CN110222129B - Credit evaluation algorithm based on relational database - Google Patents
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- CN110222129B CN110222129B CN201910521418.XA CN201910521418A CN110222129B CN 110222129 B CN110222129 B CN 110222129B CN 201910521418 A CN201910521418 A CN 201910521418A CN 110222129 B CN110222129 B CN 110222129B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24564—Applying rules; Deductive queries
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G06F16/25—Integrating or interfacing systems involving database management systems
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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Abstract
The invention discloses a credit evaluation algorithm based on a relational database, which belongs to the technical field of computer application, and realizes the arrangement of basic data and credit evaluation of system personnel by realizing an algorithm method based on a definition of a storage process in the database, and the algorithm is realized by differentiating different relational databases based on a compiling rule of the storage process; the implementation mode is as follows: processing and sorting a series of business index items according to different pretreatment rules; integrating the scoring condition of the related specific indexes according to the processing algorithms of different index items; and then, carrying out score integration according to the scoring mode and rules of the dimension to which each index belongs, and finishing out a final credit evaluation score so as to realize credit evaluation of the main body. The invention can calculate the evaluation result customized based on the requirement in a small-sized relation system, and the credit evaluation process supports configuration, has higher usability and expandability, and is suitable for credit evaluation when the data quantity is insufficient to support big data operation.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to a credit evaluation algorithm based on a relational database.
Background
With rapid development of the internet and gradual maturity of technologies such as cloud computing, big data, mobile internet, internet of things and the like, government authorities, financial institutions, internet companies, public service industries and the like accumulate rich data resources, the data resources can be fully utilized in big data environments, association relations between the data and credit are mined, and more full utilization of credit investigation data can be promoted.
The credit evaluation algorithm supported by big data, such as the Almond credit and the Beijing Dong Baibar credit, has higher research and development and maintenance cost, is not suitable for the algorithm evaluation of small projects or personnel systems, and is not suitable for the credit evaluation condition that the data quantity is small and the big data operation is not supported sufficiently.
Disclosure of Invention
The technical task of the invention is to provide a credit evaluation algorithm based on a relational database, which can calculate an evaluation result customized based on requirements in a small relational system, and the credit evaluation process supports configuration, has high usability and greatly improves the scoring speed and efficiency.
The technical scheme adopted for solving the technical problems is as follows:
the credit evaluation algorithm based on the relational database realizes the arrangement of basic data and credit evaluation of system personnel by realizing an algorithm method based on the definition of a storage process in the database, realizes differentiation for different relational databases based on the compiling rule of the storage process, and can meet the algorithm realization of multiple databases such as mysql, oracle and the like; the implementation mode is as follows:
processing and sorting a series of business index items according to different pretreatment rules;
integrating the scoring condition of the related specific indexes according to the processing algorithms of different index items;
and then, carrying out score integration according to the scoring mode and rules of the dimension to which each index belongs, and finishing out a final credit evaluation score so as to realize credit evaluation of the main body.
The stored procedure is a set of SQL statements stored in the database for performing a specific function in a large database system, the call after the first compilation does not need to be compiled again, and the user performs it by specifying the name of the stored procedure and giving the parameters if the stored procedure has parameters. The stored procedure is an important object in the database.
The algorithm is based on the custom basic arrangement of basic data and the custom configuration of related algorithms, and can calculate an evaluation result customized based on requirements in a small-sized relation system to realize the process configuration of credit evaluation.
Preferably, the bottom index item of the algorithm is data integration, and a result set meeting the scoring requirement is integrated and stored in an index item result table by preprocessing a group of data.
The data integration mode of the bottom index item is as follows: analyzing the index item 1, calculating an original value, loading a scoring standard and obtaining a score; analyzing the index item 2 from the index item 1 to the original value and loading the scoring standard to obtain the score; until the index item n is calculated, the scoring standard is loaded, and the score is obtained; and returns a scoring condition.
Starting credit rating, model, dimension, index and index item 1..n, and defining a model scoring mode, a dimension scoring mode and an index scoring mode;
data integration of the bottom index item, index loading index item 1..n score, index score calculation, index 1 overall score return, dimension loading index 1..n score, dimension score calculation, dimension 1 overall score return, model loading dimension 1..n score, model overall score calculation, and credit rating end.
Preferably, the processing SQL language of the processing rule is stored in the index item rule table, and the main algorithm process processes the index item in advance before the scoring algorithm is carried out so as to meet the usability.
Preferably, each index item corresponds to a scoring card, when the algorithm scores the index item, the algorithm rules are defined and processed to meet the requirements of different users according to the scoring basis recorded in the scoring card corresponding to each index item, and the scoring process performs scoring sentence splicing by reading the corresponding index of the scoring card through splicing form selection.
Specifically, the case wire statement is used to implement the algorithm custom configuration.
Preferably, when the grading card is configured, grading card rules are pre-configured, judgment conditions in different if sentences in the algorithm process are used, the definition of the algorithm is overrule type or step type according to the form of the algorithm, and finally the grading card is inserted into a unified result table.
Specifically, the overrule algorithm is if.. The stepwise algorithm is if..
Preferably, after the configuration of the grading card is completed, the main algorithm invokes an index grading algorithm and a dimension grading algorithm to carry out sorting weighting, a final credit evaluation total score is calculated, a weighting process algorithm reads a preset weighting rule, and different integration rules, such as summation functions sum, average value avg and the like, are realized through aggregation functions in a database.
Preferably, the algorithm dimension calculation rule supports infinite iteration, and the number of times to loop is judged by judging the highest level of the current model.
Specifically, defining the dimension as the end dimension to identify whether the algorithm is completed, performing dimension hierarchy iteration by reading the level of the algorithm, inserting the score into a unified result table, and finally performing data weighted summarization.
By implementing a limit on the final score of the score, direct grading after triggering conditions for a partial model can be achieved.
Compared with the prior art, the credit evaluation algorithm based on the relational database has the following beneficial effects:
the algorithm realizes the arrangement of basic data and credit evaluation of system personnel by realizing a group of algorithm methods defined based on a storage process in a database, has relatively simple algorithm process, and is suitable for primary selection of credit evaluation when the data quantity is insufficient to support big data operation.
The algorithm is based on the custom basic arrangement of basic data and the custom configuration of related algorithms, and can calculate an evaluation result customized based on requirements in a small-sized relation system; meanwhile, when the data structure is changed and adjusted, the algorithm does not need to be supported by developers, any service personnel understanding the service in the current evaluation field can realize the process configuration of credit evaluation, so that the algorithm has higher usability and expandability, and the scoring rate and efficiency are greatly improved.
Drawings
FIG. 1 is a flow chart of a relational database based credit rating algorithm of the present invention;
FIG. 2 is an exemplary diagram of processing rules for the index item in an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples.
A credit evaluation algorithm based on a relational database is realized by realizing an algorithm method based on a storage process definition in the database, realizing the arrangement of basic data and credit evaluation of system personnel, realizing differentiation for different relational databases based on a compiling rule of the storage process, and meeting the algorithm realization of multiple databases such as mysql, oracle and the like.
The stored procedure is a set of SQL statements stored in the database for performing a specific function in a large database system, the call after the first compilation does not need to be compiled again, and the user performs it by specifying the name of the stored procedure and giving the parameters if the stored procedure has parameters. The stored procedure is an important object in the database.
The credit evaluation is based on a set of related index system, and the credit evaluation of a certain main body is realized through the evaluation process of scoring a set of business data of each main body, and the algorithm is realized as follows:
processing and sorting a series of business index items according to different pretreatment rules;
integrating the scoring condition of the related specific indexes according to the processing algorithms of different index items;
and then, carrying out score integration according to the scoring mode and rules of the dimension to which each index belongs, and finishing out a final credit evaluation score so as to realize credit evaluation of the main body.
The bottom index item of the algorithm is data integration, and a result set meeting the scoring requirement is integrated and stored in an index item result table through preprocessing a group of data. In order to meet the usability, the processing SQL language of the processing rule is stored in an index item rule table, and the main algorithm process pre-processes the index item before the scoring algorithm is performed. The processing rules of the index items are shown in fig. 2.
When the algorithm scores the index items, the scoring cards corresponding to the index items are scored according to scoring bases recorded in the scoring cards corresponding to the index items, algorithm rules are defined and processed to meet the requirements of different users, the algorithm rules are selected in a splicing mode, scoring sentences are spliced by reading the corresponding indexes of the scoring cards in the scoring process, and the case wire sentences are used for achieving the algorithm custom configuration effect.
When the grading card is configured, grading card rules are pre-configured, and the grading card rules are inserted into a unified result table according to judgment conditions in different if sentences in the algorithm process and the algorithm definition in a overrule mode or a step mode in the algorithm mode.
The overrule algorithm is if.. The stepwise algorithm is if..
The data integration mode of the bottom index item is as follows: analyzing the index item 1, calculating an original value, loading a scoring standard and obtaining a score; analyzing the index item 2 from the index item 1 to the original value and loading the scoring standard to obtain the score; until the index item n is calculated, the scoring standard is loaded, and the score is obtained; and returns a scoring condition.
Fig. 1 is a schematic flow chart of the algorithm.
Starting credit rating, model, dimension, index and index item 1..n, and defining a model scoring mode, a dimension scoring mode and an index scoring mode;
data integration of the bottom index item, index loading index item 1..n score, index score calculation, index 1 overall score return, dimension loading index 1..n score, dimension score calculation, dimension 1 overall score return, model loading dimension 1..n score, model overall score calculation, and credit rating end.
After the grading card is configured, the main algorithm invokes an index grading algorithm and a dimension grading algorithm to carry out sorting weighting, a final credit evaluation total score is calculated, a weighting process algorithm reads a pre-configured weighting rule, and different integration rules, such as summation functions sum, average value avg and the like, are realized through aggregation functions in a database.
The algorithm dimension calculation rule can be iterated infinitely, and the number of times to be circulated is judged by judging the highest level of the current model. Defining the dimension as the end dimension to identify whether the algorithm is completed or not, performing dimension hierarchy iteration by reading the level of the algorithm, inserting the score into a unified result table, and finally performing data weighted summarization.
By implementing a limit on the final score of the score, direct grading after triggering conditions for a partial model can be achieved.
The algorithm is based on the custom basic arrangement of basic data and the custom configuration of related algorithms, the evaluation result customized based on the requirement can be calculated in a small-sized relation system, when the data structure is changed and adjusted, the algorithm does not need to be supported by developers, any business personnel understanding the business in the current evaluation field can realize the process configuration of credit evaluation, the algorithm process is relatively simple, and the algorithm is suitable for credit evaluation when the data quantity is insufficient to support large data operation.
The present invention can be easily implemented by those skilled in the art through the above specific embodiments. It should be understood that the invention is not limited to the particular embodiments described above. Based on the disclosed embodiments, a person skilled in the art may combine different technical features at will, so as to implement different technical solutions.
Claims (6)
1. A credit evaluation algorithm based on a relational database is characterized in that the algorithm realizes the arrangement of basic data and credit evaluation of system personnel by realizing an algorithm method based on a definition of a storage process in the database, and is realized by differentiating different relational databases based on a compiling rule of the storage process; the implementation mode is as follows:
processing and sorting a series of business index items according to different pretreatment rules;
integrating the scoring condition of the related specific indexes according to the processing algorithms of different index items;
then score integration is carried out according to the scoring mode and rules of the dimension to which each index belongs, and final credit evaluation scores are arranged, so that credit evaluation of a main body is realized;
the bottom index item is data integration, and a result set meeting the scoring requirement is integrated and stored in an index item result table through preprocessing a group of data;
each index item corresponds to a scoring card, an algorithm rule is defined and processed, the algorithm rule is selected in a splicing mode, and the scoring process carries out scoring sentence splicing by reading the corresponding index of the scoring card; when the grading card is configured, grading card rules are pre-configured, the grading card rules are overrule or step-type algorithm definition according to the form of the algorithm through judging conditions in different if sentences in the algorithm process, and finally the grading card rules are inserted into a unified result table; the main algorithm invokes an index scoring algorithm and a dimension scoring algorithm to carry out sorting weighting, a final credit evaluation total score is calculated, a weighting process algorithm reads a preset weighting rule, and different integral rules are realized through an aggregation function in a database.
2. The relational database-based credit rating algorithm of claim 1, wherein the processing SQL language of the processing rules is stored in an index item rule table, and the main algorithm process preprocesses the index item before proceeding with the scoring algorithm.
3. The relational database-based credit rating algorithm of claim 1, wherein the algorithm custom configuration is implemented using case wire statements.
4. A relational database based credit rating algorithm according to claim 1, characterized in that the overrule algorithm is if.. The stepwise algorithm is if..
5. A relational database based credit rating algorithm as in claim 1, wherein the algorithm dimension calculation rules support infinite iterations, the number of loops being determined by determining the highest level of the current model.
6. The relational database-based credit rating algorithm of claim 5, wherein the final dimension is defined to identify whether the algorithm is complete, the hierarchy of dimensions is iterated by reading the hierarchy of the algorithm, the scores are inserted into a unified results table, and finally the data weighted summary is performed.
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| CN112418600A (en) * | 2020-10-15 | 2021-02-26 | 重庆市科学技术研究院 | Enterprise policy scoring method and system based on index set |
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