Reservoir-lake-river system extreme flood simulation and flood control risk assessment method, system and device and storage medium

Abstract

The invention discloses a reservoir-lake-river system extreme flood simulation and flood control risk assessment method, system and device and a storage medium, and the method comprises the steps: firstly recognizing a risk source factor of a historical extreme flood event, depicting the spatial distribution uncertainty of the risk source factor through a Vine Copula function, and generating a risk source factor sample through Latin hypercube sampling; then, the samples are reduced based on a Neural Gas algorithm, and a risk source factor scene tree is constructed; generating an extreme flood hygrograph scene by combining historical flood data and adopting a same-multiple-ratio scaling method; and then a joint flood control optimization scheduling model is constructed, flood control risks of the reservoir and the lake are evaluated, and the evaluation effect is analyzed. The system is simple in structure and accurate and stable in risk assessment, fine risk assessment of flood disaster high-incidence areas is facilitated, and theoretical support of extreme flood risk assessment can be provided for areas lacking data.

Description

Technical Field

[0001] The present invention belongs to the technical field of flood control scheduling, and particularly relates to a method, system, device and storage medium for simulating extreme floods and evaluating flood control risks in a reservoir-lake-river system. Background Art

[0002] Extreme flood events have extremely high destructive potential. Their scale and intensity often exceed the design standards of flood control projects, which may lead to extensive damage, casualties, and disruptions to the economy and society. The occurrence of such events is usually triggered by the accumulation of multiple risk source factors, which exhibit significant spatio-temporal distribution uncertainties, making it difficult to accurately predict extreme flood events. In addition, due to the scarcity of extreme flood events, relying solely on historical flood data for flood control risk assessment has certain limitations. Traditional flood prediction methods often struggle to accurately forecast these uncertain extreme flood events, further increasing the difficulty of flood control risk assessment. Although it is impossible to completely prevent the occurrence of extreme flood events, effective risk assessment methods can identify high-risk factors and take flood control optimization scheduling measures, thereby effectively reducing flood disasters.

[0003] In existing research, analytical methods and stochastic simulation methods have been widely used in flood control risk assessment of reservoir systems. Analytical methods rely on simplifying risk characteristics and assuming model conditions, and evaluate flood control risks through integration or differentiation methods. However, in a complex reservoir-lake-river system, due to the complex characteristics of its hydraulic connection and hydrological features, it is difficult to apply analytical methods to simulate extreme floods and evaluate flood control risks in a reservoir-lake-river system. The stochastic simulation method first fits the marginal distribution function of risk source factors, then randomly samples from the marginal distribution function to generate risk source factor samples, and finally inputs the risk source factor samples into the joint flood control optimization scheduling model to randomly simulate the flood scheduling process and conduct flood control risk assessment.

[0004] However, there are still some challenges in the stochastic simulation of extreme flood events and flood control risk assessment. First, in the stage of characterizing and describing extreme flood events, due to the small sample size of historical extreme flood events, the historically occurred extreme flood events cannot fully represent all possible extreme flood events. Therefore, numerical simulation methods are needed to enrich the sample library of extreme flood events. Second, the occurrence of extreme flood events is usually the result of the combined accumulation of multiple risk source factors, and there are significant spatio-temporal distribution uncertainties in these risk source factors. Therefore, flood control risk assessment needs to consider the uncertain relationships among multiple risk source factors. Third, flood control scheduling requires a rapid and accurate response to extreme flood events. Therefore, the calculation time of flood control risk assessment must be controlled within a small range to meet the needs of actual flood control scheduling. Therefore, aiming at the characteristics of spatio-temporal combination uncertainties of multiple risk source factors in areas lacking historical flood data, how to construct an accurate comprehensive flood control risk assessment method for extreme flood events is a scientific problem that needs to be solved urgently. Summary of the Invention

[0005] Object of the Invention: The object of the present invention is to provide a method, system, device and storage medium for extreme flood simulation and flood control risk assessment of a reservoir-lake-river system, so as to improve the accuracy and stability of flood control risk assessment for extreme flood events and provide a scientific decision-making reference for flood control scheduling.

[0006] Technical Solution: A method for extreme flood simulation and flood control risk assessment of a reservoir-lake-river system according to the present invention includes the following steps: S1. Collect historical flood data and flood control project data of the reservoir-lake-river system, count historical extreme flood events, identify risk source factors that cause historical extreme flood events, and the risk source factors include upstream inflow flood volume, inflow flood volume into the lake in the interval, and the highest water level of the outer river. Calculate each type of risk source factor in the historical flood data of each year by the annual maximum value method; S2. Fit marginal distribution functions for each type of risk source factor, construct corresponding two-dimensional Copula functions and conditional Copula functions, generate a Vine Copula function, generate probability samples of risk source factors by the Latin hypercube sampling method, calculate the joint probability of each group of risk source factor probability samples, screen out risk source factor probability samples with joint probabilities lower than the extreme flood event probability threshold, and use the marginal distribution function to perform inverse transformation on them to generate risk source factor samples; S3. Use the K-Means method to cluster the risk source factor samples into different groups, use the Neural Gas algorithm to construct a risk source factor scenario tree for each group, calculate the extreme flood scenario probability, and use the same ratio scaling method to couple historical flood data to generate extreme flood hydrograph scenarios; S4. Construct a joint flood control optimization scheduling model for the reservoir-lake-river system with the objective of minimizing the average excess flood volume of the lake during the entire scheduling period. Use the extreme flood hydrograph scenario as the input data for this model, simulate the joint flood control scheduling plan for extreme floods, analyze the situations where the safety storage thresholds of the reservoir and the lake are exceeded in the joint flood control scheduling plan for extreme floods, evaluate the flood control risks of the reservoir and the lake, and use the Continuous Ranked Probability Score (CRPS) to evaluate the results of the flood control risks of the reservoir and the lake.

[0007] Furthermore, in step S1, the annual maximum method is used to calculate each type of risk source factor in the historical flood data of each year, including: Based on the historical flood data, extract the maximum flood event that occurred in each year, and calculate the corresponding upstream inflow to the reservoir, inflow to the lake from the intermediate area, and the highest water level of the outer river. The calculation formulas are as follows: , where, is the upstream inflow to the reservoir in the historical flood data of the th year, , is the year index corresponding to the historical flood data, is the total number of years of the historical flood data, is the th reservoir's historical upstream inflow rate at the th time period in the th year, is the total number of reservoirs, is the total scheduling duration; , where, is the inflow to the lake from the intermediate area in the historical flood data of the th year, is the historical inflow to the lake from the intermediate area at the th time period in the th year; , where, is the highest water level of the outer river in the historical flood data of the th year, is the historical outer river water level at the th time period in the th year.

[0008] Furthermore, the generation method of the Vine Copula function in step S2 is as follows: (1) Based on the upstream inflow flood volume, the inflow flood volume into the lake in the intermediate section, and the highest water level in the external river, which are three types of risk source factors, the maximum likelihood estimation method is used to fit the marginal distribution function for each type of risk source factor; the expression of the marginal distribution function of the risk source factor is: , where is the vector of the -th type of risk source factor, , , is the -th type of risk source factor in the historical flood data of the -th year, , is the year index corresponding to the historical flood data, is the total number of years of the historical flood data, is the -corresponding marginal distribution function, is the -th type of risk source factor's marginal distribution function value; (2) Calculate the Kendall rank correlation coefficient between various risk source factors, and construct a two-dimensional Copula function based on the marginal distribution function values of the risk source factors, the Kendall rank correlation coefficient, and the Bayesian information criterion BIC. The expression is: , where represents the two-dimensional Copula function of the joint distribution between the -th type and the -th type of risk source factors, , is the vector of the -th type of risk source factor, and its marginal distribution function is , is the marginal distribution function value of the -th type of risk source factor; (3) Based on the marginal distribution function and the two-dimensional Copula function of the risk source factors, construct a conditional Copula function. The conditional Copula function is used to calculate the conditional joint distribution function of the -th type of risk source factor under the condition of given the remaining -th type of risk source factors. The calculation formula of the conditional Copula function is: , where is the joint Copula function of the risk source factors from the 1st type to the -th type, , , are the marginal distribution function values of the first type, the second type, and the type of risk source factors, respectively; (4) Construct a Vine Copula structure based on the two-dimensional Copula function and the conditional Copula function. Use the Akaike information criterion AIC and the maximum likelihood estimation method for structure selection and parameter estimation. Construct the Vine Copula structure by nesting the two-dimensional Copula function and the conditional Copula function layer by layer. The first layer of the Vine Copula structure consists of two-dimensional Copula functions, and the second layer is connected by conditional Copula functions. The calculation formula of the Vine Copula function is: , where is the -dimensional Vine Copula function, which is used to describe the dependence relationship between the types of risk source factors, represents the iteration of the hierarchical structure, represents that under the given set of conditional risk source factors , the th and the th type of conditional Copula function of risk source factors, is the total number of categories of risk source factors.

[0009] Furthermore, the method for generating risk source factor samples in step S2 is as follows: (1) Based on the marginal distribution function of risk source factors, combined with the two-dimensional Copula function, the conditional Copula function, and the Vine Copula structure, use the Latin hypercube sampling method to generate risk source factor probability samples , , , is the probability sample of the first type of risk source factor, is the probability sample of the second type of risk source factor, is the probability sample of the third type of risk source factor; (2) According to the Vine Copula structure and the principle of probability mutual exclusion, the joint probability calculation formula of risk source factor probability samples is: , where represents , , 's joint probability, represents the Vine Copula function, and the input is the marginal distribution function values of the corresponding risk source factors, The marginal distribution function value of the risk source factor of the first category is , and the Vine Copula function with the marginal distribution function values of the second and third categories being 1 (the maximum value), The marginal distribution function value of the risk source factor of the second category is , and the Vine Copula function with the marginal distribution function values of the first and third categories being 1, The marginal distribution function value of the risk source factor of the third category is , and the Vine Copula function with the marginal distribution function values of the first and second categories being 1, The marginal distribution function values of the risk source factors of the first and second categories are and , and the Vine Copula function with the marginal distribution function value of the third category being 1, The marginal distribution function values of the risk source factors of the first and third categories are and , and the Vine Copula function with the marginal distribution function value of the second category being 1, The marginal distribution function values of the risk source factors of the second and third categories are and , and the Vine Copula function with the marginal distribution function value of the first category being 1, The marginal distribution function values of the risk source factors of the first, second, and third categories are , and of the Vine Copula function; (3) Based on the probability threshold of the extreme flood event and the joint probability of the risk source factor probability samples , screen the probability samples that meet the following conditions: , Take the risk source factor probability samples that meet the above conditions as the risk source factor probability samples corresponding to the extreme flood event; Based on the marginal distribution function, perform inverse transformation on the risk source factor probability samples corresponding to the extreme flood event to generate risk source factor samples.

[0010] Further, step S3 includes the following steps: S31. Based on the generated risk source factor samples, determine the optimal number of clusters 𝑅 according to the elbow method, and use the K-Means method to divide the risk source factor samples; S32. Calculate the variance of each type of risk source factor based on the risk source factors of historical flood data. Take the risk source factor with the smallest variance as the root node in the first stage and the risk source factor with the largest variance as the leaf node in the final stage, thereby determining the positions of the risk source factors in the scenario tree. Predefine the structure of the scenario tree, then randomly select risk source factor samples as the initial node values of the scenario tree, and calculate the initial Euclidean distance between the risk source factor samples and the nodes of the scenario tree. Through iterative optimization using the Neural Gas algorithm, dynamically adjust the node values of the scenario tree to minimize the overall Euclidean distance from the risk source factor samples to the nodes of the scenario tree. Finally, based on the optimized scenario tree, calculate the probabilities of different scenarios; S33. Based on the grouped risk source factor samples obtained by partitioning, repeat step S32 to construct a separate scenario tree for each group; the structure of the scenario tree for each group and the calculation of the probabilities of the scenarios are both based on the risk source factor sample data of that group. Calculate the scenarios and their corresponding probabilities in all the scenario trees, and these scenarios constitute the extreme flood scenarios; S34. According to the probability threshold of the extreme flood event , the grouped data, and the scenario trees constructed for each group, calculate that if the probability of the th scenario tree is under the condition that the extreme flood event occurs, then the probability of the th scenario tree is ; each scenario tree contains extreme flood scenarios. Then, under the condition that the extreme flood event and the group occur, the probability of the occurrence of the extreme flood scenario is . Based on the conditional probability formula, the probability of the extreme flood scenario is calculated as follows: S35. Adopt the same ratio scaling method to couple the extreme flood scenarios with the historical flood data to construct the extreme flood hydrograph scenarios; the extreme flood hydrograph scenarios are used as the input data for the joint flood control and optimization scheduling model of the reservoir-lake-river system to simulate the joint flood control scheduling plan under extreme flood conditions.

[0011] Furthermore, the expression of the objective function in step S4 is: , where, is the objective function of the joint flood control and optimization scheduling model, is the initial storage volume of the lake at the beginning of the th time period, is the total scheduling duration, is the safety storage volume threshold of the lake; The model constraints include: reservoir water balance constraint, reservoir characteristic water level constraint, initial / final water level constraint, reservoir discharge capacity, outlet variable range constraint, reservoir-lake water balance constraint, and external river water level constraint; Input the generated extreme flood hydrograph scenarios into the joint flood control optimal operation model of the reservoir-lake-river system, and use LINGO software to calculate and solve, simulate the joint flood control operation plan for extreme floods, and calculate the highest storage volume of the reservoir corresponding to each operation plan and the highest storage volume of the lake 。

[0012] Furthermore, the methods for evaluating the flood control risks of the reservoir and the lake in step S4 are as follows: Evaluating the flood control risk of the reservoir: The flood control risk of the reservoir is defined as: the weighted sum of probabilities that the highest storage volume of the reservoir exceeds its flood control safety threshold in all joint flood control operation plans for extreme floods. Its calculation formula is: , where, is the flood control risk of the reservoir, is the number of scenarios, is the number of scenario trees, is the th scenario tree, and the th extreme flood scenario. The storage volume of the rd reservoir at the beginning of the time period calculated by the joint flood control optimal operation model for the extreme flood joint flood control operation plan, is the rd reservoir's corresponding flood control safety threshold, , is the total number of reservoirs, is the total operation duration, is the indicator function, is the th scenario tree, and the th extreme flood scenario's probability; Evaluating the flood control risk of the lake: The flood control risk of the lake is defined as: the weighted sum of probabilities that the highest storage volume of the lake exceeds its flood control safety threshold in all joint flood control operation plans for extreme floods. Its calculation formula is: , where, is the flood control risk of the lake, is the th scenario tree, and the th extreme flood scenario. Under the condition of the extreme flood hydrograph scenario calculated by the joint flood control optimal operation model, at the The lake storage at the beginning of the time period, is the flood control safety threshold corresponding to the lake's safety water level; Evaluate the effect of risk assessment: Based on the fitted marginal distribution function of risk source factors, use Monte Carlo random sampling to generate random probability samples of risk source factors, and generate random samples of risk source factors based on the inverse transformation of the marginal distribution function of risk source factors. Then, couple historical flood data to generate extreme flood stochastic hydrograph scenarios, and input them into the joint flood control optimization scheduling model of the reservoir-lake-river system to calculate the Monte Carlo joint flood control scheduling plan. Based on this plan, conduct reservoir flood risk assessment and lake flood risk assessment to obtain the Monte Carlo values of reservoir flood risk and lake flood risk, and use them as risk true values. Use the continuous ranked probability score to evaluate the effect of risk assessment. The lower the value of CRPS, the better the effect of risk assessment.

[0013] The system corresponding to the method includes: A risk source factor calculation unit, which is used to collect historical flood data and flood control project data of the reservoir-lake-river system, count historical extreme flood events, identify risk source factors that cause historical extreme flood events, and the risk source factors include upstream inflow, inflow into the lake from the intermediate area, and the highest water level of the outer river. Use the annual maximum method to calculate each type of risk source factor in the historical flood data of each year; A risk source factor sample generation unit, which is used to fit the marginal distribution function for each type of risk source factor, construct the corresponding two-dimensional Copula function and conditional Copula function, generate the Vine Copula function, generate risk source factor probability samples through the Latin hypercube sampling method, calculate the joint probability of each group of risk source factor probability samples, screen out the risk source factor probability samples with a joint probability lower than the extreme flood event probability threshold, and use the marginal distribution function to perform inverse transformation on them to generate risk source factor samples; An extreme flood hydrograph scenario generation unit, which is used to cluster risk source factor samples into different groups by using the K-Means method, construct a risk source factor scenario tree for each group by using the Neural Gas algorithm, calculate the extreme flood scenario probability, and use the same ratio scaling method to couple historical flood data to generate extreme flood hydrograph scenarios; A scheduling plan generation and evaluation unit is used to construct a joint flood control optimal scheduling model for a reservoir-lake-river system with the objective of minimizing the average excess flood volume of the lake during the entire scheduling period. Taking the extreme flood hydrograph scenario as the input data of the model, it simulates the extreme flood joint flood control scheduling plan, analyzes the situations where the safety storage thresholds of the reservoir and the lake are exceeded in the extreme flood joint flood control scheduling plan, evaluates the flood control risks of the reservoir and the lake, and uses the Continuous Ranked Probability Score (CRPS) to evaluate the results of the flood control risks of the reservoir and the lake.

[0014] An electronic device for storing and executing the above method, including a memory, a processor, and a computer program / instructions stored on the memory and executable on the processor. When the computer program / instructions are executed by the processor, the steps of the extreme flood simulation and flood control risk assessment method for the reservoir-lake-river system are implemented.

[0015] A computer-readable storage medium for storing and executing the above method. The computer-readable storage medium stores computer instructions, which are used to execute the steps of the extreme flood simulation and flood control risk assessment method for the reservoir-lake-river system when called.

[0016] Advantageous effects: Compared with the prior art, the significant technical effects of the present invention are as follows: By using Vine Copula to characterize the joint distribution of multi-risk source factors, the accuracy of extreme flood event simulation is significantly improved. Using the scenario tree to effectively reduce the samples of risk source factors improves the calculation efficiency of flood control risk assessment, meets the timeliness requirements of actual scheduling, and provides reliable data support and decision-making basis for formulating scientific and efficient flood control joint scheduling strategies. Description of the Drawings

[0017] Figure 1 is the flowchart of the method of the present invention; Figure 2 is the flowchart of extreme flood scenario generation and structure construction based on Vine Copula and Neural Gas algorithm; where (a) is the Vine Copula structure, (b) is the samples of risk source factors, (c) is the structure of the predefined scenario tree, and (d) is R trees of scenario tree. Detailed Embodiments

[0018] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some well-known technical features in the art are not described. The order of the relevant steps in the present invention is not restrictive, that is, those skilled in the art can adjust it. The order in the present invention is a case-based writing method rather than a restrictive description.

[0019] The present invention proposes a method for simulating extreme floods and evaluating flood control risks in a reservoir-lake-river system under uncertain conditions. This method first identifies and extracts risk source factors by statistically analyzing historical extreme flood events, and calculates the risk source factors in historical flood data. Subsequently, the marginal distribution functions of the risk source factors are respectively fitted, a two-dimensional Copula function and a conditional Copula function are constructed, and then a Vine Copula structure is derived to model the dependence structure between multiple risk source factors. Calculate the Vine Copula function and generate probability samples of risk source factors accordingly. Through joint probability calculation and inverse transformation of the marginal distribution function, risk source factor samples conforming to the characteristics of historical extreme flood events are obtained. Then, the generated risk source factor samples are clustered and divided, and a risk source factor scenario tree is constructed using the Neural Gas algorithm to calculate the probability of extreme flood scenarios. On this basis, historical flood data is coupled to generate extreme flood hydrograph scenarios. Finally, a joint flood control optimization scheduling model for the reservoir-lake-river system is constructed, and the joint flood control scheduling plan for extreme floods is simulated. The flood control risks of the reservoir and the lake are evaluated through the simulation results, and the effectiveness of the flood control risk assessment is analyzed. The method of the present invention has a clear structure and reasonable steps, can stably and accurately evaluate flood risks, and is especially suitable for flood control and disaster reduction work in flood-prone areas. At the same time, this method has strong applicability, can be applied to various hydrological conditions and basins with different terrain structures, and can also provide a reliable reference for extreme flood simulation and risk assessment in areas lacking data, having important engineering application value and theoretical significance.

[0020] As Figure 1 shown, a method for simulating extreme floods and evaluating flood control risks in a reservoir-lake-river system according to the present invention includes the following steps: S1. Identification of risk source factors; Collect historical flood data and flood control project data of the reservoir-lake-river system, statistically analyze historical extreme flood events, and identify the risk source factors that cause historical extreme flood events. Among them, the risk source factors include three categories: upstream reservoir inflow, lateral inflow into the lake, and the highest water level of the external river. The annual maximum value method is used to calculate each type of risk source factor in the historical flood data of each year.

[0021] Specifically, it includes the following steps: S11. Collect and organize data; Determine the study area, and collect historical flood data and flood control project data of the reservoir-lake-river system through channels such as hydrological monitoring stations, basin management agencies, and historical documents.

[0022] S12. Statistically analyze historical extreme flood events; Based on the historical flood data, set the recurrence period according to flood control standards or risk management requirements, and calculate the probability threshold of extreme flood events using the recurrence period analysis method. The calculation formula is as follows: (1), where, is the probability threshold of extreme flood events, is the recurrence period set based on historical flood data.

[0023] According to the calculated probability threshold, statistically analyze historical extreme flood events with a probability of occurrence less than this probability threshold.

[0024] S13. Identify the risk source factors that led to historical extreme flood events; Based on historical extreme flood events, identify the key factors (i.e., risk source factors) that led to historical extreme flood events. The identified risk source factors include: upstream reservoir inflow (the total amount of flood entering the reservoir, which determines the reservoir operation pressure), lateral inflow to the lake (the amount of flood flowing into the lake within the lake catchment area, which affects the lake water level change), and the highest water level of the external river (the peak water level of the external river during the flood period, which affects the flood discharge capacity).

[0025] S14. Calculate the risk source factors of historical flood data using the annual maximum value method; Based on historical flood data, extract the maximum flood event that occurred each year, and calculate its corresponding upstream reservoir inflow, lateral inflow to the lake, and the highest water level of the external river. The calculation formulas are as follows: (2), where, is the upstream reservoir inflow in the historical flood data of the th year, , is the year index corresponding to the historical flood data, is the total number of years of historical flood data, is the th year, is the th reservoir, is the historical upstream inflow at the th time period, is the total number of reservoirs, ; (3), Among them, is the inflow flood volume into the lake in the interval of the historical flood data for the th year, is the historical inflow flow rate into the lake in the interval in the th year during the time period, is the vector of the inflow flood volume into the lake in the interval, ; (4), Among them, is the highest water level of the outer river in the historical flood data for the th year, is the historical water level of the outer river in the th year during the time period, is the vector of the highest water level of the outer river, .

[0026] S2. Generate risk source factor samples based on the Vine Copula function; For each type of risk source factor, fit the marginal distribution function, construct the corresponding two-dimensional Copula function and conditional Copula function, generate the Vine Copula function, generate the risk source factor probability samples through the Latin hypercube sampling method, calculate the joint probability of each group of samples, screen out the risk source factor probability samples with a joint probability lower than the extreme flood event probability threshold, and use the marginal distribution function to perform inverse transformation on them to generate risk source factor samples.

[0027] Specifically, it includes the following steps: S21. Fit the marginal distribution function of each type of risk source factor; Based on the three types of risk source factors (inflow flood volume from the upper reaches, inflow flood volume into the lake in the interval, and the highest water level of the outer river) calculated in step S14, use the maximum likelihood estimation method to fit the marginal distribution function for each type of risk source factor. The candidate marginal distribution functions include the generalized extreme value distribution function, normal distribution function, exponential distribution function, gamma distribution function, and lognormal distribution function. Based on the candidate marginal distribution functions, perform optimization through the Kolmogorov-Smirnov (KS) test and Akaike information criterion (AIC), and select the candidate marginal distribution function that passes the KS test and has the minimum AIC value as the marginal distribution function of this risk source factor. The expression of the marginal distribution function of the risk source factor is as follows: (5), Among them, is the vector of the th type of risk source factor, , , is the -th risk source factor in the historical flood data of the -th category, is the corresponding marginal distribution function, is the value of the marginal distribution function of the -th category of risk source factors, and its value range is [0, 1].

[0028] S22. Construct a two-dimensional Copula function; Calculate the Kendall rank correlation coefficient between various risk source factors, and construct a candidate set of two-dimensional Copula functions based on the values of the marginal distribution functions of the risk source factors and the Kendall rank correlation coefficient. The candidate set of two-dimensional Copula functions includes the Clayton Copula function, the Frank Copula function, and the Gumbel Copula function. Based on the candidate set of two-dimensional Copula functions, use the Bayesian information criterion (BIC) to evaluate the fitting effect of each candidate two-dimensional Copula function, and finally select the two-dimensional Copula function with the smallest BIC value as the n -th and k -th category of risk source factors. The expression of the two-dimensional Copula function is as follows: (6), where represents the two-dimensional Copula function of the joint distribution between the -th and -th category of risk source factors, , is the vector of the -th category of risk source factors, and its marginal distribution function is , is the value of the marginal distribution function of the -th category of risk source factors.

[0029] S23: Construct a conditional Copula function; Based on the marginal distribution functions of the risk source factors and the two-dimensional Copula function, construct a conditional Copula function. The conditional Copula function is used to calculate the conditional joint distribution function of the -th category of risk source factors under the condition of given the remaining -th category of risk source factors. The calculation formula of the conditional Copula function is as follows: (7), where is from the 1st to the The joint Copula function of class risk source factors 、 、 are the marginal distribution function values of the first-class, second-class and class risk source factors respectively, and the partial derivative is used to calculate the conditional Copula function value corresponding to the class risk source factor under the condition of given .

[0030] S24. Calculate the Vine Copula function; To characterize the high-dimensional correlation structure of the three types of risk source factors, a Vine Copula structure is constructed based on the two-dimensional Copula function and the conditional Copula function. The Akaike information criterion (AIC) and the maximum likelihood estimation method are used for structure selection and parameter estimation. The Vine Copula structure is constructed by nesting the two-dimensional Copula function and the conditional Copula function layer by layer. The Vine Copula structure is shown in Figure 2 (a) as follows. The first layer consists of two-dimensional Copula functions, and the second layer is connected by conditional Copula functions. The calculation formula of the Vine Copula function is as follows: (8), where is the -dimensional Vine Copula function, which is used to describe the dependence relationship between class risk source factors, represents the iteration of the hierarchical structure, represents the conditional Copula function of the class and the class risk source factors under the condition of the given conditional risk source factor set , is the total number of categories of risk source factors.

[0031] S25. Generate probability samples of risk source factors; Based on the marginal distribution function of risk source factors, combined with the two-dimensional Copula function, the conditional Copula function and the Vine Copula structure, the Latin hypercube sampling method is used to generate probability samples of risk source factors.

[0032] Specifically, it includes the following sub-steps: S25a. On the value range [0,1] of the marginal distribution function value of the first risk source factor, use Latin hypercube sampling to generate the sample value of the marginal distribution function of the first risk source factor ; S25b. Based on , calculate the sample values of the marginal distribution function of the second risk source factor through the two-dimensional Copula function and its corresponding conditional Copula function ; S25c. Given , , calculate the sample values of the marginal distribution function of the third risk source factor according to the corresponding conditional Copula function in the Vine Copula structure ; S25d. And so on, generate the sample values of the marginal distribution functions of risk source factors layer by layer from top to bottom in the Vine Copula structure , , …, . The sample values of the marginal distribution functions of these risk source factors form the probability samples of the risk source factors.

[0033] S26. Calculate the joint probability of the probability samples of the risk source factors; According to the Vine Copula structure and the principle of probability mutual exclusion, the joint probability calculation formula of the probability samples of the risk source factors is as follows: (9), where represents , , 's joint probability, represents the Vine Copula function, and the input is the values of the marginal distribution functions of the corresponding risk source factors. represents that the value of the marginal distribution function of the first type of risk source factor is , and the values of the marginal distribution functions of the second and third types are 1 (the maximum value) of the Vine Copula function. represents that the value of the marginal distribution function of the second type of risk source factor is , and the values of the marginal distribution functions of the first and third types are 1 of the Vine Copula function. represents that the value of the marginal distribution function of the third type of risk source factor is , and the values of the marginal distribution functions of the first and second types are 1 of the Vine Copula function. represents that the values of the marginal distribution functions of the first and second types of risk source factors are and , and the value of the marginal distribution function of the third type is 1 of the Vine Copula function. The marginal distribution function values of the risk source factors of the first and third categories are and , and the Vine Copula function with the marginal distribution function value of 1 for the second category The marginal distribution function values of the risk source factors of the second and third categories are and , and the Vine Copula function with the marginal distribution function value of 1 for the first category The marginal distribution function values of the risk source factors of the first, second, and third categories are , and of the Vine Copula function.

[0034] S27. Generate risk source factor samples based on the inverse transformation of the marginal distribution function; Based on the probability threshold of the extreme flood event in step S12 and the joint probability of the risk source factor probability samples calculated in step S26 , screen the probability samples that meet the following conditions: (10), Take the risk source factor probability samples that meet the above conditions as the risk source factor probability samples corresponding to the extreme flood event.

[0035] Based on the marginal distribution function calculated in step S21, perform inverse transformation on the risk source factor probability samples corresponding to the extreme flood event to generate risk source factor samples, as shown in Figure 2 (b) below. The inverse transformation formula is: (11), where is the inverse function of the marginal distribution function of the th risk source factor, is the vector of risk source factor samples, , is the th sample of the th category of risk source factors, , and

[0036] S3. Generate a set of extreme flood hydrograph scenarios based on the Neural Gas algorithm; The K-Means method is used to cluster the risk source factor samples into different groups, the Neural Gas algorithm is used to construct the risk source factor scenario tree for each group, the extreme flood scenario probability is calculated, and the historical flood data is coupled by the same ratio scaling method to generate the extreme flood hydrograph scenario.

[0037] Specifically, it includes the following steps: S31. Divide the risk source factor samples into different groups; Based on the risk source factor samples generated in step S27, in order to facilitate the subsequent modeling of the scenario tree, these samples need to be clustered and divided into different groups.

[0038] Determine the optimal number of clusters 𝑅 according to the elbow method. The elbow method draws the relationship curve between the number of clusters and the within-cluster sum of squares (WCSS), and selects the number of clusters corresponding to the point where the decrease of WCSS begins to flatten out as the optimal number of clusters. After determining 𝑅, use the K-Means method to divide the risk source factor samples. This algorithm iterates through the following sub-steps: S31a. Initialization: Randomly select risk source factor samples as the initial group centers; S31b. Assign samples: Assign each risk source factor sample to the group center with the closest Euclidean distance to it; S31c. Update centers: Calculate the mean of the samples in each group and use this mean as the new group center; S31d. Judge convergence: If the group centers no longer change significantly, or reach the preset maximum number of iterations, the algorithm terminates; otherwise, return to step S31b to continue the iteration.

[0039] This process aims to minimize the sum of the squared errors of all risk source factor samples within the groups to their group centers, ensuring that the clustering results have good compactness and separation. Then the number of samples in all groups should satisfy the following relationship: (12), where, is the number of risk source factor samples contained in the th group.

[0040] S32. Use the Neural Gas algorithm to construct the risk source factor scenario tree; The scenario tree consists of nodes, starting from the root node in the first stage, gradually branching backward, and reaching the leaf nodes in the final stage. Each leaf node corresponds to a scenario. According to the risk source factors of the historical flood data calculated in step S14, the variance of each type of risk source factor is calculated. The risk source factor with the smallest variance is used as the root node in the first stage, and the risk source factor with the largest variance is used as the leaf node in the final stage, thereby determining the position of the risk source factor in the scenario tree. The structure of the scenario tree is predefined, and then risk source factor samples are randomly selected as the initial node values of the scenario tree, and the initial Euclidean distance between the risk source factor samples and the nodes of the scenario tree is calculated. Through iterative optimization using the Neural Gas algorithm, the node values of the scenario tree are dynamically adjusted to minimize the overall Euclidean distance from the risk source factor samples to the nodes of the scenario tree. Finally, based on the optimized scenario tree, the probabilities of different scenarios are calculated.

[0041] Specifically, it includes the following sub-steps: S32a. Predefine the structure of the scenario tree; According to the Neural Gas algorithm, the structure of the scenario tree is predefined. As shown in (c) below, the root node, intermediate nodes, and leaf nodes of the scenario tree are represented by matrices. Then the Figure 2 th scenario tree generated by the th group can be represented as: (13), where the columns of the matrix correspond to different nodes (i.e., the root node, intermediate nodes, and leaf nodes), each column of nodes corresponds to a type of risk source factor, the number of rows of the matrix corresponds to the number of leaf nodes of the scenario tree, that is, each row represents a scenario. represents the th scenario tree, represents the th scenario of the th scenario tree, represents the node value of the th scenario tree, is the number of scenarios in the scenario tree, is the number of nodes included in each scenario.

[0042] S32b. Initialize the node values of the scenario tree; In the th group, randomly select risk source factor samples as the initial node values of the scenario tree. Its calculation formula is as follows: (14), where is the node value of the th scenario tree, is selected from the Risk source factor samples of a group Samples randomly selected from , .

[0043] Step S32c, Euclidean distance calculation; Calculate the node values of the scenario tree to the risk source factor samples The Euclidean distance is calculated as follows: (15), where is the node value of the scenario tree to the risk source factor sample The Euclidean distance, , after calculating the Euclidean distance, sort the Euclidean distance in descending order, and record the sorted Euclidean distance sequence in the array for subsequent node value updates.

[0044] Step S32d, iteratively update the node values of the scenario tree based on the Neural Gas algorithm; To minimize the distance between the risk source factor samples and the node values of the constructed scenario tree, by adjusting the node values of the scenario tree to gradually approach the risk source factor samples, the node values of the scenario tree are iteratively updated, and corrected according to the Euclidean distance sorting of each node value and the risk source factor samples and the number of iterations. The adjustment process follows the following formula: (16), (17), (18), (19), where is the adjustment amount of the node value of the scenario tree in one iteration process, is the step size function, which determines the overall step size of the changes of all node values of the scenario tree in each iteration, and are step size parameters (the initial step size is set to 5, and the final step size is set to 1), is the current iteration number, , the step size decreases as the iteration number increases, is the maximum iteration number ( is set to 10000), is the local step size function, which determines the step size of the changes of the node values of a single scenario tree and To adapt to the parameters To adapt to the function, according to the node values of the scenario tree and the risk source factor samples The order of the Euclidean distances provides adaptation values for the node values of each scenario tree.

[0045] At the iteration number of the node values of the scenario tree are updated according to The update formula is as follows: (20), where is the node value of the th iteration of the th scenario tree, is the node value of the th iteration of the th scenario tree.

[0046] Repeat steps S32c and S32d. In each iteration, the node values of the scenario tree need to be adjusted according to these formulas until the maximum iteration number is reached.

[0047] Step S32e, calculate the probability of each scenario in the scenario tree; Calculate the probability of each scenario in the scenario tree based on the ratio of the number of risk source factor samples closest to each scenario to the total number of risk source factor samples in the group. The calculation formula is as follows: (21), where is the probability of the th scenario of the th scenario tree, is the counting function, which calculates the number of that meet the conditions, , is the index of the risk source factor sample, that is, the th in the group has risk source factor samples, is the Euclidean distance, is the total number of scenarios in the scenario tree.

[0048] S33, generate extreme flood scenarios; Based on the risk source factor sample groups divided in step S31, repeat step S32 to construct a scenario tree for each group separately, as Figure 2As shown in (d) of the Chinese text. The scenario tree structure and scenario probability calculation for each group are based on the sample data of risk source factors in that group. The scenarios and corresponding probabilities in all scenario trees are calculated, and these scenarios constitute the extreme flood scenarios.

[0049] S34. Calculate the probability of extreme flood scenarios; According to the probability threshold of extreme flood events in step S12 , the groups divided in step S31, and the scenario trees constructed for each group in step S33, calculate the probability of each scenario tree as , then under the condition that the extreme flood event occurs, the probability of the th scenario tree is . Each scenario tree contains extreme flood scenarios. Then, under the condition that the extreme flood event and the group occur, the probability of the extreme flood scenario occurring is . Based on the conditional probability formula, the probability of the extreme flood scenario is calculated as follows: (22), S35. Generate the extreme flood hydrograph scenarios; There is a large uncertainty in the spatio-temporal distribution of extreme flood events. Since extreme flood events are scarce in historical flood data, it is difficult to accurately characterize and calibrate the uncertainty of their spatio-temporal distribution. Extreme flood scenarios can only reflect the uncertainty of their spatial distribution and have not yet reflected the time-course evolution characteristics of extreme flood events. At the same time, it is difficult to meet the requirements of the joint flood control optimization scheduling model for the input data of flood hydrographs. Therefore, the same ratio scaling method is used to couple the extreme flood scenarios with historical flood data to construct extreme flood hydrograph scenarios, so as to comprehensively characterize the uncertainty of the time-course and spatial distribution of extreme flood events and provide reliable support for subsequent extreme flood simulation and flood control risk assessment.

[0050] Specifically, it includes the following sub-steps: Step S35a: Based on the root node value (i.e., the highest water level in the outer river) of the extreme flood scenario and the historical outer river water level at the th year and the th time period in step S14, use the same ratio scaling method to calculate the outer river water level of the extreme flood scenario at the th year and the th time period, and the calculation formula is as follows: ​(twenty three), Step S35b: Based on extreme flood scenarios The intermediate node value of (i.e. upstream inflow volume) and step S14 Year The reservoir is in Historical upstream inflow flow during the period , using the same-ratio scaling method to calculate extreme flood scenarios No. Year The reservoir is in Upstream inflow during the period , the formula is as follows: (twenty four), in, It is the unit conversion factor for converting flow to flood volume.

[0051] Step S35c: Based on extreme flood scenarios The leaf node value of (i.e. the flood volume entering the lake) and step S14 In the Historical interval inflow to the lake during the period , using the same-ratio scaling method to calculate extreme flood scenarios No. Year The flow rate into the lake during the period , the calculation formula is as follows: (25), Step S35d: Arrange the upstream inflow, interval inflow and river water level obtained in steps S34a, S34b and S34c in chronological order to form the process line data under the extreme flood scenario, thereby forming an extreme flood process line scenario. The extreme flood process line scenario generated by the calculation is not only consistent with the statistical characteristics of historical flood data, but also inherits the probability of extreme flood scenarios. ,These extreme flood process line scenarios are used as input data for the ,joint flood control optimization operation model of the reservoir-lake-river ,system to simulate the joint flood control operation scheme under ,extreme flood conditions.

[0052] S4. Extreme flood event simulation and flood risk assessment; Build a joint flood control optimal operation model for the reservoir-lake-river system, use the extreme flood hydrograph scenario as the input data of the model, simulate the joint flood control operation plan for extreme floods, analyze the situations where the safety storage thresholds of the reservoir and the lake are exceeded in the joint flood control operation plan for extreme floods, evaluate the flood control risks of the reservoir and the lake, and use the Continuous Ranked Probability Score (CRPS) to evaluate the results of the flood control risks of the reservoir and the lake.

[0053] Specifically, it includes the following steps: S41. Build a joint flood control optimal operation model for the reservoir-lake-river system; The objective function of the joint flood control optimal operation model for the reservoir-lake-river system is to minimize the average excess flood volume of the lake during the entire operation period, and the calculation formula is: (26), Where, is the objective function of the joint flood control optimal operation model, is the storage volume of the lake at the beginning of the period, is the total operation duration, is the safety storage threshold of the lake.

[0054] The constraint conditions of the joint flood control optimal operation model for the reservoir-lake-river system are: (a) Reservoir water balance constraint: (27), Where, is the storage volume of the th reservoir at the beginning of the period, is the storage volume of the th reservoir at the beginning of the period, and are respectively the inflow and outflow of the th reservoir during the period, is the time step.

[0055] (b) Reservoir characteristic water level constraint: (28), Where, is the water level of the th reservoir during the period, and are respectively the maximum limit water level and the minimum limit water level of the th reservoir during the period.

[0056] (c) Initial / final water level constraint: (29), where is the water level of the th reservoir at the initial time ; is the water level of the th reservoir at the final time ; and are the initial water level and the final water level of the th reservoir set according to the flood control operation requirements, respectively.

[0057] (d) Reservoir discharge capacity: (30), where is the discharge capacity corresponding to the water level of the th reservoir at the time period.

[0058] (e) Outflow variation range constraint: (31), where is the variation range limit value of the outflow between adjacent time periods of the th reservoir, is the outflow of the th reservoir at the time period.

[0059] (f) Lake water volume balance constraint: (32), where is the lake storage at the beginning of the time period, is the lake storage at the beginning of the time period, is the outflow of the th reservoir at the time period, and are the inflow into the lake from the catchment area and the outflow from the lake during the time period, respectively.

[0060] (g) Outer river water level constraint: (33), where is the outflow from the lake during the time period, is The lake water level during the time period, is the water level of the external river during the time period. When the lake water level is higher than the water level of the external river, the lake outflow is a functional relationship related to the lake water level and the water level of the external river , and when the water level of the external river is higher than the lake water level, the lake outflow is equal to 0.

[0061] S42: Simulate the joint flood control operation plan for extreme floods; Input the extreme flood hydrograph scenario generated in step S35 into the joint flood control optimization model of the reservoir-lake-river system, and use LINGO software to calculate and solve it to simulate the joint flood control operation plan for extreme floods, and calculate the maximum storage volume of the reservoir corresponding to each operation plan and the maximum storage volume of the lake . Since the generation of these joint flood control operation plans is based on the extreme flood hydrograph scenario, each operation plan not only reflects the operation characteristics of different extreme flood hydrograph scenarios, but also inherits the probability attributes of the extreme flood hydrograph scenario.

[0062] S43: Evaluate the flood control risk of the reservoir; The flood control risk of the reservoir is measured by the probability that the maximum storage volume of the reservoir during the operation period exceeds its corresponding flood control safety threshold. Therefore, the flood control risk of the reservoir can be defined as: the weighted sum of probabilities that the maximum storage volume of the reservoir exceeds its flood control safety threshold in all joint flood control operation plans for extreme floods. The calculation formula is: (34), where, is the flood control risk of the reservoir, is the number of scenarios, is the number of scenario trees, is the th extreme flood scenario of the th reservoir in the extreme flood joint flood control operation plan calculated by the joint flood control optimization model at the beginning of the time period, is the flood control safety threshold corresponding to the th reservoir, is the th probability of the th

[0063] S44: Evaluate the flood control risk of the lake; The flood control risk of a lake is measured by the probability that the maximum water storage of the lake during the scheduling period exceeds its corresponding flood control safety threshold. Therefore, the flood control risk of the lake can be defined as the sum of the weighted probabilities that the maximum water storage of the lake exceeds its flood control safety threshold in all extreme flood joint flood control scheduling schemes. The calculation formula is as follows: (35), where is the flood control risk of the lake, is the th scenario tree, the initial water storage of the lake at the th time period under the extreme flood hydrograph scenario calculated by the joint flood control optimization model for the th extreme flood scenario,

[0064] S45: Evaluate the effect of risk assessment; To verify the stability and reliability of the risk assessment results, the risk assessment value is compared with the risk true value. Since it is difficult to directly obtain the true values of the reservoir flood control risk and the lake flood control risk, based on the marginal distribution function of the risk source factors fitted in step S21, Monte Carlo random sampling is used to generate random probability samples of the risk source factors, and random samples of the risk source factors are generated based on the inverse transformation of the marginal distribution function of the risk source factors. Then, historical flood data is coupled to generate extreme flood random hydrograph scenarios, which are input into the joint flood control optimization model of the reservoir-lake-river system to calculate the Monte Carlo joint flood control scheduling scheme. Based on this scheme, reservoir flood control risk assessment and lake flood control risk assessment are carried out to obtain the Monte Carlo value of the reservoir flood control risk ( ) and the Monte Carlo value of the lake flood control risk ( ), which are used as the risk true values. The continuous ranked probability score (CRPS) is used to evaluate the effect of risk assessment. The calculation formula is as follows: (36), (37), where is the continuous ranking score function, is the cumulative probability distribution function, is the Monte Carlo value of the risk assessment, is the numerical value of the risk assessment, is the cumulative probability distribution function of the risk assessment value, is the th Heaviside function of the th type of risk, is the th type of risk for the th assessment, The Heaviside function of the second type of risk in the second evaluation. The lower the value of CRPS, the better the effect of risk assessment.

[0065] The system of the present invention includes: A risk source factor calculation unit, which is used to collect historical flood data and flood control project data of the reservoir-lake-river system, count historical extreme flood events, identify risk source factors that cause historical extreme flood events, and the risk source factors include upstream inflow, inflow into the lake in the interval, and the highest water level of the outer river. The annual maximum value method is used to calculate each type of risk source factor in the historical flood data of each year; A risk source factor sample generation unit, which is used to fit marginal distribution functions for each type of risk source factor, construct corresponding two-dimensional Copula functions and conditional Copula functions, generate a Vine Copula function, generate risk source factor probability samples through the Latin hypercube sampling method, calculate the joint probability of each group of risk source factor probability samples, screen out risk source factor probability samples with a joint probability lower than the extreme flood event probability threshold, and use the marginal distribution function to perform inverse transformation on them to generate risk source factor samples; An extreme flood hydrograph scenario generation unit, which is used to cluster risk source factor samples into different groups by using the K-Means method, construct a risk source factor scenario tree for each group by using the Neural Gas algorithm, calculate the extreme flood scenario probability, and generate an extreme flood hydrograph scenario by coupling historical flood data using the same ratio scaling method; A scheduling plan generation and evaluation unit, which is used to construct a joint flood control optimization scheduling model of the reservoir-lake-river system with the objective of minimizing the average excess flood volume of the lake during the entire scheduling period, use the extreme flood hydrograph scenario as the input data of the model, simulate the extreme flood joint flood control scheduling plan, analyze the situation where the safety storage thresholds of the reservoir and the lake are exceeded in the extreme flood joint flood control scheduling plan, evaluate the reservoir flood risk and the lake flood risk, and use the continuous ranked probability score CRPS to evaluate the results of the reservoir flood risk and the lake flood risk.

[0066] The electronic device of the present invention includes a memory, a processor, and a computer program / instructions stored on the memory and executable on the processor. When the computer program / instructions are executed by the processor, the steps of the method for simulating extreme floods and assessing flood control risks of the reservoir-lake-river system are implemented.

[0067] The computer-readable storage medium of the present invention stores computer instructions, and when the computer instructions are called, they are used to execute the steps of the method for simulating extreme floods and assessing flood control risks of the reservoir-lake-river system.

Claims

1. A method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system, characterized in that It includes the following steps: S1. Collect the historical flood data and flood control project data of the reservoir-lake-river system, count the historical extreme flood events, identify the risk source factors that cause the historical extreme flood events. The risk source factors include the upstream inflow into the reservoir, the inflow into the lake from the intermediate area, and the highest water level of the external river. Use the annual maximum value method to calculate each type of risk source factor in the historical flood data of each year; S2. Fit the marginal distribution function for each type of risk source factor, construct the corresponding two-dimensional Copula function and conditional Copula function, generate the Vine Copula function, generate the probability samples of the risk source factors through the Latin hypercube sampling method, calculate the joint probability of each group of risk source factor probability samples, screen out the risk source factor probability samples with the joint probability lower than the probability threshold of the extreme flood event, and use the marginal distribution function to perform inverse transformation on them to generate the risk source factor samples; S3. Use the K-Means method to cluster the risk source factor samples into different groups, use the Neural Gas algorithm to construct the risk source factor scenario tree for each group, calculate the extreme flood scenario probability, and use the same ratio scaling method to couple the historical flood data to generate the extreme flood hydrograph scenario; S4. Construct the joint flood control optimization scheduling model of the reservoir-lake-river system with the goal of minimizing the average excess flood volume of the lake during the entire scheduling period. Use the extreme flood hydrograph scenario as the input data of this model, simulate the extreme flood joint flood control scheduling plan, analyze the situation where the safety storage thresholds of the reservoir and the lake are exceeded in the extreme flood joint flood control scheduling plan, evaluate the flood control risks of the reservoir and the lake, and use the continuous ranked probability score CRPS to evaluate the results of the flood control risks of the reservoir and the lake.

2. The method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system according to claim 1, wherein In step S1, the annual maximum value method is used to calculate each type of risk source factor in the historical flood data of each year, including: Based on the historical flood data, extract the maximum flood event that occurs each year, and calculate the corresponding upstream inflow into the reservoir, the inflow into the lake from the intermediate area, and the highest water level of the external river. The calculation formulas are as follows: ， Among them, is the upstream inflow volume of the historical flood in the th year, is the th year's th reservoir's historical upstream inflow rate during the time period, is the total number of reservoirs, is the total dispatching duration; ， Among them, is the inflow volume into the lake in the interval in the historical flood data of the th year, is the historical inflow rate into the lake in the interval in the th year during the th time period; ， Among them, is the highest water level of the outer river in the historical flood data of the th year, is the historical water level of the outer river in the th year during the time period.

3. A method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system according to claim 1, characterized in that The method for generating the Vine Copula function in step S2 is: (1) Based on the three types of risk source factors, namely the upstream inflow into the reservoir, the inflow into the lake from the intermediate area, and the highest water level of the external river, use the maximum likelihood estimation method to fit the marginal distribution function for each type of risk source factor; the expression of the marginal distribution function of the risk source factor is: ， Among them, is the vector of the type of risk source factors, , , is the type of risk source factors in the year's historical flood data, , is the year index corresponding to the historical flood data, is the total number of years of the historical flood data, is the corresponding marginal distribution function, is the value of the marginal distribution function of the type of risk source factors; (2) Calculate the Kendall rank correlation coefficient between each type of risk source factor, and construct a two-dimensional Copula function based on the marginal distribution function values of the risk source factors, the Kendall rank correlation coefficient, and the Bayesian information criterion BIC. The expression is: ， Among them, represents the two-dimensional Copula function of the joint distribution between the -th type and the -th type of risk source factors, , is the vector of the -th type of risk source factors, and its marginal distribution function is , is the value of the marginal distribution function of the -th type of risk source factors; (3) Based on the marginal distribution function of risk source factors and the two-dimensional Copula function, construct a conditional Copula function, which is used to calculate the conditional joint distribution function of the -th type of risk source factors under the condition of given other -th type of risk source factors. The calculation formula of the conditional Copula function is as follows: ， Among them, is the joint Copula function of risk source factors from the 1st to the th category, , , are the marginal distribution function values of the 1st, 2nd, and th category of risk source factors respectively; (4) Construct a Vine Copula structure based on two-dimensional Copula functions and conditional Copula functions. Use the Akaike Information Criterion (AIC) and maximum likelihood estimation method for structure selection and parameter estimation. Build the Vine Copula structure by nesting two-dimensional Copula functions and conditional Copula functions layer by layer. The first layer of the Vine Copula structure consists of two-dimensional Copula functions, and the second layer is connected by conditional Copula functions. The calculation formula of the Vine Copula function is: ， Among them, is the d-dimensional Vine Copula function, which is used to describe the dependence relationship between types of risk source factors. represents the iteration of the hierarchical structure, indicating the conditional Copula function of the -th and -th types of risk source factors under the given set of conditional risk source factors, and is the total number of categories of risk source factors.

4. A method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system according to claim 1, characterized in that The method for generating risk source factor samples in step S2 is as follows: (1) Based on the marginal distribution function of risk source factors, combined with the two-dimensional Copula function, conditional Copula function, and VineCopula structure, the Latin hypercube sampling method is used to generate probability samples of risk source factors , , , is the probability sample of the first type of risk source factor, is the probability sample of the second type of risk source factor, is the probability sample of the third type of risk source factor; (2) According to the Vine Copula structure and the principle of probability mutual exclusion, the joint probability calculation formula of risk source factor probability samples is: ， Among them, represents , , the joint probability of represents the Vine Copula function, and the input is the marginal distribution function values of the corresponding risk source factors. represents that the marginal distribution function value of the first type of risk source factor is , and the Vine Copula function with the marginal distribution function values of the second and third types being 1. represents that the marginal distribution function value of the second type of risk source factor is , and the Vine Copula function with the marginal distribution function values of the first and third types being 1. represents that the marginal distribution function value of the third type of risk source factor is , and the VineCopula function with the marginal distribution function values of the first and second types being 1. represents that the marginal distribution function values of the first and second types of risk source factors are and , and the Vine Copula function with the marginal distribution function value of the third type being 1. represents that the marginal distribution function values of the first and third types of risk source factors are and , and the Vine Copula function with the marginal distribution function value of the second type being 1. represents that the marginal distribution function values of the second and third types of risk source factors are and , and the VineCopula function with the marginal distribution function value of the first type being 1. represents the Vine Copula function with the marginal distribution function values of the first, second, and third types of risk source factors being , and ; (3) Probability threshold based on extreme flood events and the joint probability of the probability samples of risk source factors , and screen the probability samples that meet the following conditions: ， Take the risk source factor probability samples that meet the above conditions as the risk source factor probability samples corresponding to extreme flood events; Based on the marginal distribution function, perform inverse transformation on the risk source factor probability samples corresponding to extreme flood events to generate risk source factor samples.

5. A method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system according to claim 1, characterized in that, Step S3 includes the following steps: S31. Based on the generated risk source factor samples, determine the optimal number of clusters 𝑅 according to the elbow method, and use the K-Means method to divide the risk source factor samples; S32. According to the risk source factors of historical flood data, calculate the variance of each type of risk source factor. Take the risk source factor with the smallest variance as the root node in the first stage, and the risk source factor with the largest variance as the leaf node in the final stage, so as to determine the position of the risk source factor in the scenario tree. Pre-define the structure of the scenario tree, then randomly select risk source factor samples as the initial node values of the scenario tree, and calculate the initial Euclidean distance between the risk source factor samples and the nodes of the scenario tree. Through iterative optimization of the Neural Gas algorithm, dynamically adjust the node values of the scenario tree to minimize the overall Euclidean distance from the risk source factor samples to the nodes of the scenario tree. Finally, based on the optimized scenario tree, calculate the probabilities of different scenarios; S33. Based on the divided groups of risk source factor samples, repeat step S32 to construct a scenario tree for each group separately; the structure of the scenario tree and the calculation of scenario probabilities for each group are based on the risk source factor sample data of that group. Calculate the scenarios and corresponding probabilities in all scenario trees, and these scenarios constitute extreme flood scenarios; S34. According to the probability threshold of extreme flood events , the groups divided and the scenario trees constructed for each group, calculate the probability of each scenario tree as . Then, under the condition that an extreme flood event occurs, the probability of the -th scenario tree is ; each scenario tree contains extreme flood scenarios. Then, under the condition that the extreme flood event and the group occur, the probability of the extreme flood scenario occurring is . Based on the conditional probability formula, the probability expression of the extreme flood scenario is: ， Among them, is the probability of the extreme flood scenario; S35. Use the same ratio scaling method to couple the extreme flood scenarios with historical flood data to construct extreme flood hydrograph scenarios; the extreme flood hydrograph scenarios are used as the input data of the joint flood control optimization scheduling model for the reservoir-lake-river system to simulate the joint flood control scheduling plan under extreme flood conditions.

6. A method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system according to claim 1, characterized in that, The expression of the objective function in step S4 is: ， Among them, is the objective function of the joint flood control optimal operation model, is the storage volume of the lake at the beginning of time period, is the total operation duration, is the threshold of the safe storage volume of the lake; The model constraint conditions include: reservoir water balance constraint, reservoir characteristic water level constraint, initial / final water level constraint, reservoir discharge capacity, discharge variation range constraint, reservoir-lake water balance constraint, and outer river water level constraint; Input the generated extreme flood hydrograph scenarios into the joint flood control optimal operation model of the reservoir-lake-river system, use the LINGO software to calculate and solve, simulate the extreme flood joint flood control operation plan, and calculate the maximum storage volume of the reservoir corresponding to each operation plan and the maximum storage volume of the lake .

7. A method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system according to claim 1, characterized in that The methods for evaluating reservoir flood risk and lake flood risk in step S4 are: Evaluate the reservoir flood risk: The flood control risk of the reservoir is defined as: the sum of the weighted probabilities that the maximum storage volume of the reservoir exceeds its flood control safety threshold in all joint flood control operation plans for extreme floods. The calculation formula is as follows: ， Among them, is the flood control risk of the reservoir, is the number of scenarios, is the number of scenario trees, is the th scenario tree, and the th extreme flood scenario. The th reservoir's initial storage volume at the beginning of the time period, is the th reservoir's corresponding flood control safety threshold, , is the total number of reservoirs, is the total scheduling duration, is the indicator function, is the th scenario tree, and the th extreme flood scenario's probability; Evaluate the flood control risk of the lake: The flood control risk of the lake is defined as: the sum of the weighted probabilities that the maximum storage volume of the lake exceeds its flood control safety threshold in all joint flood control operation plans for extreme floods. The calculation formula is as follows: ， Among them, is the flood control risk of the lake, is the th scenario tree, and the th extreme flood scenario. Under the scenario conditions of the extreme flood hydrograph calculated by the joint flood control optimization scheduling model, the lake storage volume at the beginning of the th period, is the flood control safety threshold corresponding to the safe water level of the lake; Evaluate the effect of risk assessment: Based on the fitted marginal distribution function of risk source factors, Monte Carlo random sampling is used to generate random probability samples of risk source factors, and random samples of risk source factors are generated based on the inverse transformation of the marginal distribution function of risk source factors. Then, historical flood data is coupled to generate extreme flood stochastic hydrograph scenarios, which are input into the joint flood control optimization scheduling model of the reservoir-lake-river system to calculate the Monte Carlo joint flood control operation plans. Based on these plans, the flood control risk of the reservoir and the flood control risk of the lake are evaluated to obtain the Monte Carlo values of the flood control risk of the reservoir and the Monte Carlo values of the flood control risk of the lake, which are used as the risk true values. The continuous ranked probability score is used to evaluate the effect of risk assessment. The lower the value of CRPS, the better the effect of risk assessment.

8. A reservoir-lake-river system extreme flood simulation and flood control risk assessment system, characterized in that, Including: A risk source factor calculation unit, which is used to collect historical flood data and flood control project data of the reservoir-lake-river system, count historical extreme flood events, identify risk source factors that cause historical extreme flood events. The risk source factors include upstream inflow, inflow into the lake from the intermediate area, and the highest water level of the outer river. The annual maximum value method is used to calculate each type of risk source factor in the historical flood data of each year. A risk source factor sample generation unit, which is used to fit the marginal distribution function for each type of risk source factor, construct the corresponding two-dimensional Copula function and conditional Copula function, generate the Vine Copula function, generate random probability samples of risk source factors through the Latin hypercube sampling method, calculate the joint probability of each group of risk source factor probability samples, screen out the risk source factor probability samples with joint probabilities lower than the extreme flood event probability threshold, and use the marginal distribution function to perform inverse transformation on them to generate risk source factor samples. An extreme flood hydrograph scenario generation unit, which is used to cluster risk source factor samples into different groups by using the K-Means method, construct a risk source factor scenario tree for each group by using the Neural Gas algorithm, calculate the extreme flood scenario probability, and use the same ratio scaling method to couple historical flood data to generate extreme flood hydrograph scenarios. A scheduling plan generation and evaluation unit, which is used to construct a joint flood control optimization scheduling model of the reservoir-lake-river system with minimizing the average excess flood volume of the lake during the entire scheduling period as the objective function, use the extreme flood hydrograph scenarios as the input data of this model, simulate the joint flood control operation plans for extreme floods, analyze the situations where the safety storage volume thresholds of the reservoir and the lake are exceeded in the joint flood control operation plans for extreme floods, evaluate the flood control risk of the reservoir and the flood control risk of the lake, and use the continuous ranked probability score CRPS to evaluate the results of the flood control risk of the reservoir and the flood control risk of the lake.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program / instructions stored in the memory and executable on the processor. When the computer program / instructions are executed by the processor, the steps of the method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system according to any one of claims 1-7 are implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions which, when called, are used to execute the steps of the method for simulating extreme floods and assessing flood control risks in a reservoir-lake-river system according to any one of claims 1-7.

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