AI-based cascade reservoir ecological scheduling system

Abstract

This invention relates to the field of water resource management technology, specifically to an artificial intelligence-based cascade reservoir ecological scheduling system, comprising a multi-source data integration and processing module, an ecological impact prediction module, a scheduling strategy optimization module, an adaptive water quality management module, and an intelligent user interaction module. Specifically: the multi-source data integration and processing module collects various data from meteorological stations, hydrological stations, and ecological monitoring points; the ecological impact prediction module constructs an ecological impact model; the scheduling strategy optimization module formulates reservoir scheduling strategies; the adaptive water quality management module adjusts the reservoir's outflow and regulation strategies; and the intelligent user interaction module provides a human-machine interface. This invention, by integrating multi-source real-time data, applying reinforcement learning to optimize scheduling strategies, and providing an efficient user interface, significantly improves the real-time responsiveness and decision-making efficiency of reservoir management, while ensuring optimal utilization of water resources and protection of the ecological environment.

Description

Technical Field

[0001] This invention relates to the field of water resources management technology, and in particular to an artificial intelligence-based cascade reservoir ecological scheduling system. Background Technology

[0002] Cascade reservoir systems play a crucial role in achieving efficient water resource utilization 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 performance. Currently, most reservoir management systems rely on static scheduling schemes, which 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 shortcomings in data processing and user interaction, such as low data integration efficiency, unfriendly user interfaces, and an inability to provide sufficient decision support, thus limiting the real-time nature and effectiveness of dynamic optimization and strategy adjustment in reservoir management.

[0003] In view of these shortcomings of the prior art, the present invention aims to solve several technical problems in the cascade reservoir ecological scheduling system. First, the prior art has failed 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 type of 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 interface. Summary of the Invention

[0005] To achieve the above objectives, this invention provides an artificial intelligence-based cascade reservoir ecological scheduling system.

[0006] An AI-based cascade reservoir ecological scheduling system includes a multi-source data integration and processing module, an ecological impact prediction module, a scheduling strategy optimization module, an adaptive water quality management module, and an intelligent user interaction module; among which:

[0007] Multi-source data integration and processing module: used to collect various data from meteorological stations, hydrological stations and ecological monitoring points, and to preprocess 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 rate, water quality indicators and meteorological data;

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

[0009] The scheduling strategy optimization module receives the prediction report from the ecological impact prediction module, uses reinforcement learning algorithms to formulate reservoir scheduling strategies, and dynamically adjusts the water release plan based on 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 and processing module and the prediction results of the ecological impact prediction module, it automatically adjusts the reservoir's outflow and regulation strategy through an adaptive algorithm;

[0011] Intelligent User Interaction Module: This module provides a human-machine interface, enabling operators to view data and results from various modules in real time, including multi-source data, ecological impact predictions, scheduling strategies, and water quality adjustment plans.

[0012] Furthermore, the multi-source data integration and 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 at regular intervals through a sensor network, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow rate, 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 rate 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 from the data collection unit and performs preliminary cleaning operations, including outlier removal, missing data filling, and time series error correction. Outlier removal involves identifying and deleting erroneous data through statistical analysis and comparison with historical data. Missing data filling uses interpolation methods to complete the missing data. Time series error correction involves aligning asynchronous data to ensure that all data has a consistent timestamp.

[0015] Data fusion unit: It fuses the preprocessed multi-source data and uses a weighted average algorithm to synthesize the 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 for each data point, thereby optimizing the accuracy and consistency of the data.

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

[0017] Furthermore, the data fusion unit includes:

[0018] Data standardization: Standardizing data from different sources eliminates the impact of different data scales; the standardization formula is: Among them, X i For the original data from the i-th data source, μ i Let σ be the mean of the data from the i-th data source. i Let X be the standard deviation of the data from the i-th data source. i ′ represents the standardized data from the i-th data source;

[0019] Determine data source weights: Assign weights based on the credibility and importance of each data source. If there are n data sources, then the weight of each data source is w. i And it satisfies the following constraints:

[0020] Weighted data calculation: The standardized data is synthesized using a weighted average algorithm to calculate the weighted data values. The formula for calculating the weighted average is as follows: Among them, X w w represents the weighted data values. i Let X be the weight of the i-th data source. i ′ represents the standardized data from the i-th data source;

[0021] Destandardization: The weighted data is destandardized to restore it to its original scale. The destandardization formula is: X f =X w ·σ+μ, where X f X represents the final merged data value. w The values ​​are weighted, where 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 and processing module, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow rate, water quality indicators and meteorological data, and transmits the data to the model building unit;

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

[0025] Impact Analysis Unit: Using the constructed ecological impact model, this unit analyzes the specific impacts of different reservoir scheduling schemes on the ecosystem. By simulating each scheduling scheme, it assesses its potential impact on biological habitats, water quality changes, and ecosystem health, and generates analytical data.

[0026] Report generation unit: Based on the data provided by the impact analysis unit, it generates an ecological impact prediction report, which includes the evaluation results of each scheduling scheme 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. Each particle represents a combination of parameters for an ecological impact model. 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) represents the initial position and velocity of the i-th particle, respectively, x min and x max Let v be the minimum and maximum values ​​at the given positions. min and v max These represent 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 based on the objective function of the ecological impact model. The fitness function represents the quality of the model parameters, and the fitness calculation formula is: f i =F(x) i ), where f i Let F(x) be the fitness value of the i-th particle. i ) is the objective function of the ecological impact model;

[0032] Step 3: Based on the optimal position p experienced by the particle itself i Given the global optimal position g, update the particle's velocity and position using the following formula:

[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) represents the velocity and position of the i-th particle at time t+1, w is the inertial weight, c1 and c2 are learning factors, and r1 and r2 are random numbers;

[0035] Step 4: Re-evaluate the fitness of each particle and update the global optimal position. If the current particle's fitness is better than its historical optimal fitness, then update the particle's historical optimal position; if the current particle's fitness is better than the global optimal fitness, then 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 parameter combination of the constructed ecological impact model.

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

[0038] Input of scheduling scheme: Input different reservoir scheduling schemes, each scheme including specific water release plans and operating parameters;

[0039] Model simulation operation: The ecological impact model is used to simulate each scheduling scheme. Based on the input scheduling scheme and preprocessed 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 rate and water quality indicators.

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

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

[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] Prediction Report Receiving Unit: Used to receive and parse the prediction reports generated by the ecological impact prediction module, including data on the impact of different scheduling schemes on biological habitats, water quality and ecosystem health;

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

[0045] Policy generation unit: The initial scheduling policy is generated 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 action, and maxQ(s′,a′) is the optimal value function in the new state.

[0046] Reward Function Definition Unit: Define a reward function r to evaluate the effectiveness of the scheduling strategy. The reward function comprehensively considers water resource utilization efficiency, ecological impact, and environmental protection standards. The specific formula is as follows:

[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 of environmental protection standards, and w1, w2, and w3 are weighting coefficients.

[0048] Policy optimization unit: Iteratively optimizes the scheduling policy using reinforcement learning algorithms, continuously updates the Q-value function, and selects the optimal scheduling policy by simulating various scheduling schemes to maximize the reward function. The specific formula is as follows:

[0049] π(s)=argmax a Q(s, a), where π(s) is the optimal policy in state s, and argmax is... a This indicates choosing action 'a' that maximizes Q(s, a);

[0050] Dynamic adjustment unit: dynamically adjusts 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 acquisition: Through the multi-source data integration and processing module, current environmental data, including water level, flow rate, rainfall, water temperature, pH value, dissolved oxygen and water quality indicators, are collected in real time;

[0053] Status Update: Based on real-time collected environmental data, update the current system status. The status update formula is: S t ={L t Q t R t T t P t O t W t}, where S t As the current state, L t Q represents the current water level. t For the current traffic, R t T represents the current rainfall. t Given the current water temperature, P t The current pH value, O t For the current dissolved oxygen, W t Current water quality indicators;

[0054] Strategy application: Based on the updated current state S t Given the optimized scheduling strategy π(S), calculate the required water release volume and time. The strategy application formula is: A t =π(S) t ), where A t The water release action that needs to be taken now includes the amount of water released and the timing of the release;

[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 Where E is the actual water release volume, and L is the ecological demand coefficient. t The current water level is L. min The minimum water level required to maintain ecosystem balance;

[0056] Water release plan adjustment: based on the calculated water release volume V t With the optimized scheduling strategy, the water release plan is dynamically adjusted to determine the specific water release time and volume;

[0057] Implement water release: Implement 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 status assessment 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 and processing module, including water temperature, pH value, dissolved oxygen, water quality indicators, and prediction results from the ecological impact prediction module;

[0060] Status Assessment Unit: Based on received real-time data and prediction results, assesses the current water quality status of the reservoir. The status assessment includes determining whether the water quality meets standards and whether adjustments to the reservoir's discharge flow are necessary. The specific assessment formula is as follows: Where ΔW is the discharge flow state deviation, W i,t Let W be the discharge value of the i-th reservoir at time t. i,s Let ΔW be the target original reservoir discharge value, and n be the number of cascade reservoirs. When ΔW exceeds the preset threshold ΔW... ttreshold In such cases, it is necessary to adjust the discharge flow of the cascade reservoirs;

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

[0062] Execution unit: Used to implement specific outbound flow adjustment operations based on the generated outbound flow adjustment strategy.

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

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

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

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

[0067] Interface generation unit: Generates 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: Provides user operation and control functions, enabling operators to view data and results from 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 through click and drag operations.

[0069] The beneficial effects of this invention are:

[0070] This invention integrates real-time data from multiple sources, including hydrological, meteorological, and ecological monitoring data, enabling the system to provide more accurate and real-time data analysis. This allows for immediate assessment and adjustment of the reservoir's operational status. This highly integrated data processing capability enables reservoir managers to respond quickly to environmental changes, optimize water resource allocation, and ensure maximum water utilization and maintenance of ecological balance.

[0071] This invention employs advanced reinforcement learning algorithms to automatically optimize scheduling strategies, allowing the system to dynamically adjust water release plans under constantly changing environmental conditions. This adaptive adjustment capability not only improves the efficiency of water resource utilization but also reduces 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 also protecting the ecosystem surrounding the reservoir.

[0072] This invention provides operators with an intuitive and easy-to-use interface through an intelligent user interaction module, enabling them to monitor and manage various indicators and statuses of the reservoir in real time. Through graphical and data visualization, operators can more easily understand complex data and trends, thereby making more reasonable and effective decisions. This not only improves operational efficiency but also enhances the accuracy of decision-making, further improving the performance and reliability of the entire reservoir management system. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a schematic diagram of a cascade reservoir ecological scheduling system according to an embodiment of the present invention;

[0075] Figure 2 This is a schematic diagram of the ecological impact prediction module in an embodiment of the present invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0077] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0078] like Figures 1-2 As shown, the AI-based cascade reservoir ecological scheduling system includes a multi-source data integration and processing module, an ecological impact prediction module, a scheduling strategy optimization module, an adaptive water quality management module, and an intelligent user interaction module; among which:

[0079] Multi-source data integration and processing module: used to collect various data from meteorological stations, hydrological stations and ecological monitoring points, and preprocess and unify the format through data fusion technology to provide high-quality and high-precision input data for subsequent modules. Various data include water temperature, pH value, dissolved oxygen, rainfall, water level, flow rate, water quality indicators and meteorological data;

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

[0081] The scheduling strategy optimization module receives the prediction report from the ecological impact prediction module, uses reinforcement learning algorithms to formulate reservoir scheduling strategies, and dynamically adjusts the water release plan according to the ecological impact model and environmental protection standards to achieve optimal use of water resources and minimize adverse impacts on the ecosystem.

[0082] Adaptive Water Quality Management Module: Based on the data provided by the multi-source data integration and processing module and the prediction results of the ecological impact prediction module, the module automatically adjusts the reservoir's outflow and regulation strategy through adaptive algorithms to optimize water quality management.

[0083] Intelligent User Interaction Module: This module provides a human-machine interface, enabling operators to view data and results from various modules in real time, including multi-source data, ecological impact predictions, scheduling strategies, and water quality adjustment plans.

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

[0085] Data Collection Unit: This unit collects specific data periodically from meteorological stations, hydrological stations, and ecological monitoring points via a sensor network. This data includes water temperature, pH value, dissolved oxygen, rainfall, water level, flow rate, water quality indicators, and meteorological data. The sensor network comprises water temperature and dissolved oxygen sensors, pH sensors, rainfall sensors, water level and flow rate sensors, and water quality indicator sensors. Specifically, water temperature and dissolved oxygen sensors are installed at different depths within the reservoir to monitor vertical water quality changes; pH sensors are installed at the inlet and outlet; rainfall sensors are installed at key rainfall monitoring points around the reservoir; water level and flow rate sensors are installed at the main inlet and outlet of the reservoir; water quality indicator sensors are distributed in potentially polluted areas; and meteorological data is acquired through meteorological stations installed around the reservoir. All sensors transmit data in real-time to a central processing server via a wireless communication network.

[0086] Data preprocessing unit: Receives data from the data collection unit and performs preliminary cleaning operations, including outlier removal, missing data filling, and time series error correction. Outlier removal involves identifying and deleting obvious erroneous data through statistical analysis and comparison with historical data. Missing data filling uses interpolation methods to complete the missing data. Time series error correction ensures that all data has a consistent timestamp by aligning asynchronous data in time.

[0087] Data fusion unit: It fuses the preprocessed multi-source data and uses a weighted average algorithm to synthesize the 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 for each data point, thereby optimizing the accuracy and consistency of the data.

[0088] Format conversion unit: Based on the needs of the system and subsequent modules, the fused data is converted into standard formats, including JSON or XML, to ensure seamless integration 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 and processing module can effectively provide the system with high-quality and high-precision input data, ensuring a solid and reliable data input foundation for the entire cascade reservoir ecological scheduling system.

[0089] The data fusion unit includes:

[0090] Data standardization: Standardizing data from different sources eliminates the impact of different data scales; the standardization formula is: Among them, X i For the original data from the i-th data source, μ i Let σ be the mean of the data from the i-th data source. i Let X be the standard deviation of the data from the i-th data source.i ′ represents the standardized data from the i-th data source;

[0091] Determine data source weights: Assign weights based on the credibility and importance of each data source. If there are n data sources, then the weight of each data source is w. i And it satisfies the following constraints:

[0092] Weighted data calculation: The standardized data is synthesized using a weighted average algorithm to calculate the weighted data values. The formula for calculating the weighted average is as follows: Among them, X w w represents the weighted data values. i Let X be the weight of the i-th data source. i ′ represents the standardized data from the i-th data source;

[0093] Destandardization: The weighted data is destandardized to restore it to its original scale. The destandardization formula is: X f =X w ·σ+μ, where X f X represents the final merged data value. w The weighted data values ​​are represented by σ, which is the average of the standard deviations of all data sources, and μ, which is the average of the means of all data sources. Through detailed steps and formula descriptions, the data fusion unit effectively integrates multiple data from different data sources using a weighted average algorithm to calculate the optimal value for each data point. This method can improve the accuracy and consistency of the data, ensuring high-quality data support for subsequent ecological impact prediction and scheduling strategy optimization, thereby enhancing 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 and processing module, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow rate, water quality indicators and meteorological data, and transmits the data to the model building unit;

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

[0097] Impact Analysis Unit: Using the constructed ecological impact model, this unit analyzes the specific impacts of different reservoir scheduling schemes on the ecosystem. By simulating each scheduling scheme, it assesses its potential impact on biological habitats, water quality changes, and ecosystem health, and generates detailed analytical data.

[0098] Report generation unit: Based on the data provided by the impact analysis unit, it generates an ecological impact prediction report. The report includes the evaluation results of each scheduling scheme and the specific impact on each ecological indicator, and ensures that the format and content of the report meet the decision-making needs and are easy for the scheduling strategy optimization module to use.

[0099] The model building unit includes constructing ecological impact models, including:

[0100] Step 1: Initialize the particle swarm, including setting the initial position and velocity of the particles. Each particle represents a combination of parameters for an ecological impact model. 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) represents the initial position and velocity of the i-th particle, respectively, x min and x max Let v be the minimum and maximum values ​​at the given positions. min and v max These represent 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 based on the objective function of the ecological impact model. The fitness function represents the quality of the model parameters, and the fitness calculation formula is: f i =F(x) i ), where f i Let F(x) be the fitness value of the i-th particle. i ) is the objective function of the ecological impact model;

[0104] Step 3: Based on the optimal position p experienced by the particle itself i Given the global optimal position g, update the particle's velocity and position using the following formula:

[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) represents the velocity and position of the i-th particle at time t+1, w is the inertial weight, c1 and c2 are learning factors, and r1 and r2 are random numbers;

[0107] Step 4: Re-evaluate the fitness of each particle and update the global optimal position. If the current particle's fitness is better than its historical optimal fitness, then update the particle's historical optimal position; if the current particle's fitness is better than the global optimal fitness, then 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 final globally optimal position g is the parameter combination of the constructed ecological impact model. Through the above steps, the model building unit can efficiently search for and determine the optimal parameter combination of the ecological impact model. This method can significantly improve the prediction accuracy and adaptability of the model, providing a solid data foundation for subsequent ecological impact analysis and scheduling strategy optimization, thereby effectively improving the overall performance and decision support capability of the cascade reservoir ecological scheduling system.

[0109] The impact analysis unit specifically includes:

[0110] Input of scheduling scheme: Input different reservoir scheduling schemes. Each scheme includes a specific water release plan 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: The ecological impact model is used to simulate each scheduling scheme. Based on the input scheduling scheme and preprocessed 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 rate and water quality indicators.

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

[0113] The specific formula is calculated as follows:

[0114] Habitat area change rate: Where ΔA is the rate of change of habitat area, A t A0 represents the habitat area under the current scheduling plan, while A0 represents the habitat area under the baseline plan.

[0115] Water quality compliance rate: Among them, C w For water quality compliance rate, N c To meet the water quality standards, N t This refers to the total number of water quality monitoring sessions.

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

[0117] Impact Assessment: Based on monitored changes in ecological parameters, a quantitative assessment of the ecological impact of each scheduling scheme is conducted. The assessment includes the impact on habitat, the degree of water quality change, and the comprehensive impact on the overall ecosystem health. Specifically, ecological indicators are used for quantification, such as changes in habitat area change rate, water quality compliance rate, and 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, w3 are the 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] Prediction Report Receiving Unit: Used to receive and parse the prediction reports generated by the ecological impact prediction module, including data on the impact of different scheduling schemes on biological habitats, water quality and ecosystem health;

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

[0121] Policy generation unit: The initial scheduling policy is generated 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 action, and maxQ(s′,a′) is the optimal value function in the new state.

[0122] Reward Function Definition Unit: Define a reward function r to evaluate the effectiveness of the scheduling strategy. The reward function comprehensively considers water resource utilization efficiency, ecological impact, and environmental protection standards. The specific formula is as follows:

[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 of environmental protection standards, and w1, w2, and w3 are weighting coefficients.

[0124] Policy optimization unit: Iteratively optimizes the scheduling policy using reinforcement learning algorithms, continuously updates the Q-value function, and selects the optimal scheduling policy by simulating various scheduling schemes to maximize the reward function. The specific formula is as follows:

[0125] π(s)=argmax a Q(s, a), where π(s) is the optimal policy in state s, and argmax is... a This indicates choosing action 'a' that maximizes Q(s, a);

[0126] Dynamic Adjustment Unit: Based on current environmental data and optimized scheduling strategies, the water release plan is dynamically adjusted to ensure optimal utilization of water resources and protection of the ecosystem. Through the combination of the above units, the scheduling strategy optimization module can efficiently formulate and optimize reservoir scheduling strategies to ensure optimal utilization of water resources and protection of the ecosystem. The optimized scheduling strategies provided by this module help improve the efficiency and sustainability of water resource management and significantly enhance the overall performance and decision support capabilities of the cascade reservoir ecological scheduling system.

[0127] The dynamic adjustment unit includes:

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

[0129] Status Update: Based on real-time collected environmental data, update the current system status. The status update formula is: S t ={L t Q tR t T t P t O t W t}, where S t As the current state, L t Q represents the current water level. t For the current traffic, R t T represents the current rainfall. t Given the current water temperature, P t The current pH value, O t For the current dissolved oxygen, W t Current water quality indicators;

[0130] Strategy application: Based on the updated current state S t Given the optimized scheduling strategy π(S), calculate the required water release volume and time. The strategy application formula is: A t =π(S) t ), where A t The water release action that needs to be taken now includes the amount of water released and the timing of the release;

[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 E represents the actual water release volume, E represents the ecological demand coefficient (pre-set based on specific ecological needs), and L represents the actual water release volume. t The current water level is L. min The minimum water level required to maintain ecosystem balance;

[0132] Water release plan adjustment: based on the calculated water release volume V t Based on the optimized scheduling strategy, the water release plan is dynamically adjusted to determine the specific release time and volume; the water release plan adjustment formula is as follows: P t ={T start T end V t}, where P t For the water release plan, T start T is the start time for water release. end V is the end time of water release. t This refers to the amount of water discharged.

[0133] Implementing water release: Based on the adjusted water release plan, specific water release operations are carried out to ensure that the water release process meets the requirements of the scheduling strategy, guaranteeing 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 based on 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 support capabilities of the cascade reservoir ecological scheduling system.

[0134] The adaptive water quality management module includes a data receiving unit, a status assessment unit, an adjustment strategy generation unit, and an execution unit; among which:

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

[0136] Status Assessment Unit: Based on received real-time data and prediction results, assesses the current water quality status of the reservoir. The status assessment includes determining whether the water quality meets standards and whether adjustments to the reservoir's discharge flow are necessary. The specific assessment formula is as follows: Where ΔW is the discharge flow state deviation, W i,t Let W be the discharge value of the i-th reservoir at time t. i,s Let ΔW be the target original reservoir discharge value, and n be the number of cascade reservoirs. When ΔW exceeds the preset threshold ΔW... threshold In such cases, it is necessary to adjust the discharge flow of the cascade reservoirs;

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

[0138] Outbound flow calculation: Among them, Q t Let K be the outbound flow rate at time t. p ,K i and K d These are the proportional, integral, and differential coefficients, respectively; T is the target water quality parameter value; and C... t C represents the current water quality parameter value. i These are the water quality parameter values ​​from a previous time point;

[0139] Adjustment time calculation: Assuming the adjustment time interval ΔT is a constant, the outflow rate is recalculated and adjustment is performed within each time interval. The formula is: T next =T current +ΔT, where T next For the next adjustment time, Tcurrent ΔT represents the current time, and ΔT represents the preset time interval.

[0140] Execution Unit: Used to implement specific outflow regulation operations based on the generated outflow regulation strategy. Through the design of the above units, the adaptive water quality management module can automatically adjust the reservoir's outflow and regulation strategy based on real-time data and prediction results through adaptive algorithms. This method ensures that the water quality always meets ecological and human water use standards, effectively protects the ecosystem, and significantly improves the overall performance and decision 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 generation unit, an interface generation unit, and an interaction control unit; wherein:

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

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

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

[0145] Interface generation unit: Generates 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 simple and clear, so that operators can quickly obtain key information.

[0146] Interactive control unit: Provides user operation and control functions, enabling operators to view data and results from each module in real time. Operation and control functions include data query, filtering, sorting, zooming in and out, allowing operators to interact with the system through click and drag operations.

[0147] This 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 this invention should be included within the scope of protection of this invention.

Claims

1. An artificial intelligence-based cascade reservoir ecological scheduling system, characterized in that, It includes a multi-source data integration and processing module, an ecological impact prediction module, a scheduling strategy optimization module, an adaptive water quality management module, and an intelligent user interaction module; among which: Multi-source data integration and processing module: used to collect various data from meteorological stations, hydrological stations and ecological monitoring points, and to preprocess 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 rate, water quality indicators and meteorological data; Ecological Impact Prediction Module: Based on the data processed by the multi-source data integration and processing module, an ecological impact model is constructed to analyze the impact of different reservoir scheduling schemes on biological habitats, water quality and ecosystem health, and to generate a prediction report; The scheduling strategy optimization module receives the prediction report from the ecological impact prediction module, uses reinforcement learning algorithms to formulate reservoir scheduling strategies, and dynamically adjusts the water release plan based on the ecological impact model and environmental protection standards. Adaptive water quality management module: Based on the data provided by the multi-source data integration and processing module and the prediction results of the ecological impact prediction module, it automatically adjusts the reservoir's outflow and regulation strategy through an adaptive algorithm; Intelligent User Interaction Module: This module provides a human-machine interface, enabling operators to view data and results from various modules in real time, including multi-source data, ecological impact predictions, scheduling strategies, and water quality adjustment plans.

2. The cascade reservoir ecological scheduling system based on artificial intelligence according to claim 1, characterized in that, The multi-source data integration and 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 at regular intervals through a sensor network, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow rate, 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 rate 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 from the data collection unit and performs preliminary cleaning operations, including outlier removal, missing data filling, and time series error correction. Outlier removal involves identifying and deleting erroneous data through statistical analysis and comparison with historical data. Missing data filling uses interpolation methods to complete the missing data. Time series error correction involves aligning asynchronous data to ensure that all data has a consistent timestamp. Data fusion unit: It fuses the preprocessed multi-source data and uses a weighted average algorithm to synthesize the 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 for each data point, thereby optimizing the accuracy and consistency of the data. Format conversion unit: Converts the merged data into a standard format, including JSON or XML.

3. The cascade reservoir ecological scheduling system based on artificial intelligence according to claim 2, characterized in that, The data fusion unit includes: Data standardization: Standardizing data from different sources eliminates the impact of different data scales; the standardization formula is: Among them, X i For the original data from the i-th data source, μ i Let σ be the mean of the data from the i-th data source. i Let X be the standard deviation of the data from the i-th data source. i ′ represents the standardized data from the i-th data source; Determine data source weights: Assign weights based on the credibility and importance of each data source. If there are n data sources, then the weight of each data source is w. i And it satisfies the following constraints: Weighted data calculation: The standardized data is synthesized using a weighted average algorithm to calculate the weighted data values. The formula for calculating the weighted average is as follows: Among them, X w w represents the weighted data values. i Let X be the weight of the i-th data source. i ′ represents the standardized data from the i-th data source; Destandardization: The weighted data is destandardized to restore it to its original scale. The destandardization formula is: X f =X w ·σ+μ, where X f X represents the final merged data value. w The values ​​are weighted, where 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 cascade reservoir ecological scheduling system based on artificial intelligence according to claim 1, 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 and processing module, including water temperature, pH value, dissolved oxygen, rainfall, water level, flow rate, water quality indicators and meteorological data, and transmits the data to the model building unit; Model building unit: Based on the received data, an ecological impact model is built using the particle swarm optimization algorithm, and the optimal solution is found by continuously adjusting the model parameters to simulate the impact of reservoir scheduling on biological habitats, water quality and ecosystem health; Impact Analysis Unit: Using the constructed ecological impact model, this unit analyzes the specific impacts of different reservoir scheduling schemes on the ecosystem. By simulating each scheduling scheme, it assesses its potential impact on biological habitats, water quality changes, and ecosystem health, and generates analytical data. Report generation unit: Based on the data provided by the impact analysis unit, it generates an ecological impact prediction report, which includes the evaluation results of each scheduling scheme and the specific impact on each ecological indicator.

5. The cascade reservoir ecological scheduling system based on artificial intelligence according to claim 4, characterized in that, The model building unit includes: Step 1: Initialize the particle swarm, including setting the initial position and velocity of the particles. Each particle represents a combination of parameters for an ecological impact model. 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) represents the initial position and velocity of the i-th particle, respectively, x min and x max Let v be the minimum and maximum values ​​at the given positions. min and v max These represent 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 based on the objective function of the ecological impact model. The fitness function represents the quality of the model parameters, and the fitness calculation formula is: f i =F(x) i ), where f i Let F(x) be the fitness value of the i-th particle. i ) is the objective function of the ecological impact model; Step 3: Based on the optimal position p experienced by the particle itself i Given the global optimal position g, update the particle's velocity and position using the following formula: 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) represents the velocity and position of the i-th particle at time t+1, w is the inertial weight, c1 and c2 are learning factors, and r1 and r2 are random numbers; Step 4: Re-evaluate the fitness of each particle and update the global optimal position. If the current particle's fitness is better than its historical optimal fitness, then update the particle's historical optimal position; if the current particle's fitness is better than the global optimal fitness, then 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 parameter combination of the constructed ecological impact model.

6. The cascade reservoir ecological scheduling system based on artificial intelligence according to claim 4, characterized in that, The impact analysis unit specifically includes: Input of scheduling scheme: Input different reservoir scheduling schemes, each scheme including specific water release plans and operating parameters; Model simulation operation: The ecological impact model is used to simulate each scheduling scheme. Based on the input scheduling scheme and preprocessed 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 rate and water quality indicators. Ecological parameter monitoring: During the simulation operation, changes in ecological parameters are monitored in real time, and changes in biological habitats, water quality, and overall ecosystem health status are recorded under each scheduling scheme; the monitoring content includes fish habitat area, water quality compliance rate, and ecosystem biodiversity index; Impact assessment: Based on the monitored changes in ecological parameters, the ecological impact of each scheduling scheme is quantitatively assessed. The assessment includes the impact on biological habitats, the degree of water quality change, and the comprehensive impact on the overall health of the ecosystem.

7. The cascade reservoir ecological scheduling system based on artificial intelligence according to claim 1, 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: Prediction Report Receiving Unit: Used to receive and parse the prediction reports generated by the ecological impact prediction module, including data on the impact of different scheduling schemes on biological habitats, water quality and ecosystem health; State representation unit: The current state of the reservoir and the data in the ecological impact prediction report are represented as the input state of the reinforcement learning algorithm. The state representation includes the current water level, flow rate, rainfall, water quality indicators and predicted ecological impact of the reservoir. Policy generation unit: The initial scheduling policy is generated 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 action, and maxQ(s′,a′) is the optimal value function in the new state. Reward Function Definition Unit: Define a reward function r to evaluate the effectiveness of the scheduling strategy. The reward function comprehensively considers water resource utilization efficiency, ecological impact, and environmental protection standards. The specific formula is as follows: 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 of environmental protection standards, and w1, w2, w3 are weighting coefficients. Policy optimization unit: Iteratively optimizes the scheduling policy using reinforcement learning algorithms, continuously updates the Q-value function, and selects the optimal scheduling policy by simulating various scheduling schemes to maximize the reward function. The specific formula is as follows: π(s)=argmax a Q(s, a), where π(s) is the optimal policy in state s, and argmax is... a This indicates choosing action 'a' that maximizes Q(s, a); Dynamic adjustment unit: dynamically adjusts the water release plan based on current environmental data and optimized scheduling strategies.

8. The cascade reservoir ecological scheduling system based on artificial intelligence according to claim 7, characterized in that, The dynamic adjustment unit includes: Real-time environmental data acquisition: Through the multi-source data integration and processing module, current environmental data, including water level, flow rate, rainfall, water temperature, pH value, dissolved oxygen and water quality indicators, are collected in real time; Status Update: Based on real-time collected environmental data, update the current system status. The status update formula is: S t ={L t Q t R t T t P t O t W t }, where S t As the current state, L t Q represents the current water level. t For the current traffic, R t T represents the current rainfall. t Given the current water temperature, P t The current pH value, O t For the current dissolved oxygen, W t Current water quality indicators; Strategy application: Based on the updated current state S t Given the optimized scheduling strategy π(S), calculate the required water release volume and time. The strategy application formula is: A t =π(S) t ), where A t The water release action that needs to be taken now includes the amount of water released and the timing of the release; 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 Where E is the actual water release volume, and L is the ecological demand coefficient. t The current water level is L. min The minimum water level required to maintain ecosystem balance; Water release plan adjustment: based on the calculated water release volume V t With the optimized scheduling strategy, the water release plan is dynamically adjusted to determine the specific water release time and volume; Implement water release: Implement specific water release operations according to the adjusted water release plan.

9. The cascade reservoir ecological scheduling system based on artificial intelligence according to claim 1, characterized in that, The adaptive water quality management module includes a data receiving unit, a status assessment 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 and processing module, including water temperature, pH value, dissolved oxygen, water quality indicators, and prediction results from the ecological impact prediction module; Status Assessment Unit: Based on received real-time data and prediction results, assesses the current water quality status of the reservoir. The status assessment includes determining whether the water quality meets standards and whether adjustments to the reservoir's discharge flow are necessary. The specific assessment formula is as follows: Where ΔW is the discharge flow state deviation, W i,t Let W be the discharge value of the i-th reservoir at time t. i,s Let ΔW be the target original reservoir discharge value, and n be the number of cascade reservoirs. When ΔW exceeds the preset threshold ΔW... threshold In such cases, it is necessary to adjust the discharge flow of the cascade reservoirs; Adjustment strategy generation unit: Generates outflow adjustment strategy based on adaptive algorithm; determines the water quality parameters that need to be adjusted, and calculates the optimal outflow and adjustment time based on the current water quality status and prediction results; Execution unit: Used to implement specific outbound flow adjustment operations based on the generated outbound flow adjustment strategy.

10. The cascade reservoir ecological scheduling system based on artificial intelligence according to claim 1, characterized in that, The intelligent user interaction module includes a data receiving unit, a data processing unit, a graphics generation unit, an interface generation unit, and an interaction control unit; wherein: Data receiving unit: Used to receive data and results from the multi-source data integration and processing module, the ecological impact prediction module, the scheduling strategy optimization module and the adaptive water quality management module. The received data includes water temperature, pH value, dissolved oxygen, water quality indicators, ecological impact prediction results, optimized scheduling strategies and water quality adjustment plans. Data processing unit: processes and integrates the received data, unifies the data format of each module, and corrects the timestamps; Graphics generation unit: Utilizes data visualization technology to transform processed data into visual graphics and charts, including real-time monitoring charts, trend charts, bar charts, and pie charts; Interface generation unit: Generates 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: Provides user operation and control functions, enabling operators to view data and results from 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 through click and drag operations.

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