Open channel high-flow interference water level control method based on storage compensation and improved PID coupling

Abstract

The invention discloses an open channel high-flow interference water level control method based on storage compensation and improved PID coupling, and the method comprises the following steps: collecting channel operation parameters in real time, including current flow, water level fluctuation amplitude, predicted demand and on-way resistance coefficient distribution; dynamically constructing a channel damping effect model based on the on-way resistance coefficient distribution, calculating water level control response delay time, and generating a preposed adjustment coefficient; dynamically adjusting the actual participation volume according to the maximum effective storage volume and the maximum effective storage volume of the stagnant storage area; a compensation control quantity is generated by the input improved PID controller; and the compensation control quantity is executed to adjust the opening degree of the channel gate, and dynamic suppression of water level fluctuation is achieved. The water level stability and the control effect of the open channel can be improved.

Description

Technical Field

[0001] This invention relates to the field of water level control technology, and in particular to a method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling. Background Technology

[0002] Open channels are widely used in agricultural irrigation, water resource allocation, and urban water conveyance systems. Their water level stability directly impacts water use efficiency and operational safety in downstream areas. However, existing water level control methods still have significant limitations in responding to sudden disturbances. Especially under conditions of drastic flow fluctuations, supply-demand mismatches, or sudden changes in operating conditions, water levels are prone to violent fluctuations, posing challenges to system stability and control efficiency.

[0003] Existing water level control methods largely rely on traditional proportional-integral-derivative (PID) control algorithms, failing to fully consider the dynamic characteristics and control lag issues in water conveyance systems. Under large flow disturbances, these methods are prone to delayed response and control instability. Furthermore, in terms of control strategy, current methods often lack a coupled design between feedforward and compensation mechanisms, failing to dynamically adjust the control output based on the disturbance propagation process and actual participation capacity, thus making it difficult to achieve precise regulation of water level deviations. Particularly in long-distance water conveyance channels, control response often exhibits delays, and existing methods lack proactive adaptive adjustment mechanisms for these delays, limiting the system's control accuracy and robustness under complex operating conditions.

[0004] Therefore, there is an urgent need for a water level control method that has adaptive adjustment capabilities, can fully integrate the system's own storage capacity, and can cope with large flow disturbances, so as to improve the water level stability and control effect of open channels during sudden disturbances. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for controlling water level in open channels with high flow disturbances based on storage compensation and improved PID coupling, comprising the following steps: Real-time collection of target channel operating parameters, including current flow rate, water level fluctuation range, predicted demand, and distribution of resistance coefficient along the channel; Based on the distribution of friction coefficients along the channel, a channel damping effect model is dynamically constructed to calculate the water level control response delay time and generate the pre-adjustment coefficient. Based on the current flow rate, the water level fluctuation range, and the predicted demand, the maximum effective storage volume of the storage area is calculated using an adaptive algorithm, and the actual participation volume of the maximum effective storage volume in the current adjustment cycle is dynamically adjusted. The pre-adjustment coefficient is combined with the actual participating volume input to improve the PID controller and generate a compensation control quantity. The compensation control quantity is executed to adjust the opening of the channel gate, thereby achieving dynamic suppression of water level fluctuations.

[0007] As a preferred embodiment of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling described in this invention, the specific steps for dynamically constructing a channel damping effect model based on the friction coefficient distribution include: The distribution of friction coefficients along the friction path is used as the structural input feature of the model; The actual flow rate, water level, and response delay time between upstream and downstream of each segment of the channel are collected during different scheduling periods and used as dynamic labels for model training samples. Construct a modeling framework with time-aware capabilities, preferably a deep neural network model that integrates convolutional neural networks and long short-term memory network structures; The deep neural network is trained end-to-end using training sample data, enabling it to learn the damping propagation law in channel hydrodynamics and output the control response delay time of each channel segment. Based on the delay time, a pre-adjustment coefficient is generated to complete the construction of the channel damping effect model.

[0008] As a preferred embodiment of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling described in this invention, wherein: the expression for the pre-adjustment coefficient is:

[0009] in: This is the pre-adjustment coefficient; The flow response time constant reflects the system's response inertia; The baseline flow rate set for the system; Current traffic; The water level control response delay time is calculated using the following formula: L represents the total length of the channel. The wave propagation speed is calculated based on the drag coefficient distribution. The formula for calculating wave propagation speed is: ,in, The drag coefficients for each segment are: To correspond to the drag coefficient of each segment The actual physical length, that is, the length of the channel section within the range of the resistance coefficient.

[0010] As a preferred embodiment of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling described in this invention, the formula for calculating the maximum effective storage volume is:

[0011] in, , , These are the weighting coefficients. This indicates the system's responsiveness to instantaneous traffic. This indicates the sensitivity to water level fluctuations. This indicates a preference for compensation for the predicted supply-demand gap, and + + =1; This represents a function that takes positive values, i.e. Its meaning is: when the predicted demand... Higher than the current traffic If the value is positive, the term takes the positive difference; otherwise, the term is 0.

[0012] As a preferred embodiment of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling described in this invention, the step of calculating the maximum effective storage volume of the stagnation area using the adaptive algorithm is as follows: Based on historical operational data and channel topographic information, a three-dimensional feature curve of water level-area-volume for the water storage area is extracted. Extract the current operating status input features as input variables for the adaptive algorithm; wherein, the operating status input features include, but are not limited to, current channel flow, water level fluctuation range, predicted demand change, adjustment cycle and upstream and downstream control targets; Construct an adaptive function mapping structure to establish the mapping relationship between the current perturbation conditions and the optimal adjustable volume; Calculate the maximum effective storage volume of the stagnation zone under the constraints of current flow load and predicted demand changes; Introducing dynamic adjustment coefficient Calculate the actual participating volume for the current adjustment cycle.

[0013] As a preferred embodiment of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling described in this invention, wherein: the dynamic adjustment coefficient The expression is:

[0014] in, This refers to the baseline water level fluctuation range or threshold. The baseline flow rate; , To adjust the weighting coefficients.

[0015] As a preferred embodiment of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling described in this invention, wherein: the compensation control quantity of the improved PID controller The calculation formula is as follows:

[0016] in: To compensate for the control input, the output signal of the PID controller is improved to adjust the opening of the channel gate. This indicates the current water level deviation, which is the difference between the current collected water level and the target set water level, representing the degree of deviation. To account for response latency With traffic The pre-adjustment coefficient of the coupling relationship is used to correct and improve the response sensitivity of the PID controller; The actual participating volume in the current period is defined as follows: , used to dynamically adjust the controller output; This is the deviation integral term, representing the cumulative value of the water level deviation over time, used to eliminate steady-state deviation; The deviation change rate represents the trend of water level changes and is used to respond to disturbances in advance. The proportional gain coefficient characterizes the system's response to the current water level deviation. The degree of immediate response; The integral gain coefficient characterizes the system's response speed to accumulated deviations and can reduce steady-state error; The differential gain coefficient characterizes the system's ability to respond to water level change trends and is used for predictive regulation.

[0017] As a preferred embodiment of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling described in this invention, wherein the predicted demand is obtained through any of the following methods: ARIMA model prediction based on historical upstream water inflow data; The output of the neural network demand forecasting model is combined with weather forecasts.

[0018] As a preferred embodiment of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling described in this invention, the steps for obtaining the friction coefficient distribution are as follows: The entire channel is divided into N segments with essentially the same physical properties; Based on the known geometric parameters and structural type of each segment, a preliminary estimate is made using Manning's formula:

[0019] in, The drag coefficient, This refers to the Manning roughness of this section of the channel; The hydraulic radius; The slope of this section of the channel; By installing flow meters and water level gauges at the cross-section, and combining the measured values ​​of flow velocity, water level, and flow rate, the drag coefficient of each segment is adjusted in real time using numerical inversion or least squares fitting methods. ; By splicing together all the segmented drag coefficients, a complete friction coefficient distribution along the friction path is formed. .

[0020] This invention also provides an open channel high-flow-rate disturbance water level control system based on storage compensation and improved PID coupling, applied to the aforementioned open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling, comprising: The data acquisition unit is used to acquire the current flow of the channel in real time. The model calculation unit is used to construct a damping effect model based on the distribution of friction coefficient and calculate the response delay time and the pre-adjustment coefficient. A volume calculation unit is used to calculate the maximum effective storage volume and the actual participating volume, and to adjust it based on a dynamic adjustment coefficient. A PID control unit, wherein the PID controller is used to generate a compensation control quantity based on the pre-adjustment coefficient, the actual participating volume and the water level deviation; An execution control unit is used to control the channel gate actuator according to the compensation control amount to realize closed-loop regulation and control of water level.

[0021] The beneficial effects of this invention are: 1. In this invention, a channel damping effect model is dynamically constructed, and the pre-adjustment coefficient is calculated based on the distribution of friction coefficient, water level control response delay time and current flow rate. This effectively solves the problem of lag, advance or lag in response of traditional PID control in open channels, and improves the controller's adaptability and response sensitivity to wave propagation characteristics.

[0022] 2. This invention introduces a modeling method that separates the maximum effective storage volume from the actual participating volume, combining the water level fluctuation amplitude and the predicted supply-demand difference. By adaptively adjusting and dynamically determining the participation level of the storage area, the system achieves refined compensation and optimal resource utilization when dealing with large-volume disturbances.

[0023] 3. This invention introduces the pre-adjustment coefficient and the actual participating volume into the improved PID controller to construct a new compensation control quantity control structure. Without changing the traditional PID architecture, it achieves coordinated adjustment of multi-dimensional disturbance factors, enhancing the control system's rapid response capability and disturbance suppression effect to sudden water level fluctuations. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart of the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0028] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0029] Example 1 Reference Figure 1 As an embodiment of the present invention, a method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling is provided, comprising the following steps: S1: Real-time collection of channel operating parameters, including current flow rate, water level fluctuation range, predicted demand, and distribution of resistance coefficients along the channel.

[0030] It should be noted that common channel types mainly include natural channels, artificial channels, and composite channels. Natural channels include naturally formed water conveyance paths such as rivers and streams; artificial channels are man-made water conveyance channels with regular cross-sections, mainly including earthen canals, concrete canals, and lined canals (such as cement-lined and stone-lined canals); composite channels are channel structures with retention areas or regulating ponds, capable of temporarily storing water and regulating flow. This application uses artificial open channels, especially lined or concrete channel systems with gates and storage capacity. These channels are commonly found in regional water diversion projects, urban regulating open channel systems, and industrial park water supply systems. These channels have well-defined cross-sections, regular structures, and can accommodate sensor equipment, facilitating real-time acquisition and feedback control of flow and water levels.

[0031] It should be noted that different channels may collect different operating parameters. For example, natural rivers often collect flow velocity, water level, water temperature, turbidity, and water quality; earthen canals often collect water level, flow rate, seepage rate, and slope stability. This application focuses on lined or concrete channels in artificial open channels, collecting current flow rate, water level fluctuation amplitude, predicted demand, and friction coefficient distribution. The current flow rate reflects the system's water conveyance state and is the core parameter for flow changes; the water level fluctuation amplitude reflects the intensity of disturbance and is a key indicator for determining whether a compensation mechanism needs to be activated; the predicted demand improves response foresight and avoids delayed adjustments; and the friction coefficient distribution reflects the physical nature of channel transmission lag and can be directly used to construct a channel damping effect model. In contrast, other parameters such as turbidity, sediment content, seepage rate, and water quality, while potentially affecting the water conveyance process, have limited impact on water level regulation in the short term and do not possess real-time control significance.

[0032] In one specific implementation, the channel operating parameters are obtained as follows: The current flow rate is obtained by installing ultrasonic flow meters or radar flow velocity sensors at the channel cross-section. The water level fluctuation range is measured at the channel cross-section using a water level gauge (such as a pressure water level gauge or an ultrasonic water level gauge). Demand forecasts can be obtained through any of the following methods: It can be based on the ARIMA model to predict based on historical upstream water inflow data and the output of a neural network demand prediction model that combines meteorological forecasts; The steps to obtain the friction coefficient distribution are as follows: The entire channel is divided into N segments with basically the same physical properties (such as every 100-500 meters) to ensure that the cross-sectional shape, material and slope of each segment are relatively stable. Based on the known geometric parameters and structural type of each segment, a preliminary estimate is made using Manning's formula:

[0033] in, The Manning roughness of this channel section can be obtained by referring to the manual or through actual measurement. The hydraulic radius (= wetted perimeter area / wetted perimeter length) can be calculated based on the cross-sectional geometry. The slope of this section of the channel; By installing flow meters and water level gauges at key cross-sections, and combining the measured values ​​of flow velocity, water level and flow rate, the resistance coefficient Ri of each section is adjusted in real time using numerical inversion or least squares fitting methods to make it more consistent with the current channel operation status. By splicing together all the segmented drag coefficients, a complete friction coefficient distribution along the friction path is formed. This serves as the input basis for channel damping modeling and water level delay prediction.

[0034] It should be noted that critical sections refer to representative measurement locations in the channel system that are highly sensitive to changes in flow and water level. The following types of sections are typically selected as critical sections for setting up measurement points: Nodes where the hydraulic boundaries change, such as the starting point, branching point, and confluence point of the channel; At points where the cross-sectional geometry, material, or slope changes abruptly, such as when a trapezoid changes into a rectangle or where the roughness changes significantly; High-frequency flow disturbances occur at points such as irrigation inlets, outlets, and locations near storage facilities. Representative sections within each set of standard segments, such as one representative section every 200-500 meters, are selected for monitoring.

[0035] By installing water level gauges at the aforementioned key cross-sections and Flow meters can improve the accuracy and representativeness of resistance coefficient inversion, ensuring that the friction coefficient distribution model is closer to the actual operating conditions.

[0036] It should be noted that physical properties include cross-sectional shape, material, and slope. Cross-sectional shape refers to the geometric structure of the channel's cross-section, such as rectangular, trapezoidal, circular, or U-shaped, which determines the relationship between flow rate and water depth and affects the calculation of the hydraulic radius. Material includes the channel lining or material type; different materials directly affect the roughness (Manning coefficient), thus influencing flow resistance. Slope, the elevation difference along the flow direction, is a crucial parameter driving gravity flow, affecting flow velocity and energy gradient.

[0037] In one specific implementation, the steps for obtaining predicted demand based on ARIMA model prediction using historical upstream water inflow data can be as follows: First, acquire historical water inflow sequences at the upstream section, including water inflow data for multiple time periods (e.g., the past 30 days or multiple scheduling cycles). Then, preprocess the collected data to ensure it meets the prerequisites for ARIMA modeling. Second, establish an ARIMA model based on the preprocessed flow data. Then, train the ARIMA model using membership data and perform prediction tests on a reserved validation set to evaluate prediction accuracy. Third, use the trained ARIMA model to continuously predict future water inflow values, and calculate the demand for the future time period based on the predicted flow and downstream water transfer plan.

[0038] In a specific implementation, the steps for obtaining the predicted demand from the output of a neural network demand forecasting model based on weather forecasts can be as follows: First, collect meteorological data highly correlated with water resource demand, including rainfall, evaporation, temperature, humidity, wind speed, and historical flow and water level data. Then, process these data from different dimensions uniformly and construct combined features as model input. Second, use a long short-term memory network as the framework for the neural network demand forecasting model. This framework may include an input layer, several hidden layers, and an output layer, with the output being the demand at the predicted time. Third, train the neural network demand forecasting model using historical samples. Fourth, deploy the trained neural network demand forecasting model. When using it, input the current weather forecast value and the system's historical operating data to output the predicted demand for the future in real time.

[0039] In summary, the purpose of step S1 is to acquire basic operational information reflecting the water conveyance status, disturbance intensity, forward regulation demand, and transmission damping effect of the artificial open channel by collecting four key parameters in real time: current flow rate, water level fluctuation amplitude, predicted demand, and distribution of friction coefficient. This provides comprehensive, accurate, and quantifiable data support for subsequent construction of dynamic damping models, calculation of response delay time, determination of regulation volume, and execution of compensation control, thereby ensuring that the entire control method has good real-time performance, adaptability, and predictability in complex high-flow-rate disturbance scenarios.

[0040] S2: Based on the distribution of the friction coefficient along the channel, a channel damping effect model is dynamically constructed, the water level control response delay time is calculated, and a pre-adjustment coefficient is generated.

[0041] In a specific implementation, the specific steps for dynamically constructing a channel damping effect model based on the distribution of friction coefficients along the channel include: The friction coefficient distribution obtained in the preceding steps is used as the structural input feature of the model; The actual flow rate, water level, and response delay time between upstream and downstream of each segment of the channel are collected during different scheduling periods and used as dynamic labels for model training samples. A modeling framework with time-aware capabilities is constructed, preferably a deep neural network model that integrates convolutional neural networks and long short-term memory network structures, to capture the nonlinear and time-varying characteristics of disturbances during their propagation in the channel; The deep neural network is trained end-to-end using training sample data, enabling it to learn the damping propagation law in channel hydrodynamics and output the control response delay time of each channel segment. Based on the delay time, a pre-adjustment coefficient is generated to complete the construction of the channel damping effect model.

[0042] It should be noted that the deep neural network model integrates the structures of convolutional neural networks and long short-term memory networks. The integration steps include: First, defining the input feature structure, which involves uniformly formatting the structural parameter information of multiple segmented channels (including friction coefficients, cross-sectional shape, slope, etc.) and corresponding time-series scheduling data (such as actual flow and water level changes in each time period) into tensor form of input data, and clearly defining the dimensional structure and number of channels of the input data to meet the modeling needs of the subsequent neural network; Second, constructing a convolutional feature extraction module, which involves designing a feature extraction network composed of multiple one-dimensional or two-dimensional convolutional layers to extract the local spatial features and adjacency influence relationships of channel structural features; and setting the convolutional kernel size, stride, and activation function (such as ReLU) according to the input feature dimension to improve the network's ability to express differences in structural features; Third, constructing a time-series modeling module, which involves connecting at least one layer of long short-term memory network after the convolutional feature extraction module to capture the propagation pattern of channel disturbances in the time dimension; and configuring... The system employs several key parameters: First, it sets the number of memory units, the dimension of hidden layers, and a masking mechanism for the input time series to support historical data input of varying lengths. Second, it establishes a feature fusion and output module, where the time-series features output from the time-series modeling module are input into a set of fully connected layers for fusion processing. The output layer structure is set according to the control objective, preferably an intermediate output structure that predicts the water level control response delay time or can be used for subsequent adjustment parameters (such as pre-adjustment coefficients). Third, it sets the loss function and optimization strategy, selecting a loss function suitable for the control delay prediction task, preferably the mean square error between the predicted value and the actual response delay. The learning rate, regularization term, and training batch size are then set in conjunction with the Adam optimizer to achieve effective updating of model parameters. Fourth, it completes the encapsulation and initialization configuration of the fusion model, integrating the above modules to construct the neural network model and setting the initial parameters, input / output interfaces, training process, and pre-processing steps, ultimately forming a deep fusion model with end-to-end learning capabilities.

[0043] In one specific implementation, the expression for the pre-adjustment coefficient is:

[0044] in, This is the pre-adjustment coefficient. The flow response time constant reflects the system's response inertia and is obtained empirically or through calibration. The baseline flow rate set for the system is usually the average flow rate during normal operation; The response delay time is calculated using the following formula: Where L is the total length of the channel. The wave propagation speed is calculated based on the drag coefficient distribution. The formula for calculating the wave propagation speed is: ,in, The drag coefficients for each segment are: To correspond to the drag coefficient of each segment The actual physical length, that is, the length of the channel section within the range of the resistance coefficient.

[0045] For example: Assuming the total length of the open channel is L = 1000 meters, the section from the control gate to the target water level control section is divided into N = 4 segments with different hydraulic characteristics. The parameters of each segment are as follows:

[0046] Calculate the resistance coefficient of each canal section according to Manning's formula. Calculate each canal section The values ​​are as follows: Segment number 1: Segment number 2: Segment number 3: Segment number 4: ; Then, calculate the wave propagation speed. The calculation process is as follows: Each section of the canal and Multiplication:

[0047] therefore, Finally, calculate the response delay time. The calculation process is as follows: Let the distance between the target control point and the gate be L = 1000 m, then:

[0048] In summary, the segmented drag coefficients obtained by calculating based on the Manning formula... Combined with the length of each section of the canal A damping effect model reflecting the actual hydraulic propagation characteristics was successfully constructed. Calculations show that the current hydraulic disturbance propagation velocity is approximately 0.143 m / s, corresponding to a control response delay of approximately 6993 seconds (about 1.94 hours), which is the feedforward regulation coefficient of the downstream control system. The generation provides a precise basis.

[0049] To illustrate further, when =6993 seconds; =2.5m 3 / s; =5.0m 3 / s; =45s, substitute:

[0050] Then at that moment This indicates that the current system participates in regulation with only a normal feedforward response, which is suitable for situations with large response delays and slow disturbance changes, in order to avoid overcompensation that could cause water level fluctuations.

[0051] In summary, step 2 above constructs a channel damping effect model with time-sensing capabilities based on the distribution of friction coefficients along the channel. This model characterizes the transmission path and time delay of disturbances in heterogeneous channel structures, thereby achieving dynamic modeling of the channel's hydraulic lag characteristics. This enhances the control system's ability to predict disturbance responses under complex flow regimes and structural differences, and provides physical support and data-driven basis for subsequent compensation control strategies. Furthermore, based on the constructed channel damping effect model, a pre-adjustment coefficient is generated. This aims to use the response delay time output by the model to pre-correct and quantify the impact of disturbances about to be transmitted downstream, guiding the improved PID controller to achieve proactive adjustment for disturbance pre-response. This effectively alleviates control lag and water level fluctuations caused by channel lag, improving the real-time performance, stability, and adjustment accuracy of water level control.

[0052] S3: Based on the current flow rate, water level fluctuation range, and predicted demand, calculate the maximum effective storage volume of the storage area using an adaptive algorithm, and dynamically adjust the actual participation volume of the maximum effective storage volume in the current regulation cycle.

[0053] It should be noted that the adaptive algorithm refers to an adaptive function mapping structure, which can be constructed using rule models, regression functions, neural networks, or other modeling methods with nonlinear mapping capabilities. After inputting key parameters such as the current flow rate of the channel, the amplitude of water level fluctuations, the predicted change in demand, and the adjustment cycle, it can output the estimated maximum effective storage volume of the storage area, and further adjust the actual storage volume to participate in the regulation based on the fluctuation trend.

[0054] In one specific implementation, the steps for calculating the maximum effective storage volume of the stagnation region using an adaptive algorithm are as follows: Based on historical operational data and channel topography information, a three-dimensional feature curve of water level-area-volume in the storage area is extracted to describe the static maximum volume response relationship under different water levels. Extract the current operating status input features as input variables for the adaptive algorithm; wherein, the operating status input features include, but are not limited to, current channel flow, water level fluctuation range, predicted demand change, adjustment cycle and upstream and downstream control targets; Construct an adaptive function mapping structure based on rule-based reasoning, autoregressive models, or lightweight neural networks (such as GRU / RNN) to establish the mapping relationship between the current perturbation conditions and the optimal adjustable volume; Under the constraints of current flow load and predicted demand changes, calculate the maximum storage volume of the retention area that can participate in water level regulation without affecting the safety of upstream water storage and the downstream regulation capacity. Introducing dynamic adjustment coefficient Calculate the actual participating volume for the current adjustment cycle.

[0055] In one specific implementation, constructing the adaptive function mapping structure includes the following steps: The maximum available adjustment volume that has been verified to be effective within the corresponding time period in the historical data is used as the output label to form supervised learning sample pairs, thus forming an input-output mapping training set. Based on the system's dynamic characteristics and real-time requirements, a mapping model is constructed by selecting a rule-based reasoning model, an autoregressive model, or a lightweight neural network model. The selected model is trained using historical sample data, and the model parameters are optimized through the loss function so that the output can accurately approximate the actual adjustment capacity. If neural network modeling is used, cross-validation, normalization mechanism and learning rate decay strategy can be introduced to improve the stability and robustness of the model. The trained mapping model was validated using an independent dataset to evaluate its accuracy in predicting the maximum effective storage volume under different perturbation scenarios.

[0056] It should be noted that the independent dataset refers to a subset of data extracted from the historical operating database but not used in the training of the adaptive function mapping model. This dataset can be distinguished from the training set by time period, operating condition type, or traffic fluctuation characteristics to ensure that it is independent of the training samples in terms of data source and does not overlap. It is used to verify the accuracy and stability of the model's estimate of the maximum effective storage volume under new perturbation conditions.

[0057] It should be noted that a retention area refers to an area in an open channel system that can temporarily store water to buffer fluctuations in flow and water level, including but not limited to: regulating ponds and spillways on both sides or downstream of the channel; reservoirs in front of controllable water level dams; and segmented branch canals or slow-flow sections designed to absorb or release part of the water. In this invention, the retention area is equivalent to a "hydraulic buffer" of the system, which can help stabilize the water level by "participating in regulation" when the water level is disturbed or there is a sudden flow surge.

[0058] In summary, by constructing an adaptive function mapping model based on rule-based reasoning, autoregressive models, or lightweight neural networks, it is possible to dynamically estimate the maximum effective storage volume of the storage area available for regulation in the current cycle, taking into account multi-dimensional disturbance information such as current channel flow, water level fluctuation amplitude, predicted demand changes, and regulation cycle. Furthermore, by combining the water level-area-volume characteristics with upstream and downstream control target constraints, it can autonomously adjust the actual participating volume to meet the balance between storage and release and have dynamic response capabilities, thereby improving the system's matching and timeliness of water level regulation strategies under large flow disturbances.

[0059] In one specific implementation, the expression for the maximum effective storage volume is:

[0060] in, Maximum effective storage volume; , , These are the weighting coefficients. This indicates the system's responsiveness to instantaneous traffic. This indicates the sensitivity to water level fluctuations. This indicates a preference for compensation for the predicted supply-demand gap, and + + =1, which can be trained or adjusted based on historical working conditions; This represents a function that takes positive values, i.e. Its meaning is: when the predicted demand... Higher than the current traffic When demand is less than or equal to supply, this item takes its positive difference; otherwise (demand is less than or equal to supply), this item is 0. The reason for this setting is that when... When the value of the term is negative, the actual volume of the participating capacity decreases, which does not conform to the actual control intention of the storage participation and may even cause improper negative feedback, resulting in the risk of unexpected flow leakage.

[0061] This formula, by introducing a weighted combination of three factors—current flow rate, water level fluctuation, and future supply-demand deviation—and using a positive function to avoid risk disturbances caused by over-compensation in forecasting, achieves dynamic, controllable, and robust estimation of storage capacity, providing a regulatory basis for open channel water level control that balances timeliness and safety.

[0062] In one specific implementation, the actual participating volume expression is:

[0063] in, This is a dynamic adjustment coefficient, and its value is based on... and The combination function is obtained, and its expression is:

[0064] in, This refers to the baseline water level fluctuation range or threshold. The baseline flow rate; , To adjust the weighting coefficients, and satisfy the following conditions: ; This is the ratio of the current water level fluctuation to the baseline fluctuation, representing the sensitivity of the system to regulation and the intensity of disturbances. The supply-demand gap ratio under a unit base flow indicates the urgency of the future supply-demand imbalance. If the predicted demand exceeds the supply, it incentivizes participation in storage regulation; otherwise, it is 0. This setting is to ensure the adjustment coefficient Always keep ≤1 to prevent over-adjustment; when the disturbance is small, <1, actual volume involved It decreases, entering a conservative adjustment mode.

[0065] This formula constructs a dynamic adjustment coefficient by normalizing the current water level fluctuation range with the predicted supply-demand gap and assigning adjustment weights. This allows for the full release of available capacity to quickly respond to control demands when there are severe disturbances or significant supply-demand imbalances, while actively suppressing the intensity of regulation when the operating state is stable or the prediction error is large, effectively preventing the risk of over-regulation. This achieves a balance between the accuracy, flexibility, and safety of regulation response in the process of water level control in the storage area.

[0066] For example, suppose: Current traffic =12.0m 3 / s; Current water level fluctuation range =0.18m; Predicting future time periods ( =10min) Demand m 3 / s; Set the hysteresis response weighting coefficient: =0.3、 =0.5、 =0.2; Baseline water level fluctuation range ; Baseline flow gap ; The weighting coefficient is =0.6, =0.4; Then, calculate the maximum effective storage volume. :

[0067] Substitute the data:

[0068] calculate:

[0069] Then, calculate the dynamic adjustment coefficient. :

[0070] Substitute the data into the calculation: =0.72 =0.6 so:

[0071] Therefore, the actual volume involved .

[0072] The actual value of the unit volume can be converted into the actual amount of water required for water level regulation. Here, the unit volume is defined as 1 m³. 3 Using this as a standard, 2.88 units of volume can be converted to 2.88m³. 3 This water volume can be directly used to guide the control of sluice gate opening or the decision-making of water storage areas, thereby achieving precise dynamic water level regulation.

[0073] In summary, step S3 constructs an adaptive function mapping structure based on rule-based reasoning, autoregressive models, or lightweight neural networks. It dynamically estimates the maximum effective storage volume of the storage area using disturbance information such as current channel flow, water level fluctuation amplitude, predicted demand changes, and regulation cycle. Combined with fluctuation trends, it adjusts the actual participation volume within the current regulation cycle. This achieves dynamic matching and controllable release of the storage area's ability to participate in regulation under water level disturbance conditions, ensuring that the regulation response has good timeliness and adaptability while meeting the constraints of storage-discharge balance and upstream and downstream control targets.

[0074] S4: Combine the pre-adjustment coefficient with the actual volume input to improve the PID controller and generate a compensation control quantity.

[0075] Specifically, the formula for calculating the compensation control quantity of the improved PID controller is as follows:

[0076] in: To compensate for the control quantity, the unit is dimensionless or expressed as a percentage. It is an improved PID controller output signal used to adjust the opening of the channel gate, reflecting the comprehensive adjustment force. This indicates the current water level deviation, which is the difference between the current collected water level and the target set water level. It represents the degree of deviation and is expressed in meters (m). To account for response latency With traffic The pre-adjustment coefficient for coupling relationship, in dimensionless form, is used to correct and improve the response sensitivity of the PID controller. The actual participating volume in the current period is defined as follows: Used to dynamically adjust the controller output, the unit is m. 3 ; This is the deviation integral term, representing the cumulative value of the water level deviation over time, used to eliminate steady-state deviation, with units of m·s; The deviation rate indicates the trend of water level change (fast or slow), used for early response to disturbances, in m / s; The proportional gain coefficient, dimensionless or % / m, characterizes the system's response to the current water level deviation. The degree of immediate response; The integral gain coefficient, with units of 1 / s or % / (m·s), characterizes the system's response speed to cumulative deviation and can reduce steady-state error; The differential gain coefficient, in units of s or %·s / m, characterizes the system's ability to respond to water level change trends and is used for predictive regulation.

[0077] It should be noted that, , , It can be a fixed value, an experimental setting value based on historical operating data, or dynamically adjusted according to the system operating status using an adaptive function.

[0078] It should also be noted that traditional PID controllers are a linear feedback control method based on fixed proportional-integral-derivative coefficients. They fail to fully consider the nonlinear characteristics of flow disturbances, system response time delays, and dynamic changes in control resources (such as storage capacity) during the water level regulation process in open channels. Therefore, if traditional PID controllers are used directly, the following problems may occur: First, they cannot identify the water level response delay caused by friction resistance or geometric characteristics in the channel, which can easily lead to a situation where "the signal precedes the regulation," causing system hysteresis and oscillation. Second, traditional PID controllers output control quantities based on the absolute value of the error, without considering the currently available "control resources" (i.e., the actual water body that can participate in regulation). When the adjustable capacity is insufficient, they still forcibly output large regulation commands, which can easily cause gate malfunctions, overflow risks, or over-regulation of water levels.

[0079] The improved PID controller introduces a dynamic pre-regulation coefficient on the basis of the traditional PID controller. With actual participating volume Two modulation factors enable the controller to dynamically adjust control sensitivity and control strength based on the sensing system response delay and the state of adjustment resources, thereby significantly improving the following performance aspects: More stable response: through pre-adjustment coefficient The compensation for time delay effect avoids misjudgment caused by traffic disturbance; Precise adjustment: through actual participation in volume Limit the output intensity to match the control action with the available capacity of the storage area, and prevent over-adjustment or waste of adjustment resources; More adaptable: The system can automatically adjust the control strategy according to changes in flow rate, water level, and demand, adapting to dynamic and complex working conditions and ensuring the safety and timeliness of regulation.

[0080] In summary, the improved PID controller not only solves the problems of sluggish response and regulation mismatch of traditional controllers in open water systems, but also significantly improves the controller's adaptability to large flow disturbances and resource constraints while ensuring system stability.

[0081] For example: As already known from the preceding text: =0.5; =2.88m 3 ; set up: =0.8; =0.1; =0.05; =0.12m; =0.5 m·s; =-0.01m / s; in: =Target water level - Actual current water level = 0.12m; =0.5 m·s, the calculation process is as follows: Assuming the control cycle is sampling every 10 seconds, the deviation values ​​for the last 5 times are as follows: Current time t=0.12; t-10s=0.12; t-20s=0.13; t-30s=0.11; t-40s=0.10; but:

[0082] For ease of calculation, the normalized value is taken as 0.5 m·s; The calculation process is as follows: Assuming a control period of 10 seconds, the current deviation =0.12m, previous deviation =0.13m; but: This indicates that the water level deviation is decreasing at a rate of 1 cm per second.

[0083] Substitute:

[0084]

[0085] In the current cycle, the compensation control quantity calculated by the controller is approximately 0.21. This value can be directly mapped to a standardized instruction. For example, if the control system uses 0-1 proportional control, it means that the gate opening needs to be adjusted to a unit adjustment range of 21%. If the control system adjusts the outflow rate by 10L / s per unit control quantity, it means that a quantity adjustment instruction of approximately 2.1L / s needs to be output.

[0086] It should be noted that in step S4, the pre-adjustment coefficient is... With actual participating volume An improved PID controller is introduced, which can actively sense the time delay of the water level response when outputting the compensation control quantity, avoid premature adjustment that causes system oscillation, and dynamically adjust the adjustment intensity according to the actual availability of the current storage resources. This significantly enhances the stability, accuracy and adaptability of the water level control process, and effectively solves the risk of excessive adjustment or response error of traditional PID controllers.

[0087] S5: Execute the compensation control quantity to adjust the opening of the channel gate, thereby achieving dynamic suppression of water level fluctuations.

[0088] In one specific implementation, the specific steps for executing the compensation control quantity to adjust the channel gate opening are as follows: First: Establish a gate control execution model.

[0089] The compensation control quantity, in units of L / s or standardized proportions, is converted into a specific gate opening command ΔH, in units of cm or percentage. This conversion can be achieved in any of the following ways: establishing a mapping function between the compensation control quantity and the gate opening command ΔH based on the local hydraulic structure characteristics of the channel (such as weir height, channel width, and gate shape) and the regulation response curve; or forming a correspondence rule between the compensation control quantity and the gate opening based on field measured data through offline fitting or online learning.

[0090] Second: Set the opening adjustment rhythm and interval according to the control response level.

[0091] To avoid system oscillations or overflow risks caused by excessively rapid adjustments, the minimum adjustment step size and minimum execution interval of the gate action are set according to the current control level and disturbance intensity. For example, when the disturbance amplitude is small, such as water level changes within ±5cm, the opening change is set to no more than 5%, and the execution interval is no less than 30s. When the disturbance is large or the predicted demand fluctuation is significant, the adjustment step size (e.g., 10%) and response cycle (e.g., 10s) can be appropriately increased to enhance the response capability.

[0092] Third: Send adjustment commands to the gate actuator.

[0093] The calculated target opening degree ΔH is used as a control command. An adjustment signal is sent to the electric actuator of the channel gate through the PLC control system or remote execution terminal. The signal can be: an analog voltage signal, suitable for proportional controllers; or a digital pulse control command, suitable for stepper or relay mechanisms.

[0094] Fourth: Execute the gate action and provide real-time feedback on the execution status.

[0095] After receiving the control signal, the gate begins its opening / closing action, and monitors the current gate opening degree in real time through built-in position sensors (such as potentiometers, encoders, laser displacement gauges, etc.). The system feeds back the opening status to the upper control system in real time and records the following key parameters: current gate position (%), opening / closing speed (cm / s), execution completion time; and execution error (deviation between target opening degree and actual opening degree) for subsequent control compensation.

[0096] Fifth: Collect the water level response after adjustment and evaluate the adjustment effect.

[0097] After the gate operation is completed, the system continuously collects data on water level changes at key downstream sections and evaluates the regulation effect within a set time window (e.g., 1-3 minutes). If the water level still does not reach the target range or the response delay exceeds expectations, the next round of compensation control quantity can be recalculated, forming a closed-loop control process.

[0098] Sixth: Record control execution data and dynamically correct the control model.

[0099] The process data of each round of control, including inputs (disturbance parameters, predicted demand), outputs (compensation control quantity, target opening degree), and feedback (actual water level response, opening degree error), are archived and used to correct control model parameters, such as pre-adjustment coefficients, in order to improve the accuracy and adaptability of the next cycle of adjustment.

[0100] By performing the above steps, it can be ensured that the control system can stably and accurately execute compensation control commands even under conditions of response delay and storage capacity limitations. Simultaneously, this method enables real-time feedback and automatic adjustment, allowing the channel system to effectively control water levels and quickly respond to adjustment needs even in complex situations such as sudden flow changes or drastic water level fluctuations, thereby ensuring the safety of the water conveyance process and the timeliness of scheduling.

[0101] In summary, this invention effectively solves the problems of lag, lead, or lag in response of traditional PID control in open channels by dynamically constructing a channel damping effect model and calculating the pre-regulation coefficient based on the distribution of friction coefficients along the channel, water level control response delay time, and current flow rate. This improves the controller's adaptability and response sensitivity to fluctuation propagation characteristics. This invention also introduces a separate modeling method for the maximum effective storage volume and the actual participating volume, combined with the water level fluctuation amplitude and predicted supply-demand difference. By adaptively adjusting and dynamically determining the participation level of the storage area, the system achieves refined compensation regulation and optimal resource utilization when dealing with large flow disturbances. This invention introduces both the pre-regulation coefficient and the actual participation volume into the improved PID controller, constructing a new compensation control structure. Without altering the traditional PID architecture, it achieves coordinated regulation of multi-dimensional disturbance factors, enhancing the control system's rapid response to sudden water level fluctuations and its disturbance suppression effect.

[0102] Example 2, the second embodiment of the present invention, provides an open channel high-flow-rate disturbance water level control system based on storage compensation and improved PID coupling, applied to the open channel high-flow-rate disturbance water level control method based on storage compensation and improved PID coupling in Example 1, mainly including: The data acquisition unit is used to acquire the current flow rate, water level fluctuation range, predicted demand, and distribution of resistance coefficient along the channel in real time. The model calculation unit is used to construct a damping effect model based on the friction coefficient distribution and to calculate the response delay time and the pre-adjustment coefficient. A volume calculation unit is used to calculate the maximum effective storage volume and the actual participating volume, and to adjust it based on a dynamic adjustment coefficient. A PID control unit, wherein the PID controller is used to generate a compensation control quantity based on the pre-adjustment coefficient, the actual participating volume and the water level deviation; An execution control unit is used to control the channel gate actuator according to the compensation control amount to realize closed-loop regulation and control of water level.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling water level in open channels with high flow disturbance based on storage compensation and improved PID coupling, characterized in that, Includes the following steps: Real-time collection of target channel operating parameters, including current flow rate, water level fluctuation range, predicted demand, and distribution of resistance coefficient along the channel; Based on the distribution of friction coefficients along the channel, a channel damping effect model is dynamically constructed to calculate the water level control response delay time and generate the pre-adjustment coefficient. Based on the current flow rate, the water level fluctuation range, and the predicted demand, the maximum effective storage volume of the storage area is calculated using an adaptive algorithm, and the actual participation volume of the maximum effective storage volume in the current adjustment cycle is dynamically adjusted. The pre-adjustment coefficient is combined with the actual participating volume input to improve the PID controller and generate a compensation control quantity. The compensation control quantity is executed to adjust the opening of the channel gate, thereby achieving dynamic suppression of water level fluctuations.

2. The method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling as described in claim 1, characterized in that: The specific steps for dynamically constructing a channel damping effect model based on the aforementioned friction coefficient distribution include: The distribution of friction coefficients along the friction path is used as the structural input feature of the model; The actual flow rate, water level, and response delay time between upstream and downstream of each segment of the channel are collected during different scheduling periods and used as dynamic labels for model training samples. Construct a modeling framework with time-aware capabilities, preferably a deep neural network model that integrates convolutional neural networks and long short-term memory network structures; The deep neural network is trained end-to-end using training sample data, enabling it to learn the damping propagation law in channel hydrodynamics and output the control response delay time of each channel segment. Based on the delay time, a pre-adjustment coefficient is generated to complete the construction of the channel damping effect model.

3. The method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling as described in claim 2, characterized in that: The expression for the pre-adjustment coefficient is: in: This is the pre-adjustment coefficient; The flow response time constant reflects the system's response inertia; The baseline flow rate set for the system; Current traffic; The water level control response delay time is calculated using the following formula: L represents the total length of the channel. The wave propagation speed is calculated based on the drag coefficient distribution. The formula for calculating wave propagation speed is: ,in, The drag coefficients for each segment are: To correspond to the drag coefficient of each segment The actual physical length, that is, the length of the channel section within the range of the resistance coefficient.

4. The method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling as described in claim 1, characterized in that: The steps for calculating the maximum effective storage volume of the stagnation region using the adaptive algorithm are as follows: Based on historical operational data and channel topographic information, a three-dimensional feature curve of water level-area-volume for the water storage area is extracted. Extract the current operating status input features as input variables for the adaptive algorithm; wherein, the operating status input features include, but are not limited to, current channel flow, water level fluctuation range, predicted demand change, adjustment cycle and upstream and downstream control targets; Construct an adaptive function mapping structure to establish the mapping relationship between the current perturbation conditions and the optimal adjustable volume; Calculate the maximum effective storage volume of the stagnation zone under the constraints of current flow load and predicted demand changes; Introducing dynamic adjustment coefficient Calculate the actual participating volume for the current adjustment cycle.

5. The method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling as described in claim 4, characterized in that: The formula for calculating the maximum effective storage volume is as follows: in, , , These are the weighting coefficients. This indicates the system's responsiveness to instantaneous traffic. This indicates the sensitivity to water level fluctuations. This indicates a preference for compensation for the predicted supply-demand gap, and + + =1; This represents a function that takes positive values, i.e. Its meaning is: when the predicted demand... Higher than the current traffic If the value is positive, the term takes the positive difference; otherwise, the term is 0.

6. The method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling as described in claim 4, characterized in that: The formula for calculating the actual volume involved is as follows: in, This is a dynamic adjustment coefficient, and its value is based on... and The combination function is obtained, and its expression is: in, This refers to the baseline water level fluctuation range or threshold. The baseline flow rate; , To adjust the weighting coefficients, and satisfy the following conditions: ; This is the ratio of the current water level fluctuation to the baseline fluctuation, representing the sensitivity of the system to regulation and the intensity of disturbances. The supply-demand gap ratio under a unit base flow indicates the urgency of the future supply-demand imbalance. If the predicted demand exceeds the supply, it incentivizes participation in storage regulation; otherwise, it is 0.

7. The method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling as described in claim 1, characterized in that: The compensation control quantity of the improved PID controller The calculation formula is as follows: in: To compensate for the control input, the output signal of the PID controller is improved to adjust the opening of the channel gate. This indicates the current water level deviation, which is the difference between the current collected water level and the target set water level, representing the degree of deviation. To account for response latency With traffic The pre-adjustment coefficient of the coupling relationship is used to correct and improve the response sensitivity of the PID controller; The actual participating volume in the current period is defined as follows: , used to dynamically adjust the controller output; This is the deviation integral term, representing the cumulative value of the water level deviation over time, used to eliminate steady-state deviation; The deviation change rate represents the trend of water level changes and is used to respond to disturbances in advance. The proportional gain coefficient characterizes the system's response to the current water level deviation. The degree of immediate response; The integral gain coefficient characterizes the system's response speed to accumulated deviations and can reduce steady-state error; The differential gain coefficient characterizes the system's ability to respond to water level change trends and is used for predictive regulation.

8. The method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling as described in claim 1, characterized in that: The predicted demand is obtained through any of the following methods: ARIMA model prediction based on historical upstream water inflow data; The output of the neural network demand forecasting model is combined with weather forecasts.

9. The method for controlling water level in open channels with large flow disturbances based on storage compensation and improved PID coupling as described in claim 1, characterized in that: The steps for obtaining the friction coefficient distribution are as follows: The entire channel is divided into N segments with essentially the same physical properties; Based on the known geometric parameters and structural type of each segment, a preliminary estimate is made using Manning's formula: in, The drag coefficient, This refers to the Manning roughness of this section of the channel; The hydraulic radius; The slope of this section of the channel; By installing flow meters and water level gauges at the cross-section, and combining the measured values ​​of flow velocity, water level, and flow rate, the drag coefficient of each segment is adjusted in real time using numerical inversion or least squares fitting methods. ; By splicing together all the segmented drag coefficients, a complete friction coefficient distribution along the friction path is formed. .

10. A water level control system for large flow disturbances in an open channel based on storage compensation and improved PID coupling, applied to the water level control method for large flow disturbances in an open channel based on storage compensation and improved PID coupling as described in any one of claims 1-9, characterized in that, include: The data acquisition unit is used to acquire the current flow rate, water level fluctuation range, predicted demand, and distribution of resistance coefficient along the channel in real time. The model calculation unit is used to construct a damping effect model based on the friction coefficient distribution and to calculate the response delay time and the pre-adjustment coefficient. A volume calculation unit is used to calculate the maximum effective storage volume and the actual participating volume, and to adjust it based on a dynamic adjustment coefficient. A PID control unit, wherein the PID controller is used to generate a compensation control quantity based on the pre-adjustment coefficient, the actual participating volume and the water level deviation; An execution control unit is used to control the channel gate actuator according to the compensation control amount to realize closed-loop regulation and control of water level.

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