CN111191091A - Data classification method and system - Google Patents
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- CN111191091A CN111191091A CN201911395075.3A CN201911395075A CN111191091A CN 111191091 A CN111191091 A CN 111191091A CN 201911395075 A CN201911395075 A CN 201911395075A CN 111191091 A CN111191091 A CN 111191091A
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Abstract
The invention relates to a data classification method and a system, wherein the method comprises the following steps: obtaining data to be classified, wherein the data comprises an enterprise name and a plurality of characteristic variables; determining the industry to which the enterprise belongs according to the enterprise name, and calling out the classification rule of the industry to which the enterprise belongs from a rule base; and classifying according to the extracted classification rule and the numerical values of the plurality of characteristic variables. By the method or the system, the enterprise data can be classified more accurately, and then a corresponding method or model can be adopted in a targeted manner during further application, so that the data processing result is more accurate.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data classification method and a data classification system suitable for enterprise credit risk early warning.
Background
Based on the big data era, the system can effectively help enterprises or others to create more value by collecting and analyzing various data generated in enterprise operation. For example, by analyzing the hot-market type and buying crowd of a product, a business may be helped to make more accurate product marketing strategies. For another example, analysis of the transaction data of the enterprise can help the enterprise form a credit profile, which helps the enterprise to perform financing or loan. Taking the enterprise credit assessment as an example, the traditional credit assessment model performs credit assessment on an enterprise through a model taking methods such as logistic regression and discriminant analysis as main methods, and although the enterprise credit risk can be evaluated, the data of the traditional credit assessment model mainly depends on transaction data, so that the traditional credit assessment model has good reliability for large enterprises with large amount of transaction data, and for small companies lacking loan experience and transaction behaviors, the credit records of the small companies are automatically regarded as large credit risks and further influence financing or loan of the small companies. Therefore, by classifying the enterprises, different credit assessment models are adopted based on different types of enterprises, and the accuracy of credit assessment can be improved. However, the current enterprise classification is generally classified according to indexes such as enterprise workers, income, and total assets, which is a rough classification method, and such classification method is not suitable for various applications such as enterprise credit assessment, and cannot improve the accuracy of the assessment result.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a data classification method and system suitable for enterprise credit risk early warning.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
a method of classifying data comprising the steps of:
obtaining data to be classified from a database, wherein the data comprises an enterprise name and a plurality of characteristic variables;
determining the industry to which the enterprise belongs according to the enterprise name, and calling out the classification rule of the industry to which the enterprise belongs from a rule base;
and classifying according to the extracted classification rule and the numerical values of the plurality of characteristic variables.
In the method, different classification rules are adopted for different industries, the industries to which the industries belong are determined according to enterprise names during classification, and then classification is performed based on the classification rules of the industries, so that the method has higher accuracy compared with the traditional method that a single classification mode of enterprise scale is adopted for all the industries. And corresponding application processing is carried out based on the classified data, so that the processing result is more accurate and referential.
As a preferred embodiment, the step of classifying according to the extracted classification rule and the numerical values of the plurality of feature variables includes:
and judging whether the numerical values of all the characteristic variables simultaneously meet the classification condition of a certain class or not according to the sequence of the classes from high to low by taking the requirement of the classification condition as a reference, classifying the characteristic variables into the class if the numerical values of all the characteristic variables simultaneously meet the classification condition of the certain class, and classifying the characteristic variables into the class next to the class if the numerical values of any one of the characteristic variables do not meet the classification condition of the class.
On the other hand, an embodiment of the present invention also provides a data classification system, including:
the system comprises a first memory, a second memory and a third memory, wherein the first memory is provided with a database and is used for storing data to be classified, and the data comprises an enterprise name and a plurality of characteristic variables;
the second memory is configured with a rule base and used for storing classification rules of various industries;
and the classification equipment with the processor is in data communication with the first memory and the second memory respectively, and is used for acquiring data to be classified from the first memory, determining the industry to which the enterprise belongs according to the enterprise name, calling out the classification rule of the industry to which the enterprise belongs from the rule base of the second memory, and classifying according to the called out classification rule and the numerical values of the characteristic variables.
Compared with the prior art, the method or the system can classify the enterprise data more accurately, and then a corresponding method or model can be adopted pertinently during further application, so that the data processing result is more accurate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flowchart of a data classification method according to an embodiment.
FIG. 2 is a flow chart of another data classification method in an embodiment.
FIG. 3 is a diagram of a data classification system according to an embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 3, the present embodiment provides a data classification system, which includes a first memory, a second memory and a classification device with a processor, wherein the classification device is respectively in communication with the first memory and the second memory for data transmission. The classification device can be a PC, a notebook computer, a server and other devices with data processing functions.
The first memory is configured with a database for storing data to be classified, wherein the data comprises an enterprise name and a plurality of characteristic variables. The second memory is configured with a rule base for storing classification rules of various industries.
And the classification equipment acquires the data to be classified from the first memory, determines the industry to which the enterprise belongs according to the enterprise name, calls out the classification rule of the industry to which the enterprise belongs from the rule base of the second memory, and classifies the data according to the called out classification rule and the numerical values of the characteristic variables.
The embodiment also provides a method for realizing data classification based on the system. Referring to fig. 2, the data classification method includes the following steps:
s100, obtaining data to be classified, wherein the data comprises an enterprise name and a plurality of characteristic variables.
The number of feature variables may vary for different applications, such as the aforementioned applications for enterprise credit assessment, feature variable registration capital and number of one degree associates. It is to be understood that the data herein includes not only the name of the characteristic variable but also the specific numerical value of the characteristic variable. For example, a business named AAA has a registered capital of 200 ten thousand and a degree of associator 15, then the data described in this step includes: AAA, registered capital 200 ten thousand, and a number of associators of 15.
S200, determining the industry to which the enterprise belongs according to the enterprise name, and calling out the classification rule of the industry to which the enterprise belongs from the rule base.
There may be different industry divisions for different applications, such as 21 categories for applications for corporate credit assessment, and other divisions for other applications, such as production manufacturing (including agriculture, forestry, animal husbandry, fishery, mining, manufacturing, construction, finance, land based), profitable services (including power, thermal, gas and water production and supply, wholesale and retail, transportation, warehousing and postal, lodging and catering, information transfer, software and information technology services, rental and business services, cultural, sports and entertainment), non-profitable services (including scientific and technical services, water conservancy, environmental and public facilities management, residential, repair and other services, education, health and social work, public administration, social security and social organization, international organization) and other of these 4 major categories.
And S300, classifying according to the extracted classification rule and the numerical values of the plurality of characteristic variables.
When classifying, with the requirement level of the classification condition as a reference, it is first determined whether the values of all the feature variables satisfy the classification condition of a certain class at the same time in the order of class from high to low, and if the values satisfy the classification condition at the same time, the classification is performed on the class.
The enterprise data may be classified based on different applications, and the data classification method of the present invention will be described below by taking the enterprise credit evaluation application as an example only.
Referring to fig. 1, the data classification method provided in the present embodiment includes the following steps based on the enterprise credit evaluation:
s10, obtaining data to be classified, wherein the data comprises business names, registered capital and one-degree associator quantity. The related party refers to a party having a relationship with the enterprise, and may be a natural person or an enterprise, the relationship may be an arbitrary role (such as a manager, a director, etc.), a stockholder, an investment, etc., and once the relationship means that the related party is directly related to the enterprise, for example, company a is an investor of company B, and for example, zhang san is a stockholder of company B.
And S20, determining the industry to which the enterprise belongs according to the enterprise name, and calling the classification rule of the industry to which the enterprise belongs. In this embodiment, the types of industries are classified into 21 types, which are: agriculture, forestry, animal husbandry, fisheries, mining, manufacturing, power, thermal, gas and water production and supply, construction, wholesale and retail, transportation, warehousing and postal, lodging and catering, information transmission, software and information technology services, financial, land-based, leasing and business services, scientific research and technical services, water, environmental and public facilities management, residential, repair and other services, educational, health and social operations, cultural, sports and entertainment, public management, social security and social organisation, international organisation, and others. Of course, there may be different ways of partitioning based on different applications.
And S30, classifying according to the extracted classification rule, the registered capital and the number of first degree associator.
For the application of enterprise credit assessment, classified characteristic variables refer to registered capital and the number of one-degree related parties, but different classification rules are adopted for different industries, so that data can be classified more accurately. When classifying, the requirement of the classification condition is used as a reference, firstly, whether the registration capital and the number of the first degree related parties simultaneously satisfy the classification condition of a certain class is judged according to the sequence of the classes from high to low, if the registration capital and the number of the first degree related parties simultaneously satisfy the classification condition of the certain class, the classification is carried out, and if the registration capital and the number of the first degree related parties cannot satisfy the classification condition of the certain class simultaneously, namely, if any one of the characteristic variables does not satisfy the classification condition of the certain class, the classification is carried out. That is, for each classification, if only one (any one) of the two feature variables, i.e., the registered capital and the number of one-degree-of-relevance parties, satisfies the classification condition of the class, the classification is performed as the next class of the class.
For example, for agriculture, forestry, animal husbandry and fishery, if the registered capital is more than or equal to 2000 ten thousand and the number of related parties per degree is more than or equal to 38, the large-scale enterprise is classified; classifying the enterprise as a small and medium-sized enterprise if the registered capital is between 360 and 2000 ten thousand and the number of the one-degree related parties is between 6 and 38; and if the registered capital is less than 360 ten thousand and the number of the one-degree related parties is less than 6, classifying the small micro-enterprise. For example, a small-to-medium enterprise is classified as a registered capital of 2000 ten thousand or more and a number of related parties of 6 to 38. If the registered capital is more than or equal to 2000 ten thousand and the number of the one-degree related parties is less than 6, the enterprise is classified as a medium-sized or small enterprise.
Based solely on the application of the enterprise credit evaluation, classification rules for various industries are illustrated in this embodiment, as shown in the following table. However, it is easy to understand that the classification rule is only directed to the application of enterprise credit assessment and is obtained through experiments, the classification rule is only an example, different classification rules can be formulated based on different applications and different requirements, and the method of the present invention only provides such an idea.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. A method of classifying data, comprising the steps of:
obtaining data to be classified from a database, wherein the data comprises an enterprise name and a plurality of characteristic variables;
determining the industry to which the enterprise belongs according to the enterprise name, and calling out the classification rule of the industry to which the enterprise belongs from a rule base;
and classifying according to the extracted classification rule and the numerical values of the plurality of characteristic variables.
2. The method of claim 1, wherein the step of classifying according to the retrieved classification rules and the values of the plurality of feature variables comprises:
and judging whether the numerical values of all the characteristic variables simultaneously meet the classification condition of a certain class or not according to the sequence of the classes from high to low by taking the requirement of the classification condition as a reference, classifying the characteristic variables into the class if the numerical values of all the characteristic variables simultaneously meet the classification condition of the certain class, and classifying the characteristic variables into the class next to the class if the numerical values of any one of the characteristic variables do not meet the classification condition of the class.
3. The method of claim 1, wherein the characteristic variables comprise registered capital and number of one degree associators.
4. A system for classifying data, comprising:
the system comprises a first memory, a second memory and a third memory, wherein the first memory is provided with a database and is used for storing data to be classified, and the data comprises an enterprise name and a plurality of characteristic variables;
the second memory is configured with a rule base and used for storing classification rules of various industries;
and the classification equipment with the processor is in data communication with the first memory and the second memory respectively, and is used for acquiring data to be classified from the first memory, determining the industry to which the enterprise belongs according to the enterprise name, calling out the classification rule of the industry to which the enterprise belongs from the rule base of the second memory, and classifying according to the called out classification rule and the numerical values of the characteristic variables.
5. The system according to claim 4, wherein the classification device determines whether the values of all the feature variables satisfy the classification condition of a certain category in descending order of category with reference to the requirement level of the classification condition when performing classification, classifies the category as the category if the values of all the feature variables satisfy the classification condition of the certain category at the same time, and classifies the category as the next category of the category if the values of any one of the feature variables do not satisfy the classification condition of the category.
6. The system of claim 4, wherein the characteristic variables include registered capital and number of one degree associators.
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