Electric power security risk identification method and device, computer equipment and readable storage medium

Abstract

The invention relates to an electric power security risk identification method and device, computer equipment and a readable storage medium. The method comprises the following steps: constructing an accident tree of a target electric power operation scene; the accident tree comprises a top event representing an accident and a plurality of potential basic events evolved into the accident; according to the accident tree, determining a numerical relationship between the occurrence probability of each basic event and the occurrence probability of the top event; obtaining the probability of occurrence of each basic event through expert scores; and based on the probability of occurrence of each basic event and the numerical relationship, determining the probability of occurrence of the top event in the target power operation scene. According to the method, the power production safety risk assessment method based on the fuzzy accident tree is adopted, the comprehensive advantages of accident tree identification risk factors are fused, risk quantitative assessment is reasonably achieved, and power operation scene safety risk portrait analysis is achieved.

Description

Technical Field

[0001] The present application relates to the field of power safety technology, and in particular to a power safety risk identification method, device, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] As a large energy enterprise, the power industry is indispensable in people's production and life. However, power company employees face many risks during their work. Depending on the work scenario, the risk influencing factors may vary, which may lead to personal danger.

[0003] For the safety production risk assessment of the power industry, the existing risk identification methods often rely on experience or intuitive judgment, lack of systematicity and standardization, and may lead to the neglect or misjudgment of potential risks. In addition, traditional risk assessment methods are usually difficult to quantify the impact of basic events on the probability of top events, thus affecting the scientific nature of accident prevention and emergency management decisions. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for identifying power safety risks in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for identifying power safety risks. The method comprises:

[0006] Constructing an accident tree of a target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into the accident;

[0007] According to the accident tree, determining the numerical relationship between the probability of occurrence of each of the basic events and the probability of occurrence of the top event; and obtaining the probability of occurrence of each of the basic events through expert scoring;

[0008] Based on the occurrence probabilities of the basic events and the numerical relationships, the probability of the top event occurring in the target power operation scenario is determined.

[0009] In one embodiment, obtaining the occurrence probability of each basic event through expert scoring includes:

[0010] Obtain the authoritative quantitative value of each expert's score and the consistency coefficient between each expert and other experts' scores;

[0011] The authoritative quantitative value and consistency coefficient of each expert's score are weighted to obtain the score weight of each expert;

[0012] For each basic event, the scoring values ​​of the experts on the basic event and the scoring weights of the experts are aggregated to obtain the probability of occurrence of the basic event.

[0013] In one embodiment, obtaining the authoritative quantitative value of each expert's score includes:

[0014] Obtain the importance of multiple factors affecting expert ratings;

[0015] Based on the importance of each influencing factor and the preset fuzzy rules, determine the initial quantitative value of each expert's authority under each influencing factor;

[0016] According to the initial quantitative value of each expert's authority under each influencing factor, the total score of each expert and the total score of all experts are calculated;

[0017] For each expert, the authority quantification value of the expert is determined according to the total score of the expert and the total scores of all experts.

[0018] In one embodiment, obtaining the consistency coefficient between each expert and other experts' scores includes:

[0019] Obtaining the fuzzy importance of each expert's score; the fuzzy importance of the score includes the importance of the expert's score under multiple influencing factors;

[0020] Based on the fuzzy importance and authority quantified value of the score corresponding to each expert, the consistency coefficient between each expert and the scores of other experts is determined.

[0021] In one embodiment, determining the consistency coefficient between each expert's scores and other experts' scores based on the fuzzy importance and authority quantization value of the scores corresponding to each expert includes:

[0022] Defuzzifying the fuzzy importance of the rating to obtain the actual importance;

[0023] Based on the actual importance and the authority quantified value corresponding to each expert, a consistency coefficient between each expert and the scores of other experts is determined.

[0024] In one embodiment, for each basic event, the scoring values ​​of the basic event given by each expert and the scoring weights of each expert are aggregated to obtain the occurrence probability of the basic event, including:

[0025] For each basic event, the scoring values ​​of each expert on the basic event and the scoring weights of each expert are aggregated to obtain the importance of the basic event;

[0026] The importance of the basic event is converted into a probability to obtain the occurrence probability of the basic event.

[0027] In a second aspect, the present application also provides a device for identifying power safety risks. The device comprises:

[0028] An accident tree construction module is used to construct an accident tree of a target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into the accident;

[0029] A first determination module is used to determine the numerical relationship between the occurrence probability of each of the basic events and the occurrence probability of the top event according to the accident tree; and to obtain the occurrence probability of each of the basic events through expert scoring;

[0030] The second determination module is used to determine the probability of the top event occurring in the target power operation scenario based on the occurrence probability of each of the basic events and the numerical relationship.

[0031] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0032] Constructing an accident tree of a target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into the accident;

[0033] According to the accident tree, determining the numerical relationship between the probability of occurrence of each of the basic events and the probability of occurrence of the top event; and obtaining the probability of occurrence of each of the basic events through expert scoring;

[0034] Based on the occurrence probabilities of the basic events and the numerical relationships, the probability of the top event occurring in the target power operation scenario is determined.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0036] Constructing an accident tree of a target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into the accident;

[0037] According to the accident tree, determining the numerical relationship between the probability of occurrence of each of the basic events and the probability of occurrence of the top event; and obtaining the probability of occurrence of each of the basic events through expert scoring;

[0038] Based on the occurrence probabilities of the basic events and the numerical relationships, the probability of the top event occurring in the target power operation scenario is determined.

[0039] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0040] Constructing an accident tree of a target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into the accident;

[0041] According to the accident tree, determining the numerical relationship between the probability of occurrence of each of the basic events and the probability of occurrence of the top event; and obtaining the probability of occurrence of each of the basic events through expert scoring;

[0042] Based on the occurrence probabilities of the basic events and the numerical relationships, the probability of the top event occurring in the target power operation scenario is determined.

[0043] The above-mentioned power safety risk identification method, device, computer equipment, storage medium and computer program product construct an accident tree of the target power operation scenario; determine the numerical relationship between the probability of occurrence of each basic event and the probability of occurrence of the top event according to the accident tree; and obtain the probability of occurrence of each basic event through expert scoring; based on the probability of occurrence and numerical relationship of each basic event, determine the probability of occurrence of the top event in the target power operation scenario. This method adopts a power production safety risk assessment method based on fuzzy accident tree, integrates the advantages of accident tree in identifying comprehensive risk factors, reasonably realizes risk quantitative assessment, and realizes safety risk portrait analysis of power operation scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a flow chart of a method for identifying power safety risks in one embodiment;

[0045] Figure 2 A schematic diagram of a flow chart for obtaining the occurrence probability of each basic event in an embodiment;

[0046] Figure 3 A schematic diagram of an accident tree in a high-fall scenario in one embodiment;

[0047] Figure 4 is a structural block diagram of a power safety risk identification device in one embodiment;

[0048] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] In one embodiment, Figure 1 As shown, a method for identifying power safety risks is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster consisting of multiple servers. In this embodiment, the method includes the following steps:

[0051] Step S110, constructing an accident tree of the target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into accidents.

[0052] The target power operation scenario may be any scenario in the power field, for example, a high-fall scenario.

[0053] Among them, the accident tree can be used to represent the path of potential events evolving into accidents in the target power operation scenario.

[0054] In the specific implementation, after determining the target power operation scenario, the serious accidents that may occur in the target power operation scenario are defined as the top events of the fault tree. The causes behind the top events are analyzed, and all potential basic events are listed. It can be understood that there may be intermediate events between the basic events and the top events, and the resulting fault tree is a multi-level tree diagram.

[0055] Step S120, determining the numerical relationship between the occurrence probability of each basic event and the occurrence probability of the top event according to the accident tree; and obtaining the occurrence probability of each basic event through expert scoring.

[0056] In the specific implementation, the corresponding probability calculation model can be established according to the logical relationship between the basic events and the top events in the accident tree. For the "AND" relationship, the multiplication rule can be used; for the "OR" relationship, the addition rule can be used. For example, when the basic events A and B are in an AND relationship, P=P(A)×P(B); when the basic events A and B are in an OR relationship, P=P(A)+P(B).

[0057] When determining the probability of occurrence of each basic event, an expert team consisting of multiple experts can be formed to score each basic event separately, and the scores of each expert can be integrated to obtain the probability of occurrence of each basic event.

[0058] Step S130, based on the occurrence probability and numerical relationship of each basic event, determine the probability of the top event occurring in the target power operation scenario.

[0059] Specifically, after obtaining the probability of occurrence of each basic event and the numerical relationship between the probability of occurrence of each basic event and the probability of occurrence of the top event, the probability of occurrence of each basic event can be substituted into the numerical relationship. The numerical relationship can be regarded as a calculation model to calculate the probability of occurrence of the top event, that is, the probability of the accident represented by the top event occurring in the target power operation scenario, thereby realizing the quantification of the risk in the target power operation scenario.

[0060] In the above-mentioned power safety risk identification method, an accident tree of the target power operation scenario is constructed; based on the accident tree, the numerical relationship between the probability of occurrence of each basic event and the probability of occurrence of the top event is determined; and the probability of occurrence of each basic event is obtained through expert scoring; based on the probability of occurrence and numerical relationship of each basic event, the probability of occurrence of the top event in the target power operation scenario is determined. This method adopts a power production safety risk assessment method based on fuzzy accident tree, integrates the advantages of accident tree in identifying comprehensive risk factors, reasonably realizes risk quantitative assessment, and realizes safety risk portrait analysis of power operation scenarios.

[0061] It is understandable that although the underlying logic and rules in the power production operation scenario are deduced through methods such as accident trees, and the risk factors that lead to accidents are comprehensively traversed, it lacks a reasonable quantitative interpretation method. The determination of each risk factor relies on the probability of basic events. However, the number of accident samples in the power industry is limited, and frequency statistics alone cannot meet the requirements of basic event probability statistics. In addition, due to the lack of sufficient accident samples, it is usually difficult to use statistical data to represent the probability of basic events. If expert scoring is directly used, there are problems of strong subjectivity and randomness, and it may not be possible to fully characterize the objective importance of basic events. Therefore, this application takes the above-mentioned issues into consideration and proposes a fuzzy importance determination method that takes into account the consistency of expert scoring to quantify the probability of basic events. Specifically, in an exemplary embodiment, Figure 2 As shown, in the above step S120, the occurrence probability of each basic event is obtained through expert scoring, including:

[0062] Step S210, obtaining the authoritative quantitative value of each expert's score and the consistency coefficient between each expert and other experts' scores;

[0063] Step S220, weighting the authority quantification value and consistency coefficient of each expert's score to obtain the score weight of each expert;

[0064] Step S230: for each basic event, the scoring values ​​of the experts on the basic event and the scoring weights of the experts are aggregated to obtain the occurrence probability of the basic event.

[0065] In a specific implementation, for each expert, the authoritative quantitative value of his / her score and the consistency coefficient with other experts' scores can be determined, and the authoritative quantitative value and consistency coefficient of each expert's score can be further weighted to obtain the score weight of each expert. For any basic event, after each expert scores the basic event, the score value of each expert is weighted with its score weight to obtain the weighted score of each expert for the basic event, and the weighted scores of all experts are summed to obtain the probability of occurrence of the basic event. Each basic event is processed in this way, thereby obtaining the probability of occurrence of each basic event.

[0066] In this embodiment, the method quantifies the probability of occurrence of basic events based on the identification of risk factors based on the accident tree, combined with the authority of expert scoring and the consistency of opinions, which can improve the objective accuracy of the determined probability of occurrence of basic events, thereby improving the reliability of risk identification results in power operation scenarios.

[0067] In an exemplary embodiment, the above-mentioned step S210, obtaining the authoritative quantitative value of each expert's score, includes: obtaining the importance of multiple influencing factors that affect the expert's score; determining the initial authoritative quantitative value of each expert under each influencing factor based on the importance of each influencing factor and preset fuzzy rules; calculating the total score of each expert and the total score of all experts according to the initial authoritative quantitative value of each expert under each influencing factor; for each expert, determining the authoritative quantitative value of the expert according to the total score of the expert and the total score of all experts.

[0068] Specifically, in the process of scoring the importance of basic events, different experts express their opinions on the importance of basic events based on their own experience and knowledge. The expert scoring results are converted into the corresponding fuzzy set M s = (M 1 , M 2 , M 3 , M 4 ), the fuzzy importance is {VB, VS, PS, PB}, which represent the four dimensions of {very low, low, relatively high, high} respectively, and their corresponding relationships are shown in Table 1.

[0069] Table 1 Fuzzy rule settings

[0070]

[0071] Different experts may have different opinions on the probability of accidents caused by the same risk factor, which is usually related to factors such as their professional titles, work experience and educational background, which can be regarded as influencing factors affecting expert scores. Due to the different views and professional knowledge expressed by the expert team, the uncertainty of expert opinions is caused. Therefore, considering the authority of expert scores and the similarity (or consistency) of scoring opinions in the scoring process can make the obtained risk factor probability importance more objective and fair. Generally speaking, the scores of experts with senior experience and professional background have higher authority, and a higher consensus on scores indicates that the opinions of the expert are more consistent with those of other experts. Therefore, when the authority and consistency of expert opinions are high, the evaluation results of the importance of risk factors are closer to the actual situation. In this application, the authority of the scoring experts is mainly considered in the following factors: professional title, experience and educational background, as shown in Table 2. It should be noted that in different application scenarios, other influencing factors can also be set according to needs.

[0072] Table 2 Rating importance of different impact categories

[0073]

[0074] Based on the importance of the expert titles, experience and educational background in Table 2, the scores of different experts on the importance of risk factors are considered differently. The authority of each expert's score is the ratio of the total score after considering the above factors to the sum of the total scores of all experts, as shown in formula (1).

[0075] (1)

[0076] In the formula, S e (k) represents the quantitative value of the authority of the kth expert; S f (k) represents the initial value of the authority of the kth expert; n represents the number of influencing factor categories; and m represents the number of experts in the scoring team.

[0077] The initial value of each expert’s authority is calculated based on the fuzzy rules in Table 1 according to the scoring importance in Table 2, and then normalized by formula (1) to obtain the authority of the expert in the scoring team.

[0078] In this embodiment, multiple influencing factors affecting the expert scoring are considered, and the importance of each influencing factor is considered; based on the importance of each influencing factor and the preset fuzzy rules, the authoritative quantified initial value of each expert under each influencing factor is determined; according to the authoritative quantified initial value of each expert under each influencing factor, the total score of each expert and the total score of all experts are calculated respectively; for each expert, the authoritative quantified value of the expert is determined according to the total score of the expert and the total score of all experts. In this way, a more accurate authoritative quantified value can be obtained.

[0079] In an exemplary embodiment, the above step S210, obtaining the consistency coefficient between each expert's scores and those of other experts, includes: obtaining the fuzzy importance of each expert's scores; the fuzzy importance of scores includes the importance of the expert's scores under multiple influencing factors; based on the fuzzy importance of scores and the authority quantification value corresponding to each expert, determining the consistency coefficient between each expert's scores and those of other experts.

[0080] Specifically, considering the consistency of expert ratings, the consistency between the vth expert and the bth expert is calculated as shown in formula (2), and their absolute consistency is further calculated as shown in formula (3).

[0081] (2)

[0082] (3)

[0083] In the formula, C v,b is the consistency coefficient between the vth expert and the bth expert; l is the level of importance, such as professional title, educational background, and work experience, including 3 levels of importance. i 、b i , i=1,2,...,l is the fuzzy importance of expert rating. v,b The value range of is 0~1, and the closer it is to 1, the higher the consistency of the two experts' scores on the impact of risk factors. is the absolute consistency coefficient.

[0084] In addition, the first formula in formula (3) indicates that the scoring result is corrected according to the consistency, that is, the score value corrected based on consistency is obtained. The second formula indicates normalization, because the final result is to calculate the weight, so normalization is required to reflect the authority of the expert in the scoring team.

[0085] Combining the authoritative quantitative value of formula (1) with the absolute consistency coefficient of expert scores in formula (3), we can obtain the score weight of each expert on the risk factor as shown in formula (4).

[0086] (4)

[0087] In the formula, a is the weight for measuring the authority of expert ratings and the consistency of expert ratings. The larger the value of a is, the more emphasis is placed on the individual ratings of the experts and the less attention is paid to the consistency of opinions. By appropriately selecting the value of a, the consistency of expert opinions can be taken into account in the selection of risk factor importance. The value of a ranges from 0 to 1.

[0088] In an exemplary embodiment, the step of determining the consistency coefficient between each expert and other experts' scores based on the fuzzy importance and authoritative quantitative value of the scores corresponding to each expert includes: defuzzifying the fuzzy importance of the scores to obtain the actual importance; determining the consistency coefficient between each expert and other experts' scores based on the actual importance and authoritative quantitative value corresponding to each expert. Specifically, the fuzzy importance of the scores can be defuzzified using a defuzzification method, for example, the actual importance can be calculated using the centroid method, the maximum membership method, etc. Further, based on the actual importance and authoritative quantitative value corresponding to each expert, the consistency coefficient between each expert and other experts' scores is determined, taking into account the subjectivity and uncertainty of the scores to enhance the reliability of subsequent scoring results.

[0089] Further, in an exemplary embodiment, the above step S230 aggregates the score values ​​of each expert on the basic event and the score weights of each expert for each basic event to obtain the probability of occurrence of the basic event, including: for each basic event, aggregating the score values ​​of each expert on the basic event and the score weights of each expert to obtain the importance of the basic event; and performing probability conversion processing on the importance of the basic event to obtain the probability of occurrence of the basic event.

[0090] Specifically, for each basic event, the score of each expert on the basic event and the score weight of each expert are weighted to obtain the importance of the basic event, as shown in formula (5).

[0091] (5)

[0092] In the formula, F e is the final importance weight (weight value) of the risk factor; F(s,m) is the score given by the mth expert to the sth risk factor; s is the risk factor category; and m is the number of experts in the scoring team.

[0093] Furthermore, in order to quantify the probability of accidents caused by different risk factors, a conversion function is introduced to convert the importance of risk factors into the probability of accidents, as shown in formula (6).

[0094] (6)

[0095] Where P(s) is the true probability of an accident caused by the sth risk factor (basic event) based on the expert score.

[0096] Based on equations (1) to (6), the probability of the basic event X that combines the authority of expert ratings and the consistency of ratings can be obtained.

[0097] In the above embodiment, the fuzzy opinion aggregation method is used to integrate the degree of consensus of expert opinions into the calculation of expert weights, thereby reducing the error caused by each expert's independent scoring.

[0098] In one embodiment, in order to facilitate those skilled in the art to understand the embodiments of the present application, the following will be described with reference to specific examples of the accompanying drawings. Figure 3 , shows a schematic diagram of an accident tree in a high-fall scenario, which can provide a comprehensive assessment of the path of potential events evolving into accidents in power operation scenarios. It can be understood that the construction of an accident tree is not the only one, and the ultimate goal is to be able to characterize the process of the accident in this scenario.

[0099] for Figure 1 The fault tree shown in the figure is based on the probability calculation method of when two events A and B are in an AND relationship, P=P(A)×P(B); when two events A and B are in an OR relationship, P=P(A)+P(B). The fault tree events are expressed using the Boolean algebra method, and the result is:

[0100] (7)

[0101] According to the Boolean expression obtained based on the accident tree structure in this scenario, the probability of the top event T can be obtained by estimating the probability of the basic event X. Therefore, the probability of the basic event X that integrates the authority of expert scoring and the consistency of scoring can be obtained based on equations (1) to (6). Based on equation (7), the minimum cut set of the top event T and the intermediate event M can be obtained, which is a combination of the basic event X. Combining the probability of the basic event X, the probability of the top event and the intermediate event can be further obtained, thereby obtaining the risk assessment results of each dimension under the target power operation scenario, which can provide a reference for the quantitative assessment of power risk.

[0102] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0103] Based on the same inventive concept, the embodiment of the present application also provides a power safety risk identification device for implementing the power safety risk identification method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power safety risk identification device embodiments provided below can refer to the limitations of the power safety risk identification method above, and will not be repeated here.

[0104] In one embodiment, Figure 4 As shown, a power safety risk identification device is provided, including: an accident tree construction module 410, a first determination module 420 and a second determination module 430, wherein:

[0105] The accident tree construction module 410 is used to construct an accident tree of the target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into accidents;

[0106] The first determination module 420 is used to determine the numerical relationship between the probability of occurrence of each basic event and the probability of occurrence of the top event according to the accident tree; and obtain the probability of occurrence of each basic event through expert scoring;

[0107] The second determination module 430 is used to determine the probability of occurrence of the top event in the target power operation scenario based on the occurrence probability and numerical relationship of each basic event.

[0108] In one embodiment, the first determination module 420 is also used to obtain the authoritative quantitative value of each expert's score and the consistency coefficient between each expert and other experts' scores; weight the authoritative quantitative value and consistency coefficient of each expert's score to obtain the score weight of each expert; for each basic event, aggregate the score value of each expert on the basic event and the score weight of each expert to obtain the probability of occurrence of the basic event.

[0109] In one embodiment, the first determination module 420 is also used to obtain the importance of multiple influencing factors that affect the expert's score; based on the importance of each influencing factor and preset fuzzy rules, determine the initial quantitative value of the authority of each expert under each influencing factor; calculate the total score of each expert and the total score of all experts according to the initial quantitative value of the authority of each expert under each influencing factor; for each expert, determine the quantitative value of the expert's authority according to the total score of the expert and the total score of all experts.

[0110] In one embodiment, the first determination module 420 is also used to obtain the fuzzy importance of the score of each expert; the fuzzy importance of the score includes the importance of the expert's score under multiple influencing factors; based on the fuzzy importance of the score and the authority quantification value corresponding to each expert, the consistency coefficient of each expert's score with other experts' scores is determined.

[0111] In one embodiment, the first determination module 420 is further used to defuzzify the fuzzy importance of the rating to obtain the actual importance; based on the actual importance and the authoritative quantitative value corresponding to each expert, determine the consistency coefficient between each expert and the ratings of other experts.

[0112] In one embodiment, the first determination module 420 is also used to aggregate the scoring values ​​of each expert on the basic event and the scoring weights of each expert for each basic event to obtain the importance of the basic event; and perform probability conversion processing on the importance of the basic event to obtain the probability of occurrence of the basic event.

[0113] Each module in the above-mentioned power safety risk identification device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0114] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for identifying power safety risks is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0115] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0116] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0117] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0118] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0119] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0120] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0121] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for identifying power safety risks, characterized in that: The method comprises: Constructing an accident tree of a target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into the accident; According to the accident tree, determining the numerical relationship between the probability of occurrence of each of the basic events and the probability of occurrence of the top event; and obtaining the probability of occurrence of each of the basic events through expert scoring; Based on the occurrence probabilities of the basic events and the numerical relationships, the probability of the top event occurring in the target power operation scenario is determined.

2. The method according to claim 1, characterized in that The method of obtaining the occurrence probability of each basic event through expert scoring includes: Obtain the authoritative quantitative value of each expert's score and the consistency coefficient between each expert and other experts' scores; The authoritative quantitative value and consistency coefficient of each expert's score are weighted to obtain the score weight of each expert; For each basic event, the scoring values ​​of the experts on the basic event and the scoring weights of the experts are aggregated to obtain the probability of occurrence of the basic event.

3. The method according to claim 2, characterized in that The authoritative quantitative values ​​of the expert scores are obtained, including: Obtain the importance of multiple factors affecting expert ratings; Based on the importance of each influencing factor and the preset fuzzy rules, determine the initial quantitative value of each expert's authority under each influencing factor; According to the initial quantitative value of each expert's authority under each influencing factor, the total score of each expert and the total score of all experts are calculated; For each expert, the authority quantification value of the expert is determined according to the total score of the expert and the total scores of all experts.

4. The method according to claim 2, characterized in that: Obtain the consistency coefficient between each expert and other experts, including: Obtaining the fuzzy importance of each expert's score; the fuzzy importance of the score includes the importance of the expert's score under multiple influencing factors; Based on the fuzzy importance and authority quantified value of the score corresponding to each expert, the consistency coefficient between each expert and the scores of other experts is determined.

5. The method according to claim 4, characterized in that Determining the consistency coefficient between each expert's scores and other experts' scores based on the fuzzy importance and authority quantified value of the scores corresponding to each expert includes: Defuzzifying the fuzzy importance of the rating to obtain the actual importance; Based on the actual importance and the authority quantified value corresponding to each expert, a consistency coefficient between each expert and the scores of other experts is determined.

6. The method according to claim 2, characterized in that For each basic event, the scoring values ​​of the experts on the basic event and the scoring weights of the experts are aggregated to obtain the probability of occurrence of the basic event, including: For each basic event, the scoring values ​​of each expert on the basic event and the scoring weights of each expert are aggregated to obtain the importance of the basic event; The importance of the basic event is converted into a probability to obtain the occurrence probability of the basic event.

7. A power safety risk identification device, characterized in that: The device comprises: An accident tree construction module is used to construct an accident tree of a target power operation scenario; the accident tree includes a top event representing an accident and multiple basic events that potentially evolve into the accident; A first determination module is used to determine the numerical relationship between the occurrence probability of each of the basic events and the occurrence probability of the top event according to the accident tree; and to obtain the occurrence probability of each of the basic events through expert scoring; The second determination module is used to determine the probability of the top event occurring in the target power operation scenario based on the occurrence probability of each of the basic events and the numerical relationship.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power safety risk identification method described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power safety risk identification method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the power safety risk identification method described in any one of claims 1 to 6 are implemented.

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