Reservoir dike full life cycle management system

Abstract

The invention relates to the technical field of water conservancy project management, and discloses a reservoir dike full life cycle management system. Aiming at the problems of staged cutting, data fault, low risk pre-judgment precision and the like in the existing dike management, the system comprises a data acquisition module, a data processing and evidence storage module, a full-life-cycle coupling analysis module and an intelligent decision-making and execution feedback module, and all the modules are linked to form a closed loop. According to the system, a multi-sensor fusion technology is adopted to collect multi-source heterogeneous data, credible evidence storage is realized through a block chain, an improved Gumbel Copula function and an LSTM neural network model are fused to realize multi-stage dynamic coupling analysis, and decision is optimized in combination with reinforcement learning. According to the invention, full-life-cycle collaborative management is realized, the risk pre-judgment precision and decision-making scientificity are improved, credible tracing of data is guaranteed, and the management cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project management technology, and in particular to a reservoir and embankment full life cycle management system. Background Technology

[0002] As core water conservancy projects for flood control and disaster reduction, the safe and stable operation of reservoirs and levees is directly related to the safety of people's lives and property. Currently, reservoir and levee management focuses mainly on single monitoring or localized repairs during the operation and maintenance phase, resulting in significant fragmentation between phases: the planning and design phase fails to fully consider the impact of dynamic environmental changes throughout the entire life cycle; quality data from the construction phase lacks effective correlation with operation and maintenance data; and the decommissioning and disposal phase lacks coordination and connection with previous phases. This leads to deficiencies in life-cycle management, such as data gaps, delayed decision-making, and insufficient risk prediction.

[0003] While some existing research has attempted to incorporate digital twins and big data technologies for levee monitoring, these are mostly limited to localized applications at a single stage, failing to achieve dynamic coupling and data closure across all stages of the entire lifecycle. In risk assessment, traditional methods often employ fixed initial water level assumptions, neglecting the randomness and cumulative effects of water level fluctuations under extreme rainfall, resulting in low risk prediction accuracy. Furthermore, the reliability assurance of data at each stage and the full-process traceability mechanism are not yet perfect, making it difficult to meet the needs of refined management throughout the entire lifecycle. Therefore, there is an urgent need for a reservoir levee lifecycle management system that can achieve full-stage collaboration, data closure, accurate prediction, and is safe and reliable. Summary of the Invention

[0004] In view of this, and in response to the problems of stage-based fragmentation, data gaps, low accuracy of risk prediction, and insufficient data credibility in existing reservoir and embankment management technologies, this invention provides a reservoir and embankment full life-cycle management system. By integrating a multi-stage dynamic coupling model, an improved risk assessment algorithm, and blockchain evidence storage technology, it achieves collaborative management of the entire process of reservoir and embankment management from planning and design to decommissioning and disposal, improves the scientific and accurate nature of management decisions, and ensures the safety and stability of embankment projects throughout their entire life cycle.

[0005] The technical solution of this invention is implemented as follows: A reservoir and embankment full life cycle management system includes a data acquisition module, a data processing and storage module, a full life cycle coupling analysis module, an intelligent decision-making module, and an execution feedback module. The modules are linked together in sequence to form a closed-loop management. The data acquisition module collects multi-source heterogeneous data from each stage of the entire life cycle of the reservoir and embankment, and uses multi-sensor fusion technology to achieve full coverage and high-precision perception. The data processing and evidence storage module preprocesses the collected data and then uses a blockchain consortium blockchain to achieve trusted evidence storage and traceability, while combining quantum encryption to ensure data transmission security. The full lifecycle coupling analysis module constructs a hierarchical digital twin model, and integrates the improved GumbelCopula function and the LSTM neural network model to achieve four-stage dynamic coupling analysis. The intelligent decision-making module generates decision-making schemes for each stage based on the results of coupling analysis and optimizes them using reinforcement learning algorithms. The execution feedback module pushes the decision-making plan and collects execution effect data to feed back to the system to complete the closed-loop iteration.

[0006] Preferably, the data acquisition module employs multi-sensor fusion technology including distributed fiber optic sensors, electrochemical impedance spectroscopy sensors, millimeter-wave radar, and satellite remote sensing equipment, wherein the structural deformation monitoring accuracy reaches the millimeter level, and the seepage detection sensitivity reaches 10. -9 S / cm; The collected multi-source heterogeneous data includes: historical meteorological data of the watershed during the planning and design phase, geological survey data, material performance parameters, and design specification parameters; Data on incoming construction materials, construction procedure parameters, quality acceptance data, and on-site environmental monitoring data during the construction phase; Real-time hydrological data, dike structure monitoring data, equipment operating status data, and maintenance record data during the operation and maintenance phase; Data on structural deterioration, ecological and environmental impact, and demolition construction parameters during the decommissioning and disposal phase.

[0007] Preferably, the preprocessing process of the data processing and evidence storage module uses edge computing technology to complete data noise reduction, normalization and data fusion, and uses principal component analysis (PCA) algorithm to screen key feature data; the blockchain evidence storage adopts a consortium blockchain node network, realizes automated verification and evidence storage of data upload through smart contracts, and is equipped with a quantum encryption gateway to realize end-to-end encryption of data transmission.

[0008] Preferably, the coupling analysis process of the full life-cycle coupling analysis module in the planning and design phase is as follows: An extreme hydrological scenario simulation model is constructed based on the improved Gumbel Copula function; historical meteorological data and real-time forecast data of the watershed are integrated to generate a random water level sequence conforming to a joint distribution, providing accurate hydrological boundary conditions for the design of the dike structure; the expression of the improved Gumbel Copula function is: ,in, , These are the marginal distribution function values ​​of rainfall and water level, respectively. The correlation coefficient is obtained by the maximum likelihood estimation method and is used to capture the tail correlation between extreme rainfall and water level fluctuations.

[0009] Preferably, the coupling analysis process of the full life cycle coupling analysis module in the construction-operation and maintenance phase is as follows: Material performance data and quality acceptance data from the construction phase are imported into a hierarchical digital twin model to construct a correlation mapping relationship between construction quality and structural service performance; during the operation phase, real-time collected structural monitoring data is dynamically compared with the digital twin model, and the structural deterioration trend is predicted using an LSTM neural network model. The deterioration prediction expression of the LSTM neural network model is: ,in, Let t be the degree of structural degradation. The initial degradation level, For material activation energy, The gas constant is As the reference temperature, This refers to the change in ambient temperature. Let i be the influence coefficient of the i-th type of environmental factor. Let be the value of the i-th type of environmental factor at time t.

[0010] Preferably, the coupling analysis process of the full life cycle coupling analysis module in the decommissioning and disposal stage is as follows: based on the structural deterioration trend data and ecological environment monitoring data during the operation stage, a multi-objective decision-making model is constructed to comprehensively evaluate the indicators of three dimensions: structural safety threshold, ecological restoration requirements and economic costs, and output the optimal decommissioning and disposal plan.

[0011] Preferably, the decision-making schemes generated by the intelligent decision-making module at each stage specifically include: optimization schemes for dike structural parameters, material selection schemes, and construction process suggestions in the planning and design stage; suggestions for adjusting construction parameters and quality rectification schemes in the construction stage; graded early warning information and precise maintenance schemes in the operation and maintenance stage, wherein the precise maintenance schemes include maintenance timing, maintenance scope, and maintenance processes; and suggestions for decommissioning timing, demolition construction schemes, and ecological restoration schemes in the decommissioning and disposal stage.

[0012] Preferably, the execution terminal of the execution feedback module includes a construction monitoring platform, an operation and maintenance management terminal, and a decommissioning disposal command system. The execution terminal supports the visualization and automated execution of decision-making schemes. The automated execution is achieved by triggering maintenance equipment scheduling through smart contracts.

[0013] Preferably, the random water level sequence generated by the extreme hydrological scenario simulation model is used to replace the traditional fixed initial water level assumption, providing a dynamic hydrological boundary basis for the design of core parameters such as the height and thickness of the dike structure.

[0014] Preferably, the structural monitoring data includes deformation data, seepage data, stress data, and crack data of the embankment structure, and the dynamic comparison error between the structural monitoring data and the digital twin model is controlled within 5%.

[0015] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions: I. Innovative implementation of dynamic coupling management across multiple stages of the entire life cycle: Through a hierarchical digital twin model and an improved Copula function, the problem of phased fragmentation in traditional dike management is broken, enabling data interaction and collaborative decision-making throughout the entire process of planning and design, construction, operation and maintenance, and decommissioning, thereby improving the systematicness and coherence of management. II. Enhancing the accuracy of risk prediction and the scientific nature of decision-making: The improved GumbelCopula function can accurately capture the randomness and cumulative effects of extreme hydrological scenarios, the LSTM neural network model can accurately predict the trend of structural degradation, and the combination of credible data stored in the blockchain makes the decision-making scheme more data-supported, with an early warning accuracy rate of over 98%. III. Ensuring Data Security and Trustworthy Traceability: By using blockchain consortium blockchain and quantum encryption technology, the system achieves tamper-proof storage and secure transmission of data throughout its entire lifecycle, solving the trust issue in multi-stage data sharing and meeting the audit and supervision requirements of water conservancy project management. IV. Reduce life cycle management costs: Through accurate prediction and precise maintenance, unnecessary maintenance investment can be reduced; through full-stage collaborative optimization, construction costs and decommissioning costs can be reduced, and the service life of dike projects can be extended. It is estimated that the life cycle cost can be reduced by more than 30%.

[0016] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the system module structure of the present invention; Figure 2 This is a schematic diagram of the full lifecycle coupling analysis stage of the present invention; Figure 3This is a schematic diagram of the data processing and evidence storage process of the present invention; Figure 4 This is a schematic diagram of the intelligent decision-making and execution feedback closed loop of the present invention. Detailed Implementation

[0019] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0020] It is important to note that terms such as "first," "second," "symmetric," and "array" are used only to distinguish between descriptive and positional descriptions and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified with terms such as "first" or "symmetric" may explicitly or implicitly include one or more of that feature; similarly, when the quantity of certain features is not limited by words such as "two" or "three," it should be noted that such features also explicitly or implicitly include one or more features. In this invention, unless otherwise explicitly specified and limited, terms such as "installation," "connection," and "fixation" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral molding; they can refer to a mechanical connection, a direct connection, a welding connection, or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the accompanying drawings and specific circumstances.

[0021] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0022] like Figure 1-4 As shown, this invention provides a reservoir embankment full life cycle management system, including a data acquisition module, a data processing and storage module, a full life cycle coupling analysis module, an intelligent decision-making module, and an execution feedback module. Each module is linked sequentially, and real-time data interaction between modules is achieved through standardized data interfaces. The closed-loop management process is as follows: the data acquisition module transmits data of all stages to the data processing and storage module. After processing and storage, the data is synchronized to the full life cycle coupling analysis module for in-depth analysis. The analysis results are pushed to the intelligent decision-making module to generate a solution. The execution feedback module feeds back the solution execution effect data to the data processing and storage module, realizing a virtuous cycle of data iteration and solution optimization.

[0023] The closed-loop management model breaks down the information barriers of traditional independent operation of each module, ensures the continuous flow and full reuse of data throughout the entire lifecycle, improves the overall collaborative operation efficiency of the system, provides full-chain data support for management decisions at each stage, and avoids one-sided decision-making. The data acquisition module collects multi-source heterogeneous data from all stages of the reservoir's entire life cycle. It employs multi-sensor fusion technology to achieve full-area coverage. This technology integrates and calibrates signals collected by different types of sensors through data-level fusion algorithms, eliminating measurement biases from individual sensors. Distributed fiber optic sensors are deployed along the entire dike area, electrochemical impedance spectroscopy sensors are embedded in key seepage prevention structures, millimeter-wave radar is deployed on the dike top to monitor water level dynamics, and satellite remote sensing enables macroscopic deformation monitoring of a large area of ​​the dike, forming a three-dimensional acquisition network of "point-line-surface".

[0024] Full coverage ensures no monitoring blind spots, and high-precision sensing guarantees data accuracy, providing a high-quality data foundation for subsequent analysis and decision-making, and effectively avoiding risk misjudgments caused by missing or incorrect data.

[0025] The data processing and evidence storage module preprocesses the collected data and then uses a blockchain consortium blockchain for trusted evidence storage and traceability, combined with quantum encryption to ensure data transmission security. The preprocessed key data will generate a unique data fingerprint, which will be uploaded to the blockchain along with metadata such as data source, collection time, and processing process. The consortium blockchain nodes cover multiple entities including construction, operation and maintenance, and supervision, enabling multi-party joint supervision and traceability of data. Quantum encryption uses quantum key distribution technology, and a dynamic key is generated for each data transmission, eliminating the risk of key cracking.

[0026] Blockchain-based evidence storage ensures that data is tamper-proof and traceable, solving the data trust problem in multi-party collaboration. Quantum encryption provides an extremely high level of security for data transmission, protecting the privacy and security of core data in water conservancy projects and complying with industry regulations. The full lifecycle coupling analysis module constructs a hierarchical digital twin model, integrating an improved Gumbel Copula function and an LSTM neural network model to achieve four-stage dynamic coupling analysis. The hierarchical digital twin model is divided into four sub-models according to "planning and design - construction - operation and maintenance - decommissioning". Each sub-model achieves parameter linkage and dynamic updates through data interfaces. The analysis results of the improved Gumbel Copula function and the LSTM neural network model serve as the core basis for data interaction between sub-models, realizing deep coupling of analysis at each stage.

[0027] The hierarchical structure makes the model more targeted and scalable. The dynamic coupling analysis breaks the limitations of traditional independent modeling of each stage, improves the systematicness and accuracy of the whole life cycle analysis, and provides a model for accurately grasping the state of the entire dike project. The intelligent decision-making module generates decision schemes for each stage based on the results of coupling analysis and optimizes the reinforcement learning algorithm with the goal of "optimal decision effect". It uses the safety indicators, cost indicators and efficiency indicators of each stage as the core parameters of the reward function. By continuously learning the decision effect under different scenarios, it dynamically adjusts the decision logic and parameter thresholds.

[0028] Reinforcement learning enables decision-making schemes to have self-optimization capabilities, adapting to the needs of dike management under different watersheds and climatic conditions, improving the adaptability and scientific nature of decision-making schemes, and reducing the subjectivity of human decision-making. The execution feedback module pushes decision-making plans and collects execution effect data to complete the closed-loop iteration. The plan push adopts a dual mode of "terminal pop-up window + SMS reminder" to ensure that the relevant responsible persons receive it in a timely manner. The execution effect data collection combines real-time sensor monitoring and manual acceptance records to ensure the comprehensiveness and authenticity of the data. The feedback data is preprocessed and directly used to optimize the parameters and models of each module.

[0029] The dual-push mode ensures the timely execution of decision-making plans, multi-channel data collection ensures the reliability of feedback data, and closed-loop iteration enables the system to be continuously optimized and improve management level.

[0030] The data acquisition module employs multi-sensor fusion technology, including distributed fiber optic sensors, electrochemical impedance spectroscopy sensors, millimeter-wave radar, and satellite remote sensing equipment. The structural deformation monitoring accuracy reaches the millimeter level, and the seepage detection sensitivity reaches 10. -9 S / cm; the spatial resolution of the distributed fiber optic sensor reaches 1m, enabling continuous monitoring of dike structure deformation; the measurement frequency range of the electrochemical impedance spectroscopy sensor is 10... -2 ~10 6 Hz can accurately capture changes in the electrical properties of the seepage medium, thereby reflecting the seepage state; millimeter-wave radar has a measurement distance accuracy of ±0.1m, which can monitor the dynamic changes in water level in real time.

[0031] The high precision of each sensor ensures the accuracy of key monitoring indicators, providing precise data support for structural safety assessment. Millimeter-level deformation monitoring and high-sensitivity seepage detection can detect potential structural hazards in advance, improving the timeliness of risk warnings. The collected multi-source heterogeneous data includes: historical meteorological data of the watershed during the planning and design phase, geological survey data, material performance parameters, and historical meteorological data of the design watershed covering more than 50 years of time-series data such as rainfall, temperature, and wind speed. Geological survey data includes detailed data such as soil type, bearing capacity, and groundwater distribution. Material performance parameters include core indicators such as concrete strength and soil compaction. Design specification parameters strictly follow current water conservancy engineering design standards.

[0032] Comprehensive planning and design phase data provides a foundation for analysis and decision-making in subsequent phases, ensuring the scientific validity and feasibility of the design scheme. It also provides data on incoming construction materials, construction process parameters, quality acceptance data, and on-site environmental monitoring data for deviation analysis in the subsequent construction and operation and maintenance phases. The data on incoming construction materials includes the test results of indicators such as strength and moisture content of raw materials such as cement, sand, gravel, and steel bars; the parameters of construction procedures cover the number of compaction passes and paving thickness of embankment filling, and the vibration time and curing temperature of concrete pouring; the quality acceptance data includes the acceptance records and test reports of each sub-project; and the on-site environmental monitoring data records environmental factors such as temperature, humidity, and rainfall in real time during the construction process.

[0033] Detailed construction phase data can accurately reflect the construction quality and process, providing a basis for construction traceability for structural performance evaluation in the subsequent operation and maintenance phase. It facilitates the investigation of real-time hydrological data, dike structure monitoring data, equipment operation status data, and maintenance record data during the operation and maintenance phase caused by construction problems. Real-time hydrological data includes dynamic data such as water level, flow velocity, and flow rate. Dike structure monitoring data covers core safety indicators such as deformation, seepage, stress, and cracks. Equipment operation status data includes operating parameters and fault records of monitoring sensors, drainage equipment, etc. Maintenance record data records in detail the time, scope, process, and effect of each maintenance.

[0034] Real-time and comprehensive operation and maintenance data can reflect the operational status of dike projects in a timely manner, providing data support for risk warning and precise maintenance. Maintenance record data provides historical experience for the optimization of subsequent maintenance plans. Data on structural deterioration during the decommissioning and disposal stage, ecological and environmental impact data, and demolition construction parameters include structural deterioration data such as concrete strength reduction and soil shear strength reduction. Ecological and environmental impact data covers monitoring results of ecological indicators such as soil, water, and vegetation around the dike. Demolition construction parameter data includes demolition methods, construction progress, and waste disposal plans.

[0035] Comprehensive data from the decommissioning process provides a solid basis for developing the optimal decommissioning plan, ensuring the safety, environmental protection, and economy of the decommissioning process, and also providing a reference for the decommissioning management of similar projects in the future.

[0036] The data processing and evidence storage module employs edge computing technology for data denoising, normalization, and data fusion. Principal Component Analysis (PCA) is used to select key feature edge computing nodes, which are deployed near each monitoring site to achieve localized data processing. Denoising utilizes wavelet thresholding to effectively remove noise data caused by environmental interference. Normalization uses the min-max standardization method to uniformly map sensor data of different magnitudes to the [0,1] interval. Data fusion employs a weighted average fusion algorithm, allocating weights based on the accuracy and reliability of each sensor. The PCA algorithm calculates the covariance matrix of the data and extracts principal components with a variance exceeding 90% as key feature data.

[0037] Edge computing reduces bandwidth pressure and latency in data transmission, improving the real-time performance of data processing; noise reduction and normalization improve data quality, and data fusion enhances data reliability; the PCA algorithm effectively reduces dimensionality, decreasing the computational load of subsequent analysis and improving the efficiency of blockchain notarization. The blockchain notarization system utilizes a consortium blockchain node network, using smart contracts to automate data upload verification and notarization. It also incorporates a quantum encryption gateway to achieve end-to-end data transmission. The consortium blockchain nodes consist of construction units, operation and maintenance units, water conservancy regulatory departments, and third-party testing institutions, each with equal regulatory authority. Smart contracts pre-set data verification rules, including data format, source legality, and accuracy compliance; only verified data can be notarized. The quantum encryption gateway is deployed at both ends of the data transmission process, providing encrypted protection throughout the entire transmission process and preventing data theft or tampering during transmission.

[0038] The multi-node architecture of the consortium blockchain ensures the fairness and comprehensiveness of data supervision, while smart contracts automate and standardize data storage, improving storage efficiency. The quantum encryption gateway provides absolute security for data transmission, further enhancing data security and credibility. The coupling analysis process of the full lifecycle coupling analysis module in the planning and design phase is as follows: An extreme hydrological scenario simulation model is constructed based on an improved Gumbel Copula function. Historical meteorological data and real-time forecast data from the basin are integrated to generate a random water level sequence conforming to a joint distribution, providing an accurate hydrological extreme hydrological scenario simulation model for dike structure design. First, the historical meteorological data of the basin is fitted with a probability distribution to determine the marginal distribution functions of rainfall and water level. Then, a joint distribution model of the two is constructed using the improved Gumbel Copula function. Real-time forecast data uses short-term meteorological and hydrological forecast data, which are dynamically fused with historical data through a sliding window fusion algorithm to generate multiple sets of random water level sequences with different return periods.

[0039] The improved Gumbel Copula function can accurately capture the tail correlation between extreme rainfall and water level fluctuations, improving the accuracy of extreme hydrological scenario simulations. Random water level sequences provide a more realistic dynamic hydrological boundary for dike structure design, avoiding design deviations caused by traditional fixed initial water level assumptions and improving the safety of the design scheme. The expression for the improved Gumbel Copula function is: Where u1 and u2 are the marginal distribution function values ​​of rainfall and water level, respectively, and θ is the correlation coefficient, which is solved by the maximum likelihood estimation method to capture the tail correlation between extreme rainfall and water level fluctuations. The maximum likelihood estimation method constructs a likelihood function and solves for the value of θ that maximizes the likelihood function. The solution process adopts the Newton-Raphson iterative algorithm to ensure the accuracy and efficiency of the solution. In practical applications, the function parameters are calibrated with a large amount of historical data to further improve the adaptability of the function to the hydrological characteristics of specific watersheds.

[0040] A precise correlation coefficient θ can accurately characterize the coupling relationship between rainfall and water level, especially the tail correlation in extreme cases, making the generated random water level sequence more valuable for practical reference. This provides a reliable full life-cycle coupling analysis module for levee structure design. The coupling analysis process in the construction-operation and maintenance phase is as follows: Material performance data and quality acceptance data from the construction phase are imported into a hierarchical digital twin model to establish a correlation between construction quality and structural service performance. The import process employs deep integration of BIM technology and the digital twin model, accurately associating the data of each sub-project in the construction phase with the corresponding components in the digital twin model. The correlation mapping relationship is constructed using machine learning algorithms, with construction quality data as input features and structural service performance indicators from the operation phase as output labels, to train and generate a mapping model.

[0041] The integration of BIM and digital twins enables precise correlation and visualization of construction data. The correlation mapping relationship can accurately locate key construction factors affecting structural service performance, providing a direct basis for tracing defects during the operation and maintenance phase. During the operation phase, real-time collected structural monitoring data is dynamically compared with the digital twin model, and the structural deterioration trend is predicted through an LSTM neural network model. The dynamic comparison uses a deviation analysis algorithm to calculate the absolute and relative deviations between real-time monitoring data and model prediction data, setting multiple deviation thresholds. When the deviation exceeds the threshold, an early warning is automatically triggered. The LSTM neural network model adopts a multi-layer hidden layer structure. The input data includes historical structural monitoring data, construction quality data, environmental impact data, etc. Through time-series training, it captures the patterns of structural deterioration and achieves the prediction of structural deterioration trends over the next 3-5 years.

[0042] Dynamic comparison can promptly detect anomalies in the structural operating state and provide early warnings of potential risks; the time-series prediction capability of the LSTM neural network model can accurately grasp the structural degradation pattern, providing precise time nodes and maintenance basis for preventive maintenance, avoiding the high costs of post-repair repairs. The degradation prediction expression of the LSTM neural network model is as follows: Where D(t) is the structural degradation degree at time t, D0 is the initial degradation degree, and E a Let E be the material activation energy, R be the gas constant, T be the reference temperature, ΔT be the change in ambient temperature, Ki be the influence coefficient of the i-th type of environmental factor, and Fi(t) be the material activation energy E of the i-th type of environmental factor at time t. a Based on the type of main materials of the dike, the environmental factor influence coefficient Ki is determined through experiments and calibrated with a large amount of experimental data and field monitoring data, covering a variety of environmental factors such as temperature, humidity, freeze-thaw cycle, and water flow scouring. In practical applications, dynamic prediction of D(t) is achieved by updating Fi(t) in real time.

[0043] This expression comprehensively considers the impact of material properties and environmental factors on structural deterioration, making the prediction results more scientific and accurate, and providing a precise mathematical basis for quantitatively assessing the structural safety status.

[0044] The coupling analysis process of the full life cycle coupling analysis module in the decommissioning and disposal stage is as follows: Based on the structural deterioration trend data and ecological environment monitoring data during the operation stage, a multi-objective decision-making model is constructed. This model comprehensively evaluates indicators across three dimensions: structural safety threshold, ecological restoration requirements, and economic costs. The optimal decommissioning and disposal multi-objective decision-making model is output using a combined weighting method of the analytic hierarchy process (AHP) and the entropy weighting method. The AHP determines the subjective weights of each dimension's indicators based on expert experience, while the entropy weighting method determines the objective weights based on the data's dispersion. The final weight is the weighted average of the two methods. Structural safety threshold indicators include structural bearing capacity and anti-sliding stability; ecological restoration requirements include water quality improvement and habitat restoration; and economic cost indicators include demolition costs, restoration costs, and maintenance costs. By calculating the comprehensive score of each candidate scheme, the optimal scheme with the highest score is selected.

[0045] The combined weighting method takes into account both subjective experience and objective data, improving the rationality of indicator weights; multi-dimensional indicator evaluation avoids the one-sidedness of single-objective decision-making, ensuring that the optimal decommissioning and disposal plan can achieve synergistic optimization of safety, ecology, and economy, and is consistent with the decision-making plans generated by the green water conservancy intelligent decision-making module at each stage. Specifically, the plan includes: optimization schemes for dike structural parameters, material selection schemes, and construction process recommendations in the planning and design stage; optimization schemes for structural parameters, including optimized values ​​for core parameters such as dike height, top width, and slope ratio; material selection schemes, recommending suitable concrete, soil, and other material types based on geological conditions and hydrological environment; and construction process recommendations, recommending the optimal construction methods and technical points for different sub-projects.

[0046] Precise planning and design decisions provide a scientific basis for subsequent construction, ensuring the feasibility and economy of the design and reducing changes and rework during construction. Construction parameter adjustment suggestions and quality rectification plans are provided during the construction phase. The parameter adjustment suggestions address quality deviations that occur during construction, providing specific adjustment values ​​for parameters such as the number of compaction passes and paving thickness. The quality rectification plans clearly define the scope, process, standards, and acceptance requirements for rectification, ensuring that the quality meets standards after rectification.

[0047] Real-time adjustments to construction parameters and quality rectification suggestions can promptly correct construction deviations, ensure construction quality, avoid later structural defects caused by quality issues, and reduce rework costs. The operation and maintenance phase includes tiered early warning information and precise maintenance plans. The precise maintenance plans include maintenance timing, maintenance scope, and tiered early warning information divided into four levels: blue, yellow, orange, and red, corresponding to different risk levels, clearly defining the warning scope and emergency response measures. Maintenance timing is determined based on structural deterioration prediction results, and the maintenance scope is precisely located to specific defective areas. Recommended maintenance processes include appropriate reinforcement, seepage prevention, and repair technologies.

[0048] Tiered early warning systems enable managers to quickly grasp risk levels and key areas for action. Precise maintenance plans avoid blind maintenance, improve maintenance efficiency, reduce maintenance costs, and extend the service life of dikes. During the decommissioning phase, recommendations for decommissioning timing, dismantling construction plans, and ecological restoration are determined based on structural deterioration trends and safety thresholds. Dismantling construction plans specify the dismantling sequence, methods, safety protection measures, and waste disposal plans. Ecological restoration plans include specific measures such as vegetation planting, water purification, and habitat reconstruction.

[0049] Scientifically sound decommissioning timing recommendations avoid the resource waste of premature decommissioning and the safety risks of delayed decommissioning. Standardized dismantling and construction plans ensure construction safety and environmental friendliness, while ecological restoration plans restore the ecological value of the dikes after decommissioning. The execution feedback module's execution terminal includes a construction monitoring platform, an operation and maintenance management terminal, and a decommissioning command system. The execution terminal supports the visualization and automated execution of decision-making plans. Automated execution is triggered by smart contracts. The visualization of maintenance is presented intuitively using charts, 3D models, and other formats, showcasing the core content and implementation requirements of the decision-making plan. The smart contracts have preset equipment scheduling rules. Upon receiving an execution command, they automatically send scheduling signals to the corresponding maintenance equipment, enabling automatic start-up, shutdown, and parameter adjustment of the equipment.

[0050] Visualization enhances the readability and execution efficiency of decision-making schemes, while automated execution reduces human intervention, improves the timeliness and accuracy of execution, and lowers the cost of manual operation. The random water level sequence generated by the extreme hydrological scenario simulation model is used to replace the traditional fixed initial water level assumption, providing dynamic hydrological boundary basis for the design of core parameters such as the height and thickness of the dike structure. The random water level sequence covers extreme water level conditions with different return periods (such as 50-year, 100-year, and 200-year return periods), providing a more comprehensive hydrological boundary consideration for structural parameter design. Designers can select the corresponding water level sequence for parameter optimization according to different risk levels.

[0051] The dynamic hydrological boundary assumption, compared to the traditional fixed initial water level assumption, better reflects the actual hydrological evolution, improving the safety and rationality of the dike structure design and enabling the dike to better cope with extreme floods of varying intensities. Structural monitoring data includes deformation, seepage, stress, and crack data of the dike structure. The dynamic comparison error between the structural monitoring data and the digital twin model is controlled within 5%. Deformation data includes vertical settlement and horizontal displacement; seepage data includes seepage flow and seepage pressure; stress data includes soil stress and concrete structure stress; and crack data includes crack width, length, and depth. Dynamic comparison error control is achieved through periodic sensor calibration and optimization of digital twin model parameters.

[0052] Comprehensive structural monitoring data can reflect the safety status of the dike structure from all angles. A dynamic comparison error of less than 5% ensures the consistency between the monitoring data and the model, improving the accuracy of structural status assessment and risk warning.

[0053] In this embodiment, the present invention operates as follows: Data Acquisition Phase: This system initiates full lifecycle data acquisition through its data acquisition module. During the planning and design phase, it collects nearly 50 years of historical meteorological data, geological survey data, and levee material performance parameters for a specific watershed. During construction, distributed fiber optic sensors and electrochemical impedance spectroscopy sensors are deployed at key locations such as the levee fill layer and clay core wall. Simultaneously, drones are used to collect construction progress imagery data, and real-time parameters such as material compaction and moisture content, as well as on-site environmental data, are obtained. During operation, millimeter-wave radar monitors water level changes, satellite remote sensing acquires levee deformation data across the entire area, and seepage sensors collect groundwater seepage data. During decommissioning, data on levee structural crack distribution, concrete strength decay, and surrounding ecological environment are collected. Throughout the entire process, multi-sensor fusion technology achieves full coverage and millimeter-level deformation and 10... -9 High-precision sensing of S / cm seepage sensitivity.

[0054] Data processing and evidence storage stage: The collected multi-source heterogeneous data is first transmitted to the data processing and evidence storage module. The edge computing nodes complete noise reduction, normalization and data fusion preprocessing. Then, the PCA algorithm is used to screen out key feature data such as water level change rate and structural deformation. Subsequently, the key data after each stage of preprocessing are uploaded to the blockchain consortium chain composed of the construction unit, operation and maintenance unit and regulatory department. The legality and integrity of the data are automatically verified by smart contracts and evidence storage is completed. The data transmission process is encrypted end-to-end through a quantum encryption gateway to ensure data security and reliable traceability.

[0055] Full life cycle coupling analysis phase: Processed and stored data are synchronized to the full life cycle coupling analysis module. This module conducts multi-stage dynamic coupling analysis through a hierarchical digital twin model: In the planning and design phase, based on the improved Gumbel Copula function, the correlation coefficient θ=1.8 is calculated by inputting historical data of watershed rainfall and water level, generating a random water level sequence under extreme hydrological scenarios, providing accurate hydrological boundaries for levee height optimization; In the construction phase, construction quality data is imported into the digital twin model to build a correlation mapping relationship with structural service performance; In the operation phase, real-time structural monitoring data is dynamically compared with the model, and the deterioration degree of a certain levee section is predicted after 3 years by inputting seepage rate and temperature change data into the LSTM neural network model; In the decommissioning and disposal phase, the deterioration data and ecological environment data from the operation phase are combined, and a multi-objective decision model is used to comprehensively evaluate safety, ecological, and economic indicators to determine the optimal decommissioning time after 35 years of service.

[0056] Intelligent Decision-Making Phase: Based on the results of coupled analysis and combined with preset safety thresholds, the intelligent decision-making module generates decision-making schemes for each stage: In the planning and design phase, it outputs optimized parameters for increasing the embankment height by 0.5m, along with recommendations for the selection of supporting materials and construction techniques; in the construction phase, it outputs parameter adjustment suggestions to increase the number of compaction cycles from 6 to 8 times to address the insufficient compaction of a certain embankment section; in the operation phase, it outputs graded early warning information and grouting reinforcement and maintenance schemes based on the deterioration prediction results; in the decommissioning phase, it outputs demolition construction schemes and ecological restoration schemes for soil covering and greening, while continuously optimizing each scheme through reinforcement learning algorithms.

[0057] Execution Feedback Phase: The execution feedback module pushes decision-making plans for each stage to corresponding execution terminals such as the construction monitoring platform and operation and maintenance management terminal, supporting the visualization of the plans; the construction unit optimizes the construction process based on parameter adjustment suggestions, the operation and maintenance unit carries out grouting reinforcement construction, and the decommissioning phase implements demolition and ecological restoration according to the plan; after execution, the module collects effect data such as the degradation degree after reinforcement decreasing to 0.2, and feeds it back to the data processing and evidence storage module to realize closed-loop iteration of full life cycle management and ensure the continuous optimization and operation of the system.

[0058] The following are several other specific embodiments of the application of this invention: Example 1: Example of a mountain reservoir embankment project This embodiment takes a reservoir embankment project in a mountainous canyon as an example. This area has steep terrain, concentrated rainfall, and frequent short-duration heavy rainfall, which easily triggers flash floods and a surge in reservoir water levels. The embankment faces the risk of strong water erosion and slope instability. The full life-cycle management system of this invention is applied, and the specific implementation steps are as follows: Data acquisition module deployment: During the planning and design phase, nearly 45 years of historical meteorological data for the mountainous area (with a focus on short-term heavy rainfall records), canyon topographic and geological survey data (including rock layer distribution and landslide hazard points), and performance parameters of erosion-resistant embankment materials are collected. During the construction phase, distributed fiber optic sensors, high-frequency displacement sensors, and electrochemical impedance spectroscopy sensors are deployed at erosion-prone areas such as the water-facing slope, shoulders, and toe of the embankment to collect data on the compaction degree, erosion resistance, and slope gradient of construction materials. Topographic data of the construction area is obtained through a combination of UAV aerial photography and laser scanning, and rainfall and wind speed data of the construction environment are collected simultaneously. During the operation phase, millimeter-wave radar is deployed to monitor changes in the peak flood level in the reservoir in real time. Satellite remote sensing is used to monitor landslide hazards in the surrounding mountains of the embankment. Ultrasonic flow velocity sensors are added to monitor the water flow erosion velocity, and seepage data of the embankment body is collected in conjunction with seepage sensors. During the decommissioning phase, data on embankment structural erosion and deterioration (such as the depth of scour at the toe and the amount of concrete wear on the water-facing slope), mountain ecological environment monitoring data (including vegetation coverage and soil erosion), and data on the impact of demolition construction on the canyon topography are collected.

[0059] Data Processing and Storage: Real-time noise reduction is performed on the collected data through edge computing nodes. The PCA algorithm is used to filter out key feature data such as short-term heavy rainfall, flood peak water level change rate, water flow scour velocity, and slope displacement (feature variance ratio ≥ 93%). Data at each stage is uploaded to a blockchain consortium chain composed of construction units, operation and maintenance units, mountain water conservancy supervision departments, and geological disaster prevention agencies. The timeliness and integrity of the data are verified through smart contracts (the delay for short-term heavy rainfall data is required to be ≤ 5 minutes). Quantum encryption is used to ensure the stability of data transmission under complex terrain, realizing trusted storage and traceability of data throughout the entire life cycle.

[0060] Full lifecycle coupling analysis: Planning and Design Phase: Based on the improved Gumbel Copula function, historical data on short-term heavy rainfall, flash flood flow, and reservoir water level in the region are input. The correlation coefficient θ=2.0 is calculated to generate a random water level-flow joint sequence under the scenario of flash floods caused by short-term heavy rainfall and a surge in reservoir water level. Based on this, the thickness of the scour-resistant revetment on the water-facing slope of the embankment is optimized (by adding a 1.0m reinforced concrete revetment), the slope ratio of the embankment (adjusted to 1:2.5), and the parameters of the energy dissipation facilities are improved to enhance scour resistance and anti-sliding stability. During the construction phase: Data such as the impact strength of construction materials and the compaction degree of the slope were imported into a hierarchical digital twin model and compared with the design parameters. It was found that the compaction degree of the slope toe of a certain section of the embankment was insufficient (the design requirement was ≥95%, but the actual measurement was only 92%). The output parameter adjustment suggestions were made (using a heavy roller to compact the slope, increasing the number of compaction times from 7 to 10, and adding a filter layer). After the rectification, the quality data met the design requirements. Operation and maintenance phase: Real-time collected data on short-term heavy rainfall, flood peak water level, water flow scour velocity, and slope displacement are dynamically compared with the digital twin model. The LSTM neural network model is used to predict the structural deterioration trend. The input data includes data on flash flood events, construction quality, and environmental data from the past 8 years. It is predicted that the deterioration degree of the water-facing slope of a certain embankment section will reach 0.37 after 4 years (the safety threshold is 0.4), of which water flow scour contributes 70% of the deterioration degree. The output is a red-level early warning information and a targeted maintenance plan (using anti-erosion and wear-resistant coating spraying + slope toe riprap reinforcement). Decommissioning and disposal phase: Combining data on scour and deterioration during operation with monitoring data on the mountain ecological environment, a multi-objective decision-making model is constructed. The combined weighting method of the analytic hierarchy process and the entropy weighting method is used (structural safety weight 45%, mountain ecological protection weight 35%, economic cost weight 20%). After comprehensive evaluation, the optimal decommissioning time is determined to be after 28 years of service. The "partial reinforcement and renovation + construction of mountain flood ecological flood discharge channel" plan is output, which clarifies the scope of renovation, low-disturbance construction technology and vegetation restoration plan for the ecological flood discharge channel.

[0061] Intelligent decision-making and execution feedback: The decision-making plan at each stage is pushed to the construction monitoring platform and the operation and maintenance emergency command terminal. The construction unit optimizes the slope toe construction process according to the adjustment suggestions, and the operation and maintenance unit completes the emergency reinforcement construction within 24 hours after the warning. During the decommissioning stage, the transformation and ecological construction are implemented according to the plan. Data on the effects of the reinforcement are collected, such as the structural deterioration rate being reduced to 0.20 and the response time for mountain flood defense being shortened by 40% after 4 years. The data is fed back to the system to achieve closed-loop iteration.

[0062] In this embodiment, through the application of this system, the accuracy rate of short-term heavy rainfall and flash flood warning for the mountain reservoir embankment project reached 99.3%, the incidence rate of structural defects caused by water erosion decreased by 78%, the full life cycle management cost decreased by 30%, and the service life was extended by 5 years, verifying the effectiveness of this system in complex mountainous terrain and high-incidence flash flood scenarios.

[0063] Example 2: Implementation of Dike Engineering in Plain Polder Areas This embodiment takes a dike project in a plain polder area as an example. This area mainly undertakes the task of flood control and drainage for farmland. There is a large amount of arable land around the dike, with a high groundwater level and high soil moisture content, which can easily lead to seepage in the dike body and settlement of soft soil foundation. The full life cycle management system of the present invention is applied, and the specific implementation steps are as follows: Data acquisition module deployment: During the planning and design phase, nearly 50 years of historical meteorological data (including long rainfall records during the plum rain season), soft soil foundation geological survey data (including soil moisture content and bearing capacity), and performance parameters of seepage-proof dike materials are collected for the polder area. During the construction phase, distributed fiber optic sensors, pore water pressure sensors, and electrochemical impedance spectroscopy sensors are deployed at key locations such as the dike body, foundation, and seepage-proof wall to collect data on the compaction degree of construction materials, seepage-proof performance, and foundation treatment parameters. Construction progress images are collected via drones, and groundwater level and rainfall data of the construction environment are collected simultaneously. During the operation phase, millimeter-wave radar is deployed to monitor water level changes in the polder area, and satellite remote sensing is used to obtain settlement data of the entire dike area. Seepage data of the dike body and pore water pressure data of the foundation are collected in combination with seepage sensors and pore water pressure sensors. During the decommissioning phase, data on seepage and deterioration of the dike structure (such as the damaged area of ​​the seepage-proof wall and the amount of seepage in the dike body), monitoring data of the farmland ecological environment in the polder area (including soil fertility and groundwater quality), and data on the impact of demolition construction on the farmland irrigation system are collected.

[0064] Data Processing and Evidence Preservation: The collected data is denoised and normalized using edge computing nodes. The PCA algorithm is used to filter out key characteristic data such as groundwater level changes, levee seepage, foundation settlement, and pore water pressure. Data from each stage is uploaded to a blockchain consortium chain composed of construction units, operation and maintenance units, agricultural and rural departments, and water conservancy regulatory departments. The legality of the data is verified through smart contracts, and quantum encryption is used to ensure the security of data transmission. This achieves tamper-proof evidence preservation and traceability of data throughout its entire lifecycle, providing reliable data support for farmland water conservancy supervision.

[0065] Full lifecycle coupling analysis: Planning and design phase: Based on the improved Gumbel Copula function, the historical data of rainfall, groundwater level and water level in the polder area during the plum rain season are input, and the correlation coefficient θ=1.5 is solved to generate a random water level sequence under the scenario of long rainfall during the plum rain season superimposed on rising groundwater level. Based on this, the depth of the dike seepage prevention wall is optimized (deepened to 8m) and the thickness of the seepage prevention coating on the dike body is increased (a 0.6m bentonite seepage prevention layer is added) to improve seepage prevention performance. During the construction phase: Data such as the seepage prevention performance of construction materials and the compaction degree of the foundation are imported into a hierarchical digital twin model and compared with the design parameters. It is found that there are gaps in the construction of the seepage prevention wall in a certain section of the embankment. A rectification plan is output (using high-pressure grouting to fill the gaps and re-laying the seepage prevention coating). After rectification, the quality data meets the seepage prevention design requirements. Operation and maintenance phase: Real-time data such as groundwater level, embankment seepage, and foundation settlement are dynamically compared with the digital twin model. The LSTM neural network model is used to predict the structural deterioration trend. The input data includes data from the past 10 years of plum rain season, construction quality data, and farmland irrigation impact data. It is predicted that the deterioration degree of a certain embankment section will reach 0.39 after 6 years (the safety threshold is 0.4). Among them, seepage factors contribute 68% of the deterioration degree. The output is a yellow-level early warning information and maintenance plan (using composite geomembrane laying + foundation grouting reinforcement technology). Decommissioning and disposal phase: Combining leakage and deterioration data from the operational phase with farmland ecological data from the polder area, a multi-objective decision-making model is constructed. The combined weighting method of the analytic hierarchy process and the entropy weighting method is used (structural safety weight 35%, farmland ecological protection weight 40%, economic cost weight 25%). After comprehensive evaluation, the optimal decommissioning time is determined to be after 33 years of service. The "segmented dismantling of the dike + integration of farmland irrigation canal system" plan is output, which clarifies the dismantling scope, farmland-friendly dismantling process, and irrigation canal system optimization plan.

[0066] Intelligent decision-making and execution feedback: The decision-making plan at each stage is pushed to the construction monitoring platform and operation and maintenance management terminal. The construction unit optimizes the seepage prevention construction according to the rectification plan, and the operation and maintenance unit completes the seepage prevention and reinforcement construction. During the decommissioning stage, the demolition and irrigation canal system are integrated according to the plan. Data on the effects of the reinforcement are collected, such as the structural deterioration rate being reduced to 0.23 and the irrigation efficiency of farmland in the polder area being increased by 25% after 6 years. The data is fed back to the system to achieve closed-loop iteration.

[0067] In this embodiment, through the application of this system, the accuracy rate of early warning of seepage during the plum rain season of the plain polder project reached 98.8%, the incidence of structural defects caused by seepage decreased by 72%, the full life cycle management cost decreased by 29%, and the service life was extended by 4 years, which verified the adaptability of this system in the plain soft soil area and farmland protection scenario.

[0068] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A reservoir levee full life cycle management system, characterized in that, It includes a data acquisition module, a data processing and storage module, a full lifecycle coupled analysis module, an intelligent decision-making module, and an execution feedback module. These modules work together in sequence to form a closed-loop management system. The data acquisition module collects multi-source heterogeneous data from each stage of the entire life cycle of the reservoir and embankment, and uses multi-sensor fusion technology to achieve full coverage and high-precision perception. The data processing and evidence storage module preprocesses the collected data and then uses a blockchain consortium blockchain to achieve trusted evidence storage and traceability, while combining quantum encryption to ensure data transmission security. The full lifecycle coupling analysis module constructs a hierarchical digital twin model, and integrates the improved GumbelCopula function and the LSTM neural network model to achieve four-stage dynamic coupling analysis. The intelligent decision-making module generates decision-making schemes for each stage based on the results of coupling analysis and optimizes them using reinforcement learning algorithms. The execution feedback module pushes the decision-making plan and collects execution effect data to feed back to the system to complete the closed-loop iteration.

2. The reservoir levee full life cycle management system according to claim 1, characterized in that: The data acquisition module employs multi-sensor fusion technology, including distributed fiber optic sensors, electrochemical impedance spectroscopy sensors, millimeter-wave radar, and satellite remote sensing equipment. The structural deformation monitoring accuracy reaches the millimeter level, and the seepage detection sensitivity reaches 10⁻⁶. -9 S / cm; The collected multi-source heterogeneous data includes: historical meteorological data of the watershed during the planning and design phase, geological survey data, material performance parameters, and design specification parameters; Data on incoming construction materials, construction procedure parameters, quality acceptance data, and on-site environmental monitoring data during the construction phase; Real-time hydrological data, dike structure monitoring data, equipment operating status data, and maintenance record data during the operation and maintenance phase; Data on structural deterioration, ecological and environmental impact, and demolition construction parameters during the decommissioning and disposal phase.

3. The reservoir levee full life cycle management system according to claim 1, characterized in that: The data processing and evidence storage module employs edge computing technology for data noise reduction, normalization, and data fusion during preprocessing, and uses principal component analysis (PCA) algorithm to screen key feature data. The blockchain evidence storage uses a consortium blockchain node network, and uses smart contracts to achieve automated verification and evidence storage of uploaded data. It also incorporates a quantum encryption gateway to achieve end-to-end encryption of data transmission.

4. The reservoir levee full life cycle management system according to claim 1, characterized in that: The coupling analysis process of the full life cycle coupling analysis module in the planning and design phase is as follows: An extreme hydrological scenario simulation model is constructed based on the improved Gumbel Copula function; historical meteorological data and real-time forecast data of the watershed are integrated to generate a random water level sequence conforming to a joint distribution, providing accurate hydrological boundary conditions for the design of the dike structure; the expression of the improved Gumbel Copula function is: ,in, , These are the marginal distribution function values ​​of rainfall and water level, respectively. The correlation coefficient is obtained by the maximum likelihood estimation method and is used to capture the tail correlation between extreme rainfall and water level fluctuations.

5. A reservoir levee full life cycle management system according to claim 1, characterized in that: The coupling analysis process of the full life cycle coupling analysis module in the construction-operation and maintenance phase is as follows: Material performance data and quality acceptance data from the construction phase are imported into a hierarchical digital twin model to construct a correlation mapping relationship between construction quality and structural service performance; during the operation phase, real-time collected structural monitoring data is dynamically compared with the digital twin model, and the structural deterioration trend is predicted using an LSTM neural network model. The deterioration prediction expression of the LSTM neural network model is: ,in, Let t be the degree of structural degradation. The initial degradation level, For material activation energy, The gas constant is... As the reference temperature, This refers to the change in ambient temperature. Let i be the influence coefficient of the i-th type of environmental factor. Let be the value of the i-th type of environmental factor at time t.

6. The reservoir levee full life cycle management system according to claim 1, characterized in that: The coupling analysis process of the full life cycle coupling analysis module in the decommissioning and disposal stage is as follows: Based on the structural deterioration trend data and ecological environment monitoring data during the operation stage, a multi-objective decision-making model is constructed to comprehensively evaluate the indicators of three dimensions: structural safety threshold, ecological restoration requirements and economic costs, and output the optimal decommissioning and disposal plan.

7. A reservoir levee life-cycle management system according to claim 1, characterized in that: The decision-making modules generated at each stage specifically include: optimization schemes for dike structural parameters, material selection schemes, and construction process suggestions during the planning and design stage; suggestions for adjusting construction parameters and quality rectification schemes during the construction stage; graded early warning information and precise maintenance schemes during the operation and maintenance stage, wherein the precise maintenance schemes include maintenance timing, maintenance scope, and maintenance processes; and suggestions for decommissioning timing, demolition construction schemes, and ecological restoration schemes during the decommissioning and disposal stage.

8. A reservoir levee full life cycle management system according to claim 1, characterized in that: The execution terminal of the execution feedback module includes a construction monitoring platform, an operation and maintenance management terminal, and a decommissioning disposal command system. The execution terminal supports the visualization and automated execution of decision-making schemes. The automated execution is achieved by triggering maintenance equipment scheduling through smart contracts.

9. A reservoir levee full life cycle management system according to claim 1, characterized in that: The random water level sequence generated by the extreme hydrological scenario simulation model is used to replace the traditional fixed initial water level assumption, providing a dynamic hydrological boundary basis for the design of core parameters such as the height and thickness of the dike structure.

10. A reservoir levee full life cycle management system according to claim 1, characterized in that: The structural monitoring data includes deformation data, seepage data, stress data, and crack data of the embankment structure. The dynamic comparison error between the structural monitoring data and the digital twin model is controlled within 5%.

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