A method, device, equipment and readable storage medium for measuring risk-weighted assets

Abstract

The present invention provides a risk-weighted asset measurement method, which relates to the field of risk-weighted asset calculation. The method includes information acquisition, data preprocessing and blocking, and calling multiple computing units to form distributed computing. The method converts the entire measurement process into a data simplification and blocking processing and multi-unit concurrent computing mode, wherein the data simplification and blocking preprocessing step divides the original information into simplified versions of multiple block information, and then the multiple block information is respectively handed over to multiple computing units for processing, thereby realizing multi-threaded synchronous processing. The method has strong scalability and can use multiple computing units according to actual conditions, and has strong flexibility.

Description

Technical Field

[0001] The present invention relates to the field of risk-weighted asset calculation, and in particular to a risk-weighted asset measurement system, device, equipment and readable storage medium. Background Art

[0002] The business scope of Risk-Weighted Assets (RWA) calculation is now wide-ranging, and its data sources involve 11 systems, including corporate credit processes, retail pools, and market risk management.

[0003] To calculate RWA based on these extensive data sources and huge amounts of data, traditional risk-weighted asset measurement systems often measure risk-weighted assets in batches. This approach results in complex storage procedures and a high degree of coupling within the program. This makes troubleshooting and modification difficult when problems arise, making it difficult to update and maintain. Summary of the Invention

[0004] The present invention aims to provide a risk-weighted asset measurement method, apparatus, device, and readable storage medium to improve the above-mentioned problems. To achieve the above-mentioned objectives, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present application provides a risk-weighted asset measurement method, comprising: obtaining first information, the first information including database data, the database data including each debt item, the number corresponding to each debt item, and basic data required for risk-weighted asset measurement; establishing a data preprocessing block data model based on the first information, solving the above-mentioned data preprocessing block data model to obtain third information, the third information including multiple block information, each block information including the numbers corresponding to multiple debt items; calling multiple computing units, assigning a block information to each computing unit, each computing unit establishing a data block measurement model based on the first information and one block information, solving the data block measurement model to obtain fourth information, the fourth information including each debt item and the risk-weighted assets corresponding to each debt item.

[0006] Furthermore, the data preprocessing block data model is established based on the first information, including: calling a table processing mathematical model, taking the first information as input information of the table processing mathematical model, solving the table processing mathematical model, the output result of the table processing mathematical model is second information, and the second information includes a table made from the first information; establishing a block mathematical model based on the second information, solving the block mathematical model to obtain third information, and the third information includes multiple block information, each block information includes the numbers corresponding to multiple debts.

[0007] Furthermore, the establishment of a block mathematical model based on the second information includes: obtaining fifth information, the fifth information including block configuration information, the block configuration information including sorting by the number corresponding to each debt item; calling a sorting mathematical model; using the fifth information and the second information as input information of the sorting mathematical model, solving the sorting mathematical model, the output result of the sorting data model is sixth information, the sixth information including a sorting table in which the second information is sorted according to the fifth information; establishing a data splitting data model based on the fifth information, using the sixth information as the input condition of the data splitting data model, the calculation result of the data splitting data model including multiple block information, each block information including the numbers of two debt items; solving the data splitting data model.

[0008] Furthermore, before establishing a data block measurement model using the second information and one of the block information, it includes: caching the basic data required for the risk-weighted asset measurement in a redis database component, wherein the basic data required for the risk-weighted asset measurement includes parameters required for the risk-weighted asset measurement and rules required for the risk-weighted asset measurement.

[0009] Furthermore, each of the calculation units establishes a data block measurement model based on the first information and the block information, including: calling a sorting and filling mathematical model, taking one of the block information as input information of the sorting and filling mathematical model, solving the sorting and filling mathematical model, and the calculation result of the sorting and filling mathematical model includes a continuous numbering table, the minimum number of the continuous numbering table is the same as the minimum number in the input block information, and the maximum number of the continuous numbering table is the same as the maximum number in the input block information; calling a data retrieval mathematical model, taking the continuous numbering table as input information of the data retrieval mathematical model, solving the data retrieval mathematical model, and the output result of the data retrieval mathematical model is the seventh information, and the seventh information The seventh information includes the debt corresponding to the number in the consecutive numbering table and the relevant information corresponding to the debt; calling the verification mathematical model; using the seventh information as the input information of the verification mathematical model, solving the verification mathematical model, and the output result of the verification mathematical model is verification information, and the verification information includes but is not limited to the compliance and non-compliance of the relevant information corresponding to each of the debts; if the verification information is non-compliant, adding a mark to the debt corresponding to the verification information; if the verification information is compliant, establishing a risk-weighted asset calculation mathematical model based on the seventh information, the risk-weighted asset calculation mathematical model, solving the risk-weighted asset calculation mathematical model, and the calculation result of the risk-weighted assets is the risk-weighted assets corresponding to each of the debts.

[0010] Furthermore, the establishment of a mathematical model for calculating risk-weighted assets based on the seventh information includes: caching the basic data required for the risk-weighted asset measurement into a redis database component, wherein the basic data required for the risk-weighted asset measurement includes the parameters required for the risk-weighted asset measurement and the rules required for the risk-weighted asset measurement; obtaining eighth information from the redis database component, wherein the eighth information includes the parameters required for the risk-weighted asset measurement and the rules required for the risk-weighted asset measurement; establishing a first mathematical model based on the eighth information, and using the seventh information as input information of the first mathematical model; and solving the first mathematical model, wherein the calculation result of the first mathematical model is the risk-weighted asset corresponding to each of the debt items.

[0011] Furthermore, each of the computing units feeds back ninth information, which includes but is not limited to completion of the computation; in response to the ninth information, uncalculated block information is allocated to the computing unit that feeds back the ninth information until there is no uncalculated block information.

[0012] In the second aspect, the present application also provides a risk-weighted asset measurement device, including a first data acquisition module, a data preprocessing block module and a risk-weighted asset cluster calculation module, the first data acquisition module is used to obtain first information, the first information includes database data, the database data includes each debt item, the number corresponding to each debt item and the basic data required for risk-weighted asset measurement; it is used to establish a data preprocessing block data model based on the first information, and solve the above-mentioned data preprocessing block data model to obtain third information, the third information includes multiple block information, and one block information is assigned to each calculation unit, each block information includes the numbers corresponding to multiple debt items; the risk-weighted asset cluster calculation module is used to call multiple calculation units, each calculation unit establishes a data block measurement model based on the first information and one block information, and solves the data block measurement model to obtain fourth information, the fourth information includes each debt item and the risk-weighted assets corresponding to each debt item.

[0013] Furthermore, the data preprocessing block module includes: a table processing module and a block calculation module. The table processing module is used to call a table processing mathematical model, use the first information as the input information of the table processing mathematical model, solve the table processing mathematical model, and the output result of the table processing mathematical model is second information. The second information includes a table made of the first information; the block calculation module is used to establish a block mathematical model based on the second information, solve the block mathematical model to obtain third information, and the third information includes multiple block information, and each block information includes the numbers corresponding to multiple debts.

[0014] Furthermore, the block calculation module includes: a second data acquisition module, a data splitting module and a sorting calculation module, the second data acquisition module is used to obtain the fifth information, the fifth information includes block configuration information, and the block configuration information includes sorting by the number corresponding to each debt item; the sorting calculation module is used to call the sorting mathematical model, use the fifth information and the second information as input information of the sorting mathematical model, solve the sorting mathematical model, and the output result of the sorting data model is sixth information, and the sixth information includes a sorting table in which the second information is sorted according to the fifth information; the data splitting module is used to establish a data splitting data model based on the fifth information, use the sixth information as the input condition of the data splitting data model, and the calculation result of the data splitting data model includes multiple block information, each block information includes the numbers of two debt items, and solve the data splitting data model.

[0015] Furthermore, the risk-weighted asset measurement device includes a cache module, which is used to cache the basic data required for the risk-weighted asset measurement into a redis database component. The basic data required for the risk-weighted asset measurement includes parameters required for the risk-weighted asset measurement and rules required for the risk-weighted asset measurement.

[0016] Furthermore, the risk-weighted asset cluster calculation module includes: a sorting and filling calculation module, a verification calculation module, a first logic judgment model, a second logic judgment model and a data retrieval calculation module. The sorting and filling calculation module is used to call the sorting and filling mathematical model, and uses one of the block information as the input information of the sorting and filling mathematical model to solve the sorting and filling mathematical model. The calculation result of the sorting and filling mathematical model includes a continuous numbering table, the minimum number of the continuous numbering table is the same as the minimum number in the input block information, and the maximum number of the continuous numbering table is the same as the maximum number in the input block information; the data retrieval calculation module is used to call the data retrieval mathematical model, and uses the continuous numbering table as the input information of the data retrieval mathematical model to solve the data retrieval mathematical model. The output result of the data retrieval mathematical model is the seventh information, and the seventh The information includes the debt items corresponding to the numbers in the consecutive numbering table and the relevant information corresponding to the debt items; the verification calculation module is used to call the verification mathematical model, use the seventh information as the input information of the verification mathematical model, and solve the verification mathematical model. The output result of the verification mathematical model is the verification information, and the verification information includes but is not limited to the compliance and non-compliance of the relevant information corresponding to each of the debt items; the first logical judgment model is used to determine whether the verification information is non-compliant, and then add a mark to the debt item corresponding to the verification information; the second logical judgment model is used to determine whether the verification information is compliant, and then establish a risk-weighted asset calculation mathematical model based on the seventh information, the risk-weighted asset calculation mathematical model, solve the risk-weighted asset calculation mathematical model, and the calculation result of the risk-weighted asset calculation mathematical model is the risk-weighted asset corresponding to each of the debt items.

[0017] Furthermore, the second logical judgment model includes: a risk-weighted asset calculation module, a third data acquisition module and a cache module. The cache module is used to cache the basic data required for the risk-weighted asset measurement into the redis database component. The basic data required for the risk-weighted asset measurement includes the parameters required for the risk-weighted asset measurement and the rules required for the risk-weighted asset measurement. The third data acquisition module is used to obtain the eighth information from the redis database component. The eighth information includes the parameters required for the risk-weighted asset measurement and the rules required for the risk-weighted asset measurement. The risk-weighted asset calculation module is used to establish a first mathematical model based on the eighth information, use the seventh information as the input information of the first mathematical model, and solve the first mathematical model. The calculation result of the first mathematical model is the risk-weighted assets corresponding to each of the debt items.

[0018] Furthermore, the risk-weighted asset measurement device includes: an allocation module, which is used to respond to each of the calculation units feeding back ninth information, wherein the ninth information includes but is not limited to completion of calculation; and allocate uncalculated block information to the calculation unit that feeds back the ninth information until there is no uncalculated block information.

[0019] In a third aspect, the present application further provides a risk-weighted asset measurement device, comprising:

[0020] Memory for storing computer programs;

[0021] A processor is configured to implement the risk-weighted asset measurement steps when executing the computer program.

[0022] In a fourth aspect, the present application also provides a readable storage medium having a computer program stored thereon, and the computer program implements the steps of the risk-weighted asset measurement method when executed by a processor.

[0023] The beneficial effects of the present invention are:

[0024] First, the present invention transforms the entire processing process into a data simplification and block processing and multi-unit concurrent computing model. The data simplification and block preprocessing step divides the original information into multiple blocks of information, where each block information is simplified information. The multiple blocks are then handed over to multiple computing units for processing, realizing multi-threaded synchronous processing. It has strong scalability and can use multiple computing units according to actual conditions, achieving the purpose of distributed computing.

[0025] Second, the multiple blocks of information in the present invention are simplified versions of information. Their actual data volume is small, but the amount of data they contain is large. Distributing them to multiple computing units does not consume a large amount of resources. Furthermore, the information exchange process with the computing units is fast, with low resource consumption. Furthermore, the process of decompressing the implicit information within the computing units consumes little resource for the computing units.

[0026] 3. The present invention simplifies data into block preprocessing and multi-unit concurrent calculation methods to meet concurrent metering needs while completing complex calculation processes and huge calculation amounts, reducing the coupling degree of different mathematical models, facilitating subsequent maintenance processes, reducing human maintenance costs, and also facilitating porting to different platforms in practice.

[0027] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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 embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 A flowchart of the risk-weighted asset measurement method described in this application;

[0030] Figure 2 This is a schematic diagram of the structure of the risk-weighted asset measurement device described in this application;

[0031] Figure 3 This is a structural diagram of the data preprocessing block module described in this application;

[0032] Figure 4 This is a schematic diagram of the structure of the block calculation module described in this application;

[0033] Figure 5 This is a schematic diagram of the structure of the risk-weighted asset cluster calculation module described in this application;

[0034] Figure 6 This is a schematic diagram of the structure of the second logic judgment module described in this application;

[0035] Figure 7This is a schematic diagram of the structure of the risk-weighted asset system testing equipment described in an embodiment of the present invention.

[0036] Markings in the figure: 1. First data acquisition module; 2. Data preprocessing and block module; 21. Table processing module; 22. Block calculation module; 221. Second data acquisition module; 222. Sorting calculation module; 223. Data splitting module; 3. Cache module; 4. Risk-weighted asset cluster calculation module; 41. Sorting and filling calculation module; 42. Data retrieval calculation module; 43. Verification calculation module; 44. First logical judgment model; 45. Second logical judgment model; 451. Third data acquisition module; 452. Risk-weighted asset calculation module; 5. Data output module; 6. Allocation module. DETAILED DESCRIPTION

[0037] To make the purposes, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures. Furthermore, in the description of the present invention, the terms "first," "second," etc. are used only to distinguish the description and are not to be understood as indicating or implying relative importance.

[0039] Example 1:

[0040] See also Figure 1 In this embodiment, a risk-weighted asset measurement method is provided. The method includes steps S100, S200, S300, S400, S500, and S600:

[0041] S100: Obtain first information, where the first information includes database data. The database data includes each debt item, the serial number corresponding to each debt item, and basic data required for risk-weighted asset measurement.

[0042] It should be noted that the number corresponding to each debt item in this step is a UUID number. Those skilled in the art can choose other unique identifiers as needed. No specific restrictions are made in this application. The basic data required for risk-weighted asset measurement include but are not limited to the parameters and rules required for risk-weighted asset measurement. It is common knowledge among those skilled in the art and will not be elaborated in this application.

[0043] S200. Establish a data preprocessing block data model based on the first information, and solve the data preprocessing block data model to obtain third information. The third information includes multiple block information, and each block information includes serial numbers corresponding to multiple debt items.

[0044] It can be understood that this step is to pre-process the first information, and its purpose is to reduce the resources consumed by the server scheduling. Specifically, this step also includes step S210 and step S220:

[0045] S210, calling the table processing mathematical model, the first information as the input information of the table processing mathematical model, solving the table processing mathematical model, the output result of the table processing mathematical model is the second information, the second information includes a table made of the first information.

[0046] It should be noted that the function of the processing mathematical model called in this step is to output the data to a table to be processed, which can better facilitate subsequent processing. The specific steps are common knowledge in the field and are not specifically limited in this application. At the same time, the information operated on in this step is. At the same time, in this step, in order to facilitate the segmentation of the table, in this step, the relevant information is aggregated and processed according to the debt granularity and spliced ​​into a wide table.

[0047] S220: Establish a block mathematical model based on the second information, and solve the block mathematical model to obtain third information, where the third information includes multiple pieces of block information, each piece of block information including serial numbers corresponding to multiple debt items;

[0048] Specifically, the specific number of blocks in this step depends on the total amount of data in the database and the amount of data contained in each block. This is determined based on actual needs, so this application does not impose specific restrictions and only provides illustrative examples. More details about this step are provided below. Specifically, this step includes steps S221, S222, S223, S224, and S225:

[0049] S221. Acquire fifth information, the fifth information including block configuration information, the block configuration information including a sequence number corresponding to each debt item;

[0050] It is understood that the separation configuration information mentioned in this step is the configuration information for splitting all debt items in the database. Specifically, the configuration information includes but is not limited to the determination of the total amount of debt information required to be included in each block of information and the sorting requirements for the table of the first information.

[0051] S222, calling a sorting mathematical model;

[0052] It should be noted that the sorting mathematical model used in this step is common knowledge in this field. It sorts the table according to a certain preset condition. In this application, it is sorted by UUID number. Whether it is in ascending or descending order will not affect the next steps, so those skilled in the art can choose any sorting method, and this application does not make specific restrictions.

[0053] S223: Use the fifth information and the second information as input information of a sorting mathematical model to solve the sorting mathematical model. The output result of the sorting mathematical model is sixth information, which includes a sorting table in which the second information is sorted according to the fifth information.

[0054] It can be understood that after this step, the table obtained in step S210 will become a sorted table sorted according to the fifth information as the sorting requirement.

[0055] S224: Establish a data splitting data model based on the sixth information, wherein a calculation result of the data splitting data model includes multiple pieces of block information, each piece of block information includes the serial numbers of two debt items;

[0056] It is understandable that after this step, the sorting table will be divided into multiple blocks of information, each of which is a part of the table sorted according to the fifth information. The numbers in each block of information do not overlap with each other, and after being recombined, they can form the numbers in the complete sorting. In order to reduce the resource consumption caused by system scheduling and information dissemination during implementation, in this embodiment, each block of information only contains the numbers of two debt items. Specifically, the numbers between the two debt items are omitted and not transmitted. That is, only a small amount of data needs to be transmitted through this step, reducing the I / O consumption problem in actual use. In addition, it can meet the needs of multi-line concurrent computing without causing equipment bottleneck problems. In practice, it can well expand the equipment using this method as the business logic.

[0057] S225, solving the data splitting data model;

[0058] S300. Before establishing a data block measurement model using the second information and the third information, the method includes: caching basic data required for risk-weighted asset measurement in a redis database component, where the basic data required for risk-weighted asset measurement includes parameters and rules required for risk-weighted asset measurement.

[0059] It can be understood that the purpose of this step is to pre-cache the parameters and rules required for risk-weighted asset measurement. This step can be performed at any time between S400 and S100. The purpose of this step is to pre-cache repeated data that is used repeatedly and in large quantities in a device that can be read at high speed. This can reduce unnecessary database reading time in the subsequent risk-weighted asset measurement, reduce the I / O pressure of the database, and improve the overall processing speed of the risk-weighted asset measurement.

[0060] S400: Call multiple computing units, assign a block of information to each computing unit, each computing unit establishes a data block measurement model based on the first information and the block of information, and solves the data block measurement model to obtain fourth information, where the fourth information includes each debt item and the risk-weighted assets corresponding to each debt item.

[0061] It is understandable that in this step, formal risk-weighted asset measurement will be performed based on the information processed in step S255. In this application, the original information is split into multiple block information, so in this embodiment, multiple computing units can be used to perform subsequent calculations, that is, the block information is distributed to multiple computing units, and each computing unit processes different block information at the same time. Through multi-threaded processing, the time for risk-weighted measurement processing can be effectively shortened. At the same time, it is understandable that in the step, multiple data units can also be used to distribute block information at different times to achieve the technical effect that multiple computing units will not access the database at the same time, realize the database staggered I / O process, and achieve that each computing unit will not be idle waiting for data reading, further improving the processing efficiency of this application. It should be noted that this step describes the processing method within a computing unit. Specifically, this step includes step S410, step S420, step S430, step S440, step S450 and step S460:

[0062] S410. Retrieve a sorting and filling mathematical model, use a block of information as input information of the sorting and filling mathematical model, solve the sorting and filling mathematical model, and the calculation result of the sorting and filling mathematical model includes a consecutive numbering table, the minimum number of the consecutive numbering table is the same as the minimum number in the input block information, and the maximum number of the consecutive numbering table is the same as the maximum number in the input block information.

[0063] It is understandable that in this step, the hidden information in the block information is supplemented to complete the omitted number. This supplementation process is simple and easy to implement, consumes very little computing power of the computing unit, and can achieve the effect of "instant completion".

[0064] S420 calls a data retrieval mathematical model, uses the sequential number table as input information of the data retrieval mathematical model, solves the data retrieval mathematical model, and outputs the data retrieval mathematical model as seventh information, the seventh information including the debt item corresponding to the number in the sequential number table and related information corresponding to the debt item;

[0065] It can be understood that in this step, the purpose is to retrieve the relevant information corresponding to each debt item corresponding to the consecutive number table from the database based on the processing result of S410, where the relevant information corresponding to each debt item includes but is not limited to data related to multiple systems such as corporate credit, retail pools, market risk management, credit reserves, general ledgers, core banking business, personal credit, collateral management, enterprise resource management finance, fund transaction background, international cards, etc. Since it is common knowledge known to those skilled in the art, it will not be elaborated here.

[0066] S430, calling a verification mathematical model;

[0067] It should be noted that the verification mathematical model is common knowledge to those skilled in the art and is not specifically limited in this application.

[0068] S440: Using the seventh information as input information for a verification mathematical model, solving the verification mathematical model, and outputting verification information as an output result of the verification mathematical model. The verification information includes, but is not limited to, whether the relevant information corresponding to each debt item is compliant or non-compliant.

[0069] It can be understood that this step is to verify the compliance of the relevant information corresponding to each debt item in the continuous numbering table that needs to be processed. It is to reduce calculation errors in subsequent calculations, which may cause errors in the overall calculation process and improve the adaptability of this embodiment to non-compliant data.

[0070] S450: If the verification information is non-compliant, a mark is added to the debt item corresponding to the verification information and the mark is entered back into the database;

[0071] It is understandable that this step can effectively remind maintenance personnel which data is incorrect by marking non-compliant debts, facilitating subsequent modifications.

[0072] S460: If the verification information is compliant, establish a risk-weighted asset calculation mathematical model based on the seventh information, solve the risk-weighted asset calculation mathematical model, and use the risk-weighted asset calculation mathematical model as a calculation result, whereby the risk-weighted asset corresponding to each debt item is calculated.

[0073] Specifically, this step also includes step S461, step S462, step S463 and step S464:

[0074] S461. Obtain eighth information from the redis database component, where the eighth information includes parameters and rules required for risk-weighted asset measurement.

[0075] It is understandable that this step will cooperate with S300 to achieve rapid reading of parameters and rules required for risk-weighted asset measurement, which can further improve the calculation efficiency of the calculation unit.

[0076] S462: Establish a first mathematical model based on the eighth information, using the seventh information as input information of the first mathematical model;

[0077] It should be noted that the steps of establishing the first mathematical model are common knowledge in this field, which is to establish how to measure risk-weighted assets and will not be repeated in this application.

[0078] S463. Solve the first mathematical model, where the calculation result of the first mathematical model is the risk-weighted assets corresponding to each debt item;

[0079] S464. The calculation unit feeds back ninth information, including but not limited to calculation completion.

[0080] S500: Input the fourth information into the database.

[0081] S600 : In response to the ninth information, distribute uncalculated block information to the calculation unit that feeds back the ninth information until there is no uncalculated block information.

[0082] It can be understood that the purpose of this step is to reduce the number of computing units used, and to realize continuous calculation of the computing unit by having the computing unit feedback the information that has been calculated, and then allocating an unprocessed block information to the computing unit, and finally realizing the consumption of computing information by multiple blocks of information, and each computing unit will execute S500 again when receiving the unprocessed block information.

[0083] Through the above steps of this embodiment, the entire processing process is transformed into data simplification and pre-processing into multiple blocks of information. These blocks are then processed separately by multiple computing units, ultimately achieving multi-threaded synchronous processing. This provides strong scalability, allowing the use of multiple computing units based on actual circumstances, and offers high flexibility. Furthermore, each block of information is a simplified version of the information, with a small actual data volume but a large amount of implicit data. Distributing it to multiple computing units does not consume significant resources, and information exchange is rapid. Furthermore, the process of extracting implicit information from the computing units consumes minimal resources. Ultimately, this achieves concurrent metering requirements while completing complex calculations and large computational workloads. This approach also reduces the coupling between different mathematical models, facilitating subsequent maintenance and reducing labor costs. It also facilitates porting across different platforms. In actual use, this distributed risk-weighted asset metering engine can meter 3 million data items in 5 minutes, a 5-6 times reduction in computation time compared to the original.

[0084] Example 2:

[0085] Corresponding to the above method embodiment, this embodiment further provides a risk-weighted asset measurement device. The risk-weighted asset measurement device described below and the risk-weighted asset measurement method described above can be referenced to each other.

[0086] like Figures 2 to 6 As shown, a risk-weighted asset measurement device includes: a first data acquisition module 1, a data pre-processing and blocking module 2, a cache module 3, a risk-weighted asset cluster calculation module 4 and a data output module 5:

[0087] The first data acquisition module 1 is used to acquire first information. The first information includes database data. The database data includes each debt item, the number corresponding to each debt item, and basic data required for risk-weighted asset measurement.

[0088] The data preprocessing block module 2 is used to establish a data preprocessing block data model based on the first information, and solve the above data preprocessing block data model to obtain third information. The third information includes multiple block information, and each block information includes the numbers corresponding to multiple debt items.

[0089] The data pre-processing block module 2 includes: a table processing module 21 and a block calculation module 22:

[0090] The table processing module 21 is used to call the table processing mathematical model, use the first information as input information of the table processing mathematical model, solve the table processing mathematical model, and the output result of the table processing mathematical model is second information, which includes a table made from the first information.

[0091] The block calculation module 22 is used to establish a block mathematical model based on the second information, and solve the block mathematical model to obtain third information. The third information includes multiple block information, and each block information includes serial numbers corresponding to multiple debt items.

[0092] The block calculation module 22 includes a second data acquisition module 221, a sorting calculation module 222 and a data splitting module 223:

[0093] The second data acquisition module 221 is used to acquire fifth information, which includes block configuration information. The block configuration information includes a sequence of numbers corresponding to each debt item.

[0094] The sorting calculation module 222 is used to call the sorting mathematical model, use the fifth information and the second information as input information of the sorting mathematical model, solve the sorting mathematical model, and the output result of the sorting data model is the sixth information, which includes a sorting table in which the second information is sorted according to the fifth information.

[0095] The data splitting module 223 is used to establish a data splitting data model based on the fifth information, and use the sixth information as the input condition of the data splitting data model. The calculation result of the data splitting data model includes multiple block information, each block information includes the numbers of two debt items, and solves the data splitting data model.

[0096] The cache module 3 is used to cache the basic data required for risk-weighted asset measurement into the redis database component. The basic data required for risk-weighted asset measurement includes parameters and rules required for risk-weighted asset measurement.

[0097] The risk-weighted asset cluster calculation module 4 is used to call multiple calculation units, each of which establishes a data block measurement model based on the first information and a block information, and solves the data block measurement model to obtain fourth information, which includes each debt item and the risk-weighted assets corresponding to each debt item.

[0098] The risk-weighted asset cluster calculation module 4 includes a sorting and filling calculation module 41, a data retrieval calculation module 42, a verification calculation module 43, a first logic judgment model 44, and a second logic judgment model 45:

[0099] The sorting and filling calculation module 41 is used to call the sorting and filling mathematical model, take a block of information as the input information of the sorting and filling mathematical model, and solve the sorting and filling mathematical model. The calculation result of the sorting and filling mathematical model includes a continuous numbering table, the minimum number of the continuous numbering table is the same as the minimum number in the input block information, and the maximum number of the continuous numbering table is the same as the maximum number in the input block information.

[0100] The data retrieval calculation module 42 is used to call the data retrieval mathematical model, use the consecutive numbering table as the input information of the data retrieval mathematical model, solve the data retrieval mathematical model, and the output result of the data retrieval mathematical model is the seventh information, which includes the debt corresponding to the number in the consecutive numbering table and the relevant information corresponding to the debt.

[0101] The verification calculation module 43 is used to call the verification mathematical model, use the seventh information as the input information of the verification mathematical model, solve the verification mathematical model, and the output result of the verification mathematical model is the verification information. The verification information includes but is not limited to the compliance and non-compliance of the relevant information corresponding to each debt.

[0102] The first logic judgment model 44 is used to judge that if the verification information is non-compliant, a mark is added to the debt item corresponding to the verification information and the mark is input back into the database.

[0103] The second logic judgment model 45 is used to determine if the verification information is compliant, then establish a risk-weighted asset calculation mathematical model based on the seventh information, solve the risk-weighted asset calculation mathematical model, and the calculation result of the risk-weighted asset calculation mathematical model is the risk-weighted assets corresponding to each of the debt items.

[0104] The second logic judgment model 45 includes a third data acquisition module 451 and a risk-weighted asset calculation module 452:

[0105] The third data acquisition module 451 is used to obtain eighth information from the redis database component, where the eighth information includes parameters and rules required for risk-weighted asset measurement.

[0106] The risk-weighted asset calculation module 452 is configured to establish a first mathematical model based on the eighth information, use the seventh information as input information of the first mathematical model, and solve the first mathematical model. The calculation result of the first mathematical model is the risk-weighted asset corresponding to each debt item.

[0107] The allocation module 6 is configured to allocate uncalculated block information to the computing unit that has fed back the ninth information in response to each computing unit feeding back the ninth information, wherein the ninth information includes but is not limited to completion of the computation; until there is no uncalculated block information.

[0108] The data output module 5 is used to input the fourth information into the database.

[0109] Example 3:

[0110] Corresponding to the above method embodiment, this embodiment further provides a risk-weighted asset measurement device. The risk-weighted asset measurement device described below and the risk-weighted asset measurement method described above can refer to each other.

[0111] Figure 7 FIG. 8 is a block diagram of a risk-weighted asset measurement device 800 according to an exemplary embodiment. Figure 7 As shown, the risk-weighted asset measurement device 800 may include: a processor 801 , a memory 802 , and may further include one or more of a multimedia component 803 , an input / output (I / O) interface 804 , and a communication component 805 .

[0112] Processor 801 is used to control the overall operation of risk-weighted asset measurement device 800 to complete all or part of the steps in the risk-weighted asset measurement method described above. Memory 402 is used to store various types of data to support the operation of risk-weighted asset measurement device 800. This data may include, for example, instructions for any application or method operating on risk-weighted asset measurement device 800, as well as application-related data, such as contact information, sent and received messages, images, audio, and video. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which may include a keyboard, mouse, buttons, etc. These buttons may be virtual or physical. The communication component 805 is used for wired or wireless communication between the risk-weighted asset measurement device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof, may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0113] In an exemplary embodiment, the risk-weighted asset measurement device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the above-mentioned risk-weighted asset measurement method.

[0114] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When executed by a processor, the program instructions implement the steps of the risk-weighted asset measurement method described above. For example, the computer-readable storage medium may be the aforementioned memory 802 including the program instructions. The program instructions may be executed by the processor 801 of the risk-weighted asset measurement device 800 to implement the risk-weighted asset measurement method described above.

[0115] Example 4:

[0116] Corresponding to the above method embodiment, this embodiment further provides a readable storage medium. The readable storage medium described below and the risk-weighted asset measurement method described above can refer to each other.

[0117] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the risk-weighted asset system testing method of the above method embodiment.

[0118] The readable storage medium may specifically be any readable storage medium capable of storing program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0119] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0120] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0121] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for measuring risk-weighted assets, characterized in that: include: Obtaining first information, where the first information includes database data, where the database data includes each debt item, a number corresponding to each debt item, and basic data required for risk-weighted asset measurement; Establishing a data preprocessing block data model based on the first information, solving the data preprocessing block data model to obtain third information, the third information including multiple block information, each block information including serial numbers corresponding to multiple debt items; Invoking multiple computing units, assigning a block of information to each computing unit, each computing unit establishing a data block measurement model based on the first information and the block of information, and solving the data block measurement model to obtain fourth information, the fourth information including each debt item and the risk-weighted assets corresponding to each debt item; Each of the calculation units establishes a data block metering model according to the first information and one of the block information, including: Retrieving a sorting and filling mathematical model, taking one of the block information as input information of the sorting and filling mathematical model, and solving the sorting and filling mathematical model, wherein a calculation result of the sorting and filling mathematical model includes a consecutive number table, a minimum number in the consecutive number table is the same as the minimum number in the input block information, and a maximum number in the consecutive number table is the same as the maximum number in the input block information; Invoking a data retrieval mathematical model, using the sequential numbering table as input information of the data retrieval mathematical model, solving the data retrieval mathematical model, whereby an output result of the data retrieval mathematical model is seventh information, the seventh information including the debt item corresponding to the number in the sequential numbering table and related information corresponding to the debt item; Call the verification mathematical model; Using the seventh information as input information of a verification mathematical model, solving the verification mathematical model, and outputting verification information as an output result of the verification mathematical model, the verification information including compliance and non-compliance of relevant information corresponding to each debt item; If the verification information is non-compliant, adding a mark to the debt item corresponding to the verification information; If the verification information is compliant, a risk-weighted asset calculation mathematical model is established based on the seventh information, the risk-weighted asset calculation mathematical model is solved, and the calculation result of the risk-weighted asset calculation mathematical model is the risk-weighted asset corresponding to each of the debt items; The step of establishing a risk-weighted asset calculation mathematical model based on the seventh information includes: Cache the basic data required for the risk-weighted asset measurement in a redis database component, wherein the basic data required for the risk-weighted asset measurement includes parameters and rules required for the risk-weighted asset measurement; Obtaining eighth information from the redis database component, the eighth information including parameters required for the risk-weighted asset measurement and rules required for the risk-weighted asset measurement; establishing a first mathematical model based on the eighth information, and using the seventh information as input information of the first mathematical model; Solve a first mathematical model, where the calculation result of the first mathematical model is the risk-weighted assets corresponding to each of the debt items.

2. The risk-weighted asset measurement method according to claim 1, characterized in that: The step of establishing a data preprocessing block data model according to the first information includes: Invoking a table processing mathematical model, taking the first information as input information of the table processing mathematical model, solving the table processing mathematical model, where an output result of the table processing mathematical model is second information, where the second information includes a table made from the first information; A block mathematical model is established based on the second information, and the block mathematical model is solved to obtain third information, wherein the third information includes multiple pieces of block information, and each piece of the block information includes serial numbers corresponding to multiple debt items.

3. The risk-weighted asset measurement method according to claim 2, characterized in that: The step of establishing a block mathematical model according to the second information includes: Acquire fifth information, the fifth information including block configuration information, the block configuration information including a sequence number corresponding to each debt item; Calling the sorting mathematical model; The fifth information and the second information are used as input information of the sorting mathematical model, and the sorting mathematical model is solved. The output result of the sorting mathematical model is sixth information, and the sixth information includes a sorting table in which the second information is sorted according to the fifth information; Establishing a data splitting data model based on the fifth information, using the sixth information as an input condition of the data splitting data model, wherein a calculation result of the data splitting data model includes a plurality of block information, each of the block information includes the serial numbers of two debt items; Solve the data splitting data model.

4. The risk-weighted asset measurement method according to claim 1, characterized in that: After obtaining the fourth information by solving the data block metering model, the method further includes: Each of the computing units feeds back ninth information, wherein the ninth information includes completion of computing; In response to the ninth information, uncalculated block information is distributed to the calculation unit that feeds back the ninth information until there is no uncalculated block information.

5. A risk-weighted asset measurement device, characterized in that: include: A first data acquisition module is configured to acquire first information, wherein the first information includes database data, the database data including each debt item, a serial number corresponding to each debt item, and basic data required for risk-weighted asset measurement; a data preprocessing and blocking module, configured to establish a data preprocessing and blocking data model based on the first information, and solve the data preprocessing and blocking data model to obtain third information, wherein the third information includes a plurality of block information, each of which includes serial numbers corresponding to a plurality of debt items; a risk-weighted asset cluster calculation module, configured to call a plurality of calculation units, assign a block of information to each of the calculation units, each of the calculation units establishing a data block measurement model based on the first information and the block of information, and solving the data block measurement model to obtain fourth information, the fourth information including each debt item and the risk-weighted asset corresponding to each debt item; The risk-weighted asset cluster calculation module includes: a sorting and filling calculation module, configured to call a sorting and filling mathematical model, use the block information as input information of the sorting and filling mathematical model, and solve the sorting and filling mathematical model, wherein the calculation result of the sorting and filling mathematical model includes a consecutive number table, the minimum number in the consecutive number table is the same as the minimum number in the input block information, and the maximum number in the consecutive number table is the same as the maximum number in the input block information; a data retrieval calculation module, configured to call a data retrieval mathematical model, use the sequential numbering table as input information of the data retrieval mathematical model, and solve the data retrieval mathematical model, wherein the output result of the data retrieval mathematical model is seventh information, wherein the seventh information includes the debt item corresponding to the number in the sequential numbering table and related information corresponding to the debt item; a verification calculation module, configured to invoke a verification mathematical model, use the seventh information as input information of the verification mathematical model, and solve the verification mathematical model, wherein an output result of the verification mathematical model is verification information, wherein the verification information includes compliance and non-compliance of relevant information corresponding to each of the debt items; A first logic judgment model is used to determine that if the verification information is non-compliant, then add a mark to the debt item corresponding to the verification information; a second logic judgment model, configured to determine if the verification information is compliant, establish a risk-weighted asset calculation mathematical model based on the seventh information, solve the risk-weighted asset calculation mathematical model, and use the risk-weighted asset calculation mathematical model as a calculation result, wherein the risk-weighted asset calculation mathematical model is the risk-weighted asset corresponding to each debt item; The second logic judgment model includes: A cache module is used to cache the basic data required for the risk-weighted asset measurement into a redis database component, wherein the basic data required for the risk-weighted asset measurement includes parameters and rules required for the risk-weighted asset measurement. a third data acquisition module, configured to acquire eighth information from the redis database component, the eighth information including parameters and rules required for the risk-weighted asset measurement; The risk-weighted asset calculation module is used to establish a first mathematical model based on the eighth information, use the seventh information as input information of the first mathematical model, and solve the first mathematical model. The calculation result of the first mathematical model is the risk-weighted assets corresponding to each of the debt items.

6. The risk-weighted asset measurement device according to claim 5, characterized in that: The data preprocessing block module includes: a table processing module, configured to call a table processing mathematical model, use the first information as input information of the table processing mathematical model, solve the table processing mathematical model, and output a result of the table processing mathematical model as second information, wherein the second information includes a table made from the first information; The block calculation module is used to establish a block mathematical model based on the second information, and solve the block mathematical model to obtain third information, wherein the third information includes multiple block information, and each block information includes serial numbers corresponding to multiple debt items.

7. The risk-weighted asset measurement device according to claim 6, characterized in that: The block calculation module includes: A second data acquisition module is configured to acquire fifth information, wherein the fifth information includes block configuration information, and the block configuration information includes a sequence number corresponding to each debt item; a ranking calculation module, configured to call a ranking mathematical model, use the fifth information and the second information as input information of the ranking mathematical model, solve the ranking mathematical model, and output a sixth information from the ranking mathematical model, wherein the sixth information includes a ranking table in which the second information is ranked according to the fifth information; A data splitting module is used to establish a data splitting data model based on the fifth information, and use the sixth information as the input condition of the data splitting data model. The calculation result of the data splitting data model includes multiple block information, each block information includes the numbers of two debt items, and solves the data splitting data model.

8. The risk-weighted asset measurement device according to claim 5, characterized in that: The risk-weighted asset measurement device includes: The allocation module is configured to respond to each of the computing units feeding back ninth information, wherein the ninth information includes completion of the calculation; and allocate uncalculated block information to the computing unit feeding back the ninth information until there is no uncalculated block information.

9. A risk-weighted asset measurement device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of risk-weighted asset measurement according to any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the risk-weighted asset measurement method according to any one of claims 1 to 4.

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