Cascade reservoir ecological scheduling system based on artificial intelligence

Abstract

The invention relates to the technical field of water resource management, in particular to a cascade reservoir ecological scheduling system based on artificial intelligence, which comprises a multi-source data integrated processing module, an ecological influence prediction module, a scheduling strategy optimization module, a self-adaptive water quality management module and an intelligent user interaction module. Wherein the multi-source data integrated processing module is used for collecting various data of a meteorological station, a hydrometric station and an ecological monitoring point; the ecological influence prediction module is used for constructing an ecological influence model; the scheduling strategy optimization module is used for formulating a reservoir scheduling strategy; the self-adaptive water quality management module is used for adjusting the reservoir outlet flow and the adjusting strategy of the reservoir; and the intelligent user interaction module provides a human-computer interface. According to the invention, by integrating multi-source real-time data, applying reinforcement learning to optimize the scheduling strategy and providing an efficient user interaction interface, the real-time responsiveness and decision-making efficiency of reservoir management are significantly improved, and optimal utilization of water resources and protection of the ecological environment are ensured at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource management, and in particular to an artificial intelligence-based ecological dispatching system for cascade reservoirs. Background Art

[0002] Cascade reservoir systems play a key role in achieving efficient use of water resources and protecting ecological balance. However, due to the complexity of reservoir operation, including the variability of hydrological, meteorological and ecological factors, traditional reservoir management methods often fail to achieve optimal levels. Currently, most reservoir management systems rely on static scheduling schemes that often lack the ability to respond to real-time environmental changes, leading to uneven water resource utilization, ecological damage or water quality problems. In addition, traditional systems also have deficiencies in data processing and user interaction, such as low data integration efficiency, unfriendly operating interface, and inability to provide sufficient decision support, which limits the real-time and effectiveness of dynamic optimization and strategy adjustment of reservoir management.

[0003] In view of these deficiencies in the prior art, the present invention aims to solve multiple technical problems in the ecological scheduling system of cascade reservoirs. First, the prior art fails to effectively integrate information from multiple data sources, including real-time hydrological, meteorological and ecological data, and lacks an effective mechanism to integrate these data to support real-time decision-making. Second, the existing reservoir scheduling strategy lacks flexibility and cannot adjust the reservoir water release strategy according to real-time data to cope with rapidly changing environmental conditions and ecological needs.

[0004] Therefore, it is necessary to develop a new cascade reservoir management system that can respond to environmental changes in real time, automatically optimize scheduling strategies, and improve operational efficiency and decision-making quality through an advanced user interaction interface. Summary of the invention

[0005] Based on the above objectives, the present invention provides an artificial intelligence-based cascade reservoir ecological scheduling system.

[0006] The artificial intelligence-based cascade reservoir ecological dispatching system includes a multi-source data integration processing module, an ecological impact prediction module, a dispatching strategy optimization module, an adaptive water quality management module, and an intelligent user interaction module; among them:

[0007] Multi-source data integration processing module: used to collect various data from meteorological stations, hydrological stations and ecological monitoring points, and pre-process and convert them into a unified format through data fusion technology. The various data include water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data;

[0008] Ecological impact prediction module: Based on the data processed by the multi-source data integration processing module, an ecological impact model is constructed to analyze the impact of different reservoir operation schemes on biological habitats, water quality and ecosystem health, and generate a prediction report;

[0009] Dispatching strategy optimization module: receives the forecast report from the ecological impact prediction module, uses the reinforcement learning algorithm to formulate the reservoir dispatching strategy, and dynamically adjusts the water release plan according to the ecological impact model and environmental protection standards;

[0010] Adaptive water quality management module: Based on the data provided by the multi-source data integration processing module and the prediction results of the ecological impact prediction module, the outflow and regulation strategy of the reservoir are automatically adjusted through an adaptive algorithm;

[0011] Intelligent User Interaction Module: Used to provide a human-machine interface, enabling operators to view the data and results of each module in real time, including multi-source data, ecological impact prediction, scheduling strategies and water quality adjustment plans.

[0012] Furthermore, the multi-source data integration processing module includes a data collection unit, a data preprocessing unit, a data fusion unit and a format conversion unit; wherein:

[0013] Data collection unit: used to collect specific data from meteorological stations, hydrological stations and ecological monitoring points through a sensor network at regular intervals, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data. The sensor network includes water temperature and dissolved oxygen sensors, pH value sensors, rainfall sensors, water level and flow sensors and water quality indicator sensors. The meteorological data is obtained through meteorological stations installed around the reservoir. All sensors transmit data to the central processing server in real time through a wireless communication network;

[0014] Data preprocessing unit: Receives data transmitted by the data collection unit and performs preliminary cleaning operations. Data cleaning includes removing outliers, filling missing data, and correcting time series errors. Removing outliers involves identifying and deleting erroneous data through statistical analysis and historical data comparison. Filling missing data involves using interpolation methods to complete missing data. Correcting time series errors involves time alignment of asynchronous data to ensure that all data has consistent timestamps.

[0015] Data fusion unit: fuses the pre-processed multi-source data and uses a weighted average algorithm to synthesize data from different sources. The weighted average algorithm assigns different weights according to the credibility and importance of each data source and calculates the optimal value of each data point, thereby optimizing the accuracy and consistency of the data;

[0016] Format conversion unit: converts the fused data into a standard format, including JSON or XML.

[0017] Furthermore, the data fusion unit includes:

[0018] Data standardization: Standardize data from different sources to eliminate the impact of different data scales; the standardization formula is: Among them, X i is the original data of the ith data source, μ i is the mean of the data from the ith data source, σ i is the standard deviation of the data from the ith data source, X i ′ is the standardized data of the i-th data source;

[0019] Determine the weight of data sources: Assign weights according to the credibility and importance of each data source. If there are n data sources, the weight of each data source is w i , and the following constraints are met:

[0020] Weighted data calculation: Apply the weighted average algorithm to the standardized data for synthesis processing and calculate the weighted data value. The calculation formula for weighted average is: Among them, X w is the weighted data value, w i is the weight of the i-th data source, X i ′ is the standardized data of the i-th data source;

[0021] De-standardization: De-standardize the weighted data to restore it to the original data scale. The de-standardization formula is: X f =X w ·σ+μ, where X f is the final fused data value, X w is the weighted data value, σ is the average of the standard deviations of all data sources, and μ is the average of the means of all data sources.

[0022] Furthermore, the ecological impact prediction module includes a data input unit, a model building unit, an impact analysis unit and a report generation unit; wherein:

[0023] Data input unit: receives standardized data input from the multi-source data integration processing module, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data, and passes the data to the model building unit;

[0024] Model building unit: Based on the received data, the particle swarm optimization algorithm is used to build an ecological impact model, and the optimal solution is found by continuously adjusting the model parameters to simulate the impact of reservoir operation on biological habitats, water quality and ecosystem health;

[0025] Impact Analysis Unit: Use the constructed ecological impact model to analyze the specific impacts of different reservoir operation plans on the ecosystem. By simulating each operation plan, evaluate its potential impact on biological habitats, water quality changes and ecosystem health, and generate analysis data;

[0026] Report generation unit: Based on the data provided by the impact analysis unit, an ecological impact prediction report is generated. The report includes the evaluation results of each scheduling plan and the specific impact on each ecological indicator.

[0027] Furthermore, the model building unit includes:

[0028] Step 1: Initialize the particle swarm, including setting the initial position and velocity of the particles, where each particle represents a combination of ecological impact model parameters. The initialization formula is:

[0029] x i (0) = x min +(x max -x min )·rand();

[0030] v i (0) = v min +(v max -v min )·rand(); where x i (0) and v i (0) are the initial position and velocity of the ith particle, x min and x max are the minimum and maximum values ​​of the position, respectively, v min and v max are the minimum and maximum values ​​of the speed respectively, and rand() is a random number generation function;

[0031] Step 2: Evaluate the fitness of each particle according to the objective function of the ecological impact model. The fitness function is used to indicate the quality of the model parameters. The fitness calculation formula is: f i =F(x i ), where f i is the fitness value of the ith particle, F(x i ) is the objective function of the ecological impact model;

[0032] Step 3: According to the optimal position p experienced by the particle itself i And the global optimal position g, update the particle's speed and position, the update formula is:

[0033] v i (t+1)=w·v i (t)+c1·r1·(pi -x i (t))+c2·r2·(gx i (t));

[0034] x i (t+1)=x i (t)+v i (t+1); where v i (t+1) and x i (t+1) are the velocity and position of the ith particle at time t+1, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers;

[0035] Step 4: Evaluate the fitness of each particle again and update the global optimal position. If the fitness of the current particle is better than its historical optimal fitness, update the historical optimal position of the particle; if the fitness of the current particle is better than the global optimal fitness, update the global optimal position;

[0036] Step 5: Repeat steps 2 to 4 until the preset number of iterations is reached or the fitness function converges to the target value. The final global optimal position g is the constructed ecological impact model parameter combination.

[0037] Furthermore, the impact analysis unit specifically includes:

[0038] Operation plan input: input different reservoir operation plans, each plan includes specific water release plan and operation parameters;

[0039] Model simulation operation: Use the ecological impact model to simulate each operation plan. Based on the input operation plan and pre-processed multi-source data, the model dynamically simulates the reservoir ecosystem, including changes in ecological parameters such as water temperature, pH value, dissolved oxygen, rainfall, water level, flow and water quality indicators;

[0040] Ecological parameter monitoring: During the simulation operation, the changes in ecological parameters are monitored in real time, and the changes in biological habitats, water quality and overall ecosystem health status under each scheduling scheme are recorded; the monitoring content includes the area of ​​fish habitats, the compliance rate of water quality indicators and the biodiversity index of the ecosystem;

[0041] Impact assessment: Based on the monitored changes in ecological parameters, the ecological impact of each scheduling option is quantitatively assessed, including the impact on biological habitats, the degree of water quality change, and the combined impact on the overall ecosystem health.

[0042] Furthermore, the scheduling strategy optimization module includes a prediction report receiving unit, a state representation unit, a strategy generation unit, a reward function definition unit, a strategy optimization unit and a dynamic adjustment unit; wherein:

[0043] Forecast report receiving unit: used to receive and analyze the forecast report generated by the ecological impact forecast module, including the impact data of different scheduling schemes on biological habitats, water quality and ecosystem health;

[0044] State representation unit: represents the current state of the reservoir and the data in the ecological impact prediction report as the input state of the reinforcement learning algorithm. The state representation includes the current water level, flow, rainfall, water quality indicators and predicted ecological impact of the reservoir.

[0045] Strategy generation unit: Generate the initial scheduling strategy using the Q learning algorithm. The specific formula is: Q(s, a)←Q(s, a)+α[r+γmaxQ(s′, a′)-Q(s, a)], where Q(s, a) is the value function of taking action a in state s, α is the learning rate, r is the reward value, γ is the discount factor, s′ is the new state after taking the action, and maxQ(s′, a′) is the optimal value function in the new state;

[0046] Reward function definition unit: defines the reward function r, which is used to evaluate the effect of the scheduling strategy. The reward function comprehensively considers water resource utilization efficiency, ecological impact and environmental protection standards. The specific formula is:

[0047] r = w1·U+w2·(1-E)+w3·C, where U is the water resource utilization rate, E is the ecological impact index, C is the compliance with environmental protection standards, and w1, w2, and w3 are weight coefficients;

[0048] Strategy optimization unit: Use reinforcement learning algorithm to iteratively optimize scheduling strategy, continuously update Q value function, and select the optimal scheduling strategy by simulating multiple scheduling schemes to maximize the reward function. The specific formula is:

[0049] π(s)=argmax a Q(s, a), where π(s) is the optimal policy in state s, argmax a represents the selection of action a that maximizes Q(s, a);

[0050] Dynamic adjustment unit: Dynamically adjust the water release plan based on current environmental data and optimized scheduling strategies.

[0051] Furthermore, the dynamic adjustment unit includes:

[0052] Real-time environmental data collection: Through the multi-source data integration processing module, the current environmental data is collected in real time, including water level, flow, rainfall, water temperature, pH value, dissolved oxygen and water quality indicators;

[0053] Status update: Update the current system status based on the real-time collected environmental data. The status update formula is: S t ={L t , Q t , R t , T t , P t , O t , W t}, where S t is the current state, L t is the current water level, Q t is the current flow rate, R t is the current rainfall, T t is the current water temperature, P t is the current pH value, O t is the current dissolved oxygen, W t is the current water quality index;

[0054] Strategy application: Based on the updated current state S t And the optimized dispatching strategy π(S), calculate the required water release amount and time, the strategy application formula is: A t =π(S t ), where A t The water release action that needs to be taken at present, including the water release amount and time;

[0055] Water release calculation: Based on the current reservoir capacity and ecological needs, calculate the actual water release volume V t , the calculation formula is: V t =E·(L t -L min ), where V t is the actual water release, E is the ecological demand coefficient, L t is the current water level, L min the minimum water level required to maintain a balanced ecosystem;

[0056] Adjustment of water release plan: Based on the calculated water release volume V t With the optimized dispatching strategy, the water release plan is adjusted dynamically to determine the specific time and amount of water release;

[0057] Implement water release: carry out specific water release operations according to the adjusted water release plan.

[0058] Furthermore, the adaptive water quality management module includes a data receiving unit, a state evaluation unit, an adjustment strategy generation unit and an execution unit; wherein:

[0059] Data receiving unit: used to receive real-time data from the multi-source data integration processing module, including water temperature, pH value, dissolved oxygen, water quality indicators, and the prediction results of the ecological impact prediction module;

[0060] Status evaluation unit: Based on the received real-time data and prediction results, the current water quality status of the reservoir is evaluated. The status evaluation includes judging whether the water quality meets the standards and whether the discharge flow of the reservoir needs to be adjusted. The specific evaluation formula is: Among them, ΔW is the leakage flow state deviation, W i,t is the discharge value of the i-th reservoir at the current time t, W i,s is the discharge value of the target original reservoir, n is the number of cascade reservoirs, when ΔW exceeds the preset threshold ΔW ttreshold When the discharge of cascade reservoirs needs to be adjusted;

[0061] Adjustment strategy generation unit: Generates outflow flow adjustment strategy based on adaptive algorithm; determines the water quality parameters that need to be adjusted, and calculates the optimal outflow flow and adjustment time according to the current water quality status and prediction results;

[0062] Execution unit: used to implement specific outbound traffic regulation operations according to the generated outbound traffic regulation strategy.

[0063] Furthermore, the intelligent user interaction module includes a data receiving unit, a data processing unit, a graphics generating unit, an interface generating unit and an interaction control unit; wherein:

[0064] Data receiving unit: used to receive data and results from the multi-source data integration processing module, ecological impact prediction module, scheduling strategy optimization module and adaptive water quality management module. The received data include water temperature, pH value, dissolved oxygen, water quality index, ecological impact prediction results, optimized scheduling strategy and water quality adjustment plan;

[0065] Data processing unit: processes and integrates the received data, unifies the data format of each module and corrects the timestamp;

[0066] Graphics generation unit: uses data visualization technology to convert processed data into visual graphics and charts, including real-time monitoring charts, trend charts, bar charts and pie charts;

[0067] Interface generation unit: Generates a human-computer interaction interface to display processed data and graphics. The interface includes multiple views and labels to display the data and results of each module.

[0068] Interactive control unit: used to provide user operation and control functions, so that operators can view the data and results of each module in real time. The operation and control functions include data query, screening, sorting, zooming in and out, so that operators can interact with the system by clicking and dragging.

[0069] Beneficial effects of the present invention:

[0070] The present invention, by integrating real-time data from multiple sources, including hydrological, meteorological and ecological monitoring data, the system can provide more accurate and real-time data analysis, thereby realizing instant evaluation and adjustment of the reservoir operation status. This highly integrated data processing capability enables reservoir managers to quickly respond to environmental changes, optimize water resource allocation, ensure maximum water utilization and maintain ecological balance.

[0071] The present invention automatically optimizes the scheduling strategy by adopting an advanced reinforcement learning algorithm, allowing the system to dynamically adjust the water release plan under changing environmental conditions. This adaptive adjustment capability not only improves the utilization efficiency of water resources, but also reduces the ecological damage and water quality problems caused by improper scheduling. Through continuous learning and optimization of intelligent algorithms, the system can ensure the safe operation of the reservoir while protecting the ecological system around the reservoir.

[0072] The present invention provides operators with an intuitive and easy-to-use operating interface through an intelligent user interaction module, enabling them to monitor and manage various indicators and status of the reservoir in real time. Through the means of graphics and data visualization, operators can more easily understand complex data and trends, thereby making more reasonable and effective decisions, which not only improves operational efficiency, but also enhances the accuracy of decision-making, and further improves the performance and reliability of the entire reservoir management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0074] Figure 1 A schematic diagram of an ecological dispatching system for cascade reservoirs according to an embodiment of the present invention;

[0075] Figure 2 Schematic diagram of an ecological impact prediction module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0076] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0077] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. The words "include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0078] like Figure 1-Figure 2 As shown in the figure, the ecological dispatching system of cascade reservoirs based on artificial intelligence includes a multi-source data integration processing module, an ecological impact prediction module, a dispatching strategy optimization module, an adaptive water quality management module and an intelligent user interaction module; among which:

[0079] Multi-source data integration processing module: used to collect various data from meteorological stations, hydrological stations and ecological monitoring points, and pre-process and convert them into a unified format through data fusion technology, providing high-quality and high-precision input data for subsequent modules. The various data include water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data;

[0080] Ecological impact prediction module: Based on the data processed by the multi-source data integration processing module, an ecological impact model is constructed to analyze the impact of different reservoir operation schemes on biological habitats, water quality and ecosystem health, and generate a prediction report;

[0081] Scheduling strategy optimization module: Receives the forecast report from the ecological impact prediction module, uses the reinforcement learning algorithm to formulate reservoir scheduling strategies, and dynamically adjusts the water release plan based on the ecological impact model and environmental protection standards to achieve optimal utilization of water resources and minimize adverse effects on the ecosystem;

[0082] Adaptive water quality management module: Based on the data provided by the multi-source data integration processing module and the prediction results of the ecological impact prediction module, the outflow flow and regulation strategy of the reservoir are automatically adjusted through an adaptive algorithm to optimize water quality management;

[0083] Intelligent User Interaction Module: Used to provide a human-machine interface, enabling operators to view the data and results of each module in real time, including multi-source data, ecological impact prediction, scheduling strategies and water quality adjustment plans.

[0084] The multi-source data integration processing module includes a data collection unit, a data preprocessing unit, a data fusion unit and a format conversion unit; wherein:

[0085] Data collection unit: used to collect specific data from meteorological stations, hydrological stations and ecological monitoring points through a sensor network at regular intervals, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data. The sensor network includes water temperature and dissolved oxygen sensors, pH value sensors, rainfall sensors, water level and flow sensors, and water quality indicator sensors; specifically, water temperature and dissolved oxygen sensors are installed at different depths of the reservoir to monitor vertical water quality changes; pH value sensors are installed at the water inlet and outlet; rainfall sensors are installed at major rainfall monitoring points around the reservoir; water level and flow sensors are installed at the main water inlet and outlet of the reservoir; water quality indicator sensors are distributed in areas that may be polluted, and meteorological data are obtained through meteorological stations installed around the reservoir. All sensors transmit data to the central processing server in real time through a wireless communication network;

[0086] Data preprocessing unit: Receives data transmitted by the data collection unit and performs preliminary cleaning operations. Data cleaning includes removing outliers, filling missing data, and correcting time series errors. Removing outliers involves identifying and deleting obvious erroneous data through statistical analysis and historical data comparison. Filling missing data involves using interpolation methods to complete missing data. Correcting time series errors involves time alignment of asynchronous data to ensure that all data has consistent timestamps.

[0087] Data fusion unit: fuses the pre-processed multi-source data and uses a weighted average algorithm to synthesize data from different sources. The weighted average algorithm assigns different weights according to the credibility and importance of each data source and calculates the optimal value of each data point, thereby optimizing the accuracy and consistency of the data;

[0088] Format conversion unit: According to the needs of the system and subsequent modules, the fused data is converted into standard formats, including JSON or XML, to ensure seamless connection with the ecological impact prediction module and other modules; through the precise configuration and coordinated operation of the above units, the multi-source data integration processing module can effectively provide the system with high-quality and high-precision input data, ensuring that the data input foundation of the entire cascade reservoir ecological scheduling system is solid and reliable.

[0089] The data fusion unit includes:

[0090] Data standardization: Standardize data from different sources to eliminate the impact of different data scales; the standardization formula is: Among them, X i is the original data of the ith data source, μ i is the mean of the data from the ith data source, σ i is the standard deviation of the data from the ith data source, Xi ′ is the standardized data of the i-th data source;

[0091] Determine the weight of data sources: Assign weights according to the credibility and importance of each data source. If there are n data sources, the weight of each data source is w i , and the following constraints are met:

[0092] Weighted data calculation: Apply the weighted average algorithm to the standardized data for synthesis processing and calculate the weighted data value. The calculation formula for weighted average is: Among them, X w is the weighted data value, w i is the weight of the i-th data source, X i ′ is the standardized data of the i-th data source;

[0093] De-standardization: De-standardize the weighted data to restore it to the original data scale. The de-standardization formula is: X f =X w ·σ+μ, where X f is the final fused data value, X w is the weighted data value, σ is the average of the standard deviations of all data sources, and μ is the average of the means of all data sources; through detailed steps and formula descriptions, the data fusion unit uses a weighted average algorithm to effectively integrate multiple data from different data sources and calculate the optimal value of each data point. This method can improve the accuracy and consistency of the data, ensure high-quality data support for subsequent ecological impact prediction and scheduling strategy optimization, and thus enhance the overall performance and reliability of the system.

[0094] The ecological impact prediction module includes a data input unit, a model building unit, an impact analysis unit, and a report generation unit; among which:

[0095] Data input unit: receives standardized data input from the multi-source data integration processing module, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data, and passes the data to the model building unit;

[0096] Model building unit: Based on the received data, the particle swarm optimization algorithm (PSO) is used to build an ecological impact model, and the optimal solution is found by continuously adjusting the model parameters to simulate the impact of reservoir operation on biological habitats, water quality and ecosystem health. The model building process includes parameter initialization, fitness evaluation, particle position update and global optimal solution search;

[0097] Impact Analysis Unit: Analyze the specific impacts of different reservoir operation plans on the ecosystem using the constructed ecological impact model. By simulating each operation plan, evaluate its potential impact on biological habitats, water quality changes and ecosystem health, and generate detailed analysis data;

[0098] Report generation unit: Generates an ecological impact forecast report based on the data provided by the impact analysis unit. The report includes the evaluation results of each scheduling plan and the specific impact on each ecological indicator. It also ensures that the format and content of the report meet the decision-making needs and is convenient for use by the scheduling strategy optimization module.

[0099] The construction of ecological impact models in the model building unit includes:

[0100] Step 1: Initialize the particle swarm, including setting the initial position and velocity of the particles, where each particle represents a combination of ecological impact model parameters. The initialization formula is:

[0101] x i (0) = x min +(x max -x min )·rand();

[0102] v i (0) = v min +(v max -v min )·rand(); where x i (0) and v i (0) are the initial position and velocity of the ith particle, x min and x max are the minimum and maximum values ​​of the position, respectively, v min and v max are the minimum and maximum values ​​of the speed respectively, and rand() is a random number generation function;

[0103] Step 2: Evaluate the fitness of each particle according to the objective function of the ecological impact model. The fitness function is used to indicate the quality of the model parameters. The fitness calculation formula is: f i =F(x i ), where f i is the fitness value of the ith particle, F(x i ) is the objective function of the ecological impact model;

[0104] Step 3: According to the optimal position p experienced by the particle itself i And the global optimal position g, update the particle's speed and position, the update formula is:

[0105] v i (t+1)=w·vi (t)+c1·r1·(p i -x i (t))+c2·r2·(gx i (t));

[0106] x i (t+1)=x i (t)+v i (t+1); where v i (t+1) and x i (t+1) are the velocity and position of the ith particle at time t+1, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers;

[0107] Step 4: Evaluate the fitness of each particle again and update the global optimal position. If the fitness of the current particle is better than its historical optimal fitness, update the historical optimal position of the particle; if the fitness of the current particle is better than the global optimal fitness, update the global optimal position;

[0108] Step 5: Repeat steps 2 to 4 until the preset number of iterations is reached or the fitness function converges to the target value. The global optimal position g finally obtained is the constructed ecological impact model parameter combination. Through the above steps, the model construction unit can efficiently search and determine the optimal parameter combination of the ecological impact model. This method can significantly improve the prediction accuracy and adaptability of the model, and provide a solid data foundation for subsequent ecological impact analysis and scheduling strategy optimization, thereby effectively improving the overall performance and decision-making support capabilities of the cascade reservoir ecological scheduling system.

[0109] The impact analysis unit specifically includes:

[0110] Scheduling scheme input: input different reservoir scheduling schemes, each of which includes specific water release plans and operating parameters. These scheme data are provided by the scheduling strategy optimization module and serve as input to the impact analysis unit;

[0111] Model simulation operation: Use the ecological impact model to simulate each operation plan. Based on the input operation plan and pre-processed multi-source data, the model dynamically simulates the reservoir ecosystem, including changes in ecological parameters such as water temperature, pH value, dissolved oxygen, rainfall, water level, flow and water quality indicators;

[0112] Ecological parameter monitoring: During the simulation operation, the changes in ecological parameters are monitored in real time, and the changes in biological habitats, water quality and overall ecosystem health status under each scheduling scheme are recorded; the monitoring content includes the area of ​​fish habitats, the compliance rate of water quality indicators and the biodiversity index of the ecosystem;

[0113] The specific calculation formula is as follows:

[0114] Habitat area change rate: Among them, ΔA is the habitat area change rate, A t is the habitat area under the current scheduling scheme, and A0 is the habitat area under the baseline scheme;

[0115] Water quality compliance rate: Among them, C w is the water quality compliance rate, N c The number of water quality monitoring times that meet the standards, N t is the total number of water quality monitoring times;

[0116] Biodiversity Index: Among them, D is the biodiversity index, A is the total number of species, and p i is the relative abundance of the i-th species;

[0117] Impact assessment: Based on the monitored changes in ecological parameters, the ecological impact of each scheduling scheme is quantitatively assessed, including the impact on biological habitats, the degree of water quality changes, and the comprehensive impact on the overall ecosystem health; specific ecological indicators are used for quantification, such as the change rate of habitat area, the rate of water quality compliance, and the change of biodiversity index; the specific formula is as follows: Comprehensive ecological impact index: E = w1·ΔA+w2·C w +w3·D, where E is the comprehensive ecological impact index, and w1, w2, and w3 are weight coefficients of different ecological parameters.

[0118] The scheduling strategy optimization module includes a prediction report receiving unit, a state representation unit, a strategy generation unit, a reward function definition unit, a strategy optimization unit, and a dynamic adjustment unit; wherein:

[0119] Forecast report receiving unit: used to receive and analyze the forecast report generated by the ecological impact forecast module, including the impact data of different scheduling schemes on biological habitats, water quality and ecosystem health;

[0120] State representation unit: represents the current state of the reservoir and the data in the ecological impact prediction report as the input state of the reinforcement learning algorithm. The state representation includes the current water level, flow, rainfall, water quality indicators and predicted ecological impact of the reservoir.

[0121] Strategy generation unit: Use the Q-Learning Algorithm to generate the initial scheduling strategy. The specific formula is: Q(S, a)←Q(s, a)+α[r+γmaxQ(s′, a′)-Q(s, a)], where Q(s, a) is the value function of taking action a in state s, α is the learning rate, r is the reward value, γ is the discount factor, s′ is the new state after taking the action, and maxQ(s′, a′) is the optimal value function in the new state;

[0122] Reward function definition unit: defines the reward function r, which is used to evaluate the effect of the scheduling strategy. The reward function comprehensively considers water resource utilization efficiency, ecological impact and environmental protection standards. The specific formula is:

[0123] r = w1·U+w2·(1-E)+w3·C, where U is the water resource utilization rate, E is the ecological impact index, C is the compliance with environmental protection standards, and w1, w2, and w3 are weight coefficients;

[0124] Strategy optimization unit: Use reinforcement learning algorithm to iteratively optimize scheduling strategy, continuously update Q value function, and select the optimal scheduling strategy by simulating multiple scheduling schemes to maximize the reward function. The specific formula is:

[0125] π(s)=argmax a Q(s, a), where π(s) is the optimal policy in state s, argmax a represents the selection of action a that maximizes Q(s, a);

[0126] Dynamic adjustment unit: Dynamically adjust the water release plan according to the current environmental data and the optimized scheduling strategy to ensure the optimal utilization of water resources and the protection of the ecosystem; through the combination of the above units, the scheduling strategy optimization module can efficiently formulate and optimize the reservoir scheduling strategy to ensure the optimal utilization of water resources and the protection of the ecosystem. The optimized scheduling strategy provided by this module helps to improve the efficiency and sustainability of water resources management, and significantly enhance the overall performance and decision-making support capabilities of the cascade reservoir ecological scheduling system.

[0127] The dynamic adjustment unit includes:

[0128] Real-time environmental data collection: Through the multi-source data integration processing module, the current environmental data, including water level, flow, rainfall, water temperature, pH value, dissolved oxygen and water quality indicators, are collected in real time, and these data are passed as input to the dynamic adjustment unit;

[0129] Status update: Update the current system status based on the real-time collected environmental data. The status update formula is: S t ={L t , Q t, R t , T t , P t , O t , W t}, where S t is the current state, L t is the current water level, Q t is the current flow rate, R t is the current rainfall, T t is the current water temperature, P t is the current pH value, O t is the current dissolved oxygen, W t is the current water quality index;

[0130] Strategy application: Based on the updated current state S t And the optimized dispatching strategy π(S), calculate the required water release amount and time, the strategy application formula is: A t =π(S t ), where A t The water release action that needs to be taken at present, including the water release amount and time;

[0131] Water release calculation: Based on the current reservoir capacity and ecological needs, calculate the actual water release volume V t , the calculation formula is: V t =E·(L t -L min ), where V t is the actual water release volume, E is the ecological demand coefficient (pre-set according to specific ecological needs), L t is the current water level, L min the minimum water level required to maintain a balanced ecosystem;

[0132] Adjustment of water release plan: Based on the calculated water release volume V t And the optimized scheduling strategy, dynamically adjust the water release plan, determine the specific water release time and water release amount; the water release plan adjustment formula is as follows: t ={T start , T end , V t}, where P t For the water release plan, T start is the start time of water release, T end is the end time of water release, V t is the amount of water released;

[0133] Implementation of water release: According to the adjusted water release plan, implement specific water release operations to ensure that the water release process meets the requirements of the scheduling strategy and ensures the rational use of water resources and the protection of the ecosystem; through the above steps, the dynamic adjustment unit can dynamically adjust the water release plan according to the current environmental data and the optimized scheduling strategy. This method ensures the optimal use of water resources, meets ecological needs, effectively protects the ecosystem, and significantly improves the overall performance and decision-making support capabilities of the cascade reservoir ecological scheduling system.

[0134] The adaptive water quality management module includes a data receiving unit, a state evaluation unit, an adjustment strategy generation unit and an execution unit; wherein:

[0135] Data receiving unit: used to receive real-time data from the multi-source data integration processing module, including water temperature, pH value, dissolved oxygen, water quality indicators, and the prediction results of the ecological impact prediction module. These data provide basic input for the adaptive algorithm;

[0136] Status evaluation unit: Based on the received real-time data and prediction results, the current water quality status of the reservoir is evaluated. The status evaluation includes judging whether the water quality meets the standards and whether the discharge flow of the reservoir needs to be adjusted. The specific evaluation formula is: Among them, ΔW is the leakage flow state deviation, W i,t is the discharge value of the i-th reservoir at the current time t, W i,s is the discharge value of the target original reservoir, n is the number of cascade reservoirs, when ΔW exceeds the preset threshold ΔW threshold When the discharge of cascade reservoirs needs to be adjusted;

[0137] Adjustment strategy generation unit: Generates outflow flow adjustment strategy based on adaptive algorithm; determines the water quality parameters that need to be adjusted, and calculates the optimal outflow flow and adjustment time according to the current water quality status and prediction results;

[0138] Outbound flow calculation: Among them, Q t is the outbound flow at time t, K p ,K i and K d are proportional, integral and differential coefficients respectively, T is the target water quality parameter value, C t is the current water quality parameter value, C i is the water quality parameter value at the previous moment;

[0139] Adjustment time calculation: Assume that the adjustment time interval ΔT is a fixed value, then recalculate the outflow flow and perform adjustment in each time interval. The formula is: T next =T current +ΔT, where T next is the next adjustment time, Tcurrent is the current time, ΔT is the preset time interval;

[0140] Execution unit: used to implement specific outflow flow regulation operations according to the generated outflow flow regulation strategy; through the design of the above units, the adaptive water quality management module can automatically adjust the outflow flow and regulation strategy of the reservoir through an adaptive algorithm based on real-time data and prediction results. This method ensures that the water quality always meets the ecological and human water use standards, effectively protects the ecosystem, and significantly improves the overall performance and decision-making support capabilities of the cascade reservoir ecological scheduling system.

[0141] The intelligent user interaction module includes a data receiving unit, a data processing unit, a graphics generating unit, an interface generating unit and an interaction control unit; wherein:

[0142] Data receiving unit: used to receive data and results from the multi-source data integration processing module, ecological impact prediction module, scheduling strategy optimization module and adaptive water quality management module. The received data include water temperature, pH value, dissolved oxygen, water quality index, ecological impact prediction results, optimized scheduling strategy and water quality adjustment plan;

[0143] Data processing unit: processes and integrates the received data to ensure the accuracy and consistency of the data before display. Specifically, the data format of each module is unified and the timestamp is corrected so that data from different sources can be compared and analyzed on the same interface;

[0144] Graphics generation unit: uses data visualization technology to convert processed data into visual graphics and charts, including real-time monitoring charts, trend charts, bar charts and pie charts;

[0145] Interface generation unit: Generates a human-computer interaction interface to display processed data and graphics. The interface includes multiple views and labels to display the data and results of each module. The interface design should be concise and clear to facilitate operators to quickly obtain key information;

[0146] Interactive control unit: used to provide user operation and control functions, allowing operators to view the data and results of each module in real time. The operation and control functions include data query, filtering, sorting, zooming in and out, allowing operators to interact with the system by clicking and dragging.

[0147] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The ecological dispatching system of cascade reservoirs based on artificial intelligence is characterized by: It includes multi-source data integration processing module, ecological impact prediction module, scheduling strategy optimization module, adaptive water quality management module and intelligent user interaction module; among which: Multi-source data integration processing module: used to collect various data from meteorological stations, hydrological stations and ecological monitoring points, and pre-process and convert them into a unified format through data fusion technology. The various data include water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data; Ecological impact prediction module: Based on the data processed by the multi-source data integration processing module, an ecological impact model is constructed to analyze the impact of different reservoir operation schemes on biological habitats, water quality and ecosystem health, and generate a prediction report; Dispatching strategy optimization module: receives the forecast report from the ecological impact prediction module, uses the reinforcement learning algorithm to formulate the reservoir dispatching strategy, and dynamically adjusts the water release plan according to the ecological impact model and environmental protection standards; Adaptive water quality management module: Based on the data provided by the multi-source data integration processing module and the prediction results of the ecological impact prediction module, the outflow and regulation strategy of the reservoir are automatically adjusted through an adaptive algorithm; Intelligent User Interaction Module: Used to provide a human-machine interface, enabling operators to view the data and results of each module in real time, including multi-source data, ecological impact prediction, scheduling strategies and water quality adjustment plans.

2. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 1 is characterized in that: The multi-source data integration processing module includes a data collection unit, a data preprocessing unit, a data fusion unit and a format conversion unit; wherein: Data collection unit: used to collect specific data from meteorological stations, hydrological stations and ecological monitoring points through a sensor network at regular intervals, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data. The sensor network includes water temperature and dissolved oxygen sensors, pH value sensors, rainfall sensors, water level and flow sensors and water quality indicator sensors. The meteorological data is obtained through meteorological stations installed around the reservoir. All sensors transmit data to the central processing server in real time through a wireless communication network; Data preprocessing unit: Receives data transmitted by the data collection unit and performs preliminary cleaning operations. Data cleaning includes removing outliers, filling missing data, and correcting time series errors. Removing outliers involves identifying and deleting erroneous data through statistical analysis and historical data comparison. Filling missing data involves using interpolation methods to complete missing data. Correcting time series errors involves time alignment of asynchronous data to ensure that all data has consistent timestamps. Data fusion unit: fuses the pre-processed multi-source data and uses a weighted average algorithm to synthesize data from different sources. The weighted average algorithm assigns different weights according to the credibility and importance of each data source and calculates the optimal value of each data point, thereby optimizing the accuracy and consistency of the data; Format conversion unit: converts the fused data into a standard format, including JSON or XML.

3. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 2 is characterized in that: The data fusion unit comprises: Data standardization: Standardize data from different sources to eliminate the impact of different data scales; the standardization formula is: Among them, X i is the original data of the ith data source, μ i is the mean of the data from the ith data source, σ i is the standard deviation of the data from the ith data source, X i ′ is the standardized data of the i-th data source; Determine the weight of data sources: Assign weights according to the credibility and importance of each data source. If there are n data sources, the weight of each data source is w i , and the following constraints are met: Weighted data calculation: Apply the weighted average algorithm to the standardized data for synthesis processing and calculate the weighted data value. The calculation formula for weighted average is: Among them, X w is the weighted data value, w i is the weight of the i-th data source, X i ′ is the standardized data of the i-th data source; De-standardization: De-standardize the weighted data to restore it to the original data scale. The de-standardization formula is: X f =X w ·σ+μ, where X f is the final fused data value, X w is the weighted data value, σ is the average of the standard deviations of all data sources, and μ is the average of the means of all data sources.

4. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 1 is characterized in that: The ecological impact prediction module includes a data input unit, a model building unit, an impact analysis unit and a report generation unit; wherein: Data input unit: receives standardized data input from the multi-source data integration processing module, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow, water quality indicators and meteorological data, and passes the data to the model building unit; Model building unit: Based on the received data, the particle swarm optimization algorithm is used to build an ecological impact model, and the optimal solution is found by continuously adjusting the model parameters to simulate the impact of reservoir operation on biological habitats, water quality and ecosystem health; Impact Analysis Unit: Use the constructed ecological impact model to analyze the specific impacts of different reservoir operation plans on the ecosystem. By simulating each operation plan, evaluate its potential impact on biological habitats, water quality changes and ecosystem health, and generate analysis data; Report generation unit: Based on the data provided by the impact analysis unit, an ecological impact prediction report is generated. The report includes the evaluation results of each scheduling plan and the specific impact on each ecological indicator.

5. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 4 is characterized in that: The model building unit comprises: Step 1: Initialize the particle swarm, including setting the initial position and velocity of the particles, where each particle represents a combination of ecological impact model parameters. The initialization formula is: x i (0)=x min +(x max -x min )·rand(); v i (0) = v min +(v max -v min )·rand(); where x i (0) and v i (0) are the initial position and velocity of the ith particle, x min and x max are the minimum and maximum values ​​of the position, respectively, v min and v max are the minimum and maximum values ​​of the speed respectively, and rand() is a random number generation function; Step 2: Evaluate the fitness of each particle according to the objective function of the ecological impact model. The fitness function is used to indicate the quality of the model parameters. The fitness calculation formula is: f i =F(x i ), where f i is the fitness value of the ith particle, F(x i ) is the objective function of the ecological impact model; Step 3: According to the optimal position p experienced by the particle itself i And the global optimal position g, update the particle's speed and position, the update formula is: v i (t+1)=w·v i (t)+c1·r1·(p i -x i (t))+c2·r2·(g-x i (t)); x i (t+1)=x i (t)+v i (t+1); where v i (t+1) and x i (t+1) are the velocity and position of the ith particle at time t+1, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers; Step 4: Evaluate the fitness of each particle again and update the global optimal position. If the fitness of the current particle is better than its historical optimal fitness, update the historical optimal position of the particle; if the fitness of the current particle is better than the global optimal fitness, update the global optimal position; Step 5: Repeat steps 2 to 4 until the preset number of iterations is reached or the fitness function converges to the target value. The final global optimal position g is the constructed ecological impact model parameter combination.

6. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 4 is characterized in that: The impact analysis unit specifically includes: Operation plan input: input different reservoir operation plans, each plan includes specific water release plan and operation parameters; Model simulation operation: Use the ecological impact model to simulate each operation plan. Based on the input operation plan and pre-processed multi-source data, the model dynamically simulates the reservoir ecosystem, including changes in ecological parameters such as water temperature, pH value, dissolved oxygen, rainfall, water level, flow and water quality indicators; Ecological parameter monitoring: During the simulation operation, the changes in ecological parameters are monitored in real time, and the changes in biological habitats, water quality and overall ecosystem health status under each scheduling scheme are recorded; the monitoring content includes the area of ​​fish habitats, the compliance rate of water quality indicators and the biodiversity index of the ecosystem; Impact assessment: Based on the monitored changes in ecological parameters, the ecological impact of each scheduling option is quantitatively assessed, including the impact on biological habitats, the degree of water quality change, and the combined impact on the overall ecosystem health.

7. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 1 is characterized in that: The scheduling strategy optimization module includes a prediction report receiving unit, a state representation unit, a strategy generation unit, a reward function definition unit, a strategy optimization unit and a dynamic adjustment unit; wherein: Forecast report receiving unit: used to receive and analyze the forecast report generated by the ecological impact forecast module, including the impact data of different scheduling schemes on biological habitats, water quality and ecosystem health; State representation unit: represents the current state of the reservoir and the data in the ecological impact prediction report as the input state of the reinforcement learning algorithm. The state representation includes the current water level, flow, rainfall, water quality indicators and predicted ecological impact of the reservoir. Strategy generation unit: Generate the initial scheduling strategy using the Q learning algorithm. The specific formula is: Q(s, a)←Q(s, a)+α[r+γmaxQ(s′, a′)-Q(s, a)], where Q(s, a) is the value function of taking action a in state s, α is the learning rate, r is the reward value, γ is the discount factor, s′ is the new state after taking the action, and maxQ(s′, a′) is the optimal value function in the new state; Reward function definition unit: defines the reward function r, which is used to evaluate the effect of the scheduling strategy. The reward function comprehensively considers water resource utilization efficiency, ecological impact and environmental protection standards. The specific formula is: r=w1·U+w2·(1-E)+w3·C, where U is the water resource utilization rate, E is the ecological impact index, C is the compliance with environmental protection standards, and w1, w2, and w3 are weight coefficients; Strategy optimization unit: Use reinforcement learning algorithm to iteratively optimize scheduling strategy, continuously update Q value function, and select the optimal scheduling strategy by simulating multiple scheduling schemes to maximize the reward function. The specific formula is: π(s)=argmax a Q(s, a), where π(s) is the optimal policy in state s, argmax a represents the selection of action a that maximizes Q(s, a); Dynamic adjustment unit: Dynamically adjust the water release plan based on current environmental data and optimized scheduling strategies.

8. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 7 is characterized in that: The dynamic adjustment unit comprises: Real-time environmental data collection: Through the multi-source data integration processing module, the current environmental data is collected in real time, including water level, flow, rainfall, water temperature, pH value, dissolved oxygen and water quality indicators; Status update: Update the current system status based on the real-time collected environmental data. The status update formula is: S t ={L t , Q t , R t , T t , P t , O t , W t }, where S t is the current state, L t is the current water level, Q t is the current flow rate, R t is the current rainfall, T t is the current water temperature, P t is the current pH value, O t is the current dissolved oxygen, W t is the current water quality index; Strategy application: Based on the updated current state S t And the optimized dispatching strategy π(S), calculate the required water release amount and time, the strategy application formula is: A t =π(S t ), where A t The water release action that needs to be taken at present, including the water release amount and time; Water release calculation: Based on the current reservoir capacity and ecological needs, calculate the actual water release volume V t , the calculation formula is: V t =E·(L t -L min ), where V t is the actual water release, E is the ecological demand coefficient, L t is the current water level, L min the minimum water level required to maintain a balanced ecosystem; Adjustment of water release plan: Based on the calculated water release volume V t With the optimized dispatching strategy, the water release plan is adjusted dynamically to determine the specific time and amount of water release; Implement water release: carry out specific water release operations according to the adjusted water release plan.

9. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 1 is characterized in that: The adaptive water quality management module includes a data receiving unit, a state evaluation unit, an adjustment strategy generation unit and an execution unit; wherein: Data receiving unit: used to receive real-time data from the multi-source data integration processing module, including water temperature, pH value, dissolved oxygen, water quality indicators, and the prediction results of the ecological impact prediction module; Status evaluation unit: Based on the received real-time data and prediction results, the current water quality status of the reservoir is evaluated. The status evaluation includes judging whether the water quality meets the standards and whether the discharge flow of the reservoir needs to be adjusted. The specific evaluation formula is: Among them, ΔW is the leakage flow state deviation, W i,t is the discharge value of the i-th reservoir at the current time t, W i,s is the discharge value of the target original reservoir, n is the number of cascade reservoirs, when ΔW exceeds the preset threshold ΔW threshold When the discharge of cascade reservoirs needs to be adjusted; Adjustment strategy generation unit: Generates outflow flow adjustment strategy based on adaptive algorithm; determines the water quality parameters that need to be adjusted, and calculates the optimal outflow flow and adjustment time according to the current water quality status and prediction results; Execution unit: used to implement specific outbound traffic regulation operations according to the generated outbound traffic regulation strategy.

10. The artificial intelligence-based cascade reservoir ecological dispatching system according to claim 1 is characterized in that: The intelligent user interaction module includes a data receiving unit, a data processing unit, a graphics generating unit, an interface generating unit and an interaction control unit; wherein: Data receiving unit: used to receive data and results from the multi-source data integration processing module, ecological impact prediction module, scheduling strategy optimization module and adaptive water quality management module. The received data include water temperature, pH value, dissolved oxygen, water quality index, ecological impact prediction results, optimized scheduling strategy and water quality adjustment plan; Data processing unit: processes and integrates the received data, unifies the data format of each module and corrects the timestamp; Graphics generation unit: uses data visualization technology to convert processed data into visual graphics and charts, including real-time monitoring charts, trend charts, bar charts and pie charts; Interface generation unit: Generates a human-computer interaction interface to display processed data and graphics. The interface includes multiple views and labels to display the data and results of each module. Interactive control unit: used to provide user operation and control functions, so that operators can view the data and results of each module in real time. The operation and control functions include data query, screening, sorting, zooming in and out, so that operators can interact with the system by clicking and dragging.

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