CN115879819A - Enterprise credit evaluation method and device - Google Patents
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Abstract
The application relates to an enterprise credit assessment method and device. The method comprises the following steps: obtaining an evaluation model of an enterprise to be evaluated and first-class enterprise data; acquiring second type enterprise data related to the first type enterprise data from a local and/or third-party database according to the first type enterprise data; determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data; and evaluating the credit of the enterprise to be evaluated based on the target evaluation index. According to the method, the evaluation model and the first class of enterprise data sent by the client are obtained, the associated second class of enterprise data is obtained according to the first class of enterprise data, the target evaluation index of the enterprise to be evaluated in the evaluation model is determined based on the first class of enterprise data and the second class of enterprise data, the credit of the enterprise to be evaluated is further evaluated, and the problems that when the credit of the enterprise to be evaluated is artificially and subjectively evaluated, the evaluation result is large in difference and the evaluation result is inaccurate are solved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for evaluating enterprise credit.
Background
The enterprise credit assessment can help enterprises to know the credit risk of the enterprises, can be used as a credit portrait of the enterprises, provides credit service for market activities such as financial credit, bid and tender, business cooperation and the like, and meets the increasing credit service requirements of the market.
Most of the existing enterprise credit evaluation methods artificially and subjectively evaluate the credit of the enterprise to be evaluated according to enterprise information and related data of the enterprise to be evaluated, and the experience relevance of the evaluation personnel is large, so that the enterprise credit evaluation result is inaccurate.
Disclosure of Invention
Accordingly, it is desirable to provide a method and an apparatus for enterprise credit assessment capable of improving accuracy of enterprise credit assessment results.
In a first aspect, the present application provides an enterprise credit assessment method. The method comprises the following steps:
responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database;
determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
In one embodiment, obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database comprises:
determining a data acquisition time period according to the evaluation model;
determining the identity of the enterprise to be evaluated according to the first type of enterprise data;
and acquiring second type enterprise data related to the first type enterprise data from a local and/or third-party database according to the identity and the data acquisition time period.
In one embodiment, determining a target evaluation index hit by an enterprise to be evaluated in an evaluation model according to a first type of enterprise data and a second type of enterprise data includes:
screening credit evaluation data from the first type of enterprise data and the second type of enterprise data;
and taking the reference evaluation index hit by the credit evaluation data in the evaluation model as a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
In one embodiment, the evaluating the credit of the enterprise to be evaluated according to the target evaluation index includes:
determining the index grade, the index type and the index hit times of a target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit times of the target evaluation index.
In one embodiment, if the number of the target evaluation indexes is multiple, the method for evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit frequency of the target evaluation index includes:
determining an index value corresponding to each target evaluation index according to the index type and index hit times of each target evaluation index through an evaluation model;
determining a weight value corresponding to each target evaluation index according to the index grade of each target evaluation index through an evaluation model;
determining an index score corresponding to each target evaluation index according to the index value and the weight value corresponding to each target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index score corresponding to each target evaluation index.
In one embodiment, determining, by an evaluation model, an index value corresponding to each target evaluation index according to the index type and the index hit frequency of each target evaluation index includes:
determining a hit frequency threshold value according to the index type of each target evaluation index through an evaluation model;
and determining the index value corresponding to each target evaluation index according to the hit frequency threshold value and the index hit frequency of each target evaluation index.
In a second aspect, the application further provides an enterprise credit evaluation device. The device includes:
the response module is used for responding to an enterprise evaluation request sent by the client and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
the acquisition module is used for acquiring second-type enterprise data related to the first-type enterprise data from a local and/or third-party database;
the determining module is used for determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and the evaluation module is used for evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor realizes the following steps when executing the computer program:
responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database;
determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
In a fourth aspect, the present application further provides a computer-readable storage medium. A computer-readable storage medium on which a computer program is stored which, when executed by a processor, performs the steps of:
responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database;
determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
In a fifth aspect, the present application further provides a computer program product. Computer program product comprising a computer program which, when executed by a processor, performs the steps of:
responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database;
determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
According to the enterprise credit evaluation method and device, when an enterprise evaluation request sent by a client is obtained, an evaluation model and first-class enterprise data of an enterprise to be evaluated are obtained; acquiring second enterprise data related to the first enterprise data from a local and/or third-party database according to the first enterprise data; determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data; and evaluating the credit of the enterprise to be evaluated based on the target evaluation index. According to the method and the device, the evaluation model and the first type of enterprise data sent by the client are obtained, the associated second type of enterprise data is obtained according to the first type of enterprise data, the target evaluation index of the enterprise to be evaluated in the evaluation model is automatically determined based on the first type of enterprise data and the second type of enterprise data, and then the credit of the enterprise to be evaluated is evaluated, so that the problems that when the credit of the enterprise to be evaluated is artificially and subjectively evaluated, the evaluation result is large in difference, and the evaluation result is inaccurate are solved.
Drawings
Fig. 1 is an application environment diagram of the enterprise credit evaluation method provided in this embodiment;
fig. 2 is a schematic flowchart of a first enterprise credit assessment method provided in this embodiment;
fig. 3 is a schematic flowchart of acquiring second-type enterprise data according to this embodiment;
fig. 4 is a schematic flowchart of a process of determining a target evaluation index according to this embodiment;
fig. 5 is a schematic flow chart illustrating a first process of evaluating credit of an enterprise to be evaluated based on a target evaluation index according to this embodiment;
fig. 6 is a schematic flow chart illustrating a second process of evaluating the credit of an enterprise to be evaluated based on a target evaluation index according to this embodiment;
fig. 7 is a schematic flowchart of a second enterprise credit assessment method according to this embodiment;
fig. 8 is a block diagram illustrating a first enterprise credit evaluation device according to this embodiment;
fig. 9 is a block diagram illustrating a second enterprise credit evaluation device according to this embodiment;
fig. 10 is a block diagram illustrating a third exemplary embodiment of an enterprise credit evaluation device;
fig. 11 is a block diagram illustrating a fourth enterprise credit evaluation device according to the present embodiment;
fig. 12 is an internal structural diagram of the computer device provided in the present embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The enterprise credit assessment method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein client 102 communicates with server 104 over a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104, or may be located on the cloud or other network server. Specifically, a user can input an evaluation model and first-class enterprise data through the client 102, send an enterprise evaluation request to the server 104, and respond to the enterprise evaluation request of the client 102, and the server 104 obtains the evaluation model and the first-class enterprise data of an enterprise to be evaluated; acquiring second type enterprise data related to the first type enterprise data from a local and/or third party database; determining a target evaluation index of the enterprise to be evaluated in the evaluation model based on the first type of enterprise data and the second type of enterprise data; and evaluating the credit of the enterprise to be evaluated by using the target evaluation index. The client 102 may be, but is not limited to, various smart devices such as a personal computer, a notebook computer, a smart phone, and a tablet computer. The server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In one embodiment, an enterprise credit assessment method is provided, which is described by taking the method as an example applied to the server in fig. 1, as shown in fig. 2, and includes the following steps:
s201, responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request.
The enterprise evaluation request refers to a request sent by a user from a client for evaluating enterprise credit; the evaluation model is a model which is sent by a user through a client and is used for credit evaluation of an enterprise to be evaluated, and can comprise an acquisition time period, evaluation indexes and the like; the first type of enterprise data refers to enterprise data sent by a user and related to an enterprise to be assessed, and is used for assessing credit of the enterprise to be assessed.
An optional implementation manner of this embodiment is: and acquiring a data packet of an enterprise evaluation request sent by a client, analyzing the data packet, and acquiring an evaluation model and first-class enterprise data of the enterprise to be evaluated.
Another optional implementation manner of this embodiment is: the method comprises the steps of obtaining a request for evaluating enterprise credit sent by a user in the form of a webpage, and obtaining an evaluation model and first-class enterprise data of an enterprise to be evaluated from an attachment of the webpage.
S202, second-type enterprise data related to the first-type enterprise data are obtained from a local and/or third-party database.
Wherein, the local database is a database residing in a machine running the client application; the third-party database refers to a database of other enterprises which can be used for acquiring enterprise data of the enterprise to be evaluated, such as an enterprise check database, a judicial agency database and the like; the second type of enterprise data refers to enterprise data obtained from local and/or third party databases based on the first type of enterprise data.
Optionally, in this embodiment, the enterprise identity is obtained from the first type of enterprise data, and local and/or third-party database search is performed based on the enterprise identity, so as to obtain the second type of enterprise data associated with the first type of enterprise data. The enterprise identity is an identity used for representing the identity of an enterprise, such as an enterprise name, a contact address, a registered address, a unified social credit code, a legal representative and the like.
S203, determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data.
The target evaluation index refers to an index of hit evaluation model conditions in the first type of enterprise data and the second type of enterprise data.
An optional implementation manner of this embodiment is: and acquiring original evaluation indexes from the first-class enterprise data and the second-class enterprise data, matching the original evaluation indexes with reference evaluation indexes in the evaluation model, and determining target evaluation indexes in the hit reference evaluation indexes from the original evaluation indexes. The method for obtaining the original evaluation index may be a keyword analysis or semantic analysis. The original evaluation index refers to an evaluation index obtained from the first type of enterprise data and the second type of enterprise data, and the reference evaluation index refers to an evaluation index in the evaluation model.
And S204, evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
An optional implementation manner of this embodiment is: and evaluating the credit of the enterprise to be evaluated according to the number of the target evaluation indexes. For example, the larger the number of target evaluation indexes (administrative penalty, negative public opinion and loss of credit are performed, etc.), the worse the credit of the enterprise to be evaluated.
Another optional implementation manner of this embodiment is: and evaluating the credit of the enterprise to be evaluated according to the severity level of the target evaluation index, wherein the higher the severity level of the target evaluation index is, the worse the credit of the enterprise to be evaluated is. For example, taking the administrative penalty as an example, the greater the number of times of the administrative penalty, the higher the severity level is, and the worse the credit of the enterprise to be assessed is.
According to the enterprise credit evaluation method, when an enterprise evaluation request sent by a client is received, an evaluation model and first-class enterprise data of an enterprise to be evaluated are obtained; acquiring second enterprise data related to the first enterprise data from a local and/or third-party database according to the first enterprise data; determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data; and evaluating the credit of the enterprise to be evaluated based on the target evaluation index. According to the method and the device, the evaluation model and the first type of enterprise data sent by the client are obtained, the associated second type of enterprise data is obtained according to the first type of enterprise data, the target evaluation index of the enterprise to be evaluated in the evaluation model is determined based on the first type of enterprise data and the second type of enterprise data, and then the credit of the enterprise to be evaluated is evaluated, so that the problems that when the credit of the enterprise to be evaluated is artificially and subjectively evaluated, the evaluation result is large in difference, and the evaluation result is inaccurate are solved.
In one embodiment, in order to obtain the second type of enterprise data, as shown in fig. 3, an alternative implementation of S202 includes:
s301, determining a data acquisition time period according to the evaluation model.
The data collection period refers to a time limit of the first type of enterprise data and the second type of enterprise data, for example, data within one year.
Optionally, the evaluation model of this embodiment includes a preset data acquisition period region, where the data acquisition period region is used to indicate a data acquisition period, and at this time, the embodiment may determine the data acquisition period according to the data acquisition period region in the evaluation model.
S302, according to the first type of enterprise data, identity identification of the enterprise to be evaluated is determined.
An optional implementation manner of this embodiment is: the method comprises the steps of obtaining first-class enterprise data, carrying out keyword identification of identity identification on the first-class enterprise data, determining identity identification fields in the first-class enterprise data, and further obtaining the identity identification of an enterprise to be evaluated from the identity identification fields.
Another optional implementation manner of this embodiment is: inputting the first type of enterprise data into a neural network model, analyzing and identifying the first type of enterprise data by the neural network model, and acquiring the identity of the enterprise to be evaluated.
And S303, acquiring second-class enterprise data related to the first-class enterprise data from a local and/or third-party database according to the identity and the data acquisition time period.
Optionally, in this embodiment, a first screening condition is established according to the identity identifier, a second screening condition is established according to the data acquisition time period, and second-type enterprise data associated with the first-type enterprise data is acquired from the local and/or third-party database according to the first screening condition and the second screening condition.
The embodiment determines a data acquisition period according to the evaluation model, determines the identity of the enterprise to be evaluated according to the first type of enterprise data, and acquires the second type of enterprise data associated with the first type of enterprise data according to the data acquisition period and the identity.
In one embodiment, in order to quickly determine the target evaluation index, as shown in fig. 4, an optional implementation of S203 includes:
s401, screening credit evaluation data from the first type of enterprise data and the second type of enterprise data.
The credit evaluation data refers to data used for evaluating the credit of the enterprise to be evaluated.
An optional implementation manner of this embodiment is: the method comprises the steps of obtaining first-class enterprise data and second-class enterprise data, conducting semantic analysis on the first-class enterprise data and the second-class enterprise data, and screening credit evaluation data from the first-class enterprise data and the second-class enterprise data based on semantic analysis results.
Another optional implementation manner of this embodiment is: and determining search keywords related to the credit evaluation data, and performing keyword search on the first type of enterprise data and the second type of enterprise data based on the search keywords related to the credit evaluation data so as to screen out the credit evaluation data.
S402, taking the reference evaluation index hit by the credit evaluation data in the evaluation model as a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
Optionally, in this embodiment, the credit evaluation data is matched with the reference evaluation index in the evaluation model, and if the matching is successful, it indicates that the reference index is hit, so as to determine all hit reference evaluation indexes, which are used as target evaluation indexes hit by the enterprise to be evaluated in the evaluation model. Wherein, the reference evaluation index can be determined by the client in advance at the client and recorded in the evaluation model.
In the embodiment, the credit evaluation data is screened from the first-class enterprise data and the second-class enterprise data, and the target evaluation index is determined based on the credit evaluation data, so that the determination efficiency of the target evaluation index is improved.
In one embodiment, in order to accurately evaluate the credit of the enterprise to be evaluated, as shown in fig. 5, S204 is an alternative implementation, and includes:
s501, determining the index grade, the index type and the index hit frequency of the target evaluation index.
The index grade refers to a grade which is determined by a user in advance according to the importance degree of the evaluation index, such as a first-class index and a second-class index, wherein the first-class index has a heavier weight when the enterprise credit is evaluated, namely the corresponding weight is larger; the index type refers to the type of target evaluation index, such as administrative penalty, loss of credit performed, negative public opinion, and the like; the index hit number refers to the number of times the target evaluation index is hit, for example, the number of times the administrative penalty is 6, that is, the target evaluation index is hit 6 times.
Optionally, in this embodiment, the target evaluation index is matched with the reference evaluation index of each index level according to the reference evaluation index corresponding to the index level preset by the client, so as to determine the index level corresponding to each target evaluation index; semantic analysis is carried out on the target evaluation index, the type (such as administrative penalty, negative public opinion and the like) of the target evaluation index is determined according to the semantic analysis result, and the index hit frequency is determined according to the hit frequency of the target evaluation index.
And S502, evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit times of the target evaluation index.
An optional implementation manner of this embodiment is: and inputting the index grade, the index type and the index hit times of the target evaluation index into the trained neural network model, and evaluating the credit of the enterprise to be evaluated by the neural network model based on the index grade, the index type and the index hit times.
Another optional implementation manner of this embodiment is: determining index values of the target evaluation indexes (if the number of the target evaluation indexes is multiple, the index values of different target evaluation indexes can be the same or different), determining the weighted value of each target evaluation index according to the index grade, the index type and the index hit frequency of the target evaluation index, calculating the product of the index value and the weighted value of the target evaluation index, and evaluating the credit of the enterprise to be evaluated according to the product result.
According to the embodiment, the credit of the enterprise to be evaluated is evaluated by determining the index grade, the index type and the index hit times of the target evaluation index and based on the index grade, the index type and the index hit times of the target evaluation index, so that the evaluation result is more accurate.
Based on the foregoing embodiment, if the number of the target evaluation indexes is multiple, in order to accurately evaluate the credit of the enterprise to be evaluated, as shown in fig. 6, another optional implementation manner of S502 is as follows:
s601, determining index values corresponding to the target evaluation indexes according to the index types and the index hit times of the target evaluation indexes through the evaluation model.
The index value is a value of a target evaluation index determined according to the index type and the index hit frequency.
An optional implementation manner of this embodiment is: performing semantic analysis on the index type, determining the severity of the index type according to the semantic analysis result, and determining an index value according to the severity, wherein the index value is more serious than the index value corresponding to the product short-term delayed delivery, for example, the index value is more serious than the index value corresponding to the product short-term delayed delivery when the index value is determined; the index value of the target evaluation index can be determined by dividing the index hit frequency intervals in advance, setting a corresponding index value in each interval and determining the corresponding interval according to the index hit frequency.
Another optional implementation manner of this embodiment is: determining a hit frequency threshold value according to the index types of all target evaluation indexes through an evaluation model; and determining the index value corresponding to each target evaluation index according to the hit frequency threshold value and the index hit frequency of each target evaluation index. For example, the target evaluation index is 'administrative penalty', a hit frequency threshold of 'administrative penalty' is preset to be 5 times, and if the hit frequency of the 'administrative penalty' index is less than or equal to 5 times, the index value is 0; if the index hit number of the 'administration penalty' is higher than 5, the index value is 6.
S602, determining the weight value corresponding to each target evaluation index according to the index grade of each target evaluation index through the evaluation model.
The weight value is a value determined based on the degree of importance of the target evaluation index.
Optionally, in this embodiment, the index level of each target evaluation index is obtained, the importance degree of the index level is determined, and the weight value of the index level is determined according to the importance degree. The higher the importance degree is, the higher the weight value is, for example, the weight value corresponding to the first class of indicators is higher than the weight value corresponding to the second class of indicators.
S603, determining the index score corresponding to each target evaluation index according to the index value and the weight value corresponding to each target evaluation index.
The index score is a score determined based on the index value and the weight value corresponding to each target evaluation index.
Optionally, in this embodiment, a product of the index value corresponding to each target evaluation index and the weight value is calculated according to the index value corresponding to each target evaluation index and the weight value, and a result of the product of the index value corresponding to each target evaluation index and the weight value is used as an index score corresponding to each target evaluation index.
And S604, evaluating the credit of the enterprise to be evaluated according to the index score corresponding to each target evaluation index.
Optionally, in this embodiment, the total index score is obtained by summing the index scores corresponding to the target evaluation indexes, and the credit of the enterprise to be evaluated is evaluated according to the total index score.
In this embodiment, an index value corresponding to each target evaluation index is determined according to the index type and the index hit frequency of each target evaluation index, a weight value corresponding to each target evaluation index is determined according to the index grade of each target evaluation index, a product result of the index value and the weight value of each target evaluation index can be used as an index score of each target evaluation index, the index scores of each target evaluation index are summed to obtain an index total score, and the credit of an enterprise to be evaluated is evaluated based on the index total score, so that the evaluation result is more accurate.
In one embodiment, as shown in fig. 7, an alternative implementation of the enterprise credit evaluation method is as follows:
s701, responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request.
S702, determining a data acquisition time period according to the evaluation model.
And S703, determining the identity of the enterprise to be evaluated according to the first type of data.
S704, according to the identity and the data acquisition time period, second-class enterprise data related to the first-class enterprise data are obtained from a local and/or third-party database.
S705, credit evaluation data is screened from the first type of enterprise data and the second type of enterprise data.
And S706, taking the reference evaluation index hit by the credit evaluation data in the evaluation model as a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
And S707, determining the index grade, the index type and the index hit frequency of the target evaluation index.
S708, determining a hit frequency threshold value according to the index type of each target evaluation index through the evaluation model.
And S709, determining the index value corresponding to each target evaluation index according to the hit frequency threshold value and the index hit frequency of each target evaluation index.
And S7010, determining the weight value corresponding to each target evaluation index according to the index grade of each target evaluation index through the evaluation model.
And S7011, determining an index score corresponding to each target evaluation index according to the index value and the weight value corresponding to each target evaluation index.
And S7012, evaluating the credit of the enterprise to be evaluated according to the index scores corresponding to the target evaluation indexes.
According to the method and the device, the evaluation model and the first type of enterprise data sent by the client are obtained, the associated second type of enterprise data is obtained according to the first type of enterprise data, the target evaluation index of the enterprise to be evaluated in the evaluation model is automatically determined based on the first type of enterprise data and the second type of enterprise data, and then the credit of the enterprise to be evaluated is evaluated, so that the problems that when the credit of the enterprise to be evaluated is artificially and subjectively evaluated, the evaluation result is large in difference, and the evaluation result is inaccurate are solved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides an enterprise credit evaluation device for realizing the enterprise credit evaluation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more embodiments of the enterprise credit assessment device provided below may refer to the limitations on the enterprise credit assessment method in the above description, and details are not repeated here.
In one embodiment, as shown in fig. 8, there is provided an enterprise credit evaluation device 1 including: a response module 10, an acquisition module 20, a determination module 30 and an evaluation module 40, wherein:
the response module 10 is configured to, in response to an enterprise assessment request sent by a client, obtain an assessment model and first-class enterprise data of an enterprise to be assessed from the enterprise assessment request.
An obtaining module 20 is configured to obtain a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database.
And the determining module 30 is configured to determine, according to the first type of enterprise data and the second type of enterprise data, a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
And the evaluation module 40 is used for evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
In one embodiment, based on fig. 8, as shown in fig. 9, the obtaining module 20 in fig. 8 further includes:
a first determining unit 201, configured to determine a data acquisition period according to the evaluation model.
The second determining unit 202 is configured to determine, according to the first type of data, an identity of the enterprise to be evaluated.
The obtaining unit 203 is configured to obtain, according to the identity and the data collection period, second type enterprise data associated with the first type enterprise data from a local and/or third-party database.
In one embodiment, based on fig. 8, as shown in fig. 10, the determining module 30 in fig. 8 further includes:
a screening unit 301, configured to screen the first type of enterprise data and the second type of enterprise data for credit evaluation data.
And a third determining unit 302, configured to use the reference evaluation index hit by the credit evaluation data in the evaluation model as a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
In one embodiment, based on fig. 8, as shown in fig. 11, the evaluation module 40 in fig. 8 further includes:
a fourth determination unit 401 for determining the index level, the index kind, and the index hit number of the target evaluation index.
And the evaluation unit 402 is configured to evaluate the credit of the enterprise to be evaluated according to the index level, the index type and the index hit frequency of the target evaluation index.
In one embodiment, as shown in fig. 11, if the number of target evaluation indicators is multiple, the evaluation unit 402 in fig. 11 is specifically configured to: determining an index value corresponding to each target evaluation index according to the index type and index hit times of each target evaluation index through an evaluation model; determining a weight value corresponding to each target evaluation index according to the index grade of each target evaluation index through an evaluation model; determining an index score corresponding to each target evaluation index according to the index value and the weight value corresponding to each target evaluation index; and evaluating the credit of the enterprise to be evaluated according to the index score corresponding to each target evaluation index.
In one embodiment, the evaluation unit 402 in fig. 11 is further configured to: determining a hit frequency threshold value according to the index type of each target evaluation index through an evaluation model; and determining the index value corresponding to each target evaluation index according to the hit frequency threshold value and the index hit frequency of each target evaluation index.
The various modules in the enterprise credit evaluation device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 12. The computer device includes a processor, a memory, an Input/Output interface (I/O for short), and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing relevant data of the enterprise to be evaluated. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement an enterprise credit assessment method.
Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database;
determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database, comprising:
determining a data acquisition time period according to the evaluation model;
determining the identity of the enterprise to be evaluated according to the first type of data;
and acquiring second type enterprise data related to the first type enterprise data from a local and/or third party database according to the identity and the data acquisition time period.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data, wherein the target evaluation index comprises the following steps:
screening credit evaluation data from the first type of enterprise data and the second type of enterprise data;
and taking the reference evaluation index hit by the credit evaluation data in the evaluation model as a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: according to the target evaluation index, evaluating the credit of the enterprise to be evaluated, comprising the following steps:
determining the index grade, the index type and the index hit times of a target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit times of the target evaluation index.
In one embodiment, the processor when executing the computer program further performs the steps of: if the number of the target evaluation indexes is multiple, evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit times of the target evaluation indexes, and the method comprises the following steps:
determining index values corresponding to the target evaluation indexes according to the index types and the index hit times of the target evaluation indexes through an evaluation model;
determining a weight value corresponding to each target evaluation index according to the index grade of each target evaluation index through an evaluation model;
determining an index score corresponding to each target evaluation index according to the index value and the weighted value corresponding to each target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index score corresponding to each target evaluation index.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining the index value corresponding to each target evaluation index according to the index type and the index hit frequency of each target evaluation index through an evaluation model, wherein the method comprises the following steps:
determining a hit frequency threshold value according to the index type of each target evaluation index through an evaluation model;
and determining the index value corresponding to each target evaluation index according to the hit frequency threshold value and the index hit frequency of each target evaluation index.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database;
determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database, comprising:
determining a data acquisition time period according to the evaluation model;
determining the identity of the enterprise to be evaluated according to the first type of data;
and acquiring second type enterprise data related to the first type enterprise data from a local and/or third-party database according to the identity and the data acquisition time period.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a target evaluation index hit by the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data, wherein the target evaluation index comprises the following steps:
screening credit evaluation data from the first type of enterprise data and the second type of enterprise data;
and taking the reference evaluation index hit by the credit evaluation data in the evaluation model as a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the target evaluation index, evaluating the credit of the enterprise to be evaluated, comprising the following steps:
determining the index grade, the index type and the index hit times of a target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit times of the target evaluation index.
In one embodiment, the computer program when executed by the processor further performs the steps of: if the number of the target evaluation indexes is multiple, evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit frequency of the target evaluation indexes, wherein the evaluation comprises the following steps:
determining an index value corresponding to each target evaluation index according to the index type and index hit times of each target evaluation index through an evaluation model;
determining a weight value corresponding to each target evaluation index according to the index grade of each target evaluation index through an evaluation model;
determining an index score corresponding to each target evaluation index according to the index value and the weight value corresponding to each target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index score corresponding to each target evaluation index.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining the index value corresponding to each target evaluation index according to the index type and the index hit frequency of each target evaluation index through an evaluation model, wherein the method comprises the following steps:
determining a hit frequency threshold value according to the index type of each target evaluation index through an evaluation model;
and determining the index value corresponding to each target evaluation index according to the hit frequency threshold value and the index hit frequency of each target evaluation index.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of:
responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database;
determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database, comprising:
determining a data acquisition time period according to the evaluation model;
determining the identity of the enterprise to be evaluated according to the first type of data;
and acquiring second type enterprise data related to the first type enterprise data from a local and/or third-party database according to the identity and the data acquisition time period.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a target evaluation index hit by the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data, wherein the target evaluation index comprises the following steps:
screening credit evaluation data from the first type of enterprise data and the second type of enterprise data;
and taking the reference evaluation index hit by the credit evaluation data in the evaluation model as a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the target evaluation index, evaluating the credit of the enterprise to be evaluated, comprising the following steps:
determining the index grade, the index type and the index hit times of a target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit times of the target evaluation index.
In one embodiment, the computer program when executed by the processor further performs the steps of: if the number of the target evaluation indexes is multiple, evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit times of the target evaluation indexes, and the method comprises the following steps:
determining index values corresponding to the target evaluation indexes according to the index types and the index hit times of the target evaluation indexes through an evaluation model;
determining a weight value corresponding to each target evaluation index according to the index grade of each target evaluation index through an evaluation model;
determining an index score corresponding to each target evaluation index according to the index value and the weight value corresponding to each target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index score corresponding to each target evaluation index.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining the index value corresponding to each target evaluation index according to the index type and the index hit frequency of each target evaluation index through an evaluation model, wherein the method comprises the following steps:
determining a hit frequency threshold value according to the index types of all target evaluation indexes through an evaluation model;
and determining the index value corresponding to each target evaluation index according to the hit frequency threshold value and the index hit frequency of each target evaluation index.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A method for enterprise credit assessment, the method comprising:
responding to an enterprise evaluation request sent by a client, and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
obtaining a second type of enterprise data associated with the first type of enterprise data from a local and/or third party database;
determining a target evaluation index of the enterprise to be evaluated in an evaluation model according to the first type of enterprise data and the second type of enterprise data;
and evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
2. The method of claim 1, wherein the obtaining the second type of enterprise data associated with the first type of enterprise data from a local and/or third party database comprises:
determining a data acquisition time period according to the evaluation model;
determining the identity of the enterprise to be evaluated according to the first type of enterprise data;
and acquiring second type enterprise data related to the first type enterprise data from a local and/or third-party database according to the identity and the data acquisition time period.
3. The method of claim 1, wherein determining a target evaluation index hit by the enterprise to be evaluated in the evaluation model based on the first type of enterprise data and the second type of enterprise data comprises:
screening the first type of enterprise data and the second type of enterprise data for credit evaluation data;
and taking the reference evaluation index hit by the credit evaluation data in the evaluation model as a target evaluation index hit by the enterprise to be evaluated in the evaluation model.
4. The method according to claim 1, wherein the evaluating credit of the business to be evaluated according to the target evaluation index comprises:
determining the index grade, the index type and the index hit times of the target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index grade, the index type and the index hit times of the target evaluation index.
5. The method of claim 4, wherein if the number of the target evaluation indicators is multiple, evaluating the credit of the enterprise to be evaluated according to the indicator level, the indicator type and the indicator hit number of the target evaluation indicator comprises:
determining index values corresponding to the target evaluation indexes according to the index types and the index hit times of the target evaluation indexes through an evaluation model;
determining a weight value corresponding to each target evaluation index according to the index grade of each target evaluation index through an evaluation model;
determining an index score corresponding to each target evaluation index according to the index value and the weight value corresponding to each target evaluation index;
and evaluating the credit of the enterprise to be evaluated according to the index score corresponding to each target evaluation index.
6. The method of claim 5, wherein the determining, by the evaluation model, the index value corresponding to each target evaluation index according to the index type and the index hit frequency of each target evaluation index comprises:
determining a hit frequency threshold value according to the index type of each target evaluation index through an evaluation model;
and determining the index value corresponding to each target evaluation index according to the hit frequency threshold value and the index hit frequency of each target evaluation index.
7. An enterprise credit evaluation device, comprising:
the response module is used for responding to an enterprise evaluation request sent by a client and acquiring an evaluation model and first-class enterprise data of an enterprise to be evaluated from the enterprise evaluation request;
the acquisition module is used for acquiring second-class enterprise data related to the first-class enterprise data from a local and/or third-party database;
the determining module is used for determining a target evaluation index of the enterprise to be evaluated in the evaluation model according to the first type of enterprise data and the second type of enterprise data;
and the evaluation module is used for evaluating the credit of the enterprise to be evaluated according to the target evaluation index.
8. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program performs the steps of the enterprise credit assessment method of any one of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the enterprise credit assessment method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the enterprise credit assessment method according to any one of claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362940A (en) * | 2023-04-01 | 2023-06-30 | 山东联信数字科技有限公司 | Enterprise credit assessment method, system, device and storage medium based on big data analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109934431A (en) * | 2017-12-15 | 2019-06-25 | 上海特易信息科技有限公司 | A kind of credit estimation method and system |
| CN111915155A (en) * | 2020-07-13 | 2020-11-10 | 上海冰鉴信息科技有限公司 | Small and micro enterprise risk level identification method and device and computer equipment |
| CN113988671A (en) * | 2021-11-02 | 2022-01-28 | 城云科技（中国）有限公司 | Enterprise credit risk assessment method, device and application |
| CN114282840A (en) * | 2021-12-31 | 2022-04-05 | 联通(广东)产业互联网有限公司 | Enterprise operation safety risk assessment method, system, equipment and medium |
| CN115392787A (en) * | 2022-09-28 | 2022-11-25 | 中国建设银行股份有限公司 | Enterprise risk assessment method, device, equipment, storage medium and program product |
-
2022
- 2022-12-30 CN CN202211720800.1A patent/CN115879819A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109934431A (en) * | 2017-12-15 | 2019-06-25 | 上海特易信息科技有限公司 | A kind of credit estimation method and system |
| CN111915155A (en) * | 2020-07-13 | 2020-11-10 | 上海冰鉴信息科技有限公司 | Small and micro enterprise risk level identification method and device and computer equipment |
| CN113988671A (en) * | 2021-11-02 | 2022-01-28 | 城云科技（中国）有限公司 | Enterprise credit risk assessment method, device and application |
| CN114282840A (en) * | 2021-12-31 | 2022-04-05 | 联通(广东)产业互联网有限公司 | Enterprise operation safety risk assessment method, system, equipment and medium |
| CN115392787A (en) * | 2022-09-28 | 2022-11-25 | 中国建设银行股份有限公司 | Enterprise risk assessment method, device, equipment, storage medium and program product |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116362940A (en) * | 2023-04-01 | 2023-06-30 | 山东联信数字科技有限公司 | Enterprise credit assessment method, system, device and storage medium based on big data analysis |
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