Quantitative grading fuzzy evaluation method for risk of barrier lake

Abstract

The invention discloses a barrier lake risk quantitative grading fuzzy evaluation method. The method comprises the following steps: determining a barrier lake risk evaluation factor set and a barrier lake risk evaluation grade set; calculating the membership degree of each evaluation factor in the evaluation factor set to each evaluation grade in the evaluation grade set, and constructing a membership degree matrix; calculating the weight of each evaluation factor in each hierarchy based on the importance scoring condition of pairwise comparison of the evaluation factors in each hierarchy, and finally integrating to obtain a final weight vector; and determining a barrier lake risk evaluation grade based on the membership matrix and the weight vector. The barrier lake risk quantitative grading fuzzy evaluation method provided by the invention solves the problem of inconsistent evaluation factor weight assignment by experts, the evaluation index system and grading are reasonable, the information acquisition method is feasible, the weight vector is scientific, and the risk grade evaluation result is reliable. The method has good applicability and is suitable for risk grade evaluation of all barrier lakes.

Description

Technical Field

[0001] The invention relates to the technical field of barrier lake risk management, and in particular to a barrier lake risk quantitative grading fuzzy evaluation method. Background Art

[0002] A landslide-dammed lake is formed when landslides, mudslides and other factors block the river channel and cause water to accumulate. Its harm not only floods the upstream, but also seriously threatens the lives and property of people downstream and the safety of major infrastructure after its collapse.

[0003] The United States Geological Survey found that the lifespan of 73 barrier lakes around the world was less than 1 year for 85% of them. Chinese scholars found that the lifespan of 352 breached barrier lakes around the world was less than 1 year for 84.4% of the barrier lakes, less than 1 month for 68.2% of the barrier lakes, and less than 1 day for 29.8%. It can be seen that the lifespan of barrier lakes varies greatly and the window period for emergency disposal is short. How to scientifically classify the risk of barrier lakes is the key to avoiding blind rescue. According to the definition of risk in ISO31000:2009 International Standard for Risk Management, the risk of barrier lakes can be expressed as the product of the probability of breach of barrier lakes and the breach loss, that is, R = PC, where R is the risk of barrier lakes; P is the probability of breach of barrier lakes; and C is the breach loss of barrier lakes. The former is mainly manifested in the danger of barrier bodies, and the higher the danger, the greater the probability of breach; the latter is mainly manifested in the losses caused by barrier lakes and breach floods.

[0004] Determining the weights of risk assessment factors for landslide-dammed lakes is the key to the risk level assessment method for landslide-dammed lakes. In the actual risk grading of landslide-dammed lakes, the following situations often occur: (1) All experts believe that the first indicator is more important than the second indicator, but the importance levels are inconsistent. For example, 8 out of 10 experts believe that the first indicator is more important than the second indicator, with an importance level of 0.8, and the other two experts also believe that the first indicator is more important than the second indicator, but the importance level is 0.7; (2) Some experts believe that the first indicator is more important than the second indicator, but the remaining experts believe that the two indicators are equally important. For example, 7 experts believe that the first indicator is more important than the second indicator, with an importance level of 0.6, but the other two experts believe that the two indicators are equally important; (3) Some experts believe that the first indicator is more important than the second indicator, and the remaining experts do not make any evaluation. For example, 8 experts believe that the first indicator is more important than the second indicator, with an importance level of 0.7, but the other two experts do not make any evaluation due to their lack of in-depth knowledge and understanding of the two indicators to be evaluated.

[0005] In traditional methods, whether it is the judgment matrix or the fuzzy preference membership matrix, it is impossible to describe the above three situations at the same time, resulting in the inability to quantify the weights of the evaluation indicators. Summary of the invention

[0006] In order to overcome the shortcomings of the above-mentioned technology, the present invention aims at the problem that neither the judgment matrix nor the fuzzy preference membership matrix in the traditional method can quantify the weights of the evaluation indicators of the risk level of the barrier lake. A fuzzy evaluation method for quantitative grading of the risk of the barrier lake is proposed to solve the problem of inconsistent weight assignment of evaluation factors by experts, realize quantitative grading of the risk of the barrier lake, and avoid blind emergency disposal.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0008] A fuzzy evaluation method for quantitative classification of barrier lake risk, which is special in that it includes the following steps:

[0009] 1) Determine the risk assessment factor set U and the risk assessment level set V of the dammed lake = {v 1 , v 2 , ..., v p};

[0010] 2) Based on the assignment of the degree to which each evaluation factor belongs to each evaluation level, the degree of membership of each evaluation factor in the evaluation factor set U to each evaluation level in the evaluation level set V is calculated, and a membership matrix R is constructed;

[0011] 3) Based on the importance scores of the evaluation factors in each level, a fuzzy evaluation matrix is ​​constructed and the weights of each evaluation factor in each level are calculated, and finally the final weight vector W is obtained by integration;

[0012] 4) Based on the membership matrix R and the weight vector W, determine the risk assessment level of the barrier lake.

[0013] Furthermore, the evaluation factor set U includes a dam body risk evaluation factor set D = {d 1 , d 2 , ..., d m} and the barrier lake loss evaluation factor set L = {l 1 , l 2 ,……,l n}.

[0014] Furthermore, the evaluation factors in the landslide hazard evaluation factor set D include the volume of the landslide lake, the amount of water entering the lake, the particle size of the landslide body, and the volume of the landslide body.

[0015] Furthermore, the evaluation factors in the barrier lake loss evaluation factor set L include dangerous population, town size, infrastructure scale, and environmental modulus.

[0016] Furthermore, in the barrier lake risk assessment level set, p = 4, V = {v 1 , v 2 , v 3 , v4}, where v 1 、v 2 、v 3 、v 4 They represent level I, level II, level III, and level IV, which are low risk, medium risk, high risk, and extremely high risk respectively.

[0017] Furthermore, in step 2), the membership degree r of the i-th evaluation factor to the k-th evaluation level ik The calculation methods include:

[0018] The scores of each evaluation factor are divided into four assignment intervals, which are [α i1 =0,α i2 =3]、(α i2 =3,α i3 =5]、(α i3 =5,α i4 =7]、(α i4 =7,α i5 =9];

[0019] Assign a value to the degree to which the ith evaluation factor belongs to the kth evaluation level, and calculate the degree of membership based on the assignment interval and the assignment situation; among them, the degree of membership of the ith evaluation factor to the kth evaluation level is r ik The calculation formula is as follows:

[0020]

[0021] In the formula, x ik is the score of the degree to which the i-th evaluation factor belongs to the k-th evaluation level, i=1, 2, 3..., 8; k=1, 2, 3, 4.

[0022] Furthermore, the volume of the barrier lake, the amount of water entering the lake, the particle size of the barrier body, the volume of the barrier body, and the population at risk are assigned using linear interpolation method; the three evaluation factors of town scale, infrastructure scale, and environmental modulus are assigned based on the quantified number of affected towns, facilities, and ecological environments.

[0023] As a preferred solution, the method for calculating the weight of each evaluation factor in each level in step 3) includes the following steps:

[0024] 3.1) Establish fuzzy evaluation matrix B:

[0025]

[0026] Among them, b 11 , b 12 ,......,b 1f are the first evaluation factor j in the same level respectively.1 Relative to the 1st, 2nd, ..., fth evaluation factor j 1 、j 2 ,......,j f The importance scores of , and so on;

[0027] The yth evaluation factor j y Relative to the zth evaluation factor j z The importance of scoring yz is {(α 1 , β 1 ), (α 2 , β 2 ),...,(α θ , β θ )},β 1 , β 2 ,......,β θ are the yth evaluation factor j y Relative to the zth evaluation factor j z The importance index is α 1 , α 2 ,......,α θ The proportion of 1 +β 2 +......+β θ ≤1, where (α 1 , β 1 ) represents the scoring result for the yth evaluation factor j y Relative to the zth evaluation factor j z The importance index is α 1 The proportion of the number of scores to the total number of scores is β 1 ;

[0028] 3.2) Convert the fuzzy evaluation matrix B into the weight matrix C

[0029] The fuzzy evaluation matrix Convert to weight matrix The conversion method is as follows:

[0030] The yth evaluation factor j y Relative to the zth evaluation factor j z The weight c yz =α 1 ×β 1 +α 2 ×β 2 +......+α θ ×β θ ;

[0031] 3.3) Based on the importance ranking of each evaluation factor, the weight matrix is ​​transposed into the weight ranking matrix C T :

[0032]

[0033] in, Representative evaluation factor Relative to the evaluation factor The weight of Representative evaluation factor Relative to the evaluation factor The weight of, and so on, Representative evaluation factor Relative to the evaluation factor The weight of each row increases from the first column to the last column. is the evaluation factor j 1 、j 2 ,......,j f The corresponding evaluation factors are sorted from most important to least important;

[0034] 3.4) Obtaining evaluation factors based on constraints The weight of the evaluation factor j can be obtained 1 、j 2 ,......,j f The weight of

[0035] 3.5) Calculate the weights of the evaluation factors at each level in the barrier lake risk evaluation factor set U through 3.1) to 3.4).

[0036] Furthermore, in step 3.4), the constraints are as follows:

[0037]

[0038] And so on;

[0039] in For evaluation factors The weight of For evaluation factors The weight of For evaluation factors The weight of .

[0040] Furthermore, the final weight vector W is obtained by processing the weights of the evaluation factors in each level using matrix multiplication.

[0041] Further, step 4) comprises the following steps:

[0042] 4.1) Calculate the barrier lake risk assessment level vector G based on the membership matrix R and the weight vector W, where G = W × R = [g 1 , g 2 ,......,g p ],g p is the pth evaluation level v in the risk evaluation level set of barrier lake p A vector of

[0043] 4.2) Based on the barrier lake risk level evaluation level vector G, the risk level is determined according to the objective function grade(); the calculation formula of the objective function grade() is as follows:

[0044]

[0045] The present invention also provides a fuzzy evaluation system for quantitative grading of barrier lake risk, which is used to implement the above-mentioned fuzzy evaluation method for quantitative grading of barrier lake risk, and comprises:

[0046] A data storage module is used to store the assignment of the degree to which each evaluation factor in the barrier lake risk evaluation factor set belongs to each evaluation level in the barrier lake risk evaluation level set, and the importance scoring results between the evaluation factors in each level;

[0047] A data processing module, used for constructing a membership matrix R and a weight vector W, and performing calculations based on the membership matrix R and the weight vector W, so as to determine the risk assessment level of the barrier lake;

[0048] The data output module is used to output the final determined risk assessment level of the barrier lake.

[0049] The present invention also provides a computer device, including a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load the at least one program to execute the above-mentioned fuzzy evaluation method for quantitative grading of barrier lake risk.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a fuzzy evaluation method for quantitative grading of barrier lake risk. Aiming at the problem that neither the judgment matrix nor the fuzzy preference membership matrix in traditional methods can quantify the weights of barrier lake risk level evaluation indicators, a fuzzy evaluation method for quantitative grading of barrier lake risk is proposed, which solves the problem of inconsistent weight assignment of evaluation factor weights by experts.

[0052] The fuzzy evaluation method for quantitative classification of barrier lake risk proposed in the present invention has a reasonable evaluation index system and classification, a feasible information acquisition method, a scientific weight vector, and a reliable risk level evaluation result. The method has good applicability and is suitable for risk level evaluation of all barrier lakes. DETAILED DESCRIPTION

[0053] In order to better explain the present invention, the main contents of the present invention are further explained below in conjunction with specific embodiments, but the contents of the present invention are not limited to the following embodiments.

[0054] The method of the present invention is described below by taking two barrier lakes: barrier lake A and barrier lake B as examples.

[0055] The invention provides a fuzzy evaluation method for quantitative classification of barrier lake risk, comprising the following steps:

[0056] 1) Determine the risk assessment factor set U and the risk assessment level set V of the dammed lake = {v 1 , v 2 ,......,v p}, construct a mathematical model for quantitative evaluation of barrier lake risk classification.

[0057] The mathematical model for quantitative evaluation of landslide lake risk classification includes three parts, as shown in the following formula: the first part is the model objective function; the second part is the data set of the model: including the evaluation factor set U and the evaluation level set V; the third part is the operator set of the model: including membership calculation function, weight calculation function, fuzzy operator function, etc., to solve the problem of weight vector quantization and evaluation level quantization.

[0058] Objective function: grade()

[0059] Data Collection

[0060] U=[D,L]=[{d 1 , d 2 , ..., d m},{l 1 , l 2 ,……,l n}]

[0061] V={v 1 , v 2 , ..., v p}

[0062] Operator Set

[0063]

[0064] Where:

[0065] U is the risk assessment factor set of the barrier lake;

[0066] D is the set of dam hazard assessment factors, which includes m elements, namely, d 1 d 2 ,......,d m In this specific implementation, m=4, d 1 d 2 d 3 d 4 They are the volume of the barrier lake, the amount of water entering the lake, the particle size of the barrier body, and the volume of the barrier body;

[0067] L is a set of evaluation factors for the loss of the barrier lake, which includes n elements, namely l 1 , l 2 ,……,l n In this specific implementation, n = 4, l 1 , l 2 , l 3 , l 4 They correspond to the population at risk, town size, infrastructure scale, and environmental modulus respectively;

[0068] V is the risk assessment level set of the barrier lake, which includes p elements, namely v 1 、v 2 ,……,v p In this specific embodiment, p = 4, v 1 、v 2 、v 3 、v 4 They correspond to level I, level II, level III, and level IV, which represent low risk, medium risk, high risk, and extremely high risk respectively;

[0069] For extremely high and high risks, engineering measures such as digging diversion channels and non-engineering measures such as joint scheduling of upstream and downstream reservoirs can be used to deal with them; for low risks, no measures are required for the time being; for medium risks, measures will be adopted based on actual conditions.

[0070] R is the membership matrix of the barrier lake risk assessment factor set U to the barrier lake risk assessment level set V; ik is the membership degree of the ith evaluation factor to the kth evaluation level; x i is the value assigned to the degree to which the ith evaluation factor belongs to a certain evaluation level; a ik is the boundary value of the evaluation factor assignment interval;

[0071] W is the weight vector corresponding to the barrier lake risk assessment factor set U; i is the weight of the i-th evaluation factor; f 1 (w) is the weight conversion formula;

[0072] G is the risk assessment level vector of the barrier lake, g p is the pth evaluation level v in the risk evaluation level set of barrier lake p In this specific implementation, G includes 4 elements, namely g 1 , g 2 , g 3 , g 4 ; max() is the maximum value function.

[0073] 2) Based on the assignment of the degree to which each evaluation factor belongs to each evaluation level, the degree of membership of each evaluation factor in the evaluation factor set U to each evaluation level in the evaluation level set V is calculated, and the membership matrix R is constructed.

[0074] According to the above mathematical model, there are eight evaluation factors in the risk assessment of barrier lakes. Each evaluation factor can be divided into four intervals, and the corresponding assignment intervals are: [α i1 =0,α i2 =3]、(α i2 =3,α i3 =5]、(α i3 =5,α i4 =7]、(α i4 =7,α i5 =9]. Among them, the volume of the barrier lake is d 1 , water volume entering the lake 2 , particle size of dam body d 3 , dam volume d 4 , dangerous population 1 The five factors can be directly assigned using linear interpolation. 2 、Infrastructure scale 3 、Environmental modulus l 4 The three indicators can be assigned values ​​based on the number of towns, facilities, and ecological environments affected by quantification. The membership matrix R (8×4) The membership degree r of the i-th evaluation factor to the k-th evaluation level ik The calculation and matrix construction are shown below.

[0075]

[0076] 3) Construct the weight vector W. Calculate the weights of each evaluation factor in each level, including the weights of each evaluation factor in the landslide hazard evaluation factor set, the weights of each evaluation factor in the landslide lake loss evaluation factor set, and the weights of the landslide hazard and the landslide lake loss in the landslide lake risk evaluation factor set, and finally integrate them to get the final weight vector.

[0077] The calculation method of the weight of each evaluation factor in each level is as follows: first, establish a fuzzy evaluation matrix B, which can fully feedback the preferences given by experts, and the sum of the matrix elements can be less than 1; second, convert the matrix B into a weight matrix C; third, based on the ranking of the indicator weights, transpose the weight matrix into a weight ranking matrix C T ; Finally, the weights of indicators in each level are obtained based on the indicator weight and constraints such as equal to 1. The specific operations are as follows:

[0078] 3.1) Establish fuzzy evaluation matrix B:

[0079]

[0080] Among them, b 11 , b 12 ,......,b 1f are the first evaluation factor j in the same level respectively. 1 Relative to the 1st, 2nd, ..., fth evaluation factor j 1 、j 2 ,......,j f The importance score of b, and so on; b 11 , b 22 ,......,b ff They are all {(0.50, 1.0)}, that is, all scoring results are that the importance of a certain evaluation factor relative to itself is 0.5.

[0081] The yth evaluation factor j y Relative to the zth evaluation factor j z The importance of scoring yz is {(α 1 , β 1 ), (α 2 , β 2 ),...,(α θ , β θ )},β 1 , β 2 ,......,β θ are the yth evaluation factor j y Relative to the zth evaluation factor j z The importance index is α 1 , α 2 ,......,α θ The proportion of 1 +β 2 +......+β θ ≤1, where (α 1 , β 1 ) represents the scoring result for the yth evaluation factor j yRelative to the zth evaluation factor j z The importance index is α 1 The proportion of the number of scores to the total number of scores is β 1 .

[0082] Taking the hazard assessment factor of landslide dam as an example, 10 experts were selected to evaluate the hazard factor of landslide dam. 1 d 2 d 3 d 4 The importance of the four indicators is scored. The scoring rules are:

[0083] The importance of each indicator relative to itself is scored as 0.5;

[0084] The importance of each indicator relative to other indicators is scored in d 1 and d 2 For example: If we think d 1 Than d 2 If the importance is high, then d 1 Relative to d 2 The importance index interval is (0.5, 1]; if it is considered that d 1 Than d 2 If the importance is low, then d 1 Relative to d 2 The importance index interval is [0, 0.5).

[0085] The same expert 1 Relative to d 2 The importance index and d 2 Relative to d 1 The sum of the importance indices is 1.

[0086]

[0087] The data in the first row are d 1 Relative to d 1 d 2 d 3 d 4 The importance score of d is given in the following order. Taking the first row and second column as an example to explain the meaning of the parameter, it means: 2 experts out of 10 think that d 1 D 2 The importance is poor, the importance index is 0.45, and 8 experts think that d 1 D 2 The importance is high, with 3 experts believing that the importance index is 0.55, 3 experts believing that the importance index is 0.6, 1 expert believing that the importance index is 0.65, and 1 expert believing that the importance index is 0.9. The other parameters are similar and will not be elaborated.

[0088] 3.2) Convert matrix B to weight matrix C

[0089]

[0090] The conversion method is: the yth evaluation factor j y Relative to the zth evaluation factor j z The weight c yz =α 1 ×β 1 +α 2 ×β 2 +......+α θ ×β θ .

[0091] The above 10 experts 1 d 2 d 3 d 4 The fuzzy evaluation matrix B established after scoring the importance of the four evaluation factors is converted into the weight matrix C as follows:

[0092]

[0093] Take the first row and second column element as an example to illustrate the conversion process:

[0094] 0.45×0.2+0.55×0.3+0.6×0.3+0.65×0.1+0.9×0.1=0.590. The other parameters are deduced accordingly and will not be elaborated herein.

[0095] 3.3) Based on the importance ranking of each evaluation factor, the weight matrix is ​​transposed into the weight ranking matrix C T , sort the evaluation factors from large to small according to their importance:

[0096]

[0097] in, Representative evaluation factor Relative to the evaluation factor The weight of Representative evaluation factor Relative to the evaluation factor The weight of, and so on, Representative evaluation factor Relative to the evaluation factor The weights of each row increase from the first column to the last column. is the evaluation factor j 1 、j 2 ,......,jf The corresponding evaluation factors are sorted from large to small according to their importance.

[0098] Step 3.2) 1 d 2 d 3 d 4 The weight matrix C of the four evaluation factors is transposed to obtain the following weight ranking matrix C T :

[0099]

[0100] The first line is the evaluation factor Relative to the evaluation factor The second line is the evaluation factor Relative to the evaluation factor 's weight, and so on. is the evaluation factor d 1 d 2 d 3 d 4 According to the order of importance from large to small, the corresponding evaluation factors are:

[0101] 3.4) Calculate the weights of each level based on the constraints, which include:

[0102]

[0103] And so on;

[0104] in For evaluation factors The weight of For evaluation factors The weight of For evaluation factors The weight of .

[0105] Obtained by the above method The weight of j 1 、j 2 ,......,j f The weight w 1 、w 2 、......、w f .

[0106] Since the importance of each indicator relative to itself is scored as 0.5, b 11 、b 22 ,......,b ffare all {(0.50, 1.0)}, then c 11 、c 22 ,......,c ff are both 0.500, and are all 0.500, so the weight sorting matrix C in step 3.3) T Calculate d 1 d 2 d 3 d 4 The weights of the four indicators are subject to the following constraints:

[0107]

[0108] Calculated from the above formula, the four indicators d 1 d 2 d 3 d 4 The weights w are 0.32875, 0.24375, 0.24875 and 0.17875 respectively.

[0109] 3.5) Calculate the weights of the evaluation factors in each level through 3.1) to 3.4) to obtain the four indicators of the landslide hazard evaluation factor d 1 d 2 d 3 d 4 The weights of the four indicators in the barrier lake loss evaluation factor are 0.32875, 0.24375, 0.24875 and 0.17875. 1 , l 2 , l 3 , l 4 The weights of the dam hazard D and the breach loss L are 0.525 and 0.475 respectively.

[0110] 3.6) Matrix multiplication is used to integrate the weights of each level. The eight indicator weight value vectors W = [0.173, 0.128, 0.131, 0.094, 0.186, 0.127, 0.108, 0.053] are obtained.

[0111] 4) Based on the membership matrix R and weight vector W, determine the risk assessment level of the barrier lake

[0112] After calculation, the corresponding membership R matrix of barrier lake A and barrier lake B is as follows.

[0113]

[0114] The barrier lake risk level evaluation level vector G is calculated based on the membership matrix R and the weight vector W:

[0115] G 甲 =W×R 甲 =[g 1 =0.645g 2 =0.309g 3 =0.046g 4 =0.000]

[0116] G 乙 =W×R 乙 =[g 1 =0.331g 2 =0.460g 3 =0.119g 4 =0.091]

[0117] Based on the risk level evaluation vector G of the barrier lake, the risk level is determined according to the grade() function. The risk levels of barrier lake A and barrier lake B are level I and level II, respectively.

[0118] grade() 甲 =max(g 1 , g 2 , g 3 , g 4 ) = g 1 = Level I

[0119] grade() 乙 =max(g 1 , g 2 , g 3 , g 4 ) = g 2 =Level Ⅱ.

[0120] It can be concluded that the risk level of impounded lake A is level I, corresponding to a low risk level; the risk level of impounded lake B is level II, corresponding to a medium risk level.

[0121] In this specific implementation, a fuzzy evaluation system for quantitative grading of barrier lake risk is also provided to implement the above-mentioned fuzzy evaluation method for quantitative grading of barrier lake risk, including:

[0122] A data storage module is used to store the assignment of the degree to which each evaluation factor in the barrier lake risk evaluation factor set belongs to each evaluation level in the barrier lake risk evaluation level set, and the importance scoring results between the evaluation factors in each level;

[0123] A data processing module, used for constructing a membership matrix R and a weight vector W, and performing calculations based on the membership matrix R and the weight vector W, so as to determine the risk assessment level of the barrier lake;

[0124] The data output module is used to output the final determined risk assessment level of the barrier lake.

[0125] In this specific embodiment, a computer device is also provided, including a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load the at least one program to execute the above-mentioned fuzzy evaluation method for quantitative grading of barrier lake risk. The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A fuzzy evaluation method for quantitative classification of barrier lake risk, characterized by: The following steps are involved: 1) Determine the risk assessment factor set U and the risk assessment level set V of the dammed lake = {v1, v2, ..., v p }; 2) Based on the assignment of the degree to which each evaluation factor belongs to each evaluation level, the degree of membership of each evaluation factor in the evaluation factor set U to each evaluation level in the evaluation level set V is calculated, and a membership matrix R is constructed; 3) Based on the importance scores of the evaluation factors in each level, a fuzzy evaluation matrix is ​​constructed and the weights of each evaluation factor in each level are calculated, and finally the final weight vector W is obtained by integration; 4) Based on the membership matrix R and the weight vector W, determine the risk assessment level of the barrier lake.

2. The fuzzy evaluation method for quantitative classification of barrier lake risk according to claim 1 is characterized by: The evaluation factor set U includes the dam body risk evaluation factor set D = {d1, d2, ..., d m } and the barrier lake loss evaluation factor set L = {l1,l2,…,l n }.

3. The fuzzy evaluation method for quantitative classification of barrier lake risk according to claim 2 is characterized by: The evaluation factors in the landslide hazard evaluation factor set D include the volume of the landslide lake, the amount of water entering the lake, the particle size of the landslide body, and the volume of the landslide body.

4. The fuzzy evaluation method for quantitative classification of barrier lake risk according to claim 3 is characterized by: The evaluation factors in the barrier lake loss evaluation factor set L include dangerous population, town scale, infrastructure scale, and environmental modulus.

5. The fuzzy evaluation method for quantitative classification of barrier lake risk according to claim 4 is characterized by: In the set of risk assessment levels for the barrier lake, p=4, V={v1, v2, v3, v4}, where v1, v2, v3, and v4 represent level I, level II, level III, and level IV, respectively, representing low risk, medium risk, high risk, and extremely high risk, respectively.

6. The fuzzy evaluation method for quantitative classification of barrier lake risk according to claim 5 is characterized by: In step 2), the membership degree r of the i-th evaluation factor to the k-th evaluation level ik The calculation methods include: The scores of each evaluation factor are divided into four assignment intervals, which are [α i1 =0,α i2 =3]、(α i2 =3,α i3 =5]、(α i3 =5,α i4 =7]、(α i4 =7,α i5 =9]; Assign a value to the degree to which the ith evaluation factor belongs to the kth evaluation level, and calculate the degree of membership based on the assignment interval and the assignment situation; among them, the degree of membership of the ith evaluation factor to the kth evaluation level is r ik The calculation formula is as follows: In the formula, x ik is the score of the degree to which the i-th evaluation factor belongs to the k-th evaluation level, i=1, 2, 3..., 8; k=1, 2, 3, 4.

7. The fuzzy evaluation method for quantitative classification of barrier lake risk according to claim 6 is characterized by: The volume of the barrier lake, the amount of water entering the lake, the particle size of the barrier body, the volume of the barrier body and the population at risk are assigned using linear interpolation method; the three evaluation factors of town scale, infrastructure scale and environmental modulus are assigned based on the quantified number of affected towns, facilities and ecological environment.

8. The fuzzy evaluation method for quantitative classification of barrier lake risk according to any one of claims 1 to 7, characterized in that: The method for calculating the weight of each evaluation factor in each level in step 3) includes the following steps: 3.1) Establish fuzzy evaluation matrix B: Among them, b 11 , b 12 ,......,b 1f are the first evaluation factor j1 relative to the first, second, ..., fth evaluation factors j1, j2, ..., j in the same level, respectively. f The importance scores of , and so on; The yth evaluation factor j y Relative to the zth evaluation factor j z The importance of scoring yz is {(α1,β1),(α2,β2),...,(α θ ,β θ )}, β1, β2, ..., β θ are the yth evaluation factor j y Relative to the zth evaluation factor j z The importance indexes are α1, α2, ..., α θ The proportion of β1+β2+......+β θ ≤1, where (α1, β1) represents the scoring result for the yth evaluation factor j y Relative to the zth evaluation factor j z The proportion of the number of scores with the importance index α1 to the total number of scores is β1; 3.2) Convert the fuzzy evaluation matrix B into the weight matrix C The fuzzy evaluation matrix Convert to weight matrix The conversion method is as follows: The yth evaluation factor j y Relative to the zth evaluation factor j z The weight c yz =α1×β1+α2×β2+……+α θ ×β θ ; 3.3) Based on the importance ranking of each evaluation factor, the weight matrix is ​​transposed into the weight ranking matrix C T : in, Representative evaluation factor Relative to the evaluation factor The weight of Representative evaluation factor Relative to the evaluation factor The weight of, and so on, Representative evaluation factor Relative to the evaluation factor The weight of each row increases from the first column to the last column. are the evaluation factors j1, j2, ..., j f The corresponding evaluation factors are sorted from most important to least important; 3.4) Obtaining evaluation factors based on constraints The weights of the evaluation factors j1, j2, ..., j f The weight of 3.5) Calculate the weights of the evaluation factors at each level in the barrier lake risk evaluation factor set U through 3.1) to 3.4).

9. The fuzzy evaluation method for quantitative classification of barrier lake risk according to claim 8 is characterized by: In step 3.4), the constraints are as follows: And so on; in For evaluation factors The weight of For evaluation factors The weight of For evaluation factors The weight of .

10. The fuzzy evaluation method for quantitative classification of barrier lake risk according to claim 1 is characterized by: The final weight vector W is obtained by processing the weights of the evaluation factors in each level using matrix multiplication.

11. The fuzzy evaluation method for quantitative classification of barrier lake risk according to any one of claims 1 to 10, characterized in that: Step 4) comprises the following steps: 4.1) Calculate the barrier lake risk assessment level vector G based on the membership matrix R and the weight vector W, where G = W × R = [g1, g2, ..., g p ],g p is the pth evaluation level v in the barrier lake risk evaluation level set p A vector of 4.2) Based on the barrier lake risk level evaluation level vector G, the risk level is determined according to the objective function grade(); the calculation formula of the objective function grade() is as follows:

12. A fuzzy evaluation system for quantitative classification of barrier lake risk, characterized by: The method for implementing the quantitative classification fuzzy evaluation of the barrier lake risk as described in any one of claims 1 to 11 comprises: A data storage module is used to store the assignment of the degree to which each evaluation factor in the barrier lake risk evaluation factor set belongs to each evaluation level in the barrier lake risk evaluation level set, and the importance scoring results between the evaluation factors in each level; A data processing module, used for constructing a membership matrix R and a weight vector W, and performing calculations based on the membership matrix R and the weight vector W, so as to determine the risk assessment level of the barrier lake; The data output module is used to output the final determined risk assessment level of the barrier lake.

13. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load the at least one program to execute the fuzzy evaluation method for quantitative grading of barrier lake risk as described in any one of claims 1 to 11.

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