Intelligent scheduling and compensation benefit optimization method for cascade hydropower station group facing ecological flow guarantee

Abstract

The application discloses a cascade hydropower station group intelligent scheduling and compensation benefit optimization method for ecological flow guarantee, and relates to the technical field of hydraulic engineering management. The application can accurately guarantee the ecological flow demand of the key section of the river basin, maintain the integrity of the aquatic habitat and the biodiversity, effectively solve the problem that the ecological flow is squeezed and difficult to quantify in the traditional cascade scheduling, optimize the power generation efficiency of the cascade hydropower station group through intelligent scheduling, balance the ecological protection cost and the hydropower development benefit by using the compensation mechanism, quantify the optimization effect by using the three-dimensional evaluation system, reduce the contradiction and conflict of the stakeholders, realize the win-win of ecological protection and economic development, and ensure that the method is adapted to different river basin characteristics and scheduling scenes through the whole-process simulation and parameter optimization of the typical river basin case in the model verification and iteration module. The system management module guarantees the efficient coordination of the user authority, data security and module interaction, and improves the operability and long-term operation reliability of the method.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project management technology, specifically to a method for intelligent scheduling and compensation benefit optimization of cascade hydropower station groups for ecological flow protection. Background Technology

[0002] With the large-scale development of cascade hydropower stations in my country's river basins, how to achieve efficient utilization of hydropower resources while ensuring the health of downstream river ecosystems has become a core challenge for integrated river basin management.

[0003] The existing cascade hydropower station group dispatching technology has the following main drawbacks: The scheduling objectives are often singular, focusing primarily on maximizing power generation efficiency while neglecting the dynamic demands of downstream ecological flow. Static control modes with fixed flow thresholds are frequently employed, making it difficult to adapt to the differentiated needs of river ecosystems under different seasons and hydrological cycles. This can easily lead to ecological problems such as river flow interruption and habitat destruction, highlighting the problem of singular scheduling objectives. Furthermore, each power station often formulates its own scheduling strategy, lacking the support of intelligent collaborative algorithms based on a basin-wide perspective. This results in low water resource allocation efficiency and an inability to achieve global optimization of ecological flow protection and hydropower utilization, indicating insufficient cascade coordination. The lack of a scientific ecological value quantification model leads to a low match between ecological protection investment and returns, hindering the effective mobilization of power station operators, local governments, and ecological protection stakeholders to participate in ecological flow protection. This restricts the sustainability of the ecological and economic synergy of cascade hydropower station groups and results in an imperfect compensation mechanism. Therefore, this invention provides an intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow protection. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for intelligent scheduling and compensation benefit optimization of cascade hydropower station groups for ecological flow protection, in order to solve the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent scheduling and compensation benefit optimization of cascade hydropower station groups for ecological flow assurance, comprising the following steps: S1. Multi-source data acquisition and preprocessing: Collect real-time operation data, hydrological and meteorological data, and ecological monitoring data of the cascade hydropower station group in the basin, and clean and normalize the data to build a standardized basin database. S2. Construct a dynamic ecological flow demand model: Based on the seasonal characteristics, hydrological cycle and key ecological processes of the watershed ecosystem, a dynamic ecological flow demand prediction model is established using machine learning algorithms to output the ecological flow target range under different time periods and hydrological conditions, replacing the traditional fixed threshold control mode. S3. Training of Multi-Objective Cascade Coordinated Scheduling Model: With ecological flow guarantee, power generation benefit maximization, and water resource utilization efficiency as objective functions, a deep reinforcement learning algorithm is introduced to construct a coordinated scheduling model for a cascade hydropower station group. The model is trained and optimized by simulating different scheduling scenarios to generate a globally optimal set of cascade scheduling strategies. S4. Execute real-time collaborative scheduling and feedback: Based on the cloud-edge collaborative architecture, the optimized scheduling strategy is sent to the control systems of each power station to realize real-time data interaction and scheduling command coordination among cascade power stations; and a scheduling effect feedback mechanism is established to monitor the downstream ecological flow compliance, power generation revenue and water resource utilization efficiency in real time, and dynamically adjust scheduling parameters. S5. Construct an ecological compensation benefit optimization mechanism: Use the conditional valuation method combined with the ecosystem service value accounting model to quantify the ecological benefits brought by ecological flow guarantee; based on the quantitative results, design a diversified compensation mechanism, including the proportion of power generation revenue to be extracted into the ecological compensation fund, government ecological subsidies, and horizontal compensation between upstream and downstream areas of the basin, and establish a linkage mechanism between ecological contribution and benefit distribution to stimulate the enthusiasm of all stakeholders to participate in ecological flow guarantee. S6. Model Validation and Iterative Optimization: A typical cascade hydropower station group in a river basin is selected for case validation. The ecological flow compliance rate, power generation benefits and the operation effect of the compensation mechanism before and after optimization are compared and analyzed. Based on the validation results, the model parameters and compensation mechanism are iteratively optimized to ensure the practicality and stability of the method.

[0006] Preferably, the multi-source data acquisition and preprocessing specifically includes the following steps: S11. Multi-source data classification and acquisition: Real-time acquisition of dynamic parameters such as water level, output, reservoir capacity, and gate opening of each power station through intelligent sensors; Hydrological and meteorological data such as rainfall, runoff, water temperature, and wind speed are collected through hydrological and meteorological stations deployed within the basin; Ecological monitoring data such as ecological flow and water quality parameters are collected through fixed-point monitoring stations in the downstream river channel. S12. Data cleaning: Perform quality checks on the collected raw data, fill in missing values ​​and remove outliers, and deduplicate duplicate data. S13. Data normalization processing: Perform feature scale normalization processing on the cleaned data; S14. Construct a standardized watershed database: Store the cleaned and normalized multi-source data in a unified format, establish an index linking the data, and achieve efficient data retrieval and access.

[0007] Preferably, the construction of the dynamic ecological flow demand model specifically includes the following steps: S21. Correlation analysis between ecological factors and flow: Combining key ecological processes within the watershed, analyze the correlation between various indicators in ecological monitoring data and flow, and screen out key factors that have a significant impact on ecological flow demand. S22. Model input feature selection: Seasonal features, hydrological cycle parameters and key ecological factors are extracted from preprocessed hydrological and meteorological data and ecological monitoring data as model input features; S23. Machine learning model training and parameter optimization: Long short-term memory network or gradient boosting tree is used to train the model using historical ecological flow demand data and corresponding input features. Model parameters are optimized through grid search, cross-validation and other methods. S24. Model Validation and Dynamic Correction: The trained model is validated using an independent watershed ecological flow monitoring dataset to assess the deviation between the prediction results and actual ecological needs, and the model structure or parameters are adjusted based on feedback from watershed ecological experts. S25. Dynamic Ecological Flow Target Range Output: The model receives real-time hydrological and meteorological data and ecological monitoring data as input, and outputs the ecological flow target range for different time periods and under different hydrological conditions, replacing the traditional fixed threshold and providing a dynamic basis for the intelligent scheduling of cascade hydropower station groups.

[0008] Preferably, the training of the multi-objective tiered cooperative scheduling model specifically includes the following steps: S31. Constructing objective functions: Establish functions for maximizing power generation benefits and optimizing water resource utilization efficiency under ecological flow guarantee constraints, respectively. Set weight coefficients for each objective based on the actual needs of the basin, and establish a multi-objective collaborative optimization objective function system. S32. Constructing the state space and action space: The state space includes real-time water level, reservoir capacity, and power output of each cascade power station, real-time hydrological and meteorological data of the basin, dynamic ecological flow target range, and key ecological factors; the action space includes scheduling decision variables such as the adjustment range of gate opening of each power station, power output allocation scheme, and cross-power station water resource allocation. S33. Construct a reward function: Transform the ecological flow compliance rate, power generation revenue growth value, and water resource utilization rate into a comprehensive reward signal. Set a gradient penalty term for ecological flow deviating from the target range, and set positive rewards for power generation efficiency improvement and efficient water resource utilization to balance the conflict between multiple objectives. S34. Deep reinforcement learning model training: A tiered scheduling agent is constructed using a near-end policy optimization algorithm to simulate the scheduling process under scenarios such as high water season, normal water season, low water season and extreme hydrological events. The policy network parameters are updated through continuous interaction between the agent and the environment to improve the adaptability of scheduling decisions. S35. Model Validation and Strategy Iteration: The trained model is validated using historical watershed scheduling data and independently run datasets. The performance of the strategy in terms of ecological flow guarantee rate, power generation efficiency and water resource utilization efficiency is evaluated. The target weights or model structure are adjusted in combination with feedback from water conservancy experts to achieve iterative optimization of the strategy. S36. Generation of Global Optimal Scheduling Strategy Set: Integrate the optimal scheduling decisions under different hydrological scenarios and ecological needs to form a standardized tiered collaborative scheduling strategy library, which supports dispatchers to quickly match and call the optimal strategy based on the real-time watershed status.

[0009] Preferably, the execution of real-time collaborative scheduling and feedback specifically includes the following steps: S41. Issue cloud-edge collaborative scheduling instructions: Based on the global optimal scheduling strategy set stored in the cloud platform, combined with the real-time operation data uploaded by the edge nodes, the scheduling instructions are accurately issued to the local controllers of each power station through a low-latency communication protocol. S42. Real-time data interaction and status synchronization: Edge nodes continuously collect operational status data such as water level, reservoir capacity, and power output of each power station, as well as monitoring data such as downstream ecological flow and water quality. The data is then uploaded to the cloud platform through an encrypted channel. The cloud platform integrates and processes the multi-source real-time data and updates the basin status space synchronously. S43. Real-time monitoring of dispatching effectiveness: Real-time display of key indicators such as downstream ecological flow compliance rate, power generation of each power station, and water resource utilization efficiency; setting multi-level abnormal early warning thresholds, when ecological flow deviates from the target range, power generation revenue is lower than expected, or water resource waste exceeds the threshold, an automatic early warning is triggered and pushed to the dispatcher's terminal; S44. Parameter Feedback and Dynamic Adjustment: Based on real-time monitoring data, calculate evaluation indicators such as ecological flow guarantee rate, power generation efficiency improvement rate, and water resource utilization rate; if the indicators do not meet the preset targets, combine the online learning module of the deep reinforcement learning model to dynamically adjust the target weight coefficient or action space constraints in the scheduling strategy, generate the corrected scheduling instructions, and issue them for execution. S45. Generate scheduling logs and performance evaluation reports: Automatically record the issuance time, execution results, and indicator changes of each scheduling instruction to form a traceable scheduling log; periodically (e.g., weekly, monthly) generate scheduling performance evaluation reports to analyze the comprehensive performance of ecological flow guarantee, power generation benefits, and water resource utilization, providing data support for subsequent model optimization and strategy iteration.

[0010] Preferably, the construction of the ecological compensation benefit optimization mechanism specifically includes the following steps: S51. Quantitative assessment of ecological benefits: Based on the classification system of watershed ecosystem service value, the benefits brought by ecological flow guarantee are divided into four categories: supply services, regulation services, support services and cultural services; the market value method is used to calculate the value of water resource supply, the substitution cost method is used to calculate the value of water purification cost savings, the travel cost method is used to calculate the value of ecotourism, and the contingent valuation method is combined to obtain the public's willingness to pay for ecological protection, and the comprehensive results of ecological benefit quantification are formed. S52. Differentiated formulation of compensation standards: Based on the quantitative value of ecological benefits, the reduced power generation revenue of cascade power stations due to ecological dispatch, the differences in economic development level and ecological contribution between upstream and downstream areas of the basin, a tiered compensation standard shall be formulated; the basic compensation amount of upstream power stations shall be determined according to the ecological flow discharge and compliance rate, the horizontal compensation ratio of downstream beneficiary areas shall be calculated according to the ecological service value they enjoy, and the government subsidy standard shall be dynamically adjusted with reference to the regional ecological protection investment and performance evaluation results.

[0011] Preferably, the model verification and iterative optimization specifically include the following steps: S61. Case Basin Selection and Data Preparation: Select basins with typical ecological characteristics, complete cascade hydropower station layout, and sufficient data accumulation as verification objects; collect historical hydrological and meteorological data, cascade hydropower station operation data, ecological monitoring data, and existing dispatch records for the basin over the past 5-10 years to construct a verification dataset; simultaneously acquire auxiliary data such as basin ecological protection planning, power generation revenue statistics, and upstream and downstream economic development levels to support multi-dimensional verification; S62. Model full-process simulation and control group setup: The methodology of S1-S5 is applied to the case watershed, and multi-source data preprocessing, dynamic ecological flow demand model construction, multi-objective cascade collaborative scheduling model training, real-time scheduling execution and ecological compensation mechanism simulation are completed in sequence; a control group is set up to conduct parallel simulation and record the changes of key indicators of the two groups under different hydrological scenarios. S63. Multi-dimensional verification index evaluation: Construct a three-dimensional evaluation system of ecology, economy, and mechanism; use statistical methods such as paired-samples t-test and analytic hierarchy process to quantify the difference between the optimization group and the control group and verify the effectiveness of the method.

[0012] A smart scheduling and compensation benefit optimization system for cascade hydropower station groups aimed at ensuring ecological flow includes: Data acquisition and preprocessing module: used to acquire real-time operational data, hydrological and meteorological data, and ecological monitoring data of the cascade hydropower station group within the basin; Ecological Demand Analysis Module: This module combines the seasonal characteristics, hydrological cycles, and key ecological processes of the watershed ecosystem to analyze the correlation between various indicators and flow in ecological monitoring data in order to screen key influencing factors. The collaborative scheduling model training module is used to construct a multi-objective function system with the goals of ensuring ecological flow, maximizing power generation benefits, and optimizing water resource utilization efficiency. It defines a state space that includes the operating status of cascade power stations, hydrological and meteorological data, and dynamic ecological flow targets, as well as an action space that includes gate opening adjustment and output allocation. It designs a comprehensive reward function that integrates ecological achievement rewards and deviation penalties, and uses deep reinforcement learning algorithms to train the cascade collaborative scheduling agent to generate a set of globally optimal scheduling strategies under different hydrological scenarios. Real-time scheduling and feedback module: Based on cloud-edge collaborative architecture, the optimized scheduling strategy is distributed to the local controller of each power station to realize real-time data interaction and status synchronization between cascade power stations, monitor the downstream ecological flow compliance rate, power generation revenue and water resource utilization efficiency in real time, set multi-level abnormal early warning thresholds and trigger automatic early warning, and dynamically adjust scheduling parameters in combination with online learning module to generate traceable scheduling logs and periodic effect evaluation reports. Ecological compensation optimization module: It is used to quantify the value of ecosystem services such as supply services and regulation services brought about by ecological flow guarantee. It combines the power generation revenue reduced by ecological dispatch of cascade power stations, the economic development level of upstream and downstream areas and the difference in ecological contribution to formulate hierarchical compensation standards, design a diversified compensation mechanism such as the extraction of ecological compensation fund from power generation revenue, government ecological subsidies, and horizontal compensation between upstream and downstream areas of the basin, and establish a linkage mechanism between ecological contribution and revenue distribution. Model Validation and Iteration Module: This module is used to select typical watersheds to construct validation datasets, apply the methodology to the case watersheds for full-process simulation and set up control groups, construct a three-dimensional evaluation system of ecology, economy and mechanism, use statistical methods to quantify the optimization effect, and iteratively optimize the model parameters and compensation mechanism based on the validation results to ensure the practicality and stability of the method. System Management Module: Used to manage user permissions, securely encrypt data, record system logs, and coordinate interactions between modules. It supports model parameter configuration, policy library updates, and visual display of performance evaluation reports, ensuring stable system operation and efficient maintenance.

[0013] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent scheduling and compensation benefit optimization method for a cascade hydropower station group oriented towards ecological flow protection as described in any of the preceding claims.

[0014] An electronic device, comprising: Memory, used to store computer programs; A processor, when executing the computer program, implements the intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow protection as described in any of the above.

[0015] Beneficial effects Compared with the prior art, the present invention has the following advantages: This method can accurately guarantee the ecological flow demand of key sections in a watershed, maintain the integrity of aquatic habitats and biodiversity, and effectively solve the problems of squeezed ecological flow and difficulty in quantifying and satisfying it in traditional cascade scheduling. It optimizes the power generation efficiency of cascade hydropower station groups through intelligent scheduling, balances the costs of ecological protection and the benefits of hydropower development through compensation mechanisms, and quantifies the optimization effect through a three-dimensional evaluation system, reducing conflicts among stakeholders and achieving a win-win situation for ecological protection and economic development. The model verification and iteration module ensures that the method is adaptable to different watershed characteristics and scheduling scenarios through full-process simulation and parameter optimization of typical watershed cases. The system management module ensures efficient coordination of user permissions, data security and module interaction, improving the operability and long-term operational reliability of the method. Attached Figure Description

[0016] Figure 1 This is a flowchart of the process of this invention; Figure 2 This is a flowchart of the multi-source data acquisition and preprocessing process in this invention; Figure 3 This is a flowchart illustrating the process of constructing a dynamic ecological flow demand model in this invention. Figure 4 This is a flowchart illustrating the training process of the multi-objective tiered collaborative scheduling model in this invention. Figure 5 This is a flowchart illustrating the process of performing real-time collaborative scheduling and feedback in this invention; Figure 6 This is a flowchart illustrating the process of constructing an ecological compensation benefit optimization mechanism in this invention. Figure 7 This is a flowchart of the model verification and iterative optimization process in this invention; Figure 8 This is the system architecture diagram of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1-7 A method for intelligent scheduling and compensation benefit optimization of cascade hydropower station groups for ecological flow protection includes the following steps: S1. Multi-source data acquisition and preprocessing: Collect real-time operation data, hydrological and meteorological data, and ecological monitoring data of the cascade hydropower station group in the basin, and clean and normalize the data to build a standardized basin database. S2. Construct a dynamic ecological flow demand model: Based on the seasonal characteristics, hydrological cycle and key ecological processes of the watershed ecosystem, a dynamic ecological flow demand prediction model is established using machine learning algorithms to output the ecological flow target range under different time periods and hydrological conditions, replacing the traditional fixed threshold control mode. S3. Training of Multi-Objective Cascade Coordinated Scheduling Model: With ecological flow guarantee, power generation benefit maximization, and water resource utilization efficiency as objective functions, a deep reinforcement learning algorithm is introduced to construct a coordinated scheduling model for a cascade hydropower station group. The model is trained and optimized by simulating different scheduling scenarios to generate a globally optimal set of cascade scheduling strategies. S4. Execute real-time collaborative scheduling and feedback: Based on the cloud-edge collaborative architecture, the optimized scheduling strategy is sent to the control systems of each power station to realize real-time data interaction and scheduling command coordination among cascade power stations; and a scheduling effect feedback mechanism is established to monitor the downstream ecological flow compliance, power generation revenue and water resource utilization efficiency in real time, and dynamically adjust scheduling parameters. S5. Construct an ecological compensation benefit optimization mechanism: Use the conditional valuation method combined with the ecosystem service value accounting model to quantify the ecological benefits brought by ecological flow guarantee; based on the quantitative results, design a diversified compensation mechanism, including the proportion of power generation revenue to be extracted into the ecological compensation fund, government ecological subsidies, and horizontal compensation between upstream and downstream areas of the basin, and establish a linkage mechanism between ecological contribution and benefit distribution to stimulate the enthusiasm of all stakeholders to participate in ecological flow guarantee. S6. Model Validation and Iterative Optimization: A typical cascade hydropower station group in a river basin is selected for case validation. The ecological flow compliance rate, power generation benefits and the operation effect of the compensation mechanism before and after optimization are compared and analyzed. Based on the validation results, the model parameters and compensation mechanism are iteratively optimized to ensure the practicality and stability of the method.

[0019] Specifically, multi-source data acquisition and preprocessing includes the following steps: S11. Multi-source data classification and acquisition: Real-time acquisition of dynamic parameters such as water level, output, reservoir capacity, and gate opening of each power station through intelligent sensors; Hydrological and meteorological data such as rainfall, runoff, water temperature, and wind speed are collected through hydrological and meteorological stations deployed within the basin; Ecological monitoring data such as ecological flow and water quality parameters are collected through fixed-point monitoring stations in the downstream river channel. S12. Data cleaning: Perform quality checks on the collected raw data, fill in missing values ​​and remove outliers, and deduplicate duplicate data. S13. Data normalization processing: Perform feature scale normalization processing on the cleaned data; S14. Construct a standardized watershed database: Store the cleaned and normalized multi-source data in a unified format, establish an index linking the data, and achieve efficient data retrieval and access.

[0020] The above-described methods and steps enable multi-source data acquisition and preprocessing to provide high-quality, standardized data source support for subsequent model building and scheduling decisions. Categorized acquisition ensures comprehensive data coverage, while data cleaning and normalization effectively eliminate noise, missing values, and format differences in the original data. The constructed standardized watershed database achieves unified storage and efficient correlation retrieval of multi-source data, laying a solid data foundation for accurate prediction of dynamic ecological flow demand models, training and optimization of multi-objective tiered collaborative scheduling models, and effective operation of real-time scheduling feedback mechanisms. This ensures the scientific validity and feasibility of the entire intelligent scheduling and compensation benefit optimization method.

[0021] Specifically, constructing a dynamic ecological flow demand model includes the following steps: S21. Correlation analysis between ecological factors and flow: Combining key ecological processes within the watershed, analyze the correlation between various indicators in ecological monitoring data and flow, and screen out key factors that have a significant impact on ecological flow demand. S22. Model input feature selection: Seasonal features, hydrological cycle parameters and key ecological factors are extracted from preprocessed hydrological and meteorological data and ecological monitoring data as model input features; S23. Machine learning model training and parameter optimization: Long short-term memory network or gradient boosting tree is used to train the model using historical ecological flow demand data and corresponding input features. Model parameters are optimized through grid search, cross-validation and other methods. S24. Model Validation and Dynamic Correction: The trained model is validated using an independent watershed ecological flow monitoring dataset to assess the deviation between the prediction results and actual ecological needs, and the model structure or parameters are adjusted based on feedback from watershed ecological experts. S25. Dynamic Ecological Flow Target Range Output: The model receives real-time hydrological and meteorological data and ecological monitoring data as input, and outputs the ecological flow target range for different time periods and under different hydrological conditions, replacing the traditional fixed threshold and providing a dynamic basis for the intelligent scheduling of cascade hydropower station groups.

[0022] The above-described methods and steps enable the construction of a dynamic ecological flow demand model that overcomes the limitations of traditional fixed ecological flow thresholds, achieving dynamic and accurate prediction of ecological flow demand. Key influencing factors are screened through ecological factor correlation analysis to ensure the relevance and effectiveness of the model input; advanced machine learning algorithms are used to train and optimize the model, improving the accuracy and reliability of the prediction results; and dynamic corrections are made based on expert feedback, further enhancing the model's adaptability to the actual needs of the watershed ecosystem.

[0023] Specifically, the training of the multi-objective tiered cooperative scheduling model includes the following steps: S31. Constructing objective functions: Establish functions for maximizing power generation benefits and optimizing water resource utilization efficiency under ecological flow guarantee constraints, respectively. Set weight coefficients for each objective based on the actual needs of the basin, and establish a multi-objective collaborative optimization objective function system. S32. Constructing the state space and action space: The state space includes real-time water level, reservoir capacity, and power output of each cascade power station, real-time hydrological and meteorological data of the basin, dynamic ecological flow target range, and key ecological factors; the action space includes scheduling decision variables such as the adjustment range of gate opening of each power station, power output allocation scheme, and cross-power station water resource allocation. S33. Construct a reward function: Transform the ecological flow compliance rate, power generation revenue growth value, and water resource utilization rate into a comprehensive reward signal. Set a gradient penalty term for ecological flow deviating from the target range, and set positive rewards for power generation efficiency improvement and efficient water resource utilization to balance the conflict between multiple objectives. S34. Deep reinforcement learning model training: A tiered scheduling agent is constructed using a near-end policy optimization algorithm to simulate the scheduling process under scenarios such as high water season, normal water season, low water season and extreme hydrological events. The policy network parameters are updated through continuous interaction between the agent and the environment to improve the adaptability of scheduling decisions. S35. Model Validation and Strategy Iteration: The trained model is validated using historical watershed scheduling data and independently run datasets. The performance of the strategy in terms of ecological flow guarantee rate, power generation efficiency and water resource utilization efficiency is evaluated. The target weights or model structure are adjusted in combination with feedback from water conservancy experts to achieve iterative optimization of the strategy. S36. Generation of Global Optimal Scheduling Strategy Set: Integrate the optimal scheduling decisions under different hydrological scenarios and ecological needs to form a standardized tiered collaborative scheduling strategy library, which supports dispatchers to quickly match and call the optimal strategy based on the real-time watershed status.

[0024] The above-described methods enable the multi-objective cascade collaborative scheduling model training to effectively balance the conflicts between ecological flow guarantee, power generation benefits, and water resource utilization efficiency. By constructing a comprehensive state space and action space, the model accurately captures the operational status and scheduling decision dimensions of the cascade hydropower station group. A rationally designed reward function guides the agent to make optimal decisions that take into account multiple objectives in complex scenarios. The application of deep reinforcement learning algorithms enhances the model's adaptability to different hydrological cycles and extreme events. The final generated global optimal scheduling strategy set provides a scientific and efficient decision-making basis for the real-time collaborative scheduling of cascade power stations, ensuring that power generation benefits and water resource utilization efficiency are maximized while meeting dynamic ecological flow demands, thus laying a core technological foundation for the sustainable operation of the cascade hydropower station group.

[0025] Specifically, performing real-time collaborative scheduling and providing feedback includes the following steps: S41. Issue cloud-edge collaborative scheduling instructions: Based on the global optimal scheduling strategy set stored in the cloud platform, combined with the real-time operation data uploaded by the edge nodes, the scheduling instructions are accurately issued to the local controllers of each power station through a low-latency communication protocol. S42. Real-time data interaction and status synchronization: Edge nodes continuously collect operational status data such as water level, reservoir capacity, and power output of each power station, as well as monitoring data such as downstream ecological flow and water quality. The data is then uploaded to the cloud platform through an encrypted channel. The cloud platform integrates and processes the multi-source real-time data and updates the basin status space synchronously. S43. Real-time monitoring of dispatching effectiveness: Real-time display of key indicators such as downstream ecological flow compliance rate, power generation of each power station, and water resource utilization efficiency; setting multi-level abnormal early warning thresholds, when ecological flow deviates from the target range, power generation revenue is lower than expected, or water resource waste exceeds the threshold, an automatic early warning is triggered and pushed to the dispatcher's terminal; S44. Parameter Feedback and Dynamic Adjustment: Based on real-time monitoring data, calculate evaluation indicators such as ecological flow guarantee rate, power generation efficiency improvement rate, and water resource utilization rate; if the indicators do not meet the preset targets, combine the online learning module of the deep reinforcement learning model to dynamically adjust the target weight coefficient or action space constraints in the scheduling strategy, generate the corrected scheduling instructions, and issue them for execution. S45. Generate scheduling logs and performance evaluation reports: Automatically record the issuance time, execution results, and indicator changes of each scheduling instruction to form a traceable scheduling log; periodically (e.g., weekly, monthly) generate scheduling performance evaluation reports to analyze the comprehensive performance of ecological flow guarantee, power generation benefits, and water resource utilization, providing data support for subsequent model optimization and strategy iteration.

[0026] The aforementioned methods and steps enable real-time collaborative scheduling and feedback, achieving real-time, collaborative, and dynamic closed-loop management of the cascade hydropower station group's scheduling. The cloud-edge collaborative architecture ensures low-latency and accurate issuance of scheduling commands and efficient interaction and synchronization of multi-source data. Real-time monitoring and multi-level anomaly early warning mechanisms can quickly detect issues such as ecological flow deviations, power generation revenue fluctuations, and water resource utilization anomalies. Parameter feedback and dynamic adjustment functions continuously optimize scheduling strategies through an online learning module, while scheduling logs and periodic evaluation reports provide traceable evidence for subsequent model iterations and strategy improvements. This process effectively connects model training with actual operation, ensuring that dynamic ecological flow demands are met in real time. Simultaneously, it maximizes power generation benefits and water resource utilization efficiency under the premise of ecological protection, promoting a dynamic balance between ecological protection and economic benefits for the cascade hydropower station group, and providing crucial operational support for the sustainable development of the basin.

[0027] Specifically, the construction of an ecological compensation benefit optimization mechanism includes the following steps: S51. Quantitative assessment of ecological benefits: Based on the classification system of watershed ecosystem service value, the benefits brought by ecological flow guarantee are divided into four categories: supply services, regulation services, support services and cultural services; the market value method is used to calculate the value of water resource supply, the substitution cost method is used to calculate the value of water purification cost savings, the travel cost method is used to calculate the value of ecotourism, and the contingent valuation method is combined to obtain the public's willingness to pay for ecological protection, and the comprehensive results of ecological benefit quantification are formed. S52. Differentiated formulation of compensation standards: Based on the quantitative value of ecological benefits, the reduced power generation revenue of cascade power stations due to ecological dispatch, the differences in economic development level and ecological contribution between upstream and downstream areas of the basin, a tiered compensation standard shall be formulated; the basic compensation amount of upstream power stations shall be determined according to the ecological flow discharge and compliance rate, the horizontal compensation ratio of downstream beneficiary areas shall be calculated according to the ecological service value they enjoy, and the government subsidy standard shall be dynamically adjusted with reference to the regional ecological protection investment and performance evaluation results.

[0028] The above-described methods and steps enable the construction of an ecological compensation benefit optimization mechanism to effectively address the problem of uneven distribution of benefits in ecological flow guarantee, providing a scientific and quantitative basis for ecological compensation. Through multi-dimensional classification and accounting of ecological benefits, the bottleneck of ambiguous benefits in traditional compensation is broken, establishing a clear correspondence between ecological contributions and economic compensation. Differentiated compensation standards fully consider the actual contributions and economic differences of cascade hydropower stations and upstream and downstream regions, ensuring reasonable compensation for upstream power stations' ecological scheduling while guiding downstream beneficiary regions to proactively assume ecological protection responsibilities. Simultaneously, a dynamically adjusted government subsidy mechanism further enhances the flexibility and adaptability of the compensation policy. This mechanism transforms the external benefits of ecological protection into quantifiable economic incentives, effectively stimulating the enthusiasm of various stakeholders to participate in ecological flow guarantee, promoting the coordinated progress of ecological protection and economic development within the basin, and providing a long-term guarantee for the sustainable operation and eco-friendly scheduling of cascade hydropower station groups.

[0029] Specifically, model validation and iterative optimization include the following steps: S61. Case Basin Selection and Data Preparation: Select basins with typical ecological characteristics, complete cascade hydropower station layout, and sufficient data accumulation as verification objects; collect historical hydrological and meteorological data, cascade hydropower station operation data, ecological monitoring data, and existing dispatch records for the basin over the past 5-10 years to construct a verification dataset; simultaneously acquire auxiliary data such as basin ecological protection planning, power generation revenue statistics, and upstream and downstream economic development levels to support multi-dimensional verification; S62. Model full-process simulation and control group setup: The methodology of S1-S5 is applied to the case watershed, and multi-source data preprocessing, dynamic ecological flow demand model construction, multi-objective cascade collaborative scheduling model training, real-time scheduling execution and ecological compensation mechanism simulation are completed in sequence; a control group is set up to conduct parallel simulation and record the changes of key indicators of the two groups under different hydrological scenarios. S63. Multi-dimensional verification index evaluation: Construct a three-dimensional evaluation system of ecology, economy, and mechanism; use statistical methods such as paired-samples t-test and analytic hierarchy process to quantify the difference between the optimization group and the control group and verify the effectiveness of the method.

[0030] The above-described methods and steps enable model validation and iterative optimization to comprehensively verify the feasibility and effectiveness of the methodology in real-world watershed scenarios. Through full-process simulations of typical watersheds and parallel comparisons with control groups, combined with a three-dimensional evaluation system encompassing ecology, economy, and mechanisms, and scientific statistical methods, shortcomings and deficiencies in model parameters, scheduling strategies, and compensation mechanisms are accurately identified. Based on the validation results, targeted iterative optimizations are performed on the prediction accuracy of the dynamic ecological flow demand model, the strategy adaptability of the multi-objective cascade collaborative scheduling model, and the incentive effect of the ecological compensation mechanism. This continuously improves the scientific rigor, practicality, and stability of the methodology, ensuring its optimal performance under different hydrological conditions, ecological demands, and economic scenarios, providing reliable technical support for the eco-friendly scheduling and sustainable operation of cascade hydropower station groups.

[0031] Please see Figure 8 A smart scheduling and compensation benefit optimization system for cascade hydropower station groups aimed at ensuring ecological flow includes: Data acquisition and preprocessing module: used to acquire real-time operational data, hydrological and meteorological data, and ecological monitoring data of the cascade hydropower station group within the basin; Ecological Demand Analysis Module: This module is used to analyze the correlation between various indicators and flow in ecological monitoring data by combining the seasonal characteristics, hydrological cycle and key ecological processes of the watershed ecosystem in order to screen key influencing factors. The collaborative scheduling model training module is used to construct a multi-objective function system with the goals of ensuring ecological flow, maximizing power generation benefits, and optimizing water resource utilization efficiency. It defines a state space that includes the operating status of cascade power stations, hydrological and meteorological data, and dynamic ecological flow targets, as well as an action space that includes gate opening adjustment and output allocation. It designs a comprehensive reward function that integrates ecological achievement rewards and deviation penalties, and uses deep reinforcement learning algorithms to train the cascade collaborative scheduling agent to generate a set of globally optimal scheduling strategies under different hydrological scenarios. Real-time scheduling and feedback module: Based on cloud-edge collaborative architecture, the optimized scheduling strategy is distributed to the local controller of each power station to realize real-time data interaction and status synchronization between cascade power stations, monitor the downstream ecological flow compliance rate, power generation revenue and water resource utilization efficiency in real time, set multi-level abnormal early warning thresholds and trigger automatic early warning, and dynamically adjust scheduling parameters in combination with online learning module to generate traceable scheduling logs and periodic effect evaluation reports. Ecological compensation optimization module: It is used to quantify the value of ecosystem services such as supply services and regulation services brought about by ecological flow guarantee. It combines the power generation revenue reduced by ecological dispatch of cascade power stations, the economic development level of upstream and downstream areas and the difference in ecological contribution to formulate hierarchical compensation standards, design a diversified compensation mechanism such as the extraction of ecological compensation fund from power generation revenue, government ecological subsidies, and horizontal compensation between upstream and downstream areas of the basin, and establish a linkage mechanism between ecological contribution and revenue distribution. Model Validation and Iteration Module: This module is used to select typical watersheds to construct validation datasets, apply the methodology to the case watersheds for full-process simulation and set up control groups, construct a three-dimensional evaluation system of ecology, economy and mechanism, use statistical methods to quantify the optimization effect, and iteratively optimize the model parameters and compensation mechanism based on the validation results to ensure the practicality and stability of the method. System Management Module: Used to manage user permissions, securely encrypt data, record system logs, and coordinate interactions between modules. It supports model parameter configuration, policy library updates, and visual display of performance evaluation reports, ensuring stable system operation and efficient maintenance.

[0032] Working principle: The system first integrates real-time operational data, hydrological and meteorological data, and ecological monitoring data from the cascade hydropower stations within the basin through a data acquisition and preprocessing module. After cleaning and standardization, a structured dataset is formed. Subsequently, the ecological demand analysis module, based on this dataset and considering the seasonal characteristics and key ecological processes of the basin's ecosystem, identifies the core factors affecting ecological flow and determines dynamic ecological flow targets for different time periods. The collaborative scheduling model training module, aiming to ensure ecological flow, maximize power generation efficiency, and optimize water resource utilization, constructs a multi-objective function and uses deep reinforcement learning algorithms to train an agent, generating a globally optimal scheduling strategy set adapted to different hydrological scenarios. Finally, the real-time scheduling and feedback module, relying on a cloud-edge collaborative architecture, distributes the optimization strategies to the local controllers of each power station. The system enables real-time data interaction and status synchronization among cascade power stations, while continuously monitoring key indicators such as downstream ecological flow compliance rate and power generation revenue. It triggers anomaly warnings and dynamically adjusts scheduling parameters through online learning. The ecological compensation optimization module quantifies the ecosystem service value brought by ecological flow guarantee, and formulates hierarchical compensation standards and diversified compensation mechanisms based on power generation revenue loss and differences in contributions from various parties in the basin, achieving linkage between ecological contribution and revenue distribution. The model verification and iteration module evaluates the optimization effect from ecological, economic, and mechanism dimensions through simulation of typical basin cases and comparison with control groups, and iteratively updates model parameters and compensation mechanisms based on verification results. The system management module provides full-process user access control, data security protection, and visualization support to ensure efficient collaboration and stable operation of all modules in the system.

[0033] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the intelligent scheduling and compensation benefit optimization method for a cascade hydropower station group oriented towards ecological flow protection, as described above.

[0034] An electronic device, comprising: Memory, used to store computer programs; A processor, used to execute computer programs, implements any of the above-mentioned methods for intelligent scheduling and compensation benefit optimization of cascade hydropower station groups for ecological flow protection.

[0035] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent scheduling and compensation benefit optimization of cascade hydropower station groups for ecological flow protection, characterized in that, The steps include the following: S1. Multi-source data acquisition and preprocessing: Collect real-time operation data, hydrological and meteorological data, and ecological monitoring data of the cascade hydropower station group in the basin, and clean and normalize the data to build a standardized basin database. S2. Construct a dynamic ecological flow demand model: Based on the seasonal characteristics, hydrological cycle and key ecological processes of the watershed ecosystem, a dynamic ecological flow demand prediction model is established using machine learning algorithms to output the ecological flow target range under different time periods and hydrological conditions, replacing the traditional fixed threshold control mode. S3. Training of Multi-Objective Cascade Coordinated Scheduling Model: With ecological flow guarantee, power generation benefit maximization, and water resource utilization efficiency as objective functions, a deep reinforcement learning algorithm is introduced to construct a coordinated scheduling model for a cascade hydropower station group. The model is trained and optimized by simulating different scheduling scenarios to generate a globally optimal set of cascade scheduling strategies. S4. Execute real-time collaborative scheduling and feedback: Based on the cloud-edge collaborative architecture, the optimized scheduling strategy is sent to the control systems of each power station to realize real-time data interaction and scheduling command coordination among cascade power stations; and a scheduling effect feedback mechanism is established to monitor the downstream ecological flow compliance, power generation revenue and water resource utilization efficiency in real time, and dynamically adjust scheduling parameters. S5. Construct an ecological compensation benefit optimization mechanism: Use the conditional valuation method combined with the ecosystem service value accounting model to quantify the ecological benefits brought by ecological flow guarantee; based on the quantitative results, design a diversified compensation mechanism, including the proportion of power generation revenue to be extracted into the ecological compensation fund, government ecological subsidies, and horizontal compensation between upstream and downstream areas of the basin, and establish a linkage mechanism between ecological contribution and benefit distribution to stimulate the enthusiasm of all stakeholders to participate in ecological flow guarantee. S6. Model Validation and Iterative Optimization: A typical cascade hydropower station group in a river basin is selected for case validation. The ecological flow compliance rate, power generation benefits and the operation effect of the compensation mechanism before and after optimization are compared and analyzed. Based on the validation results, the model parameters and compensation mechanism are iteratively optimized to ensure the practicality and stability of the method.

2. The intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow guarantee as described in claim 1, characterized in that, The multi-source data acquisition and preprocessing specifically includes the following steps: S11. Multi-source data classification and acquisition: Real-time acquisition of dynamic parameters such as water level, output, reservoir capacity, and gate opening of each power station through intelligent sensors; Hydrological and meteorological data such as rainfall, runoff, water temperature, and wind speed are collected through hydrological and meteorological stations deployed within the basin; Ecological monitoring data such as ecological flow and water quality parameters are collected through fixed-point monitoring stations in the downstream river channel. S12. Data cleaning: Perform quality checks on the collected raw data, fill in missing values ​​and remove outliers, and deduplicate duplicate data. S13. Data normalization processing: Perform feature scale normalization processing on the cleaned data; S14. Construct a standardized watershed database: Store the cleaned and normalized multi-source data in a unified format, establish an index linking the data, and achieve efficient data retrieval and access.

3. The intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow guarantee as described in claim 1, characterized in that, The construction of the dynamic ecological flow demand model specifically includes the following steps: S21. Correlation analysis between ecological factors and flow: Combining key ecological processes within the watershed, analyze the correlation between various indicators in ecological monitoring data and flow, and screen out key factors that have a significant impact on ecological flow demand. S22. Model input feature selection: Seasonal features, hydrological cycle parameters and key ecological factors are extracted from preprocessed hydrological and meteorological data and ecological monitoring data as model input features; S23. Machine learning model training and parameter optimization: Long short-term memory network or gradient boosting tree is used to train the model using historical ecological flow demand data and corresponding input features. Model parameters are optimized through grid search, cross-validation and other methods. S24. Model Validation and Dynamic Correction: The trained model is validated using an independent watershed ecological flow monitoring dataset to assess the deviation between the prediction results and actual ecological needs, and the model structure or parameters are adjusted based on feedback from watershed ecological experts. S25. Dynamic Ecological Flow Target Range Output: The model receives real-time hydrological and meteorological data and ecological monitoring data as input, and outputs the ecological flow target range for different time periods and under different hydrological conditions, replacing the traditional fixed threshold and providing a dynamic basis for the intelligent scheduling of cascade hydropower station groups.

4. The intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow guarantee as described in claim 1, characterized in that, The training of the multi-objective hierarchical cooperative scheduling model specifically includes the following steps: S31. Constructing objective functions: Establish functions for maximizing power generation benefits and optimizing water resource utilization efficiency under ecological flow guarantee constraints, respectively. Set weight coefficients for each objective based on the actual needs of the basin, and establish a multi-objective collaborative optimization objective function system. S32. Constructing the state space and action space: The state space includes real-time water level, reservoir capacity, and power output of each cascade power station, real-time hydrological and meteorological data of the basin, dynamic ecological flow target range, and key ecological factors, etc. The action space includes scheduling decision variables such as the adjustment range of the gate opening of each power station, the power output allocation scheme, and the amount of water resources allocated between power stations; S33. Construct a reward function: Transform the ecological flow compliance rate, power generation revenue growth value, and water resource utilization rate into a comprehensive reward signal. Set a gradient penalty term for ecological flow deviating from the target range, and set positive rewards for power generation efficiency improvement and efficient water resource utilization to balance the conflict between multiple objectives. S34. Deep reinforcement learning model training: A tiered scheduling agent is constructed using a near-end policy optimization algorithm to simulate the scheduling process under scenarios such as high water season, normal water season, low water season and extreme hydrological events. The policy network parameters are updated through continuous interaction between the agent and the environment to improve the adaptability of scheduling decisions. S35. Model Validation and Strategy Iteration: The trained model is validated using historical watershed scheduling data and independently run datasets. The performance of the strategy in terms of ecological flow guarantee rate, power generation efficiency and water resource utilization efficiency is evaluated. The target weights or model structure are adjusted in combination with feedback from water conservancy experts to achieve iterative optimization of the strategy. S36. Generation of Global Optimal Scheduling Strategy Set: Integrate the optimal scheduling decisions under different hydrological scenarios and ecological needs to form a standardized tiered collaborative scheduling strategy library, which supports dispatchers to quickly match and call the optimal strategy based on the real-time watershed status.

5. The intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow guarantee as described in claim 1, characterized in that, The process of performing real-time collaborative scheduling and providing feedback specifically includes the following steps: S41. Issue cloud-edge collaborative scheduling instructions: Based on the global optimal scheduling strategy set stored in the cloud platform, combined with the real-time operation data uploaded by the edge nodes, the scheduling instructions are accurately issued to the local controllers of each power station through a low-latency communication protocol. S42. Real-time data interaction and status synchronization: Edge nodes continuously collect operational status data such as water level, reservoir capacity, and power output of each power station, as well as monitoring data such as downstream ecological flow and water quality. The data is then uploaded to the cloud platform through an encrypted channel. The cloud platform integrates and processes the multi-source real-time data and updates the basin status space synchronously. S43. Real-time monitoring of dispatching effectiveness: Real-time display of key indicators such as downstream ecological flow compliance rate, power generation of each power station, and water resource utilization efficiency; setting multi-level abnormal early warning thresholds, when ecological flow deviates from the target range, power generation revenue is lower than expected, or water resource waste exceeds the threshold, an automatic early warning is triggered and pushed to the dispatcher's terminal; S44. Parameter Feedback and Dynamic Adjustment: Based on real-time monitoring data, calculate evaluation indicators such as ecological flow guarantee rate, power generation efficiency improvement rate, and water resource utilization rate. If the target is not met, the target weight coefficient or action space constraint in the scheduling strategy is dynamically adjusted by combining the online learning module of the deep reinforcement learning model, and the corrected scheduling instruction is generated and issued for execution. S45. Generate scheduling logs and performance evaluation reports: Automatically record the issuance time, execution results, and indicator changes of each scheduling instruction to form a traceable scheduling log; periodically (e.g., weekly, monthly) generate scheduling performance evaluation reports to analyze the comprehensive performance of ecological flow guarantee, power generation benefits, and water resource utilization, providing data support for subsequent model optimization and strategy iteration.

6. The intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow guarantee as described in claim 1, characterized in that, The specific steps involved in constructing the ecological compensation benefit optimization mechanism are as follows: S51. Quantitative assessment of ecological benefits: Based on the classification system of watershed ecosystem service value, the benefits brought by ecological flow guarantee are divided into four categories: supply services, regulation services, support services and cultural services; the market value method is used to calculate the value of water resource supply, the substitution cost method is used to calculate the value of water purification cost savings, the travel cost method is used to calculate the value of ecotourism, and the contingent valuation method is combined to obtain the public's willingness to pay for ecological protection, and the comprehensive results of ecological benefit quantification are formed. S52. Differentiated formulation of compensation standards: Based on the quantitative value of ecological benefits, the reduced power generation revenue of cascade power stations due to ecological dispatch, the differences in economic development level and ecological contribution between upstream and downstream areas of the basin, a tiered compensation standard shall be formulated; the basic compensation amount of upstream power stations shall be determined according to the ecological flow discharge and compliance rate, the horizontal compensation ratio of downstream beneficiary areas shall be calculated according to the ecological service value they enjoy, and the government subsidy standard shall be dynamically adjusted with reference to the regional ecological protection investment and performance evaluation results.

7. The intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow guarantee as described in claim 1, characterized in that, The model validation and iterative optimization specifically include the following steps: S61. Case Basin Selection and Data Preparation: Select basins with typical ecological characteristics, complete cascade hydropower station layout, and sufficient data accumulation as verification objects; collect historical hydrological and meteorological data, cascade hydropower station operation data, ecological monitoring data, and existing scheduling records of the basin over the past 5-10 years to construct a verification dataset; Simultaneously acquire auxiliary data such as watershed ecological protection planning, power generation revenue statistics, and upstream and downstream economic development levels to provide support for multi-dimensional verification; S62. Model full-process simulation and control group setup: The methodology of S1-S5 is applied to the case watershed, and multi-source data preprocessing, dynamic ecological flow demand model construction, multi-objective cascade collaborative scheduling model training, real-time scheduling execution and ecological compensation mechanism simulation are completed in sequence; a control group is set up to conduct parallel simulation and record the changes of key indicators of the two groups under different hydrological scenarios. S63. Multi-dimensional verification index evaluation: Construct a three-dimensional evaluation system of ecology, economy, and mechanism; use statistical methods such as paired-samples t-test and analytic hierarchy process to quantify the difference between the optimization group and the control group and verify the effectiveness of the method.

8. A smart scheduling and compensation benefit optimization system for cascade hydropower station groups aimed at ecological flow protection, characterized in that: include: Data acquisition and preprocessing module: used to acquire real-time operational data, hydrological and meteorological data, and ecological monitoring data of the cascade hydropower station group within the basin; Ecological Demand Analysis Module: This module combines the seasonal characteristics, hydrological cycles, and key ecological processes of the watershed ecosystem to analyze the correlation between various indicators and flow in ecological monitoring data in order to screen key influencing factors. The collaborative scheduling model training module is used to construct a multi-objective function system with the goals of ensuring ecological flow, maximizing power generation benefits, and optimizing water resource utilization efficiency. It defines a state space that includes the operating status of cascade power stations, hydrological and meteorological data, and dynamic ecological flow targets, as well as an action space that includes gate opening adjustment and output allocation. It designs a comprehensive reward function that integrates ecological achievement rewards and deviation penalties, and uses deep reinforcement learning algorithms to train the cascade collaborative scheduling agent to generate a set of globally optimal scheduling strategies under different hydrological scenarios. Real-time scheduling and feedback module: Based on cloud-edge collaborative architecture, the optimized scheduling strategy is distributed to the local controller of each power station to realize real-time data interaction and status synchronization between cascade power stations, monitor the downstream ecological flow compliance rate, power generation revenue and water resource utilization efficiency in real time, set multi-level abnormal early warning thresholds and trigger automatic early warning, and dynamically adjust scheduling parameters in combination with online learning module to generate traceable scheduling logs and periodic effect evaluation reports. Ecological compensation optimization module: It is used to quantify the value of ecosystem services such as supply services and regulation services brought about by ecological flow guarantee. It combines the power generation revenue reduced by ecological dispatch of cascade power stations, the economic development level of upstream and downstream areas and the difference in ecological contribution to formulate hierarchical compensation standards, design a diversified compensation mechanism such as the extraction of ecological compensation fund from power generation revenue, government ecological subsidies, and horizontal compensation between upstream and downstream areas of the basin, and establish a linkage mechanism between ecological contribution and revenue distribution. Model Validation and Iteration Module: This module is used to select typical watersheds to construct validation datasets, apply the methodology to the case watersheds for full-process simulation and set up control groups, construct a three-dimensional evaluation system of ecology, economy and mechanism, use statistical methods to quantify the optimization effect, and iteratively optimize the model parameters and compensation mechanism based on the validation results to ensure the practicality and stability of the method. System Management Module: Used to manage user permissions, securely encrypt data, record system logs, and coordinate interactions between modules. It supports model parameter configuration, policy library updates, and visual display of performance evaluation reports, ensuring stable system operation and efficient maintenance.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow protection as described in any one of claims 1-8.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; The processor, when executing the computer program, implements the intelligent scheduling and compensation benefit optimization method for cascade hydropower station groups oriented towards ecological flow protection as described in any one of claims 1-8.

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