Fuzzy evaluation method for quantitative classification of dammed lake risk

Abstract

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

Description

Fuzzy Evaluation Method for Quantitative Classification of Landslide-Damaged Lake Risk Technical Field

[0001] This invention relates to the field of landslide dam risk management technology, specifically to a fuzzy evaluation method for quantitative classification of landslide dam risks. Background Technology

[0002] Determining the weights of risk assessment factors for landslide dammed lakes is crucial for assessing the risk level of landslide dammed lakes. In actual landslide dammed lake risk classification, the following situations often occur: (1) All experts believe that the first indicator is more important than the second indicator, but the degree of importance is 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. The other two experts also believe that the first indicator is more important than the second indicator, but the degree of importance 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. However, 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, but 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. However, the other two experts do not make any evaluation because they lack in-depth understanding of the two indicators to be evaluated.

[0003] Traditional methods, whether using judgment matrices or fuzzy preference membership matrices, cannot simultaneously describe all three scenarios, resulting in the inability to quantify the weights of evaluation indicators. Summary of the Invention

[0004] To overcome the shortcomings of the above-mentioned technologies, this invention addresses the problem that traditional methods, whether using judgment matrices or fuzzy preference membership matrices, cannot quantify the weights of indicators for assessing the risk level of landslide dammed lakes. It proposes a fuzzy evaluation method for quantitative classification of landslide dammed lake risks, resolving the inconsistency in the weight assignments of evaluation factors by experts, achieving quantitative classification of landslide dammed lake risks, and avoiding blind emergency response.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A fuzzy evaluation method for quantitative risk classification of landslide dammed lakes, characterized by the following steps:

[0007] 1) Determine the set of risk assessment factors U and the set of risk assessment levels V for landslide-dammed lakes. ;

[0008] 2) Based on the assignment of values ​​for the degree to which each evaluation factor belongs to each evaluation level, calculate the membership degree of each evaluation factor in the evaluation factor set U to each evaluation level in the evaluation level set V, and construct the membership degree matrix R.

[0009] 3) Based on the importance scores of pairwise comparisons of evaluation factors within each level, a fuzzy evaluation matrix is ​​constructed and the weights of each evaluation factor within each level are calculated. Finally, the weight vector W is obtained by integrating the results.

[0010] 4) Based on the membership matrix R and weight vector W, determine the risk assessment level of the landslide dammed lake.

[0011] Furthermore, the evaluation factor set U includes the landslide dam hazard evaluation factor set D= The set of evaluation factors for landslide dammed lake losses, L= .

[0012] Furthermore, the evaluation factors in the set D of the landslide dam risk assessment factors include the volume of the landslide dam lake, the amount of water flowing into the lake, the particle size of the landslide dam, and the volume of the landslide dam.

[0013] Furthermore, the evaluation factors in the set L of the landslide dam loss evaluation factors include the at-risk population, town size, infrastructure scale, and environmental modulus.

[0014] Furthermore, in the aforementioned risk assessment level set for landslide dammed lakes, p=4, V= ,in, These represent Level I, Level II, Level III, and Level IV, respectively, which are low risk, medium risk, high risk, and extremely high risk.

[0015] Furthermore, in step 2), the membership degree r of the i-th evaluation factor to the k-th evaluation level is... ik The calculation methods include:

[0016] The scores for each evaluation factor are divided into four assignment intervals, namely [α], ... i1 =0, α i2 =3]、(α i2 =3, α i3 =5]、(α i3 =5, α i4 =7]、(α i4 =7, α i5 =9];

[0017] Assign a value to the degree to which the i-th evaluation factor belongs to the k-th evaluation level, and calculate the membership degree based on the assignment range and assignment situation; where the membership degree r of the i-th evaluation factor to the k-th evaluation level is... ik The calculation formula is as follows:

[0018] = ; = ;

[0019] = ; = ;

[0020] In the formula, Let be the score representing the degree to which the i-th evaluation factor belongs to the k-th evaluation level, where i = 1, 2, 3, ..., 8; k = 1, 2, 3, 4.

[0021] Furthermore, the volume of the landslide dammed lake, the amount of water flowing into the lake, the particle size of the landslide dam, the volume of the landslide dam, and the population at risk are assigned values ​​using linear interpolation; the three evaluation factors of town size, infrastructure scale, and environmental modulus are assigned values ​​based on the quantification of the number of affected towns, facilities, and ecological environments.

[0022] As a preferred option, the method for calculating the weight of each evaluation factor within each level in step 3) includes the following steps:

[0023] 3.1) Establish fuzzy evaluation matrix B:

[0024] B=

[0025] Among them, b 11 b 12 ... b 1f These represent the first evaluation factor j1 within the same level relative to the first, second, ..., fth evaluation factors j1, j2, ..., j... f The importance scores are assigned sequentially;

[0026] The y-th evaluation factor j y Relative to the z-th evaluation factor j z Importance rating (b) yz for{( , ),( , ),......,( , )}, , ... The y-th evaluation factor j y Relative to the z-th evaluation factor j z Importance indicators are , ... The proportion, + +......+ 1, among which, ( , () represents the score result of the y-th evaluation factor j y Relative to the z-th evaluation factor j z Importance indicators are The percentage of scores given was [percentage missing] of the total scores. ;

[0027] 3.2) Convert the fuzzy evaluation matrix B into a weight matrix C.

[0028] The fuzzy evaluation matrix B= Convert to weight matrix The conversion method is as follows:

[0029] The y-th evaluation factor j y Relative to the z-th evaluation factor j z weight = × + × +......+ × ;

[0030] 3.3) Based on the importance ranking of each evaluation factor, transpose the weight matrix into a weight ranking matrix C. T :

[0031] = ,

[0032] in, ... Representative evaluation factors Relative to evaluation factors ... The weight, ... Representative evaluation factors Relative to evaluation factors ... The weights, and so on, ... Representative evaluation factors Relative to evaluation factors , , ...... The weights are calculated as follows: the weights of the first column in each row increase sequentially from the last column to the first column. , , ...... For evaluation factors j1, j2, ..., j f The corresponding evaluation factors are sorted from largest to smallest according to their importance;

[0033] 3.4) Obtaining evaluation factors based on constraints , , ...... By determining the weights, we can obtain the evaluation factors j1, j2, ..., j f The weights;

[0034] 3.5) Calculate the weights of the evaluation factors within each level of the risk assessment factor set U of the landslide dammed lake through steps 3.1) to 3.4).

[0035] Furthermore, in step 3.4), the constraints are as follows:

[0036] + + +......+ =1; - = - ; - = - ; - = - And so on; - = - .

[0037] in Evaluation factors weights, Evaluation factors Weights, ... Evaluation factors The weight.

[0038] Furthermore, the final weight vector W is obtained by processing the weights of the evaluation factors within each level using matrix multiplication.

[0039] Further, step 4) includes the following steps:

[0040] 4.1) Calculate the risk assessment level vector G of the landslide dammed lake based on the membership matrix R and the weight vector W, where G = W × R = , The p-th assessment level in the set of risk assessment levels for landslide dammed lakes ;

[0041] 4.2) Based on the risk level assessment vector G of the landslide dammed lake, the risk level is determined according to the objective function grade(); the calculation formula of the objective function grade() is as follows:

[0042] grade() .

[0043] This invention also provides a fuzzy evaluation system for quantitative classification of landslide dammed lake risks, used to implement the above-mentioned fuzzy evaluation method for quantitative classification of landslide dammed lake risks, comprising:

[0044] The data storage module is used to store the assignment of the degree to which each evaluation factor in the set of risk assessment factors of the landslide dam belongs to each evaluation level in the set of risk assessment levels of the landslide dam, and the importance scoring results between each pair of evaluation factors within each level.

[0045] The data processing module is used to construct a membership matrix R and a weight vector W, and to perform calculations based on the membership matrix R and the weight vector W to determine the risk assessment level of the landslide dammed lake.

[0046] The data output module is used to output the final determined risk assessment level of the landslide dammed lake.

[0047] 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-described fuzzy evaluation method for quantitative classification of landslide dam risks.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] This invention provides a fuzzy evaluation method for quantitative classification of landslide dammed lake risks. It addresses the problem that traditional methods, whether using judgment matrices or fuzzy preference membership matrices, cannot quantify the weights of evaluation indicators for landslide dammed lake risk levels. This method solves the problem of inconsistent weight assignments by experts to evaluation factors.

[0050] The fuzzy evaluation method for quantitative risk classification of landslide-dammed lakes proposed in this invention has a reasonable evaluation index system and classification, feasible information acquisition methods, scientific weight vectors, and reliable risk level evaluation results. This method has good applicability and is suitable for risk level evaluation of all landslide-dammed lakes. Detailed Implementation

[0051] To better explain the present invention, the main contents of the present invention are further illustrated below with reference to specific embodiments, but the contents of the present invention are not limited to the following embodiments.

[0052] The method of the present invention will be explained below using two landslide-dammed lakes: landslide-dammed lake A and landslide-dammed lake B as examples.

[0053] This invention provides a fuzzy evaluation method for quantitative classification of landslide dammed lake risks, comprising the following steps:

[0054] 1) Determine the set of risk assessment factors U and the set of risk assessment levels V for landslide-dammed lakes. A mathematical model for quantitative evaluation of the risk classification of landslide dammed lakes was constructed.

[0055] The mathematical model for quantitative assessment of landslide dam risk classification consists of three parts, as shown in the following formula: Part 1 is the objective function of the model; Part 2 is the data set of the model, including the set of evaluation factors U and the set of evaluation levels V; Part 3 is the set of operators of the model, including the membership calculation function, weight calculation function, fuzzy operator function, etc., to solve the problems of weight vector quantification and evaluation level quantification.

[0056]

[0057] In the formula:

[0058] U represents the set of risk assessment factors for landslide dammed lakes;

[0059] D is the set of risk assessment factors for the landslide dam, which includes m elements, namely d1, d2, ..., dm. m In this specific embodiment, m=4, and d1, d2, d3, and d4 are the volume of the landslide dammed lake, the amount of water flowing into the lake, the particle size of the landslide dam, and the volume of the landslide dam, respectively.

[0060] L represents the set of evaluation factors for the loss of the landslide dammed lake, which includes n elements, namely l1, l2, ..., l n In this specific implementation, n=4, and l1, l2, l3, and l4 correspond to the dangerous population, town size, infrastructure scale, and environmental modulus, respectively.

[0061] V represents the set of risk assessment levels for the landslide dammed lake, which includes p elements, namely v1, v2, ..., v p In this specific implementation, p=4, and v1, v2, v3, and v4 correspond to Level I, Level II, Level III, and Level IV, respectively, which are low risk, medium risk, high risk, and extremely high risk.

[0062] For extremely high and high risks, engineering measures such as excavating diversion channels and non-engineering measures such as joint scheduling of upstream and downstream reservoirs can be used for disposal; for low risks, no disposal is required for the time being; for medium risks, measures should be adopted according to the actual situation.

[0063] R is the membership matrix from the set of risk assessment factors U of the landslide dammed lake to the set of risk assessment levels V of the landslide dammed lake; r ik x represents the membership degree of the i-th evaluation factor to the k-th evaluation level; i Assign a value to the degree to which the i-th evaluation factor belongs to a certain evaluation level; a ik Assign boundary values ​​to the evaluation factors within their assigned range;

[0064] W is the weight vector corresponding to the set of risk assessment factors U for the landslide dammed lake; w i Let f1(w) be the weight of the i-th evaluation factor; f1(w) is the weight transformation formula.

[0065] G represents the risk assessment level vector for the landslide dammed lake. The p-th assessment level in the set of risk assessment levels for landslide dammed lakes In this specific implementation, G is a vector containing four elements: g1, g2, g3, and g4; max() is the function for finding the maximum value.

[0066] 2) Based on the assignment of values ​​for the degree to which each evaluation factor belongs to each evaluation level, calculate the membership degree of each evaluation factor in the evaluation factor set U to each evaluation level in the evaluation level set V, and construct the membership degree matrix R.

[0067] Based on the above mathematical model, the risk assessment of the landslide dammed lake comprises eight evaluation factors, each of which can be divided into four intervals, corresponding to the following value intervals: [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 five factors of landslide dammed lake volume d1, inflow water volume d2, landslide dam particle size d3, landslide dam volume d4, and at-risk population l1 can be directly assigned values ​​using linear interpolation. The three indicators of town size l2, infrastructure scale l3, and environmental modulus l4 can be assigned values ​​based on the quantification of the number of affected towns, facilities, and ecological environments, with 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 in the following formula.

[0068] ;

[0069] = ; = ;

[0070] = ; = .

[0071] 3) Construct the weight vector W. Calculate the weight of each evaluation factor within each level, including the weight of each evaluation factor in the landslide dam hazard evaluation factor set, the weight of each evaluation factor in the landslide dam loss evaluation factor set, and the weights of landslide dam hazard and landslide dam loss in the landslide dam risk evaluation factor set. Finally, integrate these weights to obtain the final weight vector.

[0072] The calculation method for the weights of each evaluation factor within each level is as follows: First, establish a fuzzy evaluation matrix B, which can comprehensively reflect the preferences given by experts, and the sum of the matrix elements can be less than 1; second, convert 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 the indicators within each level are obtained based on constraints such as the sum of the indicator weights equaling 1. The specific operation is as follows:

[0073] 3.1) Establish fuzzy evaluation matrix B:

[0074] B=

[0075] Among them, b 11 b 12 ... b 1f These represent the first evaluation factor j1 within the same level relative to the first, second, ..., fth evaluation factors j1, j2, ..., j... f The importance scores are assigned sequentially; b 11 b 22 ... b ff All scores are {(0.50, 1.0)}, meaning that all scores indicate that a certain evaluation factor is 0.5 in importance relative to itself.

[0076] The y-th evaluation factor j y Relative to the z-th evaluation factor j z Importance rating (b) yz for{( , ),( , ),......,( , )}, , ... The y-th evaluation factor j y Relative to the z-th evaluation factor j zImportance indicators are , ... The proportion, + +......+ 1, among which, ( , () represents the score result of the y-th evaluation factor j y Relative to the z-th evaluation factor j z Importance indicators are The percentage of scores given was [percentage missing] of the total scores. .

[0077] Taking the risk assessment factors of landslide dams as an example, 10 experts were selected to score the importance of four indicators: d1, d2, d3, and d4. The scoring rules are as follows:

[0078] Each indicator is rated as 0.5 based on its relative importance to itself.

[0079] The importance score of each indicator relative to other indicators: Taking d1 and d2 as examples: If d1 is considered to be more important than d2, then the importance index range of d1 relative to d2 is (0.5, 1]; if d1 is considered to be less important than d2, then the importance index range of d1 relative to d2 is [0, 0.5].

[0080] The sum of the importance indicators of d1 relative to d2 and the importance indicators of d2 relative to d1 for the same expert is 1.

[0081]

[0082] The data in the first row represents the importance scores of d1 relative to d1, d2, d3, and d4, and so on. Taking the element in the second column of the first row as an example, it illustrates the meaning of the parameters: Out of 10 experts, 2 experts consider d1 less important than d2, with an importance index of 0.45; 8 experts consider d1 more important than d2, with 3 experts considering an importance index of 0.55, 3 experts considering an importance index of 0.6, 1 expert considering an importance index of 0.65, and 1 expert considering an importance index of 0.9. Other parameters follow the same pattern and will not be elaborated further.

[0083] 3.2) Convert matrix B into weight matrix C

[0084] C= ,

[0085] The conversion method is as follows: the y-th evaluation factor j y Relative to the z-th evaluation factor j zweight = × + × +......+ × .

[0086] The weight matrix C obtained after transforming the fuzzy evaluation matrix B, which was established by the above 10 experts scoring the importance of the four evaluation factors d1, d2, d3, and d4, is as follows:

[0087]

[0088] The transformation process is illustrated using the element in the first row and second column as an example:

[0089] 0.45×0.2+0.55×0.3+0.6×0.3+0.65×0.1+0.9×0.1=0.590, and the other parameters follow the same logic, which will not be elaborated further.

[0090] 3.3) Based on the importance ranking of each evaluation factor, transpose the weight matrix into a weight ranking matrix C. T The evaluation factors are ranked from most important to least important:

[0091] = ,

[0092] in, ... Representative evaluation factors Relative to evaluation factors ... The weight, ... Representative evaluation factors Relative to evaluation factors ... The weights, and so on, ... Representative evaluation factors Relative to evaluation factors , , ...... The weights are assigned sequentially, from the first column to the last column in each row. For example... < <...< . , ... For evaluation factors j1, j2, ..., j f The corresponding evaluation factors are sorted from most important to least important.

[0093] Transpose the weight matrix C of the four evaluation factors d1, d2, d3, and d4 in step 3.2) to obtain the following weight ranking matrix C. T :

[0094]

[0095] The first row contains the evaluation factors. Relative to evaluation factors The weights are shown in the first row, and the second row represents the evaluation factors. Relative to evaluation factors The weights, and so on. The evaluation factors d1, d2, d3, and d4 are sorted according to their importance from highest to lowest. =d1, =d3, =d2, =d4.

[0096] 3.4) Calculate the weights of each level based on constraints, including:

[0097] + + +......+ =1; - = - ; - = - ; - = - And so on; - = - .

[0098] in Evaluation factors weights, Evaluation factors Weights, ... Evaluation factors The weight.

[0099] Obtained through the above method , , ...... By determining the weights, we can obtain j1, j2, ..., j f The weights w1, w2, ..., w f .

[0100] Since each indicator is rated 0.5 based on its relative importance to itself, b 11 b 22 ... b ff If all are {(0.50, 1.0)}, then , ... Both are 0.500, and , ... All are 0.500, therefore, the weight sorting matrix C in step 3.3) is used. T Calculate the weights of the four indicators d1, d2, d3, and d4, with the following constraints:

[0101]

[0102] Calculated from the above formula, the weights w of the four indicators d1, d2, d3, and d4 are 0.32875, 0.24375, 0.24875, and 0.17875, respectively.

[0103] 3.5) By calculating the weights of the evaluation factors within each level through steps 3.1) to 3.4), the weights of the four indicators d1, d2, d3, and d4 in the landslide dam risk evaluation factor are 0.32875, 0.24375, 0.24875, and 0.17875, respectively; the weights of the four indicators l1, l2, l3, and l4 in the landslide dam loss evaluation factor are 0.393, 0.268, 0.228, and 0.113, respectively; and the weights of landslide dam risk D and breach loss L are 0.525 and 0.475, respectively.

[0104] 3.6) Matrix multiplication is used to integrate the weights of each level. The calculated weight vector of 8 indicators is W = [0.173, 0.128, 0.131, 0.094, 0.186, 0.127, 0.108, 0.053].

[0105] 4) Based on the membership matrix R and weight vector W, determine the risk assessment level of the landslide dammed lake.

[0106] The membership degree R matrix for landslide dammed lake A and landslide dammed lake B is calculated as follows.

[0107]

[0108] The risk level assessment vector G of the landslide dammed lake is calculated based on the membership degree matrix R and the weight vector W:

[0109] G 甲 =W×R 甲 =[g1=0.645 g2=0.309 g3=0.046 g4=0.000]

[0110] G 乙 =W×R 乙 =[g1=0.331 g2=0.460 g3=0.119 g4=0.091]

[0111] Based on the risk level assessment vector G of the landslide dammed lake, the risk level is determined by the grade() function. The risk levels of landslide dammed lake A and landslide dammed lake B are Level I and Level II, respectively.

[0112] grade() 甲 =max(g1, g2, g3, g4)=g1=Level I

[0113] grade() 乙 =max(g1, g2, g3, g4)=g2=Level II.

[0114] Therefore, the risk level of landslide dammed lake A is Level I, corresponding to a low risk level; and the risk level of landslide dammed lake B is Level II, corresponding to a medium risk level.

[0115] In this specific embodiment, a fuzzy evaluation system for quantitative classification of landslide dammed lake risks is also provided to implement the aforementioned fuzzy evaluation method for quantitative classification of landslide dammed lake risks, including:

[0116] The data storage module is used to store the assignment of the degree to which each evaluation factor in the set of risk assessment factors of the landslide dam belongs to each evaluation level in the set of risk assessment levels of the landslide dam, and the importance scoring results between each pair of evaluation factors within each level.

[0117] The data processing module is used to construct a membership matrix R and a weight vector W, and to perform calculations based on the membership matrix R and the weight vector W to determine the risk assessment level of the landslide dammed lake.

[0118] The data output module is used to output the final determined risk assessment level of the landslide dammed lake.

[0119] In this specific embodiment, a computer device is also provided, including a memory and a processor. 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-described fuzzy evaluation method for quantitative classification of landslide dam risks. The above are merely preferred embodiments of the present invention and are 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 within the scope of protection of the present invention.

Claims

1. A fuzzy evaluation method for quantitative classification of landslide dammed lake risks, characterized in that: Includes the following steps: 1) Determine the set of risk assessment factors U and the set of risk assessment levels V for landslide-dammed lakes. 2) Based on the assignment of the degree to which each evaluation factor belongs to each evaluation level, calculate the membership degree of each evaluation factor in the evaluation factor set U to each evaluation level in the evaluation level set V, and construct the membership degree matrix R; 3) Based on the importance scores of pairwise comparisons of evaluation factors within each level, construct a fuzzy evaluation matrix and calculate the weight of each evaluation factor within each level, and finally integrate to obtain the final weight vector W; where b represents the importance score of the y-th evaluation factor relative to the z-th evaluation factor in the fuzzy evaluation matrix. yz for{( , ),( , ),......,( , )}， 、 、......、 The importance indices of the y-th evaluation factor relative to the z-th evaluation factor are respectively: 、 、......、 The proportion, + +......+ 1; The method for calculating the weight of each evaluation factor within each level includes: converting the fuzzy evaluation matrix into a weight matrix C, where the weight of the y-th evaluation factor relative to the z-th evaluation factor is... = × + × +......+ × Based on the importance ranking of each evaluation factor, the weight matrix is ​​transposed into a weight ranking matrix C. T ; Solve for the weights of each evaluation factor based on the constraints; 4) Determine the risk assessment level of the landslide dam based on the membership matrix R and the weight vector W.

2. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to claim 1, characterized in that: The evaluation factor set U includes the landslide dam hazard evaluation factor set D= The set of evaluation factors for landslide dammed lake losses, L= 。 3. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to claim 2, characterized in that: The evaluation factors in the set of risk assessment factors D for landslide dams include the volume of the landslide dammed lake, the amount of water flowing into the lake, the particle size of the landslide dam, and the volume of the landslide dam.

4. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to claim 3, characterized in that: The evaluation factors in the set L of the landslide dam loss assessment factors include the at-risk population, town size, infrastructure scale, and environmental modulus.

5. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to claim 4, characterized in that: In the set of risk assessment levels for the landslide dammed lake, p=4, V= ,in, These represent Level I, Level II, Level III, and Level IV, respectively, which are low risk, medium risk, high risk, and extremely high risk.

6. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to claim 5, characterized in that: In step 2), the membership degree r of the i-th evaluation factor to the k-th evaluation level is... ik The calculation method includes: dividing the scores of each evaluation factor into four assignment intervals, wherein the 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]; Assign a value to the degree to which the i-th evaluation factor belongs to the k-th evaluation level, and calculate the membership degree based on the assignment range and assignment situation; where, the membership degree r of the i-th evaluation factor to the k-th evaluation level is... ik The calculation formula is as follows: In the formula, Let be the score representing the degree to which the i-th evaluation factor belongs to the k-th evaluation level, where i = 1, 2, 3, ..., 8; k = 1, 2, 3, 4.

7. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to claim 6, characterized in that: The volume of the landslide dammed lake, the amount of water flowing into the lake, the particle size of the landslide dam, the volume of the landslide dam, and the population at risk were assigned values ​​using linear interpolation. The three evaluation factors, namely town size, infrastructure scale, and environmental modulus, were assigned values ​​based on the quantification of the number of affected towns, facilities, and ecological environments.

8. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to any one of claims 1 to 7, characterized in that: Step 3) The calculation method for the weights of each evaluation factor within each level includes the following steps: 3.1) Establishing the fuzzy evaluation matrix B: B = Among them, b 11 b 12 ... b 1f These represent the first evaluation factor j1 within the same level relative to the first, second, ..., fth evaluation factors j1, j2, ..., j... f The importance scores are assigned sequentially; the y-th evaluation factor j y Relative to the z-th evaluation factor j z Importance rating (b) yz for{( , ),( , ),......,( , )}， 、 、......、 The y-th evaluation factor j y Relative to the z-th evaluation factor j z Importance indicators are 、 、......、 The proportion, + +......+ 1, among which, ( , () represents the score result of the y-th evaluation factor j y Relative to the z-th evaluation factor j z Importance indicators are The percentage of scores given was [percentage missing] of the total scores. 3.2) Convert the fuzzy evaluation matrix B into a weight matrix C. (The fuzzy evaluation matrix B = ...) Convert to weight matrix The conversion method is as follows: the y-th evaluation factor j y Relative to the z-th evaluation factor j z weight = × + × +......+ × 3.3) Based on the importance ranking of each evaluation factor, the weight matrix is ​​transposed into a weight ranking matrix C. T : = ,in, 、......、 Representative evaluation factors Relative to evaluation factors 、......、 The weight, 、......、 Representative evaluation factors Relative to evaluation factors 、......、 The weights, and so on, 、......、 Representative evaluation factors Relative to evaluation factors 、 、....... The weights are calculated as follows: the weights of the first column in each row increase sequentially from the last column to the first column. 、 、....... For evaluation factors j1, j2, ..., j f The evaluation factors are sorted from most important to least important; 3.4) Evaluation factors are obtained based on constraints. 、 、....... By determining the weights, we can obtain the evaluation factors j1, j2, ..., j f 3.5) Calculate the weights of the evaluation factors within each level of the risk assessment factor set U of the landslide dammed lake through 3.1)~3.4).

9. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to claim 8, characterized in that: In step 3.4), the constraints are as follows: ＋ ＋ ＋......＋ =1； － = － ； － = － ； － = － And so on; － = － ;in Evaluation factors weights, Evaluation factors Weights, ... Evaluation factors The weight.

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

11. The fuzzy evaluation method for quantitative classification of landslide dammed lake risk according to any one of claims 1 to 7, characterized in that: Step 4) includes the following steps: 4.1) Calculate the risk assessment level vector G of the landslide dammed lake based on the membership matrix R and the weight vector W, where G = W × R = ， The p-th assessment level in the set of risk assessment levels for landslide dammed lakes 4.2) Based on the risk level assessment vector G of the landslide dammed lake, the risk level is determined according to the objective function grade(); the calculation formula of the objective function grade() is as follows: grade() 。 12. A fuzzy evaluation system for quantitative classification of landslide dammed lake risks, characterized in that: The method for quantitative classification and fuzzy evaluation of landslide dammed lake risk according to any one of claims 1 to 11 comprises: a data storage module for storing the assignment of the degree to which each evaluation factor in the set of landslide dammed lake risk evaluation factors belongs to each evaluation level in the set of landslide dammed lake risk evaluation levels, and the importance scoring results between each pair of evaluation factors within each level; a data processing module 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 to determine the risk evaluation level of the landslide dammed lake; and a data output module for outputting the finally determined risk evaluation level of the landslide dammed lake.

13. A computer device, characterized in that: The system includes 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 classification of landslide dam risks according to any one of claims 1 to 11.

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