CN115759850A - Enterprise credit evaluation method and device, electronic equipment and storage medium - Google Patents
Enterprise credit evaluation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application is applicable to the technical field of data processing, and provides an enterprise credit evaluation method, an enterprise credit evaluation device, electronic equipment and a storage medium. The method comprises the following steps: determining a preset number of characteristic indexes according to electric power influence factors influencing enterprise credit; the characteristic indexes influence the credit level of the enterprise; constructing a classic domain and a section domain of the characteristic indexes according to the preset credit grade grades and the characteristic indexes of the enterprises; the credit level indicates the credit level of the enterprise; carrying out standardization processing on the classical domain based on the section domain to obtain a processed classical domain; acquiring an index value of the enterprise to be evaluated, and determining a credit evaluation grade corresponding to the enterprise to be evaluated according to the processed classical domain and the index value; the index value corresponds to the characteristic index. According to the method and the device, the enterprise credit can be measured from the aspect of the electric power data of the enterprise, and the accuracy of the enterprise credit evaluation is improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an enterprise credit evaluation method and device, electronic equipment and a storage medium.
Background
The enterprise credit has important significance in the aspects of financing and fund transfer of enterprises. The traditional enterprise credit evaluation method generally measures the credit of an enterprise from the dimensions of loan stream, repayment information, mortgage guarantee, operation conditions and the like of the enterprise, and lacks the measurement of the credit of the enterprise from the aspect of electric power data of the enterprise, while the dimensions of default electricity stealing, electricity charge quantity, electricity utilization trend and the like of the enterprise can also reflect the operation conditions of the enterprise, so that the traditional enterprise credit evaluation method has inaccurate conditions.
The electric power enterprise is an important component of an energy supply chain, and the electric power data of the enterprises at the upstream and the downstream of the electric power enterprise can be acquired by the electric power enterprise to measure the operation conditions of the enterprises, namely, the credit of the enterprises is evaluated according to the electric power information dimensions such as default electricity stealing, electricity charge quantity, electricity utilization trend and the like of the enterprises, so that the accuracy of the credit of the enterprises is improved, the financing period of the enterprises with qualified credit of the enterprises can be shortened, the fund transfer of the enterprises is accelerated, and the operation pressure is relieved.
Disclosure of Invention
In view of this, embodiments of the present application provide an enterprise credit evaluation method, an apparatus, an electronic device, and a storage medium, so as to solve the technical problem that the conventional enterprise credit evaluation method lacks the measure of enterprise credit from the aspect of power data of an enterprise, resulting in inaccurate enterprise credit evaluation.
In a first aspect, an embodiment of the present application provides an enterprise credit evaluation method, including: determining a preset number of characteristic indexes according to electric power influence factors influencing enterprise credit; the characteristic indexes influence the credit level of the enterprise; constructing a classical domain and a section domain of a characteristic index according to the preset credit grade of the enterprise and the characteristic index; the credit level indicates the credit level of the enterprise; carrying out standardization processing on the classical domain based on the section domain to obtain a processed classical domain; acquiring an index value of the enterprise to be evaluated, and determining a credit evaluation grade corresponding to the enterprise to be evaluated according to the processed classical domain and the index value; the index value corresponds to the characteristic index.
In one possible implementation of the first aspect, the power influencing factor is plural; according to the electric power influence factor that influences the enterprise credit, confirm the characteristic index of preset quantity, include: processing the electric power influence factors influencing the enterprise credit by using a Logistic regression model, and determining the weight of each electric power influence factor; and selecting a preset number of electric power influence factors from the electric power influence factors as characteristic indexes according to the weight of each electric power influence factor.
In a possible implementation manner of the first aspect, before determining the preset number of characteristic indicators according to the electric power influence factors influencing the enterprise credit, the method includes: acquiring historical power data of a plurality of enterprises; and performing data cleaning processing on the historical power data, and extracting power influence factors according to the processed historical power data.
In one possible implementation manner of the first aspect, performing data cleansing processing on historical power data includes: performing elimination redundant value processing on historical power data; performing complement missing value processing on the historical power data subjected to the redundancy value elimination processing; and carrying out abnormal value processing on the historical power data after the missing value completion processing.
In a possible implementation manner of the first aspect, the preset number is J, and the credit rating is divided into I levels; the classical domain is:
in the formula, R i Indicating a level i credit rating of N i Corresponding classical domain object element matrix, c j Represents the jth characteristic index, (a) ij ,b ij ) Representing a standard magnitude range corresponding to the jth characteristic index under the ith credit level; the section area is:
wherein R represents a section domain matter element matrix, N represents a credit level, (a) j ,b j ) And the extension value range corresponding to the jth characteristic index is shown.
In a possible implementation manner of the first aspect, normalizing the classical domain based on the section domain includes: carrying out normalization processing on the classical domain based on a normalization formula and a section domain; the normalized formula is:
in the formula, x ij Denotes a ij Or b ij ，x i ' j Indicating the corresponding normalized a ij Or b ij 。
In a possible implementation manner of the first aspect, determining a credit evaluation level corresponding to the enterprise to be evaluated according to the processed classical domain and the index value includes: carrying out standardization processing on the index value to obtain a processed index value; determining credit levels corresponding to the processed index values based on the processed classical domains; summing the weights of the characteristic indexes corresponding to the processed index values with the same credit grade to obtain at least one weight sum; the sum of weights corresponds to a credit level; and taking the weight and the maximum credit rating as the credit evaluation rating corresponding to the enterprise to be evaluated.
In a second aspect, an embodiment of the present application provides an enterprise credit evaluation apparatus, including:
the determining module is used for determining the preset number of characteristic indexes according to the electric power influence factors influencing the enterprise credit; the characteristic indexes influence the credit level of the enterprise;
the construction module is used for constructing a classical domain and a section domain of the characteristic index according to the preset credit grade and the characteristic index of the enterprise; the credit level indicates the credit level of the enterprise;
the processing module is used for carrying out standardization processing on the classical domain based on the section domain to obtain a processed classical domain;
the evaluation module is used for acquiring an index value of the enterprise to be evaluated and determining a credit evaluation grade corresponding to the enterprise to be evaluated according to the processed classical domain and the index value; the index value corresponds to the characteristic index.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor executes the computer program to implement the enterprise credit evaluation method according to any one of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for evaluating enterprise credit according to any one of the first aspect is implemented.
In a fifth aspect, the present application provides a computer program product, which when run on an electronic device, causes the electronic device to execute the enterprise credit evaluation method of any one of the above first aspects.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
According to the enterprise credit evaluation method, the device, the electronic equipment and the storage medium, the preset number of characteristic indexes are determined according to the electric power influence factors influencing the enterprise credit, the classical domain and the section domain of the characteristic indexes are constructed according to the classification and the characteristic indexes of the preset credit level of the enterprise, the classical domain is subjected to normalized processing based on the section domain to obtain the processed classical domain, the index value of the enterprise to be evaluated is obtained, the credit evaluation level corresponding to the enterprise to be evaluated is determined according to the processed classical domain and the index value, the characteristic indexes influencing the enterprise credit are determined according to the electric power data of the enterprise, the enterprise credit of the enterprise is evaluated by adopting a simplified matter element extension method based on the characteristic indexes, the enterprise credit can be measured from the aspect of the electric power data of the enterprise, and the accuracy of enterprise credit evaluation is improved.
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 specification.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart diagram illustrating an enterprise credit evaluation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an enterprise credit evaluation device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described more clearly with reference to specific examples. The following examples will assist those skilled in the art in further understanding the role of the present application, but are not intended to limit the application in any way. It should be noted that numerous variations and modifications could be made by those skilled in the art without departing from the spirit of the application. All falling within the scope of protection of the present application.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
In the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
In addition, "a plurality" mentioned in the embodiments of the present application should be construed as two or more.
Fig. 1 is a schematic flowchart of an enterprise credit evaluation method according to an embodiment of the present application. As shown in fig. 1, the method in the embodiment of the present application may include:
Wherein, the characteristic indexes influence the credit level of the enterprise. The influence factor of the electric power is multiple.
In a possible implementation manner, before step 101, specifically, the method may include: acquiring historical power data of a plurality of enterprises; and performing data cleaning processing on the historical power data, and extracting power influence factors according to the processed historical power data.
Alternatively, the historical power data may be power data of a plurality of enterprises within a preset time period, for example, power data of a plurality of enterprises from the last year to the present. The historical electric power data comprise electric power information such as card numbers, electric quantity and electric charge, default electricity stealing and electricity generating information of enterprises, wherein each piece of historical electric power data comprises electric power information such as card numbers, electric quantity and electric charge, default electricity stealing and electricity generating information of a certain enterprise at a certain time point in a preset time period.
In some examples, the data cleansing processing on the historical power data may specifically include: removing redundant values from historical power data; performing complement missing value processing on the historical power data subjected to the redundancy value elimination processing; and carrying out abnormal value processing on the historical power data after the missing value completion processing.
Optionally, the uniqueness of the historical power data is detected, and the repeated data of the enterprise at a certain time point is removed according to the card number corresponding to the enterprise.
Illustratively, normal historical power data within a certain period of time is taken as a training set, a K-neighbor interpolation method is adopted for missing values in the historical power data after the redundant value elimination processing, and an average value of two feature values nearest to the missing values in the training set is taken as the missing values to complete data.
Optionally, the boxed graph is used to determine an abnormal value in the historical power data after the completion missing value processing, and the whole historical power data corresponding to the abnormal value is deleted to obtain the processed historical power data.
For example, as can be seen from the foregoing embodiment, the historical power data includes power information such as card numbers, power amount and electricity charges, illegal electricity stealing, power generation information, and the like of the enterprises, and a plurality of power influencing factors can be extracted according to the processed power data.
For example, according to the power utilization failure times and power failure times in the historical power data, power failure analysis is extracted as one of the power influence factors, which reflects the defects in the internal management of the enterprise, particularly in the aspect of safety production; extracting the electricity fee payment level as one of the electric power influence factors according to the electricity fee balance in the historical electric power data, the payment times and the difference degree between the amount and the average level in the industry, wherein the difference degree reflects whether an enterprise has normal operation cost expenditure within a preset time period; according to the times and the degree of the information loss behaviors such as arrearage, overdue, default electricity utilization and the like in the historical power data, the default electricity utilization information is extracted to serve as one of the power influence factors, and the power influence factors reflect the credit level of the power utilization aspect of enterprises; according to the change condition of the short-term power consumption, the medium-term power consumption and the long-term power consumption in the historical power data compared with the power consumption in the last year, the power consumption trend is extracted as one of the power influence factors, and the power consumption trend reflects the production operation scale increase and shrinkage condition and the potential development trend of enterprises in the short-term, medium-term and long-term situations.
In a possible implementation manner, in step 101, the method specifically includes:
and processing the electric power influence factors influencing the enterprise credit by using a Logistic regression model, and determining the weight of each electric power influence factor.
And selecting a preset number of electric power influence factors from the electric power influence factors as characteristic indexes according to the weight of each electric power influence factor.
Exemplarily, a credit risk result of an enterprise in a preset time period is used as a dependent variable, wherein a risk occurrence value is 1, a risk non-occurrence value is 0, all electric power influence factors are used as independent variables, and a weight corresponding to each electric power influence factor is determined by using a Logistic regression model, wherein the weight indicates the influence degree of the corresponding electric power influence factor on the enterprise credit, and the larger the weight value is, the larger the influence of the electric power influence factor on the enterprise credit is. The credit risk result may be determined according to whether the enterprise has a credit risk within a preset time period.
Optionally, the weights of the electric power influence factors are sorted from large to small, and the electric power influence factors of the preset number are selected as characteristic indexes having significant influence on enterprise credit.
And 102, constructing a classical domain and a section domain of the characteristic indexes according to the preset credit grade grades and the characteristic indexes of the enterprises.
Wherein the credit rating indicates the level of the enterprise credit.
Optionally, enterprise credit of the enterprise is evaluated by using a simplified matter element extension method. Specifically, the preset credit rating of the enterprise can be divided into I levels, the characteristic indexes can be J, enterprise credits of the enterprise to be evaluated are used as object elements to be evaluated, the credit rating is used as objects to be evaluated, and the classical domain and the section domain of the characteristic indexes are constructed according to the credit rating and the characteristic indexes.
The classical domain can be expressed as:
in the formula, R i Indicating a level i credit rating of N i Corresponding classical domain element matrix, I =1,2, …, I, c j J =1,2, …, J, (a) ij ,b ij ) And representing a standard quantity value range corresponding to the jth characteristic index under the ith credit level, wherein the standard quantity value range can be manually determined according to the comprehensive level of the corresponding characteristic indexes of the enterprises in the nationwide range.
The section area is:
wherein R represents section domain matter element matrix, N represents credit rating, (a) j ,b j ) Representing the extension value range corresponding to the jth characteristic index, wherein a 1 Is a i1 Minimum value of (1), b 1 Is b is i1 Of (d) is also a j Is a ij Minimum value of (1), b j Is b is ij The maximum value of (a), that is,
and 103, carrying out normalization processing on the classical domain based on the section domain to obtain the processed classical domain.
Illustratively, each characteristic index corresponds to (a) due to its meaning ij ,b ij ) The numerical value of (A) is greatly different, and the pair of (a) is required ij And b ij And carrying out normalization processing, wherein the classical domain can be normalized based on a normalization formula and a section domain.
The normalized formula is:
in the formula, x ij Denotes a ij Or b ij ，x i ' j Indicating the corresponding normalized a ij Or b ij 。
And 104, acquiring an index value of the enterprise to be evaluated, and determining a credit evaluation grade corresponding to the enterprise to be evaluated according to the processed classical domain and the index value.
The index values of the enterprises to be evaluated correspond to the characteristic indexes one to one, and the index values can be manually determined according to the correlation levels of the corresponding characteristic indexes of the enterprises to be evaluated.
The evaluation object element can be expressed as:
in the formula, R 0 Representing the object to be evaluated, i.e. the enterprise credit of the enterprise, N representing the credit rating, v j And j represents the value of the j index and corresponds to the j characteristic index.
In a possible implementation manner, in step 104, specifically, the method may include:
s1, conducting standardization processing on the index value to obtain the processed index value.
And S2, determining the credit level corresponding to each processed index value based on the processed classical domain.
And S3, summing the weights of the characteristic indexes corresponding to the processed index values with the same credit level to obtain at least one weight sum, wherein the weight sum corresponds to the credit level.
And S4, taking the weight and the maximum credit rating as the credit evaluation rating corresponding to the enterprise to be evaluated.
Optionally, the index value is normalized based on the normalized formula and the section area in step 103 of the foregoing embodiment, and specific implementation processes and principles are referred to the foregoing embodiment, which is not described herein again.
For example, the credit level corresponding to each processed index value is determined based on the standard magnitude range corresponding to the corresponding feature index in the processed classic domain, for example, for the 3 rd processed index value corresponding to the 3 rd feature index, based on the standard magnitude range corresponding to the 3 rd feature index in the processed classic domain, it is determined that (a) is under the 2 nd credit level 23 ,b 23 ) And the credit level corresponding to the index value after the 3 rd processing is the 2 nd level.
Optionally, after determining the credit rating corresponding to each processed index value, if the credit ratings are the same, determining that the credit rating is the credit evaluation rating corresponding to the enterprise to be evaluated; and if the credit levels are at least two, adding the weights corresponding to the processed index values of the same credit level according to the weights of the characteristic indexes corresponding to the processed index values to obtain at least two weight sums, and taking the weight sum with the maximum credit level as the credit evaluation level corresponding to the enterprise to be evaluated. Wherein the weight of the characteristic index is determined in step 101 in the previous embodiment.
A simple example is that in the enterprise credit evaluation of the enterprise a to be evaluated, the credit rating of the enterprise is divided into 3 levels, the feature indexes are 6, and the processed classical domain is represented as:
in the formula, R 1 ' means level 1 Credit level N 1 Corresponding processed classic domain object matrix, R' 2 Representing a level 2 credit rating of N 2 Corresponding processed classical domain object element matrix, R 3 ' means level 3 Credit level N 3 Corresponding processed classical domain voxel matrix.
And determining 6 processed index values respectively corresponding to the 1 st level, the 2 nd level, the 1 st level, the 3 rd level and the 1 st level of the credit level based on the processed classical domain, calculating to obtain the 1 st level weight and the maximum corresponding credit level according to the weights of the 6 processed index values corresponding to the characteristic indexes, and determining the credit evaluation level of the enterprise A to be evaluated as the 1 st level credit level.
According to the enterprise credit evaluation method provided by the embodiment of the application, the preset number of characteristic indexes are determined according to the electric power influence factors influencing the enterprise credit, the classic domain and the section domain of the characteristic indexes are constructed according to the grades and the characteristic indexes of the preset credit level of the enterprise, the classic domain is subjected to normalized processing based on the section domain to obtain the processed classic domain, the index value of the enterprise to be evaluated is obtained, the credit evaluation level corresponding to the enterprise to be evaluated is determined according to the processed classic domain and the index value, the characteristic indexes influencing the enterprise credit are determined according to the electric power data of the enterprise, the enterprise credit of the enterprise is evaluated by adopting a simplified matter element extension method based on the characteristic indexes, the enterprise credit can be measured from the electric power data aspect of the enterprise, and the accuracy of enterprise credit evaluation is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by functions and internal logic of the process, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 2 is a schematic structural diagram of an enterprise credit evaluation device according to an embodiment of the present application. As shown in fig. 2, the enterprise credit evaluation apparatus provided in this embodiment may include: a determination module 201, a construction module 202, a processing module 203 and an evaluation module 204.
The determining module 201 is configured to determine a preset number of characteristic indexes according to an electric power influence factor influencing an enterprise credit; the characteristic indexes influence the credit level of the enterprise;
the building module 202 is used for building a classic domain and a section domain of the characteristic indexes according to the preset credit rating and the characteristic indexes of the enterprise; the credit level indicates the credit level of the enterprise;
the processing module 203 is configured to perform normalization processing on the classical domain based on the section domain to obtain a processed classical domain;
the evaluation module 204 is used for acquiring an index value of the enterprise to be evaluated, and determining a credit evaluation grade corresponding to the enterprise to be evaluated according to the processed classical domain and the index value; the index value corresponds to the characteristic index.
Optionally, the electric power influence factor is multiple; the determining module 201 is specifically configured to: processing the electric power influence factors influencing the enterprise credit by using a Logistic regression model, and determining the weight of each electric power influence factor; and selecting a preset number of electric power influence factors from the electric power influence factors as characteristic indexes according to the weight of each electric power influence factor.
Optionally, the determining module 201 is further specifically configured to: acquiring historical power data of a plurality of enterprises; and performing data cleaning processing on the historical power data, and extracting power influence factors according to the processed historical power data.
Optionally, the determining module 201 is further specifically configured to: removing redundant values from historical power data; performing complement missing value processing on the historical power data subjected to the redundancy value elimination processing; and carrying out abnormal value processing on the historical power data after the missing value completion processing.
Optionally, the processing module 203 is specifically configured to: and carrying out normalization processing on the classical domain based on a normalization formula and the section domain.
Optionally, the evaluation module 204 is specifically configured to: carrying out standardization processing on the index value to obtain a processed index value; determining credit levels corresponding to the processed index values based on the processed classical domains; carrying out summation operation on the weights of the characteristic indexes corresponding to the processed index values with the same credit level to obtain at least one weight sum; the sum of weights corresponds to a credit level; and taking the weight and the maximum credit rating as the credit evaluation rating corresponding to the enterprise to be evaluated.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 3, the electronic apparatus 300 of this embodiment includes: a processor 310, a memory 320, wherein the memory 320 stores a computer program 321 that can be run on the processor 310. The processor 310, when executing the computer program 321, implements the steps in any of the various method embodiments described above, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 310, when executing the computer program 321, implements the functions of each module/unit in each device embodiment described above, for example, the functions of the modules 201 to 204 shown in fig. 2.
Illustratively, the computer program 321 may be divided into one or more modules/units, which are stored in the memory 320 and executed by the processor 310 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 321 in the electronic device 300.
Those skilled in the art will appreciate that fig. 3 is merely an example of an electronic device and is not meant to be limiting and may include more or fewer components than those shown, or some components may be combined, or different components such as input output devices, network access devices, buses, etc.
The Processor 310 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 320 may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device, or an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device. The memory 320 may also include both an internal storage unit and an external storage device of the electronic device. The memory 320 is used for storing computer programs and other programs and data required by the electronic device. The memory 320 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other ways. For example, the above-described apparatus/electronic device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (10)
1. An enterprise credit evaluation method, comprising:
determining a preset number of characteristic indexes according to electric power influence factors influencing enterprise credit; the characteristic indexes influence the credit level of the enterprise;
constructing a classical domain and a section domain of the characteristic index according to the preset credit grade of the enterprise and the characteristic index; the credit level indicates the level of the enterprise credit;
carrying out standardization processing on the classical domain based on the section domain to obtain a processed classical domain;
acquiring an index value of the enterprise to be evaluated, and determining a credit evaluation grade corresponding to the enterprise to be evaluated according to the processed classical domain and the index value; the index value corresponds to the characteristic index.
2. The enterprise credit evaluation method of claim 1, wherein the power influencing factor is plural; the method for determining the characteristic indexes of the preset quantity according to the electric power influence factors influencing the enterprise credit comprises the following steps:
processing the electric power influence factors influencing the enterprise credit by using a Logistic regression model, and determining the weight of each electric power influence factor;
and selecting a preset number of electric power influence factors from the electric power influence factors as characteristic indexes according to the weight of each electric power influence factor.
3. The method according to claim 2, wherein before determining the predetermined number of characteristic measures according to the power influence factors influencing the enterprise credit, the method comprises:
acquiring historical power data of a plurality of enterprises;
and performing data cleaning processing on the historical power data, and extracting power influence factors according to the processed historical power data.
4. The enterprise credit evaluation method of claim 3, wherein the data cleansing of the historical power data comprises:
removing redundant values from the historical power data;
performing complement missing value processing on the historical power data subjected to the redundancy value elimination processing;
and carrying out abnormal value processing on the historical power data after the missing value completion processing.
5. The enterprise credit evaluation method of claim 1, wherein the predetermined number is J, and the credit rating is classified as I;
the classical domain is:
in the formula, R i Indicating a level i credit rating of N i Corresponding classical domain object matrix, c j Represents the jth characteristic index, (a) ij ,b ij ) Representing a standard magnitude range corresponding to the jth characteristic index under the ith credit level;
the section domains are as follows:
wherein R represents a section domain matter element matrix, N represents a credit level, (a) j ,b j ) And the extension value range corresponding to the jth characteristic index is shown.
6. The enterprise credit assessment method of claim 5, wherein said normalizing said classical domain based on said segment domain comprises:
carrying out normalization processing on the classical domain based on a normalization formula and the section domain;
the normalized formula is:
in the formula, x ij Denotes a ij Or b ij ，x i ' j Indicating the corresponding normalized a ij Or b ij 。
7. The enterprise credit evaluation method according to any one of claims 1 to 6, wherein the determining the credit evaluation level corresponding to the enterprise to be evaluated according to the processed classical domain and the index value comprises:
carrying out standardization processing on the index value to obtain a processed index value;
determining credit levels corresponding to the processed index values based on the processed classical domains;
carrying out summation operation on the weights of the characteristic indexes corresponding to the processed index values with the same credit level to obtain at least one weight sum; the weighted sum corresponds to a credit level;
and taking the weight and the maximum credit rating as the credit evaluation rating corresponding to the enterprise to be evaluated.
8. An enterprise credit evaluation device, comprising:
the determining module is used for determining the preset number of characteristic indexes according to the electric power influence factors influencing the enterprise credit; the characteristic indexes influence the credit level of the enterprise;
the construction module is used for constructing a classical domain and a section domain of the characteristic index according to the preset credit grade of the enterprise and the characteristic index; the credit level indicates the level of the enterprise credit;
the processing module is used for carrying out standardization processing on the classical domain based on the section domain to obtain a processed classical domain;
the evaluation module is used for acquiring an index value of the enterprise to be evaluated and determining a credit evaluation grade corresponding to the enterprise to be evaluated according to the processed classical domain and the index value; the index value corresponds to the characteristic index.
9. An electronic device comprising a memory and a processor, the memory having stored therein a computer program operable on the processor, wherein the processor, when executing the computer program, implements the enterprise credit assessment method of any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the enterprise credit assessment method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116128375A (en) * | 2023-03-29 | 2023-05-16 | 东莞先知大数据有限公司 | User water credit abnormity determination method and device, electronic equipment and medium |
| CN116128375B (en) * | 2023-03-29 | 2023-08-18 | 东莞先知大数据有限公司 | User water credit abnormity determination method and device, electronic equipment and medium |
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