Dam system project full life cycle dynamic optimization planning method and system

Abstract

The invention relates to the technical field of water conservancy project management, in particular to a dam system project full life cycle dynamic optimization planning method and system, and the method comprises the steps: constructing a digital twinborn body fusing a geologic body stress field, a storage capacity time-varying function and an ecological constraint boundary; generating a topological relation between an initial dam body space coordinate set and a flood discharge channel based on a topological time-varying fitness algorithm; in the operation period, deformation time sequence data and osmotic pressure gradient data of the dam body monitoring terminal are received, and a risk entropy feedback model is input to dynamically generate a gate opening degree regulation and control instruction set; and in the decommissioning stage, a decommissioning cost conduction function and a reservoir capacity release curve are constructed in combination with the material consumption list and the dam body aging damage model, and a full-life-cycle decision packet capable of feeding back and updating initial planning is formed. According to the invention, collaborative optimization of dam body structure safety, operation stability and decommissioning economic and environmental benefits can be realized, and the intelligent management level of the whole life cycle of dam system engineering is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water conservancy engineering management, in particular to a dam system engineering full life cycle dynamic optimization planning method and system. BACKGROUND

[0002] As important infrastructure for water resource regulation, power generation, flood control and ecological maintenance, dam system engineering involves complex geological, hydrological, structural and ecological factors in its planning, construction and operation management. Traditional dam planning usually relies on static geological exploration data and historical hydrological records for dam site selection and structure arrangement, and relies on fixed gate scheduling table for water level regulation during operation period, while lacking systematic evaluation mechanism in the decommissioning stage. In recent years, with the development of digital twin technology, topology optimization algorithm and full life cycle management concept, more and more researches try to introduce multi-source data fusion, dynamic feedback mechanism and simulation prediction method into hydraulic structure management to improve the adaptability, intelligence and sustainability of dam system engineering in different life cycle stages.

[0003] However, the existing technology still has many limitations in the full life cycle management of dam system engineering: firstly, the initial planning stage often lacks a unified modeling method integrating geological body stress field, hydrological nonlinear reservoir capacity response and ecological constraint factors, resulting in difficulty in balancing structural stability and ecological sensitivity in dam layout; secondly, the operation period control mechanism is mostly preset logic, lacking real-time response capability to deformation behavior and seepage pressure anomaly, making it difficult to achieve dynamic safety control of topological linkage structure; thirdly, the decommissioning stage lacks a multi-factor decision model based on material performance degradation trend and recycling value evaluation, which cannot realize the integrated trade-off of economy, environment and stability. SUMMARY

[0004] The present application provides a dam system engineering full life cycle dynamic optimization planning method and system, a dynamic optimization planning method and system covering the whole process from dam planning, operation to decommissioning, and a truly full life cycle closed-loop management system of dam system engineering is constructed.

[0005] The dam system engineering full life cycle dynamic optimization planning method comprises the following steps: S1: Obtain geological exploration data, hydrological history data and existing dam structure parameters of the target river basin, and construct a digital twin including geological body stress field, reservoir capacity time-varying function and ecological constraint boundary; S2: Based on the digital twin, generate an initial planning scheme of dam system engineering through a topology time-varying fitness algorithm, and output an optimization scheme including dam spatial coordinate set, flood discharge channel topology relationship and material quantity list; S3: receiving deformation time series data and seepage pressure gradient data of the dam monitoring terminal in the running period, inputting the spillway channel topological relationship in the optimization scheme into the risk entropy value feedback model, and dynamically generating a gate opening control instruction set; S4: constructing a retirement cost conduction function according to the material quantity list of the optimization scheme, combining a dam aging damage model, outputting a whole life cycle decision package including a demolition time sequence scheme, a reservoir capacity release curve and a material recycling value, and feeding back to S2 to update the initial planning scheme.

[0006] Optionally, the S1 comprises: S11: inputting the geological exploration data into a three-dimensional geological modeling system, and generating a geological body stress field including a rock layer permeability coefficient matrix and a fault space coordinate through discretization processing; S12: based on the rock layer permeability coefficient matrix in the geological body stress field, combining the water level-flow relationship in the hydrological history data, establishing a reservoir capacity time-varying function reflecting the nonlinear change of reservoir capacity with water level; S13: analyzing the river flow velocity time series record and the historical inundation image in the hydrological history data, extracting the inundation sensitive parameters of the vegetation coverage area, and forming an ecological constraint boundary including a minimum flow velocity threshold and a maximum inundation period; S14: coordinate system unification fusion of the geological body stress field, the reservoir capacity time-varying function and the ecological constraint boundary, and the existing dam structure parameters, outputting a digital twin integrated with multiple physical fields.

[0007] Optionally, the S11 comprises: S111: standardizing the drilling core strength and seismic wave velocity profile in the geological exploration data, and outputting a standardized geological parameter set; S112: inputting the standardized geological parameter set into a fault activity evaluation model, calculating the fault displacement probability distribution under the action of reservoir water pressure, and generating a fault space coordinate with risk weight; S113: dividing the finite element grid based on the fault space coordinate, solving the initial ground stress distribution by using the rock mass constitutive equation, and outputting the geological body stress field including the rock layer permeability coefficient matrix.

[0008] Optionally, the S2 comprises: S21: discretizing the geological body stress field in the digital twin into an unstructured finite element grid with a rock layer permeability coefficient matrix, and outputting a finite element grid model; S22: traversing the connection path between the boundary nodes of the finite element grid model, screening a line segment set satisfying the minimum dam height constraint, and generating a dam space coordinate set including coordinate elevation information; S23: based on the dam body spatial coordinate set, the seepage path redundancy of each dam body combination is calculated by a topological time-varying fitness algorithm, a flood discharge channel topological relationship including gate linkage logic is generated in the principle of maximum redundancy, and a material consumption list including the volume of corresponding dam body is associated to form an initial planning scheme.

[0009] Optionally, the S23 comprises: S231: extracting a geological fault activation factor of the spatial coordinates of the fault in the geological body stress field, combining the path length in the flood discharge channel topological relationship, and calculating the seepage path redundancy of each flood discharge channel; S232: taking the sum of the seepage path redundancy as the objective function, iteratively adjusting the dam axis angle and connection order in the dam body spatial coordinate set, and updating the flood discharge channel topological relationship; S233: according to the dam body connection structure in the updated flood discharge channel topological relationship, matching the standard dam body structure parameter template, and outputting the material consumption list including the concrete volume and steel type.

[0010] Optionally, the S3 comprises: S31: receiving real-time sensor data uploaded by dam body monitoring terminals deployed on each dam body, separating and extracting deformation time series data reflecting dam body deflection changes and seepage pressure gradient data reflecting foundation seepage pressure; S32: converting the deformation time series data into a dam body deflection spectrum, combining the gate linkage logic in the flood discharge channel topological relationship, inputting a risk entropy value feedback model to calculate the system instability probability under the current topological relationship, and outputting a risk entropy value set with weight coefficients and spatial position labels; S33: if any entropy value in the risk entropy value set exceeds a preset threshold, according to the spatial mapping relationship between the seepage pressure gradient data and the flood discharge channel topological relationship, dynamically generating a gate opening control instruction set including gate number, opening value and execution time sequence.

[0011] Optionally, the S32 comprises: S321: Fourier transforming the deformation time series data to extract the main frequency offset and harmonic distortion rate of the deflection spectrum; S322: coupling the main frequency offset with the gate linkage logic in the flood discharge channel topological relationship, calculating the topological failure probability through a Markov chain state transition equation, and generating an initial risk entropy value set with weight coefficients; S323: based on the gradient distribution of the seepage pressure gradient data in the dam foundation grid unit, applying a seepage-stress coupling correction coefficient to the initial risk entropy value set, and outputting a risk entropy value set with weight coefficients and spatial position labels.

[0012] Optionally, the S4 comprises: S41: input the material quantity list in the optimization scheme into the dam aging damage model, combine the environmental corrosion data of the target basin, calculate the steel corrosion rate and concrete carbonation depth, and generate a time-stamped material performance degradation curve; S42: based on the material performance degradation curve, establish a retirement cost transmission function including demolition energy consumption coefficient, residual value recovery rate and environmental governance cost, output the demolition timing scheme and material recycling value corresponding to different retirement years; S43: coupling simulation of the demolition timing scheme and the storage time-varying function in the digital twin, predicting the process of water level drop in the reservoir area, generating the reservoir capacity release curve, and integrating the material recycling value to form a full life cycle decision package including economic and environmental evaluation indicators, and feeding back to S2 to update the initial planning scheme.

[0013] Optionally, the S42 comprises: S421: according to the steel corrosion critical point in the material performance degradation curve, triggering the retirement node with the structure stability threshold, generating the demolition timing scheme including the blasting timing and mechanical demolition sequence; S422: matching the demolition method in the demolition timing scheme with the material type, calling the material recycling price database, outputting the material recycling value with metal content purity index; S423: integrating the energy consumption data of the demolition timing scheme, material recycling value and ecological restoration cost parameters, constructing a retirement cost transmission function reflecting the nonlinear relationship between time and cost.

[0014] The dam system engineering full life cycle dynamic optimization planning system is used to realize the dam system engineering full life cycle dynamic optimization planning method, comprising the following modules: Geology and hydrology data modeling module: used to obtain geological exploration data, hydrological history data and existing dam structure parameters of the target basin, and construct a digital twin including geologic body stress field, reservoir capacity time-varying function and ecological constraint boundary; Initial planning optimization module: used to generate dam spatial coordinate set, flood discharge channel topological relationship and material quantity list based on the digital twin through topological time-varying fitness algorithm, forming the initial planning scheme of the dam system engineering; Running period dynamic regulation module: used to receive deformation time series data and seepage pressure gradient data uploaded by dam monitoring terminal, calculate system instability probability based on risk entropy value feedback model, and output corresponding gate opening regulation instruction set; Retirement period feedback simulation module: used to input the material quantity list into the dam aging damage model, calculate the material performance degradation curve, establish the retirement cost transmission function, generate the demolition timing scheme, reservoir capacity release curve and material recycling value, form the full life cycle decision package and feedback to the initial planning optimization module for updating.

[0015] The beneficial effects of the present application are: The present application realizes the coordinate system unification of multi-source heterogeneous data and the multi-physical field coupling modeling by constructing the digital twin model of the fusion geologic body stress field, the reservoir capacity time-varying function and the ecological constraint boundary. Compared with the traditional dam site selection scheme which relies on a single geologic parameter or a static hydrological chart, the method can identify the fault activity, the rock layer permeability difference and the ecological sensitive area in the early planning stage, avoid the conflict between the dam layout and the ecological constraint, improve the adaptability and environmental coordination of the planning point selection, and lay a precise data foundation for the subsequent structure topology optimization.

[0016] The present application proposes a topology time-varying fitness algorithm which combines the dam spatial coordinate set and the redundancy index of the flood discharge channel, dynamically optimizes the dam connection sequence and the gate linkage logic with the goal of maximizing the redundancy, receives the dam deformation time series data and the seepage pressure gradient data in real time through the risk entropy feedback model in the running period, couples the main frequency offset, the state transition probability and the seepage-stress correction, and realizes the dynamic regulation and control of the flood discharge strategy. Compared with the existing static preset scheduling table, the mechanism can actively respond to local instability signs, effectively reduce the system failure probability and improve the operation safety redundancy.

[0017] The present application deduces the aging process of steel and concrete by integrated analysis of material performance degradation curve and environmental corrosion data, generates a demolition time sequence scheme based on the stability threshold trigger, further combines the material recycling value and the ecological restoration cost, constructs a retirement cost transmission function, and couples it with the reservoir capacity time-varying function to generate a reservoir capacity release curve. The scheme enables the retirement scheme to have fine cost prediction ability and resource recycling maximization potential, and at the same time updates the initial planning through the feedback mechanism to realize the whole life cycle closed-loop linkage of design-operation-retirement phase, significantly improving the economy and sustainability of the dam system project. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0019] Figure 1 The method flowchart of the embodiment of the present application is shown in the figure. Figure 2 The system flowchart of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0020] The application will be described in detail below with reference to the drawings and specific embodiments. It should be noted that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be implemented by those skilled in the art for some known technologies; and the drawings are only used to describe the embodiments in more detail, and are not intended to specifically limit the application.

[0021] It should be noted that in the specification, "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like indicate that the described embodiment can include a specific feature, structure or property, but not necessarily every embodiment includes the specific feature, structure or property. In addition, when a specific feature, structure or property is described in combination with an embodiment, it should be within the knowledge of those skilled in the related art to realize such a feature, structure or property in combination with other embodiments (whether or not explicitly described).

[0022] Generally, the terms can be understood at least in part from the context of their use. For example, depending on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular or can be used to describe combinations of features, structures, or characteristics, in the plural, that are collectively equivalent in the singular. In addition, the term "based on" can be understood as not necessarily intending to convey a set of exclusive factors, but instead, at least in part, depending on the context, allowing the existence of other factors not necessarily explicitly described.

[0023] As shown in Figure 1 The dam system engineering full life cycle dynamic optimization planning method includes the following steps: S1: Obtain the geological exploration data, hydrological history data and existing dam structure parameters of the target basin, and construct a digital twin including the stress field of the geological body, the time-varying function of the reservoir capacity, and the ecological constraint boundary, which includes the following steps: S11: Input the geological exploration data into the three-dimensional geological modeling system, and generate the stress field of the geological body including the rock layer permeability coefficient matrix and the fault spatial coordinates through discretization processing, specifically: First, load the obtained geological exploration data as input data into the three-dimensional geological modeling system. The geological exploration data includes borehole core strength, seismic wave velocity profile, fault distribution map, and rock layer burial structure map, covering the physical properties and spatial structure information of the rock mass in the target basin area. The system uses a spatial data block division mechanism to discretize the original data, forming a structured and analyzable multi-dimensional parameter set, providing a basis for subsequent model calculation.

[0024] S111: Standardize the drilling core strength and seismic wave velocity profile in the geological exploration data. The standardization process includes: normalizing the uniaxial compressive strength, splitting tensile strength and wave velocity horizon corresponding values in the drilling core samples, after removing outliers, taking the median as the reference, generating a standardized geological parameter set. The standardization process aims to eliminate the scale deviation caused by lithology difference between different measurement points, and improve the stability and comparability of the subsequent model.

[0025] S112: Input the above standardized geological parameter set into the fault activity evaluation model, which is established based on the coupling relationship between regional fault structure and water pressure sensitivity. The evaluation model simulates the response behavior of the fault under the loading scenario of reservoir water pressure according to the fault trend, fault surface friction coefficient, surrounding rock fracture expansion trend and other information, and calculates the fault displacement probability distribution under the action of reservoir water pressure. The output results of the model include fault displacement trend index and its corresponding spatial coordinates, combined with the displacement probability density distribution, to generate fault spatial coordinates with risk weight value, that is, each fault position is attached with the probability weight of significant displacement under a certain water pressure condition, and the coordinate set will be an important structure constraint input for subsequent stress field calculation.

[0026] S113: Based on the generated fault spatial coordinates, perform finite element mesh division to construct a structure-solvable three-dimensional geological model. The mesh division considers the fault zone position and different rock layer boundaries to achieve the boundary consistency of the mesh element and the geological structure. Then, the initial geostress state is solved in each element by applying the rock mass constitutive model. The rock mass constitutive model sets parameters such as elastic modulus, Poisson's ratio and density according to the lithology category, and outputs the initial stress tensor field of each element by considering the terrain loading, water pressure distribution and self-weight action. At the same time, the permeability difference in each element is estimated to form a rock layer permeability coefficient matrix containing different rock layers and different spatial regions. Finally, the output geological body stress field includes stress state distribution data in three-dimensional space, rock layer permeability coefficient matrix and fault risk spatial coordinate set, which provides a geological foundation field for the construction of digital twin.

[0027] S12: Based on the rock layer permeability coefficient matrix in the geological body stress field, and combined with the water level-flow relationship in the hydrological historical data, a reservoir capacity time-varying function reflecting the nonlinear change of reservoir capacity with water level is established.

[0028] This step mainly determines the permeation characteristic region of the reservoir foundation and the surrounding slope based on the rock permeability coefficient matrix obtained in the stress field of the geological body, and combines the hydrological historical data of the target region, including the water level-flow relationship curve recorded by the station in previous years, the reservoir operation scheduling log, the water level jump event and the corresponding reservoir capacity change information, etc., to construct a reservoir capacity time-varying function. The reservoir capacity time-varying function is a function form describing the change of the reservoir capacity with the water level, reflecting the lag storage capacity and discharge response of the water body under different geological backgrounds. The function structure is constructed in a polynomial fitting form, has a nonlinear response characteristic, and has the following expression: ; wherein, represents the reservoir capacity at time , represents the reservoir water level at the corresponding time, and are fitting coefficients obtained by least square fitting according to the water level-reservoir capacity historical data; the function is used to dynamically simulate the change behavior of the reservoir capacity in different storage stages, and supports real-time calculation in the dam body operation period.

[0029] S13: Analyzing the river flow speed time sequence record and the historical inundation image in the hydrological historical data, extracting the inundation sensitive parameters of the vegetation coverage region, and forming the ecological constraint boundary including the minimum flow speed threshold and the maximum inundation period.

[0030] In this step, the flow speed time sequence record of the main river in the target basin is extracted from the hydrological historical data, and the intersection relationship between the vegetation coverage region and the flood inundation region is determined by superimposing the normalized vegetation index NDVI through image processing algorithm combined with the water-logged region image data collected by the remote sensing platform. By using the intersection analysis, the sensitivity of different vegetation types to hydrological disturbance is evaluated, and the ecological response threshold is extracted, including: the minimum flow speed threshold sensitive to the river flow speed, and the maximum inundation period that the vegetation community can tolerate.

[0031] The minimum flow speed threshold is defined as the minimum river flow speed value required to maintain the basic function of the ecological system without being damaged, and the value is obtained by statistically analyzing the relationship between the NDVI decay rate and the flow speed in the historical extreme drought period. The maximum inundation period is based on the longest duration of a certain region being continuously inundated in the sequence of consecutive water-logged images, and exceeding this period is easy to cause root rot and ecological function degradation. The above two indexes constitute the core parameters of the ecological constraint boundary, which are used to demarcate the ecological constraint space region that must be met by the dam system engineering operation.

[0032] S14: Coordinate system unification and fusion of the geological body stress field, the reservoir capacity time-varying function and the ecological constraint boundary, and the existing dam structure parameters, to output the digital twin body integrated with multiple physical fields.

[0033] ​In this step, the existing dam structure parameters are called, including dam type structure form, dam spatial coordinates, cross-section size, construction material grade, foundation burial depth, and dam foundation seepage control structure form, etc. information as the structure reference data. Through the unified coordinate conversion module, the spatial grid of the geological body stress field, the river coverage area of the ecological constraint boundary, and the water level elevation curve involved in the reservoir capacity function are all projected into the dam structure coordinate system to realize the unified processing of the coordinate system. In the fusion process, the scale of each physical field data is normalized and spatially matched to eliminate the data alignment errors caused by resolution differences and ensure the spatial position correspondence consistency between each data.

[0034] The fusion result is output as a complete digital twin, including the geological body stress field in three-dimensional space, the reservoir capacity time-varying function based on the comprehensive response of water level-flow-permeability, the ecological constraint boundary with clear threshold limit, and the dam structure parameter set linked with them. This digital twin model will serve as the basis for the whole process simulation of the subsequent dam system engineering initial planning and operation period regulation.

[0035] S2: Based on the digital twin, the initial planning scheme of the dam system engineering is generated through the topological time-varying fitness algorithm, and the optimization scheme including the dam spatial coordinate set, the flood discharge channel topological relationship, and the material quantity list is output, which is specifically: S21: Discretize the geological body stress field in the digital twin into an unstructured finite element grid with rock layer permeability coefficient matrix, and output the finite element grid model: Firstly, the geological body stress field information in the digital twin is called, including the stratum distribution, lithology classification, fault spatial coordinates, and rock layer permeability coefficient matrix. Delaunay triangulation algorithm is used for unstructured grid division in three-dimensional geological space. The fault position is taken as a forced boundary constraint in the division process to ensure that the grid division meets the structural continuity requirement. Each grid element is nested with rock layer permeability coefficient value and maintains the same physical properties as its spatial position.

[0036] After the division is completed, the finite element grid model is established, which includes node number, three-dimensional coordinates, rock layer number, local permeability value, and stress field initial value. This finite element grid model will serve as the basis structure for dam spatial layout and seepage path analysis.

[0037] S22: Traverse the connection path between the boundary nodes of the finite element grid model, select the line segment set that meets the minimum dam height constraint, and generate the dam spatial coordinate set including coordinate elevation information; In this step, for the boundary nodes of the finite element grid model, the spatial connection between the nodes is extracted pair by pair, and the vertical elevation difference is calculated. Set the minimum dam height threshold When the height difference of the connection line segment is greater than or equal to the threshold, the line segment is retained as a selectable dam foundation section.

[0038] After the line segment screening, the three-dimensional coordinates of its start and end points are extracted 、 , which are marked as dam space candidate coordinates. Further spatial clustering is performed on the set of line segments that meet the requirements, and grouping generates continuous dam axis segments. The set of all selected line segments constitutes the dam space coordinate set, which includes the start and end coordinates of each dam axis, the corresponding elevation, and the geological unit number, which is used for subsequent topological optimization.

[0039] S23: Based on the dam space coordinate set, the seepage path redundancy of each dam combination is calculated by a topological time-varying fitness algorithm to generate a flood discharge channel topology including gate linkage logic, and associate the material consumption list of the corresponding dam volume to form an initial planning scheme, which is described as follows: S231: Extract the geological fault activation factor of the fault space coordinates in the geological body stress field, and calculate the seepage path redundancy of each flood discharge channel by combining the path length in the flood discharge channel topology; In this step, the fault space coordinates are extracted from the geological body stress field, and the geological fault activation factor of each fault is calculated. This factor is used to measure the risk of seepage or slip induced by the fault under water pressure. It is defined as follows: ; wherein, is the geological fault activation factor, is the additional shear stress on the fault plane under operating conditions, is the critical shear strength of the fault, which is estimated by fault gouge thickness, friction coefficient and normal stress.

[0040] Combined with the path length of each channel in the flood discharge channel topology , the seepage path redundancy is defined as: ; wherein, is the seepage path redundancy of the th flood discharge channel, is the total length of the channel path, is the activation factor of the th fault on the channel path, is the fault weight coefficient, considering the influence of its size and depth, is the total number of faults crossed by the channel path. This formula reflects that the longer the path, the fewer or more stable the fault, the higher the redundancy.

[0041] S232: Iteratively adjust the dam axis angle and connection order in the dam spatial coordinate set to maximize the sum of seepage path redundancy as the objective function, and update the flood discharge channel topology relationship; In this step, a topology time-varying fitness algorithm is constructed to maximize the sum of seepage path redundancy as the objective function, and dynamically optimize the dam combination mode. Set the initial dam spatial coordinate set as , each group represents the start and end coordinates of a dam section. By iteratively adjusting the dam axis angle and its connection order in the dam spatial coordinate set, a better topology combination is explored. The objective function is defined as follows: ; Where, is the axis angle between the th dam section and the adjacent dam section, is the permutation and combination of dam connection order, is the seepage path redundancy of the th flood discharge channel, which is affected by and , is the number of all feasible flood discharge channels; Iterative search is performed through genetic algorithm or ant colony algorithm. When the objective function converges or reaches the set number of rounds, the optimal topology structure is output.

[0042] S233: According to the updated dam connection structure in the flood discharge channel topology relationship, match the standard dam structure parameter template, and output the material consumption list including concrete volume and steel type; This step matches the dam connection structure defined in the updated flood discharge channel topology relationship against the standard dam structure parameter template library. Each dam structure template contains parameters such as dam type (such as gravity dam, roller compacted concrete dam, etc.), cross-section structure size, configuration reinforcement proportion, and construction unit module division.

[0043] Combined with the three-dimensional size information of each dam section in the dam spatial coordinate set, the dam concrete demand is calculated according to the volume formula, and the steel type and quantity are generated combined with the reinforcement ratio in the template. Finally, a complete material consumption list is formed, including: Concrete volume of each dam section, steel type and quality of main stress parts, joint sealing material and its demand of structure connection parts.

[0044] This material consumption list, together with the dam spatial coordinate set and the flood discharge channel topology relationship, constitutes the initial planning scheme of the dam system project, which serves as the basic parameter reference for dynamic regulation and control in the subsequent operation period and retirement period.

[0045] S3: During operation, it receives deformation time-series data and seepage gradient data from the dam monitoring terminal, inputs the flood discharge channel topology from the optimized scheme into the risk entropy value feedback model, and dynamically generates a set of gate opening control instructions, specifically: S31: Receive real-time sensor data uploaded by dam monitoring terminals deployed on each dam body, and separate and extract deformation time series data reflecting dam body deflection changes and seepage gradient data reflecting foundation seepage pressure. In this step, the system first receives the raw data stream collected by sensors through the dam monitoring terminal, which consists of strain gauges, displacement gauges, tilt sensors, and seepage pressure sensors deployed in various dam sections. The raw data includes multidimensional mechanical response data, displacement curves, and pore water pressure sequences. The system performs data structuring based on sensor type identifiers and timestamps, and distinguishes different data channels according to the equipment calibration model.

[0046] Specifically, by filtering and resampling, the tilt angle change and displacement data are combined to generate deformation time series data, which is used to characterize the dam body deflection trend. At the same time, the pore water pressure gradient signal of the dam foundation is reconstructed by space-time interpolation to form seepage pressure gradient data describing the seepage pressure gradient change. This data will be used for seepage-stress coupling correction in subsequent risk modeling.

[0047] S32: Convert deformation time series data into dam body deflection spectrum, combine gate linkage logic in flood discharge channel topology, input risk entropy value feedback model to calculate system instability probability under current topology, and output risk entropy value set with weight coefficient and spatial location label; In this step, the system converts the received deformation time-series data into a frequency domain representation, generates the dam deflection spectrum, and couples it with the gate linkage logic in the flood discharge channel topology for modeling. The risk entropy value feedback model is then input to calculate the current instability risk of the system topology. This process includes: S321: Perform Fourier transform on the deformation time series data to extract the dominant frequency offset and harmonic distortion rate of the deflection spectrum; First, the deformation time series data... Perform a Fast Fourier Transform (FFT) to obtain the spectral representation. Extract the main frequency component from it. With system design frequency Offset: ; And calculate the harmonic distortion rate. (Total Harmonic Distortion): ; in, For the first The amplitude of the first frequency component, The fundamental amplitude, For the analysis order, This is the main frequency offset. To reflect the degree of nonlinear distortion in the deformation spectrum, it is used to identify potential unstructured deformations.

[0048] S322: Couple the main frequency offset with the gate linkage logic in the flood discharge channel topology, calculate the topology failure probability through the Markov chain state transition equation, and generate an initial risk entropy value set with weighted coefficients. Based on the gate linkage logic defined in the flood discharge channel topology, a state transition diagram is constructed. The dominant frequency offset is used as the initial disturbance source to trigger the gate response mechanism. Let the gate state set be... Each state represents a linked combination. Define the Markov state transition probability matrix. The state transition probability is based on the following function: ; in, Indicates the state of the gate Transition to state The probability, This is the disturbance sensitivity coefficient. This indicates the main frequency offset. express and The structural connectivity distance between them This is the normalization factor.

[0049] The cumulative probability of the system transitioning from a normal state to an unstable state is calculated using the aforementioned state transition matrix, thereby generating an initial set of risk entropy values. ,in, For the corresponding spatial location labels of the dam structure, This represents the risk weight coefficient for the corresponding node, with a value between [0,1].

[0050] S323: Based on the gradient distribution of seepage pressure gradient data in the grid cells of the dam foundation, apply a seepage-stress coupling correction coefficient to the initial risk entropy value set, and output a risk entropy value set with weight coefficients and spatial location labels; In this step, the osmotic pressure gradient data is retrieved. Based on the grid distribution information, the seepage gradient value of the region where each risk node is located is calculated, and a correction function is introduced. The initial risk entropy value is adjusted. The correction function is defined as follows: ; in, For the first The seepage-stress coupling correction coefficient for each node. The magnitude of the seepage pressure gradient at the node location. This is the seepage sensitivity weighting coefficient, and its value is set according to the dam foundation structure classification.

[0051] The final set of corrected risk entropy values ​​is as follows: ; in, For spatial location labels, This is the corrected risk weighting coefficient, used to determine whether a regulatory instruction is triggered.

[0052] S33: If any entropy value in the risk entropy value set exceeds the preset threshold, a set of gate opening control instructions, including gate number, opening value, and execution sequence, is dynamically generated based on the spatial mapping relationship between the seepage pressure gradient data and the topology of the flood discharge channel. In this step, the system adjusts the set of corrected risk entropy values. Perform a point-by-point search; if any ,in If a preset risk response threshold is set, the control process will be triggered.

[0053] First, establish a spatial mapping function between the seepage pressure gradient data and the topological relationship of the flood discharge channel. Used to locate risk nodes to specific control gate numbers. .

[0054] Next, based on the current status of the gate and its historical execution records, combined with the estimated load of the operating environment, a gate opening control instruction set is dynamically generated, including the following: Gate number : The flood discharge unit corresponding to the triggered area mapping; Opening value Determined based on inversion calculations of seepage pressure; Execution sequence : These are the start and end times of the control, in seconds.

[0055] The control command set is sent to the gate actuator through the dam control system and the entire execution feedback is recorded for subsequent dynamic feedback optimization.

[0056] S4: Based on the material usage list of the optimized plan, and combined with the dam aging and damage model, construct the decommissioning cost transmission function, outputting a full life-cycle decision package including the dismantling sequence plan, reservoir capacity release curve, and material recycling value, and feed it back to S2 to update the initial planning plan, specifically: S41: Input the material usage list from the optimized scheme into the dam aging and damage model, combine it with the environmental corrosion data of the target watershed, calculate the steel corrosion rate and concrete carbonation depth, and generate time-stamped material performance degradation curves. In this step, the material usage list output from the optimization scheme is called, including parameters such as the volume of concrete used in each dam section, the type and layout ratio of reinforcing steel, and the surface treatment grade, and then input into the dam aging and damage model. The dam aging and damage model is a multi-factor coupled model that considers the synergistic effects of material aging, environmental corrosion, and load.

[0057] For the steel component, the model receives environmental corrosion data from the target watershed, such as annual average humidity, SO2 concentration, pH value, and chloride ion content. Based on the steel type (carbon steel or alloy steel), it calls the corrosion rate calculation module and outputs the corrosion depth per unit time. The calculation structure employs an empirical regression corrosion rate model. ; in, For the corrosion rate of steel, Relative humidity, Chloride ion concentration, The regression coefficient is preset based on the steel grade through the experimental database.

[0058] For the concrete component, an environmental parameter-driven carbonation depth prediction model is used, considering the concrete water-cement ratio, surface cover thickness, and alkali-aggregate reaction, to generate a time-dependent carbonation depth evolution trajectory. The carbonation depth is expressed using the following model: ; in, For carbonization depth, The carbonation coefficient is set based on the concrete mix design and exposure level. For time.

[0059] The final output is a set of time-stamped material property degradation curves, which describe the attenuation rate of the effective cross section of steel and the carbonation development trend of the concrete protective layer in different dam sections, providing a basis for the safety assessment of demolition.

[0060] S42: Based on the material performance degradation curve, establish a decommissioning cost transmission function that includes the dismantling energy consumption coefficient, residual value recovery rate, and environmental remediation costs. Output the dismantling sequence schemes and material recycling values ​​corresponding to different decommissioning years, specifically including: S421: Based on the critical point of steel corrosion in the material property degradation curve, trigger the decommissioning node with the structural stability threshold, and generate a demolition sequence scheme including blasting sequence and mechanical demolition sequence. The system first analyzes the corrosion trend of steel in the material performance degradation curve, locates the point at which the weakening degree of the corrosion section reaches the lower limit of structural stability, and records it as the structural critical node. Using a safety reserve factor less than 1.5 or complete carbonization of the concrete protective layer as triggering conditions, the system determines that the dam section has entered the decommissioning stage.

[0061] To address the spatial layout, dam structure, and adaptability of demolition techniques for different dam sections, a systematic plan was developed for the blasting sequence and mechanical demolition order. The blasting sequence design must consider energy conduction interference between adjacent dam sections and employ delayed blasting logic to avoid structural resonance. The mechanical demolition order is planned based on the dam height and platform accessibility, proceeding from the free surface to the foundation in reverse order to ensure construction stability and the integrity of material recovery.

[0062] S422: Match the demolition method and material type in the demolition sequence plan, call the material recycling price database, and output the material recycling value with metal content and purity indicators. The system maps the different work units in the demolition sequence plan, based on their construction methods, demolition methods, and corresponding material types, to a structural material database to obtain a suitable recycling path. For the reinforcing steel, the system estimates the metal content purity index according to the type and surface corrosion level, and calculates the proportion of recyclable metal per unit mass. For the concrete, the system determines whether it can be used for recycled aggregate reuse based on the carbonation level and impurity content.

[0063] By accessing the material recycling price database, the system outputs a list of recycling values ​​for various materials, including the recycling unit price, recyclable quantity, and corresponding metal purity for each type of material. The generated results include time stamps for subsequent cost function coupling.

[0064] S423: Integrate energy consumption data, material recycling value, and ecological restoration cost parameters of the dismantling sequence scheme, and construct a decommissioning cost transmission function that reflects the nonlinear relationship between time and cost; In this step, the system extracts the type, power level, and operation time of construction machinery for key nodes from the demolition sequence plan, and estimates the equipment energy consumption cost of the demolition process by combining it with carbon emission factors. Simultaneously, a decommissioning cost transmission function is constructed by superimposing the material recycling value and ecological restoration cost indicators: ; in, For time Total decommissioning costs For equipment energy consumption costs, The corresponding costs of environmental governance and ecological restoration, The value of recycled materials.

[0065] This function reflects the differences in economic benefits at different retirement time points, and supports users in setting retirement years according to different strategies to achieve life cycle decision-making oriented towards minimizing costs.

[0066] S43: Couple the demolition timeline scheme with the time-varying function of reservoir capacity in the digital twin for simulation, predict the reservoir water level decline process, generate the reservoir capacity release curve, and integrate the material recycling value to form a full life cycle decision package including economic and environmental assessment indicators, which is then fed back to S2 to update the initial planning scheme. In this step, the system cascades the aforementioned demolition time sequence scheme with the reservoir capacity time-varying function in the digital twin, applies hydraulic boundary changes to different dam section blasting or demolition nodes according to the time sequence, and dynamically simulates the change process of reservoir capacity structure.

[0067] The water level drop process is modeled by continuous profile retreat, taking into account the instantaneous release and natural rebound of water after the dam section blasting, and outputting the corresponding reservoir capacity release curve to describe the reservoir capacity reduction rate and the final water storage balance state per unit time.

[0068] By combining the reservoir capacity release curve with the material recycling value list, the system integrates economic assessment indicators (such as unit reservoir capacity revenue and decommissioning cost recovery ratio) and environmental indicators (such as total carbon emissions and ecological restoration area) to form a full life cycle decision package, which is then fed back to S2 as feedback input to optimize and iterate the initial planning scheme.

[0069] like Figure 2 As shown, the dam system engineering life cycle dynamic optimization planning system is used to implement the above-mentioned dam system engineering life cycle dynamic optimization planning method, and includes the following modules: Geological and hydrological data modeling module: used to acquire geological exploration data, hydrological historical data and existing dam structure parameters of the target watershed, and to construct a digital twin including geological stress field, reservoir capacity time-varying function and ecological constraint boundary; Initial planning optimization module: Based on digital twins, it generates the dam body spatial coordinate set, flood discharge channel topology relationship and material usage list through topology time-varying fitness algorithm to form the initial planning scheme of dam system project; Dynamic control module during operation: It is used to receive deformation time series data and seepage pressure gradient data uploaded by the dam monitoring terminal, calculate the system instability probability based on the risk entropy value feedback model, and output the corresponding gate opening control instruction set. The decommissioning feedback simulation module is used to input the material usage list into the dam aging and damage model, calculate the material performance degradation curve, establish the decommissioning cost transmission function, generate the dismantling sequence plan, reservoir capacity release curve and material recycling value, form a full life cycle decision package and feed it back to the initial planning and optimization module for updating.

[0070] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0071] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic optimization planning method for the entire life cycle of dam system engineering, characterized in that, Includes the following steps: S1: Obtain geological exploration data, hydrological historical data, and existing dam structure parameters of the target watershed, and construct a digital twin including geological stress field, reservoir capacity time-varying function, and ecological constraint boundary; S2: Based on the digital twin, an initial planning scheme for the dam system project is generated through a topology time-varying fitness algorithm, and an optimized scheme including the dam body spatial coordinate set, the topological relationship of the flood discharge channel, and the material usage list is output. S3: During operation, receive deformation time series data and seepage pressure gradient data from the dam body monitoring terminal, input the flood discharge channel topology relationship in the optimization scheme into the risk entropy value feedback model, and dynamically generate a set of gate opening control instructions; S4: Based on the material usage list of the optimization scheme, construct the decommissioning cost transmission function in conjunction with the dam aging and damage model, output a full life cycle decision package including the demolition sequence scheme, reservoir capacity release curve, and material recycling value, and feed it back to S2 to update the initial planning scheme.

2. The dynamic optimization planning method for the entire life cycle of dam system engineering according to claim 1, characterized in that, S1 includes: S11: Input the geological exploration data into the three-dimensional geological modeling system, and generate a geological stress field including the rock layer permeability coefficient matrix and fault spatial coordinates through discretization processing; S12: Based on the rock strata permeability coefficient matrix in the stress field of the geological body, and combined with the water level-discharge relationship in the hydrological historical data, establish a time-varying function of reservoir capacity that reflects the nonlinear change of reservoir capacity with water level; S13: Analyze the time-series records of river flow velocity and historical inundation images in the hydrological historical data, extract inundation-sensitive parameters of vegetation-covered areas, and form ecological constraint boundaries including minimum flow velocity threshold and maximum inundation cycle. S14: The geological stress field, the reservoir capacity time-varying function, and the ecological constraint boundary are integrated with the existing dam structure parameters to form a coordinate system and output a digital twin integrating multiple physics fields.

3. The dynamic optimization planning method for the entire life cycle of dam system engineering according to claim 2, characterized in that, S11 includes: S111: Standardize the borehole core strength and seismic wave velocity profiles in the geological exploration data and output a standardized geological parameter set; S112: Input the standardized geological parameter set into the fault activity assessment model, calculate the probability distribution of fault displacement under reservoir water pressure, and generate fault spatial coordinates with risk weights. S113: Based on the spatial coordinates of the fault, divide the finite element mesh, use the rock mass constitutive equation to solve the initial geostress distribution, and output the geological stress field including the rock layer permeability coefficient matrix.

4. The dynamic optimization planning method for the entire life cycle of dam system engineering according to claim 3, characterized in that, S2 includes: S21: Discretize the stress field of the geological body in the digital twin into an unstructured finite element mesh with rock strata permeability coefficient matrix, and output the finite element mesh model; S22: Traverse the connection paths between the boundary nodes of the finite element mesh model, filter the set of line segments that satisfy the minimum dam height constraint, and generate a spatial coordinate set of the dam body including coordinate elevation information; S23: Based on the dam body spatial coordinate set, the redundancy of the seepage path of each dam body combination is calculated by the topology time-varying fitness algorithm. The topology relationship of the flood discharge channel, including the gate linkage logic, is generated according to the principle of maximizing redundancy. The material consumption list of the corresponding dam body volume is then output to form the initial planning scheme.

5. The dynamic optimization planning method for the entire life cycle of dam system engineering according to claim 4, characterized in that, S23 includes: S231: Extract the geological fault activation factor of the spatial coordinates of the fault in the stress field of the geological body, and calculate the seepage path redundancy of each flood discharge channel in combination with the path length in the topological relationship of the flood discharge channel. S232: Taking the maximization of the sum of the redundancy of the seepage paths as the objective function, iteratively adjust the included angle and connection sequence of the dam axis in the dam body spatial coordinate set, and update the topology of the flood discharge channel; S233: Based on the updated flood discharge channel topology, match the standard dam structure parameter template and output a material usage list including concrete volume and steel type.

6. The dynamic optimization planning method and system for the entire life cycle of dam system engineering according to claim 5, characterized in that, S3 includes: S31: Receive real-time sensor data uploaded by dam monitoring terminals deployed on each dam body, and separate and extract deformation time series data reflecting dam body deflection changes and seepage gradient data reflecting foundation seepage pressure. S32: Convert the deformation time series data into a dam body deflection spectrum, combine it with the gate linkage logic in the topology of the flood discharge channel, input the risk entropy value feedback model to calculate the system instability probability under the current topology, and output a set of risk entropy values ​​with weighted coefficients and spatial location labels; S33: If any entropy value in the risk entropy value set exceeds a preset threshold, a gate opening control instruction set including gate number, opening value, and execution sequence is dynamically generated based on the spatial mapping relationship between the seepage pressure gradient data and the topology of the flood discharge channel.

7. The dynamic optimization planning method for the entire life cycle of dam system engineering according to claim 6, characterized in that, S32 includes: S321: Perform Fourier transform on the deformation time series data to extract the dominant frequency offset and harmonic distortion rate of the deflection spectrum; S322: Couple the main frequency offset with the gate linkage logic in the flood discharge channel topology, calculate the topology failure probability through the Markov chain state transition equation, and generate an initial risk entropy value set with weighted coefficients. S323: Based on the gradient distribution of the seepage pressure gradient data in the dam foundation grid cells, apply a seepage-stress coupling correction coefficient to the initial risk entropy value set, and output a risk entropy value set with weighted coefficients and spatial location labels.

8. The dynamic optimization planning method for the entire life cycle of dam system engineering according to claim 7, characterized in that, S4 includes: S41: Input the material usage list in the optimization scheme into the dam aging damage model, combine it with the environmental corrosion data of the target watershed, calculate the steel corrosion rate and concrete carbonation depth, and generate a material performance degradation curve with timestamps. S42: Based on the material performance degradation curve, establish a decommissioning cost transmission function that includes the demolition energy consumption coefficient, residual value recovery rate and environmental governance cost, and output the demolition sequence scheme and material recycling value corresponding to different decommissioning years; S43: Couple the demolition timing scheme with the reservoir capacity time-varying function in the digital twin to predict the reservoir water level drop process, generate the reservoir capacity release curve, and integrate the material recycling value to form a full life cycle decision package including economic and environmental assessment indicators, and feed it back to S2 to update the initial planning scheme.

9. The dynamic optimization planning method for the entire life cycle of dam system engineering according to claim 8, characterized in that, S42 includes: S421: Based on the critical point of steel corrosion in the material property degradation curve, trigger the decommissioning node with the structural stability threshold, and generate a demolition sequence scheme including blasting sequence and mechanical demolition sequence; S422: Match the demolition method and material type in the demolition sequence plan, call the material recycling price database, and output the material recycling value with metal content purity index; S423: Integrate the energy consumption data, material recycling value, and ecological restoration cost parameters of the demolition timing scheme to construct a decommissioning cost transmission function that reflects the nonlinear relationship between time and cost.

10. A dynamic optimization planning system for the entire life cycle of dam system engineering, used to implement the dynamic optimization planning method for the entire life cycle of dam system engineering as described in any one of claims 1-9, characterized in that, Includes the following modules: Geological and hydrological data modeling module: used to acquire geological exploration data, hydrological historical data and existing dam structure parameters of the target watershed, and to construct a digital twin including geological stress field, reservoir capacity time-varying function and ecological constraint boundary; Initial planning optimization module: Based on the digital twin, it generates the dam body spatial coordinate set, flood discharge channel topology relationship and material usage list through the topology time-varying fitness algorithm to form the initial planning scheme of the dam system project; Dynamic control module during operation: It is used to receive deformation time series data and seepage pressure gradient data uploaded by the dam monitoring terminal, calculate the system instability probability based on the risk entropy value feedback model, and output the corresponding gate opening control instruction set. The decommissioning feedback simulation module is used to input the material usage list into the dam aging and damage model, calculate the material performance degradation curve, establish the decommissioning cost transmission function, generate the dismantling sequence plan, reservoir capacity release curve and material recycling value, form a full life cycle decision package and feed it back to the initial planning and optimization module for updating.

Images