Adaptive optimization method and system for intelligent water resource scheduling based on digital twinning

Abstract

The application provides a water resource intelligent scheduling adaptive optimization method and system based on digital twinning, the method comprises the following steps: constructing a water network dynamics characteristic mirror atlas of a target river basin, and constructing a multi-dimensional digital twinning model; performing multi-scenario dynamic simulation and modeling; constructing a water resource intelligent scheduling adaptive optimization model containing a multi-target elastic coordination domain; solving the water resource intelligent scheduling adaptive optimization model by using an optimization algorithm; correcting an initial scheduling scheme; and transmitting a final scheduling scheme to an execution terminal to trigger a twin body cognitive autonomous evolution and fidelity self-healing mechanism of the multi-dimensional digital twinning model. The application enables the scheduling system to continuously adapt to changes in hydro-meteorological conditions and changes in economic development needs.

Description

Technical Field

[0001] This invention belongs to the field of water resource scheduling technology, specifically relating to an adaptive optimization method and system for intelligent water resource scheduling based on digital twins. Background Technology

[0002] Watershed water resource allocation involves a complex system with multiple objectives, levels, and projects. Traditional allocation methods are difficult to coordinate the relationships between multiple objectives such as flood control, water supply, irrigation, ecology, and power generation. The formulation of allocation plans often relies on experience and lacks scientific quantitative decision support.

[0003] Existing technologies employ data collection and digital twin model construction for dynamic simulation of irrigation district water resources. However, this method only addresses irrigation district water distribution scenarios and fails to solve the comprehensive scheduling problem involving multiple water sources, users, and objectives at the watershed scale. Furthermore, it lacks an adaptive optimization mechanism for scheduling schemes and the dynamic updating capability of the digital twin model. Another approach involves constructing a reservoir digital twin based on multi-source data fusion to achieve accurate virtual-real mapping of reservoir operating conditions. However, this method primarily focuses on scheduling decisions for a single reservoir and fails to encompass the complex scenarios of coordinated scheduling of multiple projects within a watershed, including reservoir groups, pond and dam groups, and canal systems. Moreover, its optimization objectives do not fully reflect the multi-dimensional evaluation requirements under the concept of high-quality development. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a digital twin-based intelligent water resource scheduling adaptive optimization method and system, thereby overcoming the shortcomings of the prior art.

[0005] In a first aspect, the present invention provides an adaptive optimization method for intelligent water resource scheduling based on digital twins, the method comprising: Acquire hydrological, meteorological, and comprehensive data of the target watershed, and preprocess the hydrological, meteorological, and comprehensive data; Based on the preprocessed hydrological, meteorological, and integrated data, a water network dynamics feature mirror map of the target watershed is constructed, and a multi-dimensional digital twin model of the water resources system is constructed based on the water network dynamics feature mirror map. Based on the aforementioned multi-dimensional digital twin model and the introduction of a prediction-driven look-ahead rolling simulation mechanism, and employing a collaborative adaptive look-ahead time-domain compression strategy, multi-scenario dynamic simulation is performed to generate simulation results of the system's operating state in future multi-period periods. Based on the simulation results, the optimization objectives are to optimize the preset development index, the water supply and demand balance index, and the water resource spatial equilibrium coefficient. In addition, water supply resilience constraints under extreme conditions are introduced to construct an intelligent water resource scheduling adaptive optimization model that includes a multi-objective elastic coordination domain. An optimization algorithm is used to solve the intelligent water resource scheduling adaptive optimization model to obtain an initial scheduling scheme; The initial scheduling scheme is virtually pre-simulated and evaluated using the multi-dimensional digital twin model. Based on the results of the pre-simulation and evaluation, the Pareto front solution set and multi-objective conflict degree of the initial scheduling scheme are calculated. The initial scheduling scheme is then modified after adjusting the multi-objective conflict degree to generate the final scheduling scheme. The final scheduling scheme is transmitted to the execution terminal, and execution feedback data is collected in real time. The operational health index of the multi-dimensional digital twin model is periodically calculated to determine whether the operational health index is lower than a preset threshold. If so, the twin cognitive autonomy evolution and fidelity self-healing mechanism of the multi-dimensional digital twin model is triggered, and the multi-dimensional digital twin model is updated according to the execution feedback data.

[0006] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a multi-dimensional digital twin model, accurate virtual-real mapping and dynamic simulation of the water resource system are achieved, providing a highly reliable simulation basis for scheduling decisions; with the maximum high-quality development index and the optimal water resource supply-demand balance index as joint optimization objectives, multi-objective collaborative optimization of water resource scheduling is achieved through multi-dimensional requirements; the scheduling scheme is virtually rehearsed and evaluated through the digital twin model, reducing decision-making risks; and the digital twin model is dynamically updated based on execution feedback data, forming a closed-loop adaptive optimization mechanism that senses updates, enabling the scheduling system to continuously adapt to changes in hydrological and meteorological conditions and economic development needs.

[0007] Furthermore, the step of constructing a mirror map of the hydrodynamic characteristics of the target watershed based on the preprocessed hydrological, meteorological, and integrated data includes: Extract the spatial locations of each hydrological node, water conservancy project node, and water user node within the target watershed, as well as the connection relationships and scheduling affiliations of the water user nodes, to obtain the physical topology layer; The propagation delay time, friction loss coefficient, and storage response elastic coefficient of the quantified water flow are extracted to form a multidimensional dynamic parameter matrix; The physical topology layer and the multidimensional dynamic parameter matrix are correlated and fused to construct a water network dynamic feature mirror map containing static structural information and dynamic response feature information.

[0008] Furthermore, prior to the step of employing a collaborative adaptive look-ahead time-domain compression strategy for multi-scenario dynamic simulation, the method further includes: Set the baseline length of the rolling optimization time window and the baseline rolling step size; The prediction confidence curve of medium- and long-term runoff forecast data within the current time window is evaluated in real time, and the time-domain compression coefficient is dynamically calculated based on the prediction confidence curve. The calculation expression is as follows: ; In the formula, Represents the time-domain compression factor. express The confidence level of the prediction at any given time. , These represent the minimum and maximum prediction confidence levels, respectively. The reference length and the reference rolling step size are dynamically adjusted based on the time-domain compression coefficient to obtain an adaptive look-ahead time domain, expressed as: ; In the formula, express The time-adjusted look-ahead time domain length, This indicates the baseline length of the rolling optimization time window. This represents the preset time-domain adjustment sensitivity coefficient.

[0009] Furthermore, the functional expression of the joint optimization objective is: ; In the formula, This represents the value of the joint optimization objective function. , , , These represent the weight coefficients of the three different sub-objectives and the penalty term, respectively. Indicating a high-quality development index, This represents the regional water resources supply and demand balance index. Indicates the spatial balance coefficient of water resources. This indicates a multi-objective conflict penalty item. This represents a vector of decision variables.

[0010] Furthermore, the boundary constraint expression of the multi-objective elastic coordination domain is: ; ; In the formula, Indicates the first The current function value of each sub-objective. Indicates the first The preset elastic relaxation coefficient for each sub-target has a range of values. , Indicates the first The optimal value of each sub-objective under ideal conditions.

[0011] Furthermore, prior to the step of triggering the self-evolution of the twin's cognitive autonomy and the fidelity self-healing mechanism of the multi-dimensional digital twin model, the method further includes: When the operational health index is lower than the preset threshold, locate the key deviation sub-region or key mutual feedback relationship chain with the largest accumulated deviation in the virtual mapping layer of the multi-dimensional digital twin model. For the identified key deviation sub-regions, based on the latest accumulated long-term series execution feedback data, a transfer learning method is used to identify the physical law drift in the key deviation sub-regions; Based on the identification results of the physical law drift, the corresponding causal strength coefficient in the multi-dimensional digital twin model is re-estimated, and the behavioral rule set of the twin proxy object corresponding to the key deviation sub-region is corrected. The modified set of behavioral rules and the re-estimated causal strength coefficients are then reintegrated into the virtual mapping layer to complete the adaptive reconstruction of the cognitive logic of the virtual mapping layer.

[0012] Furthermore, the step of updating the multi-dimensional digital twin model based on the execution feedback data includes: The gate opening, pump station operating status, water level changes, and flow rate data of the execution terminal are collected in real time to generate an execution feedback dataset. The execution feedback dataset is compared with the results of the virtual pre-simulation of the multi-dimensional digital twin model to perform a deviation analysis and calculate the simulation deviation value of each key node. Based on the simulated deviation value, the Kalman filter algorithm is used to perform online smoothing correction on the key operating parameters in the digital twin model, and the results of the twin's cognitive autonomy evolution, the re-estimated causal strength coefficient, and the corrected behavioral rule set are updated in the multidimensional digital twin model to update the multidimensional digital twin model.

[0013] Secondly, the present invention also provides a water resource intelligent scheduling adaptive optimization system based on digital twins, the system comprising: The acquisition and processing module is used to acquire hydrological, meteorological, and comprehensive data of the target watershed and to preprocess the hydrological, meteorological, and comprehensive data. The first construction module is used to construct a water network dynamics feature mirror map of the target watershed based on the preprocessed hydrological, meteorological and comprehensive data, and to construct a multi-dimensional digital twin model of the water resources system based on the water network dynamics feature mirror map. The simulation module is used to perform multi-scenario dynamic simulation based on the multi-dimensional digital twin model and the prediction-driven look-ahead rolling simulation mechanism, and to generate simulation results of the system's operating status in the future for multiple time periods by adopting a collaborative adaptive look-ahead time domain compression strategy. The second construction module is used to construct an intelligent water resource scheduling adaptive optimization model that includes a multi-objective elastic coordination domain, based on the simulation results, with the optimization objectives being the optimal preset development index, the optimal water resource supply and demand balance index, and the maximization of the water resource spatial equilibrium coefficient. It also introduces water supply resilience constraints under extreme working conditions. The solution module is used to solve the intelligent water resource scheduling adaptive optimization model using an optimization algorithm to obtain an initial scheduling scheme; The calculation correction module is used to perform virtual pre-simulation and evaluation of the initial scheduling scheme through the multi-dimensional digital twin model, calculate the Pareto front solution set and multi-objective conflict degree of the initial scheduling scheme based on the results of the pre-simulation and evaluation, and correct the initial scheduling scheme after adjusting the multi-objective conflict degree to generate the final scheduling scheme. The transmission and acquisition module is used to transmit the final scheduling scheme to the execution terminal and collect execution feedback data in real time. The judgment update module is used to periodically calculate the operational health index of the multi-dimensional digital twin model, determine whether the operational health index is lower than a preset threshold, and if so, trigger the twin cognitive autonomous evolution and fidelity self-healing mechanism of the multi-dimensional digital twin model, and update the multi-dimensional digital twin model according to the execution feedback data.

[0014] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described adaptive optimization method for intelligent water resource scheduling based on digital twins.

[0015] Fourthly, the present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described adaptive optimization method for intelligent water resource scheduling based on digital twins. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1This is a flowchart of the adaptive optimization method for intelligent water resource scheduling based on digital twins in the first embodiment of the present invention; Figure 2 This is a structural block diagram of the intelligent water resource scheduling adaptive optimization system based on digital twins in the second embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention.

[0018] Explanation of key component symbols: 10. Acquisition and Processing Module; 20. First Construction Module; 30. Simulation Module; 40. Second Construction Module; 50. Solving Module; 60. Calculation and Correction Module; 70. Transmission and Acquisition Module; 80. Judgment and Update Module; 90. Bus; 91. Processor; 92. Memory; 93. Communication interface.

[0019] The embodiments of the present invention will be further described below with reference to the accompanying drawings. Detailed Implementation

[0020] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0021] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] Example 1 Please see Figure 1 The figure shows an adaptive optimization method for intelligent water resource scheduling based on digital twins in the first embodiment of the present invention, the method comprising steps S1 to S8: S1, acquire hydrological, meteorological and comprehensive data of the target watershed, and preprocess the hydrological, meteorological and comprehensive data; Understandably, this step forms the data foundation for the entire scheduling method. The hydrological, meteorological, and comprehensive data include, but are not limited to: daily precipitation, evaporation, runoff, water level, and water quality indicators at various hydrological stations within the basin; meteorological forecast data (such as precipitation and temperature forecasts for the next 7-15 days); medium- and long-term runoff prediction data (monthly runoff predictions for the next few months based on models such as LSTM); operational status data of water conservancy projects (reservoir water level, gate opening, pumping station power, and canal flow); and socio-economic data (population, GDP, industrial structure, irrigated area, crop planting structure, etc.).

[0024] During preprocessing, outliers caused by gross errors and sensor malfunctions are removed using statistical anomaly detection algorithms (such as the 3σ principle and interquartile range). Missing data is imputed (linear interpolation or imputation based on historical averages). Data from different sources and sampling frequencies are timestamped and standardized to a daily or decadal scale. Indicators with different dimensions are normalized or standardized. The purpose of preprocessing is to ensure the accuracy, completeness, and consistency of the input data, providing reliable data support for subsequent digital twin modeling and simulation.

[0025] S2, construct a water network dynamics feature mirror map of the target watershed based on the preprocessed hydrological, meteorological and comprehensive data, and construct a multi-dimensional digital twin model of the water resources system based on the water network dynamics feature mirror map; In this embodiment, the water network dynamics feature mirror map is not a static water system map in the traditional sense, but a high-dimensional dynamic structured database that integrates the natural water cycle topology of the watershed, the scheduling relationship of artificial water conservancy projects, and the spatiotemporal response delay characteristics, attenuation characteristics, and regulation elasticity of water flow when it is transmitted between multiple elements.

[0026] It should be noted that, in this embodiment, the multi-dimensional digital twin model includes a physical entity layer, a virtual mapping layer constructed based on multi-dimensional mutual feedback virtual conjugate mapping, and a dynamic simulation layer. In this embodiment, the expression for the multi-dimensional digital twin model is: ; In the formula, The overall operator representing a multi-dimensional digital twin model represents the complete mapping chain from the physical world to the virtual space and then to the simulation output; express The global simulation state matrix of the virtual space at any given time includes the water level, flow rate, and storage capacity of each node; Represents an operator; Indicates the initial time. The measured global state matrix of the physical entity. express The external decision input vector at any given time (gate opening, pump station flow, water distribution plan, etc.). Represents the conjugate causality strength matrix, encoding the strength of multidimensional feedback relationships between twin proxy objects; This represents the vector of adjustable parameters of the model (Manning coefficient, evaporation coefficient, leakage coefficient, delay time, etc.). This indicates the length of the look-ahead time domain determined by the collaborative adaptive look-ahead time domain compression strategy.

[0027] It should be explained that traditional digital twin models are mostly single water transfer mappings (based solely on the Saint-Venant equation or water balance). This invention upgrades the mapping relationship to a three-dimensional conjugate structure: on the basis of linear hydraulic connection (water transfer), a nonlinear water quality dispersion connection (controlled by the dispersion coefficient tensor of the convection-diffusion equation) and an economic-ecological value trade-off connection (controlled by the marginal utility substitution rate matrix) are superimposed. These three are linked by a conjugate causal strength matrix. Uniform encoding, its elements Simultaneously reflecting the physical influence direction, intensity, and response time characteristics between nodes, this invention optimizes the one-dimensional physical mapping into a multi-dimensional coupled mapping of hydrology, ecology, and economy. Traditional digital twin models are mostly unidirectional mappings (physical → virtual), while this invention... Embedded bidirectional feedback link This allows the model parameters to be continuously corrected online based on the measured data, achieving bidirectional dynamic alignment between the physical entity and the virtual model.

[0028] Specifically, step S2 includes steps S21 to S23: S21, extract the spatial location of each hydrological node, water conservancy project node, and water user node within the target watershed, as well as the connection relationship and scheduling affiliation relationship of the water user nodes, to obtain the physical topology layer. It should be noted that, through GIS spatial analysis and water conservancy project ledgers, the geographical locations, upstream and downstream connections, and management affiliations of entities such as reservoirs, ponds, canals, sluice gates, pumping stations, hydrological stations, and water intakes within the basin are clearly defined, forming a clear water system network map, i.e., the physical topology layer. This layer provides a spatial framework for subsequent dynamic parameter matching.

[0029] S22, extract the propagation delay time, friction loss coefficient and storage response elastic coefficient of the quantified water flow to form a multidimensional dynamic parameter matrix; It should be noted that, based on historical hydrological data and hydraulic models, for each river segment or channel with upstream and downstream connections, the average propagation delay time required for water flow to reach the downstream node (e.g., how many hours after upstream water release to reach the downstream section) is calculated; the proportion of flow attenuation caused by factors such as seepage, evaporation, and water intake along the route is statistically analyzed to obtain the flow attenuation coefficient; for lakes, reservoirs, and ponds with regulation and storage functions, the ratio of water level change amplitude to inflow rate when facing inflow fluctuations is analyzed to obtain the regulation and storage response elasticity coefficient. These parameters together constitute a multidimensional dynamic parameter matrix describing the laws of water flow movement.

[0030] S23, the physical topology layer and the multidimensional dynamic parameter matrix are associated and fused to construct the water network dynamic feature mirror map containing static structural information and dynamic response feature information; It should be noted that by linking and fusing the physical topology layer structure with the dynamic parameter matrix, each connection edge in the graph acquires dynamic properties such as delay, attenuation, and storage elasticity. This provides some physical content for the digital twin model, significantly improving the realism and reliability of subsequent simulations.

[0031] S3. Based on the multi-dimensional digital twin model and the prediction-driven look-ahead rolling simulation mechanism, and using a collaborative adaptive look-ahead time-domain compression strategy, multi-scenario dynamic simulation is performed to generate simulation results of the system's operating state in the future for multiple time periods. It should be explained that the prospective rolling optimization concept of introducing model predictive control (MPC) enables the scheduling system to have foresight, rather than simply relying on current measured data to make a passive response.

[0032] Specifically, step S3 includes steps S31 to S33: S31, set the baseline length of the rolling optimization time window and the baseline rolling step size; It needs to be explained that setting a fixed initial value, such as a baseline length, is important. =90 days (forecast window for the next three months), baseline rolling step =10 days (optimized again every 10 days). This is the initial reference for subsequent adaptive adjustments.

[0033] S32, Real-time evaluation of the prediction confidence curve of medium- and long-term runoff forecast data within the current time window, and dynamic calculation of the time-domain compression coefficient based on the prediction confidence curve. The calculation expression is as follows: ; In the formula, Represents the time-domain compression factor. express The confidence level of the prediction at any given time. , These represent the minimum and maximum prediction confidence levels, respectively. It should be noted that when the current prediction confidence level is close to the historical high, the compression coefficient approaches 0; when the confidence level is close to the historical low, the compression coefficient approaches 1. This design allows the length of the look-ahead window to be intelligently expanded or contracted according to the reliability of the forecast.

[0034] S33, dynamically adjust the reference length and the reference rolling step size according to the time-domain compression coefficient to obtain the adaptive look-ahead time domain, the expression of which is: ; In the formula, express The time-adjusted look-ahead time domain length, This indicates the baseline length of the rolling optimization time window. This represents the preset time-domain adjustment sensitivity coefficient; It should be noted that when the accuracy of meteorological and hydrological forecast information is high, the system adopts a shorter look-ahead window and concentrates computing resources for fine-grained decision-making. When forecast uncertainty increases (such as during periods of frequent extreme weather events), the system automatically lengthens the look-ahead window, increasing the safety redundancy space of the scheduling scheme, thereby achieving a synergistic optimal balance between computational efficiency and decision robustness. This forecast confidence-driven time-domain compression strategy is a key innovation of this invention, distinguishing it from traditional fixed-window rolling optimization.

[0035] S4. Based on the simulation results, the optimization objectives are to optimize the preset development index, the water supply and demand balance index, and the water resource spatial equilibrium coefficient. In addition, water supply resilience constraints under extreme conditions are introduced to construct an intelligent water resource scheduling adaptive optimization model that includes a multi-objective elastic coordination domain. It should be noted that the preset development index is the High-quality Development Index, which comprehensively evaluates the quality of regional development from four dimensions: social, economic, resource, and ecological. The water resource supply and demand balance index is the ratio of regional water demand to water supply, representing the overall balance. The water resource spatial equilibrium coefficient is used to quantify the dispersion of water allocation among sub-regions; the closer it is to 1, the more equitable the allocation.

[0036] In this embodiment, the functional expression of the joint optimization objective is: ; In the formula, This represents the value of the joint optimization objective function. , , , These represent the weight coefficients of the three different sub-objectives and the penalty term, respectively. Indicating a high-quality development index, This represents the regional water resources supply and demand balance index. Indicates the spatial balance coefficient of water resources. This indicates a multi-objective conflict penalty item. Represents a vector of decision variables; The boundary constraint expression for the multi-objective elastic coordination domain is: ; ; In the formula, Indicates the first The current function value of each sub-objective. Indicates the first The preset elastic relaxation coefficient for each sub-target has a range of values. , Indicates the first The optimal value of each sub-objective under ideal conditions.

[0037] It is worth noting that, in this embodiment, the expression for the intelligent water resource scheduling adaptive optimization model, which includes a multi-objective elastic coordination domain, is as follows: ; ; In the formula, This represents the overall operator of the intelligent water resource scheduling adaptive optimization model. Represents a vector of decision variables (water supply allocation from each water source to each water user, lake water level processes, pump station flow rates, etc.). This represents the multi-objective elastic coordination domain, which is the set of all feasible solutions that satisfy the boundary relaxation constraints and the toughness constraints. Indicates a penalty for multi-objective conflict; This indicates that for all sub-targets That is, the constraint must apply to every optimization sub-objective simultaneously; Indicates the maximum depth of water supply disruption under extreme conditions (activated only when entering extreme conditions). This represents the preset maximum permissible threshold for damage depth. The intelligent water resource scheduling adaptive optimization model, incorporating a multi-objective flexible coordination domain, achieves several key improvements over traditional water resource scheduling optimization. First, it introduces the concept of a flexible coordination domain, allowing each optimization objective to fluctuate within a tolerance range near its optimal value, significantly improving the engineering feasibility and social acceptability of the decision-making scheme. Second, it constructs a four-dimensional collaborative objective system integrating a high-quality development index, supply and demand balance, spatial equilibrium, and conflict penalties. Third, it designs a dynamic adaptive weight adjustment mechanism, enabling the model to automatically switch priorities based on regional supply and demand tensions and spatial allocation fairness. Fourth, it superimposes a hard constraint on water supply resilience under extreme conditions on top of the flexible domain. Through these improvements, the model, while maintaining optimal comprehensive benefits under normal operating conditions, significantly enhances its resilience against extreme climate events and its adaptability to different hydrological conditions.

[0038] It should be explained that in this embodiment, it is not required that all sub-objectives simultaneously reach their respective absolute optimal values ​​(which is often impossible in engineering). Instead, each sub-objective is allowed to fluctuate within an acceptable tolerance range around its optimal value, and this tolerance range is determined by the relaxation coefficient. All feasible solutions that satisfy the above boundary constraints together constitute a multi-objective flexible coordination domain. Within this domain, any scheduling scheme is considered an acceptable satisfactory solution, without the need to forcibly pursue a unique mathematically optimal solution. This effectively alleviates the Pareto conflict problem commonly encountered in multi-objective optimization, making the final decision scheme more engineering-feasible.

[0039] S5, The water resource intelligent scheduling adaptive optimization model is solved using an optimization algorithm to obtain an initial scheduling scheme; It should be noted that an improved adaptive differential evolution algorithm is used for the solution. The algorithm uses the ten-day water level process of each lake, the water intake flow of each pumping station, and the water intake flow of each branch gate as decision variables. Under the premise of satisfying water balance constraints, reservoir capacity constraints, flow capacity constraints, ecological flow constraints, and extreme condition resilience constraints, it searches for the combination of decision variables that maximizes the joint objective function value. To improve convergence speed and global search capability, the algorithm divides the population into three subpopulations—dominant, intermediate, and inferior—based on fitness, using different mutation factors and crossover probabilities for each subpopulation to achieve adaptive parameter control. The set of decision variable values ​​obtained from the solution constitutes the initial scheduling scheme.

[0040] S6. The initial scheduling scheme is virtually pre-simulated and evaluated using the multi-dimensional digital twin model. Based on the results of the pre-simulation and evaluation, the Pareto front solution set and multi-objective conflict degree of the initial scheduling scheme are calculated. The initial scheduling scheme is then modified after adjusting the multi-objective conflict degree to generate the final scheduling scheme. It should be noted that the initial scheduling scheme is placed into a digital twin model for high-precision simulation to model its execution effect in actual hydrological processes. Then, the performance of the scheme on each sub-objective is extracted, and the difference between it and its ideal optimal value is evaluated to calculate the multi-objective conflict degree. If the conflict degree exceeds a preset threshold, a fuzzy membership compromise mediation mechanism is activated: the non-dominated solution with the highest overall satisfaction is selected from the Pareto front solution set to replace the original initial solution. Through the process from virtual simulation to conflict assessment to compromise mediation, a scheduling scheme that satisfies engineering constraints and achieves a good balance among the objectives is finally obtained—the final scheduling scheme. This avoids directly applying the optimal solution of the mathematical model to actual engineering without verification, significantly reducing decision-making risk.

[0041] S7, transmit the final scheduling scheme to the execution terminal and collect execution feedback data in real time; It should be noted that, through the Industrial Internet of Things (IIoT) interface, data such as time-period gate opening commands, pump station start-up and shutdown plans, and water diversion flow targets from the final scheduling scheme are sent to various execution terminals (gate control system, pump station automation system, etc.). Simultaneously, sensors such as water level gauges, flow meters, and gate position gauges deployed at key nodes transmit actual execution data back in real time at minute or hourly frequencies, forming a feedback dataset that provides experimental basis for subsequent online model calibration.

[0042] S8. Periodically calculate the operational health index of the multi-dimensional digital twin model, determine whether the operational health index is lower than a preset threshold, and if so, trigger the twin cognitive autonomy evolution and fidelity self-healing mechanism of the multi-dimensional digital twin model, and update the multi-dimensional digital twin model according to the execution feedback data. It should be explained that, in this embodiment, the definition of the operational health index is: ; In the formula, Indicates the operational health index, Indicates the attenuation sensitivity coefficient. This represents the cumulative average absolute error between the simulated water level and the actual water level at key nodes over the most recent scheduling cycles. When the value is below a preset threshold (e.g., 0.75), it indicates that the simulation accuracy of the digital twin model has significantly decreased, and simple parameter tuning can no longer correct it, requiring the initiation of a deeper cognitive evolution.

[0043] Specifically, step S8 includes steps S81 to S87: S81, when the operational health index is lower than the preset threshold, locate the key deviation sub-region or key mutual feedback relationship chain with the largest deviation accumulation in the virtual mapping layer of the multi-dimensional digital twin model. In this embodiment, the multi-dimensional digital twin model includes a physical entity layer, a virtual mapping layer constructed based on multi-dimensional mutual feedback virtual conjugate mapping, and a dynamic simulation layer; It should be noted that when the operational health index is lower than the preset threshold, the model deviation source tracing and diagnosis program is launched. Through the deviation contribution quantification analysis of each twin agent object, the key sub-region or key mutual feedback relationship chain with the largest deviation accumulation in the virtual mapping layer is located. It should be explained that by ranking the deviation contribution of each twin agent object, the main areas that cause model inaccuracies (such as the largest deviation in the runoff simulation of a certain tributary) or the most critical multidimensional feedback relationship chains (such as the inaccurate simulation of the conflict between ecological water demand and agricultural water use downstream of a certain reservoir) are identified.

[0044] S82, for the located key deviation sub-region, based on the latest accumulated long-term sequence execution feedback data, the transfer learning method is used to identify the physical law drift in the key deviation sub-region; It should be noted that, for the identified key deviation sub-regions, based on the latest accumulated long-term series execution feedback data, transfer learning methods are used to identify elements in the key sub-regions where physical law drift has occurred. It should be explained that transfer learning utilizes existing domain knowledge (historical patterns) to assist the learning of new domains (patterns after recent changes), effectively identifying physical law drift caused by river erosion and deposition, land use changes, and adjustments to reservoir scheduling rules. For example, it may be discovered that the previously applicable water level-discharge relationship curve is no longer accurate, necessitating the introduction of a new functional form.

[0045] S83, based on the identification results of the physical law drift, re-estimate the causal strength coefficients corresponding to the multi-dimensional digital twin model, and correct the behavior rule set of the twin proxy object corresponding to the key deviation sub-region; It should be noted that, based on the identification results of physical law drift, the corresponding causal strength coefficients in the conjugate causal strength matrix are re-estimated, and the behavioral rule set of the twin proxy object in that sub-region is corrected. It should be explained that the causal strength coefficients are elements of the conjugate causal strength matrix in the virtual mapping layer, quantitatively describing the strength and direction of the influence between pairwise variables. When physical laws drift, these coefficients must be recalculated; simultaneously, the behavioral rules of the proxy object (such as scheduling functions and leakage rules) are modified accordingly to match the new laws.

[0046] S84, the modified set of behavioral rules and the re-estimated causal strength coefficients are re-integrated into the virtual mapping layer to complete the adaptive reconstruction of the cognitive logic of the virtual mapping layer; It should be noted that the modified set of behavioral rules and the updated conjugate causal strength matrix are reintegrated into the virtual mapping layer to complete the adaptive reconstruction of the cognitive logic of the virtual mapping layer; the updated local rules and coefficients are then re-embedded into the overall causal network of the virtual mapping layer to complete the process from local modification to global fusion. This operation is essentially a reconstruction of the cognitive logic of the digital twin model, rather than simply adjusting a few numerical parameters, thereby achieving cognitive autonomous evolution.

[0047] S85, collects in real time the gate opening, pump station operating status, water level changes and flow data of the execution terminal to generate an execution feedback dataset; S86, Perform a deviation analysis between the execution feedback dataset and the virtual pre-simulation results of the multi-dimensional digital twin model, and calculate the simulation deviation value of each key node; It should be noted that by continuously receiving the measured data transmitted back from the sensors, a complete execution record of the current scheduling cycle is formed. The differences between the simulated water level and the actual water level, and between the simulated flow rate and the actual flow rate within the same time period are compared, and the absolute deviation and relative deviation are calculated to provide input for subsequent filtering correction.

[0048] S87. Based on the simulated deviation value, the Kalman filter algorithm is used to perform online smooth correction on the key operating parameters in the digital twin model, and the results of the twin's cognitive autonomous evolution, the re-estimated causal strength coefficient, and the corrected behavioral rule set are updated to the multidimensional digital twin model to update the multidimensional digital twin model. It should be noted that Kalman filtering can effectively fuse observed values ​​and model predictions to achieve optimal recursive estimation of model parameters in noisy environments. By combining online smoothing correction (parameter-level update) with cognitive evolution results (structure and rule-level update), multi-level joint updates of the digital twin model are realized, ensuring both short-term accuracy and long-term adaptability.

[0049] In summary, the adaptive optimization method for intelligent water resource scheduling based on digital twins in the above embodiments of the present invention constructs a multi-dimensional digital twin model containing a mirror map of water network dynamic characteristics, introduces an adaptive time-domain compressed rolling simulation mechanism driven by forecast confidence, establishes a joint optimization model containing a multi-objective elastic coordination domain, and designs a self-evolving cognitive evolution and fidelity self-healing mechanism for the twin based on operational health indicators. This forms a closed-loop intelligent scheduling system integrating forward-looking prediction, flexible decision-making, virtual simulation, online execution, deviation feedback, and model self-healing. This method effectively improves the adaptability, fairness, resilience, and long-term fidelity of water resource scheduling in the face of climate change uncertainties and multi-objective conflicts, significantly outperforming existing static digital twins and fixed rolling optimization schemes.

[0050] Example 2 Please see Figure 2 The figure shows a digital twin-based intelligent water resource scheduling adaptive optimization system according to a second embodiment of the present invention. The system includes: The acquisition and processing module 10 is used to acquire hydrological, meteorological and comprehensive data of the target watershed and to preprocess the hydrological, meteorological and comprehensive data. The first construction module 20 is used to construct a water network dynamics feature mirror map of the target watershed based on the preprocessed hydrological, meteorological and comprehensive data, and to construct a multi-dimensional digital twin model of the water resources system based on the water network dynamics feature mirror map. The simulation module 30 is used to perform multi-scenario dynamic simulation based on the multi-dimensional digital twin model and the prediction-driven look-ahead rolling simulation mechanism, and to generate simulation results of the system's operating status in the future for multiple time periods by adopting a collaborative adaptive look-ahead time domain compression strategy. The second construction module 40 is used to construct an intelligent water resource scheduling adaptive optimization model containing a multi-objective elastic coordination domain, based on the simulation results, with the optimization of a preset development index, the optimal water resource supply and demand balance index, and the maximization of the water resource spatial equilibrium coefficient as joint optimization objectives, and by introducing water supply resilience constraints under extreme conditions; wherein, the functional expression of the joint optimization objective is: ; In the formula, This represents the value of the joint optimization objective function. , , , These represent the weight coefficients of the three different sub-objectives and the penalty term, respectively. Indicating a high-quality development index, This represents the regional water resources supply and demand balance index. Indicates the spatial balance coefficient of water resources. This indicates a multi-objective conflict penalty item. Represents a vector of decision variables; The boundary constraint expression for the multi-objective elastic coordination domain is: ; ; In the formula, Indicates the first The current function value of each sub-objective. Indicates the first The preset elastic relaxation coefficient for each sub-target has a range of values. , Indicates the first The optimal value of each sub-objective under ideal conditions; The solution module 50 is used to solve the intelligent water resource scheduling adaptive optimization model using an optimization algorithm to obtain an initial scheduling scheme; The calculation correction module 60 is used to perform virtual pre-playing and evaluation of the initial scheduling scheme through the multi-dimensional digital twin model, calculate the Pareto front solution set and multi-objective conflict degree of the initial scheduling scheme based on the results of the pre-playing and evaluation, and correct the initial scheduling scheme after adjusting the multi-objective conflict degree to generate the final scheduling scheme. The transmission and acquisition module 70 is used to transmit the final scheduling scheme to the execution terminal and collect execution feedback data in real time. The judgment update module 80 is used to periodically calculate the operational health index of the multi-dimensional digital twin model, determine whether the operational health index is lower than a preset threshold, and if so, trigger the twin cognitive autonomous evolution and fidelity self-healing mechanism of the multi-dimensional digital twin model, and update the multi-dimensional digital twin model according to the execution feedback data.

[0051] In some alternative embodiments, the first building module 20 includes: The first extraction unit is used to extract the spatial location of each hydrological node, water conservancy project node, and water user node within the target watershed, as well as the connection relationship and scheduling affiliation of the water user nodes, in order to obtain the physical topology layer. The second extraction unit is used to extract the propagation delay time, friction loss coefficient, and storage response elastic coefficient of the quantified water flow to form a multidimensional dynamic parameter matrix. The fusion construction unit is used to associate and fuse the physical topology layer with the multidimensional dynamic parameter matrix to construct the water network dynamic feature mirror map containing static structural information and dynamic response feature information.

[0052] In some alternative embodiments, the simulation module 30 includes: The setting unit is used to set the baseline length of the rolling optimization time window and the baseline rolling step size; The evaluation unit is used to evaluate the prediction confidence curve of medium- and long-term runoff prediction data within the current time window in real time, and dynamically calculate the time-domain compression coefficient based on the prediction confidence curve. The calculation expression is as follows: ; In the formula, Represents the time-domain compression factor. express The confidence level of the prediction at any given time. , These represent the minimum and maximum prediction confidence levels, respectively. The adjustment unit is used to dynamically adjust the reference length and the reference rolling step size according to the time-domain compression coefficient to obtain an adaptive look-ahead time domain, expressed as: ; In the formula, express The time-adjusted look-ahead time domain length, This indicates the baseline length of the rolling optimization time window. This represents the preset time-domain adjustment sensitivity coefficient.

[0053] In some optional embodiments, the determination update module 80 includes: The positioning unit is used to locate the key deviation sub-region or key mutual feedback relationship chain with the largest accumulated deviation in the virtual mapping layer of the multi-dimensional digital twin model when the operating health index is lower than the preset threshold. The identification unit is used to identify the physical law drift in the key deviation sub-region based on the latest accumulated long-term series feedback data and by using a transfer learning method for the located key deviation sub-region. The correction is applied to which element, which is used to re-estimate the causal strength coefficient corresponding to the multi-dimensional digital twin model based on the identification result of the drift of the physical law, and to correct the behavior rule set of the twin agent object corresponding to the key deviation sub-region; The fusion unit is used to re-fuse the modified set of behavioral rules and the re-estimated causal strength coefficients into the virtual mapping layer to complete the adaptive reconstruction of the cognitive logic of the virtual mapping layer. The data acquisition unit is used to collect data on the gate opening, pump station operating status, water level changes, and flow rate of the execution terminal in real time, so as to generate an execution feedback dataset. The analysis unit is used to perform deviation analysis between the execution feedback dataset and the virtual pre-simulation results of the multi-dimensional digital twin model, and to calculate the simulation deviation value of each key node. The update unit is used to perform online smooth correction of key operating parameters in the digital twin model based on the simulated deviation value using the Kalman filter algorithm, and update the results of the twin's cognitive autonomy evolution, the re-estimated causal strength coefficient, and the corrected behavioral rule set to the multidimensional digital twin model, so as to update the multidimensional digital twin model.

[0054] The functions or operation steps implemented by the above modules and units are largely the same as those in the above method embodiments, and will not be repeated here.

[0055] The water resource intelligent scheduling adaptive optimization system based on digital twin provided in this invention has the same implementation principle and technical effects as the aforementioned method embodiments. For the sake of brevity, any parts not mentioned in the system embodiments can be referred to the corresponding content in the aforementioned method embodiments.

[0056] Example 3 The third embodiment of the present invention also proposes an electronic device, please refer to [link / reference]. Figure 3 The image shows an electronic device according to a third embodiment of the present invention.

[0057] The electronic device may include a processor 91 and a memory 92 storing computer program instructions.

[0058] Specifically, the processor 91 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement this application.

[0059] The memory 92 may include a mass storage device for data or instructions. For example, and not limitingly, the memory 92 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 92 may include removable or non-removable (or fixed) media. Where appropriate, the memory 92 may be internal or external to a data processing device. In a particular embodiment, the memory 92 is non-volatile memory. In a particular embodiment, the memory 92 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0060] The memory 92 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 91.

[0061] The processor 91 reads and executes the computer program instructions stored in the memory 92 to implement the adaptive optimization method for intelligent water resource scheduling based on digital twins in the first embodiment described above.

[0062] In some embodiments, the electronic device may further include a communication interface 93 and a bus 90. For example, Figure 3 As shown, the processor 91, memory 92, and communication interface 93 are connected through bus 90 and complete communication with each other.

[0063] The communication interface 93 is used to enable communication between the various modules, devices, units, and / or equipment in this application. The communication interface 93 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0064] Bus 90 includes hardware, software, or both, that couples components of a device together. Bus 90 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 90 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 90 may include one or more buses. Although this application describes and illustrates a specific bus, this application considers any suitable bus or interconnection.

[0065] The electronic device can acquire a digital twin-based intelligent water resource scheduling adaptive optimization system and execute the digital twin-based intelligent water resource scheduling adaptive optimization method of this embodiment.

[0066] Furthermore, in conjunction with the adaptive optimization method for intelligent water resource scheduling based on digital twins in Embodiment 1 above, this application can provide a storage medium for implementation. This storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement the adaptive optimization method for intelligent water resource scheduling based on digital twins in Embodiment 1 above.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A digital twin-based intelligent water resource scheduling adaptive optimization method, characterized in that, The method includes: Acquire hydrological, meteorological, and comprehensive data of the target watershed, and preprocess the hydrological, meteorological, and comprehensive data; Based on the preprocessed hydrological, meteorological, and integrated data, a water network dynamics feature mirror map of the target watershed is constructed, and a multi-dimensional digital twin model of the water resources system is constructed based on the water network dynamics feature mirror map. Based on the aforementioned multi-dimensional digital twin model and the introduction of a prediction-driven look-ahead rolling simulation mechanism, and employing a collaborative adaptive look-ahead time-domain compression strategy, multi-scenario dynamic simulation is performed to generate simulation results of the system's operating state in future multi-period periods. Based on the simulation results, the optimization objectives are to optimize the preset development index, the water supply and demand balance index, and the water resource spatial equilibrium coefficient. In addition, water supply resilience constraints under extreme conditions are introduced to construct an intelligent water resource scheduling adaptive optimization model that includes a multi-objective elastic coordination domain. An optimization algorithm is used to solve the adaptive optimization model for intelligent water resource scheduling to obtain an initial scheduling scheme; The initial scheduling scheme is virtually pre-simulated and evaluated using the multi-dimensional digital twin model. Based on the results of the pre-simulation and evaluation, the Pareto front solution set and multi-objective conflict degree of the initial scheduling scheme are calculated. The initial scheduling scheme is then modified after adjusting the multi-objective conflict degree to generate the final scheduling scheme. The final scheduling scheme is transmitted to the execution terminal, and execution feedback data is collected in real time. The operational health index of the multi-dimensional digital twin model is periodically calculated to determine whether the operational health index is lower than a preset threshold. If so, the twin cognitive autonomy evolution and fidelity self-healing mechanism of the multi-dimensional digital twin model is triggered, and the multi-dimensional digital twin model is updated according to the execution feedback data.

2. The adaptive optimization method for intelligent water resource scheduling based on digital twins according to claim 1, characterized in that, The steps of constructing a mirror map of the hydrodynamic characteristics of the target watershed based on the preprocessed hydrological, meteorological, and integrated data include: Extract the spatial locations of each hydrological node, water conservancy project node, and water user node within the target watershed, as well as the connection relationships and scheduling affiliations of the water user nodes, to obtain the physical topology layer; The propagation delay time, friction loss coefficient, and storage response elastic coefficient of the quantified water flow are extracted to form a multidimensional dynamic parameter matrix; The physical topology layer and the multidimensional dynamic parameter matrix are correlated and fused to construct a water network dynamic feature mirror map containing static structural information and dynamic response feature information.

3. The adaptive optimization method for intelligent water resource scheduling based on digital twins according to claim 1, characterized in that, Before the step of employing a collaborative adaptive look-ahead time-domain compression strategy for multi-scenario dynamic simulation, the method further includes: Set the baseline length of the rolling optimization time window and the baseline rolling step size; The prediction confidence curve of medium- and long-term runoff forecast data within the current time window is evaluated in real time, and the time-domain compression coefficient is dynamically calculated based on the prediction confidence curve. The calculation expression is as follows: ； In the formula, Represents the time-domain compression factor. express The confidence level of the prediction at any given time. , These represent the minimum and maximum prediction confidence levels, respectively. The reference length and the reference rolling step size are dynamically adjusted based on the time-domain compression coefficient to obtain an adaptive look-ahead time domain, expressed as: ； In the formula, express The time-adjusted look-ahead time domain length, This indicates the baseline length of the rolling optimization time window. This represents the preset time-domain adjustment sensitivity coefficient.

4. The adaptive optimization method for intelligent water resource scheduling based on digital twins according to claim 1, characterized in that, The functional expression of the joint optimization objective is: ； In the formula, This represents the value of the joint optimization objective function. , , , These represent the weight coefficients of the three different sub-objectives and the penalty term, respectively. Indicating a high-quality development index, This represents the regional water resources supply and demand balance index. This represents the spatial balance coefficient of water resources. This indicates a multi-objective conflict penalty item. This represents a vector of decision variables.

5. The adaptive optimization method for intelligent water resource scheduling based on digital twins according to claim 1, characterized in that, The boundary constraint expression for the multi-objective elastic coordination domain is: ； ； In the formula, Indicates the first The current function value of each sub-objective. Indicates the first The preset elastic relaxation coefficient for each sub-target has a range of values. , Indicates the first The optimal value of each sub-objective under ideal conditions.

6. The adaptive optimization method for intelligent water resource scheduling based on digital twins according to claim 1, characterized in that, Before the step of triggering the self-evolution and fidelity self-healing mechanism of the multi-dimensional digital twin model's cognitive processes, the method further includes: When the operational health index is lower than the preset threshold, locate the key deviation sub-region or key mutual feedback relationship chain with the largest accumulated deviation in the virtual mapping layer of the multi-dimensional digital twin model. For the identified key deviation sub-regions, based on the latest accumulated long-term series execution feedback data, a transfer learning method is used to identify the physical law drift in the key deviation sub-regions; Based on the identification results of the physical law drift, the corresponding causal strength coefficient in the multi-dimensional digital twin model is re-estimated, and the behavioral rule set of the twin proxy object corresponding to the key deviation sub-region is corrected. The modified set of behavioral rules and the re-estimated causal strength coefficients are then reintegrated into the virtual mapping layer to complete the adaptive reconstruction of the cognitive logic of the virtual mapping layer.

7. The adaptive optimization method for intelligent water resource scheduling based on digital twins according to claim 1, characterized in that, The step of updating the multi-dimensional digital twin model based on the execution feedback data includes: The gate opening, pump station operating status, water level changes, and flow rate data of the execution terminal are collected in real time to generate an execution feedback dataset. The execution feedback dataset is compared with the results of the virtual pre-simulation of the multi-dimensional digital twin model to perform a deviation analysis and calculate the simulation deviation value of each key node. Based on the simulated deviation value, the Kalman filter algorithm is used to perform online smoothing correction on the key operating parameters in the digital twin model, and the results of the twin's cognitive autonomy evolution, the re-estimated causal strength coefficient, and the corrected behavioral rule set are updated in the multidimensional digital twin model to update the multidimensional digital twin model.

8. A water resource intelligent scheduling adaptive optimization system based on digital twins, characterized in that, The system includes: The acquisition and processing module is used to acquire hydrological, meteorological, and comprehensive data of the target watershed and to preprocess the hydrological, meteorological, and comprehensive data. The first construction module is used to construct a water network dynamics feature mirror map of the target watershed based on the preprocessed hydrological, meteorological and comprehensive data, and to construct a multi-dimensional digital twin model of the water resources system based on the water network dynamics feature mirror map. The simulation module is used to perform multi-scenario dynamic simulation based on the multi-dimensional digital twin model and the prediction-driven look-ahead rolling simulation mechanism, and to generate simulation results of the system's operating status in the future for multiple time periods by adopting a collaborative adaptive look-ahead time domain compression strategy. The second construction module is used to construct an intelligent water resource scheduling adaptive optimization model that includes a multi-objective elastic coordination domain, based on the simulation results, with the optimization objectives being the optimal preset development index, the optimal water resource supply and demand balance index, and the maximization of the water resource spatial equilibrium coefficient. It also introduces water supply resilience constraints under extreme working conditions. The solution module is used to solve the intelligent water resource scheduling adaptive optimization model using an optimization algorithm to obtain an initial scheduling scheme; The calculation correction module is used to perform virtual pre-simulation and evaluation of the initial scheduling scheme through the multi-dimensional digital twin model, calculate the Pareto front solution set and multi-objective conflict degree of the initial scheduling scheme based on the results of the pre-simulation and evaluation, and correct the initial scheduling scheme after adjusting the multi-objective conflict degree to generate the final scheduling scheme. The transmission and acquisition module is used to transmit the final scheduling scheme to the execution terminal and collect execution feedback data in real time. The judgment update module is used to periodically calculate the operational health index of the multi-dimensional digital twin model, determine whether the operational health index is lower than a preset threshold, and if so, trigger the twin cognitive autonomous evolution and fidelity self-healing mechanism of the multi-dimensional digital twin model, and update the multi-dimensional digital twin model according to the execution feedback data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the adaptive optimization method for intelligent water resource scheduling based on digital twins as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the adaptive optimization method for intelligent water resource scheduling based on digital twins as described in any one of claims 1 to 7.

Images