CN112598430A - Intelligent credit evaluation optimization method and system - Google Patents
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Abstract
The invention discloses an intelligent credit evaluation optimization method and system, wherein the method comprises the following steps: collecting historical data of a power market trading subject in the aspects of performance willingness, performance capability and performance, repayment capability rating and financial guarantee in a power market; calling a credit management server, and calculating a credit evaluation score of the electric power market trading subject based on historical data; calculating the upper limit and the lower limit of the overall credit evaluation score threshold range of the electric power market; and classifying the trading bodies of the electric power market and adjusting the trading quotas. The method optimizes and eliminates the influence of the ineffectiveness factors on the overall credit behavior of the market trading subject by calculating the upper limit and the lower limit of the reasonable value range of the overall credit score of the electric power market.
Description
Technical Field
The invention relates to an intelligent credit evaluation optimization method and system, and belongs to the technical field of credit evaluation optimization.
Background
In order to accelerate the construction of a credit system of a power market main body, strengthen credit management and build an honest power market environment, each power trading center develops credit evaluation research of the power market main body in recent years and constructs a credit evaluation index system aiming at four types of power market main bodies, namely a power generation enterprise, a power selling enterprise, a power user and a power grid enterprise. The credit rating of the electric power market main body is evaluated based on the credit rating by performing credit rating on the indexes of the electric power market main body such as finance, credit status, marketable trading ability, trading management, contract management, settlement management and the like.
In order to better perform credit evaluation, transaction credit guarantee and negative behavior observation work of the whole market body, a credit evaluation method and a credit evaluation system of the electric power market transaction body, which flexibly match various transaction rules, need to be established, and the electric power market credit management work of each electric power transaction center of the southern power grid is comprehensively supported to be efficiently and accurately developed.
Meanwhile, the conventional credit evaluation method of the electric power market trading subject is to punish and reward the market trading subject according to the absolute value of the credit evaluation of the electric power market trading subject. This simple method does not take into account the effect of external invariance factors on the overall credit behavior of the market trading entity. For example, rainfall is reduced due to weather reasons, most hydraulic power plants cannot complete contract electric quantity in time, so that credit scoring is generally low, and the rationality of credit evaluation cannot be well reflected.
Disclosure of Invention
The invention aims to overcome the technical defects in the prior art and provides an intelligent credit evaluation optimization method and system, which comprise a credit management system, a settlement system, a short message system, a client server system and a trading center system. The client service end system collects historical data of the electric power market trading main body in the aspects of the electric power market such as the performance desire, performance capability and performance, repayment capability rating, financial guarantee and the like, calls the credit management system, calculates the credit evaluation score of the electric power market trading main body and calculates the range of the reasonable value of the overall credit score of the electric power market. And according to the range of the reasonable value of the integral credit score, performing reward and punishment on the electric power market trading main body, wherein the credit evaluation score is lower than the electric power market trading main body with the lower limit of the reasonable value range, so that the trading quota is reduced, and the credit evaluation score is higher than the electric power market trading main body with the upper limit of the reasonable value range, so that the trading quota is increased. The invention eliminates the influence of the ineffectiveness factors on the overall credit behavior of the market trading subject by calculating the upper limit and the lower limit of the reasonable value range of the overall credit score, and embodies the fairness and justice of credit evaluation.
The invention specifically adopts the following technical scheme: an intelligent credit evaluation optimization method comprises the following steps:
step SS 1: collecting historical data of a power market trading subject in the aspects of performance willingness, performance capability and performance, repayment capability rating and financial guarantee in a power market;
step SS 2: calling a credit management server, and calculating a credit evaluation score of the electric power market trading subject based on historical data;
step SS 3: calculating the upper limit and the lower limit of the overall credit evaluation score threshold range of the electric power market;
step SS 4: and classifying the trading bodies of the electric power market and adjusting the trading quotas.
As a preferred embodiment, the electric power market trading subject in the step SS1 includes electric power consumers, power generation enterprises, power selling companies, and power grid enterprises; the historical data in the step SS1 is obtained from an electric power market trading server, a settlement server and a market management system; the historical data collected in the step SS1 is data in a credit evaluation period; the sampling frequency is to acquire all data required by the credit evaluation at one time; according to the rules of credit evaluation, a credit evaluation score is calculated for each market trading entity based on the collected historical data.
As a preferred embodiment, the credit management server in step SS2 is loaded with a public interface class of credit evaluation algorithm module, and the public interface class of credit evaluation algorithm module is loaded with a data collection method and a credit evaluation calculation method; the credit evaluation algorithm module executes a plurality of aspects of assets of the transaction body, fund examples of legal persons, transaction performance rate and integrity of registration data to evaluate the transaction credit of the transaction body; each credit evaluation algorithm module inherits the public interface class of the credit evaluation algorithm module and realizes a specific data acquisition method and a credit evaluation calculation method; and dynamically loading a credit evaluation algorithm module by using a java reflection mechanism, calling a data acquisition method and a credit evaluation calculation method of the credit evaluation algorithm module, and obtaining the credit evaluation score of the electric power market trading subject through a return value of the credit evaluation calculation method.
As a preferred embodiment, step SS3 specifically includes:
calculate the average of all electricity market trading subject credits:
wherein pij represents the jth credit evaluation score of the ith electric power market trading subject, n represents the number of the electric power market trading subjects, and m represents the number of the credit evaluation rules;
then, calculating the standard deviation sigma of credit evaluation scores of all the electric power market trading subjects;
and finally, calculating an upper limit pmax and a lower limit pmin of the overall credit evaluation score threshold range of the power market.
As a preferred embodiment, step SS4 specifically includes: classifying the trading subjects of the electric power market according to the upper limit and the lower limit of the reasonable value range of the overall credit evaluation score of the electric power market obtained by calculation; the classification of the electric power market trading bodies refers to the adjustment of the trading bodies outside the upper limit and the lower limit of the score threshold range according to the upper limit and the lower limit of the credit evaluation score threshold range; and the trading quota of the electric power market trading main body with the credit evaluation score lower than the lower limit of the credit evaluation score threshold range is reduced, and the trading quota of the electric power market trading main body with the credit evaluation score higher than the upper limit of the credit evaluation score threshold range is increased.
The invention also provides an intelligent credit evaluation optimization system, which comprises:
a historical data acquisition module to perform: collecting historical data of a power market trading subject in the aspects of performance willingness, performance capability and performance, repayment capability rating and financial guarantee in a power market;
a credit evaluation management module for performing: calling a credit management server, and calculating a credit evaluation score of the electric power market trading subject based on historical data;
a credit evaluation calculation module to perform: calculating the upper limit and the lower limit of the overall credit evaluation score threshold range of the electric power market;
a transaction coordination adjustment module for performing: and classifying the trading bodies of the electric power market and adjusting the trading quotas.
As a preferred embodiment, the electric power market trading subject includes electric power consumers, power generation enterprises, power selling companies and power grid enterprises; the historical data is obtained from an electric power market trading server, a settlement server and a market management system; the historical data is data sampled in a credit evaluation period; the sampling frequency is to acquire all data required by the credit evaluation at one time; according to the rules of credit evaluation, a credit evaluation score is calculated for each market trading entity based on the collected historical data.
As a preferred embodiment, the credit management server is loaded with a public interface class of a credit evaluation algorithm module, and the public interface class of the credit evaluation algorithm module is loaded with a data acquisition method and a credit evaluation calculation method; the credit evaluation algorithm module executes a plurality of aspects of assets of the transaction body, fund examples of legal persons, transaction performance rate and integrity of registration data to evaluate the transaction credit of the transaction body; each credit evaluation algorithm module inherits the public interface class of the credit evaluation algorithm module and realizes a specific data acquisition method and a credit evaluation calculation method; and dynamically loading a credit evaluation algorithm module by using a java reflection mechanism, calling a data acquisition method and a credit evaluation calculation method of the credit evaluation algorithm module, and obtaining the credit evaluation score of the electric power market trading subject through a return value of the credit evaluation calculation method.
As a preferred embodiment, the credit evaluation calculating module specifically includes:
calculate the average of all electricity market trading subject credits:
wherein pij represents the jth credit evaluation score of the ith electric power market trading subject, n represents the number of the electric power market trading subjects, and m represents the number of the credit evaluation rules;
then, calculating the standard deviation sigma of credit evaluation scores of all the electric power market trading subjects;
and finally, calculating an upper limit pmax and a lower limit pmin of the overall credit evaluation score threshold range of the power market.
As a preferred embodiment, the transaction coordination adjustment module specifically includes: classifying the trading subjects of the electric power market according to the upper limit and the lower limit of the reasonable value range of the overall credit evaluation score of the electric power market obtained by calculation; the classification of the electric power market trading bodies refers to the adjustment of the trading bodies outside the upper limit and the lower limit of the score threshold range according to the upper limit and the lower limit of the credit evaluation score threshold range; and the trading quota of the electric power market trading main body with the credit evaluation score lower than the lower limit of the credit evaluation score threshold range is reduced, and the trading quota of the electric power market trading main body with the credit evaluation score higher than the upper limit of the credit evaluation score threshold range is increased.
The invention achieves the following beneficial effects: compared with the prior art, the credit evaluation algorithm module public interface type, the data acquisition method and the credit evaluation calculation method are realized, so that each credit evaluation algorithm module can be dynamically loaded by the system, and the calling of various evaluation indexes is supported. The influence of the ineffectiveness factors on the overall credit behavior of the market trading body is eliminated by calculating the upper limit and the lower limit of the reasonable value range of the overall credit score, and the fairness and justice of credit evaluation are embodied. The method comprises the steps of firstly calculating the credit evaluation score of a trading subject of the electric power market based on historical data, and finally obtaining the credit value based on the upper limit and the lower limit of the reasonable value range of the overall credit evaluation score of the electric power market.
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FIG. 1 is a schematic diagram of the topology of the preferred embodiment of the intelligent credit evaluation optimization method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1: as shown in fig. 1, the present invention provides an intelligent credit evaluation optimization method, which includes the following steps:
step SS 1: collecting historical data of a power market trading subject in the aspects of performance willingness, performance capability and performance, repayment capability rating and financial guarantee in a power market;
step SS 2: calling a credit management server, and calculating a credit evaluation score of the electric power market trading subject based on historical data;
step SS 3: calculating the upper limit and the lower limit of the overall credit evaluation score threshold range of the electric power market;
step SS 4: and classifying the trading bodies of the electric power market and adjusting the trading quotas.
As a preferred embodiment, the electric power market trading subject in the step SS1 includes electric power consumers, power generation enterprises, power selling companies, and power grid enterprises; the historical data in the step SS1 is obtained from an electric power market trading server, a settlement server and a market management system; the historical data collected in the step SS1 is data in a credit evaluation period; the sampling frequency is to acquire all data required by the credit evaluation at one time; according to the rules of credit evaluation, a credit evaluation score is calculated for each market trading entity based on the collected historical data.
Optionally, the credit management server in step SS2 is loaded with a public interface class of a credit evaluation algorithm module, where the public interface class of the credit evaluation algorithm module is loaded with a data collection method and a credit evaluation calculation method; the credit evaluation algorithm module executes a plurality of aspects of assets of the transaction body, fund examples of legal persons, transaction performance rate and integrity of registration data to evaluate the transaction credit of the transaction body; each credit evaluation algorithm module inherits the public interface class of the credit evaluation algorithm module and realizes a specific data acquisition method and a credit evaluation calculation method; and dynamically loading a credit evaluation algorithm module by using a java reflection mechanism, calling a data acquisition method and a credit evaluation calculation method of the credit evaluation algorithm module, and obtaining the credit evaluation score of the electric power market trading subject through a return value of the credit evaluation calculation method.
Optionally, step SS3 specifically includes:
calculate the average of all electricity market trading subject credits:
wherein pij represents the jth credit evaluation score of the ith electric power market trading subject, n represents the number of the electric power market trading subjects, and m represents the number of the credit evaluation rules;
then, calculating the standard deviation sigma of credit evaluation scores of all the electric power market trading subjects;
and finally, calculating an upper limit pmax and a lower limit pmin of the overall credit evaluation score threshold range of the power market.
Optionally, step SS4 specifically includes: classifying the trading subjects of the electric power market according to the upper limit and the lower limit of the reasonable value range of the overall credit evaluation score of the electric power market obtained by calculation; the classification of the electric power market trading bodies refers to the adjustment of the trading bodies outside the upper limit and the lower limit of the score threshold range according to the upper limit and the lower limit of the credit evaluation score threshold range; and the trading quota of the electric power market trading main body with the credit evaluation score lower than the lower limit of the credit evaluation score threshold range is reduced, and the trading quota of the electric power market trading main body with the credit evaluation score higher than the upper limit of the credit evaluation score threshold range is increased.
Example 2: the invention also provides an intelligent credit evaluation optimization system, which comprises:
a historical data acquisition module to perform: collecting historical data of a power market trading subject in the aspects of performance willingness, performance capability and performance, repayment capability rating and financial guarantee in a power market;
a credit evaluation management module for performing: calling a credit management server, and calculating a credit evaluation score of the electric power market trading subject based on historical data;
a credit evaluation calculation module to perform: calculating the upper limit and the lower limit of the overall credit evaluation score threshold range of the electric power market;
a transaction coordination adjustment module for performing: and classifying the trading bodies of the electric power market and adjusting the trading quotas.
As a preferred embodiment, the electric power market trading subject includes electric power consumers, power generation enterprises, power selling companies and power grid enterprises; the historical data is obtained from an electric power market trading server, a settlement server and a market management system; the historical data is data sampled within a credit evaluation period, and is generally 1 month or 1 quarter; the sampling frequency is to acquire all data required by the credit evaluation at one time; according to the rules of credit evaluation, a credit evaluation score is calculated for each market trading entity based on the collected historical data.
As a preferred embodiment, the credit management server is loaded with a public interface class of a credit evaluation algorithm module, and the public interface class of the credit evaluation algorithm module is loaded with a data acquisition method and a credit evaluation calculation method; the credit evaluation algorithm module executes a plurality of aspects of assets of the transaction body, fund examples of legal persons, transaction performance rate and integrity of registration data to evaluate the transaction credit of the transaction body; each credit evaluation algorithm module inherits the public interface class of the credit evaluation algorithm module and realizes a specific data acquisition method and a credit evaluation calculation method; and dynamically loading a credit evaluation algorithm module by using a java reflection mechanism, calling a data acquisition method and a credit evaluation calculation method of the credit evaluation algorithm module, and obtaining the credit evaluation score of the electric power market trading subject through a return value of the credit evaluation calculation method. "inheritance" is a term for object-oriented programming, and can be understood as the process by which a class obtains methods and properties from another class. If class b inherits class a, then class b possesses the properties and methods of class a. Inheritance uses extensions keys.
Further, the method and formula for calculating the credit evaluation score pij of the electric power market trading entity are calculated according to the published "electric power market entity credit management implementation rules".
Further, multiple credit evaluation algorithm modules may be loaded simultaneously.
As a preferred embodiment, the credit evaluation calculating module specifically includes:
calculate the average of all electricity market trading subject credits:
wherein pij represents the jth credit evaluation score of the ith electric power market trading subject, n represents the number of the electric power market trading subjects, and m represents the number of the credit evaluation rules;
then, calculating the standard deviation sigma of credit evaluation scores of all the electric power market trading subjects;
and finally, calculating an upper limit pmax and a lower limit pmin of the overall credit evaluation score threshold range of the power market.
As a preferred embodiment, the transaction coordination adjustment module specifically includes: classifying the trading subjects of the electric power market according to the upper limit and the lower limit of the reasonable value range of the overall credit evaluation score of the electric power market obtained by calculation; the classification of the electric power market trading bodies refers to the adjustment of the trading bodies outside the upper limit and the lower limit of the score threshold range according to the upper limit and the lower limit of the credit evaluation score threshold range; and the trading quota of the electric power market trading main body with the credit evaluation score lower than the lower limit of the credit evaluation score threshold range is reduced, and the trading quota of the electric power market trading main body with the credit evaluation score higher than the upper limit of the credit evaluation score threshold range is increased.
Further, the credit evaluation score of the electric power market trading subject is as follows: pij. Further, the transaction quota can be reduced or increased, and can be set to be in the range of 10% -20% according to the experience of engineering implementation.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. An intelligent credit evaluation optimization method is characterized by comprising the following steps:
step SS 1: collecting historical data of a power market trading subject in the aspects of performance willingness, performance capability and performance, repayment capability rating and financial guarantee in a power market;
step SS 2: calling a credit management server, and calculating a credit evaluation score of the electric power market trading subject based on historical data;
step SS 3: calculating the upper limit and the lower limit of the overall credit evaluation score threshold range of the electric power market;
step SS 4: and classifying the trading bodies of the electric power market and adjusting the trading quotas.
2. The intelligent credit evaluation optimization method of claim 1, wherein the electricity market transaction subjects of the step SS1 include electricity consumers, electricity generation enterprises, electricity selling companies and power grid enterprises; the historical data in the step SS1 is obtained from an electric power market trading server, a settlement server and a market management system; the historical data collected in the step SS1 is data in a credit evaluation period; the sampling frequency is to acquire all data required by the credit evaluation at one time; according to the rules of credit evaluation, a credit evaluation score is calculated for each market trading entity based on the collected historical data.
3. The intelligent credit evaluation optimization method of claim 1, wherein the credit management server in step SS2 is loaded with a common interface class of credit evaluation algorithm modules, and the common interface class of credit evaluation algorithm modules is loaded with a data collection method and a credit evaluation calculation method; the credit evaluation algorithm module executes a plurality of aspects of assets of the transaction body, fund examples of legal persons, transaction performance rate and integrity of registration data to evaluate the transaction credit of the transaction body; each credit evaluation algorithm module inherits the public interface class of the credit evaluation algorithm module and realizes a specific data acquisition method and a credit evaluation calculation method; and dynamically loading a credit evaluation algorithm module by using a java reflection mechanism, calling a data acquisition method and a credit evaluation calculation method of the credit evaluation algorithm module, and obtaining the credit evaluation score of the electric power market trading subject through a return value of the credit evaluation calculation method.
4. The intelligent credit evaluation optimization method of claim 1, wherein the step SS3 specifically comprises:
calculate the average of all electricity market trading subject credits:
wherein pij represents the jth credit evaluation score of the ith electric power market trading subject, n represents the number of the electric power market trading subjects, and m represents the number of the credit evaluation rules;
then, calculating the standard deviation sigma of credit evaluation scores of all the electric power market trading subjects;
and finally, calculating an upper limit pmax and a lower limit pmin of the overall credit evaluation score threshold range of the power market.
5. The intelligent credit evaluation optimization method of claim 1, wherein the step SS4 specifically comprises: classifying the trading subjects of the electric power market according to the upper limit and the lower limit of the reasonable value range of the overall credit evaluation score of the electric power market obtained by calculation; the classification of the electric power market trading bodies refers to the adjustment of the trading bodies outside the upper limit and the lower limit of the score threshold range according to the upper limit and the lower limit of the credit evaluation score threshold range; and the trading quota of the electric power market trading main body with the credit evaluation score lower than the lower limit of the credit evaluation score threshold range is reduced, and the trading quota of the electric power market trading main body with the credit evaluation score higher than the upper limit of the credit evaluation score threshold range is increased.
6. An intelligent credit evaluation optimization system, comprising:
a historical data acquisition module to perform: collecting historical data of a power market trading subject in the aspects of performance willingness, performance capability and performance, repayment capability rating and financial guarantee in a power market;
a credit evaluation management module for performing: calling a credit management server, and calculating a credit evaluation score of the electric power market trading subject based on historical data;
a credit evaluation calculation module to perform: calculating the upper limit and the lower limit of the overall credit evaluation score threshold range of the electric power market;
a transaction coordination adjustment module for performing: and classifying the trading bodies of the electric power market and adjusting the trading quotas.
7. The intelligent credit evaluation optimization system of claim 6, wherein the electricity market trading entity comprises electricity consumers, electricity generation enterprises, electricity selling companies, and power grid enterprises; the historical data is obtained from an electric power market trading server, a settlement server and a market management system; the historical data is data sampled in a credit evaluation period; the sampling frequency is to acquire all data required by the credit evaluation at one time; according to the rules of credit evaluation, a credit evaluation score is calculated for each market trading entity based on the collected historical data.
8. The intelligent credit evaluation optimization system of claim 6, wherein the credit management server is loaded with a public interface class of credit evaluation algorithm modules, the public interface class of credit evaluation algorithm modules being loaded with a data collection method and a credit evaluation calculation method; the credit evaluation algorithm module executes a plurality of aspects of assets of the transaction body, fund examples of legal persons, transaction performance rate and integrity of registration data to evaluate the transaction credit of the transaction body; each credit evaluation algorithm module inherits the public interface class of the credit evaluation algorithm module and realizes a specific data acquisition method and a credit evaluation calculation method; and dynamically loading a credit evaluation algorithm module by using a java reflection mechanism, calling a data acquisition method and a credit evaluation calculation method of the credit evaluation algorithm module, and obtaining the credit evaluation score of the electric power market trading subject through a return value of the credit evaluation calculation method.
9. The intelligent credit evaluation optimization system of claim 6, wherein the credit evaluation calculation module specifically comprises:
calculate the average of all electricity market trading subject credits:
wherein pij represents the jth credit evaluation score of the ith electric power market trading subject, n represents the number of the electric power market trading subjects, and m represents the number of the credit evaluation rules;
then, calculating the standard deviation sigma of credit evaluation scores of all the electric power market trading subjects;
and finally, calculating an upper limit pmax and a lower limit pmin of the overall credit evaluation score threshold range of the power market.
10. The intelligent credit evaluation optimization system of claim 1, wherein the trade coordination adjustment module specifically comprises: classifying the trading subjects of the electric power market according to the upper limit and the lower limit of the reasonable value range of the overall credit evaluation score of the electric power market obtained by calculation; the classification of the electric power market trading bodies refers to the adjustment of the trading bodies outside the upper limit and the lower limit of the score threshold range according to the upper limit and the lower limit of the credit evaluation score threshold range; and the trading quota of the electric power market trading main body with the credit evaluation score lower than the lower limit of the credit evaluation score threshold range is reduced, and the trading quota of the electric power market trading main body with the credit evaluation score higher than the upper limit of the credit evaluation score threshold range is increased.
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| CN107563589A (en) * | 2017-07-26 | 2018-01-09 | 广东电力交易中心有限责任公司 | A kind of electricity market main body credit management system and management method |
| CN108399453A (en) * | 2018-01-24 | 2018-08-14 | 国家电网公司 | A kind of Electric Power Customer Credit Rank Appraisal method and apparatus |
| CN110889750A (en) * | 2019-12-06 | 2020-03-17 | 昆明电力交易中心有限责任公司 | Electric power market trading subject credit evaluation method |
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