Methods and systems for intelligent gate systems that link groundwater and river / lake ecological water levels

Abstract

This invention provides a method and system for intelligent gate control of groundwater-river / lake ecological water levels. The method includes: acquiring target data collected in real time through a sensing layer; the target data includes surface water level and flow data, groundwater level data, and meteorological data; using a coupled hydrological model in the decision layer, based on the target data, rolling predictions of the state changes of surface water and groundwater under different gate opening scenarios within a future set time period; based on the predicted state changes and preset ecological control targets, optimization calculations are performed through an intelligent control algorithm in the decision layer to generate an optimal gate opening command; the optimal gate opening command is sent to the execution layer to drive the gate to adjust to the target opening, and the actual gate opening is obtained for feedback. In this method, the rolling optimization of gate opening through a coupled hydrological model and intelligent algorithm improves the ecological flow guarantee rate and effectively enhances the ecological protection level and system adaptability.

Description

Technical Field

[0001] This invention relates to the field of linkage control technology, and in particular to a method and system for intelligent gate linkage control of groundwater-river and lake ecological water levels. Background Technology

[0002] The coordinated regulation of groundwater and river / lake ecological water levels aims to break away from the limitations of traditional water management that focuses solely on water quality, treating groundwater and surface water as an organically unified whole for collaborative management. Its significance lies in achieving holistic restoration and protection of regional aquatic ecosystems through intelligent coordinated control. This not only significantly improves the ecological flow guarantee rate and effectively curbs groundwater over-extraction and river / lake ecological degradation, but also represents a key technological pathway for transforming water resource management from an extensive and passive approach to a precise and forward-looking one, holding profound implications for ensuring water security and ecological sustainability.

[0003] Among related technologies, regulation techniques mainly rely on human experience or automatic control based on fixed thresholds: manual methods suffer from lag and strong subjectivity; fixed threshold methods are rigid, unable to incorporate dynamic meteorological factors such as rainfall and evaporation, and cannot predict future hydrological conditions, thus constituting passive response control. None of these methods achieve the linkage simulation and optimization of groundwater and surface water, resulting in low ecological flow guarantee rates and insufficient forward-looking regulation, making it difficult to meet the high standards required for overall regional water ecological restoration and protection. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method and system for intelligent gate control of groundwater-river and lake ecological water levels. By coupling hydrological models and intelligent algorithms to continuously optimize the gate opening, the ecological flow guarantee rate is improved, and the ecological protection level and system adaptability are effectively enhanced.

[0005] In a first aspect, embodiments of the present invention provide a method for intelligent gate control of groundwater-river-lake ecological water levels, applied to an intelligent gate system for groundwater-river-lake ecological water level control. The system includes a perception layer, a decision layer, and an execution layer. The method includes: acquiring target data collected in real time through the perception layer; the target data includes: surface water level and flow data, groundwater level data, and meteorological data; using a coupled hydrological model in the decision layer, based on the target data, rollingly predicting the state changes of surface water and groundwater under different gate opening scenarios within a future set time period; based on the predicted state changes and preset ecological control targets, performing optimization calculations through an intelligent control algorithm in the decision layer to generate an optimal gate opening command; sending the optimal gate opening command to the execution layer to drive the gate to adjust to the target opening, and obtaining the actual gate opening for feedback.

[0006] In a preferred embodiment of the present invention, the aforementioned sensing layer includes a surface water monitoring unit, a groundwater monitoring unit, and a meteorological and hydrological unit; acquiring target data collected in real time through the sensing layer includes: collecting water level and flow data of upstream and downstream sections of the gate through the surface water monitoring unit; collecting groundwater level data along the coast through the groundwater monitoring unit; and collecting real-time meteorological data and rainfall forecast data for future periods through the meteorological and hydrological unit.

[0007] In a preferred embodiment of the present invention, the aforementioned decision layer is deployed on an edge computing gateway; using the coupled hydrological model in the decision layer, based on target data, the state changes of surface water and groundwater under different gate opening scenarios within a future set time period are predicted in a rolling manner, including: using the currently collected target data as the initial state and boundary conditions of the coupled hydrological model; using different assumed gate openings as control inputs, and using upstream inflow and rainfall forecasts as feedforward disturbance inputs; running the coupled hydrological model, and outputting downstream flow prediction sequences and groundwater level prediction sequences corresponding to each assumed gate opening.

[0008] In a preferred embodiment of the present invention, the above-mentioned optimization calculation through the intelligent control algorithm in the decision layer to generate the optimal gate opening command includes: constructing a target optimization function that includes ecological flow tracking error, groundwater level deviation threshold and gate action stability penalty term; obtaining a target gate opening sequence by rolling solution of the target optimization function based on the downstream flow prediction sequence and the groundwater level prediction sequence; the target gate opening sequence is the gate opening sequence in the future control time domain that minimizes the value of the target optimization function; and determining the first opening value in the target gate opening sequence as the current optimal gate opening command.

[0009] In a preferred embodiment of the present invention, the objective optimization function is: ;in, Let i be the downstream cross-sectional flow predicted by the model at time i in the future; The set ecological target flow rate; The key point groundwater level predicted by the model at time i in the future; The set threshold for groundwater ecological water level; denoted as , where is the change in gate opening; W, V, and R are positive definite weight matrices.

[0010] In a preferred embodiment of the present invention, the above method further includes an adaptive adjustment step: calculating the recent actual ecological flow guarantee rate; comparing the actual ecological flow guarantee rate with a preset guarantee rate target value to obtain a comparison result; and dynamically adjusting the weight of the ecological flow tracking error term in the objective optimization function based on the comparison result.

[0011] In a preferred embodiment of the present invention, the execution layer includes a gate controller and a high-precision feedback device; driving the gate to adjust to the target opening degree and obtaining the actual opening degree of the gate for feedback includes: sending the optimal gate opening degree command to the gate controller; the gate controller driving the gate hoist to adjust the gate to the target opening degree; measuring the actual opening degree of the gate through the high-precision feedback device and feeding it back to the decision layer to verify the execution accuracy.

[0012] Secondly, embodiments of the present invention also provide a groundwater-river-lake ecological water level linkage regulation intelligent gate system, used to realize the above-mentioned groundwater-river-lake ecological water level linkage regulation intelligent gate method in the first aspect, comprising: a sensing layer, used to collect surface water level and flow data, groundwater level data and meteorological data in real time; a decision layer, communicatively connected to the sensing layer, having a built-in coupled hydrological model and intelligent control algorithm, used to perform rolling prediction and optimization decision based on the data of the sensing layer, and generate the optimal gate opening command; and an execution layer, communicatively connected to the decision layer, used to receive commands and drive the gate action, while simultaneously feeding back the actual gate opening.

[0013] Thirdly, embodiments of the present invention also provide a device for a groundwater-river-lake ecological water level linkage control intelligent gate, applied to a groundwater-river-lake ecological water level linkage control intelligent gate system. The system includes a sensing layer, a decision layer, and an execution layer. The device includes: a target data acquisition module, used to acquire target data collected in real time through the sensing layer; the target data includes: surface water level and flow data, groundwater level data, and meteorological data; a state change prediction module, used to use a coupled hydrological model in the decision layer, based on the target data, to predict the state changes of surface water and groundwater under different gate opening scenarios within a future set time period; an optimal gate opening command generation module, used to generate an optimal gate opening command based on the predicted state changes and preset ecological control targets, through intelligent control algorithms in the decision layer; and an actual opening feedback module, used to send the optimal gate opening command to the execution layer to drive the gate to adjust to the target opening, and to obtain the actual opening of the gate for feedback.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are invoked and executed by a processor, the computer-executable instructions cause the processor to implement the method for groundwater-river-lake ecological water level linkage regulation of the intelligent gate described in the first aspect.

[0015] The embodiments of the present invention bring the following beneficial effects:

[0016] This invention provides a method and system for the coordinated regulation of groundwater and river / lake ecological water levels using intelligent gates. By acquiring target data collected in real-time through a sensing layer, and based on this data, the system continuously predicts the state changes of surface water and groundwater under different gate opening scenarios within a set future time period. Based on the predicted state changes and preset ecological control targets, an intelligent control algorithm in the decision-making layer performs optimization calculations to generate an optimal gate opening command. This optimal gate opening command is then sent to the execution layer to drive the gate to adjust to the target opening, and the actual gate opening is obtained as feedback. This approach, by coupling a hydrological model and an intelligent algorithm to continuously optimize the gate opening, improves the ecological flow guarantee rate and effectively enhances the ecological protection level and system adaptability.

[0017] Other features and advantages of this disclosure will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0018] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for the coordinated regulation of groundwater and river / lake ecological water levels, provided in an embodiment of the present invention;

[0021] Figure 2 A flowchart illustrating another method for the intelligent gate for groundwater-river-lake ecological water level linkage regulation provided in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of an intelligent gate system for the linkage regulation of groundwater and river / lake ecological water levels, provided in an embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram of upstream and downstream water level and flow rate observation data provided in an embodiment of the present invention.

[0024] Figure 5 A schematic diagram of meteorological observation data provided in an embodiment of the present invention;

[0025] Figure 6This is a schematic diagram comparing the measured groundwater level and the simulated groundwater level of the MODFLOW model provided in this embodiment of the invention.

[0026] Figure 7 A schematic diagram of the structure of a smart gate device for groundwater-river-lake ecological water level linkage regulation provided in an embodiment of the present invention;

[0027] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions 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, 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.

[0029] The coordinated regulation of groundwater and river / lake ecological water levels aims to break away from the limitations of traditional water management that focuses solely on water quality, treating groundwater and surface water as an organically unified whole for collaborative management. Its significance lies in achieving holistic restoration and protection of regional aquatic ecosystems through intelligent coordinated control. This not only significantly improves the ecological flow guarantee rate and effectively curbs groundwater over-extraction and river / lake ecological degradation, but also represents a key technological pathway for transforming water resource management from an extensive and passive approach to a precise and forward-looking one, holding profound implications for ensuring water security and ecological sustainability.

[0030] In the field of groundwater-river-lake ecological water level regulation, related technologies mainly rely on human experience or automatic control based on fixed thresholds. Manual regulation has a serious lag in response, depends on individual subjective judgment, and cannot cope with complex and ever-changing hydrological conditions; while fixed threshold control has achieved basic automation, its strategy is rigid, and it can only make passive and post-event responses, lacking the ability to predict dynamic factors such as rainfall and evaporation. In essence, it is a passive control mode.

[0031] The fundamental flaws of these traditional methods lie in their "separation of land and water" and "lack of intelligence." They typically focus only on a single indicator of surface water, neglecting the dynamics of groundwater levels that are inextricably linked to the health of rivers and lakes, thus disrupting the integrity of the aquatic ecosystem. Furthermore, due to the lack of an intelligent decision-making core based on hydrological model simulation and prediction, the system cannot anticipate future changes and intervene in advance, resulting in low and unstable ecological flow guarantees, failing to meet the demands of high-standard ecological protection.

[0032] Based on this, the present invention provides a method and system for intelligent gate control of groundwater-river-lake ecological water levels. This system acquires target data collected in real-time through a sensing layer. Based on this target data, it continuously predicts the state changes of surface water and groundwater under different gate opening scenarios within a set future time period. Based on the predicted state changes and preset ecological control targets, it performs optimization calculations through an intelligent control algorithm in the decision layer to generate an optimal gate opening command. This optimal gate opening command is then sent to the execution layer to drive the gate to adjust to the target opening, and the actual gate opening is obtained for feedback. In this approach, by coupling a hydrological model and an intelligent algorithm to continuously optimize the gate opening, the ecological flow guarantee rate is improved, effectively enhancing the ecological protection level and the system's adaptive capability.

[0033] To facilitate understanding of this embodiment, a method for intelligent gate for groundwater-river-lake ecological water level linkage regulation disclosed in this embodiment of the invention will first be described in detail.

[0034] Example 1

[0035] This invention provides a method for the coordinated regulation of groundwater and river / lake ecological water levels using an intelligent gate. Figure 1 A flowchart illustrating a method for the coordinated regulation of groundwater and river / lake ecological water levels, provided in an embodiment of the present invention. Figure 1 As shown, the method for intelligent gate linkage regulation of groundwater-river-lake ecological water level is applied to an intelligent gate system for groundwater-river-lake ecological water level linkage regulation. The system includes a sensing layer, a decision-making layer, and an execution layer. The method may include the following steps:

[0036] Step S101: Obtain target data collected in real time through the perception layer.

[0037] The target data includes: surface water level and flow data, groundwater level data, and meteorological data.

[0038] The sensing layer includes surface water monitoring units, groundwater monitoring units, and meteorological and hydrological units.

[0039] Specifically, acquiring target data collected in real time through the sensing layer can include: collecting water level and flow data at upstream and downstream sections of the gate through the surface water monitoring unit; collecting groundwater level data along the coast through the groundwater monitoring unit; and collecting real-time meteorological data and rainfall forecast data for future periods through the meteorological and hydrological unit.

[0040] Step S102: Using the coupled hydrological model in the decision-making layer, based on the target data, the state changes of surface water and groundwater under different gate opening scenarios within a set future time period are predicted in a rolling manner.

[0041] Specifically, by utilizing the coupled hydrological model in the decision-making layer, and based on target data, rolling predictions can be made of the state changes of surface water and groundwater under different gate opening scenarios within a set future time period. This can include: using the currently collected target data as the initial state and boundary conditions of the coupled hydrological model; using different assumed gate openings as control inputs, and upstream inflow and rainfall forecasts as feedforward disturbance inputs; running the coupled hydrological model, and outputting downstream flow prediction sequences and groundwater level prediction sequences corresponding to each assumed gate opening.

[0042] The state-space form of the coupled hydrological model is as follows: Characterizes the system state It also includes surface water levels. and groundwater level ,Right now This mathematically enables a unified description and prediction of the dynamic linkage between groundwater and surface water, thereby allowing for the dynamic simulation of the comprehensive impact of different gate openings on the water system in the future.

[0043] Observation equation: .

[0044] in, Let k be the system's state vector at time k, containing the surface water level. and groundwater level ; This represents the control input, namely the gate opening. ; For measurable disturbances, upstream inflow Rainfall P and other parameters are used as feedforward terms to improve the model's prediction accuracy. The observed values ​​are the surface water and groundwater levels measured by the sensors; the A, B, E, and C model matrices are obtained through system identification or linearization of the physical model, and describe the dynamic relationship between the system state, control input, external disturbances, and observed values. , These are process noise and observation noise, respectively.

[0045] Step S103: Based on the predicted state changes and the preset ecological control objectives, the optimal gate opening command is generated by the intelligent control algorithm in the decision-making layer through optimization calculation.

[0046] Among them, ecological control targets include: downstream ecological flow Q_target ≥ 0.8 m³ / s, etc.

[0047] Step S104: Send the optimal gate opening command to the execution layer to drive the gate to adjust to the target opening and obtain the actual gate opening for feedback.

[0048] The execution layer includes a gate controller and a high-precision feedback device.

[0049] Specifically, driving the gate to adjust to the target opening degree and obtaining the actual opening degree of the gate for feedback may include: sending the optimal gate opening degree command to the gate controller; the gate controller driving the gate hoist to adjust the gate to the target opening degree; and measuring the actual opening degree of the gate through a high-precision feedback device and feeding it back to the decision-making level to verify the execution accuracy.

[0050] The method for intelligent gate control linking groundwater and river / lake ecological water levels provided in this invention can acquire target data collected in real time through the sensing layer. Based on the target data, it continuously predicts the state changes of surface water and groundwater under different gate opening scenarios within a set future time period. Based on the predicted state changes and preset ecological control targets, it performs optimization calculations through intelligent control algorithms in the decision layer to generate an optimal gate opening command. The optimal gate opening command is then sent to the execution layer to drive the gate to adjust to the target opening, and the actual gate opening is obtained for feedback. In this method, by coupling a hydrological model and an intelligent algorithm to continuously optimize the gate opening, the ecological flow guarantee rate is improved, effectively enhancing the ecological protection level and the system's adaptive capability.

[0051] Example 2

[0052] This invention also provides another method for the coordinated regulation of groundwater and river / lake ecological water levels using intelligent gates; this method is based on the method described in the above embodiments; the method focuses on the specific implementation of generating the optimal gate opening command through optimization calculations using intelligent control algorithms in the decision-making layer.

[0053] Figure 2 A flowchart of another method for groundwater-river-lake ecological water level linkage regulation intelligent gate provided by an embodiment of the present invention is shown below. Figure 2 As shown, the optimization calculation performed by the intelligent control algorithm in the decision-making layer to generate the optimal gate opening command may include the following steps:

[0054] Step S201: Construct an objective optimization function that includes ecological flow tracking error, groundwater level deviation threshold, and gate action stability penalty term.

[0055] Among them, the intelligent control algorithm preferably uses Model Predictive Control (MPC) as the "decision engine," which solves for the future in each control cycle k. Optimal control sequence in the prediction time domain .

[0056] Solving an innovative multi-objective optimization function through rolling: The optimal gate opening command is calculated, taking into account three major objectives: ecological flow, groundwater level, and stable equipment operation. Thus, long-term ecological management goals can be transformed into an immediate optimization problem that needs to be minimized in each control cycle. By continuously solving this function, the system behavior is driven to continuously tend towards a high guarantee rate.

[0057] in, Let i be the downstream cross-sectional flow predicted by the model at time i in the future; The set ecological target flow rate; The key point groundwater level predicted by the model at time i in the future; The set threshold for groundwater ecological water level; This represents the change in the gate opening. W, V, and R are positive definite weight matrices used to weigh the importance of different objectives, including: W is the ecological flow tracking weight, which is set relatively high to ensure that the flow prioritizes the target; V is the groundwater level protection weight to prevent excessive damage to groundwater while ensuring flow; and R is the control action smoothness weight to avoid frequent opening and closing of gates and protect equipment. For prediction in the time domain; To control the time domain ( ).

[0058] Furthermore, it also includes an adaptive adjustment step: calculating the recent actual ecological flow guarantee rate; comparing the actual ecological flow guarantee rate with the preset guarantee rate target value to obtain the comparison result; and dynamically adjusting the weight of the ecological flow tracking error term in the objective optimization function based on the comparison result.

[0059] Among them, real-time monitoring of the recent actual ecological flow guarantee rate And through the formula The weights in the optimization function are dynamically adjusted, enabling the system to learn and correct itself. This endows the system with self-learning and adaptability by calculating the current guarantee rate in real time. and the target guarantee rate By comparing the target value of 95% guarantee rate, the ecological flow tracking weight W in the control algorithm is dynamically adjusted to ensure that the system can actively maintain the set high ecological guarantee rate.

[0060] Step S202: Based on the downstream flow prediction sequence and the groundwater level prediction sequence, the objective optimization function is solved in a rolling manner to obtain the objective gate opening sequence.

[0061] The target gate opening sequence is the gate opening sequence in the future control time domain that minimizes the value of the target optimization function.

[0062] Step S203: Determine the first opening value in the target gate opening sequence as the current optimal gate opening command.

[0063] The intelligent control algorithm compares the model's predictions with the ecological objectives, and evaluates all scenarios by solving an optimization problem aimed at minimizing the objective optimization function J. Finally, it calculates the optimal gate opening command that best balances ecological flow assurance, groundwater level maintenance, and stable equipment operation.

[0064] Example 3

[0065] Corresponding to the above method embodiments, this invention provides an intelligent gate system for the coordinated regulation of groundwater and river / lake ecological water levels. Figure 3 This is a schematic diagram of the structure of an intelligent gate device for groundwater-river-lake ecological water level linkage regulation provided in an embodiment of the present invention, as shown below. Figure 3 As shown:

[0066] The perception layer is a comprehensive, three-dimensional system that acts as the "nerve endings" of the entire system. It consists of three highly coordinated units:

[0067] (1) Surface water monitoring unit: Radar level gauges, Doppler flow meters, and integrated level-flow monitoring stations are deployed upstream and downstream of the sluice gate to accurately monitor the water conditions of rivers and lakes directly affected by sluice gate regulation. Among them, radar level gauges are non-contact measurement devices installed on stable supports on the bank to monitor the water level in front of the sluice gate (water storage level) and behind the sluice gate (tailwater level) in real time; Doppler flow meters are installed at the bottom or side of the river channel to accurately measure the cross-sectional flow velocity and calculate the real-time flow rate in combination with the water level data. This is the most direct indicator for assessing the ecological flow guarantee rate; integrated level-flow monitoring station: at key cross-sections, water level and flow velocity sensors can be integrated into one unit to directly output flow data.

[0068] (2) Groundwater monitoring unit, which captures groundwater level dynamics in real time through monitoring wells and internal pressure gauges deployed along the shore. Among them, the filter pipe section of the automatic groundwater monitoring well corresponds to the main aquifer, and the wellhead is equipped with a protective cover to prevent human damage and natural interference; the submersible pressure gauge is placed in the groundwater monitoring well and converts the hydrostatic pressure into water level value.

[0069] (3) Meteorological and hydrological unit, which connects to meteorological stations to obtain real-time and forecast data such as rainfall, temperature, humidity, wind speed, and evaporation, captures external driving factors, and provides forward-looking input for model prediction.

[0070] The green power supply solution of solar energy and batteries is given priority. All monitoring data is transmitted to the decision-making level in real time and wirelessly through IoT technologies such as 4G / 5G and NB-IoT, forming an integrated dynamic monitoring network of "surface-underground-meteorology", providing a complete and timely data foundation for subsequent intelligent decision-making.

[0071] The decision-making layer, acting as the "intelligent brain" of the entire system, undertakes core computing and decision-making functions. Its physical carrier is an embedded edge computing gateway deployed at the gate station. This edge computing model ensures that the system can still operate independently and reliably with local computing power even in extreme situations such as network anomalies, while reducing data transmission latency and bandwidth pressure.

[0072] The intelligence of the decision-making level stems from two core software modules that operate internally: a coupled hydrological model and intelligent control algorithms.

[0073] (1) The coupled hydrological model is the "prediction engine" of the system. It is a simplified groundwater-surface water coupled numerical model, which is often expressed mathematically in state-space form as follows: Characterizes the system state It also includes surface water levels. and groundwater level ,Right now This mathematically enables a unified description and prediction of the dynamic linkage between groundwater and surface water, thereby allowing for the dynamic simulation of the comprehensive impact of different gate openings on the water system in the future.

[0074] Observation equation: .

[0075] in, Let k be the system's state vector at time k, containing the surface water level. and groundwater level ; This represents the control input, namely the gate opening. ; For measurable disturbances, upstream inflow Rainfall P and other parameters are used as feedforward terms to improve the model's prediction accuracy. The observed values ​​are the surface water and groundwater levels measured by the sensors; the A, B, E, and C model matrices are obtained through system identification or linearization of the physical model, and describe the dynamic relationship between the system state, control input, external disturbances, and observed values. , These are process noise and observation noise, respectively.

[0076] (2) The intelligent control algorithm preferably uses model predictive control (MPC) as the "decision engine" to solve the future in each control cycle k. Optimal control sequence in the prediction time domain .

[0077] Solving an innovative multi-objective optimization function through rolling: The optimal gate opening command is calculated, taking into account three major objectives: ecological flow, groundwater level, and stable equipment operation. Thus, long-term ecological management goals can be transformed into an immediate optimization problem that needs to be minimized in each control cycle. By continuously solving this function, the system behavior is driven to continuously tend towards a high guarantee rate.

[0078] in, Let i be the downstream cross-sectional flow predicted by the model at time i in the future; The set ecological target flow rate; The key point groundwater level predicted by the model at time i in the future; The set threshold for groundwater ecological water level; This represents the change in the gate opening. W, V, and R are positive definite weight matrices used to weigh the importance of different objectives, including: W is the ecological flow tracking weight, which is set relatively high to ensure that the flow prioritizes the target; V is the groundwater level protection weight to prevent excessive damage to groundwater while ensuring flow; and R is the control action smoothness weight to avoid frequent opening and closing of gates and protect equipment. For prediction in the time domain; To control the time domain ( ).

[0079] To achieve the core indicator of "ecological guarantee rate ≥ 95%", the decision-making body also includes an adaptive module. This module monitors the recent actual ecological flow guarantee rate in real time. And through the formula The weights in the optimization function are dynamically adjusted, enabling the system to learn and correct itself. This endows the system with self-learning and adaptability by calculating the current guarantee rate in real time. and the target guarantee rate By comparing the target value of 95% guarantee rate, the ecological flow tracking weight W in the control algorithm is dynamically adjusted to ensure that the system can actively maintain the set high ecological guarantee rate.

[0080] The execution layer, the final actuator of the entire system, is responsible for accurately, reliably, and safely executing every control command issued by the decision-making layer, directly driving the gate to move, thereby achieving physical regulation of river and lake water levels. It mainly comprises three key components:

[0081] (1) The intelligent gate body, such as electric, hydraulic or screw-type hoist, serves as the final flow regulation device, and the change of its opening directly controls the size of the flow cross section.

[0082] (2) Gate controller, usually based on PLC, is responsible for receiving and converting decision instructions, driving the gate hoist to run, and supporting automatic, manual and remote control modes.

[0083] (3) High-precision feedback device, mainly an absolute encoder, is used to monitor and provide feedback on the accurate opening degree of the gate in real time. It includes: upper limit switch and lower limit switch for physical protection to prevent the gate from exceeding the mechanical travel range and causing equipment damage; torque / thrust sensor (optional but recommended) to monitor the load of the hoist in real time. When the load increases abnormally (such as the gate being stuck or there is a foreign object below), it can immediately trigger a protective shutdown and alarm to avoid equipment damage.

[0084] In the actual application of this system, (1) the system is started and parameters are configured. The core task is to set clear ecological control targets, namely the ecological flow target values ​​of key sections. groundwater ecological water level threshold Meanwhile, calibration parameters reflecting local geological and hydrological characteristics are loaded into the coupled hydrological model in the decision-making layer, and initial parameters such as prediction time domain, control time domain and optimization weight of the model prediction control algorithm are set. (2) After the system enters the running state, the perception layer begins to continuously and synchronously collect multi-dimensional data, including surface water level and flow rate upstream and downstream of the gate, groundwater level of monitoring wells along the shore, and access to real-time meteorological and hydrological information. All these data are transmitted to the decision-making layer in real time through the Internet of Things, providing a comprehensive data foundation for intelligent decision-making. (3) The decision-making layer starts core calculation based on real-time data. The coupled hydrological model uses the latest data as the initial condition and boundary to perform multi-scenario simulations of future time periods (such as 24 hours). Its essence is to carry out a series of "if...then..." deductions to predict the dynamic response process of groundwater and river and lake hydrology under different gate opening scenarios. (4) In this stage, the intelligent control algorithm compares the model's prediction results with the ecological target, and evaluates all scenarios by solving an optimization problem aimed at minimizing the target optimization function J, and finally calculates the optimal gate opening command that best balances ecological flow assurance, groundwater level maintenance and stable equipment operation. (5) After receiving the command, the execution layer drives the gate controller to precisely adjust the gate to the target opening; at the same time, the high-precision encoder feeds back the actual opening value to the decision layer in real time, forming an inner-loop closed-loop control that ensures execution accuracy. (6) The system takes this as the starting point and enters the next control cycle, repeating steps two to five to achieve continuous rolling optimization. More importantly, a background adaptive module will calculate the recent ecological assurance rate online. If it falls below the target of 95%, the weights in the objective function are dynamically optimized through an adaptive adjustment law, enabling the system to self-correct its behavior and thus ensuring that the long-term ecological guarantee rate remains stable above 95%.

[0085] For ease of understanding, an application scenario and specific implementation are provided corresponding to the above embodiments:

[0086] 1. Application scenarios and problems.

[0087] A seasonal river in North China, approximately 50 kilometers long, serves as an irrigation channel for farmland and a vital part of the regional ecological conservation efforts. In recent years, due to reduced upstream flow and inefficient water resource allocation, the lower reaches of the river frequently experience flow interruptions during the dry season. This has led to riverbed ecological degradation and a continuous decline in groundwater levels along the riverbanks, seriously threatening regional ecological security. Traditional artificial sluice gate control methods are unable to accurately respond to complex hydrological changes, with an ecological flow guarantee rate of only about 75%.

[0088] 2. System hardware deployment.

[0089] (1) Deployment of the perception layer.

[0090] ① Surface water monitoring unit: 50 meters upstream of the control gate, a radar water level gauge (measurement accuracy ±2mm) and a Doppler flow meter (measurement accuracy ±1%) are installed to monitor the upstream water level and flow rate.

[0091] At a key ecological section 100 meters downstream of the sluice gate, another radar level gauge and Doppler flow meter were installed to monitor the downstream water level and ecological flow after regulation. Figure 4 This is a schematic diagram of upstream and downstream water level and flow rate observation data provided in an embodiment of the present invention, as shown in the following figure. Figure 4 As shown (displayed as weekly average data).

[0092] ② Groundwater Monitoring Unit: Along both banks of the river, three monitoring profiles are set up at distances of 50 meters, 150 meters, and 300 meters from the riverbank, with a total of six groundwater monitoring wells. The wells are 15 meters deep, and the filter pipes correspond to the main aquifers. Each well is equipped with a submersible pressure level gauge (measurement accuracy ±5mm) for real-time monitoring of groundwater level dynamics.

[0093] ③ Meteorological and hydrological unit: A small automatic weather station is installed on the top of the gate control room to monitor rainfall, temperature, humidity, and evaporation. Figure 5 This is a schematic diagram of meteorological observation data provided in an embodiment of the present invention, specifically as follows: Figure 5 As shown (displayed as weekly average data). Quantitative Rainfall Forecast (QPF) data for the next 24 hours was accessed via a 4G network from the local meteorological bureau.

[0094] (2) Deployment by the decision-making level.

[0095] An industrial-grade embedded edge computing gateway (configuration: 4-core ARM processor, 4GB RAM, 64GB storage) is installed in the gate control room. This gateway has a built-in 4G communication module and Ethernet port, and is responsible for running the coupled hydrological model and intelligent control algorithm.

[0096] (3) Execution layer configuration.

[0097] Intelligent gate body: adopts electric screw-type gate with opening and closing force of 100kN, stroke of 2 meters, and good self-locking performance.

[0098] Gate controller: A programmable logic controller (PLC) (model: Siemens S7-1200) is used to receive instructions (4-20mA analog signal) from the edge computing gateway and control the gate opening and closing motor.

[0099] Feedback device: An absolute encoder (model: XX-ABS360, resolution 0.1mm) is installed on the drive shaft of the gate hoist to provide real-time feedback on the precise opening degree of the gate. Upper and lower limit switches are also provided as hardware protection.

[0100] 3. Software and model configuration.

[0101] (1) Construction and calibration of coupled hydrological model.

[0102] A simplified groundwater-surface water coupling model is deployed in the edge computing gateway. This model is based on the MODFLOW model kernel and is further developed and simplified to generalize the study area into a two-dimensional unconfined aquifer, and the river flow is described by the linearized Saint-Venant equation.

[0103] The model was calibrated and validated using hydrological monitoring data from the past year to ensure that it can accurately simulate key processes such as river seepage and groundwater recharge. Figure 6 This is a schematic diagram comparing the measured groundwater level and the simulated groundwater level of the MODFLOW model provided in this embodiment of the invention, as shown in the following figure. Figure 6 As shown (using weekly average data), the calibrated model has a Nash efficiency coefficient (NSE) of over 0.85.

[0104] (2) Setting ecological control targets.

[0105] Ecological flow target ( Based on the Tennant method and in combination with the needs of local aquatic organisms, the ecological base flow at the downstream key section is set at 0.8 m³ / s.

[0106] Groundwater level threshold ( To protect the vegetation along the riverbank, the groundwater depth of the monitoring well located 50 meters from the riverbank must not be less than 4 meters.

[0107] (3) Intelligent control algorithm parameter settings

[0108] Control algorithm: Model predictive control (MPC) is adopted.

[0109] Prediction time domain ( ): 24 hours.

[0110] Control Time Domain ( ): 6 hours.

[0111] Control cycle: 1 hour.

[0112] Optimize the objective function weights: ecological flow tracking weight W: initial value set to 10.0; groundwater level protection weight V: initial value set to 5.0; control action smoothness weight R: initial value set to 2.0.

[0113] Working process and steps (taking a typical control cycle as an example).

[0114] (1) Step S1: Data collection and transmission (08:00 daily).

[0115] The sensing layer is activated, collecting upstream water level (2.5 meters), upstream flow rate (1.2 m³ / s), downstream water level (1.8 meters), downstream flow rate (0.9 m³ / s), water level data from 6 groundwater wells, as well as information on no rainfall in the past 24 hours and a forecast of no rainfall in the next 24 hours monitored by the meteorological station.

[0116] All data is packaged and encrypted via the 4G network before being transmitted to the edge computing gateway.

[0117] (2) Step S2: Model prediction and scenario simulation.

[0118] The coupled hydrological model in the edge computing gateway uses all currently collected data as the initial state to make rolling predictions for the next 24 hours.

[0119] The model simulates three scenarios:

[0120] ①Scenario A: The gate remains at its current opening (1.0 meter). The prediction results show that after 12 hours, the downstream flow will drop to 0.75 m³ / s (below the target of 0.8 m³ / s), and the groundwater level will slowly decrease.

[0121] ②Scenario B: The gate opening is increased to 1.2 meters. The prediction results show that the downstream flow will rise immediately, but it will cause the upstream water level to drop too quickly and the benefit to groundwater recharge will not be obvious.

[0122] ③Scenario C: The gate opening is reduced to 0.8 meters. The prediction results show that, with appropriate water storage, the downstream flow can be stabilized at about 0.82 m³ / s after 6 hours and can be maintained at no less than the target value for the next 18 hours. At the same time, it has a continuous recharge effect on groundwater.

[0123] (3) Step S3: Intelligent optimization decision.

[0124] The MPC control algorithm calls the optimization solver to evaluate all the above scenarios and calculate the corresponding objective function J value.

[0125] Calculations show that scenario C has the smallest objective function J value because it best balances the objectives of ecological flow assurance and groundwater maintenance, and the gate operation is smooth.

[0126] Therefore, the algorithm generates the optimal decision: adjust the gate opening from the current 1.0 meter to 0.8 meters.

[0127] (4) Step S4: Instruction execution and feedback.

[0128] The edge computing gateway sends the instruction (target opening of 0.8 meters) to the PLC via a 4-20mA analog signal.

[0129] The PLC drives the electric gate hoist to move the gate to an opening of 0.8 meters.

[0130] The absolute encoder monitors in real time and feeds back the actual opening of 0.799 meters to the PLC and edge computing gateway to confirm that the operation was successful and a closed loop was formed. The system status is then updated.

[0131] (5) Step S5: Adaptive learning (runs in the background)

[0132] The system backend calculates the ecological traffic guarantee rate for the past 30 days every 24 hours. Suppose that on a certain day, the calculation yielded... =93% (below the 95% target), the adaptive module according to the formula The traffic weight W was automatically increased from 10.0 to 10.1. This makes the controller slightly more inclined to prioritize traffic in subsequent adjustments.

[0133] (6) Implementation results.

[0134] Through the aforementioned continuous closed-loop regulation, the system operated stably on the river for a full hydrological year. Statistical results showed that the ecological flow guarantee rate significantly increased from 75% before implementation to 97.8%. The average groundwater level in key areas along the river rose by 0.35 meters, effectively curbing the trend of ecological degradation.

[0135] By constructing a groundwater-surface water coupling model, the traditional "separate land and water" control mode has been completely transformed, achieving holistic and coordinated optimization of the regional aquatic ecosystem. Employing a model predictive control (MPC) algorithm, the system can continuously optimize gate opening based on real-time data and future weather forecasts, enabling proactive regulation to prevent problems before they occur, thereby increasing the ecological flow guarantee rate to over 95%. It possesses adaptive learning capabilities, dynamically adjusting control strategies according to the guarantee rate achieved, demonstrating strong robustness. The deep integration of IoT sensing, hydrological mechanism simulation, and automatic control technologies has formed a closed-loop intelligent solution, significantly improving the precision and automation of water resource management.

[0136] Example 4

[0137] Corresponding to the above method embodiments, this invention provides a device for intelligent gate for groundwater-river-lake ecological water level linkage regulation. Figure 7 This is a schematic diagram of the structure of a smart gate device for groundwater-river-lake ecological water level linkage regulation provided in an embodiment of the present invention, as shown below. Figure 7 As shown, the device for the intelligent gate for groundwater-river-lake ecological water level linkage regulation is applied to an intelligent gate system for groundwater-river-lake ecological water level linkage regulation. The system includes a sensing layer, a decision-making layer, and an execution layer. The device may include:

[0138] The target data acquisition module 401 is used to acquire target data collected in real time through the perception layer; the target data includes: surface water level and flow data, groundwater level data, and meteorological data.

[0139] The state change prediction module 402 is used to predict the state changes of surface water and groundwater under different gate opening scenarios within a set time period in the future, based on target data and the coupled hydrological model in the decision layer.

[0140] The optimal gate opening command generation module 403 is used to generate the optimal gate opening command by performing optimization calculations based on predicted state changes and preset ecological control objectives through intelligent control algorithms in the decision layer.

[0141] The actual opening feedback module 404 is used to send the optimal gate opening command to the execution layer to drive the gate to adjust to the target opening and to obtain the actual opening of the gate for feedback.

[0142] The intelligent gate device for groundwater-river / lake ecological water level linkage regulation provided in this invention can acquire target data collected in real time through the sensing layer. Based on the target data, it continuously predicts the state changes of surface water and groundwater under different gate opening scenarios within a set future time period. Based on the predicted state changes and preset ecological control targets, it performs optimization calculations through intelligent control algorithms in the decision layer to generate the optimal gate opening command. The optimal gate opening command is then sent to the execution layer to drive the gate to adjust to the target opening, and the actual gate opening is obtained for feedback. In this method, by coupling the hydrological model and the intelligent algorithm to continuously optimize the gate opening, the ecological flow guarantee rate is improved, effectively enhancing the ecological protection level and the system's adaptive capability.

[0143] In some embodiments, the sensing layer includes a surface water monitoring unit, a groundwater monitoring unit, and a meteorological and hydrological unit; the target data acquisition module is further configured to collect water level and flow data of the upstream and downstream sections of the gate through the surface water monitoring unit; collect groundwater level data along the coast through the groundwater monitoring unit; and collect real-time meteorological data and rainfall forecast data for future periods through the meteorological and hydrological unit.

[0144] In some embodiments, the decision layer is deployed on an edge computing gateway; the state change prediction module is also used to use the currently collected target data as the initial state and boundary conditions of the coupled hydrological model; to use different assumed gate openings as control inputs, and upstream inflow and rainfall forecasts as feedforward disturbance inputs; to run the coupled hydrological model and output downstream flow prediction sequences and groundwater level prediction sequences corresponding to each assumed gate opening.

[0145] In some embodiments, the optimal gate opening command generation module is further configured to construct a target optimization function that includes ecological flow tracking error, groundwater level deviation threshold, and gate action stability penalty term; to obtain a target gate opening sequence by rolling the solution of the target optimization function based on the downstream flow prediction sequence and the groundwater level prediction sequence; the target gate opening sequence is the gate opening sequence in the future control time domain that minimizes the value of the target optimization function; and to determine the first opening value in the target gate opening sequence as the current optimal gate opening command.

[0146] In some embodiments, the objective optimization function is: ;in, Let i be the downstream cross-sectional flow predicted by the model at time i in the future; The set ecological target flow rate; The key point groundwater level predicted by the model at time i in the future; The set threshold for groundwater ecological water level; denoted as , where is the change in gate opening; W, V, and R are positive definite weight matrices.

[0147] In some embodiments, the optimal gate opening instruction generation module is also used to calculate the recent actual ecological flow guarantee rate; compare the actual ecological flow guarantee rate with the preset guarantee rate target value to obtain a comparison result; and dynamically adjust the weight of the ecological flow tracking error term in the objective optimization function based on the comparison result.

[0148] In some embodiments, the execution layer includes a gate controller and a high-precision feedback device; the actual opening degree feedback module is further used to send the optimal gate opening degree command to the gate controller; the gate controller drives the gate hoist to adjust the gate to the target opening degree; the actual gate opening degree is measured by the high-precision feedback device and fed back to the decision layer to verify the execution accuracy.

[0149] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0150] Example 5

[0151] This invention also provides an electronic device for operating the aforementioned method of intelligent gate for coordinated regulation of groundwater and river / lake ecological water levels; see also Figure 8 The diagram shows the structure of an electronic device, which includes a memory 500 and a processor 501. The memory 500 stores one or more computer instructions, which are executed by the processor 501 to realize the above-mentioned method of intelligent gate for groundwater-river-lake ecological water level linkage regulation.

[0152] Furthermore, Figure 8 The electronic device shown also includes a bus 502 and a communication interface 503. The processor 501, the communication interface 503, and the memory 500 are connected via the bus 502.

[0153] The memory 500 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 503 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 502 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0154] Processor 501 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 501 or by instructions in software form. Processor 501 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 500, and processor 501 reads information from memory 500 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0155] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-mentioned method for the intelligent gate for the linkage regulation of groundwater-river and lake ecological water levels. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0156] The computer program product of the method for intelligent gate for groundwater-river-lake ecological water level linkage regulation provided in the embodiments of the present invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0157] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0158] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0159] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0161] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent gate control of groundwater-river-lake ecological water levels, characterized in that, An intelligent gate system for coordinated regulation of groundwater and river / lake ecological water levels is applied, the system comprising a sensing layer, a decision-making layer, and an execution layer; the method includes: Acquire target data collected in real time through the sensing layer; the target data includes: surface water level and flow data, groundwater level data, and meteorological data; The decision layer is deployed on the edge computing gateway; using the coupled hydrological model in the decision layer, based on the target data, the state changes of surface water and groundwater under different gate opening scenarios within a future set time period are predicted in a rolling manner. The state-space form of the coupled hydrological model is described above. for: Characterizes the system state It also includes surface water levels. and groundwater level ,Right now ; Observation equation: ;in, Let k be the system's state vector at time k, containing the surface water level. and groundwater level ; This represents the control input, namely the gate opening. ; For measurable disturbances; These are the observed values, i.e., the surface water and groundwater levels measured by the sensors; the A, B, E, and C model matrices are obtained through system identification or by linearizing the physical model. , These are process noise and observation noise, respectively. Based on the predicted state changes and the preset ecological control objectives, the optimal gate opening command is generated through optimization calculations performed by the intelligent control algorithm in the decision-making layer. The optimization calculation includes constructing and solving a multi-objective optimization function: ; in, Let i be the downstream cross-sectional flow predicted by the model at time i in the future; The set ecological target flow rate; The key point groundwater level predicted by the model at time i in the future; The set threshold for groundwater ecological water level; V represents the change in gate opening; W, V, and R are positive definite weight matrices. The optimal gate opening command is sent to the execution layer to drive the gate to adjust to the target opening and to obtain the actual gate opening for feedback.

2. The method according to claim 1, characterized in that, The sensing layer includes a surface water monitoring unit, a groundwater monitoring unit, and a meteorological and hydrological unit; acquiring target data collected in real time through the sensing layer includes: The surface water monitoring unit collects water level and flow data at the upstream and downstream sections of the gate. The groundwater monitoring unit collects groundwater level data along the coast. The meteorological and hydrological unit collects real-time meteorological data and rainfall forecast data for future periods.

3. The method according to claim 1, characterized in that, The method of utilizing a coupled hydrological model in the decision-making layer to predict the state changes of surface water and groundwater under different gate opening scenarios within a future set time period, based on the target data, includes: The currently collected target data is used as the initial state and boundary conditions of the coupled hydrological model; Different assumed gate openings are used as control inputs, and upstream inflow and rainfall forecasts are used as feedforward disturbance inputs; Run the coupled hydrological model to output the downstream flow prediction sequence and the groundwater level prediction sequence corresponding to each assumed gate opening degree.

4. The method according to claim 3, characterized in that, The process of optimizing and calculating the gate opening command through intelligent control algorithms in the decision-making layer includes: Construct an objective optimization function that includes ecological flow tracking error, groundwater level deviation threshold, and gate action stability penalty term; The target gate opening sequence is obtained by rolling the solution of the objective optimization function based on the downstream flow prediction sequence and the groundwater level prediction sequence; the target gate opening sequence is the gate opening sequence in the future control time domain that minimizes the value of the objective optimization function; The first opening value in the target gate opening sequence is determined as the current optimal gate opening command.

5. The method according to claim 1, characterized in that, The execution layer includes a gate controller and a high-precision feedback device; the process of driving the gate to adjust to the target opening degree and obtaining the actual opening degree of the gate for feedback includes: The optimal gate opening command is sent to the gate controller; The gate controller drives the gate opening and closing mechanism to adjust the gate to the target opening degree; The actual opening degree of the gate is measured by the high-precision feedback device and fed back to the decision-making level to verify the execution accuracy.

6. A groundwater-river-lake ecological water level linkage regulation intelligent gate system, used to implement the groundwater-river-lake ecological water level linkage regulation intelligent gate as described in any one of claims 1-5, characterized in that, include: The sensing layer is used to collect real-time data on surface water level and flow, groundwater level, and meteorological data. The decision-making layer, which is communicatively connected to the perception layer, has a built-in coupled hydrological model and intelligent control algorithm, which is used to perform rolling prediction and optimization decision-making based on the data of the perception layer and generate the optimal gate opening command. The execution layer, which is communicatively connected to the decision layer, is used to receive the instructions and drive the gate to move, while also providing feedback on the actual opening degree of the gate.

7. A device for intelligent gate for groundwater-river-lake ecological water level linkage regulation, characterized in that, The intelligent gate system for groundwater-river-lake ecological water level linkage regulation, as described in claim 6, comprises a sensing layer, a decision-making layer, and an execution layer; the device comprises: The target data acquisition module is used to acquire target data collected in real time through the perception layer; the target data includes: surface water level and flow data, groundwater level data, and meteorological data. The state change prediction module is used to use the coupled hydrological model in the decision-making layer to predict the state changes of surface water and groundwater under different gate opening scenarios within a set future time period based on the target data. The optimal gate opening command generation module is used to generate the optimal gate opening command by performing optimization calculations based on predicted state changes and preset ecological control objectives through intelligent control algorithms in the decision-making layer. The actual opening feedback module is used to send the optimal gate opening command to the execution layer to drive the gate to adjust to the target opening and to obtain the actual opening of the gate for feedback.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method for groundwater-river-lake ecological water level linkage regulation of the intelligent gate as described in any one of claims 1 to 5.

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