product of the target product code traffic volume ratio and the target product code traffic cost conversion ratio, and because in the channel cost settlement, the key index for the product is the traffic volume of the product and the traffic cost conversion ratio of the product, the importance degree of each product code (i.e. the corresponding product) can be represented more reasonably by the way of calculating the weight.
In one embodiment, the Euler algorithm may be used to determine the distance between a single user representation and all traffic samples, as follows:
wherein D is ij X is the distance between user representation i and business sample j i Weighting the channel type corresponding to user portrait i, X j For the weight of the channel type corresponding to the service sample j, Y i For the weight of the service type corresponding to the user portrait i, Y j For the service type corresponding to service sample jWeights, Z of (2) i Weights coded for the product corresponding to user representation i, Z j And (5) coding the weight of the product corresponding to the service sample j.
It should be noted that other methods may be used to determine the distance between a single user representation and all business samples, which is not limited herein.
The distance between a single user portrait and all service samples is calculated through the Euler algorithm, so that the method is convenient and quick and is convenient to calculate.
Fig. 5 is a flowchart of a channel fee settlement method according to an embodiment of the present application. Referring to fig. 5, in one embodiment, a method for updating channel fee service codes corresponding to a single user image according to distance according to an embodiment of the present application may include:
501. sorting the distances between a single user figure and all service samples from small to large, and selecting channel cost service codes of the first N sorted distance corresponding samples as candidate channel cost service codes;
wherein N is an integer of 5 or more.
The smaller the distance between a single user representation and a business sample, the closer the single user representation is to the business sample, i.e., the more similar the single user representation is to the business sample.
502. And updating the channel cost service codes corresponding to the single user picture by using the candidate channel cost service codes with the largest quantity in the N candidate channel cost service codes.
For example, the channel fee business codes of the first 5 samples corresponding to the distances in the sequence are selected as candidate channel fee business codes, and if the 5 candidate channel fee business codes are respectively SH1780001, SH1010001, SH2000001, SH1780001 and SH1780001, the value "SH1780001" with the largest number of candidate channel fee business codes is taken according to the "minority-compliance majority" principle of the K-nearest neighbor algorithm, and the single user portrait is labeled with "SH1780001", so that the updating of the channel fee business codes corresponding to the single user portrait is completed.
It should be noted that if there are a plurality of candidate channel fee service codes with the largest number among the 5 candidate channel fee service codes, for example, the 5 candidate channel fee service codes are respectively SH1780001, SH1010001, SH2000001, SH1780001 and SH1010001, and SH1780001 and SH1010001 are two, then the optional SH1780001 or SH1010001 updates the channel fee service codes corresponding to the single user image.
In the embodiment, the channel fee business codes corresponding to the single user images are updated by selecting the business sample closest to the single user image and selecting the channel fee business codes corresponding to the business sample and having the largest quantity, so that a plurality of user images can be classified, similar user images are clustered together, repeated settlement of the similar user images is reduced, and the settlement efficiency is improved.
Fig. 6 is a flowchart illustrating a channel fee settlement method according to an embodiment of the present application. Referring to fig. 6, in one embodiment, a method for distributing user portrait data corresponding to updated different channel fee service codes to different rediss for channel fee settlement according to an embodiment of the present application may include:
601. acquiring channel fee settlement rules according to the updated different channel fee business codes;
602. And carrying out channel fee settlement on the user portrait data received by different Redis in parallel according to the channel fee settlement rule.
Different Redis receive user portrait data of different categories (namely different channel fee business codes), and each Redis carries out channel fee settlement on the user portrait data of the corresponding category in parallel according to the corresponding channel fee settlement rule, so that the channel fee settlement efficiency can be improved without mutual interference.
According to the embodiment, channel fee settlement is carried out on the user portrait data received by different Redis in parallel, so that the settlement rules can be operated by different Redis at the same time, and the channel fee settlement efficiency is improved.
The channel fee settlement device provided by the embodiment of the application is described below, and the channel fee settlement device described below and the channel fee settlement method described above can be referred to correspondingly.
Fig. 7 is a schematic structural diagram of a channel fee settlement device according to an embodiment of the present application. Referring to fig. 7, an embodiment of the present application provides a channel fee settlement apparatus, which may include:
a user portrayal generation module 701 for: establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits; the information of each order includes: channel type, service type, product code, and channel fee service code;
A weighting module 702, configured to: encoding weights for said channel types, said business types, and said products in said plurality of user portraits;
a service sample generation module 703, configured to: correlating the channel fee service code with the corresponding weight of at least one channel type, the corresponding weight of at least one service type and the corresponding weight of at least one product code to obtain at least one service sample;
a distance calculation module 704, configured to: determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples;
channel fee service code update module 705 for: updating the channel fee business codes corresponding to the single user portrait according to the distance;
channel fee settlement module 706 for: and distributing the user portrait data corresponding to the updated business codes of different channel fees to different Redis for channel fee settlement.
According to the channel expense settlement device provided by the embodiment, discrete data of each order in a business system are structured into user images, data association inquiry is reduced, at least one business sample is obtained according to the association of channel expense business codes with weights of at least one channel type corresponding to the channel expense business codes, weights of at least one business type and weights of at least one product code, the distance between a single user image and all business samples is determined, channel expense business codes corresponding to the single user image are updated according to the distance, finally user image data corresponding to different updated channel expense business codes are distributed to different Redis for channel expense settlement, the user image data of different categories are equally distributed to different Redis according to a certain rule, all data in one user image only exist in the same Redis, bandwidth resource consumption caused by access of the Redis instance to data is reduced in the channel expense settlement process, the problem of association operation and the instance data cross-access process can be effectively solved, and the settlement efficiency is improved.
In one embodiment, the weighting module 702 is specifically configured to:
determining the duty ratio of the type business volume of the target channel according to the ratio of the total business volume of the type business volume of the target channel to the total business volume; the target channel type is any channel type in the plurality of user portraits;
calculating the ratio of the successful total amount to the total amount of the target channel type service according to the target channel type service charge, and determining the conversion ratio of the target channel type service charge;
and weighting the target channel type according to the product of the target channel type service volume duty ratio and the target channel type service cost conversion ratio.
In one embodiment, the weighting module 702 is specifically configured to:
determining the duty ratio of the target business type business volume according to the ratio of the total business volume of the target business type to the total business volume; the target service type is any service type in the plurality of user portraits;
calculating the ratio of the successful total amount to the total amount of the target service type service according to the target service type service cost, and determining the conversion ratio of the target service type service cost;
and weighting the target service type according to the product of the service volume duty ratio of the target service type and the service cost conversion ratio of the target service type.
In one embodiment, the weighting module 702 is specifically configured to:
determining the code traffic volume duty ratio of the target product according to the ratio of the code traffic volume of the target product to the total traffic volume; the target product code is any one of the product codes in the plurality of user portraits;
calculating the ratio of the successful total amount to the total amount of the target product coding service according to the target product coding service cost, and determining the conversion ratio of the target product coding service cost;
and weighting the target product code according to the product of the target product code service volume duty ratio and the target product code service cost conversion ratio.
In one embodiment, the distance calculation module 704 is specifically configured to:
determining the distance between a single user portrait and all service samples according to an Euler algorithm, wherein the Euler algorithm is as follows:
wherein D is ij X is the distance between user representation i and business sample j i Weighting the channel type corresponding to user portrait i, X j For the weight of the channel type corresponding to the service sample j, Y i For the weight of the service type corresponding to the user portrait i, Y j Z is the weight of the service type corresponding to the service sample j i Weights coded for the product corresponding to user representation i, Z j And (5) coding the weight of the product corresponding to the service sample j.
In one embodiment, the channel fee service code update module 705 is specifically configured to:
sorting the distances between a single user portrait and all service samples from small to large, and selecting channel cost service codes of the first N sorted distance corresponding samples as candidate channel cost service codes, wherein N is an integer greater than or equal to 5;
and updating the channel fee business codes corresponding to the single user portrait by using the channel fee business codes candidate with the largest quantity in the N channel fee business codes candidate.
In one embodiment, the channel fee settlement module 706 is specifically configured to:
obtaining channel fee settlement rules according to the updated different channel fee business codes;
and carrying out channel fee settlement on the user portrait data received by different Redis in parallel according to the channel fee settlement rule.
Fig. 8 illustrates a physical structure diagram of an electronic device, as shown in fig. 8, which may include: processor 810, communication interface (Communication Interface) 820, memory 830, and communication bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through communication bus 840. The processor 810 may call a computer program in the memory 830 to perform the steps of the channel fee settlement method, including, for example:
Establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits; the information of each order includes: channel type, service type, product code, and channel fee service code;
encoding weights for said channel types, said business types, and said products in said plurality of user portraits;
correlating the channel fee service code with the corresponding weight of at least one channel type, the corresponding weight of at least one service type and the corresponding weight of at least one product code to obtain at least one service sample;
determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples;
updating the channel fee business codes corresponding to the single user portrait according to the distance;
and distributing the user portrait data corresponding to the updated channel fee business codes to different Redis for channel fee settlement.
Further, the logic instructions in the memory 830 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present application further provide a computer program product, where the computer program product includes a computer program, where the computer program may be stored on a non-transitory computer readable storage medium, where the computer program when executed by a processor is capable of executing the steps of the channel fee settlement method provided in the foregoing embodiments, for example, including:
Establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits; the information of each order includes: channel type, service type, product code, and channel fee service code;
encoding weights for said channel types, said business types, and said products in said plurality of user portraits;
correlating the channel fee service code with the corresponding weight of at least one channel type, the corresponding weight of at least one service type and the corresponding weight of at least one product code to obtain at least one service sample;
determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples;
updating the channel fee business codes corresponding to the single user portrait according to the distance;
and distributing the user portrait data corresponding to the updated channel fee business codes to different Redis for channel fee settlement.
In another aspect, embodiments of the present application further provide a processor-readable storage medium storing a computer program for causing a processor to execute the steps of the method provided in the above embodiments, for example, including:
establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits; the information of each order includes: channel type, service type, product code, and channel fee service code;
encoding weights for said channel types, said business types, and said products in said plurality of user portraits;
correlating the channel fee service code with the corresponding weight of at least one channel type, the corresponding weight of at least one service type and the corresponding weight of at least one product code to obtain at least one service sample;
determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples;
Updating the channel fee business codes corresponding to the single user portrait according to the distance;
and distributing the user portrait data corresponding to the updated channel fee business codes to different Redis for channel fee settlement.
The processor-readable storage medium may be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic storage (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), semiconductor storage (e.g., ROM, EPROM, EEPROM, nonvolatile storage (NAND FLASH), solid State Disk (SSD)), and the like.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A channel fee settlement method, comprising:
establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits; the information of each order includes: channel type, service type, product code, and channel fee service code;
encoding weights for said channel types, said business types, and said products in said plurality of user portraits;
correlating the channel fee service code with the corresponding weight of at least one channel type, the corresponding weight of at least one service type and the corresponding weight of at least one product code to obtain at least one service sample;
determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples;
updating the channel fee business codes corresponding to the single user portrait according to the distance;
and distributing the user portrait data corresponding to the updated channel fee business codes to different Redis for channel fee settlement.
2. The channel fee settlement method as set forth in claim 1, wherein said weighting the channel type, the service type, and the product code in the plurality of user portraits comprises:
determining the duty ratio of the type business volume of the target channel according to the ratio of the total business volume of the type business volume of the target channel to the total business volume; the target channel type is any channel type in the plurality of user portraits;
calculating the ratio of the successful total amount to the total amount of the target channel type service according to the target channel type service charge, and determining the conversion ratio of the target channel type service charge;
and weighting the target channel type according to the product of the target channel type service volume duty ratio and the target channel type service cost conversion ratio.
3. The channel fee settlement method as set forth in claim 1, wherein said weighting the channel type, the service type, and the product code in the plurality of user portraits comprises:
determining the duty ratio of the target business type business volume according to the ratio of the total business volume of the target business type to the total business volume; the target service type is any service type in the plurality of user portraits;
Calculating the ratio of the successful total amount to the total amount of the target service type service according to the target service type service cost, and determining the conversion ratio of the target service type service cost;
and weighting the target service type according to the product of the service volume duty ratio of the target service type and the service cost conversion ratio of the target service type.
4. The channel fee settlement method as set forth in claim 1, wherein said weighting the channel type, the service type, and the product code in the plurality of user portraits comprises:
determining the code traffic volume duty ratio of the target product according to the ratio of the code traffic volume of the target product to the total traffic volume; the target product code is any one of the product codes in the plurality of user portraits;
calculating the ratio of the successful total amount to the total amount of the target product coding service according to the target product coding service cost, and determining the conversion ratio of the target product coding service cost;
and weighting the target product code according to the product of the target product code service volume duty ratio and the target product code service cost conversion ratio.
5. The channel fee settlement method according to claim 1, wherein the determining the distance between a single user representation and all service samples from the weights of the channel type, the service type, and the product code corresponding to the user representation and the channel type, the service type, and the product code corresponding to the service sample comprises:
Determining the distance between a single user portrait and all service samples according to an Euler algorithm, wherein the Euler algorithm is as follows:
wherein D is ij X is the distance between user representation i and business sample j i Weighting the channel type corresponding to user portrait i, X j For the weight of the channel type corresponding to the service sample j, Y i For the weight of the service type corresponding to the user portrait i, Y j Z is the weight of the service type corresponding to the service sample j i Weights coded for the product corresponding to user representation i, Z j And (5) coding the weight of the product corresponding to the service sample j.
6. The channel fee settlement method as set forth in claim 1, wherein said updating said channel fee service code corresponding to a single one of said user portraits based on said distance comprises:
sorting the distances between a single user portrait and all service samples from small to large, and selecting channel cost service codes of the first N sorted distance corresponding samples as candidate channel cost service codes, wherein N is an integer greater than or equal to 5;
and updating the channel fee business codes corresponding to the single user portrait by using the channel fee business codes candidate with the largest quantity in the N channel fee business codes candidate.
7. The channel fee settlement method as set forth in claim 1, wherein said distributing updated user portrayal data corresponding to different ones of said channel fee service codes to different rediss for channel fee settlement comprises:
obtaining channel fee settlement rules according to the updated different channel fee business codes;
and carrying out channel fee settlement on the user portrait data received by different Redis in parallel according to the channel fee settlement rule.
8. A channel fee settlement apparatus, comprising:
the user portrait generation module is used for: establishing information of each order in the service system as a user portrait to obtain a plurality of user portraits; the information of each order includes: channel type, service type, product code, and channel fee service code;
a weighting module, configured to: encoding weights for said channel types, said business types, and said products in said plurality of user portraits;
a service sample generation module, configured to: correlating the channel fee service code with the corresponding weight of at least one channel type, the corresponding weight of at least one service type and the corresponding weight of at least one product code to obtain at least one service sample;
A distance calculation module for: determining the distance between a single user portrait and all service samples according to the weights of the channel types, the weights of the service types, the weights of the product codes, the weights of the channel types, the weights of the service types and the weights of the product codes corresponding to the service samples;
the channel fee business code updating module is used for: updating the channel fee business codes corresponding to the single user portrait according to the distance;
a channel fee settlement module for: and distributing the user portrait data corresponding to the updated business codes of different channel fees to different Redis for channel fee settlement.
9. An electronic device comprising a processor and a memory storing a computer program, characterized in that the processor implements the steps of the channel fee settlement method of any one of claims 1 to 7 when executing the computer program.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the channel fee settlement method of any one of claims 1 to 7.
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| CN202210626394.6A CN117217749A (en) | 2022-06-02 | 2022-06-02 | Channel fee settlement method and device |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210626394.6A CN117217749A (en) | 2022-06-02 | 2022-06-02 | Channel fee settlement method and device |
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