CN111291089A - Service processing method and device - Google Patents
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Abstract
The present specification provides a service processing method and a device, wherein the service processing method includes: acquiring user data of a service platform and determining service processing conditions of each service in the service platform; splitting the user data into subdata based on the service processing condition; reading service subdata corresponding to the service from the subdata according to the multi-bin tasks corresponding to the service, and performing service data processing on the service subdata to obtain service processing data; and updating the user data based on the service processing data.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a service processing method. The present specification also relates to a business processing apparatus, a computing device, and a computer-readable storage medium.
Background
With the development of computer technology, data processing amount and processing efficiency become more and more efficient, and with the development of data processing efficiency, services which can be brought to users by a service platform corresponding to data processing become better and better. In a big data background, a model of a bin often contains many complex calculation rules, and a model can obtain an expected data output result only by processing the whole amount of data in one task instance, but only a part of the data in the whole amount of data needs to be processed by the model of the bin, and the rest of the data in the whole amount of data does not need to be calculated through the complex rules, and at this time, the processing of the rest of the data in the whole amount of data wastes much time and occupies a large amount of hardware resources, so a time-saving and resource-saving scheme is needed to solve the above problems.
Disclosure of Invention
In view of this, the embodiments of the present specification provide a service processing method. The present specification also relates to a service processing apparatus, a computing device, and a computer-readable storage medium, so as to solve the technical defects in the prior art.
According to a first aspect of embodiments of the present specification, there is provided a service processing method, including:
acquiring user data of a service platform and determining service processing conditions of each service in the service platform;
splitting the user data into subdata based on the service processing condition;
reading service subdata corresponding to the service from the subdata according to the multi-bin tasks corresponding to the service, and performing service data processing on the service subdata to obtain service processing data;
and updating the user data based on the service processing data.
Optionally, the updating the user data based on the service processing data includes:
updating the service subdata based on the service processing data, and determining an updating time node;
and updating an original time node corresponding to original subdata which is not processed by service data in the subdata according to the update time node, wherein the original subdata and the service subdata form the user data.
Optionally, after the step of obtaining the user data of the service platform and determining the service processing condition of each service in the service platform is executed, and before the step of splitting the user data into the sub-data based on the service processing condition is executed, the method further includes:
determining a timestamp corresponding to the user data, and judging whether the timestamp is consistent with a preset time node or not;
if so, performing service data processing on the user data according to the warehouse tasks corresponding to the services to obtain full service processing data, and updating the user data based on the full service processing data;
if not, executing the step of splitting the user data into sub data based on the service processing conditions.
Optionally, the splitting the user data into sub-data based on the service processing condition includes:
reading service data related to the service in the user data based on the service type of the service;
and splitting the service data into the subdata according to the service processing condition.
Optionally, the performing service data processing on the service sub data to obtain service processing data includes:
performing service data processing on the service subdata according to the warehouse tasks of the service to obtain target data;
and reading original data which is not processed by service data in the subdata based on the target data, and merging the target data and the original data into the service processing data.
Optionally, the performing service data processing on the service sub data to obtain service processing data includes:
determining a multi-bin model corresponding to the multi-bin task;
inputting the service subdata into the multi-bin model, and performing service prediction related to the service on a user corresponding to the service subdata to obtain the service processing data output by the multi-bin model.
Optionally, the service includes at least one of the following:
the system comprises a quota consumption service, a quota extraction service, a mutual aid project service, a balance storage service and a guarantee project service.
Optionally, in a case that the service is a credit consumption service, the method includes:
determining the using condition of the quota consuming service;
splitting the user data into sub-user data based on the limit using condition;
reading the credit sub-data corresponding to the credit consuming service in the sub-user data according to the warehouse counting task corresponding to the credit consuming service, and carrying out credit data processing on the credit sub-data to obtain credit processing data;
and updating the user data based on the quota processing data.
According to a second aspect of embodiments herein, there is provided a service processing apparatus, including:
the system comprises an acquisition user data module, a service platform and a service processing module, wherein the acquisition user data module is configured to acquire user data of the service platform and determine service processing conditions of each service in the service platform;
a split user data module configured to split the user data into sub-data based on the service processing condition;
a service data processing module configured to read service subdata corresponding to the service from the subdata according to the multi-bin task corresponding to the service, and perform service data processing on the service subdata to obtain service processing data;
an update user data module configured to update the user data based on the service processing data.
Optionally, the module for updating user data includes:
the first updating unit is configured to update the service sub data based on the service processing data and determine an updating time node;
and a second updating unit configured to update, according to the update time node, an original time node corresponding to original sub-data that is not processed by service data in the sub-data, where the original sub-data and the service sub-data constitute the user data.
Optionally, the apparatus further comprises:
the judging module is configured to determine a timestamp corresponding to the user data and judge whether the timestamp is consistent with a preset time node;
if yes, operating a full data processing module;
the full data processing module is configured to perform service data processing on the user data according to the warehouse tasks corresponding to the services to obtain full service processing data, and update the user data based on the full service processing data;
and if not, operating the split user data module.
Optionally, the splitting the user data module includes:
a read service data unit configured to read service data related to the service in the user data based on a service type to which the service belongs;
and the splitting service data unit is configured to split the service data into the subdata according to the service processing condition.
According to a third aspect of embodiments herein, there is provided a computing device comprising:
a memory and a processor;
the memory is to store computer-executable instructions, and the processor is to execute the computer-executable instructions to:
acquiring user data of a service platform and determining service processing conditions of each service in the service platform;
splitting the user data into subdata based on the service processing condition;
reading service subdata corresponding to the service from the subdata according to the multi-bin tasks corresponding to the service, and performing service data processing on the service subdata to obtain service processing data;
and updating the user data based on the service processing data.
According to a fourth aspect of embodiments herein, there is provided a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the business processing method.
In the service processing method provided in an embodiment of this specification, before service data is processed, user data of a service platform is obtained, a service processing condition of each service in the service platform is determined, the user data is split based on the service processing condition to obtain sub-data, service sub-data corresponding to the service is read from the sub-data according to a number-bin task corresponding to the service, then the service sub-data is processed to obtain the service processing data, and finally the user data is updated based on the service processing data, so that in a process of performing corresponding service data processing for the number-bin task corresponding to the service, data to be processed can be split from the full amount of user data to be processed, and data not requiring service data processing is not processed, and furthermore, the data processing efficiency can be effectively improved, the operation of the multi-bin tasks is accelerated, and simultaneously the effect of saving hardware resources is achieved, so that the service data processing process is more time-saving and resource-saving.
Drawings
Fig. 1 is a flowchart of a service processing method provided in an embodiment of the present specification;
fig. 2 is a processing flow chart of a service processing method applied in a payment service platform according to an embodiment of the present specification;
fig. 3 is a schematic structural diagram of a service processing apparatus according to an embodiment of the present disclosure;
fig. 4 is a block diagram of a computing device according to an embodiment of the present disclosure.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, as those skilled in the art will be able to make and use the present disclosure without departing from the spirit and scope of the present disclosure.
The terminology used in the description of the one or more embodiments is for the purpose of describing the particular embodiments only and is not intended to be limiting of the description of the one or more embodiments. As used in one or more embodiments of the present specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It will be understood that, although the terms first, second, etc. may be used herein in one or more embodiments to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first can also be referred to as a second and, similarly, a second can also be referred to as a first without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
First, the noun terms to which one or more embodiments of the present specification relate are explained.
Counting bins: the data warehouse is a platform for developing, publishing, operating and managing big data; the method aims to provide decision support for enterprises or users by constructing an analysis-oriented integrated data environment.
And (3) counting the bins: one issued to the corresponding data warehouse, and operable with separate internal logic data processing tasks.
A bin counting model: a collection of complete independent custom development scripts or programs that run on several bins of tasks.
In the present specification, a service processing method is provided, and the present specification relates to a service processing apparatus, a computing device, and a computer-readable storage medium, which are described in detail in the following embodiments one by one.
Fig. 1 shows a flowchart of a service processing method according to an embodiment of the present specification, which specifically includes the following steps:
step 102: the method comprises the steps of obtaining user data of a service platform and determining service processing conditions of each service in the service platform.
In an embodiment of the present specification, the service platform is specifically a platform that provides services for a user, and each service in the corresponding service platform may be a limit consumption service, a limit extraction service, a mutual aid project service, a balance storage service, a guarantee project service, a catering booking service, a living payment service, and the like; the user data specifically refers to data acquired by the service platform and required by the user for each service.
For example, under the condition that the service platform provides a quota consumption service and a balance storage service, wherein the quota consumption service refers to a quota for shopping consumption provided by the service platform for the user, and the user only needs to pay for the consumption quota according to a rule of the quota consumption service, the balance storage service refers to a service for storing funds provided by the service platform for the user, and the user can use the funds in the balance at any time so as to be convenient for the user to use; the user data of the user collected by the service platform is data related to the limit consumption service and the balance storage service, and can be data corresponding to the upper limit of the user consumption limit, data corresponding to funds stored by the user, data corresponding to the fund frequency in the user use balance, and the like.
Based on this, the limit extraction service refers to a borrowing support service provided by the service platform for the user, specifically, the user can borrow through the service platform and pay according to the rules of the limit extraction service and borrow interest on time; the mutual aid project service is that the service platform provides mutual aid project service for users, specifically, the users can help other users suffering from diseases through the service platform, and when the users are ill and wounded, the users can also seek help of other users through the service platform; the guarantee project service refers to a service that a user can participate in a guarantee project through a service platform, and specifically refers to a guarantee service that the user can obtain the guarantee project service through purchasing the guarantee project.
In specific implementation, because services provided by each service in the service platform to the user are different, the service platform collects user data of the user and includes data with different dimensions, such as attribute data of the user, consumption data of the user, professional data of the user, and the like; the users corresponding to each service are users belonging to the service platform relative to the service platform, and the user data corresponding to the total number of users of the service platform is also large in quantity; for example, a service platform has a service a, a service B and a service C, where the service a has a user a and a user B, the service B has a user C and a user d, and the service C has a user a and a user d, so the users of the service platform have a user a, a user B, a user C and a user d, and the user data acquired by the corresponding service platform includes data of the user a, the user B, the user C and the user d relative to the service a, the service B and the service C;
when each service needs to process user data corresponding to the full amount of users of the service platform in the process of processing service data aiming at the corresponding warehouse tasks, the service data processing result obtained by each service is also the processing result aiming at the full amount of user data, and at the moment, the service data processing process of the service is also carried out on the user data corresponding to the users irrelevant to the service; along with the above example, in the process of service-related prediction of the user by the service a, corresponding service data processing is performed on the data corresponding to the user a, the user b, the user c and the user d, and at this time, the service data processing of the service a is also performed on the user c and the user d irrespective to the service a; however, since the data volume of the full amount of user data is large, it will take much time and more hardware resources to complete the service processing process corresponding to the service, and thus, the efficiency of service data processing can be effectively improved by reducing the running time and hardware resources in the service data processing process.
In order to improve the processing efficiency of the service data processing process for the multi-bin tasks corresponding to the services and reduce the hardware resources consumed in the service data processing process, the service processing method provided by this specification obtains the user data of the service platform, determines the service processing conditions of each service in the service platform at the same time, splits the user data based on the service processing conditions to obtain the subdata, reads the service subdata corresponding to the services from the subdata according to the multi-bin tasks corresponding to the services, processes the service data for the service subdata to obtain the service processing data, updates the user data based on the service processing data, and can split the data to be processed from the full amount of user data to process in the process of processing the corresponding service data for the multi-bin tasks corresponding to the services, and the data which does not need the service data processing does not need to be processed correspondingly, so that the data processing efficiency can be effectively improved, the operation of the multi-bin tasks is accelerated, and the effect of saving hardware resources is achieved, so that the service data processing process is more time-saving and resource-saving.
In practical application, if it is required to improve the processing efficiency of service data processing in the service processing process, it is preferred that user data is split according to the service processing conditions of the service, where the service processing conditions are used to split user data of a user related to the service from the user data; the service processing condition can be understood as that user data of a user related to the service can be selected from all users of the service platform to perform a subsequent service data processing process; along with the above example, in the process of service-related prediction of the user by service a, the user data of the user a and the user b related to the service a may be selected to perform subsequent service data processing, and the service processing condition corresponding to the service a is the service data processing process corresponding to the warehouse task of the service a performed by the user who has selected to participate in the service a among the total users of the service platform.
In addition, the service processing condition may be determined according to historical action data of the user for each service, that is, when the user has historical action data for the service, it may be determined that the user is related to the service, and in the process of performing subsequent splitting of the user data, the user data corresponding to the user may be determined as the service sub data corresponding to the service.
Further, on the basis of acquiring the user data of the service platform and determining the service processing conditions of each service, in order to avoid inaccurate results of subsequent service data processing caused by the change of the user data, the user data may be fully flushed at a preset time node, that is, the service data processing of the warehouse tasks corresponding to the services is performed on the full user data, in one or more embodiments of this embodiment, the service data processing of the full user data is performed, and the specific description contents are as follows:
determining a timestamp corresponding to the user data, and judging whether the timestamp is consistent with a preset time node or not;
if so, performing service data processing on the user data according to the warehouse tasks corresponding to the services to obtain full service processing data, and updating the user data based on the full service processing data;
if not, go to step 104.
Specifically, a timestamp corresponding to the user data is determined, where the timestamp is used to indicate that the user data is already present, completed, and verifiable at a certain specific time, and then it is determined whether the timestamp is consistent with a preset time node, where determining whether the timestamp is consistent with the preset time node specifically means determining whether the time node corresponding to the user data reaches an effective time node of full flushing;
if so, it is indicated that the user data at the moment needs to perform service data processing for the warehouse counting task corresponding to the service, the user data is subjected to service data processing according to the warehouse counting task corresponding to the service to obtain full service processing data, the user data is updated according to the full service processing data, and if not, it is indicated that the user data at the moment does not reach a preset time node, and then the subsequent step 104 is executed.
For example, the service platform comprises credit consumption service and mutual aid project service, wherein the total users of the service platform have 10 hundred million, and the credit consumption service has 5 hundred million users; the method comprises the steps that a user can shop through an available quota provided by a service platform in quota consumption service, the quota consumption service needs to carry out quota lifting prediction on the user using the service at the moment, a corresponding warehouse task is quota lifting prediction, the time node corresponding to user data is determined to be 20 years, 12 months and 1 day through a time stamp of the user data in a user database of a service platform, the time stamp is determined to be consistent with a preset time node at the moment, the quota consumption service needs to carry out quota lifting prediction on the whole user data corresponding to 10 billion users at the moment, the number of 5 billion users of the quota consumption service is determined to be increased to 5.5 users according to a service data processing result, wherein the number of the newly added user (the user newly added to the service platform) is 0.1 billion, the user data in the user database is updated, the updated user data indicates that the service platform has 10.1 billion users, the credit consumption service has 5.5 hundred million users, so that the user database has user data corresponding to 10.1 hundred million users.
By carrying out full-volume service data processing on the user data according to the multi-bin tasks corresponding to the services at the preset time nodes, the condition that the processing result of carrying out service data processing corresponding to the services by other time nodes is inaccurate due to the change of the user data is avoided, the accuracy of the service data processing process is effectively improved, and more accurate service processing results can be made for the multi-bin tasks.
Step 104: and splitting the user data into subdata based on the service processing condition.
Specifically, on the basis of obtaining the user data of the service platform and determining the service processing conditions of each service, the user data is further split according to the service processing conditions to obtain the sub-data, wherein all the sub-data can be merged into the user data, and only the service sub-data related to the service can be processed for the multi-bin task corresponding to the subsequent service by splitting the user data, so that the service data processing efficiency is improved.
In practical applications, in the process of splitting the user data, service processing conditions corresponding to each service are different, so that splitting ratios are also different, the description will describe the splitting of the user data with a service processing condition corresponding to a service, and other services may refer to each other in the process of splitting the user data according to the service processing conditions, which is not described in detail herein.
Further, in the process of splitting the user data according to the service processing condition, in order to accelerate the processing efficiency of subsequent service data, data related to the service may be read from the user data and split, and then subsequent service data processing may be performed, in one or more embodiments of this embodiment, a specific process of splitting the data is as follows:
reading service data related to the service in the user data based on the service type of the service;
and splitting the service data into the subdata according to the service processing condition.
Specifically, the service type to which the service belongs is determined, then the service data related to the service is read from the user data, at this time, the service data can be determined to be the data of the service corresponding to the full number of users of the service slip, and finally, the service data is split according to the service processing conditions of the service, so that the sub data can be obtained; in practical application, the service data is composed of all the subdata.
For example, the user data of the service platform includes attribute data, consumption data and occupational data of the user, and the service platform has 10 hundred million users, under the condition that the credit consumption service in the service platform needs to predict the credit increase probability of the user, the consumption data related to the credit consumption service is read in the user data, and the credit consumption is determined to be smaller than 1000 yuan as a first interval according to the service processing conditions of the credit consumption service, the credit consumption is greater than or equal to 1000 yuan and smaller than 2000 yuan as a second interval, the credit consumption is greater than or equal to 2000 yuan as a third interval, the consumption data corresponding to the 10 hundred million users is split according to the service processing conditions, the first consumption subdata corresponding to the user in the first interval is determined, the second consumption subdata corresponding to the user in the second interval, the third interval corresponds to the third consumption subdata of the user, the user data not participating in the credit consumption service is fourth consumption subdata, the consumption data corresponding to the 10 hundred million users consists of first consumption subdata, second consumption subdata, third consumption subdata and fourth consumption subdata.
In the process of splitting data, in order to further improve the processing efficiency of subsequent service data processing, in the splitting process, the service data related to the service is read from the user data and split according to the service processing conditions, so that subdata needing to be subsequently processed and subdata needing not to be processed can be extracted from the user data, the subdata needing to be subsequently processed can be conveniently and directly read for processing the service data, and a large amount of service data processing time is saved.
Step 106: and reading the service subdata corresponding to the service from the subdata according to the multi-bin tasks corresponding to the service, and processing the service data of the service subdata to obtain service processing data.
Specifically, on the basis of splitting the user data into the sub-data, further, a multi-bin task corresponding to the service is determined first, where the multi-bin task is specifically a task for processing the sub-data of the current service, the service sub-data corresponding to the service is read from the sub-data according to the multi-bin task, and finally, the service data is processed according to the multi-bin task, so as to obtain the service processing data.
Based on this, the service subdata specifically refers to a task that needs to perform service data processing according to the several-bin tasks in the service, and the corresponding service processing data may be data obtained after the service subdata is processed by service data, or data obtained after the service subdata is processed by service data and data obtained after the service data is not processed by the service data are combined; for example, the service sub data is consumption data of the user, the warehouse task is used for predicting the probability of increasing the consumption amount of the user, corresponding prediction service data processing is carried out on the consumption data according to the warehouse task, and the specific details of the probability of increasing the consumption amount of the user are obtained and are the service processing data.
Further, on the basis of determining the several-bin task corresponding to the service and the service sub-data related to the service, the service sub-data also needs to be processed, and after the service data is processed, data that is not processed by the service data and data that is processed by the service data need to be merged, and then subsequent user data update is performed, in one or more embodiments of this embodiment, in order to ensure that all user data of the service platform can be updated, the following method may be implemented:
performing service data processing on the service subdata according to the warehouse tasks of the service to obtain target data;
and reading original data which is not processed by service data in the subdata based on the target data, and merging the target data and the original data into the service processing data.
Specifically, the target data is data obtained through the service data processing, and the original data is data that is not processed by the service data in the sub-data, based on which, the service data is processed according to the multi-bin task of the service to obtain the target data, and then the original data that is not processed by the service data is read from the data sub-data based on the target data, and the service processing data is obtained by combining the original data and the target data.
According to the above example, the consumption data of 10 hundred million users are split according to the credit consumption service to obtain first consumption subdata, second consumption subdata, third consumption subdata and fourth consumption subdata, then the consumption subdata is processed according to the number bin task (predicting the credit increasing probability of the users) of the credit consumption service, the first consumption subdata, the second consumption subdata and the third consumption subdata are determined to be related to the credit consumption service, then the service data processing is carried out according to the first consumption subdata, the second consumption subdata and the third consumption subdata, namely the credit increasing probability of the users corresponding to the first consumption subdata, the second consumption subdata and the third consumption subdata is predicted to obtain target data corresponding to the credit consumption service, wherein the target data is data corresponding to the credit increasing probability of each user of the consumption service, at this time, the fourth consumption subdata is determined as original data, and service processing data of the quota consumption service, namely data corresponding to the probability of increasing the quota by 10 billion users, can be determined by combining the original data and the target data.
The service data processing is carried out on the service subdata related to the service according to the warehouse tasks to obtain the target data, and then the target data and the original data are combined to obtain the service processing data, so that the data of all users of the service platform aiming at the service can be updated in the subsequent updating process of the user data, and the condition that time nodes are inconsistent in the user data of the service platform is avoided.
In a specific implementation, in the process of performing service data processing on the service sub data according to the multi-bin task, the service sub data is input to a multi-bin model corresponding to the multi-bin task to perform service prediction related to a service, and in one or more implementations of this embodiment, a specific implementation manner of the service data processing process is as follows:
determining a multi-bin model corresponding to the multi-bin task;
inputting the service subdata into the multi-bin model, and performing service prediction related to the service on a user corresponding to the service subdata to obtain the service processing data output by the multi-bin model.
Specifically, a multi-bin model corresponding to the multi-bin task is determined, and service prediction related to the service is performed on a user corresponding to the service subdata by inputting the service subdata into the multi-bin model, so that the service processing data corresponding to the user and output by the multi-bin model is obtained.
In practical applications, the bin model is a model related to the business prediction, and the description is not limited herein.
According to the above example, the consumption data of 10 hundred million users are split according to the credit consumption service to obtain first consumption subdata, second consumption subdata, third consumption subdata and fourth consumption subdata, then the consumption subdata is processed according to the number bin task (predicting the probability of increasing credit of users) of the credit consumption service, the first consumption subdata, the second consumption subdata and the third consumption subdata are determined to be related to the credit consumption service, then the service data processing is carried out according to the first consumption subdata, the second consumption subdata and the third consumption subdata, namely the credit increase probability of users corresponding to the first consumption subdata, the second consumption subdata and the third consumption subdata is predicted, and the first consumption subdata, the second consumption subdata and the third consumption subdata are input into the number bin model corresponding to the consumption service, and predicting the probability of increasing the quota of the user corresponding to the first consumption subdata, the second consumption subdata and the third consumption subdata to obtain the probability of increasing the quota of each user output by the multi-bin model, namely the service processing data.
Step 108: and updating the user data based on the service processing data.
Specifically, on the basis of performing service data processing on the service sub data according to the multi-bin tasks to obtain the service processing data, the user data may be further updated according to the service processing data.
Further, in the process of updating the user data, in order to enable all the user data corresponding to the service platform to be updated so as to achieve consistency of time nodes corresponding to data in the user data, in one or more embodiments of this embodiment, the user data may be updated by updating the service sub data in the sub data and also updating the original sub data, where the specific implementation manner is as follows:
updating the service subdata based on the service processing data, and determining an updating time node;
and updating an original time node corresponding to original subdata which is not processed by service data in the subdata according to the update time node, wherein the original subdata and the service subdata form the user data.
Specifically, since the user data is split into the sub-data, and the service sub-data is obtained by reading the sub-data, that is, the user data may be understood as being composed of the service sub-data and other sub-data (original sub-data), where the original sub-data specifically refers to data that has not been processed by the service data in the sub-data, so that in the process of updating the user data, the service sub-data needs to be updated according to the service processing data, and at this time, there will be time nodes where the original sub-data has not been updated, which causes inconsistency of time nodes of all corresponding data in the user data, and in order to avoid inconvenience caused by the situation that the service of the service platform is processed by other service data, the original sub-data can also be updated;
based on this, the method for updating the original sub-data specifically refers to updating the time node of the original sub-data, and specifically includes determining an update time node after the service processing data updates the service sub-data, and updating the time node of the original sub-data based on the time node, so that the time nodes of the full amount of data contained in the updated user data are all consistent.
According to the use example, after the first consumption subdata, the second consumption subdata and the third consumption subdata are determined to be processed through the service data of the quota consumption service, service processing data are obtained, at the moment, the service processing data and the fourth consumption subdata exist, the first consumption subdata, the second consumption subdata and the third consumption subdata in the consumption data are updated according to the service processing data, the updating time node is determined to be 20 x years, 12 months, 11 days, 09 hours, 00 minutes and 01 seconds, the time node corresponding to the fourth consumption subdata is updated based on the updating time node, 20 x years, 12 months, 10 days, 09 hours, 00 minutes and 01 seconds, the updated consumption data are composed of the fourth consumption subdata and the service processing data, and the time nodes are consistent.
In the process of updating the user data based on the service processing data, the service subdata is updated through the service processing data, and then the original subdata is updated through the update time node of the service subdata, so that the full data contained in the user data can be updated, the time nodes corresponding to the user data are ensured to be the same, and the convenience of each service of a service platform in the process of processing other service data and the accuracy of service data processing are improved.
In specific implementation, when the service is a credit consumption service, the service processing method specifically includes the following steps:
step 1: determining the using condition of the quota consuming service;
step 2: splitting the user data into sub-user data based on the limit using condition;
and step 3: reading the credit sub-data corresponding to the credit consuming service in the sub-user data according to the warehouse counting task corresponding to the credit consuming service, and carrying out credit data processing on the credit sub-data to obtain credit processing data;
and 4, step 4: and updating the user data based on the quota processing data.
Specifically, referring to the above description, when it is determined that the service is the credit consumption service, determining a credit usage condition of the credit consumption service, where the credit usage condition specifically refers to splitting the user data according to a credit consumption service opening condition, splitting the user data into sub-user data, where the sub-user data includes data corresponding to a credit consumption service opening user and data corresponding to a credit consumption service non-opening user;
and reading the quota data corresponding to the quota consumption service in the sub-user data according to a number bin task corresponding to the quota consumption service, and performing quota data processing on the quota data according to the number bin task to obtain quota processing data, wherein the quota data processing corresponding to the number bin task can be used for predicting the probability of increasing the quota of the user using the quota consumption service, the quota processing data is data corresponding to the probability details of increasing the quota of the user, and finally, the user data is updated through the quota processing data, namely, the data corresponding to the quota increasing probability of the user in the user data of the service platform is changed.
The service processing method provided by the present specification obtains user data of a service platform, determines service processing conditions of each service in the service platform, splits the user data based on the service processing conditions to obtain the sub-data, reads service sub-data corresponding to the service from the sub-data according to a number of bin tasks corresponding to the service, processes the service data to obtain the service processing data, and updates the user data based on the service processing data, so that data to be processed can be split from the total amount of user data to be processed in the process of performing corresponding service data processing for the number of bin tasks corresponding to the service, thereby achieving the effect of effectively improving data processing efficiency and saving hardware resources while accelerating the operation of the number of bin tasks, therefore, the service data processing process is more time-saving and resource-saving, and in order to improve the accuracy of service data processing, service-related service data processing is performed on the full amount of user data at a preset time node, so that the accuracy of service data processing is ensured.
The following will further describe the service processing method by taking an application of the service processing method provided in this specification in a payment service platform as an example with reference to fig. 2. Fig. 2 shows a processing flow chart of a service processing method applied in a payment service platform according to an embodiment of the present specification, which specifically includes the following steps:
step 202: the method comprises the steps of obtaining full user data of a payment service platform and determining service processing conditions of fund storage services in the payment service platform.
Specifically, the payment service platform comprises a fund storage service, a quota consumption service and a mutual aid project service, wherein the fund storage service specifically refers to a service for providing fund storage service for a user, and the user can store funds on the payment service platform, so that the user can pay for consumption in a shopping or ordering scene conveniently;
based on this, the payment service platform has 10 hundred million users, so the user data corresponding to the 10 hundred million users is stored in the user database of the payment service platform, and at this time, the payment service platform needs to predict the storage upper limit of the fund storage service used by the user, that is, predict the probability that the user reaches 1 ten thousand yuan through the fund storage service, wherein the service processing condition of the fund storage service is to open the fund storage service.
Step 204: determining the time stamp of the full amount of user data, and judging whether the time stamp is consistent with a preset time node; if yes, go to step 206; if not, go to step 210.
Step 206: and performing service data processing on the full-volume user data according to the warehouse tasks corresponding to the fund storage service to obtain full-volume service processing data.
Step 208: and updating the full user data according to the full service processing data.
Specifically, under the condition that the timestamp is determined to be consistent with the preset time node, it is indicated that business data processing corresponding to a multi-bin task of fund storage business needs to be carried out on full-amount user data at the moment, namely, the probability that fund storage quota reaches 1 ten thousand yuan needs to be predicted on 10 hundred million users, and full-amount business processing data of the 10 hundred million users are obtained;
based on the method, the data of the full-volume user is updated according to the data processed by the full-volume service, and the probability that 10 hundred million users store the fund amount of 1 ten thousand yuan at the payment service platform through the fund storage service is determined.
Step 210: and splitting the full amount of user data into sub-user data according to the service processing conditions.
Specifically, according to the method for opening the fund storage service, user data corresponding to 10 hundred million users is split into first user data relevant to the fund storage service and second user data irrelevant to the fund storage service, and 2 hundred million users of the users relevant to the fund storage service are determined.
Step 212: and reading fund subdata related to the fund storage service in the subdue data according to the warehouse tasks of the fund storage service.
Step 214: and carrying out fund data processing on the fund sub-data according to the plurality of the bin tasks to obtain service processing data.
Specifically, under the condition that the full amount of user data is split into first user data related to the fund storage service and second user data unrelated to the fund storage service, the first user data is fund subdata, and the fund subdata is data related to the fund storage service corresponding to 2 hundred million users;
based on the above, the fund sub-data is subjected to fund data processing according to a plurality of bins, namely, the probability that the fund storage amount of the 2 hundred million users exceeds 1 ten thousand yuan is predicted according to the fund sub-data, and the obtained business processing data is the data corresponding to the probability that the fund storage amount of the 2 hundred million users exceeds 1 ten thousand yuan.
Step 216: and merging the service processing data with the original sub-data in the sub-user data which is not processed by the fund data.
Step 218: and determining target user data according to the merging result, and updating the full user data.
Specifically, the full amount of user data is composed of original subdata and fund subdata, and at this time, service processing data processed by the fund data and the original subdata not processed by the fund data are combined to obtain target user data;
based on the method, when the full-volume user data is updated, the business processing data in the target user data can be selected to update the fund subdata in the full-volume user data, the update time node is obtained, the original time node of the original subdata is updated based on the update time node, the full-volume user data can be updated based on the business processing data and the original subdata, and the updated full-volume user data can indicate the detailed data with the probability that the fund storage amount of each user exceeds 1 ten thousand yuan.
The service processing method provided by the specification can split the data to be processed from the full amount of user data for processing, so that the data processing efficiency can be effectively improved, the operation of the multi-bin task is accelerated, and the effect of saving hardware resources is achieved, so that the service data processing process is more time-saving and resource-saving, and in order to improve the accuracy of service data processing, the service data related to the full amount of user data can be processed at the preset time node, so that the accuracy of service data processing is ensured.
Corresponding to the above method embodiment, this specification further provides an embodiment of a service processing apparatus, and fig. 3 shows a schematic structural diagram of a service processing apparatus provided in an embodiment of this specification. As shown in fig. 3, the apparatus includes:
an acquire user data module 302 configured to acquire user data of a service platform and determine service processing conditions of each service in the service platform;
a split user data module 304 configured to split the user data into sub-data based on the service processing condition;
a service data processing module 306, configured to read the service sub data corresponding to the service from the sub data according to the multi-bin task corresponding to the service, and perform service data processing on the service sub data to obtain service processing data;
an update user data module 308 configured to update the user data based on the traffic processing data.
In an alternative embodiment, the update user data module 308 includes:
the first updating unit is configured to update the service sub data based on the service processing data and determine an updating time node;
and a second updating unit configured to update, according to the update time node, an original time node corresponding to original sub-data that is not processed by service data in the sub-data, where the original sub-data and the service sub-data constitute the user data.
In an optional embodiment, the service processing apparatus further includes:
the judging module is configured to determine a timestamp corresponding to the user data and judge whether the timestamp is consistent with a preset time node;
if yes, operating a full data processing module;
the full data processing module is configured to perform service data processing on the user data according to the warehouse tasks corresponding to the services to obtain full service processing data, and update the user data based on the full service processing data;
if not, the split user data module 304 is run.
In an optional embodiment, the split user data module 304 includes:
a read service data unit configured to read service data related to the service in the user data based on a service type to which the service belongs;
and the splitting service data unit is configured to split the service data into the subdata according to the service processing condition.
In an optional embodiment, the service data processing module 306 includes:
the first processing unit is configured to perform service data processing on the service sub data according to the warehouse tasks of the service to obtain target data;
a merging unit configured to read original data that is not processed by service data in the sub-data based on the target data, and merge the target data and the original data into the service processing data.
In an optional embodiment, the service data processing module 306 includes:
a determining model unit configured to determine a bin model corresponding to the bin task;
and the second processing unit is configured to input the service subdata into the multi-bin model, perform service prediction related to the service on a user corresponding to the service subdata, and obtain the service processing data output by the multi-bin model.
In an optional embodiment, the service includes at least one of:
the system comprises a quota consumption service, a quota extraction service, a mutual aid project service, a balance storage service and a guarantee project service.
In an optional embodiment, in a case that the service is a credit consumption service, the service processing apparatus includes:
the determining module is configured to determine the using condition of the quota consuming service;
the splitting module is configured to split the user data into sub-user data based on the limit using condition;
the processing module is configured to read the credit sub-data corresponding to the credit consuming service in the sub-user data according to the warehouse counting task corresponding to the credit consuming service, and perform credit data processing on the credit sub-data to obtain credit processing data;
and the updating module is configured to update the user data based on the quota processing data.
The service processing apparatus provided in this specification obtains user data of a service platform, determines service processing conditions of each service in the service platform, splits the user data based on the service processing conditions to obtain the sub-data, reads service sub-data corresponding to the service from the sub-data according to a number of bin tasks corresponding to the service, processes the service data to obtain the service processing data, and updates the user data based on the service processing data, so that data to be processed can be split from the total amount of user data to be processed in a process of performing corresponding service data processing for the number of bin tasks corresponding to the service, thereby effectively improving data processing efficiency, and achieving an effect of saving hardware resources while accelerating operation of the number of bin tasks, therefore, the service data processing process is more time-saving and resource-saving, and in order to improve the accuracy of service data processing, service-related service data processing is performed on the full amount of user data at a preset time node, so that the accuracy of service data processing is ensured.
The foregoing is a schematic scheme of a service processing apparatus according to this embodiment. It should be noted that the technical solution of the service processing apparatus and the technical solution of the service processing method belong to the same concept, and details that are not described in detail in the technical solution of the service processing apparatus can be referred to the description of the technical solution of the service processing method.
FIG. 4 illustrates a block diagram of a computing device 400 provided according to an embodiment of the present description. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. Processor 420 is coupled to memory 410 via bus 430 and database 450 is used to store data.
Computing device 400 also includes access device 440, access device 440 enabling computing device 400 to communicate via one or more networks 460. Examples of such networks include the Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. The access device 440 may include one or more of any type of network interface (e.g., a Network Interface Card (NIC)) whether wired or wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) wireless interface, a worldwide interoperability for microwave access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a bluetooth interface, a Near Field Communication (NFC) interface, and so forth.
In one embodiment of the present description, the above-described components of computing device 400, as well as other components not shown in FIG. 4, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 4 is for purposes of example only and is not limiting as to the scope of the present description. Those skilled in the art may add or replace other components as desired.
Computing device 400 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smartphone), wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 400 may also be a mobile or stationary server.
Wherein processor 420 is configured to execute the following computer-executable instructions:
acquiring user data of a service platform and determining service processing conditions of each service in the service platform;
splitting the user data into subdata based on the service processing condition;
reading service subdata corresponding to the service from the subdata according to the multi-bin tasks corresponding to the service, and performing service data processing on the service subdata to obtain service processing data;
and updating the user data based on the service processing data.
The above is an illustrative scheme of a computing device of the present embodiment. It should be noted that the technical solution of the computing device and the technical solution of the service processing method belong to the same concept, and details that are not described in detail in the technical solution of the computing device can be referred to the description of the technical solution of the service processing method.
An embodiment of the present specification also provides a computer readable storage medium storing computer instructions that, when executed by a processor, are operable to:
acquiring user data of a service platform and determining service processing conditions of each service in the service platform;
splitting the user data into subdata based on the service processing condition;
reading service subdata corresponding to the service from the subdata according to the multi-bin tasks corresponding to the service, and performing service data processing on the service subdata to obtain service processing data;
and updating the user data based on the service processing data.
The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium belongs to the same concept as the technical solution of the service processing method, and details that are not described in detail in the technical solution of the storage medium can be referred to the description of the technical solution of the service processing method.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The computer instructions comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, or the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present disclosure is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present disclosure. Further, those skilled in the art should also appreciate that the embodiments described in this specification are preferred embodiments and that acts and modules referred to are not necessarily required for this description.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The preferred embodiments of the present specification disclosed above are intended only to aid in the description of the specification. Alternative embodiments are not exhaustive and do not limit the invention to the precise embodiments described. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the specification and its practical application, to thereby enable others skilled in the art to best understand the specification and its practical application. The specification is limited only by the claims and their full scope and equivalents.
Claims (14)
1. A service processing method comprises the following steps:
acquiring user data of a service platform and determining service processing conditions of each service in the service platform;
splitting the user data into subdata based on the service processing condition;
reading service subdata corresponding to the service from the subdata according to the multi-bin tasks corresponding to the service, and performing service data processing on the service subdata to obtain service processing data;
and updating the user data based on the service processing data.
2. The traffic processing method according to claim 1, wherein said updating the user data based on the traffic processing data comprises:
updating the service subdata based on the service processing data, and determining an updating time node;
and updating an original time node corresponding to original subdata which is not processed by service data in the subdata according to the update time node, wherein the original subdata and the service subdata form the user data.
3. The service processing method according to claim 1, after the step of obtaining user data of a service platform and determining a service processing condition of each service in the service platform is executed, and before the step of splitting the user data into sub-data based on the service processing condition is executed, further comprising:
determining a timestamp corresponding to the user data, and judging whether the timestamp is consistent with a preset time node or not;
if so, performing service data processing on the user data according to the warehouse tasks corresponding to the services to obtain full service processing data, and updating the user data based on the full service processing data;
if not, executing the step of splitting the user data into sub data based on the service processing conditions.
4. The service processing method according to claim 1, wherein the splitting the user data into sub-data based on the service processing condition includes:
reading service data related to the service in the user data based on the service type of the service;
and splitting the service data into the subdata according to the service processing condition.
5. The service processing method according to claim 1, wherein the performing service data processing on the service sub data to obtain service processing data includes:
performing service data processing on the service subdata according to the warehouse tasks of the service to obtain target data;
and reading original data which is not processed by service data in the subdata based on the target data, and merging the target data and the original data into the service processing data.
6. The service processing method according to claim 1, wherein the performing service data processing on the service sub data to obtain service processing data includes:
determining a multi-bin model corresponding to the multi-bin task;
inputting the service subdata into the multi-bin model, and performing service prediction related to the service on a user corresponding to the service subdata to obtain the service processing data output by the multi-bin model.
7. The traffic processing method according to claim 1, wherein the traffic comprises at least one of:
the system comprises a quota consumption service, a quota extraction service, a mutual aid project service, a balance storage service and a guarantee project service.
8. The service processing method according to claim 7, in case that the service is a credit consumption service, the method includes:
determining the using condition of the quota consuming service;
splitting the user data into sub-user data based on the limit using condition;
reading the credit sub-data corresponding to the credit consuming service in the sub-user data according to the warehouse counting task corresponding to the credit consuming service, and carrying out credit data processing on the credit sub-data to obtain credit processing data;
and updating the user data based on the quota processing data.
9. A traffic processing apparatus, comprising:
the system comprises an acquisition user data module, a service platform and a service processing module, wherein the acquisition user data module is configured to acquire user data of the service platform and determine service processing conditions of each service in the service platform;
a split user data module configured to split the user data into sub-data based on the service processing condition;
a service data processing module configured to read service subdata corresponding to the service from the subdata according to the multi-bin task corresponding to the service, and perform service data processing on the service subdata to obtain service processing data;
an update user data module configured to update the user data based on the service processing data.
10. The traffic processing apparatus according to claim 9, wherein the update user data module comprises:
the first updating unit is configured to update the service sub data based on the service processing data and determine an updating time node;
and a second updating unit configured to update, according to the update time node, an original time node corresponding to original sub-data that is not processed by service data in the sub-data, where the original sub-data and the service sub-data constitute the user data.
11. The traffic processing apparatus of claim 9, the apparatus further comprising:
the judging module is configured to determine a timestamp corresponding to the user data and judge whether the timestamp is consistent with a preset time node;
if yes, operating a full data processing module;
the full data processing module is configured to perform service data processing on the user data according to the warehouse tasks corresponding to the services to obtain full service processing data, and update the user data based on the full service processing data;
and if not, operating the split user data module.
12. The traffic processing apparatus of claim 9, wherein the split subscriber data module comprises:
a read service data unit configured to read service data related to the service in the user data based on a service type to which the service belongs;
and the splitting service data unit is configured to split the service data into the subdata according to the service processing condition.
13. A computing device, comprising:
a memory and a processor;
the memory is to store computer-executable instructions, and the processor is to execute the computer-executable instructions to:
acquiring user data of a service platform and determining service processing conditions of each service in the service platform;
splitting the user data into subdata based on the service processing condition;
reading service subdata corresponding to the service from the subdata according to the multi-bin tasks corresponding to the service, and performing service data processing on the service subdata to obtain service processing data;
and updating the user data based on the service processing data.
14. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the traffic processing method of any of claims 1 to 8.
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