Reservoir flood discharge scheduling method and device, electronic equipment, storage medium and product

Abstract

The invention relates to the technical field of reservoir flood discharge scheduling, and discloses a reservoir flood discharge scheduling method and device, electronic equipment, a storage medium and a product, and the reservoir flood discharge scheduling method comprises the steps: predicting the precipitation amount of a prediction time period according to the current typhoon position, the current typhoon intensity and the precipitation distribution radius of a target area; obtaining precipitation prediction time sequence data; according to the precipitation prediction time sequence data, the reservoir inflow of the target reservoir is predicted, and reservoir inflow prediction time sequence data is obtained; constructing a risk optimization objective function and an optimization constraint condition according to the reservoir water storage, the maximum safe reservoir water storage, the reservoir flood discharge optimization variable, the minimum safe reservoir water storage and the maximum capacity of the flood discharge facility; and according to the optimization constraint condition and the risk optimization objective function, generating the target reservoir flood discharge amount time sequence data, the rainfall is predicted in combination with the typhoon path and the typhoon intensity, the accuracy of rainfall prediction is improved, and the subsequently determined flood discharge amount is more accurate.

Description

Technical Field

[0001] This invention relates to the field of reservoir flood discharge scheduling technology, specifically to reservoir flood discharge scheduling methods, devices, electronic equipment, storage media, and products. Background Technology

[0002] With the increasing frequency and intensity of typhoons year by year, reservoirs in typhoon-prone areas need to release water in a timely manner during typhoon weather to prevent excessive pressure on the reservoirs and serious disasters such as dam failure. Therefore, it is necessary to schedule the release of water from reservoirs in a timely manner.

[0003] In related technologies, the reservoir discharge scheduling method involves predicting rainfall for a future period, generating a reservoir discharge scheduling plan in advance based on the rainfall, and then executing the plan in the future. However, this method only considers the impact of rainfall caused by weather conditions on reservoir discharge, resulting in an inaccurate and unrealistic reservoir discharge scheduling plan. Summary of the Invention

[0004] This invention provides a reservoir flood discharge scheduling method, apparatus, electronic device, storage medium, and product to solve the problem that reservoir flood discharge scheduling methods in related technologies are not accurate enough and do not conform to actual conditions.

[0005] In a first aspect, the present invention provides a reservoir flood discharge scheduling method, comprising: predicting precipitation for a predicted time period based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, thereby obtaining precipitation prediction time series data; and predicting the inflow of the target reservoir for a predicted time period based on the precipitation prediction time series data, the catchment area, runoff coefficient, confluence time, and initial soil moisture index of the target reservoir in the target area, thereby obtaining the inflow prediction time series data. The process involves: 1) Obtaining time-series data on the target reservoir's water storage, maximum safe water storage, risk weight coefficients, and flood discharge optimization variables; 2) Constructing optimization constraints based on the target reservoir's water storage, inflow forecast time-series data, flood discharge optimization variables, maximum safe water storage, minimum safe water storage, and maximum capacity of the flood discharge facilities; and 3) Generating time-series data on the target reservoir's flood discharge volume based on these constraints and the risk optimization objective function, enabling reservoir flood discharge scheduling based on this data.

[0006] The reservoir discharge scheduling method of this invention predicts precipitation for a given time period based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area. This yields time-series precipitation forecast data. By integrating multi-dimensional typhoon parameters and precipitation characteristic parameters, and considering the impact of typhoon path and intensity on precipitation, the prediction of precipitation for the forecast period is more accurate and better reflects actual conditions. Furthermore, this invention predicts the inflow into the target reservoir for a given time period based on the precipitation forecast time-series data, the target reservoir's catchment area, runoff coefficient, confluence time, and initial soil moisture index. This yields inflow forecast time-series data. Based on the precipitation forecast results, combined with reservoir catchment characteristics and hydrological parameters, the time-series changes in inflow are accurately deduced, transforming precipitation information into reservoir inflow for scheduling. This invention constructs a risk optimization objective function based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficients, and reservoir discharge optimization variables. By introducing risk weight coefficients, the risk balance requirement for flood discharge scheduling is transformed into a quantifiable objective function, upgrading flood discharge decision-making from empirical judgment to a risk-quantified optimization problem. Furthermore, this invention constructs optimization constraints based on the target reservoir's water storage capacity, inflow forecast time-series data, reservoir discharge optimization variables, maximum safe water storage capacity, minimum safe water storage capacity, and maximum capacity of the flood discharge facilities, clarifying both hard and dynamic constraints for reservoir scheduling. This invention generates time-series data of target reservoir discharge volume based on optimization constraints and a risk optimization objective function. This data is then used for reservoir discharge scheduling. Through the optimization framework of constraints and the objective function, dynamic, precise, and adaptable discharge scheduling is achieved. Compared with related technologies, this invention comprehensively considers the impact of typhoon intensity and path on precipitation, obtaining more accurate precipitation forecasts. These forecasts are then used to predict inflow. This invention, through a risk optimization objective function and multi-dimensional constraints, provides dual protection for reservoir capacity safety, discharge facility safety, and downstream flood control safety, effectively preventing typhoon-induced flood risks. The discharge volume determined by the risk optimization objective function and multi-dimensional constraints is more accurate and feasible. This invention accurately predicts reservoir discharge volumes in advance, adapting to rapid changes in typhoon weather when discharge regulation is needed, ensuring reservoir safety and preventing typhoon impacts.

[0007] In one optional implementation, precipitation for a prediction period is predicted based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, to obtain precipitation prediction time-series data. This includes: predicting the typhoon intensity for the prediction period based on the typhoon intensity variation function, the current typhoon intensity, and the typhoon intensity attenuation coefficient, to obtain typhoon intensity time-series data; predicting the typhoon path for the prediction period based on the path evolution function, the typhoon intensity time-series data, the current typhoon location, and the typhoon path correction coefficient, to obtain typhoon path time-series data; and performing spatial interpolation processing based on the typhoon path time-series data, precipitation intensity coefficient, and precipitation distribution radius to obtain precipitation prediction time-series data.

[0008] In one optional implementation, the inflow of the target reservoir during the prediction period is predicted based on the precipitation prediction time series data, the catchment area of ​​the target reservoir in the target area, the runoff coefficient, the confluence time, and the initial soil moisture index, to obtain the inflow prediction time series data. This includes: integrating the precipitation prediction time series data and obtaining the average precipitation time series data for the prediction period based on the quotient of the integration result and the catchment area; adjusting the average precipitation time series data based on the confluence time to obtain the target average precipitation time series data; and transforming the target average precipitation time series data by volume unit to obtain the transformation result; obtaining the target soil moisture index by summing 1 and the initial soil moisture index; and obtaining the inflow prediction time series data for the prediction period by multiplying the transformation result, the target soil moisture index, the runoff coefficient, and the catchment area.

[0009] In one optional implementation, a risk optimization objective function is constructed based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficient, and reservoir discharge optimization variables. This includes: obtaining a squared result by squaring the difference between the target reservoir's water storage capacity and its maximum safe water storage capacity; obtaining a first risk relationship by multiplying the squared result by the risk weight coefficient; obtaining a target risk weight coefficient by squaring the difference between the squared result and the risk weight coefficient; obtaining a second risk relationship by multiplying the squared result by the target risk weight coefficient; integrating the sum of the first and second risk relationships to obtain a total risk relationship; and minimizing the total risk relationship to obtain the risk optimization objective function.

[0010] In one optional implementation, the optimization constraints include water storage source constraints, water storage range constraints, and flood discharge constraints. Based on the target reservoir's water storage, inflow forecast time-series data, flood discharge optimization variables, maximum safe water storage, minimum safe water storage, and maximum capacity of the flood discharge facilities, the optimization constraints are constructed as follows: a water storage source constraint is constructed based on the fact that the rate of change of the target reservoir's water storage over time equals the difference between the inflow forecast time-series data and the flood discharge optimization variables; a water storage range constraint is constructed based on the fact that the target reservoir's water storage is between the maximum safe water storage and the minimum safe water storage; and a flood discharge constraint is constructed based on the fact that the flood discharge optimization variables are between 0 and the maximum capacity of the flood discharge facilities.

[0011] In one optional implementation, the time series data of the target reservoir discharge volume is generated based on the optimization constraints and the risk optimization objective function, including: performing optimization iteration on the reservoir discharge optimization variables based on the optimization constraints and the risk optimization objective function until the time series data of the target reservoir discharge volume is obtained.

[0012] Secondly, the present invention provides a reservoir flood discharge scheduling device, comprising: a precipitation prediction unit, used to predict precipitation for a prediction period based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, to obtain precipitation prediction time series data; and an inflow prediction unit, used to predict the inflow of the target reservoir for a prediction period based on the precipitation prediction time series data, the catchment area of ​​the target reservoir in the target area, runoff coefficient, confluence time, and initial soil moisture index, to obtain inflow prediction time series data; The objective function construction unit is used to construct a risk optimization objective function based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficient, and reservoir discharge optimization variables. The constraint construction unit is used to construct optimization constraints based on the target reservoir's water storage capacity, inflow forecast time series data, reservoir discharge optimization variables, maximum safe water storage capacity, minimum safe water storage capacity, and maximum capacity of the discharge facilities. The discharge control unit is used to generate time series data of the target reservoir's discharge volume based on the optimization constraints and the risk optimization objective function, so as to carry out reservoir discharge scheduling based on the target reservoir's discharge volume time series data.

[0013] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the reservoir flood discharge scheduling method described in the first aspect or any corresponding embodiment thereof.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the reservoir flood discharge scheduling method described in the first aspect or any corresponding embodiment thereof.

[0015] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the reservoir flood discharge scheduling method described in the first aspect or any corresponding embodiment. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the reservoir flood discharge scheduling method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of the reservoir flood discharge scheduling method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the reservoir flood discharge scheduling method according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the workflow of a reservoir flood discharge scheduling system according to an embodiment of the present invention; Figure 6 This is a structural block diagram of a reservoir flood discharge scheduling device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0020] 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 technical features indicated. Thus, 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.

[0021] As an optional application scenario of this invention, such as Figure 1 As shown, the reservoir flood discharge scheduling system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0022] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0023] The frequency and intensity of typhoons are showing a trend of increasing year by year, and they are characterized by extreme transientity and uncertainty. The reservoir flood discharge scheduling methods in related technologies suffer from the following technical shortcomings: First, reliance on a single factor: these technologies mainly rely on rainfall forecasts, without comprehensively considering key factors such as typhoon path and wind speed; Second, insufficient forecast accuracy: currently, there is a lack of multi-factor coupled analysis models, making it difficult to accurately predict the spatiotemporal distribution characteristics of typhoon rainfall; Third, response lag: flood discharge decision-making adjustment cycles are long, making it difficult to adapt to the rapid changes in typhoon weather; Fourth, weak risk control: there is a lack of dynamic assessment of downstream carrying capacity, resulting in overly conservative or aggressive flood discharge decisions; Fifth, insufficient system coordination: meteorological forecasts, hydrological models, and flood discharge control systems operate independently, lacking an integrated decision-making mechanism.

[0024] This invention provides a reservoir flood discharge scheduling method that improves the accuracy of rainfall prediction by combining typhoon path and typhoon intensity, thereby making the subsequent determined flood discharge volume more accurate.

[0025] According to an embodiment of the present invention, a method for scheduling flood discharge from a reservoir is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] This embodiment provides a reservoir flood discharge scheduling method, which can be used with computer equipment. Figure 2 This is a first flowchart of a reservoir flood discharge scheduling method according to an embodiment of the present invention, as follows: Figure 2 As shown, the process includes the following steps: Step S201: Based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, the precipitation for the prediction period is predicted to obtain the precipitation prediction time series data.

[0027] The target area is a specific geographical area that includes the target reservoir and its complete catchment basin, and is affected by the typhoon, requiring precipitation forecasting and flood discharge scheduling. For example, the target area covers the upstream catchment area of ​​the reservoir and the downstream area affected by flood discharge, with the regional boundary based on the basin topography and the typhoon's impact range.

[0028] In some optional implementations, the current typhoon position is the precise geographical coordinates of the typhoon center (or typhoon eye) at the start of the forecast, which can be represented by latitude and longitude; the current typhoon intensity is a quantitative indicator of the typhoon's level at the start of the forecast, including the current typhoon central pressure and the current typhoon maximum wind speed; the typhoon intensity attenuation coefficient is used to quantify the rate at which the typhoon weakens during its movement, and its value can be between 0 and 1, with a larger value indicating slower attenuation; the typhoon path correction coefficient is used to correct the deviation between the predicted typhoon path and the actual path, and can be set based on historical typhoon path error patterns; the precipitation intensity coefficient is used to characterize the intensity of precipitation caused by the typhoon, and is proportional to the current typhoon intensity; the precipitation distribution radius refers to the radius of the effective coverage area of ​​precipitation under the influence of the typhoon.

[0029] In some optional implementations, the forecast period can be the next 72 hours, with a forecast made every hour; the precipitation forecast time series data is time series data composed of the precipitation forecast values ​​for every hour of the next 72 hours.

[0030] In some optional implementations, precipitation for the prediction period is predicted based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, to obtain precipitation prediction time series data. This includes: inputting the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area into a trained typhoon multi-element coupled prediction model to obtain precipitation prediction time series data for the prediction period.

[0031] Step S202: Based on the precipitation forecast time series data, the catchment area, runoff coefficient, confluence time, and initial soil moisture index of the target reservoir in the target area, the inflow of the target reservoir during the forecast period is predicted to obtain the inflow forecast time series data.

[0032] Among them, the target reservoir is a specific reservoir that needs to control flood risks and ensure safe operation through flood discharge scheduling; the catchment area of ​​the target reservoir refers to the total area of ​​all catchment areas upstream of the target reservoir; the runoff coefficient represents the proportion of precipitation in the target area that is converted into surface runoff, and the value can be between 0 and 1, with a larger value indicating a higher proportion of precipitation converted into runoff; the confluence time refers to the total time it takes for precipitation in the target area to flow into the target reservoir from its point of origin (such as the upstream of the catchment area) through surface / subsurface runoff; the initial soil moisture index is used to characterize the soil moisture level in the target reservoir catchment area at the start of the prediction, and the value is between 0 and 1, with 0 being extremely dry and 1 being saturated.

[0033] In some optional implementations, the inflow of the target reservoir during the prediction period is predicted based on the precipitation prediction time series data, the catchment area, runoff coefficient, confluence time, and initial soil moisture index of the target reservoir in the target area, to obtain the inflow prediction time series data. This includes: inputting the precipitation prediction time series data, the catchment area, runoff coefficient, confluence time, and initial soil moisture index of the target reservoir in the target area into the trained inflow prediction model to obtain the inflow prediction time series data.

[0034] Step S203: Construct a risk optimization objective function based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficient, and reservoir flood discharge optimization variables.

[0035] Among them, the reservoir storage capacity refers to the total amount of water actually stored in the target reservoir at a certain moment; the maximum safe storage capacity of the reservoir refers to the highest storage capacity threshold that ensures the structural safety and flood control safety of the target reservoir; the risk weight coefficient is used to quantify the importance of the risk of exceeding the safe storage capacity of the reservoir in the optimization objective; the reservoir discharge optimization variable is the optimization variable solved by the embodiment of the present invention, that is, the discharge volume at each time node (one time node per hour) within the predicted time period.

[0036] Step S204: Based on the target reservoir's water storage capacity, inflow forecast time series data, reservoir discharge optimization variables, maximum safe water storage capacity, minimum safe water storage capacity, and maximum capacity of the discharge facilities, construct optimization constraints.

[0037] Among them, the minimum safe water storage capacity of the reservoir refers to the minimum water storage threshold that ensures the basic functions and structural safety of the target reservoir; the maximum capacity of the flood discharge facility is the maximum amount of water that the flood discharge system of the target reservoir can safely discharge per unit time under the design standard, which is the physical upper limit of the flood discharge capacity.

[0038] Step S205: Based on the optimization constraints and risk optimization objective function, generate time series data of the target reservoir discharge volume, so as to carry out reservoir discharge scheduling based on the time series data of the target reservoir discharge volume.

[0039] The reservoir discharge scheduling method provided in this embodiment predicts precipitation for a given period based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area. This yields time-series precipitation forecast data. By integrating multi-dimensional typhoon parameters and precipitation characteristic parameters, and considering the impact of typhoon path and intensity on precipitation, the prediction of precipitation for the forecast period is more accurate and better reflects actual conditions. This embodiment also predicts the inflow into the target reservoir for a given period based on the precipitation forecast time-series data, the target reservoir's catchment area, runoff coefficient, confluence time, and initial soil moisture index. This yields inflow forecast time-series data. Based on the precipitation forecast results, combined with reservoir catchment characteristics and hydrological parameters, the temporal changes in inflow are accurately deduced, transforming precipitation information into reservoir inflow for scheduling. This invention constructs a risk optimization objective function based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficient, and reservoir discharge optimization variables. By introducing risk weight coefficients, the risk balance requirement for discharge scheduling is transformed into a quantifiable objective function, upgrading discharge decision-making from empirical judgment to a risk-quantified optimization problem. Furthermore, this invention constructs optimization constraints based on the target reservoir's water storage capacity, inflow forecast time-series data, reservoir discharge optimization variables, maximum safe water storage capacity, minimum safe water storage capacity, and maximum discharge facility capacity, clarifying both hard and dynamic constraints for reservoir scheduling. This invention generates time-series data of target reservoir discharge volume based on optimization constraints and a risk optimization objective function. Reservoir discharge scheduling is then performed based on this data. Through the optimization framework of constraints and the objective function, dynamic, precise, and adaptable discharge scheduling is achieved. Compared with related technologies, this invention comprehensively considers the impact of typhoon intensity and path on precipitation, obtaining more accurate precipitation forecasts. These forecasts are then used to predict inflow. This invention, through a risk optimization objective function and multi-dimensional constraints, provides dual protection for reservoir capacity safety, discharge facility safety, and downstream flood control safety, effectively preventing typhoon-induced flood risks. The discharge volume determined by the risk optimization objective function and multi-dimensional constraints is more accurate and feasible. This invention accurately predicts reservoir discharge volumes in advance, adapting to rapid changes in typhoon weather when discharge regulation is needed, ensuring reservoir safety and preventing typhoon impacts.

[0040] This embodiment provides a reservoir flood discharge scheduling method, which can be used with computer equipment. Figure 3 This is a second flowchart of the reservoir flood discharge scheduling method according to an embodiment of the present invention, as follows: Figure 3 As shown, the process includes the following steps: Step S301: Based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, the precipitation for the prediction period is predicted to obtain the precipitation prediction time series data.

[0041] Specifically, step S301 includes: Step S3011: Based on the typhoon intensity variation function, the typhoon intensity for the prediction period is predicted according to the current typhoon intensity and the typhoon intensity attenuation coefficient, so as to obtain the typhoon intensity time series data.

[0042] In some optional implementations, the typhoon intensity time-series data includes typhoon central pressure time-series data and typhoon maximum wind speed time-series data; the typhoon intensity variation function includes the typhoon central pressure variation function and the typhoon maximum wind speed variation function; for example, the relationship between the typhoon intensity data (typhoon central pressure data and typhoon maximum wind speed data) at each time point in the typhoon intensity time-series data is determined as follows:

[0043]

[0044] in, This is the central air pressure data for the typhoon, in hectopascals (hPa). This is the current air pressure data at the center of the typhoon. This is the current maximum wind speed data for the typhoon. This is the typhoon intensity attenuation coefficient. () represents the typhoon center pressure variation function, used to characterize the dynamic variation law of typhoon center pressure. This is the maximum wind speed data for the typhoon, in meters per second. This is a function representing the change in the maximum wind speed of a typhoon, used to characterize the dynamic attenuation or change of the maximum wind speed. For predicting the time point.

[0045] Step S3012: Based on the path evolution function, the typhoon path for the prediction period is predicted according to the typhoon intensity time series data, the current typhoon position, and the typhoon path correction coefficient, to obtain the typhoon path time series data.

[0046] In some optional implementations, the typhoon track time series data includes typhoon track longitude time series data and typhoon track latitude time series data. The formula for determining the typhoon track longitude data and typhoon track latitude data at each time point in the prediction period is as follows:

[0047]

[0048] in, This is the longitude data for the typhoon's path, in degrees. This is the latitude data for the typhoon's path, in degrees. This is the path longitude evolution function. This is the path latitude evolution function. This indicates the current longitude of the typhoon. This indicates the current latitude of the typhoon. This is the air pressure data at the center of the typhoon. This is the maximum wind speed data for the typhoon. This is the correction factor for the typhoon's path. This refers to the predicted time point within the predicted time period.

[0049] Step S3013: Spatial interpolation is performed based on the typhoon path time series data, precipitation intensity coefficient, and precipitation distribution radius to obtain the precipitation prediction time series data.

[0050] In some optional implementations, the formula for determining the precipitation forecast data for each time point in the precipitation forecast time series data for the forecast period is:

[0051] in, For predicting time points ,Location( Precipitation forecast data, in millimeters. This is the precipitation intensity coefficient. It is an exponential function. This is the longitude data for the typhoon's path. This is the latitude data for the typhoon's path. This indicates the current longitude of the typhoon. This indicates the current latitude of the typhoon. This represents the radius of precipitation distribution, expressed in kilometers.

[0052] Step S302: Based on the precipitation forecast time series data, the catchment area, runoff coefficient, confluence time and initial soil moisture index of the target reservoir in the target area, the inflow of the target reservoir during the forecast period is predicted to obtain the inflow forecast time series data.

[0053] Specifically, step S302 includes: Step S3021: Integrate the precipitation forecast time series data, and obtain the average precipitation time series data for the forecast period based on the quotient of the integration result and the watershed area.

[0054] The formula for determining the average precipitation data at each time point in the time series data for the prediction period is as follows:

[0055] in, For predicting time points Average precipitation data, in millimeters. The drainage area is expressed in square kilometers. For time points ,Location( The precipitation forecast data is integrated with the precipitation forecast data of the target reservoir's catchment area.

[0056] Step S3022: Adjust the time series data of average precipitation according to the confluence time to obtain the time series data of target average precipitation, and transform the volume unit of the time series data of target average precipitation to obtain the transformation result.

[0057] Step S3023: Based on the sum of 1 and the initial soil moisture index, the target soil moisture index is obtained. Based on the product of the transformation result, the target soil moisture index, the runoff coefficient, and the watershed area, the predicted inflow time series data for the predicted time period is obtained.

[0058] In some optional implementations, the formula for determining the inbound flow data at each time point in the predicted inbound flow time series data for the predicted time period is:

[0059] in, For predicting time points Inbound traffic data, The runoff coefficient reflects the proportion of precipitation that is converted into runoff. For the drainage area, For time points Average precipitation data, This represents the runoff time, expressed in hours, reflecting the delay effect between precipitation and flow rate. This represents the initial soil moisture index.

[0060] Step S303: Construct a risk optimization objective function based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficient, and reservoir flood discharge optimization variables.

[0061] Specifically, step S303 includes: Step S3031: Based on the square of the difference between the target reservoir's water storage capacity and the reservoir's maximum safe water storage capacity, obtain the square result. Based on the product of the square result and the risk weight coefficient, obtain the first risk relationship.

[0062] Step S3032: Based on the difference between 1 and the risk weight coefficient, the target risk weight coefficient is obtained. Based on the product of the square of the reservoir flood discharge optimization variable and the target risk weight coefficient, the second risk relationship is obtained.

[0063] Step S3033: Integrate the sum of the first risk relation and the second risk relation to obtain the total risk relation.

[0064] Step S3034: Minimize the total risk relationship to obtain the risk optimization objective function.

[0065] In some optional implementations, the expression for the risk optimization objective function is:

[0066] in, Optimize the objective function for risk. To identify the optimization variables that minimize the risk optimization objective function, At the current time point, To predict the time point, For risk weighting coefficients, For predicting time points The target reservoir's water storage capacity, in cubic meters. To the maximum safe water storage capacity of the reservoir, For predicting time points The optimized variable for reservoir discharge is expressed in cubic meters per second.

[0067] Step S304: Based on the target reservoir's water storage capacity, inflow forecast time series data, reservoir discharge optimization variables, maximum safe water storage capacity, minimum safe water storage capacity, and maximum capacity of the discharge facilities, construct optimization constraints.

[0068] Specifically, step S304 includes: Step S3041: Based on the fact that the rate of change of the reservoir's water storage over time is equal to the difference between the predicted inflow time series data and the reservoir's flood discharge optimization variables, a water storage source constraint is constructed.

[0069] In some alternative implementations, the expression for the water storage source constraint is:

[0070] in, The target reservoir's water storage capacity. To predict the time point, For predicting time points Inbound traffic data, For predicting time points Optimization variables for reservoir flood discharge.

[0071] Step S3042: Based on the fact that the water storage capacity of the target reservoir is between the maximum safe water storage capacity and the minimum safe water storage capacity, construct a water storage capacity range constraint.

[0072] In some alternative implementations, the expression for the water storage range constraint is:

[0073] in, For predicting time points The target reservoir's water storage capacity The minimum safe water storage capacity of the reservoir. This is the maximum safe water storage capacity of the reservoir.

[0074] Step S3043: Based on the fact that the reservoir flood discharge optimization variable is between 0 and the maximum capacity of the flood discharge facility, construct the flood discharge volume constraint.

[0075] In some alternative implementations, the expression for the flood discharge constraint is:

[0076] in, For predicting time points Optimization variables for reservoir flood discharge The maximum capacity unit for flood discharge facilities is cubic meters per second.

[0077] Step S305: Based on the optimization constraints and risk optimization objective function, generate time series data of the target reservoir discharge volume, so as to carry out reservoir discharge scheduling based on the time series data of the target reservoir discharge volume.

[0078] Specifically, step S305 includes: Step S3051: Based on the optimization constraints, and according to the risk optimization objective function, the optimization variables for reservoir discharge are iterated and optimized until the time series data of the target reservoir discharge volume are obtained.

[0079] Numerical optimization algorithms (such as gradient descent or dynamic programming) can be used to perform optimization iterations to obtain the time series data of the target reservoir's flood discharge.

[0080] The reservoir flood discharge scheduling method provided in this embodiment realizes multi-factor collaborative prediction and analysis of typhoon path, rainfall and wind speed, high-precision dynamic prediction of reservoir inflow, rolling optimization and real-time dynamic adjustment of flood discharge volume, dual-objective optimization of reservoir safety and downstream flood control, and intelligent and adaptive flood discharge decision-making.

[0081] This embodiment provides a reservoir flood discharge scheduling method, which can be used with computer equipment. Figure 4This is a third flowchart of the reservoir flood discharge scheduling method according to an embodiment of the present invention, as follows: Figure 4 As shown, the process includes the following steps: Real-time data collection from meteorological satellites, ground meteorological stations, hydrological stations, and reservoir operations is performed, and spatiotemporal alignment and data fusion are conducted to construct a multi-element dataset. Based on numerical weather prediction models and historical typhoon path analysis, the probability distribution of typhoon paths in the next 72 hours is predicted. A rainfall-wind speed-topography coupled model is established to predict the spatiotemporal distribution characteristics of typhoon rainfall. A distributed hydrological model, combined with real-time soil moisture and land cover data, is used to calculate the runoff in different sub-basins, considering the impact of wind speed on runoff velocity, to predict the reservoir inflow process in the next 72 hours. With reservoir safety level, downstream flood control capacity, and flood discharge stability as constraints, a model predictive control algorithm is used to continuously solve for the optimal flood discharge process line in the next 24 hours, updating the decision scheme every 3 hours. The opening of the flood discharge gates is automatically controlled to achieve precise regulation of the flood discharge volume. The flood discharge effect and reservoir water level changes are monitored in real time to form a closed-loop control system. The predicted and actual hydrological responses are compared to evaluate the decision-making effect. Based on the evaluation results, the model parameters are automatically corrected to improve the accuracy of subsequent predictions and realize the system's self-learning capability.

[0082] This embodiment provides a reservoir flood discharge scheduling system, which can be used with computer equipment. Figure 5 This is a flowchart of the reservoir flood discharge scheduling system according to an embodiment of the present invention, as follows: Figure 5 As shown, the system includes: a typhoon multi-factor coupled prediction module 501, an inflow dynamic prediction module 502, and a flood discharge optimization decision module 503.

[0083] The typhoon multi-factor coupled prediction module 501 includes: wind path probability prediction model, spatiotemporal distribution coupling analysis of rainfall and wind speed, modeling of the rainfall enhancement effect of watershed topography, and fusion processing of multi-source meteorological data.

[0084] The inflow dynamic prediction module 502 includes: distributed hydrological model construction, real-time soil saturation monitoring, runoff velocity-wind speed correlation model, and inflow prediction for the next 72 hours.

[0085] The flood discharge volume optimization decision module 503 includes: multi-objective dynamic optimization algorithm, risk constraint modeling, flood discharge gate collaborative control, and rolling optimization and feedback mechanism.

[0086] This embodiment also provides a reservoir flood discharge scheduling device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0087] This embodiment provides a reservoir flood discharge scheduling device, such as... Figure 6 As shown, it includes: The precipitation prediction unit 601 is used to predict the precipitation for the prediction period based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, and obtain precipitation prediction time series data.

[0088] The inflow prediction unit 602 is used to predict the inflow of the target reservoir during the prediction period based on the precipitation prediction time series data, the catchment area of ​​the target reservoir in the target area, the runoff coefficient, the confluence time and the initial soil moisture index, and to obtain the inflow prediction time series data.

[0089] The objective function construction unit 603 is used to construct a risk optimization objective function based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficient, and reservoir flood discharge optimization variables.

[0090] The constraint construction unit 604 is used to construct optimization constraints based on the target reservoir's water storage capacity, inflow prediction time series data, reservoir discharge optimization variables, maximum safe water storage capacity, minimum safe water storage capacity, and maximum capacity of the discharge facilities.

[0091] The flood discharge control unit 605 is used to generate time series data of the target reservoir's flood discharge volume based on optimization constraints and risk optimization objective function, so as to carry out reservoir flood discharge scheduling based on the time series data of the target reservoir's flood discharge volume.

[0092] In some alternative implementations, the precipitation prediction unit 601 belongs to Figure 5 The typhoon multi-factor coupled prediction module 501 and the inflow prediction unit 602 belong to the category of... Figure 5 The inflow dynamic prediction module 502, objective function construction unit 603, constraint condition construction unit 604, and flood discharge control unit 605 belong to the category of Figure 5 The flood discharge volume optimization decision module 503 in the middle.

[0093] In some alternative implementations, the precipitation prediction unit 601 includes: The typhoon intensity prediction subunit is used to predict the typhoon intensity for the prediction period based on the typhoon intensity variation function, the current typhoon intensity, and the typhoon intensity attenuation coefficient, thus obtaining typhoon intensity time series data.

[0094] The typhoon track prediction subunit is used to predict the typhoon track for a prediction period based on the track evolution function, the typhoon intensity time series data, the current typhoon position, and the typhoon track correction coefficient, thus obtaining the typhoon track time series data.

[0095] The precipitation prediction subunit is used to perform spatial interpolation based on the typhoon path time series data, precipitation intensity coefficient, and precipitation distribution radius to obtain the precipitation prediction time series data.

[0096] In some optional implementations, the inbound flow prediction unit 602 includes: The average precipitation determination sub-unit is used to perform integral processing on the precipitation forecast time series data, and obtain the average precipitation time series data for the forecast period based on the quotient of the integral processing result and the watershed area.

[0097] The unit transformation subunit is used to adjust the average precipitation time series data according to the confluence time to obtain the target average precipitation time series data, and to perform volume unit transformation on the target average precipitation time series data to obtain the transformation result.

[0098] The inflow prediction subunit is used to obtain the target soil moisture index by summing 1 with the initial soil moisture index, and to obtain the inflow prediction time series data for the prediction period by multiplying the transformation result, the target soil moisture index, the runoff coefficient and the watershed area.

[0099] In some optional implementations, the objective function construction unit 603 includes: The first risk relationship determination sub-unit is used to obtain the square result by the square of the difference between the target reservoir's water storage capacity and the reservoir's maximum safe water storage capacity. The first risk relationship is obtained by multiplying the square result with the risk weight coefficient.

[0100] The second risk relationship determines the sub-unit, which is used to obtain the target risk weight coefficient based on the difference between 1 and the risk weight coefficient, and to obtain the second risk relationship based on the product of the square of the reservoir flood discharge optimization variable and the target risk weight coefficient.

[0101] The total risk relation is defined by a sub-unit, which is used to integrate the sum of the first risk relation and the second risk relation to obtain the total risk relation.

[0102] The minimization subunit is used to minimize the total risk relationship to obtain the risk optimization objective function.

[0103] In some alternative implementations, the constraint construction unit 604 includes: The water storage source constraint construction sub-unit is used to construct water storage source constraints based on the fact that the rate of change of the target reservoir's water storage over time is equal to the difference between the predicted inflow time series data and the reservoir's flood discharge optimization variables.

[0104] The water storage range constraint construction sub-unit is used to construct the water storage range constraint based on the fact that the water storage of the target reservoir is between the maximum safe water storage capacity and the minimum safe water storage capacity of the reservoir.

[0105] The flood discharge constraint construction sub-unit is used to construct flood discharge constraints based on the reservoir flood discharge optimization variable being between 0 and the maximum capacity of the flood discharge facility.

[0106] In some alternative implementations, the flood discharge control unit 605 includes: The flood discharge control subunit is used to perform optimization iteration on the reservoir flood discharge optimization variables based on optimization constraints and risk optimization objective function until the time series data of the target reservoir flood discharge volume is obtained.

[0107] The reservoir flood discharge scheduling device provided in this embodiment of the invention can execute the reservoir flood discharge scheduling method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0108] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0109] The following is a detailed reference. Figure 7 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 701, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 702 or a program loaded from memory 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0110] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0111] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the reservoir flood discharge scheduling method of the embodiments of the present invention.

[0112] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0113] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the reservoir flood discharge scheduling method shown in the above embodiments is implemented.

[0114] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0115] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for scheduling flood discharge from a reservoir, characterized in that, The method includes: Based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, the precipitation for the prediction period is predicted to obtain the precipitation prediction time series data. Based on the precipitation forecast time series data, the catchment area, runoff coefficient, confluence time and initial soil moisture index of the target reservoir in the target area, the inflow of the target reservoir during the forecast time period is predicted to obtain the inflow forecast time series data. Based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficient, and reservoir flood discharge optimization variables, a risk optimization objective function is constructed. Based on the target reservoir's water storage capacity, the predicted inflow time series data, the reservoir's flood discharge optimization variables, the reservoir's maximum safe water storage capacity, the reservoir's minimum safe water storage capacity, and the maximum capacity of the flood discharge facilities, optimization constraints are constructed. Based on the optimization constraints and the risk optimization objective function, time-series data of the target reservoir discharge volume is generated, so as to carry out reservoir discharge scheduling based on the time-series data of the target reservoir discharge volume.

2. The method according to claim 1, characterized in that, The precipitation forecast for the target area is obtained by predicting the precipitation over the forecast period based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius, including: Based on the typhoon intensity variation function, the typhoon intensity for the predicted time period is predicted according to the current typhoon intensity and the typhoon intensity attenuation coefficient, thus obtaining typhoon intensity time series data. Based on the path evolution function, the typhoon path for the predicted time period is predicted according to the typhoon intensity time series data, the current typhoon position, and the typhoon path correction coefficient, thus obtaining the typhoon path time series data. Spatial interpolation is performed based on the typhoon path time series data, the precipitation intensity coefficient, and the precipitation distribution radius to obtain the precipitation prediction time series data.

3. The method according to claim 1 or 2, characterized in that, The method involves predicting the inflow into the target reservoir during the prediction period based on the predicted precipitation time series data, the catchment area, runoff coefficient, confluence time, and initial soil moisture index of the target reservoir in the target area, thereby obtaining inflow prediction time series data, including: The precipitation forecast time series data is integrated, and the average precipitation time series data for the forecast period is obtained based on the quotient of the integration result and the watershed area. The average precipitation time series data is adjusted according to the confluence time to obtain the target average precipitation time series data, and the volume unit of the target average precipitation time series data is transformed to obtain the transformation result. The target soil moisture index is obtained by summing 1 with the initial soil moisture index. The inflow forecast time series data for the forecast period is obtained by multiplying the transformation result, the target soil moisture index, the runoff coefficient, and the watershed area.

4. The method according to claim 1 or 2, characterized in that, The step involves constructing a risk optimization objective function based on the target reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficients, and reservoir discharge optimization variables, including: The squared result is obtained by squared the difference between the water storage capacity of the target reservoir and the maximum safe water storage capacity of the reservoir. The first risk relationship is obtained by multiplying the squared result with the risk weight coefficient. The target risk weight coefficient is obtained based on the difference between 1 and the risk weight coefficient. The second risk relationship is obtained by multiplying the square of the reservoir flood discharge optimization variable with the target risk weight coefficient. Integrating the sum of the first risk relation and the second risk relation yields the total risk relation. The total risk relationship is minimized to obtain the risk optimization objective function.

5. The method according to claim 1, characterized in that, The optimization constraints include constraints on water storage source, water storage range, and flood discharge capacity; the optimization constraints are constructed based on the target reservoir's water storage capacity, the predicted inflow time series data, the reservoir flood discharge optimization variables, the reservoir's maximum safe water storage capacity, the reservoir's minimum safe water storage capacity, and the maximum capacity of the flood discharge facilities, including: Based on the fact that the rate of change of the reservoir storage capacity of the target reservoir over time is equal to the difference between the predicted inflow time series data and the reservoir discharge optimization variable, the storage capacity source constraint is constructed. Based on the fact that the water storage capacity of the target reservoir is between the maximum safe water storage capacity and the minimum safe water storage capacity of the reservoir, the water storage capacity range constraint is constructed; The flood discharge constraint is constructed based on the fact that the reservoir flood discharge optimization variable is between 0 and the maximum capacity of the flood discharge facility.

6. The method according to claim 1 or 2, characterized in that, The step of generating time-series data of the target reservoir discharge volume based on the optimization constraints and the risk optimization objective function includes: Based on the aforementioned optimization constraints and according to the aforementioned risk optimization objective function, the optimization variables for reservoir discharge are iteratively optimized until the time-series data of the target reservoir discharge volume are obtained.

7. A reservoir flood discharge scheduling device, characterized in that, The device includes: The precipitation prediction unit is used to predict the precipitation for the prediction period based on the current typhoon location, current typhoon intensity, typhoon intensity attenuation coefficient, typhoon path correction coefficient, precipitation intensity coefficient, and precipitation distribution radius of the target area, and obtain precipitation prediction time series data. The inflow prediction unit is used to predict the inflow of the target reservoir during the prediction period based on the precipitation prediction time series data, the catchment area, runoff coefficient, confluence time and initial soil moisture index of the target reservoir in the target area, so as to obtain the inflow prediction time series data. The objective function construction unit is used to construct a risk optimization objective function based on the reservoir's water storage capacity, maximum safe water storage capacity, risk weight coefficient, and reservoir flood discharge optimization variables. The constraint construction unit is used to construct optimization constraints based on the reservoir storage capacity, the predicted inflow time series data, the reservoir discharge optimization variables, the maximum safe storage capacity, the minimum safe storage capacity, and the maximum capacity of the discharge facilities of the target reservoir. The flood discharge control unit is used to generate time-series data of the target reservoir's flood discharge volume based on the optimization constraints and the risk optimization objective function, so as to carry out reservoir flood discharge scheduling based on the time-series data of the target reservoir's flood discharge volume.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the reservoir flood discharge scheduling method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the reservoir flood discharge scheduling method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the reservoir flood discharge scheduling method according to any one of claims 1 to 6.

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