Intelligent control method and system for gates in water conservancy projects

Abstract

This application relates to the field of gate control in water conservancy projects, and discloses an intelligent gate control method and system for water conservancy projects. The method includes: collecting and processing water level difference pressure data on both sides of the gate to obtain a smooth curve and a load deviation detection mechanism; analyzing response delay and torque fluctuation to trigger dynamic damping compensation; analyzing the impact of uneven load on torque efficiency to obtain brake pad wear and oil pressure attenuation parameters; measuring braking response delay and torque distribution deviation to initiate heat accumulation monitoring and compensation; detecting brake clearance and disc surface roughness, and adjusting braking force output based on brake fluid viscosity; controlling the gate using the adjusted parameters, monitoring the impact of moment of inertia on positioning accuracy, and obtaining position deviation and stability results; updating the detection mechanism and damping model when the deviation exceeds a threshold to form a closed-loop system. This application solves the problems of low positioning accuracy and poor stability caused by load fluctuations and brake attenuation in gate control, improving control accuracy and system reliability.

Description

Technical Field

[0001] This application relates to the field of machine tool monitoring technology, and in particular to a gate intelligent control method and system for water conservancy projects. Background Technology

[0002] Gate control systems in water conservancy projects are core equipment for precise water resource allocation and flood control and drainage. By adjusting the gate opening to control flow capacity, they directly affect the operational efficiency and safety of water conservancy projects. With the increasing complexity of hydrological environments, modern gate control systems need to possess the ability to rapidly sense and adaptively regulate dynamic water levels and load changes. Currently, this field is gradually introducing intelligent sensing, real-time analysis, and automatic control technologies to improve the system's response speed and control accuracy under unsteady conditions.

[0003] Existing gate control technologies suffer from several shortcomings: First, most systems exhibit lag in response to dynamic load fluctuations caused by water level differences, lacking effective real-time monitoring and compensation mechanisms. This leads to problems such as uneven torque and increased vibration during gate operation, affecting positioning accuracy and equipment lifespan. Second, traditional control methods often employ fixed parameters, failing to dynamically adjust based on braking system conditions (such as friction pad wear, oil pressure decay, and heat accumulation). This results in unstable braking force output, particularly under frequent start-stop or high-load conditions, potentially causing overheating, reduced efficiency, or even failure. Furthermore, existing solutions often focus on optimizing single components, failing to integrate load detection, damping compensation, braking correction, and motion control into a closed-loop system. This results in poor overall system adaptability and an inability to maintain stable performance during rapid water level changes. For example, while some PID-based control systems can achieve basic regulation, parameter tuning is difficult when facing nonlinear and time-varying hydraulic loads, easily leading to overshoot or oscillation. Meanwhile, some solutions incorporating intelligent algorithms are computationally complex and lack real-time performance, making them difficult to apply in practical engineering. These defects collectively limit the reliability and accuracy of the gate control system in complex environments.

[0004] To address the above deficiencies, this application combines dynamic load monitoring, adaptive damping compensation, and closed-loop braking optimization to solve the problems of low control accuracy and poor stability of the gate under dynamic water levels due to load fluctuations and braking attenuation, thereby improving the system's adaptive capability, positioning accuracy, and operational reliability. Summary of the Invention

[0005] This application provides a gate intelligent control method and system for water conservancy projects. By combining dynamic load monitoring, adaptive damping compensation and closed-loop braking optimization, it solves the problems of low control accuracy and poor stability of gates caused by load fluctuations and braking attenuation under dynamic water levels, and improves the system's adaptive capability, positioning accuracy and operational reliability.

[0006] In a first aspect, this application provides a method for intelligent gate control in water conservancy projects, the method comprising: Step S101: Collect water level difference pressure change data on both sides of the gate through pressure sensor, perform noise reduction processing on the water level difference pressure change data, and obtain a smooth water level difference pressure change curve and load deviation detection mechanism. Step S102: Perform time-domain analysis on the water level difference pressure change curve according to the load deviation detection mechanism, calculate the gate opening response delay time and load torque fluctuation amplitude. If the load torque fluctuation amplitude exceeds the preset fluctuation threshold, trigger the compensation calculation process for the change of dynamic damping coefficient. Step S103: Use the dynamic damping coefficient change compensation algorithm to analyze the impact of uneven load distribution on torque transmission efficiency loss, and obtain brake friction pad wear assessment data and brake oil pressure attenuation characteristic parameters. Step S104: Based on the brake oil pressure attenuation characteristic parameters, measure the brake response time delay and braking torque distribution deviation. When the braking torque distribution deviation exceeds the allowable deviation range, activate the monitoring and compensation mechanism for brake heat accumulation effect. Step S105: Based on the monitoring data of the brake heat accumulation effect, detect the brake clearance adjustment error and the change in brake disc surface roughness, and adjust the braking force output parameters in combination with the brake fluid viscosity-temperature characteristic parameters. Step S106: Control the gate movement using the adjusted braking force output parameters, monitor the impact of load inertia moment changes on gate positioning accuracy, and obtain the final positioning deviation and motion stability evaluation results of the gate. Step S107: When the final positioning deviation of the gate exceeds the preset accuracy requirement threshold, update the parameter settings of the load deviation detection mechanism and the calculation model of the dynamic damping coefficient change to form a closed-loop optimized gate intelligent control system.

[0007] Secondly, this application provides a gate intelligent control system for water conservancy projects, used to implement the gate intelligent control method for water conservancy projects, the system comprising: The data acquisition and processing module is used to acquire water level difference pressure change data on both sides of the gate through pressure sensors, and to process the water level difference pressure change data for noise to obtain a smooth water level difference pressure change curve and basic data for the load deviation detection mechanism. The load fluctuation analysis module is used to perform time-domain analysis on the water level difference pressure change curve according to the load deviation detection mechanism, calculate the gate opening response delay time and load torque fluctuation amplitude, and trigger the compensation calculation process of dynamic damping coefficient change when the load torque fluctuation amplitude exceeds the preset fluctuation threshold. The torque efficiency analysis module is used to analyze the impact of uneven load distribution on torque transmission efficiency loss using a dynamic damping coefficient change compensation algorithm, and to obtain brake friction pad wear assessment data and brake oil pressure attenuation characteristic parameters. The braking deviation monitoring module is used to measure the braking response time delay and braking torque distribution deviation based on the brake oil pressure attenuation characteristic parameters, and to activate the monitoring and compensation mechanism for braking heat accumulation effect when the braking torque distribution deviation exceeds the allowable deviation range. The braking force correction module is used to detect the brake clearance adjustment error and the change in brake disc surface roughness based on the monitoring data of the brake heat accumulation effect, and adjust the braking force output parameters in combination with the brake fluid viscosity-temperature characteristic parameters. The motion control evaluation module is used to control the gate motion using the adjusted braking force output parameters, monitor the impact of load inertia moment changes on the gate positioning accuracy, and obtain the final positioning position deviation and motion stability evaluation results of the gate. The closed-loop optimization module is used to update the parameter settings of the load deviation detection mechanism and the calculation model of the dynamic damping coefficient change when the deviation of the gate position exceeds the preset accuracy requirement threshold, so as to form a closed-loop optimized gate intelligent control system.

[0008] This application proposes an intelligent gate control method and system for water conservancy projects, solving the problems of low control accuracy and poor stability caused by load fluctuations and braking attenuation under dynamic water levels, and improving the system's adaptability, positioning accuracy, and operational reliability. Compared with the prior art, the beneficial effects of the technical solution of this application are at least as follows: First, by collecting water level difference pressure data through pressure sensors and using Kalman filtering algorithm for noise reduction, environmental noise interference is effectively filtered out, and a smooth water level difference pressure change curve is obtained, providing a high-precision data basis for load deviation detection. Second, by performing time-domain analysis on the water level difference pressure curve based on the load deviation detection mechanism, the gate opening response delay time and load torque fluctuation amplitude can be accurately calculated, thus realizing early identification and active compensation for uneven load distribution. Third, the dynamic damping coefficient variation compensation algorithm was used to analyze the impact of uneven load distribution on torque transmission efficiency loss, and real-time monitoring and evaluation of brake friction pad wear and brake oil pressure decay characteristics were realized. Fourth, by establishing a monitoring and compensation mechanism for the braking heat accumulation effect, and combining the brake fluid viscosity-temperature characteristic correction algorithm to dynamically adjust the braking force output parameters, the impact of braking system thermal fade on control performance is effectively suppressed. Fifth, by monitoring the impact of load inertia moment changes on gate positioning accuracy in real time, and updating detection parameters and calculation models in a closed loop when position deviation exceeds limits, an adaptive and optimized intelligent control system is formed, which improves the long-term operational stability of the system. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating the intelligent gate control method for water conservancy projects described in this application. Figure 2 This is a schematic diagram of the process for adjusting the braking force output parameters during the intelligent control of gates used in water conservancy projects in this application; Figure 3 This is a comparison chart of the gate positioning performance in this application; Figure 4 This is a comparison diagram of the gate positioning error in this application; Figure 5 This is a schematic diagram of the gate intelligent control system used in water conservancy projects in this application. Detailed Implementation

[0011] This application provides a method and system for intelligent gate control in water conservancy projects. The terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0012] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the intelligent gate control method for water conservancy projects in this application includes: Step S101: Collect water level difference pressure change data on both sides of the gate through pressure sensor, perform noise reduction processing on the water level difference pressure change data, and obtain a smooth water level difference pressure change curve and load deviation detection mechanism.

[0013] In one specific embodiment, step S101 may specifically include the following steps: The pressure sensors installed on both sides of the gate collect real-time data on the pressure difference between the two sides of the gate. The random noise in the water level difference pressure change data is filtered using the Kalman filter algorithm to obtain the filtered water level difference pressure change signal. A smooth water level difference pressure change curve is generated based on the filtered water level difference pressure change signal. The load deviation characteristic parameters are extracted from the smooth water level difference pressure change curve and used as the basic data for the load deviation detection mechanism. If the load deviation characteristic parameter shows abnormal fluctuations, it is determined to be residual noise, and the Kalman filter algorithm is iterated to further smooth the smooth water level difference pressure change curve until the load deviation characteristic parameter has no abnormal fluctuations.

[0014] Specifically, in the gate control system of a hydraulic engineering project, pressure sensors installed on both sides of the gate continuously capture the pressure signal caused by the water level difference at a reference sampling frequency of 10 times per second. When the water flow velocity in the area where the gate is located exceeds 2 meters per second, the system automatically increases the sampling frequency to 20 times per second. This dynamic sampling mechanism ensures that a sufficient density of raw data points can be obtained under high flow velocity conditions such as flood season, effectively avoiding the loss of water level difference pressure characteristics due to insufficient sampling. The collected analog pressure signal is quantized into a digital sequence by an analog-to-digital conversion module, forming a raw signal data set, which completely records the dynamic change process of the water pressure difference on both sides of the gate. The collected raw water level difference pressure change data includes environmental noise such as wind and wave disturbances and sensor noise. This noise can mask the true pressure change trend and lead to misjudgment of load deviation. Therefore, a Kalman filter algorithm is used for noise reduction. During Kalman filtering initialization, a state vector is set to represent the estimated pressure signal, the covariance matrix reflects the uncertainty of this estimate, the state transition matrix adopts an identity matrix form, the process noise covariance is set to 0.1, and the measurement noise covariance is set to 0.5. The filtering process iterates through two steps: prediction and update. The prediction step calculates the prior state estimate for the current time based on the state estimate from the previous time step, and updates the prior covariance estimate. The update step calculates the Kalman gain to balance the reliability of the prior estimate and the actual measurement value, and then combines the actual measurement value to correct the prior state estimate, obtaining the posterior state estimate and updating the covariance matrix. After 10 iterations, the mean square error of the filtered water level difference pressure change signal is reduced from 0.5 in the original data to below 0.1, effectively suppressing random noise and solving the technical problem of inaccurate load fluctuation perception caused by signal noise interference in traditional control.

[0015] Based on the filtered water level difference pressure change signal, a smooth water level difference pressure change curve is generated using spline interpolation. This process uses the timestamp as the independent variable and the filtered pressure value as the dependent variable to construct a cubic spline function. ,in For time variables, These are undetermined coefficients. To ensure the overall smoothness of the curve, it is necessary to guarantee that the curve is continuous at adjacent data nodes, and that both the first and second derivatives are continuous. This avoids the broken-line fluctuations that may occur with simple linear interpolation, ensuring that the curve truly reflects the continuous change trend of water level difference pressure over time. When extracting characteristic parameters representing the load state from the smoothed curve, a 10-minute sliding time window is set, and the pressure peak, valley, and slope change rate within the window are calculated. The slope change rate is obtained by calculating the ratio sequence of pressure difference to time difference between adjacent data points and then averaging the absolute values. These three parameters constitute a feature vector [pressure peak, valley, slope change rate], which serves as input data for the load deviation detection mechanism. The difference between the peak and valley values ​​directly reflects the intensity of load fluctuations, while the slope change rate reflects the severity of load changes. When an abnormality is detected in the characteristic parameters, i.e., the difference between the peak and valley values ​​exceeds 5 kPa or the slope change rate is greater than 0.2 kPa / s, the system determines that there is residual noise. At this point, the iterative filtering process is initiated. The process noise covariance matrix is ​​adjusted to 0.005 to enhance the filtering strength or the number of iterations is increased (from 10 to 20), and the Kalman filter algorithm is re-executed. The iterative process continues until the characteristic parameters meet the stability conditions: peak-to-valley difference ≤ 5 kPa and slope change rate ≤ 0.2 kPa / s, ensuring that the output smooth curve truly reflects the water level difference variation.

[0016] This technical solution addresses the issue of data acquisition integrity under different flow rate conditions through adaptive sampling frequency adjustment. The optimized application of the Kalman filter algorithm effectively suppresses environmental noise interference, and the feature parameter extraction and iterative verification mechanism ensures the reliability of the load detection data.

[0017] Step S102: Perform time-domain analysis on the water level difference pressure change curve according to the load deviation detection mechanism, calculate the gate opening response delay time and load torque fluctuation amplitude. If the load torque fluctuation amplitude exceeds the preset fluctuation threshold, trigger the compensation calculation process for the change of dynamic damping coefficient.

[0018] In one specific embodiment, step S102 may specifically include the following steps: Based on the load deviation detection mechanism, a time-domain analysis is performed on the smooth water level difference pressure change curve; The gate opening response delay time is calculated based on the time domain analysis, and the gate opening response delay time is the time difference from the input signal of gate control to the actual output response of the gate. The load torque fluctuation amplitude is calculated based on the time-domain analysis, and the load torque fluctuation amplitude is the difference between the peak and valley values ​​of the smooth water level difference pressure change curve; If the load torque fluctuation exceeds the preset fluctuation threshold, the fluctuation frequency parameter is extracted from the smooth water level difference pressure change curve. The compensation calculation process for the change in dynamic damping coefficient is initiated using the aforementioned fluctuation frequency parameter as the trigger condition.

[0019] Specifically, when performing time-domain analysis on the smoothed water level difference pressure change curve based on the load deviation detection mechanism, the system first establishes a data sequence of time-pressure correspondence. This sequence originates from the smoothed curve after Kalman filtering and cubic spline interpolation, with the data sampling interval remaining constant at 100 milliseconds. During the time-domain analysis, the system identifies characteristic time points within each control cycle, including the moment the control command is issued. and the actual start time of the gate's movement By calculating the difference between these two time points The gate opening response delay time is obtained. In practical applications, this delay time is usually between 0.5 seconds and 3 seconds, and the specific value depends on the current state of the gate's mechanical transmission mechanism.

[0020] The load torque fluctuation amplitude is calculated using a sliding time window analysis method. The system selects a 30-second time window and searches for local extreme points on the smooth curve within this window. The peak value is calculated by comparing the pressure values ​​at adjacent extreme points. Valley value Pressure difference between This pressure difference is determined by a pre-calibrated conversion factor. (Value range 0.8~1.2 Nm / kPa) Convert to load torque fluctuation amplitude value Conversion factor The value was determined based on the gate structure parameters and hydraulic system characteristics, and was obtained through fitting experimental data. The system's preset fluctuation threshold... The value is set as a percentage of the gate's rated torque, typically 15% of the rated torque. This applies when load torque fluctuations are detected. Exceed At this time, the system initiates the frequency feature extraction process. This process performs a Fast Fourier Transform on the pressure curve within the current time window, with a transform length N of 256 points and a sampling frequency of [missing information]. The frequency is 10Hz. The main frequency components are identified through spectral analysis, and the frequency component with the largest amplitude is extracted as the fluctuation frequency parameter. The frequency extraction range is limited to 0.1Hz to 2Hz, covering the water flow pulsation frequencies commonly found in hydraulic engineering.

[0021] Fluctuation frequency parameter This serves as a trigger signal, activating the compensation calculation process for changes in the dynamic damping coefficient. The compensation calculation is based on an established second-order system model, whose transfer function includes an adjustable damping ratio parameter. The compensation algorithm is based on the frequency parameter. Calculate the target damping ratio The calculation formula is: Where "0.3" is the baseline value of the damping coefficient, "0.2" is the damping coefficient compensation gain, and the reference frequency is... A frequency of 0.5Hz is used. This calculation formula ensures that a larger damping is used to suppress oscillations at low frequencies and a smaller damping is used to maintain the system response speed at high frequencies. The target damping ratio output by the compensation calculation process is... The signal is transmitted to the gate's hydraulic control system, where the damping coefficient is adjusted in real time by regulating the opening of the throttle valve in the hydraulic circuit. The throttle valve control signal is calculated using a proportional-integral algorithm, with the proportional coefficient... Set to 0.8, integration time Set to 2 seconds. This closed-loop control mechanism based on time-domain analysis and frequency detection effectively solves the system oscillation problem caused by load fluctuations in traditional gate control, and improves the stability of gate operation.

[0022] The entire process establishes a direct mapping relationship from pressure data to control parameters: the time-domain characteristics of the smooth curve are converted into delay time and fluctuation amplitude parameters; exceeding the fluctuation amplitude triggers frequency analysis; and the frequency parameters drive the calculation of damping coefficient compensation. This data-driven method overcomes the limitations of fixed-parameter control in adapting to dynamic load changes, enabling the system to automatically adjust its control characteristics according to actual operating conditions. By monitoring and compensating for load fluctuations in real time, the system significantly reduces the mechanical vibration of the gate during regulation, extends the service life of the equipment, and improves the accuracy and reliability of water level control.

[0023] Step S103: Use the dynamic damping coefficient change compensation algorithm to analyze the impact of uneven load distribution on torque transmission efficiency loss, and obtain brake friction pad wear assessment data and brake oil pressure attenuation characteristic parameters.

[0024] In one specific embodiment, step S103 may specifically include the following steps: A dynamic damping coefficient variation compensation algorithm is used to quantify the uneven load distribution during gate operation and calculate the torque transmission efficiency loss. Based on the torque transmission efficiency loss and the friction coefficient variation model, the wear level assessment data of the brake friction pad is obtained, and the wear level of the brake friction pad is determined by the friction coefficient variation model. Brake oil pressure attenuation characteristic parameters, including oil pressure attenuation rate and attenuation curve slope, are extracted from the brake friction pad wear assessment data. Determine whether the brake oil pressure attenuation characteristic parameter exceeds the preset normal range; if so, adjust the parameters of the dynamic damping coefficient change compensation algorithm. The load distribution unevenness is requantified by the adjusted dynamic damping coefficient change compensation algorithm, and the brake friction pad wear assessment data and brake oil pressure attenuation characteristic parameters are further updated.

[0025] Specifically, during gate operation, the dynamic damping coefficient change compensation algorithm quantifies the uneven load distribution by analyzing hydraulic system pressure data and gate displacement data. The system collects real-time pressure values ​​from the left and right hydraulic cylinders and calculates the absolute difference between the real-time pressure values ​​of the left and right hydraulic cylinders to obtain the pressure deviation. This deviation value is positively correlated with the load distribution unevenness. Simultaneously, the gate operating speed is monitored. Calculate the theoretical driving torque (in (the system's inherent coefficients), through the actual output torque The ratio of torque to theoretical torque is used to calculate torque transmission efficiency. Efficiency loss is defined as... This parameter directly reflects the degree of energy loss caused by uneven load. It is based on the calculated torque transmission efficiency loss. The system invokes a friction coefficient variation model to assess wear. This model establishes efficiency loss. With coefficient of friction Mapping relationship: The initial friction coefficient Take 0.35, the proportionality coefficient The value is set to 0.02. The wear assessment data W is obtained through integration: The integral period is the single complete operation of the gate. Wear levels are determined based on the numerical range of wear data W: W < 50 is considered light wear, 50 ≤ W < 150 is moderate wear, and W ≥ 150 is heavy wear. These data provide a quantitative basis for braking system maintenance.

[0026] When extracting brake fluid pressure fade characteristic parameters from wear assessment data, the system analyzes the fluid pressure drop curve during braking. Fluid pressure fade rate. The formula is calculated using linear regression. ,in This is the pressure drop value. The time interval. Slope of the decay curve. The parameters were obtained using a quadratic polynomial fitting method, with a required goodness of fit of 0.95 or higher. Specifically, oil pressure data was selected within 2 seconds of braking initiation, with a sampling frequency of 20Hz to ensure accurate parameter extraction. The system presets the normal range for the oil pressure attenuation characteristic parameters: attenuation rate. The attenuation curve slope should be between 0.5 and 2.0 MPa / s. The absolute value should be less than 0.8. When a parameter is detected to be outside this range, the gain parameter of the dynamic damping coefficient change compensation algorithm is automatically adjusted. The adjustment rule is: when... At >2.0 MPa / s, the damping coefficient compensation gain Adjusted to ;when When the gain is less than -0.8, the gain is adjusted to... This adaptive adjustment mechanism ensures that the system maintains stable control performance under different wear conditions.

[0027] When requantifying load distribution unevenness using the adjusted dynamic damping coefficient change compensation algorithm, the weighting coefficient of the system pressure deviation is updated, increasing the weighting ratio of recent data to 70%. The torque transmission efficiency loss is recalculated. Used to update wear assessment data Among them, the update coefficient The value is set to 0.1. Based on the updated wear data, the oil pressure decay characteristic parameters are extracted again, forming a closed-loop optimization process. Through this iterative update mechanism, the system can track the changes in the braking system status in real time, providing accurate operating parameters for intelligent gate control.

[0028] This technical solution addresses the problem of accelerated brake system wear caused by uneven load in traditional gate control by establishing a quantitative relationship between load distribution, torque efficiency, friction and wear, and hydraulic characteristics. The dynamic parameter adjustment mechanism overcomes the limitations of fixed parameter control in adapting to changes in equipment conditions, achieving adaptive optimization based on actual operating conditions. Wear assessment and hydraulic characteristic monitoring provide data support for preventative maintenance, helping to extend equipment lifespan and improve system reliability.

[0029] Step S104: Based on the brake oil pressure attenuation characteristic parameters, measure the brake response time delay and braking torque distribution deviation. When the braking torque distribution deviation exceeds the allowable deviation range, activate the monitoring and compensation mechanism for brake heat accumulation effect.

[0030] In one specific embodiment, measuring the braking response time delay and braking torque distribution deviation in step S104 may specifically include the following steps: The brake oil pressure attenuation characteristic parameters are sampled in real time, and the start time and response time of the braking system receiving the braking command are recorded to determine the braking response time delay. At the same time, the real-time torque values ​​of the left and right braking mechanisms are collected by torque sensors, the difference between the real-time torque values ​​of the left and right braking mechanisms is calculated, and the braking torque distribution deviation value is determined. Determine whether the braking response time delay and the braking torque distribution deviation are abnormal. If so, remeasure the braking response time delay and the braking torque distribution deviation.

[0031] Specifically, in the scenario of gate control in hydraulic engineering, the measurement of braking response time delay and braking torque distribution deviation relies on the real-time monitoring module and sensor components of the braking system, with the brake oil pressure attenuation characteristic parameter as the core monitoring object. Real-time sampling of the brake oil pressure attenuation characteristic parameter is performed using a pressure sensor installed in the brake oil circuit, with a sampling frequency set to 50 times per second to ensure the capture of the subtle attenuation process of the oil pressure over time. The sampled data includes the oil pressure value at different times and the corresponding timestamp. The start time of the braking system receiving the braking command is recorded, with the time when the control system issues the "start braking" electrical signal as the starting point. Simultaneously, the response moment when the brake fluid pressure in the brake fluid circuit begins to rise is monitored by the oil pressure sensor. The time difference between the two is the braking response time delay. For example, if For 1000ms, If the sampling frequency is 1080ms, the braking response time delay is 80ms. In scenarios with urgent braking needs, such as during the flood season, the sampling frequency can be increased to 100 times per second to avoid misjudging the response time due to excessively large sampling intervals.

[0032] Simultaneously, real-time torque values ​​of the left and right braking mechanisms are collected using torque sensors. These sensors must be symmetrically mounted on the drive shafts of the left and right brake wheels, with a sampling frequency consistent with the hydraulic pressure sampling frequency (50 or 100 times per second) to ensure data synchronization over time. The collected real-time torque values ​​of the left and right sides are denoted as follows: and (Unit: N·m), calculate the absolute difference between the two values; this difference is the braking torque distribution deviation. Assume that at a certain moment... 280 N·m If the torque is 265 N·m, then the braking torque distribution deviation is 15 N·m. If the gate size is large (e.g., width exceeds 10 m), torque sensors can be added at different transmission nodes of the left and right braking mechanisms. The average of multiple sets of differences can be taken as the final deviation value, reducing measurement errors caused by the failure of a single sensor. By directly collecting the torque on the left and right sides and calculating the difference, quantitative monitoring of the braking torque distribution status can be achieved, avoiding brake wear or gate jamming caused by torque imbalance.

[0033] When determining whether there are abnormalities in the braking response time delay and braking torque distribution deviation values, corresponding abnormality judgment thresholds need to be preset. The threshold setting needs to be combined with the gate model and operating conditions: the normal threshold for braking response time delay is set to 100ms in a normal water level regulation scenario, and can be relaxed to 120ms during the flood season due to the increased braking load; the normal threshold for braking torque distribution deviation value is set to 5% of the gate's rated braking torque. If the gate's rated braking torque is 500 N·m, then the deviation threshold is 25 N·m. The measured braking response time delay is compared with the normal threshold for the corresponding scenario. If the delay value exceeds the threshold (e.g., a measured value of 110ms in a normal scenario exceeds the 100ms threshold), or the braking torque distribution deviation value exceeds the deviation threshold (e.g., a measured value of 30 N·m exceeds the 25 N·m threshold), then an abnormality is determined to exist. At this point, the measurement process needs to be restarted. During the remeasurement, the sensor connection status must be checked first (e.g., whether the hydraulic pressure sensor's oil circuit is unobstructed, and whether the torque sensor is loose). The sampling frequency should be temporarily increased to 150 times per second to increase the data sampling volume and improve measurement accuracy. The braking response time delay and braking torque distribution deviation should be recalculated until three consecutive measurement results are within the normal threshold range, ensuring data reliability. This anomaly detection and retesting mechanism addresses the problem of untimely correction of measurement data errors in traditional control. By setting thresholds and retesting procedures, it avoids misadjustment of braking parameters due to measurement deviations, ensuring the stability of the braking system output and thus improving gate positioning accuracy.

[0034] In one specific embodiment, the activation of the monitoring and compensation mechanism for the braking heat accumulation effect in step S104 may specifically include the following steps: If the braking torque distribution deviation exceeds the allowable deviation range, then the attenuation peak value is extracted from the brake oil pressure attenuation characteristic parameter; The monitoring and compensation mechanism for the braking heat accumulation effect is activated using the attenuation peak value as a trigger signal. A heat compensation factor is generated based on the monitoring and compensation mechanism and applied to the optimization of the braking response time delay; The braking torque distribution deviation value is recalculated using the optimized braking response time delay. Based on the recalculated braking torque distribution deviation value, it is determined whether to continue compensation.

[0035] Specifically, in the scenario of gate control in water conservancy projects, the activation of the monitoring and compensation mechanism for the braking heat accumulation effect requires a comparison between the braking torque distribution deviation and the allowable deviation range. When the braking torque distribution deviation exceeds the allowable deviation range (e.g., the allowable deviation range is set to 10~25 N·m, and the measured deviation value of 30 N·m exceeds this range), the attenuation peak value is extracted from the brake oil pressure attenuation characteristic parameters. When extracting the attenuation peak value, the brake oil pressure attenuation curve needs to be analyzed, and the highest pressure value before the oil pressure drops from the initial peak value in the curve is selected as the attenuation peak value. For example, if the brake oil pressure initially rises to 180 kPa and then begins to attenuate, this 180 kPa is the attenuation peak value. The extraction process needs to be combined with the time series of oil pressure sampling data to eliminate false peak values ​​caused by instantaneous pulse interference. For example, if the instantaneous pressure reaches 185 kPa at a certain moment in the sampling data, but subsequent data quickly drops back to 180 kPa and continues to attenuate, then 180 kPa is taken as the attenuation peak value.

[0036] The extracted attenuation peak value is used as a trigger signal to initiate the monitoring and compensation mechanism for brake heat accumulation. During the activation process, the attenuation peak value needs to be transmitted to the brake system's heat monitoring module. Simultaneously, a temperature sensor installed near the brake disc (sampling frequency set to twice per second, measurement range -20℃ to 200℃) is activated to collect real-time brake disc surface temperature data, constructing a temperature-time curve to reflect the brake heat accumulation. At the same time, the hydraulic pressure compensation module is activated to prepare for the subsequent generation of a heat compensation factor. Abnormal operating conditions of the brake system are identified through the attenuation peak value, and monitoring and compensation are initiated promptly to prevent continuous heat accumulation from deteriorating brake performance. When generating the heat compensation factor based on the monitoring and compensation mechanism, the real-time brake disc temperature and attenuation peak value collected by the temperature sensor must be combined. A reference temperature is set (e.g., ambient temperature 25℃). When the real-time brake disc temperature is higher than the reference temperature, the compensation coefficient is calculated by multiplying the temperature difference by the attenuation peak value. This is then combined with the brake system's heat-hydraulic pressure influence coefficient (e.g., set to 0.002) to generate the heat compensation factor. For example, with a peak brake attenuation of 180 kPa and a real-time brake disc temperature of 45℃ (a difference of 20℃ from the reference temperature), the compensation coefficient is 20 × 180 = 3600, and the heat compensation factor is 3600 × 0.002 = 7.2. The generated heat compensation factor is applied to optimize the brake response time delay. The optimization method is to multiply the original brake response time delay by (1 - heat compensation factor / 100). If the original delay is 80 ms, the optimized delay is 80 × (1 - 7.2 / 100) = 74.24 ms. Adjusting the delay time using the heat compensation factor reduces brake lag caused by heat.

[0037] The braking torque distribution deviation is recalculated using the optimized braking response time delay. During recalculation, the previous torque sensor sampling data (sampling frequency maintained at 50 times per second) is used. The torque sampling time window is adjusted based on the optimized delay time; for example, if the original time window was 100ms before optimization, it is adjusted to 74.24ms after optimization. This ensures that the torque data matches the optimized braking response timing, resulting in a new braking torque distribution deviation value (e.g., the deviation value is reduced to 22 N·m after adjustment). Based on the recalculated braking torque distribution deviation value, it is determined whether further compensation is needed. The criterion is whether the value is within the allowable deviation range. If the recalculated deviation value is within the allowable deviation range (e.g., 22 N·m is within the range of 10~25 N·m), compensation stops. If it still exceeds the allowable deviation range (e.g., the recalculated deviation value is 28 N·m), the process returns to the step of extracting the attenuation peak, regenerating the heat compensation factor (at this time, the compensation factor needs to be adjusted according to the new temperature data, e.g., if the brake disc temperature rises to 50℃, the compensation factor is adjusted to 8.5), optimizing the braking response time delay again and recalculating the deviation value until the deviation value falls within the allowable deviation range, forming a closed-loop compensation logic. Through multiple iterations, the braking torque distribution is ensured to be balanced, mitigating the impact of heat accumulation on the braking system and ensuring the stability of the brake.

[0038] Step S105: Based on the monitoring data of the braking heat accumulation effect, detect the brake clearance adjustment error and the change in brake disc surface roughness, and adjust the braking force output parameters in combination with the brake fluid viscosity-temperature characteristic parameters.

[0039] In one specific embodiment, step S105 may specifically include the following steps: Based on the temperature change and heat accumulation value in the monitoring data, the braking clearance adjustment error of the gate braking mechanism is detected; Simultaneously extract the surface contour feedback information of the brake disc from the monitoring data, and analyze the changes in the surface roughness of the brake disc. The preset brake fluid viscosity-temperature characteristic parameters are retrieved and combined with the real-time temperature values ​​in the monitoring data to establish a mapping relationship between temperature and braking force output. Based on the brake clearance adjustment error, the change in brake disc surface roughness, and the mapping relationship, the braking force output parameters are dynamically adjusted to match the brake force output with the gate braking requirements.

[0040] Specifically, please refer to Figure 2In the operation scenario of a gate braking system in a hydraulic engineering project, monitoring data on the braking heat accumulation effect serves as the core support. The first step is to detect the braking clearance adjustment error by focusing on the temperature change and the accumulated heat value. A temperature sensor mounted on the brake caliper continuously collects temperature data of the friction pair contact area at a frequency of 1Hz. The temperature change rate ΔT is obtained by calculating the temperature difference between adjacent sampling points, thus reflecting the rate of heat generation during braking. The accumulated heat value... It is then obtained through integration, and the integration formula is: ,in The thermal conductivity coefficient is taken as 45 W / m. 2 ·K, The effective friction area of ​​the braking mechanism is taken as 0.02m². 2 The integral period covers a single complete braking process (such as the 15-second duration from the start of braking to complete stop of the gate). For example, during a braking process, The curve that changes over time, after integration, combined with... and The final value is obtained. The value is 8 MJ. Brake clearance adjustment error. The relationship was calculated by establishing a correlation between the heat accumulation value and the calculated value. proportionality coefficient Based on the specific model of the gate braking mechanism, it was calibrated through bench testing. The calculated Substituting 8MJ into the relation, we can obtain... By establishing a quantitative correlation model, the braking clearance adjustment error can be accurately captured, avoiding a decrease in braking performance due to abnormal clearance and ensuring the stability of the initial mechanical state during the braking process.

[0041] While detecting brake clearance adjustment errors, the system simultaneously acquires surface contour feedback information of the brake disc via a laser displacement sensor. The laser displacement sensor uses five sampling points evenly distributed radially on the brake disc at a high sampling frequency of 500Hz to collect three-dimensional contour data of the brake disc surface from all directions, avoiding the limitations of data from a single sampling point. After filtering the acquired contour data, the arithmetic mean deviation of the contour is calculated. The value is used to assess the change in surface roughness, and the calculation formula is: Where L is the sampling length (8 mm), and Z(x) is the offset of each point on the profile relative to the baseline. For example, the profile data collected from a certain measuring point is calculated to obtain... Compare it with the initial roughness of the brake disc (such as in its brand new state). By comparison, the roughness change was obtained. The system simultaneously establishes the roughness variation. Correlation model with braking frequency ,in Take the wear coefficient as , This represents the cumulative number of braking actions. If the cumulative number of braking actions is 50, substituting it into the model yields... When detected Exceeding the threshold Time (such as after long-term operation) The system automatically determines the surface condition of the brake disc that requires attention and schedules maintenance accordingly. Quantified roughness data provides a basis for brake disc maintenance and replacement, preventing brake vibration caused by surface deterioration and ensuring smooth braking.

[0042] The system retrieves preset brake fluid viscosity-temperature characteristic parameters from the database. These parameters were obtained through prior experimental measurements and stored in the form of data tables, with viscosity being the core parameter. The relationship between temperature Te and temperature is expressed as follows: The reference viscosity Take 120 cSt (corresponding to the reference temperature) =20℃), temperature coefficient Take 0.035℃ -1 Combined with real-time collected brake fluid temperature values (The measurement range covers -10℃ to 120℃, such as during a braking event) (at 40℃), substituting the values ​​into the formula, the actual viscosity at the current temperature can be calculated. .

[0043] Based on the calculated actual viscosity The system generates braking force output correction coefficients through interpolation calculations. The calculation formula is ,in The reference viscosity is 80 cSt, and the proportionality coefficient is... Take 0.6. Substituting =59.6cSt, we get... By calculating the correlation between viscosity and temperature, the braking force correction coefficient is dynamically adjusted to ensure the baseline stability of the braking force output under different temperature conditions. This is combined with the brake clearance adjustment error obtained from testing. Variation in surface roughness of brake disc and braking force output correction coefficient The braking force output parameters are dynamically adjusted. Target braking force. At the benchmark braking force (Based on a gate load setting of 8000N) a composite correction is performed, and the correction formula is as follows: ,in The maximum allowable gap is set at 2mm. The maximum permissible roughness is set at 25 μm. =0.096mm For example, substituting into the formula yields... The corrected braking force output parameters are sent to the brake actuator via the CAN bus. The actuator adjusts the hydraulic system pressure according to the instructions to ensure that the actual braking force matches the output parameters. To maintain consistency and ensure that the braking effect matches the mechanical state and ambient temperature conditions under the current working conditions, this solves the problem of unstable braking force output caused by thermal effects and wear in traditional braking systems, improves the control accuracy and reliability of the gate braking process, and extends the service life of the braking system.

[0044] Step S106: Control the gate movement using the adjusted braking force output parameters, monitor the impact of load inertia moment changes on gate positioning accuracy, and obtain the final positioning deviation and motion stability evaluation results of the gate.

[0045] In one specific embodiment, step S106 may specifically include the following steps: The adjusted braking force output parameters are sent to the gate actuator to drive the gate to open or close. Real-time acquisition of dynamic data on load moment of inertia during gate movement, and analysis of the impact of changes in load moment of inertia on gate positioning accuracy; After the gate completes its movement, the final actual position of the gate is obtained, and the difference between the final actual position and the preset target position is calculated to obtain the deviation of the final position of the gate. The system synchronously collects velocity change data, acceleration fluctuation data, and vibration frequency data during the gate's movement. Combined with the gate's final positioning deviation, it generates a gate movement stability assessment result.

[0046] Specifically, the adjusted braking force output parameters are transmitted digitally to the gate hydraulic actuator via a fieldbus. The core is establishing the correspondence between the parameters and the actuator input current, achieved through the formula: Actuator pressure control valve input current = proportional coefficient × target braking force after thermal compensation and wear correction. The proportional coefficient is fixed at 0.08 A / kN. For example, if the target braking force after thermal compensation and wear correction is 180 kN, substituting into the formula yields the actuator pressure control valve input current = 0.08 A / kN × 180 kN = 14.4 A. Upon receiving the 14.4 A current command, the actuator drives the pressure control valve to adjust the hydraulic system pressure, stabilizing it within the 12~18 MPa range. This pressure then pushes the brake shoes against the brake disc to generate braking force, controlling the gate to open or close at a speed of 0.1~0.3 m / s, ensuring precise matching of the braking force output to the current load demand.

[0047] The system collects data through a torque sensor (sampling frequency 100Hz, measurement range 0~500 N·m) and an encoder (sampling frequency 100Hz, resolution 0.1°) mounted on the drive shaft. The load moment of inertia calculation first requires calculating the angular acceleration using the encoder data: taking the rotational speed at two adjacent sampling moments, assuming a sampling time interval of 1 / 100 = 0.01 s, the angular acceleration is calculated as (current rotational speed value - previous rotational speed value) / sampling time interval. Then, combining this with the real-time torque value collected by the torque sensor, the load moment of inertia is obtained according to the rotational motion moment of inertia calculation logic (load moment of inertia = real-time torque value / angular acceleration). For example, if the torque sensor collects a real-time torque value of 136.5 N·m at a certain moment, and the encoder collects a current rotational speed value of 2.1 rad / s and a previous rotational speed value of 2.0 rad / s, the calculated angular acceleration is (2.1 - 2.0) / 0.01 = 10 rad / s. 2 Furthermore, the load moment of inertia is calculated to be 136.5 / 10 = 13.65 kg·m. 2 The impact on positioning accuracy was then analyzed: the theoretical moment of inertia of the gate was determined (based on the gate's self-weight of 3000 kg and the force-bearing area of ​​20 m²). 2 Based on the current water level difference of 2m, the value is taken as 12.5 kg·m. 2 Calculate the change in moment of inertia = |load moment of inertia - theoretical moment of inertia|, then calculate the influence coefficient = change in moment of inertia / theoretical moment of inertia. If the influence coefficient exceeds the threshold of 0.15 (e.g., in this example, change in moment of inertia = 1.15 kg·m),... 2 The influence coefficient is 1.15 / 12.5 = 0.092, which is within the normal range; if the load moment of inertia is 14.5 kg·m 2 Change in moment of inertia = 2 kg·m 2 If the influence coefficient is 0.16, it exceeds the threshold. The system determines that the change in load inertia moment may cause positioning deviation and records the inertia moment curve for that period.

[0048] After the gate completes its movement, an absolute encoder (0.1mm resolution, measurement range 0~10m) acquires the final actual positioning position. The preset target positioning position (e.g., set to 6.8500m based on flood discharge requirements) is retrieved from the control system database, and the deviation is calculated using the formula: "Position deviation = |Final actual positioning position - Preset target positioning position|". For example, if the encoder measures a final actual positioning position of 6.8508m, the positioning deviation is |6.8508 - 6.8500| = 0.0008m = 0.8mm. This deviation value is stored in real-time in the system for subsequent parameter optimization, addressing the problem of insufficient accuracy in traditional position detection. Multi-dimensional dynamic data is collected synchronously: velocity data is converted from the rotational speed collected by the encoder (gate linear velocity = rotational speed × gate drive radius, with the gate drive radius taken as 0.5m), with a sampling interval of 50ms; acceleration data is directly collected by a triaxial accelerometer (range ±10g, sampling frequency 200Hz) to obtain x, y, and z-axis acceleration values; vibration frequency data is obtained by performing a Fast Fourier Transform (FFT) on the acceleration signal (1024 FFT sampling points, frequency resolution 0.2Hz) to extract the main vibration frequency. These data, along with the positioning deviation, are then substituted into the stability assessment logic: first, the standard deviation of velocity is calculated (the square root of the variance of 50 sets of velocity data, e.g., 0.02m / s), and the standard deviation of acceleration is calculated (the square root of the variance of 100 sets of acceleration data, e.g., 0.04m / s). 2 The average vibration amplitude (take the average amplitude corresponding to the main vibration frequency, such as 0.08g) is then used to calculate the comprehensive score by weighting the average amplitude according to the standard deviation of velocity (0.3, standard deviation of acceleration (0.3), average vibration amplitude (0.2), and position deviation (0.2) respectively). For example, if the standard deviation of velocity is 0.02, the standard deviation of acceleration is 0.04, the mean vibration amplitude is 0.08, and the deviation of positioning position is 0.8mm, the score is calculated as follows: "Score = (1 - standard deviation of velocity / 0.1) × 30 + (1 - standard deviation of acceleration / 0.2) × 30 + (1 - mean vibration amplitude / 0.5) × 20 + (1 - deviation of positioning position / 5) × 20". The score is (1 - 0.2) × 30 + (1 - 0.2) × 30 + (1 - 0.16) × 20 + (1 - 0.16) × 20 = 81.6 points. After quantification into a score range of 0 to 100, a score above 70 is considered stable, and a score below 70 is marked as abnormal.

[0049] Step S107: When the final positioning deviation of the gate exceeds the preset accuracy requirement threshold, update the parameter settings of the load deviation detection mechanism and the calculation model of the dynamic damping coefficient change to form a closed-loop optimized gate intelligent control system.

[0050] In one specific embodiment, step S107 may specifically include the following steps: Determine whether the final positioning deviation of the gate exceeds a preset accuracy requirement threshold. If so, generate an updated parameter set based on the motion stability assessment results. The parameter settings of the load deviation detection mechanism are adjusted using the updated parameter set, including threshold and sensitivity modifications; The coefficients of the calculation model for the change of the dynamic damping coefficient are adjusted by updating the parameter set. The adjusted load deviation detection mechanism parameter settings and the adjusted dynamic damping coefficient change calculation model are fed back to the gate control core unit to form a closed-loop optimized intelligent gate control system.

[0051] Specifically, the system compares the final positioning deviation of the gate with a preset accuracy threshold, which is set to ±15 mm according to the gate specifications. The system compares the positioning deviation calculated in step S106 (e.g., 18 mm) with this threshold. If the deviation exceeds the threshold, the parameter update process is automatically triggered. The parameter update process uses the motion stability evaluation score (value range 0~100) as the main input and uses a linear interpolation method to map the motion stability evaluation score into specific parameter adjustment amounts, generating an updated parameter set.

[0052] For the load deviation detection mechanism, the system adjusts the detection threshold and sensitivity coefficient by updating the parameter set. When the motion stability assessment score is below 75 (e.g., motion stability assessment score = 70), the detection threshold adjustment is calculated as "detection threshold adjustment amount = -0.1 × (80 - motion stability assessment score) kPa", resulting in a detection threshold adjustment amount of -0.1 × (80 - 70) = -1 kPa, meaning the detection threshold is lowered by 1 kPa. Simultaneously, the sensitivity coefficient adjustment is calculated as "sensitivity coefficient adjustment amount = 0.05 × (80 - motion stability assessment score)", resulting in a sensitivity coefficient adjustment amount of 0.05 × 10 = 0.5, meaning the sensitivity coefficient is increased by 0.5. This adjustment allows the detection mechanism to identify load fluctuations earlier when stability is poor, while avoiding false triggers caused by excessively low thresholds, thus solving the problems of fixed parameters and insufficient sensitivity in traditional detection mechanisms.

[0053] The coefficients of the dynamic damping coefficient change calculation model are adjusted synchronously. The frequency response coefficient in the model is corrected according to the formula: "Adjusted frequency response coefficient = Original frequency response coefficient × [1 + 0.01 × (motion stability assessment score - 80)]". If the motion stability assessment score is 85 (higher than 80), then the adjusted frequency response coefficient = original frequency response coefficient × [1 + 0.01 × 5] = 1.05 × original frequency response coefficient, increasing the coefficient to enhance system damping; if the motion stability assessment score is 65 (lower than 70), then the adjusted frequency response coefficient = original frequency response coefficient × [1 + 0.01 × (-15)] = 0.85 × original frequency response coefficient, decreasing the coefficient to improve response speed. Simultaneously, the damping ratio reference value is adjusted within ±10% to ensure the system maintains appropriate damping characteristics under different stability states, solving the problem of fixed coefficients and poor adaptability in traditional models. The adjusted parameters are fed back to the gate control core unit via the data bus. The core unit applies them to the next control cycle, and the parameter update adopts a smooth transition strategy, gradually replacing the old parameters over three control cycles to avoid system oscillations caused by sudden parameter changes. This closed-loop parameter update mechanism establishes a feedback path between control results and parameters. When changes in water flow or component wear lead to a decline in control performance, the strategy can be adaptively adjusted, solving the problem that traditional fixed parameters cannot adapt to changes in operating conditions and suffer from decreased accuracy. This achieves online parameter self-tuning, extends the lifespan of the system's precision control, and reduces manual intervention.

[0054] Please see Figure 3 , Figure 3 This is a comparison chart of the gate positioning performance in this application; Figure 3 The comparison of gate positioning performance over a time span of 0–400 seconds is shown, including the gate trajectory, and the gate position changes over time for the traditional method and the proposed method. This demonstrates that, compared to the traditional method, the proposed method exhibits superior performance in terms of position tracking and consistency during gate positioning, achieving more accurate gate positioning and showcasing its significant advantages in gate positioning performance.

[0055] Please see Figure 4 , Figure 4 This is a comparison diagram of the gate positioning error in this application; Figure 4 The comparison of the absolute positioning error between the traditional method and the proposed method within a time range of 0 to 400 seconds demonstrates that the proposed method is superior to the traditional method in controlling positioning error, effectively reducing the absolute positioning error and exhibiting higher positioning accuracy and stability.

[0056] Please see Figure 5 The following describes the intelligent gate control system for water conservancy projects in the embodiments of this application. The intelligent gate control system 500 for water conservancy projects includes: The data acquisition and processing module 501 is used to acquire water level difference pressure change data on both sides of the gate through a pressure sensor, and to process the water level difference pressure change data for noise to obtain a smooth water level difference pressure change curve and basic data for the load deviation detection mechanism. The load fluctuation analysis module 502 is used to perform time-domain analysis on the water level difference pressure change curve according to the load deviation detection mechanism, calculate the gate opening response delay time and load torque fluctuation amplitude, and trigger the compensation calculation process of dynamic damping coefficient change when the load torque fluctuation amplitude exceeds the preset fluctuation threshold. The torque efficiency analysis module 503 is used to analyze the impact of uneven load distribution on torque transmission efficiency loss using a dynamic damping coefficient change compensation algorithm, and to obtain brake friction pad wear assessment data and brake oil pressure attenuation characteristic parameters. The braking deviation monitoring module 504 is used to measure the braking response time delay and braking torque distribution deviation based on the brake oil pressure attenuation characteristic parameters, and to activate the monitoring and compensation mechanism for braking heat accumulation effect when the braking torque distribution deviation exceeds the allowable deviation range. The braking force correction module 505 is used to detect the brake clearance adjustment error and the change in brake disc surface roughness based on the monitoring data of the brake heat accumulation effect, and adjust the braking force output parameters in combination with the brake fluid viscosity-temperature characteristic parameters. The motion control evaluation module 506 is used to control the gate motion using the adjusted braking force output parameters, monitor the influence of load inertia moment changes on the gate positioning accuracy, and obtain the final positioning position deviation and motion stability evaluation results of the gate. The closed-loop optimization module 507 is used to update the parameter settings of the load deviation detection mechanism and the calculation model of the dynamic damping coefficient change when the deviation of the gate position exceeds the preset accuracy requirement threshold, so as to form a closed-loop optimized gate intelligent control system.

[0057] Through the collaborative efforts of the aforementioned components, the system constructs a comprehensive intelligent control system encompassing water level difference and pressure sensing, load fluctuation analysis, braking status monitoring, and closed-loop parameter optimization. This enables real-time sensing of the gate's operating status and dynamic optimization of control parameters. Specifically: The data acquisition and processing module 501 collects water level difference pressure data at a sampling frequency of 10Hz using pressure sensors installed on both sides of the gate. It then uses a Kalman filter algorithm for noise reduction, generating a smooth water level difference pressure curve to provide accurate raw data support for the system. The load fluctuation analysis module 502 receives the processed data and performs time-domain analysis within a 30-second time window, calculating the gate opening response delay time and load torque fluctuation amplitude. When the load torque fluctuation amplitude exceeds a preset threshold, the torque efficiency analysis module 503 is immediately triggered to start dynamic damping compensation calculation. Based on a dynamic damping coefficient compensation algorithm, the torque efficiency analysis module 503 calculates the target damping ratio according to the fluctuation frequency parameter, quantifies the impact of uneven load distribution on transmission efficiency, and simultaneously evaluates the wear degree of the brake friction pads using a friction coefficient variation model. The wear assessment data and oil pressure attenuation characteristic parameters are then transmitted to the brake deviation monitoring module 504. The braking deviation monitoring module 504 monitors the brake fluid pressure decay characteristics in real time at a sampling frequency of 50Hz. When the braking torque distribution deviation exceeds the allowable range, the thermal compensation mechanism of the braking force correction module 505 is activated. The braking force correction module 505 dynamically adjusts the braking force output parameters by fusing multiple parameters, combining the brake clearance adjustment error, the change in brake disc surface roughness, and the brake fluid viscosity characteristics, and sends the results to the actuator via the CAN bus. The motion control evaluation module 506 receives the adjusted braking force parameters and collects drive shaft torque and angular acceleration data at a frequency of 100Hz during gate movement, calculating the load inertia moment in real time. The final positioning position deviation is measured by a high-precision position sensor, and a motion stability evaluation score is generated by combining speed, acceleration, and vibration data. The closed-loop optimization module 507 generates an updated parameter set based on the position deviation and stability score using a linear interpolation method, automatically adjusts the load deviation detection threshold and sensitivity, and corrects the frequency response coefficient of the damping calculation model. The optimized parameters are smoothly transferred to each functional module within three control cycles, completing the closed-loop control loop. This modular collaborative architecture ensures the accuracy and timeliness of information transmission between modules through standardized data interfaces, significantly improving the system's dynamic response capability and control precision.

[0058] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications 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 this application.

Claims

1. A gate intelligent control method for hydraulic engineering, characterized in that, Includes the following steps: Step S101: Collect water level difference pressure change data on both sides of the gate through pressure sensor, perform noise reduction processing on the water level difference pressure change data, and obtain a smooth water level difference pressure change curve and load deviation detection mechanism. Step S102: Perform time-domain analysis on the water level difference pressure change curve according to the load deviation detection mechanism, calculate the gate opening response delay time and load torque fluctuation amplitude. If the load torque fluctuation amplitude exceeds the preset fluctuation threshold, trigger the compensation calculation process for the change of dynamic damping coefficient. Step S103: Analyze the impact of uneven load distribution on torque transmission efficiency loss using a dynamic damping coefficient change compensation algorithm, and obtain brake friction pad wear assessment data and brake oil pressure attenuation characteristic parameters. Step S104: Based on the brake oil pressure attenuation characteristic parameters, measure the brake response time delay and braking torque distribution deviation. When the braking torque distribution deviation exceeds the allowable deviation range, activate the monitoring and compensation mechanism for brake heat accumulation effect. Step S105: Based on the monitoring data of the brake heat accumulation effect, detect the brake clearance adjustment error and the change in brake disc surface roughness, and adjust the braking force output parameters in combination with the brake fluid viscosity-temperature characteristic parameters. Step S106: Control the gate movement using the adjusted braking force output parameters, monitor the impact of load inertia moment changes on gate positioning accuracy, and obtain the final positioning position deviation and motion stability evaluation results of the gate. Step S107: When the final positioning deviation of the gate exceeds the preset accuracy requirement threshold, update the parameter settings of the load deviation detection mechanism and the calculation model of the dynamic damping coefficient change to form a closed-loop optimized gate intelligent control system.

2. The method of claim 1, wherein, Step S101 includes: The pressure sensors installed on both sides of the gate collect real-time data on the pressure difference between the two sides of the gate. The random noise in the water level difference pressure change data is filtered using the Kalman filter algorithm to obtain the filtered water level difference pressure change signal. A smooth water level difference pressure change curve is generated based on the filtered water level difference pressure change signal. The load deviation characteristic parameters are extracted from the smooth water level difference pressure change curve and used as the basic data for the load deviation detection mechanism. If the load deviation characteristic parameter shows abnormal fluctuations, it is determined to be residual noise, and the Kalman filter algorithm is iterated to further smooth the smooth water level difference pressure change curve until the load deviation characteristic parameter has no abnormal fluctuations.

3. The method of claim 2, wherein, Step S102 includes: Based on the load deviation detection mechanism, a time-domain analysis is performed on the smooth water level difference pressure change curve; The gate opening response delay time is calculated based on the time domain analysis, and the gate opening response delay time is the time difference from the input signal of gate control to the actual output response of the gate. The load torque fluctuation amplitude is calculated based on the time-domain analysis, and the load torque fluctuation amplitude is the difference between the peak and valley values ​​of the smooth water level difference pressure change curve; If the load torque fluctuation exceeds the preset fluctuation threshold, the fluctuation frequency parameter is extracted from the smooth water level difference pressure change curve. The compensation calculation process for the change in dynamic damping coefficient is initiated using the aforementioned fluctuation frequency parameter as the trigger condition.

4. The method according to claim 1, characterized in that, Step S103 includes: A dynamic damping coefficient variation compensation algorithm is used to quantify the uneven load distribution during gate operation and calculate the torque transmission efficiency loss. Based on the torque transmission efficiency loss and the friction coefficient variation model, the wear level assessment data of the brake friction pad is obtained, and the wear level of the brake friction pad is determined by the friction coefficient variation model. Brake oil pressure attenuation characteristic parameters, including oil pressure attenuation rate and attenuation curve slope, are extracted from the brake friction pad wear assessment data. Determine whether the brake oil pressure attenuation characteristic parameter exceeds the preset normal range; if so, adjust the parameters of the dynamic damping coefficient change compensation algorithm. The load distribution unevenness is requantified by the adjusted dynamic damping coefficient change compensation algorithm, and the brake friction pad wear assessment data and brake oil pressure attenuation characteristic parameters are further updated.

5. The method according to claim 1, characterized in that, The step S104, which measures the braking response time delay and braking torque distribution deviation, includes: The brake oil pressure attenuation characteristic parameters are sampled in real time, and the start time and response time of the braking system receiving the braking command are recorded to determine the braking response time delay. At the same time, the real-time torque values ​​of the left and right braking mechanisms are collected by torque sensors, the difference between the real-time torque values ​​of the left and right braking mechanisms is calculated, and the braking torque distribution deviation value is determined. Determine whether the values ​​of the braking response time delay and the braking torque distribution deviation are abnormal. If so, remeasure the values ​​of the braking response time delay and the braking torque distribution deviation.

6. The method according to claim 5, characterized in that, The step S104 involves activating a monitoring and compensation mechanism for the braking heat accumulation effect, including: If the braking torque distribution deviation exceeds the allowable deviation range, then the attenuation peak value is extracted from the brake oil pressure attenuation characteristic parameter; The monitoring and compensation mechanism for the braking heat accumulation effect is activated using the attenuation peak value as a trigger signal. A heat compensation factor is generated based on the monitoring and compensation mechanism and applied to the optimization of the braking response time delay; The braking torque distribution deviation value is recalculated using the optimized braking response time delay. Based on the recalculated braking torque distribution deviation value, it is determined whether to continue compensation.

7. The method according to claim 1, characterized in that, Step S105 includes: Based on the temperature change and heat accumulation value in the monitoring data, the braking clearance adjustment error of the gate braking mechanism is detected; Simultaneously extract the surface contour feedback information of the brake disc from the monitoring data, and analyze the changes in the surface roughness of the brake disc. The preset brake fluid viscosity-temperature characteristic parameters are retrieved and combined with the real-time temperature values ​​in the monitoring data to establish a mapping relationship between temperature and braking force output. Based on the brake clearance adjustment error, the change in brake disc surface roughness, and the mapping relationship, the braking force output parameters are dynamically adjusted to match the brake force output with the gate braking requirements.

8. The method according to claim 1, characterized in that, Step S106 includes: The adjusted braking force output parameters are sent to the gate actuator to drive the gate to open or close. Real-time acquisition of dynamic data on load moment of inertia during gate movement, and analysis of the impact of changes in load moment of inertia on gate positioning accuracy; After the gate completes its movement, the final actual position of the gate is obtained, and the difference between the final actual position and the preset target position is calculated to obtain the deviation of the final position of the gate. The system synchronously collects velocity change data, acceleration fluctuation data, and vibration frequency data during the gate's movement. Combined with the gate's final positioning deviation, it generates a gate movement stability assessment result.

9. The method according to claim 8, characterized in that, Step S107 includes: Determine whether the final positioning deviation of the gate exceeds a preset accuracy requirement threshold. If so, generate an updated parameter set based on the motion stability assessment results. The parameter settings of the load deviation detection mechanism are adjusted using the updated parameter set, including threshold and sensitivity modifications; The coefficients of the calculation model for the change of the dynamic damping coefficient are adjusted by updating the parameter set. The adjusted load deviation detection mechanism parameter settings and the adjusted dynamic damping coefficient change calculation model are fed back to the gate control core unit to form a closed-loop optimized intelligent gate control system.

10. A gate intelligent control system for water conservancy projects, used to implement the gate intelligent control method for water conservancy projects as described in any one of claims 1 to 9, characterized in that, The aforementioned intelligent gate control system for water conservancy projects includes: The data acquisition and processing module is used to acquire water level difference pressure change data on both sides of the gate through pressure sensors, and to process the water level difference pressure change data for noise to obtain a smooth water level difference pressure change curve and basic data for the load deviation detection mechanism. The load fluctuation analysis module is used to perform time-domain analysis on the water level difference pressure change curve according to the load deviation detection mechanism, calculate the gate opening response delay time and load torque fluctuation amplitude, and trigger the compensation calculation process of dynamic damping coefficient change when the load torque fluctuation amplitude exceeds the preset fluctuation threshold. The torque efficiency analysis module is used to analyze the impact of uneven load distribution on torque transmission efficiency loss using a dynamic damping coefficient change compensation algorithm, and to obtain brake friction pad wear assessment data and brake oil pressure attenuation characteristic parameters. The braking deviation monitoring module is used to measure the braking response time delay and braking torque distribution deviation based on the brake oil pressure attenuation characteristic parameters, and to activate the monitoring and compensation mechanism for braking heat accumulation effect when the braking torque distribution deviation exceeds the allowable deviation range. The braking force correction module is used to detect the brake clearance adjustment error and the change in brake disc surface roughness based on the monitoring data of the brake heat accumulation effect, and adjust the braking force output parameters in combination with the brake fluid viscosity-temperature characteristic parameters. The motion control evaluation module is used to control the gate motion using the adjusted braking force output parameters, monitor the impact of load inertia moment changes on the gate positioning accuracy, and obtain the final positioning position deviation and motion stability evaluation results of the gate. The closed-loop optimization module is used to update the parameter settings of the load deviation detection mechanism and the calculation model of the dynamic damping coefficient change when the deviation of the gate position exceeds the preset accuracy requirement threshold, so as to form a closed-loop optimized gate intelligent control system.

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