Giant disaster risk data processing method and device and electronic equipment

Abstract

The invention provides a huge disaster risk data processing method and device and electronic equipment, and is applied to the technical field of data processing. According to the invention, by acquiring the reinsurance huge disaster risk detail data for the specified dangerous unit dimension at the specified deadline, the target insurance data of which the original self-remaining amount is greater than the preset amount threshold can be analyzed and screened out in detail, and then the address and the price of each subject matter are accurately acquired. And reasonable splitting is performed according to the insurance policy product type, so that each insurance object data can reflect the real risk exposure condition, and the accuracy and processing efficiency of the huge disaster risk exposure data are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device and electronic equipment for processing catastrophic risk data. Background Art

[0002] In the management of large-scale catastrophe risk, cumulative catastrophe risk data suffers from gaps, uneven granularity, and a lack of unified standards. This is particularly true for blanket insurance data. Blanket insurance involves numerous different risk targets, a wide geographical distribution, and complex industries, making it difficult for insurance companies to directly obtain accurate data.

[0003] Currently, lists of blanket insurance targets are often stored in the image center as a variety of attachments. These lists are formatted inconsistently and mixed with other information, making them difficult to organize and standardize. Furthermore, some clients, due to commercial confidentiality concerns, withhold or obscure key property data, resulting in incomplete data. Existing catastrophic risk data processing relies primarily on manual labor. Staff must manually extract data from the image center, organize it into Excel, match the insured amount with the address, and manually adjust complex situations to generate a detailed cumulative catastrophic risk breakdown for the blanket insurance policy. This overall process is inefficient and prone to errors, hindering insurers' accurate assessment of actual risks and the effectiveness of their risk management.

[0004] Therefore, how to efficiently process catastrophic risk exposure data and improve the accuracy of catastrophic risk exposure data has become a technical problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method, device and electronic device for processing catastrophic risk data that overcomes or at least partially solves the above problems. The technical solution is as follows:

[0006] A method for processing catastrophe risk data, comprising:

[0007] Obtaining detailed reinsurance catastrophe risk data for a specified risk unit dimension at a specified cutoff time point, wherein the detailed reinsurance catastrophe risk data includes at least one insurance data item, wherein the insurance data item includes a policy number, a policy position number, administrative region information, and an original retention amount corresponding to the administrative region information for the specified risk unit dimension;

[0008] Filtering target insurance data whose original retention amount is greater than or equal to a preset amount threshold from the reinsurance catastrophe risk detailed data, and adding the target insurance data to the policy pool;

[0009] For any target insurance data in the policy pool: obtaining a policy subject list file corresponding to the target insurance data uploaded by a user, wherein the policy subject list file includes at least one insurance subject data, and the insurance subject data includes an address and a price of the subject matter;

[0010] Calculate the split price corresponding to each of the subject matter addresses: when there are multiple pieces of the insurance subject data in the policy subject list file whose subject matter addresses share the same subject matter price, determine the policy product type of the insurance data corresponding to the policy subject list file, and use the provincial, municipal and city value share data corresponding to the policy product type to split the shared subject matter price into the corresponding multiple subject matter addresses of the insurance subject data, so that the subject matter address of each piece of the insurance subject data in the policy subject list file corresponds to a separate split price.

[0011] A catastrophic risk data processing device includes: a reinsurance catastrophic risk detailed data acquisition unit, a target insurance data screening unit, a policy subject list file acquisition unit and a split price calculation unit.

[0012] The reinsurance catastrophe risk detail data acquisition unit is configured to acquire reinsurance catastrophe risk detail data for a specified risk unit dimension at a specified cutoff time point, wherein the reinsurance catastrophe risk detail data includes at least one insurance data item, wherein the insurance data includes a policy number, a policy position number, administrative region information, and an original retention amount corresponding to the administrative region information for the specified risk unit dimension;

[0013] The target insurance data screening unit is configured to screen out target insurance data whose original retention amount is greater than or equal to a preset amount threshold from the reinsurance catastrophe risk detailed data, and add the target insurance data to the policy pool;

[0014] The policy subject list file obtaining unit is configured to obtain, for any target insurance data in the policy pool: a policy subject list file corresponding to the target insurance data uploaded by a user, wherein the policy subject list file includes at least one piece of insurance subject data, and the insurance subject data includes an address and a price of an underlying object;

[0015] The split price calculation unit is used to calculate the split price corresponding to each of the subject matter addresses: when there are multiple pieces of insurance subject data in the policy subject list file that share the same subject matter price, determine the policy product type of the insurance data corresponding to the policy subject list file, and use the provincial, municipal and city value share data corresponding to the policy product type to split the shared subject matter price into the corresponding multiple subject matter addresses of the insurance subject data, so that the subject matter address of each piece of insurance subject data in the policy subject list file corresponds to a separate split price.

[0016] An electronic device comprising at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other via the bus; and the processor is configured to call program instructions in the memory to execute the method for processing catastrophic risk data.

[0017] By means of the above technical solution, the present invention provides a method, device and electronic device for processing catastrophe risk data, which obtains reinsurance catastrophe risk detail data for a specified risk unit dimension at a specified deadline, wherein the reinsurance catastrophe risk detail data includes at least one insurance data, wherein the insurance data includes a policy number, a risk position number, administrative region information and an original retention amount corresponding to the administrative region information under the specified risk unit dimension; filters out target insurance data whose original retention amount is greater than or equal to a preset amount threshold from the reinsurance catastrophe risk detail data, and adds the target insurance data to the policy pool; for any target insurance data in the policy pool: obtains a policy subject list document corresponding to the target insurance data uploaded by the user; The insurance policy subject list file includes at least one insurance subject data, and the insurance subject data includes an object address and an object price; the split price corresponding to each object address is calculated: when there are multiple insurance subject data in the insurance policy subject list file whose object addresses share the same object price, the insurance product type of the insurance data corresponding to the insurance policy subject list file is determined, and the shared object price is split into the object addresses of the corresponding multiple insurance subject data using the provincial, municipal and city value ratio data corresponding to the policy product type, so that the object address of each insurance subject data in the insurance policy subject list file corresponds to a separate split price. By acquiring detailed data on reinsured catastrophe risks for a specified risk unit dimension at a specified deadline, the present invention can conduct detailed analysis and screening of target insurance data with original retention amounts greater than or equal to a preset amount threshold. Furthermore, by accurately acquiring the address and price of each subject matter and reasonably splitting it according to the type of insurance policy product, it ensures that the data of each insurance subject matter can reflect the actual risk exposure situation, thereby significantly improving the accuracy and processing efficiency of catastrophe risk exposure data.

[0018] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0020] Figure 1A schematic diagram showing a flow chart of an implementation of a method for processing catastrophic risk data provided by an embodiment of the present invention;

[0021] Figure 2 A flowchart illustrating a first specific implementation of a method for processing catastrophe risk data provided by an embodiment of the present invention is shown;

[0022] Figure 3 A flow chart showing a second specific implementation of the method for processing catastrophe risk data provided by an embodiment of the present invention is shown;

[0023] Figure 4 A schematic diagram showing the structure of a catastrophic risk data processing device provided by an embodiment of the present invention is shown;

[0024] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0025] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0026] In the practice of CAT risk management at Continental, the quality and completeness of accumulated CAT risk data directly impact the accuracy of risk analysis and decision-making. However, current accumulated CAT risk data generally suffers from missing data, uneven granularity, and a lack of unified standards. Data on blanket insurance business is particularly problematic, presenting the most prominent and complex pain point. As a special type of business among direct insurance companies, blanket insurance policies typically cover a large number of different risk targets, compared to common auto insurance business, with a wide geographical distribution and diverse industry types. Because it is difficult for insurance companies to directly connect and extract accurate data, Continental currently uses the method of entering blanket insurance target list information into the imaging center as an attachment to the policy. The file formats vary, including Excel, PDF, and images, with inconsistent file names and mixed recording with other policy attachments. This makes it difficult to ensure that all blanket insurance businesses provide a list of targets with a uniform format and consistent granularity.

[0027] Furthermore, some clients withhold or obscure key property data due to commercial confidentiality or internal management considerations, resulting in significant gaps or ambiguity in the data obtained by insurance companies. These issues pose significant challenges to the compilation and accuracy of accumulated catastrophe risk data.

[0028] Currently, the processing of catastrophic risk accumulation data relies primarily on manual operations. Staff must first retrieve relevant data from the imaging center, manually organize it into Excel spreadsheets, and match the insured amount with address information one by one. For complex situations, such as when one insured amount corresponds to multiple addresses, manual data allocation and adjustment based on the underwriting basis are still required to ultimately generate detailed catastrophic risk accumulation data for the blanket policy. This detailed data is then manually integrated into the company's overall catastrophic risk accumulation system. This manual processing method is not only labor-intensive and inefficient, but also prone to data distortion due to the coarse data granularity, lack of support from scientific data models, and the principle of indiscriminate processing. This can cause significant deviations between the insurance company's catastrophic risk accumulation data and the actual risk situation, impacting the scientific nature and effectiveness of risk management.

[0029] Based on this, an embodiment of the present invention provides a method for processing catastrophe risk data. This method obtains detailed reinsurance catastrophe risk data for a specific risk unit dimension at a specified deadline, screens out target insurance data with original retention exceeding a preset threshold, and accurately extracts the address and price of each underlying asset by combining it with a user-uploaded policy subject list file. By rationally splitting the shared underlying asset prices and basing this on policy product type and provincial, prefectural, and municipal value percentage data, it ensures that each insurance subject data accurately reflects the actual risk exposure, thereby improving the accuracy and processing efficiency of catastrophe risk exposure data.

[0030] like Figure 1 FIG. 1 is a flow chart of an implementation of a method for processing catastrophic risk data provided by an embodiment of the present invention. The method may include:

[0031] S100: Obtain detailed reinsurance catastrophe risk data for a specified risk unit dimension at a specified cutoff time point, wherein the detailed reinsurance catastrophe risk data includes at least one insurance data item, wherein the insurance data includes a policy number, a policy position number, administrative region information, and an original retention amount corresponding to the administrative region information for the specified risk unit dimension.

[0032] The designated cutoff time is the time point for data collection or analysis, and subsequent data processing and presentation are based on this time point. The designated cutoff time point can be the last day of a month or the last day of a quarter.

[0033] The designated risk unit dimension refers to a classification used to assess and analyze specific types of risks. Optionally, the designated risk unit dimension may include earthquake, typhoon, and other catastrophic risks.

[0034] Among them, reinsurance catastrophe risk detail data refers to detailed risk information related to catastrophes, usually including original policies, risk assessments and loss forecasts.

[0035] Among them, insurance data refers to specific information related to the insurance contract, including policyholder information, policy terms, insurance amount and insurance period.

[0036] The policy number uniquely identifies an insurance contract. It can be used to distinguish different insurance contracts across different product types. For example, "PG" designates engineering insurance, "PQ" designates corporate property insurance, and "PJ" designates household property insurance. Each policy is assigned a policy number, which links all insured items and insured sums under the contract.

[0037] Among them, the risk number refers to the number in the insurance contract that indicates specific insurance liability or coverage.

[0038] Among them, administrative area information refers to administrative division data related to a specific geographical location, including divisions at the province, autonomous region, and municipality levels.

[0039] Among them, the original retention refers to the part of the risk that the insurance company chooses to retain during the underwriting process, that is, the amount of risk that is not transferred to the reinsurance company.

[0040] S110. Target insurance data with original retention amounts greater than or equal to a preset amount threshold is selected from the reinsurance catastrophe risk detail data, and the target insurance data is added to the policy pool.

[0041] A preset monetary threshold refers to a specific monetary limit set during data screening to determine which data items meet specific conditions or criteria. In reinsurance catastrophe risk management, preset monetary thresholds are used to screen insurance data that meets risk management requirements.

[0042] Specifically, an embodiment of the present invention may provide an insurance data screening interface. In response to a user inputting a preset amount threshold in the insurance data screening interface, target insurance data having an original retention amount greater than or equal to the preset amount threshold is screened out from the reinsurance catastrophe risk detail data, and the target insurance data is added to the policy pool.

[0043] For ease of understanding, an example is given here: assuming that the obtained reinsurance catastrophe risk detailed data is as shown in Table 1, if the original retention amount is 100 million, the filtered target insurance data can be shown in Table 2.

[0044] Table 1

[0045]

[0046] Table 2

[0047]

[0048] S120. For any target insurance data in the policy pool: obtain a policy subject list file corresponding to the target insurance data uploaded by the user, wherein the policy subject list file includes at least one insurance subject data, and the insurance subject data includes an address of the subject matter and a price of the subject matter.

[0049] The policy subject list file contains detailed information about all insured items covered by the blanket policy. It typically includes multiple fields, such as the address of the item and the corresponding insured amount, to clarify the scope of insurance liability and risk exposure.

[0050] Among them, insurance subject data refers to detailed information related to specific insurance subject matter.

[0051] The address of the subject matter refers to a detailed description of the physical location or location of the specific subject matter, including information such as street and city.

[0052] The subject matter price refers to the insured amount corresponding to the insured object, i.e., the maximum amount of compensation the insurance company will bear in the event of an insured event, as stipulated in the insurance contract. In a blanket insurance policy, one insured amount can correspond to a single or multiple addresses, reflecting the risk value and coverage of each subject matter.

[0053] For example, assume that the policy data corresponding to the policy number "PQYA24310921***22" in Table 2 has a policy subject list file as shown in Table 3, which contains 12 pieces of insurance subject data.

[0054] Table 3

[0055]

[0056] The embodiment of the present invention can identify the address column containing the address of the subject matter and the insurance amount column containing the price of the subject matter in each worksheet page of the insurance policy subject list file; if the address column and / or insurance amount column in any worksheet page fails to be identified, the user is prompted to select the address column and insurance amount column on the worksheet page; the address information in the address column and the insurance amount information in the insurance amount column are parsed, the correspondence between each group of insurance amount and address is confirmed, and the insurance amount and address structured data are obtained.

[0057] Specifically, when processing a policy subject list file, this embodiment of the present invention automatically identifies the columns containing the subject matter's address and price within each worksheet to accurately extract key data. If the address column or the insured amount column cannot be clearly located within any worksheet, the user is prompted to manually select the corresponding column within that worksheet to ensure accurate data parsing.

[0058] Understandably, in practice, the formatting of user-uploaded policy list files is often non-standard. For example, column headings may be inconsistent, missing, or incorrect, and the address and coverage information columns may be positioned in different locations, sometimes confusing or omitted. This makes it difficult for automatic recognition algorithms to accurately determine which columns correspond. This non-standard formatting prevents automatic data extraction and parsing, requiring users to manually specify the address and coverage columns to ensure the accuracy of subsequent data processing and the mapping between coverage and address.

[0059] The embodiment of the present invention parses and matches the data in the selected address column and insurance amount column to confirm the address of the subject matter corresponding to each subject matter price, thereby constructing a clear correspondence between the insurance amount and the address, and finally forming structured original insurance amount-address data to facilitate subsequent price splitting.

[0060] S130. Calculate the split price corresponding to each subject matter address: When there are multiple insurance subject matter data in the policy subject matter list file that share the same subject matter price, determine the policy product type of the insurance data corresponding to the policy subject matter list file, and use the provincial, municipal and city value share data corresponding to the policy product type to split the shared subject matter price into the corresponding multiple subject matter addresses of the insurance subject matter data, so that the subject matter address of each insurance subject matter data in the policy subject matter list file corresponds to a separate split price.

[0061] Policy product types refer to the categories of insurance products designed by insurance companies to meet different insurance needs, such as engineering insurance, corporate property insurance, and household property insurance. Each policy product type covers different risk characteristics and coverage, which affects risk assessment and insured amount distribution. In this embodiment of the present invention, the corresponding policy product type can be determined based on the policy number of the target master policy.

[0062] The provincial, municipal, and city value share data refers to the proportion of the total asset value held by each province, prefecture, and city (prefecture-level city) for a specific insurance product type. This value share reflects the relative weight of each province, prefecture, and city in the risk exposure of that insurance product type and is a key indicator for regional risk distribution and cumulative risk analysis.

[0063] For example: Based on the insurance policy subject list file shown in Table 3, the subject matter addresses of the six insurance subject data with serial numbers 3, 4, 5, 6, 7 and 8 share the same subject matter price. At this time, it is necessary to use the provincial, municipal and city value share data corresponding to the insurance policy product type to split the shared subject matter price of 10,000 yuan into the subject matter addresses of these six insurance subject data. The split prices corresponding to the subject matter addresses of each insurance subject data in the insurance policy subject list file can be shown in Table 4.

[0064] Table 4

[0065]

[0066] The present invention provides a method for processing catastrophe risk data. By acquiring detailed reinsurance catastrophe risk data for a specified risk unit dimension at a specified cutoff time point, the method can analyze and screen in detail target insurance data with original retention amounts greater than or equal to a preset amount threshold. By accurately acquiring the address and price of each subject matter and reasonably splitting it according to the type of insurance policy product, it ensures that the data of each insurance subject matter can reflect the actual risk exposure situation, thereby significantly improving the accuracy and processing efficiency of catastrophe risk exposure data.

[0067] Optional, based on Figure 1 The method shown, such as Figure 2 FIG. 5 is a flow chart of a first specific implementation of a method for processing catastrophic risk data according to an embodiment of the present invention. After step S130, the method may further include:

[0068] S200. Utilize the total price of the policy subject list file and the split price corresponding to each subject matter address to obtain the price ratio corresponding to each subject matter address.

[0069] The total price refers to the total insurance amount covered by the insurance contract, which is calculated by adding up all the individual insured amounts in the policy's subject matter list. The total price represents the total risk exposure of the policy across all underlying assets.

[0070] Understandably, while a blanket insurance policy typically identifies a single administrative address as the primary address, it may cover multiple specific insured addresses, each with its own insured sum. To fully reflect the total value of the policy's risk and facilitate subsequent scientific and efficient calculations, the sums insured for all these addresses must be tallied and aggregated to arrive at the policy's total price.

[0071] In actual applications, the embodiment of the present invention can calculate the total price by summarizing the insured amounts of each subject in the policy subject list file. However, due to reasons such as data quality or file format, the total price calculated by the system may be incomplete or biased. To ensure the accuracy of the total price, technicians can manually adjust the system calculation results based on the total price provided by the user, or choose to only summarize the insured amounts of the top 20 subjects with higher insured amounts, so as to obtain a more realistic total price, thereby effectively improving the accuracy and flexibility of the total price statistics.

[0072] Specifically, the embodiment of the present invention can divide the split price by the total price to calculate the price ratio corresponding to each target address. For example, based on the split prices shown in Table 4, the calculated price ratio corresponding to each target address can be shown in Table 5.

[0073] Table 5

[0074]

[0075] S210. Using the price ratio corresponding to each subject matter address, the original retention amount is split among the subject matter addresses to obtain a first split retention amount corresponding to each subject matter address.

[0076] Specifically, embodiments of the present invention can calculate the first post-split retention amount for each object address by multiplying the original retention amount by the price percentage corresponding to each object address. For example, based on the price percentages shown in Table 5, assuming the original retention amount is 150 million, the calculated first post-split retention amount for each object address can be shown in Table 6.

[0077] Table 6

[0078]

[0079] S220: Analyze the target province, city, and district corresponding to each target object address.

[0080] For example: based on the object addresses of each insurance object data shown in Table 6, the parsed target provinces and cities include Beijing, Zhengzhou, Henan Province, Kaifeng, Henan Province, Zhumadian, Henan Province, Nanyang, Henan Province and Xinyang, Henan Province.

[0081] S230. Calculate the second retention amount after splitting corresponding to each target province, prefecture-level city, by using the first retention amount after splitting corresponding to each target object address.

[0082] For example: Based on the first post-splitting self-retention amount corresponding to each target address shown in Table 6, the second post-splitting self-retention amount corresponding to Beijing is approximately RMB 31.02 million, the second post-splitting self-retention amount corresponding to Zhengzhou City, Henan Province is approximately RMB 25.86 million, the second post-splitting self-retention amount corresponding to Kaifeng City, Henan Province is approximately RMB 46.575 million, the second post-splitting self-retention amount corresponding to Zhumadian City, Henan Province is approximately RMB 10.35 million, the second post-splitting self-retention amount corresponding to Nanyang City, Henan Province is approximately RMB 15.51 million, and the second post-splitting self-retention amount corresponding to Xinyang City, Henan Province is approximately RMB 20.685 million.

[0083] S240. Obtain a change in the retention amount corresponding to each target province, prefecture-level city by using the original retention amount and the second split retention amount corresponding to each target province, prefecture-level city.

[0084] For example, based on Table 2, the original retention for policy number "PQYA24310921***22" in Beijing is 150 million yuan. After splitting, the second-split retention for each target province, prefecture, and city is calculated based on Table 6. The corresponding retention for Beijing is 31.02 million yuan, resulting in a -118.98 million yuan change. Zhengzhou, Kaifeng, Zhumadian, Nanyang, and Xinyang, Henan, do not have corresponding original retentions in Table 2. Therefore, the corresponding retention changes for Zhengzhou, Kaifeng, Zhumadian, Nanyang, and Xinyang, Henan, are +25.86 million yuan, +46.575 million yuan, +10.35 million yuan, +15.51 million yuan, and +20.685 million yuan, respectively.

[0085] By calculating the split price of each target address, the embodiment of the present invention can analyze the price ratio, thereby accurately splitting the original retention amount into each address, parsing the target province, city and district information, and counting the retention amount of each province, city and district, making it possible to calculate the change in the retention amount, which helps to provide a reliable basis for the statistics of catastrophic risk data in different dimensions, thereby facilitating more scientific resource allocation and risk assessment.

[0086] Optional, in the above Figure 2 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, the method may further include:

[0087] Obtain risk exposure statistical conditions input by the user, wherein the risk exposure statistical conditions include risk exposure name, assessment end year and month, risk exposure type and summary dimension; use multiple insurance data matching the risk exposure statistical conditions to calculate risk exposure summary data under the risk exposure statistical conditions; based on the policy number, determine at least one insurance data to be applied that has an intersection in the multiple insurance data matching the risk exposure statistical conditions and the policy pool to form a set of insurance data to be designated; determine the designated insurance data selected by the user in the set of insurance data to be designated; use the change in retention amount corresponding to each target province, prefecture-level city calculated from each designated insurance data in the policy pool to update the risk exposure summary data to obtain updated risk exposure summary data.

[0088] Risk exposure statistical conditions refer to a set of specific standards and parameters set during risk assessment and statistics. These conditions are used to filter and match relevant insurance data to accurately analyze and summarize risk exposure.

[0089] The risk exposure name is a label or name used to identify a specific risk exposure.

[0090] The assessment cutoff year and month refers to the time point used for risk assessment, typically expressed in the format of "year-month". This time point is used to define the validity and relevance of statistical data, ensuring that risk exposure assessments are based on the most up-to-date and reliable data.

[0091] Among them, risk exposure type refers to the classification method of different risk exposures, which corresponds to the risk unit dimension.

[0092] Aggregation dimensions refer to the specific dimensions or classification criteria used when aggregating risk exposure data. Aggregation dimensions can include time, region, insurance product type, and insurance amount, allowing for comprehensive risk exposure analysis from various perspectives.

[0093] Aggregate risk exposure data refers to the aggregated risk exposure information for all relevant policies within a specific insurance product type and coverage area within a statistical period. Cumulative risk exposure data includes the cumulative sum insured, insurance liability, and risk characteristics for each policy in each target province, prefecture, and city. It reflects the overall scale and distribution of risk borne by insurance companies within that region and product range.

[0094] Specifically, in this embodiment of the present invention, when a user clicks the "Apply Policy Pool Split" button on the exposure information management interface, the user is redirected to the policy selection interface. Here, the user can enter risk exposure statistics criteria for preliminary screening to extract the insurance data to be applied. The user then selects specific insurance data from these targeted insurance data and clicks the "Confirm Split" button. This updates the exposure summary data using the changes in retention for each target province, prefecture, and city, calculated from the specified insurance data.

[0095] For example: Assuming that the risk exposure summary data before the update is as shown in Table 7, the risk exposure summary data is updated based on the change in the retention amount corresponding to each target province, city, and prefecture-level city, that is, the change in the retention amount is added to the retention amount of the target province, city, and prefecture-level city under the policy product type in the risk exposure summary data. The updated risk exposure summary data can be shown in Table 8.

[0096] Table 7

[0097]

[0098] Table 8

[0099]

[0100] The embodiment of the present invention first uses user-defined risk exposure statistical conditions to more accurately identify and match insurance data related to specific risks, then adds statistics and application of changes in retention amounts, so that risk exposure summary data can reflect changes in insurance data in real time. Then, a comprehensive analysis of insurance data is performed based on multiple dimensions (such as risk exposure name, assessment end year and month, risk exposure type, and summary dimension). The changes in retention amounts for provinces, prefectures, and cities calculated using specified insurance data in the maintained policy pool are used to effectively update the risk exposure summary data, thereby improving the accuracy of the risk exposure summary data.

[0101] Optional, in the above Figure 2 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, before step S200, the method further includes:

[0102] Verify whether each object address matches any real province or real city; for the object address that does not match any real province: remove the object address from the policy object list file; for the object address that does not match any real city: create a virtual city category under the real province that matches the object address, classify the object address into the virtual city category, and count the split price corresponding to the object address into the real province that matches the object.

[0103] Specifically, the embodiment of the present invention can identify and determine the specific prefecture-level administrative region to which the address of the target object belongs through address resolution technology or Geographic Information System (GIS) tools, thereby verifying whether each target object address matches any real province or real city.

[0104] The virtual city category refers to a city that does not exist in reality. For example, the embodiment of the present invention may classify the target object address that does not match any real city into the created “other cities”.

[0105] To facilitate understanding, let's use an example: Assume that the policy's subject matter list file contains four subject matter addresses and their corresponding prices: "A street in Chaoyang District, Beijing, price 1 million yuan," "A street in Pudong New Area, Shanghai, price 1.5 million yuan," "A street in Fantasy District, Beijing, price 2 million yuan," and "A virtual block in a virtual city in a virtual province, price 1.2 million yuan." Since "A virtual block in a virtual city in a virtual province, price 1.2 million yuan" doesn't match any real province, this subject matter address is deleted. Since "A street in Fantasy District, Beijing, price 2 million yuan" doesn't match any real city, but does match Beijing, this subject matter address can be classified as "Other cities," and the split amount can be counted towards Beijing's split retention amount.

[0106] In the process of acquiring and processing the reinsurance catastrophe risk detail data, the embodiment of the present invention can ensure the accuracy and validity of the policy subject list by verifying whether each subject matter address matches any real province or real city. For subject matter addresses that do not match the real province or city, timely elimination or reclassification to other cities can avoid potential risk assessment errors. This process lays a solid foundation for the subsequent price ratio calculation, so that the price after the split of each subject matter address can accurately reflect its actual risk value, thereby improving the reliability of risk exposure summary data and the accuracy of risk management, and ultimately helping insurance companies make more reasonable decisions in pricing and claims, reducing financial losses and operational risks caused by inaccurate information.

[0107] Optional, based on Figure 1 The method shown, such as Figure 3 As shown, a flowchart of a second specific implementation of the method for processing catastrophe risk data provided by an embodiment of the present invention is shown, and step S130 may specifically include:

[0108] S300: Determine the policy product type of the insurance data based on the policy number of the insurance data corresponding to the policy subject list file.

[0109] Specifically, embodiments of the present invention can identify the policy number of insurance data. When the policy number of the insurance data is identified as including the "PG" identifier, the policy product type of the insurance data is determined to be engineering insurance. When the policy number of the insurance data is identified as including the "PQ" identifier, the policy product type of the insurance data is determined to be corporate property insurance. When the policy number of the insurance data is identified as including the "PJ" identifier, the policy product type of the insurance data is determined to be household property insurance.

[0110] S310. Obtain provincial, municipal, and city value share data corresponding to the policy product type, wherein the provincial, municipal, and city value share data are calculated from asset replacement value data aggregated by province, municipal, and city in a pre-built catastrophic risk exposure database.

[0111] Among them, the catastrophe risk exposure database is a risk management database pre-built by insurance companies. It collects asset information and its replacement value covering different provinces, cities and different types of insurance products. It is specifically used to analyze and evaluate the potential impact and losses of major disasters (such as typhoons, earthquakes and floods) on assets.

[0112] Asset replacement value (ARV) data refers to the estimated amount of funds required to rebuild or restore the insured assets after a disaster. This data measures the actual economic value of assets and helps accurately reflect insurance liabilities and risk exposure.

[0113] S320. Using the provincial, municipal, and city value ratio data, split the shared subject matter price into the subject matter addresses of the corresponding multiple insurance subject matter data.

[0114] The embodiment of the present invention identifies the corresponding insurance product type based on the policy number, and then obtains the value share information of provinces, prefectures and cities through the asset replacement value data summarized in the catastrophe risk exposure database, so as to ensure that the price of each target address reflects its actual risk exposure when splitting the target price.

[0115] Optional, in the above Figure 3 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, step S310 may specifically include:

[0116] When the policy product type is corporate property insurance or engineering insurance, the value share of the first province, city and prefecture is obtained, where the value share of the first province, city and prefecture is calculated based on the commercial replacement value data and industrial replacement value data summarized by province, city and prefecture in the pre-built catastrophe risk exposure database.

[0117] Among them, corporate property insurance, also known as enterprise property insurance, refers to insurance products that provide protection against corporate property risks, covering assets such as factories, equipment, and inventory, and preventing property losses caused by risks such as fire and natural disasters.

[0118] Among them, engineering insurance, also known as engineering insurance, refers to insurance products provided for engineering losses and related liabilities caused by accidents, natural disasters, etc. during the construction process of construction projects.

[0119] Among them, commercial replacement value data refers to the total replacement cost of commercial assets (such as shops, office buildings, commercial facilities, etc.) in the catastrophe risk exposure database, which is calculated by province, city and municipality.

[0120] Among them, industrial replacement value data refers to the total replacement cost of industrial assets (such as factories, production lines, machinery and equipment, etc.) in the catastrophe risk exposure database, which is calculated by province, city and district.

[0121] Among them, the first province-level city value share is the share of a specific province-level city in the overall replacement value calculated based on commercial replacement value data and industrial replacement value data.

[0122] Specifically, the embodiment of the present invention can extract the commercial replacement value and industrial replacement value data corresponding to the policy from a pre-built catastrophe risk exposure database, provided that the policy product type corresponding to the policy number is corporate property insurance or engineering insurance, and calculate the first province, city, and district value share according to the split ratio of the sum of the commercial replacement value data and industrial replacement value data summarized by provinces, cities, and districts, so as to reasonably distribute the common subject matter prices in the policy subject list to the target province, city, and district according to the first province, city, and district value share, thereby ensuring that the risk exposure summary data reflects the actual asset distribution and risk characteristics.

[0123] In an embodiment of the present invention, when the policy product type is corporate property insurance or engineering insurance, the commercial replacement value and industrial replacement value data summarized by province, city and municipality in a pre-built catastrophic risk exposure database are used to calculate the value share of the first province, city and municipality, and then the common subject matter price is scientifically and reasonably split into each target province, city and municipality, thereby effectively combining the differences in policy product types with the asset distribution characteristics of provinces, cities and municipalities, improving the accuracy of the common subject matter price split and the authenticity of risk exposure, further realizing efficient processing of catastrophic risk data and improving the accuracy of risk exposure summary data.

[0124] Optional, in the above Figure 3 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, step S310 may specifically include:

[0125] When the policy product type is home property insurance, the second provincial, municipal and city value ratio is obtained, where the second provincial, municipal and city value ratio is calculated based on the residential replacement value data summarized by province, municipality and city in the pre-constructed catastrophe risk exposure database.

[0126] Among them, home insurance, also known as household property insurance, refers to an insurance product that provides protection for personal homes and their attached property. It mainly covers residential buildings and their internal household property, and the risk of property loss caused by fire, theft, natural disasters, etc.

[0127] Among them, residential replacement value data refers to the total replacement cost of personal residences and their ancillary facilities summarized by province, city and municipality in the catastrophe risk exposure database.

[0128] Among them, the second provincial and municipal value share is based on the residential replacement value data, calculating the proportion of residential assets in a specific province, municipality, and city in the overall residential assets.

[0129] Specifically, when the policy product type corresponding to the policy number is home property insurance, the embodiment of the present invention extracts residential replacement value data summarized by province, city, and district from a pre-built catastrophic risk exposure database, calculates the proportion of the residential asset value of each province, city, and district to the overall residential asset value, that is, the second province, city, and district value proportion, and then, based on the second province, city, and district value proportion, splits and allocates the shared underlying asset price to each target province, city, and district, to ensure that the risk exposure summary data matches the scale of the residential asset value.

[0130] In the embodiment of the present invention, in the case of home property insurance product types, the second provincial and municipal value ratio calculated using residential replacement value data can more scientifically and reasonably reflect the distribution characteristics of residential assets in various provinces and cities, thereby achieving a reasonable split of the common underlying asset price into each target province and city, improving the accuracy of the common underlying asset price split and the authenticity of risk exposure, further achieving efficient processing of risk exposure summary data and improving the reliability of policy insured amounts.

[0131] Optional, in the above Figure 1 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, step S100 may specifically include:

[0132] Based on the policy maintenance screening conditions entered by the user on the policy pool maintenance interface, the reinsurance catastrophe risk detail data that meets the policy maintenance screening conditions is filtered out in the big object storage system. The policy maintenance screening conditions include the specified cutoff time point and the specified risk unit dimension entered by the user.

[0133] Among them, the large object storage system is a cloud storage architecture used to store and manage large-scale data. In reinsurance catastrophe risk management, the large object storage system can effectively store and process large amounts of insurance data, improve data retrieval efficiency, and enable users to quickly screen out reinsurance catastrophe risk details that meet policy maintenance conditions, thereby supporting accurate risk assessment and decision-making.

[0134] The policy pool maintenance interface is a user interface that allows users to manage and filter policy data. In this interface, users can enter various filtering criteria (such as cutoff time and risk unit dimension) to extract the corresponding reinsurance catastrophe risk details from the big object storage system.

[0135] This embodiment of the present invention efficiently filters out qualified reinsurance catastrophe risk detail data within a large object storage system by responding to user-entered filtering criteria in the policy pool maintenance interface, including specifying a cutoff time point and risk unit dimensions. This ensures the timeliness and pertinence of the acquired reinsurance catastrophe risk detail data, providing reliable foundational data for subsequent data processing. Through precise filtering, users can clearly focus on specific risk units of interest and obtain reinsurance catastrophe risk detail data closely related to their business needs, improving work efficiency and decision-making accuracy.

[0136] Optional, in the above Figure 1 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, step S120 may specifically include:

[0137] In response to the user selecting the target insurance data in the policy list displaying multiple insurance data, the policy details of the target insurance data are loaded and the interface is jumped to the policy adjustment interface; in the policy adjustment interface, the policy subject list file uploaded by the user through the file upload control is obtained.

[0138] The Policy Adjustment screen is a user interface used to display and edit detailed information about a selected policy. Within this screen, users can modify, add, or delete policy information, and upload relevant documents (such as a policy subject list).

[0139] The embodiment of the present invention uses a policy adjustment interface to allow users to easily upload a policy subject list file corresponding to the target insurance data through a file upload control, thereby not only improving the convenience of user operation, but also ensuring that the uploaded policy subject list file is effectively matched with the specific policy, thereby promoting the accuracy and completeness of the data.

[0140] Although the operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0141] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0142] Corresponding to the above method embodiment, the embodiment of the present invention also provides a catastrophic risk data processing device, the structure of which is as follows: Figure 4As shown, the system may include: a reinsurance catastrophe risk detail data acquisition unit 10, a target insurance data screening unit 20, a policy subject list file acquisition unit 30 and a split price calculation unit 40.

[0143] The reinsurance catastrophe risk detail data acquisition unit 10 is used to acquire the reinsurance catastrophe risk detail data for a specified risk unit dimension at a specified deadline, wherein the reinsurance catastrophe risk detail data includes at least one insurance data, wherein the insurance data includes a policy number, a policy position number, administrative region information, and an original retention amount corresponding to the administrative region information in the specified risk unit dimension.

[0144] The target insurance data screening unit 20 is used to screen out target insurance data whose original retention amount is greater than or equal to a preset amount threshold from the reinsurance catastrophe risk detailed data, and add the target insurance data to the policy pool.

[0145] The policy subject list file obtaining unit 30 is used for obtaining, for any target insurance data in the policy pool, a policy subject list file corresponding to the target insurance data uploaded by the user, wherein the policy subject list file includes at least one insurance subject data, and the insurance subject data includes the address of the subject matter and the price of the subject matter.

[0146] The split price calculation unit 40 is used to calculate the split price corresponding to each subject matter address: when there are multiple insurance subject matter data in the policy subject matter list file that share the same subject matter price, determine the policy product type of the insurance data corresponding to the policy subject matter list file, and use the provincial, municipal and city value share data corresponding to the policy product type to split the shared subject matter price into the corresponding multiple insurance subject matter data addresses, so that the subject matter address of each insurance subject data in the policy subject matter list file corresponds to a split price separately.

[0147] Optionally, the catastrophic risk data processing device may also include: a price ratio obtaining unit, a first split retention amount obtaining unit, a target province, city and district analysis unit, a second split retention amount obtaining unit, a retention amount change obtaining unit and a risk exposure summary data updating unit.

[0148] The price ratio obtaining unit is used to obtain the price ratio corresponding to each object address by using the total price of the policy object list file and the split price corresponding to each object address after the split price calculation unit 40 calculates the split price corresponding to each object address.

[0149] The first post-split retention amount obtaining unit is used to split the original retention amount into each subject matter address using the price ratio corresponding to each subject matter address, and obtain the first post-split retention amount corresponding to each subject matter address.

[0150] The target province, city and district parsing unit is used to parse out the target province, city and district corresponding to each target object address.

[0151] The second post-split retention amount obtaining unit is used to calculate the second post-split retention amount corresponding to each target province, prefecture-level city, by using the first post-split retention amount corresponding to each target object address.

[0152] The retention amount change obtaining unit is used to obtain the retention amount change corresponding to each target province, prefecture-level city by using the original retention amount and the second split retention amount corresponding to each target province, prefecture-level city.

[0153] Optionally, the catastrophic risk data processing device may further include: a policy pool application unit.

[0154] The policy pool application unit is used to obtain the risk exposure statistical conditions input by the user, wherein the risk exposure statistical conditions include the risk exposure name, the assessment end year and month, the risk exposure type and the summary dimension; use multiple insurance data matching the risk exposure statistical conditions to calculate the risk exposure summary data under the risk exposure statistical conditions; based on the policy number, determine at least one insurance data to be applied that has an intersection in the multiple insurance data matching the risk exposure statistical conditions and the policy pool to form a set of insurance data to be designated; determine the designated insurance data selected by the user in the set of insurance data to be designated; use the change in the retention amount corresponding to each target province, prefecture-level city calculated from the designated insurance data in the policy pool to update the risk exposure summary data to obtain updated risk exposure summary data.

[0155] Optionally, the catastrophic risk data processing device may further include: a provincial, municipal and city verification unit.

[0156] The province, city and prefecture verification unit is used for the price ratio obtaining unit to use the total price of the policy subject list file and the split price corresponding to each subject matter address to obtain the price ratio corresponding to each subject matter address, and verify whether each subject matter address matches any real province or real city; for the subject matter address that does not match any real province: remove the subject matter address from the policy subject list file; for the subject matter address that does not match any real city: create a virtual city category under the real province that matches the subject matter address, classify the subject matter address into the virtual city category, and count the split price corresponding to the subject matter address into the real province that matches the subject matter.

[0157] Optionally, the post-splitting price calculation unit 40 may include: a policy product type determination subunit, a provincial, municipal and city value ratio data acquisition subunit and a subject matter price splitting subunit.

[0158] The policy product type determination subunit is used to determine the policy product type of the insurance data based on the policy number of the insurance data corresponding to the policy subject list file.

[0159] The sub-unit for obtaining provincial, municipal and city value share data is used to obtain provincial, municipal and city value share data corresponding to the policy product type, wherein the provincial, municipal and city value share data are calculated based on the asset replacement value data summarized by province, municipal and city in the pre-built catastrophe risk exposure database.

[0160] The subject matter price splitting sub-unit is used to use the provincial, municipal and city value ratio data to split the common subject matter price into the subject matter addresses of the corresponding multiple insurance subject matter data.

[0161] Optionally, the sub-unit for obtaining the provincial, municipal and city value share data can be specifically used to obtain the first provincial, municipal and city value share when the policy product type is corporate property insurance or engineering insurance, wherein the first provincial, municipal and city value share is calculated from the commercial replacement value data and industrial replacement value data summarized by province, municipal and city in the pre-constructed catastrophe risk exposure database.

[0162] Optionally, the sub-unit for obtaining the provincial, municipal and city value ratio data can be specifically used to obtain the second provincial, municipal and city value ratio when the policy product type is home property insurance, wherein the second provincial, municipal and city value ratio is calculated based on the residential replacement value data summarized by province, municipal and city in the pre-constructed catastrophe risk exposure database.

[0163] Optionally, the reinsurance catastrophe risk detail data acquisition unit 10 can be specifically used to filter out reinsurance catastrophe risk detail data that meets the policy maintenance screening conditions entered by the user in the policy pool maintenance interface in the large object storage system, wherein the policy maintenance screening conditions include a specified deadline time point and a specified risk unit dimension entered by the user.

[0164] Optionally, the policy subject list file obtaining unit 30 can be specifically used to load the policy details of the target insurance data and jump to the policy adjustment interface in response to the user's selection operation of the target insurance data in the policy list displaying multiple insurance data; in the policy adjustment interface, obtain the policy subject list file uploaded by the user through the file upload control.

[0165] The present invention provides a catastrophic risk data processing device, which is used to obtain detailed reinsurance catastrophic risk data for a specified risk unit dimension at a specified deadline, and can analyze and screen in detail the target insurance data whose original retention amount is greater than or equal to a preset amount threshold. Then, by accurately obtaining the address and price of each subject matter and reasonably splitting it according to the type of insurance policy product, it is ensured that the data of each insurance subject matter can reflect the actual risk exposure situation, thereby significantly improving the accuracy and processing efficiency of catastrophic risk exposure data.

[0166] Regarding the apparatus in the above embodiment, the specific manner in which each unit performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0167] The catastrophic risk data processing device includes a processor and a memory. The reinsurance catastrophic risk detailed data acquisition unit 10, the target insurance data screening unit 20, the policy subject list file acquisition unit 30, and the split price calculation unit 40 are all stored as program units in the memory. The processor executes the program units stored in the memory to implement corresponding functions.

[0168] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured. By adjusting kernel parameters, detailed reinsurance catastrophe risk data for a specified risk unit dimension at a specified cutoff time is obtained and added to the policy pool. This allows for detailed analysis and screening of target insurance data with original retention amounts exceeding a preset threshold. By accurately obtaining the address and price of each underlying asset and rationally segmenting it based on policy product type, this ensures that the data for each insured asset reflects the true risk exposure, significantly improving the accuracy and processing efficiency of catastrophe risk exposure data.

[0169] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon, which implements the method for processing catastrophic risk data when executed by a processor.

[0170] An embodiment of the present invention provides a processor, which is used to run a program, wherein the method for processing catastrophic risk data is executed when the program is run.

[0171] like Figure 5As shown, an embodiment of the present invention provides an electronic device 1000, which includes at least one processor 1001, at least one memory 1002 connected to the processor 1001, and a bus 1003. The processor 1001 and the memory 1002 communicate with each other via the bus 1003. The processor 1001 is configured to invoke program instructions stored in the memory 1002 to execute the aforementioned method for processing catastrophic risk data. The electronic device herein may be a server, a PC, a PAD, a mobile phone, or the like.

[0172] The present invention also provides a computer program product which, when executed on an electronic device, is adapted to execute the program steps of the method for processing data subject to catastrophic risk.

[0173] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatuses, electronic devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable device to produce a machine, so that the instructions executed by the processor of the computer or other programmable device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0174] In a typical configuration, an electronic device includes one or more processors (CPUs), a memory, and a bus. The electronic device may also include an input / output interface, a network interface, and the like.

[0175] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip. Memory is an example of a computer-readable medium.

[0176] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0177] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0178] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0179] In the description of the present invention, it should be understood that if the terms "up", "down", "front", "back", "left" and "right" are used to indicate directions or positional relationships, they are based on the directions or positional relationships shown in the accompanying drawings. They are only used to facilitate the description of the present invention and simplify the description, and do not indicate or imply that the positions or elements referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limitations of the present invention.

[0180] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. It should also be noted that the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, commodity, or device comprising the element.

[0181] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0182] The above are merely embodiments of the present invention and are not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be within the scope of the present invention.

Claims

1. A method for processing catastrophic risk data, characterized in that: include: Obtaining detailed reinsurance catastrophe risk data for a specified risk unit dimension at a specified cutoff time point, wherein the detailed reinsurance catastrophe risk data includes at least one insurance data item, wherein the insurance data item includes a policy number, a policy position number, administrative region information, and an original retention amount corresponding to the administrative region information for the specified risk unit dimension; Filtering target insurance data whose original retention amount is greater than or equal to a preset amount threshold from the reinsurance catastrophe risk detailed data, and adding the target insurance data to the policy pool; For any target insurance data in the policy pool: obtaining a policy subject list file corresponding to the target insurance data uploaded by a user, wherein the policy subject list file includes at least one insurance subject data, and the insurance subject data includes an address and a price of the subject matter; Calculate the split price corresponding to each of the subject matter addresses: when there are multiple pieces of the insurance subject data in the policy subject list file whose subject matter addresses share the same subject matter price, determine the policy product type of the insurance data corresponding to the policy subject list file, and use the provincial, municipal and city value share data corresponding to the policy product type to split the shared subject matter price into the corresponding multiple subject matter addresses of the insurance subject data, so that the subject matter address of each piece of the insurance subject data in the policy subject list file corresponds to a separate split price.

2. The method according to claim 1, characterized in that After calculating the split price corresponding to each of the subject matter addresses, the method further includes: Using the total price of the policy subject list document and the split price corresponding to each of the subject matter addresses, obtain the price ratio corresponding to each of the subject matter addresses; Using the price ratio corresponding to each of the subject matter addresses, the original retention amount is split among the respective subject matter addresses to obtain a first split retention amount corresponding to each of the subject matter addresses; Parsing the target province, city or district corresponding to each of the subject matter addresses; Using the first post-split retention amount corresponding to each of the target addresses, calculate the second post-split retention amount corresponding to each of the target provinces, prefectures, and cities; The original retention amount and the second split retention amount corresponding to each of the target provinces, prefectures, and cities are used to obtain the change in the retention amount corresponding to each of the target provinces, prefectures, and cities.

3. The method according to claim 2, characterized in that Also includes: Obtaining risk exposure statistical conditions input by a user, wherein the risk exposure statistical conditions include risk exposure name, assessment end year and month, risk exposure type, and summary dimension; Utilizing a plurality of insurance data matching the risk exposure statistical conditions, calculating risk exposure summary data under the risk exposure statistical conditions; Based on the insurance policy number, determining at least one insurance data to be applied that has an intersection from the multiple insurance data matching the risk exposure statistical condition and the insurance policy pool to form a set of insurance data to be designated; Determining the designated insurance data selected by the user from the set of insurance data to be designated; The risk exposure summary data is updated using the change in the retention amount corresponding to each target province, prefecture-level city, calculated from the designated insurance data in the policy pool, to obtain the updated risk exposure summary data.

4. The method according to claim 2, characterized in that Before obtaining the price ratio corresponding to each of the subject matter addresses by using the total price of the policy subject matter list document and the split price corresponding to each of the subject matter addresses, the method further includes: Verify whether each of the subject matter addresses matches any real province or real city; For the object address that does not match any of the real provinces: remove the object address from the policy object list file; For the target object address that does not match any real city: create a virtual city category under the real province that the target object address matches, divide the target object address into the virtual city category, and count the split price corresponding to the target object address to the real province that the target object matches.

5. The method according to claim 1, wherein Determining the policy product type of the insurance data corresponding to the policy subject list file, and using the provincial, municipal, and city value ratio data corresponding to the policy product type, splitting the shared subject matter price into the subject matter addresses of the corresponding multiple pieces of insurance subject matter data includes: Determining the policy product type of the insurance data based on the policy number of the insurance data corresponding to the policy subject list file; Obtaining provincial, municipal, and city-level value share data corresponding to the policy product type, wherein the provincial, municipal, and city-level value share data is calculated from asset replacement value data aggregated by province, municipal, and city level in a pre-built catastrophic risk exposure database; The province, city and district value ratio data are used to split the shared subject matter price into the subject matter addresses of the corresponding multiple pieces of insurance subject matter data.

6. The method according to claim 5, characterized in that The obtaining of the provincial, municipal and city value share data corresponding to the policy product type includes: When the policy product type is corporate property insurance or engineering insurance, obtaining the first province-city value ratio, wherein the first province-city value ratio is calculated based on the commercial replacement value data and industrial replacement value data summarized by province, city, and prefecture in the pre-constructed catastrophe risk exposure database; And / or, when the policy product type is home property insurance, the second provincial, municipal and city value ratio is obtained, wherein the second provincial, municipal and city value ratio is calculated based on the residential replacement value data summarized by province, municipal and city in the pre-constructed catastrophe risk exposure database.

7. The method according to claim 1, characterized in that The acquisition of detailed reinsurance catastrophe risk data for a specified risk unit dimension at a specified cutoff time point includes: Based on the policy maintenance screening conditions entered by the user in the policy pool maintenance interface, detailed reinsurance catastrophe risk data that meets the policy maintenance screening conditions is screened out in the large object storage system, wherein the policy maintenance screening conditions include the specified cutoff time point and the specified risk unit dimension entered by the user.

8. The method according to claim 1, characterized in that The obtaining of the policy subject list file corresponding to the target insurance data uploaded by the user includes: In response to a user selecting the target insurance data from a list of insurance policies displaying multiple insurance data items, loading detailed insurance policy information of the target insurance data and jumping to an insurance policy adjustment interface; In the insurance policy adjustment interface, the insurance policy subject list file uploaded by the user through the file upload control is obtained.

9. A catastrophic risk data processing device, characterized in that: include: Reinsurance catastrophe risk detailed data acquisition unit, target insurance data screening unit, policy subject list file acquisition unit and split price calculation unit, The reinsurance catastrophe risk detail data acquisition unit is configured to acquire reinsurance catastrophe risk detail data for a specified risk unit dimension at a specified cutoff time point, wherein the reinsurance catastrophe risk detail data includes at least one insurance data item, wherein the insurance data includes a policy number, a policy position number, administrative region information, and an original retention amount corresponding to the administrative region information for the specified risk unit dimension; The target insurance data screening unit is configured to screen out target insurance data whose original retention amount is greater than or equal to a preset amount threshold from the reinsurance catastrophe risk detailed data, and add the target insurance data to the policy pool; The policy subject list file obtaining unit is configured to obtain, for any target insurance data in the policy pool: a policy subject list file corresponding to the target insurance data uploaded by a user, wherein the policy subject list file includes at least one piece of insurance subject data, and the insurance subject data includes an address and a price of an underlying object; The split price calculation unit is used to calculate the split price corresponding to each of the subject matter addresses: when there are multiple pieces of insurance subject data in the policy subject list file that share the same subject matter price, determine the policy product type of the insurance data corresponding to the policy subject list file, and use the provincial, municipal and city value share data corresponding to the policy product type to split the shared subject matter price into the corresponding multiple subject matter addresses of the insurance subject data, so that the subject matter address of each piece of insurance subject data in the policy subject list file corresponds to a separate split price.

10. An electronic device, characterized in that: The electronic device includes at least one processor, and at least one memory and bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the catastrophic risk data processing method according to any one of claims 1 to 8.

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