Multi-objective optimization reservoir gate strategy recommendation method and system

Abstract

The invention discloses a multi-objective optimization reservoir gate strategy recommendation method and system, and relates to the reservoir scheduling and data processing technology, and the method comprises the steps: obtaining the landform data of main stream and branch stream coverage in a basin coverage range in advance, and collecting the hydrological data of a basin; according to the topographic data, determining an influence factor of the collected hydrological data on a dam section; establishing a scheduling optimization target of the current gate; establishing a constraint condition of the scheduling optimization target; solving an optimal solution set by adopting a simulated annealing algorithm according to the scheduling optimization target and the constraint condition, and dynamically updating influence factors according to the monitored hydrological data in the solving process so as to realize self-adaptive adjustment; and outputting a reservoir gate opening and closing strategy according to a solving result. The method is used for processing hydrological data more finely, introducing historical operation experience, and accurately triggering an optimization process, so that the operation efficiency and the operation safety of the reservoir are improved.

Description

Technical Field

[0001] This application relates to the fields of reservoir scheduling and data processing technology, and in particular to a method and system for recommending reservoir gate strategies through multi-objective optimization. Background Technology

[0002] The optimal scheduling of reservoir gates is a core issue in the field of water resources management. Its goal is usually to ensure flood control safety while taking into account the comprehensive benefits of multiple objectives such as water supply, power generation, and ecology.

[0003] In existing technical solutions, the typical implementation method is as follows: collect key hydrological data of reservoirs and watersheds, such as water level, flow rate and rainfall; establish a target optimization model and solve it.

[0004] Existing methods typically use raw hydrological data collected from discrete monitoring points directly, or perform only simple averaging. However, significant spatial differences exist in the topography and geomorphology (such as slope, catchment area, and distance from dams) within a watershed, profoundly affecting the generation, collection, and propagation of hydrological data. For example, a sub-watershed located on a steep slope with a large catchment area and close to a dam will have a much greater impact on the dam cross-section from a unit of rainfall than a sub-watershed located on a gentle slope with a small catchment area and far away. Existing technologies generally ignore this spatial heterogeneity modulated by topographic data, causing the data input to the optimization model to fail to accurately reflect the overall state of the watershed. This results in subsequent strategy recommendations being based on distorted data, akin to "blind men describing an elephant," significantly reducing their accuracy and reliability.

[0005] Existing technologies fail to make explicit this tacit knowledge based on historical operating patterns and use it as a constraint to guide the search of optimization algorithms. This leads to optimization algorithms potentially finding some strange solutions that, while satisfying physical constraints, violate actual operating experience and have poor executability. Summary of the Invention

[0006] This application provides a method and system for recommending reservoir gate strategies with multi-objective optimization, which processes hydrological data more precisely, incorporates historical operating experience, and accurately triggers the optimization process, thereby improving reservoir operating efficiency and safety.

[0007] This application proposes a multi-objective optimization method for recommending reservoir gate strategies, including: The topographic data of the main stream and tributaries within the watershed coverage area are obtained in advance, and the hydrological data of the watershed are collected, including water level, flow rate and rainfall. Based on the topographic data, determine the influence factors of the collected hydrological data on the dam cross section; Based on the influencing factors and the hydrological data, and considering the preconditions for gate opening, a scheduling optimization target for the current gate is established. Establish constraints for the scheduling optimization objective, wherein constraints based on historical operating patterns are introduced into the constraints. Based on the scheduling optimization objective and the constraints, the simulated annealing algorithm is used to solve for the optimal solution set. Furthermore, during the solution process, the influencing factors are dynamically updated based on the monitored hydrological data to achieve adaptive adjustment. The reservoir gate opening and closing strategy is output based on the solution results.

[0008] Optionally, based on the topographic data, the factors influencing the collected hydrological data on the dam cross-section include: Based on the slope data in the terrain data, the main stream and tributaries within the watershed coverage area are divided into multiple sub-watersheds, and basic sub-weights are configured for each sub-watershed under the same slope level. Based on the aforementioned topographic data, determine the corresponding basic catchment area for each sub-basin; The sub-weight of the impact of any sub-basin on the dam cross-section is calculated as follows: in, For sub-basins i Sub-weights of the impact on the dam cross-section , For the configured basic sub-weights, , For sub-basins i, j Basic catchment area For sub-basins i Distance to the dam cross-section Let N be the distance reference, and N be the number of sub-basins.

[0009] Optionally, determining the influence factors of the collected hydrological data on the dam cross-section based on the topographic data further includes: Sub-basins are cascaded according to the natural location relationship of the main stream or tributaries; Based on the influence sub-weights of each sub-basin, the influence weight of any entire main stream or tributary on the dam cross section is determined. Furthermore, a slope adjustment coefficient is configured according to the coverage relationship of the sub-basins in any main stream or tributary. The slope adjustment coefficient is used to describe the influence of sub-basins whose slopes are greater than a specified threshold contained in any main stream or tributary. The larger the catchment area and the longer the sub-basin, the larger the slope adjustment coefficient. Based on the determined influence weights and the slope adjustment coefficients, the influence factors of any main stream or tributary on the dam cross section are determined.

[0010] Optionally, based on the influencing factors and the hydrological data, the preconditions for opening the gate include: Determine the sub-basins of the main stream or tributaries covered by the rainfall range in the hydrological data; For the covered sub-basins, flow increment prediction is performed based on the corresponding rainfall data in the hydrological data; Based on the predicted flow increment results and the corresponding influencing factors, determine the impact of the flow increment on any of the main streams or tributaries. Based on the calculated impact of the flow increment and the base flow of each main stream or tributary, determine the possible flow at the dam cross section. If the possible flow rate exceeds a preset threshold, determine the preconditions for opening the gate.

[0011] Optionally, determining the sub-basin of the main stream or tributary covered by the rainfall range in the hydrological data includes: Based on the aforementioned topographic data, a buffer zone associated with any sub-basin is determined, and a dynamic catchment area is established as follows: in, For the adjusted byte stream i Dynamic catchment area For sub-basins i Basic catchment area The average rainfall intensity over the coverage area, Where K is the critical value for rainfall intensity, and K is the soil permeability coefficient. , This refers to the beach area coefficient; The dynamic catchment area is used as the runoff generation area to predict the flow increment based on the corresponding rainfall in the hydrological data.

[0012] Optionally, the scheduling optimization objectives for the current gates may include: Defined as minimizing a comprehensive objective function F as: in, , , These are the objective functions for flood control, ecological flow, and power generation, respectively. , , These are dynamic weights, , , It is determined by the ratio between the matching similarity between the current influencing factors and the characteristics of possible flows and the different hydrological characteristics in the historical model library, and the overall hydrological similarity.

[0013] Optionally, the constraints for establishing the scheduling optimization objective include: establishing physical and security constraints. Flood control safety constraints: , Let t be the reservoir water level. This refers to the water level limit for the reservoir. Gate capacity constraints: , Let t be the gate opening calculated at time t. , This is the converted gate opening limit value; Downflow fluctuation constraints: | - | ,in , They are respectively , The flow rate at any given moment, This refers to the discharge flow limit.

[0014] Optionally, the constraints for establishing the scheduling optimization objective include: establishing constraints based on historical operating patterns. A historical operation mode library is constructed. Based on the operation data and the matching results of dynamic weight calculation, scheduling cases under similar hydrological scenarios are extracted to form a scheduling strategy set. Define a dynamic envelope and calculate the timeframe for each moment within the current scheduling period based on the matched historical cases. The statistical envelope of the reservoir water level and downstream flow is as follows: in, This is the lowest water level among historical cases of the same period. This represents the highest traffic volume among historical cases during the same period. The mean, Standard deviation ， This is an adjustable coefficient. for Downstream flow at any given moment The constraints are established as follows: Optionally, the influencing factors are dynamically updated based on the monitored hydrological data during the solution process to achieve adaptive adjustment, including: determining the dynamically adjusted catchment area based on real-time monitored hydrological data, and repeatedly determining the weights of the influencing sub-factors to dynamically update the influencing factors.

[0015] This application also proposes a multi-objective optimized reservoir gate strategy recommendation system, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the aforementioned multi-objective optimized reservoir gate strategy recommendation method.

[0016] The proposed solution determines the impact factors of the collected hydrological data on the dam cross-section based on the topographic data of the reservoir basin. In a specific example, the impact factors can be adjusted in real time to adapt to different hydrological conditions and fit the actual operation. The method of this application can process hydrological data more precisely, introduce historical operating experience, and accurately trigger the optimization process, thereby improving the reservoir's operating efficiency and safety.

[0017] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of the basic process of the reservoir gate strategy recommendation method for multi-objective optimization in this embodiment. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0020] This application proposes a multi-objective optimization method for recommending reservoir gate strategies, such as... Figure 1 As shown, it includes the following steps: In step S101, topographic data covering the main stream and tributaries within the watershed coverage area are acquired in advance, and hydrological data of the watershed, including water level, flow rate, and rainfall, are collected. In this specific example, the topographic data can be used for a long period after acquisition, while the hydrological data is acquired in real time based on actual operational conditions.

[0021] In step S102, the influence factor of the collected hydrological data on the dam cross section is determined based on the topographic data. In subsequent examples of this application, the core is to design an adaptive influence factor based on the topography, thereby greatly improving the output accuracy of hydrological data and reducing the amount of data processing.

[0022] In step S103, based on the influencing factors and the hydrological data, and considering the preconditions for opening the gate, a scheduling optimization target for the current gate is established. In some examples, the scheduling optimization target can be a combination of multiple operational requirements, which can be described by a function.

[0023] In step S104, constraints are established for the scheduling optimization objective, including constraints based on historical operating patterns. In this application, when designing specific constraints, historical operating patterns are further considered, and adaptive feasible region constraints are designed, rather than simply designing upper and lower limit constraints. This makes the solved strategy more reasonable and helpful.

[0024] In step S105, the optimal solution set is obtained by using the simulated annealing algorithm according to the scheduling optimization objective and the constraints. During the solution process, the influencing factors are dynamically updated based on the monitored hydrological data to achieve adaptive adjustment. In a specific example, the dynamic updating of the influencing factors enables the target river to be identified more accurately, thereby solving the problem of missed or misjudged cases caused by fixed ranges or simple superposition in traditional methods.

[0025] In step S106, the reservoir gate opening and closing strategy is output based on the solution results.

[0026] The proposed solution determines the impact factors of the collected hydrological data on the dam cross-section based on the topographic data of the reservoir basin. In a specific example, the impact factors can be adjusted in real time to adapt to different hydrological conditions and fit the actual operation. The method of this application can process hydrological data more precisely, introduce historical operating experience, and accurately trigger the optimization process, thereby improving the reservoir's operating efficiency and safety.

[0027] In some embodiments of this application, determining the influence factors of the collected hydrological data on the dam cross-section based on the topographic data includes: Based on the slope data in the topographic data, the main stream and tributaries within the watershed coverage area are divided into multiple sub-watersheds. Basic sub-weights are then assigned to each sub-watershed within the same slope level. In a specific example, slope intervals are defined, such as: Interval 1: 0°-5° (gentle slope), Interval 2: 5°-15° (medium slope), Interval 3: >15° (steep slope). Basic sub-weights are assigned to each slope interval. This is based on the impact of slope on water flow velocity: steep slopes have faster water flow and a greater impact on dam cross-sections; gentle slopes have slower water flow and a smaller impact.

[0028] Based on the topographic data, a corresponding basic catchment area is determined for each sub-basin. In a specific example, the basic catchment area can be the area within the basin's coverage. In subsequent examples, the basic catchment area is dynamically adjusted according to rainfall conditions to achieve an adaptive influence factor. This application, by dividing the basin slope and setting basic sub-weights, and by configuring the basic catchment area, can be used to quantitatively characterize the key factors affecting gate scheduling.

[0029] The sub-weight of the impact of any sub-basin on the dam cross-section is calculated as follows: in, For sub-basins i Sub-weights of the impact on the dam cross-section , For the configured basic sub-weights, , For sub-basins i, j Basic catchment area For sub-basins i Distance to the dam cross-section Let N be the distance reference, and N be the number of sub-basins.

[0030] In some embodiments of this application, determining the influence factors of the collected hydrological data on the dam cross-section based on the topographic data further includes: Sub-basins are cascaded according to the natural positional relationship of the main stream or tributaries, which means restoring the sub-basins to the natural relationship of the main stream or tributaries.

[0031] Based on the influence sub-weights of each sub-basin, the influence weight of any entire main stream or tributary on the dam cross-section is determined. Furthermore, a slope adjustment coefficient is configured according to the coverage relationship of sub-basins within any main stream or tributary. This slope adjustment coefficient describes the influence of sub-basins whose slopes exceed a specified threshold within any main stream or tributary; the larger the catchment area and the longer the sub-basin, the larger the slope adjustment coefficient. In a specific example, the influence weight of any entire main stream or tributary on the dam cross-section can be determined by averaging the influence sub-weights of each sub-basin. This example further designs a slope adjustment coefficient to characterize the influence of tributaries with steeper slopes on the basin, thus better reflecting the actual watershed hydrological conditions.

[0032] Based on the determined influence weights and the slope adjustment coefficients, the influence factor of any main stream or tributary on the dam cross section is determined. In some examples, the slope adjustment coefficient can be added to the influence weights in the form of a deviation value, or it can be a separate coefficient multiplied with the influence weights to obtain the influence factor on the dam cross section.

[0033] In some embodiments of this application, determining the preconditions for opening the gate based on the influencing factors and the hydrological data includes: In some examples, the sub-basins of the main stream or tributary covered by the rainfall range in the hydrological data can be determined based on the rainfall range of the basin. The sub-basins that actually have an impact on the main stream or tributary due to rainfall can be determined based on the intersection between the rainfall range and each basic catchment area.

[0034] For the covered sub-basins, flow increment prediction is performed based on the corresponding rainfall in the hydrological data. In a specific example, this application only performs flow increment prediction for sub-basins covered by rainfall. For areas without rainfall, the base flow value is maintained. In this way, the analysis can focus on areas covered by rainfall. Flow increment prediction can be performed using curve fitting or LSTM, without specific limitations.

[0035] Based on the predicted flow increment and the corresponding influencing factors, the impact of the flow increment on any of the main streams or tributaries is determined. In a specific example, the predicted flow increment can be multiplied by the influencing factors to obtain the true impact of the tributary's flow increment on the dam cross section.

[0036] Based on the calculated impact of the flow increment and the base flow of each main stream or tributary, the possible flow of the dam section is determined. In some examples, the impact of the flow increment and the base flow are accumulated to obtain the possible flow. If the possible flow exceeds a preset threshold, the preconditions for opening the gate are determined.

[0037] In some embodiments of this application, determining the sub-basin of the main stream or tributary covered by the rainfall range in the hydrological data includes: Based on the aforementioned topographic data, a buffer zone associated with any sub-basin is determined, and a dynamic catchment area is established as follows: in, For the adjusted byte stream i Dynamic catchment area For sub-basins i Basic catchment area The average rainfall intensity over the coverage area, Where K is the critical value for rainfall intensity, and K is the soil permeability coefficient. , This refers to the beach area coefficient. This application dynamically adjusts the catchment area of ​​sub-basins based on rainfall intensity, making it particularly suitable for large-scale watershed analysis. Through slope division and dynamic catchment area design, it can greatly improve the accuracy of potential flow prediction and enhance the reliability of calculation results.

[0038] The dynamic catchment area is used as the runoff generation area to predict the flow increment based on the corresponding rainfall in the hydrological data.

[0039] In some embodiments of this application, establishing the scheduling optimization objective for the current gate includes: Defined as minimizing a comprehensive objective function F as: in, , , These are the objective functions for flood control, ecological flow, and power generation, determined based on actual dispatching needs. , , These are dynamic weights, , , It is determined by the ratio between the similarity between the current influencing factors and potential flow characteristics and the matching similarity with different hydrological characteristics in the historical model library, and the overall hydrological similarity. For example, , To determine the similarity between the current hydrological scenario and reservoir patterns during historical flood seasons, For total similarity, , similar.

[0040] In some embodiments of this application, the constraints for establishing the scheduling optimization objective include: establishing physical and security constraints. Flood control safety constraints: , Let t be the reservoir water level. This refers to the water level limit for the reservoir. Gate capacity constraints: , Let t be the gate opening calculated at time t. , This is the converted gate opening limit value; Downflow fluctuation constraints: | - | ,in , They are respectively , The flow rate at any given moment, This refers to the discharge flow limit.

[0041] In some embodiments of this application, the constraints for establishing the scheduling optimization objective include: establishing constraints based on historical operating patterns. A historical operation mode library is constructed. Based on the operation data and the matching results of dynamic weight calculation, scheduling cases under similar hydrological scenarios are extracted to form a scheduling strategy set. Each case includes the reservoir water level process line and the discharge flow process line during its scheduling period. Define a dynamic envelope and calculate the timeframe for each moment within the current scheduling period based on the matched historical cases. The statistical envelope of the reservoir water level and downstream flow is as follows: in, This is the lowest water level among historical cases of the same period. The maximum flow rate during the same historical period (determined based on the reservoir water level process curve and the discharge flow process curve during the case scheduling period). The mean, Standard deviation ， This is an adjustable coefficient. for Downstream flow at any given moment; The constraints are established as follows: By establishing constraints based on historical operating patterns, a search space verified by history is defined for the solution process, effectively preventing the algorithm from obtaining strange solutions that satisfy hard constraints but are contrary to common sense and experience.

[0042] In some embodiments of this application, the dynamic updating of the influencing factors based on the monitored hydrological data during the solution process to achieve adaptive adjustment includes: determining the dynamically adjusted catchment area based on the real-time monitored hydrological data, and repeatedly determining the weights of the influencing sub-factors to dynamically update the influencing factors.

[0043] The method in this application adopts a dynamic weight allocation mechanism based on historical pattern matching, which enables the multi-objective trade-off process to adapt to the focus of the current hydrological situation, and solves the problem of rigid strategies and inability to keep up with the times caused by traditional methods that rely on fixed weights or manual adjustments.

[0044] This application also proposes a multi-objective optimized reservoir gate strategy recommendation system, including a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the steps of the aforementioned multi-objective optimized reservoir gate strategy recommendation method.

[0045] Furthermore, although exemplary embodiments have been described herein, their scope includes any and all embodiments based on this disclosure that have equivalent elements, modifications, omissions, combinations (e.g., schemes involving intersections of various embodiments), adaptations, or changes. They are not limited to the examples described in this specification or during the implementation of this application, and such examples are to be construed as non-exclusive.

[0046] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. Other embodiments can be used by those skilled in the art when reading the above description.

[0047] The above embodiments are merely exemplary embodiments of this disclosure. Those skilled in the art can make various modifications or equivalent substitutions to this invention within the scope of the disclosure, and such modifications or equivalent substitutions should also be considered to fall within the protection scope of this invention.

Claims

1. A multi-objective optimization method for recommending reservoir gate strategies, characterized in that, include: The topographic data of the main stream and tributaries within the watershed coverage area are obtained in advance, and the hydrological data of the watershed are collected, including water level, flow rate and rainfall. Based on the topographic data, determine the impact factors of the collected hydrological data on the dam cross-section; Based on the influencing factors and the hydrological data, and considering the preconditions for gate opening, a scheduling optimization target for the current gate is established. Establish constraints for the scheduling optimization objective, wherein constraints based on historical operating patterns are introduced into the constraints. Based on the scheduling optimization objective and the constraints, the simulated annealing algorithm is used to solve for the optimal solution set. Furthermore, during the solution process, the influencing factors are dynamically updated based on the monitored hydrological data to achieve adaptive adjustment. The reservoir gate opening and closing strategy is output based on the solution results.

2. The multi-objective optimization reservoir gate strategy recommendation method as described in claim 1, characterized in that, Based on the topographic data, the factors influencing the dam cross-section determined by the collected hydrological data include: Based on the slope data in the terrain data, the main stream and tributaries within the watershed coverage area are divided into multiple sub-watersheds, and basic sub-weights are configured for each sub-watershed under the same slope level. Based on the aforementioned topographic data, determine the corresponding basic catchment area for each sub-basin; The sub-weight of the impact of any sub-basin on the dam cross-section is calculated as follows: in, For sub-basins i Sub-weights of the impact on the dam cross-section , For the configured basic sub-weights, , For sub-basins i, j Basic catchment area For sub-basins i Distance to the dam cross-section Let N be the distance reference, and N be the number of sub-basins.

3. The multi-objective optimization reservoir gate strategy recommendation method as described in claim 2, characterized in that, Based on the aforementioned topographic data, the factors influencing the collected hydrological data on the dam cross-section also include: Sub-basins are cascaded according to the natural location relationship of the main stream or tributaries; Based on the influence sub-weights of each sub-basin, the influence weight of any entire main stream or tributary on the dam cross section is determined. Furthermore, a slope adjustment coefficient is configured according to the coverage relationship of the sub-basins in any main stream or tributary. The slope adjustment coefficient is used to describe the influence of sub-basins whose slopes are greater than a specified threshold contained in any main stream or tributary. The larger the catchment area and the longer the sub-basin, the larger the slope adjustment coefficient. Based on the determined influence weights and the slope adjustment coefficients, the influence factors of any main stream or tributary on the dam cross section are determined.

4. The multi-objective optimization reservoir gate strategy recommendation method as described in claim 3, characterized in that, Based on the aforementioned influencing factors and the aforementioned hydrological data, the preconditions for the gate opening are determined to include: Determine the sub-basins of the main stream or tributaries covered by the rainfall range in the hydrological data; For the covered sub-basins, flow increment prediction is performed based on the corresponding rainfall data in the hydrological data; Based on the predicted flow increment results and the corresponding influencing factors, determine the impact of the flow increment on any of the main streams or tributaries. Based on the calculated impact of the flow increment and the base flow of each main stream or tributary, determine the possible flow at the dam cross section. If the possible flow rate exceeds a preset threshold, determine the preconditions for opening the gate.

5. The multi-objective optimization reservoir gate strategy recommendation method as described in claim 4, characterized in that, Determining the sub-basins of the main stream or tributaries covered by the rainfall range in the hydrological data includes: Based on the aforementioned topographic data, a buffer zone associated with any sub-basin is determined, and a dynamic catchment area is established as follows: in, For the adjusted byte stream i Dynamic catchment area For sub-basins i Basic catchment area The average rainfall intensity over the coverage area, Where K is the critical value for rainfall intensity, and K is the soil permeability coefficient. , This refers to the beach area coefficient; The dynamic catchment area is used as the runoff generation area to predict the flow increment based on the corresponding rainfall in the hydrological data.

6. The multi-objective optimization reservoir gate strategy recommendation method as described in claim 4, characterized in that, The current gate scheduling optimization objectives include: Defined as minimizing a comprehensive objective function F as: in, , , These are the objective functions for flood control, ecological flow, and power generation, respectively. , , These are dynamic weights, , , It is determined by the ratio between the matching similarity between the current influencing factors and the characteristics of possible flows and the different hydrological characteristics in the historical model library, and the overall hydrological similarity.

7. The multi-objective optimization reservoir gate strategy recommendation method as described in claim 6, characterized in that, The constraints for establishing the scheduling optimization objective include: establishing physical and security constraints. Flood control safety constraints: , Let t be the reservoir water level. This refers to the water level limit for the reservoir. Gate capacity constraints: , Let t be the gate opening calculated at time t. , This is the converted gate opening limit value; Downflow fluctuation constraints: | - | ,in , They are respectively , The flow rate at any given moment, This refers to the discharge flow limit.

8. The multi-objective optimization reservoir gate strategy recommendation method as described in claim 7, characterized in that, The constraints for establishing the scheduling optimization objective include: establishing constraints based on historical operating patterns. A historical operation mode library is constructed. Based on the operation data and the matching results of dynamic weight calculation, scheduling cases under similar hydrological scenarios are extracted to form a scheduling strategy set. Define a dynamic envelope and calculate the timeframe for each moment within the current scheduling period based on the matched historical cases. The statistical envelope of the reservoir water level and downstream flow is as follows: in, This is the lowest water level among historical cases of the same period. The highest traffic among historical cases during the same period, The mean, Standard deviation ， This is an adjustable coefficient. for Downstream flow at any given moment The constraints are established as follows: 。 9. The multi-objective optimization reservoir gate strategy recommendation method as described in claim 8, characterized in that, The process of solving the problem involves dynamically updating the influencing factors based on the monitored hydrological data to achieve adaptive adjustment. This includes determining the dynamically adjusted catchment area based on real-time monitored hydrological data and repeatedly determining the weights of the influencing sub-factors to dynamically update the influencing factors.

10. A multi-objective optimization reservoir gate strategy recommendation system, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the steps of the multi-objective optimization reservoir gate strategy recommendation method as described in any one of claims 1 to 9.

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