Reprotection data processing method and device, equipment and medium

Abstract

The invention discloses a reinsurance data processing method and device, equipment and a medium, and the method comprises the steps: obtaining a combined reinsurance data table according to insurance policy information, the combined reinsurance data table comprises a plurality of groups of reinsurance data, and the reinsurance data is marked with a first rule label, a second rule label and a third rule label; according to the first rule label and the second rule label, first-level calculation circulation is carried out on the reinsurance data to obtain first-level reinsurance data calculation results, and according to the third rule label, the first-level reinsurance data calculation results are classified, summarized and stored to obtain result data; and summarizing the result data into a reinsurance result data table to serve as a basis for reinsurance and reinsurance of the insurance policy. Through the method, reinsurance and reinsurance calculation has better calculation configuration flexibility in a big data framework.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a reinsurance data processing method, device, equipment and medium. Background Art

[0002] After underwriting a risk, an insurance company may face huge potential losses. Through reinsurance, the insurance company can transfer part of the risk to the reinsurance company, thereby improving its financial stability and capital efficiency. Among them, an important part of the reinsurance business is the reinsurance calculation. At present, under the existing technology, the industry is still using Oracle database for reinsurance calculation operations. However, with the increase in the amount of data to be processed, the increase in operation and maintenance costs, and the need for system scalability, the reinsurance calculation must be migrated to the big data architecture to meet the needs. During the data migration process, the solidified processing logic in the Oracle database cannot be directly applied to the big data environment, resulting in the lack of flexibility in reinsurance calculation and calculation configuration. Summary of the invention

[0003] The embodiments of the present invention provide a reinsurance data processing method, device, equipment and medium, aiming to solve the problem of lack of flexibility in reinsurance calculation and calculation configuration caused by the fact that the processing logic solidified in the Oracle database cannot be directly applied to the big data environment during reinsurance calculation.

[0004] In a first aspect, an embodiment of the present invention provides a reinsurance data processing method, comprising: acquiring a combined reinsurance data table according to policy information, the combined reinsurance data table comprising multiple groups of reinsurance data, the reinsurance data being marked with a first rule tag, a second rule tag, and a third rule tag; performing a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying, aggregating, and storing the first-level reinsurance data calculation result according to the third rule tag to obtain result data; and aggregating the result data into a reinsurance result data table as a basis for reinsurance of the policy.

[0005] In a second aspect, an embodiment of the present invention further provides a reinsurance data processing device, comprising the units of the reinsurance data processing method as described above.

[0006] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to execute the steps of the above-mentioned reinsurance data processing method.

[0007] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the steps of the above-mentioned reinsurance data processing method can be implemented.

[0008] Compared with the prior art, the present invention has the following beneficial effects:

[0009] In the technical solution of the present invention, after obtaining the combined reinsurance data table, the reinsurance data is calculated according to the first rule tag and the second rule tag marked on the reinsurance data to obtain the calculation result, and the first-level reinsurance data calculation result is classified, summarized and stored according to the third rule tag marked on the reinsurance data to obtain the result data. Finally, after the calculation is completed, the result data is summarized into a reinsurance result data table as a basis for reinsurance of the policy. This technical solution enables the reinsurance calculation to have better calculation configuration flexibility in the big data architecture. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0011] Figure 1 A flowchart of the reinsurance data processing method provided by the present invention;

[0012] Figure 2 A first sub-flow chart of the reinsurance data processing method provided by the present invention;

[0013] Figure 3 A sub-flow chart of the first sub-flow chart of the reinsurance data processing method provided by the present invention;

[0014] Figure 4 A second sub-flow chart of the reinsurance data processing method provided by the present invention;

[0015] Figure 5 A third sub-flow chart of the reinsurance data processing method provided by the present invention;

[0016] Figure 6 A schematic block diagram of units of the reinsurance data processing device provided by the present invention;

[0017] Figure 7 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0019] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0020] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0021] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0022] In order to solve the problem that the processing logic solidified in the Oracle database cannot be directly applied to the big data environment in the prior art, resulting in the lack of flexibility in reinsurance calculation and calculation configuration, the present invention invents the following reinsurance data processing method, referring to Figures 1 to 5 , which includes:

[0023] S110. Acquire a combined reinsurance data table according to the insurance policy information, wherein the combined reinsurance data table includes multiple groups of reinsurance data, and the reinsurance data are marked with a first rule tag, a second rule tag, and a third rule tag.

[0024] In this embodiment, the so-called policy information refers to the original insurance policy that has the risk to be transferred, including personal insurance policies, property insurance policies and liability insurance policies. According to the actual situation, some insurance policies can be identified as having a high potential claim risk, so the demand for reinsurance and cession is relatively large. Such policies are original insurance policies with risks. The so-called rule label refers to a characteristic value used to indicate that the policy data needs to be calculated according to a certain order or rule. Different policies have various related data, including basic policy information, underwriting strategies and risk information. In order to perform reinsurance and cession calculations and to make the calculation results more accurate through the intervention of multi-source data information as much as possible, the data needs to be aggregated and combined. After the different data are aggregated and combined, the combined reinsurance data table can be obtained. It should be emphasized that the multi-source data information in the policy needs to be processed by data integration, cleaning, conversion, etc. before it can be used in the reinsurance and cession calculation method in the embodiment of the present invention. Since reinsurance business is divided into multiple types such as percentage reinsurance, excess reinsurance, and limit percentage reinsurance, these reinsurances will have different priority relationships under the same customer or the same policy. For example, excess reinsurance may rely on the results of percentage reinsurance calculation data before the result value can be calculated. Therefore, it is necessary to assign rule labels to the reinsurance data to facilitate the computer's selection of calculation rules and summary and classification of the final calculation results when performing reinsurance calculations.

[0025] In one embodiment, referring to Figure 4 As shown, the step of obtaining the combined reinsurance data table according to the policy information includes:

[0026] S111, listing a reinsurance relationship configuration table according to the policy information to define the priority relationship between reinsurance types;

[0027] S112, listing the policy data table according to the policy information and collecting the basic data of the policy;

[0028] S113: Associating the reinsurance relationship configuration table with the insurance policy data table to obtain a combined reinsurance data table.

[0029] The so-called reinsurance relationship configuration table refers to a table that defines the priority relationship between reinsurance types, and the so-called policy data table refers to a table that collects the basic data of the policy. In order to obtain clear policy basic data and the priority relationship between policy reinsurance types through the program, it is necessary to separate the reinsurance relationship configuration table that defines the priority relationship between reinsurance types and the policy data that records the basic data of the policy and obtain them through different programs, and then associate the reinsurance relationship configuration table and the policy data table to establish the calculation logic in the table to obtain the combined reinsurance data table. The solution of this embodiment can manage the calculation order through the configuration table, quickly adjust the calculation order, and adapt to different reinsurance needs. At the same time, the logic of the reinsurance relationship configuration table and the policy data table acquisition program and the combined reinsurance data table association generation program used in the embodiment is clearer, which is convenient for error checking and maintenance. In actual operation, this embodiment also improves the readability and maintainability of the code and reduces operation and maintenance costs.

[0030] In the process of implementing the implementation scheme, the policy information and reinsurance data from different sources are integrated, and big data technologies such as Apache Kafka or Apache NiFi can be used to realize real-time data inflow and batch processing. The original policy data obtained needs to be cleaned, including deduplication, format standardization and missing value processing to ensure the data quality in the table. Then use a tag management system, such as Apache Hive or Elasticsearch, to further mark and classify the reinsurance data to achieve tag management. Among them, the extensible tag system allows new rule tags to be added dynamically to facilitate subsequent calculations. According to the first and second rule tags, the reinsurance data is grouped and integrated. After that, the grouping operation in the big data framework, such as Apache Spark, can be used to implement group processing and improve computing efficiency.

[0031] In order to explain the embodiments of the present invention more clearly, the following examples are provided:

[0032] Since big data does not have the index concept of relational databases, it is not possible to find external table data for each order. Therefore, when using it, the data needed will be first unified and associated and merged into a wide table to facilitate subsequent algorithm calculations.

[0033] Reinsurance relationship configuration table (A1):

[0034]

[0035] The combination serial number in the reinsurance relationship configuration table A1 is the first rule label, and the priority is the second rule label. The first rule label and the second rule label specify the logical order of reinsurance calculation. At the same time, the reinsurance relationship configuration table A1 also contains the contract, insurance type, liability, reinsurance type and self-limit algorithm number. These labels involve the calculation procedures, associated logic or calculation weights that the computer needs to apply during specific calculations. In the actual calculation process, these labels will affect the specific calculation results. When listed in the form of label entries in the table, the user can change and modify them at any time according to needs. In order to facilitate inspection and subsequent editing, in some cases, the reinsurance relationship configuration table A1 also contains the self-limit algorithm description, so that the user can intuitively obtain the actual calculation logic of the algorithm.

[0036] Policy Data Sheet (A2):

[0037]

[0038] The customer number in the policy data table A2 is the third rule tag referred to in the embodiment of the present invention, and the third tag exists as the address basis for data classification and aggregation. At the same time, the policy data table A2 also includes the policy number, the main and additional contract number, the insurance type, the contract, the liability, the premium and the risk coverage. These tags are the specific information of the policy. In order to facilitate computer reading and information storage, a representative number is assigned to each information value or information set, so that the computer can quickly index the relevant information and the relevant calculation program model when running the calculation program.

[0039] Combined reinsurance data table (A3):

[0040]

[0041] Combined reinsurance data table A3 is an intermediate table after combining reinsurance relationship configuration table A1 and policy data table A2 and sorting them according to customer number, combination sequence number, priority, policy number, and primary and secondary contract number. The so-called intermediate table means that it is only used as a process tool for reinsurance re-reporting calculation and has no guiding significance for reinsurance result analysis. At this time, combined reinsurance data table A3 can be used for reinsurance calculation.

[0042] S120, performing a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying, summarizing and storing the first-level reinsurance data calculation result according to the third rule tag to obtain result data.

[0043] The computer calculates the reinsurance data according to the defined rule tags, and performs the first-level calculation loop according to the first rule tags and the second rule tags. The so-called first-level calculation loop refers to a sequential calculation loop based on the serial numbers of the first rule tags and the second rule tags. When the serial number of the first rule tag is the maximum value, the calculation loop stops and the calculation result of the first-level reinsurance data is obtained. After the calculation result of the first-level reinsurance data is obtained, the result needs to be classified and summarized based on the third rule tag for further calculation and processing. In order to facilitate the computer to call the calculation result of the first-level reinsurance data when performing further calculation and processing, it is also necessary to store it in the target location.

[0044] In actual operation, distributed computing frameworks such as Apache Spark or Flink are used to process the reinsurance data in parallel, using their computing power to perform calculation cycles, thereby reducing calculation time. Aggregation functions are then used to summarize the results. Data warehouse technology such as Amazon Redshift or Google BigQuery is then used to store the summary results in the target location to support subsequent queries and analysis.

[0045] In one embodiment, referring to Figure 2 As shown, the step of performing a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying and aggregating the first-level reinsurance data calculation result according to the third rule tag, and storing it in a target location includes:

[0046] S121, performing a first-level calculation cycle on the reinsurance data according to the serial number of the first rule label to obtain result data, wherein the first-level calculation cycle includes a second calculation cycle, and performing the second calculation cycle on the reinsurance data according to the serial number of the second rule label to obtain a second-level reinsurance data calculation result;

[0047] S122, summarizing the calculation results of the second-level reinsurance data based on the third rule tag and storing them in a target location;

[0048] S123, recording the final result of the second calculation cycle under the serial number of the same first rule label as the first-level reinsurance data calculation result of the serial number, and classifying and summarizing the first-level reinsurance data calculation result according to the third rule label, and storing it to the target location.

[0049] In this embodiment, the so-called second calculation loop refers to a sequential calculation loop based on the second rule tag. When the second rule tag sequence number is the maximum value, the calculation loop stops and the second-level reinsurance data calculation result is obtained. In this embodiment, in order to further refine the logical level of the calculation so that the calculation result is more in line with the ideal reinsurance result, within the first-level calculation loop, for each group, the second calculation loop is performed according to the sequence number of the second rule tag. After the second-level reinsurance data calculation result is obtained, the result needs to be classified and summarized based on the third rule tag for further calculation and processing. In order to facilitate the computer to call the second-level reinsurance data calculation result when further calculation and processing are performed, it is also necessary to store it in the target location. After the second-level calculation loop is completed, the final result obtained is the first-level reinsurance data calculation result of the first rule tag to which the second rule tag belongs. At this time, the first-level reinsurance data calculation result is classified and summarized according to the third rule tag, and stored in the target location for the convenience of further calculation and processing by the computer. This hierarchical calculation and aggregation method makes reinsurance data processing more systematic, can handle complex reinsurance data more effectively, ensure the accuracy and traceability of the data, and thus provide a more reliable basis for reinsurance decisions.

[0050] In one embodiment, referring to Figure 2 After the step of aggregating the calculation result of the second-level reinsurance data based on the third rule tag and storing it in the target location, the method further includes:

[0051] S124: Generate an intermediate reinsurance data table by associating the calculation result of the first-level reinsurance data with the combined reinsurance data table based on the third rule tag.

[0052] In the process of associating the calculation result of the first-level reinsurance data with the combined reinsurance data table according to the third rule tag, an intermediate reinsurance data table is also generated for further data processing and analysis. Furthermore, the intermediate reinsurance data table, as a temporary data storage method, can help program designers obtain a basis for simplifying complex calculation processes during later inspections, optimize program design, and enable data to be flexibly applied in different calculation cycles.

[0053] In one embodiment, referring to Figure 3 The step of aggregating the calculation result of the second-level reinsurance data based on the third rule tag and storing it in a target location includes:

[0054] S1221, the calculation results of the second-level reinsurance data are summarized based on the third rule tag, and the summary results are stored in the real-time result field for reference when the next second calculation cycle is calculated;

[0055] S1222, the calculation result of the first-level reinsurance data is associated with the combined reinsurance data table based on the third rule tag;

[0056] S1223. The calculation result of the second-level reinsurance data is associated with the combined reinsurance data table based on the third rule label to generate an intermediate reinsurance data table.

[0057] In this embodiment, the so-called real-time result field refers to a field used to display or store real-time data in a data processing, database management or real-time data analysis system. By storing the summary results in the real-time result field, the latest status or changes of the data can be reflected immediately without waiting for batch processing or regular updates, which is convenient for data calls during the next second calculation cycle. At the same time, it is also convenient for the calculation monitoring system to intervene to review and detect the calculation process. When an error occurs in the calculation result, the computer will make corresponding feedback based on the judgment of the calculation monitoring system on the error, and inform the user of the source of the error, so that the user can check and correct the corresponding calculation program and data table. In order to further calculate,

[0058] In the process of associating the calculation result of the second-level reinsurance data with the combined reinsurance data table according to the third rule tag, an intermediate reinsurance data table is also generated for further data processing and analysis. Furthermore, the intermediate reinsurance data table, as a temporary data storage method, can help program designers obtain a basis for simplifying complex calculation processes during later inspections, optimize program design, and enable data to be flexibly applied in different calculation cycles.

[0059] In one embodiment, referring to Figure 5 The step of performing a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying and aggregating the first-level reinsurance data calculation result according to the third rule tag and storing it in a target location includes:

[0060] S12a, performing a first-level calculation cycle on the reinsurance data according to the first combination serial number of the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying and aggregating the first-level reinsurance data calculation result according to the third rule tag, and storing it to a target location, wherein the first rule tag includes a plurality of combination serial numbers for sequential calculation;

[0061] S12b, performing a first-level calculation cycle on the reinsurance data according to the second combination serial number of the first rule label and the second rule label to obtain a first-level reinsurance data calculation result, and classifying and summarizing the first-level reinsurance data calculation result according to the third rule label, and storing it to a target location until all combination serial numbers of the first rule label are calculated.

[0062] In the present embodiment, during the specific implementation, according to the first combination serial number of the first rule tag and the second rule label, a first-level calculation cycle is performed on the reinsurance data to obtain the calculation result of the first-level reinsurance data. Then, according to the third rule label, the calculation result of the first-level reinsurance data is classified and summarized, and the summarized result is stored in the target location. Then, according to the second combination serial number of the first rule label and the second rule label, the above-mentioned first-level calculation cycle is repeated to obtain a new calculation result of the first-level reinsurance data, and it is still classified, summarized and stored according to the third rule label. After that, the calculation of all combination serial numbers is completed, and the process continues until all combination serial numbers in the first rule label are calculated, ensuring that the data corresponding to each combination serial number is processed and stored. Through the method steps of this embodiment, comprehensive and systematic processing of reinsurance data can be achieved, ensuring the integrity and accuracy of the data, and providing a reliable basis for subsequent analysis and decision-making.

[0063] In actual operation, if Apache Spark is used as the computing framework, a loop structure can be created to traverse each combination number of the first rule label. Use Spark's map and reduce functions to implement parallel computing, and calculate the reinsurance data according to the second rule label, and write the corresponding Spark job. After obtaining the calculation results of the first-level reinsurance data, use Spark SQL to perform classification and aggregation, and use SQL queries or DataFrame's groupBy method to perform aggregation operations (such as SUM, AVG, etc.) according to the third rule label. Store the classification and aggregation results to the target location, such as HDFS, databases (such as Apache Hive, Apache HBase), or data warehouses (such as Amazon Redshift). After completing the processing of one combination number, enter the calculation of the next combination number, using Spark's loop calculation method, such as using foreach or writing custom iteration logic, until all combination numbers are processed.

[0064] S130, summarizing the result data into a reinsurance result data table as a basis for reinsurance of the policy.

[0065] The final results are summarized and presented in the form of reinsurance result data tables. In actual operation, the final classification and summary results are stored in HDFS or data warehouse for subsequent analysis. At the same time, data visualization tools (such as Tableau and PowerBI) can be used to connect to the result storage to generate visualization reports and dashboards to help business analysis and decision-making as a basis for reinsurance.

[0066] In order to explain the embodiment of the present invention more clearly, in combination with the reinsurance relationship configuration table A1, the policy data table A2 and the combined reinsurance data table A3 given above, the following is an example:

[0067] The first level calculation loop with the first rule label 1 is performed on the combined reinsurance data table A3, and the second level calculation loop with the first rule label 1 and the second rule label 1 is performed. At this time, the table of reinsurance data that needs to be calculated is as follows:

[0068] Calculation intermediate process (P1):

[0069]

[0070] After completing the calculation of the above table, the customer number is used as the key to format and aggregate the results, and the results are stored in the real-time result field. The real-time calculation temporary storage table is as follows:

[0071] Real-time calculation temporary storage table (L1):

[0072]

[0073] When performing the second level calculation cycle with the first rule tag being 1 and the second rule tag being 1, data is provided through the above-mentioned real-time calculation temporary storage table L1. At this time, the table of reinsurance data that needs to be calculated for the second level calculation cycle with the first rule tag being 2 and the second rule tag being 1 is as follows:

[0074] Calculate the intermediate process table (P2):

[0075]

[0076] In the calculation intermediate process table P2, it can be explained that the 1 in the brackets of max(100-(1),0) is the retention amount result of the first row of the first cycle, the 1.5 in the brackets of max(100-(1.5),0) is the retention amount of policy number A2 in the third row of the first cycle, and the 0.9 in the brackets of max(100-(0.9),0) is the retention amount of policy number A3 in the fourth row of the first cycle.

[0077] After completing the calculation of the intermediate process table P2, the customer number is used as the key to format and aggregate the results, and the results are stored in the real-time result field. The real-time calculation temporary storage table is obtained as follows:

[0078] Real-time calculation temporary storage table (L2):

[0079]

[0080] For the real-time calculation temporary storage table L2 and the second-level calculation loop with the first rule label being 2 and the second rule label being 1, it should be pointed out that due to the particularity of the content of this example, the two results are merged.

[0081] When performing the second level calculation cycle with the first rule tag being 3 and the second rule tag being 1, data is provided through the above-mentioned real-time calculation temporary storage table L2. At this time, the table of reinsurance data that needs to be calculated for the second level calculation cycle with the first rule tag being 3 and the second rule tag being 1 is as follows:

[0082] Calculate the intermediate process table (P3):

[0083]

[0084] The result data are summarized to obtain the following reinsurance result data table:

[0085] Final result table (A4):

[0086]

[0087] The final result table A4 is the reinsurance result data table, which can be used as the basis for reinsurance of the policies in the table. By analyzing the reinsurance result data table, insurance companies can identify the patterns of different types of risks, so as to better assess potential risks and reinsure and transfer risks. In addition, based on historical reinsurance results, insurance companies can also price more accurately to ensure that premiums can cover risks and costs. At the same time, according to market changes and historical data, the pricing strategy of reinsurance can be adjusted to improve competitiveness and profitability.

[0088] Figure 6 FIG. 1 is a schematic block diagram of a reinsurance data processing device provided by an embodiment of the present invention. Figure 6 As shown, corresponding to the above reinsurance data processing method, the present invention also provides a reinsurance data processing device. The reinsurance data processing device includes a unit for executing the above reinsurance data processing method, and the device can be configured in a desktop computer, a tablet computer, a smart phone, and other terminals. For details, please refer to Figure 6 , the reinsurance data processing device comprises:

[0089] a reinsurance data table acquisition unit, configured to acquire a combined reinsurance data table according to the policy information, wherein the combined reinsurance data table includes reinsurance data, wherein the reinsurance data is marked with a first rule tag, a second rule tag, and a third rule tag; a reinsurance calculation unit, configured to perform a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and to classify and summarize the first-level reinsurance data calculation result according to the third rule tag, and store it in a target location; and a reinsurance result output unit, configured to summarize the result data into a reinsurance result data table as a basis for reinsurance of the policy.

[0090] Among them, the reinsurance calculation unit is used to obtain a combined reinsurance data table according to the policy information; the reinsurance calculation unit is used to calculate according to the reinsurance data to obtain the reinsurance data calculation result; the reinsurance result output unit is used to summarize the result data into a reinsurance result data table.

[0091] The reinsurance data processing device can be implemented in the form of a computer program. Figure 7 Runs on the computer device shown.

[0092] See also Figure 7 , Figure 7 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a desktop computer, a tablet computer, a smart phone, etc. The server may be an independent server or a server cluster composed of multiple servers.

[0093] See also Figure 7 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0094] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a reinsurance data processing method.

[0095] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0096] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a reinsurance data processing method.

[0097] The network interface 505 is used to communicate with other devices over the network. Those skilled in the art will appreciate that Figure 7 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0098] The processor 502 is used to run a computer program 5032 stored in the memory to implement the steps of the above method.

[0099] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0100] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0101] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the above method.

[0102] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0103] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0104] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0105] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0107] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A reinsurance data processing method, characterized in that: include: Acquire a combined reinsurance data table according to the policy information, wherein the combined reinsurance data table includes multiple groups of reinsurance data, and the reinsurance data are marked with a first rule label, a second rule label, and a third rule label; Performing a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying, aggregating and storing the first-level reinsurance data calculation result according to the third rule tag to obtain result data; The result data are summarized in the reinsurance result data table as a basis for reinsurance of the policy.

2. The reinsurance data processing method according to claim 1, characterized in that: Performing a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying, aggregating and storing the first-level reinsurance data calculation result according to the third rule tag to obtain result data, including: Performing a first-level calculation cycle on the reinsurance data according to the serial number of the first rule label to obtain result data, wherein the first-level calculation cycle includes a second calculation cycle, and performing the second calculation cycle on the reinsurance data according to the serial number of the second rule label to obtain a second-level reinsurance data calculation result; Aggregate the calculation results of the second-level reinsurance data based on the third rule tag and store them in a target location; The final result of the second calculation cycle under the serial number of the same first rule label is recorded as the first-level reinsurance data calculation result of the serial number, and the first-level reinsurance data calculation result is classified and summarized according to the third rule label and stored in the target location.

3. The reinsurance data processing method according to claim 2, characterized in that: The step of aggregating the calculation results of the second-level reinsurance data based on the third rule tag and storing the data in a target location comprises: Summarize the calculation results of the second-level reinsurance data based on the third rule tag, and store the summary results in the real-time result field for use in the next calculation of the second calculation cycle; Associating the calculation result of the second-level reinsurance data with the combined reinsurance data table based on the third rule tag; A secondary intermediate reinsurance data table is generated based on the calculation result of the second-level reinsurance data and the result of associating it with the combined reinsurance data table based on the third rule label.

4. The reinsurance data processing method according to claim 2, characterized in that: After the step of aggregating the calculation result of the second-level reinsurance data based on the third rule tag and storing it in the target location, the method further includes: An intermediate reinsurance data table is generated based on the calculation result of the first-level reinsurance data and the result of associating the result with the combined reinsurance data table based on the third rule label.

5. The reinsurance data processing method according to claim 1, characterized in that: The step of obtaining the combined reinsurance data table according to the insurance policy information includes: According to the policy information, a reinsurance relationship configuration table is listed to define the priority relationship between reinsurance types; According to the policy information, a policy data table is listed to collect the basic data of the policy; The reinsurance relationship configuration table and the policy data table are associated to obtain a combined reinsurance data table.

6. The reinsurance data processing method according to any one of claims 1 to 5, characterized in that: The step of performing a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying, aggregating and storing the first-level reinsurance data calculation result according to the third rule tag to obtain result data includes: Performing a first-level calculation cycle on the reinsurance data according to the first combination serial number of the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and classifying and aggregating the first-level reinsurance data calculation result according to the third rule tag, and storing it in a target location, wherein the first rule tag includes a plurality of combination serial numbers for sequential calculation; A first-level calculation cycle is performed on the reinsurance data according to the second combination serial number of the first rule label and the second rule label to obtain a first-level reinsurance data calculation result, and the first-level reinsurance data calculation result is classified and summarized according to the third rule label and stored in a target location until all combination serial numbers of the first rule label are calculated.

7. A reinsurance data processing device, characterized in that: A unit comprising the reinsurance data processing method according to any one of claims 1 to 6.

8. The reinsurance data processing device according to claim 7, characterized in that: include: A reinsurance data table acquisition unit, configured to acquire a combined reinsurance data table according to the policy information, wherein the combined reinsurance data table includes reinsurance data, and the reinsurance data is marked with a first rule tag, a second rule tag, and a third rule tag; a reinsurance calculation unit, configured to perform a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result, and to classify and summarize the first-level reinsurance data calculation result according to the third rule tag, and store it in a target location; The reinsurance result output unit is used to summarize the result data into a reinsurance result data table as a basis for reinsurance of the insurance policy.

9. A computer device, characterized in that: The computer device comprises a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to execute the steps of the method as claimed in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 can be implemented.

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