Method for evaluating safe operation state of dam structure

Abstract

The application discloses a kind of dam structure's safe operation state's evaluation method, comprising the following steps, S1, construction evaluation system and data modeling;S2, subjective and objective combination weighting determines constant weight;S3, dynamic weight correction based on grey correlation analysis;S4, set pair cloud model construction and cloud connection degree calculation;S5, comprehensive evaluation and result output.The application sets up the new evaluation framework of "dynamic weight+set pair cloud model evaluation", realizes the self-adaptive adjustment of index importance, solves the problem that traditional static weight is disconnected with dynamic working condition, makes weight distribution and dam actual working nature match, fuses set pair analysis method and normal cloud model to construct set pair cloud model, based on set pair theory "same-different-opposite" principle, defines the same, difference and opposite relationship of evaluation index and safety level, realizes revealing the contribution degree and uncertainty source quantization of each index to safety level, improves the delicacy and explainability of evaluation result.

Description

Technical Field

[0001] This invention relates to the field of dam monitoring technology, and more specifically, to a method for evaluating the safe operating status of a dam structure. Background Technology

[0002] Current dam safety assessment typically follows a process of safety assessment indicator selection, weight determination, and model evaluation, forming a conventional technical system that combines subjective weighting, objective weighting, and single evaluation models. In practical applications, data on seepage, stress-strain, displacement, and crack opening are collected using sensors such as piezometers, strain gauges, and displacement gauges on the dam body and foundation, combined with environmental parameters such as upstream reservoir water level and ambient temperature. Subjective weighting methods such as the Analytic Hierarchy Process (AHP) and Delphi method, or objective weighting methods such as entropy weighting and coefficient of variation, are used to determine indicator weights. Some methods integrate subjective and objective weights using simple combination weighting. Finally, based on evaluation models such as fuzzy comprehensive evaluation, principal component analysis, weighted set pair analysis, and single cloud models, a conclusion on the dam's safety level (normal, basically normal, slightly abnormal, etc.) is drawn, providing a reference for daily dam operation and maintenance, periodic inspections, and emergency assessments.

[0003] Problems with the use of existing technology: (i) The static and fixed weights of the indicators cannot adapt to dynamic and complex working conditions. Dams operate in complex and variable environments for extended periods, and the impact of various safety indicators on dam safety varies significantly under different operating conditions (e.g., seepage indicators are more important during flood season than under normal conditions, and stress-strain indicators are more critical during low-temperature seasons). Furthermore, dam materials exhibit time-varying characteristics such as aging and strength reduction with increasing service life, thus altering the logical correlation of indicator importance. Traditional subjective weighting methods, objective weighting methods, and combined weighting methods mostly rely on static weight assumptions, failing to capture the time-varying characteristics of weights as the environment and dam condition change, leading to a disconnect between evaluation results and the actual working performance of the dam. (ii) Insufficient ability to handle uncertainty, and lack of robustness and interpretability in evaluation. The classification of safety levels is ambiguous, meaning that the safety status of a dam is not absolutely "safe" or "unsafe," but rather contains many intermediate states that are "neither here nor there" (such as "basically normal but requiring enhanced monitoring"). Existing single evaluation models have limitations; for example, fuzzy comprehensive evaluation can only handle fuzziness, and principal component analysis can only handle randomness. Single evaluation methods cannot take into account both types of uncertainty, resulting in an insufficiently precise and accurate judgment of the safety status. Summary of the Invention

[0004] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide an evaluation method for the safe operation status of a dam structure. By setting a novel evaluation framework of "dynamic weights + set pair cloud model evaluation," the method achieves adaptive adjustment of the importance of indicators, solves the problem of the disconnect between traditional static weights and dynamic operating conditions, and makes the weight allocation match the actual working behavior of the dam body. The method integrates set pair analysis and normal cloud model to construct a set pair cloud model. Based on the "identity-difference-opposition" principle of set pair theory, the method defines the identity, difference, and opposition relationships between evaluation indicators and safety levels, thereby revealing the contribution of each indicator to the safety level and the source of uncertainty, improving the precision and interpretability of the evaluation results, and solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the safe operating status of a dam structure, comprising the following steps: S1. Constructing the evaluation system and data modeling A three-tiered evaluation index system of deformation, stress, and seepage was established, and anomaly removal, missing data interpolation, and standardization were performed on the monitoring data; a three-dimensional finite element model of the dam body was constructed based on the COMSOL platform. S2. Determining constant weights by combining subjective and objective weighting methods. Subjective weights are calculated using the analytic hierarchy process (AHP) and objective weights are calculated using the entropy weight method. By optimizing the model and fusing the subjective and objective weights, an initial constant weight matrix for each indicator is obtained. S3. Dynamic weight correction based on grey relational analysis Using environmental quantities as the reference sequence and monitoring data as the comparison sequence, the grey relational coefficient and global relational degree are calculated; variable weight coefficients are generated through the relational degree, and the constant weights are dynamically adjusted to obtain a variable weight matrix that changes with the working conditions. S4. Set-to-Cloud Model Construction and Cloud Connectivity Calculation The safety status is divided into 5 levels, and the cloud parameters of each level are determined based on set pair theory and normal cloud model. Cloud droplets are generated and the cloud connectivity of single indicators is calculated. Combined with variable weights, the cloud connectivity of the monitoring effect layer and the comprehensive effect layer is obtained. S5. Comprehensive Evaluation and Result Output The weighted and integrated cloud connectivity effect is used to calculate the overall security evaluation index and determine the security level. Analyze the distribution and uncertainty of cloud connectivity, and output the evaluation results and trends through chart visualization.

[0006] In a preferred embodiment, step S1 specifically includes the following steps: S1.1 Constructing an evaluation index system, dividing it into three levels of indicators including a comprehensive effect layer, a monitoring effect layer, and a monitoring quantity layer, and clarifying the physical meaning and evaluation scope of each indicator; S1.2 Data preprocessing: Outliers are removed from the collected raw data using the 3σ criterion, missing values ​​are filled in using linear interpolation, and then the data of different dimensional indicators are transformed into dimensionless data in the [0,1] interval using a standardization formula to eliminate the interference of dimensional differences on subsequent calculations; S1.3 Build a finite element model. Construct a three-dimensional finite element model of the dam body and foundation on the COMSOL platform. Divide the elements and nodes according to the set bedrock modeling range, set the corresponding boundary conditions, and input the mechanical parameters of the dam body and foundation materials for subsequent physical mechanism constraints and index numerical verification.

[0007] In a preferred embodiment, based on the standardized data preprocessed in step S1, a combined weighting strategy of analytic hierarchy process (AHP) and entropy weighting method is used in step S2 to calculate the initial constant weights of each evaluation index. The specific steps of S2 are as follows: S2.1 Subjective weight calculation: Dam engineering experts were organized to conduct pairwise comparisons of indicators at the same level, establishing a judgment matrix based on a 1-9 scale. Corresponding matrices were constructed for different indicator levels such as deformation, stress, and seepage. The judgment matrices were then subjected to a consistency check, calculating the consistency ratio CR using the corresponding formula. Consistency was only satisfied when CR ≤ 0.1; otherwise, the judgment matrix was adjusted until the standard was met. The largest eigenvector of the judgment matrix was solved, and after normalization, the subjective weight vectors of each indicator were obtained. ; S2.2 Objective weight calculation: Based on the standardized monitoring data matrix, the information entropy value of each indicator is calculated. Then, the objective weight vector of each indicator is derived through the entropy weight formula. ; S2.3 Combined weight optimization introduces the Euclidean distance function to quantify the difference between subjective and objective weights, constructs an optimization objective function, sets constraints, and solves for the optimal allocation coefficients using the formula... Calculate the initial constant weights of each indicator to form a constant weight matrix, laying the foundation for the next step of dynamic correction.

[0008] In a preferred embodiment, based on the constant weights obtained in step S2, step S3 uses grey relational analysis to quantify the correlation between monitoring data and environmental quantities, thereby achieving dynamic weight adjustment to adapt to changes in operating conditions. The specific steps of S3 are as follows: S3.1 Determine the reference sequence and comparison sequence, using key environmental quantities such as upstream reservoir water level and ambient temperature as the reference sequence. The standardized monitoring data from each monitoring point were used as a comparison sequence. ; S3.2 Grey Relational Coefficient Calculation: Set the resolution coefficient ξ=0.5, and calculate the correlation coefficient between the comparison sequence and the reference sequence at each data point using the corresponding formula to accurately reflect the degree of correlation between the two at a single moment. S3.3 Global Correlation Degree Calculation: The average correlation coefficient of each data point is taken to obtain the global correlation degree between each monitoring indicator and the environmental quantity. ; S3.4 Dynamic weight adjustment constructs an exponential state variable function to adjust the global correlation. Variable weight coefficients are generated, and the constant weights obtained in step S2 are dynamically adjusted to finally obtain a variable weight vector that changes with working conditions and time. Among them, the indicators with greater correlation are assigned higher weights, realizing real-time adaptive updating of weights and forming the final variable weight matrix to adapt to the dynamic operating conditions of the dam.

[0009] In a preferred embodiment, step S4 is as follows: S4.1 Determine the evaluation level and cloud model parameters, classifying the dam's safety status into five levels: normal, basically normal, slightly abnormal, severely abnormal, and malignantly abnormal. Based on design specifications and engineering experience, determine the index range for each level. Based on the similarity-dissimilarity-inverse principle of set pair theory, calculate the numerical characteristics of the cloud model for each level, including the expected value. ,entropy hyperentropy ,in Take the midpoint of the grade interval. Calculated based on interval length Set the value to 0.01-0.05; S4.2 generates cloud droplets and calculates the cloud connectivity of a single indicator, runs the forward cloud generator 1000 times to generate cloud droplets for each indicator at the corresponding level; calculates the cloud connectivity of each monitoring indicator at different security levels, and quantifies the consistency, difference, and opposition between the indicator and the level; S4.3 Cloud connectivity standardization and hierarchical aggregation: The cloud connectivity of single indicators is normalized. Combined with the variable weight matrix obtained in step S3, a weighted fusion method is adopted to calculate the comprehensive cloud connectivity from the monitoring quantity layer upwards, sequentially calculating the comprehensive cloud connectivity of the monitoring effect layer and the comprehensive effect layer, forming cloud connectivity matrices at each level. During the aggregation process, the calculation results of the finite element model built in step S1 are simultaneously verified to ensure that the evaluation results conform to the physical and mechanical laws of the dam body.

[0010] In a preferred embodiment, based on the comprehensive cloud connectivity at each level obtained in step S4, the safety evaluation index is calculated and the dam safety status is determined in step S5. Simultaneously, an uncertainty analysis of the evaluation results is output. The specific operation steps are as follows: S5.1 Overall comprehensive cloud connectivity calculation: The cloud connectivity of the comprehensive effect layer is weighted and summed to obtain the comprehensive cloud connectivity of the overall structural safety of the dam, which corresponds to 5 safety levels. The S5.2 security evaluation index is calculated using a weighted summation method, which involves multiplying the cloud connectivity of each level by the set level coefficient and then summing the results to obtain the security evaluation index. S5.3 Safety level determination: The safety level of the dam body is determined based on the evaluation index range. At the same time, the distribution characteristics of cloud connectivity are analyzed to clarify the contribution of each indicator to different levels and the sources of uncertainty. The S5.4 results are visualized, with bar charts showing the cloud connectivity at each level and time evolution curves for safety levels, clearly presenting the safety status and changing trends of the dam body, providing an intuitive basis for subsequent verification and updates.

[0011] The technical effects and advantages of this invention are as follows: This invention, by setting up a novel evaluation framework of "dynamic weighting + set-pair cloud model evaluation", has the following advantages: (i) it constructs a dynamic variable weighting mechanism that integrates subjective and objective factors. A dynamic weighting strategy integrating subjective and objective weighting with grey relational analysis correction is adopted. Subjective weights based on expert experience are obtained through the analytic hierarchy process (AHP), while objective weights driven by data are calculated based on information entropy theory. Furthermore, the optimal allocation coefficients are solved based on the Euclidean distance optimization model, and the initial constant weights are obtained by integration. The correlation between monitoring data and environmental quantities (such as reservoir water level) is quantified by grey relational analysis, and an exponential state variable function is constructed. The initial constant weights are dynamically corrected through a variable weighting formula, generating a variable weight vector that changes with working conditions and time. This achieves adaptive adjustment of the importance of indicators, solves the problem of the disconnect between traditional static weights and dynamic working conditions, and makes the weight allocation match the actual working behavior of the dam. (ii) Propose a set-pair cloud model to collaboratively handle complex uncertainties. This paper integrates set pair analysis with the normal cloud model to construct a set pair cloud model. Based on the "identity-difference-opposition" principle of set pair theory, it defines the identity, difference, and opposition relationships between evaluation indicators and safety levels. The contribution of each relationship is quantified through set pair connections. The numerical characteristics (expectation value, entropy, hyperentropy) of the normal cloud model are used to characterize the randomness and fuzziness in the evaluation process. A forward cloud generator is run to generate cloud droplets, and the cloud connectivity degree is calculated and standardized. Combined with a dynamic variable weight matrix, the cloud connectivity degree is obtained by weighted fusion at each level: "monitoring quantity layer - monitoring effect layer - comprehensive effect layer". This method reveals the contribution of each indicator to the safety level and quantifies the source of uncertainty, thereby improving the precision and interpretability of the evaluation results. Attached Figure Description

[0012] Figure 1This is a schematic diagram illustrating the change process of the water pressure component in the finite element calculation during the implementation of the method of the present invention; Figure 2 This is a schematic diagram showing the evaluation results of cloud connectivity and structural response status during dam operation in the process of implementing the method of the present invention. Figure 3 This is a mesh model diagram of the finite element analysis of the overall dam structure of the present invention. Figure 4 This is a finite element pressure analysis cloud diagram of the overall dam structure of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] As attached Figure 1 To be continued Figure 4 The method for evaluating the safe operation status of a dam structure, as shown, includes the following steps: S1, constructing an evaluation system and data modeling. A three-tiered evaluation index system of deformation, stress, and seepage was established, and anomaly removal, missing data interpolation, and standardization were performed on the monitoring data; a three-dimensional finite element model of the dam body was constructed based on the COMSOL platform. S2. Determining constant weights by combining subjective and objective weighting methods. Subjective weights are calculated using the analytic hierarchy process (AHP) and objective weights are calculated using the entropy weight method. By optimizing the model and fusing the subjective and objective weights, an initial constant weight matrix for each indicator is obtained. S3. Dynamic weight correction based on grey relational analysis Using environmental quantities as the reference sequence and monitoring data as the comparison sequence, the grey relational coefficient and global relational degree are calculated; variable weight coefficients are generated through the relational degree, and the constant weights are dynamically adjusted to obtain a variable weight matrix that changes with the working conditions. S4. Set-to-Cloud Model Construction and Cloud Connectivity Calculation The safety status is divided into 5 levels, and the cloud parameters of each level are determined based on set pair theory and normal cloud model. Cloud droplets are generated and the cloud connectivity of single indicators is calculated. Combined with variable weights, the cloud connectivity of the monitoring effect layer and the comprehensive effect layer is obtained. S5. Comprehensive Evaluation and Result Output The weighted and integrated cloud connectivity effect is used to calculate the overall security evaluation index and determine the security level. Analyze the distribution and uncertainty of cloud connectivity, and output the evaluation results and trends through chart visualization.

[0015] S1 specifically includes the following steps: S1.1 Constructing an evaluation index system, dividing the comprehensive effect layer (deformation, stress, seepage), monitoring effect layer (horizontal displacement, vertical displacement, dam strain, etc.), and monitoring quantity layer (specific data of each monitoring point) into three levels of indicators, clarifying the physical meaning and evaluation scope of each indicator; S1.2 Data preprocessing, using the 3σ criterion to remove outliers from the collected raw data, using linear interpolation to fill in missing values, and then using a standardization formula to convert the data of different dimension indicators into dimensionless data in the [0,1] interval, eliminating the interference of dimensional differences on subsequent calculations; S1.3 Build a finite element model. Construct a three-dimensional finite element model of the dam body and foundation on the COMSOL platform. Divide the elements and nodes according to the set bedrock modeling range, set the corresponding boundary conditions (fixed bottom surface, rolling support around the perimeter, constant head boundary between upstream and downstream, etc.), and input the mechanical parameters of the dam body and foundation materials (elastic modulus, Poisson's ratio, permeability coefficient, etc.) for subsequent physical mechanism constraints and index numerical verification.

[0016] Based on the standardized data preprocessed in step S1, a combined weighting strategy of the Analytic Hierarchy Process (AHP) and entropy weighting method is used in step S2 to calculate the initial constant weights of each evaluation index. The specific steps of S2 are as follows: S2.1 Subjective weight calculation: Dam engineering experts are organized to conduct pairwise comparisons of the same level of indicators and establish a judgment matrix based on a scale of 1-9. Corresponding matrices are constructed for different index levels such as deformation, stress, and seepage. The consistency of the judgment matrix is ​​checked, and the consistency ratio CR is calculated using the corresponding formula. The consistency requirement is met only when CR ≤ 0.1; otherwise, the judgment matrix is ​​adjusted until the standard is met. The largest eigenvector of the judgment matrix is ​​solved, and the subjective weight vector of each index is obtained after normalization. S2.2 Objective weight calculation: Based on the standardized monitoring data matrix, the information entropy value of each indicator is calculated. (The smaller the entropy value, the greater the amount of effective information in the indicator), and then the objective weight vector of each indicator is derived through the entropy weight formula. S2.3 Combined weight optimization introduces the Euclidean distance function to quantify the difference between subjective and objective weights, constructs an optimization objective function (the core of which is to minimize the sum of the squared differences between the combined weights and the subjective and objective weights), and sets constraints (allocation coefficients). , The sum is 1 and After obtaining the optimal allocation coefficients, the formula is used... Calculate the initial constant weights of each indicator to form a constant weight matrix, laying the foundation for the next step of dynamic correction.

[0017] Based on the constant weights obtained in step S2, step S3 uses grey relational analysis (GRA) to quantify the correlation between monitoring data and environmental quantities, thereby dynamically adjusting the weights to adapt to changes in operating conditions. The parameters and logic of the original text are used throughout the process. The specific steps of S3 are as follows: S3.1 Determine the reference sequence and comparison sequence, using key environmental parameters such as upstream reservoir water level and ambient temperature as the reference sequence. The standardized monitoring data from each monitoring point were used as a comparison sequence. ; S3.2 Grey Relational Coefficient Calculation: Set the resolution coefficient ξ=0.5, and calculate the correlation coefficient between the comparison sequence and the reference sequence at each data point using the corresponding formula to accurately reflect the degree of correlation between the two at a single moment. S3.3 Global Correlation Degree Calculation: The average correlation coefficient of each data point is taken to obtain the global correlation degree between each monitoring indicator and the environmental quantity. ; S3.4 Dynamic weight adjustment constructs an exponential state variable function to adjust the global correlation. Variable weight coefficients are generated, and the constant weights obtained in the second step are dynamically adjusted to ultimately obtain a variable weight vector that varies with operating conditions and time. Indicators with higher correlation (such as the flood season seepage index in the original example) are assigned higher weights, achieving real-time adaptive updates of the weights and forming the final variable weight matrix to adapt to the dynamic operating conditions of the dam. The specific steps of S4 are as follows: S4.1 Determine the evaluation level and cloud model parameters, classifying the dam's safety status into five levels: normal, basically normal, slightly abnormal, severely abnormal, and malignant abnormal. Based on design specifications and engineering experience, determine the index range for each level. Based on the same-dissimilar-inverse principle of set pair theory, calculate the digital characteristics of the cloud model for each level. (Expected value) ,entropy hyperentropy ),in Take the midpoint of the grade interval. Calculated based on interval length Set the value to 0.01-0.05 (adjust according to the fuzzy threshold); S4.2 generates cloud droplets and calculates the cloud connectivity of a single indicator. It runs the forward cloud generator 1000 times (to ensure the reliability of the results) to generate cloud droplets for each indicator at the corresponding level. It calculates the cloud connectivity of each monitoring indicator at different security levels and quantifies the consistency, difference, and opposition between the indicator and the level (monitoring data within the level range is considered to be of the same relationship, adjacent ranges are considered to be of different relationship, and the rest are considered to be of opposition). S4.3 Cloud connectivity standardization and hierarchical aggregation: The cloud connectivity of single indicators is normalized (ensuring that the sum of connectivity at each level is 1). Combined with the variable weight matrix obtained in step three, a weighted fusion method is adopted to calculate the comprehensive cloud connectivity from the monitoring quantity layer upwards, sequentially calculating the comprehensive cloud connectivity of the monitoring effect layer and the comprehensive effect layer, forming cloud connectivity matrices at each level. During the aggregation process, the calculation results of the finite element model built in step one are simultaneously verified to ensure that the evaluation results conform to the physical and mechanical laws of the dam body.

[0018] Based on the comprehensive cloud connectivity at each level obtained in step S4, the safety evaluation index is calculated and the dam safety status is determined in step S5. At the same time, the uncertainty analysis of the evaluation results is output. The specific operation steps are as follows: S5.1 Overall comprehensive cloud connectivity calculation: The cloud connectivity of the comprehensive effect layer (deformation, stress, seepage) is weighted and summed according to the weights in the original example to obtain the comprehensive cloud connectivity of the overall structural safety of the dam body, which corresponds to 5 safety levels. The S5.2 security evaluation index is calculated using a weighted summation method, which involves multiplying the cloud connectivity of each level by the set level coefficient and then summing the results to obtain the security evaluation index. S5.3 Safety level determination: The safety level of the dam body is determined based on the evaluation index range. At the same time, the distribution characteristics of cloud connectivity are analyzed to clarify the contribution of each indicator to different levels and the sources of uncertainty. The S5.4 results are visualized, with bar charts showing the cloud connectivity at each level and time evolution curves for safety levels, clearly presenting the safety status and changing trends of the dam body, providing an intuitive basis for subsequent verification and updates.

[0019] In step S2, the subjective weighting strategy is a key component in determining the weights of the structural response state evaluation indicators for the safe operation of concrete dams. In this work, a subjective weighting strategy based on relative importance was adopted to establish a judgment matrix. ( That is, the number of indicators evaluated at a certain level), in the matrix, The values ​​were determined by dam engineering experts based on the relative importance of specific indicators, as shown in Table 1.

[0020] In determining the weights of the main indicators, it is necessary to ensure the rationality of the results. According to the consistency theory of judgments in multi-criteria decision analysis, the consistency of the judgment matrix must be checked before the weights are calculated. This paper constructs a consistency ratio (CR). When CR ≤ 0.1, the judgment matrix is ​​considered to meet the consistency requirements; otherwise, the judgment matrix will be adjusted until the requirements are met. The expression for calculating CR is as follows:

[0021] in , and These are the order of the matrix and the largest eigenvalue, respectively. It is the average consistency index. Table 2 lists the average random consistency index values ​​for judgment matrices of different orders.

[0022] Table 2. Average value of random consistency index

[0023] After passing the consistency check, the judgment matrix is ​​obtained. Maximum eigenvector Then, the weight vector is obtained after normalization. , The expression is as follows: in, It determines the order of a matrix. Is it related to the current indicators? The sequence number of all indicators to be compared pairwise. In step S2, the objective weighting strategy determines the importance of the evaluation indicators based on the characteristics of the indicator values. According to entropy theory, the smaller the entropy value of the indicator sequence, the greater the amount of effective information provided by the indicator, and therefore the greater the weight should be; conversely, the greater the entropy value, the smaller the weight should be. Therefore, we determine the target weight based on the calculation result of the indicator information entropy.

[0024] The standardized matrix of monitoring quantities, which serves as an evaluation index for the safe operation status of concrete dam structures, is represented as follows: In the formula Indicates the first The first evaluation indicator Subsequent monitoring values; and These represent the number of evaluation indicators and the sample size, respectively.

[0025] According to the information entropy theory, the first Information entropy value of each evaluation indicator for in It is a coefficient related to the monitoring value of the evaluation indicator.

[0026] Based on the principle of entropy weight, from each standardized evaluation index The weights of each evaluation indicator are quantified from the information entropy, as shown below:

[0027] in, It is the first The information entropy value of each indicator.

[0028] To comprehensively utilize the advantages of both subjective and objective weighting strategies, a function based on Euclidean distance is introduced. The optimal combination is achieved through model optimization. The specific process for determining the combined weights is as follows: 1. Define the Euclidean distance function between the subjective weight vector and the objective weight vector to quantify the difference between the subjective and objective weight results. This function is expressed as: in, 1. Indicate the number of indicators. 2. Construct the combined weights. And assign coefficients to satisfy the normalization condition; in, and These are the allocation coefficients for the subjective weighting method and the objective weighting method, respectively. .

[0029] 3. Constructed by minimizing the sum of squared differences between the combined weights and the subjective objective weights. The optimization objective function is shown below. Optimal allocation coefficients. and Solve accordingly; in, It is the first The combined weights of the evaluation indicators, wherein, in step S4, to ensure the reliability of the cloud connectivity of the indicators at the dam monitoring point layer under a specific level, it is necessary to run... Second-rate( The cloud connectivity obtained after normalization from the forward cloud generator (number of times). As shown below:

[0030] in This indicates the length of the indicator sequence. Since the sum of cloud connectivity scores at different levels is usually not equal to 1, making the cloud connectivity scores of different indicators incomparable, it is necessary to standardize the cloud connectivity scores at each level. The specific format is as follows: in This represents the number of connections. Considering the connection degree and the established evaluation rating, in this paper... , It is the first The measured monitoring values ​​of each evaluation indicator It is the serial number of the evaluation level. Indicates the first The first indicator for the first Standardized cloud connectivity at each level.

[0031] By combining the determined combined weight matrix with the comprehensive cloud connectivity of each indicator, the comprehensive cloud connectivity of the dam structure safety operation status evaluation system is calculated. Its expression is as follows: in, It is the first Comprehensive cloud connectivity at each security level It is the total number of evaluation indicators. It is the first The combined weights of the evaluation indicators It is the first The first indicator for the first Standardized cloud connectivity at each level.

[0032] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change. Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the safe operating status of a dam structure, characterized in that: Includes the following steps, S1. Constructing the evaluation system and data modeling Establish a three-layer evaluation index system for deformation, stress, and seepage, and perform anomaly removal, missing data interpolation, and standardization processing on the monitoring data; A three-dimensional finite element model of the dam body was constructed based on the COMSOL platform; S2. Determining constant weights by combining subjective and objective weighting methods. Subjective weights are calculated using the analytic hierarchy process (AHP) and objective weights are calculated using the entropy weight method. By optimizing the model and integrating subjective and objective weights, an initial constant weight matrix for each indicator is obtained; S3. Dynamic weight correction based on grey relational analysis Using environmental parameters as the reference sequence and monitoring data as the comparison sequence, the grey relational coefficient and global relational degree are calculated. By generating variable weight coefficients through correlation, the constant weights are dynamically adjusted to obtain a variable weight matrix that changes with the working conditions. S4. Set-to-Cloud Model Construction and Cloud Connectivity Calculation The safety status is divided into 5 levels, and the cloud parameters of each level are determined based on set pair theory and normal cloud model. Cloud droplets are generated and the cloud connectivity of single indicators is calculated. Combined with variable weights, the cloud connectivity of the monitoring effect layer and the comprehensive effect layer is obtained. S5. Comprehensive Evaluation and Result Output The weighted and integrated cloud connectivity effect is used to calculate the overall security evaluation index and determine the security level. Analyze the distribution and uncertainty of cloud connectivity, and output the evaluation results and trends through chart visualization.

2. The method for evaluating the safe operation status of a dam structure according to claim 1, characterized in that: Step S1 specifically includes the following steps: S1.1 Construct an evaluation index system, divide it into three levels of indicators including the comprehensive effect layer, the monitoring effect layer, and the monitoring quantity layer, and clarify the physical meaning and evaluation scope of each indicator; S1.2 Data preprocessing: Outliers are removed from the collected raw data using the 3σ criterion, missing values ​​are filled in using linear interpolation, and then the data of different dimensional indicators are transformed into dimensionless data in the [0,1] interval using a standardization formula to eliminate the interference of dimensional differences on subsequent calculations; S1.3 Build a finite element model. Construct a three-dimensional finite element model of the dam body and foundation on the COMSOL platform. Divide the elements and nodes according to the set bedrock modeling range, set the corresponding boundary conditions, and input the mechanical parameters of the dam body and foundation materials for subsequent physical mechanism constraints and index numerical verification.

3. The method for evaluating the safe operation status of a dam structure according to claim 2, characterized in that: Based on the standardized data preprocessed in step S1, a combined weighting strategy of analytic hierarchy process (AHP) and entropy weighting method is used in step S2 to calculate the initial constant weights of each evaluation index. The specific steps of S2 are as follows: S2.1 Subjective weight calculation: Dam engineering experts were organized to conduct pairwise comparisons of indicators at the same level, establishing a judgment matrix based on a 1-9 scale. Corresponding matrices were constructed for different indicator levels such as deformation, stress, and seepage. The judgment matrices were then subjected to a consistency check, calculating the consistency ratio CR using the corresponding formula. Consistency was only satisfied when CR ≤ 0.1; otherwise, the judgment matrix was adjusted until the standard was met. The largest eigenvector of the judgment matrix was solved, and after normalization, the subjective weight vectors of each indicator were obtained. ; S2.2 Objective weight calculation: Based on the standardized monitoring data matrix, the information entropy value of each indicator is calculated. Then, the objective weight vector of each indicator is derived through the entropy weight formula. ; S2.3 Combined weight optimization introduces the Euclidean distance function to quantify the difference between subjective and objective weights, constructs an optimization objective function, sets constraints, and solves for the optimal allocation coefficients using the formula... Calculate the initial constant weights of each indicator to form a constant weight matrix, laying the foundation for the next step of dynamic correction.

4. The method for evaluating the safe operation status of a dam structure according to claim 1, characterized in that: Based on the constant weights obtained in step S2, step S3 uses grey relational analysis to quantify the correlation between monitoring data and environmental quantities, thereby achieving dynamic weight adjustment to adapt to changes in operating conditions. The specific steps of S3 are as follows: S3.1 Determine the reference sequence and comparison sequence, using key environmental quantities such as upstream reservoir water level and ambient temperature as the reference sequence. The standardized monitoring data from each monitoring point were used as a comparison sequence. ; S3.2 Grey Relational Coefficient Calculation: Set the resolution coefficient ξ=0.5, and calculate the correlation coefficient between the comparison sequence and the reference sequence at each data point using the corresponding formula to accurately reflect the degree of correlation between the two at a single moment. S3.3 Global Correlation Degree Calculation: The average correlation coefficient of each data point is taken to obtain the global correlation degree between each monitoring indicator and the environmental quantity. ; S3.4 Dynamic weight adjustment constructs an exponential state variable function to adjust the global correlation. Variable weight coefficients are generated, and the constant weights obtained in step S2 are dynamically adjusted to finally obtain a variable weight vector that changes with working conditions and time. Among them, the indicators with greater correlation are assigned higher weights, realizing real-time adaptive updating of weights and forming the final variable weight matrix to adapt to the dynamic operating conditions of the dam.

5. The method for evaluating the safe operation status of a dam structure according to claim 1, characterized in that: The specific steps of step S4 are as follows: S4.1 Determine the evaluation level and cloud model parameters, classifying the dam's safety status into five levels: normal, basically normal, slightly abnormal, severely abnormal, and malignantly abnormal. Based on design specifications and engineering experience, determine the index range for each level. Based on the similarity-dissimilarity-inverse principle of set pair theory, calculate the numerical characteristics of the cloud model for each level, including the expected value. ,entropy hyperentropy ,in Take the midpoint of the grade interval. Calculated based on interval length Set the value to 0.01-0.05; S4.2 generates cloud droplets and calculates the cloud connectivity of a single indicator. It runs the forward cloud generator 1000 times to generate cloud droplets for each indicator at the corresponding level. It calculates the cloud connectivity of each monitoring indicator at different security levels and quantifies the consistency, differences, and opposition between indicators and levels. S4.3 Cloud connectivity standardization and hierarchical aggregation: The cloud connectivity of single indicators is normalized. Combined with the variable weight matrix obtained in step S3, a weighted fusion method is adopted to calculate the comprehensive cloud connectivity from the monitoring quantity layer upwards, sequentially calculating the comprehensive cloud connectivity of the monitoring effect layer and the comprehensive effect layer, forming cloud connectivity matrices at each level. During the aggregation process, the calculation results of the finite element model built in step S1 are simultaneously verified to ensure that the evaluation results conform to the physical and mechanical laws of the dam body.

6. The method for evaluating the safe operation status of a dam structure according to claim 1, characterized in that: Based on the comprehensive cloud connectivity at each level obtained in step S4, the safety evaluation index is calculated and the dam safety status is determined in step S5. At the same time, the uncertainty analysis of the evaluation results is output. The specific operation steps are as follows: S5.1 Overall comprehensive cloud connectivity calculation: The cloud connectivity of the comprehensive effect layer is weighted and summed to obtain the comprehensive cloud connectivity of the overall structural safety of the dam, which corresponds to 5 safety levels. The S5.2 security evaluation index is calculated using a weighted summation method, which involves multiplying the cloud connectivity of each level by the set level coefficient and then summing the results to obtain the security evaluation index. S5.3 Safety level determination: The safety level of the dam body is determined based on the evaluation index range. At the same time, the distribution characteristics of cloud connectivity are analyzed to clarify the contribution of each indicator to different levels and the sources of uncertainty. The S5.4 results are visualized, with bar charts showing the cloud connectivity at each level and time evolution curves for safety levels, clearly presenting the safety status and changing trends of the dam body, providing an intuitive basis for subsequent verification and updates.

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