A method and device for determining the security requirement level of power transaction data and a medium

Abstract

The application discloses a power transaction data security requirement grade determination method and device and a medium thereof, relates to the electrical engineering technical field, and is used for providing a reasonable security grade quantification system for power transaction data, aiming at the problem that there is no reasonable quantification system for the security requirement of power transaction data, providing a power transaction data security requirement grade determination method, which comprises the following steps: obtaining various data indexes representing the information value and the degree of vulnerability of power transaction data, realizing the risk judgment of power transaction data by a hierarchical fuzzy Petri network, and then obtaining the final credibility of power transaction data; obtaining an initial state matrix representing the risk probability of each data index by each evaluation vector, and obtaining a final state matrix by the weight of each data index; and finally quantitatively determining the security requirement grade of power transaction data and providing guidance for subsequent security protection work.

Description

Technical Field

[0001] This application relates to the field of electrical engineering technology, and in particular to a method, apparatus and medium for determining the security requirement level of power transaction data. Background Technology

[0002] With the development of the electricity market and the intensive implementation of market-based transactions, a multi-cycle, multi-product trading system has been formed. For a wide range of upstream and downstream enterprises and market participants, electricity trading data is a crucial foundation for business operations, revenue generation, and decision-making in the electricity spot market. Electricity trading data has fundamentally changed the trading and operation models of a large number of upstream and downstream enterprises and their market participants, helping many companies make more informed trading decisions.

[0003] Currently, electricity trading data disclosure strictly adheres to the requirements of documents issued by energy regulatory agencies and is conducted through the electricity market information disclosure platform. Electricity trading data is categorized into four types based on security needs: public information, public data, private information, and information subject to disclosure requests. However, due to the diverse types and massive volume of electricity trading data involved in information disclosure, and the need to ensure the validity and privacy of this data, there is a lack of research on data security protection in the electricity market. A reasonable quantitative security assessment system has not been established for existing electricity trading data, making it impossible to determine its security needs and consequently, to establish corresponding protection mechanisms. This will limit the further development of the electricity market in the future, and market participants' data will face the risk of leakage, thereby disrupting market operations.

[0004] Therefore, those skilled in the art urgently need a method for determining the security requirements of power trading data to solve the problem that there is currently no reasonable quantitative system for the security requirements of power trading data, which makes it impossible to establish corresponding protection mechanisms. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, and medium for determining the security requirement level of power transaction data, thereby providing a reasonable security level quantification system for power transaction data, so that each power transaction data can be protected accordingly based on its security requirements.

[0006] To address the aforementioned technical problems, this application provides a method for determining the security requirement level of power transaction data, comprising:

[0007] This document describes the acquisition of various data indicators from power trading data sent by power trading terminals, as well as the relationships between these indicators. The data indicators include primary and secondary data indicators. Primary data indicators include data information value, data format, and data vulnerability. Secondary data indicators include market participation level, asset integration density, data content, data structuring level, data attack vulnerability, and impact on market clearing. Power trading data is derived from each primary data indicator according to transition rules, and each primary data indicator is derived from each secondary data indicator according to transition rules. Each transition rule corresponds one-to-one with each data indicator.

[0008] The event credibility and evaluation vector corresponding to each data indicator, as well as the transition credibility corresponding to each transition rule, are obtained from the expert knowledge base. The evaluation vector is used to characterize the probability of different risk levels of the data indicators.

[0009] Based on the relationships between various data indicators, determine the weights corresponding to each data indicator; based on the credibility of each event and the credibility of each change, determine the final credibility of the power trading data; based on each evaluation vector, determine the initial state matrix.

[0010] The final state matrix is ​​determined based on the initial state matrix, each weight, and the final confidence level.

[0011] Based on the final state matrix and the preset risk level evaluation matrix, the security requirement level of power trading data is determined.

[0012] Preferably, determining the security requirement level of power trading data based on the final state matrix and the preset risk level evaluation matrix includes: determining the event occurrence risk value of the power trading data based on the final state matrix and the risk level evaluation matrix; determining the risk assessment value of the power trading data based on the event occurrence risk value and the final credibility; and determining the security requirement level of the power trading data based on the risk assessment value.

[0013] Preferably, determining the security requirement level of electricity trading data based on risk assessment values ​​includes: the types of electricity trading data acquired, and the corresponding market privacy requirements; wherein, market privacy requirements are the requirements raised by market participants for further security protection of electricity trading data when providing it; and determining the security requirement level of electricity trading data based on the risk assessment value, data types, and market privacy requirements.

[0014] Preferably, the security requirement levels include: first security requirement, second security requirement, third security requirement, and fourth security requirement; and the security requirement levels of the first security requirement, second security requirement, third security requirement, and fourth security requirement increase sequentially.

[0015] Correspondingly, based on the risk assessment value, data type, and market privacy requirements of electricity trading data, the security requirement level for electricity trading data is determined as follows:

[0016] When the data type of electricity trading data is public information or open information, and the risk assessment value is less than or equal to the first preset value, it is determined whether there is a market privacy requirement. If there is, the security requirement level of the electricity trading data is determined to be the first security requirement; if not, the security requirement level of the electricity trading data is determined to be the second security requirement.

[0017] When the data type of electricity trading data is public information or open information, and the risk assessment value is greater than the first preset value, the security requirement level of the electricity trading data is determined to be the second security requirement.

[0018] When the data type of electricity trading data is private information or information to be disclosed, if the risk assessment value is less than or equal to the first preset value and there is no market privacy requirement, the security requirement level of the electricity trading data is determined to be the second security requirement; if the risk assessment value is greater than the first preset value, the security requirement level of the electricity trading data is determined to be the fourth security requirement; otherwise, the security requirement level of the electricity trading data is determined to be the third security requirement.

[0019] Preferably, determining the weight of each data indicator based on the relationship between them includes: determining the relative importance of each pair of data indicators using a 1-9 proportional scaling method, and establishing a judgment matrix accordingly; the judgment matrix is ​​as follows:

[0020]

[0021] a ij Indicates data indicator a i For data indicator a j The relative importance of the judgment matrix, where n represents the order of the judgment matrix;

[0022] The weights of each data indicator are determined according to the first and second formulas;

[0023] The first formula is:

[0024]

[0025] The second formula is:

[0026]

[0027] m i W represents an intermediate variable. i Indicates data indicator a i The weight.

[0028] Preferably, the method further includes: calculating the consistency of the judgment matrix; if the consistency of the judgment matrix is ​​not established, then returning to the step of determining the relative importance of each pair of data indicators according to the 1-9 proportional scaling method, and establishing the judgment matrix accordingly.

[0029] To address the aforementioned technical problems, this application also provides a device for determining the security requirement level of power transaction data, comprising:

[0030] The data indicator acquisition module is used to acquire various data indicators of the power trading data sent by the power trading terminal, as well as the relationships between these data indicators. These data indicators include primary and secondary data indicators. Primary data indicators include data information value, data format, and data vulnerability. Secondary data indicators include market participation level, asset integration density, data content, data structuring level, data attack vulnerability, and impact on market clearing. Power trading data is derived from each primary data indicator according to transition rules, and each primary data indicator is derived from each secondary data indicator according to transition rules. Each transition rule corresponds one-to-one with each data indicator.

[0031] The expert knowledge base acquisition module is used to obtain the event credibility and evaluation vector corresponding to each data indicator, as well as the transition credibility corresponding to each transition rule, from the expert knowledge base; among them, the evaluation vector is used to characterize the probability of the data indicator appearing at different risk levels;

[0032] The final state matrix preparation module is used to determine the weight of each data indicator based on the relationship between them; to determine the final credibility of the power trading data based on the credibility of each event and each transition; and to determine the initial state matrix based on each evaluation vector.

[0033] The final state matrix determination module is used to determine the final state matrix based on the initial state matrix, each weight, and the final confidence level.

[0034] The security requirement level determination module is used to determine the security requirement level of power trading data based on the final state matrix and the preset risk level evaluation matrix.

[0035] Preferably, it further includes: a consistency judgment module, used to calculate the consistency of the judgment matrix; if the consistency of the judgment matrix is ​​not established, it returns to the step of determining the relative importance of each pair of data indicators according to the 1-9 proportional scaling method and establishing the judgment matrix accordingly.

[0036] To address the aforementioned technical problems, this application also provides a device for determining the security requirement level of power transaction data, comprising:

[0037] Memory, used to store computer programs;

[0038] A processor is used to implement the steps of the method for determining the security requirement level of power trading data as described above when executing a computer program.

[0039] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for determining the security requirement level of power trading data as described above.

[0040] This application provides a method for determining the security requirement level of power trading data. By acquiring primary data indicators characterizing the information value and vulnerability of power trading data, as well as secondary data indicators constituting each primary data indicator, the risk assessment of power trading data is achieved using a hierarchical fuzzy Petri network. Specifically, each data indicator is a repository within the Petri network, which has a three-layer architecture. Secondary data indicators represent the lowest-level repository, and corresponding transition rules are used to represent the primary data indicators of the next higher-level repository. Similarly, primary data indicators use corresponding transition rules to represent the power trading data of the repository at the next higher level. Power trading data is analyzed layer by layer upwards, based on the event credibility of each storage facility, the change credibility of the corresponding change rules, and the weight of each lower-level storage facility within its parent facility, to obtain the final credibility of the power trading data. Then, using evaluation vectors to characterize the probability of different risk levels for each data indicator, an initial state matrix representing the risk probability of each data indicator is obtained, and the final state matrix is ​​derived through the weights of each data indicator. Finally, the security requirement level of the power trading data is determined, providing guidance for subsequent security protection work, reducing the risk of power trading data leakage, and contributing to the normal operation of the power trading market.

[0041] The device for determining the security requirements level of power transaction data and the computer-readable storage medium provided in this application correspond to the above-mentioned method and have the same effect. Attached Figure Description

[0042] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart of a method for determining the security requirement level of power transaction data provided by the present invention;

[0044] Figure 2 This is a structural diagram of the lower-layer fuzzy Petri network provided by the present invention;

[0045] Figure 3This is a structural diagram of the upper-layer fuzzy Petri network provided by the present invention;

[0046] Figure 4 A structural diagram of a device for determining the security requirement level of power transaction data provided by the present invention;

[0047] Figure 5 A structural diagram of another device for determining the security requirement level of power transaction data provided by the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0049] The core of this application is to provide a method, device, and medium for determining the security requirement level of power transaction data.

[0050] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] This application aims to construct a reasonable quantitative system for the security requirement levels of electricity trading data, and provides a method for determining the security requirement levels of electricity trading data, such as... Figure 1 As shown, it includes:

[0052] S11: Obtain the various data indicators of the power trading data sent by the power trading terminal, as well as the relationships between the various data indicators.

[0053] The data indicators include primary data indicators and secondary data indicators. Primary data indicators include data information value, data form, and data vulnerability. Secondary data indicators include the degree of participation in market transactions, the tightness of asset integration, data content, the degree of data structuring, the possibility of data being attacked, and the impact on market clearing. Electricity transaction data is obtained from each primary data indicator according to the change rules, and each primary data indicator is obtained from each secondary data indicator according to the change rules. Each change rule corresponds one-to-one with each data indicator.

[0054] The specific correspondence between each primary data indicator and each secondary data indicator is as follows: the value of data information is composed of factors such as the degree of participation in market transactions and the closeness of asset integration; the data form is composed of factors such as the data content and the degree of data structuring; and the data vulnerability is composed of factors such as the possibility of data being attacked and the impact on market clearing.

[0055] The relationships between the aforementioned data indicators include the correspondence between the primary and secondary data indicators. Based on this correspondence, when establishing a Petri network, the databases formed by primary data indicators are the upper-level databases of the databases formed by secondary data indicators. Electricity trading data, in turn, is the upper-level database of primary data indicators. The specific Petri network structure is as follows: Figure 2 As shown.

[0056] Petri nets: As a working model for describing asynchronous concurrent operations of system elements, Petri nets have been widely used in many fields such as computer science. Petri nets are an effective modeling tool for describing, analyzing and designing complex systems from a process perspective. They can naturally describe system characteristics such as concurrency, conflict, synchronization, and resource contention, and have execution control mechanisms. They also have theoretical rigor supported by formal steps and mathematical graph theory.

[0057] S12: Obtain the event credibility and evaluation vector corresponding to each data indicator, and the transition credibility corresponding to each transition rule from the expert knowledge base.

[0058] The evaluation vector is used to characterize the probability of different risk levels appearing in data indicators. For example, for one type of electricity trading data—market participant declared electricity energy—a possible evaluation vector for the secondary data indicator of market participation level is (0.7 0.3 0 0 0); and for another type of electricity trading data—entity operating information—a possible evaluation vector for the secondary data indicator of market participation level is (0.1 0.1 0.2 0.3 0.3). The evaluation vectors are determined by expert evaluation and pre-stored in an expert knowledge base for retrieval when needed.

[0059] S13: Determine the weight of each data indicator based on the relationship between them; determine the final credibility of the power trading data based on the credibility of each event and each change; determine the initial state matrix based on each evaluation vector.

[0060] As can be seen from the above, step S13 consists of three sub-steps, which are used to determine the weights of each data indicator, the final credibility of the power trading data, and the initial state matrix, respectively, and there is no order among them.

[0061] Specifically, regarding how to determine the final credibility of electricity trading data, the following is an explanation:

[0062] As can be seen from the above, the Petri network formed by various data indicators of electricity trading data is as follows: Figure 2As shown, the repository is divided into three layers. The credibility of the upper-level repository can be determined by the credibility of the lower-level repository and the credibility of its corresponding transition rule. This process is repeated layer by layer until the final credibility of the top-level repository is obtained. Furthermore, the credibility of the upper-level repository is determined by the credibility of the lower-level repository and the credibility of its corresponding transition rule. To achieve this, where p g1 The credibility of the upper-level library, p s For the credibility of the lower-level library, μ s The credibility of the transfer from a lower-level repository to an upper-level repository.

[0063] Specifically, regarding how to determine the weights corresponding to each data indicator, this embodiment also provides a preferred implementation scheme, which uses the hierarchical analysis method to determine the weights corresponding to each data indicator, specifically including:

[0064] S131: Determine the relative importance of each pair of data indicators using the 1-9 proportional scaling method, and establish a judgment matrix accordingly; wherein, the judgment matrix is:

[0065]

[0066] a ij Indicates data indicator a i For data indicator a j The relative importance of the judgment matrix is ​​given by n, where n represents the order of the judgment matrix.

[0067] S132: Calculate the relative weights W of each element using the root method. i .

[0068] The specific calculation formula is as follows:

[0069]

[0070]

[0071] Where, m i W represents an intermediate variable. i Indicates data indicator a i The weight.

[0072] Correspondingly, since the judgment matrix may not satisfy consistency when determining it, thus affecting the accuracy of the results obtained in subsequent steps, this embodiment, based on the above embodiment, also provides a preferred implementation scheme. The above method further includes:

[0073] S133: Calculate the consistency of the judgment matrix. If the consistency of the judgment matrix is ​​not valid, return to step S131.

[0074] This reduces the problem of inconsistent results in obtaining the judgment matrix negatively impacting subsequent steps in determining the security requirement level of power trading data, and further improves the accuracy of the method provided in this application in determining the security requirement level of power trading data.

[0075] Furthermore, step S133 specifically includes:

[0076] First, calculate the largest eigenvalue λ of the judgment matrix. max :

[0077]

[0078] Next, calculate the consistency index (CI) of the judgment matrix:

[0079]

[0080] If CI < 0.1, it indicates that the consistency of the judgment matrix is ​​valid.

[0081] S14: Determine the final state matrix based on the initial state matrix, each weight, and the final confidence level.

[0082] Specifically, step S14 includes:

[0083] S141: After determining the initial state matrix M(0), let k = 0, and the confidence level of the transition input factor being true is IN. T The reliability of the transition-derived output library is M(k). Where IN:P→T is the transition input matrix, and IN={a ij}, a ij ∈{0,1}; OUT:T→P is the transition output matrix, where OUT={β ij}, β ij ∈{0,1}.

[0084] S142: Calculate the next state after the transition:

[0085] S143: Determine whether M(k+1) = M(k). If not, let k = k+1 and return to step S142. If yes, stop the calculation and determine the final state matrix M(k).

[0086] S15: Determine the security requirement level of power trading data based on the final state matrix and the preset risk level evaluation matrix.

[0087] Similarly, a preferred embodiment of step S15 includes:

[0088] S151: Determine the event occurrence risk value of the power trading data based on the final state matrix and the risk level evaluation matrix.

[0089] Based on the final state matrix M(k) and the risk level assessment matrix Q T The evaluation index F for each event in the storage facility was calculated, and Q T The risk level assessment matrix is ​​Q = (10, 8, 6, 4, 2), where larger values ​​indicate higher risk levels. F is an n-dimensional vector, and the last element of the vector corresponds to the final data of the information risk, i.e., the risk value of the event, denoted as f. g .

[0090] S152: Determine the risk assessment value of electricity trading data based on the event occurrence risk value and final credibility.

[0091] Then from S=ω g f g Determine the risk assessment value for electricity trading data, where ω g S represents the final credibility obtained from step S13 above, and S is the final data risk assessment value.

[0092] S153: Determine the security requirement level of electricity trading data based on risk assessment values.

[0093] The risk level assessment matrix Q in step S151 above T It can be seen that, regarding risk level, Q T Risk is divided into 5 levels: 0-2, 2-4, 4-6, 6-8, and 8-10, representing an increasing level of risk. Similarly, the risk level assessment matrix Q is derived from this. T The final data risk assessment value S obtained also ranges from 0 to 10, and the larger the value, the higher the risk. Based on this, a risk assessment value that characterizes the risk of power trading data can be obtained, and then the security requirement level of power trading data can be determined according to the risk assessment value.

[0094] To further improve the accuracy of the security requirements for electricity trading data, this embodiment, based on the above embodiments, also provides a preferred solution, wherein step S153 specifically includes:

[0095] The types of data used to acquire electricity trading data, and the corresponding market privacy requirements.

[0096] Among them, market privacy requirements refer to the requirements of market participants for further security protection of electricity trading data when providing electricity trading data. These requirements are provided by the market participants, and in this application, the presence or absence of market privacy requirements is mainly used as one of the influencing factors in determining the security requirement level of electricity trading data.

[0097] The security requirement level of electricity trading data is determined based on the risk assessment value, data type, and market privacy requirements.

[0098] Regarding the specific judgment rules, a preferred implementation method is as follows:

[0099] The security requirement levels are: first security requirement, second security requirement, third security requirement, and fourth security requirement, with the security requirement levels increasing sequentially.

[0100] When the data type of electricity trading data is public information or publicly available information, and the risk assessment value is less than or equal to the first preset value, it is determined whether there is a market privacy requirement; if so, the security requirement level of the electricity trading data is determined to be the first security requirement; if not, the security requirement level of the electricity trading data is determined to be the second security requirement.

[0101] When the data type of electricity trading data is public information or open information, and the risk assessment value is greater than the first preset value, the security requirement level of the electricity trading data is determined to be the second security requirement.

[0102] When the data type of electricity trading data is private information or information to be disclosed, if the risk assessment value is less than or equal to the first preset value and there is no market privacy requirement, the security requirement level of the electricity trading data is determined to be the second security requirement; if the risk assessment value is greater than the first preset value, the security requirement level of the electricity trading data is determined to be the fourth security requirement; otherwise, the security requirement level of the electricity trading data is determined to be the third security requirement.

[0103] Similarly, the rules for determining the security requirement level of electricity trading data mentioned above can also be implemented by establishing a Petri network, such as... Figure 3 The figure shows a six-tuple FPN model, where:

[0104] FPN = (P, T, IN, OUT, F, W)

[0105] P = {p1, p2, ..., p} n Let} be the finite set of the library; T = {t1, t2, ..., t} n} is a finite set of transitions; IN:P→T is the transition input matrix, IN={a ij}, a ij ∈{0,1}; OUT:T→P is the transition output matrix, OUT={β ij}, β ij ∈{0,1};F:T→[0,1] is the transition confidence function, F(t) j )=μ j (j=1,2,…,m),μ jFor fuzzy rule t j The confidence level; W:P→[0,1] represents the location p. i The credibility function, W(p) i )={ω i}. At the same time, for the purpose of... Figure 2 The Petri network shown is used for differentiation. Figure 2 The Petri network shown becomes a lower-level fuzzy Petri network. Figure 3 The Petri network shown becomes an upper-level fuzzy Petri network.

[0106] As described above, this application discloses a method for determining the security requirement level of power trading data. It establishes a Petri network based on fuzzy reasoning using various data indicators reflecting the value and risk of power trading data. This yields a final credibility rating characterizing the risk status of the power trading data. Combining an initial matrix determined by evaluation vectors and weights determined by the relationships between various data indicators, a final state matrix is ​​derived through fuzzy reasoning. This provides a quantitative indicator of the risk status of power trading data, enabling the determination of its security requirement level and laying a foundation for subsequent security protection in power trading centers. Furthermore, this fuzzy reasoning-based method for determining security requirement levels is suitable for complex data in power trading centers, easily handles uncertain evaluation indicators, and allows for reasonable security requirement classification. It also possesses good parallel processing capabilities, demonstrating better performance in describing and analyzing the fuzziness and concurrency of risk events.

[0107] To further illustrate the method for determining the security requirement level of power transaction data provided in this application, the following explanation is based on a practical application scenario:

[0108] 1. Based on the above data indicators, establish as follows: Figure 2 The Petri network shown represents the market participation level, asset correlation, data volume, structure level, data attack risk, and data impact on market clearing. a P represents information value. b To represent the data format, P c P represents data vulnerability. g This represents the risk of data information; the corresponding ω value represents the credibility of the propositions in the database. Since propositions P1-P6 in the database are not events, but factors that form indicators, there is no distinction in credibility. Here, the credibility ω of the database is set to 1. Assume the database change rule t i The credibility is μ i For ease of explanation, let's assume the following matrix represents the confidence levels of the nine transition rules:

[0109] μ=(0.9,0.8,0.9,0.85,0.9,0.8,0.9,0.8,0.9)

[0110] 2. The final credibility of the repository is determined using an event credibility reasoning algorithm, i.e., the final credibility, derived from w(p). g1 ) = max(p s *μ s It can be known that the library is P. a P b P c The credibility matrix is ​​w = (0.9, 0.9, 0.9). Using this credibility matrix and the last three elements of the transition rule matrix, we can see that the final credibility is 0.81.

[0111] 3. Solve for the indicators based on actual data. This example uses the electricity energy curve declared by market entities and their business information as examples to illustrate the indicator solution. Let the risk level evaluation matrix be Q = (10, 8, 6, 4, 2). Appendix Table 1 constructs a level interval quantification table using warehouse P1 as an example. In the table, A represents the electricity energy curve declared by the market entity, and B represents the business information of the entity.

[0112] Table 1. Quantitative Table of P1 Level Range for Warehouses

[0113] Assessment Level Evaluation Vector (A) Evaluation Vector (B) high 0.7 0.1 higher 0.3 0.1 middle 0 0.2 lower 0 0.3 Low 0 0.3

[0114] By analogy, the evaluation vectors of each storage facility are obtained to obtain the initial state matrix M(0), and the initial state matrix M of the market entity's declared electricity energy curve. A (0) and the initial state matrix M of the main business information B (0) is as follows:

[0115]

[0116] as well as

[0117]

[0118] Then, the weights of each data indicator are determined according to the above method. Since the method for determining the weights has already provided a clear calculation method, it will not be repeated here in this embodiment. For ease of explanation, it is assumed that the weights of each data indicator are equal, that is, the transition confidence vector V is:

[0119]

[0120] Based on steps S141 to S143 above, the final state matrix M(k) is obtained:

[0121]

[0122] as well as

[0123]

[0124] Therefore, based on the last row of data in the final state matrix, the information risk assessment vector of the market entity's declared electricity energy curve is (0.73, 0.2, 0.07, 0, 0), and the information risk assessment vector of the entity's operating information is (0.14, 0.05, 0.15, 0.28, 0.38).

[0125] Therefore, the weighted average results are as follows: the information risk index of the market entity's declared electricity energy curve is 9.32; the information risk index of the entity's business information is 4.58.

[0126] Therefore, the risk level of the information obtained from the market entity's declared electricity energy curve is 8-10, which, according to the preset risk level evaluation matrix, is high risk; while the entity's business information is in the 4-6 range, which is medium risk.

[0127] 4. Considering the actual situation, the market privacy requirements for electricity energy curves and business information declared by market entities are generally none or low. For simplicity, we will assume none for the subsequent steps. As can be seen from the specific rules for determining electricity trading data mentioned above, electricity energy curves declared by market entities are high-risk data. According to the disclosure mechanism, this data type is private information, therefore the security protection level for this type of electricity trading data should be the fourth security requirement. Business information is medium-risk, and according to the disclosure mechanism, this data type is public information, therefore the security protection level for this type of electricity trading data should be the second security requirement. This completes the process of determining the security requirement level for electricity trading data.

[0128] The above embodiments provide a detailed description of a method for determining the security requirement level of power trading data. This application also provides an embodiment corresponding to a device for determining the security requirement level of power trading data. It should be noted that this application describes the device embodiment from two perspectives: one based on functional modules, and the other based on hardware.

[0129] From the perspective of functional modules, this embodiment provides a preferred implementation scheme, including:

[0130] The data indicator acquisition module 21 is used to acquire various data indicators of the power trading data sent by the power trading terminal, as well as the relationships between the data indicators. The data indicators include primary data indicators and secondary data indicators. Primary data indicators include data information value, data form, and data vulnerability. Secondary data indicators include the degree of participation in market transactions, the tightness of asset integration, data content, the degree of data structuring, the possibility of data attack, and the impact on market clearing. The power trading data is obtained from each primary data indicator according to the change rules, and each primary data indicator is obtained from each secondary data indicator according to the change rules. Each change rule corresponds one-to-one with each data indicator.

[0131] The expert knowledge base acquisition module 22 is used to acquire the event credibility and evaluation vector corresponding to each data indicator, as well as the change credibility corresponding to each change rule, from the expert knowledge base; wherein, the evaluation vector is used to characterize the probability of the data indicator having different risk levels.

[0132] The final state matrix preparation module 23 is used to determine the weight of each data indicator based on the relationship between them; to determine the final credibility of the power trading data based on the credibility of each event and the credibility of each transition; and to determine the initial state matrix based on each evaluation vector.

[0133] The final state matrix determination module 24 is used to determine the final state matrix based on the initial state matrix, each weight, and the final confidence level.

[0134] The security requirement level determination module 25 is used to determine the security requirement level of power trading data based on the final state matrix and the preset risk level evaluation matrix.

[0135] Preferably, the power transaction data security requirement level determination device provided in this embodiment further includes:

[0136] The consistency judgment module is used to calculate the consistency of the judgment matrix. If the consistency of the judgment matrix is ​​not met, the process returns to the step of determining the relative importance of each pair of data indicators according to the 1-9 proportional scaling method and establishing the judgment matrix accordingly.

[0137] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0138] Figure 5 A structural diagram of a device for determining the security requirements level of power transaction data, as provided in another embodiment of this application, is shown below. Figure 5 As shown, a device for determining the security requirement level of power transaction data includes: a memory 30 for storing computer programs;

[0139] The processor 31 is used to execute a computer program to implement the steps of a method for determining the security requirement level of power transaction data as described in the above embodiment.

[0140] The device for determining the security requirement level of power transaction data provided in this embodiment may include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0141] The processor 31 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 31 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 31 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 31 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0142] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 30 is used to store at least the following computer program 301, which, after being loaded and executed by the processor 31, is capable of implementing the relevant steps of the method for determining the security requirement level of power trading data disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, and the storage method may be temporary or permanent storage. The operating system 302 may include Windows, Unix, Linux, etc. The data 303 may include, but is not limited to, a method for determining the security requirement level of power trading data.

[0143] In some embodiments, a power transaction data security requirement level determination device may further include a display screen 32, an input / output interface 33, a communication interface 34, a power supply 35, and a communication bus 36.

[0144] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on a power transaction data security requirement level determination device and may include more or fewer components than shown.

[0145] This application provides an apparatus for determining the security requirement level of power transaction data, including a memory and a processor. When the processor executes a program stored in the memory, it can implement the following method: a method for determining the security requirement level of power transaction data.

[0146] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0147] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0148] The foregoing provides a detailed description of a method, apparatus, and medium for determining the security requirement level of power transaction data provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0149] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for determining the security requirement level of electricity trading data, characterized in that, include: This document describes a method for acquiring various data indicators of power trading data sent from a power trading terminal, as well as the relationships between these indicators. The data indicators include primary and secondary data indicators. Primary data indicators include data information value, data format, and data vulnerability. Secondary data indicators include market participation level, asset integration density, data content, data structuring level, data attack vulnerability, and impact on market clearing. The power trading data is derived from each of the primary data indicators according to transition rules, and each primary data indicator is derived from each of the secondary data indicators according to these transition rules. Each transition rule corresponds one-to-one with each of the data indicators. The correspondence between the primary and secondary data indicators is as follows: data information value corresponds to market participation level and asset integration density; data format corresponds to data content and data structuring level; and data vulnerability corresponds to data attack vulnerability and impact on market clearing. Obtain the event credibility and evaluation vector corresponding to each of the data indicators, and the transition credibility corresponding to each of the transition rules from the expert knowledge base; wherein, the evaluation vector is used to characterize the probability of the data indicators having different risk levels; The relative importance of each pair of data indicators is determined using the 1-9 scaling method, and a judgment matrix is ​​established accordingly; the judgment matrix is ​​as follows: ； Indicates the data indicators For the data indicators The relative importance; Calculate the consistency of the judgment matrix. If the consistency of the judgment matrix is ​​not met, return to the step of determining the relative importance of each pair of data indicators according to the 1-9 scaling method and establishing the judgment matrix accordingly. The weights of each data indicator are determined according to the first and second formulas; The first formula is: ； The second formula is: ； To represent an intermediate variable, Indicates the data indicators The weights, where n represents the order of the judgment matrix; The final credibility of the power trading data is determined based on the credibility of each event and the credibility of each transition; the initial state matrix is ​​determined based on each evaluation vector. The final state matrix is ​​determined based on the initial state matrix, each of the weights, and the final credibility. Based on the final state matrix and the preset risk level evaluation matrix, the event occurrence risk value of the power trading data is determined; Based on the event occurrence risk value and the final credibility, the risk assessment value of the power trading data is determined; The data types of the electricity trading data and the corresponding market privacy requirements are obtained; wherein, the market privacy requirements are the requirements of market participants for further security protection of the electricity trading data when providing the electricity trading data; The security requirement level of the electricity trading data is determined based on the risk assessment value, the data type, and the market privacy requirements.

2. The method for determining the security requirement level of power transaction data according to claim 1, characterized in that, The security requirement levels include: first security requirement, second security requirement, third security requirement, and fourth security requirement; and the security requirement levels of the first security requirement, second security requirement, third security requirement, and fourth security requirement increase sequentially. Correspondingly, determining the security requirement level of the power trading data based on the risk assessment value, data type, and market privacy requirements includes: When the data type of the power trading data is public information or open information, and the risk assessment value is less than or equal to the first preset value, it is determined whether there is a market privacy requirement. If there is, the security requirement level of the power trading data is determined to be the first security requirement. If not, the security requirement level of the power trading data is determined to be the second security requirement. When the data type of the power trading data is public information or open information, and the risk assessment value is greater than the first preset value, the security requirement level of the power trading data is determined to be the second security requirement; When the data type of the power trading data is private information or information to be disclosed, if the risk assessment value is less than or equal to the first preset value and there is no market privacy requirement, then the security requirement level of the power trading data is determined to be the second security requirement; if the risk assessment value is greater than the first preset value, then the security requirement level of the power trading data is determined to be the fourth security requirement; otherwise, the security requirement level of the power trading data is determined to be the third security requirement.

3. A device for determining the security requirement level of power transaction data, characterized in that, include: The data indicator acquisition module is used to acquire various data indicators of the power trading data sent by the power trading terminal, as well as the relationships between these data indicators. The data indicators include primary data indicators and secondary data indicators. Primary data indicators include data information value, data form, and data vulnerability. Secondary data indicators include market participation level, asset integration density, data content, data structuring degree, data attack vulnerability, and impact on market clearing. The power trading data is obtained from each of the primary data indicators according to transition rules, and each primary data indicator is obtained from each of the secondary data indicators according to the transition rules. Each transition rule corresponds one-to-one with each of the data indicators. The correspondence between the primary and secondary data indicators is as follows: data information value corresponds to market participation level and asset integration density; data form corresponds to data content and data structuring degree; and data vulnerability corresponds to data attack vulnerability and impact on market clearing. The expert knowledge base acquisition module is used to acquire, from the expert knowledge base, the event credibility and evaluation vector corresponding to each of the data indicators, and the transition credibility corresponding to each of the transition rules; wherein, the evaluation vector is used to characterize the probability of the data indicators having different risk levels; The final state matrix preparation module is used for: The relative importance of each pair of data indicators is determined using the 1-9 scaling method, and a judgment matrix is ​​established accordingly; the judgment matrix is ​​as follows: ； Indicates the data indicators For the data indicators The relative importance; Calculate the consistency of the judgment matrix. If the consistency of the judgment matrix is ​​not met, return to the step of determining the relative importance of each pair of data indicators according to the 1-9 scaling method and establishing the judgment matrix accordingly. The weights of each data indicator are determined according to the first and second formulas; The first formula is: ； The second formula is: ； To represent an intermediate variable, Indicates the data indicators The weights, where n represents the order of the judgment matrix; the final credibility of the power trading data is determined based on the credibility of each event and the credibility of each transition; the initial state matrix is ​​determined based on each evaluation vector; The final state matrix determination module is used to determine the final state matrix based on the initial state matrix, each of the weights, and the final confidence level. The security requirement level determination module is used to determine the event occurrence risk value of the power trading data based on the final state matrix and a preset risk level evaluation matrix; determine the risk assessment value of the power trading data based on the event occurrence risk value and the final credibility; obtain the data type of the power trading data and the corresponding market privacy requirements; wherein, the market privacy requirements are the requirements for further security protection of the power trading data raised by market participants when providing the power trading data; and determine the security requirement level of the power trading data based on the risk assessment value, the data type, and the market privacy requirements.

4. A device for determining the security requirement level of power transaction data, characterized in that, include: Memory, used to store computer programs; A processor, used to implement the steps of the method for determining the security requirement level of power trading data as described in claim 1 or 2 when executing the computer program.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for determining the security requirement level of power trading data as described in claim 1 or 2.

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