WO2018233308A1 - 统计指标处理方法、装置、终端设备及可读存储介质 - Google Patents

统计指标处理方法、装置、终端设备及可读存储介质 Download PDF

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WO2018233308A1
WO2018233308A1 PCT/CN2018/076517 CN2018076517W WO2018233308A1 WO 2018233308 A1 WO2018233308 A1 WO 2018233308A1 CN 2018076517 W CN2018076517 W CN 2018076517W WO 2018233308 A1 WO2018233308 A1 WO 2018233308A1
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statistical
period
dimension
indicator
summary model
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PCT/CN2018/076517
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English (en)
French (fr)
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王海平
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平安科技（深圳）有限公司
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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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G06Q40/08—Insurance

Definitions

  • the present application belongs to the field of data processing technologies, and in particular, to a statistical indicator processing method, apparatus, terminal device, and readable storage medium.
  • the operational data of life insurance is divided into information of policy, information of insurance, information of policyholder or insured, claim information, channel information, preservation acceptance information, underwriting information, account information, and margin information.
  • the main function of the actuarial subsystems is to analyze the operational data of life insurance and obtain the corresponding statistical indicators.
  • the statistical indicators include: cash value, premium, insurance amount, accumulated contribution amount, claims, partial collection amount, critical illness payment amount, account value, policy period and so on.
  • the existing actuarial subsystems determine the same statistical indicator, its statistical dimension and statistical cycle are likely to be similar, and repeated statistics not only waste system resources, but also increase subsequent demand development and data consistency check. Difficulties and labor costs.
  • the monthly settlement process of the reserve determines the cash value from the policy information, the insurance information, the preservation acceptance information, and the account information, and is integrated (All In When the One, AIO) system determines the cash value, its statistical dimension is similar to that of the reserve system. However, since the AIO system cannot directly use the cash value of the reserve system, it must be re-stated according to the reserve system. Counting it once, wasting resources.
  • the embodiment of the present application provides a method, a device, a terminal device, and a readable storage medium for processing a statistical indicator, so as to solve the problem of waste of resources caused by recalculating the same statistical indicator in the prior art.
  • a first aspect of the embodiment of the present application provides a method for processing a statistical indicator, including:
  • the statistical indicator is calculated and stored according to the statistical dimension and the statistical period.
  • a second aspect of the embodiments of the present application provides a statistical indicator processing apparatus, including:
  • a statistical indicator determining unit for determining statistical indicators having the same statistical dimension and statistical period in each subsystem of the actuarial calculation
  • a statistical dimension and a period storage unit configured to store a statistical dimension and a statistical period corresponding to the statistical indicator
  • the statistical indicator calculation unit is configured to calculate and store the statistical indicator according to the statistical dimension and the statistical period.
  • a third aspect of an embodiment of the present application provides a terminal device including a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, the processor executing the computer The following steps are implemented when the instructions are readable:
  • the statistical indicator is calculated and stored according to the statistical dimension and the statistical period.
  • a fourth aspect of the embodiments of the present application provides a computer readable storage medium storing computer readable instructions that, when executed by a processor, implement the following steps:
  • the statistical indicator is calculated and stored according to the statistical dimension and the statistical period.
  • the statistical indicators are calculated and stored according to the statistical dimension and the statistical period, and the statistical indicators have the same statistical dimension and statistical period in each subsystem of the actuarial calculation, it is convenient for each subsystem of the subsequent actuarial calculation to directly call the calculated statistical indicators, thereby A statistical indicator, the actuarial subsystems do not need to be re-stated again, which greatly saves system resources and labor costs.
  • FIG. 1 is a flowchart of a method for processing a statistical indicator provided by an embodiment of the present application
  • FIG. 2 is a structural diagram of a statistical indicator processing apparatus according to an embodiment of the present application.
  • FIG. 3 is a schematic diagram of a terminal device according to an embodiment of the present application.
  • FIG. 1 is a flowchart of a method for processing a statistical indicator provided by an embodiment of the present application, which is described in detail as follows:
  • Step S101 Determine statistical indicators having the same statistical dimension and a statistical period in each subsystem of the actuarial calculation.
  • the statistical indicators of the same statistical dimension and statistical period in each subsystem of the actuarial calculation include at least one of the following: basic insured amount, effective insured amount, purchased shares, cash value, claim amount, receiving amount, bonus, survival gold, account value , reduction of increase, reserve, amount of heavy illness payment, amount of disability payment, number of payment, accumulated payment amount, policy year, payment year, etc.
  • the statistical dimensions include: policy, insurance, type of insurance, (insurance or insured) occupation, (insured or insured) age, (insured or insured) gender, policy year, service personnel, Insurance status, channels, payment, etc.
  • the statistical cycle includes: day, month, half year, year, and so on.
  • the method includes:
  • the usage frequency of the statistical indicators having the same dimension and the statistical period in each subsystem of the actuarial calculation is counted, and the use frequency level of the statistical indicator is divided according to the statistical result. For example, the frequency of use of statistical indicators is divided into high, medium, and low.
  • the specified use frequency level usually refers to the level corresponding to the statistical indicator with lower frequency. For example, when the specified usage frequency level is "Low", the statistical indicator with the low frequency level is excluded.
  • the method includes:
  • A22 Obtain a policy status corresponding to a statistical indicator converted into a specified format, and exclude a statistical indicator whose policy status is invalid. Specifically, when the policy status is detected as N, F, and C, it is determined that the policy status is invalid, and the statistical indicator corresponding to the invalid policy status is excluded.
  • Step S102 Store a statistical dimension and a statistical period corresponding to the statistical indicator.
  • the specific policy data corresponding to the statistical dimension and the statistical period is stored.
  • step S102 includes:
  • the association extracts the granularity of the granularity to the statistical number of the policy number and the statistical period including the hour, minute, and second date, and stores the extracted statistical data and the policy data corresponding to the statistical period to the A preliminary summary model.
  • the statistical dimension with the granularity of the policy number is the finest statistical dimension. Since the primary summary model saves the policy data under a large number of statistical dimensions, it can satisfy the application of the required list.
  • the association extracts the statistical dimension including the policyholder, the insured, and the insurance category, and associates the statistical period including the hour, minute, and second date, and the statistical dimension to be extracted and
  • the policy data corresponding to the statistical period is stored in the advanced summary model.
  • the advanced summary model there is no need to save the statistical dimension of the policy number, and only the statistical dimensions including the policyholder, the insured, and the insurance category can be retained to reduce the amount of data.
  • the determined storage model is a primary summary model.
  • the statistical indicators corresponding to the daily data are stored in the format of the primary summary model at 0:00 am; after the primary summary model is completed, the primary is filtered according to the format of the advanced summary model. Summarize the model to get the statistical dimensions and statistical periods that need to be deposited into the advanced summary model.
  • the user can retrieve the corresponding statistical dimension and the policy data corresponding to the corresponding statistical period from the two models, since all the repeated statistical indicators are All are unified from the data model, so the system structure is clear, data reusability, code readability is greatly improved, and manual development and maintenance costs are reduced, and system resource consumption is reduced.
  • C1 counts the number of calls of the primary summary model and the advanced summary model over a period of time.
  • the primary summary model stores detailed statistical dimensions and statistical periods, while the advanced summary model storage is a rough statistical dimension and statistical period.
  • Two different storage methods can meet different needs, for example, if needed Accurate analysis can directly call the policy data stored in the primary summary model. If the interval is too long and does not require too much information, the policy data stored in the advanced summary model is called.
  • the specific statistical dimensions and statistical periods stored in the advanced summary model can be updated periodically to ensure that the updated primary summary model and advanced summary model are more realistic. Specifically, the number of calls of the primary summary model and the advanced summary model is calculated separately for a period of time.
  • the statistical dimension and the statistical period of the primary summary model invoked are There is deleted information. If yes, the statistical dimension of the retention and the number of times of the statistical period are counted, and the information of the statistical dimension and the statistical period whose number reaches a certain threshold is added to the advanced summary model.
  • step S102 includes:
  • the photographing data is generated according to the statistical dimension and the statistical period corresponding to the statistical index, and the generated photographing data is stored to ensure the integrity of the data (such as the current month data), and meet the application requirement that the monthly data needs to analyze the policy status.
  • Step S103 Calculate and store the statistical indicator according to the statistical dimension and the statistical period.
  • the different statistical indicators are calculated in advance according to the statistical dimensions and statistical periods required for calculating different statistical indicators, so that the statistical indicators can be directly invoked subsequently.
  • the statistical indicator photograph data may be generated according to the calculated statistical indicators, and the photograph data is stored.
  • the method includes: receiving a call instruction of a statistical indicator, and calling a corresponding statistical indicator according to the call instruction.
  • the statistical indicators having the same statistical dimension and the statistical period in each subsystem of the actuarial calculation are determined, the statistical dimension and the statistical period corresponding to the statistical indicator are stored, and the statistics are calculated and stored according to the statistical dimension and the statistical period. index. Since the statistical indicators are calculated and stored according to the statistical dimension and the statistical period, and the statistical indicators have the same statistical dimension and statistical period in each subsystem of the actuarial calculation, it is convenient for each subsystem of the subsequent actuarial calculation to directly call the calculated statistical indicators, thereby A statistical indicator, the actuarial subsystems do not need to be re-stated again, which greatly saves system resources and labor costs.
  • FIG. 2 is a schematic structural diagram of a statistical indicator processing apparatus according to an embodiment of the present application. For convenience of description, only the part related to the implementation is shown.
  • the statistical indicator processing apparatus 2 includes: a statistical indicator determining unit 21, The statistical dimension and period storage unit 22 and the statistical index calculation unit 23. among them:
  • the statistical indicator determining unit 21 is configured to determine statistical indicators having the same statistical dimension and a statistical period in each subsystem of the actuarial calculation.
  • the statistical indicators of the same statistical dimension and statistical period in each subsystem of the actuarial calculation include at least one of the following: basic insured amount, effective insured amount, purchased shares, cash value, claim amount, receiving amount, bonus, survival gold, account value , reduction of increase, reserve, amount of heavy illness payment, amount of disability payment, number of payment, accumulated payment amount, policy year, payment year, etc.
  • the statistical dimensions include: policy, insurance, type of insurance, (insurance or insured) occupation, (insured or insured) age, (insured or insured) gender, policy year, service personnel, Insurance status, channels, payment, etc.
  • the statistical indicator processing apparatus includes:
  • the frequency level division module is configured to collect the frequency of use of the statistical indicators having the same dimension and the statistical period in each subsystem of the actuarial calculation, and divide the use frequency level of the statistical indicator according to the statistical result.
  • the statistical indicator culling module is used to eliminate statistical indicators that use the frequency level as the specified frequency level.
  • the specified use frequency level usually refers to the level corresponding to the statistical indicator with lower frequency.
  • the statistical indicator processing apparatus includes:
  • a format conversion module for converting the format of the remaining statistical indicators into a specified format. Specifically, the statistical indicators remaining after performing the culling operation are first classified, and according to different requirements of the different types of formats, the format of the statistical indicators is converted into a specified format required by the category to which it belongs.
  • the statistical indicator culling module of the invalid state is used to obtain the policy status corresponding to the statistical indicator converted into the specified format, and the statistical indicator whose policy status is invalid is excluded.
  • the statistical dimension and period storage unit 22 is configured to store a statistical dimension and a statistical period corresponding to the statistical indicator.
  • the specific policy data corresponding to the statistical dimension and the statistical period is stored.
  • the camera data is first generated according to the statistical dimension and the statistical period corresponding to the statistical indicator, and the generated camera data is stored.
  • the statistical dimension and period storage unit 22 includes:
  • the storage model determining module is configured to determine a statistical model corresponding to the statistical indicator and a storage model of the statistical period.
  • the statistical period is in units of months, it is determined whether the current date is before the specified date, such as determining whether it is before the 4th of the current month, and if so, the determined storage model is a primary summary model.
  • the statistical indicators corresponding to the daily data are stored in the format of the primary summary model at 0:00 am; after the primary summary model is completed, the primary is filtered according to the format of the advanced summary model. Summarize the model to get the statistical dimensions and statistical periods that need to be deposited into the advanced summary model.
  • the primary summary model data storage module is configured to: when the storage model is a primary summary model, correlate the statistical dimension with the granularity accurate to the policy number and the statistical period including the hour, minute, and second date, and the statistical dimension and the statistical period to be taken out The corresponding policy data is stored to the primary summary model.
  • the high-level summary model data storage module is configured to: when the storage model is a high-level summary model, associate the statistical dimension including the policyholder, the insured, and the insurance category, and associate the statistical period including the hour, minute, and second date
  • the policy data corresponding to the extracted statistical dimension and the statistical period is stored in the advanced summary model. Specifically, in the advanced summary model, there is no need to save the statistical dimension of the policy number, and only the statistical dimensions including the policyholder, the insured, and the insurance category can be retained to reduce the amount of data.
  • the statistical indicator processing apparatus includes:
  • the call count statistics unit is used to separately count the number of calls of the primary summary model and the advanced summary model over a period of time.
  • An information adding unit of the advanced summary model configured to determine a statistical dimension and a statistical period of the primary summary model of the call when the difference between the number of calls of the primary summary model and the number of calls of the advanced summary model is greater than a preset difference threshold Whether there is deleted information, and when there is deleted information in the statistical dimension of the primary summary model and the statistical period of the call, the statistically retained statistical dimension and the number of times of the statistical period information are retained, and the number of times retained is greater than the preset number threshold. Information on statistical dimensions and statistical period statistical dimensions and statistical periods is added to the advanced summary model.
  • the statistical indicator calculation unit 23 is configured to calculate and store the statistical indicator according to the statistical dimension and the statistical period.
  • the different statistical indicators are calculated in advance according to the statistical dimensions and statistical periods required for calculating different statistical indicators, so that the statistical indicators can be directly invoked subsequently.
  • the statistical indicator photograph data may be generated according to the calculated statistical indicators, and the photograph data is stored.
  • the statistical indicator processing apparatus includes: a call instruction receiving unit, configured to receive a call instruction of a statistical indicator, and invoke a corresponding statistical indicator according to the call instruction.
  • the statistical indicator is calculated and stored according to the statistical dimension and the statistical period, and the statistical indicator has the same statistical dimension and the statistical period in each subsystem of the actuarial calculation, it is convenient for the subsequent actuarial calculation subsystem to directly call the calculated Statistical indicators, so that for the same statistical indicator, the actuarial subsystems do not need to be re-stated again, which greatly saves system resources and labor costs.
  • FIG. 3 is a schematic diagram of a terminal device according to an embodiment of the present application.
  • the terminal device 3 of this embodiment includes a processor 30, a memory 31, and computer readable instructions 32 stored in the memory 31 and operable on the processor 30.
  • the processor 30 executes the computer readable instructions 32, the steps in the foregoing embodiments of the respective statistical index processing methods are implemented, such as steps S101 to S103 shown in FIG.
  • the processor 30 executes the computer readable instructions 32
  • the functions of the modules/units in the various apparatus embodiments described above are implemented, such as the functions of the modules 21 to 23 shown in FIG. 2.
  • the computer readable instructions 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30, To complete this application.
  • the computer readable instructions 32 may be divided into a statistical indicator determining unit, a statistical dimension and a periodic storage unit, and a statistical indicator computing unit. The specific functions of each module are as follows:
  • a statistical indicator determining unit for determining statistical indicators having the same statistical dimension and statistical period in each subsystem of the actuarial calculation
  • a statistical dimension and a period storage unit configured to store a statistical dimension and a statistical period corresponding to the statistical indicator
  • the statistical indicator calculation unit is configured to calculate and store the statistical indicator according to the statistical dimension and the statistical period.
  • the terminal device 3 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the terminal device may include, but is not limited to, the processor 30 and the memory 31. It will be understood by those skilled in the art that FIG. 3 is only an example of the terminal device 3, does not constitute a limitation of the terminal device 3, may include more or less components than those illustrated, or combine some components, or different components.
  • the terminal device may further include an input/output device, a network access device, a bus, and the like.
  • the so-called processor 30 can be a central processing unit (Central Processing Unit, CPU), can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3.
  • the memory 31 may also be an external storage device of the terminal device 3, for example, a plug-in hard disk equipped on the terminal device 3, a smart memory card (SMC), and a secure digital (SD). Card, flash card (Flash Card) and so on.
  • the memory 31 may also include both an internal storage unit of the terminal device 3 and an external storage device.
  • the memory 31 is configured to store the computer readable instructions and other programs and data required by the terminal device.
  • the memory 31 can also be used to temporarily store data that has been output or is about to be output.
  • each functional unit and module in the foregoing system may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be implemented by hardware.
  • Formal implementation can also be implemented in the form of software functional units.
  • the specific names of the respective functional units and modules are only for the purpose of facilitating mutual differentiation, and are not intended to limit the scope of protection of the present application.
  • the disclosed device/terminal device and method may be implemented in other manners.
  • the device/terminal device embodiments described above are merely illustrative.
  • the division of the modules or units is only a logical function division.
  • there may be another division manner for example, multiple units.
  • components may be combined or integrated into another system, or some features may be omitted or not performed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated modules/units if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the processes in the foregoing embodiments, and may also be implemented by computer readable instructions, which may be stored in a computer readable storage medium.
  • the computer readable instructions when executed by a processor, may implement the steps of the various method embodiments described above.
  • the computer readable medium can include any entity or device capable of carrying the computer readable instruction code, a recording medium, a USB flash drive, a removable hard drive, a magnetic disk, an optical disk, a computer memory, a read only memory (ROM, Read-Only) Memory), random access memory (RAM, Random) Access Memory), electrical carrier signals, telecommunications signals, and software distribution media.
  • ROM Read Only memory
  • RAM Random Access Memory
  • electrical carrier signals telecommunications signals
  • telecommunications signals and software distribution media. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer readable media Does not include electrical carrier signals and telecommunication signals.

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Abstract

统计指标处理方法、装置、终端设备及可读存储介质，所述方法包括：确定精算各个子系统中具有相同统计维度及统计周期的统计指标；存储所述统计指标对应的统计维度及统计周期；根据所述统计维度及统计周期计算并存储所述统计指标。通过上述方法，对同一个统计指标，精算各个子系统无需都重新统计一遍，极大节省了系统资源及人工成本。

Description

统计指标处理方法、装置、终端设备及可读存储介质
本申请要求于2017年6月21日提交中国专利局、申请号为CN 201710478076.9、发明名称为“统计指标处理方法、装置及终端设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于数据处理技术领域，尤其涉及统计指标处理方法、装置、终端设备及可读存储介质。
 背景技术
寿险的运营数据分为保单的信息、险种的信息、投保人或被保人的信息、理赔信息、渠道信息、保全受理信息、核保信息、帐户信息、保证金信息等。精算各个子系统的主要功能是分析寿险的运营数据，得到对应的统计指标。其中，统计指标包括：现金价值、保费、保额、累计缴费金额、理赔金、部分领取金额、重疾给付金额、帐户价值、保单期数等。但现有的精算各个子系统在确定同一个统计指标时，其统计维度和统计周期极可能都是类似的，而重复的统计不但浪费系统资源，也给后续的需求开发、数据一致性核查增加了难度及人工成本。
比如在确定现金价值这一统计指标时，准备金的月结流程分块从保单信息、险种信息、保全受理信息、账号信息中确定现金价值，而一体（All In One，AIO）系统确定现金价值时，其统计维度与准备金系统的类似，但由于该AIO系统又不能直接使用准备金系统的现金价值这一数据，因此还得按准备金系统的统计方式重新统计一遍，从而浪费了资源。
 技术问题
现有技术中存在对同一统计指标需要重新计算一遍所导致的资源浪费的问题
 技术解决方案
有鉴于此，本申请实施例提供了统计指标处理方法、装置、终端设备及可读存储介质，以解决现有技术中对同一统计指标需要重新计算一遍所导致的资源浪费的问题。
本申请实施例的第一方面提供了一种统计指标处理方法，包括：
确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
存储所述统计指标对应的统计维度及统计周期；
根据所述统计维度及统计周期计算并存储所述统计指标。
本申请实施例的第二方面提供了一种统计指标处理装置，包括：
统计指标确定单元，用于确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
统计维度及周期存储单元，用于存储所述统计指标对应的统计维度及统计周期；
统计指标计算单元，用于根据所述统计维度及统计周期计算并存储所述统计指标。
本申请实施例的第三方面提供了一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机可读指令，所述处理器执行所述计算机可读指令时实现如下步骤：
确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
存储所述统计指标对应的统计维度及统计周期；
根据所述统计维度及统计周期计算并存储所述统计指标。
本申请实施例的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可读指令，所述计算机可读指令被处理器执行时实现如下步骤：
确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
存储所述统计指标对应的统计维度及统计周期；
根据所述统计维度及统计周期计算并存储所述统计指标。
 有益效果
本申请实施例与现有技术相比存在的有益效果是：
由于根据统计维度及统计周期计算并存储统计指标，且该统计指标在精算各个子系统中具有相同统计维度及统计周期，因此，便于后续精算各个子系统直接调用已计算的统计指标，从而对同一个统计指标，精算各个子系统无需都重新统计一遍，极大节省了系统资源及人工成本。
 附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种统计指标处理方法的流程图；
图2是本申请实施例提供的一种统计指标处理装置的结构图；
图3是本申请实施例提供的终端设备的示意图。
 本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
为了说明本申请所述的技术方案，下面通过具体实施例来进行说明。
图1示出了本申请实施例提供的一种统计指标处理方法的流程图，详述如下：
步骤S101、确定精算各个子系统中具有相同统计维度及统计周期的统计指标。
其中，精算各个子系统中具有相同统计维度、统计周期的统计指标包括以下至少一个：基本保额、有效保额、购买份数、现金价值、理赔金额、领取金额、红利、生存金、账号价值、减清增额、准备金、重疾给付金额、残疾给付金额、缴费次数、累加缴费金额、保单年度、缴费年度等。
其中，统计维度包括：保单、险种、险种类别、（投保人或被保人）职业、（投保人或被保人）年龄、（投保人或被保人）性别、保单生效年度、服务人员、险种状态、渠道、缴别等。
其中，统计周期包括：日、月、半年、年等。
可选地，为了获得用户需要的统计指标，则在所述步骤S101之后，包括：
A1、统计所述精算各个子系统中具有相同维度及统计周期的统计指标的使用频率，并根据统计结果划分所述统计指标的使用频率级别。例如，将统计指标的使用频率级别划分为高、中、低。
A2、剔除使用频率级别为指定使用频率级别的统计指标。其中，指定使用频率级别通常是指使用频率较低的统计指标对应的级别。例如，在指定使用频率级别为“低”时，则剔除使用频率级别为低的统计指标。
可选地，为了提高确定的统计指标的使用频率，则在所述A2之后，包括：
A21、将剩余的统计指标的格式转换为指定格式。具体地，先将执行剔除操作后剩余的统计指标进行归类，再根据归类结果，例如，根据不同类别对格式的不同要求，统计指标的格式转换为其所属类别要求的指定格式。由于统计指标包括单位、日期格式、年龄层级、缴次层级等多个分量，因此，将统计指标的格式转换为指定格式是指将统计指标的单位、日期格式、年龄层级、缴次层级等的格式转换为指定格式。
A22、获取转换为指定格式的统计指标对应的保单状态，剔除保单状态为无效的统计指标。具体地，当检测到保单状态为N、F、C时，判定该保单状态为无效，并剔除无效的保单状态对应的统计指标。
步骤S102、存储所述统计指标对应的统计维度及统计周期。
具体地，存储统计维度及统计周期对应的具体的保单数据。
可选地，所述步骤S102包括：
B1、确定所述统计指标对应的统计维度及统计周期的存储模型。
B2、在所述存储模型为初级汇总模型时，关联取出粒度精确到保单号的统计维度及包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型。粒度为保单号的统计维度是最细的统计维度，由于该初级汇总模型通过保存完善的大量的统计维度下的保单数据，因此能够满足需要清单的应用使用。
B3、在所述存储模型为高级汇总模型时，关联取出包括投保人、被保人和险种类别的统计维度，以及，关联取出包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型。具体地，高级汇总模型中，无需保存保单号这一统计维度，可以仅保留包括投保人、被保人和险种类别的统计维度以减少数据量。
可选地，上述B1中，在统计周期以月为单位时，则判断当前日期是否在指定日期之前，如判断是否在当月的4号之前，若是，则确定的存储模型为初级汇总模型。此外，为了减少存储时对日常工作的影响，则在凌晨0点时按照初级汇总模型的格式存入日数据对应的统计指标；在完成初级汇总模型后，按照高级汇总模型的格式过滤所述初级汇总模型，得到需存入高级汇总模型的统计维度和统计周期。
当在初级汇总模型和高级汇总模型中存入对应的统计维度和统计周期后，用户可从这两个模型中调取相应统计维度和相应统计周期对应的保单数据，由于把所有重复统计的指标都统一改为从数据模型出，因此使得系统结构分明，数据复用性，代码的可读性都大大提高，并且减少人工的开发维护成本，以及减少系统资源占用。
可选地，在所述B2和B3之后，包括：
C1、分别统计一段时间内初级汇总模型和高级汇总模型的调用次数。
C2、在所述初级汇总模型的调用次数与所述高级汇总模型的调用次数的差大于预设差值阈值时，判断调用的初级汇总模型的统计维度及统计周期是否存在被删除的信息，并在调用的初级汇总模型的统计维度及统计周期存在被删除的信息时，统计保留的统计维度及统计周期的信息的次数，将保留的次数大于预设次数阈值的统计维度及统计周期统计维度及统计周期的信息增加到高级汇总模型中。
上述C1和C2中，初级汇总模型存储的是详细的统计维度及统计周期，而高级汇总模型存储是粗略的统计维度及统计周期，两种不同的存储方式能够满足不同的需求，例如，若需要精确分析则可以直接调用该初级汇总模型存储的保单数据，若间隔时间太久，不需要过多的信息量，则调用高级汇总模型存储的保单数据。当然，高级汇总模型存储的具体统计维度及统计周期可定时更新，以保证更新后的初级汇总模型和高级汇总模型更贴合实际需求。具体地，分别统计一段时间内初级汇总模型和高级汇总模型的调用次数，在初级汇总模型的调用次数明显高于高级汇总模型的调用次数时，判断调用的初级汇总模型的统计维度及统计周期是否存在被删除的信息，若是，统计保留的统计维度及统计周期的信息的次数，并将次数达到一定阈值的统计维度及统计周期的信息增加到高级汇总模型中。
可选地，所述步骤S102包括：
根据所述统计指标对应的统计维度及统计周期生成拍照数据，并存储生成的拍照数据，以确保数据（如当月数据）的完整性，满足月数据需要分析保单状态的应用需求。
步骤S103、根据所述统计维度及统计周期计算并存储所述统计指标。
具体地，预先根据计算不同统计指标所需的统计维度和统计周期计算该不同统计指标，以便后续直接调用统计指标。
当然，在计算了统计指标后，可根据计算的统计指标生成统计指标拍照数据，并存储该拍照数据。
可选地，在步骤S103之后，包括，接收统计指标的调用指令，根据该调用指令调用对应的统计指标。
本申请实施例中，确定精算各个子系统中具有相同统计维度及统计周期的统计指标，存储所述统计指标对应的统计维度及统计周期，根据所述统计维度及统计周期计算并存储所述统计指标。由于根据统计维度及统计周期计算并存储统计指标，且该统计指标在精算各个子系统中具有相同统计维度及统计周期，因此，便于后续精算各个子系统直接调用已计算的统计指标，从而对同一个统计指标，精算各个子系统无需都重新统计一遍，极大节省了系统资源及人工成本。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
图2示出了本申请实施例提供的一种统计指标处理装置的结构示意图，为便于说明，仅示出了与本实施相关的部分，该统计指标处理装置2包括：统计指标确定单元21、统计维度及周期存储单元22、统计指标计算单元23。其中：
统计指标确定单元21，用于确定精算各个子系统中具有相同统计维度及统计周期的统计指标。
其中，精算各个子系统中具有相同统计维度、统计周期的统计指标包括以下至少一个：基本保额、有效保额、购买份数、现金价值、理赔金额、领取金额、红利、生存金、账号价值、减清增额、准备金、重疾给付金额、残疾给付金额、缴费次数、累加缴费金额、保单年度、缴费年度等。
其中，统计维度包括：保单、险种、险种类别、（投保人或被保人）职业、（投保人或被保人）年龄、（投保人或被保人）性别、保单生效年度、服务人员、险种状态、渠道、缴别等。
可选地，为了获得用户需要的统计指标，所述统计指标处理装置包括：
使用频率级别划分模块，用于统计所述精算各个子系统中具有相同维度及统计周期的统计指标的使用频率，并根据统计结果划分所述统计指标的使用频率级别。
统计指标剔除模块，用于剔除使用频率级别为指定使用频率级别的统计指标。其中，指定使用频率级别通常是指使用频率较低的统计指标对应的级别。
可选地，为了提高确定的统计指标的使用频率，所述统计指标处理装置包括：
格式转换模块，用于将剩余的统计指标的格式转换为指定格式。具体地，先将执行剔除操作后剩余的统计指标进行归类，再根据不同类别对格式的不同要求，统计指标的格式转换为其所属类别要求的指定格式。
无效状态的统计指标剔除模块，用于获取转换为指定格式的统计指标对应的保单状态，剔除保单状态为无效的统计指标。
统计维度及周期存储单元22，用于存储所述统计指标对应的统计维度及统计周期。
具体地，存储统计维度及统计周期对应的具体的保单数据，可选地，在存储时，首先根据所述统计指标对应的统计维度及统计周期生成拍照数据，再存储生成的拍照数据。
所述统计维度及周期存储单元22包括：
存储模型确定模块，用于确定所述统计指标对应的统计维度及统计周期的存储模型。可选地，在统计周期以月为单位时，则判断当前日期是否在指定日期之前，如判断是否在当月的4号之前，若是，则确定的存储模型为初级汇总模型。此外，为了减少存储时对日常工作的影响，则在凌晨0点时按照初级汇总模型的格式存入日数据对应的统计指标；在完成初级汇总模型后，按照高级汇总模型的格式过滤所述初级汇总模型，得到需存入高级汇总模型的统计维度和统计周期。
初级汇总模型数据存储模块，用于在所述存储模型为初级汇总模型时，关联取出粒度精确到保单号的统计维度及包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型。
高级汇总模型数据存储模块，用于在所述存储模型为高级汇总模型时，关联取出包括投保人、被保人和险种类别的统计维度，以及，关联取出包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型。具体地，高级汇总模型中，无需保存保单号这一统计维度，可以仅保留包括投保人、被保人和险种类别的统计维度以减少数据量。
可选地，所述统计指标处理装置包括：
调用次数统计单元，用于分别统计一段时间内初级汇总模型和高级汇总模型的调用次数。
高级汇总模型的信息增加单元，用于在所述初级汇总模型的调用次数与所述高级汇总模型的调用次数的差大于预设差值阈值时，判断调用的初级汇总模型的统计维度及统计周期是否存在被删除的信息，并在调用的初级汇总模型的统计维度及统计周期存在被删除的信息时，统计保留的统计维度及统计周期的信息的次数，将保留的次数大于预设次数阈值的统计维度及统计周期统计维度及统计周期的信息增加到高级汇总模型中。
统计指标计算单元23，用于根据所述统计维度及统计周期计算并存储所述统计指标。
具体地，预先根据计算不同统计指标所需的统计维度和统计周期计算该不同统计指标，以便后续直接调用统计指标。
当然，在计算了统计指标后，可根据计算的统计指标生成统计指标拍照数据，并存储该拍照数据。
可选地，所述统计指标处理装置包括：调用指令接收单元，用于接收统计指标的调用指令，根据该调用指令调用对应的统计指标。
本申请实施例中，由于根据统计维度及统计周期计算并存储统计指标，且该统计指标在精算各个子系统中具有相同统计维度及统计周期，因此，便于后续精算各个子系统直接调用已计算的统计指标，从而对同一个统计指标，精算各个子系统无需都重新统计一遍，极大节省了系统资源及人工成本。
图3是本申请实施例提供的终端设备的示意图。如图3所示，该实施例的终端设备3包括：处理器30、存储器31以及存储在所述存储器31中并可在所述处理器30上运行的计算机可读指令32。所述处理器30执行所述计算机可读指令32时实现上述各个统计指标处理方法实施例中的步骤，例如图1所示的步骤S101至S103。或者，所述处理器30执行所述计算机可读指令32时实现上述各装置实施例中各模块/单元的功能，例如图2所示模块21至23的功能。
示例性的，所述计算机可读指令32可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器31中，并由所述处理器30执行，以完成本申请。例如，所述计算机可读指令32可以被分割成统计指标确定单元、统计维度及周期存储单元、统计指标计算单元，各模块具体功能如下：
统计指标确定单元，用于确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
统计维度及周期存储单元，用于存储所述统计指标对应的统计维度及统计周期；
统计指标计算单元，用于根据所述统计维度及统计周期计算并存储所述统计指标。
所述终端设备3可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述终端设备可包括，但不仅限于，处理器30、存储器31。本领域技术人员可以理解，图3仅仅是终端设备3的示例，并不构成对终端设备3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，例如所述终端设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器30可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器 (Digital Signal Processor，DSP)、专用集成电路 (Application Specific Integrated Circuit，ASIC)、现成可编程门阵列 (Field-Programmable Gate Array，FPGA) 或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器31可以是所述终端设备3的内部存储单元，例如终端设备3的硬盘或内存。所述存储器31也可以是所述终端设备3的外部存储设备，例如所述终端设备3上配备的插接式硬盘，智能存储卡（Smart Media Card, SMC），安全数字（Secure Digital, SD）卡，闪存卡（Flash Card）等。进一步地，所述存储器31还可以既包括所述终端设备3的内部存储单元也包括外部存储设备。所述存储器31用于存储所述计算机可读指令以及所述终端设备所需的其他程序和数据。所述存储器31还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/终端设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机可读指令来指令相关的硬件来完成，所述的计算机可读指令可存储于一计算机可读存储介质中，该计算机可读指令在被处理器执行时，可实现上述各个方法实施例的步骤。所述计算机可读介质可以包括：能够携带所述计算机可读指令代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器（ROM，Read-Only Memory）、随机存取存储器（RAM，Random Access Memory）、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (20)

  1. 一种统计指标处理方法，其特征在于，包括：
    确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
    存储所述统计指标对应的统计维度及统计周期；
    根据所述统计维度及统计周期计算并存储所述统计指标。
  2. 如权利要求1所述的统计指标处理方法，其特征在于，在所述确定精算各个子系统中具有相同统计维度及统计周期的统计指标之后，包括：
    统计所述精算各个子系统中具有相同维度及统计周期的统计指标的使用频率，并根据统计结果划分所述统计指标的使用频率级别；
    剔除使用频率级别为指定使用频率级别的统计指标。
  3. 如权利要求2所述的统计指标处理方法，其特征在于，在所述剔除使用频率级别为指定使用频率级别的统计指标之后，包括：
    将剩余的统计指标的格式转换为指定格式；
    获取转换为指定格式的统计指标对应的保单状态，剔除保单状态为无效的统计指标。
  4. 如权利要求1所述的统计指标处理方法，其特征在于，所述存储所述统计指标对应的统计维度及统计周期，包括：
    确定所述统计指标对应的统计维度及统计周期的存储模型；
    在所述存储模型为初级汇总模型时，关联取出粒度精确到保单号的统计维度及包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型；
    在所述存储模型为高级汇总模型时，关联取出包括投保人、被保人和险种类别的统计维度，以及，关联取出包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型。
  5. 如权利要求4所述的统计指标处理方法，其特征在于，在所述将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型以及在所述将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型之后，包括：
    分别统计一段时间内初级汇总模型和高级汇总模型的调用次数；
    在所述初级汇总模型的调用次数与所述高级汇总模型的调用次数的差大于预设差值阈值时，判断调用的初级汇总模型的统计维度及统计周期是否存在被删除的信息，并在调用的初级汇总模型的统计维度及统计周期存在被删除的信息时，统计保留的统计维度及统计周期的信息的次数，将保留的次数大于预设次数阈值的统计维度及统计周期统计维度及统计周期的信息增加到高级汇总模型中。
  6.  一种统计指标处理装置，其特征在于，包括：
    统计指标确定单元，用于确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
    统计维度及周期存储单元，用于存储所述统计指标对应的统计维度及统计周期；
    统计指标计算单元，用于根据所述统计维度及统计周期计算并存储所述统计指标。
  7. 如权利要求6所述的统计指标处理装置，其特征在于，所述统计指标处理装置包括：
    使用频率级别划分模块，用于统计所述精算各个子系统中具有相同维度及统计周期的统计指标的使用频率，并根据统计结果划分所述统计指标的使用频率级别；
    统计指标剔除模块，用于剔除使用频率级别为指定使用频率级别的统计指标。
  8. 如权利要求7所述的统计指标处理装置，其特征在于，所述统计指标处理装置包括：
    格式转换模块，用于将剩余的统计指标的格式转换为指定格式。
    无效状态的统计指标剔除模块，用于获取转换为指定格式的统计指标对应的保单状态，剔除保单状态为无效的统计指标。
  9. 如权利要求6所述的统计指标处理装置，其特征在于，所述统计维度及周期存储单元包括：
    存储模型确定模块，用于确定所述统计指标对应的统计维度及统计周期的存储模型；
    初级汇总模型数据存储模块，用于在所述存储模型为初级汇总模型时，关联取出粒度精确到保单号的统计维度及包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型；
    高级汇总模型数据存储模块，用于在所述存储模型为高级汇总模型时，关联取出包括投保人、被保人和险种类别的统计维度，以及，关联取出包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型。
  10. 如权利要求9所述的统计指标处理装置，其特征在于，所述统计指标处理装置包括：
    调用次数统计单元，用于分别统计一段时间内初级汇总模型和高级汇总模型的调用次数。
    高级汇总模型的信息增加单元，用于在所述初级汇总模型的调用次数与所述高级汇总模型的调用次数的差大于预设差值阈值时，判断调用的初级汇总模型的统计维度及统计周期是否存在被删除的信息，并在调用的初级汇总模型的统计维度及统计周期存在被删除的信息时，统计保留的统计维度及统计周期的信息的次数，将保留的次数大于预设次数阈值的统计维度及统计周期统计维度及统计周期的信息增加到高级汇总模型中。
  11. 一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机可读指令，其特征在于，所述处理器执行所述计算机可读指令时实现如下步骤：
    确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
    存储所述统计指标对应的统计维度及统计周期；
    根据所述统计维度及统计周期计算并存储所述统计指标。
  12. 如权利要求11所述的终端设备，其特征在于，在所述确定精算各个子系统中具有相同统计维度及统计周期的统计指标之后，包括：
    统计所述精算各个子系统中具有相同维度及统计周期的统计指标的使用频率，并根据统计结果划分所述统计指标的使用频率级别；
    剔除使用频率级别为指定使用频率级别的统计指标。
  13. 如权利要求12所述的终端设备，其特征在于，在所述剔除使用频率级别为指定使用频率级别的统计指标之后，包括：
    将剩余的统计指标的格式转换为指定格式；
    获取转换为指定格式的统计指标对应的保单状态，剔除保单状态为无效的统计指标。
  14. 如权利要求11所述的终端设备，其特征在于，所述存储所述统计指标对应的统计维度及统计周期，包括：
    确定所述统计指标对应的统计维度及统计周期的存储模型；
    在所述存储模型为初级汇总模型时，关联取出粒度精确到保单号的统计维度及包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型；
    在所述存储模型为高级汇总模型时，关联取出包括投保人、被保人和险种类别的统计维度，以及，关联取出包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型。
  15. 如权利要求14所述的终端设备，其特征在于，在所述将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型以及在所述将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型之后，包括：
    分别统计一段时间内初级汇总模型和高级汇总模型的调用次数；
    在所述初级汇总模型的调用次数与所述高级汇总模型的调用次数的差大于预设差值阈值时，判断调用的初级汇总模型的统计维度及统计周期是否存在被删除的信息，并在调用的初级汇总模型的统计维度及统计周期存在被删除的信息时，统计保留的统计维度及统计周期的信息的次数，将保留的次数大于预设次数阈值的统计维度及统计周期统计维度及统计周期的信息增加到高级汇总模型中。
  16. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可读指令，其特征在于，所述计算机可读指令被处理器执行时实现如下步骤：
    确定精算各个子系统中具有相同统计维度及统计周期的统计指标；
    存储所述统计指标对应的统计维度及统计周期；
    根据所述统计维度及统计周期计算并存储所述统计指标。
  17. 如权利要求16所述的计算机可读存储介质，其特征在于，在所述确定精算各个子系统中具有相同统计维度及统计周期的统计指标之后，包括：
    统计所述精算各个子系统中具有相同维度及统计周期的统计指标的使用频率，并根据统计结果划分所述统计指标的使用频率级别；
    剔除使用频率级别为指定使用频率级别的统计指标。
  18. 如权利要求17所述的计算机可读存储介质，其特征在于，在所述剔除使用频率级别为指定使用频率级别的统计指标之后，包括：
    将剩余的统计指标的格式转换为指定格式；
    获取转换为指定格式的统计指标对应的保单状态，剔除保单状态为无效的统计指标。
  19. 如权利要求16所述的计算机可读存储介质，其特征在于，所述存储所述统计指标对应的统计维度及统计周期，包括：
    确定所述统计指标对应的统计维度及统计周期的存储模型；
    在所述存储模型为初级汇总模型时，关联取出粒度精确到保单号的统计维度及包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型；
    在所述存储模型为高级汇总模型时，关联取出包括投保人、被保人和险种类别的统计维度，以及，关联取出包括时、分、秒日期的统计周期，将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型。
  20. 如权利要求19所述的计算机可读存储介质，其特征在于，在所述将取出的统计维度及统计周期对应的保单数据存储到所述初级汇总模型以及在所述将取出的统计维度及统计周期对应的保单数据存储到所述高级汇总模型之后，包括：
    分别统计一段时间内初级汇总模型和高级汇总模型的调用次数；
    在所述初级汇总模型的调用次数与所述高级汇总模型的调用次数的差大于预设差值阈值时，判断调用的初级汇总模型的统计维度及统计周期是否存在被删除的信息，并在调用的初级汇总模型的统计维度及统计周期存在被删除的信息时，统计保留的统计维度及统计周期的信息的次数，将保留的次数大于预设次数阈值的统计维度及统计周期统计维度及统计周期的信息增加到高级汇总模型中。
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