Power data risk assessment method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a power data risk assessment method and device, electronic equipment and a storage medium. The method comprises the following steps: for any preset secondary index, determining a probability hesitant fuzzy element of the preset secondary index based on an evaluation value input by each expert according to to-be-evaluated power data; then determining an evaluation weighted positive ideal distance and an evaluation weighted negative ideal distance based on the probability hesitant fuzzy element and a predetermined weight of each preset secondary index; and finally, determining the risk state of the to-be-assessed power data according to the assessment weighted positive ideal distance and the assessment weighted negative ideal distance. Based on this, during evaluation, a probability hesitant fuzzy element is introduced as a data basis of evaluation, so that the subjectivity of expert evaluation is eliminated to a certain extent, and the objectivity and accuracy of an evaluation result are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing technology, and in particular to a method, device, electronic device and storage medium for power data risk assessment. Background Art

[0003] Power data mainly includes grid operation and equipment monitoring data, power marketing data, power company management data, etc., showing the characteristics of large data scale, rich data types, and high data real-time. It contains huge potential and value in social governance, public services, and business innovation. However, power data has high risk of leakage, is difficult to control, and is easy to become a target of cyber attacks.

[0004] At present, risk assessment of power data is usually based on expert decision-making information. However, due to their subjective consciousness, experts often have large differences in their opinions on safety risk assessment, which will lead to insufficient expression of expert decision-making information and may cause the risk assessment results to be not objective enough, thereby reducing the accuracy of the assessment. Summary of the invention

[0005] The embodiments of the present application provide a power data risk assessment method, device, electronic device and storage medium to reduce the adverse effects of subjectivity in the expert evaluation process and improve the objectivity and accuracy of the evaluation results.

[0006] In a first aspect, an embodiment of the present application provides a method for risk assessment of power data, the method comprising:

[0007] For any preset secondary indicator, the probability hesitant fuzzy element of the preset secondary indicator is determined based on the evaluation value input by each expert according to the power data to be evaluated; the expert is a professional who participates in evaluating the risk of the power data to be evaluated;

[0008] Based on the probability hesitant fuzzy element and the predetermined weights of each preset secondary index, an evaluation weighted positive ideal distance and an evaluation weighted negative ideal distance are determined;

[0009] The risk status of the power data to be evaluated is determined based on the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance.

[0010] In a second aspect, an embodiment of the present application further provides a power data risk assessment device, the device comprising:

[0011] The first determination module is used to determine the probability hesitant fuzzy element of any preset secondary indicator based on the evaluation value input by each expert according to the power data to be evaluated; the expert is a professional who participates in evaluating the risk of the power data to be evaluated;

[0012] A second determination module is used to determine the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance based on the probabilistic hesitant fuzzy element and the predetermined weights of each preset secondary indicator;

[0013] The evaluation module is used to determine the risk status of the power data to be evaluated according to the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance.

[0014] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0015] one or more processors;

[0016] a storage device for storing one or more programs,

[0017] When one or more programs are executed by one or more processors, the one or more processors implement the power data risk assessment method provided in any embodiment of the present application.

[0018] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements a power data risk assessment method as provided in any embodiment of the present application.

[0019] The technical solution of the embodiment of the present application, for any preset secondary indicator, determines the probability hesitant fuzzy element of the preset secondary indicator based on the evaluation value input by each expert according to the power data to be evaluated; then determines the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance based on the probability hesitant fuzzy element and the pre-determined weights of each preset secondary indicator; finally, determines the risk status of the power data to be evaluated based on the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance. Based on this, when conducting the evaluation, the probability hesitant fuzzy element will be introduced as the data basis for the evaluation, thereby eliminating the subjectivity of the expert evaluation to a certain extent, thereby improving the objectivity and accuracy of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a power data risk assessment method provided in Example 1 of the present application;

[0021] Figure 2 A schematic diagram of a topological structure of a preset indicator set provided in Example 1 of the present application;

[0022] Figure 3 A schematic diagram of the structure of a power data risk assessment device provided in Example 2 of the present application;

[0023] Figure 4 A schematic diagram of the structure of an electronic device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0024] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0025] Embodiment 1

[0026] Figure 1 A flow chart of the power data risk assessment method provided in Example 1 of the present application is shown in FIG. Figure 1 As shown, the power data risk assessment method provided in this embodiment can be applied to the server, and specifically may include the following steps:

[0027] Step 101: For any preset secondary indicator, determine the probability hesitant fuzzy element of the preset secondary indicator based on the evaluation value input by each expert according to the power data to be evaluated.

[0028] In this step, the experts are professionals who participate in the assessment of the risk of the power data to be assessed, and the preset secondary indicators are indicators with a secondary topology in the preset indicator set. It should be noted that the preset indicator set contains multiple preset primary indicators, and each preset primary indicator contains multiple preset secondary indicators.

[0029] In a specific example, the topology of the preset indicator set can be referred to Figure 2 , Figure 2 A schematic diagram of the topological structure of a preset indicator set provided in Example 1 of the present application.

[0030] like Figure 2 As shown, the preset indicator set includes 6 preset first-level indicators, namely: data collection security, data transmission security, data storage security, data processing security, data exchange security and data destruction security.

[0031] Under each preset first-level indicator, there are several preset second-level indicators, such as Figure 2 As shown in the figure, the data collection security indicators include three preset secondary indicators: data collection tools, data classification, and data labels. Similarly, data storage security includes data transmission encryption, transmission interface security, and network credibility; data storage security includes data backup and recovery, storage medium security, and database encryption; data processing security includes data desensitization, data processing environment, and data fusion; data exchange security includes data interface security, data distribution security, and access permission control; data destruction security includes storage medium destruction and data software destruction.

[0032] For the convenience of representation, the preset first-level index is represented by Ii Indicates that the preset secondary index is I ij Indicates, where i = 1, 2, 3, ..., 6; j is a positive integer. For details, please refer to Figure 2 The identifier after the name of each indicator.

[0033] In this step, experts mainly evaluate the preset secondary indicators in the preset indicator set and input the evaluation values ​​of each preset secondary indicator.

[0034] Specifically, in this step, multiple experts can evaluate each preset secondary indicator based on the preset security risk level and input the corresponding evaluation value. Then, for any preset secondary indicator, the number of experts with the same evaluation value for the indicator is determined, the distribution probability of each evaluation value is determined, and the evaluation value and its corresponding distribution probability are used as the probability hesitant fuzzy element of the preset secondary indicator.

[0035] In a specific example, the aforementioned security risk levels may be shown in Table 1 below.

[0036] Table 1

[0037]

[0038] Based on the above process, the evaluation values ​​of 25 experts can be used to obtain the probability hesitant fuzzy elements of the preset secondary indicators as shown in Table 3 below.

[0039] Table 3

[0040]

[0041]

[0042] Take I 11 For example, there are two evaluation values ​​0.3 and 0.1. For 0.3, 40% of the experts gave this score, that is, 0.4, and 60% of the experts gave the evaluation value of 0.1, and its distribution probability is 0.6.

[0043] Step 102: Determine the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance based on the probability hesitant fuzzy element and the predetermined weights of each preset secondary index.

[0044] Before this step is performed, the weight of each preset secondary indicator needs to be determined in advance. Specifically, the weight of each preset secondary indicator in the preset indicator set can be determined based on the indicator importance scale input by each expert according to the preset indicator set.

[0045] The indicator importance scale can include two parts, one part is the importance scale value, and the other part is the hesitant judgment probability corresponding to the importance scale value. Both parts are input by experts. The importance scale value can be made by referring to the scale table in Table 3 below. The hesitant judgment probability is the judgment probability that the expert subjectively believes when he or she inputs the importance scale value.

[0046] Table 3

[0047]

[0048] As can be seen from Table 3, in this embodiment, it is necessary to compare the indicators in pairs.

[0049] Specifically, when determining the weights of the preset secondary indicators, we can first construct a first-level indicator judgment matrix and a second-level indicator judgment matrix corresponding to each expert based on the indicator importance scale input by each expert according to the preset indicator set; then use the first-level indicator judgment matrix and the second-level indicator judgment matrix corresponding to each expert to determine the weights of each preset secondary indicator.

[0050] Among them, when constructing the first-level indicator judgment matrix and the second-level indicator judgment matrix, the first-level indicator judgment matrix corresponding to each expert can be constructed according to the indicator importance scale input by each expert according to the first-level indicator preset in the preset indicator set; and then, the second-level indicator judgment matrix corresponding to each expert under each preset first-level indicator can be constructed according to the indicator importance scale input by each expert according to the second-level indicator preset in the preset indicator set.

[0051] For the first-level indicator judgment matrix, in this embodiment, for any target preset first-level indicator, the important scale value between the target preset first-level indicator input by any target expert and each preset first-level indicator and the hesitant judgment probability corresponding to the important scale value are obtained.

[0052] In a specific example, taking the aforementioned 6 preset first-level indicators as an example, the expert can input the importance of I1 compared to I1, the importance of I1 compared to I2, ..., the importance of I1 compared to I6.

[0053] By analogy, complete the input of the importance scale values ​​of the importance between the preset first-level indicators and the corresponding hesitant judgment probabilities.

[0054] Then, the importance scale value corresponding to each preset first-level indicator and the hesitant judgment probability corresponding to the importance scale value are determined as the indicator importance scale of the target preset first-level indicator for the target expert; and then, based on the indicator importance scale of each preset first-level indicator input by the target expert, the first-level indicator judgment matrix corresponding to the target expert is constructed.

[0055] In a specific example, the first-level indicator judgment matrix input by the target expert is as follows:

[0056]

[0057] Among them, taking the element in the first row and second column as an example, the target expert gave two importance scales, 0.3 and 0.1, for the importance of I1 compared to I2, and he believed that the probability of 0.7 tends to 0.3, and the remaining probability of 0.3 tends to 0.1.

[0058] For the secondary indicator judgment matrix, in this embodiment, for the target preset secondary indicator under any target preset first-level indicator, the importance scale value between the target preset secondary indicator and each preset secondary indicator input by any target expert and the hesitant judgment probability corresponding to the important scale value are obtained; the important scale value corresponding to each preset secondary indicator and the hesitant judgment probability corresponding to the important scale value are determined as the indicator importance scale of the target expert for the target preset secondary indicator; based on the indicator importance scale of each preset secondary indicator input by the target expert, the secondary indicator judgment matrix corresponding to the target expert under the target preset first-level indicator is constructed.

[0059] It should be noted that this process is similar to the preset first-level indicators, and will not be repeated here. In a specific example, the second-level indicator judgment matrix of the target experts for the preset second-level indicators under each preset first-level indicator can be given as follows:

[0060]

[0061] When determining the weights of each preset secondary indicator by using the first-level indicator judgment matrix and the second-level indicator judgment matrix corresponding to each expert, the first-level indicator judgment matrix and the second-level indicator judgment matrix can be aggregated to obtain the importance evaluation results of each preset first-level indicator and the importance evaluation results of each preset second-level indicator in the preset indicator set; according to the importance evaluation results of each preset first-level indicator and the importance evaluation results of each preset second-level indicator, determine the corresponding weights of each preset second-level indicator.

[0062] During aggregation, the first-level indicator judgment matrix corresponding to each expert can be aggregated to obtain the importance evaluation result of each preset first-level indicator; then the second-level indicator judgment matrix corresponding to each expert can be aggregated to obtain the importance evaluation result of each preset second-level indicator.

[0063] For the aggregation of the first-level indicator judgment matrix, we can first aggregate the first-level indicator judgment matrix corresponding to any expert row by row to obtain the expert's evaluation results for each preset first-level indicator; then use the probability hesitant fuzzy set arithmetic mean operator to aggregate the evaluation results of all experts on any preset first-level indicator to obtain the importance assessment results of the preset first-level indicator.

[0064] Among them, row-by-row aggregation can be achieved by referring to the following formula:

[0065]

[0066] Where n represents the number of indicators, The probability hesitant fuzzy element representing the relative importance of the i-th indicator and the j-th indicator given by the k-th expert, γ ijk represents the degree of membership, i.e., the important scale value, p ijk represents the corresponding probability, that is, the hesitant judgment probability, and λ represents the set Scale.

[0067] Based on the above formula, taking the first row of H1 as an example, the specific aggregation formula is as follows:

[0068]

[0069] In this way, we can get the evaluation results of the target expert for each indicator, and then aggregate the evaluation results of all experts on a certain indicator to get the evaluation result of the importance of the indicator. The specific results can be obtained with the help of the following formula:

[0070] Among them, m is the number of experts.

[0071] For the preset secondary indicators, the same process is used. For the secondary indicator judgment matrix corresponding to any expert, row-by-row aggregation is performed to obtain the expert's evaluation results for each preset secondary indicator. Using the probability hesitant fuzzy set arithmetic mean operator, for any preset secondary indicator, the evaluation results of all experts on the preset secondary indicators are aggregated to obtain the importance assessment results of the preset secondary indicators.

[0072] After obtaining the importance evaluation results of the above-mentioned preset first-level indicators and the importance evaluation results of the preset second-level indicators, the probabilistic hesitant fuzzy set scoring function can be used to determine the hierarchical weight of each preset first-level indicator according to the importance evaluation results of each preset first-level indicator; then the probabilistic hesitant fuzzy set scoring function can be used to determine the hierarchical weight of each preset second-level indicator according to the importance evaluation results of each preset second-level indicator; finally, the weight of each preset second-level indicator is determined based on the indicator structure between the preset first-level indicators and the second-level indicators, the hierarchical weight of each preset first-level indicator, and the hierarchical weight of each preset second-level indicator.

[0073] Among them, the hierarchical weight can be determined based on the following formula:

[0074]

[0075] Then, for any preset secondary indicator, the product of the hierarchical weight of the preset primary indicator to which the preset secondary indicator belongs and the hierarchical weight of the preset secondary indicator is determined as the weight of the preset secondary indicator.

[0076] Specifically, the weight of the preset secondary indicator can be determined by using the following formula:

[0077]

[0078] After the weights are determined, the probability hesitant fuzzy elements can be used to construct a probability hesitant fuzzy judgment matrix, and the positive ideal solution and the negative ideal solution can be established based on the probability hesitant fuzzy elements.

[0079] In a specific example, the positive ideal solution and the negative ideal solution are as follows:

[0080]

[0081] in,

[0082] Then, the modified Hamming distance is used to determine the evaluation weighted positive ideal distance between the positive ideal solution and the probability hesitant fuzzy judgment matrix based on the weights of each preset secondary indicator.

[0083] The specific calculation can be based on the following formula:

[0084] Among them, w j The weight of the j-th preset secondary indicator

[0085] The modified Hamming distance is used to determine the evaluation weighted negative ideal distance between the negative ideal solution and the probability hesitant fuzzy judgment matrix based on the weights of each preset secondary indicator.

[0086] The specific calculation can be based on the following formula:

[0087]

[0088] Step 103: Determine the risk status of the power data to be evaluated according to the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance.

[0089] In this step, the risk value can be determined by the following formula, and then the corresponding risk status can be determined: Among them, the larger the Ω value, the greater the power data security risk.

[0090] In this embodiment,

[0091] For any preset secondary indicator, the probability hesitant fuzzy element of the preset secondary indicator is determined based on the evaluation value input by each expert based on the power data to be evaluated; then, based on the probability hesitant fuzzy element and the pre-determined weights of each preset secondary indicator, the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance are determined; finally, according to the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance, the risk status of the power data to be evaluated is determined. Based on this, when conducting the evaluation, the probability hesitant fuzzy element will be introduced as the data basis for the evaluation, thereby eliminating the subjectivity of the expert evaluation to a certain extent, and thus improving the objectivity and accuracy of the evaluation results.

[0092] Embodiment 2

[0093] Figure 3 This is a schematic diagram of the structure of a power data risk assessment device provided in the second embodiment of the present application. The power data risk assessment device provided in the embodiment of the present application can execute the power data risk assessment method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. The device can be implemented in software and / or hardware, such as Figure 3 As shown, the power data risk assessment device specifically includes: a first determination module 301 , a second determination module 302 , and an assessment module 303 .

[0094] The first determination module is used to determine the probability hesitant fuzzy element of any preset secondary indicator based on the evaluation value input by each expert according to the power data to be evaluated;

[0095] A second determination module is used to determine the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance based on the probabilistic hesitant fuzzy element and the predetermined weights of each preset secondary index;

[0096] The evaluation module is used to determine the risk status of the power data to be evaluated according to the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance.

[0097] Embodiment 3

[0098] Figure 4 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present application is shown in FIG. Figure 4 As shown, the electronic device includes a processor 410, a memory 420, an input device 430, and an output device 440; the number of the processor 410 in the electronic device can be one or more. Figure 4 A processor 410 is taken as an example; the processor 410, the memory 420, the input device 430 and the output device 440 in the electronic device can be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0099] The memory 420 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the power data risk assessment method in the embodiment of the present invention. The processor 410 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 420, that is, to implement the above-mentioned power data risk assessment method:

[0100] For any preset secondary indicator, the probability hesitant fuzzy element of the preset secondary indicator is determined based on the evaluation value input by each expert according to the power data to be evaluated;

[0101] Based on the probability hesitant fuzzy element and the predetermined weights of each preset secondary index, an evaluation weighted positive ideal distance and an evaluation weighted negative ideal distance are determined;

[0102] The risk status of the power data to be evaluated is determined based on the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance.

[0103] The memory 420 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 420 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 420 may further include a memory remotely arranged relative to the processor 410, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0104] Embodiment 8

[0105] Embodiment 8 of the present application further provides a storage medium containing computer executable instructions, which are used to execute a power data risk assessment method when executed by a computer processor:

[0106] For any preset secondary indicator, the probability hesitant fuzzy element of the preset secondary indicator is determined based on the evaluation value input by each expert according to the power data to be evaluated;

[0107] Based on the probability hesitant fuzzy element and the predetermined weights of each preset secondary index, an evaluation weighted positive ideal distance and an evaluation weighted negative ideal distance are determined;

[0108] The risk status of the power data to be evaluated is determined based on the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance.

[0109] Of course, the storage medium containing computer executable instructions provided in the embodiment of the present application, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the power data risk assessment method provided in any embodiment of the present application.

[0110] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present application can be implemented with the help of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0111] It is worth noting that in the embodiment of the above-mentioned search device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0112] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for risk assessment of power data, characterized in that: The method comprises: For any preset secondary indicator, the probability hesitant fuzzy element of the preset secondary indicator is determined based on the evaluation value input by each expert according to the power data to be evaluated; the expert is a professional who participates in evaluating the risk of the power data to be evaluated; Determine an evaluation weighted positive ideal distance and an evaluation weighted negative ideal distance based on the probability hesitant fuzzy element and the predetermined weights of the preset secondary indicators; The risk status of the power data to be evaluated is determined according to the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance.

2. The method according to claim 1, characterized in that The preset secondary indicator belongs to a preset indicator set. Before determining the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance based on the probabilistic hesitant fuzzy element and the predetermined weight of the preset secondary indicator, the method further includes: The weights of the preset secondary indicators are determined in advance based on the indicator importance scale input by each expert according to the preset indicator set.

3. The method according to claim 2, characterized in that The step of determining the weight of the preset secondary indicator based on the indicator importance scale input by each expert according to the preset indicator set in advance includes: According to the indicator importance scale input by each expert based on the preset indicator set, the first-level indicator judgment matrix and the second-level indicator judgment matrix corresponding to each expert are constructed; The weights of the preset secondary indicators are determined by using the primary indicator judgment matrix and the secondary indicator judgment matrix corresponding to each expert.

4. The method according to claim 3, characterized in that The method constructs a first-level indicator judgment matrix and a second-level indicator judgment matrix corresponding to each expert according to the indicator importance scale input by each expert according to the preset indicator set, including: According to the indicator importance scale input by each expert based on the preset first-level indicators, a first-level indicator judgment matrix corresponding to each expert is constructed; According to the indicator importance scales input by each expert based on the preset secondary indicators, the corresponding secondary indicator judgment matrices are constructed.

5. The method according to claim 4, characterized in that The first-level indicator judgment matrix corresponding to each expert is constructed according to the indicator importance scale input by each expert according to the preset first-level indicator set, including: Obtaining an important scale value between any of the target preset first-level indicators and each preset first-level indicator and a hesitant judgment probability corresponding to the important scale value; Determine the importance scale value corresponding to each of the preset first-level indicators and the hesitant judgment probability corresponding to the importance scale value as the indicator importance scale of the target expert for the target preset first-level indicator; A first-level indicator judgment matrix corresponding to the target expert is constructed based on the indicator importance scale of each preset first-level indicator input by the target expert.

6. The method according to claim 4, characterized in that According to the indicator importance scale input by each expert based on the preset secondary indicator set, a secondary indicator judgment matrix corresponding to each expert under each preset primary indicator is constructed, including: For any target preset secondary indicator under any target preset primary indicator, obtain the important scale value between the target preset secondary indicator and each preset secondary indicator input by any target expert and the hesitant judgment probability corresponding to the important scale value; Determine the importance scale value corresponding to each preset secondary indicator and the hesitant judgment probability corresponding to the importance scale value as the indicator importance scale of the target expert for the target preset secondary indicator; Based on the indicator importance scales of the preset secondary indicators, a secondary indicator judgment matrix corresponding to the target experts under the target preset primary indicators is constructed.

7. The method according to claim 3, characterized in that The method of using the first-level indicator judgment matrix and the second-level indicator judgment matrix corresponding to each expert to determine the weight of each preset second-level indicator includes: Aggregating the first-level indicator judgment matrix and the second-level indicator judgment matrix to obtain an importance evaluation result of each preset first-level indicator in the preset indicator set and an importance evaluation result of each preset second-level indicator; According to the importance evaluation result of each preset first-level indicator and the importance evaluation result of each preset second-level indicator, the weight corresponding to each preset second-level indicator is determined.

8. The method according to claim 7, characterized in that The aggregating the first-level indicator judgment matrix and the second-level indicator judgment matrix to obtain the importance evaluation result of each preset first-level indicator in the preset indicator set and the importance evaluation result of each preset second-level indicator includes: Aggregate the first-level indicator judgment matrix corresponding to each expert to obtain the importance evaluation result of each preset first-level indicator; The secondary indicator judgment matrix corresponding to each expert is aggregated to obtain the importance evaluation result of each preset secondary indicator.

9. The method according to claim 8, characterized in that The first-level indicator judgment matrix corresponding to each expert is aggregated to obtain the importance evaluation result of each preset first-level indicator, including: For the first-level indicator judgment matrix corresponding to any expert, perform row-by-row aggregation to obtain the evaluation results of the expert for each preset first-level indicator; By using the probability hesitant fuzzy set arithmetic mean operator, for any preset first-level indicator, the evaluation results of all experts on the preset first-level indicator are aggregated to obtain the importance evaluation result of the preset first-level indicator.

10. The method according to claim 8, characterized in that The secondary indicator judgment matrix corresponding to each expert is aggregated to obtain the importance evaluation result of each preset secondary indicator, including: For the secondary indicator judgment matrix corresponding to any expert, perform row-by-row aggregation to obtain the evaluation results of the expert for each preset secondary indicator; By using the probability hesitant fuzzy set arithmetic mean operator, for any preset secondary indicator, the evaluation results of all experts on the preset secondary indicator are aggregated to obtain the importance evaluation result of the preset secondary indicator.

11. The method according to claim 7, characterized in that Determining the weight corresponding to each preset secondary indicator according to the importance evaluation result of each preset primary indicator and the importance evaluation result of each preset secondary indicator includes: Using a probability hesitant fuzzy set scoring function, the hierarchical weight of each preset first-level indicator is determined according to the evaluation result of the importance of each preset first-level indicator; Using a probability hesitant fuzzy set scoring function, the hierarchical weight of each preset secondary indicator is determined according to the evaluation result of the importance of each preset secondary indicator; The weight of each preset secondary indicator is determined based on the indicator structure between the preset primary indicator and the secondary indicator, the hierarchical weight of each preset primary indicator and the hierarchical weight of each preset secondary indicator.

12. The method according to claim 11, characterized in that The determining of the weight of each preset secondary indicator based on the indicator structure between the preset primary indicator and the secondary indicator, the hierarchical weight of each preset primary indicator and the hierarchical weight of each preset secondary indicator includes: For any preset secondary indicator, the product of the hierarchical weight of the preset primary indicator to which the preset secondary indicator belongs and the hierarchical weight of the preset secondary indicator is determined as the weight of the preset secondary indicator.

13. The method according to claim 1, characterized in that The step of determining the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance based on the probability hesitant fuzzy element and the predetermined weights of each of the preset secondary indicators comprises: constructing a probability hesitant fuzzy judgment matrix using probability hesitant fuzzy elements, and establishing a positive ideal solution and a negative ideal solution based on the probability hesitant fuzzy elements; Determine the evaluation weighted positive ideal distance between the positive ideal solution and the probability hesitant fuzzy judgment matrix based on the weight of each of the preset secondary indicators by using a modified Hamming distance; By using a modified Hamming distance, an evaluation weighted negative ideal distance between the negative ideal solution and the probability hesitant fuzzy judgment matrix is ​​determined based on the weights of the preset secondary indicators.

14. A power data risk assessment device, characterized in that: The device comprises: A first determination module is used to determine, for any preset secondary indicator, a probability hesitant fuzzy element of the preset secondary indicator based on the evaluation values ​​input by each expert according to the power data to be evaluated; the expert is a professional who participates in evaluating the risk of the power data to be evaluated; A second determination module is used to determine an evaluation weighted positive ideal distance and an evaluation weighted negative ideal distance based on the probability hesitant fuzzy element and the predetermined weights of each of the preset secondary indicators; An evaluation module is used to determine the risk status of the power data to be evaluated according to the evaluation weighted positive ideal distance and the evaluation weighted negative ideal distance.

15. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the power data risk assessment method as described in any one of claims 1-13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the power data risk assessment method as described in any one of claims 1-13 is implemented.

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