Power transmission and transformation overhead line engineering construction risk nonlinear fuzzy comprehensive evaluation method and system based on improved FBWM-CRITIC method

Abstract

The invention discloses a power transmission and transformation overhead line project construction risk nonlinear fuzzy comprehensive evaluation method and system based on an improved FBWM-CRITIC method, and the method comprises the steps: constructing a power transmission and transformation project construction safety risk evaluation index system, obtaining the combination weight of evaluation indexes in the evaluation index system through an improved FBWM-CRITIC combination weighting method, and carrying out the nonlinear fuzzy comprehensive evaluation of the power transmission and transformation overhead line project construction risk. Obtaining a membership degree vector by utilizing an expert evaluation method, and constructing a fuzzy relation matrix; based on the combined weight and the fuzzy relation matrix, obtaining an index layer relation matrix by using a nonlinear fuzzy comprehensive evaluation method, and processing the index layer relation matrix to obtain a comprehensive evaluation result vector; and adopting a maximum membership degree principle for the comprehensive evaluation result vector to obtain a risk evaluation result of power transmission and transformation project construction. The construction safety of the power transmission and transformation project is effectively improved, the accident occurrence probability is reduced, and smooth proceeding of the project and personnel safety are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power transmission and transformation engineering construction technology, specifically to a nonlinear fuzzy comprehensive assessment method and system for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method. Background Technology

[0002] The construction of power transmission and transformation projects includes civil engineering, electrical engineering, overhead line engineering, and cable line engineering. It involves multiple aspects such as high-altitude operations, large machinery operation, and electrical equipment installation. In particular, the construction of substation renovation and expansion projects and transmission line projects is characterized by proximity to live conductors, movement, crossing of live lines, crossing of highways and railways, and crossing of rivers and lakes. The construction environment is extremely complex and the safety risks are relatively high. It is a highly comprehensive and systematic project.

[0003] The construction environment of power transmission and transformation projects is harsh, the operations are complex, and there are numerous influencing factors, making them highly susceptible to safety accidents during construction. The LEC risk assessment method, currently used in the industry under the "Regulations for Safety Risk Management of Power Transmission and Transformation Engineering Construction" (Q / GDW12152-2021), relies on subjective judgments of the L, E, and C value ranges. The evaluation standards are relatively general and difficult to scientifically quantify the complex and uncertain risk factors in overhead line construction. Therefore, the fuzzy uncertainty, discrete data, and nonlinearity in the safety evaluation process of power transmission and transformation engineering construction lead to inaccurate evaluation results, resulting in the failure to promptly identify and eliminate safety hazards during construction, ultimately causing engineering quality problems and safety accidents. Summary of the Invention

[0004] The purpose of this invention is to provide a nonlinear fuzzy comprehensive assessment method and system for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method. This method reduces the subjective arbitrariness of experts and also reduces the excessive reliance of objective methods on raw data, while maintaining the rationality and scientific nature of the computational workload.

[0005] This invention adopts the following technical solution: a nonlinear fuzzy comprehensive assessment method for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method, comprising the following steps:

[0006] S1. Construct a construction safety risk assessment index system for power transmission and transformation projects, which includes primary evaluation indicators and secondary evaluation indicators.

[0007] S2. The combined weights of the evaluation indicators in the construction safety risk evaluation index system of power transmission and transformation projects are obtained by using the improved FBWM-CRITIC combined weighting method.

[0008] S3. The expert evaluation method is used to process the safety risk evaluation index system for power transmission and transformation engineering construction, obtain the membership vector, and construct the fuzzy relation matrix.

[0009] S4. Based on the combined weights in step S2 and the fuzzy relation matrix in step S3, a first-level nonlinear comprehensive evaluation is performed using the nonlinear fuzzy comprehensive evaluation method to obtain the index layer relation matrix. The index layer relation matrix is ​​then processed to obtain the comprehensive evaluation result vector.

[0010] S5. Apply the maximum membership principle to the comprehensive evaluation result vector to obtain the risk assessment results of power transmission and transformation project construction.

[0011] Furthermore, in step S1, the construction of the safety risk assessment index system for power transmission and transformation engineering construction includes the following:

[0012] The risk assessment indicators in the revised HFACS (Human Factors Analysis and Classification System) framework were screened through data collection, expert consultation, and questionnaires to obtain four primary evaluation indicators. These four primary evaluation indicators were then further subdivided into 15 secondary evaluation indicators. The primary and secondary evaluation indicators together constitute the safety risk assessment indicator system for power transmission and transformation engineering construction.

[0013] The primary evaluation indicators include the impact of enterprise organization (C1), safety supervision (C2), on-site operation-related factors (C3), and construction personnel-related factors (C4).

[0014] The secondary evaluation indicators are specifically: Corporate organizational impact C1, including organizational structure and responsibilities C. 11 Safety production investment C 12 Safety Management Procedures C 13 Safety supervision C2 includes risk monitoring and early warning C 21 Supervision and management violations C 22 1. Work Plan Arrangement C 23 On-site operation-related factors C3 include basic construction conditions C 31 Construction technical measures C 32 Construction machinery C 33 Construction environment C 34 Construction Social Environment C 35 Construction personnel-related factors C4 include violations of operating procedures C 41 Skill error C 42 Intuition and Decision Errors (C) 43 Personnel quality C 44 .

[0015] Furthermore, in step S2, the combined weights are obtained as follows:

[0016] The combined weights include subjective weights and objective weights.

[0017] The subjective weights of the primary evaluation indicators are obtained using FBWM (Fuzzy best-worst multi-criteria decision-making method), specifically as follows:

[0018] Based on the primary evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction, the optimal and worst evaluation indicators among the primary evaluation indicators are determined according to the opinions of the expert group. The optimal evaluation indicator is compared with other primary evaluation indicators, and the worst evaluation indicator is compared with other primary evaluation indicators. Fuzzy linguistic variables are used to determine the importance of the primary evaluation indicators.

[0019] The membership degree of the primary evaluation index is calculated using the membership function. The specific formula is as follows:

[0020]

[0021] Where l, m, and u all represent triangular fuzzy numbers, and l < m < u. Let represent the membership function, and x represent the score of the primary evaluation indicator.

[0022] A fuzzy vector A is constructed based on fuzzy linguistic variables to compare the optimal evaluation index with other primary evaluation indices. B And the fuzzy vector A comparing the worst-case evaluation index with other first-level evaluation indices. W The specific formula is as follows:

[0023]

[0024] in, This represents the fuzzy contrast between the optimal evaluation index and the j-th other primary evaluation index, where n represents the total number of primary evaluation indicators. This represents the fuzzy contrast between the worst evaluation index and the j-th other first-level evaluation index.

[0025] The standard fuzzy index weights are obtained through a goal programming model, using the following formula:

[0026]

[0027] in, w represents minimizing fuzzy bias. B w represents the standard fuzzy index weight of the optimal evaluation index. jw represents the standard fuzzy index weight of the j-th other first-level evaluation index. W R(w) represents the standard fuzzy index weight of the worst-case evaluation index. j ) represents the subjective weight of the j-th other primary evaluation indicator, l j m j u j Each represents the triangular fuzzy number of the j-th other first-level evaluation index.

[0028] The subjective weights of the first-level evaluation indicators are obtained by processing the standard fuzzy index weights using the hierarchical mean integral representation method. The specific formula is as follows:

[0029]

[0030] in, Let l represent the subjective weight of the i-th primary evaluation indicator. i m i u i Each represents the triangular fuzzy number of the i-th primary evaluation index.

[0031] By comparing the fuzzy linguistic variables of the optimal and worst evaluation indicators, and combining them with the consistency index C, the formula is used. Perform a consistency check; when the consistency ratio C R If <0.1, then the consistency test is passed.

[0032] The objective weights of the secondary evaluation indicators are obtained using the improved CRITIC (Criteria Importance Through Inter-criteria Correlation) method, specifically as follows:

[0033] Given D samples to be evaluated and Q secondary evaluation indicators, establish an initial indicator data matrix M = (b dq ); where b dq This represents the element in the d-th row and q-th column of the initial indicator data matrix, which is the q-th secondary evaluation indicator in the d-th sample to be evaluated.

[0034] The initial index data matrix is ​​dimensionless to obtain the standardized index data matrix; the volatility of the elements in the standardized index data matrix is ​​calculated using the following formula:

[0035]

[0036] Among them, F q b' represents the volatility of the q-th secondary evaluation indicator. dq This represents the element in the d-th row and q-th column of the standardized index data matrix. This represents the mean of the q-th secondary evaluation indicator.

[0037] The specific formula for calculating the conflict matrix and information content of the secondary evaluation indicators is as follows:

[0038]

[0039] D q =F q ×A q

[0040] Where, r dq b represents the element in the d-th row and q-th column of the conflict matrix. dk This represents the element in the d-th row and k-th column of the initial index data matrix. Let A represent the mean of the k-th secondary evaluation indicator. q D represents the conflict of the q-th secondary evaluation indicator. q This represents the information content of the q-th secondary evaluation indicator.

[0041] The specific formula for calculating the weight of secondary evaluation indicators is as follows:

[0042]

[0043] Where, p dq This represents the weight of the q-th secondary evaluation indicator in the d-th sample to be evaluated.

[0044] The specific formula for calculating the information entropy value of the secondary evaluation indicators is as follows:

[0045]

[0046] Among them, e q Let ξ represent the information entropy value of the q-th secondary evaluation indicator, and let ξ represent a constant.

[0047] The specific formula for the objective weights of the secondary evaluation indicators is as follows:

[0048]

[0049] in, This represents the objective weight of the q-th secondary evaluation indicator.

[0050] The specific formula for the combined weights of the evaluation indicators is as follows:

[0051]

[0052] Among them, w g This represents the combined weight of the g-th evaluation indicator.

[0053] Furthermore, in step S3, constructing the fuzzy relation matrix includes the following:

[0054] Based on the characteristics of power transmission and transformation engineering construction, a risk assessment set was constructed, and the evaluation indicators were divided into 5 levels: N = {low risk, lower risk, medium risk, higher risk, high risk}.

[0055] The primary indicator risk factor set is U, where U = {U1, U2, U3, U4); the secondary indicator risk factor set is Ui, where U1 = {u 11 ,u 12 ,u 13 ), U2={u 21 ,u 22 ,u 23}、U3={u 31 ,u 32 ,u 33 ,u 34 ,u 35 ), U4={u 41 ,u 42 ,u 43 ,u 44 ); where U i Let u represent the set of risk factors for the i-th primary evaluation indicator, i = 1, 2, 3, 4. 13 This represents the risk factors of the third secondary evaluation indicator of the first primary evaluation indicator, u. 23 This represents the risk factors of the third secondary evaluation indicator, which is the second primary evaluation indicator. 35 This represents the risk factors of the fifth secondary evaluation indicator, which is the third primary evaluation indicator. 44 This indicates the risk factors of the fourth secondary evaluation indicator, which is the fourth primary evaluation indicator.

[0056] Based on the five levels in the risk assessment set, the evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction are scored using the expert evaluation method, resulting in a membership vector. The specific expression is as follows:

[0057] S = [S g1 ,S g2 ,...,S g5 ]

[0058] Among them, S g1 This represents the membership vector of the g-th evaluation index in the power transmission and transformation project construction safety risk evaluation index system to the first risk level in the risk evaluation set.

[0059] Based on the membership vector of each evaluation index to the risk comment set in the safety risk evaluation index system for power transmission and transformation engineering construction, the membership vectors corresponding to each risk level are combined to obtain the fuzzy relationship matrix between the risk comment set and the risk level.

[0060] Furthermore, in step S4, the comprehensive evaluation result vector includes the following:

[0061] Based on the combined weights, a nonlinear fuzzy comprehensive evaluation method is used to obtain the nonlinear fuzzy relation moment synthesis operator, the specific formula of which is:

[0062]

[0063] Where f represents the nonlinear fuzzy relation moment composition operator, g = 1, 2, ..., G, G represents the total number of evaluation indicators, and w g ≥0 and s g Let Λ represent the element in the g-th column of the fuzzy relation matrix, and let Λ represent the vector of coefficients indicating the degree of prominence of the evaluation indicators, where Λ = [λ1, λ2, ..., λ]. g ,...,λ G ], λ g Let λ represent the coefficient of the prominent influence of the g-th evaluation indicator, and let λ represent the maximum value among the coefficients of the prominent influence of the evaluation indicators. λ = max(λ1, λ2, ..., λ) G ).

[0064] The fuzzy relation matrix is ​​processed using a nonlinear fuzzy relation moment synthesis operator to obtain the index layer relation matrix, and then the index layer relation matrix is ​​transformed using the following formula:

[0065] E' i'j' =10*E i'j'

[0066] Among them, E' i'j' E represents the element in the i'-th row and j'-th column of the transformed index layer relation matrix. i'j' This represents the element in the i'th row and j'th column of the indicator layer relation matrix.

[0067] The values ​​of the transformed indicator layer relationship matrix are normalized to obtain the comprehensive evaluation result vector.

[0068] Furthermore, this invention also proposes a nonlinear fuzzy comprehensive assessment system for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method, comprising:

[0069] The evaluation index system construction module is used to construct an evaluation index system for the construction safety risks of power transmission and transformation projects. This system includes primary evaluation indicators and secondary evaluation indicators.

[0070] The combined weight acquisition module is used to obtain the combined weights of evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction through the improved FBWM-CRITIC combined weighting method.

[0071] The matrix acquisition module is used to process the evaluation index system for construction safety risks of power transmission and transformation projects using the expert evaluation method, obtain the membership vector, and construct the fuzzy relation matrix.

[0072] The evaluation result acquisition module is used to combine weights and fuzzy relation matrices, and to perform a first-level nonlinear comprehensive evaluation using a nonlinear fuzzy comprehensive evaluation method to obtain an index-level relation matrix. The index-level relation matrix is ​​then processed to obtain a comprehensive evaluation result vector. The comprehensive evaluation result vector is then processed using the maximum membership principle to obtain the risk evaluation result of the power transmission and transformation project construction.

[0073] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the nonlinear fuzzy comprehensive assessment method for construction risks of overhead power transmission and transformation projects based on the improved FBWM-CRITIC method.

[0074] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the nonlinear fuzzy comprehensive assessment method for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method.

[0075] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0076] 1. The safety risk assessment index system for power transmission and transformation engineering construction constructed in this invention can achieve comprehensive and multi-faceted evaluation while maintaining the rationality and scientific nature of the calculations.

[0077] 2. This invention not only reduces the subjective arbitrariness of experts, but also reduces the excessive reliance of objective methods on original data, making up for the shortcomings of the single weighting method, thereby improving authenticity and scientific rigor. Attached Figure Description

[0078] Figure 1 This is a flowchart illustrating the overall implementation of the present invention.

[0079] Figure 2 This is a structural diagram of the safety risk assessment index system for power transmission and transformation engineering construction of the present invention. Detailed Implementation

[0080] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0081] To achieve the above objectives, this invention proposes a nonlinear fuzzy comprehensive assessment method for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method, such as... Figure 1 As shown, the specific steps are as follows:

[0082] S1, such as Figure 2 As shown, a construction safety risk assessment index system for power transmission and transformation projects is constructed, which includes primary and secondary evaluation indicators. Specifically:

[0083] Through data collection, expert consultation, and questionnaires, combined with past accident data and on-site investigations, the risk assessment indicators in the revised HFACS (Human Factors Analysis and Classification System) framework were screened to obtain four primary evaluation indicators. These four primary evaluation indicators were further subdivided into 15 secondary evaluation indicators. The primary and secondary evaluation indicators constitute the safety risk assessment indicator system for power transmission and transformation engineering construction.

[0084] The primary evaluation indicators include the impact of enterprise organization (C1), safety supervision (C2), on-site operation-related factors (C3), and construction personnel-related factors (C4).

[0085] The secondary evaluation indicators are specifically: Corporate organizational impact C1, including organizational structure and responsibilities C. 11 Safety production investment C 12 Safety Management Procedures C 13 Safety supervision C2 includes risk monitoring and early warning C 21 Supervision and management violations C 22 1. Work Plan Arrangement C 23 On-site operation-related factors C3 include basic construction conditions C 31 Construction technical measures C 32 Construction machinery C 33 Construction environment C 34 Construction Social Environment C 35 Construction personnel-related factors C4 include violations of operating procedures C 41 Skill error C 42 Intuition and Decision Errors (C) 43 Personnel quality C 44 .

[0086] S2. The combined weights of the evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction are obtained through the improved FBWM-CRITIC combined weighting method. Specifically:

[0087] The combined weights include subjective weights and objective weights.

[0088] The subjective weights of the primary evaluation indicators are obtained using FBWM (Fuzzy best-worst multi-criteria decision-making method), specifically as follows:

[0089] Based on the primary evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction, the optimal and worst evaluation indicators among the primary evaluation indicators are determined according to the opinions of the expert group. The optimal evaluation indicator is compared with other primary evaluation indicators, and the worst evaluation indicator is compared with other primary evaluation indicators. The importance of the primary evaluation indicators is determined using fuzzy linguistic variables as shown in Table 1.

[0090] Table 1. Transformation rules and consistency indices for fuzzy linguistic variables.

[0091] Fuzzy linguistic variables Membership function C Equally important (1，1，1) 3.00 More important (2 / 3,1,3 / 2) 3.80 Very important (3 / 2,2,5 / 2) 5.29 Very important (5 / 2,3,7 / 2) 6.69 Extremely important (7 / 2,4,9 / 2) 8.04

[0092] The membership degree of the primary evaluation index is calculated using the membership function. The specific formula is as follows:

[0093]

[0094] Where l, m, and u all represent triangular fuzzy numbers, and l < m < u. Let represent the membership function, and x represent the score of the primary evaluation indicator.

[0095] A fuzzy vector A is constructed based on fuzzy linguistic variables to compare the optimal evaluation index with other primary evaluation indices. B And the fuzzy vector A comparing the worst-case evaluation index with other first-level evaluation indices. W The specific formula is as follows:

[0096]

[0097] in, This represents the fuzzy contrast between the optimal evaluation index and the j-th other primary evaluation index, where n represents the total number of primary evaluation indicators. This represents the fuzzy contrast between the worst evaluation index and the j-th other first-level evaluation index.

[0098] It needs to be specifically pointed out This represents the fuzzy contrast between the optimal evaluation index and the optimal evaluation index. This represents the fuzzy contrast between the worst-case evaluation index and the worst-case evaluation index.

[0099] The standard fuzzy index weights are obtained through a goal programming model, using the following formula:

[0100]

[0101] in, w represents minimizing fuzzy bias. B w represents the standard fuzzy index weight of the optimal evaluation index. j w represents the standard fuzzy index weight of the j-th other first-level evaluation index. W R(w) represents the standard fuzzy index weight of the worst-case evaluation index. j ) represents the subjective weight of the j-th other primary evaluation indicator, l j m j u j Each represents the triangular fuzzy number of the j-th other first-level evaluation index.

[0102] The subjective weights of the first-level evaluation indicators are obtained by processing the standard fuzzy index weights using the hierarchical mean integral representation method. The specific formula is as follows:

[0103]

[0104] in, Let l represent the subjective weight of the i-th primary evaluation indicator. i m i u i Each represents the triangular fuzzy number of the i-th primary evaluation index.

[0105] By comparing the fuzzy linguistic variables of the optimal and worst evaluation indicators, and combining them with the consistency index C shown in Table 1, the formula is used. A consistency test was performed, and the consistency ratio C was determined. R The closer to 0, the better the consistency. Generally, C is set to... R If <0.1, then the consistency test is passed.

[0106] The objective weights of the secondary evaluation indicators are obtained using the improved CRITIC (Criteria Importance Through Inter-criteria Correlation) method, specifically as follows:

[0107] Given D samples to be evaluated and Q secondary evaluation indicators, establish an initial indicator data matrix M = (b dq ); where b dq This represents the element in the d-th row and q-th column of the initial indicator data matrix, which is the q-th secondary evaluation indicator in the d-th sample to be evaluated.

[0108] To eliminate the influence of different indicator dimensions on the results, the initial indicator data matrix is ​​dimensionless to obtain a standardized indicator data matrix; the volatility of the elements in the standardized indicator data matrix is ​​calculated using the following formula:

[0109]

[0110] Among them, F q b' represents the volatility of the q-th secondary evaluation indicator. dq This represents the element in the d-th row and q-th column of the standardized index data matrix. This represents the mean of the q-th secondary evaluation indicator.

[0111] The specific formula for calculating the conflict matrix and information content of the secondary evaluation indicators is as follows:

[0112]

[0113] D q =F q ×A q

[0114] Where, r dq b represents the element in the d-th row and q-th column of the conflict matrix. dk This represents the element in the d-th row and k-th column of the initial index data matrix. Let A represent the mean of the k-th secondary evaluation indicator. q D represents the conflict of the q-th secondary evaluation indicator. q This represents the information content of the q-th secondary evaluation indicator.

[0115] The specific formula for calculating the weight of secondary evaluation indicators is as follows:

[0116]

[0117] Where, p dq This represents the weight of the q-th secondary evaluation indicator in the d-th sample to be evaluated.

[0118] The specific formula for calculating the information entropy value of the secondary evaluation indicators is as follows:

[0119]

[0120] Among them, e q Let ξ represent the information entropy value of the q-th secondary evaluation indicator, and let ξ represent a constant.

[0121] The specific formula for the objective weights of the secondary evaluation indicators is as follows:

[0122]

[0123] in, This represents the objective weight of the q-th secondary evaluation indicator.

[0124] For the primary evaluation indicators, which involve multiple perspectives such as economy, technology, and environment, the FBWM method is used to assign weights to the primary evaluation indicators in order to reflect the evaluation purpose. Under the same primary evaluation indicator, each secondary evaluation indicator is objectively weighted using its own weights. The combined weights comprehensively consider the influence of subjective and objective weights. The combined weighting method of FBWM and the improved CRITIC method weakens the subjective arbitrariness of experts and reduces the excessive reliance of objective methods on raw data, making up for the shortcomings of single weighting methods, thereby improving the authenticity and scientific nature of the weighting in the safety evaluation system for power transmission and transformation engineering construction.

[0125] The specific formula for the combined weights of the evaluation indicators is as follows:

[0126]

[0127] Among them, w g This represents the combined weight of the g-th evaluation indicator.

[0128] S3. The expert evaluation method is used to process the safety risk assessment index system for power transmission and transformation engineering construction, obtaining the membership vector and constructing a fuzzy relation matrix. Specifically:

[0129] Constructing a risk assessment set: Assessments are qualitative descriptions of risk evaluation indicators. The assessment set is a collection of various combinations of assessments made by assessors regarding the risks in the construction process of power transmission and transformation projects. Based on the characteristics of power transmission and transformation project construction, a risk assessment set is constructed, dividing the evaluation indicators into 5 levels: N = {n1, n2, n3, n4, n5} = {low risk, relatively low risk, medium risk, relatively high risk, high risk}.

[0130] Constructing a risk factor set: The risk factor set is established based on the safety risk assessment index system for power transmission and transformation engineering construction. The primary indicator risk factor set is U, U = {U1, U2, U3, U4); the secondary indicator risk factor set is Ui, U1 = {u 11 ,u 12 ,u 13 ), U2={u 21 ,u 22 ,u 23}、U3={u 31 ,u 32 ,u 33 ,u 34 ,u 35 ), U4={u 41 ,u 42 ,u 43 ,u 44); where Ui represents the risk factor set of the i-th primary evaluation indicator, i = 1, 2, 3, 4, u 13 This represents the risk factors of the third secondary evaluation indicator of the first primary evaluation indicator, u. 23 This represents the risk factors of the third secondary evaluation indicator, which is the second primary evaluation indicator. 35 This represents the risk factors of the fifth secondary evaluation indicator, which is the third primary evaluation indicator. 44 This indicates the risk factors of the fourth secondary evaluation indicator, which is the fourth primary evaluation indicator.

[0131] Based on the five levels in the risk assessment set, the evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction are scored using the expert evaluation method, resulting in a membership vector. The specific expression is as follows:

[0132] S = [S g1 ,S g2 ,...,S g5 ]

[0133] Among them, S g1 This represents the membership vector of the g-th evaluation index in the power transmission and transformation project construction safety risk evaluation index system to the first risk level in the risk evaluation set.

[0134] Based on the membership vector of each evaluation index to the risk comment set in the safety risk evaluation index system for power transmission and transformation engineering construction, the membership vectors corresponding to each risk level are combined to obtain the fuzzy relationship matrix between the risk comment set and the risk level.

[0135] S4. Based on the combined weights in step S2 and the fuzzy relation matrix in step S3, a first-level nonlinear comprehensive evaluation is performed using the nonlinear fuzzy comprehensive evaluation method to obtain the index layer relation matrix. The index layer relation matrix is ​​then processed to obtain the comprehensive evaluation result vector. Specifically:

[0136] Compared to linear operators used to calculate the safety risk level of power transmission and transformation projects, some important indicators may be weakened. In linear operators, all risk factors are treated equally, leading to a weakening of the impact of prominent risk factors on construction safety, ultimately affecting the accuracy of the assessment results. Nonlinear calculation methods use different indices to represent the importance of different risk factors, which better reflects the actual situation on site. This approach highlights the impact of important risks while reducing the influence of less important factors. Furthermore, nonlinear fuzzy evaluation better reflects the uncertainty and ambiguity of prominent influencing factors at the construction site of power transmission and transformation projects.

[0137] Based on the combined weights, a nonlinear fuzzy comprehensive evaluation method is used to obtain the nonlinear fuzzy relation moment synthesis operator, the specific formula of which is:

[0138]

[0139] Where f represents the nonlinear fuzzy relation moment composition operator, g = 1, 2, ..., G, G represents the total number of evaluation indicators, and w g ≥0 and s g Let Λ represent the element in the g-th column of the fuzzy relation matrix, and let Λ represent the vector of coefficients indicating the degree of prominence of the evaluation indicators, where Λ = [λ1, λ2, ..., λ]. g ,...,λ G ], λ g Let λ represent the coefficient of the prominent influence of the g-th evaluation indicator, and let λ represent the maximum value among the coefficients of the prominent influence of the evaluation indicators. λ = max(λ1, λ2, ..., λ) G ).

[0140] The criteria for determining the value of the evaluation index's degree of influence coefficient are shown in Table 2.

[0141] Table 2. Criteria for Determining the Values ​​of the Evaluation Indicators' Influence Level Coefficient

[0142] Scale meaning 1 Indicator factors had no significant impact 1.5 Indicator factors have almost no significant impact 2.5 The indicator factor has a slightly prominent influence. 3.5 Indicator factors have a significant and prominent impact 4.5 Indicator factors have a strong and prominent influence 5.5 Indicator factors have an extremely prominent impact 2、3、4、5 The median of adjacent scales represents the scale between two adjacent scales.

[0143] The fuzzy relation matrix is ​​processed using a nonlinear fuzzy relation moment synthesis operator to obtain the index layer relation matrix. Since all elements in the index layer relation matrix are greater than or equal to 1, but the membership values ​​are between [0,1], the index layer relation matrix is ​​transformed using the following formula:

[0144] E' i'j' =10*E i'j'

[0145] Among them, E' i'j' E represents the element in the i'-th row and j'-th column of the transformed index layer relation matrix. i'j' This represents the element in the i'th row and j'th column of the indicator layer relation matrix.

[0146] The values ​​of the transformed indicator layer relationship matrix are normalized to obtain the comprehensive evaluation result vector.

[0147] S5. Apply the maximum membership principle to the comprehensive evaluation result vector to obtain the risk assessment results of power transmission and transformation project construction.

[0148] Example:

[0149] Taking the risk analysis of the Changqing-Wenchang overhead power line construction project of Guangdong Power Transmission and Transformation Company as an example, based on the constructed safety risk evaluation index system for power transmission and transformation project construction, the membership function is:

[0150]

[0151]

[0152] Experts evaluated and compared intermediate-level risk indicators based on the actual engineering situation and their professional knowledge. They compiled a fuzzy evaluation matrix based on the maximum and minimum risk items, and calculated the subjective weights of each level of risk indicators. According to relevant data and expert advice, the optimal evaluation indicator in the first-level evaluation index system for the construction safety risk assessment of power transmission and transformation projects is the impact of enterprise organization, while the worst evaluation indicator is safety supervision. After obtaining the original intermediate-level evaluation matrix, the weights of the intermediate-level risk indicators under expert evaluation were calculated based on each matrix element. The results are shown in Table 3.

[0153] Table 3. Fuzzy linguistic variables comparing the best and worst evaluation indicators with other first-level evaluation indicators.

[0154]

[0155] The fuzzy vector A comparing the optimal evaluation index with other primary evaluation indices. B For A B =[(1,1,1),(7 / 2,4,5 / 2),(3 / 2,1,5 / 2),(2 / 3,1,3 / 2)], the fuzzy vector A representing the worst-case evaluation index compared to other first-level evaluation indices. W For, Aw=[(7 / 2,4,9 / 2),(1,1,1),(3 / 2,2,5 / 2),(5 / 2,3,7 / 2)].

[0156] The standard fuzzy index weights for the primary evaluation indicators are:

[0157] w1 = (0.3092, 0.3577, 0.3620)

[0158] w2 = (0.1001, 0.1177, 0.1224)

[0159] w3 = (0.1712, 0.2223, 0.2517)

[0160] w4 = (0.2727, 0.3219, 0.3301)

[0161]

[0162] After processing the standard fuzzy index weights, the true weights of the first-level evaluation indicators are obtained as w1 = 0.350, w2 = 0.116, w3 = 0.219, and w4 = 0.315. BW =a 12 = (7 / 2, 4, 9 / 2). Therefore, C = 8.04. R =0.3156 / 8.04 = 0.039 < 0.1, passing the consistency test.

[0163] The results of the combined weights are shown in Table 4.

[0164] Table 4 Combined Weights

[0165]

[0166] Based on the actual construction situation of the Changqing-Wenchang overhead power line project undertaken by Guangdong Power Transmission and Transformation Company, the secondary evaluation indicators were scored to obtain corresponding score values, as shown in Table 5. The obtained score values ​​were then substituted into the membership function to obtain the membership values. For example, C... 12 The corresponding membership value is (103-100) / 20 = 0.15.

[0167] Table 5 Scoring values ​​of secondary evaluation indicators

[0168] Evaluation indicators <![CDATA[C 11 ]]> <![CDATA[C 12 ]]> <![CDATA[C 13 ]]> <![CDATA[C 21 ]]> <![CDATA[C 22 ]]> <![CDATA[C 23 ]]> <![CDATA[C 31 ]]> <![CDATA[C 32 ]]> <![CDATA[C 33 ]]> <![CDATA[C 34 ]]> <![CDATA[C 35 ]]> <![CDATA[C 41 ]]> <![CDATA[C 42 ]]> <![CDATA[C 43 ]]> <![CDATA[C 44 ]]> Rating value 110 103 90 81 75 82 89 83 85 79 75 85 82 95 85

[0169] Table 6 shows the membership values ​​for the secondary evaluation indicators. For example, C 12 The corresponding risk membership value is (120-103) / 20 = 0.85.

[0170] Table 6. Membership Degree Values ​​of Secondary Evaluation Indicators

[0171]

[0172] Based on the membership degrees of the secondary evaluation indicators in the construction of the Changqing-Wenchang overhead line project of Guangdong Power Transmission and Transformation Company obtained from Table 6, fuzzy relation matrices are constructed according to the secondary evaluation indicators corresponding to the primary evaluation indicators. The nonlinear fuzzy evaluation matrix obtained after transforming the matrix through a nonlinear operator is specifically expressed as follows:

[0173]

[0174] Based on the actual construction situation of the Changqing-Wenchang line project of Guangdong Power Transmission and Transformation Company's overhead line construction, and combined with expert opinions, the coefficient of the maximum prominent influence of the evaluation index was obtained, as shown in Tables 7 and 8.

[0175] Table 7: Coefficient of Maximum Prominent Influence of Primary Evaluation Indicators

[0176] Evaluation indicators <![CDATA[C1]]> <![CDATA[C2]]> <![CDATA[C3]]> <![CDATA[C4]]> λ 4.0 1.0 2.0 3.0

[0177] Table 8. Coefficient of Maximum Prominent Influence of Secondary Evaluation Indicators

[0178] Evaluation indicators <![CDATA[C 11 ]]> <![CDATA[C 12 ]]> <![CDATA[C 13 ]]> <![CDATA[C 21 ]]> <![CDATA[C 22 ]]> <![CDATA[C 23 ]]> <![CDATA[C 31 ]]> <![CDATA[C 32 ]]> <![CDATA[C 33 ]]> <![CDATA[C 34 ]]> <![CDATA[C 35 ]]> <![CDATA[C 41 ]]> <![CDATA[C 42 ]]> <![CDATA[C 43 ]]> <![CDATA[C 44 ]]> λ 4.0 3.0 2.0 2.0 1.0 3.0 2.0 3.0 3.0 4.0 2.0 4.0 4.0 3.0 3.0

[0179] According to Table 8, the prominent influence coefficient vectors corresponding to E1-E4 are γ1=[4.0,2.0,1.0], γ2=[2.0,1.0,3.0], γ3=[2.0,3.0,3.0,4.0,2.0], and γ4=[4.0,4.0,3.0,3.0].

[0180] The comprehensive evaluation result vectors of the first-level evaluation indicators are N1=[0,0.4393,0.4503,0.1104,0], N2=[0,0,0.0951,0.8275,0.0774], N3=[0,0,0.1836,0.7445,0.0719], and N4=[0,0,0.2840,0.7160,0].

[0181] The indicator layer relationship matrix is ​​as follows:

[0182]

[0183] The initial comprehensive evaluation result vector is obtained by performing nonlinear calculations on the indicator layer relationship matrix = [0.3337, 0.4472, 0.7597, 0.3170].

[0184] The initial comprehensive evaluation result vector is normalized to obtain the comprehensive evaluation result vector as [0.1796, 0.2407, 0.4090, 0.1707].

[0185] Based on the fuzzy comprehensive evaluation vector, the risk level of this project is classified as Level 3, with a rating of medium risk. This indicates that the overall risk of the power transmission and transformation project is at a medium level. As long as the construction quality is guaranteed to be up to standard and management is orderly, the risk of the project is controllable. The results of this invention are consistent with the actual situation on site, and the evaluation results are in line with the facts, demonstrating the accuracy of the method proposed in this invention.

[0186] This invention also proposes a nonlinear fuzzy comprehensive assessment system for construction risks of overhead power transmission line projects based on the improved FBWM-CRITIC method. The system includes an evaluation index system construction module, a combined weight acquisition module, a matrix acquisition module, an evaluation result acquisition module, and a computer program that can run on a processor. It should be noted that each module in the above system corresponds to a specific step of the method provided in this invention embodiment, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this invention embodiment.

[0187] This invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, it corresponds to the specific steps of the method provided in this invention, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this invention.

[0188] This invention also proposes a computer-readable storage medium storing a computer program. It should be noted that when the computer program is executed by a processor, it corresponds to the specific steps of the method provided in this invention, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this invention.

[0189] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A nonlinear fuzzy comprehensive assessment method for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method, characterized in that, include: S1. Construct a construction safety risk assessment index system for power transmission and transformation projects, which includes primary and secondary evaluation indicators. S2. The combined weights of the evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction are obtained by using the improved FBWM-CRITIC combined weighting method. S3. The expert evaluation method is used to process the safety risk evaluation index system for power transmission and transformation projects, obtain the membership vector, and construct the fuzzy relation matrix. S4. Based on the combined weights in step S2 and the fuzzy relation matrix in step S3, a first-level nonlinear comprehensive evaluation is performed using the nonlinear fuzzy comprehensive evaluation method to obtain the index layer relation matrix. The index layer relation matrix is ​​then processed to obtain the comprehensive evaluation result vector. S5. Apply the maximum membership principle to the comprehensive evaluation result vector to obtain the risk assessment results of power transmission and transformation project construction.

2. The nonlinear fuzzy comprehensive assessment method for construction risk of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method according to claim 1, characterized in that, In step S1, the construction of the safety risk assessment index system for power transmission and transformation engineering includes the following: The risk assessment indicators in the revised HFACS framework were screened through data collection, expert consultation, and questionnaire surveys, resulting in four primary assessment indicators. These four primary assessment indicators were further subdivided into 15 secondary assessment indicators. The primary and secondary assessment indicators together constitute the safety risk assessment indicator system for power transmission and transformation engineering construction. The primary evaluation indicators include the impact of corporate organization, safety supervision, on-site operation-related factors, and construction personnel-related factors; The specific secondary evaluation indicators are as follows: Enterprise organizational impact includes organizational structure and responsibilities, safety production investment, and safety management procedures; safety supervision includes risk monitoring and early warning, supervision and management violations, and work plan arrangements; on-site operation related factors include basic construction conditions, construction technology measures, construction machinery, construction natural environment, and construction social environment; and construction personnel related factors include violations of regulations, skill errors, intuition and decision-making errors, and personnel quality.

3. The nonlinear fuzzy comprehensive assessment method for construction risk of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method as described in claim 1, characterized in that, In step S2, the combined weights are obtained as follows: The combined weighting includes subjective weighting and objective weighting; The subjective weights of the primary evaluation indicators are obtained using FBWM, specifically as follows: Based on the primary evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction, the optimal and worst evaluation indicators among the primary evaluation indicators are determined according to the opinions of the expert group. The optimal evaluation indicator is compared with other primary evaluation indicators, and the worst evaluation indicator is compared with other primary evaluation indicators. Fuzzy linguistic variables are used to determine the importance of the primary evaluation indicators. The membership degree of the primary evaluation index is calculated using the membership function. The specific formula is as follows: Where l, m, and u all represent triangular fuzzy numbers, and l < m < u. represents the membership function, and x represents the score of the primary evaluation index; A fuzzy vector A is constructed based on fuzzy linguistic variables to compare the optimal evaluation index with other primary evaluation indices. B And the fuzzy vector A comparing the worst-case evaluation index with other first-level evaluation indices. W The specific formula is as follows: in, This represents the fuzzy contrast between the optimal evaluation index and the j-th other primary evaluation index, where n represents the total number of primary evaluation indicators. This represents the fuzzy contrast between the worst evaluation index and the j-th other first-level evaluation index; The standard fuzzy index weights are obtained through a goal programming model, using the following formula: in, w represents minimizing fuzzy bias. B w represents the standard fuzzy index weight of the optimal evaluation index. j w represents the standard fuzzy index weight of the j-th other first-level evaluation index. W R(w) represents the standard fuzzy index weight of the worst-case evaluation index. j ) represents the subjective weight of the j-th other primary evaluation indicator, l j m j u j All represent the triangular fuzzy number of the j-th other first-level evaluation index; The subjective weights of the first-level evaluation indicators are obtained by processing the standard fuzzy index weights using the hierarchical mean integral representation method. The specific formula is as follows: in, Let l represent the subjective weight of the i-th primary evaluation indicator. i m i u i Each represents the triangular fuzzy number of the i-th primary evaluation index; By comparing the fuzzy linguistic variables of the optimal and worst evaluation indicators, and combining them with the consistency index C, the formula is used. Perform a consistency check; when the consistency ratio C R If the value is less than 0.1, the consistency test is passed. The objective weights of the secondary evaluation indicators are obtained using the improved CRITIC method, specifically as follows: Given D samples to be evaluated and Q secondary evaluation indicators, establish an initial indicator data matrix M = (b dq ); where b dq This represents the element in the d-th row and q-th column of the initial indicator data matrix, that is, the q-th secondary evaluation indicator in the d-th sample to be evaluated; The initial index data matrix is ​​dimensionless to obtain the standardized index data matrix; the volatility of the elements in the standardized index data matrix is ​​calculated using the following formula: Among them, F q b' represents the volatility of the q-th secondary evaluation indicator. dq This represents the element in the d-th row and q-th column of the standardized index data matrix. This represents the mean of the q-th secondary evaluation indicator; The specific formula for calculating the conflict matrix and information content of the secondary evaluation indicators is as follows: D q =F q ×A q Where, r dq b represents the element in the d-th row and q-th column of the conflict matrix. dk This represents the element in the d-th row and k-th column of the initial index data matrix. Let A represent the mean of the k-th secondary evaluation indicator. q D represents the conflict of the q-th secondary evaluation indicator. q This represents the information content of the q-th secondary evaluation indicator; The specific formula for calculating the weight of secondary evaluation indicators is as follows: Where, p dq This represents the weight of the q-th secondary evaluation indicator in the d-th sample to be evaluated; The specific formula for calculating the information entropy value of the secondary evaluation indicators is as follows: Among them, e q Let ξ represent the information entropy value of the q-th secondary evaluation indicator, and let ξ represent a constant. The specific formula for the objective weights of the secondary evaluation indicators is as follows: in, This represents the objective weight of the q-th secondary evaluation indicator; The specific formula for the combined weights of the evaluation indicators is as follows: Among them, w g This represents the combined weight of the g-th evaluation indicator.

4. The nonlinear fuzzy comprehensive assessment method for construction risk of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method according to claim 2, characterized in that, In step S3, constructing the fuzzy relation matrix includes the following: Based on the characteristics of power transmission and transformation engineering construction, a risk assessment set was constructed, and the evaluation indicators were divided into 5 levels: N = {low risk, lower risk, medium risk, higher risk, high risk}. The primary indicator risk factor set is U, where U = {U1, U2, U3, U4); the secondary indicator risk factor set is Ui, where U1 = {u 11 ,u 12 ,u 13 ), U2={u 21 ,u 22 ,u 23 }、U3={u 31 ,u 32 ,u 33 ,u 34 ,u 35 ), U4={u 41 ,u 42 ,u 43 ,u 44 ); where Ui represents the risk factor set of the i-th primary evaluation indicator, i = 1, 2, 3, 4, u 13 This represents the risk factors of the third secondary evaluation indicator of the first primary evaluation indicator, u. 23 This represents the risk factors of the third secondary evaluation indicator, which is the second primary evaluation indicator. 35 This represents the risk factors of the fifth secondary evaluation indicator, which is the third primary evaluation indicator. 44 This indicates the risk factors of the fourth secondary evaluation indicator, which is the fourth primary evaluation indicator. Based on the five levels in the risk assessment set, the evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction are scored using the expert evaluation method, resulting in a membership vector. The specific expression is as follows: S=[S g1 ,S g2 ,...,S g5 ] Among them, S g1 This represents the membership vector of the g-th evaluation index in the power transmission and transformation project construction safety risk evaluation index system to the first risk level in the risk comment set; Based on the membership vector of each evaluation index to the risk comment set in the safety risk evaluation index system for power transmission and transformation engineering construction, the membership vectors corresponding to each risk level are combined to obtain the fuzzy relationship matrix between the risk comment set and the risk level.

5. The nonlinear fuzzy comprehensive assessment method for construction risk of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method according to claim 1, characterized in that, In step S4, the comprehensive evaluation result vector includes the following: Based on the combined weights, a nonlinear fuzzy comprehensive evaluation method is used to obtain the nonlinear fuzzy relation moment synthesis operator, the specific formula of which is: Where f represents the nonlinear fuzzy relation moment synthesis operator, w g Let w represent the combined weight of the g-th evaluation indicator, where g = 1, 2, ..., G, and G represents the total number of evaluation indicators. g ≥0 and s g Let Λ represent the element in the g-th column of the fuzzy relation matrix, and let Λ represent the vector of coefficients indicating the degree of prominence of the evaluation indicators, where Λ = [λ1, λ2, ..., λ]. g ,...,λ G ], λ g Let λ represent the coefficient of the prominent influence of the g-th evaluation indicator, and let λ represent the maximum value among the coefficients of the prominent influence of the evaluation indicators. λ = max(λ1, λ2, ..., λ) G ); The fuzzy relation matrix is ​​processed using a nonlinear fuzzy relation moment synthesis operator to obtain the index layer relation matrix, and then the index layer relation matrix is ​​transformed using the following formula: AND' i′j' =10*E i'j' Among them, E′ i'j' E represents the element in the i'-th row and j'-th column of the transformed index layer relation matrix. i'j' This represents the element in the i'th row and j'th column of the indicator layer relation matrix; The values ​​of the transformed indicator layer relationship matrix are normalized to obtain the comprehensive evaluation result vector.

6. A system applied to the nonlinear fuzzy comprehensive assessment method for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method as described in claim 1, characterized in that, include: The evaluation index system construction module is used to construct an evaluation index system for the construction safety risks of power transmission and transformation projects. This system includes primary evaluation indicators and secondary evaluation indicators. The combined weight acquisition module is used to obtain the combined weights of evaluation indicators in the safety risk assessment index system for power transmission and transformation engineering construction through the improved FBWM-CRITIC combined weighting method. The matrix acquisition module is used to process the safety risk assessment index system for power transmission and transformation engineering construction using the expert evaluation method, obtain the membership vector, and construct the fuzzy relation matrix. The evaluation result acquisition module is used to combine weights and fuzzy relation matrices, and to perform a first-level nonlinear comprehensive evaluation using a nonlinear fuzzy comprehensive evaluation method to obtain an index-level relation matrix. The index-level relation matrix is ​​then processed to obtain a comprehensive evaluation result vector. The comprehensive evaluation result vector is then processed using the maximum membership principle to obtain the risk evaluation result of the power transmission and transformation project construction.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the nonlinear fuzzy comprehensive evaluation method for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, performs the nonlinear fuzzy comprehensive assessment method for construction risks of overhead power transmission and transformation line projects based on the improved FBWM-CRITIC method, as described in any one of claims 1 to 5.

Images