Beam field system toughness evaluation method based on extension cloud model

Abstract

The invention provides a beam field system toughness evaluation method based on an extension cloud model, and the method comprises the following steps: 1, based on a toughness three-stage curve, determining beam field influence factors as five types of factors: personnel, equipment, management and environment; 2, screening influence indexes to construct a beam field system toughness evaluation index system; wherein the beam field system toughness evaluation index system comprises a first-level index, a second-level index and a third-level index; 3, calculating the combined weight of the second-level index and the third-level index; step 4, constructing an extension cloud evaluation model, then determining to-be-evaluated matter elements, dividing evaluation grades and boundaries of the evaluation grades, and finally calculating standard cloud parameters of each cloud droplet based on the evaluation grades and the boundaries; step 5, firstly calculating standard cloud membership degrees kij of three-level indexes based on the standard cloud parameters, and calculating the standard cloud membership degrees kij of the three-level indexes based on actually collected characteristic values; calculating the actual cloud membership degree of the third-level indexes; then, according to the actual cloud membership degree, the second-level index comprehensive certainty degree, the toughness grade characteristic value of the second-level index toughness, the comprehensive certainty degree of the target layer and the toughness grade characteristic value of the target layer are calculated; according to the method, the comprehensiveness of a toughness concept and the complexity of a beam field are considered, the uncertainty of indexes in a toughness evaluation system is fully considered, qualitative and quantitative indexes can be analyzed more reasonably, and an evaluation method comprehensively considering toughness influence factors is explored.

Description

Technical Field

[0001] The present invention belongs to the technical field of beam field toughness evaluation, and in particular relates to a beam field system toughness evaluation method based on an extension cloud model. Background Art

[0002] Resilient transportation construction enables transportation infrastructure and transportation systems to have sufficient capabilities to face the destructive impact of various events, which has become a major issue in national development. Conducting resilience assessment on beam yards, exploring scientific and reasonable resilience assessment methods, and seeking more efficient resilience improvement strategies are the key to improving the risk prevention and control management capabilities of beam yards and are a key link in ensuring the smoothness and safety of urban transportation.

[0003] Regarding the toughness assessment method of beam field, when scholars study toughness, they mostly evaluate toughness by analyzing a few indicators. However, as a comprehensive concept, toughness involves many factors; as a highly complex multi-level system, the beam field must also be composed of many elements; therefore, from the perspective of the composition of the beam field, exploring an assessment method that comprehensively considers the factors affecting toughness is crucial to the toughness assessment of the beam field system. Summary of the invention

[0004] The purpose of the present invention is to provide a beam field system toughness assessment method based on the extension cloud model, and to explore an assessment index system that comprehensively considers the factors affecting toughness from the perspective of the beam field system composition. The technical solutions adopted are as follows:

[0005] A beam field system toughness assessment method based on the extension cloud model includes the following steps:

[0006] Step 1: Based on the three-stage resilience curve, find the influencing indicators of each stage and obtain the data of each influencing indicator.

[0007] Specific: such as Figure 2 As shown in the figure, the concept of beam field toughness is defined as the ability of the beam field to recover to its initial state when it is impacted by an accident. The process of beam field toughness is clarified, and the process of beam field toughness is mainly divided into three stages: resistance, response and recovery.

[0008] For example, in the resistance stage, the types of indicators that affect the resistance stage (resistance ability) include: time indicators and efficiency indicators.

[0009] As a complex system, the beam yard can be divided into four elements: personnel, equipment, management, and environment. When analyzing the factors affecting toughness, we also start from these four aspects.

[0010] Step 2: Screen influencing indicators to construct a beam yard system resilience assessment index system; the beam yard system resilience assessment index system includes: primary indicators, secondary indicators and tertiary indicators;

[0011] The first-level indicator is the beam field system toughness E c , the first-level indicators include several second-level indicators, and all second-level indicators contain several third-level indicators.

[0012] Specifically: By sorting out domestic and foreign literature on beam field toughness indicators, the frequency analysis method is used to select indicators with higher frequency of appearance in the evaluation indicator library.

[0013] Combined with the opinions of experts in the rail transit field obtained by the Delphi method, the DEMATEL method was used to remove indicators unrelated to toughness and beam yard system from the indicators with higher frequency. Finally, the indicators were screened out to construct an index system for evaluating the toughness of the beam yard system.

[0014] Step 3: Calculate the combined weights of the secondary and tertiary indicators.

[0015] That is, the subjective weight obtained by the C-OWA operator is multiplied by the objective weight obtained by the CRITIC theory, and then a linear weighted normalization process is performed to obtain the combined weight of each indicator.

[0016] Step 4: Construct a cloud evaluation model, then determine the object to be evaluated, divide the evaluation level and the boundaries of the evaluation level, and finally calculate the standard cloud parameters of each cloud droplet based on the evaluation level and boundaries:

[0017] Step 5: First, based on the standard cloud parameters, calculate the standard cloud membership k of the three-level indicators ij ,Based on the actual collected eigenvalues, the actual cloud membership of the three-level indicators is calculated;

[0018] Then, according to the actual cloud membership, the comprehensive certainty of the secondary indicator, the resilience level characteristic value of the secondary indicator resilience, the comprehensive certainty of the target layer, and the resilience level characteristic value of the target layer are calculated.

[0019] Preferably, in step 2, the beam yard system toughness assessment index system includes: 4 secondary indicators and 27 tertiary indicators;

[0020] The four secondary indicators include: personnel resilience B1, equipment resilience B2, management resilience B3 and environmental resilience B4.

[0021] Preferably, in step 3, the model for calculating the combined weight is:

[0022]

[0023] in, -The weight value of the indicator q obtained using the C-OWA operator, -Objective weight values ​​of indicators obtained using CRITIC theory.

[0024] Preferably, step 4 specifically includes the following steps:

[0025] Step 4A: Establish an extension cloud evaluation model based on the matter-element extension model;

[0026] The evaluation model of Ketuo Cloud is:

[0027]

[0028] Among them, N-three-level indicators;

[0029] C n - the nth evaluation level;

[0030] Ex j -Expected parameters corresponding to evaluation level j; j = 1 to 5

[0031] En j -Evaluation level j corresponding to the entropy parameter;

[0032] He j - Evaluation level j corresponding to the hyperentropy parameter;

[0033] (Ex j ,En j , He j ) is the cloud droplet x corresponding to the evaluation level j i The cloud parameters.

[0034] That is, for the object-element to be evaluated with n indicators, the characteristic value V in the object-element extension theory is replaced by the three digital features in CM: that is, the expected parameter Ex corresponding to the evaluation level j j , Entropy parameter En corresponding to evaluation level j j And the super entropy parameter He corresponding to the evaluation level j j .

[0035] Step 4B: Determine the boundaries of the assessment levels and divide the assessment levels.

[0036] The evaluation level is divided into 5 levels, where level 1 represents the worst and level 5 represents the best;

[0037] Determine the boundaries of evaluation level j:

[0038] -The lower limit of the value interval (boundary) corresponding to the evaluation level;

[0039] -The upper limit of the value range (limit) corresponding to the evaluation level.

[0040] Step 4C: Calculate the standard cloud droplet x i Cloud parameters;

[0041]

[0042] He j =S.

[0043] Preferably, step 5 specifically includes the following steps:

[0044] Step 5A: Calculate the cloud membership k of the third-level index ij ;

[0045]

[0046] i-the number of the third-level indicator, j-the number of the evaluation level;

[0047] Among them, when k is calculated based on normal distribution random numbers ij When k ij is the standard cloud membership in the cloud map;

[0048] When k is calculated based on the actual collected indicator feature values ij When k ij is the actual cloud membership;

[0049] Step 5B, calculating the comprehensive certainty B, and then calculating the toughness grade characteristic value based on the comprehensive certainty B;

[0050] Among them, the comprehensive certainty B is:

[0051]

[0052] b Ij -The actual cloud membership of the first secondary indicator or the first primary indicator;

[0053] I-the number of the secondary indicator or primary indicator;

[0054] j- the number of the assessment level;

[0055] k ij -The actual cloud membership calculated based on the actual collected indicator characteristic values; j = 1 to 5

[0056] Based on the comprehensive certainty B of the secondary index, the toughness grade characteristic value of the secondary index is calculated;

[0057] Based on the comprehensive certainty B of the target layer, the toughness grade characteristic value of the target layer is calculated;

[0058] Toughness grade characteristic value r:

[0059]

[0060] f j Represents the score value of evaluation level j.

[0061] Compared with the prior art, the advantages of the present invention are:

[0062] (1) In order to address the problem that current toughness assessment indicators are not comprehensive, this paper builds a toughness assessment indicator system from the perspective of beam field system composition.

[0063] (2) The present invention combines the C-OWA operator with the CRITIC theory, fully considering the C-OWA operator as a subjective weight calculation method. Although it can better explain the nature of the indicator, it relies more on the subjective judgment of experts to construct the disadvantage of the combined weighting model, making the weight assignment more scientific and reasonable.

[0064] (3) The present invention combines the physical element extension theory with the cloud model to construct an extension cloud model, which fully considers the complexity of the beam field system and takes into account the fuzziness and randomness of the toughness index. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is the overall flow chart of the toughness assessment method of beam field system based on Extension Cloud model;

[0066] Figure 2 It is the influence diagram of beam yard system performance parameters on its toughness capacity;

[0067] Figure 3 It is a (partial) diagram of the membership degree cloud of the evaluation indicators. DETAILED DESCRIPTION

[0068] The following is a more detailed description of the beam field system toughness assessment method based on the extension cloud model of the present invention in conjunction with the schematic diagram, wherein the preferred embodiment of the present invention is shown, and it should be understood that those skilled in the art can modify the present invention described herein, while still achieving the advantageous effects of the present invention. Therefore, the following description should be understood as being widely known to those skilled in the art, and not as a limitation of the present invention.

[0069] like Figure 1 As shown, a beam field system toughness assessment method based on the extension cloud model includes the following steps:

[0070] Step 1: Based on the three-stage resilience curve, find the influencing indicators of each stage and obtain the value of each influencing indicator.

[0071] Step 2: Screen influencing indicators to construct a beam yard system resilience assessment index system.

[0072] The beam yard system resilience assessment index system includes: 1 first-level indicator, 4 second-level indicators, and 27 third-level indicators.

[0073] Among them, the first-level indicator is the beam field system toughness E c ;

[0074] The four secondary indicators include: personnel resilience B1, equipment resilience B2, management resilience B3 and environmental resilience B4;

[0075] The 4 secondary indicators include a total of 27 tertiary indicators.

[0076] Step 3, calculate the combined weight of indicator q;

[0077]

[0078] -The weight value of the indicator q obtained using the C-OWA operator, - The objective weight value of indicator q obtained using the CRITIC theory;

[0079] q-the number of the secondary or tertiary indicator;

[0080] m-the number of indicators q;

[0081] Step 4: Construct a cloud evaluation model, then determine the object to be evaluated, divide the evaluation levels and the boundaries of the evaluation levels, and finally calculate the standard cloud parameters of each cloud droplet based on the evaluation levels and boundaries.

[0082] Step 4A: Based on the matter-element extension model, establish an extension cloud evaluation model.

[0083] The matter-element extension model assumes that the object N has m features, which can be represented by C1, C2, …, C n To represent, the eigenvalues ​​corresponding to each feature are represented by V1, V2, …, V n To represent, an ordered triple R = (N, C, V) is formed, and its expression is:

[0084]

[0085] Where, N is the object element to be measured;

[0086] C m - mth feature;

[0087] V m - the eigenvalue of the mth feature;

[0088] Replace the eigenvalues, and the Ketuo Cloud evaluation model becomes:

[0089]

[0090] That is, in practical applications, N refers to the three-level index, C1~C n Refers to the level of three-level indicators, from level 1 to level 5.

[0091] Among them, N-three-level indicators;

[0092] C n - the nth evaluation level;

[0093] Ex j -Expected parameters corresponding to evaluation level j; j = 1 to 5

[0094] En j -Evaluation level j corresponding to the entropy parameter;

[0095] He j - Evaluation level j corresponding to the hyperentropy parameter;

[0096] (Ex j ,En j , He j ) is the cloud droplet x corresponding to the evaluation level j i Cloud parameters;

[0097] Step 4B: Determine the boundaries of the assessment levels and divide the assessment levels.

[0098] The evaluation level is divided into 5 levels, with 1 being the worst and 5 being the best.

[0099] Determine the boundaries of evaluation level j:

[0100] Step 4C: Calculate the standard cloud droplet x i The cloud parameters.

[0101]

[0102] He j =S

[0103] Among them, S represents a constant determined according to the degree of fuzziness. Combined with the research of Wu et al., this study takes the value of 0.1.

[0104] Step 5: First, based on the standard cloud parameters, calculate the standard cloud membership k of the three-level indicators ij , based on the actually collected eigenvalues, calculate the actual cloud membership of the third-level indicators;

[0105] Then, according to the actual cloud membership, the comprehensive certainty of the secondary indicator, the resilience level characteristic value of the secondary indicator resilience, the comprehensive certainty of the target layer, and the resilience level characteristic value of the target layer are calculated.

[0106] Step 5A: Calculate the cloud membership k of the third-level index ij .

[0107]

[0108] Based on any cloud droplet corresponding to each third-level indicator, the standard deviation is generated as E n ′, a normally distributed random number with expected value Ex.

[0109] Among them, it is known from the prior art that:

[0110] Let Ex = Ex j , E n ′=He j , input into MATLAB, run once to get the first output value, the output value is combined with Ex, E n ′, output the first normally distributed random number.

[0111] Then let Ex = Ex j , E n ′=He j , input it into MATLAB again, and run it again to get the second output value (different from the first output value). The second output value combines Ex, E n ′, output the second normally distributed random number.

[0112] Repeat the cycle again and finally obtain several normally distributed random numbers.

[0113] Draw a normal distribution random number in the coordinate system, k ij ) is the cloud map with coordinates. Since one cloud droplet corresponds to one cloud map, the number of cloud maps is 5.

[0114] i is the number of the three-level indicator, and j is the number of the evaluation level;

[0115] z i - Normally distributed random numbers or actually collected indicator characteristic values. The actually collected indicator characteristic values ​​are the values ​​of the influencing indicators obtained in step 1.

[0116] Based on Ex, E n ′ and normally distributed random numbers (actually collected indicator eigenvalues), solve k ij .

[0117] k ij This is the case where the third-level indicator i belongs to the evaluation level j.

[0118] Among them, when k is calculated based on normal distribution random numbers ij When k ij is the standard cloud membership in the cloud map;

[0119] When k is calculated based on the actual collected indicator feature values ij When k ij is the actual cloud membership.

[0120] That is, for the object element to be evaluated in this paper, the actual cloud membership degree k between the three-level indicators and each evaluation level is calculated. ij When the characteristic value of the indicator V can be set to be a normal distribution random number, Ex = Ex j , E n ′=He j , calculate the actual cloud membership between each third-level indicator and each level, as shown in Table 3.

[0121] Step 5B, calculate the comprehensive certainty B, and then calculate the toughness grade characteristic value based on the comprehensive certainty B.

[0122] Among them, the comprehensive certainty B is:

[0123]

[0124] b lj -The actual cloud membership of the first secondary indicator or the first primary indicator;

[0125] I-the number of the secondary indicator or primary indicator;

[0126] j- the number of the assessment level;

[0127] k m1 - The actual cloud membership of the indicator m to level 1 calculated based on the actual collected indicator feature values;

[0128] Based on the comprehensive certainty B of the secondary indicator, the toughness grade characteristic value of the secondary indicator is calculated.

[0129] Based on the comprehensive certainty B of the target layer, the toughness grade characteristic value of the target layer is calculated.

[0130] Among them, when calculating the comprehensive certainty B of the secondary index, m is the number of secondary indexes; I = 1 ~ m;

[0131] When calculating the comprehensive certainty B of the target layer, m is the number of first-level indicators, and I=1.

[0132] Toughness grade characteristic value r:

[0133]

[0134] b Ij -The actual cloud membership of the I-th secondary indicator or primary indicator to level j.

[0135] f jIt represents the score value of evaluation level j, i.e. 1, 2, 3, 4, 5. For example, the score value of evaluation level 1 is 1.

[0136] Step 1: The three-stage toughness action curve obtained by analysis is divided into before disturbance, during disturbance and after disturbance. The disturbance can be divided into resistance stage, response stage and recovery stage. The influence of beam field system performance parameters on its toughness capacity is as follows: Figure 2 .

[0137] In step 1, the factors affecting the resilience of the beam yard system include personnel, equipment, management, and environment.

[0138] The personnel include beam yard staff, dispatchers, etc.

[0139] Equipment is the basic element of the beam yard system and an important prerequisite for ensuring the safety of passengers' travel and the rational development of all work. It includes communication and signal equipment, ancillary facilities, security equipment, environmental and monitoring equipment, disaster prevention and alarm equipment, power supply equipment, elevator equipment, water supply and drainage equipment, ventilation and air-conditioning systems, etc. The stability and safety performance of the equipment directly determine the emergency response capabilities and passenger evacuation efficiency in emergency situations.

[0140] Management is also one of the components of a beam yard. A complete management system should take ensuring personal safety as its core concept, and achieve reasonable allocation of personnel, equipment, and environment throughout the life cycle of the beam yard through dynamic management policies, thereby effectively organizing people and repairing faults in a timely manner.

[0141] Compared with personnel, equipment and management, the environment is an external factor that affects the beam yard. Many subsystems rely on environmental factors, including internal and external environments. Unstable factors such as geological environment, bad weather, and public security incidents may lead to the occurrence of safety accidents.

[0142] The final index screening result of the beam field toughness evaluation index system described in step 2 is finally composed of 27 third-level evaluation indicators (third-level indicators), as shown in Table 1.

[0143] Table 1 Beam yard system toughness evaluation index system

[0144]

[0145] In step 3, the C-OWA operator is used to calculate the subjective weight of each indicator; combined with the data of Hongqiao Railway Station, the CRITIC method is used to calculate the objective weight of the indicator. The results are shown in Table 2:

[0146] Table 2 Calculation results of toughness evaluation index weights

[0147]

[0148] In step 4, the characteristic values ​​of the 27 third-level indicators are collected and sorted to form the original data set, and the result of constructing the object element to be evaluated is as follows:

[0149]

[0150] The above matrix refers to the matrix before replacing the eigenvalue. N represents the beam field toughness capacity, It represents the four secondary indicators of the object N to be evaluated, namely, personnel resilience, equipment resilience, management resilience, and environmental resilience.

[0151] Based on relevant domestic and international standards and literature research, and combined with the opinions of experts in the rail transit field, the boundary thresholds of each assessment level corresponding to the 27 indicators in the resilience assessment index system are divided.

[0152] Then, the cloud parameters of the third-level indicators of the resilience assessment system are calculated through the level limit values ​​(boundary thresholds) and formulas of each indicator, as shown in Table 3.

[0153] According to the object elements and cloud parameters to be evaluated, combined with the extension cloud model and cloud membership calculation formula, each evaluation indicator (three-level indicator) is run 3000 times in the MATLAB program to draw the standard cloud map corresponding to each three-level indicator.

[0154] Based on the indicators C1, the employee technical level reaching standard rate and C 11 Take the platform passenger flow density as an example. Figure 3 shown.

[0155] Calculate the actual cloud membership k of the three-level evaluation index ij , the results are shown in Table 3.

[0156] Table 3. Calculation results of the three-level indicator cloud parameters and actual membership

[0157]

[0158]

[0159] That is, calculate k in Table 3 i1 , bring 87, 30, and 0.1 into Calculate k in the cloud graph i1 When the normal distribution random number, 30, and 0.1 are introduced

[0160] Table 4 Calculation results of membership and grade characteristic values ​​of secondary indicators and target layers

[0161]

[0162] From the results in Table 4, we can find that:

[0163] From the perspective of the target layer, the characteristic value of the toughness assessment level of the beam field system is r=3.91. For the membership calculation results of the target layer, according to the maximum membership principle: 46.89>26.82>14.38>6.79>0.07, the site is at level 4 in the 1-5 level (level 1 is the worst and level 5 is the best) assessment level, and the overall toughness level is relatively high. That is, the maximum value of the cloud membership is selected, and the corresponding level is the assessment level of the site.

[0164] Analyzing from the secondary indicator level, the characteristic value of the station's personnel resilience level is 3.62, which is level 4 according to the maximum affiliation principle, 7.42% lower than the overall characteristic level. This shows that the evaluation results of personnel quality indicators are low and need to be strengthened.

[0165] The characteristic value of the equipment resilience level is 3.78, which is level 4 according to the maximum membership principle, 3.32% lower than the overall characteristic level. This shows that the resilience of the site's facilities and equipment is relatively low and there is room for improvement.

[0166] The characteristic value of the management resilience level is 4.52, which is level 5 according to the maximum membership principle, significantly higher than the overall characteristic level by 15.6%, indicating that the management system of the site has excellent resilience performance and meets the requirements of a resilient beam field.

[0167] The characteristic value of the environmental resilience level is 3.63, which is level 4 according to the maximum membership principle, 7.16% lower than the overall characteristic level. This shows that the environmental resilience performance of the site is relatively low and there is still room for improvement.

[0168] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A beam field system toughness assessment method based on extension cloud model, characterized in that: The following steps are involved: Step 1: Based on the three-stage resilience curve, find the influencing indicators of each stage and obtain the value of each influencing indicator; Step 2: Screen influencing indicators to construct a beam yard system resilience assessment index system; the beam yard system resilience assessment index system includes: primary indicators, secondary indicators and tertiary indicators; The first-level indicator is the beam field system toughness E c , the first-level indicators include several second-level indicators, and all second-level indicators contain several third-level indicators; Step 3: Calculate the combined weights of the secondary and tertiary indicators; Step 4: Construct a cloud evaluation model, then determine the object to be evaluated, divide the evaluation level and the boundaries of the evaluation level, and finally calculate the standard cloud parameters of each cloud droplet based on the evaluation level and boundaries: Step 5: First, based on the standard cloud parameters, calculate the standard cloud membership k of the three-level indicators ij ,Based on the actual collected eigenvalues, the actual cloud membership of the three-level indicators is calculated; Then, according to the actual cloud membership, the comprehensive certainty of the secondary indicator, the resilience level characteristic value of the secondary indicator resilience, the comprehensive certainty of the target layer, and the resilience level characteristic value of the target layer are calculated.

2. The beam field system toughness assessment method based on extension cloud model according to claim 1 is characterized in that: In step 2, the beam yard system resilience assessment index system includes: 4 secondary indicators and 27 tertiary indicators; The four secondary indicators include: personnel resilience B1, equipment resilience B2, management resilience B3 and environmental resilience B4.

3. The beam field system toughness assessment method based on extension cloud model according to claim 1 is characterized in that: In step 3, the model for calculating the combined weight is: in, -The weight value of the indicator q obtained using the C-OWA operator, -The objective weight value of indicator q obtained using the CRITIC theory.

4. The beam field system toughness assessment method based on extension cloud model according to claim 1 is characterized in that: Step 4 specifically includes the following steps: Step 4A: Establish an extension cloud evaluation model based on the matter-element extension model; The evaluation model of Ketuo Cloud is: Among them, N-three-level indicators; C n - the nth evaluation level; Ex j -Expected parameters corresponding to evaluation level j; j = 1 to 5 En j -Evaluation level j corresponding to the entropy parameter; He j - Evaluation level j corresponding to the hyperentropy parameter; (Ex j ,En j , He j ) is the cloud droplet x corresponding to the evaluation level j i Cloud parameters; Step 4B: Determine the boundaries of the assessment levels and divide the assessment levels. The evaluation level is divided into 5 levels, where level 1 represents the worst and level 5 represents the best; Determine the boundaries of evaluation level j: Step 4C: Calculate the standard cloud droplet x i Cloud parameters; He j =S。 5. The beam field system toughness assessment method based on extension cloud model according to claim 1 is characterized in that: Step 5 specifically includes the following steps: Step 5A: Calculate the cloud membership k of the third-level index ij ; i-the number of the third-level indicator, j-the number of the evaluation level; Among them, when k is calculated based on normal distribution random numbers ij When k ij is the standard cloud membership in the cloud map; When k is calculated based on the actual collected indicator feature values ij When k ij is the actual cloud membership; Step 5B, calculating the comprehensive certainty B, and then calculating the toughness grade characteristic value based on the comprehensive certainty B; Among them, the comprehensive certainty B is: b Ij -The actual cloud membership of the first secondary indicator or the first primary indicator; I-the number of the secondary indicator or primary indicator; j- the number of the assessment level; k ij -The actual cloud membership calculated based on the actual collected indicator characteristic values; j = 1 to 5 Based on the comprehensive certainty B of the secondary index, the toughness grade characteristic value of the secondary index is calculated; Based on the comprehensive certainty B of the target layer, the toughness grade characteristic value of the target layer is calculated; Toughness grade characteristic value r: f j Represents the score value of evaluation level j.

Images