Canal ecological water resource allocation method based on feedback mechanism

Abstract

The invention particularly relates to a canal ecological water resource allocation method based on a feedback mechanism, and the method comprises the steps: in a rainy season, when a water level gauge monitors the rising of a canal water level, closing or reducing the opening degree of an upstream water gate to control the canal water level in a safe range, adjusting the opening and closing states of the water gate and a check gate, and regulating and controlling the water resource allocation in a drainage basin; excessive water accumulation in a local section is avoided; data are monitored in real time to automatically adjust the working states of the sluice and the pump station, the sluice is automatically closed when the water level is too high, and downstream flood control pressure is relieved; in dry seasons, when the water level indicator displays that the water level drops to be close to or lower than the ecological flow guarantee water level, the upstream water gate is opened to increase the canal flow, or the pump station is started to pump underground water, and water in lakes or water storage facilities is injected into the canal; the water level and the flow are automatically monitored, and once the water level and the flow are lower than ecological flow threshold values, a water gate or a pump station is automatically opened for supplementing water; in the non-rainy season and the non-dry season, the water resource allocation of the canal is intelligently adjusted according to the weather forecast data in combination with the weather forecast data.

Description

Technical Field

[0001] The invention belongs to the field of resource allocation, and in particular relates to a canal ecological water resource allocation method based on a feedback mechanism. Background Art

[0002] The existing canal ecological water resource allocation method widely adopts modern information technology means, such as remote sensing monitoring, Internet of Things sensor network and big data analysis technology, to collect water level, flow, water quality and other water information in real time, and conduct in-depth analysis in combination with meteorological forecast data. This method aims to flexibly and quickly adjust the water resource allocation strategy by grasping the water state and future weather trends in real time, so as to ensure that the canal can provide sufficient ecological flow in different seasons and extreme climate conditions, so as to maintain the stability and health of the canal water ecosystem.

[0005] Especially in terms of ecological flow guarantee, modern allocation methods strive to maintain the necessary minimum flow of each section of the canal in different seasons throughout the year through refined management, so as to support the reproduction and growth of aquatic plant and animal populations and maintain the integrity of the ecological chain. However, since the canal water resources are significantly affected by natural factors such as seasonal precipitation and evaporation, especially the rainy season may cause a sharp rise in water levels and cause flood risks, while the dry season is prone to cause a rapid drop in water levels, threatening ecological water requirements. Therefore, precise regulation and balance of this seasonal difference poses a great challenge to the existing water resource allocation methods.

[0006] The reality is that, although many high-tech technologies have been applied to canal water resource management, many current configuration schemes still show certain limitations in the face of complex and changeable weather conditions and highly uncertain ecological needs. On the one hand, the lack of detailed simulation of hydrological processes and accurate prediction of future changes makes it difficult to accurately match the temporal and spatial distribution of water resources with ecological needs; on the other hand, some regulatory measures may not fully consider the diversity and complexity of ecosystems, as well as the mutual influence between different river sections, which makes it difficult to achieve both uniform and sufficient allocation of canal ecological water resources under extreme climatic conditions, and prone to local oversaturation or drought shortage. Summary of the invention

[0007] The purpose of the present invention is to provide a canal ecological water resources allocation method based on a feedback mechanism to solve the problems raised in the above background technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] The present invention provides a canal ecological water resource allocation method based on a feedback mechanism, comprising the steps of:

[0010] In the rainy season: when the water level meter detects that the water level in the canal is rising, it closes or reduces the opening degree of the upstream sluice gate to control the water level in the canal within a safe range, adjusts the switch status of the sluice gate and the regulating gate, regulates the distribution of water resources in the basin, and avoids excessive water accumulation in some areas; the real-time monitoring data automatically adjusts the working status of the sluice gate and pump station, and automatically closes the sluice gate when the water level is too high, reducing the flood control pressure downstream;

[0011] In the dry season: when the water level gauge shows that the water level has dropped to or below the ecological flow guarantee water level, the upstream sluice gate is opened to increase the canal flow, or the pump station is started to extract water from groundwater, lakes or water storage facilities and inject it into the canal; the water level and flow are automatically monitored, and once it falls below the ecological flow threshold, the sluice gate or pump station is automatically opened to replenish water;

[0012] In non-rainy and non-dry seasons, the water resource allocation of the canal is intelligently adjusted based on weather forecast data.

[0013] Furthermore, combined with weather forecast data, the canal’s water resource allocation is intelligently adjusted based on the weather forecast data. The specific process includes: predicting the canal’s water level and flow changes based on a time series analysis model;

[0014] Integrate real-time hydrological monitoring data;

[0015] Obtain historical hydrological record data for model training and model performance verification;

[0016] Synchronous access to weather forecast data, including rainfall and temperature in the next few hours to days;

[0017] Check and process missing values, and fill in missing hydrological monitoring data through interpolation and average filling;

[0018] Correct outliers to identify and address data points that significantly deviate from the normal range;

[0019] Standardize or normalize the data to ensure that different variables are on the same scale;

[0020] Transform weather forecast data into features related to water level and flow;

[0021] Construct hysteresis characteristics according to the impact of water level and flow changes in historical periods on the current state;

[0022] Use the LSTM model to prepare training sets, validation sets, and test sets, and divide the time series data into time windows;

[0023] Build a multi-layer LSTM structure and set the input dimension and output dimension;

[0024] Train the model and adjust hyperparameters;

[0025] The selected LSTM model is trained using historical data, and the model parameters are optimized to minimize the prediction error. The trained model is used to input the latest real-time hydrological data and meteorological forecast data to predict future canal water levels and flows.

[0026] Furthermore, real-time hydrological monitoring data includes water level and flow information at various monitoring points on the canal.

[0027] Furthermore, the characteristics related to water level and flow include accumulated rainfall and temperature difference.

[0028] Furthermore, we set the input dimension to contain features of multiple time steps and the output dimension to contain the predicted target variable.

[0029] Furthermore, the hyperparameters to be adjusted include learning rate, number of hidden layer units, batch size, and number of recurrent layers.

[0030] Furthermore, combined with weather forecast data, the water resource allocation of the canal is intelligently adjusted according to the weather forecast data. The specific process includes:

[0031] Based on the prediction of the future canal water level and flow, the uniformity of the canal ecological water resource allocation is calculated to evaluate the uniformity of ecological flow distribution;

[0032] The following variables are used to calculate the uniformity of the canal's ecological water resources allocation:

[0033] Q i (t): represents the predicted flow value of the i-th river section in the future time period t;

[0034] E i : represents the ecological water demand of the ith river section;

[0035] T: the length of the future forecast period, such as days or months;

[0036] N: total number of canal sections; calculate the mean flow rate of each section Calculate the flow uniformity index, coefficient of variation V Q ,

[0037] Calculate the ecological water demand satisfaction of each river section like The satisfaction is 1, otherwise it is calculated proportionally; the uniformity index U of the ecological water demand satisfaction is calculated E :

[0038]

[0039] The uniformity of flow and the satisfaction of ecological water demand are combined to construct a composite index U: U = w_1*V Q +w_2*UE Among them, w_1 and w_2 are weight factors, which are determined according to the relative importance of ecological demand and flow uniformity; then the uniformity of ecological flow distribution is evaluated by the uniformity of canal ecological water resources allocation, which is a closed-loop feedback of water resources allocation in rainy and dry seasons.

[0040] Furthermore, the uniformity of ecological flow distribution is evaluated based on the uniformity of the canal's ecological water resources allocation, and a closed-loop feedback loop is used to determine the allocation of water resources in the rainy season and the dry season. Specifically, if the composite indicator constructed by the comprehensive flow uniformity and the ecological water demand satisfaction does not meet the threshold requirement, a closed-loop feedback loop is used to determine the allocation of water resources in the rainy season and the dry season; otherwise, there is no need for a closed-loop feedback loop to determine the allocation of water resources in the rainy season and the dry season.

[0041] Beneficial effects: Compared with the background technology, this application significantly improves the adaptability and regulatory efficiency of the canal's ecological water resources in rainy and dry seasons, effectively solves the problem of uneven water resource allocation caused by weather changes, and realizes the efficient utilization of water resources and the refined guarantee of ecological flow. DETAILED DESCRIPTION

[0042] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The present application discloses a canal ecological water resource allocation method based on a feedback mechanism, comprising the following steps:

[0044] During the rainy season:

[0045] When the water level gauge detects that the water level in the canal is rising, the dispatching center closes or reduces the opening of the upstream sluice gate to control the water level in the canal within a safe range to prevent excessive water levels from damaging the ecological environment and facilities around the canal. At the same time, water storage facilities (such as reservoirs and ponds) are used to store water to make full use of the abundant water resources reserves during the rainy season.

[0046] Sluice and throttle control:

[0047] According to real-time hydrological information, adjust the opening and closing status of sluice gates and control gates to regulate water resource distribution in the basin and avoid excessive waterlogging in some areas;

[0048] Real-time monitoring data automatically adjusts the working status of sluices and pump stations, such as automatically closing sluices when the water level is too high to reduce downstream flood control pressure; in the dry season:

[0049] When the water level gauge shows that the water level has dropped to near or below the ecological flow guarantee water level, the dispatching center should open the upstream sluice gate to increase the canal flow, or start the pumping station to extract water from groundwater, lakes or water storage facilities and inject it into the canal;

[0050] If the natural water source is insufficient to maintain the ecological flow, the pumping station needs to adjust the operating power and pumping volume as needed based on the feedback from the water level meter and flow meter to ensure that the canal branch does not dry up;

[0051] Automatically monitor water level and flow, and once it falls below the ecological flow threshold, automatically open the sluice gate or pump station to replenish water;

[0052] In the non-rainy season and non-dry season, combined with weather forecast data, the water resource allocation of the canal is intelligently adjusted according to the weather forecast data:

[0053] The specific process is:

[0054] The specific process of predicting canal water level and flow changes based on time series analysis model:

[0055] Integrate real-time hydrological monitoring data, including but not limited to water level and flow information at various monitoring points on the canal;

[0056] Obtain historical hydrological record data for model training and model performance verification;

[0057] Synchronous access to weather forecast data, including rainfall and temperature in the next few hours to days;

[0058] Check and process missing values, and fill in missing hydrological monitoring data through interpolation and average filling;

[0059] Correct outliers to identify and address data points that significantly deviate from the normal range;

[0060] Standardize or normalize the data to ensure that different variables are on the same scale;

[0061] Transform weather forecast data into features related to water level and flow, such as accumulated rainfall and temperature difference;

[0062] Construct hysteresis characteristics according to the impact of water level and flow changes in historical periods on the current state;

[0063] Use the LSTM model (Long Short-Term Memory Network) to predict the future canal water level and flow: prepare training sets, validation sets, and test sets, and divide the time series data into time windows; build a multi-layer LSTM structure, set the input dimension (including features of multiple time steps) and output dimension (predicted target variable); train the model and adjust hyperparameters such as learning rate, number of hidden layer units, batch size, and number of loop layers; use historical data to train the selected LSTM model and optimize model parameters to minimize prediction errors; use the trained model to input the latest real-time hydrological data and meteorological forecast data to predict the future canal water level and flow;

[0064] Based on the prediction of the future canal water level and flow, the uniformity of the canal ecological water resource allocation is calculated to evaluate the uniformity of ecological flow distribution;

[0065] The following variables are used to calculate the uniformity of the canal's ecological water resources allocation:

[0066] Q i (t): represents the predicted flow value of the i-th river section in the future time period t;

[0067] E i : represents the ecological water demand of the ith river section;

[0068] T: the length of the future forecast period, such as days or months;

[0069] N: total number of canal sections; calculate the mean flow rate of each section Calculate the flow uniformity index, coefficient of variation V Q ,

[0070] Calculate the ecological water demand satisfaction of each river section like The satisfaction is 1, otherwise it is calculated proportionally; the uniformity index U of the ecological water demand satisfaction is calculated E :

[0071]

[0072] The uniformity of flow and the satisfaction of ecological water demand are combined to construct a composite index U: U = w_1*V Q +w_2*U E Where w_1 and w_2 are weight factors, which are determined according to the relative importance of ecological demand and flow uniformity;

[0073] Then, the uniformity of the canal's ecological water resources allocation is used to evaluate the uniformity of the ecological flow distribution, and a closed-loop feedback system is used to configure the water resources in the rainy season and the dry season. If the composite indicator constructed by the comprehensive flow uniformity and the ecological water demand satisfaction does not meet the threshold requirement, a closed-loop feedback system is used to configure the water resources in the rainy season and the dry season. Otherwise, there is no need for a closed-loop feedback system to configure the water resources in the rainy season and the dry season.

[0074] The progress and advantages of this application over the prior art are reflected in the following aspects: 1. Fine-grained dynamic regulation: The technical solution of this application introduces real-time monitoring data and weather forecast data, combined with the feedback information of the water level meter and flow meter, to achieve fine-grained and automated regulation of the canal water level and flow, and improve the accuracy and timeliness of water resource allocation. 2. Rainy season water resource utilization and flood control: In response to the risk of floods in the rainy season, this application proposes to close or reduce the opening of the upstream sluice gate when the water level rises, and use water storage facilities to store water resources, which not only ensures the safety of the ecological environment and facilities around the canal, but also effectively utilizes the abundant water resources in the rainy season. 3. Dry season ecological flow guarantee: In the dry season, when the water level drops to close to or below the ecological flow guarantee water level, this method can promptly open the upstream sluice gate to increase the flow or start the pump station to supplement the water source to ensure that the ecological flow is met. At the same time, the operating power and pumping volume of the pump station can be dynamically adjusted according to real-time monitoring data, reducing the risk of canal branches drying up. 4. Intelligent prediction and configuration: The LSTM model based on time series analysis is adopted. Through the integration, processing and analysis of real-time hydrological monitoring data, historical hydrological record data and meteorological forecast data, accurate prediction of future canal water levels and flows is achieved, providing a scientific basis for water resource allocation. 5. Evaluation of uniformity of ecological water resource allocation: This application innovatively proposes a calculation method for the uniformity of canal ecological water resource allocation, comprehensively considers the two core indicators of flow uniformity and ecological water demand satisfaction, and constructs a composite evaluation index U to ensure the uniformity of canal ecological flow distribution, making up for the problem that existing technologies do not pay enough attention to the uniformity of water resource allocation. 6. Closed-loop feedback mechanism: This scheme emphasizes the role of closed-loop feedback. According to the evaluation results of the uniformity of canal ecological water resource allocation, the water resource allocation strategy in rainy and dry seasons is adjusted in real time to ensure that under various climatic conditions, the canal can achieve effective protection of ecological flow and balanced allocation of water resources. To sum up, compared with the background technology, this application significantly improves the adaptability and regulatory efficiency of the canal's ecological water resources in rainy and dry seasons, effectively solves the problem of uneven water resource allocation caused by weather changes, and realizes the efficient utilization of water resources and the refined guarantee of ecological flow.

[0075] The embodiments that require protection in this application include:

[0076] The canal ecological water resource allocation method based on the feedback mechanism includes the following steps: in the rainy season: when the water level meter detects that the canal water level is rising, the upstream sluice gate is closed or the opening degree is reduced to control the canal water level within a safe range, the switch status of the sluice gate and the regulating gate is adjusted, and the water resource allocation in the basin is regulated to avoid excessive water accumulation in some areas; the real-time monitoring data is used to automatically adjust the working status of the sluice gate and pump station, and the sluice gate is automatically closed when the water level is too high to reduce the flood control pressure downstream;

[0077] In the dry season: when the water level gauge shows that the water level has dropped to or below the ecological flow guarantee water level, the upstream sluice gate is opened to increase the canal flow, or the pump station is started to extract water from groundwater, lakes or water storage facilities and inject it into the canal; the water level and flow are automatically monitored, and once it falls below the ecological flow threshold, the sluice gate or pump station is automatically opened to replenish water;

[0078] In non-rainy and non-dry seasons, the water resource allocation of the canal is intelligently adjusted based on weather forecast data.

[0079] Preferably, the water resource allocation of the canal is intelligently adjusted according to the weather forecast data in combination with the weather forecast data, and the specific process includes: predicting the changes in the water level and flow of the canal based on the time series analysis model;

[0080] Integrate real-time hydrological monitoring data;

[0081] Obtain historical hydrological record data for model training and model performance verification;

[0082] Synchronous access to weather forecast data, including rainfall and temperature in the next few hours to days;

[0083] Check and process missing values, and fill in missing hydrological monitoring data through interpolation and average filling;

[0084] Correct outliers to identify and address data points that significantly deviate from the normal range;

[0085] Standardize or normalize the data to ensure that different variables are on the same scale;

[0086] Transform weather forecast data into features related to water level and flow;

[0087] Construct hysteresis characteristics according to the impact of water level and flow changes in historical periods on the current state;

[0088] Use the LSTM model to prepare training sets, validation sets, and test sets, and divide the time series data into time windows;

[0089] Build a multi-layer LSTM structure and set the input dimension and output dimension;

[0090] Train the model and adjust hyperparameters;

[0091] The selected LSTM model is trained using historical data, and the model parameters are optimized to minimize the prediction error. The trained model is used to input the latest real-time hydrological data and meteorological forecast data to predict future canal water levels and flows.

[0092] Preferably, real-time hydrological monitoring data includes water level and flow information at each monitoring point of the canal. Features related to water level and flow include accumulated rainfall and temperature difference. Set the input dimension to include features of multiple time steps and the output dimension to include the predicted target variable. Adjust the hyperparameters including learning rate, number of hidden layer units, batch size, and number of loop layers.

[0093] Preferably, the water resource allocation of the canal is intelligently adjusted according to the weather forecast data in combination with the weather forecast data, and the specific process includes:

[0094] Based on the prediction of the future canal water level and flow, the uniformity of the canal ecological water resource allocation is calculated to evaluate the uniformity of ecological flow distribution;

[0095] The following variables are used to calculate the uniformity of the canal's ecological water resources allocation:

[0096] Q i (t): represents the predicted flow value of the i-th river section in the future time period t;

[0097] E i : represents the ecological water demand of the ith river section;

[0098] T: the length of the future forecast period, such as days or months;

[0099] N: total number of canal sections; calculate the mean flow rate of each section

[0100] Calculate the flow uniformity index, coefficient of variation V Q ,

[0101] Calculate the ecological water demand satisfaction of each river section like The satisfaction is 1, otherwise it is calculated proportionally; the uniformity index U of the ecological water demand satisfaction is calculated E :

[0102]

[0103] The uniformity of flow and the satisfaction of ecological water demand are combined to construct a composite index U: U = w_1*V Q +w_2*U EAmong them, w_1 and w_2 are weight factors, which are determined according to the relative importance of ecological demand and flow uniformity; then the uniformity of ecological flow allocation is evaluated by the uniformity of canal ecological water resources allocation, and closed-loop feedback is given to the allocation of water resources in rainy and dry seasons. The uniformity of ecological flow allocation is evaluated by the uniformity of canal ecological water resources allocation, and closed-loop feedback is given to the allocation of water resources in rainy and dry seasons. Specifically, if the composite index constructed by comprehensive flow uniformity and ecological water demand satisfaction does not meet the threshold requirement, closed-loop feedback is given to the allocation of water resources in rainy and dry seasons, otherwise closed-loop feedback is not required for the allocation of water resources in rainy and dry seasons.

[0104] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium can be at least one of the following media: read-only memory (English: Read-only Memory, abbreviated: ROM), RAM, magnetic disk or optical disk, etc. Various media that can store program codes.

[0105] The present application also discloses an electronic device, comprising a computer-readable storage medium, wherein the storage medium is used to store a computer program, and the computer program is used to execute the data processing steps of the above-mentioned embodiment.

[0106] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A canal ecological water resources allocation method based on feedback mechanism, characterized in that: Includes steps: In the rainy season: when the water level meter detects that the water level in the canal is rising, it closes or reduces the opening degree of the upstream sluice gate to control the water level in the canal within a safe range, adjusts the switch status of the sluice gate and the regulating gate, regulates the distribution of water resources in the basin, and avoids excessive water accumulation in some areas; the real-time monitoring data automatically adjusts the working status of the sluice gate and pump station, and automatically closes the sluice gate when the water level is too high, reducing the flood control pressure downstream; In the dry season: when the water level gauge shows that the water level has dropped to or below the ecological flow guarantee water level, the upstream sluice gate is opened to increase the canal flow, or the pump station is started to extract water from groundwater, lakes or water storage facilities and inject it into the canal; the water level and flow are automatically monitored, and once it falls below the ecological flow threshold, the sluice gate or pump station is automatically opened to replenish water; In non-rainy and non-dry seasons, the water resource allocation of the canal is intelligently adjusted based on weather forecast data.

2. The canal ecological water resource allocation method based on feedback mechanism according to claim 1 is characterized in that: Combined with weather forecast data, the canal’s water resource allocation is intelligently adjusted based on the weather forecast data. The specific process includes: predicting the canal’s water level and flow changes based on the time series analysis model; Integrate real-time hydrological monitoring data; Obtain historical hydrological record data for model training and model performance verification; Synchronous access to weather forecast data, including rainfall and temperature in the next few hours to days; Check and process missing values, and fill in missing hydrological monitoring data through interpolation and average filling; Correct outliers to identify and address data points that significantly deviate from the normal range; Standardize or normalize the data to ensure that different variables are on the same scale; Transform weather forecast data into features related to water level and flow; Construct hysteresis characteristics according to the impact of water level and flow changes in historical periods on the current state; Use the LSTM model to prepare training sets, validation sets, and test sets, and divide the time series data into time windows; Build a multi-layer LSTM structure and set the input dimension and output dimension; Train the model and adjust hyperparameters; The selected LSTM model is trained using historical data, and the model parameters are optimized to minimize the prediction error. The trained model is used to input the latest real-time hydrological data and meteorological forecast data to predict future canal water levels and flows.

3. The canal ecological water resource allocation method based on feedback mechanism according to claim 2 is characterized in that: Real-time hydrological monitoring data, including water level and flow information at each monitoring point on the canal.

4. The canal ecological water resource allocation method based on feedback mechanism according to claim 3 is characterized in that: Characteristics related to water level and flow, including accumulated rainfall and temperature difference.

5. The canal ecological water resource allocation method based on feedback mechanism according to claim 4 is characterized in that: Set the input dimension to contain features over multiple time steps and the output dimension to contain the predicted target variable.

6. The canal ecological water resource allocation method based on feedback mechanism according to claim 5 is characterized in that: The hyperparameters to be adjusted include learning rate, number of hidden layer units, batch size, and number of recurrent layers.

7. The canal ecological water resource allocation method based on feedback mechanism according to claim 6 is characterized in that: Combined with weather forecast data, the water resource allocation of the canal is intelligently adjusted according to the weather forecast data. The specific process includes: Based on the prediction of the future canal water level and flow, the uniformity of the canal ecological water resource allocation is calculated to evaluate the uniformity of ecological flow distribution; The following variables are used to calculate the uniformity of the canal's ecological water resources allocation: Q i (t): represents the predicted flow value of the i-th river section in the future time period t; E i : represents the ecological water demand of the ith river section; T: the length of the future forecast period, such as days or months; N: total number of canal sections; calculate the mean flow rate of each section Calculate the flow uniformity index, coefficient of variation V Q , Calculate the ecological water demand satisfaction of each river section like Then the satisfaction is 1, otherwise it is calculated proportionally; Calculate the uniformity index U of ecological water demand satisfaction E : The uniformity of flow and the satisfaction of ecological water demand are combined to construct a composite index U: U = w_1*V Q +w_2*U E ; where w_1 and w_2 are weight factors, which are determined according to the relative importance of ecological demand and flow uniformity; then the uniformity of ecological flow distribution is evaluated by the uniformity of canal ecological water resources allocation, which is a closed-loop feedback of water resources allocation in rainy and dry seasons.

8. The canal ecological water resource allocation method based on feedback mechanism according to claim 7 is characterized in that: The uniformity of ecological flow distribution is evaluated by the uniformity of the canal's ecological water resources allocation, and a closed-loop feedback is performed on the allocation of water resources in the rainy season and the dry season. Specifically, if the composite indicator constructed by the comprehensive flow uniformity and the ecological water demand satisfaction does not meet the threshold requirement, the closed-loop feedback is performed on the allocation of water resources in the rainy season and the dry season; otherwise, there is no need for a closed-loop feedback on the allocation of water resources in the rainy season and the dry season.

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