Water diversion scheduling method based on large water storage reservoir

Abstract

The invention discloses a water diversion scheduling method based on a large reservoir, which belongs to the field of hydraulic engineering and comprises the following steps: inputting basic information and related historical data of the large reservoir; carrying out demand prediction and water level simulation by utilizing a machine learning algorithm; formulating an optimal water diversion scheduling strategy according to the prediction result and the scheduling target; converting the scheduling strategy into an operation instruction and implementing the operation instruction; monitoring reservoir operation data in real time and performing comparative analysis; and performing feedback optimization on the scheduling strategy according to the actual operation effect. A water diversion strategy can be dynamically adjusted according to actual requirements and reservoir conditions, and optimal configuration and efficient utilization of water resources are achieved; demand prediction and water level simulation are performed by using a machine learning algorithm, so that the prediction precision and the scheduling efficiency are improved; an optimal water diversion scheduling strategy is formulated in combination with an optimization algorithm, so that the realization of a scheduling target is ensured; and the real-time monitoring and feedback optimization mechanism ensures the real-time performance and accuracy of the scheduling strategy.

Description

Technical Field

[0001] The invention relates to the field of water conservancy projects, and in particular to a water diversion scheduling method based on a large-scale reservoir. Background Art

[0002] With the development of society and the growth of population, the contradiction between water supply and demand has become increasingly prominent. Traditional water diversion scheduling methods often have problems such as mismatch between supply and demand, serious waste of water resources, and low scheduling efficiency. Especially in the management of large reservoirs, how to efficiently and reasonably schedule water resources has become an urgent problem to be solved. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a water diversion scheduling method based on a large reservoir, which can dynamically adjust the water diversion strategy according to actual needs and reservoir conditions to achieve optimal allocation and efficient utilization of water resources.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] A water diversion scheduling method based on a large reservoir is provided, which comprises the following steps:

[0006] S1. Basic data input: First, input the basic information of large reservoirs, including reservoir capacity, water level variation range, water supply area and water demand; at the same time, input relevant information such as historical hydrological data and meteorological forecast data as the basis for subsequent calculations;

[0007] S2. Demand forecasting: Based on historical water use data and meteorological forecast data, machine learning algorithms are used to forecast water demand in the future. The forecast results will serve as an important basis for the formulation of scheduling strategies.

[0008] S3. Water level simulation: Based on the current water level, inflow forecast and water usage forecast of the reservoir, the hydrological model is used to simulate the future water level changes of the reservoir. The simulation results will be used to evaluate the impact of different scheduling strategies on the reservoir water level.

[0009] S4. Scheduling strategy formulation: Based on demand forecast and water level simulation results, combined with the reservoir's scheduling objectives and constraints, the optimal water diversion scheduling strategy is formulated using an optimization algorithm; the water diversion scheduling strategy includes key parameters such as water diversion volume, water diversion time, and water diversion route;

[0010] S5. Strategy implementation and monitoring: Convert the formulated dispatching strategy into specific operating instructions to guide the water diversion work of the reservoir; at the same time, monitor and adjust the implementation of the dispatching strategy through real-time monitoring of key parameters such as the reservoir's water level and flow rate to ensure the achievement of the dispatching goals.

[0011] S6. Feedback optimization: During the execution of the scheduling strategy, actual operation data is collected and compared with the predicted data to find out the source of error and make corrections; at the same time, the scheduling strategy is continuously optimized and improved based on the actual operation results.

[0012] Further, step S1 includes:

[0013] S11: Input the reservoir capacity, water level variation range, water supply area and water demand as basic information; clarify the reservoir's geographical location, surrounding environment and its impact on the ecological environment.

[0014] S12: Collect and organize historical hydrological data of reservoirs, including rainfall, inflow, outflow, water level changes, etc.;

[0015] S13: Analyze historical data to understand the water level variation patterns, seasonal differences and dry season characteristics of the reservoir;

[0016] S14: Obtain meteorological data such as rainfall forecast and temperature forecast provided by the meteorological department; combine historical meteorological data to analyze the impact of meteorological conditions on reservoir water inflow.

[0017] Furthermore, step S2 includes: using machine learning algorithms to analyze historical water use data to find out the changing trends and seasonal patterns of water consumption; combining meteorological forecast data to predict water demand in the future.

[0018] Further, step S3 includes:

[0019] S31: Establish a reservoir water level model based on the actual situation of the reservoir; the model should be able to consider the impact of factors such as rainfall inflow, evapotranspiration, and downstream flow on the water level;

[0020] S32: Based on the water demand forecast and the current water level and inflow forecast of the reservoir, the reservoir water level model is used for simulation analysis; the water level changes in the future period are predicted, and the impact of different scheduling schemes on the reservoir water level is evaluated.

[0021] Further, step S4 includes:

[0022] S41: Clarify the dispatching objectives, such as water supply guarantee rate, ecological flow, economic benefits, etc.; determine the constraints of the dispatching strategy, such as reservoir capacity, water level variation range, downstream water demand, etc.;

[0023] S42: Use optimization algorithms: genetic algorithm, particle swarm algorithm to formulate the optimal water diversion scheduling strategy; the strategy includes key parameters such as water diversion volume, water diversion time, and water diversion route.

[0024] The beneficial effects of the present invention are:

[0025] 1. Ability to dynamically adjust water diversion strategies according to actual needs and reservoir conditions to achieve optimal allocation and efficient utilization of water resources.

[0026] 2. Use machine learning algorithms for demand forecasting and water level simulation to improve forecasting accuracy and scheduling efficiency.

[0027] 3. Combined with the optimization algorithm, the optimal water diversion scheduling strategy is formulated to ensure the realization of the scheduling objectives.

[0028] 4. Real-time monitoring and feedback optimization mechanism ensures the real-time and accuracy of the scheduling strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of the water diversion scheduling method based on large reservoirs. DETAILED DESCRIPTION

[0030] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0031] like Figure 1 As shown, a water diversion scheduling method based on a large reservoir includes the following steps:

[0032] S1. Basic data input: First, input the basic information of large-scale reservoirs, including reservoir capacity, water level variation range, water supply area and water demand; at the same time, input relevant information such as historical hydrological data and meteorological forecast data as the basis for subsequent calculations.

[0033] Step S1 includes:

[0034] S11: Input the reservoir capacity, water level variation range, water supply area and water demand as basic information; clarify the reservoir's geographical location, surrounding environment and its impact on the ecological environment.

[0035] S12: Collect and organize historical hydrological data of reservoirs, including rainfall, inflow, outflow, water level changes, etc.;

[0036] S13: Analyze historical data to understand the water level variation patterns, seasonal differences and dry season characteristics of the reservoir;

[0037] S14: Obtain meteorological data such as rainfall forecast and temperature forecast provided by the meteorological department; combine historical meteorological data to analyze the impact of meteorological conditions on reservoir water inflow.

[0038] S2. Demand forecasting: Based on historical water use data and meteorological forecast data, use machine learning algorithms to predict water demand in the future. The forecast results will serve as an important basis for the formulation of scheduling strategies. Use machine learning algorithms to analyze historical water use data to find out the changing trends and seasonal patterns of water consumption. Combined with meteorological forecast data, predict water demand in the future.

[0039] S3. Water level simulation: Based on the current water level, inflow forecast and water usage forecast of the reservoir, the hydrological model is used to simulate the future water level changes of the reservoir. The simulation results will be used to evaluate the impact of different scheduling strategies on the reservoir water level.

[0040] Step S3 includes:

[0041] S31: Establish a reservoir water level model based on the actual situation of the reservoir; the model should be able to consider the impact of factors such as rainfall inflow, evapotranspiration, and downstream flow on the water level;

[0042] S32: Based on the water demand forecast and the current water level and inflow forecast of the reservoir, the reservoir water level model is used for simulation analysis; the water level changes in the future period are predicted, and the impact of different scheduling schemes on the reservoir water level is evaluated.

[0043] S4. Scheduling strategy formulation: Based on demand forecasts and water level simulation results, combined with the reservoir's scheduling objectives and constraints, the optimization algorithm is used to formulate the optimal water diversion scheduling strategy; the water diversion scheduling strategy includes key parameters such as water diversion volume, water diversion time, and water diversion route.

[0044] Step S4 includes:

[0045] S41: Clarify the dispatching objectives, such as water supply guarantee rate, ecological flow, economic benefits, etc.; determine the constraints of the dispatching strategy, such as reservoir capacity, water level variation range, downstream water demand, etc.;

[0046] S42: Use optimization algorithms: genetic algorithm, particle swarm algorithm to formulate the optimal water diversion scheduling strategy; the strategy includes key parameters such as water diversion volume, water diversion time, and water diversion route.

[0047] S5. Strategy implementation and monitoring: Convert the formulated dispatching strategy into specific operating instructions to guide the water diversion work of the reservoir; at the same time, monitor and adjust the implementation of the dispatching strategy through real-time monitoring of key parameters such as the reservoir's water level and flow rate to ensure the achievement of the dispatching goals.

[0048] S6. Feedback optimization: During the execution of the scheduling strategy, actual operation data is collected and compared with the predicted data to find out the source of error and make corrections; at the same time, the scheduling strategy is continuously optimized and improved based on the actual operation results.

[0049] It can dynamically adjust the water diversion strategy according to actual demand and reservoir conditions to achieve optimal allocation and efficient use of water resources. It uses machine learning algorithms to predict demand and simulate water levels, which improves prediction accuracy and scheduling efficiency. It combines optimization algorithms to formulate the optimal water diversion scheduling strategy to ensure the achievement of scheduling goals. Real-time monitoring and feedback optimization mechanisms ensure the real-time and accuracy of scheduling strategies.

Claims

1. A water diversion scheduling method based on a large reservoir, characterized in that: The following steps are involved: S1. Basic data input: First, input the basic information of large reservoirs, including reservoir capacity, water level variation range, water supply area and water demand; at the same time, input relevant information such as historical hydrological data and meteorological forecast data as the basis for subsequent calculations; S2. Demand forecasting: Based on historical water use data and meteorological forecast data, machine learning algorithms are used to forecast water demand in the future. The forecast results will serve as an important basis for the formulation of scheduling strategies. S3. Water level simulation: Based on the current water level, inflow forecast and water consumption forecast of the reservoir, the hydrological model is used to simulate the future water level changes of the reservoir. The simulation results will be used to evaluate the impact of different scheduling strategies on the reservoir water level. S4. Scheduling strategy formulation: Based on demand forecast and water level simulation results, combined with the reservoir's scheduling objectives and constraints, the optimal water diversion scheduling strategy is formulated using an optimization algorithm; the water diversion scheduling strategy includes key parameters such as water diversion volume, water diversion time, and water diversion route; S5. Strategy implementation and monitoring: Convert the formulated dispatching strategy into specific operation instructions to guide the water diversion work of the reservoir; at the same time, monitor and adjust the implementation of the dispatching strategy through real-time monitoring of key parameters such as the reservoir's water level and flow rate to ensure the achievement of the dispatching goals; S6. Feedback optimization: During the execution of the scheduling strategy, actual operation data is collected and compared with the predicted data to find out the source of error and make corrections; at the same time, the scheduling strategy is continuously optimized and improved based on the actual operation results.

2. The water diversion scheduling method based on a large reservoir according to claim 1 is characterized in that: The step S1 comprises: S11: Input the reservoir capacity, water level variation range, water supply area and water demand as basic information; clarify the reservoir's geographical location, surrounding environment and its impact on the ecological environment; S12: Collect and organize historical hydrological data of reservoirs, including rainfall, inflow, outflow, water level changes, etc.; S13: Analyze historical data to understand the water level variation patterns, seasonal differences and dry season characteristics of the reservoir; S14: Obtain meteorological data such as rainfall forecast and temperature forecast provided by the meteorological department; combine historical meteorological data to analyze the impact of meteorological conditions on reservoir water inflow.

3. The water diversion scheduling method based on a large reservoir according to claim 1 is characterized in that: The step S2 includes: using a machine learning algorithm to analyze historical water consumption data to find out the changing trend and seasonal pattern of water consumption; combining with meteorological forecast data to predict water demand in the future.

4. The water diversion scheduling method based on a large reservoir according to claim 1 is characterized in that: The step S3 comprises: S31: Establish a reservoir water level model based on the actual situation of the reservoir; the model should be able to consider the impact of factors such as rainfall inflow, evapotranspiration, and downstream flow on the water level; S32: Based on the water demand forecast and the current water level and inflow forecast of the reservoir, the reservoir water level model is used for simulation analysis; the water level changes in the future period are predicted, and the impact of different scheduling schemes on the reservoir water level is evaluated.

5. The water diversion scheduling method based on a large reservoir according to claim 1 is characterized in that: The step S4 comprises: S41: Clarify the dispatching objectives, such as water supply guarantee rate, ecological flow, economic benefits, etc.; determine the constraints of the dispatching strategy, such as reservoir capacity, water level variation range, downstream water demand, etc.; S42: Use optimization algorithms: genetic algorithm, particle swarm algorithm to formulate the optimal water diversion scheduling strategy; the strategy includes key parameters such as water diversion volume, water diversion time, and water diversion route.

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