WO2019000962A1 - 收益计算方法、装置及计算机可读存储介质 - Google Patents

收益计算方法、装置及计算机可读存储介质 Download PDF

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Publication number
WO2019000962A1
WO2019000962A1 PCT/CN2018/076160 CN2018076160W WO2019000962A1 WO 2019000962 A1 WO2019000962 A1 WO 2019000962A1 CN 2018076160 W CN2018076160 W CN 2018076160W WO 2019000962 A1 WO2019000962 A1 WO 2019000962A1
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Prior art keywords
revenue
enterprise
service
data
service data
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PCT/CN2018/076160
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English (en)
French (fr)
Inventor
胡凯豪
胡伟
吕彤
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平安科技（深圳）有限公司
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Publication of WO2019000962A1 publication Critical patent/WO2019000962A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]

Definitions

  • the present invention relates to the field of data processing technologies, and in particular, to a revenue calculation method, apparatus, and computer readable storage medium.
  • the majority of wealth management products need to be calculated every day, and feedback the user accounts corresponding to the wealth management products, so that users can intuitively understand the financial management they purchase.
  • the current revenue of the product As the types of wealth management products continue to increase, each user account may involve multiple services. When calculating the user's revenue, all historical purchase data and historical sales data in different business scenarios corresponding to the user account need to be considered. .
  • the traditional way of calculating revenue is to aggregate all business data for unified calculation.
  • the complexity of calculating the income by the traditional income calculation method is increasing, which leads to an increase in the time required for the income calculation, and may cause system instability and increase the difficulty of system operation and maintenance.
  • the main object of the present invention is to provide a revenue calculation method, device and computer readable storage medium, which are intended to solve the technical problem that the time required for the revenue calculation increases as the amount of data corresponding to the wealth management product increases.
  • the present invention provides a revenue calculation method, the revenue calculation method comprising the following steps:
  • the distributed computing Hadoop platform is used to split the computing service data according to each service scenario to obtain the first service data corresponding to each service scenario;
  • the present invention also provides a revenue calculation device, the revenue calculation device comprising: a memory, a processor, and a revenue calculation program stored on the memory and operable on the processor, The step of implementing the revenue calculation method of any of the above described ones when the revenue calculation program is executed by the processor.
  • the present invention also provides a computer readable storage medium having stored thereon a revenue calculation program, the revenue calculation program being executed by a processor to implement any of the above The steps of the revenue calculation method.
  • the invention obtains the business data corresponding to the business data to be calculated and the business data to be calculated, and then uses the distributed computing Hadoop platform to split the computing business data based on each service scenario, and then uses the Hadoop platform to calculate each service based on the first service data.
  • the first revenue of the user account corresponding to the scenario is calculated, and finally, the total revenue of each user account corresponding to the service data to be calculated is calculated based on the first revenue, and the business data to be processed by the Hadoop platform is split according to the business scenario, and the split is separately calculated.
  • FIG. 1 is a schematic structural diagram of a terminal to which a revenue computing device belongs in a hardware operating environment according to an embodiment of the present invention
  • FIG. 2 is a schematic flow chart of a first embodiment of a revenue calculation method according to the present invention.
  • FIG. 3 is a schematic flowchart of a step of splitting a service data to be processed by a distributed computing Hadoop platform according to a second embodiment of the present invention
  • FIG. 4 is a schematic flowchart of a process of calculating a total revenue of each user account corresponding to the to-be-calculated service data based on the first revenue in the third embodiment of the revenue calculation method of the present invention
  • FIG. 5 is a schematic flowchart of a step of performing a verification operation on the first income obtained by calculating the fourth embodiment of the method for calculating a revenue according to the present invention
  • FIG. 6 is a schematic flowchart of a step of performing a verification operation on the first income obtained by calculating in a fifth embodiment of the method for calculating a revenue according to the present invention
  • FIG. 7 is a schematic flowchart diagram of a sixth embodiment of a revenue calculation method according to the present invention.
  • FIG. 1 is a schematic structural diagram of a terminal to which a revenue calculation device belongs in a hardware operation environment according to an embodiment of the present invention.
  • the terminal may be a PC, or may be a mobile terminal device such as a smart phone, a tablet computer, or a portable computer.
  • the terminal may include a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002.
  • the communication bus 1002 is used to implement connection communication between these components.
  • the user interface 1003 can include a display, an input unit such as a keyboard, and the optional user interface 1003 can also include a standard wired interface, a wireless interface.
  • the network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high speed RAM memory or a stable memory (non-volatile) Memory), such as disk storage.
  • the memory 1005 can also optionally be a storage device independent of the aforementioned processor 1001.
  • the terminal may further include a camera, RF (Radio) Frequency, RF) circuits, sensors, audio circuits, WiFi modules, and more.
  • sensors such as light sensors, motion sensors, and other sensors.
  • the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display according to the brightness of the ambient light, and the proximity sensor may turn off the display and/or when the mobile terminal moves to the ear. Backlighting.
  • the gravity acceleration sensor can detect the magnitude of acceleration in each direction (usually three axes), and can detect the magnitude and direction of gravity when stationary, and can be used to identify the posture of the mobile terminal (such as horizontal and vertical screen switching, Related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal can also be equipped with other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc. No longer.
  • terminal structure shown in FIG. 1 does not constitute a limitation to the terminal, and may include more or less components than those illustrated, or a combination of certain components, or different component arrangements.
  • an operating system may be included in the memory 1005 as a computer storage medium.
  • a network communication module may be included in the memory 1005 as a computer storage medium.
  • a user interface module may be included in the memory 1005 as a computer storage medium.
  • a revenue calculation program may be included in the memory 1005 as a computer storage medium.
  • the network interface 1004 is mainly used to connect to the background server and perform data communication with the background server;
  • the user interface 1003 is mainly used to connect the client (user end), and perform data communication with the client; and the processor
  • the 1001 may be used to invoke the revenue calculation program stored in the memory 1005, and execute the revenue calculation method provided by the embodiment of the present application.
  • FIG. 2 is a schematic flowchart diagram of a first embodiment of a revenue calculation method according to the present invention.
  • the revenue calculation method includes:
  • Step S10 Obtain a service scenario corresponding to the service data to be calculated and the service data to be calculated;
  • the business data to be calculated is all data related to the user's revenue, that is, the revenue-related data in all business scenarios corresponding to the user account, and the business scenario includes payment, fixed payment
  • the business scenarios of fixed-rate payments one-time payments, redemption, share redemption, installment payments, non-scheduled payments, account adjustments, separations, and out-of-plan transfers.
  • different revenue calculation times may be set according to different wealth management products, or a fixed income calculation time is set for the service data of all service scenarios, and the business data to be calculated is acquired when the current time reaches the calculation time.
  • the service data to be calculated may be obtained according to the preset rule, and then the corresponding service scenario is determined according to the to-be-calculated service data.
  • the preset rule includes the service of acquiring the preset service scenario (one or more of the foregoing service scenarios).
  • the service scenario corresponding to the service data to be calculated may be determined according to the identifier information, that is, the service scenario to which the service data in the service data to be calculated belongs.
  • the service data of the preset service scenario is associated with the identification information of each service scenario in the preset service scenario, and the service data is to be calculated, and the identifier information can be obtained by parsing the service data to be calculated, and then The identification information determines a service scenario corresponding to the service data to be calculated.
  • the obtaining the service data to be calculated according to the preset rule includes acquiring the revenue-related data corresponding to all the user accounts, or obtaining the service scenario, and then acquiring the data related to the revenue in the service scenario, that is, the service data to be calculated.
  • step S20 the distributed computing Hadoop platform is used to split the computing service data according to each service scenario to obtain the first service data corresponding to each service scenario;
  • the Hadoop platform is a distributed system infrastructure platform that can utilize clusters for high-speed distributed computing and storage.
  • the Hadoop platform is used to split the computing service data according to each service scenario.
  • the service data to be calculated includes the service to which each group of service data belongs.
  • the identification information of the scenario can be used to accurately determine the service scenario to which each group of service data belongs, and then classify the service data included in the service data to be accurately classified according to the service scenario, and classify the service data to be calculated.
  • the first service data corresponding to each service scenario is obtained, and the service data of each service scenario in the service data to be calculated is used as a group of service data, and each group of service data is used as the first service data, that is, the first service data.
  • the data includes multiple sets of service data, and each group of service data belongs to a different service scenario, so that the Hadoop platform is used to calculate the user revenue corresponding to each service scenario.
  • the service data to be calculated includes the identification information of the service scenario to which the service data belongs, and the data format of the service data corresponding to the different service scenarios is different. Therefore, the service information may be calculated according to the identification information and the data format of each service scenario.
  • the service data is split.
  • the service data to be calculated includes the service data whose identification information is the identification information of the payment scenario, and the header data and the tail data of the service data may be determined according to the data format corresponding to the payment scenario, and then the header data, The data between the tail data and the header data and the tail data is the service data of the payment scenario.
  • the service data of the payment scenario may further include other data; according to the above manner, the business data to be calculated can be accurately split to obtain each The first service data corresponding to the service scenario.
  • the type of the business scenario includes the outbound business scenario and the inbound business scenario.
  • the service scenario of the payment is an outbound business scenario
  • the business scenarios such as fixed payment, installment fixed payment, one-time payment, installment payment, and irregular payment are Inbound business scenario.
  • the service data is split, the type of the service scenario is classified, and the outbound service data corresponding to the outbound service scenario and the inbound service data corresponding to the inbound service scenario are obtained, and then the corresponding service data is determined.
  • the user account has the user account corresponding to the incoming service data at the same time, that is, whether there is a user account with both capital inflow and capital outflow, and when there is a user account with both capital inflow and capital outflow, according to the outgoing business data
  • the data of the user account and the data of the user account in the inbound business data determine whether the revenue and expenditure of the user account are offset, that is, the capital inflow and the capital outflow are the same, and if the income and expenditure are offset, the business data and the inflow are entered.
  • the data corresponding to the user account is deleted in the item service data.
  • the user account Since the revenue and expenditure of the user account are offset, the user account does not currently have a revenue, so there is no need to calculate the revenue of the user account, thereby reducing the amount of data when calculating the user income. .
  • the outgoing business data and the incoming business data are again split according to the business scenario.
  • Step S30 using the Hadoop platform to separately calculate a first revenue of a user account corresponding to each service scenario based on the first service data;
  • the Hadoop platform is used to calculate the first user account corresponding to each service scenario based on the first service data. income. Because the Hadoop platform includes multiple cluster servers, a separate cluster server can be allocated for each service scenario, so that the cluster server is only used to calculate the first service data corresponding to the service scenario, thereby greatly reducing the number of servers on the same server. The amount of data calculated in the data, which in turn reduces the time for data calculation.
  • the first income is the revenue corresponding to the user account during the current pricing period.
  • the first revenue is: the total amount of the user's input *10%* pricing
  • the number of days during the period /365, which, for general wealth management products, the corresponding pricing period is 7 days.
  • Step S40 Calculate a total revenue of each user account corresponding to the to-be-calculated service data based on the first revenue.
  • the revenue calculation method further includes:
  • Each of the user accounts is updated based on the total revenue.
  • the balance of each user account is obtained, and the corresponding user account is updated based on the calculated total income, for example, when the balance of a user account is 5W, and the total income corresponding to the user account is 500 yuan. After the update, the balance of the user account is 5. 05W yuan.
  • the account amount of each user account corresponding to the to-be-calculated service data is updated based on the total revenue.
  • the current total income and the account amount can be sent to the preset terminal corresponding to the user account by using a short message, an email, or a WeChat.
  • the revenue calculation method proposed in this embodiment obtains the service data corresponding to the service data to be calculated and the service data to be calculated, and then uses the distributed computing Hadoop platform to split the computing service data based on each service scenario, and then adopts the Hadoop platform based on the first
  • a service data separately calculates a first revenue of a user account corresponding to each service scenario, and finally calculates a total revenue of each user account corresponding to the service data to be calculated based on the first revenue, thereby implementing a Hadoop platform to process the calculated service data according to the service scenario.
  • the time taken to calculate the revenue increases the efficiency of the revenue calculation.
  • step S20 includes:
  • Step S21 Obtain a priority of each service scenario.
  • the amount of data of the business scenario corresponding to the purchase and sale is large, and is similar to the account adjustment.
  • the amount of data in the service scenario is small. Therefore, to balance the amount of data processed by each cluster server in the Hadoop platform, you can set the corresponding priority for each service scenario, and then split according to the priority to reduce the split. The difference in the amount of data between the first business data is divided, thereby improving the efficiency of data processing.
  • the priority of each service scenario is obtained, wherein the priority of each service scenario may be reasonably set according to actual conditions, for example, for historical data. For a business scenario with multiple historical scenarios, a higher priority is set, and for a business scenario with less historical data, a lower priority can be set.
  • step S22 the distributed computing Hadoop platform is used to split the computing service data according to the respective service scenarios and the priority to obtain the second service data and the third service data, where the second service data is the to-be-processed Calculating service data corresponding to each service scenario whose priority is greater than the preset priority in the service data, where the third service data is other service data except the second service data in the to-be-computed service data;
  • the Hadoop platform is used to split the service data according to the priority of each service scenario and each service scenario, and then the second service data is obtained.
  • the third service data where the second service data is the service data corresponding to each service scenario whose priority is greater than the preset priority in the service data to be calculated, and the third service data is the second service data in the service data to be calculated.
  • the priority of each of the foregoing service scenarios is compared with the preset priority, and the service scenario with the priority greater than the preset priority and the service scenario with the priority less than or equal to the preset priority are obtained, and the service data to be calculated is to be calculated.
  • the service data of each group corresponding to the service scenario with the priority being greater than the preset priority is used as the second service data, and each group of service data corresponding to the service scenario whose priority is less than or equal to the preset priority in the service data is used as The third service data, for example, when the service scenario having a priority greater than the preset priority is a plurality of service scenarios, the second service data is each group of service data corresponding to the multiple service scenarios, that is, the second service data includes multiple groups of services. Data, each group of service data belongs to a different service scenario whose priority is greater than the preset priority, and the remaining service priorities are equal to or smaller than the preset priority of each service scenario, and the corresponding service data combination is a group of data, that is, the third Business data.
  • the computing service may be split according to the priority, for example, for each service scenario in which the priority of the service data to be calculated is greater than the first preset priority, the corresponding service is used.
  • the data is a separate set of data; for the service scenario in which the priority of the service data to be calculated is less than or equal to the first preset priority and greater than the second priority, the data of the two service scenarios may be combined and split.
  • the data of the two service scenarios is a set of data. For the service scenarios whose priority is less than or equal to the second priority, the service data of all the service scenarios can be integrated into one set of data.
  • Step S23 setting the second service data and the third service data as the first service data.
  • the second service data and the third service data are obtained by splitting, and the second service data and the third service data are directly used as the first service data, so that the first service data includes a priority greater than a preset.
  • the revenue calculation method in this embodiment obtains the priority of each service scenario, and then acquires the second service data and the third service data in the to-be-calculated service data by using the Hadoop platform, and then the second service data and the third service data.
  • Set as the first service data and realize the splitting of the service data by using the priority of each service scenario, thereby greatly reducing the gap between the data amounts of the first service data of each group after splitting, and further calculating
  • the calculation time of the first service data of each group is more balanced, and the time difference between the completion time of the first service data calculation is reduced, thereby avoiding the first data amount.
  • the business data is calculated, it is wasted system resources due to long waits.
  • step S40 includes:
  • Step S41 performing a verification operation on the first income obtained by the calculation
  • the first benefit obtained by the calculation is verified to ensure the correctness of each first benefit. Sex, to avoid adverse effects on users due to data errors.
  • step S42 when the first revenue verification is passed, the total revenue of each user account corresponding to the to-be-calculated service data is calculated based on the first revenue.
  • the revenue corresponding to each user account is integrated, thereby obtaining the total revenue of each user account.
  • the revenue calculation method proposed by the embodiment is implemented by performing a verification operation on the first benefit obtained by the calculation, and then calculating the total revenue of each user account corresponding to the business data to be calculated based on the first income when the first revenue verification is passed.
  • the first income is verified to ensure the accuracy of the first income, thereby ensuring that the total revenue of each user account is accurate, improving the accuracy of the revenue calculation, and avoiding adverse effects on the user due to data errors.
  • step S41 includes:
  • Step S411 acquiring fourth service data corresponding to each of the service scenarios in the two pricing periods before the current pricing period;
  • the verification may be performed according to the historical service data, that is, the fourth service data corresponding to each service scenario in the preset pricing period before the current pricing period is obtained, so as to be
  • the fourth service data is used to verify whether the first revenue is correct.
  • the fourth service data is the service data of each service scenario corresponding to the service data to be calculated in the two pricing periods before the current pricing period.
  • the user account may be deregistered in the preset pricing period before the current pricing period. Therefore, the fourth service data corresponding to the user account in each service scenario may be directly obtained.
  • Step S412 calculating, according to the fourth service data, a second revenue of the user account in the preset pricing period
  • the second revenue includes the revenue of the user account in the first two pricing periods
  • the second income may be the revenue of the user account in the first two pricing periods, and may also include the income of the user account in each of the pricing periods corresponding to the first two pricing periods, for example, the redemption of the amount in the fourth business data.
  • the revenue of the user account in all the business scenarios in the four service data is the second income.
  • the second revenue includes a plurality of revenues of the user account, and the quantity thereof It is consistent with the number of business scenarios involved in the user account.
  • Step S413 performing a verification operation on the second revenue, and determining, when the second revenue verification is passed, that the first revenue verification is passed.
  • the second income when the second income is obtained, by comparing the second income with the historical income, when the second income is the sum of the incomes of the user accounts in the first two pricing periods, the historical income is the first two The sum of the first income of the corresponding user account during the pricing period, and the second income is the revenue of the user account in each of the pricing periods corresponding to the first two pricing periods, the historical income including the user accounts corresponding to the first two pricing periods The first gain. Specifically, determining a historical first income of the user account corresponding to each second income, and then comparing the second income with the historical first income, and when the comparison is consistent, the second income verification is passed, thereby determining the first revenue verification. by.
  • the revenue calculation method proposed in this embodiment obtains the fourth service data corresponding to each service scenario in the two pricing periods before the current pricing period, and then calculates the second revenue of the user account in the preset pricing period based on the fourth service data. And then performing a verification operation on the second revenue, wherein, when the second revenue verification is passed, determining that the first revenue verification is passed, implementing the fourth service data pair corresponding to the user account in the business scenario in the previous two pricing periods A revenue is verified, that is, when the second revenue verification obtained according to the fourth service data has no problem, the first revenue verification is passed, and the first income can be accurately verified according to the historical data, thereby improving the accuracy of the revenue calculation, and further Improve the efficiency of revenue calculations.
  • the efficiency of the revenue verification is improved by separately verifying the first revenue corresponding to each service scenario, and when the first revenue verification corresponding to a certain service scenario fails, the individual verification can be performed according to the historical data corresponding to the service scenario.
  • the calculation ensures the accuracy of the first benefit corresponding to the business scenario, and can reduce the amount of data for the revenue check, thereby improving the efficiency of the revenue check.
  • step S41 includes:
  • Step S414 acquiring fifth service data corresponding to each of the service scenarios in the previous pricing period
  • the backtracking manner when verifying the first data, is used to first determine whether the revenue corresponding to the user account in each service scenario in the previous pricing period of the current pricing period is correct. Therefore, the previous pricing is first obtained.
  • the fifth service data corresponding to each service scenario in the period wherein the fifth service data is the service data of each service scenario corresponding to the service data to be calculated in a pricing period before the current pricing period, in an embodiment, During the preset pricing period before the current pricing period, the user account may be deregistered. Therefore, the fifth service data corresponding to the user account in each service scenario may be directly obtained.
  • Step S415 calculating, according to the fifth service data, a third revenue of the user account in the previous pricing period
  • the third revenue of each user account in the previous pricing period is calculated.
  • Step S416 performing a verification operation on the third revenue, wherein, when the third revenue verification is passed, determining that the first revenue verification is passed.
  • the third income when the third income is obtained, by comparing the third income with the historical income, when the third income is consistent with the historical first income, determining that the third income verification is passed, thereby determining the first income verification.
  • the historical first revenue is the first revenue of the corresponding user account in the previous pricing period, that is, the first revenue of the user account calculated in the previous pricing period, for example, the business scenario for redemption of the amount in the fifth service data. Calculating the revenue of each user account corresponding to the business scenario by using the redemption amount in the business scenario, the total amount before redemption, and/or the interest after redemption in the fifth business data; in the fifth service data
  • the revenue of the user account in all the business scenarios is the third revenue.
  • the income includes a plurality of revenues of the user account, and the quantity thereof is related to the user account.
  • the number of business scenarios involved is the same.
  • the revenue corresponding to the user account in the scenario is checked and checked. When the check is passed, the first revenue is determined to pass the verification.
  • the revenue calculation method proposed in this embodiment obtains the fifth service data corresponding to each service scenario in the previous pricing period, and then calculates the third revenue of the user account in the previous pricing period based on the fifth service data, and then the third revenue.
  • Performing a verification operation wherein, when the third revenue verification is passed, determining that the first revenue verification is passed, realizing that the first revenue is verified according to the fifth service data corresponding to the user account in the service scenario in the previous pricing period, and thereby Accurately verifying the first benefit based on historical data, improving the accuracy of revenue calculation, and further improving the efficiency of revenue calculation.
  • the efficiency of the revenue verification is improved by separately verifying the first revenue corresponding to each service scenario, and when the first revenue verification corresponding to a certain service scenario fails, the individual verification can be performed according to the historical data corresponding to the service scenario.
  • the calculation ensures the accuracy of the first benefit corresponding to the business scenario, and can reduce the amount of data for the revenue check, thereby improving the efficiency of the revenue check.
  • the revenue calculation method further includes:
  • Step S50 Acquire, when acquiring the service data to be calculated, a service type of the service data to be calculated;
  • the service type of the service data to be calculated is determined.
  • the service type of the service data to be calculated is an annuity business.
  • Step S60 Acquire an enterprise identifier corresponding to the to-be-calculated service data when the service type is an annuity service;
  • the enterprise identifiers of all enterprises corresponding to the business data to be calculated are acquired, wherein, in the enterprise annuity Each enterprise identity corresponds to a single enterprise.
  • step S70 the distributed computing Hadoop platform is used to split the computing service data based on the enterprise identifier to obtain enterprise business data corresponding to each enterprise identifier.
  • the distributed computing Hadoop platform is used to split the computing service data according to the enterprise identifier, and the enterprise business data corresponding to each enterprise identifier is obtained, that is, the current pricing period. Business data corresponding to each enterprise.
  • Step S80 The Hadoop platform is used to calculate the enterprise revenue corresponding to each enterprise based on the enterprise business data.
  • the Hadoop platform When obtaining the enterprise business data of each enterprise, the Hadoop platform is used to calculate the enterprise revenue corresponding to each enterprise based on the enterprise business data. Since the Hadoop platform includes multiple cluster servers, each business data of each group can be allocated one. The independent cluster server makes the cluster server only used to calculate the business data of the enterprise, thereby greatly reducing the amount of data calculated in the same server, thereby reducing the time for data calculation.
  • the revenue calculation method further includes: updating the enterprise account corresponding to each enterprise based on the enterprise revenue.
  • the balance of the enterprise account corresponding to each enterprise identifier is obtained first, and then the enterprise income corresponding to each enterprise identifier is added to the balance of the enterprise account, and the corresponding enterprise account is updated.
  • the income calculation method further includes:
  • the verification operation of the enterprise income is performed, and when the enterprise revenue verification is passed, the enterprise account corresponding to each enterprise is updated based on the enterprise revenue.
  • the enterprise revenue obtained by the current calculation may be verified in a reversed manner.
  • the specific verification process refers to the fourth embodiment and the fifth embodiment, and details are not described in this embodiment.
  • the revenue calculation method of the present embodiment obtains the service type of the service data to be calculated when the service data to be calculated is acquired, and then obtains the enterprise identifier corresponding to the service data to be calculated when the service type is an annuity service, and then based on the enterprise
  • the logo uses the distributed computing Hadoop platform to split the computing business data to obtain the enterprise business data corresponding to each enterprise identifier.
  • the Hadoop platform calculates the enterprise revenue corresponding to each enterprise based on the enterprise business data, and implements the Hadoop platform treatment.
  • the calculation business data is split according to the enterprise logo, and the revenue corresponding to the split business data is separately calculated, and then the revenue summary is performed, thereby enabling the business data to be calculated to be split and calculated, and the amount of data calculated by the same server is reduced.
  • the time required for calculating the revenue of the business data to be calculated can be greatly reduced, and the efficiency of the revenue calculation is improved.
  • the present invention also provides a computer readable storage medium.
  • a computer readable storage medium stores a revenue calculation program, and the revenue calculation program is implemented by the processor to implement the steps of the revenue calculation method.
  • portions of the technical solution of the present invention that contribute substantially or to the prior art may be embodied in the form of a software product stored in a storage medium (such as a ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present invention.
  • a terminal device which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

本发明公开了一种收益计算方法，其包括：获取待计算业务数据以及所述待计算业务数据对应的业务场景；基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分；采用所述Hadoop平台基于所述第一业务数据分别计算各个业务场景所对应用户账户的第一收益；基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益。本发明还公开了一种收益计算装置及计算机可读存储介质。本发明实现了采用Hadoop平台对待计算业务数据按照业务场景进行拆分，并分别计算拆分后的业务数据对应的收益，减少了采用同一服务器计算的数据量，进而能够大大降低待计算业务数据进行收益计算的耗时，提高了收益计算的效率。

Description

收益计算方法、装置及计算机可读存储介质
本申请要求于2017年06月26日提交中国专利局、申请号为201710497395.4、发明名称为“收益计算方法、装置及计算机可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
　　　本发明涉及数据处理技术领域，尤其涉及一种收益计算方法、装置及计算机可读存储介质。
背景技术
　　　目前，在基金、定期理财、活期理财及保险等理财产品中，绝大多数理财产品的收益需要每天计算，并反馈该理财产品对应的用户账户，以使用户能够直观的了解其所购买的理财产品当前的收益。随着理财产品业务种类不断增加，每个用户账户可能会涉及多个业务，在计算用户的收益时，需要考虑该用户账户所对应的不同业务场景中所有的历史买入数据和历史卖出数据。
　　　目前，传统的收益计算方式是集合所有的业务数据进行统一计算。但是，由于数据量的逐渐增大，采用传统的收益计算方式计算收益的复杂度越来越大，导致收益计算所需要的时间增加，同时可能造成系统不稳定，增加了系统运维的难度。
　　　上述内容仅用于辅助理解本发明的技术方案，并不代表承认上述内容是现有技术。
发明内容
　　　本发明的主要目的在于提供一种收益计算方法、装置及计算机可读存储介质，旨在解决随着理财产品对应的数据量逐渐增大造成收益计算所需要的时间增加的技术问题。
　　　为实现上述目的，本发明提供收益计算方法，所述收益计算方法包括以下步骤：
　　　获取待计算业务数据以及所述待计算业务数据对应的业务场景；
　　　基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个业务场景对应的第一业务数据；
　　　采用所述Hadoop平台基于所述第一业务数据分别计算各个业务场景所对应用户账户的第一收益；
　　　基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益。
　　　此外，为实现上述目的，本发明还提供一种收益计算装置，所述收益计算装置包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的收益计算程序，所述收益计算程序被所述处理器执行时实现上述任一项所述的收益计算方法的步骤。
　　　此外，为实现上述目的，本发明还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有收益计算程序，所述收益计算程序被处理器执行时实现上述任一项所述的收益计算方法的步骤。
　　　本发明通过获取待计算业务数据以及待计算业务数据对应的业务场景，接着基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分，而后采用Hadoop平台基于第一业务数据分别计算各个业务场景所对应用户账户的第一收益，最后基于第一收益计算待计算业务数据对应的各个用户账户的总收益，实现了采用Hadoop平台对待计算业务数据按照业务场景进行拆分，并分别计算拆分后的业务数据对应的收益，而后进行收益汇总，进而使得待计算业务数据能够进行拆分计算，减少了采用同一服务器计算的数据量，进而能够大大降低待计算业务数据进行收益计算的耗时，提高了收益计算的效率。
附图说明
　　　图1是本发明实施例方案涉及的硬件运行环境中收益计算装置所属的终端的结构示意图；
　　　图2为本发明收益计算方法第一实施例的流程示意图；
　　　图3为本发明收益计算方法第二实施例中基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分的步骤的细化流程示意图；
　　　图4为本发明收益计算方法第三实施例中基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益的步骤的细化流程示意图；
　　　图5为本发明收益计算方法第四实施例中对计算获得的所述第一收益进行验证操作的步骤的细化流程示意图；
　　　图6为本发明收益计算方法第五实施例中对计算获得的所述第一收益进行验证操作的步骤的细化流程示意图；
　　　图7为本发明收益计算方法第六实施例的流程示意图。
　　　本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
　　　应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
　　　本发明提供一种收益计算装置，如图1所示，图1是本发明实施例方案涉及的硬件运行环境中收益计算装置所属的终端的结构示意图。
　　　本发明实施例终端可以是PC，也可以是智能手机、平板电脑、便携计算机等移动式终端设备。
　　　如图1所示，该终端可以包括：处理器1001，例如CPU，网络接口1004，用户接口1003，存储器1005，通信总线1002。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏（Display）、输入单元比如键盘（Keyboard），可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选的可以包括标准的有线接口、无线接口（如WI-FI接口）。存储器1005可以是高速RAM存储器，也可以是稳定的存储器（non-volatile memory），例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
　　　可选地，终端还可以包括摄像头、RF（Radio Frequency，射频）电路，传感器、音频电路、WiFi模块等等。其中，传感器比如光传感器、运动传感器以及其他传感器。具体地，光传感器可包括环境光传感器及接近传感器，其中，环境光传感器可根据环境光线的明暗来调节显示屏的亮度，接近传感器可在移动终端移动到耳边时，关闭显示屏和/或背光。作为运动传感器的一种，重力加速度传感器可检测各个方向上（一般为三轴）加速度的大小，静止时可检测出重力的大小及方向，可用于识别移动终端姿态的应用（比如横竖屏切换、相关游戏、磁力计姿态校准）、振动识别相关功能（比如计步器、敲击）等；当然，移动终端还可配置陀螺仪、气压计、湿度计、温度计、红外线传感器等其他传感器，在此不再赘述。
　　　本领域技术人员可以理解，图1中示出的终端结构并不构成对终端的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
　　　如图1所示，作为一种计算机存储介质的存储器1005中可以包括操作系统、网络通信模块、用户接口模块以及收益计算程序。
　　　在图1所示的终端中，网络接口1004主要用于连接后台服务器，与后台服务器进行数据通信；用户接口1003主要用于连接客户端（用户端），与客户端进行数据通信；而处理器1001可以用于调用存储器1005中存储的收益计算程序，并执行本申请实施例提供的收益计算方法。
　　　本发明进一步提供一种收益计算方法。参照图2，图2为本发明收益计算方法第一实施例的流程示意图。
　　　在本实施例中，该收益计算方法包括：
　　　步骤S10，获取待计算业务数据以及所述待计算业务数据对应的业务场景；
　　　在基金、定期理财、活期理财等理财产品中，绝大部分理财产品的收益需要每天计算，并反馈至该理财产品对应的用户账户，以使用户能够直观的了解其所购买的理财产品当前的收益，因此，首先确定待计算业务数据，其中，该待计算业务数据为所有涉及到用户收益的数据，即该用户账户对应的所有业务场景中与收益相关的数据，业务场景包括缴费、定额支付、分期定额支付、一次性支付、金额赎回、份额赎回、分期支付、不定期支付、账户调整、离职以及计划外转等业务场景中的一个或多个。
　　　在本实施例中，可根据不同的理财产品设置不同的收益计算时刻，或者，对所有的业务场景的业务数据设置固定的收益计算时刻，在当前时刻到达该计算时刻时，获取待计算业务数据。具体地，可以根据预设规则获取待计算业务数据，而后根据该待计算业务数据确定其对应的业务场景，例如，预设规则包括获取预设业务场景（上述一个或多个业务场景）的业务数据，进而可根据预先定义的数据传输接口得到预设业务场景的业务数据、以及预设业务场景中的各个业务场景的标识信息，并将获取到的全部业务数据作为待计算业务数据，因此，可根据标识信息确定该待计算业务数据对应的业务场景，即待计算业务数据中的各个业务数据所属的业务场景。优选地，还可以将预设业务场景的业务数据与预设业务场景中的各个业务场景的标识信息进行关联后作为待计算业务数据，可通过解析该待计算业务数据获得该标识信息，进而根据该标识信息确定待计算业务数据对应的业务场景。其中，根据预设规则获取待计算业务数据包括获取所有的用户账户所对应的与收益相关的数据，或者，还可以获取业务场景，而后获取业务场景中与收益相关的数据即待计算业务数据。
　　　步骤S20，基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个业务场景对应的第一业务数据；
　　　其中，Hadoop平台是一个分布式系统基础架构平台，其能够利用集群进行高速分布式运算和存储。
　　　在本实施例中，在获取到待计算业务数据以及对应的业务场景时，基于各个业务场景采用Hadoop平台对待计算业务数据进行拆分，具体地，待计算业务数据包括各组业务数据所属的业务场景的标识信息，通过该标识信息能够准确的确定各组业务数据所属的业务场景，进而将待计算业务数据所包含的业务数据准确的按照业务场景进行分类，并将分类后的待计算业务数据进行拆分，得到各个业务场景对应的第一业务数据，将待计算业务数据中各个业务场景的业务数据作为一组业务数据，并将各组业务数据作为第一业务数据，即第一业务数据包括多组业务数据，每一组业务数据属于不同的业务场景，以便于采用该Hadoop平台分别计算各个业务场景对应的用户收益。
　　　其中，由于待计算业务数据包括各组业务数据所属的业务场景的标识信息，并且不同的业务场景所对应的业务数据的数据格式不同，因此，可以根据各个业务场景的标识信息以及数据格式对待计算业务数据进行拆分，例如，待计算业务数据包括标识信息为缴费场景的标识信息的业务数据，可根据缴费场景对应的数据格式确定该业务数据的头数据及尾数据，进而将该头数据、尾数据、以及头数据与尾数据之间的数据即为缴费场景的业务数据，当然缴费场景的业务数据还可以包括其他数据；按照上述方式能够将待计算业务数据进行准确的拆分，得到各个业务场景对应的第一业务数据。
　　　另外，业务场景的类型包括出项业务场景及入项业务场景，例如，缴费的业务场景为出项业务场景，定额支付、分期定额支付、一次性支付、分期支付、不定期支付等业务场景为入项业务场景。在对处理业务数据拆分时，首先业务场景的类型进行分类，得到出项业务场景对应的出项业务数据，以及入项业务场景对应的入项业务数据，而后判断出项业务数据所对应的用户账户是否同时存在与入项业务数据所对应的用户账户，即确定是否存在同时有资金流入及资金流出的用户账户，在存在同时有资金流入及资金流出的用户账户时，根据出项业务数据中该用户账户的数据、以及入项业务数据中该用户账户的数据，判断该用户账户的收支是否相抵，即资金流入及资金流出相同，若收支相抵，则在出项业务数据以及入项业务数据中删除该用户账户所对应的数据，由于该用户账户的收支相抵，因此该用户账户当前不存在收益，因此无需计算该用户账户的收益，进而能够减少计算用户收益时的数据量。在将收支相抵的用户账户的数据删除以后，再按照业务场景对出项业务数据以及入项业务数据进行再次拆分。
　　　步骤S30，采用所述Hadoop平台基于所述第一业务数据分别计算各个业务场景所对应用户账户的第一收益；
　　　在本实施中，在对待计算业务数据进行拆分，得到各个业务场景所对应的各组第一业务数据之后，采用该Hadoop平台基于第一业务数据分别计算各个业务场景所对应用户账户的第一收益。由于Hadoop平台中包含多个集群服务器，因此，可为每一个业务场景分配一个独立的集群服务器，使得该集群服务器仅用于计算该业务场景对应的第一业务数据，进而能够大大减少在同一服务器中计算的数据量，进而减少数据计算的时间。
　　　其中，该第一收益为当前定价期间用户账户对应的收益，对于固定利息的业务场景，例如，年息10%的理财产品，该第一收益为：该用户的投入总金额*10%*定价期间天数/365，其中，对于一般的理财产品，其对应的定价期间为7天。
　　　步骤S40，基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益。
　　　在本实施例中，在计算得到各个业务场景中用户的第一收益时，对各个用户账户所对应的收益进行整合，进而得到各个用户账户的总收益。
　　　进一步地，在一实施例中，在步骤S40之后，收益计算方法还包括：
　　　基于所述总收益更新各个所述用户账户。
　　　具体地，获取各个用户账户的余额，并基于计算得到的总收益以及该余额更新对应的用户账户，例如，在某一用户账户的余额为5W元，该用户账户对应的总收益为500元时，更新后该用户账户的余额为5. 05W元。
　　　当然，在得到待计算业务数据所对应的各个用户账户的总收益时，基于该总收益更新该待计算业务数据所对应的各个用户账户的账户金额。当然，还可以根据各个账户的预设信息，通过短信、邮件、微信等方式发送当前总收益以及账户金额至该用户账户所对应的预设终端。
　　　本实施例提出的收益计算方法，通过获取待计算业务数据以及待计算业务数据对应的业务场景，接着基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分，而后采用Hadoop平台基于第一业务数据分别计算各个业务场景所对应用户账户的第一收益，最后基于第一收益计算待计算业务数据对应的各个用户账户的总收益，实现了采用Hadoop平台对待计算业务数据按照业务场景进行拆分，并分别计算拆分后的业务数据对应的收益，而后进行收益汇总，进而使得待计算业务数据能够进行拆分计算，减少了采用同一服务器计算的数据量，进而能够大大降低待计算业务数据进行收益计算的耗时，提高了收益计算的效率。
　　　基于第一实施例，提出本发明收益计算方法的第二实施例，参照图3，在本实施例中，步骤S20包括：
　　　步骤S21，获取各个业务场景的优先级；
　　　一般情况下，待计算业务数据中同时存在数据量较大的业务场景以及数据量较小的业务场景，例如，买入及卖出所对应的业务场景的数据量较大，而类似于账户调整等业务场景的数据量则会较小，因此，为平衡Hadoop平台中各个集群服务器所处理的数据量，可对各个业务场景设置对应的优先级，而后按照优先级进行拆分，以减小拆分后各个第一业务数据之间数据量的差距，进而提高数据处理的效率。
　　　在本实施例中，在获取到待计算业务数据以及对应的业务场景时，获取各个业务场景的优先级，其中，各个业务场景的优先级可以根据实际进行合理的设置，例如，对于历史数据较多的业务场景，设置较高的优先级，而对于历史数据较少的业务场景，可设置较低的优先级。
　　　步骤S22，基于各个业务场景及所述优先级采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得第二业务数据以及第三业务数据，其中，所述第二业务数据为所述待计算业务数据中优先级大于预设优先级的各个业务场景对应的业务数据，所述第三业务数据为所述待计算业务数据中除所述第二业务数据之外的其他业务数据；
　　　在本实施例中，在得到待计算业务数据对于的业务场景的优先级时，基于各个业务场景以及各个业务场景对于的优先级采用Hadoop平台对待计算业务数据进行拆分，进而得到第二业务数据以及第三业务数据，其中，第二业务数据为待计算业务数据中优先级大于预设优先级的各个业务场景对应的业务数据，第三业务数据为待计算业务数据中除第二业务数据之外的其他业务数据。具体地，将上述各个业务场景的优先级与预设优先级进行对比，得到优先级大于预设优先级的业务场景、以及优先级小于或等于预设优先级的业务场景，将待计算业务数据中优先级大于预设优先级的业务场景所对应的各组业务数据作为第二业务数据，将待计算业务数据中优先级小于或等于预设优先级的业务场景所对应的各组业务数据作为第三业务数据，例如，优先级大于预设优先级的业务场景为多个业务场景时，第二业务数据为该多个业务场景对应的各组业务数据，即第二业务数据包括多组业务数据，每一组业务数据属于不同的优先级大于预设优先级的业务场景，剩余的优先级等于或小于预设优先级的各个业务场景，其对应的业务数据组合为一组数据即第三业务数据。
　　　进一步地，在一实施例中，还可以根据优先级采用其他方式对待计算业务进行拆分，例如，对于待计算业务数据中优先级大于第一预设优先级的各个业务场景，其对应的业务数据为单独的一组数据；对于待计算业务数据中优先级小于或等于第一预设优先级、且大于第二优先级的业务场景，可以将其中的两个业务场景的数据进行组合拆分，即两个业务场景对于的数据为一组数据；对于待计算业务数据中优先级小于或等于第二优先级的业务场景，可以将所有该业务场景的业务数据整合为一组数据。
　　　步骤S23，将所述第二业务数据以及第三业务数据设置为所述第一业务数据。
　　　在本实施例中，在拆分获得第二业务数据以及第三业务数据，直接将该第二业务数据以及第三业务数据作为第一业务数据，进而使第一业务数据包括优先级大于预设优先级的业务场景对应的各组业务数据、以及优先级小于或等于预设优先级的业务场景的业务数据组合的一组数据即第三业务数据。
　　　本实施例提出的收益计算方法，通过获取各个业务场景的优先级，接着采用Hadoop平台在待计算业务数据中获取第二业务数据以及第三业务数据，而后将第二业务数据以及第三业务数据设置为第一业务数据，实现了利用各个业务场景的优先级对待计算业务数据进行拆分，大大减少了拆分后的各组第一业务数据之间的数据量之间的差距，进而在计算各个业务场景所对应用户账户的第一收益时，能够使得各组第一业务数据的计算时间更加均衡，减小第一业务数据计算完成时刻之间的时间差，进而避免数据量较少的第一业务数据在计算完成时，因长时间等待为浪费系统资源。
　　　基于第一实施例，提出本发明收益计算方法的第三实施例，参照图4，在本实施例中，步骤S40包括：
　　　步骤S41，对计算获得的所述第一收益进行验证操作；
　　　在本实施例中，为避免计算获得的第一收益存在错误，在计算得到各个业务场景中用户的第一收益时，对计算获得的第一收益进行验证操作，以保证各个第一收益的正确性，避免因数据错误而对用户造成不良影响。
　　　步骤S42，在所述第一收益验证通过时，基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益。
　　　在本实施例中，在第一收益验证通过时，对各个用户账户所对应的收益进行整合，进而得到各个用户账户的总收益。
　　　本实施例提出的收益计算方法，通过对计算获得的第一收益进行验证操作，接着在第一收益验证通过时，基于第一收益计算待计算业务数据对应的各个用户账户的总收益，实现了对第一收益进行验证，以保证第一收益的准确性，进而能够确保各个用户账户的总收益准确无误，提高收益计算的准确性，避免因数据错误而对用户造成不良影响。
　　　基于第三实施例，提出本发明收益计算方法的第四实施例，参照图5，在本实施例中，步骤S41包括：
　　　步骤S411，获取当前定价期间之前的两个定价期间内，各个所述业务场景对应的第四业务数据；
　　　在本实施例中，在对第一数据进行验证时，可以根据历史业务数据进行验证，即获取当前定价期间之前的预设定价期间内各个业务场景对应的第四业务数据，以便于根据该第四业务数据，验证第一收益是否正确，其中，第四业务数据为当前定价期间之前的两个定价期间内、待计算业务数据所对应的各个业务场景的业务数据。在一实施例中，由于当前定价期间之前的预设定价期间内，可能存在用户账户注销的情况，因此，可直接获取各个业务场景中用户账户对应的第四业务数据。
　　　步骤S412，基于所述第四业务数据计算所述预设定价期间内所述用户账户的第二收益；
　　　在本实施例中，在获取到第四业务数据时，计算预设定价期间内各个业务场景对应的用户账户的第二收益，该第二收益包含前两个定价期间内用户账户的收益，其中，该第二收益可以为前两个定价期间内用户账户的收益，也可以包括前两个定价期间所对应的每一个定价期间内用户账户的收益，例如，对于第四业务数据中的金额赎回的业务场景，通过第四业务数据中该业务场景中的赎回的金额、赎回前的总额和/或赎回后的利息等数据计算该业务场景所对应的各个用户账户的收益；第四业务数据中所有的业务场景中的用户账户的收益即为第二收益，对于某一涉及到多个业务场景的用户账户而言，第二收益中包括多个该用户账户的收益，其数量与该用户账户所涉及到的业务场景的数量一致。
　　　步骤S413，对所述第二收益进行验证操作，在所述第二收益验证通过时，确定所述第一收益验证通过。
　　　在本实施例中，在获得第二收益时，通过将该第二收益与历史收益进行比较，在第二收益为前两个定价期间内用户账户的收益之和时，该历史收益为前两个定价期间所对应用户账户的第一收益之和，在第二收益为前两个定价期间所对应的每一个定价期间内用户账户的收益，该历史收益包括前两个定价期间所对应用户账户的第一收益。具体地，确定各个第二收益所对应的用户账户的历史第一收益，而后将该第二收益与历史第一收益进行比较，在比较一致时，第二收益验证通过，进而确定第一收益验证通过。
　　　本实施例提出的收益计算方法，通过获取当前定价期间之前的两个定价期间内，各个业务场景对应的第四业务数据，接着基于第四业务数据计算预设定价期间内用户账户的第二收益，而后对第二收益进行验证操作，其中，在第二收益验证通过时，确定第一收益验证通过，实现了根据之前两个定价期间内业务场景中的用户账户对应的第四业务数据对第一收益进行验证，即在根据第四业务数据得到的第二收益验证无问题时，该第一收益验证通过，进而能够根据历史数据准确的验证第一收益，提高了收益计算的准确性，进一步提高了收益计算的效率。通过对各个业务场景对应的第一收益进行单独验证，提高了收益验证的效率，并且在某一业务场景对应的第一收益验证未通过时，可以根据该业务场景对应的历史数据进行单独核验以及计算，确保该业务场景对应的第一收益的准确性，能够减少收益核对的数据量，进而提高收益核对的效率。
　　　基于第三实施例，提出本发明收益计算方法的第五实施例，参照图6，在本实施例中，步骤S41包括：
　　　步骤S414，获取前一定价期间内各个所述业务场景对应的第五业务数据；
　　　在本实施例中，在对第一数据进行验证时，采用倒推的方式首先确定当前定价期间之前一个定价期间内各个业务场景中的用户账户对应的收益是否正确，因此，首先获取前一定价期间内各个业务场景对应的第五业务数据，其中，第五业务数据为当前定价期间之前的一个定价期间内、待计算业务数据所对应的各个业务场景的业务数据，在一实施例中，由于当前定价期间之前的预设定价期间内，可能存在用户账户注销的情况，因此，可直接获取各个业务场景中用户账户对应的第五业务数据。
　　　步骤S415，基于所述第五业务数据计算所述前一定价期间内所述用户账户的第三收益；
　　　在本实施例中，在获取到第五业务数据时，计算前一定价期间内各个用户账户的第三收益。
　　　步骤S416，对所述第三收益进行验证操作，其中，在所述第三收益验证通过时，确定所述第一收益验证通过。
　　　在本实施例中，在获得第三收益时，通过将该第三收益与历史收益进行比较，在第三收益与历史第一收益一致时，确定第三收益验证通过，进而确定第一收益验证通过。该历史第一收益为前一定价期间内所对应用户账户的第一收益，即前一定价期间计算获得的用户账户的第一收益，例如，对于第五业务数据中的金额赎回的业务场景，通过第五业务数据中该业务场景中的赎回的金额、赎回前的总额和/或赎回后的利息等数据计算该业务场景所对应的各个用户账户的收益；第五业务数据中所有的业务场景中的用户账户的收益即为第三收益，对于某一涉及到多个业务场景的用户账户而言，第收益中包括多个该用户账户的收益，其数量与该用户账户所涉及到的业务场景的数量一致。
　　　进一步地，在一实施例中，若第三收益中存在验证未通过的用户收益时，则确定验证未通过的用户收益对应的业务场景；获取前一定价期间之前的一个定价期间，业务场景对应的第六业务数据；并计算第六业务数据对应用户账户的第四收益；对第四收益进行验证操作，其中，在第四收益验证通过时，基于第四收益以及第六业务数据计算该业务场景中用户账户对应的收益，并进行核对操作，在核对通过时，确定第一收益进行验证通过。
　　　本实施例提出的收益计算方法，通过获取前一定价期间内各个业务场景对应的第五业务数据，接着基于第五业务数据计算前一定价期间内用户账户的第三收益，而后对第三收益进行验证操作，其中，在第三收益验证通过时，确定第一收益验证通过，实现了根据前一定价期间内业务场景中的用户账户对应的第五业务数据对第一收益进行验证，进而能够根据历史数据准确的验证第一收益，提高了收益计算的准确性，进一步提高了收益计算的效率。通过对各个业务场景对应的第一收益进行单独验证，提高了收益验证的效率，并且在某一业务场景对应的第一收益验证未通过时，可以根据该业务场景对应的历史数据进行单独核验以及计算，确保该业务场景对应的第一收益的准确性，能够减少收益核对的数据量，进而提高收益核对的效率。
　　　基于第上述实施例，提出本发明收益计算方法的第六实施例，参照图7，在本实施例中，该收益计算方法还包括：
　　　步骤S50，在获取到待计算业务数据时，获取所述待计算业务数据的业务类型；
　　　在本实施例中，在获取到待计算业务数据时，确定该待计算业务数据的业务类型，例如，在理财产品为企业年金时，该待计算业务数据的业务类型为年金业务。
　　　步骤S60，在所述业务类型为年金业务时，获取所述待计算业务数据对应的企业标识；
　　　在本实施例中，在根据待计算业务数据的业务类型确定该待计算业务数据为年金业务的业务数据时，获取该待计算业务数据所对应的所有企业的企业标识，其中，在企业年金中，每一个企业标识对应唯一一个企业。
　　　步骤S70，基于所述企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个企业标识对应的企业业务数据；
　　　在本实施例中，在获取到待计算业务数据对应的企业标识时，基于企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，得到各个企业标识对应的企业业务数据，即当前定价期间内每一个企业对应的业务数据。
　　　步骤S80，采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益。
　　　在获得每一个企业的企业业务数据时，采用Hadoop平台基于企业业务数据分别计算各个企业所对应的企业收益，由于Hadoop平台中包含多个集群服务器，因此，可为每一组企业业务数据分配一个独立的集群服务器，使得该集群服务器仅用于计算该企业业务数据，进而能够大大减少在同一服务器中计算的数据量，进而减少数据计算的时间。
　　　进一步地，在一实施例中，在步骤S80之后，该收益计算方法还包括：基于所述企业收益更新各个企业所对应的企业账户。
　　　具体地，先获取每一个企业标识对应的企业账户的余额，而后将每一个企业标识对应的企业收益与企业账户的余额相加后更新对应的企业账户。
　　　优选地，在一实施例中，在计算获得各个企业所对应的企业收益之后，该收益计算方法还包括：
　　　对所述企业收益进行验证操作，在企业收益验证通过时，基于所述企业收益更新各个企业所对应的企业账户。
　　　本实施例中，可采用倒推的方式验证当前计算获得的企业收益，具体验证过程参照第四实施例以及第五实施例，在本实施例中不再赘述。
　　　本实施例提出的收益计算方法，通过在获取到待计算业务数据时，获取待计算业务数据的业务类型，接着在业务类型为年金业务时，获取待计算业务数据对应的企业标识，而后基于企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个企业标识对应的企业业务数据，最后采用Hadoop平台基于企业业务数据分别计算各个企业所对应的企业收益，实现了采用Hadoop平台对待计算业务数据按照企业标识进行拆分，并分别计算拆分后的业务数据对应的收益，而后进行收益汇总，进而使得待计算业务数据能够进行拆分计算，减少了采用同一服务器计算的数据量，进而能够大大降低待计算业务数据进行收益计算的耗时，提高了收益计算的效率。
　　　本发明还提供一种计算机可读存储介质，在本实施例中，计算机可读存储介质上存储有收益计算程序，所述收益计算程序被所述处理器执行时实现上述收益计算方法的步骤。
　　　需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
　　　上述本发明实施例序号仅仅为了描述，不代表实施例的优劣。
　　　通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本发明各个实施例所述的方法。
　　　以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。

Claims (20)

  1. 一种收益计算方法，其特征在于，所述收益计算方法包括以下步骤：
    　　　获取待计算业务数据以及所述待计算业务数据对应的业务场景；
    　　　基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个业务场景对应的第一业务数据；
    　　　采用所述Hadoop平台基于所述第一业务数据分别计算各个业务场景所对应用户账户的第一收益；
    　　　基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益。 　　　
  2. 如权利要求1所述的收益计算方法，其特征在于，所述基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分的步骤之后，所述收益计算方法还包括：
    　　　在获取到待计算业务数据时，获取所述待计算业务数据的业务类型；
    　　　在所述业务类型为年金业务时，获取所述待计算业务数据对应的企业标识；
    　　　基于所述企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个企业标识对应的企业业务数据；
    　　　采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益。　　　
  3. 如权利要求2所述的收益计算方法，其特征在于，所述采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益的步骤之后，所述收益计算方法还包括：
    　　　基于所述企业收益更新各个企业所对应的企业账户。　　　
  4. 如权利要求1所述的收益计算方法，其特征在于，所述基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分的步骤包括：
    　　　获取各个业务场景的优先级；
    　　　采用所述Hadoop平台在所述待计算业务数据中获取第二业务数据以及第三业务数据，其中，所述第二业务数据为所述待计算业务数据中优先级大于预设优先级的各个业务场景对应的业务数据，所述第三业务数据为所述待计算业务数据中除所述第二业务数据之外的其他业务数据；
    　　　将所述第二业务数据以及第三业务数据设置为所述第一业务数据。
  5. 　　　如权利要求4所述的收益计算方法，其特征在于，所述基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分的步骤之后，所述收益计算方法还包括：
    　　　在获取到待计算业务数据时，获取所述待计算业务数据的业务类型；
    　　　在所述业务类型为年金业务时，获取所述待计算业务数据对应的企业标识；
    　　　基于所述企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个企业标识对应的企业业务数据；
    　　　采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益。　　　
  6. 如权利要求5所述的收益计算方法，其特征在于，所述采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益的步骤之后，所述收益计算方法还包括：
    　　　基于所述企业收益更新各个企业所对应的企业账户。　　　
  7. 如权利要求1所述的收益计算方法，其特征在于，所述基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益的步骤包括：
    　　　对计算获得的所述第一收益进行验证操作；
    　　　在所述第一收益验证通过时，基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益。　　　
  8. 如权利要求7所述的收益计算方法，其特征在于，所述基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分的步骤之后，所述收益计算方法还包括：
    　　　在获取到待计算业务数据时，获取所述待计算业务数据的业务类型；
    　　　在所述业务类型为年金业务时，获取所述待计算业务数据对应的企业标识；
    　　　基于所述企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个企业标识对应的企业业务数据；
    　　　采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益。　　　
  9. 如权利要求8所述的收益计算方法，其特征在于，所述采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益的步骤之后，所述收益计算方法还包括：
    　　　基于所述企业收益更新各个企业所对应的企业账户。　　　
  10. 如权利要求7所述的收益计算方法，其特征在于，所述对计算获得的所述第一收益进行验证操作的步骤包括：
    　　　获取当前定价期间之前的两个定价期间内，各个所述业务场景对应的第四业务数据；
    　　　基于所述第四业务数据计算所述预设定价期间内所述用户账户的第二收益；
    　　　对所述第二收益进行验证操作，其中，在所述第二收益验证通过时，确定所述第一收益验证通过。　　　
  11. 如权利要求10所述的收益计算方法，其特征在于，所述基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分的步骤之后，所述收益计算方法还包括：
    　　　在获取到待计算业务数据时，获取所述待计算业务数据的业务类型；
    　　　在所述业务类型为年金业务时，获取所述待计算业务数据对应的企业标识；
    　　　基于所述企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个企业标识对应的企业业务数据；
    　　　采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益。　　　
  12. 如权利要求11所述的收益计算方法，其特征在于，所述采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益的步骤之后，所述收益计算方法还包括：
    　　　基于所述企业收益更新各个企业所对应的企业账户。　　　
  13. 如权利要求7所述的收益计算方法，其特征在于，所述对计算获得的所述第一收益进行验证操作的步骤包括：
    　　　获取前一定价期间内各个所述业务场景对应的第五业务数据；
    　　　基于所述第五业务数据计算所述前一定价期间内所述用户账户的第三收益；
    　　　对所述第三收益进行验证操作，其中，在所述第三收益验证通过时，确定所述第一收益验证通过。　　　
  14. 如权利要求13所述的收益计算方法，其特征在于，所述基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分的步骤之后，所述收益计算方法还包括：
    　　　在获取到待计算业务数据时，获取所述待计算业务数据的业务类型；
    　　　在所述业务类型为年金业务时，获取所述待计算业务数据对应的企业标识；
    　　　基于所述企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个企业标识对应的企业业务数据；
    　　　采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益。　　　
  15. 如权利要求14所述的收益计算方法，其特征在于，所述采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益的步骤之后，所述收益计算方法还包括：
    　　　基于所述企业收益更新各个企业所对应的企业账户。　　　
  16. 如权利要求1所述的收益计算方法，其特征在于，所述基于所述第一收益计算所述待计算业务数据对应的各个用户账户的总收益的步骤之后，所述收益计算方法还包括：
    　　　基于所述总收益更新各个所述用户账户。　　　
  17. 如权利要求16所述的收益计算方法，其特征在于，所述基于各个业务场景采用分布式计算Hadoop平台对待计算业务数据进行拆分的步骤之后，所述收益计算方法还包括：
    　　　在获取到待计算业务数据时，获取所述待计算业务数据的业务类型；
    　　　在所述业务类型为年金业务时，获取所述待计算业务数据对应的企业标识；
    　　　基于所述企业标识采用分布式计算Hadoop平台对待计算业务数据进行拆分，以获得各个企业标识对应的企业业务数据；
    　　　采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益。　　　
  18. 如权利要求17所述的收益计算方法，其特征在于，所述采用所述Hadoop平台基于所述企业业务数据分别计算各个企业所对应的企业收益的步骤之后，所述收益计算方法还包括：
    　　　基于所述企业收益更新各个企业所对应的企业账户。　　　
  19. 一种收益计算装置，其特征在于，所述收益计算装置包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的收益计算程序，所述收益计算程序被所述处理器执行时实现如权利要求1所述的收益计算方法的步骤。　　　
  20. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有收益计算程序，所述收益计算程序被处理器执行时实现如权利要求1所述的收益计算方法的步骤。
PCT/CN2018/076160 2017-06-26 2018-02-10 收益计算方法、装置及计算机可读存储介质 WO2019000962A1 (zh)

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