Dynamic Assessment Method and System for Flood Disaster Safety Resilience of Mountain Subway Stations Based on BM-OWA Operator and Conflict Analysis

Abstract

The present invention discloses a dynamic evaluation method for flood safety resilience of mountain subway stations based on BM-OWA operator and conflict analysis, including Step 1: constructing an evaluation index system for flood safety resilience of mountain subway stations; Step 2: using the expert scoring method to score each index in the index system; Step 3: according to the weights given by each expert to each index, using the BON-OWA operator to optimize the weights to obtain the subjective weight vector of safety resilience indexes; Step 4: setting the grading intervals of each safety resilience index and constructing a resilience level evaluation set; Step 5: experts score each resilience index according to the actual situation of mountain subway stations, obtain the resilience levels of each resilience index according to the scores, and calculate the objective weight vector of safety resilience indexes; Step 6: according to the obtained subjective weight vector and objective weight vector, obtain the subjective and objective comprehensive weight vector; Step 7: using the fuzzy comprehensive evaluation method to determine the flood safety resilience level of mountain subway stations.

Description

Technical Field

[0001] The present invention relates to the technical field of subway disaster safety assessment in mountainous areas, and in particular to a dynamic assessment method and system for flood safety resilience of subway stations in mountainous areas based on the BM-OWA operator and conflict analysis. Background Technique

[0002] With the rapid development of urbanization and modernization, the underground space of modern cities has been developed and utilized unprecedentedly, and the large-scale construction of underground infrastructure has become an inevitable trend of the times. As the central nervous system of underground engineering, the subway network has become increasingly prominent in its strategic position. It not only solves the ground traffic pressure in the city, effectively alleviates traffic congestion, but also constitutes a key underground lifeline to support the normal operation of the city. However, while bringing convenience to the city, the subway network inevitably has to face the challenges of natural disasters, especially extreme climate and geological disasters such as typhoons, heavy rains, floods and even earthquakes. Therefore, the defense ability and resilience of the subway network are particularly crucial.

[0003] Flood disasters are undoubtedly one of the most impactful natural disasters. Once they occur, they often pose a fatal threat to the underground space. In particular, those subway lines and stations deep underground are very likely to be invaded and flooded by floods. In mountainous areas, the geology and terrain are complex. When designing flood prevention for subway stations in mountainous areas, factors such as terrain elevation difference, groundwater seepage and mountain body seepage also need to be considered. The drainage system requires stronger anti-seepage ability and larger drainage capacity in design.

[0004] Currently, traditional assessment methods aim to identify and quantify the risk levels of mountain subway facilities under floods and other disasters, so as to propose targeted disaster prevention and mitigation measures and emergency plans. For example, some existing assessment methods, such as the traditional evaluation method relying on expert judgment, although playing an important role to a certain extent, due to its high subjectivity, the objectivity and recognition of the assessment results are relatively low. When there are differences in expert opinions, there is a lack of effective mechanisms to reconcile or optimize these differences. On the other hand, existing technologies lack the quantification of the safety resilience of mountain subway facilities and its dynamic changes when facing disasters such as floods, and it is difficult to achieve long-term and accurate risk assessment of mountain subway facilities and the evaluation of post-disaster recovery capabilities.

[0005] Therefore, how to conduct flood safety assessment of subway stations in mountainous areas is not only an urgent need to improve the safety management level of China's rail transit industry and ensure the unobstructed urban traffic artery, but also an important topic related to the national infrastructure safety strategic deployment. Summary of the Invention

[0006] The purpose of the present invention is to provide a dynamic evaluation method for flood safety resilience of mountain subway stations based on the BM-OWA operator and conflict analysis, so as to improve the rationality of evaluation results.

[0007] To achieve the above object, the present invention provides a dynamic evaluation method for flood safety resilience of mountain subway stations based on the BM-OWA operator and conflict analysis, which is characterized by including the following steps:

[0008] Step 1: Construct an evaluation index system for flood safety resilience of mountain subway stations;

[0009] Step 2: Use the expert scoring method to score each resilience index, obtain the importance coefficient and resilience score of each resilience index, and give the time factor of each resilience index;

[0010] Step 3: Optimize the importance coefficient obtained in Step 2 using the BM-OWA operator;

[0011] Step 4: Calculate the resilience score of each resilience index after y years according to the time factor of each resilience index, where y≥0;

[0012] Step 5: Calculate the conflict coefficient of the resilience scores of each resilience index;

[0013] Step 6: According to the conflict coefficient obtained in Step 5 and the importance coefficient obtained in Step 3, correct the conflicting resilience scores;

[0014] Step 7: Calculate the total score of flood safety resilience of the currently to-be-evaluated mountain subway station according to the corrected resilience scores;

[0015] Step 8: Determine the flood safety resilience level of the mountain subway station according to the obtained total score of safety resilience.

[0016] Further, the flood safety resilience evaluation index system includes 3 primary indicators: stability U 1 , redundancy U 2 and efficiency U 3 .

[0017] Further, stability U 1 includes 6 secondary indicators: the density of drainage pipes around the mountain subway station u 1 , the number of water inlets and sumps around the mountain subway station u 2 , the slope around the mountain subway station u 3 , the waterproof performance of the entrance u 4 , the elevation of the bottom edge of the air vent u 5 , the waterproof performance of the mechanical and electrical equipment system u 6 ;

[0018] Redundancy U 2It includes 5 secondary indicators: the flood control skills of station operators u 7 , standby equipment and facilities u 8 , standby energy supply system u 9 , emergency evacuation time u 10 , the configuration of emergency rescue teams u 11 ;

[0019] Efficiency U 3 It includes 4 secondary indicators: the rationality of flood control emergency plans u 12 , the maintenance efficiency of the comprehensive maintenance center u 13 , investment in flood control and disaster reduction u 14 and the accessibility of emergency rescue u 15 .

[0020] Furthermore, the specific steps of step 2 include:

[0021] Step 21: An evaluation group consisting of m experts uses a 10-point system to score the importance of each resilience indicator respectively, and obtains an importance coefficient vector C composed of the importance coefficients of each resilience indicator j :

[0022]

[0023] Among them, represents the importance coefficient given by the m-th expert to the j-th resilience indicator;

[0024] Step 22: Combining the actual situation of mountain subway stations, use a 100-point system to score each resilience indicator, and obtain a resilience score vector A composed of the resilience scores of each resilience indicator j :

[0025]

[0026] Among them, represents the resilience score given by the m-th expert to the j-th resilience indicator;

[0027] Step 23: The m experts respectively give the time factor of each resilience indicator, and obtain a time factor vector T composed of the time factors of each resilience indicator j :

[0028]

[0029] Among them, represents the time factor given by the m-th expert to the j-th resilience indicator.

[0030] Furthermore, the specific steps of step 3 include:

[0031] Step 31: Sort the data in the importance coefficient vector C j in descending order to obtain a new sorted vector

[0032] Step 32: Calculate the weighted value v of the sorted importance coefficients using the following formula k :

[0033]

[0034] where represents the combination number of selecting k data from m data;

[0035] The vector V = [v k , v 0 , …, v 1 , …, v m-1 is composed of the weighted value v T ;

[0036] Step 33: Optimize the sorted importance coefficient vector D based on v k using the BM-OWA operator j :

[0037]

[0038] where is the (k + 1)-th largest element in D j ; is the importance coefficient of the j-th toughness index after being optimized by the BM-OWA operator; BM represents BM calculation, and OWA represents OWA calculation;

[0039] Step 34: Standardize the obtained importance coefficients of each secondary toughness index using the following formula

[0040]

[0041] where n represents the number of secondary indicators, and w j represents the standardized importance coefficient;

[0042] Step 35: Obtain the optimized importance coefficient vector W based on w j :

[0043] W = [w 1 , w 2 , …, w n T

[0044] where n represents the number of secondary indicators. ​

[0045] Furthermore, the formula for calculating the resilience score after y years in step 4 is:

[0046]

[0047] where, represents the resilience score of the jth resilience indicator given by the ith expert; represents the resilience score of the jth resilience indicator after y years given by the ith expert;

[0048] Furthermore, the specific steps of step 5 include:

[0049] Step 51: Calculate the average resilience score of each resilience indicator through the following formula

[0050]

[0051] Step 52: Calculate the average difference of each resilience indicator through the following formula

[0052]

[0053] Step 53: Correct through the following formula to obtain the conflict coefficient e of each resilience indicator j :

[0054]

[0055] where, e j is the conflict coefficient of the jth resilience indicator;

[0056] Step 54: Construct the conflict coefficient vector E = [e j , e 1 , …, e 2 , …, e n T .

[0057] Furthermore, the specific steps of step 6 include:

[0058] Step 61: According to the conflict coefficient e in the conflict coefficient vector E j , determine the resilience score to be corrected through the following two judgment conditions:

[0059] 1) When e j > 4, it is considered that there is a conflict in the resilience scores given by the experts for this resilience indicator, and go to step 62;

[0060] 2) When e j ​If it is greater than 7, it is considered that there is a serious conflict in the toughness score of the toughness index, and it proceeds to step 2 to re-perform the expert scoring;

[0061] Step 62: Judge the importance coefficient of the current toughness index. If

[0062] 1) When the importance coefficient is less than 5, the toughness score is corrected to the average value

[0063] 2) When the importance coefficient is greater than 5, the toughness score is corrected by the following formula:

[0064]

[0065] Among them, represents the corrected toughness score;

[0066] Step 63: According to Construct the corrected toughness score vector

[0067] Furthermore, the formula for calculating the total score of flood safety toughness of mountain subway stations in step 7 is:

[0068]

[0069] Among them, P represents the total score of safety toughness, and W is the importance coefficient vector.

[0070] The flood safety toughness evaluation system for mountain subway stations, implemented by using the dynamic evaluation method for flood safety toughness of mountain subway stations based on the BM-OWA operator and conflict analysis, is characterized by including:

[0071] An evaluation index system construction module, used to construct an evaluation index system for flood safety toughness of mountain subway stations, which includes first-level indicators and second-level toughness indicators;

[0072] An expert scoring module, used to score each toughness index in the evaluation index system for flood safety toughness of mountain subway stations, obtain the importance coefficient and toughness score of each toughness index, and give the time factor of each toughness index;

[0073] An importance coefficient optimization and toughness score correction module, used to optimize the obtained importance coefficient, reduce the influence of subjective factors in expert scoring, and correct the toughness score to reduce the deviation of the evaluation result;

[0074] A safety toughness level evaluation module, used to determine the flood safety toughness level of mountain subway stations according to the total safety toughness score.

[0075] Furthermore, the importance coefficient optimization and resilience score correction module includes an importance coefficient optimization unit and a resilience score correction unit;

[0076] Among them, the importance coefficient optimization unit is used to optimize the importance coefficient using the BM-OWA operator; the resilience score correction unit is used to calculate the conflict coefficient of the resilience scores of each resilience index, and correct the conflicting resilience scores according to the conflict coefficient combined with the optimized importance coefficient.

[0077] Therefore, the present invention adopts the above-mentioned dynamic assessment method for flood safety resilience of mountain subway stations based on the BM-OWA operator and conflict analysis, and has the following beneficial effects:

[0078] First of all, based on the theory of safety resilience, the present invention establishes a corresponding flood safety resilience index system for mountain subway stations. This index system includes three first-level indicators: stability, redundancy, and efficiency. Among them, stability is the basic element of safety resilience performance, which reflects the core defensive force of mountain subway stations to maintain the safety of their structures and functions when suffering from flood attacks, ensuring that the facilities can remain stable under extreme conditions; redundancy means that when the safety state of mountain subway stations is damaged by floods, they can respond and recover through spare and additional resources and measures, and maintain the ability of the system to operate normally; efficiency means that when mountain subway stations respond to flood impacts, they can quickly and effectively shorten the disaster-affected period, reduce the damage degree, and accelerate the return to the safe operation state. The three together build a solid defense line for the safety resilience system of mountain subway stations, ensuring reliability and sustainability in the face of extreme natural challenges. At the same time, multiple second-level resilience indicators are set under the three first-level indicators, which can help experts evaluate the resilience of mountain subway stations, so as to judge the current flood risk resistance ability and post-disaster recovery ability of mountain subway stations.

[0079] Again, the present invention uses the BM-OWA operator to optimize the importance coefficient of resilience indicators to weaken the influence of subjective factors in expert scoring and achieve scientific weighting of the importance coefficient of resilience indicators. At the same time, by introducing the concept of time factor and adjusting the resilience score in a timely manner, it is not only possible to score the current safety resilience level of mountain subway stations, but also to calculate the safety resilience level of mountain subway stations after n years, so as to achieve accurate assessment of the long-term and dynamic resilience of mountain subway stations;

[0080] Finally, the present invention establishes a resilience score correction mechanism. By calculating the relationship between the average difference and the average resilience score, differential correction of the conflict coefficient is implemented for the difference amplitude in different numerical ranges. Based on the optimized importance coefficient, reasonable adjustment and correction are made to the resilience scores with higher conflict and possible deviation of the evaluation results, further improving the accuracy and reliability of the evaluation results.

[0081] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0082] Figure 1 It is a flowchart of the method for evaluating the flood safety resilience of mountain area subway stations proposed by the present invention.

[0083] Figure 2 It is the index system for flood safety resilience of mountain area subway stations constructed by the present invention. Detailed Embodiments

[0084] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0085] Based on the BM-OWA operator and conflict analysis, the present invention proposes a method for evaluating the flood safety resilience of mountain area subway stations, and its work flow is as Figure 1 shown, and specifically includes the following steps:

[0086] Step 1: Construct an evaluation index system for flood safety resilience of mountain area subway stations;

[0087] Specifically, the present invention combines the operation characteristics of mountain area subway stations and the hazard characteristics of floods, and through consulting relevant research literature, "Code for Design of Subways" (GB50157-2013) and "Flood Control Standard" (GB50201-2014), establishes an evaluation index system for flood safety resilience of mountain area subway stations as Figure 2 shown.

[0088] It can be seen from Figure 2 that the evaluation index system U for flood safety resilience of mountain area subway stations includes 3 first-level indicators and 15 second-level resilience indicators. Among them, the first-level indicators include: stability U 1 , redundancy U 2 and efficiency U 3 ;

[0089] It can be seen from Figure 2 that stability U 1 includes the following second-level indicators: the density of drainage pipelines around mountain area subway stations u 1 , the number of water inlets and sumps around mountain area subway stations u 2 , the slope around mountain area subway stations u 3 , the waterproof performance of entrances and exits u 4 , the elevation of the bottom edge of the air supply opening u 5 , the waterproof performance of the mechanical and electrical equipment system u6 ;

[0090] Redundancy U 2 It includes the following secondary indicators: flood control skills of station operators u 7 , standby equipment and facilities u 8 , standby energy supply system u 9 , emergency evacuation time u 10 , emergency rescue team configuration u 11 ;

[0091] Efficiency U 3 It includes the following secondary indicators: rationality of flood control emergency plan u 12 , maintenance efficiency of the comprehensive maintenance center u 13 , investment in flood control and disaster reduction u 14 and accessibility of emergency rescue u 15 .

[0092] First, establish a scoring reference table for the above safety resilience evaluation indicators as the basis for expert scoring. The scoring reference is shown in Table 1.

[0093] Table 1 Scoring Reference Table

[0094]

[0095] Step 2: Form an evaluation group consisting of m experts. Through the expert scoring method, score the importance of each resilience indicator based on Table 1. Each expert needs to give three sets of scoring data and fill in 3 copies of Table 2;

[0096] The specific scoring steps include:

[0097] Step 21: Use a 10 - point scale to give the importance coefficient of each resilience indicator. The size of the coefficient represents the importance degree of the resilience indicator. The higher the coefficient, the more significant the importance of the indicator for the resilience construction of mountain subway stations. The definition of the importance coefficient is as follows:

[0098] The importance coefficient vector of each resilience indicator is where represents the importance coefficient given by the first expert to the j - th resilience indicator, and m represents the number of experts.

[0099] Step 22: Each expert combines the actual situation of mountain subway stations and gives the resilience score of each resilience indicator using a 50 - 100 - point scale according to Table 1; the higher the score, the better the embodiment and implementation of the resilience indicator in the actual operation of mountain subway stations, and the greater the contribution to the improvement of the overall resilience of the subway station;

[0100] The definition of the resilience score is as follows:

[0101] Resilience score vector for each resilience index where is the resilience score given by the \(i\)-th expert for the \(j\)-th resilience index;

[0102] Step 23: The \(m\) experts give the time factor for each resilience index to reflect the rate of decay or increase of the resilience score over time; the definition of the time factor is as follows:

[0103] Time factor vector for each resilience index where is the time factor given by the \(i\)-th expert for the \(j\)-th resilience index;

[0104] Step 24: Construct an expert scoring table as shown in Table 2, and the \(m\) experts fill in this table in turn. The range of the importance coefficient in the table is \((0, 10)\), and the range of the resilience score is \((50, 100)\);

[0105] Table 2 Expert Scoring Table

[0106]

[0107]

[0108] Step 3: Use the BM-OWA operator to optimize the importance coefficients in Step 2, so as to fully consider the subjective preferences of the experts, combine the coefficients given by the experts with the combination numbers, weaken the influence of subjective factors in the expert scoring, and achieve scientific weighting of the importance coefficients of the resilience indices;

[0109] The specific steps of Step 3 include:

[0110] Step 31: Sort the data in the importance coefficient vector \(C\) j in descending order to obtain a new sorted vector

[0111] Step 32: Calculate the weighted value \(v\) of the sorted importance coefficients through the following formula k :

[0112]

[0113] where represents the combination number of selecting \(k\) data from \(m\) data;

[0114] The vector \(V = [v\) k , \(v\) 0 , …, \(v\) 1 , …, \(v\) m-1 is composed of the weighted value \(v\) T , and this vector only needs to be calculated once;

[0115] Step 33: Based on v k , optimize the sorted importance coefficient vector D j using the BM-OWA operator:

[0116]

[0117] where is the (k + 1)-th largest element in D j ; is the importance coefficient of the j-th toughness index after being optimized by the BM-OWA operator; BM represents BM calculation, and OWA represents OWA calculation;

[0118] Step 34: According to the above steps, obtain the importance coefficients of all secondary toughness indices, and standardize the importance coefficients of each secondary toughness index through the following formula:

[0119]

[0120] where n represents the number of secondary indices, and w j represents the standardized importance coefficient.

[0121] Step 35: Obtain the optimized importance coefficient vector W based on w j :

[0122] W = [w 1 , w 2 , …, w n T

[0123] where n represents the number of secondary indices.

[0124] Step 4: Calculate the toughness score of the j-th toughness index after y years according to the time factor vector of each toughness index through the following formula:

[0125]

[0126] where represents the toughness score of the j-th toughness index after y years given by the i-th expert; y is a positive integer greater than or equal to 0;

[0127] Step 5: Calculate the conflict coefficient in the toughness scores after y years to find the toughness scores with large differences in expert ratings and correct the toughness analysis;

[0128] The specific steps include:

[0129] Step 51: Calculate the average toughness score of each toughness index through the following formula​

[0130]

[0131] Step 52: Calculate the average difference of each toughness index by the following formula

[0132]

[0133] Step 53: For the toughness indexes with higher scores, their conflictiveness needs to be considered emphatically and corrected by the following formula Obtain the conflict coefficient e j :

[0134]

[0135] where e j is the conflict coefficient of the jth toughness index;

[0136] From the conflict coefficient e j form the conflict coefficient vector E = [e 1 , e 2 , …, e n T ;

[0137] Step 6: According to the conflict coefficient vector E obtained in Step 5 and the importance coefficient vector W obtained in Step 3, correct the toughness scores with greater conflictiveness;

[0138] The specific steps include:

[0139] Step 61: According to the conflict coefficient e j in the vector E, determine the toughness scores to be corrected through the following two judgment conditions:

[0140] 1) When e j > 4, it is considered that there is conflictiveness in the toughness scores given by the experts for this toughness index, and then go to Step 62 to correct the toughness scores;

[0141] 2) When e j > 7, it is considered that there is serious conflictiveness in the toughness scores of this toughness index, and then go to Step 2 to re-collect and integrate the toughness scores of the experts for this toughness index;

[0142] Step 62: For the toughness scores with conflictiveness, they need to be corrected in combination with the importance coefficient:

[0143] 1) When the importance coefficient is less than 5, it is considered that this toughness index has little influence on the evaluation result, and the toughness score is corrected to the average value

[0144] ​2) When the importance coefficient is greater than 5, it is considered that the toughness index has a greater impact on the evaluation result, and the toughness score is corrected by combining the importance coefficient through the following formula:

[0145]

[0146] Among them, represents the corrected toughness score;

[0147] consists of to form the corrected toughness score vector

[0148] Step 7: Calculate the total flood safety toughness score P of the mountain subway station through the following formula:

[0149]

[0150] Among them, W is the importance coefficient vector;

[0151] Step 8: Determine the flood safety toughness level of the mountain subway station according to the P value.

[0152] Table 3 shows the toughness level division results corresponding to the toughness scores. Based on Table 3, the flood safety toughness level of the mountain subway station can be obtained according to the score P calculated by Equation (9).

[0153] Table 3

[0154] Toughness level Low toughness Lower toughness Medium toughness Higher toughness High toughness P [50，60] (60，70] (70，80] (80，90] (90，100]

[0155] Example 1

[0156] To verify the effectiveness of the method proposed in the present invention, the following numerical example simulation is carried out.

[0157] First, invite 5 experts to evaluate the importance coefficient, toughness score and time factor for each toughness index in combination with Table 1, and obtain the importance coefficient table (see Table 4), toughness score table (see Table 5) and time factor table (see Table 6) respectively;

[0158] Table 4

[0159]

[0160]

[0161] Table 5

[0162]

[0163] Table 6

[0164]

[0165]

[0166] Secondly, calculate the weighted value v k Form the vector V = [0.0625, 0.25, 0.375, 0.25, 0.0625] T ; Thirdly, calculate W = [w 1 , w 2 , …, w n T and E = [e 1 , e 2 , …, e n T , and the specific calculation results are shown in Table 7;

[0167] Table 7

[0168] n <![CDATA[W * > W <![CDATA[E * > E 1 8.356 0.074 4.24 3.468 2 8.131 0.072 4.88 3.836 3 8.225 0.073 4.16 3.361 4 8.644 0.076 3.36 2.93 5 8 0.071 3.12 2.359 6 7.225 0.064 6.16 4.484 7 6.356 0.056 2.88 2.402 8 6.644 0.059 4.56 3.228 9 7.356 0.065 6.88 5.614 10 8.644 0.076 4.08 3.533 11 7 0.062 2.48 1.974 12 7.775 0.069 2.16 1.884 13 6.775 0.06 4.24 3.273 14 7.225 0.064 2 1.68 15 6.644 0.059 1.52 1.283

[0169] Thirdly, calculate the corrected toughness score G j , and the specific calculation results are shown in Table 8;

[0170] Table 8

[0171] <![CDATA[G 1 > <![CDATA[G 2 > <![CDATA[G 3 > <![CDATA[G 4 > <![CDATA[G 5 > 1 78 82 86 75 88 2 74 71 80 83 85 3 80 84 78 88 74 4 89 90 91 82 84 5 74 75 70 80 79 6 68.349 68.349 68.349 77.251 77.251 7 80 82 88 81 86 8 70 66 65 75 78 9 76.539 86.661 86.661 76.539 86.661 10 88 92 90 81 82 11 78 82 75 83 80 12 88 85 89 90 84 13 85 76 69 76 80 14 80 85 84 88 83 15 85 86 86 84 81

[0172] Finally, let y = 0, and calculate the total flood safety resilience score P of the current mountainous area subway station through Equation (4):

[0173] P = (79.758 + 80.999 + 80.712 + 81.400 + 81.906) / 5 = 80.955

[0174] According to Table 3, it can be seen that 80.955 is in the interval (80, 90], so its corresponding resilience level is the "higher resilience" level, indicating that the current mountainous area subway station has better resistance and recovery capabilities in the event of a flood.

[0175] Embodiment 2

[0176] Based on Embodiment 1, in this embodiment, y in Equation (4) is set to 10, and the total flood safety resilience score of the mountainous area subway station after 10 years is calculated through Equation (4) to evaluate the flood safety resilience level of the mountainous area subway station after 10 years. Specifically, it includes:

[0177] (1) Calculate the flood safety resilience score of the mountainous area subway station after 10 years and E = [e 1 , e 2 , …, e n T ; ​​​

[0178] If the toughness score is less than 50, it is scored as 50; if the toughness score is greater than 100, it is scored as 100. The calculated results are shown in Table 9.

[0179] Table 9

[0180]

[0181]

[0182] (2) By using the obtained conflict coefficient vector E and combining it with the importance coefficient vector W in step 3, the toughness scores with relatively large conflicts are corrected to obtain the corrected toughness score G. The specific results are shown in Table 10.

[0183] Table 10

[0184] <![CDATA[G 1 > <![CDATA[G 2 > <![CDATA[G 3 > <![CDATA[G 4 > <![CDATA[G 5 > 1 76 80 82 72 85 2 72 67 77 80 81 3 80 84 78 88 74 4 81.833 81.833 81.833 72.567 72.567 5 74 75 70 80 79 6 58 57 51 75 59 7 87 88 96 91 97 8 70 66 65 75 78 9 67.669 76.731 76.731 67.669 76.731 10 88 92 90 81 82 11 82 82 80 93 84 12 88 85 89 90 84 13 87 81 74 82 84 14 86 89 88 96 88 15 85 86 86 84 81

[0185] (3) Calculate the total score of flood disaster safety toughness for the current mountain area subway station:

[0186] P = (78.824 + 79.459 + 79.058 + 81.572 + 80.135) / 5 = 79.810

[0187] It can be seen that the total score of flood disaster safety toughness for the front mountain area subway station is 79.810. Referring to Table 3, it can be known that it belongs to the "medium toughness" level, and it has a certain ability to resist and recover in case of flood disasters. It is still necessary to continuously pay attention to and optimize the corresponding disaster prevention and mitigation measures to ensure safe operation and toughness maintenance under various extreme conditions.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A dynamic assessment method for flood safety resilience of mountainous subway stations based on BM-OWA operator and conflict analysis is characterized by: The following steps are involved: Step 1: Construct a flood safety resilience evaluation index system for mountainous subway stations; Step 2: Use the expert scoring method to score each resilience indicator, obtain the importance coefficient and resilience score of each resilience indicator, and give the time factor of each resilience indicator; Step 3: Use the BM-OWA operator to optimize the importance coefficient obtained in step 2; Step 4: Calculate the resilience score of each resilience indicator after the yth year based on the time factor of the resilience indicator, where y ≥ 0; Step 5: Calculate the conflict coefficient of the resilience score of each resilience indicator; Step 6: According to the conflict coefficient obtained in step 5 and the importance coefficient obtained in step 3, correct the conflicting resilience score; Step 7: Calculate the total flood safety resilience score of the mountainous subway station to be evaluated based on the revised resilience score; Step 8: Determine the flood safety resilience level of the mountainous subway station based on the obtained total safety resilience score; The specific steps of step 3 include: Step 31: Transform the importance coefficient vector C j Sort the data in from large to small to get a sorted new vector Step 32: Calculate the weighted value v of the sorted importance coefficient by the following formula: k : in, Indicates the number of combinations after selecting k data from m data; By weighted value v k Composition vector V = [v0, v1, ..., v m-1 ] T ; Step 33: Based on v k , using the BM-OWA operator to optimize the sorted importance coefficient vector D j : in, Yes D j The k+1th largest element in ; is the importance coefficient of the j-th resilience index after optimization by the BM-OWA operator; BM represents BM calculation, and OWA represents OWA calculation; Step 34: The importance coefficients of each secondary toughness index are obtained by the following formula To standardize: Among them, n represents the number of secondary indicators, w j represents the standardized importance coefficient; Step 35: According to w j Get the importance coefficient vector W after composition optimization: In=[in1,in2,…,in n ] T Among them, n represents the number of secondary indicators; Step 4: The formula for calculating the resilience score after year y is: in, represents the resilience score of the jth resilience indicator given by the i-th expert; represents the resilience score of the jth resilience indicator y years later given by the i-th expert; represents the time factor given by the i-th expert to the j-th resilience indicator; The specific steps of step 5 include: Step 51: Calculate the average toughness score of each toughness indicator by the following formula: Step 52: Calculate the average difference of each toughness index by the following formula Step 53: Correct by the following formula Get the conflict coefficient e of each toughness index j : Among them, e j is the conflict coefficient of the jth resilience index; Step 54: According to e j Construct the conflict coefficient vector E = [e1, e2, ..., e n ] T ; The formula for calculating the total flood safety resilience score of mountainous subway stations in step 7 is: Among them, P represents the total score of security resilience, W is the importance coefficient vector, G j Represents the modified toughness score vector.

2. The method for dynamic assessment of flood safety resilience of mountainous subway stations based on BM-OWA operator and conflict analysis according to claim 1 is characterized in that: The specific steps of step 2 include: Step 21: An evaluation group consisting of m experts will score the importance of each toughness indicator using a 10-point system to obtain an importance coefficient vector C composed of the importance coefficients of each toughness indicator. j : in, represents the importance coefficient given by the mth expert to the jth resilience indicator; Step 22: Based on the actual situation of mountainous subway stations, use a 100-point system to score each resilience indicator and obtain a resilience score vector A composed of the resilience scores of each resilience indicator. j : in, represents the resilience score given by the mth expert to the jth resilience indicator; Step 23: m experts give the time factor of each resilience indicator respectively, and obtain the time factor vector T composed of the time factors of each resilience indicator j : in, It represents the time factor given by the m-th expert to the j-th resilience indicator.

3. The method for dynamic assessment of flood safety resilience of mountainous subway stations based on BM-OWA operator and conflict analysis according to claim 2 is characterized in that: The flood safety resilience evaluation index system includes three primary indicators: stability U1, redundancy U2 and efficiency U3; Among them, stability U1 includes 6 secondary indicators: density of drainage pipes around mountainous subway stations u1, number of water outlets and sump pits around mountainous subway stations u2, slope around mountainous subway stations u3, waterproof performance of entrances and exits u4, elevation of bottom edge of wind outlet u5, and waterproof performance of electromechanical equipment system u6; Redundancy U2 includes five secondary indicators: flood prevention skills of station operators u7, backup equipment and facilities u8, backup energy supply system u9, emergency evacuation time u10, 10 , Emergency rescue team configuration 11 ; Efficiency U3 includes 4 secondary indicators: rationality of flood emergency plan u 12 , Maintenance efficiency of integrated maintenance center 13 , investment in flood prevention and disaster reduction 14 And emergency rescue accessibility 15 .

4. The method for dynamic assessment of flood safety resilience of mountainous subway stations based on BM-OWA operator and conflict analysis according to claim 3 is characterized in that: The specific steps of step 6 include: Step 61: According to the conflict coefficient e in the conflict coefficient vector E j , the toughness score to be corrected is determined by the following two judgment conditions: 1) When e j >4, it is considered that the resilience scores given by the experts for the resilience index are conflicting, and the process goes to step 62; 2) When e j When it is greater than 7, it is considered that there is a serious conflict in the resilience score of the resilience indicator, and the process goes to step 2 to re-score by experts; Step 62: Determine the importance coefficient of the current resilience index. If 1) When the importance coefficient is less than 5, the toughness score is corrected to the average value 2) When the importance coefficient is greater than 5, the toughness score is corrected by the following formula: in, represents the modified toughness score; Step 63: According to Constructing a modified resilience score vector 5. A flood safety resilience assessment system for mountainous subway stations, implemented by using the dynamic assessment method for flood safety resilience of mountainous subway stations based on BM-OWA operator and conflict analysis as described in claim 1, characterized in that: include: An evaluation index system construction module is used to construct an evaluation index system for flood safety resilience of mountainous subway stations, which includes primary indicators and secondary resilience indicators; The expert scoring module is used to score each resilience indicator in the flood safety resilience evaluation index system for mountainous subway stations, obtain the importance coefficient and resilience score of each resilience indicator, and give the time factor of each resilience indicator; Importance coefficient optimization and resilience score correction module, which is used to optimize the obtained importance coefficient, reduce the influence of subjective factors in expert scoring, and correct the resilience score to reduce the deviation of the evaluation results; The safety resilience level assessment module is used to determine the flood safety resilience level of mountain subway stations based on the total safety resilience score.

6. The flood safety resilience assessment system for mountainous subway stations according to claim 5 is characterized by: The importance coefficient optimization and resilience score correction module includes an importance coefficient optimization unit and a resilience score correction unit; Among them, the importance coefficient optimization unit is used to optimize the importance coefficient using the BM-OWA operator; the toughness score correction unit is used to calculate the conflict coefficient of the toughness score of each toughness indicator, and correct the conflicting toughness score based on the conflict coefficient combined with the optimized importance coefficient.

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