Data real-time statistics method, device, equipment and storage medium

Abstract

Embodiments of the present disclosure provide a data real-time statistics method, device, equipment and storage medium, the method comprising: obtaining index change data and dimension change data; if the insurance policy identifier data corresponding to any index change data and the insurance policy identifier data corresponding to any dimension change data match successfully, associating and aggregating any index change data and any dimension change data to obtain first aggregation data; if the insurance policy identifier data corresponding to any index change data and the insurance policy identifier data corresponding to each dimension change data all fail to match, obtaining historical dimension data, and in the case that the insurance policy identifier data corresponding to any index change data and the insurance policy identifier data corresponding to any historical dimension data match, associating and aggregating any index change data and any historical dimension data to obtain second aggregation data; and determining real-time statistics results of insurance indexes and insurance index dimensions according to the first aggregation data and the second aggregation data.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus, device and storage medium for real-time data statistics. Background Technology

[0002] In the insurance industry, it is often necessary to perform real-time statistics on multiple dimensions of key business indicators, such as premium income and claims payouts, including settlement currency and sales channels.

[0003] Currently, Flink window technology can be used to achieve real-time statistics of multi-dimensional data of indicators. In implementation, the dimensional change stream with the business document number as the granularity and the change stream involving indicators can be set as waiting windows. After the window period expires, real-time statistics of multi-dimensional data of indicators can be performed through the direct association operation of the two change streams.

[0004] However, when the changes in dimensions and indicators are inconsistent, because the changes in dimensions and indicators are not synchronized, that is, when the indicators change, the dimensions have not yet changed, the two change streams may not be completely corresponding, which makes it impossible to achieve real-time statistics of multi-dimensional indicator data through direct correlation operations between the two change streams. Summary of the Invention

[0005] This disclosure provides a method, apparatus, device, and storage medium for real-time data statistics to address the problem in related technologies where real-time statistics of multi-dimensional indicator data cannot be achieved when dimensional changes and indicator changes are inconsistent. This is because the real-time statistics of multi-dimensional indicator data are achieved through direct correlation operations between dimensional change streams and indicator change streams.

[0006] In a first aspect, embodiments of this disclosure provide a real-time data statistics method, the method comprising: Acquire indicator change data and dimension change data; wherein, the indicator change data is generated when the insurance indicator changes; the dimension change data is generated when the dimension of the insurance indicator changes; If the policy identification data corresponding to any of the indicator change data is successfully matched with the policy identification data corresponding to any of the dimension change data, then the policy identification data corresponding to any of the indicator change data and the dimension change data are associated and aggregated to obtain the first aggregated data. If the policy identification data corresponding to any of the indicator change data fails to match the policy identification data corresponding to each of the dimension change data, then historical dimension data is obtained. If the policy identification data corresponding to any of the indicator change data matches the policy identification data corresponding to any of the historical dimension data, then the policy change data and the historical dimension data are associated and aggregated to obtain the second aggregated data. Based on the first aggregated data and the second aggregated data, real-time statistical results of insurance indicators and insurance indicator dimensions are determined.

[0007] Secondly, embodiments of this disclosure provide a real-time data statistics device, the device comprising: The first acquisition module is used to acquire indicator change data and dimension change data; wherein, the indicator change data is data generated when the insurance indicator changes; and the dimension change data is data generated when the dimension of the insurance indicator changes. The aggregation module is used to associate and aggregate the insurance policy identification data corresponding to any of the indicator change data and the insurance policy identification data corresponding to any of the dimension change data if the insurance policy identification data corresponding to any of the indicator change data is successfully matched, to obtain the first aggregated data. The second acquisition module is used to acquire historical dimension data if the insurance policy identification data corresponding to any of the indicator change data fails to match the insurance policy identification data corresponding to each of the dimension change data, and if the insurance policy identification data corresponding to any of the indicator change data matches the insurance policy identification data corresponding to any of the historical dimension data, then the module associates and aggregates the any of the indicator change data with the any of the historical dimension data to obtain the second aggregated data. The statistics module is used to determine the real-time statistical results of insurance indicators and insurance indicator dimensions based on the first aggregated data and the second aggregated data.

[0008] Thirdly, embodiments of this disclosure provide a real-time data statistics device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method described in the first aspect above.

[0009] Fourthly, embodiments of this disclosure provide a computer-readable storage medium for storing computer-executable instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0010] Fifthly, embodiments of this disclosure provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0011] The at least one technical solution provided by the embodiments of the present invention can achieve the following technical effects: In this embodiment of the invention, indicator change data and dimension change data can be obtained first. The indicator change data is generated when the insurance indicator changes, and the dimension change data is generated when the insurance indicator dimension changes. If the insurance policy identifier data corresponding to any indicator change data matches the insurance policy identifier data corresponding to any dimension change data, the indicator change data and the dimension change data can be associated and aggregated to obtain the first aggregated data. If the insurance policy identifier data corresponding to any indicator change data fails to match the insurance policy identifier data corresponding to each dimension change data, historical dimension data can be obtained. If the insurance policy identifier data corresponding to any indicator change data matches the insurance policy identifier data corresponding to any historical dimension data, the indicator change data and the historical dimension data can be associated and aggregated to obtain the second aggregated data. Then, based on the first aggregated data and the second aggregated data, the real-time statistical results of the insurance indicator and the insurance indicator dimension are determined.

[0012] As can be seen from the above embodiments, after acquiring indicator change data and dimension change data, the present invention can determine the dimension corresponding to the changed indicator from the most recently changed dimensions using the insurance policy identifier data corresponding to the indicator change data and dimension change data. If the dimension corresponding to the changed indicator cannot be determined, historical dimensions will be further acquired to determine the dimension corresponding to the changed indicator, thereby achieving real-time statistics on the dimensions of the indicator. Since real-time statistics on multi-dimensional indicator data can be achieved through historical dimensions even when dimension changes and indicator changes are inconsistent, it can effectively solve the problem in related technologies where real-time statistics on multi-dimensional indicator data cannot be achieved when dimension changes and indicator changes are inconsistent, due to the direct association operation between dimension change streams and indicator change streams to achieve real-time statistics on multi-dimensional indicator data. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in one or more embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1This is one of the flowcharts illustrating a real-time data statistics method provided in an embodiment of the present invention; Figure 2 This is a second flowchart illustrating a real-time data statistics method provided in one embodiment of the present invention. Figure 3 A schematic diagram of the module composition of a real-time data statistics device 300 provided in one embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of a real-time data statistics device provided in one embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this disclosure, and to make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0015] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0016] Please see Figure 1 , Figure 1 This is one of the flowcharts illustrating a real-time data statistics method provided in an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps: Step 102: Obtain indicator change data and dimension change data; whereby, indicator change data is the data generated when insurance indicators change; dimension change data is the data generated when the dimensions of insurance indicators change.

[0017] Step 104: If the insurance policy identifier data corresponding to any indicator change data is successfully matched with the insurance policy identifier data corresponding to any dimension change data, then the insurance policy identifier data corresponding to any indicator change data and the insurance policy identifier data corresponding to any dimension change data are associated and aggregated to obtain the first aggregated data.

[0018] Step 106: If the policy identification data corresponding to any indicator change data fails to match the policy identification data corresponding to the change data of each dimension, then obtain the historical dimension data. If the policy identification data corresponding to any indicator change data matches the policy identification data corresponding to any historical dimension data, then associate and aggregate the policy identification data corresponding to any indicator change data with the historical dimension data to obtain the second aggregated data.

[0019] Step 108: Based on the first aggregated data and the second aggregated data, determine the real-time statistical results of the insurance indicators and the dimensions of the insurance indicators.

[0020] In this embodiment of the invention, indicator change data and dimension change data can be obtained. The indicator change data can be data generated when the insurance indicator changes, and the dimension change data can be data generated when the dimension of the insurance indicator changes.

[0021] In one example, insurance data can be stored in a relational database. Then, based on existing technologies, such as the Kafka platform, the change stream of insurance data can be obtained based on the change stream of the relational database tables, resulting in indicator change data and dimension change data.

[0022] After obtaining the indicator change data and dimension change data, the insurance policy identification data corresponding to any indicator change data can be matched with the insurance policy identification data corresponding to any dimension change data. If the match is successful, the indicator change data and the dimension change data can be associated and aggregated to obtain the first aggregated data.

[0023] In one example, the latest dimension data and the first latest insurance policy identifier data corresponding to the current latest dimension data can be determined based on the dimension change data. The latest indicator data and the second latest insurance policy identifier data corresponding to the current latest indicator data can be determined based on the indicator change data. Then, any first latest insurance policy identifier data can be matched with any second latest insurance policy identifier data.

[0024] For example, you can first determine the changed dimensions based on the dimensional change data, and then store the changed dimensions and their corresponding insurance policy identifiers (e.g., insurance policy document numbers) in a Flink object. You can set an expiration period, such as 3 days, and automatically remove the data after the expiration period. Then, you can determine the changed metrics based on the metric change data, and match the insurance policy document numbers corresponding to the changed metrics with the insurance policy document numbers stored in the Flink object.

[0025] If the policy identifier data corresponding to any indicator change data matches the policy identifier data corresponding to any dimension change data, then the policy identifier data corresponding to any indicator change data and the policy identifier data corresponding to any dimension change data can be associated and aggregated to obtain the first aggregated data. Specifically, a successful match is determined when the matched policy identifier data are identical.

[0026] In one example, if any first latest insurance policy identifier data successfully matches any second latest insurance policy identifier data, then the current latest dimension data corresponding to any first latest insurance policy identifier data and the current latest indicator data corresponding to any second latest insurance policy identifier data are associated and aggregated to obtain the first aggregated data.

[0027] For example, if the insurance business document number corresponding to the changed indicator matches the insurance business document number in the Flink object, the changed indicator can be associated and aggregated with the changed dimension to obtain the first aggregated data.

[0028] In this embodiment of the invention, when performing correlation aggregation, the changed dimensional data can be aggregated with the changed indicator data to obtain indicator data with complete dimensions.

[0029] In this embodiment of the invention, if the policy identification data corresponding to any indicator change data fails to match the policy identification data corresponding to the change data of each dimension, then historical dimension data can be obtained, and the policy identification data corresponding to any indicator change data can be matched with the policy identification data corresponding to any historical dimension data.

[0030] If the policy identification data corresponding to any change in any indicator fails to match the policy identification data corresponding to changes in each dimension, it indicates that the change in indicator is inconsistent with the change in dimension. In this case, historical dimensions can be queried to achieve real-time statistics of multi-dimensional indicators.

[0031] When retrieving historical data, asynchronous queries can be used.

[0032] In one embodiment of the invention, historical dimension data may include the latest updated insurance indicator dimension data. When acquiring historical dimension data, the latest stored insurance data can be retrieved from a database updated according to a preset period. From the latest stored insurance data, the latest stored insurance policy identifier data and its corresponding latest stored insurance indicator dimension data can be obtained. Then, the insurance policy identifier data corresponding to any indicator change data can be matched with any latest stored insurance policy identifier data.

[0033] In this embodiment of the invention, if the insurance policy identification data corresponding to any indicator change data matches the insurance policy identification data corresponding to any historical dimension data, the any indicator change data can be associated and aggregated with the any historical dimension data to obtain the second aggregated data.

[0034] In one example, if the historical dimension data is the latest updated insurance indicator dimension data, then if the insurance policy identifier data corresponding to any indicator change data is successfully matched with any latest stored insurance policy identifier data, the change data of any indicator and the latest stored insurance indicator dimension data corresponding to any latest stored insurance policy identifier data can be associated and aggregated to obtain the second aggregated data.

[0035] After obtaining the first aggregated data and the second aggregated data, real-time statistical results of insurance indicators and insurance indicator dimensions can be determined based on the first aggregated data and the second aggregated data.

[0036] In one embodiment of the present invention, historical real-time statistical results of insurance indicators and insurance indicator dimensions are obtained, and real-time statistical results of insurance indicators and insurance indicator dimensions are determined based on first aggregated data, second aggregated data, and historical real-time statistical results.

[0037] For example, historical dimension data can be the latest updated insurance indicator dimension data. In cases where the indicator changes and dimension changes are inconsistent—that is, if the policy identifier data corresponding to any indicator change fails to match the policy identifier data corresponding to all dimension changes—then the latest stored insurance data is retrieved from a database updated based on a preset period. From this latest stored insurance data, the latest stored policy identifier data and its corresponding latest stored insurance indicator dimension data are obtained. If the policy identifier data corresponding to any indicator change matches the policy identifier data corresponding to any latest stored insurance indicator dimension data, then the historical real-time statistical results of the insurance indicators and insurance indicator dimensions can be obtained, such as the multi-dimensional real-time indicator summary data up to the previous day, and stored in a designated database. Then, the second aggregated data generated by aggregating any indicator change data with the latest stored insurance indicator dimension data corresponding to any latest stored policy identifier data can be stored in the designated database, and appended to the historical multi-dimensional real-time indicator summary data according to the current statistical period.

[0038] In this embodiment of the invention, if the policy identifier data corresponding to any indicator change data fails to match the policy identifier data corresponding to any historical dimension data, the change indicators that cannot be associated with the change dimension can be stored in a database storing historical dimension data and the corresponding policy identifier data. This is used as supplementary summary data for multi-dimensional real-time indicators based on the historical dimension data inventory table, and then placed in a database storing historical real-time statistical results. At this time, the current real-time statistical results of the insurance indicators and insurance indicator dimensions can be determined based on the historical real-time statistical results of the first aggregated data, the second aggregated data, and the supplementary summary data.

[0039] In one example, such as Figure 2 As shown, this example can use the Flink real-time computing framework to process metric change data (i.e., Figure 2 This invention uses the change flow of indicators (as the main thread) as its framework. By first querying the most recently changed dimension information and then asynchronously querying historical dimension information, it achieves real-time calculation of multi-dimensional indicators. Furthermore, it can use daily batch processing to correct historical data for real-time indicators, ensuring accuracy. This example is applicable to scenarios where multi-dimensional real-time indicators are based on the change trajectory of traditional relational database tables, and the tables storing dimensions and indicators are not the same, resulting in asynchronous updates. Even in such scenarios, this invention can achieve real-time multi-dimensional statistical analysis of business indicators while saving computational and storage resources.

[0040] In this embodiment of the invention, indicator change data and dimension change data can be obtained first. The indicator change data is generated when the insurance indicator changes, and the dimension change data is generated when the insurance indicator dimension changes. If the insurance policy identifier data corresponding to any indicator change data matches the insurance policy identifier data corresponding to any dimension change data, the indicator change data and the dimension change data can be associated and aggregated to obtain the first aggregated data. If the insurance policy identifier data corresponding to any indicator change data fails to match the insurance policy identifier data corresponding to each dimension change data, historical dimension data can be obtained. If the insurance policy identifier data corresponding to any indicator change data matches the insurance policy identifier data corresponding to any historical dimension data, the indicator change data and the historical dimension data can be associated and aggregated to obtain the second aggregated data. Then, based on the first aggregated data and the second aggregated data, the real-time statistical results of the insurance indicator and the insurance indicator dimension are determined.

[0041] As can be seen from the above embodiments, after acquiring indicator change data and dimension change data, the present invention can determine the dimension corresponding to the changed indicator from the most recently changed dimensions using the insurance policy identifier data corresponding to the indicator change data and dimension change data. If the dimension corresponding to the changed indicator cannot be determined, historical dimensions will be further acquired to determine the dimension corresponding to the changed indicator, thereby achieving real-time statistics on the dimensions of the indicator. Since real-time statistics on multi-dimensional indicator data can be achieved through historical dimensions even when dimension changes and indicator changes are inconsistent, it can effectively solve the problem in related technologies where real-time statistics on multi-dimensional indicator data cannot be achieved when dimension changes and indicator changes are inconsistent, due to the direct association operation between dimension change streams and indicator change streams to achieve real-time statistics on multi-dimensional indicator data.

[0042] Corresponding to the above-described real-time data statistics method, this embodiment of the invention also provides a real-time data statistics device. Figure 3This is a schematic diagram of the module composition of the real-time data statistics device 300 provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the real-time data statistics device 300 includes: The first acquisition module 301 is used to acquire indicator change data and dimension change data; wherein, the indicator change data is data generated when the insurance indicator changes; and the dimension change data is data generated when the dimension of the insurance indicator changes. The aggregation module 302 is used to associate and aggregate the insurance policy identification data corresponding to any of the indicator change data and the insurance policy identification data corresponding to any of the dimension change data if the insurance policy identification data corresponding to any of the indicator change data is successfully matched, to obtain the first aggregated data. The second acquisition module 303 is used to acquire historical dimension data if the insurance policy identification data corresponding to any of the indicator change data fails to match the insurance policy identification data corresponding to each of the dimension change data, and if the insurance policy identification data corresponding to any of the indicator change data matches the insurance policy identification data corresponding to any of the historical dimension data, then the second aggregated data is obtained by associating and aggregating the any of the indicator change data with the any of the historical dimension data. The statistics module 304 is used to determine the real-time statistical results of insurance indicators and insurance indicator dimensions based on the first aggregated data and the second aggregated data.

[0043] Optionally, the historical dimension data includes: the latest updated insurance indicator dimension data.

[0044] Optionally, the device further includes ( Figure 3 (not shown in the image) The third acquisition module 305 is used to acquire the latest stored insurance data from a database that is updated based on a preset period before associating and aggregating any of the indicator change data with any of the historical dimension data to obtain the second aggregated data. The fourth acquisition module 306 is used to acquire the latest storage insurance policy identifier data and its corresponding latest storage insurance indicator dimension data from the latest storage insurance data. The first matching module 307 is used to match the insurance policy identification data corresponding to any of the indicator change data with any of the latest stored insurance policy identification data. The second acquisition module 303 is used for: The change data of any one of the indicators is associated and aggregated with the latest stored insurance indicator dimension data corresponding to any one of the latest stored insurance policy identifier data to obtain the second aggregated data.

[0045] Optionally, the statistics module 304 is used for: Obtain historical real-time statistical results for insurance indicators and their dimensions; Based on the first aggregated data, the second aggregated data, and the historical real-time statistical results, the real-time statistical results of the insurance indicators and the insurance indicator dimensions are determined.

[0046] Optionally, the device further includes ( Figure 3 (not shown in the image) The first determining module 308 is used to determine the latest current dimension data and the first latest insurance policy identifier data corresponding to the latest current dimension data based on the dimension change data before associating and aggregating any of the indicator change data with any of the dimension change data to obtain the first aggregated data. The second determining module 309 is used to determine the latest indicator data and the second latest insurance policy identifier data corresponding to the latest indicator data based on the indicator change data. The second matching module 310 is used to match any of the first latest insurance policy identifier data with any of the second latest insurance policy identifier data; The aggregation module 302 is used for: The latest dimension data corresponding to any of the first latest insurance policy identifier data is associated and aggregated with the latest indicator data corresponding to any of the second latest insurance policy identifier data to obtain the first aggregated data.

[0047] Optionally, the second acquisition module 303 is used for: Historical dimension data is obtained through asynchronous queries.

[0048] In this embodiment of the invention, indicator change data and dimension change data can be obtained first. The indicator change data is generated when the insurance indicator changes, and the dimension change data is generated when the insurance indicator dimension changes. If the insurance policy identifier data corresponding to any indicator change data matches the insurance policy identifier data corresponding to any dimension change data, the indicator change data and the dimension change data can be associated and aggregated to obtain the first aggregated data. If the insurance policy identifier data corresponding to any indicator change data fails to match the insurance policy identifier data corresponding to each dimension change data, historical dimension data can be obtained. If the insurance policy identifier data corresponding to any indicator change data matches the insurance policy identifier data corresponding to any historical dimension data, the indicator change data and the historical dimension data can be associated and aggregated to obtain the second aggregated data. Then, based on the first aggregated data and the second aggregated data, the real-time statistical results of the insurance indicator and the insurance indicator dimension are determined.

[0049] As can be seen from the above embodiments, after acquiring indicator change data and dimension change data, the present invention can determine the dimension corresponding to the changed indicator from the most recently changed dimensions using the insurance policy identifier data corresponding to the indicator change data and dimension change data. If the dimension corresponding to the changed indicator cannot be determined, historical dimensions will be further acquired to determine the dimension corresponding to the changed indicator, thereby achieving real-time statistics on the dimensions of the indicator. Since real-time statistics on multi-dimensional indicator data can be achieved through historical dimensions even when dimension changes and indicator changes are inconsistent, it can effectively solve the problem in related technologies where real-time statistics on multi-dimensional indicator data cannot be achieved when dimension changes and indicator changes are inconsistent, due to the direct association operation between dimension change streams and indicator change streams to achieve real-time statistics on multi-dimensional indicator data.

[0050] Corresponding to the above-described real-time data statistics method, this embodiment of the invention also provides a real-time data statistics device. Figure 4 This is a schematic diagram of the hardware structure of a real-time data statistics device provided in one embodiment of the present invention.

[0051] The real-time data statistics device can be a terminal device or server, etc., provided in the above embodiments for real-time data statistics.

[0052] Real-time data statistics devices can vary significantly due to differences in configuration and performance. They may include one or more processors 401 and memory 402, with memory 402 storing one or more application programs or data. Memory 402 can be temporary or persistent storage. The application programs stored in memory 402 may include one or more modules (not shown in the figures), each module including a series of computer-executable instructions for the real-time data statistics device. Furthermore, processor 401 may be configured to communicate with memory 402, executing the series of computer-executable instructions in memory 402 on the real-time data statistics device. The real-time data statistics device may also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input / output interfaces 405, and one or more keyboards 406.

[0053] Specifically, in this embodiment, the real-time data statistics device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the real-time data statistics device, and is configured to be executed by one or more processors as described above.

[0054] In this embodiment of the invention, indicator change data and dimension change data can be obtained first. The indicator change data is generated when the insurance indicator changes, and the dimension change data is generated when the insurance indicator dimension changes. If the insurance policy identifier data corresponding to any indicator change data matches the insurance policy identifier data corresponding to any dimension change data, the indicator change data and the dimension change data can be associated and aggregated to obtain the first aggregated data. If the insurance policy identifier data corresponding to any indicator change data fails to match the insurance policy identifier data corresponding to each dimension change data, historical dimension data can be obtained. If the insurance policy identifier data corresponding to any indicator change data matches the insurance policy identifier data corresponding to any historical dimension data, the indicator change data and the historical dimension data can be associated and aggregated to obtain the second aggregated data. Then, based on the first aggregated data and the second aggregated data, the real-time statistical results of the insurance indicator and the insurance indicator dimension are determined.

[0055] As can be seen from the above embodiments, after acquiring indicator change data and dimension change data, the present invention can determine the dimension corresponding to the changed indicator from the most recently changed dimensions using the insurance policy identifier data corresponding to the indicator change data and dimension change data. If the dimension corresponding to the changed indicator cannot be determined, historical dimensions will be further acquired to determine the dimension corresponding to the changed indicator, thereby achieving real-time statistics on the dimensions of the indicator. Since real-time statistics on multi-dimensional indicator data can be achieved through historical dimensions even when dimension changes and indicator changes are inconsistent, it can effectively solve the problem in related technologies where real-time statistics on multi-dimensional indicator data cannot be achieved when dimension changes and indicator changes are inconsistent, due to the direct association operation between dimension change streams and indicator change streams to achieve real-time statistics on multi-dimensional indicator data.

[0056] Another embodiment of this disclosure also provides a computer-readable storage medium for storing computer-executable instructions that, when executed by a processor, implement the above-described process.

[0057] The storage medium in this embodiment can implement the various processes of the above-described real-time data statistics method embodiment and achieve the same effect and function, which will not be repeated here.

[0058] Another embodiment of this disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described process.

[0059] The computer program product in this embodiment can implement the various processes of the above-described real-time data statistics method embodiment and achieve the same effect and function, which will not be repeated here.

[0060] In various embodiments of this disclosure, the computer-readable storage medium includes read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc.

[0061] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0062] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0063] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0064] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing the embodiments of this disclosure, the functions of each unit can be implemented in one or more software and / or hardware.

[0065] Those skilled in the art will understand that one or more embodiments of this disclosure can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0069] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0070] One or more embodiments of this disclosure can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this disclosure can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0071] The various embodiments in this disclosure are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0072] The above description is merely an embodiment of this disclosure and is not intended to limit the scope of this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of the claims of this disclosure.

Claims

1. A real-time data statistics method, characterized in that, The method includes: Acquire indicator change data and dimension change data; wherein, the indicator change data is data generated when the insurance indicator changes; the dimension change data is data generated when the dimension of the insurance indicator changes; If the policy identification data corresponding to any of the indicator change data is successfully matched with the policy identification data corresponding to any of the dimension change data, then the policy identification data corresponding to any of the indicator change data and the dimension change data are associated and aggregated to obtain the first aggregated data. If the policy identification data corresponding to any of the indicator change data fails to match the policy identification data corresponding to each of the dimension change data, then historical dimension data is obtained. If the policy identification data corresponding to any of the indicator change data matches the policy identification data corresponding to any of the historical dimension data, then the policy change data and the historical dimension data are associated and aggregated to obtain the second aggregated data. Based on the first aggregated data and the second aggregated data, real-time statistical results of insurance indicators and insurance indicator dimensions are determined.

2. The method according to claim 1, characterized in that, The historical data includes the latest updated insurance indicator data.

3. The method according to claim 2, characterized in that, Before associating and aggregating any of the aforementioned indicator change data with any of the aforementioned historical dimension data to obtain the second aggregated data, the method further includes: Obtain the latest stored insurance data from the database that is updated based on a preset period; From the latest storage insurance data, obtain the latest storage insurance policy identification data and its corresponding latest storage insurance indicator dimension data; Match the insurance policy identifier data corresponding to any of the aforementioned indicator change data with any of the latest stored insurance policy identifier data; The step of associating and aggregating any of the aforementioned indicator change data with any of the aforementioned historical dimension data to obtain second aggregated data includes: The change data of any one of the indicators is associated and aggregated with the latest stored insurance indicator dimension data corresponding to any one of the latest stored insurance policy identifier data to obtain the second aggregated data.

4. The method according to claim 1, characterized in that, The step of determining the real-time statistical results of insurance indicators and insurance indicator dimensions based on the first aggregated data and the second aggregated data includes: Obtain historical real-time statistical results for insurance indicators and their dimensions; Based on the first aggregated data, the second aggregated data, and the historical real-time statistical results, the real-time statistical results of the insurance indicators and the insurance indicator dimensions are determined.

5. The method according to claim 1, characterized in that, Before associating and aggregating any of the aforementioned indicator change data with any of the aforementioned dimension change data to obtain the first aggregated data, the method further includes: Based on the dimensional change data, determine the latest dimensional data and the first latest insurance policy identifier data corresponding to the latest dimensional data; Based on the indicator change data, determine the latest indicator data and the second latest insurance policy identifier data corresponding to the latest indicator data; Match any of the first latest insurance policy identifier data with any of the second latest insurance policy identifier data; The step of associating and aggregating any of the indicator change data with any of the dimension change data to obtain the first aggregated data includes: The latest dimension data corresponding to any of the first latest insurance policy identifier data is associated and aggregated with the latest indicator data corresponding to any of the second latest insurance policy identifier data to obtain the first aggregated data.

6. The method according to claim 1, characterized in that, The acquisition of historical dimension data includes: Historical dimension data is obtained through asynchronous queries.

7. A real-time data statistics device, characterized in that, The device includes: The first acquisition module is used to acquire indicator change data and dimension change data; wherein, the indicator change data is data generated when the insurance indicator changes; and the dimension change data is data generated when the dimension of the insurance indicator changes. The aggregation module is used to associate and aggregate the insurance policy identification data corresponding to any of the indicator change data and the insurance policy identification data corresponding to any of the dimension change data if the insurance policy identification data corresponding to any of the indicator change data is successfully matched, to obtain the first aggregated data. The second acquisition module is used to acquire historical dimension data if the insurance policy identification data corresponding to any of the indicator change data fails to match the insurance policy identification data corresponding to each of the dimension change data, and if the insurance policy identification data corresponding to any of the indicator change data matches the insurance policy identification data corresponding to any of the historical dimension data, then the module associates and aggregates the any of the indicator change data with the any of the historical dimension data to obtain the second aggregated data. The statistics module is used to determine the real-time statistical results of insurance indicators and insurance indicator dimensions based on the first aggregated data and the second aggregated data.

8. A real-time data statistics device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store computer-executable instructions that, when executed by a processor, implement the steps of the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1 to 6.

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