Expressway tunnel group safety risk situation assessment method based on partial connection number

Abstract

The invention relates to the technical field of tunnel engineering, and discloses an expressway tunnel group safety risk situation assessment method based on a partial correlation number, which comprises the following steps of: firstly, constructing a set of complete risk assessment index system, then determining the assessment index weight of each level by using a combined weighting method combining a G1 method and a variable coefficient method, and finally, carrying out risk assessment on the risk assessment index system. And then, by constructing a similarity and difference anti-evaluation model of the risk, the five-element connection number of each evaluation index is calculated, and the quantitative description of the risk is further refined, so that the evaluation result is more accurate and specific.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel engineering, and in particular to a method for assessing the safety risk situation of a highway tunnel group based on a partial connection coefficient. Background Art

[0002] Tunnel groups have complex geology, large traffic volume, and high accident rates. Therefore, in order to promptly discover and eliminate potential safety hazards, it is necessary to conduct a safety risk assessment of highway tunnel groups.

[0003] At present, the safety risk situation assessment of highway tunnel groups mainly adopts a combination of qualitative and quantitative methods. Qualitative assessment methods include expert scoring method, fault tree analysis method, etc., which mainly determine the weight and occurrence probability of each risk factor based on the experience and judgment of experts. Quantitative assessment methods include statistical analysis method, fuzzy comprehensive evaluation method, gray system theory method, etc., which use mathematical models and calculation methods to determine the influence and occurrence probability of each risk factor. However, these methods cannot fully consider the interaction between risk factors, and there is subjectivity and uncertainty. The partial connection coefficient method can systematically quantify the degree of connection between various elements in the system, has dynamic flexibility, can adapt to different situations, and is based on mathematical principles. The results are objective and scientific, and can effectively deal with complexity and uncertainty problems. Therefore, those skilled in the art need a method for assessing the safety risk situation of highway tunnel groups based on the partial connection coefficient. Summary of the invention

[0004] The purpose of the present invention is to solve the above problems and to design a highway tunnel group safety risk situation assessment method based on the partial connection coefficient.

[0005] The technical solution of the present invention to achieve the above-mentioned purpose is as follows: the first step is to establish a risk assessment index system for highway tunnel groups, obtain data of each assessment index, pre-process the data, and invite experts in the field of transportation to conduct risk level assessment on each assessment index;

[0006] Taking the risk assessment of highway tunnel groups as the research object, in order to make the assessment index system conform to the principles of comprehensiveness and scientificity, logic and systematicity, relevance and validity, based on the characteristics of highway tunnel group safety accidents and combined with the characteristics of highway tunnel groups, four first-level indicators of "driver factors, traffic conditions factors, traffic facilities factors, and road conditions factors" were established. With reference to traffic accidents in highway tunnel groups in previous years, expert interviews and field surveys were conducted, and the four first-level indicators were divided into 11 second-level indicators to establish a highway tunnel group risk assessment index system. For details, see Figure 1 .

[0007] The calculation methods of each secondary indicator are as follows:

[0008] (1) Driver factors

[0009] ① Visual impact and psychological stress on drivers

[0010] The average fixation duration ratio is used to reflect the driver's visual impact and psychological stress. The specific formula is as follows:

[0011]

[0012] Where: t g is the total fixation duration of the driver in the tunnel group, and t is the total time the driver travels in the tunnel group section.

[0013] ②The calculation formula for fatigue driving rate is:

[0014]

[0015] Where: N p is the number of vehicles passing through the tunnel group on the highway for more than 4 hours in one day, and M is the total number of vehicles passing through the tunnel group in one day.

[0016] (2) Vehicle factors

[0017] ① The calculation formula for vehicle overspeed is:

[0018]

[0019] Where: M c is the total number of speeding vehicles passing through the tunnel group in one day, and M is the total number of vehicles passing through the tunnel group in one day.

[0020] ② The vehicle overload rate calculation formula is:

[0021]

[0022] Where: Q X is the total number of overloaded vehicles passing through the tunnel group in one day, and M is the total number of vehicles passing through the tunnel group in one day.

[0023] (3) Transportation facilities

[0024] ① The calculation formula for traffic sign information continuity is:

[0025]

[0026] In the formula, c 21 is the traffic sign information continuity evaluation index, and its value range is [0,1]. i ) is the number of continuous guidance information. If two signs indicate the same distance information in the tunnel group, it is recorded as 2, otherwise it is 0. N is the total number of guidance information.

[0027] ②The calculation formula for the rationality of tunnel lighting facilities is:

[0028]

[0029] Where, L min —Minimum brightness on the road surface in the calculation area; L av —Average road surface brightness; δ i is the weight value of the tunnel length, expressed as a percentage of the tunnel length.

[0030] ③The calculation formula for the amount of information of signs and markings is:

[0031]

[0032] Where M i The number of such signs and markings in the actual tunnel group, N is the total number of such signs and markings according to the current specifications, is the level weight, where See Fig.14 .

[0033] (4) Road condition factors

[0034] ①Road alignment consistency

[0035] The running speed consistency is selected as the index to measure the linear coordination, and the starting and ending points of the tunnel and the midpoint of the connecting section are used as the calculation sections. The calculation formula is:

[0036]

[0037] In the formula, x i 、x i+1 is the Δ|V of the adjacent sections 85 |.c 41 The closer the value is to 1, the smoother the running speed of the tunnel group section and the better the linear coordination.

[0038] ②Road surface anti-skid ability

[0039] The skid resistance index (SRI) is selected as the basis for measuring the skid resistance of the pavement. The calculation formula is:

[0040]

[0041] Where SFC is the lateral force coefficient, SRI is min Take 35, a0 and a1 are 28.6 and -0.105 respectively.

[0042] (5) Other factors

[0043] ① The calculation formula for the bad weather ratio is:

[0044]

[0045] Where D i is the total number of rainy, snowy and foggy days in the tunnel group in a year, and N is the number of days in a year.

[0046] ② The calculation formula for the vehicle model discreteness is:

[0047]

[0048] In the formula, μ is the average number of various types of vehicles, x i is the number of vehicles of the i-th type, and N is the total number of vehicle types.

[0049] Step 2: Use the G1 method to determine the subjective weight, use the coefficient of variation method to determine the objective weight, and combine the weights to determine the weights of evaluation indicators at all levels;

[0050] (1) Determine subjective weight using the G1 method

[0051] According to the importance ranking of the evaluation indicators by relevant experts, the relative importance of the indicators is compared and assigned, and the weight of the evaluation indicators is obtained through calculation. The calculation steps are as follows:

[0052] ① Determine the order relationship. The impact of each highway tunnel group risk assessment indicator is different. Invite experts in related fields to rank the risks. If indicator X1 is riskier and more important than indicator X2, then X1>X2.

[0053] ② Determine the relative importance of the evaluation indicators. Assume that an indicator of highway tunnel group risk is X k , the adjacent index is X k-1 , let index X k With indicator X k-1 The importance ratio is r k-1 / r k , then

[0054]

[0055] Where: k = 2, 3, ..., θ; θ is the number of indicators;

[0056] ③ Calculate the evaluation index weight. According to c k The value of , calculates the subjective weight z of the kth indicator k , according to the weight z k , calculate the subjective weight z of the k-1th indicator k-1 :

[0057]

[0058] zk-1 =c k × k

[0059] (2) Determine the objective weight using the coefficient of variation method

[0060] The extreme value method is used to process the original data dimensionlessly, and the ratio of the standard deviation to the mean of each indicator is calculated to reflect the degree of variation of each indicator, and then the weight of each indicator is determined. The calculation steps are as follows:

[0061] ① Assuming there are m evaluation objects and n evaluation indicators, construct the indicator original data matrix X:

[0062]

[0063] Where: X ij Get the value of the j-th evaluation indicator of the i-th evaluation object.

[0064] The data is normalized by using the extreme value method to construct the decision matrix V = (b ij ) m×n

[0065]

[0066] In the formula, max j (X ij ) and max j (X ij ) are the minimum and maximum values ​​of the jth evaluation indicator among all evaluation objects.

[0067] ② Calculate the coefficient of variation V j :

[0068]

[0069]

[0070] Where: j is the standard deviation of the jth evaluation indicator; is the average value of the jth evaluation indicator.

[0071] ③ Normalized weight e of the jth indicator j for

[0072]

[0073] (3) Combining weights to determine dynamic weight sets

[0074] According to the objective weight of the indicator to be evaluated i and subjective weight z i, the dynamic weight set w={w1,w2,…,w n}.

[0075]

[0076] Where: w i is the weight of each evaluation indicator, the sum of all weight values ​​is one, and each weight satisfies non-negativity and normalization, i=1,2,···,n.

[0077] Step 3: Construct a risk similarity, difference and opposite evaluation model and calculate the five-element connection number of each evaluation indicator;

[0078] Compare each evaluation indicator with the relevant risk standard and correspond it to the coefficient component of the five-element connection number. According to the degree of compliance with the standard, the risk set U = {u1, u2, u3, u4, u5} is divided into five levels of comments: "low risk", "relatively low risk", "medium risk", "relatively high risk" and "high risk".

[0079] Using formula O ij =P ij / Q(P ij is the number of experts who classify evaluation indicator i as risk level j, Q is the total number of evaluation experts), and the severity and probability of occurrence of each evaluation indicator are estimated. The calculated dynamic weight set w and the five-element coefficient formula give the risk similarity, difference and opposite assessment model:

[0080]

[0081] Abbreviated as

[0082] μ=a+bi+cj+dk+el

[0083] Where: μ is the comprehensive connection degree; E = [1, i, j, k, l] is the coefficient matrix of identity, difference and reciprocity; Indicates the degree to which the indicator is classified as “low risk”; Indicates the degree to which the indicator is classified as “lower risk”; Indicates that the indicator is of "medium risk"; Indicates the degree to which the indicator is considered “higher risk”; Indicates the degree to which the indicator is "high risk"; a and e are certain items; b, c, and d are uncertain items.

[0084] Step 4: Determine the risk situation and level of each assessment indicator and analyze them;

[0085] The potential of the five-element connection number μ is shi(u*), which is expressed as:

[0086]

[0087] According to shi(μ * ) value determines the system risk, if shi(μ * )>1, indicating that the system situation is "same situation", the system risk is consistent with the ideal risk standard, and the system is in a "low risk" state; if shi(μ * )=1, indicating that the system situation is "balanced", the system risk and the ideal risk standard are "equally matched", and the system is in a "medium risk" state; if shi(μ * )<1, indicating that the system situation is "counter-trend", the system risk and the ideal risk standard are in opposition, and the system is in a "high-risk" state. Such risk factors should be taken seriously, and risk control strategies should be formulated to improve and continuously reduce the system risk level.

[0088] According to the size relationship of the connection components a, b, c, d, and e of the five-element connection number, the five-element connection number situation ranking table is queried to determine the potential level of each evaluation indicator and the total risk of the system.

[0089] Step 5: Calculate the partial connection coefficients of each order of the five-element connection coefficient and conduct an in-depth analysis of the development trend of the risk;

[0090] The expressions of the partial connection coefficients of each order of the five-element connection number are:

[0091] First-order partial connection coefficient:

[0092]

[0093] Where:

[0094] Second-order partial connection coefficient:

[0095]

[0096] Where:

[0097] The third-order partial connection coefficient is:

[0098]

[0099] Where:

[0100] The fourth-order partial connection coefficient is:

[0101]

[0102] Where:

[0103] In order to clearly analyze the risk change trend, i, j, and k are assigned corresponding values ​​in the most "conservative" case to calculate the specific risk change value. When calculating the first-order partial connection coefficient, the second-order partial connection coefficient, and the third-order partial connection coefficient, i=0, j=0, k=-1; i=-1, j=-1; i=-1. When the trend is “improving”, the highway tunnel group is in a good state and the risk is reduced; when When the trend is “fuzzy”, the risk of the highway tunnel group is in the transition stage; when When the trend is "downward", the status of the highway tunnel group is deteriorating and the risk is increasing. Measures should be taken and people should be highly vigilant.

[0104] Compared with the prior art, this application has the following advantages:

[0105] 1. The present invention proposes a method for assessing the safety risk situation of a highway tunnel group based on the partial connection coefficient. By constructing a complete set of risk assessment index systems, it covers multiple key areas such as driver factors, traffic conditions, traffic facilities, road conditions, etc., ensuring the comprehensiveness of the assessment. At the same time, the combined weighting method combining the G1 method and the coefficient of variation method is used, which not only takes into account the subjective experience of experts, but also combines the objective characteristics of the data, so that the accuracy of the assessment results is significantly improved. In addition, by constructing a risk similarity, difference and opposite assessment model and calculating the five-element connection coefficient of each assessment indicator, the quantitative description of the risk is further refined, making the assessment results more accurate and specific.

[0106] 2. By calculating the partial connection coefficients of each order of the five-element connection number, the present invention can deeply explore the changing trends of risks at different times and in different situations, and provide managers with more accurate risk warning information. This risk warning method based on the partial connection coefficient not only has higher sensitivity, but also can discover potential safety hazards earlier, thus gaining more response time for managers. At the same time, through in-depth analysis of risk development trends, managers can formulate targeted risk management strategies more scientifically, optimize resource allocation, and thus more effectively reduce the safety risks of tunnel groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 It is a flow chart of the method for evaluating the safety risk situation of a highway tunnel group based on the partial connection coefficient according to the present invention;

[0108] Figure 2 It is a composition block diagram of the risk assessment index system of the present invention;

[0109] Figure 3 is a flow chart of the method for determining weights of evaluation indicators at each level according to the present invention;

[0110] Figure 4It is a value table of various evaluation indicators of the tunnel group described in the present invention;

[0111] Figure 5 It is the highway safety risk level assessment table of different indicators of the present invention;

[0112] Figure 6 is a relative importance table of the evaluation indicators described in the present invention;

[0113] Figure 7 is a relative importance table of the evaluation indicators described in the present invention;

[0114] Figure 8 is the objective weight table of each evaluation index described in the present invention;

[0115] Fig. 9 is a weight table of the evaluation indicators described in the present invention;

[0116] Fig.10 It is the risk five-element connection number and situation table of the present invention;

[0117] Fig.11 It is the first-level assessment index and system total risk potential level table of the present invention;

[0118] Fig.12 It is the partial connection coefficient and trend table of each order described in the present invention;

[0119] Fig.13 It is the partial connection coefficient and trend table of each order described in the present invention;

[0120] Fig.14 The present invention discloses a classification and weight table of traffic signs and markings for a highway tunnel group. DETAILED DESCRIPTION

[0121] Embodiment 1;

[0122] The present invention will be described in detail below in conjunction with the accompanying drawings. Figure 1-14 As shown;

[0123] This example selects six tunnel sections of the Rongwu Expressway in Hebei Province as the research object to conduct a safety risk situation assessment. The specific steps are as follows:

[0124] Step 1: Based on the on-site survey data and the collected relevant information, obtain the data of each evaluation indicator, pre-process the data, and invite experts in the transportation field to conduct risk level assessment on each evaluation indicator;

[0125] According to the on-site measurement and data investigation, the values ​​of various evaluation indicators are obtained. For details, see Figure 4 .

[0126] Invite 10 experts in the field of transportation to conduct risk level assessment on the secondary indicators of highway tunnel group safety risk situation assessment. For details, see Figure 5 .

[0127] Step 2: Use the G1 method to determine the subjective weight, use the coefficient of variation method to determine the objective weight, and combine the weights to determine the weights of evaluation indicators at all levels;

[0128] The G1 method is used to determine the subjective weight. First, relevant experts are invited to rank the importance of the evaluation indicators. The result is X 12 >X 21 >X 22 >X 23 >X 32 >X 41 >X 11 >X 42 >X 24 >X 31 >X 33 ; Then quantitatively assign values ​​to the relative importance of the evaluation indicators, see Figure 6 ;

[0129] After calculation, the subjective weights of each secondary evaluation index are shown in Figure 7 ;

[0130] The objective weight of each secondary evaluation indicator is calculated using the coefficient of variation method. For details, see Figure 8 ;

[0131] The combined weights of the secondary evaluation indicators are shown in Fig. 9 ;

[0132] In "Driver Factor X1", the secondary evaluation index "Visual Impact and Psychological Stress X 11 "Fatigue driving rate X 12 "The weight set is w1 = (0.1125, 0.8875); in "traffic condition factor X2", the secondary evaluation index "vehicle overspeed X 21 "Vehicle overload rate X 22 """The proportion of bad weather is X 23 "Model Discreteness X 24 The weight set is w2 = (0.2913, 0.3882, 0.2895, 0.0310); in the "traffic facility factor X3", the secondary evaluation index "traffic sign information continuity X 31 ""Tunnel lighting facilities rationality X 32 "Information volume of signs and markings X 33 "The weight set is w3 = (0.0617, 0.8947, 0.0436); in "Road Condition Factor X4", the secondary evaluation index "Road Linear Consistency X 41"Road skid resistance index X 42 The weight set is w4 = (0.6688, 0.3312).

[0133] Step 3: Construct a risk similarity, difference and opposite assessment model and calculate the five-element connection number of each assessment indicator and the total risk of the system;

[0134] According to the risk level assessment results and the weights of each assessment indicator, a risk similarity, difference and opposite assessment model is constructed to determine the first-level assessment indicators "driver factor X1", "traffic condition factor X2", "traffic facility factor X3", "road condition factor X4" and the five-element connection number of the total system risk.

[0135] ① Same-different-anti-evaluation model of driver factor X1:

[0136]

[0137] Among them, a1=0.02, b1=0.12, c1=0.20, d1=0.28, e1=0.38.

[0138] ② The same-different-reverse evaluation model of traffic condition factor X2:

[0139]

[0140] Among them, a2 = 0.11, b2 = 0.11, c2 = 0.20, d2 = 0.29, e2 = 0.29. ③ The same, different and opposite evaluation model of the traffic facility factor X3:

[0141]

[0142] Among them, a3 = 0.22, b3 = 0.11, c3 = 0.29, d3 = 0.19, e3 = 0.19. ④ The same-different-reverse evaluation model of the road condition factor X4:

[0143]

[0144] Among them, a4=0.13, b4=0.17, c4=0.33, d4=0.17, e4=0.20. ⑤ Same-different-reverse evaluation model of total system risk:

[0145]

[0146]

[0147] Among them, a=0.10, b=0.12, c=0.22, d=0.26, e=0.29.

[0148] Step 4: Determine the risk situation and level of each assessment indicator and analyze them;

[0149] According to shi(u * ) value determines the risk situation of each evaluation indicator, and the results are shown in Fig.10 .

[0150] According to the size relationship of the connection components a, b, c, d, and e of the five-element connection number in Table 7, the five-element connection number situation ranking table is queried to obtain the first-level evaluation indicators "driver factor X1", "traffic condition factor X2", "traffic facility factor X3", "road condition factor X4" and the potential level of the total system risk. For details, see Fig.11 ;

[0151] The results show that the overall risk situation of the system is at the reverse potential level 181, and it belongs to the strong reverse potential area, indicating that the system is in a high-risk state; among the first-level evaluation indicators, the "traffic facility factor X3" is at the same potential level 20, which belongs to the strong same potential area, indicating that the situation is good; the "traffic condition factor X2" is at the reverse potential level 153, which belongs to the slightly reverse potential area, indicating that the situation is general and should be paid attention to; the "driver factor X1" and the "road condition factor X4" are respectively at the reverse potential level 181 and the reverse potential level 175, both of which belong to the strong reverse potential area, indicating that the situation is poor and should be paid special attention to.

[0152] Step 5: Calculate the partial connection coefficients of each order of the five-element connection coefficient and conduct an in-depth analysis of the development trend of the risk;

[0153] According to the partial connection coefficient expressions of each order of the five-element connection number, the partial connection coefficients of each order of the five-element connection number are calculated respectively, and i, j, and k are assigned corresponding values ​​respectively, and the risk change value is calculated to determine the development trend of the risk. For specific results, see Fig.12 and Fig.13 .

[0154] The results show that in the first-order potential development trend, the trends of indicators such as "fatigue driving rate", "vehicle overspeed rate" and "road linear consistency" are "decreasing", the trends of indicators such as "vehicle overloading rate" and "vehicle model dispersion" are "intermediate", and the trends of six indicators such as "visual impact and psychological tension" are "increasing"; in the second-order potential development trend, the trends of all indicators are "decreasing", which indicates that the system risk is gradually weakening from the good state of the previous layer; in the third-order potential development trend, the trends of "fatigue driving rate", "vehicle overspeed rate" and "sign and marking information volume" are "decreasing", and the trends of other indicators are "increasing"; in the fourth-order potential development trend, except for the "fatigue driving rate" which is "intermediate", the trends of other indicators are "increasing".

[0155] From the perspective of the overall risk development trend of the system, it shows a downward trend at the first and second potential levels, indicating that the system situation is getting worse and the risk is increasing; it shows an upward trend at the third and fourth potential levels, indicating that the system situation is getting better and the risk is gradually decreasing, reflecting that the risk situation of the system objectively has a trend of similarities and opposites, opposites and similarities, and fluctuating forward. From the perspective of the development trend of the secondary evaluation indicators, we should focus on factors that have repeatedly shown a downward trend, such as "fatigue driving rate" and "vehicle overspeed rate".

[0156] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions. The sentence "includes an element defined by ... does not exclude the existence of other identical elements in the process, method, article or device including the element".

[0157] The above technical solutions only reflect the preferred technical solutions of the technical solutions of the present invention. Some changes that may be made to certain parts thereof by technicians in this technical field all reflect the principles of the present invention and fall within the protection scope of the present invention.

Claims

1. A method for assessing the safety risk situation of a highway tunnel group based on partial connection coefficient, characterized in that: The method comprises the following steps: Step 1: Establish a risk assessment index system for highway tunnel groups, obtain data for each assessment index, pre-process the data, and conduct risk level assessment for each assessment index; Step 2: Use the G1 method to determine the subjective weight, use the coefficient of variation method to determine the objective weight, and combine the weights to determine the weights of the evaluation indicators at all levels; Step 3: construct a risk similarity, difference and opposite assessment model and calculate the five-element connection number of each assessment indicator; Step 4: Determine the risk situation and level of each assessment indicator and analyze them; Step five, calculate the partial connection coefficients of each order of the five-element connection coefficient and conduct an in-depth analysis of the development trend of the risk.

2. The method for evaluating the safety risk situation of a highway tunnel group based on the partial connection coefficient according to claim 1 is characterized in that: The risk assessment index system of the expressway tunnel group in step 1 includes: driver factors, traffic condition factors, traffic facility factors, road condition factors and other factors.

3. The method for evaluating the safety risk situation of a highway tunnel group based on the partial connection coefficient according to claim 2 is characterized in that: The driver factors include: the driver's visual impact and psychological tension and fatigue driving rate; the traffic condition factors include: vehicle overspeed rate and vehicle overload rate; the traffic facility factors include: traffic sign information continuity, tunnel lighting facility rationality and sign and marking information volume; the road condition factors include: road linear consistency and road surface anti-skid ability; the other factors include: the proportion of severe weather and the degree of vehicle model dispersion.

4. The method for evaluating the safety risk situation of a highway tunnel group based on the partial connection coefficient according to claim 1 is characterized in that: In step 2, the process of determining the subjective weight is as follows: The relative importance of the indicators is compared and then assigned, and the weight of the evaluation indicator is obtained by calculation. The calculation steps are as follows: Determine the order relationship. The impact of each risk assessment indicator of highway tunnel groups is different. Invite experts in related fields to rank the risks. If indicator X1 is more risky and more important than indicator X2, then X1>X2. Determine the relative importance of the evaluation indicators. Assume that a certain indicator of highway tunnel group risk is X k , the adjacent index is X k-1 , let index X k With indicator X k-1 The importance ratio is r k-1 / r k , then: Where: k = 2, 3, ..., θ; θ is the number of indicators; Calculate the evaluation index weights according to c k The value of , calculates the subjective weight z of the kth indicator k , according to the weight z k , calculate the subjective weight z of the k-1th indicator k-1 : z k-1 =c k ×z k The objective weight determination process is as follows: The extreme value method is used to process the original data dimensionlessly, and the ratio of the standard deviation to the mean of each indicator is calculated to reflect the degree of variation of each indicator, and then the weight of each indicator is determined. The calculation steps are as follows: Assuming there are m evaluation objects and n evaluation indicators, construct the indicator original data matrix X: Where: X ij Take the value of the jth evaluation indicator of the i-th evaluation object; The data is normalized by using the extreme value method to construct the decision matrix V = (b ij ) m×n In the formula, min j (X ij ) and max j (X ij ) are the minimum and maximum values ​​of the jth evaluation index among all evaluation objects; Calculate the coefficient of variation V j : Where: j is the standard deviation of the jth evaluation indicator; is the average value of the jth evaluation indicator; The normalized weight e of the jth indicator j for: The determination process of the dynamic weight set is: According to the objective weight of the indicator to be evaluated i and subjective weight z i , the dynamic weight set w={w1,w2,…,w n }; Where: w i is the weight of each evaluation indicator, the sum of all weight values ​​is one, and each weight satisfies non-negativity and normalization, i=1,2,···,n.

5. The method for evaluating the safety risk situation of a highway tunnel group based on the partial connection coefficient according to claim 1 is characterized in that: The process of constructing the risk similarity, difference and opposite evaluation model and calculating the five-element connection number of each evaluation index in step 3 is as follows: Compare each evaluation index with the relevant risk standard and correspond it with the coefficient component of the five-element connection number. According to the degree of conformity with the standard, the risk set U = {u1, u2, u3, u4, u5} is divided into five levels: "low risk", "relatively low risk", "medium risk", "relatively high risk" and "high risk". Using formula O ij =P ij / Q(P ij is the number of experts who classify evaluation indicator i as risk level j, Q is the total number of evaluation experts), and the severity and probability of occurrence of each evaluation indicator are estimated; combined with formula The calculated dynamic weight set w and the five-element coefficient formula give the risk similarity, difference and opposite assessment model: It can be abbreviated as μ = a + bi + cj + dk + el Where: μ is the comprehensive connection degree; E = [1, i, j, k, l] is the coefficient matrix of identity, difference and reciprocity; Indicates the degree to which the indicator is "low risk"; Indicates the degree to which the indicator is classified as "lower risk"; Indicates the degree to which the indicator is of "medium risk"; Indicates the degree to which the indicator is considered "higher risk"; Indicates the degree to which the indicator is "high risk"; a and e are certain items; b, c, and d are uncertain items.

6. The method for evaluating the safety risk situation of a highway tunnel group based on the partial connection coefficient according to claim 1 is characterized in that: In step 4, the process of determining the situation and level of risk of the assessment indicator is as follows: The potential of the five-element connection number μ is shi(u*), and its expression is: According to shi(μ * ) value determines the system risk, if shi(μ * )>1, indicating that the system situation is "same situation", the system risk is consistent with the ideal risk standard, and the system is in a "low risk" state; if shi(μ * )=1, indicating that the system situation is "balanced", the system risk and the ideal risk standard show a "equal" trend, and the system is in a "medium risk" state; if shi(μ * )<1, indicating that the system situation is "anti-trend", the system risk and the ideal risk standard have an opposing trend, and the system is in a "high risk" state. Such risk factors should be taken seriously, and risk control strategies should be formulated to improve and continuously reduce the system risk level; According to the size relationship of the connection components a, b, c, d, and e of the five-element connection number, the five-element connection number situation ranking table is queried to determine the potential level of each evaluation indicator and the total risk of the system.

7. The method for evaluating the safety risk situation of a highway tunnel group based on the partial connection coefficient according to claim 1 is characterized in that: The partial connection coefficient expressions of each order of the five-element connection coefficient in step 5 are respectively: First-order partial connection coefficient: Where: Second-order partial connection coefficient: Where: The third-order partial connection coefficient is: Where: The fourth-order partial connection coefficient is: Where: When calculating the first-order partial connection coefficient, the second-order partial connection coefficient, and the third-order partial connection coefficient, i=0, j=0, and k=-1 are set respectively; i=-1,j=-1;i=-1; The analysis process of risk development trend is as follows: When the trend is "improving", the highway tunnel group is in a good state and the risk is reduced; when When the trend is "fuzzy", the risk of the highway tunnel group is in a transitional stage; when When the trend is "downward", the status of the highway tunnel group is deteriorating and the risk is increasing. Measures should be taken and high vigilance should be maintained.

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