Lake water resource-water safety collaborative annual-scale robust scheduling logic generation method, system and equipment and storage medium

Abstract

The invention discloses a lake water resource-water safety collaborative annual-scale robust scheduling logic generation method, system and device and a storage medium, and the system comprises a watershed hydrology-lake body hydrodynamic coupling modeling module which is used for constructing a distributed watershed hydrology model and a lake body hydrodynamic model; the comprehensive meteorological uncertainty boundary file generation module is used for obtaining a watershed meteorological boundary file and a lake body meteorological boundary file; the drainage basin hydrological simulation module is used for forming a drainage basin inflow-water taking boundary file set covering meteorological uncertainty; the lake water level control strategy generation module can obtain a plurality of water level control boundary files; a lake body hydrodynamic simulation module; and the water level control strategy determination module is used for determining a final annual scale robust water level scheduling strategy. According to the method, the spatial-temporal heterogeneity of rainfall and a multi-scene water level control strategy are comprehensively considered, the accuracy and robustness of lake water resource management are remarkably improved, and reliable operation of the system under various meteorological conditions is guaranteed.

Description

Technical Field

[0001] The present invention relates to a method for controlling the water level of a lake, and particularly to a method, system, device and storage medium for generating an annual-scale robust scheduling logic for the coordination of lake water resources and water security. Background Art

[0002] When the prior art regulates the water level of a lake, the following problems exist: Most of them use a single meteorological scenario for simulation, without fully considering the uncertainties of the total annual rainfall, total monthly rainfall, rainfall pattern, and spatial differentiation of rainfall. Usually, a single / several sets of preset rainfall amounts are multiplied by a fixed runoff coefficient to calculate the basin inflow, considering the influence of various factors such as the previous drought duration and the difference in underlying surface on the runoff yield. However, due to the uncertainty of the basin itself, the non-linear relationship between rainfall and runoff is caused. That is, affected by the combined influence of the spatio-temporal variability of rainfall and the uncertainty of the basin hydrological process, the inflow into the lake has significant uncertainty.

[0003] Moreover, the existing scheduling methods generally rely on manual experience, usually screening solutions based on limited typical hydrological years or extreme events and other preset scenarios, lacking systematic modeling and quantitative response to multi-source uncertainties such as meteorology and hydrology. Due to the lack of integration of large-scale scenario simulation and multi-objective optimization mechanisms, when the constructed scheduling strategy encounters a situation with a significant deviation from the preset scenario during actual operation, problems such as strategy failure, and obvious deficiencies in robustness and adaptability are likely to occur. Summary of the Invention

[0004] Object of the Invention: The first object of the present invention is to provide an annual-scale robust scheduling logic generation method for the coordination of lake water resources and water security that takes into account meteorological uncertainty and basin uncertainty and has high regulation accuracy and strong robustness.

[0005] The second object of the present invention is to provide an annual-scale robust scheduling logic generation system for the coordination of lake water resources and water security.

[0006] The third object of the present invention is to provide an electronic device.

[0007] The fourth object of the present invention is to provide a computer-readable storage medium.

[0008] Technical Solution: An annual-scale robust scheduling logic generation method for the coordination of lake water resources and water security disclosed by the present invention includes the following steps, S1: Collect the hydrological and geographical data of the basin to which the target lake belongs and the characteristic data of the lake body itself, construct a distributed basin hydrological model based on the hydrological and geographical data, and jointly construct a lake hydrodynamic model by using the characteristic data of the lake body itself and the output results of the distributed basin hydrological model; S2: Obtain the annual-scale predicted rainfall data and historical measured meteorological data of the basin to which the target lake belongs. Use scenario construction technology for the annual-scale predicted rainfall data and historical measured meteorological data to construct multiple boundary scenarios representing meteorological uncertainty. Based on the multiple boundary scenarios, generate a basin meteorological boundary file for use in a distributed basin hydrological model and a lake meteorological boundary file for use in a lake hydrodynamic model respectively; S3: Input the generated multiple basin meteorological boundary files into the distributed basin hydrological model respectively, and extract the time series of the water volume flowing into the lake and the time series of the water volume taken from the lake by the basin corresponding to each basin meteorological boundary file after the operation of the distributed basin hydrological model, so as to form a set of basin inflow-withdrawal boundary files covering meteorological uncertainty; S4: Based on the water level monitoring data of the lake body ontology feature data, statistically obtain the monthly end-of-month water level distribution range of the target lake. Use the grid search method for the end-of-month water level distribution range to construct multiple annual-scale water level control strategies with different regulation characteristics, and convert each annual-scale water level strategy into a water level control boundary file for the lake hydrodynamic model; S5: Form an initial combination of the lake meteorological boundary file and the basin inflow-withdrawal boundary file with the same year of historical measured meteorological data. Arrange and combine several initial combinations with several water level control boundary files respectively to obtain multiple groups of input files for the lake hydrodynamic model. Input each group of input files into the lake hydrodynamic model for annual-scale simulation, and extract the time series of the lake water level and the time series of the downstream outlet flow rate after the simulation of the lake hydrodynamic model corresponding to each group of input files; S6: Merge several groups of input files using the same annual-scale water level control strategy to obtain several input groups; Based on the time series data of the lake water level and the downstream outlet flow rate corresponding to each group of input files, and combined with relevant indicators of lake water resources and water safety, conduct a multi-objective comprehensive evaluation of the input groups, determine the input group with the optimal multi-objective comprehensive score, and extract the water level control boundary file used by this input group as the final annual-scale robust water level scheduling logic of the target lake.

[0009] Furthermore, the basin hydrological and geographical data described in step S1 includes the surface elevation data, soil type, land use, hourly meteorological data, measured basin hydrological data, spatial distribution information of drainage and water intake engineering facilities and their operating flow time series data of the basin to which the lake belongs; The lake body ontology feature data described in step S1 includes the underwater three-dimensional terrain data, lake area meteorological data, water level monitoring data and continuous monitoring data of the inflow and outflow water volume of the lake.

[0010] Further, the annual-scale predicted rainfall data described in step S2 refers to the monthly predicted rainfall amounts for the next year predicted by all prediction stations within the basin to which the target lake belongs; the historical measured meteorological data described in step S2 refers to the historical hourly rainfall data for multiple years of all rain gauge stations within the basin to which the target lake belongs and the historical hourly multi-index meteorological observation data for multiple years of all meteorological stations.

[0011] Further, the steps of generating a basin meteorological boundary file for use in a distributed basin hydrological model and a lake meteorological boundary file for use in a lake hydrodynamic model in step S2 are as follows: S21: Based on the monthly predicted rainfall amounts for the next year of each prediction station, calculate the total annual rainfall amount of each prediction station, and perform perturbation expansion on all the total annual rainfall amounts to obtain multiple total annual rainfall amount samples; S22: Calculate the weights of the historical monthly rainfall amounts of each rain gauge station and meteorological station in each historical year accounting for the total annual rainfall amount based on the historical hourly rainfall data and historical hourly multi-index meteorological observation data to form a historical monthly-scale weight sequence, and predict and generate several groups of predicted monthly-scale weight sequences including 12 months of a year; S23: Perform permutation and combination on the total annual rainfall amount samples obtained in step S21 and the predicted monthly-scale weight sequences obtained in step S22 to obtain several total annual rainfall amount sample - predicted monthly-scale weight sequence combinations, and the monthly rainfall amounts of each month of the total annual rainfall amount sample - predicted monthly-scale weight sequence combination constitute monthly rainfall time series samples; S24: Use a clustering method to perform clustering processing on all the monthly rainfall time series samples, and cluster to obtain k types of typical meteorological scenarios, and use the typical meteorological scenarios as the target rainfall time series representative samples; S25: Combine the k target monthly rainfall time series representative samples and the historical measured meteorological data to predict the hourly-scale rainfall time series of all rain gauge stations and meteorological stations in the next year; S26: Convert the hourly-scale rainfall time series of all rain gauge stations and meteorological stations within the basin to which the target lake belongs predicted in step S25 into a basin meteorological boundary file in a format conforming to the input of the distributed basin hydrological model; convert the hourly-scale rainfall time series of the rain gauge stations and meteorological stations located within the specified range of the target lake into a lake meteorological boundary file in a format conforming to the input of the lake hydrodynamic model.

[0012] Further, the method of generating several groups of predicted monthly-scale weight sequences including 12 months of a year in step S22 is as follows: Classify all the historical monthly-scale weight sequences of each rain gauge station and meteorological station in the historical measured meteorological data by month to form 12 historical rainfall weight sets; For the set of historical rainfall weights for each month, based on the binary event results of whether rainfall occurred at each rainfall station and meteorological station in that month recorded in the historical measured meteorological data, a Bernoulli distribution model is constructed to simulate the rainfall probability for that month. The expression of the Bernoulli distribution is as follows: ; where p m is the Bernoulli distribution probability; X m = 1 indicates that there is rainfall in month m; X m = 0 indicates that there is no rainfall in month m; m = 1, 2,..., 12; In the set of historical rainfall weights for each month, the subset of historical monthly rainfall weights corresponding to rainfall events is selected, and a Gamma distribution model is constructed to simulate the probability distribution of the monthly rainfall weights. The expression of the Gamma distribution is: ; where is the monthly rainfall weight for the m-th month; is the shape parameter of the Gamma distribution; is the scale parameter of the Gamma distribution; Based on the Bernoulli–Gamma joint distribution model, multiple samplings are performed to generate multiple sets of predicted monthly scale weight sequences containing 12 months of a year. The steps for a single sampling are as follows: For the m-th month, sampling is first performed from the Bernoulli distribution corresponding to month m. If the sampled X m = 1, then sampling is performed from the Gamma distribution of the subset of historical monthly rainfall weights corresponding to month m to collect the monthly rainfall weight ; if the sampled X m = 0, then let ; Repeat the collection 12 times until the monthly rainfall weights for 12 months of the next year are collected , and perform normalization on so that the sum of the normalized is 1; the normalized is the predicted monthly scale weight sequence; The steps for predicting the hourly scale rainfall time series for all rainfall stations and meteorological stations in the next year in step S25 are as follows: Calculate the ratio b of the monthly rainfall of the k representative samples of the target monthly rainfall time series to the monthly average rainfall of the basin to which the target lake belongs. The calculation formula for b is as follows: ; where q = 1, 2, 3, 4… k， is the ratio b of the q-th target monthly rainfall time series representative sample in the n-th historical year and the m-th month; is the monthly average rainfall of the m-th month of the n-th historical year of the basin to which the target lake belongs; is the rainfall in the m-th month of the q-th target monthly rainfall time series representative sample; Then, based on the ratio b, scale and map the historical hourly rainfall of the historical measured meteorological data of all rainfall stations and meteorological stations to obtain the hourly-scale rainfall time series for the next year. The calculation formula is as follows: ; where is the predicted rainfall at the h-th hour on the d-th day of the m-th month in the next year when the rainfall station or meteorological station t maps the q-th target monthly rainfall time series representative sample; is the historical rainfall at the h-th hour on the d-th day of the m-th month in the n-th year of the rainfall station or meteorological station t.

[0013] Furthermore, the steps of constructing multiple annual-scale water level control strategies with different regulation characteristics for the end-of-month water level distribution range in step S4 using the grid search method are as follows: S41: Based on the end-of-month water level distribution range of the target lake body, extract the minimum and maximum values of the end-of-month water level distribution for each month. The extraction expressions are as follows: ; where is the historical lowest water level of the end-of-month water level distribution of the target lake body in the m-th month; where is the historical highest water level of the end-of-month water level distribution of the lake body in the m-th month; N refers to the number of historical years of the historical data of the lake body's own characteristics; S42: Use the Latin hypercube sampling method to perform multiple samplings within the interval for each month to generate multiple groups of annual-scale water level control strategies covering 12 months of the whole year. The sampling expression is as follows: ; where refers to the scheduled water level obtained during the r-th sampling in the m-th month; LHS refers to the Latin hypercube sampling; refers to the annual-scale water level control strategy obtained from the r-th sampling.

[0014] Furthermore, the multi-objective comprehensive evaluation formula in step S6 is: ; where x is the number of all lake body meteorological boundary files in the current input group whose lake body water level time series are all within the set standard water level interval after the simulation of the lake body hydrodynamic model throughout the year; y is the number of lake body meteorological boundary files in the current input group whose downstream outlet flow rate time series fully meet the outlet maximum outflow capacity limit condition after the simulation of the lake body hydrodynamic model; z is the minimum value of the end-of-year lake body water level corresponding to the annual-scale simulation results of all lake body hydrodynamic models in the current input group; z min is the minimum value of the end-of-year lake body water level corresponding to the annual-scale simulation results of the lake body hydrodynamic model in all input files; z maxis the maximum value of the lake water level at the end of the year corresponding to the annual-scale simulation results of the lake hydrodynamic model in all input files; u is the total number of lake meteorological boundary files in step S2.

[0015] Based on the same inventive concept, the present invention also discloses an annual-scale robust scheduling logic generation system for lake water resources-water security collaboration, comprising: The watershed hydrology-lake hydrodynamic coupling modeling module is used to collect the hydrogeographic data of the watershed to which the target lake belongs and the lake body characteristic data, build a distributed watershed hydrological model based on the hydrogeographic data, and use the lake body characteristic data and the output results of the distributed watershed hydrological model to jointly build a lake body hydrodynamic model; The comprehensive meteorological uncertainty boundary file generation module is used to obtain the annual scale forecast rainfall data and historical measured meteorological data of the watershed to which the target lake belongs, and use the scenario construction technology to construct multiple boundary scenarios representing meteorological uncertainty for the annual scale forecast rainfall data and historical measured meteorological data. Based on the multiple boundary scenarios, the watershed meteorological boundary files for the distributed watershed hydrological model and the lake meteorological boundary files for the lake hydrodynamic model are generated respectively; The watershed hydrological simulation module is used to input the generated multiple watershed meteorological boundary files into the distributed watershed hydrological model respectively, extract the time series of water entering the lake and the time series of water taking water from the lake output by the distributed watershed hydrological model corresponding to each watershed meteorological boundary file, and form a set of watershed inflow-water taking boundary files covering meteorological uncertainties; The lake water level control strategy generation module can calculate the monthly end-of-month water level distribution range of the target lake based on the water level monitoring data of the lake body characteristic data, and use the grid search method to construct multiple annual-scale water level control strategies with differentiated regulation characteristics for the end-of-month water level distribution range, and convert each annual-scale water level strategy into a water level control boundary file of the lake body hydrodynamic model; The lake hydrodynamic simulation module can form an initial combination of lake meteorological boundary files and basin inflow-water intake boundary files that are consistent with the historical measured meteorological data year, and arrange and combine several initial combinations with several water level control boundary files to obtain multiple groups of input files of the lake hydrodynamic model. Each group of input files is input into the lake hydrodynamic model for annual scale simulation, and the lake water level time series and downstream outflow flow time series after the lake hydrodynamic model simulation corresponding to each group of input files are extracted; The water level control strategy determination module can merge several groups of input files using the same annual-scale water level control strategy to obtain several input groups; based on the lake water level time series and downstream outlet flow time series data corresponding to each group of input files, and combining relevant indicators of lake water resources and water safety, it conducts a multi-objective comprehensive evaluation of the input groups, determines the input group with the optimal multi-objective comprehensive score, and extracts the water level control boundary file used by this input group as the final annual-scale robust water level scheduling logic for the target lake body.

[0016] Based on the same inventive concept, the present invention also discloses an electronic device, including one or more processors, one or more memories, and one or more programs. The programs are stored in the memory and are configured to be executed by the processor. When the programs are loaded into the processor, the steps of the annual-scale robust scheduling logic generation method for lake water resources - water safety coordination are implemented.

[0017] Based on the same inventive concept, the present invention also discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by the processor, the processor is caused to execute the steps of the annual-scale robust scheduling logic generation method for lake water resources - water safety coordination.

[0018] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: Based on annual-scale predicted rainfall data and historical measured meteorological data, considering the spatio-temporal variability of rainfall, it generates a rainfall uncertainty interval, which is beneficial to improving the adaptability and robustness of the final annual-scale robust water level scheduling logic to climate change from the source; using a distributed watershed hydrological model to simulate the runoff generation and concentration processes within the watershed, generating uncertainty intervals for watershed inflow and water intake, systematically considering watershed uncertainty factors such as underlying surface changes, which is beneficial to improving the accuracy and stability of the final annual-scale robust water level scheduling logic.

[0019] The present invention generates an uncertainty interval for the monthly control water level based on historical measured meteorological data, and uses the Latin hypercube method for sampling to obtain a water level dynamic control method applicable to multiple scenarios, no longer relying on artificial experience and a small number of preset scenarios, which is beneficial to improving the reliability of the final annual-scale robust water level scheduling logic.

[0020] The present invention comprehensively evaluates the annual water level fluctuations, the maximum outflow capacity of the lake outlet, and the end-of-year lake water level, and screens the water level dynamic control scheme, which is beneficial to screening out the final annual-scale robust water level scheduling logic with strong applicability and high stability. Description of the Drawings

[0021] Figure 1 It is a flowchart of the method of the present invention; Figure 2Gamma distribution diagram of the monthly sampling set for January in the embodiments of the present invention; Figure 3 Gamma distribution diagram of the monthly sampling set for February in the embodiments of the present invention; Figure 4 Gamma distribution diagram of the monthly sampling set for March in the embodiments of the present invention; Figure 5 Gamma distribution diagram of the monthly sampling set for April in the embodiments of the present invention; Figure 6 Gamma distribution diagram of the monthly sampling set for May in the embodiments of the present invention; Figure 7 Gamma distribution diagram of the monthly sampling set for June in the embodiments of the present invention; Figure 8 Gamma distribution diagram of the monthly sampling set for July in the embodiments of the present invention; Figure 9 Gamma distribution diagram of the monthly sampling set for August in the embodiments of the present invention; Figure 10 Gamma distribution diagram of the monthly sampling set for September in the embodiments of the present invention; Figure 11 Gamma distribution diagram of the monthly sampling set for October in the embodiments of the present invention; Figure 12 Gamma distribution diagram of the monthly sampling set for November in the embodiments of the present invention; Figure 13 Gamma distribution diagram of the monthly sampling set for December in the embodiments of the present invention; Figure 14 Target monthly rainfall time series sample distribution diagram of the first representative scenario in the embodiments of the present invention; Figure 15 Target monthly rainfall time series sample distribution diagram of the second representative scenario in the embodiments of the present invention; Figure 16 Target monthly rainfall time series sample distribution diagram of the third representative scenario in the embodiments of the present invention; Figure 17 Target monthly rainfall time series sample distribution diagram of the fourth representative scenario in the embodiments of the present invention; Figure 18 Target monthly rainfall time series sample distribution diagram of the fifth representative scenario in the embodiments of the present invention; Figure 19 Target monthly rainfall time series sample distribution diagram of the sixth representative scenario in the embodiments of the present invention; Figure 20It is the time series sample distribution diagram of the target monthly rainfall in the 7th representative scenario of the embodiment of the present invention; Figure 21 It is the time series sample distribution diagram of the target monthly rainfall in the 8th representative scenario of the embodiment of the present invention; Figure 22 It is the time series sample distribution diagram of the target monthly rainfall in the 9th representative scenario of the embodiment of the present invention; Figure 23 It is the time series sample distribution diagram of the target monthly rainfall in the 10th representative scenario of the embodiment of the present invention; Figure 24 It is the water level control interval diagram of Lake E in December of the embodiment of the present invention; Figure 25 It is the multi-objective comprehensive scoring diagram of the input group of the embodiment of the present invention; Figure 26 It is the structural schematic diagram of the system of the present invention. Specific embodiments

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0023] Embodiment 1: A method for generating an annual-scale robust scheduling logic for the coordinated management of lake water resources and water security disclosed in the present invention, as Figure 1 shown, includes the following steps, S1: Collect the hydrological and geographical data of the basin to which the target lake belongs and the characteristic data of the lake body itself. Based on the hydrological and geographical data, construct a distributed basin hydrological model, and use the characteristic data of the lake body itself and the output results of the distributed basin hydrological model to jointly construct a lake hydrodynamic model. The distributed basin hydrological model and the lake hydrodynamic model are linked through a two-way calibration method, and an iterative feedback mechanism is adopted. When significant deviations occur in the lake hydrodynamic simulation results, the sensitive parameters of the distributed basin hydrological model are corrected in reverse to form a two-way coupling calibration closed loop. This effectively solves the problem of distorted boundary conditions in traditional single-model calibration and provides more reliable model support for annual-scale lake scheduling.

[0024] Among them, the basin hydrological and geographical data include the surface elevation data, soil type, land use, hourly meteorological data, measured basin hydrological data, spatial distribution information of drainage and water intake engineering facilities and their operating flow time series data of the basin to which the lake belongs; among them, the characteristic data of the lake body itself include the underwater three-dimensional terrain data of the lake, lake area meteorological data, water level monitoring data, and continuous monitoring data of the inflow and outflow water volume.

[0025] When constructing a distributed watershed hydrological model in existing watershed hydrological modeling software, sub-watersheds and sub-channels are delineated based on the surface elevation data of the watershed. Each sub-watershed is divided into different hydrological response units according to soil type, land use, and surface elevation data. Boundary conditions are defined based on hourly meteorological data, measured watershed hydrological data, spatial distribution information of drainage and water intake engineering facilities, and their operating flow time series data to construct a distributed watershed hydrological model. Existing watershed hydrological modeling software includes, but is not limited to, SWAT, MIKE, and LSPC.

[0026] When constructing a lake hydrodynamic model in existing lake hydrodynamic modeling software, a grid is constructed based on the underwater three-dimensional terrain data of the lake. Boundary conditions are defined according to the meteorological data of the lake area, continuous monitoring data of inflow and outflow water volumes, and the output results of the distributed watershed hydrological model to construct a lake hydrodynamic model. The output results of the distributed watershed hydrological model refer to the time series of water volume flowing into the lake and the time series of water volume withdrawn from the lake by the watershed after the distributed watershed hydrological model operates under the current conditions. The monitoring data of inflow and outflow water volumes refer to the time series of other inflow and outflow water volumes of the lake except for the time series of water volume flowing into the lake from the watershed and the time series of water volume withdrawn from the lake by the watershed. Existing lake hydrodynamic modeling software includes, but is not limited to, EFDC, Delft3D, and MIKE; and zero-dimensional, one-dimensional, two-dimensional, and three-dimensional lake hydrodynamic modeling software can all be used.

[0027] The time series of the watershed hydrological and geographical data for constructing the distributed watershed hydrological model and the lake body ontology characteristic data for constructing the lake hydrodynamic model are consistent, that is, the watershed hydrological and geographical data and the lake body ontology characteristic data of the same historical year or the same historical interval are taken. The output results of the distributed watershed hydrological model, as part of constructing the lake hydrodynamic model, can establish the hydraulic coupling relationship between the watershed and the lake body and can effectively address the uncertainty of rainfall-runoff non-linear transformation caused by differences in watershed characteristics.

[0028] S2: Obtain the annual-scale forecast rainfall data and historical measured meteorological data of the watershed to which the target lake belongs. Use scenario construction technology for the annual-scale forecast rainfall data and historical measured meteorological data to construct multiple boundary scenarios representing meteorological uncertainty, and generate a watershed meteorological boundary file for use by the distributed watershed hydrological model and a lake meteorological boundary file for use by the lake hydrodynamic model respectively based on the multiple boundary scenarios.

[0029] The annual-scale forecast rainfall data refers to the monthly forecast rainfall amounts predicted by all forecast stations within the watershed to which the target lake belongs for the next year. The historical measured meteorological data refers to the historical hourly rainfall data of all rain gauge stations and the historical hourly multi-index meteorological observation data of all meteorological stations within the watershed to which the target lake belongs for many years.

[0030] The steps to generate the basin meteorological boundary file for the distributed basin hydrological model and the lake body meteorological boundary file for the lake body hydrodynamic model are as follows: S21: Based on the monthly predicted rainfall of each prediction station in the next year, calculate the total annual rainfall of each prediction station, and perform perturbation expansion on all the total annual rainfall amounts to obtain multiple total annual rainfall amount samples.

[0031] Perform upper and lower perturbations of a specified amplitude on the total annual rainfall data of all prediction stations within the basin to which the target lake body belongs. After perturbation expansion, each prediction station will obtain 3 total annual rainfall amount samples; the number of all total annual rainfall amount samples obtained after perturbation expansion is 3 times the total number of all prediction stations. Preferably, in this embodiment, the specified amplitude is 20%, that is, perform perturbations of a 20% decrease, no perturbation, and a 20% increase.

[0032] The perturbation formula for any prediction station s is as follows: Perturbation of a 20% decrease: ; No perturbation: ; Perturbation of a 20% increase: ; where P s refers to the total annual rainfall predicted by prediction station s.

[0033] S22: Based on the historical hourly rainfall data and historical hourly multi-index meteorological observation data, calculate the weights of the historical monthly rainfall of each rainfall station and meteorological station in each historical year accounting for the total annual rainfall, form a historical monthly scale weight sequence, and predict and generate several groups of predicted monthly scale weight sequences including 12 months of a year.

[0034] The historical monthly scale weight sequence refers to the proportion of the rainfall in December of a certain year at a rainfall station or meteorological station accounting for the total annual rainfall of that year.

[0035] The calculation formula for the historical monthly rainfall weight of a certain month at any rainfall station or meteorological station t is as follows: ; where is the monthly rainfall weight of rainfall station or meteorological station t in the m-th month of the n-th historical year; is the rainfall of rainfall station or meteorological station t in the m-th month of the n-th year; is the total annual rainfall of rainfall station or meteorological station t in the n-th historical year. constitutes the historical monthly scale weight sequence of rainfall station or meteorological station t in the n-th historical year.

[0036] The method for generating several groups of predicted monthly scale weight sequences including 12 months of a year is as follows: Classify all historical monthly-scale weight sequences of each rainfall station and meteorological station in the historical measured meteorological data by month to form 12 historical rainfall weight sets; for example The historical monthly rainfall weights with the same month in form the historical rainfall weight set corresponding to month m; For each historical rainfall weight set of each month, based on the binary event results of whether rainfall occurs at each rainfall station and meteorological station recorded in the historical measured meteorological data, construct a Bernoulli distribution model to simulate the rainfall probability of that month. The expression of the Bernoulli distribution is as follows: ; where p m is the Bernoulli distribution probability; X m = 1 indicates that there is rainfall in month m; X m = 0 indicates that there is no rainfall in month m; m = 1, 2,..., 12; In each historical rainfall weight set of each month, screen out the historical monthly rainfall weight subset corresponding to the rainfall event, and construct a Gamma distribution model to simulate the probability distribution of the monthly rainfall weight. The expression of the Gamma distribution is: ; where is the monthly rainfall weight of the m-th month; is the shape parameter of the Gamma distribution; is the scale parameter of the Gamma distribution; Based on the Bernoulli–Gamma joint distribution model, perform multiple samplings to generate multiple groups of predicted monthly-scale weight sequences containing 12 months of a year. The steps of a single sampling are as follows: Sample for the m-th month. First, sample from the Bernoulli distribution corresponding to month m. If the sampled X m = 1, then sample from the Gamma distribution of the historical monthly rainfall weight subset corresponding to month m to collect the monthly rainfall weight ; if the sampled X m = 0, then let .

[0037] Repeat the collection 12 times until the monthly rainfall weights of 12 months in the future year are collected , and perform normalization on so that the sum of the normalized is 1; the normalized is the predicted monthly-scale weight sequence.

[0038] In actual application, before performing normalization, first calculate whether the sum of is 1. If the sum is 1, then the constitutes a predicted monthly-scale weight sequence; if the sum is not 1, then perform normalization on , and the obtained It is a predicted monthly-scale weight sequence.

[0039] The normalization process is as follows:

[0040] After being processed by the above formula , is a predicted monthly-scale weight sequence.

[0041] The historical monthly rainfall weights are divided into a historical rainfall weight set and a historical monthly rainfall weight subset according to the presence or absence of rainfall. First, the historical rainfall weight set is sampled, and then the historical monthly rainfall weight subset is sampled. The setting of these two stages conforms to the actual physical process: whether rainfall occurs and the amount of rainfall are two different mechanisms. During a single sampling, first sample from the historical rainfall weight set to ensure the possibility of including dry months; the Gamma distribution is used to fit the rainfall amount during rainfall, and the shape parameter (shape) and scale parameter (scale) of the Gamma can be flexibly adjusted to match the mean, variance, and skewness in the actual data. The preferred single sampling method is Monte Carlo sampling. Through a large number of random simulations, Monte Carlo sampling obtains multiple groups of predicted monthly-scale weight sequences, which not only retain the statistical laws of historical data (such as mean, variance, and extreme value probability), but also can cover possible random fluctuations, and finally provide a physically reasonable and statistically robust data basis. The predicted monthly-scale weight sequence obtained by combining the historical rainfall weight set with both rainfall and no rainfall, the historical monthly rainfall weight subset with rainfall, the Gamma distribution, and the Monte Carlo sampling method not only conforms to the true rainfall statistical characteristics but also can quantify the historical monthly rainfall weights with uncertainty.

[0042] S23: Permute the annual rainfall total samples obtained in step S21 and the predicted monthly-scale weight sequences obtained in step S22 to obtain a number of annual rainfall total sample - predicted monthly-scale weight sequence combinations, and the monthly rainfall of each month in the annual rainfall total sample - predicted monthly-scale weight sequence combination constitutes a monthly rainfall time series sample.

[0043] Monthly rainfall time series sample is expressed as follows:

[0044] where is the monthly rainfall time series sample of the i-th annual rainfall total sample and the j-th predicted monthly-scale weight sequence combination, is the monthly rainfall of the m-th month after the combination of the i-th annual rainfall total sample and the j-th predicted monthly-scale weight sequence; is the annual rainfall total of the i-th annual rainfall total sample; is the weight of the m-th month of the j-th predicted monthly-scale weight sequence.

[0045] S24: Use the clustering method to cluster all monthly rainfall time series samples, obtaining k types of typical meteorological scenarios, and use the typical meteorological scenarios as representative samples of the target rainfall time series.

[0046] Use the Kmeans clustering method to screen out k types of typical meteorological scenarios with similar characteristics as the target monthly rainfall time series samples. Using Kmeans clustering can cluster all monthly rainfall time series samples into k categories, minimizing the within-class differences and maximizing the between-class differences.

[0047] S25: Combine the k representative samples of the target monthly rainfall time series and historical measured meteorological data to predict the hourly rainfall time series of all rainfall stations and meteorological stations in the next year.

[0048] The steps to predict the hourly rainfall time series of all rainfall stations and meteorological stations in the next year are as follows: Calculate the ratio b of the monthly rainfall of the k representative samples of the target monthly rainfall time series to the monthly average rainfall of the basin where the target lake is located. The calculation formula of b is as follows: ; where q = 1, 2, 3, 4… k， is the ratio b of the qth target monthly rainfall time series representative sample in the mth month of the nth historical year; is the monthly average rainfall in the mth month of the nth historical year of the basin where the target lake is located; is the rainfall in the mth month of the qth target monthly rainfall time series representative sample; Then, based on the ratio b, scale and map the historical hourly rainfall of the historical measured meteorological data of all rainfall stations and meteorological stations to obtain the hourly rainfall time series in the next year. The calculation formula is as follows: ; where is the predicted rainfall at the hth hour on the dth day of the mth month in the next year when the rainfall station or meteorological station t is mapped to the qth target monthly rainfall time series representative sample; is the historical rainfall at the hth hour on the dth day of the mth month of the nth year of the rainfall station or meteorological station t.

[0049] Maintain the inherent spatio-temporal differences between each rain gauge station and meteorological station through the historical hourly rainfall of rain gauge stations and meteorological stations. Use the coefficient b to capture the overall deviation (such as more or less) of the predicted rainfall from the historical measured meteorological data on the basin surface, and dynamically distribute this overall deviation to each rain gauge station or meteorological station according to the historical spatial pattern through the coefficient b to ensure that the rainfall of rain gauge stations and meteorological stations changes synchronously with the forecast. The purpose of calculating the hourly rainfall time series using the coefficient b is to map the total target monthly rainfall at the basin scale of the basin where the lake is located to the hourly rainfall of rain gauge stations and meteorological stations, maintaining spatio-temporal consistency and reasonably reflecting the forecast changes.

[0050] S26: Convert the hourly rainfall time series of all rain gauge stations and meteorological stations within the basin where the target lake is located predicted in step S25 into a basin meteorological boundary file in a format that conforms to the distributed basin hydrological model; convert the hourly rainfall time series of rain gauge stations and meteorological stations within the specified range of the target lake into a lake meteorological boundary file in a format that conforms to the lake hydrodynamic model. In actual use, the specified range of the lake can be determined according to actual needs, such as the area 20 km away from the center of the lake.

[0051] Preferably, before obtaining the basin meteorological boundary file and the lake boundary meteorological file, combine the hourly rainfall time series of rain gauge stations and meteorological stations with the historical measured meteorological data excluding historical rainfall in the corresponding nth year, and then convert it into the required basin meteorological boundary file or lake meteorological boundary file. For example, when calculating the historical rainfall of the rain gauge station or meteorological station t at the hth hour of the mth month in 2019, the historical hourly meteorological data of this rain gauge station or meteorological station t excluding historical rainfall in 2019 is required.

[0052] When obtaining the basin meteorological boundary file and lake meteorological boundary file that comprehensively consider meteorological uncertainties, first, based on the predicted annual total rainfall for the next year, and perturbing the predicted annual total rainfall data up and down, generate a sample of the predicted annual total rainfall that covers the possibilities of wetter and drier conditions; second, based on the historical monthly rainfall weights, use the Bernoulli distribution to fit the distribution of rainfall occurrence, further use the Gamma distribution to fit the distribution of rainfall amount weights when there is rainfall, and then use the Monte Carlo sampling method to obtain multiple groups of predicted monthly-scale weight sequences for the 12 months of the next year; then, combine the predicted annual total rainfall sample with the predicted monthly-scale weight sequences to obtain several groups of monthly rainfall time series samples, and use the clustering method to cluster all the monthly rainfall time series samples to obtain representative target monthly rainfall time series samples; finally, based on the monthly rainfall of the representative target monthly rainfall time series samples and the monthly average rainfall of the basin where the lake is located, map the target monthly rainfall time series samples to the meteorological stations and rainfall stations in the basin to obtain multiple groups of hourly-scale rainfall time series for all rainfall stations and meteorological stations in the next year. By combining the predicted annual total rainfall for the future with the up and down perturbation of historical measured meteorological data, generate a probabilistic annual total rainfall set that covers wetter (humid), drier (drought) and normal years, reflecting the uncertainties of long-term climate prediction. Dynamically match the annual total sample (such as "wet year") with the monthly weight sample (such as "delayed rainy season") to generate physically consistent monthly rainfall time series (such as wet year + concentrated rainy season = flood risk scenario). Extract typical rainfall patterns (such as "uniform distribution type", "concentrated heavy rain type") through clustering to reduce the computational amount while retaining key extreme scenarios. Based on the spatial relationship between the monthly total and the basin average rainfall, combined with the terrain and the historical weights of the stations, decompose the monthly values into hourly rainfall at each station (such as the spatial displacement of the rainstorm center). Finally, generate multiple groups of probabilistic rainfall scenario sets with an hourly scale and spatial distribution for the next year, covering climate prediction errors, intra-year allocation uncertainties and spatial variability.

[0053] The present invention fully considers meteorological uncertainty factors, is no longer limited to a single meteorological scenario, but through multi-source data of historical measured meteorological data and predicted rainfall data, combines multiple optimization methods to generate an uncertainty interval of rainfall, obtains hourly-scale rainfall time series for a large number of rainfall stations and meteorological stations in the next year, and systematically reflects the diversity of annual total rainfall, rainfall patterns and spatial distribution, improving the adaptability and robustness of the final annual-scale robust water level scheduling logic to climate change from the source.

[0054] S3: Input the generated multiple basin meteorological boundary files into the distributed basin hydrological model respectively, extract the water volume time series of the basin flowing into the lake and the water volume time series of the basin withdrawing water from the lake output after the operation of the distributed basin hydrological model corresponding to each basin meteorological boundary file, and form a set of basin inflow-withdrawal boundary files covering meteorological uncertainties.

[0055] Preferably, in practical applications, the water volume time series of each tributary flowing into the lake body and the water intake time series of the basin from the lake are extracted from the results after the operation of the distributed watershed hydrological model; then, these data are integrated and processed. The water volumes of multiple tributaries flowing into the lake are accumulated to generate the total inflow time series into the lake, and the water intakes of each water intake are summarized to form the total water intake time series; finally, according to the input requirements of the lake hydrodynamic model, the processed inflow and water intake data are converted into a standardized boundary file format to provide the basin inflow and water intake boundary files for the lake hydrodynamic model.

[0056] Preferably, in practical applications, after the construction of the distributed watershed hydrological model in step S1 is completed, multiple identical distributed watershed hydrological models can be copied, and the number of distributed watershed hydrological models is the same as the number of basin meteorological boundary files. Multiple basin meteorological boundary files are respectively input into different distributed watershed hydrological models, and multiple distributed watershed hydrological models are driven simultaneously, which is beneficial to improving the overall operation efficiency.

[0057] The present invention fully reflects the complexity of the basin system in practical applications. By simulating the runoff generation and concentration processes under different meteorological scenarios with a distributed watershed hydrological model, capturing the non-linear relationship between rainfall and runoff, and systematically considering basin uncertainty factors such as the change of underlying surface, compared with the traditional method that usually calculates the basin inflow by multiplying a single or several preset rainfall amounts by a fixed runoff coefficient, the present invention effectively makes up for the problem of insufficient expression of the dynamic response of the traditional method to the basin.

[0058] S4: Based on the water level monitoring data of the lake body ontology characteristics data, the monthly end-of-month water level distribution range of the target lake body is statistically analyzed. The grid search method is used to construct multiple annual-scale water level control strategies with different regulation characteristics for the end-of-month water level distribution range, and each annual-scale water level strategy is converted into a water level control boundary file of the lake hydrodynamic model.

[0059] The steps of constructing multiple annual-scale water level control strategies with different regulation characteristics for the end-of-month water level distribution range by using the grid search method are as follows: S41: Based on the monthly end-of-month water level distribution range of the target lake body, the minimum value and the maximum value of the end-of-month water level distribution of each month are extracted. The minimum value and the maximum value are used to determine the feasible boundary region of the water level regulation for the corresponding month. The extraction expressions are as follows: ; where is the historical lowest water level of the end-of-month water level distribution of the mth month of the target lake body; where is the historical highest water level of the end-of-month water level distribution of the mth month of the lake body; N refers to the number of historical years of the lake body ontology characteristics data collected.

[0060] S42: The Latin hypercube sampling method is used in Multiple sampling is performed within the interval to generate multiple sets of annual scale water level control strategies covering 12 months of the year; the sampling expression is as follows: ;in It refers to the dispatching water level obtained at the rth sampling in the mth month; LHS refers to Latin hypercube sampling; Refers to the annual scale water level control strategy obtained from the rth sampling.

[0061] The present invention combines historical end-of-month water level distribution data with Latin hypercube sampling to achieve the generation of lake water level scenarios throughout the year that take into account historical fluctuations. First, the extreme values of the water levels at the end of each month in the historical water level monitoring data are statistically analyzed to determine the seasonal variation range and historical extreme conditions of water level fluctuations; then the Latin hypercube sampling (LHS) method is used to perform efficient sampling within the extreme value interval of each month to generate a large number of annual-scale water level control strategies. This method not only retains the monthly correlation of water level changes, but also ensures that the sampling results can cover the historical extreme value range, thereby systematically capturing possible water level fluctuation scenarios. The generated annual-scale water level control strategy can be further used to evaluate water resource scheduling strategies, ecological impacts, and flood control and drought relief measures under different hydrological conditions, providing a probabilistic decision-making basis for the comprehensive management of lakes.

[0062] This invention breaks through the limitations of traditional plans that rely on a small number of scenarios and manual experience. It generates a large-scale annual water level control strategy based on methods such as Latin hypercube sampling. It can explore the optimal scheduling strategy that takes into account both water resource utilization and water security in massive scenarios, and can greatly improve the reliability and scientificity of the annual water level control strategy.

[0063] S5: The lake meteorological boundary files and basin inflow-water intake boundary files that are consistent with the historical measured meteorological data years constitute an initial combination. Several initial combinations are arranged and combined with several water level control boundary files to obtain multiple groups of input files for the lake hydrodynamic model. Each group of input files is input into the lake hydrodynamic model for annual scale simulation, and the lake water level time series and downstream outlet flow time series after the lake hydrodynamic model simulation corresponding to each group of input files are extracted.

[0064] Preferably, in actual application, after the lake hydrodynamic model in step S1 is constructed, multiple identical lake hydrodynamic models can be copied, and the number of lake hydrodynamic models is consistent with the number of input files. Multiple input files are respectively input into different lake hydrodynamic models, and multiple lake hydrodynamic models are driven at the same time, which is conducive to improving the overall operating efficiency.

[0065] By constructing multiple sets of boundary conditions with spatiotemporal consistency and physical rationality, the present invention drives the lake hydrodynamic model to carry out large-scale multi-scenario simulations, comprehensively supports the water resources scheduling work of lakes under the background of future climate change, and provides a systematic, probabilistic and operable scientific basis for decision-making.

[0066] Taking the output results of the distributed watershed hydrological model, namely the watershed inflow and water intake boundary files, as a combined part of the input files can couple the watershed hydrological process and the lake hydrodynamic process, and realize a more systematic water cycle simulation. Combining the lake meteorological boundary file, the watershed inflow-water intake boundary file and the water level control boundary file to form the input files can couple meteorological uncertainties, non-linear watershed response models and large-scale water level control schemes, realize the full-chain optimization from rainfall prediction to water level control, and significantly improve the accuracy and robustness of the finally obtained annual water level control scheme.

[0067] S6: Merge several groups of input files using the same annual-scale water level control strategy to obtain several input groups; based on the lake water level time series and the downstream outlet flow rate time series data corresponding to each group of input files, and combined with the relevant indicators of lake water resources and water security, conduct a multi-objective comprehensive evaluation of the input groups, determine the input group with the optimal multi-objective comprehensive score, and extract the water level control boundary file used by this input group as the final annual-scale robust water level scheduling logic of the target lake.

[0068] The multi-objective scoring formula is: ; where x is the number of all lake meteorological boundary files in the current input group for which the annual lake water level time series after the lake hydrodynamic model simulation is all within the set standard water level range; y is the number of lake meteorological boundary files in the current input group for which the downstream outlet flow rate time series after the lake hydrodynamic model simulation fully meets the maximum outflow capacity limit condition of the outlet; z is the minimum value of the end-of-year lake water level corresponding to the annual-scale simulation results of all lake hydrodynamic models in the current input group; z min is the minimum value of the end-of-year lake water level corresponding to the annual-scale simulation results of the lake hydrodynamic model in all input files; z max is the maximum value of the end-of-year lake water level corresponding to the annual-scale simulation results of the lake hydrodynamic model in all input files; u is the total number of lake meteorological boundary files in step S2. The x, y, and z in the multi-objective scoring formula are the relevant indicators of lake water resources and water security. In actual use, the standard water level range and the maximum outflow capacity limit condition of the outlet can be set according to the actual situation.

[0069] This step selects the annual-scale robust scheduling logic that performs stably under different meteorological conditions through multi-objective scoring, enabling the selected annual-scale robust scheduling logic to have the following advantages: The setting of x can ensure that the annual water level fluctuation is within the allowable ecological / safety water level range, avoiding the risk of over-limit; the setting of y can strictly monitor the flow rate at the outlet, ensure the ecological base flow, and at the same time not exceed the maximum outflow capacity, preventing the overload of the sluice dam or the downstream flood risk; the settings of z, z min and z max can preferentially select the scheme with a higher end-of-year storage water level to ensure the water supply, ecology and other needs of the following year, and at the same time evaluate its adaptability under different hydrological year types (wet / normal / dry years). The finally selected water level dynamic control strategy provides a quantitative decision-making basis for the actual annual water resources scheduling, that is, the optimal annual regulation strategy can maintain water level safety and take into account the storage benefit in most scenarios, demonstrating high adaptability and strategy extension ability under a wide range of meteorological conditions.

[0070] The present invention can realize the whole-process optimization from rainfall forecasting to water level control, greatly improve the accuracy, adaptability and robustness of lake water level regulation, and has important engineering application value and promotion potential.

[0071] Example 2: This example discloses the steps of obtaining the water level control strategy of Lake E by using a method for generating an annual-scale robust scheduling logic for the coordination of lake water resources and water safety in Example 1.

[0072] S1: Collect the hydrological and geographical data of the basin where Lake E is located and the lake body ontology characteristic data of Lake E, construct a distributed basin hydrological model based on the hydrological and geographical data, and use the lake body ontology characteristic data and the output results of the distributed basin hydrological model to jointly construct a lake hydrodynamic model.

[0073] The basin hydrological and geographical data includes the surface elevation data, soil type, land use, hourly meteorological data, measured basin hydrological data, spatial distribution information of drainage and water intake engineering facilities and their operating flow time series data of the basin where the lake body is located; the lake body ontology characteristic data includes the underwater three-dimensional terrain data, lake area meteorological data, water level monitoring data and continuous monitoring data of the inflow and outflow water volume of the lake body.

[0074] Use the basin hydrological and water quality model inteliway-WS model software to construct a distributed basin hydrological model. Divide the basin into sub-basins and sub-channels based on the surface elevation data of the basin. Each sub-basin is divided into different hydrological response units according to the soil type, land use and surface elevation data. Define the boundary conditions according to the hourly meteorological data, measured basin hydrological data, spatial distribution information of drainage and water intake engineering facilities and their operating flow time series data, and construct a distributed basin hydrological model.

[0075] Use the hydrodynamic - water quality - water ecological model IWIND - LR model software to construct a lake hydrodynamic model. Based on the underwater three - dimensional terrain data of the lake, grids are constructed, and boundary conditions are defined according to the meteorological data of the lake area, continuous monitoring data of inflow and outflow water volumes, and the output results of the distributed watershed hydrological model, thus constructing a lake hydrodynamic model. Among them, the output results of the distributed watershed hydrological model refer to the time series of water volumes flowing into the lake from the watershed and the time series of water volumes taken from the lake by the watershed after the watershed hydrological model runs under the current conditions; the monitoring data of inflow and outflow water volumes refer to the time series of other water volumes flowing into and out of the lake except for the time series of water volumes flowing into the lake from the watershed and the time series of water volumes taken from the lake by the watershed.

[0076] The time series of the watershed data for constructing the distributed watershed hydrological model and the lake data for constructing the lake hydrodynamic model are consistent, that is, the watershed data and lake data of the same historical year or the same historical period are taken.

[0077] S2: Obtain the annual - scale forecast rainfall data and historical measured meteorological data of the watershed to which the target lake belongs. Use scenario construction technology for the annual - scale forecast rainfall data and historical measured meteorological data to construct multiple boundary scenarios representing meteorological uncertainties, and generate a watershed meteorological boundary file for use by the distributed watershed hydrological model and a lake meteorological boundary file for use by the lake hydrodynamic model respectively based on the multiple boundary scenarios.

[0078] Among them, the annual - scale forecast rainfall data is the monthly forecast rainfall amounts for the next year predicted by 3 forecast stations within the watershed to which Lake E belongs; the historical measured meteorological data is the historical hourly meteorological data for 11 years at 104 rainfall stations and 7 meteorological stations within the watershed to which Lake E belongs.

[0079] The steps to generate a watershed meteorological boundary file for use by the distributed watershed hydrological model and a lake meteorological boundary file for use by the lake hydrodynamic model are as follows: S21: Based on the monthly forecast rainfall amounts for the next year at each forecast station, calculate the total annual rainfall amounts of the 3 forecast stations, and perform perturbation expansion on all the total annual rainfall amounts to obtain multiple samples of total annual rainfall amounts.

[0080] Perform upper and lower perturbations with a 20% amplitude on the total annual rainfall amount data of the 3 forecast stations within the watershed to which Lake E belongs. After perturbation expansion, a total of 9 samples of total annual rainfall amounts are obtained.

[0081] The perturbation formula for any forecast station s is as follows: Perturbation with a 20% decrease: ; Without perturbation: ; Perturbation with a 20% increase: ; Where P s refers to the total annual rainfall amount forecast by forecast station s.

[0082] S22: Calculate the weights of historical monthly rainfall of 104 rainfall stations and 7 meteorological stations in the past 11 years accounting for the total annual rainfall based on historical hourly rainfall data and historical hourly multi - indicator meteorological observation data, forming 1221 groups of historical monthly - scale weight sequences covering 12 months of a year. Then, generate several groups of predicted monthly - scale weight sequences covering 12 months of a year based on the obtained 1221 groups of historical monthly - scale weight sequences.

[0083] The historical monthly - scale weight sequence refers to the proportion of the December rainfall of a rainfall station or a meteorological station in a certain year accounting for the total annual rainfall of that year.

[0084] The calculation formula for the historical monthly rainfall weight of any month at rainfall station or meteorological station t is as follows: ; where is the monthly rainfall weight of rainfall station or meteorological station t in the m - th month of the n - th historical year; is the rainfall of rainfall station or meteorological station t in the m - th month of the n - th year; is the total annual rainfall of rainfall station or meteorological station t in the n - th historical year. constitutes the historical monthly - scale weight sequence of the n - th historical year of rainfall station or meteorological station t.

[0085] The method for generating several groups of predicted monthly - scale weight sequences covering 12 months of a year is as follows: Classify all historical monthly - scale weight sequences of each rainfall station and meteorological station in the historical measured meteorological data by month, forming 12 historical rainfall weight sets; for example the historical monthly rainfall weights with the same month in constitute the historical rainfall weight set corresponding to the m - th month; For each historical rainfall weight set of each month, based on the binary event results of whether rainfall occurs at each rainfall station and meteorological station in this month recorded in the historical measured meteorological data, construct a Bernoulli distribution model to simulate the rainfall probability of this month. The Bernoulli distribution expression is as follows: ; where p m is the Bernoulli distribution probability; X m = 1 indicates that there is rainfall in the m - th month; X m = 0 indicates that there is no rainfall in the m - th month; m = 1, 2,..., 12; In each historical rainfall weight set of each month, screen out the subset of historical monthly rainfall weights corresponding to rainfall events and construct a Gamma distribution model to simulate the probability distribution of the rainfall weight of this month. The expression of the Gamma distribution is: ; where is the monthly rainfall weight of the m - th month; is the shape parameter of the Gamma distribution; is the scale parameter of the Gamma distribution. The Gamma distribution of the 12 historical monthly rainfall weight subsets is as Figures 2 to 13 shown.

[0086] Based on the Bernoulli–Gamma joint distribution model, multiple samplings are performed to generate multiple groups of predicted monthly scale weight sequences containing 12 months of a year. The steps of a single sampling are as follows: For the m-th month sampling, first sample from the Bernoulli distribution corresponding to the m-th month. If the sampled X m = 1, then sample from the Gamma distribution of the historical monthly rainfall weight subset corresponding to the m-th month to collect the monthly rainfall weight ; if the sampled X m = 0, then let .

[0087] Repeat the collection 12 times until the monthly rainfall weights for 12 months of the future year are collected , and perform normalization on so that the sum of the normalized is 1; the normalized is the predicted monthly scale weight sequence.

[0088] In actual application, before performing the normalization process, first calculate whether the sum of is 1. If the sum is 1, then the constitutes a predicted monthly scale weight sequence; if the sum is not 1, then perform normalization on , and the obtained after normalization is a predicted monthly scale weight sequence.

[0089] S23: Arrange and combine the 9 annual rainfall total samples obtained in S21 and the 1221 predicted monthly scale weight sequences obtained in step S22 to obtain multiple combinations of annual rainfall total samples - predicted monthly scale weight sequences, and the monthly rainfall of each month in the combination of annual rainfall total samples - predicted monthly scale weight sequences constitutes a monthly rainfall time series sample.

[0090] Monthly rainfall time series sample The expression is as follows:

[0091] Where is the monthly rainfall time series sample of the combination of the i-th annual rainfall total sample and the j-th predicted monthly scale weight sequence, is the monthly rainfall of the m-th month after the combination of the i-th annual rainfall total sample and the j-th predicted monthly scale weight sequence; is the annual rainfall total of the i-th annual rainfall total sample; is the weight of the m-th month in the j-th predicted monthly scale weight sequence.

[0092] S24: Use the clustering method to cluster all monthly rainfall time series samples, and 10 typical meteorological scenarios are obtained by clustering. These typical meteorological scenarios are used as representative samples of the target rainfall time series.

[0093] The time series distribution of the monthly rainfall of the 10 representative monthly rainfall time series samples obtained by the Kmeans clustering algorithm is as Figures 14 to 23 shown, Figures 14 to 23 The median of the monthly rainfall weights and the 25%-75% interval range in each clustering category are given in Figures 14 to 23 "A total of * samples" in

[0094] means that the clustering cluster contains * monthly rainfall time series samples.

[0095] S25: Combine the 10 representative samples of the target monthly rainfall time series and the historical measured meteorological data to predict the hourly scale rainfall time series of all rain gauge stations and meteorological stations in the next year. ; where q = 1, 2, 3, 4... k， is the ratio b of the m-th month in the q-th target monthly rainfall time series representative sample in the n-th historical year; is the monthly average rainfall of the m-th month in the n-th historical year of the basin where the target lake is located; is the rainfall of the m-th month in the q-th target monthly rainfall time series representative sample. The combination of the 11-year historical measured meteorological data and the 10 target monthly rainfall time series samples yields a total of 11 * 10 = 110 sets of hourly rainfall time series samples for the rain gauge stations and meteorological stations in the E lake basin.

[0096] Then, based on the ratio b, scale and map the historical hourly rainfall of the historical measured meteorological data of all rain gauge stations and meteorological stations to obtain the hourly scale rainfall time series in the next year. The calculation formula is as follows: ; where is the predicted rainfall of the h-th hour on the d-th day of the m-th month in the next year when the rain gauge station or meteorological station t is mapped to the q-th target monthly rainfall time series representative sample; is the historical rainfall of the h-th hour on the d-th day of the m-th month in the n-th year of the rain gauge station or meteorological station t.

[0097] S26: Convert the hourly rainfall time series of all rain gauges and meteorological stations within the basin to which the target lake belongs predicted in step S25 into a basin meteorological boundary file in a format conforming to the input of the distributed basin hydrological model; convert the hourly rainfall time series of the rain gauges and meteorological stations within 5 km of Lake E into a lake meteorological boundary file in a format conforming to the input of the lake hydrodynamic model.

[0098] And before obtaining the basin meteorological boundary file and the lake boundary meteorological file, combine the hourly rainfall time series of the rain gauges and meteorological stations with the historical measured meteorological data excluding the historical rainfall in the corresponding nth year, and then convert it into the required basin meteorological boundary file or lake meteorological boundary file.

[0099] S3: Input the generated multiple basin meteorological boundary files into the distributed basin hydrological model respectively, extract the water volume time series of the basin flowing into the lake and the water volume time series of the basin taking water from the lake output after the operation of the distributed basin hydrological model corresponding to each basin meteorological boundary file, and form a set of basin inflow - water intake boundary files covering meteorological uncertainties.

[0100] Extract the water volume time series of each tributary flowing into the lake and the water intake time series of the basin from the lake from the results output after the operation of the distributed basin hydrological model; then integrate and process these data, accumulate the water volumes of multiple inflowing tributaries to generate the total inflow - into - lake flow time series, and summarize the water intakes of each water intake to form the total water intake time series; finally, according to the input requirements of the lake hydrodynamic model, convert the processed inflow and water intake data into a standardized boundary file format to provide the basin inflow and water intake boundary files for the lake hydrodynamic model.

[0101] After the construction of the distributed basin hydrological model in step S1 is completed, copy multiple identical distributed basin hydrological models, and the number of distributed basin hydrological models is the same as the number of basin meteorological boundary files. Input the multiple basin meteorological boundary files into different distributed basin hydrological models respectively, and drive multiple distributed basin hydrological models simultaneously, which is beneficial to improving the overall operation efficiency.

[0102] S4: Based on the water level monitoring data of the lake body ontology characteristics data, statistically analyze the monthly - end water level distribution range of the target lake body, construct multiple annual - scale water level control strategies with different regulation characteristics for the monthly - end water level distribution range using the grid search method, and convert each annual - scale water level strategy into a water level control boundary file of the lake hydrodynamic model.

[0103] The steps of constructing multiple annual - scale water level control strategies with different regulation characteristics for the monthly - end water level distribution range using the grid search method are as follows: S41: Based on the monthly end - of - month water level distribution range of the target lake body, extract the minimum and maximum values of the end - of - month water level distribution for each month. The extraction expressions are as follows: ; where is the historical lowest water level of the end - of - month water level distribution of the target lake body in the m - th month; where is the historical highest water level of the end - of - month water level distribution of the lake body in the m - th month; N refers to the number of historical years of the collected lake body characteristic data.

[0104] S42: Use the Latin hypercube sampling method to conduct multiple samplings within the interval for each month to generate multiple sets of annual - scale water level control strategies covering 12 months of the whole year. The sampling expression is as follows: ; where refers to the scheduling water level obtained during the r - th sampling in the m - th month; LHS refers to Latin hypercube sampling; refers to the annual - scale water level control strategy obtained from the r - th sampling.

[0105] In this embodiment, the water level control intervals for each month of Lake E are as Figure 24 shown. 3, 3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 2 groups are sampled respectively from January to December, and a total of 3 * 3 * 3 * 3 * 3 * 3 * 3 * 4 * 4 * 3 * 3 * 2 = 629856 groups of annual - scale water level control strategies are obtained.

[0106] S5: Form initial combinations of the lake body meteorological boundary file and the basin inflow - water intake boundary file with consistent historical measured meteorological data years. Arrange and combine several initial combinations with several water level control boundary files to obtain multiple sets of input files for the lake body hydrodynamic model. Input each set of input files into the lake body hydrodynamic model for annual - scale simulation, and extract the lake body water level time series and downstream outlet flow time series after the simulation of the lake body hydrodynamic model corresponding to each set of input files.

[0107] After the construction of the lake body hydrodynamic model in step S1 is completed, a total of 110 * 629856 = 69284160 lake body hydrodynamic models are copied. Input multiple input files into different lake body hydrodynamic models respectively, and drive multiple lake body hydrodynamic models simultaneously, which is beneficial to improving the overall operation efficiency.

[0108] S6: Merge several sets of input files using the same annual - scale water level control strategy to obtain several input groups; based on the lake body water level time series and downstream outlet flow time series data corresponding to each set of input files, and combined with relevant indicators of lake water resources and water safety, conduct a multi - objective comprehensive evaluation on the input groups, determine the input group with the optimal multi - objective comprehensive score, and extract the water level control boundary file used by this input group as the final annual - scale robust water level scheduling logic of the target lake body.

[0109] The multi-objective scoring formula is: ; x is the number of all lake meteorological boundary files in the current input group whose lake water level time series after the lake hydrodynamic model simulation is within the set standard water level range throughout the year; y is the number of lake meteorological boundary files in the current input group whose downstream outflow flow time series after the lake hydrodynamic model simulation meets the maximum outflow capacity restriction condition of the outflow outlet throughout the whole process; z is the minimum value of the lake water level at the end of the year corresponding to the annual-scale simulation results of all lake hydrodynamic models in the current input group; z min is the minimum value of the lake water level at the end of the year corresponding to the annual scale simulation results of the lake hydrodynamic model in all input files; max is the maximum value of the lake water level at the end of the year corresponding to the annual-scale simulation results of the lake hydrodynamic model in all input files; u is the total number of lake meteorological boundary files in step S2.

[0110] The multi-objective comprehensive score results of all annual scale water level control candidate strategies (i.e., input groups) obtained in this example Figure 25 This embodiment finally selects the water level control boundary file with the highest multi-objective comprehensive score as the final annual-scale robust water level scheduling logic.

[0111] Example 3: A robust scheduling logic generation system for lake water resources-water security coordination at an annual scale disclosed in the present invention, such as Figure 26 As shown, it includes a watershed hydrology-lake hydrodynamic coupling modeling module, a comprehensive meteorological uncertainty boundary file generation module, a watershed hydrology simulation module, a lake water level control strategy generation module, a lake hydrodynamic simulation module and a water level control strategy determination module.

[0112] The watershed hydrology-lake hydrodynamic coupling modeling module is used to collect the hydrogeographic data of the watershed to which the target lake belongs and the lake body characteristic data, build a distributed watershed hydrological model based on the hydrogeographic data, and use the lake body characteristic data and the output results of the distributed watershed hydrological model to jointly build a lake body hydrodynamic model. The watershed hydrology-lake hydrodynamic coupling modeling module executes the content of step S1 in Example 1.

[0113] The comprehensive meteorological uncertainty boundary file generation module is used to obtain the annual scale forecast rainfall data and historical measured meteorological data of the watershed to which the target lake belongs, and use the scenario construction technology to construct multiple boundary scenarios representing meteorological uncertainty for the annual scale forecast rainfall data and the historical measured meteorological data, and generate the watershed meteorological boundary file for the distributed watershed hydrological model and the lake meteorological boundary file for the lake hydrodynamic model based on the multiple boundary scenarios. The comprehensive meteorological uncertainty boundary file generation module executes the content of step S2 in step embodiment 1.

[0114] The basin hydrological simulation module is used to separately input multiple generated basin meteorological boundary files into a distributed basin hydrological model, extract the time series of the water volume flowing into the lake body and the time series of the water volume taken from the lake body by the distributed basin hydrological model after running for each basin meteorological boundary file, and form a set of basin inflow - water intake boundary files covering meteorological uncertainties. The basin hydrological simulation module executes the content of step S3 in Embodiment 1 of the steps.

[0115] The lake - level control strategy generation module can statistically obtain the monthly - end water - level distribution range of the target lake body based on the water - level monitoring data of the lake - body ontology characteristics data, construct multiple annual - scale water - level control strategies with different regulation characteristics for the monthly - end water - level distribution range by using the grid - search method, and convert each annual - scale water - level strategy into a water - level control boundary file of the lake - body hydrodynamic model. The lake - level control strategy generation module executes the content of step S4 in Embodiment 1 of the steps.

[0116] The lake - body hydrodynamic simulation module can form an initial combination of the lake - body meteorological boundary file and the basin inflow - water intake boundary file with the same historical measured meteorological data year, perform permutation and combination of several initial combinations with several water - level control boundary files respectively to obtain multiple groups of input files of the lake - body hydrodynamic model, input each group of input files into the lake - body hydrodynamic model for annual - scale simulation, and extract the time series of the lake - body water level and the time series of the downstream outlet flow rate after the simulation of the lake - body hydrodynamic model corresponding to each group of input files. The lake - body hydrodynamic simulation module executes the content of step S5 in Embodiment 1 of the steps.

[0117] The water - level control strategy determination module can merge several groups of input files using the same annual - scale water - level control strategy to obtain several input groups; based on the time series data of the lake - body water level and the downstream outlet flow rate corresponding to each group of input files, and combined with the relevant indicators of lake water resources and water security, conduct a multi - objective comprehensive evaluation on the input groups, determine the input group with the optimal multi - objective comprehensive score, and extract the water - level control boundary file used by this input group as the final annual - scale robust water - level scheduling logic of the target lake body.

[0118] Embodiment 4: An electronic device disclosed by the present invention includes one or more processors, one or more memories, and one or more programs. The programs are stored in the memories and are configured to be executed by the processors. When the programs are loaded into the processors, the steps of the annual - scale robust scheduling logic generation method for lake water resources - water security coordination in Embodiment 1 are implemented.

[0119] Example 5: A computer-readable storage medium disclosed by the present invention stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute the steps of the annual-scale robust scheduling logic generation method for lake water resources-water security coordination in Example 1.

Claims

1. A method for generating an annual-scale robust scheduling logic for the coordination of lake water resources and water security, characterized in that: The following steps are included: S1: Collect the hydrogeographic data of the target lake’s basin and the lake’s body characteristic data, build a distributed basin hydrological model based on the hydrogeographic data, and build a lake hydrodynamic model by linking the lake’s body characteristic data and the output results of the distributed basin hydrological model; S2: Obtain the annual scale forecast rainfall data and historical measured meteorological data of the watershed to which the target lake belongs, use scenario construction technology to construct multiple boundary scenarios representing meteorological uncertainty for the annual scale forecast rainfall data and historical measured meteorological data, and generate the watershed meteorological boundary file for the distributed watershed hydrological model and the lake meteorological boundary file for the lake hydrodynamic model based on the multiple boundary scenarios; S3: Input the generated multiple basin meteorological boundary files into the distributed basin hydrological model respectively, extract the time series of water volume entering the lake and the time series of water volume withdrawn from the lake output by the distributed basin hydrological model after running the corresponding distributed basin meteorological boundary file, and form a set of basin inflow-water withdrawal boundary files covering meteorological uncertainties; S4: Based on the water level monitoring data of the lake body characteristic data, the water level distribution range of the target lake at the end of each month is statistically calculated. A grid search method is used to construct multiple annual-scale water level control strategies with differentiated regulation characteristics for the water level distribution range at the end of the month, and each annual-scale water level strategy is converted into a water level control boundary file of the lake body hydrodynamic model; S5: The lake meteorological boundary file and the basin inflow-water intake boundary file with the same historical measured meteorological data year constitute an initial combination, and several initial combinations are arranged and combined with several water level control boundary files to obtain multiple groups of input files of the lake hydrodynamic model. Each group of input files is input into the lake hydrodynamic model for annual scale simulation, and the lake water level time series and downstream outflow flow time series after the lake hydrodynamic model simulation corresponding to each group of input files are extracted; S6: Merge several groups of input files that use the same annual-scale water level control strategy to obtain several input groups; conduct a multi-objective comprehensive evaluation of the input groups based on the lake water level time series and downstream outflow flow time series data corresponding to each group of input files, and combine the lake water resources and water security-related indicators to determine the input group with the best multi-objective comprehensive score, and extract the water level control boundary file used by the input group as the final annual-scale robust water level scheduling logic for the target lake.

2. The method for generating the annual-scale robust scheduling logic for lake water resources-water security coordination according to claim 1, wherein: The basin hydrogeographic data described in step S1 include the surface elevation data, soil type, land use, hourly meteorological data, measured hydrological data of the basin, spatial distribution information of drainage and water intake facilities, and their operating flow time series data of the basin to which the lake belongs; the lake body characteristic data described in step S1 include the underwater three-dimensional terrain data of the lake, lake area meteorological data, water level monitoring data, and continuous monitoring data of inflow and outflow.

3. The annual-scale robust scheduling logic generation method for lake water resources-water security coordination according to claim 1, characterized in that: The annual-scale predicted rainfall data described in step S2 refers to the monthly predicted rainfall amounts for the next year predicted by all prediction stations within the basin to which the target lake belongs; the historical measured meteorological data described in step S2 refers to the historical hourly rainfall data for many years of all rain gauge stations within the basin to which the target lake belongs and the historical hourly multi-index meteorological observation data for many years of all meteorological stations.

4. The method for generating the annual-scale robust scheduling logic for lake water resources - water security collaboration according to claim 3, characterized in that: The steps of generating a basin meteorological boundary file for use in a distributed basin hydrological model and a lake meteorological boundary file for use in a lake hydrodynamic model in step S2 are as follows: S21: Based on the monthly predicted rainfall amounts for the next year of each prediction station, calculate the total annual rainfall amount of each prediction station, and perform perturbation expansion on all the total annual rainfall amounts to obtain multiple total annual rainfall amount samples; S22: Calculate the weights of the historical monthly rainfall amounts of each rain gauge station and meteorological station in each historical year accounting for the total annual rainfall amount based on the historical hourly rainfall data and historical hourly multi-index meteorological observation data, form a historical monthly-scale weight sequence, and predict and generate several groups of predicted monthly-scale weight sequences containing 12 months of a year; S23: Perform permutation and combination on the total annual rainfall amount samples obtained in step S21 and the predicted monthly-scale weight sequences obtained in step S22 to obtain several total annual rainfall amount sample - predicted monthly-scale weight sequence combinations, and the monthly rainfall amounts of each month in the total annual rainfall amount sample - predicted monthly-scale weight sequence combination form monthly rainfall time series samples; S24: Use a clustering method to perform clustering processing on all the monthly rainfall time series samples, cluster to obtain k types of typical meteorological scenarios, and use the typical meteorological scenarios as the representative samples of the target rainfall time series; S26: Convert the hourly-scale rainfall time series of all rain gauge stations and meteorological stations within the basin to which the target lake belongs predicted in step S25 into a basin meteorological boundary file in a format conforming to the input of the distributed basin hydrological model; Convert the hourly-scale rainfall time series of the rain gauge stations and meteorological stations located within the specified range of the target lake into a lake meteorological boundary file in a format conforming to the input of the lake hydrodynamic model. The method of generating several groups of predicted monthly-scale weight sequences containing 12 months of a year in step S22 is as follows:

5. The annual-scale robust scheduling logic generation method for lake water resources-water security collaboration according to claim 4, wherein: Classify all the historical monthly-scale weight sequences of each rain gauge station and meteorological station in the historical measured meteorological data by month to form 12 historical rainfall weight sets; Based on the Bernoulli–Gamma joint distribution model, perform multiple samplings to generate several groups of predicted monthly-scale weight sequences containing 12 months of a year. The steps of a single sampling are as follows: For the set of historical rainfall weights for each month, based on the binary event results of whether rainfall occurred at each rainfall station and meteorological station in that month recorded in historical measured meteorological data, a Bernoulli distribution model is constructed to simulate the rainfall probability for that month. The Bernoulli distribution expression is as follows: ; where p m is the Bernoulli distribution probability; X m = 1 indicates that there is rainfall in month m; X m = 0 indicates that there is no rainfall in month m; m = 1, 2,..., 12; In the historical rainfall weight set of each month, a subset of historical monthly rainfall weights corresponding to rainfall events is selected, and a Gamma distribution model is constructed to simulate the probability distribution of the rainfall weights of that month. The expression of the Gamma distribution is as follows: ; where is the monthly rainfall weight of the m-th month; is the shape parameter of the Gamma distribution; is the scale parameter of the Gamma distribution; The steps of predicting the hourly-scale rainfall time series of all rain gauge stations and meteorological stations for the next year in step S25 are as follows: Sample the m-th month. First, sample from the Bernoulli distribution corresponding to the m-th month. If the sampled X m = 1, then sample from the Gamma distribution of the historical monthly rainfall weight subset corresponding to the m-th month to collect the monthly rainfall weight ; if the sampled X m = 0, then let ; Collect 12 times repeatedly until the monthly rainfall weights for 12 months in the next year are collected , and perform normalization on , so that the sum of the normalized is 1; After normalization is the predicted monthly-scale weight sequence; The steps of constructing multiple annual-scale water level control strategies with different regulation characteristics for the end-of-month water level distribution range using a grid search method in step S4 are as follows: Calculate the ratio b of the monthly rainfall of the k representative samples of the target monthly rainfall time series to the monthly average rainfall of the basin where the target lake is located respectively. The calculation formula of b is as follows: ; where q = 1, 2, 3, 4… k， is the ratio b of the q-th representative sample of the target monthly rainfall time series in the m-th month of the n-th historical year; is the monthly average rainfall in the m-th month of the n-th historical year of the basin where the target lake is located; is the rainfall in the m-th month of the q-th representative sample of the target monthly rainfall time series; Then, based on the ratio b, the historical hourly rainfall of the historical measured meteorological data of all rainfall stations and meteorological stations is scaled and mapped to obtain the hourly-scale rainfall time series for the next year. The calculation formula is as follows: ; where is the predicted rainfall at the hth hour on the dth day of the mth month of the next year when the rainfall time series representative sample of the qth target month is mapped for the rainfall station or meteorological station t; is the historical rainfall at the hth hour on the dth day of the mth month of the nth year for the rainfall station or meteorological station t.

6. The annual-scale robust scheduling logic generation method for lake water resources-water security coordination according to claim 1, wherein: Include, S41: Based on the monthly end - of - month water level distribution range of the target lake body, extract the minimum and maximum values of the end - of - month water level distribution for each month. The extraction expressions are as follows: ; where is the historical lowest water level of the end - of - month water level distribution of the target lake body in the m - th month; where is the historical highest water level of the end - of - month water level distribution of the lake body in the m - th month; N refers to the number of historical years of the lake body's own characteristic data collected. S42: Use the Latin hypercube sampling method to perform multiple samplings within the interval to generate multiple sets of annual-scale water level control strategies covering 12 months of the whole year; the sampling expression is as follows: ; where refers to the scheduling water level obtained during the r-th sampling in the m-th month; LHS refers to Latin hypercube sampling; refers to the annual-scale water level control strategy obtained from the r-th sampling.

7. The annual-scale robust scheduling logic generation method for lake water resources-water security coordination according to claim 1, characterized in that: The multi-objective comprehensive evaluation formula in step S6 is as follows: ; where x is the number of all lake meteorological boundary files in the current input group whose annual lake water level time series after the simulation of the lake hydrodynamic model are all within the set standard water level range; y is the number of lake meteorological boundary files in the current input group whose downstream outflow port flow rate time series after the simulation of the lake hydrodynamic model fully meet the outflow port maximum outflow capacity limit condition; z is the minimum value of the lake water level at the end of the year corresponding to the annual-scale simulation results of all lake hydrodynamic models in the current input group; z min is the minimum value of the lake water level at the end of the year corresponding to the annual-scale simulation results of the lake hydrodynamic model in all input files; z max is the maximum value of the lake water level at the end of the year corresponding to the annual-scale simulation results of the lake hydrodynamic model in all input files; u is the total number of lake meteorological boundary files in step S2.

8. A system for generating an annual-scale robust scheduling logic for the coordination of lake water resources and water security, characterized in that: ​ The watershed hydrology-lake hydrodynamic coupling modeling module is used to collect the hydrogeographic data of the watershed to which the target lake belongs and the lake body characteristic data, build a distributed watershed hydrological model based on the hydrogeographic data, and use the lake body characteristic data and the output results of the distributed watershed hydrological model to jointly build a lake body hydrodynamic model; The comprehensive meteorological uncertainty boundary file generation module is used to obtain the annual scale forecast rainfall data and historical measured meteorological data of the watershed to which the target lake belongs, and use the scenario construction technology to construct multiple boundary scenarios representing meteorological uncertainty for the annual scale forecast rainfall data and historical measured meteorological data. Based on the multiple boundary scenarios, the watershed meteorological boundary files for the distributed watershed hydrological model and the lake meteorological boundary files for the lake hydrodynamic model are generated respectively; The watershed hydrological simulation module is used to input the generated multiple watershed meteorological boundary files into the distributed watershed hydrological model respectively, extract the time series of water entering the lake and the time series of water taking water from the lake output by the distributed watershed hydrological model corresponding to each watershed meteorological boundary file, and form a set of watershed inflow-water taking boundary files covering meteorological uncertainties; The lake water level control strategy generation module can calculate the monthly end-of-month water level distribution range of the target lake based on the water level monitoring data of the lake body characteristic data, and use the grid search method to construct multiple annual-scale water level control strategies with differentiated regulation characteristics for the end-of-month water level distribution range, and convert each annual-scale water level strategy into a water level control boundary file of the lake body hydrodynamic model; The lake hydrodynamic simulation module can form an initial combination of lake meteorological boundary files and basin inflow-water intake boundary files that are consistent with the historical measured meteorological data year, and arrange and combine several initial combinations with several water level control boundary files to obtain multiple groups of input files of the lake hydrodynamic model. Each group of input files is input into the lake hydrodynamic model for annual scale simulation, and the lake water level time series and downstream outflow flow time series after the lake hydrodynamic model simulation corresponding to each group of input files are extracted; The water level control strategy determination module can merge several groups of input files using the same annual scale water level control strategy to obtain several input groups; based on the lake water level time series and downstream outflow flow time series data corresponding to each group of input files, and combined with lake water resources and water security related indicators, a multi-objective comprehensive evaluation is performed on the input group to determine the input group with the best multi-objective comprehensive score, and the water level control boundary file used by the input group is extracted as the final annual scale robust water level scheduling logic for the target lake.

9. An electronic device, characterized in that: It includes one or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and are configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the annual-scale robust scheduling logic generation method for lake water resources-water security coordination are implemented according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute the steps of the method for generating the annual-scale robust scheduling logic for lake water resources-water security coordination according to any one of claims 1 to 7.

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