Risk assessment method and device, computer equipment and readable storage medium

Abstract

The invention relates to a risk assessment method and device, computer equipment and a readable storage medium. The method comprises the steps of periodically obtaining power operation data and historical risk assessment sub-factors of a power system according to a preset risk assessment frequency of the power system; determining a current risk assessment sub-factor of the power system according to the power operation data; according to the current risk assessment sub-factor and the historical risk assessment sub-factor, obtaining a risk frequency decision factor of the power system; and when the risk frequency decision factor satisfies a preset frequency switching condition, adjusting the risk assessment frequency of the power system. By adopting the method, the danger of the power system can be found in time.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a risk assessment method, apparatus, computer equipment, and readable storage medium. Background Technology

[0002] As the power system becomes more widespread, supporting power operation schemes will be adopted to ensure better operation of the power system. Power operation is to provide electricity users with a continuous, sufficient quantity of electricity that meets certain quality standards, while making full and rational use of energy and operating equipment capabilities.

[0003] In related technologies, various data generated during the operation of power systems are generally used to conduct risk assessments of the power systems. However, the risk assessment methods in these technologies suffer from the problem of untimely assessment, failing to detect potential hazards in the power system in a timely manner. Summary of the Invention

[0004] Therefore, it is necessary to provide a risk assessment method, device, computer equipment, and readable storage medium that can promptly detect hazards in power systems, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a risk assessment method, the method comprising:

[0006] Based on the current risk assessment frequency, obtain the current power system operation data and historical risk assessment sub-factors; wherein, the historical risk assessment sub-factors are used to indicate the historical risk level of the power system;

[0007] A risk assessment is performed on the power operation data to determine the current risk assessment factors for the current power system;

[0008] Based on the current risk assessment sub-factors and the historical risk assessment sub-factors, the risk frequency determination factor of the power system is obtained;

[0009] Under the condition that the risk frequency determinant meets the preset frequency switching condition, the risk assessment frequency of the power system is adjusted.

[0010] In one embodiment, the step of performing a risk assessment on the power operation data to determine the current risk assessment factors of the current power system includes:

[0011] The current power operation data is subjected to feature identification to determine the feature parameters of the current power operation data; the feature parameters include at least one of data value parameters, threat occurrence parameters, and vulnerability parameters;

[0012] The corresponding weighting coefficients are determined based on the feature parameters;

[0013] The current risk assessment factor is determined based on the feature parameters and the corresponding weight coefficients.

[0014] In one embodiment, the frequency switching condition includes the risk assessment frequency corresponding to the target assessment range being different from the current risk assessment frequency;

[0015] The adjustment of the risk assessment frequency of the power system under the condition that the risk frequency determinant factor meets the preset frequency switching condition includes:

[0016] The target assessment range corresponding to the risk frequency determinant is determined from a plurality of preset assessment ranges;

[0017] Based on a pre-built mapping relationship library, the risk assessment frequency corresponding to the target assessment range is determined; wherein, the mapping relationship library includes a variety of different mapping relationships, which are used to represent the mapping relationship between risk frequency determinants and risk assessment frequencies;

[0018] If the risk assessment frequency corresponding to the target assessment scope is different from the current risk assessment frequency, the current risk assessment frequency will be switched to the risk assessment frequency corresponding to the target assessment scope.

[0019] In one embodiment, the plurality of assessment ranges includes at least a first assessment range and a second assessment range, wherein the upper limit of the first assessment range is less than or equal to the lower limit of the second assessment range; and the risk assessment frequency corresponding to the first assessment range is less than the risk assessment frequency corresponding to the second assessment range.

[0020] In one embodiment, obtaining the risk frequency determination factor of the power system based on the current risk assessment sub-factor and the historical risk assessment sub-factor includes:

[0021] The risk level weights are determined based on the current risk assessment factors.

[0022] Based on the current risk assessment sub-factor, the historical risk assessment sub-factor, the risk level weight, and the preset weighted average weight, the risk frequency determining factor is obtained.

[0023] In one embodiment, determining the corresponding weight coefficient based on the feature parameters includes:

[0024] The target type of the power operation data is determined, and the target type includes at least a first type and a second type; the importance parameter of the power operation data of the first type is greater than the importance parameter of the power operation data of the second type.

[0025] The weighting coefficients corresponding to the feature parameters are determined according to the target type; the weighting coefficients of the power operation data of the first type are greater than or equal to the weighting coefficients of the power operation data of the second type.

[0026] Secondly, this application provides a risk assessment device, the device comprising:

[0027] The first execution module is used to obtain the current power system operation data and historical risk assessment sub-factors based on the current risk assessment frequency; wherein, the historical risk assessment sub-factors are used to indicate the historical risk level of the power system;

[0028] The second execution module is used to perform risk assessment on the power operation data in order to determine the current risk assessment factor of the current power system.

[0029] The third execution module is used to obtain the risk frequency determination factor of the power system based on the current risk assessment sub-factor and the historical risk assessment sub-factor;

[0030] The fourth execution module is used to adjust the risk assessment frequency of the power system under the condition that the risk frequency determination factor meets the preset frequency switching condition.

[0031] Thirdly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above embodiments.

[0032] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0033] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0034] The aforementioned risk assessment methods, devices, computer equipment, computer-readable storage media, and computer program products include: periodically acquiring power system operation data and historical risk assessment sub-factors based on the current risk assessment frequency of the power system; determining the current risk assessment sub-factors of the power system based on the power operation data; and using the current risk assessment sub-factors to indicate the risk level of the power system in the current period, enabling relevant technical personnel to identify hazards in the power system. Subsequently, based on the current risk assessment sub-factors and the historical risk assessment sub-factors of the power system, a risk frequency determining factor for the power system can be determined. Then, when the risk frequency determining factor meets preset frequency switching conditions, the risk assessment frequency of the power system is adjusted, thereby adaptively adjusting the risk assessment frequency of the power system, enabling timely assessment of the power system, timely detection of hazards in the power system, and providing a good monitoring and feedback mechanism for timely response strategies. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a diagram illustrating the application environment of the risk assessment method in one embodiment;

[0037] Figure 2 This is a flowchart illustrating step S102 in one embodiment;

[0038] Figure 3 This is a flowchart illustrating step S103 in one embodiment;

[0039] Figure 4 This is a flowchart illustrating step S104 in one embodiment;

[0040] Figure 5 This is a flowchart illustrating step S202 in one embodiment;

[0041] Figure 6 This is a structural block diagram of a risk assessment device in one embodiment;

[0042] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] As described in the background section, with the gradual popularization of power systems, supporting power operation schemes will be adopted in order to improve the operation of power systems. Power operation is to provide power users with a continuous, sufficient quantity of electricity and power that meets certain quality standards in the safest and most economical way, under the premise of making full and reasonable use of energy and operating equipment capacity.

[0045] During the operation of the power system, various types of data are generated, such as power operation data. Power operation data refers to various types of data generated by the dispatching agency to support dispatching operations and spot market operations.

[0046] When faced with the aforementioned power operation data, it is necessary to assess its risk. However, existing assessment methods may result in inaccurate or untimely assessments. The main reasons for this are: first, they cannot effectively assess different data types; second, they cannot adaptively adjust the assessment frequency based on the risk assessment results. Therefore, we propose a risk assessment method for the security of power operation data.

[0047] In one exemplary embodiment, please refer to Figure 1 This application provides a risk assessment method, which includes steps S101 to S104.

[0048] S101: Based on the current risk assessment frequency, obtain the current power system operation data and historical risk assessment sub-factors; among which, the historical risk assessment sub-factors are used to indicate the historical risk level of the power system.

[0049] In applications, when risk assessments of the power system have been conducted a predetermined number of times, the current risk assessment frequency can be a preset baseline risk assessment frequency. If a predetermined number of risk assessments of the power system have already been conducted, the current risk assessment frequency can be an adjusted frequency based on historical risk assessments of the power system. For example, during the first four risk assessments of the power system, the current risk assessment frequency can be the preset baseline frequency. From the fifth risk assessment onwards, the current risk assessment frequency is an adjusted frequency based on historical risk assessments of the power system. Based on the current risk assessment frequency of the power system, power system operation data can be collected periodically.

[0050] S102: Conduct a risk assessment of power operation data to determine the current risk assessment factors for the current power system.

[0051] After collecting power operation data, the data can be preprocessed, such as cleaning and noise reduction. Then, a risk assessment can be conducted on the power operation data to identify threats, analyze the likelihood of these threats, and assess the severity of vulnerabilities, thereby obtaining the current risk assessment factors for the power system.

[0052] S103: Obtain the risk frequency determinant of the power system based on the current risk assessment sub-factors and historical risk assessment sub-factors.

[0053] When a risk assessment of the power system is required, in step S101, the historical risk assessment factors of the power system in the previous few times can also be obtained. Then, based on the current risk assessment factors and the historical risk assessment factors, a long-term risk status of the power system is comprehensively assessed to obtain the risk frequency determinant factor of the power system.

[0054] S104: Adjust the risk assessment frequency of the power system under the condition that the risk frequency determinant meets the preset frequency switching condition.

[0055] In this embodiment, the magnitude of the risk frequency determinant is related to the risk assessment frequency of the power system. When the risk frequency determinant is large, it indicates that the power system is in a high-risk state for a long period, thus requiring an increase in the frequency of risk assessment to promptly detect hazards within the power system. Conversely, when the risk frequency determinant is small, it indicates that the power system is in a low-risk state for a long period, thus allowing a decrease in the frequency of risk assessment to conserve computational resources. Specifically, when the risk assessment frequency corresponding to the risk frequency determinant does not correspond to the current risk assessment frequency, the current risk assessment frequency can be adjusted.

[0056] The aforementioned risk assessment method includes periodically acquiring power system operation data and historical risk assessment sub-factors based on the current risk assessment frequency of the power system. The current risk assessment sub-factors are determined based on the operation data, indicating the risk level of the power system in the current period, enabling technical personnel to identify hazards within the power system. Subsequently, based on the current and historical risk assessment sub-factors, a risk frequency determinant factor is determined. Then, when the risk frequency determinant factor meets preset frequency switching conditions, the risk assessment frequency of the power system is adjusted. This adaptive adjustment of the risk assessment frequency allows for timely assessment of the power system, enabling the timely detection of hazards and providing a sound monitoring and feedback mechanism for prompt response strategies.

[0057] In one exemplary embodiment, please refer to Figure 2 Step S102 involves conducting a risk assessment on the power operation data to determine the current risk assessment factors for the current power system, including steps S201 and S203.

[0058] S201: Perform feature identification on the current power operation data to determine the characteristic parameters of the current power operation data; the characteristic parameters include at least one of data value parameters, threat occurrence parameters, and vulnerability parameters.

[0059] In this embodiment, the data value parameter is a quantitative assessment of the asset importance and sensitivity of power operation data; the threat occurrence parameter is an assessment of the probability of a specific threat occurring within a certain period of time, which can be determined by analyzing historical data, referring to industry statistics, and considering factors such as the current security environment; the vulnerability parameter is an assessment of the severity of the consequences that may result if the vulnerability exists in the operation and data management of the power system and is exploited by a threat.

[0060] S202: Determine the corresponding weight coefficients based on the feature parameters.

[0061] In applications, different feature parameters may have different weight coefficients. Therefore, it is necessary to determine the weight coefficients for each feature parameter separately.

[0062] S202: Determine the current risk assessment factor based on the characteristic parameters and the corresponding weight coefficients.

[0063] In this study, the data value parameter is set as A, the threat occurrence parameter as B, and the vulnerability parameter as C. Asset identification, threat identification, and vulnerability identification are performed on the power system. Data value parameter A is assigned values, ranging from 1 to 5 according to data value from low to high. Threat occurrence parameter B is assigned values ​​ranging from 0 to 1. Vulnerability parameter C is assigned values, ranging from 1 to 5 according to vulnerability severity from low to high. Then, the assigned values ​​for data value parameter A, threat occurrence parameter B, and vulnerability parameter C are substituted into the risk assessment impact formula to derive the current risk assessment sub-factor Z.

[0064] For example, the risk assessment impact formula is: Z = (λ1A) * (λ2B) * (λ3C), where A is the data parameter, B is the threat occurrence parameter, C is the vulnerability parameter, and the weighting coefficients include λ1, λ2, and λ3. Specifically, the value weight λ1 is the weighting coefficient corresponding to the data value parameter A, the threat probability weight λ2 is the weighting coefficient corresponding to the threat occurrence parameter B, and the vulnerability weight λ3 is the weighting coefficient corresponding to the vulnerability parameter C. The values ​​of λ1, λ2, and λ3 can be determined by relevant technical personnel in the application. In one example, the value range of λ1 is 0.8 to 1.2, the value of λ2 can be 1, and the value range of λ3 is 0.7 to 1.1.

[0065] In an exemplary embodiment, the risk assessment method of this application further includes the step of outputting risk warning information when the current risk assessment factor is greater than a preset risk assessment threshold.

[0066] In application, power systems can be categorized into low-risk, medium-risk, and high-risk levels, from lowest to highest. A risk assessment threshold of X is set between low and medium risk levels, and a risk assessment threshold of Y is set between medium and high risk levels. When Z < X, the power system is classified as low-risk; when X ≤ Z ≤ Y, it is classified as medium-risk; and when Y < Z, it is classified as high-risk. A risk warning message can be output when the current risk assessment factor exceeds the risk assessment threshold Y or X.

[0067] In one exemplary embodiment, please refer to Figure 3 Step S103: Based on the current risk assessment sub-factors and historical risk assessment sub-factors, obtain the risk frequency determinant of the power system, including steps S301 and S302.

[0068] S301: Determine the risk level weights based on the current risk assessment sub-factors.

[0069] In application, the risk level weight can be a risk assessment threshold X between low and medium risk levels, or a risk assessment threshold Y between medium and high risk levels. The risk level weight can be fixed at either the risk assessment threshold X or the risk assessment threshold Y, or it can be dynamically determined from either threshold X or the risk assessment threshold Y based on the current magnitude of the risk assessment sub-factors.

[0070] S302: Obtain the risk frequency determining factor based on the current risk assessment sub-factors, historical risk assessment sub-factors, risk level weights, and preset weighted average weights.

[0071] In this embodiment, there can be multiple historical risk assessment sub-factors. In application, if a large number of historical risk assessment sub-factors are used, the risk frequency determinant will be averaged out. If a small number of historical risk assessment sub-factors are used, the risk frequency determinant is prone to extremes. In one example, the first four historical risk assessment sub-factors can be obtained, and then the current risk assessment sub-factor and the first four historical risk assessment sub-factors can be used to calculate the risk frequency determinant of the power system.

[0072] For example, the formula for calculating the risk frequency determinant J is: J = γ1(Z n / X)+γ2(Z n-1 / X)+γ3(Z n-2 / X)+γ4(Z) n-3 / X)+γ5(Z n-4 / X), where Z n Z is the current risk assessment factor. n-1 Z n-2 Z n-3 and Z n-4 These are the risk assessment sub-factors from the previous four historical assessments of the power system, with X representing the risk assessment threshold between low and medium risk levels. γ1 to γ5 are the weighted average weights from the five assessments. In one example, γ1 to γ5 can have the same value, for example, all of them being 1. In another example, γ1 to γ5 can have different values; for instance, γ1=0.4, γ2=0.25, γ3=0.2, γ4=0.1, and γ5=0.05. By setting a larger weight coefficient for risk assessment sub-factors that are more recent in time, the influence of the recent power system risk status on the risk frequency determinant can be increased.

[0073] In one exemplary embodiment, the frequency switching condition includes a difference between the risk assessment frequency corresponding to the target assessment range and the current risk assessment frequency. See also... Figure 4Step S104, under the condition that the risk frequency determinant meets the preset frequency switching condition, adjusts the risk assessment frequency of the power system, including steps S401 and S403.

[0074] S401: Determine the target assessment range corresponding to the risk frequency determinant from multiple preset assessment ranges.

[0075] In application, multiple assessment ranges include at least a first assessment range and a second assessment range, the upper limit of the first assessment range is less than or equal to the lower limit of the second assessment range, and the risk assessment frequency corresponding to the first assessment range is less than the risk assessment frequency corresponding to the second assessment range.

[0076] In another example, three assessment ranges can be set: a first assessment range, a second assessment range, and a third assessment range. When the risk frequency determinant J < the first frequency assessment threshold M, the target assessment range corresponding to the risk frequency determinant is the first assessment range; when the first frequency assessment threshold M ≤ the risk frequency determinant J ≤ the second frequency assessment threshold N, the target assessment range corresponding to the risk frequency determinant is the second assessment range; and when the second frequency assessment threshold N is less than or equal to the second frequency assessment threshold N, the target assessment range corresponding to the risk frequency determinant is the third assessment range.

[0077] S402: Determine the risk assessment frequency corresponding to the target assessment scope based on the pre-built mapping relationship library.

[0078] The mapping relationship library includes various mapping relationships, which represent the mapping between assessment scope and risk assessment frequency. For example, the library stores the mapping relationships between the risk assessment frequency corresponding to the third assessment scope and the third assessment scope, the risk assessment frequency corresponding to the second assessment scope and the second assessment scope, and the risk assessment frequency corresponding to the first assessment scope and the first assessment scope. Different assessment scopes correspond to different risk assessment frequencies; the risk assessment frequency corresponding to the third assessment scope is higher than that corresponding to the second assessment scope, and the risk assessment frequency corresponding to the second assessment scope is higher than that corresponding to the first assessment scope.

[0079] S403: If the risk assessment frequency corresponding to the target assessment scope is different from the current risk assessment frequency, switch the current risk assessment frequency to the risk assessment frequency corresponding to the target assessment scope.

[0080] Next, the risk assessment frequency corresponding to the target assessment range is compared with the current risk assessment frequency. If the risk assessment frequency corresponding to the target assessment range is different from the current risk assessment frequency, the current risk assessment frequency is updated according to the risk assessment frequency corresponding to the target assessment range. For example, if the current risk assessment frequency is the risk assessment frequency corresponding to the second assessment range and the target assessment range is the third assessment range, then the risk assessment frequency corresponding to the third assessment range needs to be updated to the current risk assessment frequency.

[0081] In one exemplary embodiment, please refer to Figure 5 Step S202 involves determining the corresponding weight coefficients based on the feature parameters, including steps S501 and S502.

[0082] S501: Determine the target type of power operation data. The target type shall include at least Category I and Category II. The importance parameter of Category I power operation data shall be greater than that of Category II power operation data.

[0083] In applications, to conduct targeted risk assessments of the power system, power operation data can be categorized. Specifically, during the initial risk assessment of the power system, after collecting power operation data, its sensitivity, importance, and real-time performance can be analyzed. Furthermore, a comprehensive review of dispatching and spot market operation data is conducted. Based on the data's confidentiality, integrity, and availability, combined with its impact on dispatching and market operations, power operation data is divided into two categories according to its importance. The importance parameter refers to the degree of significance of the power operation data. Further, the first category of power operation data can be subdivided into core-level data and important-level data, while the second category can be subdivided into general-level data and publicly available data.

[0084] In one example, core-level data refers to data that, if leaked, tampered with, or lost, would directly threaten the safe and stable operation of the power grid, seriously disrupt the fair order of the spot market, and cause significant economic losses and social impact; important-level data refers to data that has a significant impact on the rational allocation of power resources and normal market transactions; general-level data refers to basic data that has a certain auxiliary role in the operation of the power system and market operations, but whose importance is relatively low; and public-level data refers to data that has been reviewed and is allowed to be disclosed to the public, such as market operation briefings and statistical reports.

[0085] S502: Determine the weighting coefficients corresponding to the feature parameters based on the target type; the weighting coefficients of the first type of power operation data are greater than or equal to the weighting coefficients of the second type of power operation data.

[0086] In applications, the weighting coefficients for different target types of power operation data can vary. For example, the weighting coefficient for the vulnerability parameters of the first type of power operation data can be greater than that for the vulnerability parameters of the second type of power operation data. This is to ensure that when evaluating the first type of power operation data, greater emphasis can be placed on the vulnerabilities present in the power system operation and data management processes. Therefore, it is necessary to determine the weighting coefficients for the characteristic parameters of the power operation data based on its target type.

[0087] In this embodiment, when conducting an initial risk assessment of the power system, a preset baseline risk frequency can be used to assess the power operation data of each target type. After accumulating a certain number of historical risk assessment sub-factors, the risk assessment frequency for each target type of power operation data can be dynamically adjusted based on the current and historical risk assessment sub-factors, thereby achieving targeted risk assessment of the power system. The risk assessment frequencies for power operation data of different target types can be the same or different.

[0088] In a detailed embodiment, power operation data can be divided into core-level data, important-level data, general-level data, and public-level data.

[0089] For each type of power operation data, based on the corresponding type of power operation data, the data value parameter A, threat occurrence parameter B, and vulnerability parameter C of the corresponding part of the power system are evaluated. The current risk assessment factor Z for the corresponding type of power operation data is then calculated based on these parameters. The preset weights for different types of power operation data can vary. For example, the value range of λ1 in the risk assessment impact formula for core-level and important-level data is 1–1.2; the value range of λ3 in the risk assessment impact formula for core-level and important-level data is 0.9–1.1; the value range of λ1 in the risk assessment impact formula for general-level and publicly available data is 0.8–1; and the value range of λ3 in the risk assessment impact formula for general-level and publicly available data is 0.7–0.9.

[0090] In the application, each type of power operation data can be set as low risk, medium risk, and high risk levels in ascending order of risk. The risk assessment thresholds for different types of power operation data can be the same or different. For example, the risk assessment threshold X for core-level data, important-level data, general-level data, and public-level data can all be 15, while the risk assessment threshold Y for core-level data, important-level data, general-level data, and public-level data can all be 20; or the risk assessment threshold X for core-level data and important-level data can be 15, the risk assessment threshold X for general-level data and public-level data can be 13, the risk assessment threshold Y for core-level data and important-level data can be 20, and the risk assessment threshold Y for general-level data and public-level data can be 18. Then, by combining the corresponding value weight λ1 and vulnerability weight λ3, the focus can be made more on different types of power operation data, and the feedback results of power operation data can be more sensitive. For example, when evaluating core-level data and important-level data, the data value and vulnerability severity can be highlighted more, which can effectively improve the rigor of data evaluation. For general-level data and public-level data, the rigor can be relatively relaxed. Under the premise of correct evaluation, the computing load of subsequent response strategies can be reduced, thereby realizing the hierarchical evaluation of data and improving the accuracy of data risk assessment.

[0091] For example, the risk assessment threshold X for core-level data, important-level data, general-level data, and public-level data can all be set to 10, and the risk assessment threshold Y for core-level data, important-level data, general-level data, and public-level data can all be set to 18. Taking a certain type of power operation data as an example, in this assessment, the data value parameter A of the important data is assigned a value of 3, the threat occurrence parameter B is assigned a value of 0.8, and the vulnerability parameter C is assigned a value of 4. When the data is core data, substituting into the risk assessment impact formula, Z = (1.2 * 3) * 0.8 * (1.1 * 4), we get Z = 12. Since 12 is greater than the threshold X of 10, it is at a medium risk level. When the data is important data, substituting into the risk assessment impact formula, Z = (1.1 * 3) * 0.8 * (1 * 4), we get Z = 10. Since 10 is equal to the threshold X of 10, it is at a medium risk level. When the data is general data, substituting into the risk assessment impact formula, Z = (0.9 * 3) * 0.8 * (0.9 * 4), we get Z = 7. Since 7 is less than the threshold X of 10, it is at a low risk level.

[0092] Subsequently, for each type of power operation data, a risk frequency determining factor J is calculated based on its current risk assessment sub-factor and the previous four historical risk assessment sub-factors. The risk frequency determining factor J is then used to determine whether the risk assessment frequency for that type of power operation data needs adjustment. This allows for adaptive adjustment of the risk assessment frequency for the corresponding type of power operation data based on changes in the risk frequency determining factor J, enabling timely assessment of the corresponding type of power operation data and providing a sound monitoring and feedback mechanism for timely response strategies.

[0093] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0094] Based on the same inventive concept, this application also provides a risk assessment apparatus for implementing the risk assessment method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more risk assessment apparatus embodiments provided below can be found in the limitations of the risk assessment method described above, and will not be repeated here.

[0095] In one exemplary embodiment, such as Figure 6 As shown, a risk assessment device is provided, comprising: a first execution module 601, a second execution module 602, a third execution module 603, and a fourth execution module 604, wherein:

[0096] The first execution module 601 is used to obtain the current power system operation data and historical risk assessment sub-factors based on the current risk assessment frequency; wherein, the historical risk assessment sub-factors are used to indicate the historical risk level of the power system.

[0097] The second execution module 602 is used to perform risk assessment on power operation data in order to determine the current risk assessment factors of the current power system.

[0098] The third execution module 603 is used to obtain the risk frequency determination factor of the power system based on the current risk assessment sub-factors and the historical risk assessment sub-factors.

[0099] The fourth execution module 604 is used to adjust the risk assessment frequency of the power system when the risk frequency determination factor meets the preset frequency switching conditions.

[0100] In an exemplary embodiment, the second execution module 602 includes: a first execution submodule, a second execution submodule, and a third execution submodule.

[0101] The first execution submodule is used to perform feature identification on the current power operation data to determine the feature parameters of the current power operation data; the feature parameters include at least one of data value parameters, threat occurrence parameters, and vulnerability parameters.

[0102] The second execution submodule is used to determine the corresponding weight coefficients based on the feature parameters.

[0103] The third execution submodule is used to determine the current risk assessment factor based on the feature parameters and the corresponding weight coefficients.

[0104] In an exemplary embodiment, the frequency switching condition includes that the risk assessment frequency corresponding to the target assessment range is different from the current risk assessment frequency; the fourth execution module 604 includes: a fourth execution submodule, a fifth execution submodule and a sixth execution submodule.

[0105] The fourth execution submodule is used to determine the target assessment range corresponding to the risk frequency determinant from multiple preset assessment ranges.

[0106] The fifth execution submodule is used to determine the risk assessment frequency corresponding to the target assessment scope based on a pre-built mapping relationship library. The mapping relationship library includes a variety of different mapping relationships, which are used to represent the mapping relationship between risk frequency determinants and risk assessment frequencies.

[0107] The sixth execution submodule is used to switch the current risk assessment frequency to the risk assessment frequency corresponding to the target assessment range when the risk assessment frequency corresponding to the target assessment range is different from the current risk assessment frequency.

[0108] In one exemplary embodiment, the multiple assessment ranges include at least a first assessment range and a second assessment range, wherein the upper limit of the first assessment range is less than or equal to the lower limit of the second assessment range, and the risk assessment frequency corresponding to the first assessment range is less than the risk assessment frequency corresponding to the second assessment range.

[0109] In an exemplary embodiment, the third execution module 603 includes a seventh execution submodule and an eighth execution submodule.

[0110] The seventh execution submodule is used to determine the risk level weight based on the current risk assessment sub-factors.

[0111] The eighth execution submodule is used to obtain the risk frequency determining factor based on the current risk assessment sub-factor, historical risk assessment sub-factor, risk level weight, and preset weighted average weight.

[0112] In one exemplary embodiment, the second execution submodule includes: a first execution unit and a second execution unit.

[0113] The first execution unit is used to determine the target type of the power operation data. The target type includes at least the first type and the second type. The importance parameter of the power operation data of the first type is greater than that of the power operation data of the second type.

[0114] The second execution unit is used to determine the weight coefficients corresponding to the feature parameters according to the target type; the weight coefficients of the first type of power operation data are greater than or equal to the weight coefficients of the second type of power operation data.

[0115] In one exemplary embodiment, the risk assessment device further includes a fifth execution module. The fifth execution module is used to output risk warning information when the current risk assessment sub-factor is greater than a preset risk assessment threshold.

[0116] Each module in the aforementioned risk assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0117] In one exemplary embodiment, this application provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method of any of the above embodiments.

[0118] This computer device can be a terminal, and its internal structure diagram can be as follows: Figure 7As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a risk assessment method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0119] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0120] In one exemplary embodiment, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0121] In one exemplary embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps of the method of any of the above embodiments.

[0122] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0123] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0124] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0125] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A risk assessment method, characterized in that, The method includes: Based on the current risk assessment frequency, obtain the current power system operation data and historical risk assessment sub-factors; wherein, the historical risk assessment sub-factors are used to indicate the historical risk level of the power system; A risk assessment is performed on the power operation data to determine the current risk assessment factors for the current power system; Based on the current risk assessment sub-factors and the historical risk assessment sub-factors, the risk frequency determination factor of the power system is obtained; Under the condition that the risk frequency determinant meets the preset frequency switching condition, the risk assessment frequency of the power system is adjusted.

2. The risk assessment method according to claim 1, characterized in that, The risk assessment of the power operation data to determine the current risk assessment factors of the current power system includes: The current power operation data is subjected to feature identification to determine the feature parameters of the current power operation data; the feature parameters include at least one of data value parameters, threat occurrence parameters, and vulnerability parameters; Determine the corresponding weight coefficients based on the feature parameters; The current risk assessment factor is determined based on the feature parameters and the corresponding weight coefficients.

3. The risk assessment method according to claim 1, characterized in that, The frequency switching conditions include the fact that the risk assessment frequency corresponding to the target assessment scope is different from the current risk assessment frequency; The adjustment of the risk assessment frequency of the power system under the condition that the risk frequency determinant factor meets the preset frequency switching condition includes: The target assessment range corresponding to the risk frequency determinant is determined from a plurality of preset assessment ranges; Based on a pre-built mapping relationship library, the risk assessment frequency corresponding to the target assessment scope is determined; wherein, the mapping relationship library includes a variety of different mapping relationships, which are used to represent the mapping relationship between the assessment scope and the risk assessment frequency; If the risk assessment frequency corresponding to the target assessment scope is different from the current risk assessment frequency, the current risk assessment frequency will be switched to the risk assessment frequency corresponding to the target assessment scope.

4. The risk assessment method according to claim 3, characterized in that, The plurality of assessment ranges include at least a first assessment range and a second assessment range, wherein the upper limit of the first assessment range is less than or equal to the lower limit of the second assessment range; and the risk assessment frequency corresponding to the first assessment range is less than the risk assessment frequency corresponding to the second assessment range.

5. The risk assessment method according to claim 1, characterized in that, The step of obtaining the risk frequency determination factor of the power system based on the current risk assessment sub-factor and the historical risk assessment sub-factor includes: The risk level weights are determined based on the current risk assessment factors. Based on the current risk assessment sub-factor, the historical risk assessment sub-factor, the risk level weight, and the preset weighted average weight, the risk frequency determining factor is obtained.

6. The risk assessment method according to claim 2, characterized in that, The step of determining the corresponding weight coefficient based on the feature parameters includes: The target type of the power operation data is determined, and the target type includes at least a first type and a second type; the importance parameter of the power operation data of the first type is greater than the importance parameter of the power operation data of the second type. The weighting coefficients corresponding to the feature parameters are determined according to the target type; the weighting coefficients of the power operation data of the first type are greater than or equal to the weighting coefficients of the power operation data of the second type.

7. A risk assessment device, characterized in that, The device includes: The first execution module is used to obtain the current power system operation data and historical risk assessment sub-factors based on the current risk assessment frequency; wherein, the historical risk assessment sub-factors are used to indicate the historical risk level of the power system; The second execution module is used to perform risk assessment on the power operation data in order to determine the current risk assessment factor of the current power system. The third execution module is used to obtain the risk frequency determination factor of the power system based on the current risk assessment sub-factor and the historical risk assessment sub-factor; The fourth execution module is used to adjust the risk assessment frequency of the power system under the condition that the risk frequency determination factor meets the preset frequency switching condition.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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