CN116128616A

CN116128616A - Method and device for calculating data processing of resource risk trial calculation and computer equipment

Info

Publication number: CN116128616A
Application number: CN202310078855.5A
Authority: CN
Inventors: 张宇阳; 赵西宁; 陈江涛
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2023-01-17
Filing date: 2023-01-17
Publication date: 2023-05-16

Abstract

The invention relates to the field of big data security, and particularly discloses a calculation method, a device and computer equipment for data processing of resource risk trial calculation. The method comprises the following steps: receiving a calculation instruction for risk trial calculation of a target resource, and analyzing the calculation instruction through a constructed processing device to obtain instruction data; reading information data and rule configuration data of the target resource from a relational database according to the instruction data; determining a target factor to be updated in a trial calculation rule according to attribute data of the target resource and the resource configuration rule; calculating the factor fluctuation amount of each target factor according to the data of the target resource contained in the instruction data; reading the current calculation result data of the target factors in the trial calculation rule, and superposing to obtain updated target factor values; and performing risk trial calculation of the target resource according to the updated target factor value to obtain a trial calculation result. The method can improve the calculation efficiency and is easy to maintain.

Description

Method and device for calculating data processing of resource risk trial calculation and computer equipment

Technical Field

The present invention relates to the field of big data security, and in particular, to a method and apparatus for computing data processing of resource risk trial calculation, and a computer device.

Background

With the development of the financial market, many new financial products are presented, so that the investment channels and directions of the financial assets become richer, and the risk of asset management is increased. Therefore, the asset management mechanism puts a partial limiting requirement on the management of assets, and when a user purchases a plurality of financial products, the user can conduct risk trial calculation according to the change of daily assets, and whether the transaction triggers risks or not is calculated in advance.

In the related art, most of the restrictive demands of the asset management institution are related to the investment ratio of the financial products, such as a high-liquidity asset ratio, a leverage ratio, an investment concentration, etc. The financial products of the asset proportion type need to acquire more data sources when being calculated, and can be calculated by using a java program mode or a batch script mode, but the problems of difficult maintenance, low efficiency and the like exist.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a method, an apparatus, and a computer device for computing data processing of resource risk trial calculation, where a streaming data processing engine and a processing apparatus for relational database construction can be constructed. The streaming data processing engine can improve the data processing efficiency, process data are stored in the relational database, and the data in the relational database can be added, deleted and modified based on the instructions.

In a first aspect, the present application provides a data processing method for resource risk trial calculation. A processing device for use in a streaming data processing engine and a relational database construction, the method comprising:

receiving a calculation instruction for risk trial calculation of a target resource, and analyzing the calculation instruction through a constructed processing device to obtain instruction data;

reading information data and rule configuration data of the target resource from a relational database according to the instruction data, determining attribute data of the target resource according to the information data, and determining a resource configuration rule related to the target resource according to the rule configuration data;

determining a target factor to be updated in a trial calculation rule according to the attribute data of the target resource and the resource configuration rule;

calculating the factor fluctuation amount of each target factor according to the data of the target resource contained in the instruction data;

reading the current calculation result data of the target factor in the trial calculation rule, and superposing the current calculation result data of the target factor and the corresponding factor fluctuation according to a preset rule to obtain an updated target factor value;

and performing risk trial calculation of the target resource according to the updated target factor value to obtain a trial calculation result.

In one embodiment, the computational flow is written by a streaming query statement.

In one embodiment, the streaming data processing engine stores intermediate data in the computational flow in memory.

In one embodiment, a rule result value is obtained according to the target factor value and the calculation rule, and the rule result value is compared with a rule threshold value to obtain a trial calculation result.

In one embodiment, the instruction data is in a lightweight data exchange format.

In one embodiment, the information data, rule configuration data and factor configuration data of the target resource are stored in the relational database, and the data in the relational database is added, deleted and modified based on instructions.

In a second aspect, the present application further provides a data processing apparatus for resource risk trial calculation, and the data processing apparatus for resource risk trial calculation is characterized in that the data processing apparatus is applied to a processing apparatus constructed by a streaming data processing engine and a relational database, and the apparatus includes:

the analysis module is used for receiving a calculation instruction for risk trial calculation on the target resource, and analyzing the calculation instruction through the constructed processing device to obtain instruction data;

the data determining module is used for reading the information data and rule configuration data of the target resource from the relational database according to the instruction data, determining attribute data of the target resource according to the information data and determining a resource configuration rule related to the target resource according to the rule configuration data;

the factor determining module is used for determining target factors to be updated in the trial calculation rule according to the attribute data of the target resources and the resource configuration rule;

a fluctuation amount determination module for calculating a factor fluctuation amount of each target factor according to data of a target resource contained in the instruction data;

the calculation module is used for reading the current calculation result data of the target factor in the trial calculation rule, and superposing the current calculation result data of the target factor and the corresponding factor fluctuation according to a preset rule to obtain an updated target factor value;

and the trial calculation module is used for carrying out risk trial calculation on the target resource according to the updated target factor value to obtain a trial calculation result.

In a third aspect, the present disclosure also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of a data processing method for resource risk trial calculation when the processor executes the computer program.

In a fourth aspect, the present disclosure also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of a data processing method for resource risk trial calculation.

In a fifth aspect, the present disclosure also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of a data processing method for resource risk trial calculation.

The data processing method for resource risk trial calculation at least comprises the following beneficial effects:

according to the embodiment scheme provided by the disclosure, a stream data processing engine and a processing device for relational database construction are constructed and are used for obtaining trial calculation results. The streaming data processing engine can improve the data processing efficiency, process data are stored in the relational database, and the data in the relational database can be added, deleted and modified based on the instructions. The calculation flow is written by a stream query statement, so that the maintenance and the change are convenient.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments or the conventional techniques of the present disclosure, the drawings required for the descriptions of the embodiments or the conventional techniques will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to the drawings without inventive effort to those of ordinary skill in the art.

FIG. 1 is an application environment diagram of a data processing method for resource risk trial calculation in one embodiment;

FIG. 2 is a flow chart of a method of data processing for resource risk trial calculation in one embodiment;

FIG. 3 is a schematic diagram of a processing device in one embodiment;

FIG. 4 is a flow chart of a method of data processing for resource risk trial calculation in one embodiment;

FIG. 5 is a flow chart of a method of data processing for resource risk trial calculation in one embodiment;

FIG. 6 is a block diagram of a data processing apparatus for resource risk trial calculation in one embodiment;

FIG. 7 is an internal block diagram of a computer device in one embodiment;

fig. 8 is an internal structural diagram of a server in one embodiment.

Detailed Description

In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element. For example, if first, second, etc. words are used to indicate a name, but not any particular order.

The embodiment of the disclosure provides a method for calculating resource data, which can be applied to an application environment as shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In some embodiments of the present disclosure, as shown in fig. 2, a data processing method for resource risk trial calculation is provided, and an example of applying the method to the server in fig. 1 to process data is described. It will be appreciated that the method may be applied to a server, and may also be applied to a system comprising a terminal and a server, and implemented by interaction of the terminal and the server.

In a specific embodiment, the method is applied to a processing device constructed by a streaming data processing engine and a relational database.

The processing device comprises a flink-based streaming data processing engine and a relational database. Flink is a framework and distributed processing engine that can be used to perform state computation on unbounded and bounded data streams. The Flink is characterized by low latency, high throughput, and consistency. The relational database may include a relational database such as hbase, oracle, mysql, and the data may be stored in a table storage manner.

S202: and receiving a calculation instruction for risk trial calculation of the target resource, and analyzing the calculation instruction through a constructed processing device to obtain instruction data.

The calculation instructions may be transmitted through message middleware, such as kafka, activeMQ, rabbitMQ, etc. And receiving a calculation instruction, and analyzing the calculation instruction through a constructed processing device to obtain instruction data. The instruction data may include an identification of the lot, a product code, a resource value, etc. The calculation instructions are generally issued according to batches, one batch can comprise one instruction or a plurality of instructions, the instructions of the same batch need to be calculated together, and the instructions of the same batch can be screened according to the identification of the batch to be calculated simultaneously, so that trial calculation results are influenced together.

S204: and reading information data and rule configuration data of the target resource from a relational database according to the instruction data, determining attribute data of the target resource according to the information data, and determining a resource configuration rule related to the target resource according to the rule configuration data.

The information data may include information of the product, an identity of the product, and the rule configuration data may include a product type, a rule threshold. In some embodiments of the disclosure, the information data of the target resource is read from a relational database according to the instruction data, the attribute data of the target resource is determined to be a stock type product, and then the stock type product is determined to belong to a public recruitment product according to the rule configuration data, wherein the resource configuration rule of the public recruitment product can be that the proportion of the public recruitment product is not lower than 20%.

S206: and determining a target factor to be updated in the trial calculation rule according to the attribute data of the target resource and the resource configuration rule.

And finding factor configuration data matched with the relational database according to the attribute data and the resource configuration rule of the target resource, and determining the target factor to be updated. In some embodiments of the present disclosure, the factor configuration data may be represented as a numerator-denominator configuration, may be a resource type, which may include high-liquidity assets, fixed-receivability assets, equity-type assets, and the like, may be applicable to asset proportion-type products. Stock class products belong to high-fluidity assets, and the target factors to be updated are high-fluidity assets.

S208: and calculating the factor fluctuation quantity of each target factor according to the data of the target resource contained in the instruction data.

The same batch may contain a plurality of calculation instructions, and the data of the target resource contained in the instruction data is calculated at the same time, so that the factor fluctuation amount of each target factor can be obtained. If the third party resource data is required to be acquired when the factor fluctuation amount is calculated, the third party resource data can be read from the redis, and the redis is used as a storage type database and can be repeatedly read in the calculation process, so that the efficiency is improved.

S210: and reading the current calculation result data of the target factor in the trial calculation rule, and superposing the current calculation result data of the target factor and the corresponding factor fluctuation according to a preset rule to obtain an updated target factor value.

The current calculation result data can be selected from the data after the transaction is finished in the previous day, the current calculation result data of the target factor and the corresponding factor fluctuation amount are overlapped according to a preset rule, and an updated target factor value can be obtained.

S212: and performing risk trial calculation of the target resource according to the updated target factor value to obtain a trial calculation result.

And carrying out risk trial calculation on the target resource by influencing the updated target factor value by the calculation instruction to obtain a trial calculation result. The trial calculation result can be output as qualified or unqualified and stored to redis.

In the data processing method for resource risk trial calculation, a processing device constructed by a streaming data processing engine and a relational database can be constructed. The streaming data processing engine can improve the data processing efficiency, process data are stored in the relational database, and the data in the relational database can be added, deleted and modified based on the instructions. The target factor to be updated can be obtained according to the calculation instruction, the risk trial calculation of the target resource is carried out on the updated target factor value, and the trial calculation result can be obtained.

FIG. 3 is a schematic diagram of a processing device in one embodiment. A streaming data processing engine and a relational database may be included.

In some embodiments of the present disclosure, the computational flow is written by streaming query statements.

The computing code can be written by flinkql, and the sql language is convenient for the user to maintain and understand. After the stream data processing engine based on the flink acquires instruction data, the factor fluctuation can be obtained through calculating codes, so that the calculated amount is reduced, and the calculation efficiency is improved.

In some embodiments of the present disclosure, S402: the streaming data processing engine stores intermediate data in the computing process in a memory.

FIG. 4 is a flow chart of a method for processing data for resource risk trial calculation in one embodiment. The Flink can store all the calculation intermediate data in the memory, so that the space is saved, and the calculation efficiency is high. In the flow calculation process, data is continuously input and continuously calculated, intermediate calculation data, temporary data and the like can be placed in a memory, so that intermediate data can be recovered when other data flows are calculated, and the calculation efficiency is improved.

In some embodiments of the present disclosure, S502: and obtaining a rule result value according to the target factor value and the calculation rule, and comparing the rule result value with a rule threshold value to obtain a trial calculation result.

FIG. 5 is a flow chart of a method for processing data for resource risk trial calculation in one embodiment. For example, the rule threshold is 20%, the calculated rule result value is lower than 20%, and the trial calculation result is non-compliance. If the calculated rule result value is higher than 20%, the trial calculation result is compliance and can include data such as product codes, identification of batches and the like.

In some embodiments of the present disclosure, the instruction data is in a lightweight data exchange format.

The lightweight data exchange format can be json format, and the data recorded by using the json format is simpler. The json format is a structured format, so that the data can be conveniently read when being analyzed, and the data can be queried through each field, and the method is applicable to various programming languages.

In some embodiments of the disclosure, the information data, rule configuration data, factor configuration data of the target resource are stored in the relational database, and data in the relational database is added, deleted and modified based on instructions.

The relational database may include information data, rule configuration data, and factor configuration data of the target resource. In some embodiments of the present disclosure hbase may be selected, which may support dynamic addition of columns, saving space, improving storage performance, and being suitable for frequent operations. The data in the relational database can be added, deleted and modified according to the instructions.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the disclosure also provides a data processing device for implementing the above-mentioned data processing method for resource risk trial calculation. The implementation scheme of the solution to the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitation in the embodiment of the data processing device for resource risk trial provided below can be referred to the limitation of the data processing method for resource risk trial above, and will not be repeated here.

The apparatus may comprise a system (including a distributed system), software (applications), modules, components, servers, clients, etc. that employ the methods described in the embodiments of the present specification in combination with the necessary apparatus to implement the hardware. Based on the same innovative concepts, embodiments of the present disclosure provide for devices in one or more embodiments as described in the following examples. Because the implementation scheme and the method for solving the problem by the device are similar, the implementation of the device in the embodiment of the present disclosure may refer to the implementation of the foregoing method, and the repetition is not repeated. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

In one embodiment, as shown in fig. 6, there is provided a data processing apparatus 600 for resource risk trial calculation, applied to a processing apparatus constructed by a streaming data processing engine and a relational database, the apparatus comprising:

the analysis module 602 is configured to receive a calculation instruction for performing risk trial calculation on a target resource, and analyze the calculation instruction through a constructed processing device to obtain instruction data;

a data determining module 604, configured to read information data and rule configuration data of the target resource from a relational database according to the instruction data, determine attribute data of the target resource according to the information data, and determine a resource configuration rule related to the target resource according to the rule configuration data;

a factor determining module 606, configured to determine, according to the attribute data of the target resource and the resource configuration rule, a target factor that needs to be updated in a trial calculation rule;

a fluctuation amount determination module 608 for calculating a factor fluctuation amount of each of the target factors based on data of target resources included in the instruction data;

the calculation module 610 is configured to read current calculation result data of the target factor in the trial calculation rule, and superimpose the current calculation result data of the target factor and the corresponding factor fluctuation according to a preset rule to obtain an updated target factor value;

and the trial calculation module 612 is configured to perform risk trial calculation on the target resource according to the updated target factor value, so as to obtain a trial calculation result.

The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.

The above-described respective modules in the data processing apparatus for resource risk trial calculation may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a data processing method for resource risk trial calculation.

In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a data processing method for resource risk trial calculation. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

It will be appreciated by those skilled in the art that the structures shown in fig. 7 and 8 are merely block diagrams of portions of structures related to the disclosed aspects and do not constitute a limitation of the computer device on which the disclosed aspects may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, implements the method of any of the embodiments of the present disclosure.

In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method described in any of the embodiments of the present disclosure.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided by the present disclosure may include at least one of non-volatile and volatile memory, among others. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided by the present disclosure may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors involved in the embodiments provided by the present disclosure may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic, quantum computing-based data processing logic, etc., without limitation thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples have expressed only a few embodiments of the present disclosure, which are described in more detail and detail, but are not to be construed as limiting the scope of the present disclosure. It should be noted that variations and modifications can be made by those skilled in the art without departing from the spirit of the disclosure, which are within the scope of the disclosure. Accordingly, the scope of the present disclosure should be determined from the following claims.

Claims

1. A data processing method for resource risk trial calculation, which is applied to a processing device constructed by a streaming data processing engine and a relational database, the method comprising:

2. The method of claim 1, wherein the computational flow is written by a streaming query statement.

3. The method of claim 1, wherein the streaming data processing engine stores intermediate data in the computational flow in memory.

4. The method of claim 1, wherein a rule result value is obtained from the target factor value and a calculation rule, and wherein the rule result value is compared with a rule threshold to obtain a trial result.

5. The method of claim 1, wherein the instruction data is in a lightweight data exchange format.

6. The method of claim 1, wherein the information data, rule configuration data, factor configuration data of the target resource are stored in the relational database, and wherein the data in the relational database is added, deleted, and modified based on instructions.

7. A data processing apparatus for resource risk trial calculation, the apparatus being adapted for use in a processing apparatus for a streaming data processing engine and a relational database construction, the apparatus comprising:

8. The apparatus of claim 7, wherein the computational flow is written by a streaming query statement.

9. The apparatus of claim 7, wherein the streaming data processing engine stores intermediate data in the computational flow in memory.

10. The apparatus of claim 7, wherein a rule result value is obtained from the target factor value and a calculation rule, and wherein the rule result value is compared with a rule threshold to obtain a trial result.

11. The apparatus of claim 7, wherein the instruction data is in a lightweight data exchange format.

12. The apparatus of claim 7, wherein the information data, rule configuration data, factor configuration data for the target resource are stored in the relational database, and wherein the data in the relational database is added, deleted, and modified based on instructions.

13. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.

14. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

15. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.