Hydraulic engineering quality safety risk management method based on digital twinborn model

Abstract

The invention provides a hydraulic engineering quality safety risk management method based on a digital twin model, and belongs to the technical field of hydraulic engineering. The method is characterized by comprising the following steps: establishing an index system of hydraulic engineering quality safety risks, determining specific indexes, and meanwhile, optimizing and adjusting the indexes in combination with the actual situation of the hydraulic engineering; a digital twin model of the water conservancy project is constructed by using data of the water conservancy project, and real-time, dynamic and comprehensive digital representation and simulation of the water conservancy project are realized; evaluating the risks according to the possibility and severity of the risks, dividing the types, levels and grades of the risks, and forming a risk evaluation report; selecting an optimal risk processing scheme according to the property and degree of the risk; and an informatization risk management platform is utilized to realize real-time monitoring, intelligent early warning, quick response and effective disposal of the quality safety risk of the water conservancy project, so that the risk prevention and coping capability is improved, and the quality safety of the water conservancy project is guaranteed.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of water conservancy engineering, in particular to a water conservancy engineering quality and safety risk management method based on a digital twin model. BACKGROUND

[0002] With the continuous development of society and the progress of science and technology, water conservancy engineering plays a vital role in people's production and life. However, due to the complexity and particularity of water conservancy engineering, there are many quality and safety risks in the construction, operation and maintenance process. Effective risk management is the key to ensuring the stable operation of water conservancy engineering and improving the quality of the project.

[0003] Traditional water conservancy engineering quality and safety risk management methods often face the following challenges: Lack of comprehensive index system: Traditional methods are difficult to fully consider the complexity of water conservancy engineering when establishing the quality and safety risk index system, resulting in a lack of comprehensiveness and systematicness in the evaluation system.

[0004] Difficulties in information acquisition and processing: Traditional data collection methods are limited by time and space limitations, making it difficult to achieve comprehensive monitoring and data acquisition throughout the entire process of water conservancy engineering, reducing the accuracy of risk assessment.

[0005] Lack of dynamic adjustment mechanism: Traditional methods are difficult to flexibly adjust the index weight according to the actual situation of different stages of water conservancy engineering, reflect the spatio-temporal changes of risk, and affect the effectiveness of risk assessment.

[0006] Risk treatment scheme selection is not scientific enough: Traditional risk treatment scheme selection lacks systematicness and scientificness, often relying on experience and judgment, making it difficult to ensure the optimal risk management effect.

[0007] To address the above challenges, the application proposes a water conservancy engineering quality and safety risk management method based on a digital twin model. SUMMARY

[0008] In order to overcome the defects of the prior art, the purpose of the application is to provide a water conservancy engineering quality and safety risk management method based on a digital twin model, which includes the following steps: Step 1: Establish an index system for water conservancy engineering quality and safety risks and determine specific indicators, and optimize and adjust the indicators in combination with the actual situation of water conservancy engineering; Step 2: Construct a digital twin model of water conservancy engineering using data related to water conservancy engineering to achieve real-time, dynamic and comprehensive digital representation and simulation of water conservancy engineering; Step 3: Evaluate risks according to the likelihood and severity of risk occurrence, classify risks by type, level and grade, and form a risk assessment report; Step 4, according to the nature and degree of risk, select the optimal risk treatment scheme; Step 5, using the information risk management platform, realize real-time monitoring, intelligent early warning, rapid response and effective disposal of water conservancy project quality and safety risk, improve risk prevention and response ability, and ensure the quality and safety of water conservancy project.

[0009] Further, the specific steps of establishing the index system of water conservancy project quality and safety risk and determining the specific index include: Determine the influencing factors and evaluation indexes of water conservancy project quality and safety risk through expert consultation, and construct the index system; Determine the weight and evaluation standard of each index by AHP; The specific steps of optimizing and adjusting the index according to the actual situation of water conservancy project include: According to the different stages of water conservancy project, dynamically update the weight and evaluation standard of index to reflect the spatio-temporal variation of risk; According to the risk evaluation results and risk prevention and control suggestions of water conservancy project, timely adjust the value range of index and optimize the target to realize the continuous improvement of risk.

[0010] Further, step 2 includes the following steps: Step 2.1, collect the data of water conservancy project, including: historical data, field data, monitoring data, video data and remote sensing data, including the data of design, construction, operation and maintenance stages of water conservancy project, as well as the related data of natural environment, social economy and water resources of water conservancy project; Step 2.2, according to the data of water conservancy project, construct the three-dimensional geometric model of water conservancy project, reflect the form, structure, material physical characteristics of water conservancy project; Step 2.3, according to the data of water conservancy project, construct the physical model of water conservancy project, reflect the running mechanism, dynamic process and function effect physical law of water conservancy project; Step 2.4, according to the data of water conservancy project, construct the intelligent model of water conservancy project, reflect the state change, risk prediction and optimal scheduling of water conservancy project; Step 2.5, integrate the three-dimensional geometric model, physical model and intelligent model of water conservancy project into a digital twin model, realize the real-time, dynamic and comprehensive digital representation and simulation of water conservancy project; Step 2.6, visualize the digital twin model, realize the intuitive display and interactive operation of water conservancy project; Step 2.7, connect the digital twin model with the entity equipment of water conservancy project, realize the synchronous update, virtual-real interaction and iterative optimization of digital twin model and physical water conservancy project.

[0011] Further, step 3 includes the following steps: Step 3.1, according to the characteristics of water conservancy project and the availability of data, select the appropriate risk assessment method; Step 3.2, according to the selected risk assessment method to establish the corresponding mathematical model, at the same time, define the index, parameter, weight and threshold value of risk, and determine the calculation formula and evaluation standard of risk; Step 3.3, get the required risk assessment data from the digital twin model of water conservancy project and input it into the risk assessment model, carry out data processing and analysis; Step 3.4, according to the output of risk assessment model, quantitative or qualitative evaluation of risk, divide the type, level and grade of risk, form risk assessment report, which should include the description, evaluation, classification and sorting of risk; Step 3.5, according to the risk assessment results and actual situation, verify and optimize the risk assessment model.

[0012] Further, step 4 includes the following steps: Step 4.1, according to the results of risk identification and analysis, list all possible risk treatment schemes, and the probability, benefit and cost parameters of each scheme, construct the benefit matrix; Step 4.2, use decision tree tool to draw different risk treatment schemes and their corresponding results, calculate the expected benefit value of each scheme, and the investment required for the implementation of each scheme, recorded as scheme A; Step 4.3, use the uncertainty decision scheme tool, according to different risk scenarios, calculate the regret value of each scheme, that is, the difference between the benefits of the scheme and the best scheme, select the risk treatment scheme with the minimum maximum regret value, recorded as scheme B; Step 4.4, use multi-criteria decision analysis tool, according to the interests and responsibilities of stakeholders, determine different decision criteria, give each criterion a certain weight, then score each risk treatment scheme, select the risk treatment scheme with the highest weighted score, recorded as scheme C; Step 4.5, compare the advantages and disadvantages of scheme A, scheme B and scheme C, select the optimal risk treatment scheme, or adjust and optimize the scheme, in order to achieve the best risk management effect and efficiency.

[0013] Further, the specific steps of step 4.2 are as follows: Analyze the possible results and probability of each scheme, and the benefit and cost of each result; Draw a decision tree, use box to represent decision node, use circle to represent opportunity node, use triangle to represent terminal node, and use branch line to represent different results and probability; The expected return value of the decision tree is calculated, from right to left, for each opportunity node, the return of the result is multiplied by the probability, and then summed to obtain the expected return value of the node, for each decision node, the branch with the maximum expected return value is selected to obtain the expected return value of the node; The scheme with the maximum expected return value is selected and recorded as scheme A; The specific steps of step 4.3 are: The benefits of each scheme under different risk scenarios are analyzed, and a benefit matrix is constructed, with each row representing a scheme and each column representing a risk scenario; The regret value of each scheme is calculated, that is, the difference between the benefit of each cell and the maximum benefit of the column, and a regret matrix is constructed, with each row representing a scheme and each column representing a risk scenario; The maximum regret value of each scheme is calculated, that is, the maximum value of each row, and the scheme with the minimum maximum regret value is selected and recorded as scheme B; The specific steps of step 4.4 are: Different decision criteria are determined to reflect the interests and responsibilities of stakeholders; Each decision criterion is assigned a certain weight to reflect its relative importance, and the sum of the weights is 1; Each scheme is scored under each decision criterion to reflect its relative performance; The weighted score of each scheme is calculated, that is, the score of each cell is multiplied by the weight of the column, and then summed, and the scheme with the highest weighted score is selected and recorded as scheme C.

[0014] Further, step 5 includes the following steps: A water conservancy project quality and safety risk database is established to form a water conservancy project quality and safety risk knowledge base; A water conservancy project quality and safety risk early warning module is established, risk early warning indicators and thresholds are set, risk early warning information is generated according to the risk assessment report, and relevant parties are notified through various channels; A water conservancy project quality and safety risk disposal module is established, and corresponding risk response measures are started according to the risk early warning information; A water conservancy project quality and safety risk feedback module is established to track and evaluate the effect of risk disposal, collect and analyze risk disposal data and experience, summarize and refine risk management experience and lessons and improvement measures, and update and improve the risk database, assessment model, early warning module and disposal module, to improve the level and efficiency of risk management.

[0015] Compared with the prior art, the present application has at least the following technical effects or advantages: The present application realizes the comprehensive management of water conservancy project quality and safety risks through the application of digital twin models and comprehensive index systems, and improves the scientificity, accuracy and real-time performance of risk management. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a water conservancy project quality and safety risk management method based on a digital twin model, as disclosed in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0018] 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.

[0019] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0020] like Figure 1 As shown, the water conservancy project quality and safety risk management method based on digital twin model includes the following steps: Step 1: Establish an indicator system for the quality and safety risks of water conservancy projects and determine specific indicators. At the same time, optimize and adjust the indicators based on the actual situation of the water conservancy projects. Step 2: Construct a digital twin model of the water conservancy project using data from the water conservancy project to achieve real-time, dynamic, and comprehensive digital representation and simulation of the water conservancy project; Step 3: Evaluate the risks based on their likelihood and severity, classify them into types, levels, and grades, and generate a risk assessment report; Step 4: Select the optimal risk management plan based on the nature and extent of the risk; Step 5: Utilize an information-based risk management platform to achieve real-time monitoring, intelligent early warning, rapid response, and effective handling of quality and safety risks in water conservancy projects, thereby improving risk prevention and response capabilities and ensuring the quality and safety of water conservancy projects.

[0021] In Step 1, the index system of water conservancy project quality and safety risk is established, and specific evaluation indexes are determined. Through expert consultation and comprehensive consideration of the characteristics of water conservancy projects, including design, construction, operation and maintenance factors at each stage, a comprehensive and scientific index system is established. Then, combined with the actual situation of water conservancy projects, through continuous monitoring and experience accumulation, the indexes are optimized and adjusted to ensure that the index system can timely reflect the risk changes and more accurately assess the quality and safety risks of water conservancy projects.

[0022] In Step 2, data related to water conservancy projects are used to build digital twin models, including the integration of three-dimensional geometric models, physical models and intelligent models. Digital twin models can realize real-time, dynamic and comprehensive digital representation and simulation of water conservancy projects. Through digital twin models, the running state of water conservancy projects under different conditions can be simulated, potential problems can be identified in advance, and more accurate data support can be provided for risk assessment.

[0023] In Step 3, based on the data of digital twin models, the quality and safety risks of water conservancy projects are evaluated. Considering the possibility and severity of risks, the types, levels and grades of risks are classified. A detailed risk assessment report is formed to provide basis for subsequent risk management decisions.

[0024] In Step 4, according to the nature and degree of risk, corresponding risk treatment schemes are developed. This may include measures to reduce the probability of risk occurrence, improve the ability to respond to risk events, and mitigate the impact of risk events. When selecting the optimal risk treatment scheme, factors such as cost, benefit, feasibility, etc. need to be considered.

[0025] In Step 5, an information-based risk management platform is established to realize real-time monitoring, intelligent early warning, rapid response and effective disposal of water conservancy project quality and safety risks through digital twin models. The platform can integrate various monitoring data, model prediction results and actual operation conditions, analyze real-time data through algorithms, and provide real-time risk status, so that management personnel can make decisions more timely, thereby improving the risk prevention and response ability of water conservancy projects and ensuring the quality and safety of the projects.

[0026] Further, the specific steps for establishing the index system of water conservancy project quality and safety risk and determining specific indexes include: The influence factors and evaluation indexes of water conservancy project quality and safety risk are determined through expert consultation, and an index system is constructed. In this step, first, the main influence factors of water conservancy project quality and safety risk are determined through expert discussion, literature research and case analysis, and the weight of each factor is determined to reflect its contribution to the overall risk. Then, the determined factors are converted into specific evaluation indexes, the hierarchical structure between indexes is established to ensure the rationality and completeness of the system, and the measurement unit and scoring standard of the index are formulated to facilitate subsequent quantitative evaluation. The weight and evaluation standard of each index are determined through analytic hierarchy process. In this step, first, the analytic hierarchy process is used to compare each index pairwise to obtain the weight matrix. Then, consistency check is performed to ensure the consistency and credibility of expert evaluation. Finally, the weight of each index is calculated to form a weight vector. The specific steps of optimizing and adjusting the index according to the actual situation of water conservancy project include: According to the different stages of water conservancy project, the weight and evaluation standard of the index are dynamically updated to reflect the temporal and spatial changes of risk. The main purpose of this step is to adjust the weight of each index based on actual monitoring data and expert experience to reflect the changes of risk with time and space. According to the risk evaluation results and risk prevention and control suggestions of water conservancy project, the value range of the index is adjusted and the target is optimized to realize the continuous improvement of risk. In the implementation process of water conservancy project, actual data is collected and fed back in time for the correction of the index and optimization of the system.

[0027] Among them, the expert consultation adopts the improved Delphi method, which specifically includes: selecting 15-25 experts with different professional backgrounds, including water conservancy engineering design experts, construction management experts, quality detection experts, risk assessment experts and operation and maintenance management experts; Through three rounds of questionnaire survey, expert opinions are collected. In the first round, open-ended questions are used to identify potential risk factors. In the second round, Likert five-level scale is used to score the importance of risk factors. In the third round, indexes with large differences are confirmed again. Set the threshold of expert opinion consistency coefficient to 0.75. When the expert opinion consistency coefficient of an index is lower than the threshold, the fourth round of expert consultation is needed until the consistency requirement is met.

[0028] The analytic hierarchy process adopts an improved group analytic hierarchy process, and the specific steps include: constructing a three-level index hierarchy structure, including a target layer, a criterion layer and an index layer; using a 1-9 scale method to construct a pair-wise comparison matrix, wherein 1 represents that two factors are equally important, and 9 represents that one factor is extremely important than another factor; determining the weight of each index by calculating the maximum eigenvalue and the eigenvector, and simultaneously performing consistency checking, and the consistency ratio CR is required to be less than 0.15; when there is a difference in the opinions of experts in group decision-making, a geometric mean method is used to synthesize the group judgment matrix; a weight sensitivity analysis model is established, and the influence degree of the key index weight on the overall evaluation result is analyzed by changing the key index weight, so as to ensure the rationality and stability of the weight distribution.

[0029] The step of dynamically updating the index weight includes: setting a weight adjustment coefficient according to different stages of the water conservancy project, focusing on technical risk and economic risk in the design stage, and the weight coefficients are 0.4 and 0.3 respectively; focusing on quality risk and safety risk in the construction stage, and the weight coefficients are 0.35 and 0.4 respectively; focusing on operation risk and environmental risk in the operation and maintenance stage, and the weight coefficients are 0.4 and 0.25 respectively; when the deviation between the risk assessment result and the actual situation exceeds 20%, the weight adjustment program is automatically triggered; the influence of recent risk events on the importance of each index is analyzed by using the sliding window technology, and the window size is set to the historical data of the last 6 months.

[0030] Further, step 2 includes the following steps: Step 2.1, collecting data of the water conservancy project, including: historical data, field data, monitoring data, video data and remote sensing data, including data of the design, construction, operation and maintenance stages of the water conservancy project, and related data of the natural environment, social economy and water resources of the water conservancy project, in order to build an index system for quality and safety risk assessment of the water conservancy project, it is necessary to collect water conservancy related data comprehensively and multi-level. This includes historical data, field data, monitoring data, video data and remote sensing data. Historical data can provide maintenance and operation records of past water conservancy projects, field data can reflect the actual status of the current project, monitoring data can help real-time monitoring of the project operation state, and video and remote sensing data can provide more comprehensive environmental information. In addition to engineering data, information such as natural environment, social economy and water resources related to the location of the water conservancy project also needs to be considered; Step 2.2, according to the data of the water conservancy project, a three-dimensional geometric model of the water conservancy project is constructed, reflecting the form, structure and material physical characteristics of the water conservancy project, through the three-dimensional geometric model, the geometric shape and spatial structure of the water conservancy project can be intuitively displayed, providing a basis for subsequent evaluation and analysis; Step 2.3, based on the data of the water conservancy project, construct the physical model of the water conservancy project, reflect the operation mechanism, dynamic process and functional effect of the water conservancy project, and the physical model can involve water flow, pressure, temperature and other parameters, describe the operation state of the water conservancy project through mathematical equations and physical laws, and provide scientific basis for quantitative evaluation; Step 2.4, based on the data of the water conservancy project, construct the intelligent model of the water conservancy project, reflect the state change, risk prediction and optimization scheduling of the water conservancy project, realize the intelligent monitoring and prediction of the water conservancy project state, and provide intelligent support for risk assessment and decision-making; Step 2.5, integrate the three-dimensional geometric model, physical model and intelligent model of the water conservancy project into a digital twin model, realize real-time, dynamic and comprehensive digital representation and simulation of the water conservancy project, and ensure the cooperation between the models to fully reflect the multi-faceted characteristics of the water conservancy project; Step 2.6, visualize the digital twin model to realize intuitive display and interactive operation of the water conservancy project, so as to provide an intuitive tool for relevant professionals to understand and analyze the state and characteristics of the water conservancy project more easily; Step 2.7, connect the digital twin model with the physical equipment of the water conservancy project to realize synchronous update, virtual-real interaction and iterative optimization between the digital twin model and the physical water conservancy project, which can be realized through real-time data transmission, sensor network and other technologies to ensure that the state of the digital twin model is consistent with that of the actual water conservancy project, providing support for real-time monitoring and decision-making.

[0031] Among them, the data collection adopts multi-source heterogeneous data fusion technology, specifically including: establishing a distributed data collection network, deploying edge computing nodes to realize real-time preprocessing and preliminary analysis of data; using Internet of Things sensor network to collect structural health monitoring data, including strain, displacement, vibration, temperature and other physical parameters, with sensor accuracy requirement reaching 0.1%FS; using multispectral and hyperspectral remote sensing images to monitor geological changes in the project area, with time resolution not less than 1 day and spatial resolution not less than 1 meter; automatically identifying abnormal behaviors and safety hazards in the construction process, with identification accuracy not less than 95%.

[0032] The construction of the three-dimensional geometric model specifically comprises: obtaining high-precision three-dimensional point cloud data of the engineering structure by laser scanning and photogrammetry technology, and the point cloud density is not less than 1000 points per square meter; establishing a multi-level detail model (LOD), including a conceptual model (LOD100), an approximate geometric model (LOD200), an accurate geometric model (LOD300), a manufacturing model (LOD400) and an actual model (LOD500); realizing rapid updating and modification of the model through parameter driving; establishing a material attribute database including physical and mechanical parameters of materials such as concrete, steel and earth materials; dynamically adjusting the grid density according to the calculation requirement, and using high-density grid in key areas and low-density grid in secondary areas to improve the calculation efficiency.

[0033] The construction of the physical model specifically comprises: establishing a fluid-structure interaction model to simulate the interaction between water flow and structure, and solving the coupling equations of the fluid domain and the solid domain by using the finite element method; establishing a thermal force coupling model to analyze the influence of temperature change on the performance of the structure, considering the effects of material thermal expansion, thermal stress and heat conduction; establishing a chemical corrosion model to simulate the deterioration process of materials in a chemical environment, and using a diffusion equation to describe the penetration law of chemical ions in the material; establishing a fatigue damage model to predict the fatigue life of the structure under cyclic loading, and using the Paris formula to describe the fatigue crack propagation law; establishing a seismic dynamics model to analyze the influence of seismic load on the engineering structure, and using the time history analysis method to calculate the dynamic response of the structure.

[0034] The construction of the intelligent model specifically comprises: establishing a time series prediction model based on a long short-term memory network (LSTM) for predicting the trend of the state of the engineering structure, the model containing 3 hidden layers with 128 neurons in each layer; establishing an image recognition model based on a convolutional neural network (CNN) for automatically identifying abnormal phenomena such as cracks and leaks on the surface of the structure, the model adopting a ResNet-50 architecture; establishing a risk classification model based on a support vector machine (SVM) for classifying risks into low, medium and high levels, and using a radial basis function as a kernel function; establishing an optimization scheduling model based on a genetic algorithm for optimizing engineering operation parameters, with a population size of 100, a crossover probability of 0.8 and a mutation probability of 0.1.

[0035] The digital twin model integration adopts a distributed architecture and cloud-edge collaboration technology, specifically including: establishing a hierarchical architecture, including a perception layer, a network layer, a data layer, a model layer, an application layer, and a representation layer; adopting a microservice architecture design, encapsulating different functional modules as independent microservices, and realizing communication and data exchange between modules through API interfaces; establishing a data synchronization mechanism, realizing real-time transmission and synchronous updating of data using message queue technology, with a data delay of no more than 100 milliseconds; establishing a model version management mechanism, supporting version control, rollback, and updating of models; deploying models in Docker containers to support elastic expansion and load balancing; establishing a security protection mechanism, using data encryption, identity authentication, and access control technologies to protect the security of data and models.

[0036] Further, step 3 includes the following steps: Step 3.1, according to the characteristics of water conservancy projects and the availability of data, selecting appropriate risk assessment methods; Step 3.2, according to the selected risk assessment method, establishing the corresponding mathematical model, defining the indicators, parameters, weights, and thresholds of risk, and determining the calculation formula and evaluation standard of risk; Step 3.3, obtaining the required risk assessment data from the digital twin model of water conservancy projects and inputting it into the risk assessment model, and performing data processing and analysis; Step 3.4, according to the output of the risk assessment model, quantitatively or qualitatively evaluating the risk, dividing the types, levels, and grades of risk, and forming a risk assessment report, which should include the description, evaluation, classification, and ordering of risk; Step 3.5, according to the risk assessment results and actual situation, verifying and optimizing the risk assessment model.

[0037] The risk assessment method selection specifically includes: establishing a risk assessment method library, including fault tree analysis, event tree analysis, fuzzy comprehensive evaluation, analytic hierarchy process, artificial neural network, and other methods; establishing a method applicability evaluation model, evaluating the applicability of each method according to factors such as project type, data availability, risk characteristics, and calculation complexity; using the TOPSIS method to sort and select candidate methods, establishing an evaluation index system including calculation accuracy, calculation efficiency, data requirements, interpretability, and practicality; establishing a method combination optimization model, using integrated learning to combine multiple methods, improving evaluation accuracy through weight allocation and result fusion; establishing a method verification mechanism, verifying the effectiveness of the method through historical case verification and cross-validation, requiring a prediction accuracy of no less than 75%.

[0038] The risk assessment model is established by using a multi-level fuzzy evaluation method, specifically including: establishing a three-level risk evaluation index system, the first-level indexes including technical risk, management risk, environmental risk, and economic risk; establishing a fuzzy membership function, using trapezoidal fuzzy numbers to represent risk levels, including very low [0, 0, 0.2, 0.3], low [0.2, 0.3, 0.4, 0.5], medium [0.4, 0.5, 0.6, 0.7], high [0.6, 0.7, 0.8, 0.9], and very high [0.8, 0.9, 1, 1]; establishing a fuzzy weight matrix, using an improved fuzzy analytic hierarchy process to determine the weights of each index, processing the fuzziness and uncertainty of the judgment matrix; establishing a fuzzy comprehensive evaluation model, using the maximum membership degree principle and the barycentric method for fuzzy reasoning and defuzzification; and establishing a risk threshold dynamic adjustment mechanism, dynamically adjusting the risk level threshold according to the actual situation of the project and historical experience.

[0039] The risk assessment data acquisition specifically includes: establishing a data preprocessing procedure, including data cleaning, denoising, standardization, normalization, and other steps, using multiple imputation methods to handle missing values, and using a density-based local outlier factor detection method to handle abnormal values; using principal component analysis to reduce data dimensionality, retaining more than 95% of the information content; using wavelet transform technology to analyze the time-frequency characteristics of the data, identifying periodic and trend changes in the data; using the Apriori algorithm to discover the association between different risk factors, setting the minimum support to 0.1 and the minimum confidence to 0.8.

[0040] In step 3.1, according to the characteristics of water conservancy projects and the availability of data, select appropriate risk assessment methods. Different water conservancy projects may require different assessment methods, including qualitative and quantitative methods. For example, you can choose an empirical method based on statistical data, a simulation based on a physical model, or a predictive model based on data mining and machine learning. Selecting the appropriate method takes into account factors such as data quality, time, and cost.

[0041] In step 3.2, according to the selected risk assessment method, establish the corresponding mathematical model. In the mathematical model, define the risk indicators, parameters, weights, and thresholds, and determine the risk calculation formula and evaluation criteria. These parameters and criteria should be set taking into account the actual situation and characteristics of water conservancy projects to ensure a comprehensive and objective reflection of the risk occurrence probability and impact.

[0042] In step 3.3, obtain the required risk assessment data from the digital twin model of the water conservancy project and input these data into the risk assessment model for processing and analysis. The digital twin model can provide real-time, dynamic data, including the state of the project, environmental conditions, operating parameters, etc., providing a high-quality data basis for risk assessment.

[0043] In step 3.4, the risk is quantitatively or qualitatively evaluated according to the output of the risk assessment model. This includes classifying the type, level and grade of the risk, and forming a detailed risk assessment report. The report should include the description, evaluation, classification and ranking of the risk, providing detailed information for subsequent risk management.

[0044] In step 3.5, the risk assessment model is verified and optimized according to the risk assessment results and actual situation. By comparing the actual risk events and the model prediction results, the accuracy of the model is verified. If necessary, the parameters and weights of the model can be adjusted to improve the accuracy and applicability of the model. This step is a continuous improvement process to ensure that the risk assessment tool can adapt to the changes and complexity of water conservancy engineering operation.

[0045] Further, step 4 includes the following steps: Step 4.1, according to the results of risk identification and analysis, list all possible risk treatment schemes, and the probability, benefit and cost parameters of each scheme, construct a benefit matrix; Step 4.2, use decision tree tool, draw different risk treatment schemes and their corresponding results, calculate the expected benefit value of each scheme, and the investment required for the implementation of each scheme, recorded as scheme A; Step 4.3, use the uncertainty decision scheme tool, according to different risk scenarios, calculate the regret value of each scheme, that is, the difference between the benefits of the scheme and the best scheme, select the risk treatment scheme with the minimum maximum regret value, recorded as scheme B; Step 4.4, use multi-criteria decision analysis tool, according to the interests and responsibilities of stakeholders, determine different decision criteria, give certain weight to each criterion, then score each risk treatment scheme, select the risk treatment scheme with the highest weighted score, recorded as scheme C; Step 4.5, compare the advantages and disadvantages of scheme A, scheme B and scheme C, select the optimal risk treatment scheme, or adjust and optimize the scheme to achieve the best risk management effect and efficiency.

[0046] The revenue matrix construction employs Monte Carlo simulation technology, specifically including: establishing a probability distribution model to describe the probability of risk events occurring, using multiple distribution forms such as Beta distribution, normal distribution, and log-normal distribution; establishing a cost-benefit calculation model, including multiple cost types such as direct cost, indirect cost, opportunity cost, and social cost, as well as multiple benefit types such as economic benefit, social benefit, and environmental benefit; conducting 10,000 random samplings using the Monte Carlo method to calculate the expected revenue and risk level of each option under different scenarios; establishing a sensitivity analysis model to analyze the impact of changes in key parameters on decision-making results and identify key risk factors; and establishing a scenario analysis framework, designing three scenarios—optimistic scenario, baseline scenario, and pessimistic scenario—to analyze the performance of each option under different scenarios.

[0047] In step 4.1, based on the results of risk identification and analysis, all possible risk management solutions are listed. For each solution, its probability, benefits, and costs are evaluated, and a benefit matrix is ​​constructed. This matrix will help to visually compare the overall effectiveness of different solutions under different scenarios.

[0048] In step 4.2, decision tree tools are used to plot different risk management options and their corresponding outcomes. The expected return for each option and the required investment for implementation are calculated. This helps to comprehensively evaluate the economic benefits and costs of the options, providing visual support for subsequent decision-making.

[0049] In step 4.3, using the uncertainty decision-making tool, the regret value of each option is calculated based on different risk scenarios; that is, the difference in payoff between the option and the optimal option. The risk management option that minimizes the maximum regret value is selected and denoted as Option B. This helps to consider the robustness and coping ability of the option under different risk scenarios.

[0050] In step 4.4, a multi-criteria decision analysis tool is used to determine different decision criteria based on the interests and responsibilities of stakeholders, and each criterion is assigned a certain weight. Then, each risk treatment option is scored, and the risk treatment option that results in the highest weighted score is selected, denoted as Option C. This helps to comprehensively consider the interests of different stakeholders and improve the overall and long-term nature of the decision.

[0051] In step 4.5, the advantages and disadvantages of options A, B, and C are comprehensively compared, considering the overall effectiveness of each option in terms of economy, risk robustness, and stakeholder interests. Based on the results of the comprehensive comparison, the optimal risk management option is selected, or the options are adjusted and optimized to achieve the best risk management effect and efficiency. This may involve trade-offs and compromises to ensure that the selected option can meet the overall objectives to the greatest extent possible in all aspects.

[0052] Further, the specific steps of step 4.2 are: Analyze the possible outcomes and probabilities of each scenario, as well as the benefits and costs of each outcome; Draw a decision tree, with boxes representing decision nodes, circles representing chance nodes, triangles representing terminal nodes, and branch lines representing different outcomes and probabilities; Calculate the expected benefit value of the decision tree, from right to left, for each chance node, multiply the benefits of the outcomes by the probabilities, and then sum them up to get the expected benefit value of the node, for each decision node, select the branch with the maximum expected benefit value to get the expected benefit value of the node; Select the scenario with the maximum expected benefit value, denoted as scenario A; The specific steps of step 4.3 are: Analyze the benefits of each scenario under different risk scenarios, and construct a benefit matrix, with each row representing a scenario and each column representing a risk scenario; Calculate the regret value of each scenario, which is the difference between the benefit of each cell and the maximum benefit of that column, and construct a regret matrix, with each row representing a scenario and each column representing a risk scenario; Calculate the maximum regret value of each scenario, which is the maximum value of each row, and select the scenario with the minimum maximum regret value, denoted as scenario B; The specific steps of step 4.4 are: Determine different decision criteria to reflect the interests and responsibilities of stakeholders; Assign a certain weight to each decision criterion to reflect its relative importance, so that the sum of the weights is 1; Score each scenario under each decision criterion to reflect its relative performance; Calculate the weighted score of each scenario, which is the score of each cell multiplied by the weight of that column, and then sum them up, and select the scenario with the highest weighted score, denoted as scenario C.

[0053] Among them, the decision tree construction specifically includes: decomposing a complex decision problem into multiple stage sub-decision problems; using information gain ratio as node splitting criterion to avoid biasing towards attributes with more values; using cost complexity pruning method to prevent overfitting, determining the optimal pruning parameter through cross-validation; using random forest algorithm to construct multiple decision trees, improving the stability and accuracy of decision-making through voting mechanism; dynamically updating the structure and parameters of the decision tree when new data and information arrive; recording each node and branch in the decision-making process to realize the interpretability and traceability of decision-making; establishing a decision tree visualization tool to display the structure and logic of the decision tree in a graphical way.

[0054] The regret value calculation adopts interval number theory and rough set theory, and specifically includes: establishing an interval number benefit matrix, using interval numbers to represent the uncertainty of benefits, and the lower bound and upper bound of the interval number representing the most pessimistic and most optimistic benefit estimates; using interval number operation rules to calculate interval regret values, including interval number addition, subtraction, and multiplication operations; establishing a rough set model to process incomplete and inconsistent decision-making information, and describing the uncertainty of decisions through upper approximation and lower approximation sets; establishing a decision-making model based on information granularity, which decomposes the decision-making problem into sub-problems of different granularities; using a grey correlation analysis method to analyze the similarity and correlation between different schemes; and establishing a regret value weight adjustment mechanism to adjust the weight coefficients of the regret values according to the risk preferences of the decision maker.

[0055] The multi-criteria decision analysis adopts an improved ELECTRE method, and specifically includes: establishing a multi-criteria evaluation index system, including quantitative indexes and qualitative indexes, determining the weights of the quantitative indexes using an objective weighting method, and determining the weights of the qualitative indexes using a subjective weighting method; establishing consistency and inconsistency indexes to analyze the superior-inferior relationship between schemes through pairwise comparison; establishing a threshold system, including a no-difference threshold, a preference threshold, and a veto threshold, for processing the fuzziness and uncertainty in decision-making; establishing a dominance relationship matrix to describe the degree of dominance and the degree of being dominated between schemes; establishing a core solution set and a stable solution set to analyze the dominance relationship between schemes through graph theory; and establishing a sensitivity analysis mechanism to analyze the influence of weight and threshold changes on the decision results.

[0056] Further, step 5 includes the following steps: A water conservancy project quality and safety risk database is established to form a water conservancy project quality and safety risk knowledge base, and the risk assessment results of steps 3 and 4 are integrated into a water conservancy project quality and safety risk database; the database should include historical risk data, parameters and results of the risk assessment model, and implementation of risk disposal, and the like; the establishment of the database helps to form a water conservancy project quality and safety risk knowledge base, and provides reference and experience accumulation for future decision-making; A water conservancy project quality and safety risk early warning module is established, risk early warning indexes and thresholds are set, risk early warning information is generated according to the risk assessment report, and relevant parties are notified through various channels; the early warning module should have real-time and flexibility, and be able to flexibly adjust the risk early warning indexes and thresholds according to the monitoring data and model prediction results; A water conservancy project quality and safety risk disposal module is established, corresponding risk response measures are started according to the risk early warning information, the risk disposal module should have an automatic or semi-automatic risk disposal process to ensure that measures can be quickly and effectively taken when a risk occurs; the risk disposal module should also include an execution tracking and recording function of the risk response scheme, for subsequent feedback and evaluation; A water conservancy project quality and safety risk feedback module is established to track and evaluate the effect of risk disposal, collect and analyze risk disposal data and experience, summarize and refine risk management experience and lessons and improvement measures, and update and improve the risk database, assessment model, early warning module and disposal module, thereby improving the level and efficiency of risk management.

[0057] The risk database is established using a graph database and a time series database mixed architecture, specifically including: establishing an ontology model to describe the concepts, attributes and relationships of the water conservancy project risk field, including risk types, risk factors, risk events, risk consequences and other entities; using a Neo4j graph database to store a risk knowledge graph; using an InfluxDB time series database to store monitoring data and historical data, supporting efficient time series data storage and query; establishing data standardization specifications, including data coding rules, data format standards, data exchange protocols and the like; establishing a data blood relationship tracking mechanism to record the source, processing process and use of data; establishing a data life cycle management mechanism, including data creation, storage, use, archiving, destruction and the like throughout the life cycle; and establishing a data security protection mechanism using data desensitization, data encryption, access control and the like to protect sensitive data.

[0058] The risk early warning module uses multi-level early warning and intelligent early warning technology, specifically including: establishing a four-level early warning system including blue early warning (general risk), yellow early warning (higher risk), orange early warning (high risk) and red early warning (extremely high risk); establishing a dynamic adjustment mechanism for early warning index threshold values to dynamically adjust the early warning threshold values according to seasonal changes, project operating states, historical experience and the like; establishing a multi-element early warning index system including absolute indexes, relative indexes, trend indexes and comprehensive indexes; using machine learning algorithms to establish early warning models including support vector machines, random forests, neural networks and the like, and improving early warning accuracy through ensemble learning; establishing an early warning information release mechanism to release early warning information through multiple channels such as SMS, email, WeChat, APP push and voice calls; and establishing early warning response time requirements with a first-level early warning response time of no more than 5 minutes, a second-level early warning response time of no more than 15 minutes, a third-level early warning response time of no more than 30 minutes and a fourth-level early warning response time of no more than 60 minutes.

[0059] The risk disposal module adopts intelligent emergency response technology, specifically including: establishing an emergency plan library, including standardized disposal processes, emergency resource allocation schemes, emergency organizational structures, etc. for different types of risks; establishing an emergency decision support system, using expert systems and case-based reasoning technology to automatically recommend the most suitable disposal scheme according to the risk type and severity; establishing an emergency resource scheduling optimization model, using genetic algorithms and particle swarm optimization algorithms to optimize the scheduling scheme of emergency personnel, equipment, and materials; establishing an emergency command coordination mechanism, achieving real-time coordination of emergency command through video conferencing, instant messaging, location services, and other technologies; establishing an emergency effect evaluation model, evaluating the effectiveness of emergency disposal by comparing the risk level before and after disposal; establishing an emergency exercise simulation system, simulating the emergency disposal process through virtual reality technology to improve emergency response capabilities; and establishing an emergency knowledge management system to accumulate and share emergency disposal experience and lessons.

[0060] The risk feedback module adopts continuous improvement and machine learning technology, specifically including: establishing a risk disposal effect evaluation index system, including response time, disposal cost, risk reduction degree, and secondary risk control indicators; establishing a disposal effect tracking mechanism to evaluate the sustainability and stability of disposal effects through regular monitoring and follow-up surveys; establishing an experience and lesson extraction algorithm to extract valuable experience and lessons from disposal reports using text mining and knowledge graph technology; establishing an improvement measure generation model to automatically generate improvement suggestions based on historical cases and expert knowledge; establishing an online learning mechanism for models to continuously update risk assessment models and early warning models through incremental learning algorithms; establishing a knowledge update mechanism to regularly update knowledge resources such as risk databases, rule bases, and case bases; and establishing a performance evaluation system to evaluate the overall effectiveness of the risk management system through key performance indicators (KPIs), including risk identification accuracy, early warning timeliness, and disposal success rate, with all indicators not less than 85%.

Claims

1. A method for quality and safety risk management of water conservancy projects based on digital twin models, characterized in that: Includes the following steps: Step 1: Establish an indicator system for the quality and safety risks of water conservancy projects and determine specific indicators. At the same time, optimize and adjust the indicators based on the actual situation of the water conservancy projects. Step 2: Construct a digital twin model of the water conservancy project using data from the water conservancy project to achieve real-time, dynamic, and comprehensive digital representation and simulation of the water conservancy project; Step 3: Evaluate the risks based on their likelihood and severity, classify them into types, levels, and grades, and generate a risk assessment report; Step 4: Select the optimal risk management plan based on the nature and extent of the risk; Step 5: Utilize an information-based risk management platform to achieve real-time monitoring, intelligent early warning, rapid response, and effective handling of quality and safety risks in water conservancy projects, thereby improving risk prevention and response capabilities and ensuring the quality and safety of water conservancy projects.

2. The method for quality and safety risk management of water conservancy projects based on digital twin models according to claim 1, characterized in that, The specific steps for establishing an indicator system for the quality and safety risks of water conservancy projects and determining specific indicators include: Through expert consultation, the influencing factors and evaluation indicators of the quality and safety risks of water conservancy projects are determined, and an indicator system is constructed. The weights and evaluation criteria for each indicator were determined using the analytic hierarchy process (AHP). The specific steps for optimizing and adjusting indicators based on the actual conditions of water conservancy projects include: The weights and evaluation criteria of the indicators are dynamically updated according to different stages of the water conservancy project to reflect the spatiotemporal changes of risk. Based on the risk assessment results and risk prevention and control recommendations for water conservancy projects, the range of values ​​for indicators and optimization targets should be adjusted in a timely manner to achieve continuous improvement in risk management.

3. The method for quality and safety risk management of water conservancy projects based on digital twin models according to claim 1, characterized in that, Step 2 includes the following steps: Step 2.1: Collect data on water conservancy projects, including historical data, field data, monitoring data, video data, and remote sensing data. This includes data from the design, construction, operation, and maintenance phases of the water conservancy projects, as well as data related to the natural environment, socio-economic conditions, and water resources of the water conservancy projects. Step 2.2: Based on the data of the water conservancy project, construct a three-dimensional geometric model of the water conservancy project to reflect its morphology, structure, and material physical characteristics. Step 2.3: Based on the data of the water conservancy project, construct a physical model of the water conservancy project to reflect the operation mechanism, dynamic process and physical laws of the functional effects of the water conservancy project; Step 2.4: Based on the data of the water conservancy project, construct an intelligent model of the water conservancy project to reflect the status changes, risk prediction and optimized scheduling of the water conservancy project; Step 2.5 integrates the three-dimensional geometric model, physical model and intelligent model of the water conservancy project into a digital twin model to realize real-time, dynamic and comprehensive digital representation and simulation of the water conservancy project; Step 2.6: Visualize the digital twin model to achieve intuitive display and interactive operation of the water conservancy project; Step 2.7 connects the digital twin model with the physical equipment of the water conservancy project to achieve synchronous updates, virtual-physical interaction, and iterative optimization between the digital twin model and the physical water conservancy project.

4. The method for quality and safety risk management of water conservancy projects based on digital twin models according to claim 1, characterized in that, Step 3 includes the following steps: Step 3.1: Select an appropriate risk assessment method based on the characteristics of the water conservancy project and the availability of data; Step 3.2: Establish the corresponding mathematical model based on the selected risk assessment method. At the same time, define the risk indicators, parameters, weights and thresholds, and determine the risk calculation formula and evaluation criteria. Step 3.3: Obtain the required risk assessment data from the digital twin model of the water conservancy project and input it into the risk assessment model for data processing and analysis; Step 3.4: Based on the output of the risk assessment model, conduct a quantitative or qualitative evaluation of the risks, classify the types, levels and grades of risks, and generate a risk assessment report. The report should include the description, evaluation, classification and ranking of the risks. Step 3.5: Validate and optimize the risk assessment model based on the risk assessment results and actual conditions.

5. The method for quality and safety risk management of water conservancy projects based on digital twin models according to claim 1, characterized in that, Step 4 includes the following steps: Step 4.1: Based on the results of risk identification and analysis, list all possible risk management solutions, as well as the probability, benefit and cost parameters of each solution, and construct a benefit matrix; Step 4.2: Using a decision tree tool, draw different risk management plans and their corresponding outcomes, calculate the expected return for each plan, and the investment required to implement each plan, denoted as Plan A; Step 4.3: Using the uncertainty decision-making tool, calculate the regret value of each option based on different risk scenarios, i.e., the difference in payoff between the option and the optimal option. Select the risk management option that minimizes the maximum regret value, denoted as Option B. Step 4.4: Using a multi-criteria decision analysis tool, different decision criteria are determined based on the interests and responsibilities of stakeholders. Each criterion is assigned a certain weight, and then each risk treatment plan is scored. The risk treatment plan that results in the highest weighted score is selected and denoted as Plan C. Step 4.5: Compare the advantages and disadvantages of options A, B, and C, and select the optimal risk management option, or adjust and optimize the options to achieve the best risk management effect and efficiency.

6. The method for quality and safety risk management of water conservancy projects based on a digital twin model according to claim 5, characterized in that, The specific steps of step 4.2 are as follows: Analyze the possible outcomes and probabilities of each option, as well as the benefits and costs of each outcome; Draw a decision tree, using boxes to represent decision nodes, circles to represent opportunity nodes, triangles to represent termination nodes, and branches to represent different outcomes and probabilities; To calculate the expected return of a decision tree, from right to left, for each opportunity node, multiply the return of the outcome by the probability and then sum them up to obtain the expected return of that node. For each decision node, select the branch with the largest expected return to obtain the expected return of that node. Choose the option with the highest expected return, denoted as Option A; The specific steps of step 4.3 are as follows: Analyze the returns of each option under different risk scenarios, construct a return matrix, with each row representing an option and each column representing a risk scenario; calculate the regret value of each option, which is the difference between the return of each cell and the maximum return of that column, construct a regret matrix, with each row representing an option and each column representing a risk scenario; calculate the maximum regret value of each option, which is the maximum value of each row, and select the option with the smallest maximum regret value, denoted as Option B; The specific steps of step 4.4 are as follows: determine different decision criteria to reflect the interests and responsibilities of stakeholders; assign a certain weight to each decision criterion to reflect its relative importance, so that the sum of the weights is 1; score each option under each decision criterion to reflect its relative performance. Calculate the weighted score for each option, which is the score of each cell multiplied by the weight of that column, then sum them up, and select the option with the highest weighted score, denoted as option C.

7. The method for quality and safety risk management of water conservancy projects based on digital twin models according to claim 1, characterized in that, Step 5 includes the following steps: Establish a database of water conservancy project quality and safety risks to form a knowledge base for water conservancy project quality and safety risks; Establish a water conservancy project quality and safety risk early warning module, set risk early warning indicators and thresholds, generate risk early warning information based on risk assessment reports, and notify relevant parties through multiple channels; Establish a module for handling quality and safety risks in water conservancy projects, and initiate corresponding risk response measures based on risk warning information; Establish a risk feedback module for the quality and safety of water conservancy projects to track and evaluate the effectiveness of risk management, collect and analyze risk management data and experience, summarize and refine lessons learned and improvement measures for risk management, update and improve the risk database, assessment model, early warning module and management module, and improve the level and efficiency of risk management.

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