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

Abstract

The embodiment of the invention provides a data real-time statistical method and device, equipment and a storage medium. The method comprises the steps that index change data and dimension change data are acquired; if the insurance policy identification data corresponding to any index change data is successfully matched with the insurance policy identification data corresponding to any dimension change data, performing association aggregation on any index change data and any dimension change data to obtain first aggregation data; if the insurance policy identification data corresponding to any index change data fails to be matched with the insurance policy identification data corresponding to each dimension change data, obtaining historical dimension data, and when the insurance policy identification data corresponding to any index change data is matched with the insurance policy identification data corresponding to any historical dimension data, determining that the insurance policy identification data corresponding to any index change data is matched with the insurance policy identification data corresponding to any historical dimension data; any index change data and any historical dimension data are associated and aggregated to obtain second aggregated data; and according to the first aggregated data and the second aggregated data, determining a real-time statistical result of the insurance index and the insurance index dimension.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a real-time data statistics method, device, equipment and storage medium. Background Art

[0002] In the insurance industry, it is usually necessary to conduct real-time statistics on some key business indicators, such as premium income, compensation expenditure and other indicators in multiple dimensions, such as settlement currency, sales channels and other dimensions.

[0003] At present, Flink window technology can be used to achieve real-time statistics of multi-dimensional data of indicators. During implementation, the dimension change stream with 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 direct association operations of the two change streams.

[0004] However, when the dimension changes and indicator changes are inconsistent, since the dimension changes and indicator changes are not synchronized, that is, when the indicator changes, the dimension has not changed yet, the two change streams may not completely correspond, resulting in the inability to achieve real-time statistics of multi-dimensional indicator data through direct association operations of the two change streams. Summary of the invention

[0005] The embodiments of the present disclosure provide a real-time data statistics method, apparatus, device and storage medium to solve the problem in the related art that real-time statistics of multi-dimensional indicator data are achieved by directly associating the dimension change stream and the indicator change stream, but real-time statistics of multi-dimensional indicator data cannot be achieved when the dimension change and the indicator change are inconsistent.

[0006] In a first aspect, an embodiment of the present disclosure provides a real-time data statistics method, the method comprising: Obtaining 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 insurance indicator dimension changes; If the insurance policy identification data corresponding to any of the indicator change data successfully matches the insurance policy identification data corresponding to any of the dimension change data, then any of the indicator change data is associated and aggregated with any of the dimension change data to obtain first aggregated 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, historical dimension data is obtained, and when 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, any of the indicator change data is associated and aggregated with any of the historical dimension data to obtain 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] In a second aspect, an embodiment of the present disclosure provides a real-time data statistics device, the device comprising: A 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 insurance indicator dimension changes; an aggregation module, configured to associate and aggregate any of the indicator change data with any of the dimension change data to obtain first aggregated data if the insurance policy identification data corresponding to any of the indicator change data successfully matches the insurance policy identification data corresponding to any of the dimension change data; A second acquisition module is used for acquiring 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 when 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, associating and aggregating any of the indicator change data with any of the historical dimension data to obtain second aggregated data; A statistical module is used to determine real-time statistical results of insurance indicators and insurance indicator dimensions based on the first aggregated data and the second aggregated data.

[0008] In a third aspect, an embodiment of the present disclosure provides a real-time data statistics device, comprising: a processor; and a memory configured to store computer-executable instructions, wherein the computer-executable instructions, when executed, enable the processor to implement the steps of the method described in the first aspect above.

[0009] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store computer-executable instructions, and the computer-executable instructions implement the steps of the method described in the first aspect when executed by a processor.

[0010] In a fifth aspect, an embodiment of the present disclosure provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0011] At least one of the above technical solutions provided by the embodiments of the present invention can achieve the following technical effects: In an embodiment of the present invention, indicator change data and dimension change data can be obtained first; wherein, the indicator change data is data generated when the insurance indicator changes; the dimension change data is data generated when the insurance indicator dimension changes, if the insurance policy identification data corresponding to any indicator change data successfully matches the insurance policy identification data corresponding to any dimension change data, then any indicator change data can be associated and aggregated with any dimension change data to obtain first aggregated data; if the insurance policy identification data corresponding to any indicator change data fails to match the insurance policy identification data corresponding to each dimension change data, then historical dimension data can be obtained, and when the insurance policy identification data corresponding to any indicator change data matches the insurance policy identification data corresponding to any historical dimension data, any indicator change data can be associated and aggregated with any historical dimension data to obtain second aggregated data, and then, based on the first aggregated data and the second aggregated data, the real-time statistical results of the insurance indicators and insurance indicator dimensions are determined.

[0012] It can be seen from the above-mentioned embodiments of the invention that after obtaining the indicator change data and the dimension change data, the embodiments of the invention can determine the dimension corresponding to the changed indicator from the dimension that has changed recently through the insurance policy identification data corresponding to the indicator change data and the dimension change data. If the dimension corresponding to the changed indicator cannot be determined, the historical dimension will be further obtained to determine the dimension corresponding to the changed indicator through the historical dimension, so as to realize real-time statistics of the indicator dimension. Since real-time statistics of multi-dimensional data of indicators can be realized through historical dimensions when the dimension change and the indicator change are inconsistent, it can effectively solve the problem in the related technology that the real-time statistics of multi-dimensional data of indicators cannot be realized when the dimension change and the indicator change are inconsistent because the real-time statistics of multi-dimensional data of indicators are realized through the direct association operation of the dimension change stream and the indicator change stream. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate one or more embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor. Figure 1One of the flow charts of a real-time data statistics method provided by an embodiment of the present invention; Figure 2 A second flow chart of a real-time data statistics method provided by an 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 A schematic diagram of the hardware structure of a real-time data statistics device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the technical parties in this technical field better understand the technical aspects of one or more embodiments of the present disclosure, and to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in combination with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0015] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0016] See also Figure 1 , Figure 1 One of the flow charts of the real-time data statistics method provided by one embodiment of the present invention is as follows: Figure 1 As shown, the method comprises the following steps: Step 102: Obtain 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 insurance indicator dimension changes.

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

[0018] Step 106: If the insurance policy identification data corresponding to any indicator change data fails to match the insurance policy identification data corresponding to each dimension change data, then the historical dimension data is obtained, and when the insurance policy identification data corresponding to any indicator change data matches the insurance policy identification data corresponding to any historical dimension data, any indicator change data is associated and aggregated with any historical dimension data to obtain second aggregated data.

[0019] Step 108: Determine real-time statistical results of the insurance indicators and insurance indicator dimensions based on the first aggregated data and the second aggregated data.

[0020] In an embodiment of the present invention, indicator change data and dimension change data may be obtained, wherein the indicator change data may be data generated when an insurance indicator changes, and the dimension change data may be data generated when an insurance indicator dimension changes.

[0021] In one example, insurance data may be stored in a relational database, and then, based on existing technologies, such as the Kafka platform, the change stream of the insurance data may be obtained based on the relational database table change stream to obtain 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, and if the match is successful, the any indicator change data can be associated and aggregated with the any dimension change data to obtain the first aggregated data.

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

[0024] For example, you can first determine the changed dimension based on the dimension change data, and store the changed dimension and its corresponding insurance policy identification data, taking the insurance business document number as an example, in the flink object. You can set the validity period, such as 3 days, and automatically clear it after the validity period expires, for example, automatically clear it after 3 days. Then, determine the changed indicator based on the indicator change data, and match the insurance business document number corresponding to the changed indicator with the insurance business document number in the flink object.

[0025] If the insurance policy identification data corresponding to any indicator change data successfully matches the insurance policy identification data corresponding to any dimension change data, then any indicator change data and any dimension change data can be associated and aggregated to obtain first aggregated data. When the matching insurance policy identification data are the same, it can be determined that the match is successful.

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

[0027] For example, if the insurance business document number corresponding to the changed indicator successfully 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 the embodiment of the present invention, when performing association aggregation, the changed dimension data and the dimensions under the changed indicator data may be aggregated to obtain indicator data with complete dimensions.

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

[0030] If the insurance policy identification data corresponding to any indicator change data fails to match the insurance policy identification data corresponding to each dimension change data, it means that the indicator change is inconsistent with the dimension change. At this time, you can query the historical dimensions to achieve real-time statistics of multi-dimensional indicators.

[0031] When obtaining historical dimension data, you can obtain it through asynchronous query.

[0032] In one embodiment of the invention, the historical dimension data may include the latest updated insurance indicator dimension data. When acquiring the historical dimension data, the latest stored insurance data in the database may be acquired from the database updated based on a preset period, and the latest stored insurance policy identification data and its corresponding latest stored insurance indicator dimension data may be acquired from the latest stored insurance data. Then, the insurance policy identification data corresponding to any indicator change data may be matched with any latest stored insurance policy identification data.

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

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

[0035] After obtaining the first aggregated data and the second aggregated data, real-time statistical results of the insurance indicators and insurance indicator dimensions may 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 the first aggregated data, the second aggregated data, and the historical real-time statistical results.

[0037] For example, the historical dimension data may be the latest updated insurance indicator dimension data. In the case where the indicator change is inconsistent with the dimension change, that is, the insurance policy identification data corresponding to any indicator change data fails to match the insurance policy identification data corresponding to each dimension change data, the latest stored insurance data in the database is obtained from the database updated based on a preset period, and the latest stored insurance policy identification data and its corresponding latest stored insurance indicator dimension data are obtained from the latest stored insurance data. If the insurance policy identification data corresponding to any indicator change data matches the insurance policy identification data corresponding to any latest stored insurance indicator dimension data, 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 as of the previous day, and stored in the 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 insurance policy identification data can be stored in the designated database and appended to the historical multi-dimensional real-time indicator summary data according to the statistical period of the day.

[0038] In an embodiment of the present invention, if the insurance policy identification data corresponding to any indicator change data fails to match the insurance policy identification data corresponding to any historical dimension data, the change indicator that cannot be associated with the change dimension can be stored in a database storing historical dimension data and insurance policy identification data corresponding to the historical dimension data, and based on the association of the historical dimension data stock table, it can be used as supplementary summary data of multi-dimensional real-time indicators and put into the database storing historical real-time statistical results. At this time, the current real-time statistical results of the insurance indicator and the insurance indicator dimension can be determined based on the first aggregated data, the second aggregated data, and the historical real-time statistical results of the supplementary summary data.

[0039] In one example, if Figure 2 As shown, this example can use the flink real-time computing framework to calculate the indicator change data (i.e. Figure 2 The indicator change flow in the data structure is taken as the main line. By first querying the dimension information of the most recent changes and then asynchronously querying the historical dimension information, real-time calculation of multi-dimensional indicators is achieved, and the historical data of the indicators can be corrected through daily batch processing to ensure the accuracy of real-time indicators. This example can be applicable to scenarios where multi-dimensional real-time indicators are based on the change trajectory of traditional relational database tables, and the table storing the dimensions is not the same table as the table storing the indicators, and the updates of the tables are not synchronized. In this scenario, the present invention can also achieve multi-dimensional real-time statistical business indicators, and can save computing resources and storage resources.

[0040] In an embodiment of the present invention, indicator change data and dimension change data can be obtained first; wherein, the indicator change data is data generated when the insurance indicator changes; the dimension change data is data generated when the insurance indicator dimension changes, if the insurance policy identification data corresponding to any indicator change data successfully matches the insurance policy identification data corresponding to any dimension change data, then any indicator change data can be associated and aggregated with any dimension change data to obtain first aggregated data; if the insurance policy identification data corresponding to any indicator change data fails to match the insurance policy identification data corresponding to each dimension change data, then historical dimension data can be obtained, and when the insurance policy identification data corresponding to any indicator change data matches the insurance policy identification data corresponding to any historical dimension data, any indicator change data can be associated and aggregated with any historical dimension data to obtain second aggregated data, and then, based on the first aggregated data and the second aggregated data, the real-time statistical results of the insurance indicators and insurance indicator dimensions are determined.

[0041] It can be seen from the above-mentioned embodiments of the invention that after obtaining the indicator change data and the dimension change data, the embodiments of the invention can determine the dimension corresponding to the changed indicator from the dimension that has changed recently through the insurance policy identification data corresponding to the indicator change data and the dimension change data. If the dimension corresponding to the changed indicator cannot be determined, the historical dimension will be further obtained to determine the dimension corresponding to the changed indicator through the historical dimension, so as to realize real-time statistics of the indicator dimension. Since real-time statistics of multi-dimensional data of indicators can be realized through historical dimensions when the dimension change and the indicator change are inconsistent, it can effectively solve the problem in the related technology that the real-time statistics of multi-dimensional data of indicators cannot be realized when the dimension change and the indicator change are inconsistent because the real-time statistics of multi-dimensional data of indicators are realized through the direct association operation of the dimension change stream and the indicator change stream.

[0042] Corresponding to the above-mentioned real-time data statistics method, the embodiment of the present invention further provides a real-time data statistics device, Figure 3A schematic diagram of the module composition of the real-time data statistics device 300 provided in an embodiment of the present invention, as shown in FIG. 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; the dimension change data is data generated when the insurance indicator dimension changes; Aggregation module 302, configured to associate and aggregate any of the indicator change data with any of the dimension change data to obtain first aggregated data if the insurance policy identification data corresponding to any of the indicator change data successfully matches the insurance policy identification data corresponding to any of the dimension change data; The second acquisition module 303 is used for acquiring 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 when 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, associating and aggregating any of the indicator change data with any of the historical dimension data to obtain second aggregated data; The statistical module 304 is used to determine the real-time statistical results of the insurance indicators and the insurance indicator dimensions according to 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 comprises ( Figure 3 not shown): A third acquisition module 305 is used to obtain the latest insurance data stored in the database from a database 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 stored insurance policy identification data and the latest stored insurance indicator dimension data corresponding thereto from the latest stored insurance data; A first matching module 307, configured 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: Any of the indicator change data is associated and aggregated with the latest stored insurance indicator dimension data corresponding to any of the latest stored insurance policy identification data to obtain second aggregated data.

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

[0046] Optionally, the device further comprises ( Figure 3 not shown): A first determination module 308 is used to determine the current latest dimension data and the first latest insurance policy identification data corresponding to the current latest dimension data according to 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 determination module 309 is used to determine the current latest indicator data and the second latest insurance policy identification data corresponding to the current latest indicator data according to the indicator change data; A second matching module 310, configured to match any of the first latest insurance policy identification data with any of the second latest insurance policy identification data; The aggregation module 302 is used to: The current latest dimension data corresponding to any of the first latest insurance policy identification data and the current latest indicator data corresponding to any of the second latest insurance policy identification data are associated and aggregated to obtain first aggregated data.

[0047] Optionally, the second acquisition module 303 is used to: Obtain historical dimension data through asynchronous query.

[0048] In an embodiment of the present invention, indicator change data and dimension change data can be obtained first; wherein, the indicator change data is data generated when the insurance indicator changes; the dimension change data is data generated when the insurance indicator dimension changes, if the insurance policy identification data corresponding to any indicator change data successfully matches the insurance policy identification data corresponding to any dimension change data, then any indicator change data can be associated and aggregated with any dimension change data to obtain first aggregated data; if the insurance policy identification data corresponding to any indicator change data fails to match the insurance policy identification data corresponding to each dimension change data, then historical dimension data can be obtained, and when the insurance policy identification data corresponding to any indicator change data matches the insurance policy identification data corresponding to any historical dimension data, any indicator change data can be associated and aggregated with any historical dimension data to obtain second aggregated data, and then, based on the first aggregated data and the second aggregated data, the real-time statistical results of the insurance indicators and insurance indicator dimensions are determined.

[0049] It can be seen from the above-mentioned embodiments of the invention that after obtaining the indicator change data and the dimension change data, the embodiments of the invention can determine the dimension corresponding to the changed indicator from the dimension that has changed recently through the insurance policy identification data corresponding to the indicator change data and the dimension change data. If the dimension corresponding to the changed indicator cannot be determined, the historical dimension will be further obtained to determine the dimension corresponding to the changed indicator through the historical dimension, so as to realize real-time statistics of the indicator dimension. Since real-time statistics of multi-dimensional data of indicators can be realized through historical dimensions when the dimension change and the indicator change are inconsistent, it can effectively solve the problem in the related technology that the real-time statistics of multi-dimensional data of indicators cannot be realized when the dimension change and the indicator change are inconsistent because the real-time statistics of multi-dimensional data of indicators are realized through the direct association operation of the dimension change stream and the indicator change stream.

[0050] Corresponding to the above-mentioned real-time data statistics method, the embodiment of the present invention further provides a real-time data statistics device, Figure 4 A schematic diagram of the hardware structure of a real-time data statistics device provided by an embodiment of the present invention.

[0051] The real-time data statistics device may be a terminal device or a server for real-time statistical data provided in the above-mentioned embodiment.

[0052] The real-time data statistics device may have relatively large differences due to different configurations or performances, and may include one or more processors 401 and memory 402, and one or more storage applications or data may be stored in the memory 402. Among them, the memory 402 may be a short-term storage or a persistent storage. The application stored in the memory 402 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the real-time data statistics device. Furthermore, the processor 401 may be configured to communicate with the memory 402, and execute a series of computer executable instructions in the 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 and 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 the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer executable instructions in the real-time data statistics device, and is configured to be executed by one or more processors.

[0054] In an embodiment of the present invention, indicator change data and dimension change data can be obtained first; wherein, the indicator change data is data generated when the insurance indicator changes; the dimension change data is data generated when the insurance indicator dimension changes, if the insurance policy identification data corresponding to any indicator change data successfully matches the insurance policy identification data corresponding to any dimension change data, then any indicator change data can be associated and aggregated with any dimension change data to obtain first aggregated data; if the insurance policy identification data corresponding to any indicator change data fails to match the insurance policy identification data corresponding to each dimension change data, then historical dimension data can be obtained, and when the insurance policy identification data corresponding to any indicator change data matches the insurance policy identification data corresponding to any historical dimension data, any indicator change data can be associated and aggregated with any historical dimension data to obtain second aggregated data, and then, based on the first aggregated data and the second aggregated data, the real-time statistical results of the insurance indicators and insurance indicator dimensions are determined.

[0055] It can be seen from the above-mentioned embodiments of the invention that after obtaining the indicator change data and the dimension change data, the embodiments of the invention can determine the dimension corresponding to the changed indicator from the dimension that has changed recently through the insurance policy identification data corresponding to the indicator change data and the dimension change data. If the dimension corresponding to the changed indicator cannot be determined, the historical dimension will be further obtained to determine the dimension corresponding to the changed indicator through the historical dimension, so as to realize real-time statistics of the indicator dimension. Since real-time statistics of multi-dimensional data of indicators can be realized through historical dimensions when the dimension change and the indicator change are inconsistent, it can effectively solve the problem in the related technology that the real-time statistics of multi-dimensional data of indicators cannot be realized when the dimension change and the indicator change are inconsistent because the real-time statistics of multi-dimensional data of indicators are realized through the direct association operation of the dimension change stream and the indicator change stream.

[0056] Another embodiment of the present disclosure further provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store computer-executable instructions, and the computer-executable instructions implement the above process when executed by a processor.

[0057] The storage medium in the embodiment of the present disclosure can implement each process of the above-mentioned real-time data statistics method embodiment and achieve the same effect and function, which will not be repeated here.

[0058] Another embodiment of the present disclosure further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above process is implemented.

[0059] The computer program product in the embodiment of the present disclosure can implement each process of the above-mentioned real-time data statistics method embodiment and achieve the same effect and function, which will not be repeated here.

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

[0061] In the 1990s, it was very clear whether the improvement of a technology was hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, 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, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0062] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

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

[0064] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of the present disclosure, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0065] It should be understood by those skilled in the art that one or more embodiments of the present disclosure may be provided as a method, system or computer program product. Therefore, one or more embodiments of the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, one or more embodiments of the present disclosure may 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] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0067] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0069] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

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

[0071] Each embodiment in the present disclosure is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0072] The above description is only an embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the scope of the claims of the present disclosure.

Claims

1. A real-time data statistics method, characterized in that: The method comprises: Obtaining 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 insurance indicator dimension changes; If the insurance policy identification data corresponding to any of the indicator change data successfully matches the insurance policy identification data corresponding to any of the dimension change data, then any of the indicator change data is associated and aggregated with any of the dimension change data to obtain first aggregated 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, historical dimension data is obtained, and when 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, any of the indicator change data is associated and aggregated with any of the historical dimension data to obtain 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 dimension data includes: the latest updated insurance indicator dimension data.

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

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

5. The method according to claim 1, characterized in that Before associating and aggregating any of the indicator change data with any of the dimension change data to obtain first aggregated data, the method further includes: Determine the current latest dimensional data and the first latest insurance policy identification data corresponding to the current latest dimensional data according to the dimensional change data; Determine the current latest indicator data and the second latest insurance policy identification data corresponding to the current latest indicator data according to the indicator change data; Matching any of the first latest insurance policy identification data with any of the second latest insurance policy identification data; The associating and aggregating any of the indicator change data with any of the dimension change data to obtain first aggregated data includes: The current latest dimension data corresponding to any of the first latest insurance policy identification data and the current latest indicator data corresponding to any of the second latest insurance policy identification data are associated and aggregated to obtain first aggregated data.

6. The method according to claim 1, characterized in that The obtaining of historical dimension data includes: Obtain historical dimension data through asynchronous query.

7. A real-time data statistics device, characterized in that: The device comprises: A 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 insurance indicator dimension changes; an aggregation module, configured to associate and aggregate any of the indicator change data with any of the dimension change data to obtain first aggregated data if the insurance policy identification data corresponding to any of the indicator change data successfully matches the insurance policy identification data corresponding to any of the dimension change data; A second acquisition module is used for acquiring 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 when 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, associating and aggregating any of the indicator change data with any of the historical dimension data to obtain second aggregated data; A statistical module is used to determine 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 implements the steps of the method according to any one of claims 1 to 6 when executed by the processor.

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

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images