A scenario generation and optimization regulation method and system for coping with extreme low water

Abstract

This invention discloses a method and system for scenario generation and optimization control in response to extreme drought, relating to the field of water resource regulation technology. The method includes: generating extreme drought scenarios based on the characteristics of extreme drought encounters; constructing a water resource regulation model by acquiring data on inter-basin water diversion projects, reservoir groups, water intake gates, and cross-sectional data, and simulating the low-water flow and water level along the affected areas; based on the water resource regulation model, obtaining the objective function using the guarantee rate and water shortage as evaluation indicators, setting constraints, and optimizing the model through a multi-layered nested optimization algorithm to generate the optimal solutions for inter-basin water diversion projects, reservoir scheduling, and water intake gates. This invention fully considers the water resource regulation capabilities of inter-basin water diversion projects, reservoir scheduling, and water intake gates, and effectively improves the water resource system's ability to cope with extreme drought by extracting the optimal regulation strategies under different extreme drought conditions.

Description

Technical Field

[0001] This invention relates to the field of water resource regulation technology, and more specifically, to a method and system for generating and optimizing regulation of extreme drought scenarios. Background Technology

[0002] At present, water resource optimization and regulation mainly relies on the joint operation of reservoir groups, combined with historical experience and real-time forecasting technology, to achieve a rational redistribution of water resources in order to meet the water quantity or flow requirements of inter-basin water diversion projects, reservoir operation, water diversion gates and stations, and key control sections.

[0003] Due to climate change, extreme droughts are becoming more frequent and intense, leading to a surge in the imbalance between water supply and demand under extreme drought conditions, posing greater challenges to water resource management. However, historical extreme drought samples are insufficient, and cases of extreme drought events are particularly rare. Existing research is limited by observational samples, conducting management analyses only based on typical dry years or consecutive dry years, and failing to adequately consider extreme droughts and their associated events. Against this backdrop, how to scientifically generate extreme drought scenarios to provide a basis for water resource management has become an urgent problem to be solved.

[0004] Traditional water management models that rely on the joint operation of reservoir groups and treat water diversion projects and inter-basin water transfer projects as boundary conditions often fail to meet water demand security targets under extreme drought conditions. Therefore, it is necessary to coordinate inter-basin water transfer projects, cascade reservoirs, and water diversion gates, prioritizing rigid demands such as domestic water use and minimum flow requirements at cross-sections through joint regulation, while appropriately relaxing restrictions on flexible demands such as industrial and agricultural water use, allowing for minor disruptions. In this context, developing differentiated regulation rules based on different extreme drought scenarios is crucial for improving the water resources system's ability to cope with extreme droughts. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide a scenario generation and optimization control technology for dealing with extreme droughts, thereby enhancing the ability of water resource systems to cope with extreme droughts.

[0006] To achieve the above technical objectives, this application provides a method for generating and optimizing control of extreme drought scenarios, comprising the following steps:

[0007] Based on the characteristics of extreme drought encounters, extreme drought scenarios are generated. By acquiring data on inter-basin water diversion projects, reservoir groups, water diversion gates and stations, and cross-sectional data, and simulating the low-water flow and water level along the route, a water resource regulation model is constructed.

[0008] Based on the water resources regulation model, the guarantee rate and water shortage amount are used as evaluation indicators to obtain the objective function and set constraints. After optimizing the model through a multi-layer nested optimization algorithm, the optimal schemes for cross-basin water diversion projects, reservoir scheduling and gate water intake are generated for extreme dry water scenarios.

[0009] Preferably, when generating extreme drought scenarios, based on the edge distribution of station traffic, the Copula function is used to establish the joint distribution of multi-station traffic, and then the extreme drought scenario is generated and the encounter probability is calculated. In this case, by specifying the extreme drought encounter probability, the combination of drought frequencies of multiple stations can be derived.

[0010] Preferably, when obtaining the probability of encounter, an extreme dry year is selected from historical data as a typical year, and the dry season process is calculated using the same amplification method based on the different frequencies of dry flow at each station.

[0011] Preferably, when constructing the water resources regulation model, the data of the inter-basin water diversion project includes: the location of the water diversion project, the maximum inflow and outflow of the water diversion project, which is the sum of gravity-flow water diversion and pumping station water diversion when the gate is fully open;

[0012] The data for the water intake gate includes: the location of the water intake gate, the type of the gate, including domestic and industrial water use, as well as the water intake elevation and maximum water intake flow rate. When the water level at the gate is lower than the water intake elevation, the maximum water intake flow rate is the maximum water intake of the pumping station; otherwise, the maximum water intake flow rate is the sum of gravity-fed water intake and pumping station water intake.

[0013] The reservoir group data includes: water level and capacity, water level and flow rate curves of each reservoir, and various constraints in the reservoir operation regulations;

[0014] Flow data includes: natural inflow to each reservoir;

[0015] Cross-sectional data includes: minimum flow requirements for each control section.

[0016] Preferably, when constructing the objective function, the amount of water transferred out of the inter-basin water diversion project is evaluated using the amount of water shortage;

[0017] For reservoir scheduling, the outflow from the reservoir should meet the minimum flow requirements that take into account power generation, navigation, water supply and ecology, and the guarantee rate index should be used for evaluation.

[0018] For water intake gate stations, the water intake flow should meet the requirements of domestic and industrial water use as much as possible, and the guarantee rate index should be used for evaluation.

[0019] For control sections, it is necessary to ensure that the flow rate at the section meets the minimum flow rate requirement, and the guarantee rate index is used for evaluation.

[0020] Preferably, when setting constraints, the constraint for inter-basin water diversion projects is: the amount of water diverted does not exceed the maximum amount of water diverted (outflow);

[0021] The constraints on reservoir operation are: water balance and water level constraints must be met.

[0022] The constraint on the water intake gate station is that its water intake shall not exceed the maximum water intake flow rate.

[0023] Preferably, when optimizing the model using a multi-layer nested optimization algorithm, the outer layer uses a heuristic algorithm to determine the water inflow (outflow) of the inter-basin water diversion project, the middle layer uses an intelligent optimization algorithm to determine the water intake of the water diversion gate, and the inner layer uses a dynamic programming algorithm to determine the reservoir outflow.

[0024] Preferably, when generating the optimal scheme for inter-basin water diversion projects, reservoir scheduling, and gate pumping, in response to extreme drought, priority should be given to ensuring the water demand of the local basin, appropriately reducing the amount of water diverted from inter-basin water diversion projects, prioritizing domestic water use through reservoir group scheduling, allowing slight disruption to production water use, and improving the flow guarantee rate of control sections, so as to respond to and regulate extreme drought.

[0025] This invention also discloses a scenario generation and optimization control system for coping with extreme drought. This system is used in the aforementioned method for scenario generation and optimization control for coping with extreme drought. The system includes:

[0026] The regulation model construction module is used to generate extreme drought scenarios based on the characteristics of extreme drought encounters. It constructs a water resource regulation model by acquiring data on inter-basin water diversion projects, reservoir groups, water diversion gates and stations, and cross-sectional data, and by simulating the low-water flow and water level along the route.

[0027] The response and regulation module is used to obtain the objective function based on the water resources regulation model, using the guarantee rate and water shortage as evaluation indicators, and setting constraints. After optimizing the model through a multi-layer nested optimization algorithm, it generates the optimal scheme for cross-basin water diversion projects, reservoir scheduling, and gate water intake for extreme drought scenarios.

[0028] The present invention discloses the following technical effects:

[0029] This invention fully considers the water resource regulation capabilities of inter-basin water diversion projects, reservoir scheduling, and water diversion gates. It employs a multi-layer nested optimization algorithm to efficiently optimize the regulation model, extract the optimal regulation strategies under different extreme drought conditions, and effectively improve the water resource system's ability to cope with extreme drought. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the method described in this invention;

[0032] Figure 2 This is a schematic diagram of a water resource system according to an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of an extreme drought process involved in an embodiment of the present invention;

[0034] Figure 4 This is a schematic diagram illustrating the evolution of low-water flow and water level prediction in an embodiment of the present invention;

[0035] Figure 5 This is a schematic diagram of a multi-layer nested optimization algorithm involved in an embodiment of the present invention;

[0036] Figure 6 This is a schematic diagram illustrating the iterative convergence process of the optimization algorithm involved in an embodiment of the present invention;

[0037] Figure 7 This is a schematic diagram of the objective function under different extreme low water conditions involved in the embodiments of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0039] This invention provides a method for generating and optimizing control of extreme drought scenarios, comprising the following steps:

[0040] (1) Generation of extreme drought scenarios: First, determine the marginal distribution of station flow; then, use the Copula function to establish the joint distribution of multi-station flow; finally, generate extreme drought scenarios and calculate the probability of encountering them;

[0041] Determining the marginal distribution: Before constructing the joint distribution using the Copula function, it is necessary to first construct the marginal distribution of traffic at each site. Available marginal distribution line types include, but are not limited to, Pearson Type III distribution, exponential distribution, generalized extreme value distribution, and log-normal distribution.

[0042] 2) Determine the joint distribution: Construct a multi-site joint distribution using the Copula function. Taking a two-dimensional Copula joint distribution as an example, available joint distributions include, but are not limited to, Clayton Copula, Frank Copula, and Gumbel Copula. The parameters of the Copula function are determined using the maximum likelihood method, and the Copula function with the best fit is selected as the multi-site joint distribution based on the minimum AIC criterion.

[0043] 3) Scenario generation and encounter probability calculation: Select the dry flow rate at each station with a dry frequency of 90%, 95%, and 98%, and calculate the encounter probability of extreme dry water for multiple stations based on the joint distribution model; similarly, by specifying the extreme dry water encounter probability, the combination of dry water frequencies for multiple stations can be derived, where the dry water frequency of a station is ≥90%.

[0044] Extremely dry years were selected from historical data as typical years, and the dry season process was calculated using the same-scale amplification method based on the different frequencies of dry flow at various stations.

[0045] This invention fully considers the characteristics of extreme drought encounters, scientifically generates extreme drought scenarios and calculates the probability of encountering them, providing a basis for the optimized regulation of water resources under extreme drought conditions.

[0046] (2) Water resource regulation model:

[0047] 1) Data collection and organization:

[0048] To establish a water resources regulation model, the data that needs to be collected includes data on inter-basin water diversion projects, reservoir groups, water diversion gates and stations, and cross-sectional data.

[0049] Data on inter-basin water diversion projects include: the location of the water diversion project, the maximum inflow and outflow of the water diversion project, and the sum of gravity-flow water diversion and pumping station water diversion when the gate is fully open;

[0050] The data for the water intake gate includes: the location of the water intake gate, the type of the gate, including domestic and industrial water use, as well as the water intake elevation and maximum water intake flow rate. When the water level at the gate is lower than the water intake elevation, the maximum water intake flow rate is the maximum water intake of the pumping station; otherwise, the maximum water intake flow rate is the sum of gravity-fed water intake and pumping station water intake.

[0051] The reservoir group data includes: water level and capacity, water level and flow rate curves of each reservoir, and various constraints in the reservoir operation regulations;

[0052] Flow data includes: natural inflow to each reservoir;

[0053] Cross-sectional data includes: minimum flow requirements for each control section.

[0054] 2) Low-water flow evolution and water level simulation model:

[0055] Hydrodynamic models are typically used to simulate low-flow rates and water levels along the route. If real-time control is required, a large amount of data can be generated based on the calibrated hydrodynamic model simulation for training machine learning surrogate models (such as LSTM neural networks), thereby significantly reducing computation time.

[0056] 3) Optimization and regulation of water resources:

[0057] Objective function:

[0058] The degree to which water quantity or flow requirements of inter-basin water diversion projects, reservoir scheduling, water diversion gates and stations, and key control sections are met is evaluated using indicators such as guarantee rate and water shortage.

[0059] The water diversion volume of inter-basin water diversion projects is evaluated using the water shortage method, as shown in the following expression:

[0060]

[0061] In the formula: This represents the water shortage during the time period of the m-th inter-basin water diversion project; This represents the water demand of the m-th inter-basin water diversion project; T represents the total number of time periods. Δt represents the outflow of water from the m-th inter-basin water diversion project during time period t; Δt represents the length of the calculation period.

[0062] For reservoir operation, the outflow from the reservoir should meet the minimum flow requirements that comprehensively consider power generation, navigation, water supply, and ecology. The guarantee rate index is used for evaluation, and the expression is as follows:

[0063]

[0064] In the formula: This represents the guarantee rate of the outflow from the nth reservoir; This represents the outflow from the nth reservoir during time period t. This represents the minimum outflow from the nth reservoir during time period t. This indicates whether the outbound flow meets the requirements; a value of 1 indicates that it meets the requirements, and a value of 0 indicates that it does not.

[0065] For water intake gate stations, the water intake flow should meet the requirements for domestic and industrial water use as much as possible. The guarantee rate index is used for evaluation, and the expression is as follows:

[0066]

[0067] In the formula: This represents the guarantee rate of water intake flow at the i-th water intake gate station; This represents the water intake flow rate of the i-th water intake gate station during the t-th time period; This represents the water intake flow requirement of the i-th water intake gate station during the t-th time period; This indicates whether the water intake flow rate meets the requirements; 1 indicates that it meets the requirements, and 0 indicates that it does not.

[0068] For control sections, it is necessary to ensure that the flow rate at the section meets the minimum flow rate requirement. The guarantee rate index is used for evaluation, and the expression is as follows:

[0069]

[0070] In the formula: This represents the guarantee rate of the flow rate at the j-th control section; This represents the flow rate at the j-th control section during time period t; This represents the minimum flow requirement for the j-th control section during time period t. This indicates whether the cross-sectional flow rate meets the requirements; a value of 1 indicates that the requirement is met, and a value of 0 indicates that the requirement is not met.

[0071] Taking into account the above evaluation indicators, the objective function of the model is constructed using the weighting method, and its expression is shown below:

[0072]

[0073] Constraint: For inter-basin water diversion projects, the amount of water diverted shall not exceed the maximum amount of water diverted (or discharged).

[0074]

[0075] In the formula: This represents the maximum inflow (outflow) of the m-th inter-basin water diversion project.

[0076] Reservoir scheduling needs to meet requirements such as water balance and water level constraints.

[0077]

[0078] Z n,t,min ≤Z n，t ≤Z n，,t,max

[0079] In the formula: V n,t+1 V represents the capacity of reservoir n at time t+1;n,t I represents the reservoir capacity of reservoir n at time t; n，t Loss represents the average inflow rate of reservoir n during time period t; n,t Z represents the water consumption of reservoir n during time period t due to evaporation and seepage, production and domestic use, and water diversion; n,t Z represents the water level of reservoir n at time t. n，t，min Z represents the lower limit of the water level in reservoir n at time t; n,t,max Let be the upper limit of the water level of reservoir n at time t.

[0080] For water intake gate stations, the water intake shall not exceed the maximum water intake flow rate:

[0081]

[0082] In the formula: This represents the maximum water diversion flow rate of the i-th water intake gate.

[0083] Optimization algorithm:

[0084] The model needs to optimize decision variables including the inflow (outflow) of the inter-basin water diversion project, the outflow from the reservoir, and the intake flow of the water intake gate. With numerous variables (MT+NT+IT), the solution is complex, and a single algorithm is insufficient to obtain a satisfactory solution. Therefore, a multi-layered nested optimization algorithm is constructed, which decomposes this complex problem into easily solvable subproblems and then integrates multiple algorithms within a unified framework. Specifically, the outer layer uses a heuristic algorithm to determine the inflow (outflow) of the inter-basin water diversion project, the middle layer uses an intelligent optimization algorithm to determine the intake flow of the water intake gate, and the inner layer uses a dynamic programming algorithm to determine the outflow from the reservoir.

[0085] For the control model constructed in this invention, multiple weight schemes can be set, and the multi-subject competition and cooperation mechanism can be analyzed based on the optimal solution obtained by optimization. For the characteristic of water intake gates prioritizing domestic water use and allowing slight damage to production water use, the water intake guarantee rate of domestic water use should be set with a higher weight.

[0086] 4) Optimized regulation strategies for extreme drought:

[0087] For different extreme drought scenarios, the multi-layer nested optimization algorithm optimizes the model to determine the optimal schemes for cross-basin water diversion projects, reservoir scheduling, and gate pumping. It analyzes and summarizes the optimal control strategies under different extreme drought scenarios and their occurrence, ultimately achieving the goal of improving the water resources system's ability to cope with extreme drought.

[0088] Example: Figures 1-7 As shown, this invention discloses a method for generating and optimizing control of scenarios to cope with extreme drought, the process of which is as follows: Figure 1 As shown, the method includes the following steps:

[0089] Step 1: Based on the flow data, establish the marginal distribution of station flow and the joint distribution of multi-site flow, generate extreme drought scenarios, and calculate the probability of encountering them; including the following sub-steps:

[0090] Step 1-1: Establish the edge distribution of traffic at each site using the Pearson Type III distribution;

[0091] Step 1-2: Construct a joint distribution of traffic across multiple sites, and determine the optimal joint distribution function based on the minimum AIC criterion;

[0092] Steps 1-3: Calculate the dry flow rate and its frequency at each station when the dry flow frequency is 90%, 95%, and 98%. Select a typical extreme dry year and use the same scale amplification method to calculate the extreme dry flow process.

[0093] Step 2: Construct a water resource regulation model, using the extreme drought scenario generated in Step 1 as model input to determine the optimal regulation scheme; this includes the following sub-steps:

[0094] Step 2-1: Collect and organize data on inter-basin water diversion projects, reservoir groups, water diversion gates and stations, and cross-sectional data;

[0095] Step 2-2: Simulate the low-flow rate and water level along the route using a hydrodynamic model or a machine learning surrogate model;

[0096] Steps 2-3: Fully consider the regulation of water resources by inter-basin water diversion projects, reservoir scheduling, and water diversion gates, construct a joint water resource regulation model, and solve it using a multi-layer nested optimization algorithm;

[0097] Steps 2-4: Use different extreme drought scenarios as model inputs to determine the optimal control scheme and analyze water resource control strategies to cope with different extreme drought scenarios.

[0098] With attachment Figure 2 Taking the water resource system shown as an example, two reservoirs, reservoir A and reservoir B, are built from upstream to downstream along the river. A cross-basin water diversion project and a water diversion gate are built between reservoirs A and B. Section A is located downstream of reservoir B, and its flow is directly affected by the outflow from reservoir B.

[0099] In step 1-1, the marginal distribution of natural flow in the A and AB intervals of reservoir is constructed using the Pearson Type III distribution, and the statistical parameters are shown in Table 1.

[0100] Table 1 Statistical parameters of Pearson type III distribution

[0101] Statistical parameters Reservoir A AB interval <![CDATA[E x ]]> 1138.59 326.87 <![CDATA[C v ]]> 0.4 0.5 <![CDATA[C s / C v ]]> 2.5 2

[0102] In steps 1-2, the Copula function is used to construct the joint distribution of the two functions. The parameters of the Copula function are determined using the maximum likelihood method, and the function with the best fitting effect is selected according to the minimum AIC criterion. The AIC values ​​of Clayton Copula, Frank Copula, and Gumbel Copula are -218.08, -200.00, and -268.58, respectively. Therefore, the Gumbel Copula function is used to establish the joint distribution, and the probability of encountering extreme drought is calculated as shown in Table 2.

[0103] Table 2 Probability of Encountering Extreme Drought

[0104]

[0105] In steps 1-3, the low-water flow rates at frequencies of 90%, 95%, and 98% are selected for reservoirs A and AB. Based on the selected extreme low-water typical cases, the extreme low-water process is calculated using the same-scale amplification method, as shown in the attached figure. Figure 3 As shown.

[0106] In step 2-1, data on inter-basin water diversion projects, reservoir groups, water diversion gates and stations, and cross-sectional data are collected.

[0107] In step 2-2, a one-dimensional hydrodynamic model is used to calculate the water level and flow rate along the friction path. The simulation results are shown in the attached figure. Figure 4 As shown in the figure, the calculation results show that the calculated values ​​of the model fit the measured values ​​well, and it can be used for the prediction and calculation of water level and flow rate.

[0108] In steps 2-3, an optimized control model is constructed, using the methods shown in the attached figure. Figure 5 The multi-layered nested algorithm shown is used for optimization and solution. The heuristic algorithm uses the pattern search method, the intelligent optimization algorithm uses the genetic algorithm, and the dynamic programming method uses the successive approximation dynamic programming algorithm.

[0109] In steps 2-4, the generated low-water scenario is input into the constructed optimization and control model to obtain the optimal solution. The algorithm iteration is shown in the attached figure. Figure 6 As shown, it can converge to the optimal solution relatively quickly. Optimal regulation for dealing with different extreme drought conditions is shown in the appendix. Figure 7 As shown, in response to extreme droughts, priority should be given to ensuring the water demand of the local basin, appropriately reducing the water outflow from inter-basin water diversion projects, prioritizing domestic water use through reservoir group scheduling, allowing for minor disruption to production water use, and improving the flow guarantee rate of control sections. This can effectively enhance the water resource system's ability to cope with extreme droughts.

[0110] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0111] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0112] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A situational generation and optimization regulation method for coping with extreme low water, characterized in that, The method comprises the following steps: According to the characteristics of extreme low water encounter, an extreme low water scenario is generated by obtaining cross-basin water diversion project data, reservoir group data, water diversion gate station data and section data, simulating the low water flow and water level along the way, and constructing a water resources regulation model; Based on the water resources regulation model, the guarantee rate and water shortage are used as evaluation indexes to obtain the objective function, and the constraints are set. After optimization of the model by a multi-layer nested optimization algorithm, the optimal scheme of cross-basin water diversion project, reservoir regulation and gate station water intake is generated for the extreme low water scenario; When generating the extreme low water scenario, the joint distribution of multi-site flow is established by using the Copula function based on the marginal distribution of site flow, and then the extreme low water scenario is generated and the encounter probability is calculated. By specifying the extreme low water encounter probability, the low water frequency combination of multiple sites is obtained; When constructing the objective function, the water diversion quantity of the cross-basin water diversion project is evaluated by the water shortage; For reservoir regulation, the reservoir outflow should meet the minimum flow requirements considering power generation, navigation, water supply and ecology, and the guarantee rate index is used for evaluation; For water diversion gate stations, the water intake flow should meet the requirements of life and production water as much as possible, and the guarantee rate index is used for evaluation; For control sections, the flow at the section should meet the minimum flow requirements, and the guarantee rate index is used for evaluation.

2. The scenario generation and optimization regulation method for coping with extreme low water according to claim 1, wherein: When obtaining the encounter probability, an extreme low water year is selected as a typical year from historical data, and the low water process is calculated by using the same ratio amplification method according to the low water flow at different frequencies of each site.

3. The scenario generation and optimization regulation method for coping with extreme low water according to claim 2, wherein: When constructing the water resources regulation model, the cross-basin water diversion project data includes the location of the water diversion project, the maximum diversion and outflow of the water diversion project, which is the sum of the self-flow diversion and pump station diversion when the gate is fully opened; The water diversion gate station data includes the location of the water diversion gate station, the type of the gate station including life and production water, and the diversion elevation and maximum diversion flow of the gate station. When the water level at the gate station is lower than the diversion elevation, the maximum diversion flow is the maximum pump station diversion. Otherwise, the maximum diversion flow is the sum of the self-flow diversion and pump station diversion; The reservoir group data includes the water level storage, water level flow curve of each reservoir, and the constraints in the reservoir regulation rules; The flow data includes the natural inflow of each reservoir; The section data includes the minimum flow requirements of each control section.

4. The scenario generation and optimization regulation method for coping with extreme low water according to claim 1, wherein: When setting the constraints, the constraint condition for the cross-basin water diversion project is that the diversion quantity should not exceed the maximum diversion quantity and should not be less than the maximum outflow quantity; The constraint condition for reservoir regulation is that the water balance and water level constraints should be met; The constraint condition for the water diversion gate station is that the water intake quantity should not exceed the maximum diversion flow.

5. The scenario generation and optimization control method for coping with extreme low water according to claim 4, characterized in that: When the model is optimized by the multi-layer nested optimization algorithm, the outer layer uses a heuristic algorithm to determine the water intake or discharge of the inter-basin water diversion project, the middle layer uses an intelligent optimization algorithm to determine the water intake of the diversion sluice station, and the inner layer uses a dynamic programming algorithm to determine the reservoir outflow.

6. The scenario generation and optimization control method for coping with extreme low water according to claim 5, characterized in that: When generating the optimal scheme of the inter-basin water diversion project, reservoir operation and sluice station water intake, in coping with extreme low water, the water demand in the basin is prioritized, the water discharge of the inter-basin water diversion project is appropriately reduced, the water intake of the sluice station is determined by the intelligent optimization algorithm, the reservoir group operation prioritizes the water supply for life, allows the water supply for production to be slightly damaged, and improves the flow guarantee rate of the control section to cope with and control extreme low water.

7. A scenario generation and optimization regulation system for coping with extreme low water, for implementing a scenario generation and optimization regulation method for coping with extreme low water according to claim 1, characterized in that, Comprise: A control model construction module for generating an extreme low water scenario according to the characteristics of extreme low water, obtaining inter-basin water diversion project data, reservoir group data, diversion sluice station data and section data, simulating the low water flow and water level along the way, and constructing a water resources control model; A coping control module for obtaining a target function based on the water resources control model, using the guarantee rate and water shortage as evaluation indexes, setting constraint conditions, and generating an optimal scheme of the inter-basin water diversion project, reservoir operation and sluice station water intake for the extreme low water scenario after optimizing the model by the multi-layer nested optimization algorithm.

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