A safety risk assessment method and system for energy storage power station

Abstract

The invention discloses a safety risk assessment method and system for an energy storage power station, belonging to the field of data processing technology. The method comprises: obtaining a safety risk assessment system for an energy storage power station; determining the initial weight of each assessment indicator; correcting the initial weight of each assessment indicator to obtain a formal weight; obtaining the scores of multiple experts on the energy storage power station under each assessment indicator; determining a scoring vector of the energy storage power station according to the credibility of the experts; combining the scoring vectors for multiple energy storage power stations into a scoring matrix, and determining a positive ideal solution vector and a negative ideal solution vector of the scoring matrix; calculating the Euclidean distance between the scoring vector of each energy storage power station and the positive ideal solution vector and the Euclidean distance between the scoring vector and the negative ideal solution vector in combination with the formal weight of each assessment indicator; calculating the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector; and determining the safety risk level of each energy storage power station.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a safety risk assessment method and system for an energy storage power station. Background Art

[0002] New energy storage facilities are conducive to improving the regulation ability of energy systems and increasing storage capacity, and have become a hot research topic of social concern. Energy storage power stations are highly energy-intensive chemical integrated equipment with high safety risks. If the battery casing or electrical equipment fails, the battery material is prone to exothermic side reactions, resulting in battery heat leakage, which can cause serious accidents such as fire or explosion of the energy storage system. Therefore, the safety risk assessment of energy storage power stations is of paramount importance.

[0003] At present, the safety risk assessment of energy storage power stations is mainly based on manual inspections. Through manual inspections, each safety risk item is checked and scored, and the safety risk is assessed based on the final score. This is time-consuming and labor-intensive, and relies on manual judgment. It is easily affected by subjective factors, resulting in poor accuracy and consistency in safety risk assessment.

[0004] With the rapid development of technologies such as pattern recognition, machine vision, and deep learning, as well as the urgent need for safety risk assessment of energy storage power stations, more and more modern technologies are being applied to energy storage power station assessments. Among them, modern technologies represented by risk matrix analysis have been rapidly applied. The risk matrix is ​​a graphical tool used to qualitatively and quantitatively quantify the likelihood and impact of risks to determine the level of risk. In energy storage power stations, risk matrix analysis can be used to determine the priority of different risks so that more targeted control measures can be taken for high-risk areas.

[0005] However, traditional risk matrices usually only consider the possibility and impact of risk events, but lack comprehensive consideration of the weights and degree of mutual influence between different risk indicators. In addition, risk matrix analysis usually only considers two dimensions (possibility and impact), and cannot effectively deal with complex risk situations with multiple dimensions and multiple indicators, resulting in low accuracy of safety risk assessment. Summary of the invention

[0006] In order to solve the problem that the prior art checks and scores each safety risk item through manual inspection, and evaluates the safety risk according to the final score, which is time-consuming and labor-intensive, and relies on manual judgment, which is easily affected by subjective factors, resulting in poor accuracy and consistency of safety risk evaluation, the traditional risk matrix usually only considers the possibility and impact of risk events, but lacks comprehensive consideration of the weights and mutual influence between different risk indicators, and the risk matrix analysis usually only considers two dimensions (possibility and impact), and cannot effectively handle complex risk situations with multiple dimensions and multiple indicators, resulting in low accuracy of safety risk evaluation. The present invention provides a safety risk evaluation method and system for energy storage power stations.

[0007] First aspect

[0008] The present invention provides a safety risk assessment method for an energy storage power station, comprising:

[0009] S101: Obtain a safety risk assessment system for an energy storage power station, where the safety risk assessment system includes multiple assessment indicators;

[0010] S102: Determine the initial weight of each evaluation indicator;

[0011] S103: According to the influence degree between the evaluation indicators, the initial weight of each evaluation indicator is modified to obtain a formal weight;

[0012] S104: Obtaining scores of multiple experts on the energy storage power station under various evaluation indicators;

[0013] S105: Determine a scoring vector for the energy storage power station based on the scores of the experts on the energy storage power station under various evaluation indicators and the credibility of the experts;

[0014] S106: combining the scoring vectors for the plurality of energy storage power stations into a scoring matrix, and determining a positive ideal solution vector and a negative ideal solution vector of the scoring matrix;

[0015] S107: Calculate the Euclidean distance between the scoring vector of each energy storage power station and the positive ideal solution vector and the Euclidean distance between the scoring vector of each energy storage power station and the negative ideal solution vector in combination with the formal weights of each evaluation index;

[0016] S108: Calculating the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector;

[0017] S109: Determine the safety risk level of each energy storage power station according to the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector.

[0018] Second aspect

[0019] The present invention provides a safety risk assessment system for an energy storage power station, which is used to execute the safety risk assessment method for the energy storage power station in the first aspect.

[0020] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0021] (1) In the present invention, multiple evaluation indicators can be considered at the same time, a weight can be assigned to each evaluation indicator, and the initial weight of each evaluation indicator can be modified according to the degree of influence between each evaluation indicator. The weights and the degree of mutual influence between different risk indicators can be comprehensively considered, and complex risk situations with multiple dimensions and multiple indicators can be effectively handled, making the security risk evaluation more comprehensive and objective, and improving the accuracy and consistency of the security risk evaluation.

[0022] (2) In the present invention, the safety risk level of each energy storage power station is automatically determined based on the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector, which can reduce the influence of subjective factors and improve the accuracy and consistency of safety risk evaluation.

[0023] (3) In the present invention, the credibility of experts is introduced. The scoring vector of the energy storage power station is determined according to the scores of each expert on the energy storage power station under each evaluation indicator and the credibility of the experts. This can improve the credibility of the score, make the quantification of the evaluation process more reliable, and improve the accuracy of the safety risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The preferred implementation modes will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0025] Figure 1 It is a flow chart of a safety risk assessment method for an energy storage power station provided by the present invention;

[0026] Figure 2 It is a structural schematic diagram of a safety risk assessment method for an energy storage power station provided by the present invention. DETAILED DESCRIPTION

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation of the present invention will be described below with reference to the accompanying drawings.

[0028] In one embodiment, the reference specification Figure 1 , which shows a schematic diagram of a safety risk assessment method for an energy storage power station provided by the present invention. Figure 2 , showing a structural schematic diagram of a safety risk assessment method for an energy storage power station provided by the present invention.

[0029] The present invention provides a safety risk assessment method for an energy storage power station, comprising:

[0030] S101: Obtain a safety risk assessment system for the energy storage power station, where the safety risk assessment system includes multiple assessment indicators.

[0031] Among them, the safety risk assessment system includes five first-level assessment indicators: battery condition, implementation of preventive measures, operating environment, geographical location and the equipment status of the safety monitoring and protection system.

[0032] The battery condition includes five secondary assessment indicators: shell damage, odor, surface cleanliness, battery available capacity and inconsistent single cell voltage.

[0033] The implementation of preventive measures includes three secondary evaluation indicators: the implementation of external short-circuit preventive measures, the implementation of external fire source preventive measures and the implementation of physical impact preventive measures.

[0034] The operating environment includes three secondary evaluation indicators: humidity, dust and ventilation.

[0035] Geographic location includes three secondary assessment indicators: climate, topography, and incidence of natural disasters.

[0036] The equipment status of the safety monitoring and protection system includes four secondary evaluation indicators: the equipment status of the battery management system, the equipment status of the safety linkage system, the equipment status of the lightning protection and grounding system, and the equipment status of the fire protection system.

[0037] It should be noted that the above evaluation indicators all have a certain influence on the safety risks of energy storage power stations.

[0038] S102: Determine the initial weight of each evaluation indicator.

[0039] In a possible implementation, S102 specifically includes:

[0040] S1021: By comparing the influence of each evaluation indicator pairwise and combining the nine-level scaling method, a discriminant matrix is ​​established:

[0041]

[0042] Among them, A represents the discriminant matrix, a ij Indicates the importance of the i-th evaluation indicator relative to the j-th evaluation indicator, a ij The value of can be determined by the nine-pole scaling method, where m represents the total number of evaluation indicators, i = 1, 2, …, m, j = 1, 2, …, m.

[0043] Among them, the nine-pole scale is a method commonly used to compare and evaluate the relative importance of different options, and is usually used for weight determination, decision analysis, and ranking of options. Select a number in a series of levels to indicate the importance of one option relative to another option for a certain criterion or factor. The levels in the nine-level scale range from 1 to 9. Among them, 1 means that the relative importance of the two options is exactly equal, and 9 means the extreme importance of one option relative to the other. 1 means equal importance, 3 means slightly biased importance, 5 means medium importance, 7 means strongly biased importance, 9 means extreme importance, and 2, 4, 6, and 8 represent intermediate states between adjacent levels, which are used to indicate the relative degree between two levels.

[0044] It should be noted that the nine-level scale method is used for pairwise comparison to quantify the importance of the evaluation indicators. This eliminates the influence of subjective judgment and makes the determination of weights more objective and standardized.

[0045] S1022: Calculate the eigenvectors and eigenvalues ​​of the discriminant matrix A:

[0046] Aw=λw→(A-λI)w

[0047] Among them, λ represents the eigenvalue of the discriminant matrix A, w represents the eigenvector of the discriminant matrix A, I represents the unit matrix, and the largest eigenvalue is recorded as λ max , and the eigenvector corresponding to the largest eigenvalue is denoted as w max , w max =(w1,w2,…,w m ).

[0048] S1023: Eigenvector w corresponding to the largest eigenvalue max Perform normalization:

[0049]

[0050]

[0051] Among them, the normalized vector The various components Represent the initial weights of each evaluation index, which can be recorded as α1, α2, …, α m .

[0052] In the present invention, the eigenvalues ​​and eigenvectors of the discriminant matrix are used to calculate the weights, which can mathematically represent the relationship between different indicators, and then determine the initial weights of each evaluation indicator based on the eigenvectors, thereby increasing the objectivity, consistency and reliability of the evaluation and reducing the errors caused by subjectivity.

[0053] S103: According to the influence degree between each evaluation indicator, the initial weight of each evaluation indicator is modified to obtain the formal weight.

[0054] It should be noted that there may be mutual influences between evaluation indicators, and some indicators may have a greater impact on other indicators. This method can systematically analyze the degree of influence between various indicators by calculating the comprehensive influence matrix, so that the weights can more accurately reflect the relative importance of the indicators.

[0055] In a possible implementation manner, S103 specifically includes:

[0056] S1031: Normalize the discriminant matrix to obtain a normalized matrix:

[0057]

[0058] Among them, F represents the normalized matrix, A represents the discriminant matrix, max represents the maximum value, and a ij Indicates the importance of the i-th evaluation indicator relative to the j-th evaluation indicator, a ij The value of can be determined by the nine-pole scaling method, where m represents the total number of evaluation indicators, i = 1, 2, …, m, j = 1, 2, …, m.

[0059] S1032: Calculate the comprehensive impact matrix based on the normalized matrix:

[0060] C=F+F 2 +…+F m =F(1-F) -1

[0061] Among them, C represents the comprehensive impact matrix, and each element in the comprehensive impact matrix is ​​denoted by c ij , c ij Represents the element value in the i-th row and j-th column of the comprehensive influence matrix.

[0062] S1033: According to the comprehensive impact matrix, calculate the influence of each evaluation indicator on other evaluation indicators and the degree of influence:

[0063]

[0064]

[0065] Among them, f i represents the influence of the i-th evaluation index, g j represents the influence of the j-th evaluation indicator, m represents the total number of evaluation indicators, i = 1, 2, ..., n, j = 1, 2, ..., n.

[0066] Among them, the influence degree is the influence degree of an indicator on other indicators, that is, the importance of one indicator to other indicators. The influence degree is the influence degree of other indicators on a certain indicator, that is, the importance of other indicators to a certain indicator.

[0067] S1034: Calculate the centrality of each evaluation indicator according to the influence and influence of each evaluation indicator:

[0068] H j =f j +g j

[0069] Among them, H j represents the centrality of the jth evaluation index, f j represents the influence of the jth evaluation index, g j Represents the influence of the j-th evaluation indicator.

[0070] It should be noted that by calculating the influence and the degree of influence, the degree of influence between indicators can be quantified and the centrality of each indicator can be determined, which helps to more comprehensively understand the role and status of each indicator in the entire system.

[0071] Among them, centrality refers to the relative importance of the evaluation indicator in the entire risk assessment system. Centrality quantifies the relative importance of the indicator in the entire assessment system by comprehensively considering the influence and influence of the indicator. The higher the centrality of the indicator, the greater the role of the indicator in the assessment, and the greater the degree of its influence on other indicators and the degree of influence by other indicators.

[0072] S1035: According to the centrality of each evaluation indicator, the initial weight of each evaluation indicator is modified to obtain the formal weight:

[0073]

[0074] Among them, β j represents the formal weight of the jth evaluation indicator, α j represents the initial weight of the jth evaluation indicator.

[0075] It should be noted that by comprehensively analyzing the relationship between evaluation indicators, as well as the influence and centrality of the indicators, the initial weights can be adjusted more accurately so that the formal weights can better reflect the relative importance of different indicators, thereby improving the reliability and accuracy of the entire risk assessment method.

[0076] In the present invention, multiple evaluation indicators can be considered at the same time, a weight can be assigned to each evaluation indicator, and the initial weight of each evaluation indicator can be modified according to the degree of influence between each evaluation indicator. The weights and the degree of mutual influence between different risk indicators can be comprehensively considered, and complex risk situations with multiple dimensions and multiple indicators can be effectively handled, making the security risk evaluation more comprehensive and objective, and improving the accuracy and consistency of the security risk evaluation.

[0077] S104: Obtain scores from multiple experts on the energy storage power station under various evaluation indicators.

[0078] S105: Determine a scoring vector for the energy storage power station based on the scores of the experts on the energy storage power station under various evaluation indicators and the credibility of the experts.

[0079] In a possible implementation manner, S105 specifically includes:

[0080] S1051: Calculate the objective parameters of each expert's scoring of the energy storage power station under each evaluation indicator according to the following formula:

[0081]

[0082] Among them, B k represents the objective parameter when the kth expert scores the energy storage power station under various evaluation indicators, e kj represents the original score of the k-th expert when scoring the j-th evaluation indicator, It represents the average of the original scores given by each expert for the jth evaluation indicator, j = 1, 2, ..., m, m represents the total number of evaluation indicators, k = 1, 2, ..., K, K represents the total number of experts.

[0083] It should be noted that calculating the average of the ratings as a reference can help determine the overall distribution of the expert ratings. This helps determine whether the ratings are concentrated in a certain range, thereby evaluating the rationality of the ratings.

[0084] S1052: Calculate the credibility of each expert according to the following formula:

[0085]

[0086] Among them, μ k represents the credibility of the kth expert.

[0087] It should be noted that by calculating the objectivity parameters and credibility of the experts, the objectivity and credibility of the expert ratings can be quantified, which helps to eliminate subjective bias in the evaluation process, thus making the evaluation results more objective and accurate.

[0088] S1053: Calculate the final score of the energy storage power station under each evaluation indicator based on the credibility of each expert and the original score when scoring each evaluation indicator:

[0089]

[0090] Among them, e j Represents the final score under the jth evaluation indicator.

[0091] It should be noted that by calculating the final scoring vector, the original scores of the experts can be combined with the objective parameters and credibility to obtain more accurate scoring results. This makes the evaluation results more accurate and helps to effectively reflect the actual situation of the energy storage power station under various evaluation indicators.

[0092] S1054: Determine the scoring vector of the energy storage power station:

[0093] e=[e1,e2,…,e j ]

[0094] Where e represents the rating vector.

[0095] Then the score vector of the i-th energy storage power station can be expressed as: e i =[e i1 ,e i2 ,…,e ij ].

[0096] It should be noted that the objectivity and credibility of expert scoring can be fully considered to improve the reliability and accuracy of the evaluation results. By integrating the opinions of different experts and combining them with a quantitative calculation process, more comprehensive, objective and accurate evaluation results can be obtained, thereby supporting better decision-making and analysis.

[0097] In the present invention, expert credibility is introduced, and the scoring vector of the energy storage power station is determined according to the scores of each expert on the energy storage power station under each evaluation indicator and the credibility of the experts. This can improve the credibility of the score, make the quantification of the evaluation process more reliable, and improve the accuracy of safety risk assessment.

[0098] S106: combining the scoring vectors for the multiple energy storage power stations into a scoring matrix, and determining a positive ideal solution vector and a negative ideal solution vector of the scoring matrix.

[0099] In a possible implementation manner, S106 specifically includes:

[0100] S1061: Combining the scoring vectors for multiple energy storage power stations into a scoring matrix:

[0101]

[0102] Among them, E represents the scoring matrix, e ij represents the final score of the i-th energy storage power station under the j-th evaluation indicator, i = 1, 2, ..., n, n represents the total number of energy storage power stations, j = 1, 2, ..., m, m represents the total number of evaluation indicators.

[0103] S1062: Determine the positive ideal solution vector and the negative ideal solution vector of the scoring matrix:

[0104]

[0105]

[0106] in, represents the positive ideal solution vector, which consists of the maximum values ​​of each row in the scoring matrix, represents the negative ideal solution vector, which consists of the minimum values ​​of each row in the scoring matrix.

[0107] It should be noted that the positive ideal solution vector represents the most ideal state in which the highest score is obtained under all evaluation indicators. For each evaluation indicator, the corresponding value in the positive ideal solution vector is the highest score among all energy storage power stations under the evaluation indicator. Furthermore, the negative ideal solution vector represents the worst state in which the lowest score is obtained under all evaluation indicators. For each evaluation indicator, the corresponding value in the negative ideal solution vector is the lowest score among all energy storage power stations under the evaluation indicator.

[0108] S107: In combination with the formal weights of the various evaluation indicators, the Euclidean distance between the scoring vector of each energy storage power station and the positive ideal solution vector and the Euclidean distance between the scoring vector and the negative ideal solution vector are calculated.

[0109] In a possible implementation, S107 specifically includes:

[0110] S1071: Calculate the Euclidean distance between the score vector of each energy storage power station and the positive ideal solution vector using the following formula:

[0111]

[0112] in, represents the Euclidean distance between the score vector of the i-th energy storage power station and the positive ideal solution vector, β j represents the formal weight of the jth evaluation indicator, e ij represents the final score of the i-th energy storage power station under the j-th evaluation indicator, represents the positive ideal solution vector, i=1,2,…,n, n represents the total number of energy storage power stations, j=1,2,…,m, m represents the total number of evaluation indicators.

[0113] It should be noted that the Euclidean distance between the energy storage power station's score vector and the positive ideal solution vector can be used to measure the degree of difference between the energy storage power station and the optimal state under each evaluation indicator. The smaller the Euclidean distance, the closer the energy storage power station's score under each evaluation indicator is to the optimal state.

[0114] S1072: Calculate the Euclidean distance between the score vector of each energy storage power station and the negative ideal solution vector using the following formula:

[0115]

[0116] in, represents the Euclidean distance between the score vector of the i-th energy storage power station and the negative ideal solution vector, represents the negative ideal solution vector.

[0117] It should be noted that the Euclidean distance between the energy storage power station's score vector and the negative ideal solution vector can be used to measure the degree of difference between the energy storage power station and the worst state under each evaluation indicator. The smaller the Euclidean distance, the closer the score of the energy storage power station under each evaluation indicator is to the worst state.

[0118] S108: Calculate the closeness between the score vector of each energy storage power station and the negative ideal solution vector.

[0119] In a possible implementation manner, S108 specifically includes:

[0120] According to the following formula, the closeness between the score vector of each energy storage power station and the negative ideal solution vector is calculated:

[0121]

[0122] Among them, τ i represents the closeness between the score vector of the i-th energy storage power station and the negative ideal solution vector. When the closeness between the score vector of the i-th energy storage power station and the negative ideal solution vector τ i The smaller it is, the closer the scoring vector of the i-th energy storage power station is to the negative ideal solution vector, and the higher the safety risk level of the i-th energy storage power station is.

[0123] In the present invention, by calculating the closeness between the energy storage power station's scoring vector and the negative ideal solution vector, it is actually quantitatively measuring the degree of closeness of each energy storage power station to the worst state under each evaluation indicator. The calculation of this closeness helps to determine the strength of each energy storage power station in terms of safety risk, thereby achieving more accurate ranking and evaluation.

[0124] S109: Determine the safety risk level of each energy storage power station according to the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector.

[0125] In a possible implementation manner, S109 specifically includes:

[0126] S1091: When the closeness τ between the score vector of the target energy storage power station and the negative ideal solution vector satisfies τ≤T1, it is determined that the safety risk level of the target energy storage power station is extremely high risk.

[0127] S1092: When the closeness τ between the score vector of the target energy storage power station and the negative ideal solution vector satisfies T1<τ≤T2, the safety risk level of the target energy storage power station is determined to be high risk.

[0128] S1093: When the closeness τ between the score vector of the target energy storage power station and the negative ideal solution vector satisfies T2<τ≤T3, the safety risk level of the target energy storage power station is determined to be medium risk.

[0129] S1094: When the closeness τ between the scoring vector of the target energy storage power station and the negative ideal solution vector satisfies τ>T3, it is determined that the safety risk level of the target energy storage power station is low risk.

[0130] Among them, T1 represents the first preset closeness, T2 represents the second preset closeness, and T3 represents the third preset closeness.

[0131] Among them, those skilled in the art can set the first preset closeness T1, the second preset closeness T2 and the third preset closeness T3 according to actual conditions, and the present invention does not limit this.

[0132] In the present invention, the safety risk level of each energy storage power station is automatically determined according to the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector, which can reduce the influence of subjective factors and improve the accuracy and consistency of safety risk evaluation.

[0133] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0134] (1) In the present invention, multiple evaluation indicators can be considered at the same time, a weight can be assigned to each evaluation indicator, and the initial weight of each evaluation indicator can be modified according to the degree of influence between each evaluation indicator. The weights and the degree of mutual influence between different risk indicators can be comprehensively considered, and complex risk situations with multiple dimensions and multiple indicators can be effectively handled, making the security risk evaluation more comprehensive and objective, and improving the accuracy and consistency of the security risk evaluation.

[0135] (2) In the present invention, the safety risk level of each energy storage power station is automatically determined based on the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector, which can reduce the influence of subjective factors and improve the accuracy and consistency of safety risk evaluation.

[0136] (3) In the present invention, the credibility of experts is introduced. The scoring vector of the energy storage power station is determined according to the scores of each expert on the energy storage power station under each evaluation indicator and the credibility of the experts. This can improve the credibility of the score, make the quantification of the evaluation process more reliable, and improve the accuracy of the safety risk assessment.

[0137] Example 2

[0138] In one embodiment, the present invention provides a safety risk assessment system for an energy storage power station, which is used to execute the safety risk assessment method for an energy storage power station in Example 1.

[0139] A safety risk assessment system for an energy storage power station provided by the present invention can implement the steps and effects of the safety risk assessment method for an energy storage power station in the above-mentioned embodiment 1. To avoid repetition, the present invention will not go into details.

[0140] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0141] (1) In the present invention, multiple evaluation indicators can be considered at the same time, a weight can be assigned to each evaluation indicator, and the initial weight of each evaluation indicator can be modified according to the degree of influence between each evaluation indicator. The weights and the degree of mutual influence between different risk indicators can be comprehensively considered, and complex risk situations with multiple dimensions and multiple indicators can be effectively handled, making the security risk evaluation more comprehensive and objective, and improving the accuracy and consistency of the security risk evaluation.

[0142] (2) In the present invention, the safety risk level of each energy storage power station is automatically determined based on the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector, which can reduce the influence of subjective factors and improve the accuracy and consistency of safety risk evaluation.

[0143] (3) In the present invention, the credibility of experts is introduced. The scoring vector of the energy storage power station is determined according to the scores of each expert on the energy storage power station under each evaluation indicator and the credibility of the experts. This can improve the credibility of the score, make the quantification of the evaluation process more reliable, and improve the accuracy of the safety risk assessment.

[0144] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A safety risk assessment method for an energy storage power station, characterized in that: include: S101: Acquire a safety risk assessment system for an energy storage power station, where the safety risk assessment system includes multiple assessment indicators; S102: Determine the initial weight of each evaluation indicator; S103: According to the influence degree between the evaluation indicators, the initial weight of each evaluation indicator is modified to obtain a formal weight; S104: Obtaining scores of multiple experts on the energy storage power station under various evaluation indicators; S105: Determine a scoring vector for the energy storage power station based on the scores of the experts on the energy storage power station under various evaluation indicators and the credibility of the experts; S106: combining the scoring vectors for the plurality of energy storage power stations into a scoring matrix, and determining a positive ideal solution vector and a negative ideal solution vector of the scoring matrix; S107: Calculate the Euclidean distance between the scoring vector of each energy storage power station and the positive ideal solution vector and the Euclidean distance between the scoring vector of each energy storage power station and the negative ideal solution vector in combination with the formal weights of the evaluation indicators; S108: Calculating the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector; S109: Determine the safety risk level of each energy storage power station according to the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector; The safety risk assessment system includes five first-level assessment indicators: battery condition, implementation of preventive measures, operating environment, geographical location and equipment of safety monitoring and protection system; battery condition includes five second-level assessment indicators: shell damage, odor, surface cleanliness, battery available capacity and single cell voltage inconsistency; implementation of preventive measures includes three second-level assessment indicators: implementation of external short circuit preventive measures, implementation of external fire source preventive measures and implementation of physical impact preventive measures; operating environment includes three second-level assessment indicators: humidity, dust and ventilation; geographical location includes three second-level assessment indicators: climate, topography and natural disaster incidence; The equipment of the safety monitoring and protection system includes four secondary evaluation indicators: the equipment of the battery management system, the equipment of the safety linkage system, the equipment of the lightning protection and grounding system, and the equipment of the fire protection system; The S103 specifically includes: S1031: Normalize the discriminant matrix to obtain a normalized matrix: Among them, F represents the normalized matrix, A represents the discriminant matrix, max represents the maximum value, and a ij Indicates the importance of the i-th evaluation indicator relative to the j-th evaluation indicator, a ij The value of is determined by the nine-pole scaling method, where m represents the total number of evaluation indicators, i = 1, 2, …, m. j=1,2,…,m; S1032: Calculate the comprehensive impact matrix according to the normalized matrix: C=F+F 2 +…+F m =F(1-F) -1 Among them, C represents the comprehensive impact matrix, and each element in the comprehensive impact matrix is ​​denoted by c ij , c ij Represents the element value of the i-th row and j-th column in the comprehensive influence matrix; S1033: According to the comprehensive impact matrix, calculate the influence of each evaluation indicator on other evaluation indicators and the degree of influence: Among them, f i represents the influence of the i-th evaluation index, g j represents the influence of the jth evaluation index, m represents the total number of evaluation indicators, i = 1, 2, ..., m, j = 1, 2, ..., m; S1034: Calculate the centrality of each evaluation indicator according to the influence and influence of each evaluation indicator: H j =f j +g j Among them, H j represents the centrality of the jth evaluation index, f j represents the influence of the jth evaluation index, g j Indicates the influence of the jth evaluation indicator; S1035: According to the centrality of each evaluation indicator, the initial weight of each evaluation indicator is modified to obtain the formal weight: Among them, β j represents the formal weight of the jth evaluation metric, α j represents the initial weight of the jth evaluation indicator; The S105 specifically includes: S1051: Calculate the objective parameters of each expert's scoring of the energy storage power station under each evaluation indicator according to the following formula: Among them, B k represents the objective parameter when the kth expert scores the energy storage power station under various evaluation indicators, e kj represents the original score of the k-th expert when scoring the j-th evaluation indicator, represents the average of the original scores of each expert for the jth evaluation indicator, j = 1, 2, ..., m, m represents the total number of evaluation indicators, k = 1, 2, ..., K, K represents the total number of experts; S1052: Calculate the credibility of each expert according to the following formula: Among them, μ k represents the credibility of the kth expert; S1053: Calculate the final score of the energy storage power station under each evaluation indicator based on the credibility of each expert and the original score when scoring each evaluation indicator: Among them, e j Represents the final score under the jth evaluation indicator; S1054: Determine the scoring vector of the energy storage power station: Among them, e represents the scoring vector; then the scoring vector of the oth energy storage power station is expressed as: The S106 specifically includes: S1061: Combining the scoring vectors for multiple energy storage power stations into a scoring matrix: Among them, E represents the scoring matrix, e oj represents the final score of the o-th energy storage power station under the j-th evaluation indicator, o = 1, 2, ..., n, n represents the total number of energy storage power stations, j = 1, 2, ..., m, m represents the total number of evaluation indicators; S1062: Determine the positive ideal solution vector and the negative ideal solution vector of the scoring matrix: in, represents the positive ideal solution vector, which consists of the maximum values ​​of each row in the scoring matrix, represents the negative ideal solution vector, which consists of the minimum values ​​of each row in the scoring matrix; The S109 is specifically: S1091: When the closeness τ between the scoring vector of the target energy storage power station and the negative ideal solution vector satisfies τ≤T1, determining that the safety risk level of the target energy storage power station is extremely high risk; S1092: When the closeness τ between the scoring vector of the target energy storage power station and the negative ideal solution vector satisfies T1<τ≤T2, determining that the safety risk level of the target energy storage power station is high risk; S1093: When the closeness τ between the scoring vector of the target energy storage power station and the negative ideal solution vector satisfies T2<τ≤T3, determining that the safety risk level of the target energy storage power station is medium risk; S1094: When the closeness τ between the scoring vector of the target energy storage power station and the negative ideal solution vector satisfies τ>T3, determining that the safety risk level of the target energy storage power station is low risk; Among them, T1 represents the first preset closeness, T2 represents the second preset closeness, and T3 represents the third preset closeness.

2. The safety risk assessment method for an energy storage power station according to claim 1, characterized in that: The S102 specifically includes: S1021: By comparing the influence of each evaluation indicator pairwise and combining the nine-level scaling method, a discriminant matrix is ​​established: Among them, A represents the discriminant matrix, a ij Indicates the importance of the i-th evaluation indicator relative to the j-th evaluation indicator, a ij The value of is determined by the nine-pole scaling method, m represents the total number of evaluation indicators, i = 1, 2, ..., m, j = 1, 2, ..., m; S1022: Calculate the eigenvectors and eigenvalues ​​of the discriminant matrix A: Aw=λw→(A-λI)w Wherein, λ represents the eigenvalue of the discriminant matrix A, w represents the eigenvector of the discriminant matrix A, I represents the unit matrix, and the largest eigenvalue is recorded as λ max , the eigenvector corresponding to the largest eigenvalue is denoted as w max , w max =(w1,w2,…,w m ); S1023: eigenvector w corresponding to the largest eigenvalue max Perform normalization: Among them, the normalized vector The various components Represent the initial weights of each evaluation index, respectively, denoted as α1, α2, …, α m .

3. The safety risk assessment method for an energy storage power station according to claim 2, characterized in that: The S107 specifically includes: S1071: Calculate the Euclidean distance between the score vector of each energy storage power station and the positive ideal solution vector using the following formula: in, represents the Euclidean distance between the score vector of the o-th energy storage power station and the positive ideal solution vector, β j represents the formal weight of the jth evaluation indicator, e oj represents the final score of the o-th energy storage power station under the j-th evaluation indicator, represents the positive ideal solution vector, o=1,2,…,n, n represents the total number of energy storage power stations, j=1,2,…,m, m represents the total number of evaluation indicators; S1072: Calculate the Euclidean distance between the score vector of each energy storage power station and the negative ideal solution vector using the following formula: in, represents the Euclidean distance between the score vector of the o-th energy storage power station and the negative ideal solution vector, represents the negative ideal solution vector.

4. The safety risk assessment method for an energy storage power station according to claim 3, characterized in that: The S108 is specifically: According to the following formula, the closeness between the scoring vector of each energy storage power station and the negative ideal solution vector is calculated: Among them, τ o represents the closeness between the score vector of the o-th energy storage power station and the negative ideal solution vector. When the closeness between the score vector of the o-th energy storage power station and the negative ideal solution vector τ o The smaller it is, the closer the scoring vector of the o-th energy storage power station is to the negative ideal solution vector, and the higher the safety risk level of the o-th energy storage power station is.

5. A safety risk assessment system for an energy storage power station, characterized in that: Used to execute the safety risk assessment method of the energy storage power station described in any one of claims 1 to 4.

Images