A method for optimizing flood season grading drought limited water level considering supply and demand

Abstract

This application provides a method for optimizing the tiered drought limit water level during the flood season, taking into account both supply and demand. It obtains the downstream control station of the target river channel, tiered water supply demand, initial tiered outflow parameters, and target tiered outflow parameters. Based on a preset low-water inflow sample and the target tiered outflow parameters, it calculates the initial tiered drought limit water level of the upstream reservoir during the flood season. It determines the drought resistance capacity parameters on the supply side based on historical reservoir operation data, and determines the target tiered drought resistance flow gap parameters on the demand side based on the actual outflow and the target tiered outflow parameters. Simultaneously, it determines the risk value of flood control loss conditions based on the reservoir's flood season operating water level and the river's excess discharge. A scheduling simulation model is constructed to perform multi-objective optimization of the initial tiered drought limit water level during the flood season, resulting in a target tiered drought limit water level that balances both supply and demand. This method is adaptable to complex hydrological scenarios such as flood season reversals and rapid shifts between flood and drought, and can collaboratively ensure downstream tiered water demand, reservoir drought resistance capacity, and flood control safety.

Description

Technical Field

[0001] This application relates to the field of water resource allocation technology, specifically to a method for optimizing flood season drought limit water levels that takes into account both supply and demand. Background Technology

[0002] In related technologies, reservoir flood season scheduling modes are mostly aimed at flood control, and scheduling strategies involve flood control safety design. The flood control limit level is the characteristic water level for reservoir flood control scheduling, and related research mainly involves phased flood control limit level optimization design methods and real-time dynamic control technology of flood season operating water levels combined with forecast information. The drought limit level is the characteristic water level corresponding to reservoir drought relief scheduling, and its application scenario is the dry season. Because the definition of the drought limit level was proposed relatively recently, existing research mainly focuses on drought limit level determination methods and functional positioning, and there is still considerable room for exploration.

[0003] Due to climate change, extreme events such as alternating droughts and floods have become more frequent in river basins in recent years. When encountering hydrological scenarios such as downstream water shortages, flood-season droughts, or rapid shifts between floods and droughts during the flood season, the lack of scientific and systematic flood-season drought limit water level management strategies makes it difficult to effectively respond to downstream water demands. With the rapid economic and social development of cities along the river downstream, the structure of water resource demand has changed; furthermore, the impact of reservoir discharge on river channel incision also leads to changes in the water level-discharge relationship at river cross-sections, making it impossible to continuously guarantee the comprehensive water demand of the river. Therefore, in related technologies, reservoirs cannot effectively control flood risks while ensuring tiered water use downstream, and cannot adapt to the comprehensive scheduling needs under complex hydrological scenarios. Summary of the Invention

[0004] This application provides a method for optimizing the flood season drought limit water level by taking into account both supply and demand, aiming to solve the problem that reservoirs cannot effectively control flood risks while ensuring downstream water use and cannot adapt to the comprehensive scheduling needs under complex hydrological scenarios.

[0005] Firstly, this application provides a method for optimizing the flood season drought limit water level by tiers, taking into account both supply and demand, including: The system acquires the control station of the target river, the tiered water supply demand of the control station, the initial tiered outflow parameters, and the target tiered outflow parameters of the upstream reservoir corresponding to the target river; the control station corresponds to the outflow section of the upstream reservoir. Based on the preset dry inflow sample and the target graded outflow parameters, preset calculations are performed to obtain the initial flood season graded drought limit water level of the upstream reservoir. Based on historical operational data of the upstream reservoir under different inflow frequencies, the drought resistance capacity parameters of the upstream reservoir are determined; the drought resistance capacity parameters are the optimization parameters of the supply side. Based on the actual outflow parameters of the upstream reservoir and the target graded outflow parameters, the target graded drought relief flow gap parameters of the upstream reservoir are determined; the target graded drought relief flow gap parameters are optimization parameters on the demand side. Based on the flood season operating water level parameters of the upstream reservoir and the additional flood volume parameters that the target river channel needs to absorb, the flood loss condition risk value of the upstream reservoir is determined; the additional flood volume parameters that need to absorb are flood volume parameters that exceed the flood control safety allowable discharge capacity of the target river channel; the flood loss condition risk value is an optimization parameter on the supply side; Based on the drought resistance capacity parameters, the target graded drought resistance flow gap parameters, and the risk value of flood control loss conditions, a simulation model is constructed to optimize the initial flood season graded drought limit water level and obtain the target flood season graded drought limit water level.

[0006] This application obtains the target river control station, tiered water supply demand, and flow parameters, and calculates the initial flood season tiered drought limit water level by combining it with the dry inflow sample, providing a scientific starting point for water level optimization that aligns with downstream demand. It then determines the drought resistance capacity parameters on the supply side, the risk value of flood control loss conditions, and the target tiered drought resistance flow gap parameters on the demand side as optimization parameters for both supply and demand sides. Based on these parameters, a simulation model is constructed for optimization, resulting in the target flood season tiered drought limit water level adapted to extreme hydrological scenarios during the flood season. This approach not only matches downstream tiered water supply demand and reduces the drought resistance flow gap, but also strictly controls flood control loss risk and ensures the reservoir's own drought resistance capacity, achieving synergistic optimization of flood control and drought resistance during the flood season and improving the overall reservoir scheduling efficiency. Attached Figure Description

[0007] Figure 1 A flowchart illustrating a flood season-based drought limit water level optimization method that takes into account both supply and demand, as provided in an embodiment of this application. Figure 2 This is a schematic diagram illustrating the statistical results of the multi-year average water storage distribution at the end of each ten-day period, using a reservoir as an example, as an example in this application. Figure 3 This is a schematic diagram illustrating the response relationship between the flood control loss condition risk value and the change in the reservoir's operating water level during the flood season, using a reservoir as an example in this application embodiment. Figure 4 This is a schematic diagram comparing the initial flood and drought water level control schemes based solely on the demand side, using a reservoir as an example in this application embodiment; Figure 5 This is a comparative schematic diagram of the flood and drought limit water level optimization schemes based on the supply and demand sides of a reservoir, taking a certain reservoir as an example in this application. Figure 6 This is another flowchart illustrating a flood season-based drought limit water level optimization method that takes into account both supply and demand, as provided in an embodiment of this application. Figure 7 This is a schematic diagram of a hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0008] 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. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0009] If a reservoir bears the dual mission of flood control in the basin and ensuring water supply security downstream during the flood season, the coordinated management of flood control and water supply becomes a key challenge in reservoir operation. Related technologies have significant limitations: on the one hand, research on these technologies primarily focuses on single-risk type regulation, such as flood control during the flood season and drought relief during the dry season. Therefore, there is a lack of operation technologies that simultaneously consider the coordination of flood control and drought relief during the flood season. On the other hand, methods for determining drought limit water levels mostly aim to ensure water demand, rarely considering both supply and demand to optimize drought limit water level methods.

[0010] This application provides a method for optimizing the drought limit water level during the flood season by taking into account both supply and demand. This method can start from both supply and demand, consider downstream water demand, the reservoir's own drought resistance capacity and system flood risk, scientifically determine the initial value of the drought limit water level during the flood season and perform multi-objective optimization. It is suitable for the flood season scheduling of reservoir projects that need to undertake comprehensive tasks such as flood control and drought resistance under complex hydrological scenarios such as flood season drought reversal and rapid shift from flood to drought.

[0011] like Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for optimizing flood season-level drought limit water levels that takes into account both supply and demand, as provided in an embodiment of this application. The method includes: Step 101: Obtain the control station of the target river, the tiered water supply demand of the control station, the initial tiered outflow parameters, and the target tiered outflow parameters of the upstream reservoir corresponding to the target river; the outflow section of the upstream reservoir corresponding to the control station.

[0012] The target river channel can be the downstream river channel relative to the upstream reservoir. The control station, also known as the control point, can be the control point of the downstream river channel, denoted as i=1,2, Let n be the number of control stations. Key control stations in the downstream river channel within the scope of downstream water supply security can be determined based on reservoir operation regulations and relevant river basin planning documents.

[0013] Tiered water supply demand can represent different levels of water demand, such as urban and rural water demand and agricultural irrigation water demand. Water demand for urban and rural water and agricultural irrigation corresponding to key river control stations can be collected. Generally, urban and rural water demand requires a 95% guarantee rate, while agricultural irrigation water demand requires a 75% guarantee rate. Urban and rural water demand is defined as primary water supply demand, and agricultural irrigation water demand as secondary water supply demand. The target water level for urban and rural water demand at downstream key river control station i is denoted as z. urban,i The target water level corresponding to the agricultural irrigation water demand is denoted as z. agric,i .

[0014] The initial tiered outflow parameter represents the initial flow required by the target river channel at the control station under tiered water supply demand. For example, the initial tiered outflow parameter can be the tiered outflow target value. The target tiered outflow parameter represents the target flow required by the target river channel at the control station under tiered water supply demand. For example, the target tiered outflow parameter can be the tiered outflow target value. The initial tiered outflow parameter can be processed by outer envelope calculation to obtain the target tiered outflow parameter of the upstream reservoir. Specifically, this application aims to ensure the water use security of all river control sections within the downstream water supply security scope. Therefore, the outer envelope of the tiered outflow target value of the reservoir corresponding to each key control station of the river is selected to obtain the tiered outflow target value of the upstream reservoir {Q}. urban Q agric If different control sections of a river have clearly defined differences in importance in relevant management regulations, then the water demand of the control stations can be used to calculate the target values ​​for the graded outflow from upstream reservoirs.

[0015] In some embodiments, obtaining the initial graded outflow parameters of the target river channel includes: Based on the preset model, the tiered water supply demand is back-engineered to the outflow section to obtain the initial tiered outflow parameters of the control station. The preset model includes the mapping relationship between the outflow sequence of the upstream reservoir at the control station and the water level sequence of the downstream river control station of the target river.

[0016] The mapping relationship can take into account the time lag effect of water flow from the reservoir to the control station, meaning that the current flow at the control station may be related to the flow at a certain time before the reservoir. Simultaneously, the mapping relationship can also consider the flow superposition effect, meaning that the flow of the target river at the control station is not only affected by the upstream reservoir, but may also be affected by the superposition of factors such as tributary inflows and inter-regional runoff.

[0017] As an example, pre-defined models such as one-dimensional dynamic physical models or machine learning models can be used to establish a mapping relationship between the flow sequence of a representative station of reservoir outflow and the water level sequence of key control station i in the downstream river channel, thereby representing the downstream water demand {z}. urban,i , z agric,i By extrapolating back to the reservoir outflow section, we obtain the graded outflow targets {q} for the corresponding key control station i in the river channel. urban,i q agric,i}

[0018] Through the above technical solution, this application can more accurately determine the initial graded outflow parameters of the control station based on the preset model and graded water supply demand, avoiding the empirical or subjective errors that may exist in related methods, and improving the accuracy of the initial graded outflow parameters.

[0019] Step 102: Based on the preset dry inflow sample and target graded outflow parameters, perform preset calculations to obtain the initial flood season graded drought limit water level of the upstream reservoir.

[0020] Among them, the drought-prone inflow sample refers to inflow samples from years prone to drought and water shortage, used to simulate hydrological scenarios under drought conditions. The initial flood season drought limit level is the drought limit level of the upstream reservoir determined based on the drought-prone inflow sample and the target graded outflow parameters. For example, the initial flood season drought limit level can be the initial scheme for the flood season drought limit level of the upstream reservoir.

[0021] As an example, for a sample of water inflow during periods of low water levels, the initial time period for estimation is taken as the start of the flood season in the upstream reservoir, and the flood control limit water level during the flood season is taken as the starting adjustment water level. The initial values ​​of the drought limit water level in the upstream reservoir are calculated in reverse chronological order for different periods of the year. This is to determine the initial values ​​of the drought limit water level in the upstream reservoir based on the tiered water supply guarantee target conditions of the downstream demand side. Without distinguishing between tiers, the same calculation formula is used for each tier to determine the initial values ​​of the drought limit water level in the upstream reservoir. The calculation formulas are shown in equations (1), (2), and (3): (1); (2); (3); In equations (1), (2), and (3): T represents the number of time periods for setting the drought limit water level during the flood season. If the data is on a ten-day scale, the time period step is ten days, and the corresponding number of drought limit water level values ​​is T+1; Z hx,T+1 This refers to the initial value of the upstream reservoir's drought limit water level, for example, the initial drought limit water level; W hx,j This represents the required water storage volume at the beginning of the j-th time period, i.e., the drought limit water level Z at the beginning of the j-th time period. hx,j The corresponding reservoir storage capacity, 1≤j≤T; W gs,jLet Q be the target value of the reservoir's tiered outflow for the downstream river channel's tiered water demand during the j-th time period. If it is the first-level water supply demand, then the value is Q. urban If it is a secondary water supply requirement, then the value is Q. agric W in,j Let f be the inflow of water into the reservoir during the j-th time period; f() is the reservoir capacity curve.

[0022] The outer envelope of all sample years is selected as the initial scheme for the tiered drought limit water level of upstream reservoirs during the flood season based on downstream demand. Specifically, for the primary water supply demand, the initial scheme for the tiered drought limit water level of reservoirs during the flood season is denoted as... For secondary water supply needs, the initial plan for the reservoir's tiered drought limit water level during the flood season is denoted as... .

[0023] In some embodiments, before performing preset calculations based on preset inflow samples and target graded outflow parameters to obtain the initial flood season graded drought limit water level of the upstream reservoir, the method further includes: Based on the tiered water supply demand, samples of water that are in a drought-prone year are selected, with the water frequency being greater than or equal to the frequency threshold. The drought-prone water samples are water samples from years when drought and water shortage are likely to occur.

[0024] In this context, water inflow frequency refers to the probability of a certain water inflow occurring within a given period. The frequency threshold is a preset percentage value, representing the degree of drought. By setting different frequency thresholds, analyses can be conducted for different levels of drought risk. As an example, for primary water supply demand, years with an inflow frequency of 90% or 95% (corresponding to the downstream river's urban and rural water supply guarantee rate target) or higher are selected as drought inflow samples. For secondary water supply demand, years with an inflow frequency of 75% or 85% (corresponding to the downstream river's agricultural irrigation water supply guarantee rate target) or higher are selected as drought inflow samples.

[0025] By employing the above technical solution, before calculating the initial flood season drought limit water level of the upstream reservoir, samples of inflow water with a frequency greater than or equal to the frequency threshold are selected based on the graded water supply demand. This ensures that the inflow scenario used to calculate the drought limit water level is a real and representative drought scenario, avoiding the problem of inaccurate initial drought limit water level due to improper sample selection. This allows the initial flood season drought limit water level to more accurately characterize the reservoir's water supply capacity and risk tolerance under different drought levels.

[0026] Step 103: Determine the drought resistance parameters of the upstream reservoir based on historical operating data under different inflow frequencies; the drought resistance parameters are the optimized parameters of the supply side.

[0027] Among them, the drought resistance parameter is used to measure the ability of upstream reservoirs to supply water to the target river channel under different inflow frequencies.

[0028] In some embodiments, drought resistance parameters include tiered water storage parameters indicating that the upstream reservoir can supply water to the target river corresponding to the tiered water supply demand, tiered time parameters indicating that the upstream reservoir can supply water to the target river, and tiered probability parameters indicating that the upstream reservoir can supply water to the target river; the drought resistance parameters of the upstream reservoir are determined based on historical operational data of the upstream reservoir under different inflow frequencies, including: Based on the historical water level status of the upstream reservoir, determine the water storage parameters for each stage and the preset storage capacity parameters of the upstream reservoir; The first difference is obtained by subtracting the values ​​of the tiered water storage parameters and the preset reservoir capacity parameters. Divide the first difference and the target graded outbound flow parameter by a division operation to obtain the graded time parameter; Based on historical operational data of upstream reservoirs at different inflow frequencies, the grading probability parameters are determined.

[0029] Among them, the tiered water storage parameter can be the tiered available water storage volume determined based on the reservoir capacity curve under different historical tiered water level states. For example, the tiered water storage parameter can be the reservoir's tiered available water volume for drought relief, referring to the tiered water storage volume above the dead water level that can be used for downstream water supply needs. It characterizes the static basic guarantee of the reservoir's tiered drought relief capacity and determines the maximum amount of water the reservoir can provide for downstream tiered drought relief. Specifically, the value of the tiered water storage parameter can be the current water storage volume, denoted as V. kh .

[0030] As an example, actual operational data of the reservoir can be collected, including measured inflow, outflow, and water level processes, as well as characteristic water levels such as dead water level and normal storage level during the scheduling process, and water level-storage capacity curves. The inflow is the initial input data for the preset model to perform water balance calculations and water level optimization. The reservoir's water level status at the end of each ten-day period is statistically calculated, including but not limited to the multi-year average water level, maximum operating water level, and minimum operating water level. Based on the water level-storage capacity curve interpolation and deducting the water storage below the dead water level, the water storage distribution above the dead water level at different inflow frequencies (e.g., inflow frequencies of 75%, 85%, 90%, and 95%) at the end of each ten-day period is calculated.

[0031] like Figure 2 As shown, Figure 2This is a schematic diagram illustrating the statistical results of the multi-year average water storage distribution at the end of each ten-day period, using a reservoir as an example, as an example in this application. It is used to demonstrate the changes in water storage of a reservoir in different years and time periods. The horizontal axis represents the ten-day period, and the vertical axis represents the water storage. The light gray area represents the range of water storage variation, characterizing the fluctuation range of the reservoir's water storage in the same historical period. The black dashed line is the multi-year average water storage curve, representing the average water storage level of the reservoir in each ten-day period during its many years of operation. By comparing the above curves with the water storage variation range and the multi-year average water storage curve, the temporal variation characteristics of the reservoir's water storage in different years, as well as the differences from the historical average level and fluctuation range, can be clearly observed.

[0032] The tiered water storage parameters correspond to the tiered operating water level parameters, which can be the actual water levels reached by the upstream reservoir during its historical operation. For example, the tiered operating water level parameters can be the current water storage capacity V of the reservoir. kh The corresponding operating water level is denoted as Z. kh The preset water level parameter corresponding to the preset reservoir capacity parameter can be understood as the benchmark or target water level set when conducting drought resistance capacity assessment. For example, the value of the preset water level parameter can be the dead water level of the reservoir, denoted as Z. dead The preset storage capacity parameter can be the dead storage capacity, denoted as V. dead .

[0033] The time parameter for grading can be the duration of drought-resistant water replenishment for a reservoir: This refers to the duration of water replenishment from the current storage capacity V, assuming no upstream inflow and no rainfall in the reservoir area. kh Corresponding operating water level Z kh Drop to dead water level Z dead The number of days that can support downstream water supply. For example, to ensure the primary water demand Q of downstream areas. urban If the reservoir outflow rate is consistently calculated according to Q... urban Release water, and determine the dead water level Z based on the reservoir capacity characteristic curve. dead Corresponding to the preset reservoir capacity (dead capacity). For the current water storage volume V. kh With dead storage capacity V dead Perform a subtraction operation to obtain the first difference value; then combine the first difference value with the primary water demand Q. urban Performing division yields the graded time parameters. This represents the number of days the reservoir can support downstream water supply if the current operating water level drops to the dead water level. This refers to the duration for which a reservoir can replenish water during drought.

[0034] It should be noted that the graded time parameter is the key to linking the reservoir's drought-resistant water storage capacity with the downstream water demand target. It represents the transformation of static water storage capacity into dynamic water replenishment duration that can support drought resistance. If the available water volume for drought resistance is larger and the downstream water demand is smaller, the water replenishment duration will be longer and the drought resistance resilience will be stronger.

[0035] The tiered probability parameter can be defined as the probability that downstream water demand can be met under different inflow frequencies (e.g., inflow frequencies of 75%, 85%, 90%, and 95%) during historical operation, targeting primary or secondary water supply needs. For example, the tiered probability parameter could be the tiered drought resistance guarantee rate. This tiered probability parameter can be obtained by statistically analyzing the proportion of times or time periods when the reservoir's actual water supply meets the tiered water supply needs under different inflow frequencies, relative to the total number of times or time periods.

[0036] It should be noted that the drought resistance capability of the graded probability parameters is reflected in the annual scale reliability. The water storage parameters and graded time parameters characterize the drought resistance capability of the reservoir at a certain moment or under a certain scenario, but cannot reflect the long-term operational stability. The graded probability parameters, on the other hand, can cover the drought resistance effect of long-term scheduling processes at different frequencies, and focus on characterizing the long-term guarantee level.

[0037] The above technical solutions refine drought resistance parameters into water storage parameters, tiered time parameters, and tiered probability parameters, making the supply-side optimization parameters of upstream reservoirs more accurate and comprehensive. Water storage parameters directly characterize the reservoir's water supply potential, tiered time parameters characterize water supply continuity, and tiered probability parameters characterize water supply reliability. These parameters can more realistically represent the reservoir's actual drought resistance capacity, thus yielding more reasonable and operational target flood season tiered drought limit water levels.

[0038] In some embodiments, the classification probability parameters are determined based on historical operational data of the upstream reservoir at different inflow frequencies, including: Obtain historical operational data of the upstream reservoir under different inflow frequencies; the historical operational data includes the actual outflow parameters of the upstream reservoir in at least two time periods; Determine whether the actual outflow parameters for each time period meet the tiered water supply requirements to obtain the first judgment result; the first judgment result includes the first result that the actual outflow parameters meet the tiered water supply requirements and the second result that the actual outflow parameters do not meet the tiered water supply requirements. The total number of time periods is obtained by summing the number of time periods corresponding to the first result; The hierarchical probability parameters are obtained by dividing the total number of time periods by the total number of time periods in the historical operation data.

[0039] To accurately assess the water supply reliability of upstream reservoirs, historical operational data under different inflow frequencies is typically obtained from hydrological observation stations and reservoir operation records, covering the actual operation of the reservoir over a relatively long period. This historical operational data can include actual outflow parameters from the upstream reservoir for at least two time periods; for example, it could be continuous outflow records for several years, measured in monthly or ten-day periods.

[0040] After acquiring historical operational data, the actual outflow parameters for each time period can be compared with the corresponding tiered water supply demands. Tiered water supply demands are water volume requirements set according to different water use priorities (e.g., urban and rural domestic water use, agricultural irrigation water use, etc.). By judging whether the actual outflow parameters for each time period meet or exceed the tiered water supply demands, a first judgment result can be obtained. Specifically, if the actual outflow parameters are greater than or equal to the tiered water supply demands, it is determined that the demand is met, and the first result is obtained; if the actual outflow parameters are less than the tiered water supply demands, it is determined that the demand is not met, and the second result is obtained.

[0041] After obtaining the water supply satisfaction status for each time period, the tiered probability parameter can be calculated. The tiered probability parameter measures the statistical probability that the upstream reservoir can successfully supply water under tiered water supply demand. In specific calculation, the total number of time periods in all historical operating data where the actual outflow parameter satisfies the tiered water supply demand (i.e., the first result is obtained) is counted and divided by the total number of time periods in the historical operating data.

[0042] As an example, the drought resistance capacity of downstream urban and rural water demand is denoted as... The drought resistance capacity corresponding to the downstream agricultural irrigation water demand is denoted as .

[0043] Through the above technical solution, this application obtains historical operational data containing actual outflow parameters for at least two time periods, determines whether the actual outflow parameters for each time period meet the tiered water supply requirements, and thus identifies successful and unsuccessful water supply instances. Based on the number of time periods that meet the requirements and the total number of time periods in the historical operational data, a tiered probability parameter is calculated. This effectively solves the problem that water supply reliability cannot be comprehensively and accurately assessed solely based on water storage parameters and tiered time parameters, making drought resistance parameters more complete and accurate. Step 104: Based on the actual outflow parameters and target graded outflow parameters of the upstream reservoir, determine the target graded drought relief flow gap parameters of the upstream reservoir; the target graded drought relief flow gap parameters are the optimization parameters of the demand side.

[0044] The target-level drought relief flow gap parameter measures the drought relief gap between the actual outflow and the target flow when the upstream reservoir meets the tiered water supply demand. For example, the target-level drought relief flow gap parameter can be the maximum drought relief flow gap for each tier.

[0045] In some embodiments, the target-level drought relief flow gap parameters of the upstream reservoir are determined based on the actual outflow parameters and the target-level outflow parameters of the upstream reservoir, including: The difference between the actual outflow parameters and the target graded outflow parameters is calculated to obtain at least one drought relief gap occurrence period of the upstream reservoir and the graded drought relief flow gap parameters corresponding to each drought relief gap occurrence period. The ranking results are obtained by sorting according to at least one graded drought relief flow gap parameter; The first graded drought relief flow gap parameter in the sorting results is determined as the target graded drought relief flow gap parameter for the upstream reservoir.

[0046] Specifically, identifying the period during which at least one drought-relief gap occurs in the upstream reservoir allows for the identification of situations where the actual outflow from the upstream reservoir cannot meet the target tiered flow requirements. This can be determined by comparing the actual outflow parameters with the target tiered outflow parameters.

[0047] Specifically, the tiered drought relief flow gap parameter corresponding to each drought relief gap occurrence period is used to quantify the specific gap between reservoir water supply and demand within the identified drought relief gap occurrence period. The tiered drought relief flow gap parameter directly characterizes the degree of water shortage during that period. The difference between the actual outflow parameter and the target tiered outflow parameter can be calculated, and this difference is determined as the tiered drought relief flow gap parameter for the drought relief gap occurrence period. Alternatively, the gap flow can be compared to the total demand during that period, expressed as a percentage, or a more comprehensive tiered drought relief flow gap parameter can be determined by combining factors such as the duration of water shortage and using weighted averaging. Furthermore, the largest tiered drought relief flow gap parameter among all drought relief gap occurrence periods can be taken as the target tiered drought relief flow gap parameter to characterize the most severe water shortage situation. Alternatively, the average, weighted average, or sum of all gap flows can be calculated. In some cases, the target tiered drought relief flow gap parameter can also be determined through statistical analysis or risk assessment models based on factors such as the frequency and duration of the gap occurrence.

[0048] Through the above technical solution, this application can accurately identify the period when the drought relief gap occurs due to insufficient water supply from the upstream reservoir, and determine the graded drought relief flow gap parameters for each gap period. This allows the target graded drought relief flow gap parameters to more accurately represent the actual water supply gap, improve the accuracy and reliability of the initial flood season graded drought limit water level optimization, and thus enable the target flood season graded drought limit water level to better balance the supply and demand sides, thereby improving the reservoir's drought relief guarantee capacity during the flood season.

[0049] In some embodiments, the difference between the actual outflow parameters and the target graded outflow parameters is calculated to obtain at least one drought relief gap occurrence period of the upstream reservoir and the graded drought relief flow gap parameters corresponding to each drought relief gap occurrence period, including: Determine whether the actual outbound flow rate parameter for each time period is less than the target graded outbound flow rate parameter to obtain the second judgment result; If the second judgment result indicates that the actual outflow rate parameter is less than the target graded outflow rate parameter, the time period corresponding to the actual outflow rate parameter is determined as the period when the drought relief gap occurs. Based on the actual outflow parameters and target graded outflow parameters corresponding to the period when the drought relief gap occurs, determine the graded drought relief flow gap parameters corresponding to the period when the drought relief gap occurs.

[0050] Specifically, by comparing the actual outflow parameters with the target tiered outflow parameters, it can be determined whether the water supply for the current period meets the demand, generating a second judgment result to record the existence of a water supply gap. When the second judgment result indicates that the actual outflow parameters cannot reach the target tiered outflow parameters, this period is marked as the drought gap occurrence period. The absolute value of the difference between the actual outflow parameters and the target tiered outflow parameters is determined as the tiered drought relief flow gap parameter corresponding to the drought gap occurrence period.

[0051] Through the above technical solution, this application can accurately determine whether there is a water supply shortage by comparing the actual outflow parameters of each time period with the target graded outflow parameters, and generate a second judgment result. By calculating the absolute value of the difference between the target graded outflow parameters and the actual outflow parameters, the graded drought relief flow gap parameters required for each drought relief gap period can be obtained. This effectively improves the accuracy and efficiency of the upstream reservoir's demand-side response during the graded drought limit water level optimization process in the flood season, ensuring that the water supply demand of the target river can still be guaranteed to the maximum extent under drought risk.

[0052] Step 105: Determine the flood loss condition risk value of the upstream reservoir based on the flood season operating water level parameters of the upstream reservoir and the flood volume parameters that the target river channel needs to absorb. The flood volume parameters that need to be absorbed are the flood volume parameters that exceed the flood control safety allowable discharge of the target river channel. The flood loss condition risk value is the optimized parameter on the supply side.

[0053] Among them, the Value at Risk of Flood Loss Conditions (CVaR) measures the risk of flood loss caused by the increased flood volume that an upstream reservoir needs to absorb at its operating water level during the flood season. α The flood loss condition risk value of an upstream reservoir can be determined based on its flood season operating water level parameters and the additional flood volume that the target river channel needs to absorb. This additional flood volume parameter represents the flood volume exceeding the target river channel's safe flood discharge capacity. The flood loss condition risk value can be assessed based on a pre-defined loss function or expert experience. For example, when the flood season operating water level exceeds a certain threshold and there is an additional flood volume that needs to be absorbed, a corresponding flood loss risk will arise.

[0054] As an example, multiple typical flood events can be collected. Based on the reservoir inflow flood design results, a set of design flood process lines can be calculated for different inflow frequencies (covering up to 50% of the reservoir's check inflow frequencies). The lower limit of the reservoir's operating water level fluctuation range during the flood season is set as the flood control limit water level, and the upper limit is set as the flood control high water level. Flood regulation calculations are performed for design flood events with different inflow frequencies based on the operating water levels during different flood seasons. The outflow process is compared with the downstream river's flood control safety allowable discharge. The additional flood volume that needs to be absorbed due to the downstream river's flood control safety allowable discharge under different combinations of starting regulation water levels and inflow frequencies during different flood seasons is statistically analyzed.

[0055] To construct a conditional risk value index for flood control losses, specifically: the decision variable is selected as the reservoir's operating water level during the flood season, y, and the random variable is the frequency of inflow water, θ. The calculated additional flood volume to be absorbed is denoted as the flood control loss function L(y,θ). According to the definition of conditional risk value index, the conditional risk value of flood control losses is calculated as shown in equation (4): (4); In equation (4): max and F α Let be the maximum value of the loss function and the function value at confidence level α, respectively, and h() be the probability density function of flood control loss. To correspond to different water inflow frequencies during drought (e.g., inflow frequencies of 75%, 85%, 90%, and 95%), the Conditional Value at Risk (CVaR) of flood control loss is... α The confidence levels α for the indicators were set to 75%, 85%, 90%, and 95%, respectively; the flood risk rate p = 1 - α. The calculated flood loss under the flood control scheduling simulation scenario in the relevant technology was used as the benchmark value.

[0056] like Figure 3 As shown, Figure 3 This diagram illustrates the response of the flood control loss conditional risk value (VCV) to changes in the reservoir's operating water level during the flood season, using a specific reservoir as an example in this application. It characterizes the correlation between the VCV and the reservoir's operating water level (e.g., the drought limit water level) and confidence level during reservoir operation. The horizontal axis represents the reservoir's flood limit water level, and the vertical axis represents the VCV. At each confidence level, the VCV shows a monotonically increasing trend with the increase of the reservoir's flood limit water level. That is, as the flood limit water level increases, the corresponding VCV gradually increases, indicating that the higher the flood limit water level, the higher the potential risk level corresponding to the operation plan. For the same reservoir flood limit water level, a higher confidence level corresponds to a higher VCV.

[0057] Step 106: Based on drought resistance capacity parameters, target graded drought resistance flow gap parameters, and flood loss condition risk value, construct a simulation model to optimize the initial flood season graded drought limit water level and obtain the target flood season graded drought limit water level.

[0058] The simulation model considers drought resistance capacity parameters, target-level drought resistance flow gap parameters, and flood control loss condition risk value to optimize the initial flood season-level drought limit water level. Optimization refers to finding the optimal solution through iterative calculations or optimization algorithms while satisfying preset constraints. The target flood season-level drought limit water level is the final level that balances both supply and demand.

[0059] In some embodiments, the simulation model includes a first objective function, a second objective function, and a third objective function; the first objective function represents maximizing the graded drought resistance capacity of the upstream reservoir; the second objective function represents minimizing the graded maximum drought resistance flow gap of the upstream reservoir; and the third objective function represents minimizing the flood control losses of the upstream reservoir during the flood season. The simulation model is constructed based on drought resistance capacity parameters, target graded drought resistance flow gap parameters, and flood control loss conditional value-of-risk (VoV) parameters, including: The tiered drought resistance weight coefficients of the simulation model are determined based on the tiered water supply demand. The inflow frequency weighting coefficients of the simulation model are determined based on the different inflow frequencies of the upstream reservoir. Based on drought resistance capacity parameters, graded drought resistance weight coefficients, and water inflow frequency weight coefficients, a first objective function is constructed. A second objective function is constructed based on the target-level drought relief flow gap parameters, the level-level drought relief weight coefficients, and the water inflow frequency weight coefficients. A third objective function is constructed based on the risk value of flood control loss conditions, the weight coefficients of drought resistance at different levels, and the weight coefficients of water inflow frequency.

[0060] Among them, the graded drought resistance weighting coefficient is used to characterize the importance of different grades of water supply demand in the optimization objective, denoted as β. k The water inflow frequency weighting coefficient is used to characterize the importance of different water inflow frequencies in the optimization objective, denoted as γ. l The first objective function, denoted as F1, characterizes the drought resistance capacity of the upstream reservoir under different levels of water supply demand. Maximizing the first objective function enables the reservoir to provide more, longer, and more reliable water supply under drought conditions. The second objective function, denoted as F2, characterizes the maximum shortfall between the actual outflow parameter and the target level outflow parameter during drought. Minimizing the second objective function reduces the potential water supply gap under the most unfavorable conditions, thereby reducing the impact of drought on downstream water supply demand. The third objective function, denoted as F3, characterizes the potential flood control losses caused by the reservoir during the flood season. Minimizing the third objective function effectively controls the risk of flooding during the flood season.

[0061] As an example, the drought resistance maximization index characterizes the long-term drought resistance benefits, and the minimum maximum drought resistance flow gap characterizes the degree of extreme water supply damage, which is used to guide the optimization of water supply towards a direction with less extreme damage and a high overall water supply guarantee rate; while the flood control loss minimization target is because the design purpose of this application is to achieve flood and drought coordination during the flood season, which requires a balance between flood control and drought resistance benefits.

[0062] The first objective function representing the maximization of drought resistance can be shown in equation (5): (5); The second objective function characterizing the minimization of the maximum drought relief flow gap can be expressed as shown in equation (6): (6); The third objective function characterizing the minimization of flood control losses can be shown in equation (7): (7); In equations (5), (6), and (7): k = urban, agric correspond to primary water demand (urban and rural water use) and secondary water demand (agricultural irrigation water use), respectively; β k The drought resistance weighting coefficients are set separately according to the priority of urban and rural irrigation water use and agricultural irrigation water use; γ l The weighting coefficients for different water inflow frequencies are l=1,2,3,4, which correspond to water inflow frequencies of 75%, 85%, 90%, and 95%, respectively.

[0063] In some embodiments, constraints can be set, including water balance constraints, reservoir water level constraints, reservoir discharge flow constraints, and hydropower station output limits. Based on long-sequence runoff, multiple typical years such as flood season drought reversal and rapid flood-drought transitions are selected using frequency calculations. Through comprehensive optimization of different typical year scenarios, Pareto front solutions can be obtained using multi-objective optimization algorithms such as the Non-dominated Sorting Genetic Algorithm (NSGA). This determines the distribution characteristics of three objectives—drought resistance capacity, maximum drought resistance flow gap, and flood control loss—under different drought limit water level combinations during the flood season. Based on a comparison with flood control and drought resistance benchmarks in related technologies, an optimized reservoir flood season drought limit water level scheme that considers both supply and demand is extracted.

[0064] The water balance constraint can be expressed as shown in equation (8): (8); In equation (8): Δt is the unit time for calculation, I t and Q t V represents the inflow and outflow of the reservoir during the time period Δt. tLet be the reservoir capacity at time t.

[0065] The reservoir water level constraint can be expressed as shown in equation (9): (9); In equation (9): Let be the upstream water level at time t. , These represent the minimum and maximum water levels allowed at time t, respectively.

[0066] The reservoir discharge flow constraint can be expressed as shown in equation (10): (10); In formula (10): , f represents the lower and upper limits of the outflow during a given time period. HQ () is the function relating upstream water level to maximum discharge capacity.

[0067] like Figure 4 and Figure 5 As shown, Figure 4 This is a schematic diagram comparing initial flood and drought water level control schemes based solely on demand, using a reservoir as an example in this application. Figure 5 This is a comparative diagram illustrating the optimization schemes of flood season drought limit water levels based on both the supply and demand sides, using a reservoir as an example in this application. For instance, to ensure the safety of downstream old irrigation areas, a flood season drought limit water level exceeding 170m, or even reaching 175m (normal storage level), based solely on the demand side, is detrimental to flood control safety. The optimized flood season drought limit water level scheme is at 165.5m, maximizing the comprehensive benefits of flood and drought control while balancing the reservoir's own drought resistance capacity and flood control safety. As another example, to ensure the safety of downstream cities and the ecosystem, a flood season drought limit water level of 145m based solely on the demand side is used. The optimized drought limit water level scheme, at 152m during the period from early August to early September, ensures controllable flood control safety, improves the reservoir's drought resistance capacity, and is more conducive to the transition to the water storage period.

[0068] Through the aforementioned technical solution, this application addresses the optimization problem of tiered drought limit water levels during the flood season, considering both supply and demand. It decomposes this problem into three independent yet interrelated objective functions: maximizing tiered drought resistance capacity, minimizing the maximum drought resistance flow gap at each tier, and minimizing flood control losses during the flood season. This allows the simulation model to more comprehensively and precisely evaluate the combined benefits of different drought limit water level schemes in terms of drought resistance and flood control safety. Furthermore, by introducing drought resistance weighting coefficients and inflow frequency weighting coefficients, this application can differentiate the weighting of each objective function based on the importance of different tiered water supply demands and the risk preference under different inflow frequencies. This prioritizes key water supply needs during the optimization process and effectively addresses adverse scenarios such as low water inflows. It better balances drought resistance capacity and flood control safety on the supply side with water supply security on the demand side, thereby improving the overall efficiency of reservoir operation and the scientific basis of decision-making.

[0069] In some embodiments, the tiered water supply demand includes primary water demand for urban and rural water use and secondary water demand for agricultural irrigation; the drought resistance weighting coefficient includes a first drought resistance weighting coefficient and a second drought resistance weighting coefficient; determining the drought resistance weighting coefficient of the simulation model based on the tiered water supply demand includes: The first drought resistance weight coefficient is determined based on the primary water demand, and the second drought resistance weight coefficient is determined based on the secondary water demand. The first drought resistance weight coefficient is greater than the second drought resistance weight coefficient.

[0070] Primary water demand refers to the water needed to ensure the basic living needs of urban and rural residents, public services, and important industrial production. It is typically given the highest priority and is directly related to social stability and people's livelihoods. Secondary water demand refers to the water needed for agricultural production activities such as crop growth and livestock breeding. While crucial for economic development, its priority may be lower than primary water demand during periods of extreme water scarcity. The first drought resistance weight coefficient corresponds to primary water demand and represents its highest priority in drought relief, denoted as β. urban The second drought resistance weighting coefficient corresponds to secondary water demand, representing the secondary priority of secondary water demand in drought resistance security, and is denoted as β. agric As an example, if the relevant watershed planning and management documents prioritize urban and rural water use and effectively guarantee agricultural irrigation water, then a β-level can be set. urban >β agric The specific weighting can be determined based on the scale and distribution of urban and rural irrigation water intake facilities along the river, or the impact of different weights on the optimization results can be considered.

[0071] The above technical solution divides the tiered water supply demand into primary water demand for urban and rural areas and secondary water demand for agricultural irrigation. Corresponding first and second drought resistance weight coefficients are determined for each, with the first drought resistance weight coefficient being greater than the second. This more accurately reflects the actual priority of different types of water demand, prioritizing the higher-priority primary water demand. For example, during periods of water scarcity, it ensures that the basic domestic water needs of urban and rural residents are not affected, while also taking into account the rational allocation of agricultural irrigation water. This improves the scientific nature of the simulation model's decision-making under complex water conditions and the effectiveness of drought relief scheduling, enabling the target flood season tiered drought limit water level to better balance supply and demand.

[0072] In some embodiments, the inflow frequency includes a first frequency, a second frequency, a third frequency, and a fourth frequency; the inflow frequency weighting coefficients include a first coefficient corresponding to the first frequency, a second coefficient corresponding to the second frequency, a third coefficient corresponding to the third frequency, and a fourth coefficient corresponding to the fourth frequency; determining the inflow frequency weighting coefficients of the simulation model based on different inflow frequencies from the upstream reservoir includes: When the first preset condition is met, the fourth coefficient is determined to be greater than the third coefficient, the third coefficient is greater than the second coefficient, and the second coefficient is greater than the first coefficient, based on the different inflow frequencies of the upstream reservoir; the first condition is to give priority to ensuring drought relief under extreme low water conditions. When the preset second condition is met, the first coefficient is determined to be greater than the fourth coefficient, the third coefficient and the second coefficient based on the different inflow frequencies of the upstream reservoir, and the fourth coefficient, the third coefficient and the second coefficient are equal; the second condition is to prioritize the drought relief guarantee under the condition of low inflow.

[0073] In this model, the first coefficient can be denoted as γ1, the second as γ2, the third as γ3, and the fourth as γ4. When predicting or facing extreme drought scenarios, to ensure the model fully considers and prioritizes addressing water supply issues caused by extreme drought during optimization, the weighted coefficient representing the water inflow frequency under extreme drought conditions (i.e., the fourth coefficient) is set to the maximum, decreasing sequentially, creating a decreasing relationship between the fourth, third, second, and first coefficients. For example, a threshold can be set; if the predicted water inflow for a future period is lower than this threshold, the first condition is met. As an example, if drought relief is more important under extreme drought conditions, then γ4 > γ3 > γ2 > γ1.

[0074] When prioritizing drought relief under conditions of low water inflow, the water inflow frequency weighting coefficient (i.e., the first coefficient) representing these conditions is set to the maximum, while other water inflow frequency weighting coefficients (the fourth, third, and second coefficients) are set to be equal to and less than the first coefficient. For example, the second condition is met when the predicted water inflow for a future period is within a low to medium range. As an example, if extreme drought occurs infrequently and the focus is on ensuring drought relief under conditions of low water inflow, the weights can be set to γ1 > γ4 = γ3 = γ2.

[0075] In some embodiments, the initial flood season graded drought limit water level includes three key node water levels: the water level at the end of the pre-flood drawdown period, the control water level during the main flood season, and the water level at the end of the post-flood storage period. Each node water level corresponds to different flood control and drought relief control objectives. For example, the water level at the end of the pre-flood drawdown period can be a control water level to ensure the emptying of reservoir capacity before the flood season, which can reduce the flood control risk during the main flood season, corresponding to the flood control objective. The control water level during the main flood season can be a control water level to balance flood control safety during the flood season and water storage capacity after the flood season, which can both ensure flood control safety during the flood season and reserve reasonable space for water storage after the flood season, corresponding to the flood control objective and the drought relief objective. The water level at the end of the post-flood storage period can be a control water level to ensure water supply and power generation needs during the dry season, which can provide stable water support for the subsequent dry season, corresponding to the drought relief objective. This application refines the node composition of the initial flood season graded drought limit water level, making the water level setting match the actual time sequence of reservoir operation, which can improve the engineering operability of the water level scheme.

[0076] In some embodiments, the method further includes: dynamically adjusting the target flood season drought limit water level based on annual inflow forecasts. The dynamic adjustment is based on short-term hydrological forecasts, medium- and long-term climate predictions, and the watershed's water supply and demand situation. For example, if the annual inflow is predicted to be low, the water level at the end of the post-flood storage period can be appropriately raised to increase the available water volume during the dry season. If the annual inflow is predicted to be high, the main flood season control water level can be appropriately lowered to reserve more flood control capacity. This application, by introducing a dynamic adjustment mechanism and combining multi-source hydrological and meteorological forecast data to optimize the target flood season drought limit water level, can improve the adaptability of the water level scheme to inflow uncertainties and enhance the flexibility and practicality of the scheduling scheme.

[0077] The following describes the flood season-based drought limit water level optimization method that takes into account both supply and demand, as provided in the embodiments of this application. Figure 6 As shown, Figure 6 This is another flowchart illustrating a method for optimizing flood season drought limit water levels that balances both supply and demand, as provided in this application embodiment. The method includes: Step 1: Determine the initial values ​​of the tiered drought limit water levels of upstream reservoirs based on different water security targets at the downstream demand side. Specifically, identify key control stations downstream of the target river channel, and define urban and rural water use as primary water supply demand and agricultural irrigation water use as secondary water supply demand. Establish a mapping relationship between reservoir outflow and downstream control station water levels to deduce the tiered reservoir outflow target values. Using the flood control limit water level during the flood season as the starting water level, the initial value of the reservoir's drought limit water level during the flood season is obtained using a reverse time series calculation method. Screen samples of slightly dry inflow corresponding to the water supply guarantee rate, calculate and take the outer envelope to form the initial tiered drought limit water level scheme.

[0078] Step 2: Assess the tiered drought resistance capacity of upstream reservoirs under operational conditions. Specifically, construct a three-dimensional drought resistance capacity index system, including the amount of water available for drought resistance, the duration of drought-resistant water replenishment, and the tiered drought resistance guarantee rate. Collect measured operational data of the reservoirs, statistically analyze water level and storage distribution, calculate drought resistance capacity, and use it as a benchmark value. Compare the actual outflow with the tiered flow targets to determine the timing of drought resistance gaps and the parameters of tiered drought resistance flow gaps.

[0079] Step 3: Analyze the graded response relationship between different operating water levels in the reservoir during the flood season and graded flood control losses. Specifically, collect typical flood events and calculate the set of design flood hydrographs under different inflow frequencies. Set up flood control calculations for the operating water level range during the flood season and calculate the additional flood volume that needs to be absorbed beyond the downstream flood control safety discharge. Construct a flood control loss function and calculate the value-at-risk of flood control loss conditions at different confidence levels.

[0080] Step 4: Optimize the reservoir's drought limit water level during the flood season by comprehensively considering both downstream water demand, upstream reservoir drought resistance capacity, and system flood risk. Specifically, establish a multi-objective simulation model with the objectives of maximizing drought resistance capacity, minimizing drought resistance flow gap, and minimizing flood control losses. Set operational constraints for the simulation model, such as water balance, water level, downstream flow, and hydropower station output. Conduct scheduling simulations in typical years and use multi-objective optimization algorithms to obtain an optimized drought limit water level scheme that takes into account both supply and demand.

[0081] It should be noted that, in the data processing stage, the technical solution of this application has strictly limited the scope of data collection to the minimum necessary to achieve the technical objectives, preventing the acquisition of irrelevant information. For any user information to be collected, the data subject will be clearly informed and their consent obtained. Furthermore, technologies such as encrypted storage and access control are employed to strengthen data security and ensure the security and compliance of the entire data processing process. The technical model and decision-making mechanism are based on objective technical parameters and do not introduce unnecessary parameters such as gender or age that may lead to discrimination, resolutely eliminating algorithmic discrimination and upholding public order and good morals. In addition, the specification fully describes the technical implementation methods, application scenarios, and compliance protection details. The claims are consistent with the content of the specification, key compliance designs are clear and verifiable, and the overall technical design is guided by the protection of public interests and adherence to social ethics, without any circumstances that harm public interests or violate public order and good morals.

[0082] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0083] To implement the method of the embodiments of this application, Figure 7 A schematic diagram of a hardware structure of an electronic device provided in an embodiment of this application, such as... Figure 7 As shown in the illustration, this application embodiment also provides an electronic device 70 that may include: a memory 701 for storing a computer program; and a processor 702 for implementing the method described above when executing the computer program. The processor 702 can implement the steps of any of the methods described above, which will not be repeated here.

[0084] Of course, in practical applications, such as Figure 7 As shown, the electronic device 70 may further include at least one network interface 703. Various components in the electronic device are coupled together via a bus system 704. It is understood that the bus system 704 is used to implement communication between these components. In addition to a data bus, the bus system 704 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 7Various buses are labeled as bus systems 704. The number of processors 702 can be at least one. A network interface 703 is used for wired or wireless communication between electronic devices and other devices. The memory 701 in this embodiment is used to store various types of data to support the operation of the electronic device. The methods disclosed in the above embodiments can be applied to or implemented by the processor 702. The processor 702 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuits in the hardware of the processor 702 or by instructions in software form. The processor 702 can be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 702 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly reflected in the combined execution of hardware and software modules in a microcontroller. The software module can reside in a storage medium located in memory 701. Processor 702 reads information from memory 701 and, in conjunction with its hardware, completes the steps of the aforementioned method. In an exemplary embodiment, electronic device 70 can be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to execute the aforementioned method.

[0085] Specifically, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, such as a memory 701 storing the computer program, which can be executed by a processor 702 to complete the aforementioned method steps. The computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disk, or CD-ROM.

[0086] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0087] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0088] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0089] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for optimizing flood season drought-limited water levels that takes into account both supply and demand, characterized in that, include: The system acquires the control station of the target river, the tiered water supply demand of the control station, the initial tiered outflow parameters, and the target tiered outflow parameters of the upstream reservoir corresponding to the target river; the control station corresponds to the outflow section of the upstream reservoir. Based on the preset dry inflow sample and the target graded outflow parameters, preset calculations are performed to obtain the initial flood season graded drought limit water level of the upstream reservoir. Based on historical operational data of the upstream reservoir under different inflow frequencies, the drought resistance capacity parameters of the upstream reservoir are determined; the drought resistance capacity parameters are the optimization parameters of the supply side. Based on the actual outflow parameters of the upstream reservoir and the target graded outflow parameters, the target graded drought relief flow gap parameters of the upstream reservoir are determined. The target-level drought relief flow gap parameters are the optimized parameters on the demand side; Based on the flood season operating water level parameters of the upstream reservoir and the additional flood volume that the target river channel needs to absorb, the flood control loss condition risk value of the upstream reservoir is determined. The flood volume parameter that needs to be absorbed is the flood volume parameter that exceeds the flood control safety allowable discharge capacity of the target river channel; the flood loss condition risk value is the supply-side optimization parameter; Based on the drought resistance capacity parameters, the target graded drought resistance flow gap parameters, and the risk value of flood control loss conditions, a simulation model is constructed to optimize the initial flood season graded drought limit water level and obtain the target flood season graded drought limit water level.

2. The method according to claim 1, characterized in that, The acquisition of the initial graded outflow parameters of the target river channel includes: Based on a preset model, the tiered water supply demand is back-calculated to the outflow section to obtain the initial tiered outflow parameters of the control station; the preset model includes the mapping relationship between the outflow sequence of the upstream reservoir at the control station and the water level sequence of the target river at the control station downstream of the control station.

3. The method according to claim 1, characterized in that, Before performing preset calculations based on preset inflow samples and target graded outflow parameters to obtain the initial flood season graded drought limit water level of the upstream reservoir, the method further includes: Based on the tiered water supply demand, water samples with a frequency greater than or equal to a frequency threshold are selected for periods of drought; these water samples are from years prone to drought and water shortage.

4. The method according to claim 1, characterized in that, The drought resistance capacity parameters include the tiered water storage parameters of the upstream reservoir's ability to supply water to the target river channel corresponding to the tiered water supply demand, the tiered time parameters of the upstream reservoir's ability to supply water to the target river channel, and the tiered probability parameters of the upstream reservoir's ability to supply water to the target river channel; determining the drought resistance capacity parameters of the upstream reservoir based on historical operational data under different inflow frequencies includes: Based on the historical water level status of the upstream reservoir, determine the graded water storage parameters and the preset storage capacity parameters of the upstream reservoir; The first difference is obtained by performing a subtraction operation on the values ​​of the graded water storage parameters and the preset reservoir capacity parameters; The first difference and the value of the target graded outbound flow parameter are divided to obtain the graded time parameter; The grading probability parameters are determined based on historical operational data of the upstream reservoir at different inflow frequencies.

5. The method according to claim 4, characterized in that, The step of determining the graded probability parameters based on historical operational data of the upstream reservoir under different inflow frequencies includes: Obtain historical operational data of the upstream reservoir under different inflow frequencies; the historical operational data includes the actual outflow parameters of the upstream reservoir in at least two time periods; Determine whether the actual outflow parameters for each time period meet the tiered water supply requirements to obtain a first determination result; the first determination result includes a first result that the actual outflow parameters meet the tiered water supply requirements and a second result that the actual outflow parameters do not meet the tiered water supply requirements; The total number of time periods is obtained by summing the number of time periods corresponding to the first result. The hierarchical probability parameter is obtained by dividing the total number of time periods by the total number of time periods in the historical operation data.

6. The method according to claim 1, characterized in that, The determination of the target-level drought relief flow gap parameters of the upstream reservoir based on the actual outflow parameters of the upstream reservoir and the target-level outflow parameters includes: The difference between the actual outflow parameters and the target graded outflow parameters is calculated to obtain at least one drought relief gap occurrence period of the upstream reservoir and the graded drought relief flow gap parameters corresponding to each drought relief gap occurrence period. The sorting results are obtained by sorting according to at least one of the aforementioned graded drought relief flow gap parameters; The first graded drought relief flow gap parameter in the sorting results is determined as the target graded drought relief flow gap parameter for the upstream reservoir.

7. The method according to claim 6, characterized in that, The step of subtracting the actual outflow parameters and the target graded outflow parameters to obtain at least one drought relief gap occurrence period of the upstream reservoir and the graded drought relief flow gap parameters corresponding to each drought relief gap occurrence period includes: Determine whether the actual outbound flow rate parameter for each time period is less than the target graded outbound flow rate parameter to obtain a second determination result; If the second judgment result indicates that the actual outflow rate parameter is less than the target graded outflow rate parameter, the time period corresponding to the actual outflow rate parameter is determined as the time period during which the drought relief gap occurs. Based on the actual outflow parameters corresponding to the period when the drought relief gap occurs and the target graded outflow parameters, the graded drought relief flow gap parameters corresponding to the period when the drought relief gap occurs are determined.

8. The method according to claim 1, characterized in that, The simulation model includes a first objective function, a second objective function, and a third objective function; the first objective function represents maximizing the graded drought resistance capacity of the upstream reservoir; the second objective function represents minimizing the graded maximum drought resistance flow gap of the upstream reservoir; and the third objective function represents minimizing the flood control losses of the upstream reservoir during the flood season. The simulation model constructed based on the drought resistance capacity parameters, the target-level drought resistance flow gap parameters, and the flood loss condition risk value includes: The tiered drought resistance weight coefficients of the simulation model are determined based on the tiered water supply demand. The inflow frequency weighting coefficient of the simulation model is determined based on the different inflow frequencies of the upstream reservoir; The first objective function is constructed based on the drought resistance capacity parameters, the graded drought resistance weight coefficients, and the water inflow frequency weight coefficients. Based on the target graded drought relief flow gap parameters, the graded drought relief weight coefficients, and the water inflow frequency weight coefficients, a second objective function is constructed. The third objective function is constructed based on the risk value of flood control loss conditions, the graded drought resistance weight coefficient, and the water inflow frequency weight coefficient.

9. The method according to claim 8, characterized in that, The tiered water supply demand includes primary water demand for urban and rural water use and secondary water demand for agricultural irrigation; the tiered drought resistance weight coefficients include a first drought resistance weight coefficient and a second drought resistance weight coefficient; determining the tiered drought resistance weight coefficients of the simulation model based on the tiered water supply demand includes: The first drought resistance weight coefficient is determined based on the primary water demand, and the second drought resistance weight coefficient is determined based on the secondary water demand, wherein the first drought resistance weight coefficient is greater than the second drought resistance weight coefficient.

10. The method according to claim 8, characterized in that, The incoming water frequency includes a first frequency, a second frequency, a third frequency, and a fourth frequency; the weighting coefficient of the incoming water frequency includes a first coefficient corresponding to the first frequency, a second coefficient corresponding to the second frequency, a third coefficient corresponding to the third frequency, and a fourth coefficient corresponding to the fourth frequency; The determination of the inflow frequency weighting coefficients for the simulation model based on different inflow frequencies of the upstream reservoir includes: When the preset first condition is met, the fourth coefficient is determined to be greater than the third coefficient based on the different water inflow frequencies of the upstream reservoir. The third coefficient is greater than the second coefficient, and the second coefficient is greater than the first coefficient. The first condition is a condition that prioritizes drought relief under extreme low water conditions. When the preset second condition is met, the first coefficient is determined to be greater than the fourth coefficient, the third coefficient and the second coefficient based on the different water inflow frequencies of the upstream reservoir, and the fourth coefficient, the third coefficient and the second coefficient are equal; the second condition is the condition for prioritizing drought relief under the condition of low water inflow.

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