Reservoir scale intelligent optimization demonstration system and method based on multi-target risk coupling

Abstract

The application is named reservoir scale intelligent optimization demonstration system and method based on multi-target risk coupling, and belongs to the technical field of intelligent decision-making of water conservancy and hydropower engineering. The technical problem to be solved is that the traditional reservoir scale demonstration relies on static scenarios, cannot quantize the multi-target conflict coupling relationship, does not systematically consider multiple uncertainties and risks, has low optimization efficiency and insufficient scheme robustness. The technical solution points are that the application sets six modules of hydrological risk modeling, multi-target coupling analysis, scale optimization decision, uncertainty propagation simulation, comprehensive resilience evaluation and intelligent demonstration integration, generates dynamic hydrological risk indexes by fusing climate scenarios and historical data, quantizes the multi-target coupling relationship, optimizes the reservoir scale parameters by combining reinforcement learning and risk constraint, and automatically generates the optimal reservoir scale demonstration scheme with high robustness through uncertainty simulation and resilience evaluation.

Description

Technical Field

[0001] This invention belongs to the field of intelligent decision-making technology for water conservancy and hydropower projects, specifically involving an intelligent optimization demonstration system and method for reservoir scale based on multi-objective risk coupling. Background Technology

[0002] As a key infrastructure for water resource regulation and flood control, the scale and design of reservoir projects are directly related to the achievement of multiple objectives, including water supply security, ecological protection, flood control capacity, and economic benefits.

[0003] Traditional methods for demonstrating the scale of reservoirs are mostly based on single objectives or static scenario analysis. They typically use empirical formulas, historical data backtesting, or deterministic optimization models to compare different options, which makes it difficult to fully reflect the uncertainties under complex hydrological and meteorological conditions and the conflicts and coupling relationships between multiple objectives.

[0004] In recent years, although some studies have introduced multi-objective optimization algorithms (such as NSGA-II and MOPSO) to coordinate the optimization of parameters such as reservoir capacity and scheduling rules, most of them have not systematically considered the multiple risk factors brought about by climate change, frequent extreme events, and dynamic socio-economic evolution. In particular, they lack quantitative modeling of the interaction mechanisms of flood control safety risks, water shortage risks, and ecological degradation risks under different scale scenarios. In addition, existing methods still have problems such as poor model adaptability, low computational efficiency, and weak human-computer interaction in terms of risk characterization, objective weight allocation, and intelligent decision support, making it difficult to support scientific decision-making under high-dimensional, nonlinear, and strongly coupled conditions.

[0005] In view of this, the present invention is hereby proposed. Summary of the Invention

[0006] To address the aforementioned technical problems in existing technologies, this invention provides an intelligent optimization demonstration system and method for reservoir size based on multi-objective risk coupling. This system solves the problems of traditional reservoir size demonstration relying on static scenarios, difficulty in quantifying multi-objective conflict coupling relationships, failure to systematically consider multiple uncertainties and risks, low optimization efficiency, and insufficient robustness of the solution.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] The first aspect is a reservoir scale intelligent optimization demonstration system based on multi-objective risk coupling, including: The hydrological risk modeling module is used to identify key hydrological risk factors affecting reservoir safety and benefits by integrating historical hydrological and meteorological data and future climate scenario data, combining extreme precipitation, drought frequency and runoff variation characteristics, and generating dynamic hydrological risk indicators through multidimensional probability density function fitting. The multi-objective coupling analysis module is used to construct a matrix of conflict and synergy relationships between objectives by combining the dynamic hydrological risk indicators with multi-dimensional objective functions of water supply, flood control, ecology and power generation. The Pareto front tracking algorithm is used to quantify the coupling strength of objectives and obtain a multi-objective risk coupling map. The scale optimization decision module is used to dynamically adjust the reservoir scale parameter space based on the multi-objective risk coupling map using a reinforcement learning framework, introduce risk tolerance constraints, optimize the three-dimensional balance point of reservoir capacity, benefits and risks, and generate a candidate set for reservoir scale optimization. The uncertainty propagation simulation module is used to employ the aforementioned candidate set for reservoir size optimization, integrate a Monte Carlo-stochastic differential equation hybrid model, simulate the propagation path of multiple uncertainties in the system (hydrological, engineering, and socio-economic), and construct a risk transmission network. The comprehensive resilience assessment module is used to utilize the risk transmission network to quantify the functional maintenance capability and recovery speed of different scale schemes under disturbance through the coupled calculation of node vulnerability index and system resilience index, and generate a reservoir system resilience score. The intelligent demonstration integration module is used to compare the matching degree of each candidate scheme with the preset resilience threshold based on the resilience score of the reservoir system, through an AI-driven multi-criteria decision fusion mechanism, automatically generate the optimal reservoir size demonstration report, evaluate the robustness and computational efficiency of the demonstration process, and obtain the final demonstration evaluation result.

[0009] Furthermore, the dynamic hydrological risk indicators include the distribution of extreme event recurrence periods, runoff variability coefficient, and probability of seasonal drought persistence; The multi-objective risk coupling map includes an inter-objective trade-off surface, conflict hotspot region identification, and synergistic gain potential region. The candidate set for reservoir size optimization includes reservoir capacity range, dead water level setting range, and flood discharge capacity configuration combination. The risk transmission network includes a node connection structure for hydrological input disturbances, engineering response delays, and social feedback loops. The reservoir system resilience score includes functional degradation rate, recovery time constant, and multi-objective collaborative stability index. The final evaluation results include the ranking of optimal solutions, verification of compliance with risk thresholds, and analysis of the time and accuracy of intelligent evaluation.

[0010] Furthermore, the hydrological risk modeling module includes: The extreme event identification submodule is used to extract extreme event sequences such as rainstorms and droughts based on historical hydrological and meteorological data, fit their probability characteristics using a generalized extreme value distribution, calculate peak flow and minimum runoff under different return periods, and generate an extreme event statistics table. The variability quantification submodule is used to quantify the nonstationarity of interannual and intra-annual runoff processes by combining the extreme event statistics table with the sliding window entropy analysis method, calculate the coefficient of variation and the location of abrupt change points, and obtain the hydrological process variability index. The risk factor fusion submodule is used to construct a multi-source risk superposition model by using the hydrological process variability index and fusing the RCP scenario weights for climate change. It also uses the Copula function to characterize the tail dependency structure between variables and generate dynamic hydrological risk indicators.

[0011] Furthermore, the multi-objective coupling analysis module includes: The objective function construction submodule is used to establish a normalized multi-objective function set by taking water supply guarantee rate, flood control peak reduction ratio, ecological base flow satisfaction and annual power generation as basic objectives, introducing fuzzy membership degree to handle the difference in objective dimensions, and forming a standardized objective vector. The conflict and cooperation analysis submodule is used to calculate the Spearman rank correlation coefficient and Shapley interaction value between target pairs using the standardized target vector, identify strong conflict and strong cooperation target pairs, and generate a target coupling strength matrix. The Pareto front tracking submodule is used to perform high-dimensional multi-objective optimization using the target coupling strength matrix and the improved NSGA-Ⅲ algorithm, dynamically track the non-dominated solution set, visualize the target trade-off boundary, and form a multi-objective risk coupling map.

[0012] Furthermore, the comprehensive fitness function formula for the reference point-guided selection mechanism used in the improved NSGA-Ⅲ algorithm is as follows:

[0013] in, Comprehensive fitness function, To optimize the total number of targets, For the first The dynamic weights of each target are determined inversely proportional to the target coupling strength. For the normalized first The objective function value, The penalty coefficient is... To constrain the degree of violation, that is, the exceeding of the limit for hydrological risk indicators beyond the risk tolerance.

[0014] Furthermore, the scale optimization decision module includes: The parameter space exploration submodule is used to define reservoir capacity, normal water level, dead water level and number of floodgates as adjustable parameters based on the multi-objective risk coupling map, construct a continuous-discrete hybrid parameter space, and introduce a Bayesian optimization surrogate model to accelerate the search. The risk constraint embedding submodule is used to transform risk indicators into inequality constraints by using the flood risk tolerance and water shortage risk limit set by the user, and embed them into the reward function of reinforcement learning to form a risk-aware policy gradient. The candidate set generation submodule is used to iteratively generate feasible solutions that satisfy multi-objective balance in the parameter space using the risk-aware policy gradient, and outputs a candidate set for reservoir size optimization after non-dominated sorting.

[0015] Furthermore, the uncertainty propagation simulation module includes: The perturbation injection submodule is used to apply random perturbations to the inflow runoff, evaporation loss, water demand prediction and equipment failure rate based on the candidate set optimized by the reservoir size, and generate a multi-scenario input sample set. The hybrid simulation submodule is used to simulate the state transitions and control responses during reservoir operation by employing Monte Carlo sampling-driven stochastic differential equation models, record the time-series trajectories of key performance indicators, and construct a system state evolution database. The network construction submodule is used to identify the dominant propagation path of disturbances from input to output based on the system state evolution database, using Granger causality test and information flow analysis, and to establish a risk transmission network that includes delay effects.

[0016] Furthermore, the comprehensive resilience assessment module includes: The vulnerability scoring submodule is used to calculate the magnitude and duration of functional loss of each node under disturbance based on the risk transmission network, introduce conditional value at risk (CVaR) to measure tail loss, and generate a node vulnerability index. The resilience submodule is used to calculate the time required for the system to recover to 90% of the baseline level by simulating the system's adaptive scheduling capability after an impact, and to form a system resilience score by combining the scheduling flexibility index. The resilience fusion submodule is used to integrate the node vulnerability index and the system resilience score. It uses Dempster-Shafer evidence theory to fuse multi-source assessment information and generate a reservoir system resilience score that covers robustness, redundancy and adaptability.

[0017] Furthermore, the intelligent argumentation integration module includes: The threshold matching submodule is used to set the function maintenance rate ≥85%, recovery time ≤6 months, and multi-objective collaborative stability index ≥0.7 as preset resilience thresholds based on the resilience score of the reservoir system, calculate the compliance distance of each candidate scheme, and generate a threshold compliance matrix. The multi-criteria decision-making submodule is used to employ an improved TOPSIS-entropy weight fusion algorithm, combining expert preference weights and data-driven weights, to comprehensively rank candidate solutions and output the optimal size solution and confidence interval. The submodule for evaluating the effectiveness of the argumentation is used to record the time, convergence algebra, and sensitivity indicators of the entire process from data input to scheme output. By comparing with traditional trial calculation methods, it quantifies the efficiency improvement ratio and robustness gain of intelligent argumentation and obtains the final argumentation evaluation results.

[0018] Secondly, the intelligent optimization demonstration method for reservoir size based on multi-objective risk coupling is applied to the aforementioned intelligent optimization demonstration system for reservoir size based on multi-objective risk coupling, including: S1. Collect historical hydrological and meteorological data and future climate scenario data, identify extreme events and quantify runoff variability, integrate multi-source risk factors, and generate dynamic hydrological risk indicators. S2. Based on the dynamic hydrological risk indicators, construct a multi-objective function for water supply, flood control, ecology and power generation, analyze the conflict and synergy among the objectives, and draw a multi-objective risk coupling map. S3. Using the multi-objective risk coupling map, embed risk tolerance constraints, optimize the reservoir scale parameters through reinforcement learning, and generate a set of candidate schemes that satisfy the balance of multiple objectives. S4. Based on the candidate scheme set, simulate the propagation path of hydrological and engineering uncertainties in the system, construct a risk transmission network, evaluate the functional maintenance and recovery capabilities of each scheme under disturbance, and generate a reservoir system resilience score. S5. Based on the reservoir system resilience score, compare the matching degree between the selected scheme and the preset resilience threshold, integrate the multi-criteria decision results, automatically generate the optimal reservoir size demonstration report, evaluate the efficiency and robustness of the demonstration process, and obtain the final demonstration evaluation result.

[0019] The beneficial effects of this invention are as follows: (1) By integrating historical data and future climate scenarios through the hydrological risk modeling module, multidimensional risk factors such as extreme precipitation, drought frequency and runoff variability are quantified, and dynamic hydrological risk indicators are generated. This overcomes the shortcomings of traditional methods that rely on static scenarios and ignore the impact of climate change, and significantly improves the risk identification and characterization capabilities of reservoir scale demonstration. (2) Through the multi-objective coupling analysis module, multi-dimensional objective functions such as water supply, flood control, ecology and power generation are constructed. Combined with the Pareto front tracking algorithm, the conflict and synergy between objectives are quantified, and a multi-objective risk coupling map is formed. This realizes scientific trade-offs and synergistic optimization under complex objective system, and enhances the systematicness and coordination of decision-making. (3) Based on reinforcement learning and risk tolerance constraints, the scale optimization decision module dynamically adjusts the reservoir scale parameters, finds the optimal balance between reservoir capacity, benefits and risks, and generates a high-quality candidate scheme set, which greatly improves the optimization efficiency and scheme feasibility, and reduces the blindness and time consumption of manual trial calculation; (4) By simulating the propagation path of multiple uncertainties in the system through the Monte Carlo-stochastic differential equation hybrid model, a risk transmission network is constructed. Combined with node vulnerability and system resilience indicators, the functional maintenance and recovery capabilities of different schemes under disturbance are quantified, providing resilience support for the long-term stable operation of the reservoir system. (5) Relying on the intelligent argumentation integration module, the entire chain of automated processing from data input, scheme generation, resilience assessment to report output is realized. Combined with the AI ​​multi-criteria decision-making mechanism, the optimal scheme is automatically recommended and the argumentation efficiency and robustness are evaluated, which significantly improves the scientific nature, transparency and decision credibility of the argumentation process. Attached Figure Description

[0020] Figure 1 This is an architecture diagram of a smart reservoir scale optimization demonstration system based on multi-objective risk coupling provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the internal structure of the hydrological risk modeling module provided in an embodiment of the present invention; Figure 3 A flowchart illustrating the workflow of the multi-objective coupling analysis module provided in this embodiment of the invention; Figure 4 A schematic diagram illustrating the combination of the uncertainty propagation simulation module and the comprehensive resilience assessment module provided in this embodiment of the invention; Figure 5 This is a functional composition diagram of the intelligent argumentation integration module provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0022] It should be noted that, unless otherwise specifically stated, the relative arrangement and numerical expressions of the components and steps described in these embodiments should not be construed as limiting the scope of the invention.

[0023] The following description of exemplary embodiments is merely illustrative and is not intended to limit the invention or its application or use in any way. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail herein, but where applicable, such techniques, methods, and apparatus should be considered part of this specification.

[0024] Example 1 See Figure 1 , Figure 1This is an architecture diagram of the intelligent optimization demonstration system for reservoir scale based on multi-objective risk coupling proposed in this invention, which may specifically include: M1, the hydrological risk modeling module, is used to identify key hydrological risk factors affecting reservoir safety and benefits by integrating historical hydrological and meteorological data with future climate scenario data, and combining extreme precipitation, drought frequency, and runoff variability characteristics through multidimensional probability density function fitting, and generating dynamic hydrological risk indicators; see reference. Figure 2 Specifically, it includes: M11, the extreme event identification submodule, is used to extract extreme event sequences such as rainstorms and droughts based on historical hydrological and meteorological data, fit their probability characteristics using a generalized extreme value distribution, calculate peak flow and minimum runoff under different return periods, and generate an extreme event statistics table. M12, the variability quantification submodule, is used to quantify the nonstationarity of interannual and intra-annual runoff processes by combining the extreme event statistics table with the sliding window entropy analysis method, calculate the coefficient of variation and the location of abrupt change points, and obtain the hydrological process variability index. M13, the risk factor fusion submodule, is used to utilize the hydrological process variability index, fuse the climate change RCP scenario weights, construct a multi-source risk superposition model, characterize the tail dependency structure between variables through the Copula function, and generate dynamic hydrological risk indicators.

[0025] Furthermore, dynamic hydrological risk indicators include the distribution of extreme event recurrence periods, runoff variability coefficient, and probability of seasonal drought persistence; multi-objective risk coupling maps include the trade-off surface between objectives, the identification of conflict hotspot areas, and potential areas for synergistic gains; the candidate set for reservoir scale optimization includes reservoir capacity ranges, dead water level setting ranges, and combinations of flood discharge capacity configurations; the risk transmission network includes the node connection structure of hydrological input disturbances, engineering response delays, and social feedback loops; the reservoir system resilience score includes the functional degradation rate, recovery time constant, and multi-objective synergistic stability index; and the final demonstration and evaluation results include the ranking of optimal schemes, verification of risk threshold compliance, and analysis of the time consumption and accuracy of intelligent demonstration.

[0026] M2, the multi-objective coupling analysis module, is used to construct a matrix of conflict and synergy relationships between objectives by combining the dynamic hydrological risk indicators with multi-dimensional objective functions of water supply, flood control, ecology, and power generation. The Pareto front tracking algorithm is used to quantify the coupling strength of the objectives, resulting in a multi-objective risk coupling map; see reference. Figure 3 Specifically, it includes: M21, Objective Function Construction Submodule, is used to establish a normalized multi-objective function set by using water supply guarantee rate, flood control peak reduction ratio, ecological base flow satisfaction and average annual power generation as basic objectives, introducing fuzzy membership degree to handle the difference in objective dimensions, and forming a standardized objective vector. M22, Conflict Co-operation Analysis Submodule, is used to calculate the Spearman rank correlation coefficient and Shapley interaction value between target pairs through the standardized target vector, identify strong conflict and strong co-operation target pairs, and generate a target coupling strength matrix; M23, the Pareto front tracking submodule, is used to perform high-dimensional multi-objective optimization using the target coupling strength matrix and the improved NSGA-Ⅲ algorithm, dynamically track the non-dominated solution set, visualize the target trade-off boundary, and form a multi-objective risk coupling map.

[0027] The comprehensive fitness function formula for the reference point-guided selection mechanism used in the improved NSGA-Ⅲ algorithm is as follows:

[0028] in, Comprehensive fitness function, To optimize the total number of targets, For the first The dynamic weights of each target are determined inversely proportional to the target coupling strength. For the normalized first The objective function value, The penalty coefficient is... To constrain the degree of violation, that is, the exceeding of the limit for hydrological risk indicators beyond the risk tolerance.

[0029] M3, the scale optimization decision module, is used to dynamically adjust the reservoir scale parameter space based on the multi-objective risk coupling map using a reinforcement learning framework, introduce risk tolerance constraints, optimize the three-dimensional balance point of reservoir capacity, benefits, and risks, and generate a candidate set for reservoir scale optimization; specifically including: M31, Parameter Space Exploration Submodule, is used to define reservoir capacity, normal water level, dead water level and number of flood discharge gates as adjustable parameters according to the multi-objective risk coupling map, construct a continuous-discrete hybrid parameter space, and introduce a Bayesian optimization surrogate model to accelerate the search. M32, the risk constraint embedding submodule, is used to transform risk indicators into inequality constraints by using the flood risk tolerance and water shortage risk limit set by the user, and embed them into the reward function of reinforcement learning to form a risk-aware policy gradient. M33, the candidate set generation submodule, is used to iteratively generate feasible solutions that satisfy multi-objective balance in the parameter space using the risk-aware policy gradient, and outputs a candidate set for reservoir size optimization after non-dominated sorting.

[0030] M4, the uncertainty propagation simulation module, is used to employ the aforementioned reservoir size optimization candidate set, integrate a Monte Carlo-stochastic differential equation hybrid model, simulate the propagation path of multiple uncertainties (hydrological, engineering, and socioeconomic) in the system, and construct a risk transmission network; see reference. Figure 4 Specifically, it includes: M41, the disturbance injection submodule, is used to apply random disturbances to the inflow runoff, evaporation loss, water demand prediction and equipment failure rate respectively based on the candidate set for reservoir scale optimization, and generate a multi-scenario input sample set; M42, the hybrid simulation submodule, is used to simulate the state transitions and control responses during reservoir scheduling using a Monte Carlo sampling-driven stochastic differential equation model, record the time-series trajectories of key performance indicators, and construct a system state evolution database. M43, Network Construction Submodule, is used to identify the dominant propagation path of disturbances from input to output based on the system state evolution database, using Granger causality test and information flow analysis, and to establish a risk transmission network that includes delay effects.

[0031] M5, the comprehensive resilience assessment module, is used to quantify the functional maintenance capability and recovery speed of different scale schemes under disturbances by coupling the node vulnerability index and the system resilience index through the risk transmission network, and to generate a reservoir system resilience score; specifically including: M51, Vulnerability Scoring Submodule, is used to calculate the magnitude and duration of functional loss of each node under disturbance based on the risk transmission network, introduce Conditional Value at Risk (CVaR) to measure tail loss, and generate node vulnerability index. M52, the recovery force submodule, is used to calculate the time required for the system to recover to 90% of the baseline level by simulating the system's adaptive scheduling capability after an impact, and to form a system resilience score by combining the scheduling flexibility index. M53, the resilience fusion submodule, is used to integrate the node vulnerability index and the system resilience score. It uses Dempster-Shafer evidence theory to fuse multi-source assessment information and generate a reservoir system resilience score that covers robustness, redundancy, and adaptability.

[0032] M6, the intelligent demonstration and integration module, is used to compare the matching degree of each candidate scheme with the preset resilience threshold based on the resilience score of the reservoir system, through an AI-driven multi-criteria decision fusion mechanism, automatically generate an optimal reservoir size demonstration report, evaluate the robustness and computational efficiency of the demonstration process, and obtain the final demonstration evaluation result; see reference. Figure 5 Specifically, it includes: M61, Threshold Matching Submodule, is used to set the function maintenance rate ≥85%, recovery time ≤6 months, and multi-objective collaborative stability index ≥0.7 as preset resilience thresholds based on the resilience score of the reservoir system, calculate the compliance distance of each candidate scheme, and generate a threshold compliance matrix. M62, the multi-criteria decision-making submodule, is used to employ an improved TOPSIS-entropy weight fusion algorithm, combining expert preference weights and data-driven weights, to comprehensively rank candidate solutions and output the optimal size solution and confidence interval. M63, the submodule for evaluating the effectiveness of argumentation, is used to record the entire process time, convergence algebra, and sensitivity indicators from data input to scheme output. By comparing with traditional trial calculation methods, it quantifies the efficiency improvement ratio and robustness gain of intelligent argumentation and obtains the final argumentation evaluation result.

[0033] This invention achieves fully automated demonstration of the entire chain, from hydrological risk identification, multi-objective coupling analysis, intelligent optimization, uncertainty propagation simulation to resilience assessment and intelligent decision-making, through the organic synergy of six major modules. It significantly improves the scientificity, robustness and efficiency of reservoir scale demonstration and is applicable to the planning and design of complex water resource systems under the background of climate change.

[0034] Example 2 The intelligent optimization demonstration method for reservoir size based on multi-objective risk coupling proposed in this invention may specifically include: S1. Collect historical hydrological and meteorological data and future climate scenario data, identify extreme events and quantify runoff variability, integrate multi-source risk factors, and generate dynamic hydrological risk indicators. S2. Based on the dynamic hydrological risk indicators, construct a multi-objective function for water supply, flood control, ecology and power generation, analyze the conflict and synergy among the objectives, and draw a multi-objective risk coupling map. S3. Using the multi-objective risk coupling map, embed risk tolerance constraints, optimize the reservoir scale parameters through reinforcement learning, and generate a set of candidate schemes that satisfy the balance of multiple objectives. S4. Based on the candidate scheme set, simulate the propagation path of hydrological and engineering uncertainties in the system, construct a risk transmission network, evaluate the functional maintenance and recovery capabilities of each scheme under disturbance, and generate a reservoir system resilience score. S5. Based on the reservoir system resilience score, compare the matching degree between the selected scheme and the preset resilience threshold, integrate the multi-criteria decision results, automatically generate the optimal reservoir size demonstration report, evaluate the efficiency and robustness of the demonstration process, and obtain the final demonstration evaluation result.

[0035] The working principle of this method will be explained in detail below using a real-world application scenario of a newly constructed large-scale water conservancy project: Execute step S1 to start the hydrological risk modeling module; The system first collects nearly 60 years of historical hydrological and meteorological data of the basin (including daily rainfall, evaporation, inflow runoff, etc.) as input, as well as two future climate scenarios, RCP4.5 and RCP8.5, released by the Intergovernmental Panel on Climate Change (IPCC).

[0036] The extreme event identification submodule uses the generalized extreme value distribution (GEV) to fit the peak flow of the rainstorm with the minimum runoff during the dry season, and obtains a statistical table of extreme events under different return periods (10 years, 50 years, 100 years). For example, after fitting with GEV, the peak flow corresponding to a 100-year return period rainstorm in this watershed is 8500 m³ / s, and the minimum monthly average runoff during the 50-year return period dry season is 120 m³ / s.

[0037] Subsequently, the variability quantification submodule, based on the statistical table, uses the sliding window entropy analysis method (with a window length of 10 years) to calculate the information entropy change rate of interannual and intra-annual runoff processes, thereby obtaining the annual runoff variation coefficient Cv=0.38 and the year of significant change 2003, forming the hydrological process variation index.

[0038] Finally, the risk factor fusion submodule combines the aforementioned variability index with the RCP scenario weights (RCP4.5 weight 0.6, RCP8.5 weight 0.4) to construct a multi-source risk superposition model. The GumbelCopula function is used to characterize the tail dependency structure between extreme precipitation and drought events, ultimately generating dynamic hydrological risk indicators, including the distribution of extreme event return periods, runoff variability coefficient 0.38, and the probability of seasonal drought persistence (the probability of being below the ecological baseflow for three consecutive months is 0.22).

[0039] Execute step S2 to start the multi-objective coupling analysis module; The objective function construction submodule uses four basic objectives: water supply guarantee rate (target value ≥ 95%), flood peak reduction ratio (defined as the ratio of flood peak reduction to the peak value of inflow flood, target value ≥ 0.4), ecological base flow satisfaction (the proportion of days meeting ecological flow standards to the total number of days in the year, target value ≥ 90%), and average annual power generation (unit: 100 million kWh). A normalized multi-objective function set is established. To eliminate dimensional differences, a triangular fuzzy membership function is introduced to standardize each objective, forming a standardized objective vector. For example, for a candidate reservoir capacity scheme, its water supply guarantee rate is 92%, corresponding to a fuzzy membership degree of 0.85; the average annual power generation is 1.8 billion kWh, with a maximum possible value of 2 billion kWh, and a normalized value of 0.9.

[0040] Based on the standardized target vector, the conflict synergy analysis submodule calculates the Spearman rank correlation coefficient between any two targets (e.g., the correlation coefficient between water supply and power generation is 0.78, indicating a strong synergistic relationship; the correlation coefficient between flood control and water supply is -0.65, indicating a strong conflict relationship), and combines it with the Shapley interaction value to quantify the interaction contribution between targets, generating a 4×4 target coupling strength matrix.

[0041] The Pareto front tracking submodule is based on this matrix and uses an improved NSGA-Ⅲ algorithm for high-dimensional optimization. This algorithm introduces a reference point guidance mechanism, and its comprehensive fitness function is:

[0042] in, To optimize the total number of targets, For the first The dynamic weight of each objective is determined inversely by the coupling strength between the objectives (e.g., if the conflict between flood control and water supply is strong, the weights of both will be increased to enhance the exploration). For the normalized first The objective function value, =10 is the penalty coefficient. To constrain the violation rate, which is the portion of the dynamic hydrological risk index that exceeds the user-defined risk tolerance (e.g., if the flood control risk tolerance is 1%, and the simulation shows an exceedance of 0.3%, then CV = 0.3), optimization is driven by a comprehensive fitness function. This enhances the diversity of solutions in areas of strong conflict (e.g., flood control-water supply) and accelerates convergence in areas of cooperation (e.g., water supply-power generation). The final output is a multi-objective risk coupling map that includes a trade-off surface, conflict hotspot area markers, and potential areas of cooperative gains.

[0043] Execute step S3 to start the scale optimization decision module; The parameter space exploration submodule defines the total reservoir capacity (a continuous variable, ranging from 500 million to 2 billion m³), ​​normal water level (a continuous variable, ranging from 150 to 200 m), dead water level (a continuous variable, ranging from 120 to 160 m), and the number of floodgates (a discrete variable, ranging from 4 to 8 gates) as adjustable parameters to construct a continuous-discrete hybrid parameter space. A Bayesian optimization surrogate model (with a Gaussian process as a prior) is introduced to accelerate the search, and 50 parameter combinations are initially sampled.

[0044] Meanwhile, users set a flood risk tolerance of 1% (i.e., the probability of a 100-year flood overflowing the dam ≤ 1%) and a water shortage risk ceiling of 5% (annual water shortage frequency ≤ 5%). The risk constraint embedding submodule transforms these indicators into inequality constraints and embeds them into the reinforcement learning reward function: when the simulation result satisfies the constraints, the reward is +1; otherwise, the reward is -λ. CV(x) thus forms a risk-aware policy gradient, driving the deep deterministic policy gradient. The algorithm iteratively explores the parameter space.

[0045] After 200 rounds of training, the candidate set generation submodule outputs 30 sets of non-dominated solutions, which constitute the candidate set for reservoir scale optimization. Each set of solutions includes specific configuration parameters for total reservoir capacity, dead water level, and flood discharge capacity.

[0046] Execute step S4 to jointly start the uncertainty propagation simulation module and the comprehensive resilience assessment module; The perturbation injection submodule applies random perturbations to the above 30 candidate schemes respectively: the inflow runoff is perturbed by a normal distribution of ±15%, the evaporation loss is perturbed by a normal distribution of ±10%, the urban water demand prediction is perturbed by a normal distribution of ±8%, and the equipment failure rate is set as a Poisson process (annual average failure number λ=0.2). The input sample set of 1000 scenarios is generated by Latin hypercube sampling.

[0047] The hybrid simulation submodule uses a Monte Carlo sampling-driven stochastic differential equation model, and the reservoir capacity state equation is:

[0048] in, For the reservoir capacity, For inbound flow, For outbound flow, The evaporation loss is used to simulate a 10-year scheduling process, recording the time-series trajectories of key performance indicators such as water supply shortage, flood peak exceeding the limit, ecological flow shortage days, and power generation deviation, and constructing a system state evolution database.

[0049] Based on this database, the network construction submodule uses Granger causality test (lag order p=3) and transfer entropy information flow analysis to identify the dominant propagation path of disturbances from input to output. For example, "inflow disturbance → reservoir capacity fluctuation → insufficient water supply → social feedback" constitutes a feedback loop with a 2-month delay. Finally, a risk transmission network is established that includes hydrological input disturbance nodes, engineering response delay nodes, and social feedback loop nodes.

[0050] The comprehensive resilience assessment module conducts quantitative assessments based on this network: The vulnerability scoring submodule calculates the functional loss of each node under perturbation, introduces the conditional value at risk (CVaR) at a 95% confidence level to measure the tail loss, and generates a node vulnerability index. The recovery submodule simulates the system's adaptive scheduling capability after the impact (such as activating emergency water sources and adjusting power generation plans), calculates the time required for the function to recover to 90% of the baseline level, and combines the scheduling flexibility index (adjustable reservoir capacity ratio) to form a system resilience score; The resilience fusion submodule uses Dempster-Shafer (DS) evidence theory to fuse the vulnerability index and resilience score, generating a reservoir system resilience score that covers robustness, redundancy and adaptability, and outputs a unique resilience score result for each group of candidate schemes.

[0051] Execute step S5 to start the intelligent argumentation integration module; The threshold matching submodule sets preset resilience thresholds: functional maintenance rate ≥ 85%, recovery time ≤ 6 months, and multi-objective collaborative stability index ≥ 0.7. It calculates the Euclidean distance between each candidate solution and the threshold, generates a threshold compliance matrix, and selects all compliant candidate solutions.

[0052] The multi-criteria decision-making submodule adopts an improved TOPSIS-entropy weight fusion algorithm: First, the objective weights (such as resilience score weight 0.4 and reservoir capacity economy weight 0.3) are calculated by the entropy weight method, and then the subjective weights determined by expert scoring (such as flood control priority weight 0.5) are combined to obtain the comprehensive weight through linear weighted fusion; then, the relative closeness of each scheme to the positive and negative ideal solutions is calculated, and the 30 schemes are comprehensively ranked to output the optimal scale scheme (total reservoir capacity 1.32 billion m³, dead water level 138 m, 6-gate flood discharge) and its 95% confidence interval (total reservoir capacity 1.28-1.36 billion m³).

[0053] The submodule for evaluating the effectiveness of the demonstration project recorded a total processing time of 4.2 hours, which is 8.9 times faster than the 15-day cycle of traditional manual calculation methods. Simultaneously, it quantified that the standard deviation of the performance fluctuation of the scheme under disturbances was reduced by 42%, significantly improving robustness. Finally, it generates the final demonstration evaluation results, including scheme optimization ranking, risk threshold compliance verification, and efficiency and accuracy analysis, and automatically generates a richly illustrated reservoir scale demonstration report.

[0054] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any changes, modifications, substitutions and variations made by those skilled in the art to the above embodiments within the scope of the present invention should be included within the protection scope of the present invention.

[0055] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A reservoir scale intelligent optimization demonstration system based on multi-objective risk coupling, characterized in that, include: The hydrological risk modeling module is used to identify key hydrological risk factors affecting reservoir safety and benefits by integrating historical hydrological and meteorological data and future climate scenario data, combining extreme precipitation, drought frequency and runoff variation characteristics, and generating dynamic hydrological risk indicators through multidimensional probability density function fitting. The multi-objective coupling analysis module is used to construct a matrix of conflict and synergy relationships between objectives by combining the dynamic hydrological risk indicators with multi-dimensional objective functions of water supply, flood control, ecology and power generation. The Pareto front tracking algorithm is used to quantify the coupling strength of objectives and obtain a multi-objective risk coupling map. The scale optimization decision module is used to dynamically adjust the reservoir scale parameter space based on the multi-objective risk coupling map using a reinforcement learning framework, introduce risk tolerance constraints, optimize the three-dimensional balance point of reservoir capacity, benefits and risks, and generate a candidate set for reservoir scale optimization. The uncertainty propagation simulation module is used to employ the aforementioned candidate set for reservoir size optimization, integrate a Monte Carlo-stochastic differential equation hybrid model, simulate the propagation path of multiple uncertainties in the system (hydrological, engineering, and socio-economic), and construct a risk transmission network. The comprehensive resilience assessment module is used to utilize the risk transmission network to quantify the functional maintenance capability and recovery speed of different scale schemes under disturbance through the coupled calculation of node vulnerability index and system resilience index, and generate a reservoir system resilience score. The intelligent demonstration integration module is used to compare the matching degree of each candidate scheme with the preset resilience threshold based on the resilience score of the reservoir system, through an AI-driven multi-criteria decision fusion mechanism, automatically generate the optimal reservoir size demonstration report, evaluate the robustness and computational efficiency of the demonstration process, and obtain the final demonstration evaluation result.

2. The intelligent optimization demonstration system for reservoir scale based on multi-objective risk coupling according to claim 1, characterized in that, The dynamic hydrological risk indicators include the distribution of the return period of extreme events, the coefficient of runoff variability, and the probability of seasonal drought persistence. The multi-objective risk coupling map includes an inter-objective trade-off surface, conflict hotspot region identification, and synergistic gain potential region. The candidate set for reservoir size optimization includes reservoir capacity range, dead water level setting range, and flood discharge capacity configuration combination. The risk transmission network includes a node connection structure for hydrological input disturbances, engineering response delays, and social feedback loops. The reservoir system resilience score includes functional degradation rate, recovery time constant, and multi-objective collaborative stability index. The final evaluation results include the ranking of optimal solutions, verification of compliance with risk thresholds, and analysis of the time and accuracy of intelligent evaluation.

3. The intelligent optimization demonstration system for reservoir scale based on multi-objective risk coupling according to claim 1, characterized in that, The hydrological risk modeling module includes: The extreme event identification submodule is used to extract extreme event sequences such as rainstorms and droughts based on historical hydrological and meteorological data, fit their probability characteristics using a generalized extreme value distribution, calculate peak flow and minimum runoff under different return periods, and generate an extreme event statistics table. The variability quantification submodule is used to quantify the nonstationarity of interannual and intra-annual runoff processes by combining the extreme event statistics table with the sliding window entropy analysis method, calculate the coefficient of variation and the location of abrupt change points, and obtain the hydrological process variability index. The risk factor fusion submodule is used to construct a multi-source risk superposition model by using the hydrological process variability index and fusing the RCP scenario weights for climate change. It also uses the Copula function to characterize the tail dependency structure between variables and generate dynamic hydrological risk indicators.

4. The intelligent optimization demonstration system for reservoir scale based on multi-objective risk coupling according to claim 1, characterized in that, The multi-objective coupling analysis module includes: The objective function construction submodule is used to establish a normalized multi-objective function set by taking water supply guarantee rate, flood control peak reduction ratio, ecological base flow satisfaction and annual power generation as basic objectives, introducing fuzzy membership degree to handle the difference in objective dimensions, and forming a standardized objective vector. The conflict and cooperation analysis submodule is used to calculate the Spearman rank correlation coefficient and Shapley interaction value between target pairs using the standardized target vector, identify strong conflict and strong cooperation target pairs, and generate a target coupling strength matrix. The Pareto front tracking submodule is used to perform high-dimensional multi-objective optimization using the target coupling strength matrix and the improved NSGA-Ⅲ algorithm, dynamically track the non-dominated solution set, visualize the target trade-off boundary, and form a multi-objective risk coupling map.

5. The intelligent optimization demonstration system for reservoir scale based on multi-objective risk coupling according to claim 4, characterized in that, The comprehensive fitness function formula for the reference point-guided selection mechanism used in the improved NSGA-Ⅲ algorithm is as follows: in, Overall fitness function, To optimize the total number of targets, For the first The dynamic weights of each target are determined inversely proportional to the target coupling strength. For the normalized first The objective function value, The penalty coefficient is... To constrain the degree of violation, that is, the exceeding of the limit for hydrological risk indicators beyond the risk tolerance.

6. The intelligent optimization demonstration system for reservoir scale based on multi-objective risk coupling according to claim 1, characterized in that, The scale optimization decision module includes: The parameter space exploration submodule is used to define reservoir capacity, normal water level, dead water level and number of floodgates as adjustable parameters based on the multi-objective risk coupling map, construct a continuous-discrete hybrid parameter space, and introduce a Bayesian optimization surrogate model to accelerate the search. The risk constraint embedding submodule is used to transform risk indicators into inequality constraints by using the flood risk tolerance and water shortage risk limit set by the user, and embed them into the reward function of reinforcement learning to form a risk-aware policy gradient. The candidate set generation submodule is used to iteratively generate feasible solutions that satisfy multi-objective balance in the parameter space using the risk-aware policy gradient, and outputs a candidate set for reservoir size optimization after non-dominated sorting.

7. The intelligent optimization demonstration system for reservoir scale based on multi-objective risk coupling according to claim 1, characterized in that, The uncertainty propagation simulation module includes: The perturbation injection submodule is used to apply random perturbations to the inflow runoff, evaporation loss, water demand prediction and equipment failure rate based on the candidate set optimized by the reservoir size, and generate a multi-scenario input sample set. The hybrid simulation submodule is used to simulate the state transitions and control responses during reservoir operation by employing Monte Carlo sampling-driven stochastic differential equation models, record the time-series trajectories of key performance indicators, and construct a system state evolution database. The network construction submodule is used to identify the dominant propagation path of disturbances from input to output based on the system state evolution database, using Granger causality test and information flow analysis, and to establish a risk transmission network that includes delay effects.

8. The intelligent optimization demonstration system for reservoir scale based on multi-objective risk coupling according to claim 1, characterized in that, The comprehensive resilience assessment module includes: The vulnerability scoring submodule is used to calculate the magnitude and duration of functional loss of each node under disturbance based on the risk transmission network, introduce conditional value at risk (CVaR) to measure tail loss, and generate a node vulnerability index. The resilience submodule is used to calculate the time required for the system to recover to 90% of the baseline level by simulating the system's adaptive scheduling capability after an impact, and to form a system resilience score by combining the scheduling flexibility index. The resilience fusion submodule is used to integrate the node vulnerability index and the system resilience score. It uses Dempster-Shafer evidence theory to fuse multi-source assessment information and generate a reservoir system resilience score that covers robustness, redundancy and adaptability.

9. The intelligent optimization demonstration system for reservoir size based on multi-objective risk coupling according to claim 1, characterized in that, The intelligent argumentation integration module includes: The threshold matching submodule is used to set the function maintenance rate ≥85%, recovery time ≤6 months, and multi-objective collaborative stability index ≥0.7 as preset resilience thresholds based on the resilience score of the reservoir system, calculate the compliance distance of each candidate scheme, and generate a threshold compliance matrix. The multi-criteria decision-making submodule is used to employ an improved TOPSIS-entropy weight fusion algorithm, combining expert preference weights and data-driven weights, to comprehensively rank candidate solutions and output the optimal size solution and confidence interval. The submodule for evaluating the effectiveness of the argumentation is used to record the time, convergence algebra, and sensitivity indicators of the entire process from data input to scheme output. By comparing with traditional trial calculation methods, it quantifies the efficiency improvement ratio and robustness gain of intelligent argumentation and obtains the final argumentation evaluation results.

10. A method for intelligent optimization and demonstration of reservoir size based on multi-objective risk coupling, characterized in that, The intelligent optimization demonstration system for reservoir size based on multi-objective risk coupling, as described in any one of claims 1-9, includes: S1. Collect historical hydrological and meteorological data and future climate scenario data, identify extreme events and quantify runoff variability, integrate multi-source risk factors, and generate dynamic hydrological risk indicators. S2. Based on the dynamic hydrological risk indicators, construct a multi-objective function for water supply, flood control, ecology and power generation, analyze the conflict and synergy among the objectives, and draw a multi-objective risk coupling map. S3. Using the aforementioned multi-objective risk coupling map, embedding risk tolerance constraints, and optimizing reservoir scale parameters through reinforcement learning to generate a set of candidate schemes that satisfy multi-objective balance; S4. Based on the candidate scheme set, simulate the propagation path of hydrological and engineering uncertainties in the system, construct a risk transmission network, evaluate the functional maintenance and recovery capabilities of each scheme under disturbance, and generate a reservoir system resilience score. S5. Based on the reservoir system resilience score, compare the matching degree between the selected scheme and the preset resilience threshold, integrate the multi-criteria decision results, automatically generate the optimal reservoir size demonstration report, evaluate the efficiency and robustness of the demonstration process, and obtain the final demonstration evaluation result.

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