Reinsurance data processing method, device, equipment and medium

Abstract

The present invention discloses a reinsurance data processing method, apparatus, device, and medium. The reinsurance data processing method includes: obtaining a combined reinsurance data table based on policy information, the combined reinsurance data table including multiple groups of reinsurance data, the reinsurance data being labeled with a first rule tag, a second rule tag, and a third rule tag; performing a first-level calculation cycle on the reinsurance data based on the first rule tag and the second rule tag to obtain first-level reinsurance data calculation results; classifying and aggregating the first-level reinsurance data calculation results based on the third rule tag to obtain result data; and aggregating the result data into a reinsurance result data table to serve as a basis for reinsurance of the policy. The above method provides greater computational configuration flexibility for reinsurance calculations in a big data architecture.

Description

Technical Field

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

[0002] After underwriting a risk, insurance companies may face significant potential losses. Through reinsurance, insurance companies can transfer a portion of this risk to reinsurers, thereby improving their financial stability and capital efficiency. A key component of reinsurance is reinsurance calculations. Currently, the industry still uses Oracle databases for this calculation. However, with the increasing volume of data required, rising operational costs, and the need for system scalability, migrating reinsurance calculations to a big data architecture is necessary to meet these requirements. During the data migration process, the fixed processing logic in Oracle databases could not be directly adapted to a big data environment, resulting in a lack of flexibility in reinsurance calculations and calculation configuration. Summary of the Invention

[0003] Embodiments of the present invention provide a reinsurance data processing method, apparatus, device, and medium, which aim to solve the problem of lack of flexibility in reinsurance calculation and calculation configuration caused by the inability of the solidified processing logic in the Oracle database to be directly applicable to the big data environment during reinsurance calculation.

[0004] In a first aspect, embodiments of the present invention provide a reinsurance data processing method, comprising: obtaining a combined reinsurance data table based on policy information, the combined reinsurance data table including 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 based on the first rule tag and the second rule tag to obtain first-level reinsurance data calculation results, classifying and aggregating the first-level reinsurance data calculation results based on the third rule tag, and storing them to obtain result data; and aggregating the result data into a reinsurance result data table to serve as a basis for reinsurance and cession 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 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; and 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 a combined reinsurance data table, the reinsurance data is calculated based on the first and second rule tags marked on the reinsurance data to obtain calculation results. The first-level reinsurance data calculation results are then classified, aggregated, and stored based on the third rule tags marked on the reinsurance data to obtain result data. Finally, after the calculations are completed, the result data is aggregated into a reinsurance result data table, which serves as the basis for reinsurance and reinsurance of the policy. This technical solution provides greater computational configuration flexibility for reinsurance and reinsurance calculations within a 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 following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these 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 This is a first sub-flowchart of the reinsurance data processing method provided by the present invention;

[0013] Figure 3 This is a sub-flowchart of the first sub-flowchart of the reinsurance data processing method provided by the present invention;

[0014] Figure 4 This is a second sub-flowchart of the reinsurance data processing method provided by the present invention;

[0015] Figure 5 This is a third sub-flowchart 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0020] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the 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 and includes any and all possible combinations of one or more of the associated listed items.

[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. Obtain 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, policy information refers to the original insurance policies with risk that need to be transferred, including personal insurance policies, property insurance policies, and liability insurance policies. Certain insurance policies may be deemed to have a high potential claim risk, resulting in a relatively high demand for reinsurance and cession. These policies are considered risky original insurance policies. Rule tags refer to characteristic values ​​that indicate that policy data must be calculated according to a specific order or rule. Different policies have a variety of relevant data, including basic policy information, underwriting strategies, and risk information. To perform reinsurance and cession calculations and maximize the accuracy of the results by integrating multiple sources of data, data aggregation and combination are necessary. After aggregation and combination of these different data, a combined reinsurance data table is generated. It is important to emphasize that the multi-source data in the policies requires data integration, cleansing, and conversion before it can be used in the reinsurance and cession calculation method of this 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 the percentage reinsurance calculation data before the result value can be calculated. Therefore, it is necessary to assign rule tags 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 based on the policy information includes:

[0026] S111. List a reinsurance relationship configuration table based on the policy information to define priority relationships between reinsurance types;

[0027] S112, listing the policy data table based on the policy information and collating the basic data of the policy;

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

[0029] The reinsurance relationship configuration table is a table that defines the priority relationships between reinsurance types, and the policy data table is a table that aggregates the basic data of insurance policies. To enable a program to clearly determine the priority relationships between basic policy data and policy reinsurance types, the reinsurance relationship configuration table, which defines the priority relationships between reinsurance types, and the policy data, which records the basic policy data, must be retrieved separately through different programs. The reinsurance relationship configuration table and the policy data table are then linked to establish the calculation logic within the table, thereby generating a combined reinsurance data table. This embodiment's solution manages the calculation sequence through the configuration table, allowing for quick adjustment to accommodate different reinsurance requirements. Furthermore, the logic of the program used in this embodiment, which links the reinsurance relationship configuration table, the policy data table acquisition process, and the combined reinsurance data table, is clearer, facilitating error checking and maintenance. In practice, this embodiment also improves code readability and maintainability, reducing operational costs.

[0030] In the specific implementation of the embodiment, policy information and reinsurance data from different sources are integrated, and big data technologies such as Apache Kafka or Apache NiFi can be utilized to achieve real-time data inflow and batch processing. The acquired raw policy data needs to be cleaned, including deduplication, format standardization, and missing value processing, to ensure data quality within the table. A tag management system such as Apache Hive or Elasticsearch is then used to further tag and classify the reinsurance data for tag management. The scalable tag system allows for the dynamic addition of new rule tags, facilitating subsequent calculations. Reinsurance data is then grouped and integrated based on the first and second rule tags. Group processing can then be implemented using group operations within big data frameworks such as Apache Spark, improving computational efficiency.

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

[0032] Since big data does not have the index concept of relational databases, it is not possible to search for external table data for each order. Therefore, when using it, the required data 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 calculations. 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. Once listed in the form of label entries in the table, users can change and modify them at any time according to their needs. To facilitate inspection and subsequent editing, in some cases, the reinsurance relationship configuration table A1 also includes a self-limit algorithm description, so that users can intuitively obtain the actual calculation logic of the algorithm.

[0036] Policy Data Sheet (A2):

[0037]

[0038] The customer number in policy data table A2 is the third rule tag referred to in this embodiment of the present invention. The third tag serves as the address basis for data classification and aggregation. Policy data table A2 also contains the policy number, principal and rider number, insurance type, contract, liability, premium, and risk coverage. These tags represent specific policy information. To facilitate computer reading and information storage, each information value or information collection is assigned a representative number, allowing the computer to quickly index relevant information and the associated calculation program model when running the calculation program.

[0039] Combined reinsurance data sheet (A3):

[0040]

[0041] Combined reinsurance data table A3 is an intermediate table formed by combining reinsurance relationship configuration table A1 and policy data table A2, sorted by customer number, combination sequence number, priority, policy number, and principal and rider number. This intermediate table serves only as a tool for calculating reinsurance reinsurance returns and does not provide guidance for analyzing reinsurance results. At this point, combined reinsurance data table A3 can be used for reinsurance and cession calculations.

[0042] S120: Perform a first-level calculation cycle on the reinsurance data according to the first rule tag and the second rule tag to obtain first-level reinsurance data calculation results, and classify, summarize, and store the first-level reinsurance data calculation results according to the third rule tag to obtain result data.

[0043] The computer calculates the reinsurance data based on defined rule tags, performing a first-level calculation loop based on the first and second rule tags. The so-called first-level calculation loop refers to a sequential calculation loop based on the sequence numbers of the first and second rule tags. When the sequence number of the first rule tag reaches its maximum value, the calculation loop terminates, resulting in the first-level reinsurance data calculation results. After obtaining the first-level reinsurance data calculation results, they are categorized and summarized based on the third rule tags for further calculation and processing. To facilitate the computer's access to the first-level reinsurance data calculation results for further calculation and processing, they are also stored in a target location.

[0044] In practice, distributed computing frameworks such as Apache Spark or Flink are used to parallelize the reinsurance data, leveraging their computing power to perform computational loops and reduce computation time. Aggregation functions are then used to aggregate the results. Data warehouse technologies such as Amazon Redshift or Google BigQuery are then used to store the aggregated results in a target location for subsequent querying 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 first-level reinsurance data calculation results, and classifying and aggregating the first-level reinsurance data calculation results according to the third rule tag and storing them in a target location includes:

[0046] S121. Perform a first-level calculation loop on the reinsurance data according to the sequence number of the first rule tag to obtain result data, wherein the first-level calculation loop includes a second calculation loop, and the second calculation loop is performed on the reinsurance data according to the sequence number of the second rule tag 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 the data in a target location;

[0048] S123: Record the final result of the second calculation cycle under the sequence number of the same first rule tag as the first-level reinsurance data calculation result of the sequence number, and classify and summarize the first-level reinsurance data calculation results according to the third rule tag, and store them in a 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 reaches the maximum value, the calculation loop terminates, resulting in the second-level reinsurance data calculation result. To further refine the calculation logic hierarchy and ensure that the calculation results more closely match the ideal reinsurance results, within the first-level calculation loop, a second calculation loop is performed for each group based on the sequence number of the second rule tag. After the second-level reinsurance data calculation results are obtained, they are categorized and aggregated based on the third rule tag to facilitate further calculation and processing. To facilitate further computation and processing by the computer, the second-level reinsurance data calculation results are stored in a target location. Upon completion of the second-level calculation loop, the final result is the first-level reinsurance data calculation result for the first rule tag to which the second rule tag belongs. At this point, the first-level reinsurance data calculation results are categorized and aggregated based on the third rule tag and stored in a target location to facilitate further computation 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 decision-making.

[0050] In one embodiment, referring to Figure 2 After 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, 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] During the process of associating the first-level reinsurance data calculation results with the combined reinsurance data table based on the third rule tag, an intermediate reinsurance data table is also generated to facilitate further data processing and analysis. Furthermore, as a temporary data storage method, the intermediate reinsurance data table can help programmers simplify complex calculation processes during subsequent review, optimize program design, and enable flexible application of data across different calculation cycles.

[0053] In one embodiment, referring to Figure 3 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 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 in the next second calculation cycle;

[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 tag 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 error by the calculation monitoring system, 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] During the process of associating the second-level reinsurance data calculation results with the combined reinsurance data table based on the third rule tag, an intermediate reinsurance data table is also generated to facilitate further data processing and analysis. Furthermore, as a temporary data storage method, the intermediate reinsurance data table can help programmers simplify complex calculation processes during subsequent review, optimize program design, and enable flexible application of data across 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 first-level reinsurance data calculation results, and classifying and aggregating the first-level reinsurance data calculation results according to the third rule tag and storing them 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 the result in a target location, wherein the first rule tag includes a plurality of combination serial numbers for sequential calculation;

[0061] S12b: Perform a first-level calculation loop on the reinsurance data according to the second combination serial number of the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result; and classify and aggregate the first-level reinsurance data calculation result according to the third rule tag, and store it in a target location until all combination serial numbers of the first rule tag are calculated.

[0062] In this embodiment, during implementation, a first-level calculation loop is performed on the reinsurance data based on the first combination number and the second rule tag of the first rule tag, thereby obtaining the first-level reinsurance data calculation results. Then, based on the third rule tag, the first-level reinsurance data calculation results are classified and summarized, and the summarized results are stored in a target location. Next, based on the second combination number and the second rule tag of the first rule tag, the first-level calculation loop is repeated to obtain new first-level reinsurance data calculation results, which are also classified, summarized, and stored based on the third rule tag. Afterwards, the calculation of all combination numbers is completed, and this process continues until all combination numbers in the first rule tag have been calculated, ensuring that the data corresponding to each combination 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 practice, if Apache Spark is used as the computing framework, a loop structure can be created to iterate over each combination number of the first rule label. Spark's map and reduce functions can be used for parallel computing. Based on the second rule label, the reinsurance data can be calculated and the corresponding Spark job can be written. After obtaining the calculation results of the first-level reinsurance data, Spark SQL can be used to perform classification and aggregation. Based on the third rule label, SQL queries or the DataFrame groupBy method can be used to perform aggregation operations (such as SUM, AVG, etc.). The classification and aggregation results can be stored in a target location, such as HDFS, a database (such as Apache Hive, Apache HBase), or a data warehouse (such as Amazon Redshift). After completing the processing of one combination number, the calculation of the next combination number can be carried out using Spark's loop calculation methods, such as foreach or custom iterative logic, until all combination numbers have been processed.

[0064] S130: Summarize the result data into a reinsurance result data table to serve as a basis for reinsurance of the policy.

[0065] The final results are summarized and presented in a reinsurance results data table. In practice, the final classified summary results are stored in HDFS or a data warehouse for subsequent analysis. Data visualization tools (such as Tableau and PowerBI) can also be used to connect to the result storage to generate visual reports and dashboards to assist in business analysis and decision-making, serving as a basis for reinsurance and cession.

[0066] To explain the embodiment of the present invention more clearly, the following examples are provided in conjunction with the reinsurance relationship configuration table A1, the policy data table A2, and the combined reinsurance data table A3.

[0067] A first-level calculation loop is performed on the combined reinsurance data table A3 with the first rule label set to 1, and a second-level calculation loop is performed with both the first rule label set to 1 and the second rule label set to 1. The reinsurance data table that needs to be calculated is shown below:

[0068] Calculation intermediate process (P1):

[0069]

[0070] After completing the calculation of the above table, the results are formatted and aggregated using the customer number as the key and stored in the real-time result field. The resulting 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 loop 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 required for the second-level calculation loop 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 seen 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 results are formatted and aggregated using the customer number as the key and stored in the real-time result field. The real-time calculation temporary storage table is as follows:

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

[0079]

[0080] Regarding 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 noted that due to the particularity of the content of this example, the two results are merged.

[0081] When performing the second-level calculation loop 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. The table of reinsurance data required for calculation in the second-level calculation loop 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 results data sheet, which serves as the basis for reinsurance of the policies listed in the table. By analyzing the reinsurance results data sheet, insurance companies can identify patterns in different risk types, thereby better assessing potential risks and subsequently reinsuring and transferring them. Furthermore, based on historical reinsurance results, insurance companies can more accurately price policies, ensuring premiums cover risks and costs. Furthermore, based on market changes and historical data, insurance companies can adjust reinsurance pricing strategies to improve competitiveness and profitability.

[0088] Figure 6 FIG 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. The device can be configured in a terminal such as a desktop computer, tablet computer, smart phone, etc. 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 based on policy information, the combined reinsurance data table including reinsurance data, the reinsurance data being 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 based on the first rule tag and the second rule tag to obtain first-level reinsurance data calculation results, and to classify and aggregate the first-level reinsurance data calculation results based on the third rule tag and store them in a target location; and a reinsurance result output unit configured to aggregate the result data into a reinsurance result data table to serve as a basis for reinsurance and reinsurance of the policy.

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

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

[0092] See also Figure 7 , Figure 7 This is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 can be a terminal or a server. The terminal can be a desktop computer, tablet computer, smartphone, or other electronic device with communication capabilities. The server can be a standalone server or a server cluster consisting of multiple servers.

[0093] See 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 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, which, when executed, may enable the processor 502 to perform 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. 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0098] The processor 502 is configured 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] Those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program includes program instructions, which can be stored in a storage medium that is computer-readable. The program instructions are executed by at least one processor in the computer system to implement the steps in the method of the above-described embodiment.

[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 performs the steps of the above method.

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

[0103] Those skilled 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 above description has generally described the composition and steps of each example according to function. 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 herein, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the various units is merely a logical functional division, and actual implementation may employ other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0105] The steps in the methods of the embodiments of the present invention may be adjusted in order, combined, or deleted as needed. The units in the devices of the embodiments of the present invention may be combined, divided, or deleted as needed. Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0106] If this integrated unit is implemented as 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, or the part that contributes to the existing technology, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, terminal, or network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present invention.

[0107] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection 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 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, including: performing a first-level calculation cycle on the reinsurance data according to the sequence number of the first rule tag 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 sequence number of the second rule tag to obtain a second-level reinsurance data calculation result; aggregating the second-level reinsurance data calculation result based on the third rule tag and storing it in a target location; recording the final result of the second calculation cycle under the sequence number of the same first rule tag as the first-level reinsurance data calculation result for that sequence number, and classifying, aggregating, and storing the first-level reinsurance data calculation result in accordance with the third rule tag to a target location; The result data are summarized into the reinsurance result data table as the basis for reinsurance and cession of the policy.

2. The reinsurance data processing method according to claim 1, 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 includes: Summarizing the calculation results of the second-level reinsurance data based on the third rule tag, and storing the summarized results in the real-time result field for use in the next 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 by associating the calculation result of the second-level reinsurance data with the combined reinsurance data table based on the third rule tag.

3. The reinsurance data processing method according to claim 1, characterized in that: After 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, the method further includes: An intermediate reinsurance data table is generated by associating the calculation result of the first-level reinsurance data with the combined reinsurance data table based on the third rule tag.

4. The reinsurance data processing method according to claim 1, wherein: The step of obtaining the combined reinsurance data table based on the policy information includes: List a reinsurance relationship configuration table based on the policy information to define the priority relationship between reinsurance types; List the policy data table based on the policy information and 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.

5. The reinsurance data processing method according to any one of claims 1 to 4, 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 first-level reinsurance data calculation results, and classifying, aggregating, and storing the first-level reinsurance data calculation results according to the third rule tag to obtain result data includes: performing a first-level calculation loop 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 the result in a target location, wherein the first rule tag includes a plurality of combination serial numbers for sequential calculation; A first-level calculation loop is performed on the reinsurance data according to the second combination serial number of the first rule tag and the second rule tag to obtain a first-level reinsurance data calculation result. The first-level reinsurance data calculation result is classified and aggregated according to the third rule tag and stored in a target location until all combination serial numbers of the first rule tag are calculated.

6. A reinsurance data processing device, characterized in that: A unit applied to the reinsurance data processing method according to any one of claims 1 to 5, comprising: 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 first-level reinsurance data calculation results, and to classify and summarize the first-level reinsurance data calculation results according to the third rule tag, and store them 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.

7. A computer device, characterized in that: The computer device includes 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 perform the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 can be implemented.

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