Electric power system credit risk management method and device and computer equipment

Abstract

The invention provides an electric power system credit risk management method. The method comprises the steps of obtaining electric power market data corresponding to an electric power system; adopting a plurality of simplified risk measurement algorithms to perform credit risk assessment on the power market data to obtain a credit risk assessment result corresponding to each simplified risk measurement algorithm; determining a credit risk level according to each credit risk assessment result; and generating a risk control strategy according to the credit risk level, and adjusting credit risk control measures of the power system according to the risk control strategy. According to the method, the power market data is evaluated by adopting a plurality of simplified risk measurement algorithms, so that the technical defects caused by the existing complex system architecture can be effectively reduced.

Description

Technical Field

[0001] The present application relates to the field of power risk technology, and in particular to a power system credit risk management method, device and computer equipment. Background Art

[0002] With the rapid development and complexity of the power market, the importance of credit risk management in the power system has become increasingly prominent. However, the existing credit risk management system faces many challenges in practical application, especially the complexity of the system architecture, which seriously restricts the efficiency and scalability of credit risk management in the power system.

[0003] At present, many power system credit risk management solutions adopt complex system architectures, involving multiple distributed modules, complex database architectures, and different computing engines. These systems usually include multiple subsystems, such as data acquisition, model training, simulation calculations, and credit exposure calculations, which require high integration and coordination. Although this complex architecture can meet the diverse risk management needs to a certain extent, it also brings many problems. First, the complexity of the system architecture requires a lot of technical support and maintenance work, which increases operating costs and reduces reliability and flexibility; second, operators need long-term training to master highly specialized technologies, and the complexity of the system configuration and debugging process further increases the difficulty of operation and management costs. Based on this, the existing technology mainly has the technical defects of complex system architecture. Summary of the invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical deficiencies, especially the technical defect of complex system architecture in the prior art.

[0005] In a first aspect, the present application provides a method for managing credit risk of a power system, the method comprising:

[0006] Obtain power market data corresponding to the power system;

[0007] Use multiple simplified risk measurement algorithms to conduct credit risk assessment on power market data respectively, and obtain the credit risk assessment results corresponding to each simplified risk measurement algorithm;

[0008] Determine the credit risk level based on the results of each credit risk assessment;

[0009] Based on the credit risk level, a risk control strategy is generated, and the credit risk control measures of the power system are adjusted according to the risk control strategy.

[0010] In one embodiment, the simplified risk measurement algorithm includes a VaR risk assessment algorithm, the formula of which is:

[0011]

[0012] in, represents the expected rate of return, represents the standard deviation of returns, Indicates a given confidence level The quantile of the standard normal distribution of .

[0013] In one embodiment, the simplified risk measurement algorithm includes a real-time data flow analysis algorithm, whose formula is:

[0014]

[0015] in, represents the electricity market price at the current time, Indicates the demand, Indicates weather conditions.

[0016] In one embodiment, the step of determining the credit risk level according to each credit risk assessment result includes:

[0017] The weighted average of each credit risk assessment result is calculated, and the credit risk level is obtained based on the weighted average calculation result.

[0018] In one embodiment, the step of generating a risk control strategy according to the credit risk level includes:

[0019] Determine the compliance requirements corresponding to the power system, and generate risk control strategies based on the credit risk level and compliance requirements.

[0020] In one embodiment, the step of adjusting the credit risk control measures of the power system according to the risk control strategy includes:

[0021] According to the risk control strategy, the fuzzy logic controller is used to adjust the credit risk control measures of the power system.

[0022] In one embodiment, the method further comprises:

[0023] Generate credit risk reports based on credit risk levels, risk control strategies and credit risk control measures.

[0024] In a second aspect, the present application provides a power system credit risk management device, the device comprising:

[0025] The power market data acquisition module is used to acquire the power market data corresponding to the power system;

[0026] A credit risk assessment result determination module is used to use multiple simplified risk measurement algorithms to perform credit risk assessment on power market data respectively, and obtain the credit risk assessment result corresponding to each simplified risk measurement algorithm;

[0027] A credit risk level determination module is used to determine the credit risk level based on various credit risk assessment results;

[0028] The credit risk control measures adjustment module is used to generate risk control strategies according to the credit risk level and adjust the credit risk control measures of the power system according to the risk control strategies.

[0029] In a third aspect, the present application provides a storage medium: the storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the power system credit risk management method as described in any one of the above embodiments.

[0030] In a fourth aspect, the present application provides a computer device, comprising: one or more processors, and a memory;

[0031] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the steps of the power system credit risk management method in any one of the above embodiments are executed.

[0032] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0033] The power system credit risk management method, device and computer equipment provided in this application can effectively reduce the technical defects brought by the existing complex system architecture by adopting multiple simplified risk measurement algorithms to evaluate the power market data. This method does not need to rely on complex distributed modules, database architecture and computing engines, simplifies the system structure, greatly reduces the workload of technical support and maintenance, reduces operating costs, and improves the reliability and flexibility of the system. At the same time, the simplified risk assessment process reduces the difficulty of operation, reduces the need for professional training of operators and management costs. Compared with traditional methods, this method is more efficient, easy to implement and expand, and can better meet the needs of the rapid development of the power market. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 A flow chart of a method for managing credit risk of a power system provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of the structure of a power system credit risk management device provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0039] The present application provides a method for managing credit risk in a power system. The following embodiments are described by taking the method applied to a computer device as an example. It is understood that the computer device can be any device with data processing functions, including but not limited to a single server, a server cluster, a personal laptop computer, a desktop computer, etc. Figure 1 As shown, the present application provides a chat corpus annotation method, the method comprising:

[0040] S101: Acquire power market data corresponding to the power system.

[0041] The power system refers to the overall system consisting of power generation, transmission, distribution and consumption facilities, which is used for the production, transmission and distribution of electricity to ensure that electricity can be supplied to users on demand. Power market data refers to various data related to the power market, including but not limited to power transaction data, market prices, supply and demand conditions, power generation and consumption expectations and other information.

[0042] In this step, various data related to the power market can be collected in real time through the interface or database connection with the power market. For example, real-time transaction price and electricity data can be obtained from the trading platform, and credit rating information of market entities can be obtained from credit assessment agencies. Furthermore, the acquired data can be pre-processed, such as denoising, standardization, formatting, etc., to ensure the validity and consistency of the data.

[0043] It is understandable that by obtaining power market data, not only can a large amount of data be obtained quickly and accurately, but also human intervention can be reduced, and the efficiency and accuracy of data collection can be improved. This process reduces delays and errors caused by manual operations, ensuring that power system management can make decisions based on the latest and most reliable data when conducting risk assessments and strategy adjustments, thereby improving the flexibility and responsiveness of the power system.

[0044] S102: Using multiple simplified risk measurement algorithms, respectively perform credit risk assessment on the power market data, and obtain credit risk assessment results corresponding to each simplified risk measurement algorithm.

[0045] Among them, the simplified risk measurement algorithm refers to the process of simplifying the complex risk measurement model in the credit risk assessment process so that it can be calculated in a more concise and efficient way while retaining the main analysis function. Simplified algorithms usually increase processing speed by reducing computational complexity and reducing the amount of data required. Credit risk assessment refers to the assessment of the risk of default or default that power market participants may face in transactions, and predicting potential risks by analyzing relevant data and factors. The credit risk assessment results are the assessment data obtained by the risk assessment algorithm, which reflects the level of credit risk of all parties in the power market.

[0046] In this step, you can first select several simplified risk measurement algorithms, which may include probability models, statistical analysis methods, regression models, rule-based risk assessment models, lightweight machine learning algorithms, etc. These algorithms have been optimized in the specific application of the power market and can efficiently process large amounts of data. After that, the power market data is input into these algorithms, and the algorithms will calculate the corresponding credit risk value based on the input data. Each simplified risk measurement algorithm independently performs credit risk assessment and generates its own assessment results. These results will reflect the degree of credit risk of different market participants in the power market. For example, assuming that there are multiple power generation companies and power users in the power market, different simplified algorithms are used to conduct credit risk assessments on these market entities. For example, for power generation companies, algorithms based on historical power generation and transaction default data can be used; for power users, algorithms based on power consumption fluctuations and payment records can be used.

[0047] It can be understood that the use of multiple simplified risk measurement algorithms to conduct credit risk assessment on power market data can effectively improve the accuracy and efficiency of risk assessment. Through the complementary effects of different algorithms, a variety of risk assessment perspectives can be covered to ensure that the assessment results are more comprehensive and more adaptable when facing complex and dynamic power markets. This method simplifies traditional complex models, making the calculation process faster and cheaper, while reducing the need for a large amount of computing resources.

[0048] S103: Determine the credit risk level based on each credit risk assessment result.

[0049] Among them, the credit risk level is a quantitative description of the credit risk status of electricity market participants or the market as a whole.

[0050] In this step, the credit risk assessment results generated by multiple simplified risk measurement algorithms are first read, which may include multiple dimensions such as default probability, potential loss amount, credit score, etc., and then these results can be comprehensively analyzed through preset rules or models. By analyzing all assessment results, the comprehensive credit risk score can be converted into a clear credit risk level, such as high risk, medium risk, low risk, etc. For example, even if a market entity has a low probability of default, it may still be judged as a high risk if its potential loss is extremely high.

[0051] It can be understood that by determining the credit risk level based on the results of each credit risk assessment, the assessment data of different algorithms can be quickly and accurately summarized, avoiding the one-sidedness of a single indicator, providing a more comprehensive and accurate risk view, and ensuring real-time response to potential risks when facing a complex electricity market environment.

[0052] S104: Generate a risk control strategy based on the credit risk level, and adjust the credit risk control measures of the power system according to the risk control strategy.

[0053] Among them, risk control strategy is a series of measures or action plans for managing and reducing credit risk, which are formulated based on the results of market risk assessment and are intended to effectively respond to and mitigate credit risks that may arise in the power system. Credit risk control measures refer to specific actions taken in the power system based on risk control strategies, such as adjusting credit limits, increasing guarantees, setting up prepayments, etc., to reduce risk exposure.

[0054] In this step, according to the determined credit risk level, the preset risk control strategy generation module is called, which automatically generates the corresponding risk control strategy based on the mapping relationship between the risk level and the control strategy. For example, for low-risk market entities, the strategy may be to maintain the existing trading conditions; for medium-risk market entities, the strategy may be to increase the transaction margin or shorten the payment cycle; for high-risk market entities, the strategy may be to suspend trading or require additional guarantees. After the strategy is generated, the credit risk control measures of the power system are adjusted according to these strategies. For example, the credit limits of both parties to the transaction can be dynamically adjusted, stricter margins can be required, and higher prepayments can be set. In addition, these measures will be updated in real time as the risk level changes to ensure that the control strategy matches the market environment and risk level. For example, if the credit risk level of a market entity rises due to market fluctuations, the computer equipment can automatically trigger a risk warning and adjust the control measures, such as suspending some of its trading rights or requiring a reassessment of its credit status.

[0055] It can be understood that generating and adjusting risk control strategies based on credit risk levels can achieve dynamic management of credit risk in the power system, that is, ensuring that more stringent control measures are taken when the risk is high, and that requirements can be appropriately relaxed when the risk is low, thereby optimizing resource allocation and reducing unnecessary costs caused by excessive intervention. In addition, the automated strategy generation and adjustment process can reduce the deviation and delay of human judgment, improve response speed, and flexibly adjust control measures according to real-time changing market conditions, so that the power system can more effectively respond to credit risks and ensure market stability and security.

[0056] In the above embodiment, by using multiple simplified risk measurement algorithms to evaluate the power market data, the technical defects caused by the existing complex system architecture can be effectively reduced. This method does not need to rely on complex distributed modules, database architecture and computing engines, simplifies the system structure, greatly reduces the workload of technical support and maintenance, reduces operating costs, and improves the reliability and flexibility of the system. At the same time, the simplified risk assessment process reduces the difficulty of operation, reduces the need for professional training of operators and management costs. Compared with traditional methods, this method is more efficient, easy to implement and expand, and can better meet the needs of the rapid development of the power market.

[0057] In one embodiment, the simplified risk measurement algorithm includes a VaR risk assessment algorithm, the formula of which is:

[0058]

[0059] in, represents the expected rate of return, represents the standard deviation of returns, Indicates a given confidence level The quantile of the standard normal distribution of .

[0060] Specifically, the VaR risk assessment algorithm provides a clear and intuitive risk measurement indicator for power system credit risk management by quantifying the maximum loss that may be incurred at a given confidence level.

[0061] In one embodiment, the simplified risk measurement algorithm includes a real-time data flow analysis algorithm, whose formula is:

[0062]

[0063] in, represents the electricity market price at the current time, Indicates the demand, Indicates weather conditions.

[0064] Specifically, the real-time data stream analysis algorithm can dynamically and in real time assess the risk level of the power system at different time points by incorporating power market prices, demand, and weather conditions into the risk measurement model. For example, by real-time monitoring of price fluctuations and demand changes, potential market risks can be warned in advance; combined with the analysis of weather conditions, it can also effectively respond to supply disruptions or demand surges caused by extreme weather. This real-time risk assessment method can quickly respond to market fluctuations, reduce delays in human judgment, and enhance the flexibility and resilience of the power market.

[0065] In one embodiment, the step of determining the credit risk level according to each credit risk assessment result includes:

[0066] The weighted average of each credit risk assessment result is calculated, and the credit risk level is obtained based on the weighted average calculation result.

[0067] Specifically, a weight is assigned to each evaluation result according to its importance or credibility. For example, some evaluation models may be more accurate under certain market conditions, so they are given higher weights. A comprehensive credit risk level is calculated by combining the various evaluation results and their corresponding weights through a weighted average formula. Finally, based on the result of the weighted average calculation, the credit risk level of the market or individual is obtained, and an operational risk level or score is formed. In an example, it is assumed that there are multiple market entities in the power market, and each market entity is evaluated by multiple algorithms to generate different credit risk indicators. The computer device can summarize these indicators and determine their credit risk level according to preset weights and thresholds. For example, for a market entity, its default probability assessment result is 10 points, the weight is 0.4, the potential loss assessment result is 20 points, the weight is 0.3, and the credit score assessment result is 15 points, the weight is 0.3, then its comprehensive credit risk score is: 10×0.4+20×0.3+15×0.3=15.5, according to the preset rules, the credit risk level of the market entity is judged to be medium risk.

[0068] In this embodiment, by performing weighted average calculation on each credit risk assessment result, it is possible to integrate information from multiple sources to obtain a more comprehensive and accurate credit risk level. This approach can balance the advantages and disadvantages of different assessment models and improve the accuracy and reliability of the assessment results. By weighted averaging, not only can the comprehensive credit risk of the market be reflected, but also the weights can be adjusted according to the specific market environment, the adaptability of the model and the credibility of historical data, thereby providing a dynamic and flexible risk assessment result.

[0069] In one embodiment, the step of generating a risk control strategy according to the credit risk level includes:

[0070] Determine the compliance requirements corresponding to the power system, and generate risk control strategies based on the credit risk level and compliance requirements.

[0071] Among them, compliance requirements refer to the laws, regulations, industry standards and regulatory requirements that the power system must comply with during operation. These requirements involve market behavior, trading rules, information disclosure, risk management and other aspects.

[0072] Specifically, first, the compliance requirements corresponding to the power system can be obtained from the compliance database or documents issued by the regulatory agency through the data interface. These requirements may include trading limits, information disclosure rules, credit rating standards, etc. For example, regulators may require power market participants to increase trading margins or limit trading scales when the credit risk level is high. Next, the obtained compliance requirements are matched with the credit risk level of the market entity for analysis. For example, if the credit risk level of a market entity is "high" and the compliance requirements stipulate that high-risk market entities must suspend transactions or provide additional guarantees, specific risk control strategies are generated based on these requirements, requiring the market entity to suspend some transactions and provide additional guarantees before resuming transactions.

[0073] In this embodiment, by combining the credit risk level and compliance requirements to generate a risk control strategy, it is not only possible to ensure that the power system complies with laws, regulations and industry standards, but also to take flexible and effective response measures under different credit risk scenarios.

[0074] In one embodiment, the step of adjusting the credit risk control measures of the power system according to the risk control strategy includes:

[0075] According to the risk control strategy, the fuzzy logic controller is used to adjust the credit risk control measures of the power system.

[0076] Among them, the fuzzy logic controller is a control system based on fuzzy logic, which makes decisions by processing uncertainty and fuzzy information. The fuzzy logic controller can effectively control under imprecise or uncertain conditions by setting fuzzy rules and reasoning mechanisms.

[0077] Specifically, the fuzzy logic controller generates appropriate control signals and adjusts the credit risk control measures of the system by inputting fuzzy parameters in the risk control strategy, such as credit risk level, market demand, market price fluctuations, etc. For example, if the credit risk in the market is high, the guarantee requirements can be increased, the credit exposure can be reduced, or the transaction method can be adjusted to reduce the risk. According to the signal output by the fuzzy logic controller, the credit risk control measures in the power system are automatically adjusted. For example, when the risk is high, the guarantee requirements can be automatically increased or the transaction amount can be reduced; when the risk is low, the control measures can be appropriately relaxed.

[0078] In this embodiment, by using the fuzzy logic controller to adjust the credit risk control measures of the power system, it is possible to flexibly and dynamically respond to complex and uncertain credit risk changes in the market. The fuzzy logic controller can make decisions quickly according to different risk scenarios when information is incomplete or inaccurate, automatically adjust credit risk control measures, reduce manual intervention, and improve response speed.

[0079] In one embodiment, the method further comprises:

[0080] Generate credit risk reports based on credit risk levels, risk control strategies and credit risk control measures.

[0081] Specifically, the device first reads the relevant data on credit risk level, risk control strategy and credit risk control measures, including the credit risk rating of the market entity, the implemented control measures and the specific content of the risk control strategy. Then, the device integrates this data into a credit risk report based on the preset report template, which can include a credit risk overview, risk control strategy, control measure implementation and risk management recommendations.

[0082] In this embodiment, by generating a credit risk report, the power system can quickly and accurately evaluate and identify potential credit risks in a complex market environment.

[0083] The following describes the power system credit risk management device provided by the embodiment of the present application. The power system credit risk management device described below and the power system credit risk management method described above can be referenced to each other. Figure 2 As shown, the present application provides a power system credit risk management device, the device comprising:

[0084] The power market data acquisition module 201 is used to acquire power market data corresponding to the power system;

[0085] The credit risk assessment result determination module 202 is used to use multiple simplified risk measurement algorithms to perform credit risk assessment on the power market data respectively, and obtain the credit risk assessment result corresponding to each simplified risk measurement algorithm;

[0086] A credit risk level determination module 203, used to determine the credit risk level according to each credit risk assessment result;

[0087] The credit risk control measure adjustment module 204 is used to generate a risk control strategy according to the credit risk level, and adjust the credit risk control measures of the power system according to the risk control strategy.

[0088] In one embodiment, the simplified risk measurement algorithm includes a VaR risk assessment algorithm, the formula of which is:

[0089]

[0090] in, represents the expected rate of return, represents the standard deviation of returns, Indicates a given confidence level The quantile of the standard normal distribution of .

[0091] In one embodiment, the simplified risk measurement algorithm includes a real-time data flow analysis algorithm, whose formula is:

[0092]

[0093] in, represents the electricity market price at the current time, Indicates the demand, Indicates weather conditions.

[0094] In one embodiment, the credit risk level determination module 203 includes:

[0095] The credit risk water bottle determination unit is used to perform weighted average calculation on each credit risk assessment result, and obtain the credit risk level according to the weighted average calculation result.

[0096] In one embodiment, the credit risk control measure adjustment module 204 includes:

[0097] The risk control strategy generation unit is used to determine the compliance requirements corresponding to the power system and generate a risk control strategy in combination with the credit risk level and the compliance requirements.

[0098] In one embodiment, the credit risk control measure adjustment module 204 includes:

[0099] The credit risk control measure adjustment unit is used to adjust the credit risk control measures of the power system using a fuzzy logic controller according to the risk control strategy.

[0100] In one embodiment, the apparatus further comprises:

[0101] The credit risk report generation module is used to generate a credit risk report based on the credit risk level, risk control strategy and credit risk control measures.

[0102] In one embodiment, the present application also provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the power system credit risk management method as described in any of the above embodiments.

[0103] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the power system credit risk management method as described in any one of the above embodiments.

[0104] Indicatively, if Figure 3 As shown, Figure 3This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 may be provided as a server. Figure 3 The computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by a memory 301, for storing instructions executable by the processing component 302, such as an application. The application stored in the memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 302 is configured to execute instructions to perform the power system credit risk management method of any of the above embodiments.

[0105] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0106] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0107] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not clearly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. Herein, "one", "one", "said", "the" and "it" may also include plural forms, unless the context clearly indicates another way. A plurality refers to at least two cases, such as 2, 3, 5 or 8, etc. "And / or" includes any and all combinations of the relevant listed items.

[0108] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0109] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing credit risk in a power system, characterized in that: The method comprises: Obtain power market data corresponding to the power system; Using multiple simplified risk measurement algorithms to perform credit risk assessment on the power market data respectively, and obtaining credit risk assessment results corresponding to each simplified risk measurement algorithm; Determining the credit risk level based on each of the credit risk assessment results; A risk control strategy is generated according to the credit risk level, and credit risk control measures of the power system are adjusted according to the risk control strategy.

2. The power system credit risk management method according to claim 1, characterized in that: The simplified risk measurement algorithm includes a VaR risk assessment algorithm, and its formula is: in, represents the expected rate of return, represents the standard deviation of returns, Indicates a given confidence level The quantile of the standard normal distribution of .

3. The power system credit risk management method according to claim 1, characterized in that: The simplified risk measurement algorithm includes a real-time data flow analysis algorithm, and its formula is: in, represents the electricity market price at the current time, Indicates the demand, Indicates weather conditions.

4. The power system credit risk management method according to claim 1, characterized in that: The step of determining the credit risk level according to each of the credit risk assessment results comprises: A weighted average calculation is performed on each of the credit risk assessment results, and the credit risk level is obtained based on the weighted average calculation result.

5. The power system credit risk management method according to claim 1, characterized in that: The step of generating a risk control strategy according to the credit risk level comprises: The compliance requirements corresponding to the power system are determined, and the risk control strategy is generated in combination with the credit risk level and the compliance requirements.

6. The power system credit risk management method according to claim 1, characterized in that: The step of adjusting the credit risk control measures of the power system according to the risk control strategy comprises: According to the risk control strategy, a fuzzy logic controller is used to adjust the credit risk control measures of the power system.

7. The power system credit risk management method according to any one of claims 1 to 6, characterized in that: The method further comprises: A credit risk report is generated based on the credit risk level, the risk control strategy and the credit risk control measures.

8. A power system credit risk management device, characterized in that: The device comprises: The power market data acquisition module is used to acquire the power market data corresponding to the power system; A credit risk assessment result determination module, used to use multiple simplified risk measurement algorithms to perform credit risk assessment on the power market data respectively, and obtain a credit risk assessment result corresponding to each simplified risk measurement algorithm; A credit risk level determination module, used to determine the credit risk level according to each of the credit risk assessment results; The credit risk control measure adjustment module is used to generate a risk control strategy according to the credit risk level, and adjust the credit risk control measures of the power system according to the risk control strategy.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the power system credit risk management method as described in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the power system credit risk management method according to any one of claims 1 to 7 are performed.

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