Comprehensive evaluation method and device for long-term seepage safety of dam

Abstract

The present invention provides a comprehensive evaluation method for the long-term seepage safety of a dam. The method includes: establishing a comprehensive evaluation index system for the seepage safety of the dam, where the evaluation index system includes multiple evaluation indexes; determining the grading interval division criteria for each evaluation index; establishing a comprehensive evaluation model for the seepage safety of the dam according to the division criteria; calculating the subjective and objective combined weights of the evaluation indexes; and calculating the comprehensive membership degree of the evaluation grade according to the comprehensive evaluation model for the seepage safety of the dam and the subjective and objective combined weights of the evaluation indexes, and determining the comprehensive evaluation grade for the seepage safety of the dam corresponding to the evaluation period. By using the solution of the present invention, the long-term seepage safety situation of the dam can be effectively evaluated, providing reliable information for daily operation and maintenance personnel and helping the operation and maintenance personnel formulate effective operation and maintenance plans.

Description

Technical Field

[0001] The present invention relates to the field of dam seepage safety monitoring in water conservancy and hydropower projects, and particularly relates to a method and device for comprehensive evaluation of long-term dam seepage safety. Background Art

[0002] The dam body structure and the geological conditions where the dam is located are usually complex, and are simultaneously affected by various factors in the external environment. Therefore, comprehensively considering the external environmental quantities and various seepage safety effect quantities such as seepage flow rate, seepage pressure, and seepage around the dam, and establishing an accurate and reliable comprehensive evaluation model for seepage safety is an important means to ensure the long-term safe and stable operation of the dam. Currently, the commonly used comprehensive evaluation models for dam seepage safety include fuzzy mathematics, extension, cloud model, set pair analysis, grey clustering, etc.

[0003] In the process of comprehensive evaluation of dam seepage safety, due to the randomness of the change of seepage safety monitoring data, the finiteness of the number of monitoring points and the amount of measured data, the limitations of researchers' cognition, and the fuzziness of the evaluation grade division, the comprehensive evaluation of seepage safety is a complex process with multiple uncertainties coexisting such as randomness, grey nature, unascertained nature, and fuzziness. Although the existing comprehensive evaluation models for seepage safety can obtain relatively reasonable evaluation results, most of them are difficult to comprehensively consider the above-mentioned multiple uncertainties, resulting in the reliability of the evaluation results needing to be improved. Summary of the Invention

[0004] The present invention provides a method and device for comprehensive evaluation of long-term dam seepage safety, so as to effectively evaluate the long-term seepage safety of the dam, provide reliable information for daily operation and maintenance personnel, and help the operation and maintenance personnel formulate effective operation and maintenance plans.

[0005] For this purpose, the present invention provides the following technical solutions:

[0006] On the one hand, the present invention provides a method for comprehensive evaluation of long-term dam seepage safety, and the method includes:

[0007] Establishing a comprehensive evaluation index system for dam seepage safety, where the evaluation index system includes multiple evaluation indexes;

[0008] Determining the grading interval division standard for each evaluation index;

[0009] Establishing a comprehensive evaluation model for dam seepage safety according to the division standard;

[0010] Calculating the subjective and objective combined weights of the evaluation indexes;

[0011] Calculating the comprehensive membership degree of the evaluation grade according to the comprehensive evaluation model for dam seepage safety and the subjective and objective combined weights of the evaluation indexes, and determining the comprehensive evaluation grade of dam seepage safety for the corresponding evaluation period.

[0012] Optionally, the evaluation index system includes monitoring indexes and / or simulation indexes;

[0013] The monitoring indexes include environmental indexes and effect quantity indexes; the environmental indexes include any one or more of the following: upstream water level, downstream water level; the effect quantity indexes include any one or more of the following: seepage flow rate, seepage pressure, seepage around the dam;

[0014] The simulation indexes include any one or more of the following: maximum hydraulic gradient of the anti-seepage curtain, elevation of the emergence point of the dam body.

[0015] Optionally, the determination of the grading interval division criteria for each evaluation index includes:

[0016] Determine the comprehensive evaluation grade of the dam seepage safety;

[0017] According to the evaluation grade, use the golden section method to determine the grading interval division criteria for each evaluation index.

[0018] Optionally, the determination of the comprehensive evaluation grade of the dam seepage safety includes: dividing the comprehensive evaluation grade of the dam seepage safety into five grades, namely: safe L1, basically safe L2, slightly dangerous L3, relatively dangerous L4, dangerous L5.

[0019] Optionally, the establishment of the comprehensive evaluation model for the dam seepage safety according to the division criteria includes:

[0020] Use the fuzzy C-means clustering FCM algorithm to determine the distribution characteristics of the evaluation index data;

[0021] According to the distribution characteristics, determine the membership degree of each evaluation index for each grade.

[0022] Optionally, the use of the FCM algorithm to determine the distribution characteristics of the evaluation index data includes:

[0023] Use the Tianying optimization AO algorithm to perform adaptive optimization on the initial clustering center of the FCM clustering algorithm as the optimized FCM clustering algorithm;

[0024] Use the optimized FCM clustering algorithm to divide the distribution interval of the evaluation index data;

[0025] According to the distribution interval of the evaluation index data, determine the distribution characteristics of the evaluation index data.

[0026] Optionally, the determination of the membership degree of each evaluation index for each grade according to the distribution characteristics includes:

[0027] Use the cloud model to optimize the possible value interval in the blind number expression and calculate the weighted cloud of each evaluation index;

[0028] According to the weighted cloud, convert the grade interval of the evaluation grade index into the grade cloud of the evaluation index;

[0029] Calculate the membership degree of each evaluation index to each grade according to the grade cloud.

[0030] Optionally, the calculation of the subjective and objective combined weight of the evaluation index includes:

[0031] Calculate the subjective weight of the evaluation index by using the subjective weighting method based on hesitant cloud - BWM;

[0032] Calculate the objective weight of the evaluation index according to the objective weighting method based on entropy weight - CRITIC;

[0033] Based on the principle of minimum relative entropy, fuse and calculate the subjective weight and objective weight of the evaluation index to obtain the subjective and objective combined weight of the evaluation index.

[0034] Optionally, the calculation of the comprehensive membership degree of the evaluation grade and the determination of the comprehensive evaluation grade of the dam seepage safety corresponding to the evaluation period include:

[0035] Perform weighted summation on the membership degree of the evaluation index to each evaluation grade and the combined weight of the evaluation index to obtain the comprehensive membership degree of the dam seepage safety state to each evaluation grade;

[0036] Determine the comprehensive evaluation grade of the dam seepage safety corresponding to the evaluation period according to the principle of maximum membership degree.

[0037] On the other hand, the present invention also provides a device for comprehensive long - term seepage safety evaluation of a dam, and the device includes:

[0038] An evaluation index system establishment module, which is used to establish a comprehensive evaluation index system for dam seepage safety, and the evaluation index system includes multiple evaluation indexes;

[0039] A grade interval division module, which is used to determine the grade interval division standard of each evaluation index;

[0040] A model establishment module, which is used to establish a comprehensive evaluation model for dam seepage safety according to the division standard;

[0041] A weight calculation module, which is used to calculate the subjective and objective combined weight of the evaluation index;

[0042] An evaluation module, which is used to calculate the comprehensive membership degree of the evaluation grade according to the comprehensive evaluation model of the dam seepage safety and the subjective and objective combined weight of the evaluation index, and determine the comprehensive evaluation grade of the dam seepage safety corresponding to the evaluation period.

[0043] On the other hand, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the steps of the comprehensive evaluation of the long-term seepage safety of the dam are executed.

[0044] The comprehensive evaluation method and device for the long-term seepage safety of the dam provided by the present invention establish a comprehensive evaluation index system for the seepage safety of the dam, determine the grading interval division standard of each evaluation index, establish a comprehensive evaluation model for the seepage safety of the dam according to this division standard, calculate the subjective and objective combined weights of the evaluation indexes, and calculate the comprehensive membership degree of the evaluation grade according to the comprehensive evaluation model for the seepage safety of the dam and the subjective and objective combined weights of the evaluation indexes, so as to determine the comprehensive evaluation grade of the seepage safety of the dam corresponding to the evaluation period. By using the solution of the present invention, the long-term seepage safety condition of the dam can be effectively evaluated, reliable information can be provided for daily operation and maintenance personnel, and it helps the operation and maintenance personnel to formulate effective operation and maintenance plans.

[0045] Furthermore, by comprehensively considering external environmental quantities and various seepage safety effect quantities such as seepage flow rate, seepage pressure, and seepage around the dam, the established comprehensive evaluation model for seepage safety is more accurate and reliable.

[0046] Furthermore, the FCM clustering algorithm optimized by the Tianying algorithm is used to independently mine the distribution characteristics of the evaluation index data, which can more objectively and reasonably divide the data distribution interval; by introducing the cloud model to improve the possible value interval in the blind number expression, various uncertainties such as randomness, fuzziness, grayness, and unascertainty in the evaluation process can be effectively considered, and a reliable evaluation of the long-term seepage safety state of the dam can be realized.

[0047] Furthermore, a subjective weighting method based on hesitant cloud - BWM is also proposed. The computationally simple BWM is used to replace the traditional Analytic Hierarchy Process (AHP) method that requires a large number of pairwise comparisons and consumes a lot of time and effort in calculation, and the advantage that the hesitant cloud language term set can consider the randomness, fuzziness, and hesitancy in the expert decision-making process is fully utilized, and the expert decision-making information is expressed more accurately.

[0048] Furthermore, an objective weighting method based on entropy weight - CRITIC method is used to calculate the objective weights of the evaluation indexes, and the subjective and objective combined weights are calculated based on the principle of minimum relative entropy. The subjective experience and cognition of experts and the objective information of index data are comprehensively considered, and more reasonable index weight coefficients can be obtained, improving the accuracy of the evaluation results. Description of the Drawings

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0050] Figure 1 is a flowchart of a method for comprehensive evaluation of long-term seepage safety of dams provided by the present invention;

[0051] Figure 2 is a schematic diagram of evaluation indicators for establishing a comprehensive evaluation index system for dam seepage safety in the method of the present invention;

[0052] Figure 3 is a schematic diagram of the process of establishing a comprehensive evaluation model for dam seepage safety in the embodiments of the present invention;

[0053] Figure 4 is a schematic diagram of the structure of a device for comprehensive evaluation of long-term seepage safety of dams provided by the present invention. Specific Embodiments

[0054] The following will detail the specific embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0055] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] Aiming at the problems that the existing comprehensive evaluation model for dam seepage safety lacks comprehensive consideration of various uncertainties such as randomness, fuzziness, grayness, and unascertainedness in the evaluation process, and most of the subjective weighting methods for evaluation indicators are difficult to accurately express the subjective information of experts, the present invention proposes a method and device for comprehensive evaluation of long-term seepage safety of dams. Based on the improved blind number theory, comprehensively considering external environmental quantities and various seepage safety effect quantities such as seepage flow, seepage pressure, and seepage around the dam, an accurate and reliable comprehensive evaluation model for seepage safety is established, and then the long-term seepage safety of the dam is effectively and accurately evaluated.

[0057] As Figure 1 shown, it is a flowchart of a method for comprehensive evaluation of long-term seepage safety of dams provided by the present invention, including the following steps:

[0058] In step 101, a comprehensive evaluation index system for the seepage safety of the dam is established, and the evaluation index system includes multiple evaluation indexes.

[0059] The evaluation index system may include monitoring indexes and / or simulation indexes. Among them:

[0060] The monitoring indexes may include, but are not limited to, any one or more of the following: environmental quantity indexes such as upstream water level and downstream water level, effect quantity indexes such as seepage flow rate, seepage pressure, and seepage around the dam, etc.

[0061] The simulation indexes may include, but are not limited to, any one or more of the following: the maximum hydraulic gradient of the anti-seepage curtain, the elevation of the emergence point of the dam body, etc.

[0062] These simulation indexes also have an important impact on the seepage safety of the dam.

[0063] For example, in some embodiments, the evaluation indexes for establishing the comprehensive evaluation index system for the seepage safety of the dam are as Figure 2 shown.

[0064] In step 102, the grading interval division criteria for each evaluation index are determined.

[0065] For example, the comprehensive evaluation grade of the dam seepage safety can be divided into five grades: {safe (L1), basically safe (L2), slightly dangerous (L3), relatively dangerous (L4), dangerous (L5)}.

[0066] In view of the problem that most current scholars usually determine the grading criteria of evaluation indexes manually according to engineering experience, with strong subjectivity and ignoring the objective distribution law of index data, in a non-limiting embodiment, based on the actual data of the comprehensive evaluation indexes of the dam seepage safety, the golden section method is used to determine the grading interval division criteria for each evaluation index, as shown in the following formula (1):

[0067]

[0068] In the formula, x min and x max respectively represent the minimum value and the maximum value of the actual data of the evaluation index, and H represents the difference between the maximum value and the minimum value in the actual data of the evaluation index.

[0069] Of course, other algorithms can also be used to determine the grading interval division criteria for each evaluation index, and the embodiments of the present invention do not make any limitations in this regard.

[0070] In step 103, a comprehensive evaluation model for the seepage safety of the dam is established according to the division criteria.

[0071] In view of the problem that the traditional blind number theory fails to effectively consider ambiguity, and the possible value interval [x1, x n of the evaluation index data distribution is usually obtained by artificial division, with strong subjectivity and it is difficult to ensure the rationality of the division result. In the embodiments of the present invention, a comprehensive evaluation model for dam seepage safety can be established based on the improved blind number theory. First, the fuzzy C - means clustering algorithm is used to determine the distribution characteristics of the evaluation index data, so as to improve the rationality of the division of the distribution interval of the evaluation index data in the traditional blind number theory. Then, by introducing the cloud model theory, the possible value interval in the traditional blind number theory is converted into cloud characteristic parameters, and the advantage that the cloud model can comprehensively consider randomness and ambiguity in the evaluation process is utilized to make up for the defect that the traditional blind number theory is difficult to effectively consider ambiguity in the evaluation process, and realize the comprehensive consideration of various uncertainties such as randomness, fuzziness, grayness and unascertainty in the evaluation process.

[0072] The specific process of establishing the comprehensive evaluation model for dam seepage safety is as follows:

[0073] 1. Use the fuzzy C - means clustering algorithm to determine the distribution characteristics of the evaluation index data.

[0074] The fuzzy C - mean (FCM) clustering algorithm is a partition - based method, which is widely used in exploratory data analysis, data mining, image retrieval and other fields. Although the FCM clustering algorithm has good clustering effect, it is sensitive to the initial clustering centers, has poor robustness, and is easy to fall into local optimal solutions. Using an intelligent optimization algorithm to adaptively optimize the initial clustering centers of the FCM clustering algorithm can effectively solve the above problems.

[0075] Therefore, in the embodiments of the present invention, the Aquila Optimization (AO) algorithm can be first used to adaptively optimize the initial clustering centers of the FCM clustering algorithm. As the optimized FCM clustering algorithm, it is used to improve the clustering effect of the FCM clustering algorithm. Then, the optimized FCM clustering algorithm is used to divide the distribution interval of the evaluation index data to improve the rationality of the division of the distribution interval of the evaluation index data in the traditional blind number theory. Finally, the distribution characteristics of the evaluation index data can be determined according to the distribution interval of the evaluation index data.

[0076] 2. Determine the membership degrees of each evaluation index for each level according to the distribution characteristics.

[0077] The blind number theory is an uncertainty theory developed on the basis of the unascertained mathematics theory, which can process composite information with multiple uncertainties coexisting or intersecting. At present, the blind number theory is widely used in the fields of water environment, construction engineering, disaster prevention and control, etc.

[0078] Although the blind number theory has good applicability in the processing of uncertain information in many fields, it is still relatively rare in the research on dam seepage safety evaluation. And relevant research shows that the traditional blind number theory can only be effectively applied to random information, grey information, unascertained information, etc., and it is difficult to deal with composite information containing fuzzy information. Therefore, improving the traditional blind number theory to enable it to comprehensively process various uncertainties such as randomness, grey property, unascertainty, and fuzziness, and introducing the improved blind number theory into the research on dam seepage safety evaluation is of great significance for obtaining objective and reasonable dam seepage safety evaluation results.

[0079] For this reason, in a non - restrictive embodiment of the present invention, by introducing the cloud model theory, the possible value interval in the traditional blind number theory is converted into cloud characteristic parameters. Utilizing the advantage of the cloud model that can comprehensively consider randomness and fuzziness in the evaluation process, it makes up for the defect that the traditional blind number theory is difficult to effectively consider fuzziness in the evaluation process, and realizes the comprehensive consideration of various uncertainties such as random, fuzzy, grey, and unascertained in the evaluation process.

[0080] At the same time, referring to Figure 3 , Figure 3 shows the schematic process diagram of establishing a comprehensive dam seepage safety evaluation model in the embodiment of the present invention.

[0081] In the embodiment of the present invention, the cloud model is used to improve the possible value interval in the blind number expression, and the specific steps are as follows:

[0082] Step 1: Convert the possible value interval [x1, x n in the traditional blind number expression {[x1, x n , f(x)} into cloud characteristic parameters (E x , E n , H e ), where E x , E n , H e represent the expectation, entropy, and hyper - entropy respectively.

[0083] Specifically, it can be calculated according to the following formulas (2) to (4):

[0084] E x =(x1 + x n ) / 2 (2)

[0085] E n =(x n - x1) / 6 (3)

[0086] H e =k (4)

[0087] Then the improved blind number theory can be expressed as {(E x, E n , H e ), f(x)} is represented as the blind number model f(x):

[0088]

[0089] In the formula, α i is the credibility corresponding to f(x) (E xi , E ni , H ei ), p is the order of f(x), is the total credibility of f(x).

[0090] Step 2: Take the credibility in the above formula (5) as the weight coefficient, and calculate the weighted cloud of each evaluation index, that is:

[0091]

[0092] Step 3: Based on the cloud model theory, convert the grade interval of the evaluation index into the grade cloud of the evaluation index according to formulas (2) to (4), and calculate the membership degree of each evaluation index to each grade according to the proximity degree calculation formula.

[0093] The calculation formulas of the proximity degree and the membership degree are as follows:

[0094]

[0095] In the formula, Ex i represents the expectation of the weighted cloud of the i-th (i = 1, 2,..., 9) evaluation index, represents the expectation of the j-th (j = 1, 2,..., 5) evaluation grade cloud, T ij represents the proximity degree of the i-th (i = 1, 2,..., 9) evaluation index to the j-th (j = 1, 2,..., 5) evaluation grade, D ij represents the membership degree of the i-th (i = 1, 2,..., 9) evaluation index to the j-th (j = 1, 2,..., 5) evaluation grade.

[0096] Step 104, calculate the subjective and objective combined weight of the evaluation index.

[0097] The determination of the weights of evaluation indicators is an important part in the comprehensive evaluation of dam seepage safety, and it plays an important role in the accuracy and reliability of the evaluation results. The existing methods for assigning weights to indicators can be roughly divided into three categories: subjective weighting methods, objective weighting methods, and subjective-objective combined weighting methods. Among them, the subjective-objective combined weighting method that can consider both the subjective experience and knowledge of experts and the objective laws of sample data is the most widely used method for assigning weights to indicators at present. However, the difficulty lies in how to accurately express expert decision-making information in the process of determining the subjective weights of evaluation indicators. The Delphi method, Analytic Hierarchy Process (AHP), Network Analytic Hierarchy Process (ANP), etc. are commonly used subjective weighting methods in evaluation research. Among them, the Analytic Hierarchy Process and its related improved methods are the most widely used.

[0098] Although the Analytic Hierarchy Process is widely used, when the number of evaluation indicators is large, the traditional Analytic Hierarchy Process requires a large number of pairwise comparison analyses, which is time-consuming and laborious and difficult to ensure the reliability of the results.

[0099] To solve the above problems, the Best-Worst Method (BWM) has been proposed in the industry. This method only needs to compare the best and worst indicators with other indicators, reducing the amount of index calculation and making it easier to ensure the consistency of experts' evaluation results for indicators. Currently, the Best-Worst Method has been widely used in the fields of evaluation, decision-making, etc. However, in the existing research on the Best-Worst Method, the original data of subjective weighting is obtained by experts' judgment based on knowledge and experience, with strong subjective randomness, and there are uncertainties such as randomness and fuzziness. Moreover, since it is difficult for experts to accurately give the importance degree of evaluation indicators when assigning weights to them, there is inevitably a hesitant psychology. The existing research on the Best-Worst Method fails to comprehensively consider the randomness, fuzziness, and hesitant psychology in the process of expert decision-making, so it is difficult to accurately express experts' evaluation information.

[0100] In order to effectively consider the randomness, fuzziness, and hesitancy in the process of experts' subjective judgment and more comprehensively handle the uncertain information in expert decision-making problems, in a non-limiting embodiment of the present invention, a subjective weighting method based on hesitant cloud - BWM can be used to calculate the subjective weights of evaluation indicators; calculate the objective weights of evaluation indicators according to the objective weighting method based on entropy weight - CRITIC; then, based on the principle of minimum relative entropy, fuse and calculate the subjective weights and objective weights of evaluation indicators to obtain the subjective-objective combined weights of evaluation indicators.

[0101] The calculation processes of the above-mentioned subjective weights, objective weights, and combined weights are described in detail below.

[0102] (1) Subjective weights

[0103] Suppose there are n comprehensive evaluation indicators for seepage safety C = {C1, C2, …, C n} and m experts, the steps to solve the subjective weight of indicators using the subjective weighting method based on hesitant cloud - BWM are as follows:

[0104] Step 1: Determine the optimal indicator (B) and the worst indicator (W) from the evaluation indicators C = {C1, C2, …, C n} according to the expert opinions.

[0105] Step 2: Establish the comparison vectors between the optimal indicator and other indicators.

[0106] In this step, compared with the traditional BWM that uses real numbers between 1 and 9 to express the importance between different evaluation indicators, the improved BWM based on hesitant cloud uses multiple linguistic terms in the hesitant cloud linguistic term set to express the importance between different evaluation indicators, so as to fully consider the randomness, fuzziness and hesitancy in expert decision - making.

[0107] For each expert, the superiority of the optimal indicator B compared with other indicators is represented by A Bj :

[0108] A Bj =(a Bj(1) , a Bj(2) , …, a Bj(h)) (9)

[0109] a Bj(i) =(Ex Bj(i) , En Bj(i) , He Bj(i) ) (10)

[0110] In the formula, a Bj(i) =(i = 1, 2, …, h; j = 1, 2, …, n) represents the linguistic terms constructed by the normal cloud model in the hesitant cloud linguistic term set, and h represents the number of linguistic term variables for comparison.

[0111] For the convenience of calculation, the linguistic term variables in the hesitant cloud linguistic term set are converted into comprehensive clouds:

[0112] A Bj =a Bj =(Ex Bj , En Bj , He Bj ) (11)

[0113] In the formula, Ex Bj , En Bj , He Bj represent the expectation, entropy and hyper - entropy respectively.

[0114] Then, the final comparison vector of the optimal indicator relative to other indicators is expressed as:

[0115] AB =(A B1 , A B2 , …, A Bn ) = (a B1 , a B2 , …, a Bn ) (12)

[0116] Step 3: Establish the comparison vector of other indicators and the worst indicator.

[0117] Use the same processing method as in Step 2 to establish the final comparison vector of other indicators relative to the worst indicator as follows:

[0118] A W =(a 1W , a 2W , …, a nW ) (13)

[0119] Step 4: Calculate the cloud weights of each indicator based on the nonlinear programming method.

[0120] min ξ

[0121]

[0122] In the formula, ξ = (Ex ξ , En ξ , He ξ ).

[0123] Let Then F Bj can be calculated according to the following formula:

[0124]

[0125] Similarly, let Then F jW is calculated as:

[0126]

[0127] To solve formula (14), substitute formula (15) and formula (16) into formula (14), and then solve the above formula to calculate the optimal weight of each indicator and the minimum maximum absolute deviation ξ * .

[0128] Step 5: Calculate the consistency index and check the consistency ratio.

[0129] When a Bj × a jW = a BW , the consistency reaches the minimum value. To make aBj ×a jW = a BW holds. Introduce a cloud number into the following formula:

[0130]

[0131] Then the above formula (17) can be rewritten as:

[0132]

[0133] For a in the above formula BW , its upper limit can be determined by the 3En rule That is:

[0134]

[0135] Therefore, formula (17) can be converted into the following formula:

[0136]

[0137] In the formula, ξ′ is a clear value.

[0138] The maximum value of the solved ξ′ can be used as the consistency index CI.

[0139] The consistency ratio CR can be checked by the following formula:

[0140]

[0141] The value range of the CR value is [0, 1]. The closer the CR value is to 0, the higher the consistency, and vice versa.

[0142] Step 6: Calculate the final subjective weight of the index.

[0143] Aggregate the optimal index weights obtained by each expert according to the following formula to obtain the final subjective weight z of the index si :

[0144]

[0145] In the formula, is the optimal weight of the i-th index of the k-th expert, and λ k is the weight of expert k, and it can be assumed that the weights of each expert are the same.

[0146] (2) Objective weight

[0147] Calculate the objective weight of the evaluation index according to the objective weighting method based on entropy weight - CRITIC. The specific calculation steps are as follows:

[0148] Step 1: Assume there are n evaluation indicators, and each evaluation indicator has m sample data. Let x ij represent the i-th sample data of the j-th evaluation indicator. Obtain the original evaluation matrix Y = (Y ij ) m×n .

[0149] First, use the Z-score function to standardize the original evaluation indicator data matrix Y = (Y ij ) m×n to obtain the standardized matrix S = (S ij ) m×n . The calculation formula is as follows:

[0150]

[0151] Then, calculate the coefficient of variation v j (j = 1, 2,..., n). The calculation formula is as follows:

[0152]

[0153] In the above formula, and σ j are the mean and standard deviation of the j-th evaluation indicator respectively.

[0154] Step 2: Calculate the Pearson correlation coefficient r kl between each evaluation indicator to represent the correlation between evaluation indicators, so as to obtain the correlation matrix R. Then further calculate the independence coefficient η j (j = 1, 2,..., n) for measuring the information independence between metrics. The calculation formula is as shown in equations (25) to (27) below:

[0155]

[0156] Step 3: Calculate the comprehensive coefficient C j of each evaluation indicator according to the coefficient of variation v j and the independence coefficient η j (j = 1, 2,..., n) as shown in the following formula:

[0157] C j = v j × η j (j = 1, 2,..., n) (28)

[0158] Step 4: Calculate the entropy value of each evaluation indicator:

[0159]

[0160] In the formula, is the feature proportion. To prevent the feature proportion f ij from having 0 and 1 values that affect the calculation result of the entropy value, S ij can be added with 0.1

[100] , as shown in Equation (30):

[0161]

[0162] Step 5: Comprehensively consider the differences, correlations, and dispersions of the evaluation index data, and calculate the objective weights of the evaluation indexes according to Equations (31) to (33)

[0163]

[0164] In the formula, and respectively represent the weights calculated by the CRITIC method and the entropy weight method. The objective weight based on the entropy value - CRITIC method is obtained by using the additive combination calculation

[0165] (3) Combined weight

[0166] After calculating the subjective weight and objective weight of the evaluation index, based on the principle of minimum relative entropy, calculate the combined weight of each evaluation index

[0167] According to the principle of minimum relative entropy, the obtained combined weight z i should be as close as possible to the subjective weight z si and the objective weight z oi . Therefore, the following objective function is constructed:

[0168]

[0169] Solve using the Lagrange multiplier method to obtain:

[0170]

[0171] Step 105: According to the membership degree of the evaluation index for each level and the subjective and objective combined weights of the evaluation index, calculate the comprehensive membership degree of the evaluation level, and determine the comprehensive evaluation level of the dam seepage safety for the corresponding evaluation period

[0172] First, perform a weighted sum of the membership degree of the evaluation index for each evaluation level (that is, the membership degree of each evaluation index for each evaluation level calculated according to Equation (8)) and the subjective and objective combined weights of the evaluation index to obtain the comprehensive membership degree of the dam seepage safety state for each evaluation level (for example: the five evaluation levels of safe, basically safe, slightly dangerous, more dangerous, and dangerous)

[0173] Then, according to the principle of maximum membership degree, the comprehensive evaluation grade of seepage safety during the evaluation period is determined, so as to realize the long-term comprehensive evaluation of dam seepage safety.

[0174] Correspondingly, the present invention also provides a device for long-term comprehensive evaluation of dam seepage safety, as Figure 4 shown, which is a schematic structural diagram of the device.

[0175] The device for long-term comprehensive evaluation of dam seepage safety in this embodiment includes the following modules:

[0176] An evaluation index system establishment module 401, which is used to establish a comprehensive evaluation index system for dam seepage safety, and the evaluation index system includes multiple evaluation indexes;

[0177] A grade interval division module 402, which is used to determine the grade interval division standard for each evaluation index;

[0178] A model establishment module 403, which is used to establish a comprehensive evaluation model for dam seepage safety according to the division standard;

[0179] A weight calculation module 404, which is used to calculate the subjective and objective combined weights of the evaluation indexes;

[0180] An evaluation module 405, which is used to calculate the comprehensive membership degree of the evaluation grade according to the comprehensive evaluation model for dam seepage safety and the subjective and objective combined weights of the evaluation indexes, and determine the comprehensive evaluation grade of dam seepage safety corresponding to the evaluation period.

[0181] For the specific implementation manners of the above modules, reference may be made to the relevant descriptions in the method embodiments of the present invention above, and details are not described herein again.

[0182] The method and device for long-term comprehensive evaluation of dam seepage safety provided by the embodiments of the present invention establish a comprehensive evaluation model for dam seepage safety, calculate the subjective and objective combined weights of the evaluation indexes, calculate the comprehensive membership degree of the evaluation grade according to the comprehensive evaluation model for dam seepage safety and the subjective and objective combined weights of the evaluation indexes, and determine the comprehensive evaluation grade of dam seepage safety corresponding to the evaluation period, so as to effectively evaluate the long-term seepage safety situation of the dam, provide reliable information for daily operation and maintenance personnel, and help the operation and maintenance personnel formulate effective operation and maintenance plans.

[0183] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0184] In the above embodiments, the descriptions of the various embodiments each have their own emphasis. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0185] In several embodiments provided by the present invention, it should be understood that the disclosed device can be implemented in other ways.

[0186] The present invention also provides a storage medium, which is a computer-readable storage medium, having a computer program stored thereon. When the computer program runs, it can execute Figure 1 or Figure 3 some or all of the steps of the method shown in. The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, etc. The storage medium may also include a non-volatile memory or a non-transitory memory, etc.

[0187] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, a computer, a server, or a data center to another website, a computer, a server, or a data center in a wired or wireless manner.

[0188] The above embodiments of the present invention have been introduced in detail. Specific implementation manners are used in this article to elaborate on the present invention. The descriptions of the above embodiments are only used to help understand the method and system of the present invention. They are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. The content of this specification should not be construed as a limitation to the present invention. Therefore, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for long-term seepage safety of dams, characterized in that, The method includes: Establishing a comprehensive evaluation index system for the seepage safety of a dam, where the evaluation index system includes multiple evaluation indexes; Determining the grading interval division criteria for each evaluation index; Establishing a comprehensive evaluation model for the seepage safety of a dam according to the division criteria; Calculating the subjective and objective combined weights of the evaluation indexes; According to the comprehensive evaluation model for the seepage safety of the dam and the subjective and objective combined weights of the evaluation indexes, calculating the comprehensive membership degree of the evaluation grade and determining the comprehensive evaluation grade of the seepage safety of the dam for the corresponding evaluation period; The calculating the subjective and objective combined weights of the evaluation indexes includes: Calculating the subjective weight of the evaluation index by using a subjective weighting method based on hesitant cloud - BWM; Calculating the objective weight of the evaluation index according to an objective weighting method based on entropy weight - CRITIC; Fusing and calculating the subjective weight and the objective weight of the evaluation index based on the principle of minimum relative entropy to obtain the subjective and objective combined weight of the evaluation index; The steps for solving the subjective weight of the index by using the subjective weighting method based on hesitant cloud - BWM are as follows: Step 1: Determine the optimal index (B) and the worst index (W) from the evaluation indices C = {C1, C2, …, C n} according to the expert opinions; Step 2: Establishing a comparison vector between the optimal index and other indexes; Step 3: Establishing a comparison vector between other indexes and the worst index; Step 4: Calculating the cloud weight of each index based on a nonlinear programming method; minξ In the formula, ξ = (Ex ξ , En ξ , He ξ ); Let Then F can be calculated according to the following formula Bj : Similarly, let then F jW is calculated as: To solve formula (14), substitute formula (15) and formula (16) into formula (14), and then solve the above formula to calculate the optimal weights of each index and the minimum maximum absolute deviation ξ * ; Step 5: Calculating the consistency index and checking the consistency ratio; Step 6: Calculating the final subjective weight of the index.

2. The comprehensive long-term seepage safety evaluation method for dams according to claim 1, wherein, The evaluation index system includes monitoring indexes and / or simulation indexes; The monitoring indexes include environmental indexes and effect quantity indexes; the environmental indexes include any one or more of the following: upstream water level, downstream water level; the effect quantity indexes include any one or more of the following: seepage flow rate, seepage pressure, seepage around the dam; The simulation indexes include any one or more of the following: maximum hydraulic gradient of the anti - seepage curtain, elevation of the dam body seepage point.

3. The comprehensive evaluation method for long-term seepage safety of a dam according to claim 1, characterized in that, The determining the grading interval division criteria for each evaluation index includes: Determining the comprehensive evaluation grade of the seepage safety of the dam; According to the evaluation grade, using the golden section method to determine the grading interval division criteria for each evaluation index.

4. The comprehensive long-term seepage safety evaluation method for dams according to claim 3, characterized in that The determining the comprehensive evaluation grade of the seepage safety of the dam includes: Dividing the comprehensive evaluation grade of the seepage safety of the dam into five grades, namely: safe L1, basically safe L2, slightly dangerous L3, relatively dangerous L4, dangerous L5.

5. The comprehensive long-term seepage safety evaluation method for dams according to claim 1, characterized in that, The establishing a comprehensive evaluation model for the seepage safety of a dam according to the division criteria includes: Using the fuzzy C - means clustering FCM algorithm to determine the distribution characteristics of the evaluation index data; Determining the membership degree of each evaluation index for each grade according to the distribution characteristics.

6. The comprehensive long-term seepage safety evaluation method for dams according to claim 5, characterized in that, The using the FCM algorithm to determine the distribution characteristics of the evaluation index data includes: Using the Tianying optimization AO algorithm to adaptively optimize the initial clustering center of the FCM clustering algorithm as the optimized FCM clustering algorithm; Using the optimized FCM clustering algorithm to divide the distribution interval of the evaluation index data; Determining the distribution characteristics of the evaluation index data according to the distribution interval of the evaluation index data.

7. The comprehensive evaluation method for long-term seepage safety of a dam according to claim 5, characterized in that The determining the membership degree of each evaluation index for each grade according to the distribution characteristics includes: Using the cloud model to optimize the possible value interval in the blind number expression and calculating the weighted cloud of each evaluation index; According to the weighted cloud, convert the grade interval of the evaluation grade index into the grade cloud of the evaluation index. Calculate the membership degree of each evaluation index to each grade according to the grade cloud.

8. The comprehensive evaluation method for long-term seepage safety of a dam according to any one of claims 1 to 7, characterized in that The calculation of the comprehensive membership degree of the evaluation grade and the determination of the comprehensive evaluation grade of the dam seepage safety corresponding to the evaluation period include: Perform weighted summation on the membership degree of the evaluation index to each evaluation grade and the combined weight of the evaluation index to obtain the comprehensive membership degree of the dam seepage safety state to each evaluation grade. Determine the comprehensive evaluation grade of the dam seepage safety corresponding to the evaluation period according to the principle of maximum membership degree.

9. A device for comprehensive long-term seepage safety evaluation of a dam, characterized in that the device includes: An evaluation index system establishment module for establishing a comprehensive evaluation index system for dam seepage safety, where the evaluation index system includes multiple evaluation indexes. A grade interval division module for determining the grade interval division standard of each evaluation index. A model establishment module for establishing a comprehensive evaluation model for dam seepage safety according to the division standard. A weight calculation module for calculating the subjective and objective combined weights of the evaluation indexes. An evaluation module for calculating the comprehensive membership degree of the evaluation grade according to the comprehensive evaluation model of the dam seepage safety and the subjective and objective combined weights of the evaluation indexes, and determining the comprehensive evaluation grade of the dam seepage safety corresponding to the evaluation period. The calculation of the subjective and objective combined weights of the evaluation indexes includes: Calculate the subjective weight of the evaluation index using the subjective weighting method based on hesitant cloud - BWM. Calculate the objective weight of the evaluation index according to the objective weighting method based on entropy weight - CRITIC. Fuse and calculate the subjective weight and objective weight of the evaluation index based on the principle of minimum relative entropy to obtain the subjective and objective combined weight of the evaluation index. The steps of solving the subjective weight of the index using the subjective weighting method based on hesitant cloud - BWM are as follows: Step 1: Determine the optimal index (B) and the worst index (W) from the evaluation indices C = {C1, C2, …, C n} according to the expert opinions; Step 2: Establish a comparison vector between the optimal index and other indexes. Step 3: Establish a comparison vector between other indexes and the worst index. Step 4: Calculate the cloud weight of each index based on the nonlinear programming method. minξ wherein, ξ = (Ex ξ , En ξ , He ξ ); Let Then F can be calculated according to the following formula Bj : Similarly, let then F jW is calculated as: To solve formula (14), substitute formula (15) and formula (16) into formula (14), and then solve the above formula to calculate the optimal weights of each index and the minimum maximum absolute deviation ξ * ; Step 5: Calculate the consistency index and check the consistency ratio. Step 6: Calculate the final subjective weight of the index.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by a processor, it executes the steps of the comprehensive long-term seepage safety evaluation method of the dam described in any one of claims 1 to 8.

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