CN115511506A - Enterprise credit rating method, device, terminal equipment and storage medium - Google Patents
Enterprise credit rating method, device, terminal equipment and storage medium Download PDFInfo
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Abstract
The application discloses a method and a device for rating enterprise credit, terminal equipment and a storage medium, wherein the method comprises the steps of obtaining credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems; determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information; determining the correlation information among all evaluation systems; fusing the evaluation preference information and the associated information to obtain fused enterprise credit measure information; clustering the credit rating result information of each enterprise according to the fused enterprise credit measurement information to obtain various clusters; and sequencing the clusters to obtain the target enterprise credit rating result of each enterprise. By mining the difference among the evaluation systems, fusing the visual angle measurement of each evaluation system, resolving the inconsistent rating results among the evaluation systems, and finally constructing the high-quality credit rating result in all-around, multi-angle and wide-field.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to a method and a device for rating an enterprise credit, a terminal device and a storage medium.
Background
The enterprise credit rating is a credit rating activity developed by taking an enterprise as a target, measures the ability and the trust degree of the enterprise to fulfill a contract related to an economic contract, indicates the credit rating by using a general symbol and announces the credit rating to the society. The credit rating organization is an important intermediary organization in the credit management industry, and reveals the credit risk of a rating object by collecting, organizing, analyzing and providing information on financial and credit-worthiness conditions of various economic entities, and reserved enterprises or personal credits through organizing professional strength.
Currently, there are many systems for rating the credit of each enterprise, each system has its own characteristics, model, preference or standard, and if a unified rating manner is adopted for rating each different enterprise system, the rating of each enterprise system cannot be accurately determined, so how to accurately rate various different types of enterprise systems is a problem that needs to be solved at present.
Disclosure of Invention
The invention aims to provide a method and a device for rating an enterprise credit, a terminal device and a storage medium, so as to solve the defects in the prior art, and the technical problem to be solved by the invention is realized by the following technical scheme.
In a first aspect, an embodiment of the present invention provides a method for rating enterprise credit, where the method includes:
acquiring credit rating result information of each enterprise under evaluation of a plurality of evaluation systems;
determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information;
determining the associated information among all evaluation systems;
fusing the evaluation preference information and the correlation information to obtain fused enterprise credit measure information;
clustering the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises;
and sequencing the clusters to obtain a target enterprise credit rating result of each enterprise.
Optionally, the obtaining credit rating result information of each enterprise under evaluation of a plurality of evaluation systems includes:
acquiring initial credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems;
and processing the initial credit rating result information to obtain processed credit rating result information, wherein the processed credit rating result information at least comprises an enterprise name set, an enterprise sequence, rating results of all evaluation systems and a rating level model set.
Optionally, the determining, according to the credit rating result information, evaluation preference information of each enterprise under each evaluation system includes:
establishing a rating preference kernel function of each evaluation system, wherein the rating preference kernel function is used for measuring the rating preference of each evaluation system;
for any evaluation system, pairing and combining the credit rating result information of each enterprise pairwise to obtain combined pairing information;
and inputting the combined pairing information into the rating preference kernel function to obtain an enterprise rating distribution result in a kernel space.
Optionally, the determining the association information between the evaluation systems includes:
determining mutual influence information between every two evaluation systems according to a pre-established mutual influence kernel function of the evaluation systems;
and determining the comprehensive influence information of any rating system on other rating systems according to a pre-established comprehensive influence kernel function of the rating systems, wherein the comprehensive influence kernel function of the rating systems is determined according to the number of the rating systems and the influence coefficient of each rating system.
Optionally, the influence coefficient of each evaluation system is determined by:
calculating a symmetric uncertainty index according to the entropy function and the conditional entropy function;
judging whether the symmetrical uncertain indexes meet preset conditions or not for any two evaluation systems and any third evaluation system except the two evaluation systems;
calculating the accumulated value of the influence coefficients of each evaluation system;
if the accumulated value of each evaluation system influence system is not 0, carrying out standardization processing on the symmetrical uncertain indexes;
and if the accumulated value of the influence systems of each evaluation system is 0, the third evaluation system is not influenced by other evaluation systems.
Optionally, the fusing the evaluation preference information and the association information to obtain fused enterprise credit measurement information includes:
and fusing the evaluation preference information and the correlation information according to a pre-established enterprise credit matching function, a pre-established enterprise credit analysis fusion function and a pre-established enterprise credit fusion measurement expression function to obtain fused enterprise credit measurement information.
Optionally, the clustering, according to the fused enterprise credit measurement information, the credit rating result information of each enterprise to obtain various clusters, each cluster at least including the enterprise rating result information of a plurality of enterprises, includes:
constructing a kernel matrix of credit similarity among enterprises;
and substituting the kernel matrix of the credit similarity among the enterprises into a preset kernel clustering objective function, and clustering the credit rating result information of each enterprise by solving the kernel clustering objective function to obtain each cluster.
In a second aspect, an embodiment of the present invention provides an apparatus for rating enterprise credit, the apparatus including:
the acquisition module is used for acquiring credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems;
the preference determining module is used for determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information;
the association module is used for determining association information among all the evaluation systems;
the fusion module is used for fusing the evaluation preference information and the correlation information to obtain fused enterprise credit measure information;
the clustering module is used for clustering the credit rating result information of each enterprise according to the fused enterprise credit measurement information to obtain various clusters, and each cluster at least comprises enterprise rating result information of a plurality of enterprises;
and the rating module is used for sequencing the clusters to obtain a target enterprise credit rating result of each enterprise.
Optionally, the obtaining module is configured to:
acquiring initial credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems;
and processing the initial credit rating result information to obtain processed credit rating result information, wherein the processed credit rating result information at least comprises an enterprise name set, an enterprise sequence, rating results of all evaluation systems and a rating level model set.
Optionally, the preference determining module is configured to:
establishing a rating preference kernel function of each evaluation system, wherein the rating preference kernel function is used for measuring the rating preference of each evaluation system;
for any evaluation system, pairing and combining the credit rating result information of each enterprise pairwise to obtain combined pairing information;
and inputting the combined pairing information into the rating preference kernel function to obtain an enterprise rating distribution result in a kernel space.
Optionally, the association module is configured to:
determining mutual influence information between every two evaluation systems according to a pre-established mutual influence kernel function of the evaluation systems;
and determining the comprehensive influence information of any rating system on other rating systems according to a pre-established comprehensive influence kernel function of the rating systems, wherein the comprehensive influence kernel function of the rating systems is determined according to the number of the rating systems and the influence coefficient of each rating system.
Optionally, the association module is configured to:
calculating a symmetric uncertainty index according to the entropy function and the conditional entropy function;
judging whether the symmetrical uncertain indexes meet preset conditions or not for any two evaluation systems and any third evaluation system except the two evaluation systems;
calculating the accumulated value of the influence coefficients of each evaluation system;
if the accumulated value of each evaluation system influencing system is not 0, carrying out standardization processing on the symmetrical uncertain indexes;
and if the accumulated value of the influence systems of the evaluation systems is 0, the third evaluation system is not influenced by other evaluation systems.
Optionally, the fusion module is configured to:
and fusing the evaluation preference information and the correlation information according to a pre-established enterprise credit matching function, a pre-established enterprise credit analysis fusion function and a pre-established enterprise credit fusion measurement representation function to obtain fused enterprise credit measurement information.
Optionally, the clustering module is configured to:
constructing a kernel matrix of credit similarity among enterprises;
and substituting the kernel matrix of the credit similarity among the enterprises into a preset kernel clustering objective function, and clustering the credit rating result information of each enterprise by solving the kernel clustering objective function to obtain each cluster.
In a third aspect, an embodiment of the present invention provides a terminal device, including: at least one processor and a memory;
the memory stores a computer program; the at least one processor executes the computer program stored by the memory to implement the method of rating enterprise credit provided by the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed, implements the method for rating enterprise credit provided in the first aspect.
The embodiment of the invention has the following advantages:
according to the method, the device, the terminal equipment and the storage medium for rating the enterprise credit, provided by the embodiment of the invention, the credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems is obtained; determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information; determining the associated information among all evaluation systems; fusing the evaluation preference information and the correlation information to obtain fused enterprise credit measure information; clustering the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises; and sequencing the clusters to obtain the credit rating result of the target enterprise of each enterprise. The embodiment of the invention forms a unified and comprehensive enterprise credit rating mode by integrating various existing enterprise credit rating systems to obtain different types of enterprise credit rating results, so that the inconsistent rating results among the various rating systems are resolved by mining the differences among the various rating systems, fusing the visual angle measurement of the various rating systems, and finally constructing the high-quality credit rating results in an all-round, multi-angle and wide-field mode.
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In order to more clearly illustrate the embodiments or prior art solutions of the present application, the drawings used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments described in the present application, and that other drawings can be obtained by those skilled in the art without inventive labor.
FIG. 1 is a flow chart of a method for rating enterprise credit in one embodiment of the present application;
FIG. 2 is a flow chart of a method for rating enterprise credit in accordance with yet another embodiment of the present application;
FIG. 3 is a block diagram of an embodiment of an apparatus for rating enterprise credit in accordance with the present invention;
fig. 4 is a schematic structural diagram of a terminal device of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following embodiments and accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
An embodiment of the invention provides an enterprise credit rating method, which is used for carrying out enterprise credit rating on different evaluation systems. The execution subject of the embodiment is a rating device for enterprise credit, and is disposed on a terminal device, for example, the terminal device at least includes a computer terminal and the like.
The multi-evaluation system enterprise credit rating fusion method considers that each evaluation system model is an unvisited core business secret and cannot be directly obtained, and simultaneously considers the difference of data required by the evaluation system and the evaluation flow, so that the fusion of each evaluation system is directly difficult. In order to solve the above contradiction, the embodiment of the present invention treats each evaluation system model and the required enterprise data as a black box, and only focuses on the enterprise credit rating result fused with each evaluation system. The enterprise credit rating results, usually expressed in the form of a, B, C, D, excellent, good, medium, and passing, are the most closely related data to the enterprise credit that can be publicly obtained and of uniform type.
The embodiment of the invention excavates various heterogeneous coupling relations among enterprise rating results, fully utilizes the complex heterogeneous coupling relations to carry out enterprise credit clustering analysis, and further constructs a comprehensive and comprehensive enterprise credit rating model. The enterprise reflects the complex heterogeneous coupling relation existing among multiple evaluation systems in the internal part of the rating results and among the rating results under each evaluation system. For example, each evaluation system has its own evaluation preference, and this preference will be reflected in the coupling relationship between the single evaluation system and the different enterprise rating results, so that the evaluation levels show specific distribution; the evaluation systems are closely related, and the result of one evaluation system may influence other evaluation systems, so that different credit rating results of a single enterprise are related. Only by fully considering the complex coupling relation of the enterprise rating results, the credit rating results of a multi-evaluation system can be effectively fused, and the complementarity of the credit rating results is utilized to eliminate the inconsistency of the credit rating results.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a method for rating enterprise credit of the present invention is shown, and the method may specifically include the following steps:
s101, obtaining credit rating result information of each enterprise under evaluation of a plurality of evaluation systems;
specifically, the terminal device may crawl credit rating result information of each enterprise under evaluation of each evaluation system from the network, and collect and sort credit rating results of each enterprise under evaluation of a plurality of evaluation systems. The rating results of a plurality of evaluation systems for each enterprise are arranged into a four-tuple formal representation:
wherein
To represent
A collection of home business entities that are,
denotes the first
The business-to-business communication system is provided with a plurality of communication devices,
representation of
A set of ratings of the individual rating systems,
denotes the first
An evaluation system is used for evaluating the evaluation of the plants,
is a collection of rating results for each rating system,
presentation evaluation System
The set of rating levels of (a) is,
is a collection of rating models of multiple evaluation systems
A business is assigned a particular rating level.
S102, determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information;
specifically, by analyzing tabular data of the credit rating result information by columns, the preference evaluated by each evaluation system is counted.
And constructing a rating preference kernel function aiming at each evaluation system to measure the rating preference of each evaluation system.
S103, determining the associated information among all the evaluation systems;
specifically, the table data of the credit rating result information is analyzed by rows, and the mutual influence and relationship among the evaluation systems are counted. And further analyzing the comprehensive influence of other evaluation systems on each evaluation system through the comprehensive influence kernel function of the evaluation systems on the basis of analyzing the mutual influence condition between each two evaluation systems.
S104, fusing the evaluation preference information and the correlation information to obtain fused enterprise credit measurement information;
specifically, through mutual influence between individual preference of a convergence evaluation system and the evaluation system, a fused enterprise credit measure representation (the credit similarity between every two enterprises is obtained) is obtained. And fusing a multi-evaluation system to form a measure representation of the credit similarity degree between enterprises under the multi-evaluation system.
S105, clustering the credit rating result information of each enterprise according to the fused enterprise credit measurement information to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises;
and S106, sequencing the clusters to obtain a target enterprise credit rating result of each enterprise.
On the basis of the expression of the enterprise credit measure, the enterprises are clustered, and various clusters are sorted according to the enterprise credit evaluation sum contained in the clusters, so that a new enterprise credit rating result is obtained.
According to the enterprise credit rating method provided by the embodiment of the invention, credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems is obtained; determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information; determining the correlation information among all evaluation systems; fusing the evaluation preference information and the associated information to obtain fused enterprise credit measure information; clustering the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises; and sequencing the clusters to obtain the credit rating result of the target enterprise of each enterprise. The embodiment of the invention integrates various existing enterprise credit evaluation systems to form a unified and comprehensive enterprise credit rating mode to obtain the credit rating results of different types of enterprises, so that the diversity among the evaluation systems is excavated, the view angle measure of the evaluation systems is fused, the inconsistent rating results among the evaluation systems are resolved, and finally the high-quality credit rating result in all-around, multi-angle and wide-field is constructed.
The embodiment of the present invention further provides a supplementary explanation for the method for rating the enterprise credit provided in the above embodiment.
Fig. 2 is a flowchart of a method for rating enterprise credit in another embodiment of the present application, and optionally, acquiring credit rating result information of each enterprise under evaluation of a plurality of evaluation systems, where the method includes:
acquiring initial credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems;
and processing the initial credit rating result information to obtain processed credit rating result information, wherein the processed credit rating result information at least comprises an enterprise name set, an enterprise sequence, rating results of all evaluation systems and a rating level model set.
Specifically, data is prepared. And summarizing and sorting the rating results of multiple enterprises by the multiple evaluation systems into a category type data table format.
And collecting and sorting the credit rating results of each enterprise under the evaluation of a plurality of evaluation systems. The rating results of a plurality of evaluation systems for each enterprise are arranged into a four-tuple formal representation:
in which
To represent
A collection of home-based businesses that,
denotes the first
The business-to-business communication system is provided with a plurality of communication devices,
representation of
The set of rating results for each rating system,
is shown as
The rating results of the individual rating systems are,
is a collection of rating results of each rating system,
presentation evaluation System
The set of rating levels of (a) is,
is a collection of rating models of multiple evaluation systems
A business is assigned a particular rating level.
Optionally, determining, according to the credit rating result information, evaluation preference information of each enterprise under each evaluation system, including:
establishing a rating preference kernel function of each evaluation system, wherein the rating preference kernel function is used for measuring the rating preference of each evaluation system;
for any evaluation system, pairing and combining the credit rating result information of each enterprise pairwise to obtain combined pairing information;
and inputting the combined pairing information into a rating preference kernel function to obtain an enterprise rating distribution result in a kernel space.
Specifically, the evaluation system prefers mining. By analyzing the tabular data by column, the preference evaluated by each evaluation system was counted.
(2) The rating preference of each rating system was analyzed. And constructing a rating preference kernel function aiming at each evaluation system to measure the rating preference of each evaluation system. For the first
The rating preference kernel for each rating system is defined as in equation (1):
wherein the content of the first and second substances,
is a "rating frequency statistical function" defined by equation (2):
in the formula (3), the first and second groups,
is the first
All rating levels in an individual rating hierarchy constitute a subset of the set. For a certain evaluation system, all enterprises are paired and combined pairwise, then the combinations are respectively input into a rating preference kernel function of the rating system defined by a formula (1), so that an enterprise rating distribution result in a kernel space can be obtained, the result contains the rating preference of the evaluation system, and the evaluation system is combined with the evaluation system interactive relationship in subsequent steps for use.
Optionally, determining association information between the evaluation systems includes:
determining the mutual influence information between each two evaluation systems according to a pre-established mutual influence kernel function of the evaluation systems;
and determining the comprehensive influence information of any rating system on other rating systems according to a pre-established comprehensive influence kernel function of the rating systems, wherein the comprehensive influence kernel function of the rating systems is determined according to the number of the rating systems and the influence coefficient of each rating system.
Alternatively, the influence coefficient of each evaluation system is determined by:
calculating a symmetric uncertainty index according to the entropy function and the conditional entropy function;
judging whether the symmetric uncertain indexes meet preset conditions or not for any two evaluation systems and any third evaluation system except the two evaluation systems;
calculating the accumulated value of the influence coefficients of each evaluation system;
if the accumulated value of each evaluation system influencing the system is not 0, carrying out standardization processing on the symmetrical uncertain indexes;
if the cumulative value of each evaluation system influencing system is 0, the third evaluation system is not influenced by other evaluation systems.
Specifically, the analysis of the interaction condition between systems is evaluated. And analyzing the table data by rows, and counting the mutual influence and relationship among the evaluation systems.
(3) And analyzing and evaluating the interaction condition among the systems. Firstly, analyzing the mutual influence condition between every two evaluation systems through an evaluation system mutual influence kernel function defined by a formula (4):
wherein the content of the first and second substances,
is an enterprise
In the first place
The evaluation scale of each evaluation system corresponds to
Evaluation level set and enterprise under individual evaluation system
In the first place
The evaluation scale of each evaluation system corresponds to
Intersection of evaluation grade sets under each evaluation system; if it is not
Then represents the first
Individual evaluation system to enterprise
And enterprises
The rating result of (1) and
the evaluation systems did not influence each other, at this time
Is provided with
； 
"information conditional probability function" defined for equation (5):
in the formula (5), the first and second groups of the chemical reaction are represented by the following formula,
is as follows
The mapping function of the rating level in each rating system to the enterprise set assigned to the rating level is defined by equation (3).
On the basis of analyzing the mutual influence condition between every two evaluation systems, the comprehensive influence kernel function of the evaluation systems defined by the formula (6) is used for further analyzing the comprehensive influence of other evaluation systems on each evaluation system
Wherein, the first and the second end of the pipe are connected with each other,
is a institute of diseaseThe number of rating systems to be considered,
is the influence coefficient of each defined evaluation system.
The specific calculation method is as follows:
a. calculating the "symmetrical uncertainty index"
：
Wherein, the first and the second end of the pipe are connected with each other,
and
respectively representing an entropy function and a conditional entropy function.
The entropy function calculation formula is as follows:
wherein the content of the first and second substances,
defined by equation (2).
The conditional entropy function calculation formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is defined by the formula (2) below,
the definition is as follows:
b. for any two evaluation systems
And
and any one evaluation system other than the above two evaluation systems
To judge "symmetrical uncertain index"
Whether the following conditions are satisfied:
if the condition is satisfied, it will
Set to 0; if the condition is not satisfied, then
Is arranged as
。
c. Calculating the integrated value of the influence coefficients of each evaluation system:
d. if it is
Then will be
Standardized to
(ii) a If it is
This means that the evaluation system is not significantly affected by other evaluation systems, and in this case, the evaluation system will be
Is arranged as
。
Optionally, the fusing the evaluation preference information and the association information to obtain fused enterprise credit measurement information includes:
and fusing the evaluation preference information and the associated information according to a pre-established enterprise credit matching function, a pre-established enterprise credit analysis fusion function and a pre-established enterprise credit fusion measurement representation function to obtain fused enterprise credit measurement information.
Specifically, the multi-evaluation system fusion representation. And (4) acquiring the fused credit measure representation of the enterprises (acquiring the credit similarity between every two enterprises) by converging the individual preference of the evaluation system and the mutual influence between the evaluation systems.
(4) And fusing a multi-evaluation system to form a measure representation of the credit similarity degree between enterprises under the multi-evaluation system. Firstly, the similarity between two enterprises is measured through the 'enterprise credit matching function' defined by the formula (11).
Wherein, the first and the second end of the pipe are connected with each other,
the definition is as follows:
then, the similarity between two enterprises is measured through the 'enterprise credit analysis fusion function' defined by the formula (13).
Wherein
And
defined by equation (1) and equation (6), respectively.
Finally, on the basis of the formula (11) and the formula (13), the similarity between two enterprises is measured through an 'enterprise credit fusion measure expression' function defined by the formula (14):
optionally, according to the fused enterprise credit measure information, clustering the credit rating result information of each enterprise to obtain various clusters, where each cluster at least includes the enterprise rating result information of multiple enterprises, including:
constructing a kernel matrix of credit similarity among enterprises;
and (3) bringing the kernel matrix of the credit similarity between the enterprises into a preset kernel clustering objective function, and clustering the credit rating result information of each enterprise by solving the kernel clustering objective function to obtain each cluster.
In particular, enterprise credit integrated ratings based on a fused representation. On the basis of the expression of the enterprise credit measure, the enterprises are clustered, and various clusters are sorted according to the enterprise credit evaluation sum contained in the clusters, so that a new enterprise credit rating result is obtained.
(5) And fusing the enterprise credit rating of a multi-evaluation system. On top of the "enterprise credit fusion measure expression" defined by equation (11), a kernel matrix of inter-enterprise credit similarity is constructed:
will matrix
And carrying out kernel clustering on the objective function (16), and clustering the credit rating of the enterprise by solving the objective function (16).
Wherein the content of the first and second substances,
is one dimension of
A matrix of values all of which are 1,
is the number of credit levels.
For clusteringThe relaxed representation of the result is,
to (1)
Line-to-line enterprise
The position of the maximum value in the row is the category of the enterprise credit level. According to the prior art, the solution of the objective function (16) is composed of
Front of (2)
And the characteristic vectors corresponding to the large characteristic values are arranged in sequence. And after the clustering result is obtained, sorting the various clusters according to the sum of the enterprise credit evaluations contained in the various clusters, thereby obtaining a new enterprise credit rating result.
The embodiment of the invention adopts a distributed statistical mode to mine the rating preference of each evaluation system; analyzing and evaluating the mutual influence among systems by adopting a co-occurrence rate statistical method; the kernel function is adopted to ensure the fusion of various relations; a clustering mode is adopted to realize the credit classification of the self-defined granularity; and the credit rating is realized by adopting a weighted voting method. An enterprise credit rating method fusing multiple evaluation systems is provided. The invention integrates the existing various enterprise credit evaluation systems to form unified and comprehensive enterprise credit rating. By mining the difference among the evaluation systems, fusing the visual angle measurement of each evaluation system and resolving the inconsistent rating results among the evaluation systems, the invention finally constructs the high-quality credit rating result in all-round, multi-angle and wide-field.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those of skill in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the embodiments of the invention.
According to the enterprise credit rating method provided by the embodiment of the invention, credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems is obtained; determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information; determining the associated information among all evaluation systems; fusing the evaluation preference information and the correlation information to obtain fused enterprise credit measure information; according to the fused enterprise credit measure information, clustering the credit rating result information of each enterprise to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises; and sequencing the clusters to obtain the credit rating result of the target enterprise of each enterprise. The embodiment of the invention forms a unified and comprehensive enterprise credit rating mode by integrating various existing enterprise credit rating systems to obtain different types of enterprise credit rating results, so that the inconsistent rating results among the various rating systems are resolved by mining the differences among the various rating systems, fusing the visual angle measurement of the various rating systems, and finally constructing the high-quality credit rating results in an all-round, multi-angle and wide-field mode.
Another embodiment of the present invention provides an enterprise credit rating apparatus, configured to execute the enterprise credit rating method provided in the foregoing embodiment.
Referring to fig. 3, a block diagram of an embodiment of the apparatus for rating enterprise credit of the present invention is shown, and the apparatus may specifically include the following modules: an obtaining module 301, a preference determining module 302, an associating module 303, a fusing module 304, a clustering module 305, and a rating module 306, wherein:
the obtaining module 301 is configured to obtain credit rating result information of each enterprise evaluated by multiple evaluation systems;
the preference determining module 302 is configured to determine evaluation preference information of each enterprise under each evaluation system according to the credit rating result information;
the association module 303 is configured to determine association information between the evaluation systems;
the fusion module 304 is configured to fuse the evaluation preference information and the association information to obtain fused enterprise credit measure information;
the clustering module 305 is configured to cluster the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, where each cluster at least includes enterprise rating result information of multiple enterprises;
the rating module 306 is configured to rank the clusters to obtain a target enterprise credit rating result of each enterprise.
The enterprise credit rating device provided by the embodiment of the invention acquires the credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems; determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information; determining the associated information among all evaluation systems; fusing the evaluation preference information and the associated information to obtain fused enterprise credit measure information; clustering the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises; and sequencing the clusters to obtain the target enterprise credit rating result of each enterprise. The embodiment of the invention forms a unified and comprehensive enterprise credit rating mode by integrating various existing enterprise credit rating systems to obtain different types of enterprise credit rating results, so that the inconsistent rating results among the various rating systems are resolved by mining the differences among the various rating systems, fusing the visual angle measurement of the various rating systems, and finally constructing the high-quality credit rating results in an all-round, multi-angle and wide-field mode.
The present invention further provides a supplementary explanation for the enterprise credit rating device provided in the above embodiment.
Optionally, the obtaining module is configured to:
acquiring initial credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems;
and processing the initial credit rating result information to obtain processed credit rating result information, wherein the processed credit rating result information at least comprises an enterprise name set, an enterprise sequence, rating results of all evaluation systems and a rating level model set.
Optionally, the determining a preference module is configured to:
establishing a rating preference kernel function of each evaluation system, wherein the rating preference kernel function is used for measuring the rating preference of each evaluation system;
for any evaluation system, pairing and combining the credit rating result information of each enterprise pairwise to obtain combined pairing information;
and inputting the combined pairing information into a rating preference kernel function to obtain an enterprise rating distribution result in a kernel space.
Optionally, the association module is configured to:
determining mutual influence information between every two evaluation systems according to a pre-established mutual influence kernel function of the evaluation systems;
and determining the comprehensive influence information of any rating system on other rating systems according to a pre-established comprehensive influence kernel function of the rating systems, wherein the comprehensive influence kernel function of the rating systems is determined according to the number of the rating systems and the influence coefficient of each rating system.
Optionally, the association module is configured to:
calculating a symmetric uncertainty index according to the entropy function and the conditional entropy function;
judging whether the symmetrical uncertain indexes meet preset conditions or not for any two evaluation systems and any third evaluation system except the two evaluation systems;
calculating the accumulated value of the influence coefficients of each evaluation system;
if the accumulated value of each evaluation system influence system is not 0, carrying out standardization processing on the symmetric uncertain indexes;
if the cumulative value of each evaluation system influence system is 0, the third evaluation system is not influenced by other evaluation systems.
Optionally, the fusion module is configured to:
and fusing the evaluation preference information and the associated information according to a pre-established enterprise credit matching function, a pre-established enterprise credit analysis fusion function and a pre-established enterprise credit fusion measurement representation function to obtain fused enterprise credit measurement information.
Optionally, the clustering module is configured to:
constructing a kernel matrix of credit similarity among enterprises;
and (3) bringing the kernel matrix of the credit similarity between the enterprises into a preset kernel clustering objective function, and clustering the credit rating result information of each enterprise by solving the kernel clustering objective function to obtain each cluster.
For the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The enterprise credit rating device provided by the embodiment of the invention obtains the credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems; determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information; determining the correlation information among all evaluation systems; fusing the evaluation preference information and the associated information to obtain fused enterprise credit measure information; clustering the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises; and sequencing the clusters to obtain the credit rating result of the target enterprise of each enterprise. The embodiment of the invention forms a unified and comprehensive enterprise credit rating mode by integrating various existing enterprise credit rating systems to obtain different types of enterprise credit rating results, so that the inconsistent rating results among the various rating systems are resolved by mining the differences among the various rating systems, fusing the visual angle measurement of the various rating systems, and finally constructing the high-quality credit rating results in an all-round, multi-angle and wide-field mode.
Still another embodiment of the present invention provides a terminal device, configured to execute the method for rating an enterprise credit provided in the foregoing embodiment.
Fig. 4 is a schematic structural diagram of a terminal device of the present invention, and as shown in fig. 4, the terminal device includes: at least one processor 401 and memory 402;
the memory stores a computer program; at least one processor executes the memory-stored computer program to implement the method for rating enterprise credit provided by the above-described embodiments.
The terminal device provided in this embodiment obtains credit rating result information of each enterprise evaluated by a plurality of evaluation systems; determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information; determining the associated information among all evaluation systems; fusing the evaluation preference information and the associated information to obtain fused enterprise credit measure information; according to the fused enterprise credit measure information, clustering the credit rating result information of each enterprise to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises; and sequencing the clusters to obtain the target enterprise credit rating result of each enterprise. The embodiment of the invention integrates various existing enterprise credit evaluation systems to form a unified and comprehensive enterprise credit rating mode to obtain the credit rating results of different types of enterprises, so that the diversity among the evaluation systems is excavated, the view angle measure of the evaluation systems is fused, the inconsistent rating results among the evaluation systems are resolved, and finally the high-quality credit rating result in all-around, multi-angle and wide-field is constructed.
Yet another embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed, the method for rating the enterprise credit provided in any one of the above embodiments is implemented.
According to the computer-readable storage medium of the embodiment, the credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems is obtained; determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information; determining the associated information among all evaluation systems; fusing the evaluation preference information and the associated information to obtain fused enterprise credit measure information; clustering the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises; and sequencing the clusters to obtain the credit rating result of the target enterprise of each enterprise. The embodiment of the invention forms a unified and comprehensive enterprise credit rating mode by integrating various existing enterprise credit rating systems to obtain different types of enterprise credit rating results, so that the inconsistent rating results among the various rating systems are resolved by mining the differences among the various rating systems, fusing the visual angle measurement of the various rating systems, and finally constructing the high-quality credit rating results in an all-round, multi-angle and wide-field mode.
It should be noted that the above detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is intended to include the plural unless the context clearly indicates otherwise. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than those illustrated or otherwise described herein.
Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
For ease of description, spatially relative terms such as "over 8230," "upper surface," "above," and the like may be used herein to describe the spatial positional relationship of one device or feature to other devices or features as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary terms "at 8230; \8230; 'above" may include both orientations "at 8230; \8230;' above 8230; 'at 8230;' below 8230;" above ". The device may also be oriented in other different ways, such as by rotating it 90 degrees or at other orientations, and the spatially relative descriptors used herein interpreted accordingly.
In the foregoing detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, like symbols typically identify like components, unless context dictates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for rating enterprise credit, the method comprising:
acquiring credit rating result information of each enterprise under evaluation of a plurality of evaluation systems;
determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information;
determining the correlation information among all evaluation systems;
fusing the evaluation preference information and the correlation information to obtain fused enterprise credit measurement information;
clustering the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, wherein each cluster at least comprises the enterprise rating result information of a plurality of enterprises;
and sequencing the clusters to obtain a target enterprise credit rating result of each enterprise.
2. The method of claim 1, wherein obtaining credit rating result information for each business under evaluation by a plurality of evaluation systems comprises:
acquiring initial credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems;
and processing the initial credit rating result information to obtain processed credit rating result information, wherein the processed credit rating result information at least comprises an enterprise name set, an enterprise sequence, rating results of each evaluation system and a rating level model set.
3. The method of claim 1, wherein determining the evaluation preference information of each enterprise under each evaluation hierarchy according to the credit rating result information comprises:
establishing a rating preference kernel function of each evaluation system, wherein the rating preference kernel function is used for measuring the rating preference of each evaluation system;
for any evaluation system, pairing and combining the credit rating result information of each enterprise pairwise to obtain combined pairing information;
and inputting the combined pairing information into the rating preference kernel function to obtain an enterprise rating distribution result in a kernel space.
4. The method according to claim 1, wherein the determining the association information between the evaluation systems comprises:
determining the mutual influence information between each two evaluation systems according to a pre-established mutual influence kernel function of the evaluation systems;
and determining the comprehensive influence information of any rating system on other rating systems according to a pre-established comprehensive influence kernel function of the rating systems, wherein the comprehensive influence kernel function of the rating systems is determined according to the number of the rating systems and the influence coefficient of each rating system.
5. The method according to claim 4, wherein the influence coefficient of each evaluation system is determined by:
calculating a symmetric uncertainty index according to the entropy function and the conditional entropy function;
judging whether the symmetrical uncertain indexes meet preset conditions or not for any two evaluation systems and any third evaluation system except the two evaluation systems;
calculating the accumulated value of the influence coefficients of each evaluation system;
if the accumulated value of each evaluation system influence system is not 0, carrying out standardization processing on the symmetrical uncertain indexes;
and if the accumulated value of the influence systems of each evaluation system is 0, the third evaluation system is not influenced by other evaluation systems.
6. The method according to claim 1, wherein the fusing the evaluation preference information and the correlation information to obtain fused enterprise credit measure information comprises:
and fusing the evaluation preference information and the correlation information according to a pre-established enterprise credit matching function, a pre-established enterprise credit analysis fusion function and a pre-established enterprise credit fusion measurement representation function to obtain fused enterprise credit measurement information.
7. The method according to claim 1, wherein the clustering credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, each cluster at least including enterprise rating result information of a plurality of enterprises, comprises:
constructing a kernel matrix of credit similarity among enterprises;
and substituting the kernel matrix of the credit similarity among the enterprises into a preset kernel clustering objective function, and clustering the credit rating result information of each enterprise by solving the kernel clustering objective function to obtain each cluster.
8. An apparatus for rating enterprise credit, the apparatus comprising:
the acquisition module is used for acquiring credit rating result information of each enterprise under the evaluation of a plurality of evaluation systems;
the preference determining module is used for determining evaluation preference information of each enterprise under each evaluation system according to the credit rating result information;
the association module is used for determining association information among all the evaluation systems;
the fusion module is used for fusing the evaluation preference information and the correlation information to obtain fused enterprise credit measure information;
the clustering module is used for clustering the credit rating result information of each enterprise according to the fused enterprise credit measure information to obtain various clusters, and each cluster at least comprises enterprise rating result information of a plurality of enterprises;
and the rating module is used for sequencing the clusters to obtain a target enterprise credit rating result of each enterprise.
9. A terminal device, comprising: at least one processor and memory;
the memory stores a computer program; the at least one processor executing the memory-stored computer program to implement the method of rating enterprise credit of any of claims 1-7.
10. A computer-readable storage medium, in which a computer program is stored which, when executed, implements the method of rating enterprise credit of any of claims 1-7.
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