Dam long-term seepage safety comprehensive evaluation method and device

Abstract

The invention provides a dam long-term seepage safety comprehensive evaluation method which comprises the following steps: establishing a dam seepage safety comprehensive evaluation index system which comprises a plurality of evaluation indexes; determining a grade interval division standard of each evaluation index; establishing a dam seepage safety comprehensive evaluation model according to the division standard; calculating subjective and objective combination weights of the evaluation indexes; and according to the dam seepage safety comprehensive evaluation model and the subjective and objective combination weight of the evaluation indexes, calculating the evaluation grade comprehensive membership degree, and determining the dam seepage safety comprehensive evaluation grade of the corresponding evaluation time period. By means of the scheme, the long-term seepage safety condition of the dam can be effectively evaluated, reliable information is provided for daily operation and maintenance personnel, and the operation and maintenance personnel are helped to make an effective operation and maintenance plan.

Description

Technical Field

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

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

[0003] In the comprehensive evaluation process of dam seepage safety, due to the randomness of changes in seepage safety monitoring data, the limited number of monitoring points and the amount of measured data, the limitations of researchers' cognition, and the ambiguity of evaluation level division, the comprehensive evaluation of seepage safety is a complex process with multiple uncertainties such as randomness, grayness, uncertainty, and ambiguity. Although the existing comprehensive evaluation models of seepage safety can produce relatively reasonable evaluation results, most of them are difficult to comprehensively consider the above-mentioned uncertainties, resulting in the reliability of the evaluation results to be improved. Summary of the invention

[0004] The present invention provides a comprehensive evaluation method and device for the long-term seepage safety of a dam, 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] To this end, the present invention provides the following technical solutions:

[0006] In one aspect, the present invention provides a comprehensive evaluation method for long-term seepage safety of a dam, the method comprising:

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

[0008] Determine the grade interval division standards for each evaluation indicator;

[0009] Establish a comprehensive evaluation model for dam seepage safety based on the classification standards;

[0010] Calculate the subjective and objective combined weights of the evaluation indicators;

[0011] According to the comprehensive evaluation model for dam seepage safety and the subjective and objective combined weights of the evaluation indicators, the comprehensive membership of the evaluation grade is calculated to determine the comprehensive evaluation grade of dam seepage safety in the corresponding evaluation period.

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

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

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

[0015] Optionally, the criteria for determining the level intervals of each evaluation indicator include:

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

[0017] According to the evaluation levels, the golden section method is used to determine the grade interval division standard of each evaluation indicator.

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

[0019] Optionally, establishing a comprehensive evaluation model for dam seepage safety according to the classification criteria includes:

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

[0021] The membership degree of each evaluation index to each level is determined according to the distribution characteristics.

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

[0023] The Tianying optimized AO algorithm is used to adaptively optimize the initial cluster center of the FCM clustering algorithm as the optimized FCM clustering algorithm;

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

[0025] The distribution characteristics of the evaluation index data are determined according to the distribution interval of the evaluation index data.

[0026] Optionally, determining the degree of membership of each evaluation indicator to each level according to the distribution characteristics includes:

[0027] The cloud model is used 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, converting the grade interval of the evaluation grade indicator into a grade cloud of the evaluation index;

[0029] The membership degree of each evaluation index of cloud computing to each level is calculated according to the level.

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

[0031] The subjective weighting method based on hesitant cloud-BWM is used to calculate the subjective weights of the evaluation indicators;

[0032] The objective weights of the evaluation indicators are calculated according to the objective weighting method based on entropy weight-CRIITIC;

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

[0034] Optionally, the calculating of the comprehensive membership of the evaluation grade to determine the comprehensive evaluation grade of the dam seepage safety in the corresponding evaluation period includes:

[0035] The membership of the evaluation index to each evaluation level is weighted and summed with the combined weight of the evaluation index to obtain the comprehensive membership of the dam seepage safety status to each evaluation level;

[0036] The comprehensive evaluation level of dam seepage safety in the corresponding evaluation period is determined according to the maximum membership principle.

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

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

[0039] The grade interval division module is used to determine the grade interval division standard of each evaluation indicator;

[0040] A model building module, used to build a comprehensive evaluation model of dam seepage safety according to the classification standard;

[0041] A weight calculation module, used to calculate the subjective and objective combined weights of the evaluation indicators;

[0042] The evaluation module is used to calculate the comprehensive membership of the evaluation level according to the comprehensive evaluation model of dam seepage safety and the subjective and objective combined weights of the evaluation indicators, and determine the comprehensive evaluation level of dam seepage safety in the corresponding evaluation period.

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

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

[0045] Furthermore, by comprehensively considering the external environment and various seepage safety effect quantities such as seepage volume, seepage pressure, and bypass seepage, the established seepage safety comprehensive evaluation model is made more accurate and reliable.

[0046] Furthermore, the FCM clustering algorithm optimized by the Sky Eagle algorithm is used to autonomously mine the distribution characteristics of the evaluation index data, which can divide the data distribution interval more objectively and reasonably; by introducing the cloud model to improve the possible value interval in the blind number expression, it can effectively consider various uncertainties such as random, fuzzy, gray and uncertain in the evaluation process, and realize the reliable evaluation of the long-term seepage safety status of the dam.

[0047] Furthermore, a subjective weighting method based on the hesitation cloud (BWM) is proposed, which replaces the traditional analytic hierarchy process (AHP) method, which requires a large number of pairwise comparisons and is computationally time-consuming and labor-intensive, with the computationally simple BWM. It also makes full use of the advantages of the hesitation cloud language terminology set that can take into account the randomness, fuzziness and hesitation in the expert decision-making process, and expresses the expert decision-making information more accurately.

[0048] Furthermore, an objective weighting method based on the entropy weight-CRITIC method is used to calculate the objective weights of the evaluation indicators, and the subjective and objective combined weights are calculated using the minimum relative entropy principle. This comprehensively considers the subjective experience and cognition of experts and the objective information of the indicator data, and can obtain more reasonable indicator weight coefficients to improve the accuracy of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

[0051] Figure 2 It is a schematic diagram of evaluation indexes used to establish a comprehensive evaluation index system for dam seepage safety in the method of the present invention;

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

[0053] Figure 4 It is a structural schematic diagram of the dam long-term seepage safety comprehensive evaluation device provided by the present invention. DETAILED DESCRIPTION

[0054] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0055] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

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

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

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

[0059] The evaluation index system may include monitoring indicators and / or simulation indicators.

[0060] The monitoring indicators may include but are not limited to any one or more of the following: environmental indicators such as upstream water level and downstream water level, and effect indicators such as seepage volume, seepage pressure, and bypass seepage.

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

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

[0063] For example, in some embodiments, the evaluation index used to establish the comprehensive evaluation index system for dam seepage safety is as follows: Figure 2 shown.

[0064] In step 102, the level interval division standard of each evaluation index is determined.

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

[0066] At present, most scholars usually determine the grading standards of evaluation indicators based on engineering experience, which is highly subjective and ignores the objective distribution law of indicator data. In a non-limiting embodiment, based on the actual data of the comprehensive evaluation index of dam seepage safety, the golden section method is used to determine the grading interval division standard of each evaluation index, as shown in the following formula (1):

[0067]

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

[0069] Of course, other algorithms may also be used to determine the level interval division standard of each evaluation indicator, which is not limited in the embodiment of the present invention.

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

[0071] The traditional blind number theory fails to effectively consider fuzziness, and the possible value interval of the evaluation index data distribution [x1,x n ] It is usually obtained by artificial division, which is highly subjective and difficult to ensure the rationality of the division results. In an embodiment 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 to improve the rationality of the distribution interval division 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 feature parameters. The cloud model can comprehensively consider the advantages of randomness and fuzziness in the evaluation process, making up for the defect that the traditional blind number theory is difficult to effectively consider the fuzziness in the evaluation process, and realizing the comprehensive consideration of various uncertainties such as randomness, fuzziness, gray and uncertainty in the evaluation process.

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

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

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

[0075] To this end, in an embodiment of the present invention, the Aquila Optimization (AO) algorithm can be first used to adaptively optimize the initial cluster center of the FCM clustering algorithm as an optimized FCM clustering algorithm to improve the clustering effect of the FCM clustering algorithm. Then, the distribution interval of the evaluation index data is divided using the optimized FCM clustering algorithm to improve the rationality of the distribution interval division 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 degree of membership of each evaluation indicator to each level based on the distribution characteristics.

[0077] Blind number theory is an uncertainty theory developed on the basis of unascertained mathematical theory, which can handle complex information with multiple coexisting or intersecting uncertainties. At present, blind number theory is widely used in water environment, construction engineering and disaster prevention and control.

[0078] Although blind number theory has good applicability in the processing of uncertainty information in many fields, it is still relatively rare in the study of dam seepage safety evaluation, and related studies have shown that traditional blind number theory can only be effectively applied to random information, gray information and unascertained information, and it is difficult to process composite information containing fuzzy information. Therefore, the traditional blind number theory is improved to enable it to comprehensively process multiple uncertainties such as randomness, grayness, uncertainty and fuzziness, and the improved blind number theory is introduced into the study of dam seepage safety evaluation, which is of great significance for obtaining objective and reasonable dam seepage safety evaluation results.

[0079] To this end, in a non-limiting embodiment of the present invention, by introducing the cloud model theory, the possible value intervals in the traditional blind number theory are converted into cloud feature parameters. The cloud model is able to comprehensively consider the randomness and fuzziness in the evaluation process, making up for the defect that the traditional blind number theory is difficult to effectively consider the fuzziness in the evaluation process, and realizing the comprehensive consideration of various uncertainties such as randomness, fuzziness, gray and uncertainty in the evaluation process.

[0080] Also refer to Figure 3 , Figure 3 A schematic diagram of the process of establishing a comprehensive evaluation model for dam seepage safety in an embodiment of the present invention is shown.

[0081] In the embodiment of the present invention, a 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: Express the traditional blind number {[x1,x n ],f(x)} in the possible value range [x1,x n ] is converted into cloud characteristic parameters (E x ,E n ,H e ), E x ,E n ,H e They represent expectation, entropy, and super entropy respectively.

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

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

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

[0086] H e =k (4)

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

[0088]

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

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

[0091]

[0092] Step 3: Based on the cloud model theory, the level interval of the evaluation index is converted into the level cloud of the evaluation index according to formula (2) to formula (4), and the membership of each evaluation index to each level is calculated according to the closeness calculation formula.

[0093] The calculation formulas for closeness and membership are as follows:

[0094]

[0095] Where, Ex i represents the expectation of the weighted cloud of the i-th (i=1,2,…,9) evaluation index, represents the expectation of the jth (j=1,2,…,5) evaluation level cloud, T ij represents the closeness of the i-th (i=1,2,…,9) evaluation index to the j-th (j=1,2,…,5) evaluation level, D ij It represents the membership of the i-th (i=1,2,…,9) evaluation index to the j-th (j=1,2,…,5) evaluation level.

[0096] Step 104, calculating the subjective and objective combined weights of the evaluation indicators.

[0097] The determination of the weights of evaluation indicators is an important part of the comprehensive evaluation of dam seepage safety, and plays an important role in the accuracy and reliability of the evaluation results. The existing indicator weighting methods can be roughly divided into three categories: subjective weighting method, objective weighting method, and subjective and objective combined weighting method. Among them, the subjective and objective combined weighting method that can take into account the subjective experience knowledge of experts and the objective laws of sample data is currently the most widely used indicator weighting method, but its difficulty lies in how to accurately express the expert decision-making information in the process of determining the subjective weights of evaluation indicators. Expert survey method (Delphi method), hierarchical analysis method (AHP method), network hierarchy analysis method (ANP method), etc. are commonly used subjective weighting methods in evaluation research. Among them, the hierarchical analysis method 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 paired comparison analyses, which is time-consuming and labor-intensive and it is difficult to ensure the reliability of the results.

[0099] In order to solve the above problems, the industry has proposed the best-worst method (BWM). This method only needs to compare the best and worst indicators with other indicators. The calculation amount of indicators is reduced, and it is easier to ensure the consistency of experts' evaluation results. At present, the best-worst method has been widely used in the fields of evaluation and decision-making. However, in the existing best-worst method research, the original data of subjective weighting is obtained by experts based on their knowledge and experience, which is highly subjective and arbitrary, and there are uncertainties such as randomness and fuzziness. In addition, it is difficult for experts to accurately give the importance of evaluation indicators when weighting evaluation indicators, and hesitation is inevitable. The existing best-worst method research fails to comprehensively consider the randomness, fuzziness and hesitation of experts in decision-making, so it is difficult to accurately express the evaluation information of experts.

[0100] In order to effectively consider the randomness, ambiguity and hesitation in the expert's subjective judgment process and more comprehensively deal with the uncertain information in the expert decision-making problem, in a non-limiting embodiment of the present invention, a subjective weighting method based on the hesitation cloud-BWM can be used to calculate the subjective weight of the evaluation index; the objective weight of the evaluation index is calculated according to the objective weighting method based on the entropy weight-CRIITIC; then, the subjective weight and objective weight of the evaluation index are fused and calculated based on the minimum relative entropy principle to obtain the subjective and objective combined weight of the evaluation index.

[0101] The calculation process of the subjective weight, objective weight, and combined weight is described in detail below.

[0102] (1) Subjective weight

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

[0104] Step 1: According to the expert opinions, the evaluation index C = {C1, C2, ..., C n}Establish the optimal indicator (B) and the worst indicator (W).

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

[0106] In this step, compared with the traditional BWM which uses real numbers between 1 and 9 to express the importance of different evaluation indicators, the improved BWM based on the hesitation cloud uses multiple language terms in the hesitation cloud language terminology set to express the importance of different evaluation indicators, so as to fully consider the randomness, ambiguity and hesitation in expert decision-making.

[0107] For each expert, the advantage of the optimal indicator B over other indicators is expressed by A Bj express:

[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 language terms in the hesitant cloud language term set constructed by the normal cloud model, and h represents the number of language term variables used for comparison.

[0111] For ease of calculation, the language term variables in the hesitant cloud language term set are converted to a synthetic cloud:

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

[0113] Where, Ex Bj 、En Bj 、He Bj They represent expectation, entropy and super 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 a comparison vector between other indicators and the worst indicator.

[0117] The same processing as in step 2 is used 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 weight of each indicator based on the nonlinear programming method.

[0120] minξ

[0121]

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

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

[0124]

[0125] Similarly, Then F jW Calculated as:

[0126]

[0127] In order 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 and maximum absolute deviation ξ * .

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

[0129] when a Bj ×a jW =a BW When , the consistency reaches the minimum. In order to make aBj ×a jW =a BW Established, a cloud Introduce 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 Right now:

[0134]

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

[0136]

[0137] Where ξ′ is a clear value.

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

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

[0140]

[0141] The CR value range 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 indicator.

[0143] The optimal indicator weight obtained by each expert is aggregated according to the following formula to obtain the final subjective weight z of the indicator: si :

[0144]

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

[0146] (2) Objective weight

[0147] The objective weights of the evaluation indicators are calculated according to the objective weighting method based on entropy weight-CRIITIC. The specific calculation steps are as follows:

[0148] Step 1: Assume that there are n evaluation indicators, each of which has m sample data, x ij represents the i-th sample data of the j-th evaluation index, and the original evaluation matrix Y is obtained according to the original data of the evaluation index = (Y ij ) m×n .

[0149] First, the Z-score function is used to calculate the original evaluation index data matrix Y = (Y ij ) m×n Perform standardization 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 of each evaluation index 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 index respectively.

[0154] Step 2: Calculate the Pearson correlation coefficient r between each evaluation index kl , to express the correlation between the evaluation indicators, so as to obtain the correlation matrix R, and then further calculate the independence coefficient η of the information independence between the measurement indicators j (j=1,2,…,n), the calculation formula is as follows (25) to (27):

[0155]

[0156] Step 3: According to the coefficient of variation v of the evaluation index j and the independence coefficient η j Calculate the comprehensive coefficient C of each evaluation index 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 index:

[0159]

[0160] In the formula, is the characteristic specific gravity, to prevent the characteristic specific gravity f ij The presence of 0 and 1 values ​​affects the entropy calculation results. ij Add 0.1 to each

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

[0161]

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

[0163]

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

[0165] (3) Combination weight

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

[0167] According to the minimum relative entropy principle, the resulting combined weight z i Should be related to the subjective weight z si and objective weight z oi As close as possible, the objective function is constructed as follows:

[0168]

[0169] Using the Lagrange multiplier method, we can get:

[0170]

[0171] Step 105, according to the membership of the evaluation index to each level and the subjective and objective combined weights of the evaluation index, the comprehensive membership of the evaluation level is calculated to determine the comprehensive evaluation level of dam seepage safety corresponding to the evaluation period.

[0172] First, the membership of the evaluation index to each evaluation level (i.e., the membership of each evaluation index to each evaluation level calculated according to formula (8)) and the subjective and objective combined weights of the evaluation index are weighted and summed to obtain the comprehensive membership of the dam seepage safety status to each evaluation level (e.g., five evaluation levels: safe, basically safe, slightly dangerous, relatively dangerous, and dangerous).

[0173] Then, the comprehensive evaluation level of seepage safety in the evaluation period is determined according to the maximum membership principle, thereby realizing the comprehensive evaluation of the long-term seepage safety of the dam.

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

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

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

[0177] The level interval division module 402 is used to determine the level interval division standard of each evaluation indicator;

[0178] A model building module 403 is used to build a comprehensive evaluation model of dam seepage safety according to the classification standard;

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

[0180] The evaluation module 405 is used to calculate the comprehensive membership of the evaluation level according to the comprehensive evaluation model of dam seepage safety and the subjective and objective combined weights of the evaluation indicators, and determine the comprehensive evaluation level of dam seepage safety in the corresponding evaluation period.

[0181] The specific implementation of each of the above modules can refer to the relevant description in the above method embodiment of the present invention, which will not be repeated here.

[0182] The method and device for comprehensive evaluation of long-term seepage safety of dams provided in the embodiments of the present invention establish a comprehensive evaluation model for seepage safety of dams, calculate the subjective and objective combined weights of evaluation indicators, calculate the comprehensive membership of the evaluation level according to the comprehensive evaluation model for seepage safety of dams and the subjective and objective combined weights of the evaluation indicators, determine the comprehensive evaluation level of seepage safety of dams corresponding to the evaluation period, realize effective evaluation of the long-term seepage safety of dams, provide reliable information for daily operation and maintenance personnel, and help operation and maintenance personnel formulate effective operation and maintenance plans.

[0183] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but 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 required by the present invention.

[0184] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can 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, on which a computer program is stored, and the computer program can be executed when it is run. Figure 1 or Figure 3 The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. The storage medium may also include a non-volatile memory (non-volatile) or a non-transitory memory, etc.

[0187] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means.

[0188] The embodiments of the present invention are described in detail above. The present invention is described in detail using specific implementation methods herein. The description of the above embodiments is only used to help understand the method and system of the present invention. It is only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should belong to the scope of protection of the present invention, and the content of this specification should not be understood as limiting the present invention. Therefore, 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 comprehensive evaluation method for long-term seepage safety of dams, characterized in that: The method comprises: Establishing a comprehensive evaluation index system for dam seepage safety, wherein the evaluation index system includes multiple evaluation indicators; Determine the grade interval division standards for each evaluation indicator; Establish a comprehensive evaluation model for dam seepage safety based on the classification standards; Calculate the subjective and objective combined weights of the evaluation indicators; According to the comprehensive evaluation model for dam seepage safety and the subjective and objective combined weights of the evaluation indicators, the comprehensive membership of the evaluation grade is calculated to determine the comprehensive evaluation grade of dam seepage safety in the corresponding evaluation period.

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

3. The comprehensive evaluation method for long-term seepage safety of dams according to claim 1 is characterized in that: The criteria for determining the grade intervals of each evaluation indicator include: Determine the comprehensive evaluation level of dam seepage safety; According to the evaluation levels, the golden section method is used to determine the grade interval division standard of each evaluation indicator.

4. The comprehensive evaluation method for long-term seepage safety of dams according to claim 3 is characterized in that: Determining the comprehensive evaluation level of dam seepage safety includes: The comprehensive evaluation level of dam seepage safety is divided into five levels, namely: safe L1, basically safe L2, slightly dangerous L3, relatively dangerous L4, and dangerous L5.

5. The comprehensive evaluation method for long-term seepage safety of dams according to claim 1 is characterized in that: The establishment of a comprehensive evaluation model for dam seepage safety according to the classification standard includes: The fuzzy C-means clustering FCM algorithm is used to determine the distribution characteristics of the evaluation index data; The membership degree of each evaluation index to each level is determined according to the distribution characteristics.

6. The comprehensive evaluation method for long-term seepage safety of dams according to claim 5 is characterized in that: The use of the FCM algorithm to determine the distribution characteristics of the evaluation index data includes: The Tianying optimized AO algorithm is used to adaptively optimize the initial cluster 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; The distribution characteristics of the evaluation index data are determined according to the distribution interval of the evaluation index data.

7. The comprehensive evaluation method for long-term seepage safety of dams according to claim 5 is characterized in that: Determining the membership of each evaluation indicator to each level according to the distribution characteristics includes: The cloud model is used to optimize the possible value interval in the blind number expression and calculate the weighted cloud of each evaluation index; According to the weighted cloud, converting the grade interval of the evaluation grade indicator into a grade cloud of the evaluation index; The membership degree of each evaluation index of cloud computing to each level is calculated according to the level.

8. The comprehensive evaluation method for long-term seepage safety of dams according to claim 1 is characterized in that: The subjective and objective combined weights of the calculation evaluation index include: The subjective weighting method based on hesitant cloud-BWM is used to calculate the subjective weights of the evaluation indicators; The objective weights of the evaluation indicators are calculated according to the objective weighting method based on entropy weight-CRIITIC; Based on the minimum relative entropy principle, the subjective weight and objective weight of the evaluation index are fused and calculated to obtain the subjective and objective combined weight of the evaluation index.

9. The comprehensive evaluation method for long-term seepage safety of a dam according to any one of claims 1 to 8, characterized in that: The calculation of the comprehensive membership of the evaluation level to determine the comprehensive evaluation level of dam seepage safety in the corresponding evaluation period includes: The membership of the evaluation index to each evaluation level is weighted and summed with the combined weight of the evaluation index to obtain the comprehensive membership of the dam seepage safety status to each evaluation level; The comprehensive evaluation level of dam seepage safety in the corresponding evaluation period is determined according to the maximum membership principle.

10. A comprehensive evaluation device for long-term seepage safety of a dam, characterized in that the device comprises: An evaluation index system establishment module is used to establish a comprehensive evaluation index system for dam seepage safety, wherein the evaluation index system includes multiple evaluation indicators; The grade interval division module is used to determine the grade interval division standard of each evaluation indicator; A model building module, used to build a comprehensive evaluation model of dam seepage safety according to the classification standard; A weight calculation module, used to calculate the subjective and objective combined weights of the evaluation indicators; The evaluation module is used to calculate the comprehensive membership of the evaluation level according to the comprehensive evaluation model of dam seepage safety and the subjective and objective combined weights of the evaluation indicators, and determine the comprehensive evaluation level of dam seepage safety in the corresponding evaluation period.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of comprehensive evaluation of long-term seepage safety of a dam as described in any one of claims 1 to 10 are executed.

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