Reservoir scheduling optimization method and system based on artificial intelligence

Abstract

The invention relates to the technical field of hydraulic engineering, and discloses a reservoir scheduling optimization method and system based on artificial intelligence, and the system comprises a data collection and preprocessing module, a prediction module, an optimization module, a decision support module and a control execution module, and also comprises an edge calculation node, a federal learning coordination module and a man-machine collaborative decision interface. Through the combination of the deep learning prediction model and the optimization algorithm, the reservoir scheduling accuracy and efficiency are improved, and the prediction reliability under the complex meteorological condition is improved. The system can automatically generate an optimal reservoir scheduling scheme according to real-time data and a prediction result, so that the cost of manual intervention is reduced, and the management efficiency is improved; edge calculation is supported, the scheduling strategy generation delay is less than 1 minute, and flood control emergency requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, specifically to a reservoir operation optimization method and system based on artificial intelligence. Background Art

[0002] Traditional reservoir operation mainly relies on manual experience and historical data for analysis and decision-making, suffering from problems such as low operation efficiency and unreasonable resource allocation. With the rapid development of artificial intelligence technology, its applications in various fields are becoming increasingly widespread, providing new solutions for reservoir operation. However, current reservoir operation systems based on artificial intelligence still face challenges such as low model accuracy and poor algorithm adaptability. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the above technical deficiencies and provide a reservoir operation optimization method and system based on artificial intelligence.

[0004] To solve the above problems, the technical solution of the present invention is as follows: The reservoir operation optimization method based on artificial intelligence includes the following steps:

[0005] S1. Real-time collect meteorological data, hydrological data and reservoir operation data of the reservoir basin, including rainfall, evaporation, soil moisture, inflow, outflow and water level;

[0006] S2. Use a machine learning model to process the meteorological data and predict rainfall and runoff within a specified future time window;

[0007] S3. Based on a deep learning model, combine the prediction results of step S2 with historical hydrological data to generate time series predictions of reservoir water level and flow;

[0008] S4. Construct a reinforcement learning environment, use the current state of the reservoir as input, use the operation strategy as the action space, design a multi-objective reward function, and dynamically generate an optimal operation strategy through a reinforcement learning algorithm;

[0009] S5. Visualize and output the optimized operation strategy through a decision support module, and provide historical data comparison and risk warning.

[0010] Further, the machine learning model includes at least one of a support vector machine, a random forest, and a gradient boosting tree.

[0011] Further, the deep learning model is a hybrid model of a long short-term memory network and a convolutional neural network, which is used to extract time series features and spatial distribution features simultaneously.

[0012] Further, the reinforcement learning algorithm is a deep Q-network or proximal policy optimization.

[0013] Furthermore, the design of the reward function includes the following objectives:

[0014] Flood control safety index: Control the water level below the flood limit water level;

[0015] Power generation benefit index: Maximize power generation;

[0016] Water supply and irrigation index: Ensure the minimum outflow;

[0017] Ecological flow index: Maintain the ecological water demand in the downstream;

[0018] A reservoir operation system based on artificial intelligence includes the following modules:

[0019] Data collection and preprocessing module: Collect multi-source data in real time through Internet of Things sensors, satellite remote sensing and weather stations, and clean, denoise and standardize the collected data;

[0020] Prediction module: Establish a prediction model, which integrates machine learning and deep learning models, trains the model through historical data and real-time data, and predicts rainfall, runoff and water level through the prediction model;

[0021] Optimization module: Generate a multi-objective optimal operation strategy based on the reinforcement learning algorithm;

[0022] Decision support module: Provide visualization interface, historical data backtracking and risk warning functions;

[0023] Control execution module: Link with the reservoir gate control system to realize the automatic execution of the operation strategy.

[0024] Furthermore, the system includes:

[0025] Edge computing nodes, deploying lightweight STGNN models to support real-time water level anomaly detection;

[0026] Federated learning coordination module, realizing collaborative training of cross-reservoir privacy data;

[0027] Human-machine collaborative decision-making interface, supporting dynamic adjustment of the weights of expert rules and AI strategies.

[0028] Furthermore, the system supports the collaborative deployment of edge computing and cloud platform. The prediction module is deployed on the cloud, and the optimization module is deployed on the edge server to reduce the response latency.

[0029] The advantages of the present invention compared with the existing technologies are as follows: The present invention provides a reservoir operation optimization method and system based on artificial intelligence. By combining a deep learning prediction model and an optimization algorithm, the accuracy and efficiency of reservoir operation are improved, and the prediction reliability under complex meteorological conditions is enhanced. The system can automatically generate an optimal reservoir operation plan according to real-time data and prediction results, reducing the cost of manual intervention and improving management efficiency; it supports edge computing, and the generation delay of the scheduling strategy is less than 1 minute, meeting the flood control emergency requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart of the present invention.

[0031] Figure 2 is a system architecture diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0033] Embodiment

[0034] 1. Data collection: Deploy meteorological sensors, water level gauges and flow meters in the target reservoir basin, and collect data once an hour and transmit it to the cloud.

[0035] 2. Rainfall prediction: Use a random forest model, input historical rainfall, temperature, and wind speed data, and predict the rainfall in the next 72 hours, with the mean absolute error ≤ 5 mm.

[0036] 3. Water level prediction: Build a hybrid prediction model, input the rainfall prediction result, the current water level and the inflow, and output the water level change curve in the next 24 hours, with a prediction accuracy of 95%.

[0037] 4. Reinforcement learning optimization: Define the state space as the current water level, inflow and future prediction data, the action space as the gate opening adjustment plan, and the reward function weights four objectives: flood control (weight 0.4), power generation (weight 0.3), water supply (weight 0.2) and ecology (weight 0.1), and train the policy network through the PPO algorithm.

[0038] 5. Decision execution: After the system generates a scheduling plan, it is confirmed by the scheduler and sent to the gate control system to realize dynamic water level regulation.

[0039] The present invention provides an optimization method and system for reservoir operation based on artificial intelligence. By combining a deep learning prediction model and an optimization algorithm, the accuracy and efficiency of reservoir operation are improved, and the prediction reliability under complex meteorological conditions is enhanced. The system can automatically generate an optimal reservoir operation plan according to real-time data and prediction results, reducing the cost of manual intervention and improving management efficiency; it supports edge computing, and the generation delay of the operation strategy is less than 1 minute, meeting the flood control emergency requirements.

[0040] The parts not disclosed in the present invention are all prior arts, and their specific structures and working principles will not be elaborated herein.

[0041] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0042] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

[0043] The above description of the present invention and its embodiments is not restrictive. What is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and design similar structural modes and embodiments without creative efforts without departing from the purpose of the present invention, they shall fall within the protection scope of the present invention.

Claims

1. An artificial intelligence-based reservoir operation optimization method, characterized in that It includes the following steps: S1. Real-time collect meteorological data, hydrological data and reservoir operation data of the reservoir basin, including rainfall, evaporation, soil humidity, inflow, outflow and water level; S2. Use a machine learning model to process the meteorological data and predict the rainfall and runoff within a specified future time window; S3. Based on a deep learning model, combine the prediction results of step S2 with historical hydrological data to generate a time series prediction of reservoir water level and flow; S4. Construct a reinforcement learning environment, use the current state of the reservoir as the input, use the scheduling strategy as the action space, design a multi-objective reward function, and dynamically generate an optimal scheduling strategy through a reinforcement learning algorithm; S5. Visualize and output the optimized scheduling strategy through a decision support module, and provide historical data comparison and risk warning.

2. The reservoir operation optimization method based on artificial intelligence according to claim 1, wherein: The machine learning model includes at least one of a support vector machine, a random forest, and a gradient boosting tree.

3. The reservoir operation optimization method based on artificial intelligence according to claim 1, characterized in that: The deep learning model is a hybrid model of a long short-term memory network and a convolutional neural network, which is used to extract time series features and spatial distribution features simultaneously.

4. The reservoir operation optimization method based on artificial intelligence according to claim 1, characterized in that: The reinforcement learning algorithm is a deep Q network or proximal policy optimization.

5. The reservoir operation optimization method based on artificial intelligence according to claim 1, characterized in that The design of the reward function includes the following objectives: Flood control safety index: Control the water level below the flood limit water level; Power generation benefit index: Maximize power generation; Water supply and irrigation index: Ensure the minimum outflow; Ecological flow index: Maintain the ecological water demand downstream.

6. An artificial intelligence-based reservoir operation system, characterized in that, It includes the following modules: Data collection and preprocessing module: Real-time collect multi-source data through Internet of Things sensors, satellite remote sensing and weather stations, and clean, denoise and standardize the collected data; Prediction module: Establish a prediction model. The prediction model integrates machine learning and deep learning models, trains the model through historical data and real-time data, and predicts rainfall, runoff and water level through the prediction model; Optimization module: Generate a multi-objective optimized scheduling strategy based on a reinforcement learning algorithm; Decision support module: Provide a visualization interface, historical data backtracking and risk warning functions; Control execution module: Link with the reservoir gate control system to realize the automatic execution of the scheduling strategy.

7. An artificial intelligence-based reservoir operation system according to claim 6, characterized in that, The system includes: Edge computing nodes, deploy a lightweight STGNN model to support real-time water level anomaly detection; Federated learning coordination module, which realizes collaborative training of cross-reservoir privacy data; Human-machine collaborative decision-making interface, which supports dynamic adjustment of the weights of expert rules and AI strategies.

8. An artificial intelligence-based reservoir operation optimization system according to claim 7, characterized in that: The system supports the collaborative deployment of edge computing and cloud platforms. The prediction module is deployed on the cloud, and the optimization module is deployed on the edge server to reduce the response latency.

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