CN112395371B - Financial institution asset classification processing method, device and readable medium - Google Patents
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Abstract
The invention discloses a financial institution asset classification processing method, which is based on big data computing frames and comprises the following steps: custom configuration asset classification dimensions; acquiring asset data and asset classification dimensions in real time; classifying the asset data according to the asset classification dimension to form an asset classification tree; and monitoring the configured asset classification dimension in real time, and updating the asset classification tree in real time after detecting the change of the asset classification dimension. The invention is based on the design of custom asset classification dimension, combines real-time data acquisition, big data calculation framework and data rapid analysis application, and achieves the purposes of automatic configuration, rapid analysis and stable output.
Description
Technical Field
The invention relates to the field of finance, in particular to a method and a device for classifying and processing assets of a financial institution and a readable medium.
Background
With the increasing development of financial markets, investment channels and targets for financial assets are becoming increasingly abundant, such as stocks, bonds, commodity futures, foreign exchange, derivatives, funds, and the like. With the rapid increase in the number of financial assets, how to pick a financial asset for asset allocation is a challenge that investors must face in the investment process, and thus classifying a financial asset has some benefits. After categorizing the financial assets, the performance of the same type of financial asset can be conveniently compared to each other, which is of great significance to combination management and financial planning.
Currently, the method for classifying financial assets in the market is fixed, namely, users need to preset some dimensions and then label the finance with different labels through operation. However, the above classification method has several drawbacks:
1. in terms of operational flexibility: the existing classification mode is relatively dead and inflexible, and only needs to realize a dimension fixed in a product. The difficulty of enumerating these dimensions is great due to the diversity, flexibility of the financial assets, and the cost of post-maintainability is high.
2. Response speed aspect: in response to the insufficient time, the existing classification schemes, such as classifying individual coupons into stock classes, bond classes, funds, etc., can hardly update the properties of the coupons quickly if the classification is wrong, and can only wait for the next day to run again.
3. Magnitude aspect: classification of mass data cannot be supported. Because of the diversity, complexity and time persistence of the financial product data, the data volume of a plurality of companies reaches PB level or even higher, and the performance requirement is difficult to reach by adopting the existing data classification method only by adopting a single-node method.
4. Data backtracking and historical running number efficiency: the data isolation is very poor, and the global data can be carried out according to the modes of total deletion and total insertion of the daily data when one dimension of the history is added, deleted and changed, so that the hidden danger of the data stability exists, and the mode also seriously influences the execution efficiency of the system.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method, a device and a readable medium for classifying and processing assets of a financial institution.
The technical scheme of the invention is as follows:
in a first aspect, a method for classifying assets of a financial institution, based on big data, includes:
Custom configuration asset classification dimensions;
Acquiring asset data and the asset classification dimension in real time;
Classifying the asset data according to the asset classification dimension to form an asset classification tree;
And monitoring the configured asset classification dimension in real time, and updating the asset classification tree in real time after detecting the change of the asset classification dimension.
According to the scheme, the element settings of the asset classification dimension comprise dimension category, dimension type, dimension name, value mode, value field, value table name/SQL and dimension value mapping.
Further, the dimension categories include bonds, cash, buybacks, futures, stocks, funds, and options.
Further, the dimension types include a character type, a date type and a numerical type.
According to the present invention of the above aspect, the asset classification tree comprises a plurality of hierarchies, each of the hierarchies comprising one or more asset dimensions.
According to the present invention of the above-described aspect, the big data calculation framework includes:
the data acquisition layer is used for acquiring the asset data;
The data processing layer is used for selecting one of HIVE, MPP and stream processing to process data according to the asset data of the processing scene;
The data storage layer is used for storing the asset data by adopting one of HDFS and HBase according to the asset data of the processing scene;
The data service layer is used for directly interacting with the user;
And the data management layer is used for carrying out unified scheduling monitoring on the data acquisition layer, the data processing layer, the data storage layer and the data service layer.
Further, the asset data includes real-time asset data and historical asset data.
Further, the data service layer comprises,
The data dimension management service is used for users to check, newly build and modify the asset classification dimension;
and the data visualization service is used for a user to view the classification result of the asset data.
In a second aspect, a computing device includes:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform a financial institution asset classification processing method as described above.
In a third aspect, a computer readable medium stores computer executable instructions for performing a method of classifying financial institution assets as described above.
According to the invention of the scheme, the beneficial effects of the invention are as follows:
The invention has the beneficial effects that:
1. the invention is based on the design of the custom asset classification dimension, combines real-time data acquisition, big data calculation framework and data rapid analysis application, and achieves the purposes of automatic configuration, rapid analysis and stable output;
2. According to the invention, a user can realize a new asset classification dimension according to the attribute of some fields or the custom SQL so as to better match flexible and diverse financial assets;
3. the invention monitors the asset classification dimension configured by the user in real time, adopts a big data computing framework to update the asset classification result in real time, so that investors in financial institutions can grasp market dynamics in time, and the investment income is improved;
4. The invention adopts a distributed big data computing framework, can easily realize parallel computation of data, and can support PB level or even larger data operation;
5. The invention drives the history data to trace back accurately along with the change record by adapting to the change of the data of the business system and processes the history data by matching with the dimension of the classification tree, thereby greatly improving the execution efficiency.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of an asset class tree of the present invention;
FIG. 3 is a frame diagram of a big data computing frame of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and embodiments:
Referring to fig. 1 and 2, the invention provides a method for classifying and processing assets of a financial institution, which is based on big data, and comprises the following steps:
Step S1: the asset classification dimension is custom configured. The element settings of the asset classification dimension comprise dimension category, dimension type, dimension name, value mode, value field, value table name/SQL, dimension value mapping, combination special calculation identification, whether time sequence, parameters and remarks exist or not. And configuring the asset classification dimension by the user according to the element setting of the asset classification dimension.
The dimension category comprises bonds, cash, buybacks, futures, stocks, funds, options and the like, and simultaneously supports the function of newly built dimension category, thereby meeting the existing flexible and changeable financial assets.
The dimension types comprise character types, date types and numerical types, and the user can select the dimension types according to different dimension types.
The value mode comprises an SQL value mode, and a user can take one industry of a certain securities main class as a classification dimension attribute according to the SQL value mode. For example, a user takes a stock's applied future industry as a classification dimension attribute according to the SQL value mode.
And setting the relation between the source value and the display value through SQL (structured query language) by dimension value mapping. As in the case of a stock claim, 801010, by mapping, the stock will show agriculture, forestry, pasture and fishing in case of the industry of the stock; what should be stored in a stock's stock, in the case of a higher industry, is 801030, by mapping, the stock application should show chemical industry.
Step S2: asset data and asset classification dimensions are acquired in real time.
Step S3: and classifying the asset data according to the asset classification dimension to form an asset classification tree. Wherein the asset classification tree comprises a plurality of hierarchies, each hierarchy comprising one or more asset dimensions.
In one embodiment, the asset classification tree includes a first hierarchy and a second hierarchy, the first hierarchy being comprised of an asset dimension that is a stock class that can be divided into different stock classes such as bonds, cash, buybacks, futures, stocks, funds, options, and the like. The second level is composed of a plurality of asset dimensions, such as securities of buyback class, and can be continuously classified at the second level, and the securities are divided into forward buyback and reverse buyback; securities, such as bond classes, may continue to be classified at a second level, and may be classified by bond type as corporate bonds, financial bonds, and the like. Of course, if desired, the asset classification tree may further include a third or even more levels of continuing classification, such as in corporate debt, and may further classify the corporate debt into a+, A, B +, B, etc. according to different rating levels.
Step S4: and monitoring the configured asset classification dimension in real time, and updating the asset classification tree in real time after detecting the change of the asset classification dimension. After each time of updating the asset classification tree and completing correct calculation, marking the asset classification tree in a classification tree table in a mode of adding a field MODIRED_FLAG, then automatically synchronizing the marked classification tree table into HIVE L0 through a timing synchronization tool, finally scheduling and executing an HIVE script, taking out data of MODIRED_FLAG=1 in the tree table, executing calculation, and only backtracking updated historical data of the asset classification tree, wherein the historical data reaches all historical classified data before one time of updating through redefining a classification label.
Referring to fig. 3, in the present embodiment, the big data computing framework includes a data acquisition layer, a data processing layer, a data storage layer, a data service layer, and a data management layer.
And the data acquisition layer is used for acquiring asset data. Wherein the asset data includes real-time asset data and historical asset data, i.e., streaming data and batch data. Incremental collection may be performed for streaming data by means of CDC, e.g. data in mysql is collected by means of the cananal service, and data is collected in kafka. For batch data, some open source plug-ins can be adopted, for example, the sqoop acquisition can be adopted for massive data, and the tool adopts the mapreduce realization principle to acquire the data; some lightweight tools, such as datax, can also be used, which are more adaptable, and those skilled in the art can choose the collection tool according to the actual situation.
And the data processing layer is used for selecting one of HIVE, MPP and stream processing to perform data processing according to the asset data of the processing scene. The HIVE of Hadoop is a distributed substitute of a traditional data warehouse, is applied to the scenes of cleaning, filtering, converting, directly summarizing and the like of data in the traditional ETL, and the higher the data quantity is, the higher the cost performance of the HIVE is. MPP (impala is adopted in the invention) is the best substitute for the traditional data warehouse by adopting a distributed architecture, provides complete support for SQL, and after HIVE is subjected to conversion analysis, fusion modeling of the data warehouse is performed by using MPP, so that the performance is more than sufficient, and the cost performance is better than that of the traditional DB 2.
And the data storage layer is used for carrying out distributed data storage on the asset data by adopting one of HDFS and HBase according to the asset data of the processing scene. The HBase is suitable for storing scenes needing real-time inquiry or updating, and the HDFS is suitable for scenes with large data batch processing.
The data service layer is used for directly interacting with users, uses some relational databases as quick query and modification of data, such as oracle, and uses spring group as an open source component. The data service layer comprises a data dimension management service for users to check, newly build and modify asset classification dimensions; the data visualization service is used for a user to check the classification result of the asset data and provide various screening conditions, such as screening according to an asset classification tree or screening according to products.
And the data management layer is used for carrying out unified scheduling monitoring on the data acquisition layer, the data processing layer, the data storage layer and the data service layer. The data management layer adopts cloudera manager as a unified scheduling monitoring tool, and supports the management of services such as various technical components (such as MapReduce, spark, HIVE, kafka) of Hadoop. In the aspect of scheduling, the OOZIE is adopted as a unified task scheduling tool, and is an enterprise-level task scheduling tool which provides the functions of UI management interface, task log viewing and the like.
The invention has the beneficial effects that:
1. the invention is based on the design of the custom asset classification dimension, combines real-time data acquisition, big data calculation framework and data rapid analysis application, and achieves the purposes of automatic configuration, rapid analysis and stable output;
2. According to the invention, a user can realize a new asset classification dimension according to the attribute of some fields or the custom SQL so as to better match flexible and diverse financial assets;
3. the invention monitors the asset classification dimension configured by the user in real time, adopts a big data computing framework to update the asset classification result in real time, so that investors in financial institutions can grasp market dynamics in time, and the investment income is improved;
4. The invention adopts a distributed big data computing framework, can easily realize parallel computation of data, and can support PB level or even larger data operation;
5. The invention drives the history data to trace back accurately along with the change record by adapting to the change of the data of the business system and processes the history data by matching with the dimension of the classification tree, thereby greatly improving the execution efficiency.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
In some possible embodiments, a computing device according to the invention may comprise at least one processing unit and at least one memory unit. Wherein the storage unit stores program code which, when executed by the processing unit, causes the processing unit to perform the steps in the method of classifying financial institution assets according to various exemplary embodiments of the invention described hereinabove. For example, the processing unit may execute the flow of the financial institution asset classification processing in steps S1 to S4 as shown in fig. 2.
In some possible embodiments, the invention provides a computer readable medium storing computer executable instructions for performing steps in the method of classifying financial institution assets according to various exemplary embodiments of the invention described above in this specification.
The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. The readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Furthermore, although the operations of the methods of the present invention are depicted in the drawings in a particular order, this is not required or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
While the invention has been described above with reference to the accompanying drawings, it will be apparent that the implementation of the invention is not limited by the above manner, and it is within the scope of the invention to apply the inventive concept and technical solution to other situations as long as various improvements made by the inventive concept and technical solution are adopted, or without any improvement.
Claims (8)
1. A financial institution asset classification processing method based on big data computing frames, comprising:
Custom configuration asset classification dimensions;
Acquiring asset data and the asset classification dimension in real time;
Classifying the asset data according to the asset classification dimension to form an asset classification tree;
Monitoring the configured asset classification dimension in real time, and updating an asset classification tree in real time after detecting the change of the asset classification dimension; after each asset classification tree is updated and correctly calculated, marking the asset classification tree in a mode of adding a field MODIRED_FLAG in a classification tree table, then automatically synchronizing the marked classification tree table into HIVE L0 through a timing synchronization tool, finally scheduling and executing an HIVE script, taking out data of MODIRED_FLAG=1 in the tree table, executing calculation, and only backtracking updated historical data of the asset classification tree, wherein the historical data reaches all historical classified data before one-time updating through redefining a classification label;
the big data calculation framework includes:
the data acquisition layer is used for acquiring the asset data;
The data processing layer is used for selecting one of HIVE, MPP and stream processing to process data according to the asset data of the processing scene;
The data storage layer is used for storing the asset data by adopting one of HDFS and HBase according to the asset data of the processing scene;
The data service layer is used for directly interacting with the user;
the data management layer is used for carrying out unified scheduling monitoring on the data acquisition layer, the data processing layer, the data storage layer and the data service layer;
The data service layer comprises:
the data dimension management service is used for users to check, newly build and modify the asset classification dimension;
and the data visualization service is used for a user to view the classification result of the asset data.
2. The method of claim 1, wherein the element settings of the asset classification dimension include dimension category, dimension type, dimension name, valuation mode, valuation field, valuation table name/SQL, dimension valuation map.
3. The method of claim 2, wherein the dimension categories include bonds, cash, buybacks, futures, stocks, funds, and options.
4. The method of claim 2, wherein the dimension types include character type, date type, and numerical type.
5. The method of claim 1, wherein the asset classification tree comprises a plurality of hierarchies, each hierarchy comprising one or more asset dimensions.
6. The method of claim 1, wherein the asset data comprises real-time asset data and historical asset data.
7. A computing device, comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of classifying financial institution assets as claimed in any of claims 1 to 6.
8. A computer readable medium storing computer executable instructions for performing the method of classifying assets for a financial institution as claimed in any of claims 1 to 6.
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