Risk quantitative analysis method and system for power digital assets

Abstract

The invention discloses a power digital asset risk quantitative analysis method and system, and relates to the technical field of power digital asset risk quantification. In a risk index classification process, human risk indexes are clearly divided into subjective risk indexes and objective risk indexes; the method is beneficial to comprehensively and systematically teasing human risk factors influencing the electric power digital assets, avoids missing important risk sources, lays a solid foundation for subsequent accurate quantitative analysis, for example, comprehensively considers moral and motivation factors of employees, is beneficial to capturing human behavior tendencies causing risks from the source, and improves the risk assessment efficiency. By means of the method, the enterprise has a clearer direction in risk prevention and control, and in the subjective and objective risk quantitative evaluation process, complex qualitative risk factors can be converted into accurate quantitative values by means of various information scoring standards, assignment standards and corresponding calculation formulas which are set in detail.

Description

Technical Field

[0001] The present invention relates to the technical field of power digital asset risk quantification, and in particular to a method and system for risk quantification analysis of power digital assets. Background Art

[0002] With the rapid development and transformation of the power industry, the risk environment it faces is becoming increasingly complex. The widespread application of information technology has made power digital assets face new threats such as cyber attacks and data leaks. The impact of internal human factors on the security of power digital assets has become increasingly prominent. Subjective factors such as employees' moral standards, economic and career advancement motivations, as well as objective factors such as the company's checks and balances mechanism, supervision and audit system, will trigger risk events and affect the security, integrity and availability of power digital assets.

[0003] At present, a risk quantification analysis method and system for power digital assets may have the following problems: 1. Traditional risk quantification analysis methods and systems for power digital assets have many shortcomings. For example, there is a lack of in-depth analysis of subjective risks in multiple dimensions, and only simple consideration of some moral and motivational factors. There is a lack of comprehensive exploration of deep-seated subjective factors such as employee psychological pressure and changes in values. This will lead to one-sided subjective risk assessment, making the overall risk quantification inaccurate and difficult to accurately locate the root causes of human risks.

[0004] 2. Most risk quantification analysis methods and systems for power digital assets lack analysis of dynamic interactive factors, and lack sufficient consideration of the dynamic interactive relationship between subjective risk and objective risk in different business scenarios and enterprise development stages. They only mechanically divide subjective and objective risks, which will cause risk quantification to be divorced from the actual situation, and thus fail to effectively respond to the complex and changeable risk conditions of power digital assets, and lack specificity and flexibility in the formulation of risk response strategies.

[0005] 3. Most risk quantification analysis methods and systems for power digital assets lack integrated analysis of external environmental factors, and fail to incorporate external environmental factors such as market competition pressure and technological innovation into the risk quantification system, which will result in risk assessment being limited to within the enterprise. As a result, when faced with external environmental shocks, power companies find it difficult to predict in advance and adjust risk response strategies in a timely manner, increasing the possibility of power digital assets suffering losses. Summary of the invention

[0006] The purpose of the present invention is to provide a risk quantification analysis method and system for power digital assets, which solves the problems existing in the background technology.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a risk quantification analysis method for power digital assets, including: Step 1, risk indicator classification: Evaluate the human risk indicators corresponding to the power digital assets in the designated power enterprise.

[0008] Step 2: Quantitative assessment of subjective and objective risks: Based on the human risk indicators of the corresponding power digital assets in the designated power enterprise, the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the designated power enterprise are analyzed respectively.

[0009] Step 3: Comprehensive risk quantification assessment: Based on the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the designated power enterprise, the comprehensive risk quantification value of the corresponding human risk in the designated power enterprise is calculated, thereby analyzing the corresponding human risk level in the designated power enterprise.

[0010] Step 4: Risk contribution assessment: Based on the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the designated power enterprise, the risk contribution of the corresponding human behavior in the designated power enterprise is analyzed.

[0011] Step 5: Risk warning: According to the corresponding human risk level and risk contribution within the designated power enterprise, the response strategy for the human risk of power digital assets is evaluated.

[0012] In a second aspect, the present invention provides a risk quantification analysis system for power digital assets, comprising: a risk indicator classification module for evaluating human risk indicators corresponding to power digital assets in a designated power enterprise.

[0013] The subjective and objective risk quantification assessment module is used to analyze the subjective risk quantification value and the objective risk quantification value of the corresponding human behavior in the specified power enterprise according to the human risk index of the corresponding power digital assets in the specified power enterprise.

[0014] The comprehensive risk quantification assessment module is used to calculate the comprehensive risk quantification value of the corresponding human risk in the specified power enterprise based on the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the specified power enterprise, thereby analyzing the corresponding human risk level in the specified power enterprise.

[0015] The risk contribution assessment module is used to analyze the risk contribution of the corresponding human behavior in the specified power enterprise according to the subjective risk quantification value and the objective risk quantification value of the corresponding human behavior in the specified power enterprise.

[0016] The risk warning module is used to evaluate the response strategy of power digital assets corresponding to human risks based on the corresponding human risk level and risk contribution within the specified power enterprise.

[0017] The beneficial effects of the present invention are: 1. A risk quantification analysis method and system for electric power digital assets provided by the present invention, in the process of risk indicator classification, by clearly dividing human risk indicators into subjective risk indicators and objective risk indicators, is conducive to comprehensively and systematically sorting out the human risk factors affecting electric power digital assets, avoiding the omission of important risk sources, and laying a solid foundation for subsequent precise quantitative analysis. For example, a comprehensive consideration of employees' ethics and motivational factors is conducive to capturing the human behavior tendencies that cause risks from the root, so that enterprises have a clearer direction in risk prevention and control.

[0018] 2. In the process of quantitative assessment of subjective and objective risks, the embodiments of the present invention, with the help of detailed setting of various information scoring standards, assignment standards and corresponding calculation formulas, are conducive to converting complex qualitative risk factors into accurate quantitative values, which not only improves the accuracy and objectivity of risk assessment, but also facilitates the comparability of risk assessment results of different enterprises or the same enterprise at different times. Enterprises can clearly understand their own status in different risk factors based on quantitative values, and then formulate improvement measures in a targeted manner to improve risk management efficiency.

[0019] 3. In the comprehensive risk quantification assessment process, the embodiment of the present invention uses a specific calculation formula to obtain a comprehensive risk quantification value based on the subjective risk quantification value and the objective risk quantification value, and further divides the human risk level, which is beneficial for enterprises to grasp the degree of human risk faced by power digital assets from an overall perspective.

[0020] 4. In the risk contribution assessment process, the embodiment of the present invention calculates the risk contribution based on the subjective risk quantification value, the objective risk quantification value and the comprehensive risk quantification value, which is conducive to accurately identifying the respective role proportions of subjective factors and objective factors in human risks, and helps enterprises to target their risk response strategies. If the subjective risk contribution is large, the focus can be on strengthening personnel management and employee training. If the objective risk contribution is large, the focus can be on optimizing the internal management process and supervision mechanism of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a schematic flow chart of the implementation steps of the present invention.

[0023] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 As shown, the present invention provides a method for quantitative risk analysis of power digital assets, the method comprising: step one, risk indicator classification: evaluating the human risk indicators corresponding to the power digital assets in a designated power enterprise.

[0026] In a specific embodiment, the evaluation specifies the human risk indicators of the corresponding power digital assets in the power enterprise, and the specific process is as follows: the human risk indicators of the corresponding power digital assets in the power enterprise are divided into subjective risk indicators and objective risk indicators, the subjective risk indicators include moral indicators and motivation indicators, and the objective risks include power checks and balances indicators and supervision and audit indicators;

[0027] Moral indicators include information on communication integrity, credit record information, and internal violation record information. Communication integrity information and credit record information are recorded as D i and R j , where i is the number corresponding to the communication integrity information, i=1, 2, ..., q, q is the number of information aspects included in the communication integrity information, q is a positive integer, j is the number corresponding to the credit record information, j=1, 2, ..., p, p is the number of information aspects included in the credit record information, p is a positive integer, the violation record information includes the number of violations and the degree of violation, the number of violations and the degree of violation are recorded as N and L, the set communication integrity information scoring standard and credit record information scoring standard are obtained from the database, the communication integrity information score and the credit record information score are obtained, and the communication integrity information score and the credit record information score are recorded as and By calculation formula:

[0028] We can obtain the quantified value Q of the communication integrity information respectively. m1 , credit record information quantification value Q m2 ,in and The weight factors corresponding to the i-th communication integrity information and the j-th credit record information are set respectively. The violation number assignment standard and violation degree assignment standard are obtained from the database, and then the violation number score S is obtained. Nand violation severity score S L , calculated by the formula: Get the quantitative value Q of internal violation record information m3 ,in and They are respectively represented as the set violation number weight factor and violation degree weight factor.

[0029] The motivation indicators include economic pressure information and career promotion pressure information. Economic pressure information includes debt-to-income ratio and additional economic burden. Career promotion pressure information includes performance ranking and scarcity of promotion quotas. The set economic pressure information scoring criteria and career promotion pressure information scoring criteria are obtained from the database to obtain the debt-to-income ratio score S. I , Additional economic burden score S B , Performance ranking score S K and promotion quota scarcity score S S , by calculating the formula Get the economic pressure information quantification value Q P1 , Career promotion pressure information quantification value Q P2 ,in and They are the set debt-to-income ratio weight factor, additional economic burden weight factor, performance ranking weight factor and promotion quota scarcity weight factor.

[0030] It should be noted that the communication integrity information includes but is not limited to the work experience integrity, educational background integrity and social reputation, and the credit record information includes but is not limited to the credit card repayment punctuality rate, the number of loan defaults and financial fraud records. The set communication integrity information scoring standard and credit record information scoring standard are obtained from the database to obtain the communication integrity information score and credit record information score. The specific process is: for example, for work experience integrity, if the employee has no bad record, he will get 10 points, if there is a minor integrity dispute, he will get 6 points, and if there is a serious integrity problem, he will get 2 points. The educational background integrity is set in the same way, such as no academic misconduct will get 8 points, and if there is minor plagiarism, he will get 4 points, etc. The social reputation is obtained by investigating the evaluation of people around, and 9 points are good, 5 points are average, and 3 points are poor. In this way, the scores of the corresponding aspects of the communication integrity information are obtained. The scoring process of the credit record information corresponding to each aspect, the scoring process of the number of violations, the scoring process of the degree of violation, the scoring process of the economic pressure information and the scoring process of the career promotion pressure information are the same as the scoring process of the communication integrity information corresponding to each aspect, and will not be elaborated here.

[0031] It should also be noted that and The values ​​of are all greater than 0 and less than 1. The entropy weight method is used to set the weight factors corresponding to the information on communication integrity, the weight factors corresponding to the credit record information, the weight factors for the number of violations, the weight factors for the degree of violations, the weight factors for the debt-to-income ratio, the weight factors for the additional economic burden, the weight factors for the performance ranking and the weight factors for the scarcity of promotion quotas. The setting process of the entropy weight method is mentioned in the patent document with publication number CN112712284A, and will not be elaborated on here.

[0032] In a specific embodiment, the analysis specifies the objective risk indicators of the corresponding power digital assets in the power enterprise, and the specific process is as follows: obtaining the scoring criteria of the data access right audit information and the scoring criteria of the permission approval process review information from the database, obtaining the data access right audit information score and the permission approval process review information score, thereby calculating the data access right audit information quantitative value Q Q1 and authority approval process review information score Q Q2 , and then calculate the quantitative value of the power check and balance index Q Q .

[0033] Obtain the scoring criteria for internal audit frequency and scope, data monitoring information, and violation handling information from the database to obtain the internal audit frequency and scope score, data monitoring information score, and violation handling information score, thereby calculating the internal audit frequency and scope information quantitative value Q S1 , data monitoring information quantization value Q S2 and the violation handling information quantification value Q S3 , and then calculate the quantitative value Q of the supervision and audit indicator S .

[0034] It should be noted that the checks and balances indicators include data access rights audit information and authority approval process review information. Data access rights audit information includes the proportion of personnel with access rights to key power digital assets and the rationality of authority level distribution. Authority approval process review information includes the number of approval links and the strictness of approval standards.

[0035] Supervisory audit indicators include internal audit frequency and scope information, data monitoring information and violation handling information. Internal audit frequency and scope information includes internal audit frequency and audit scope ratio. Data monitoring information includes monitoring coverage completeness and alarm accuracy. Violation handling information includes the average time for violation discovery and handling effectiveness.

[0036] It should also be noted that the data access rights audit information quantification value Q Q1 , Authorization Approval Process Review Information Scoring Q Q2 , Internal audit frequency and scope information quantification value Q S1 , data monitoring information quantization value QS2 and the violation handling information quantification value Q S3 The calculation process and the economic pressure information quantification value Q P1 and career promotion pressure information quantification value Q P2 The calculation formula is the same as that of the power balance indicator, Q Q and the quantitative value of the supervisory audit indicator Q S The calculation process is the same as the calculation formula for the moral index quantization value Qm and the motivation index quantization value QP, so I will not go into details here.

[0037] Step 2: Quantitative assessment of subjective and objective risks: Based on the human risk indicators of the corresponding power digital assets in the designated power enterprise, the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the designated power enterprise are analyzed respectively.

[0038] In a specific embodiment, the analysis specifies the subjective risk quantification value of the corresponding human behavior in the power enterprise. The specific analysis process is as follows: the transaction integrity information quantification value Q m1 , Quantitative value Q of internal violation record information m3 and credit record information quantification value Q m2 Substitute into the calculation formula: Get the quantitative value of moral index Qm, where and They are respectively represented by the set transaction integrity information weight factor, credit record information weight factor and internal violation record information weight factor.

[0039] Quantify the economic pressure information into Q P1 and career promotion pressure information quantification value Q P2 Substitute into the calculation formula Get the motivation index quantification value QP, where and They are respectively represented by the set economic pressure information quantification value weight factor and career promotion pressure information quantification value weight factor.

[0040] The preliminary subjective comprehensive quantitative value x is obtained by calculating the formula x=Qm+QP+λ*Qm*QP, where λ is the interaction coefficient corresponding to the preset subjective risk. The calculation formula is: The subjective risk quantification value Qsub of the corresponding human behavior in the specified power enterprise is obtained, where μ is the preset comprehensive subjective risk elasticity coefficient, e is a natural constant, and ξ is the correction factor corresponding to the set subjective risk.

[0041] It should be noted that and The values ​​of are greater than 0 and less than 1. and The setting process and The setting process is the same as , so I will not go into details here.

[0042] It should be noted that the interaction coefficient corresponding to subjective risk reflects the degree of influence of the interaction between subjective risk factors on the overall risk, which shows that different subjective factors do not exist in isolation, but are interrelated and synergistically affect the risk results. For example, the number of power digital asset risk events caused by employee moral deficiencies and economic motivations in the past five years is counted. Assuming that moral factors cause 30 risk events, economic motivation factors cause 25 events, and the two together cause 15 events, and the risk level of these events is higher than that of events caused by a single factor, the association model is constructed to obtain an interaction coefficient of 0.4, indicating that the interaction between the two has a greater impact on the risk, and when they work together, the risk will increase by 40% on the original basis.

[0043] The correction factor corresponding to the subjective risk comprehensively considers the adjustment effect of the internal and external environmental factors of the designated power enterprise on the subjective risk assessment, so that the quantified value of the subjective risk is more in line with the complex environmental conditions in which the enterprise is actually located. For example, the industry in which a certain power enterprise is located has newly issued strict data security regulations, and the enterprise is in a period of business expansion with a high employee turnover rate. Experts score according to factors such as the strictness of industry regulations, the speed of business expansion, and employee turnover rate. Assuming that the strictness of industry regulations is scored at 0.6 points, the business expansion speed is scored at 0.3 points, and the employee turnover rate is scored at 0.1 points, after weighted averaging, the weighted averaging methods include hierarchical analysis method, etc., and the correction factor is 1.3.

[0044] For example, when the economic pressure factors of employees change, the change in subjective risk is estimated based on the elasticity coefficient. For example, in power companies, the performance bonus ratio of employees is changed from the original 10% to 20%, 30% and other different levels. If the performance bonus ratio increases by 10%, the subjective risk quantification value increases by an average of 20%, then the comprehensive subjective risk elasticity coefficient is 2, which means that for every 1 unit change in this factor, the subjective risk changes by 2 units.

[0045] In a specific embodiment, the analysis specifies the objective risk quantification value of the corresponding human behavior in the power enterprise, and the specific analysis process is as follows: by calculating the formula

[0046] y=QQ+QS+ψ*QQ*QS, and get the preliminary objective comprehensive quantitative value y, where ψ is the interaction coefficient corresponding to the preset objective risk. The objective risk quantification value Qobj of the corresponding human behavior in the specified power enterprise is obtained, where θ is the preset comprehensive objective risk elasticity coefficient, e1 is a natural constant, and ξ is the correction factor corresponding to the set objective risk.

[0047] It should be noted that the setting process of the interaction coefficient corresponding to objective risk, the comprehensive objective risk elasticity coefficient and the correction factor corresponding to objective risk is the same as the setting process of the interaction coefficient corresponding to subjective risk, the comprehensive subjective risk elasticity coefficient and the correction factor corresponding to subjective risk, and will not be elaborated here.

[0048] In the process of quantitative assessment of subjective and objective risks, the embodiments of the present invention, with the help of detailed setting of various information scoring standards, assignment standards and corresponding calculation formulas, are conducive to converting complex qualitative risk factors into accurate quantitative values, which not only improves the accuracy and objectivity of risk assessment, but also facilitates the comparability of risk assessment results of different enterprises or the same enterprise at different times. Enterprises can clearly understand their own status in different risk factors based on quantitative values, and then formulate improvement measures in a targeted manner to improve risk management efficiency.

[0049] Step 3: Comprehensive risk quantification assessment: Based on the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the designated power enterprise, the comprehensive risk quantification value of the corresponding human risk in the designated power enterprise is calculated, thereby analyzing the corresponding human risk level in the designated power enterprise.

[0050] In a specific embodiment, the calculation of the comprehensive risk quantification value of the corresponding human risk in the specified power enterprise is specifically performed as follows: according to the subjective risk quantification value Qsub and the objective risk quantification value Qobj of the corresponding human behavior in the specified power enterprise, the calculation formula is:

[0051] The comprehensive risk quantification value QF of the corresponding human risk in the specified power enterprise is obtained, where They are the weight factors corresponding to the set subjective risk quantification value and the weight factors corresponding to the objective risk quantification value respectively.

[0052] It should be noted that The values ​​of are greater than 0 and less than 1.

[0053] The setting process and The setting process is the same as , so I will not go into details here.

[0054] In a specific embodiment, the analysis specifies the corresponding human risk level in the power enterprise, and the specific process is as follows: obtaining the classification standard of the corresponding human risk level in the specified power enterprise from the database, dividing the human risk level into a low human risk level, a relatively low human risk level, a medium human risk level and a high human risk level, and obtaining each standard quantitative threshold corresponding to the classification standard from the database, and recording each standard quantitative threshold as a, b and c respectively, through the human risk level discriminant formula: The corresponding human risk level QFgrade in the specified power enterprise is obtained, where QF1, QF2, QF3 and QF4 represent low human risk level, relatively low human risk level, medium human risk level and high human risk level, respectively.

[0055] In the comprehensive risk quantification assessment process, the embodiment of the present invention uses a specific calculation formula to obtain a comprehensive risk quantification value based on the subjective risk quantification value and the objective risk quantification value, and further divides the human risk level, which is beneficial for enterprises to grasp the degree of human risk faced by power digital assets from an overall perspective.

[0056] Step 4: Risk contribution assessment: Based on the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the designated power enterprise, the risk contribution of the corresponding human behavior in the designated power enterprise is analyzed.

[0057] In a specific embodiment, the risk contribution of the corresponding human behavior in the specified power enterprise is analyzed, and the specific process is as follows: according to the subjective risk quantification value, objective risk quantification value and comprehensive risk quantification value of the corresponding human behavior in the specified power enterprise, the formula is calculated. and The subjective risk contribution GQsub and objective risk contribution GQobj of the corresponding human behavior in the specified power enterprise are obtained.

[0058] In the risk contribution assessment process, the embodiment of the present invention calculates the risk contribution based on the subjective risk quantification value, the objective risk quantification value and the comprehensive risk quantification value, which is conducive to accurately identifying the respective role proportions of subjective factors and objective factors in human risks, and helps enterprises to target their risk response strategies. If the subjective risk contribution is large, the focus can be on strengthening personnel management and employee training. If the objective risk contribution is large, the focus can be on optimizing the internal management process and supervision mechanism of the enterprise.

[0059] Step 5: Risk warning: According to the corresponding human risk level and risk contribution within the designated power enterprise, the response strategy for the human risk of power digital assets is evaluated.

[0060] In a specific embodiment, the specific process of evaluating the response strategy for human risks corresponding to power digital assets is as follows: when the corresponding human risk level in the designated power enterprise is a medium human risk level and a high human risk level, according to the subjective risk contribution and objective risk contribution of the corresponding human behavior in the designated power enterprise, if the subjective risk contribution of the corresponding human behavior in the designated power enterprise is greater than or equal to the objective risk contribution, it indicates that the response strategy for the corresponding human risk in the designated power enterprise focuses on personnel management and employee training; if the subjective risk contribution of the corresponding human behavior in the designated power enterprise is less than the objective risk contribution, it indicates that the response strategy for the corresponding human risk in the designated power enterprise focuses on the internal management process and supervision mechanism of the enterprise.

[0061] See also Figure 2 As shown, a risk quantification analysis system for power digital assets includes the following modules: a risk indicator classification module, a subjective and objective risk quantification assessment module, a comprehensive risk quantification assessment module, a risk contribution assessment module, a risk warning module and a database.

[0062] The risk indicator classification module is connected to the subjective and objective risk quantification assessment module and the database respectively, the subjective and objective risk quantification assessment module is connected to the comprehensive risk quantification assessment module and the database respectively, the comprehensive risk quantification assessment module is connected to the comprehensive risk quantification assessment module and the database respectively, and the risk contribution assessment module is connected to the risk warning module.

[0063] The risk indicator classification module is used to evaluate the human risk indicators of corresponding power digital assets within a specified power enterprise.

[0064] The subjective and objective risk quantification assessment module is used to analyze the subjective risk quantification value and the objective risk quantification value of the corresponding human behavior in the specified power enterprise according to the human risk index of the corresponding power digital assets in the specified power enterprise.

[0065] The comprehensive risk quantification assessment module is used to calculate the comprehensive risk quantification value of the corresponding human risk in the specified power enterprise based on the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the specified power enterprise, thereby analyzing the corresponding human risk level in the specified power enterprise.

[0066] The risk contribution assessment module is used to analyze the risk contribution of the corresponding human behavior in the specified power enterprise according to the subjective risk quantification value and the objective risk quantification value of the corresponding human behavior in the specified power enterprise.

[0067] The risk warning module is used to evaluate the response strategy of power digital assets corresponding to human risks based on the corresponding human risk level and risk contribution within the specified power enterprise.

[0068] The present invention provides a risk quantification analysis method and system for power digital assets. In the process of risk indicator classification, by clearly dividing human risk indicators into subjective risk indicators and objective risk indicators, it is helpful to comprehensively and systematically sort out the human risk factors that affect power digital assets, avoid missing important risk sources, and lay a solid foundation for subsequent precise quantitative analysis. For example, a comprehensive consideration of employees' ethics and motivation factors is helpful to capture the human behavior tendencies that cause risks from the root, so that enterprises have a clearer direction in risk prevention and control.

[0069] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A risk quantification analysis method for power digital assets, characterized in that: include: Step 1: Risk indicator classification: Evaluate the human risk indicators corresponding to power digital assets within the designated power enterprise; Step 2: Quantitative assessment of subjective and objective risks: According to the human risk index of the corresponding power digital assets in the designated power enterprise, the subjective risk quantification value and the objective risk quantification value of the corresponding human behavior in the designated power enterprise are analyzed respectively; Step 3: Comprehensive risk quantification assessment: Based on the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the designated power enterprise, the comprehensive risk quantification value of the corresponding human risk in the designated power enterprise is calculated, thereby analyzing the corresponding human risk level in the designated power enterprise; Step 4: Risk contribution assessment: Based on the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the designated power enterprise, the risk contribution of the corresponding human behavior in the designated power enterprise is analyzed; Step 5: Risk warning: According to the corresponding human risk level and risk contribution within the designated power enterprise, the response strategy for the human risk of power digital assets is evaluated.

2. A risk quantification analysis method for power digital assets according to claim 1, characterized in that: The assessment specifies the human risk indicators of the corresponding power digital assets in the power enterprise, and the specific process is as follows: The human risk indicators corresponding to the power digital assets in the designated power enterprise are divided into subjective risk indicators and objective risk indicators. Subjective risk indicators include moral indicators and motivation indicators, and objective risks include power checks and balances indicators and supervision and audit indicators. Moral indicators include information on communication integrity, credit record information, and internal violation record information. Communication integrity information and credit record information are recorded as D i and R j , where i is the number corresponding to the communication integrity information, i=1, 2, ..., q, q is the number of information aspects included in the communication integrity information, q is a positive integer, j is the number corresponding to the credit record information, j=1, 2, ..., p, p is the number of information aspects included in the credit record information, p is a positive integer, the violation record information includes the number of violations and the degree of violation, the number of violations and the degree of violation are recorded as N and L, the set communication integrity information scoring standard and credit record information scoring standard are obtained from the database, the communication integrity information score and the credit record information score are obtained, and the communication integrity information score and the credit record information score are recorded as and By calculation formula: We can obtain the quantified value Q of the communication integrity information respectively. m1 , credit record information quantification value Q m2 ,in and The weight factors corresponding to the i-th communication integrity information and the j-th credit record information are set respectively. The violation number assignment standard and violation degree assignment standard are obtained from the database, and then the violation number score S is obtained. N and violation severity score S L , calculated by the formula: Get the quantitative value Q of internal violation record information m3 ,in and They are respectively expressed as the set violation number weight factor and violation degree weight factor; The motivation indicators include economic pressure information and career promotion pressure information. Economic pressure information includes debt-to-income ratio and additional economic burden. Career promotion pressure information includes performance ranking and scarcity of promotion quotas. The set economic pressure information scoring criteria and career promotion pressure information scoring criteria are obtained from the database to obtain the debt-to-income ratio score S. I , Additional economic burden score S B , Performance ranking score S K and promotion quota scarcity score S S , by calculating the formula Get the economic pressure information quantification value Q P1 , Career promotion pressure information quantification value Q P2 ,in and They are the set debt-to-income ratio weight factor, additional economic burden weight factor, performance ranking weight factor and promotion quota scarcity weight factor.

3. A method for quantitative risk analysis of power digital assets according to claim 2, characterized in that: The analysis specifies the subjective risk quantification value of the corresponding human behavior in the power enterprise. The specific analysis process is as follows: Quantify the communication integrity information value Q m1 , Quantitative value Q of internal violation record information m3 and credit record information quantification value Q m2 Substitute into the calculation formula: Get the quantitative value of moral index Qm, where and They are respectively represented by the set transaction integrity information weight factor, credit record information weight factor and internal violation record information weight factor; Quantify the economic pressure information into Q P1 and career promotion pressure information quantification value Q P2 Substitute into the calculation formula Get the motivation index quantification value QP, where and They are respectively represented by the set economic pressure information quantization value weight factor and career promotion pressure information quantization value weight factor; The preliminary subjective comprehensive quantitative value x is obtained by calculating the formula x=Qm+QP+λ*Qm*QP, where λ is the interaction coefficient corresponding to the preset subjective risk. The calculation formula is: The subjective risk quantification value Qsub of the corresponding human behavior in the specified power enterprise is obtained, where μ is the preset comprehensive subjective risk elasticity coefficient, e is a natural constant, and ξ is the correction factor corresponding to the set subjective risk.

4. A method for quantitative risk analysis of power digital assets according to claim 3, characterized in that: The analysis specifies the objective risk indicators of the corresponding power digital assets in the power enterprise. The specific process is as follows: Obtain the scoring criteria for data access rights audit information and the scoring criteria for permission approval process review information from the database, obtain the data access rights audit information score and the permission approval process review information score, and thus calculate the data access rights audit information quantitative value Q Q1 and authority approval process review information score Q Q2 , and then calculate the quantitative value of the power check and balance index Q Q ; Obtain the scoring criteria for internal audit frequency and scope, data monitoring information, and violation handling information from the database to obtain the internal audit frequency and scope score, data monitoring information score, and violation handling information score, thereby calculating the internal audit frequency and scope information quantitative value Q S1 , data monitoring information quantization value Q S2 and violation handling information quantification value Q S3 , and then calculate the quantitative value Q of the supervision and audit indicator S .

5. A risk quantification analysis method for power digital assets according to claim 4, characterized in that: The analysis specifies the objective risk quantification value of the corresponding human behavior in the power enterprise. The specific analysis process is as follows: The preliminary objective comprehensive quantitative value y is obtained by the calculation formula y=QQ+QS+ψ*QQ*QS, where ψ is the interaction coefficient corresponding to the preset objective risk. The calculation formula is: The objective risk quantification value Qobj of the corresponding human behavior in the specified power enterprise is obtained, where θ is the preset comprehensive objective risk elasticity coefficient, e1 is a natural constant, and ξ is the correction factor corresponding to the set objective risk.

6. A method for quantitative risk analysis of power digital assets according to claim 5, characterized in that: The specific process of calculating the comprehensive risk quantification value of the corresponding human risk in the designated power enterprise is as follows: According to the subjective risk quantification value Qsub and objective risk quantification value Qobj of the corresponding human behavior in the specified power enterprise, the formula The comprehensive risk quantification value QF of the corresponding human risk in the specified power enterprise is obtained, where They are the weight factors corresponding to the set subjective risk quantification value and the weight factors corresponding to the objective risk quantification value respectively.

7. A method for quantitative risk analysis of power digital assets according to claim 6, characterized in that: The analysis specifies the corresponding human risk level within the power enterprise, and the specific process is as follows: The classification standard of the corresponding human risk level in the specified power enterprise is obtained from the database, and the human risk level is divided into low human risk level, lower human risk level, medium human risk level and high human risk level. The quantitative threshold values ​​of each standard corresponding to the classification standard are obtained from the database, and each standard quantitative threshold value is recorded as a, b and c respectively. The discriminant formula of the human risk level is: The corresponding human risk level QFgrade in the specified power enterprise is obtained, where QF1, QF2, QF3 and QF4 represent low human risk level, relatively low human risk level, medium human risk level and high human risk level, respectively.

8. A method for quantitative risk analysis of power digital assets according to claim 7, characterized in that: The analysis specifies the risk contribution of corresponding human behaviors in the power enterprise. The specific process is as follows: According to the subjective risk quantification value, objective risk quantification value and comprehensive risk quantification value of the corresponding human behavior in the designated power enterprise, the calculation formula is then used to calculate the risk. and The subjective risk contribution GQsub and objective risk contribution GQobj of the corresponding human behavior in the specified power enterprise are obtained.

9. A method for quantitative risk analysis of power digital assets according to claim 8, characterized in that: The specific process of the response strategy for evaluating the human risk of power digital assets is as follows: When the corresponding human risk level in the designated power enterprise is a medium human risk level and a high human risk level, according to the subjective risk contribution and objective risk contribution of the corresponding human behavior in the designated power enterprise, if the subjective risk contribution of the corresponding human behavior in the designated power enterprise is greater than or equal to the objective risk contribution, it indicates that the response strategy for the corresponding human risk in the designated power enterprise focuses on personnel management and employee training; if the subjective risk contribution of the corresponding human behavior in the designated power enterprise is less than the objective risk contribution, it indicates that the response strategy for the corresponding human risk in the designated power enterprise focuses on the internal management process and supervision mechanism of the enterprise.

10. A risk quantification analysis system for power digital assets that executes the risk quantification analysis method for power digital assets according to any one of claims 1 to 9, characterized in that: Includes the following modules: A risk indicator classification module is used to evaluate the human risk indicators of corresponding power digital assets within a specified power enterprise; The subjective and objective risk quantification assessment module is used to analyze the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the specified power enterprise according to the human risk index of the corresponding power digital assets in the specified power enterprise; The comprehensive risk quantification assessment module is used to calculate the comprehensive risk quantification value of the corresponding human risk in the specified power enterprise according to the subjective risk quantification value and objective risk quantification value of the corresponding human behavior in the specified power enterprise, thereby analyzing the corresponding human risk level in the specified power enterprise; A risk contribution assessment module is used to analyze the risk contribution of the corresponding human behavior in the specified power enterprise according to the subjective risk quantification value and the objective risk quantification value of the corresponding human behavior in the specified power enterprise; The risk warning module is used to evaluate the response strategy of power digital assets corresponding to human risks based on the corresponding human risk level and risk contribution within the specified power enterprise.

Images