River flow self-adaptive regulation system

Abstract

This invention relates to the field of river flow regulation technology and discloses a river flow adaptive regulation system. The system includes modules for hydrological parameter acquisition, flow characteristic analysis, cross-sectional stability analysis, multi-source fusion judgment, and regulation parameter generation. It can be supplemented with modules for riverbed coupling analysis and dynamic feedback execution. The hydrological parameter acquisition module collects parameters to determine the risk of flow imbalance and triggers subsequent analysis; the flow characteristic analysis module extracts turbulence parameters to assess flow uniformity; the cross-sectional stability analysis module extracts morphological parameters to predict scouring and deposition trends; the multi-source fusion judgment module fuses data to generate regulation signals; the regulation parameter generation module matches strategies to generate regulation parameters; the riverbed coupling analysis module monitors the coordination between the main stream and the riverbed and corrects the fusion index; the dynamic feedback execution module adjusts the acquisition cycle and provides feedback to form a closed loop. The system achieves real-time monitoring, risk assessment, and adaptive regulation of river flow, improving the scientific rigor and accuracy of regulation.

Description

Technical Field

[0001] This invention relates to the field of river flow regulation technology, specifically to an adaptive river flow regulation system. Background Technology

[0002] In the fields of water conservancy engineering and water resources management, the rational regulation of river flow is of vital importance for flood control and drought relief, optimal allocation of water resources, protection of aquatic ecosystems, and the safe and stable operation of rivers. Traditional methods of river flow regulation mainly rely on manual monitoring and experience-based control, which have many insurmountable drawbacks.

[0003] From a monitoring perspective, traditional methods often fail to achieve real-time, dynamic, and comprehensive collection of river hydrological parameters. For example, for key data such as water level change rate, cross-sectional velocity distribution, and upstream inflow parameters (e.g., flow fluctuation amplitude, peak sediment content), manual monitoring is not only inefficient, but also struggles to guarantee the real-time nature and accuracy of the data, leading to a failure to promptly detect flow imbalance risks. Furthermore, traditional monitoring methods are insufficient in collecting and analyzing turbulent flow characteristic parameters (e.g., flow velocity standard deviation, turbulence intensity coefficient, high-frequency eddy energy ratio) and riverbed scour and deposition change parameters (e.g., riverbed elevation change, scour and deposition rate characteristic parameters), making it difficult to thoroughly assess the uniformity of flow movement and the stability of the riverbed structure.

[0004] In terms of control strategies, traditional systems lack the ability to integrate and analyze multi-source data, failing to comprehensively consider the impact of factors such as water level changes, flow characteristics, and riverbed morphology on flow adaptability. For example, relying solely on single water level or flow data for gate regulation or diversion optimization can easily lead to a one-sided control strategy, failing to achieve adaptive and precise flow control. Furthermore, traditional control systems are typically open-loop controls, lacking dynamic feedback mechanisms. They cannot adjust monitoring and control parameters in real time based on control execution results, making it difficult to form a closed-loop control link. This results in poor control effectiveness and an inability to effectively cope with complex changes in river hydrological conditions.

[0005] With the intensification of global climate change and human activities, the hydrological environment of rivers is becoming increasingly complex, with increased flow fluctuations and frequent changes in riverbed scouring and deposition. Traditional river flow control technologies are no longer sufficient to meet the needs of modern water management. Therefore, there is an urgent need to develop a new type of river flow control system capable of real-time dynamic acquisition of multi-source hydrological parameters, accurate assessment of flow imbalance risks, comprehensive analysis of flow characteristics and riverbed stability, and adaptive control and dynamic feedback. This system will improve the scientific rigor, accuracy, and effectiveness of river flow control, ensure the safe and stable operation of rivers, and achieve optimal allocation of water resources and protection of the aquatic ecosystem. Summary of the Invention

[0006] The purpose of this invention is to provide an adaptive regulation system for river flow to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a river flow adaptive regulation system, the system comprising:

[0008] The hydrological parameter acquisition module is used to dynamically collect the water level change rate, cross-sectional flow velocity distribution and upstream water parameters of the target river channel to obtain the river channel hydrological state parameter set. Based on the river channel hydrological state parameter set, the risk of flow imbalance is judged and analyzed, and an abnormal flow signal is generated. The generated abnormal flow signal triggers the collaborative monitoring command, and the water flow characteristic analysis module and cross-sectional stability analysis module are executed according to the triggered collaborative monitoring command.

[0009] The water flow characteristic analysis module is used to extract the water flow turbulence characteristic parameters of key sections of the target river channel, quantify the uniformity of water flow, and obtain the water flow uniformity index.

[0010] The cross-section stability analysis module is used to extract parameters of the morphological changes of the target river channel control section, predict the trend of the scour and deposition balance state of the riverbed structure, and obtain the cross-section stability assessment value.

[0011] The multi-source fusion judgment module is used to receive the water flow uniformity index and cross-sectional stability assessment value, perform collaborative analysis on the river flow adaptability, and generate gate regulation signals and diversion optimization signals;

[0012] The control parameter generation module is used to receive gate adjustment signals and flow guidance optimization signals, perform flow control strategy matching, and generate gate opening adjustment parameters and flow guidance correction parameters.

[0013] Preferably, the analysis and judgment of traffic imbalance risk includes:

[0014] By collecting the target river channel water level change rate parameter in real time, calculating its percentage deviation from the set change threshold, and marking it as the water level change anomaly;

[0015] Extract the mainstream velocity and sidebank velocity from the velocity distribution parameters of the target river section, calculate the absolute value of the velocity gradient of the two, and mark them as the velocity distribution characteristic value;

[0016] The upstream inflow parameters, including the flow fluctuation amplitude and peak sediment content, are collected, and normalized weighted calculations are performed to obtain the comprehensive upstream inflow index.

[0017] The abnormality of water level change, the characteristic value of flow velocity distribution, and the comprehensive index of upstream water inflow are compared with preset thresholds. When any parameter exceeds the corresponding threshold, an abnormal flow signal is generated.

[0018] Preferably, the quantitative evaluation of the uniformity of water flow includes:

[0019] Water flow turbulence data were collected using an acoustic Doppler current profiler to generate flow velocity time series plots and eddy energy spectrum distribution plots.

[0020] Extract the velocity standard deviation and turbulence intensity coefficient from the velocity time series plot, calculate their product and take the reciprocal to obtain the flow stability coefficient;

[0021] The high-frequency eddy energy ratio and energy spectrum decay rate are extracted from the eddy energy spectrum distribution map, and the geometric mean of the two is calculated and marked as the turbulence characteristic index.

[0022] The flow uniformity index is obtained by weighting and fusing the flow stability coefficient and the turbulence characteristic index.

[0023] Preferably, the trend prediction of the scour-deposition balance state of the riverbed structure includes:

[0024] Real-time data on the morphological changes of the target river channel control section are collected, and characteristic parameters of riverbed elevation change and scouring and deposition rate are extracted.

[0025] A riverbed scouring and deposition trend prediction model is constructed. The riverbed elevation change is input into the model and smoothed over time. The model outputs the probability of riverbed imbalance in the future time interval.

[0026] Extract the maximum scouring rate and average sedimentation rate from the characteristic parameters of scouring and sedimentation rate, and calculate the logarithmic value of their ratio to obtain the dynamic factor of scouring and sedimentation.

[0027] The stability assessment value of the cross section is obtained by linearly combining the probability of riverbed imbalance with the dynamic factors of scouring and deposition.

[0028] Preferably, the collaborative analysis of river flow adaptability includes:

[0029] Retrieve water level change anomaly data, set its impact weight, and obtain the water level impact compensation value through calculation and processing;

[0030] The values ​​of flow uniformity index, cross-sectional stability assessment value and water level impact compensation value are normalized and calculated to generate flow adaptability fusion index.

[0031] Set a flow adaptability judgment threshold. If the fusion index is lower than the threshold, a gate adjustment signal is generated. If it is higher than the threshold, a flow optimization signal is generated.

[0032] Preferably, the process of matching traffic control strategies includes:

[0033] If a gate adjustment signal is detected, a gate control command is triggered. Based on the command, the gate opening amplitude and adjustment rate parameters are dynamically set to generate gate opening adjustment parameters.

[0034] If a flow guidance optimization signal is detected, a flow guidance structure command is triggered. Based on the command, the deflection angle and duration parameters of the flow guidance facility are optimized and configured to generate flow guidance correction parameters.

[0035] Preferably, the system further includes:

[0036] The riverbed coupling analysis module is used to monitor the coordination between the evolution of the main channel path and the riverbed morphology, extract the main channel swing amplitude and the riverbed response delay time, and generate riverbed coupling evaluation values.

[0037] The multi-source fusion judgment module further combines the riverbed coupling evaluation value to perform a secondary correction on the flow adaptability fusion index.

[0038] Preferably, the monitoring of the coordination between the evolution of the main river channel path and the riverbed morphology includes:

[0039] The offset of the main channel centerline and the change in riverbed topography were collected by side-scan sonar. The difference in the rate of change between the two was calculated and the absolute value was taken as the amplitude of the main channel swing.

[0040] Extract the adjustment data of riverbed topography after the change of the main path, and calculate the ratio of topographic response time to change period, which is marked as riverbed response delay time;

[0041] The mainstream swing amplitude and the riverbed response delay time are weighted and summed to generate the riverbed coupling assessment value.

[0042] Preferably, the system further includes:

[0043] The dynamic feedback execution module is used to adjust the data acquisition cycle of the control system in real time based on the generated gate opening adjustment parameters and water flow guidance correction parameters, and to feed the execution results back to the hydrological parameter acquisition module to form a closed-loop control link.

[0044] Preferably, the specific execution process of the dynamic feedback execution module includes:

[0045] If the gate opening adjustment parameters involve adjusting the opening amplitude, the sampling frequency of the water level sensor should be increased simultaneously.

[0046] If the water flow guidance correction parameters involve the deflection of the guiding structure, the density of cross-sectional velocity monitoring points should be increased simultaneously.

[0047] Input the adjusted parameters into the hydrological parameter acquisition module and restart the monitoring process.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] In terms of risk monitoring and assessment, the hydrological parameter acquisition module constructs a comprehensive assessment model for flow imbalance risk by collecting multi-dimensional data such as water level change rate, cross-sectional flow velocity distribution, and upstream inflow parameters in real time. By calculating the anomaly degree of water level change, flow velocity distribution characteristic value, and upstream inflow comprehensive index, and comparing them with preset thresholds, it can identify abnormal flow signals in a timely and accurate manner, providing a reliable early warning basis for subsequent regulation. This multi-parameter fusion risk assessment mechanism overcomes the one-sidedness of traditional single-parameter monitoring, significantly improves the sensitivity and accuracy of flow imbalance risk monitoring, and can provide early warning of disaster risks such as floods and droughts, buying valuable time for emergency response.

[0050] In terms of flow characteristics and riverbed stability analysis, the flow characteristics analysis module uses an acoustic Doppler current profiler to collect turbulent flow data. By extracting parameters such as the standard deviation of flow velocity, turbulence intensity coefficient, and high-frequency eddy energy ratio, a flow uniformity assessment model is constructed. This model can quantitatively assess the uniformity of flow motion, providing a scientific basis for judging whether the river flow state is stable. The cross-sectional stability analysis module collects real-time data on riverbed elevation changes and scour / deposition rate characteristics to construct a riverbed scour / deposition trend prediction model. Combining the probability of riverbed imbalance and dynamic scour / deposition factors, it achieves trend prediction of the riverbed scour / deposition balance state. The collaborative work of these two modules enables the system to gain a deeper understanding of the dynamic changes in river flow and riverbed, providing crucial basic data support for the formulation of flow control strategies. This helps prevent riverbed morphological deterioration and levee safety hazards caused by excessive scour or siltation.

[0051] In terms of multi-source data fusion and regulation strategy generation, the multi-source fusion judgment module normalizes and fuses multi-source data such as flow uniformity index, cross-sectional stability assessment value, and water level impact compensation value to generate a flow adaptability fusion index. Based on the comparison between the fusion index and the threshold, it intelligently generates gate regulation signals or diversion optimization signals. This multi-source data collaborative analysis mechanism can comprehensively consider the impact of various factors such as river flow, riverbed morphology, and water level changes on flow adaptability, avoiding the one-sidedness and experience-based nature of traditional regulation strategies, making regulation decisions more scientific and reasonable. The regulation parameter generation module dynamically matches flow regulation strategies according to different regulation signals, generating precise gate opening adjustment parameters and flow guidance correction parameters, realizing refined regulation of river flow. For example, for gate regulation triggered by abnormal flow signals, the opening amplitude and regulation rate of the gate can be dynamically set to ensure the timeliness and effectiveness of flow regulation; for diversion optimization signals, the deflection angle and duration of diversion facilities can be optimized to improve the river flow pattern and enhance the uniformity of flow and the stability of the riverbed.

[0052] In terms of closed-loop control and dynamic feedback, the dynamic feedback execution module adjusts the data acquisition cycle in real time according to the control parameters and feeds back the execution results to the hydrological parameter acquisition module, forming a closed-loop control link. This dynamic feedback mechanism enables the system to optimize monitoring and control parameters in real time based on the control effect, achieving adaptive control of river flow. For example, when gate opening adjustment involves adjusting the opening amplitude, the sampling frequency of the water level sensor is increased simultaneously, enabling more timely monitoring of water level changes and providing real-time data support for further control. When flow guidance correction involves the deflection of the guide structure, increasing the density of cross-sectional velocity monitoring points enables more accurate understanding of changes in flow velocity distribution and evaluation of the guide optimization effect. Through this closed-loop control, the system can continuously adapt to changes in river hydrological conditions, continuously optimize control strategies, and improve the stability and reliability of control effects.

[0053] Furthermore, the introduction of the riverbed coupling analysis module, by monitoring the amplitude of the main channel swing and the riverbed response delay time, assesses the coordination between the evolution of the main channel path and the riverbed morphology, and generates a riverbed coupling assessment value. This allows for a secondary correction of the flow adaptability fusion index, further improving the system's adaptability to the overall river evolution process and the comprehensiveness of its control decisions. This multi-module collaborative and multi-dimensional analysis design concept enables the system to deeply understand the river's hydrodynamic characteristics and riverbed evolution patterns from multiple levels, achieving comprehensive and three-dimensional control of river flow. This provides strong technical support for ensuring the safe and stable operation of the river, optimizing water resource allocation, and protecting the aquatic ecological environment. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the working principle of the river flow adaptive control system described in this invention.

[0055] Figure 2 Design diagram for traffic imbalance risk assessment and analysis;

[0056] Figure 3 Design drawings for evaluating the uniformity of water flow;

[0057] Figure 4 Design diagram for adaptive collaborative analysis of river flow;

[0058] Figure 5 Design diagram for dynamic feedback execution. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Please see Figures 1-5 The present invention relates to a river flow adaptive regulation system, which includes a hydrological parameter acquisition module, a flow characteristic analysis module, a cross-sectional stability analysis module, a multi-source fusion judgment module, and a regulation parameter generation module. The specific implementation scheme is as follows:

[0061] The hydrological parameter acquisition module dynamically collects data on the river's water level change rate, cross-sectional velocity distribution (including mainstream velocity and sidebank velocity), and upstream inflow parameters (flow fluctuation amplitude and peak sediment content) through sensor arrays deployed in the target river channel (such as water level gauges, current meters, and sediment content monitors). This data forms a multi-dimensional set of river hydrological state parameters. Based on this parameter set, the system performs flow imbalance risk assessment and analysis: it calculates the percentage deviation of the water level change rate from a set threshold in real time (i.e., water level change anomaly), extracts the mainstream and sidebank velocities from the cross-sectional velocity distribution and calculates the absolute value of their velocity gradients (i.e., velocity distribution characteristic values), and performs normalized weighted calculations on the upstream inflow parameters to obtain the upstream inflow comprehensive index. When any of the above parameters exceeds a preset threshold, a flow anomaly signal is generated, triggering a collaborative monitoring command and activating the flow characteristic analysis module and the cross-sectional stability analysis module.

[0062] The flow characteristic analysis module collects turbulence data of key sections of the target river channel using an Acoustic Doppler Current Profiler (ADCP), generating flow velocity time series plots and eddy current energy spectrum distribution plots. The flow velocity standard deviation and turbulence intensity coefficient are extracted from the flow velocity time series plots, and the reciprocal of their product is calculated to obtain the flow stability coefficient. The high-frequency eddy energy proportion and energy spectrum decay rate are extracted from the eddy current energy spectrum distribution plots, and their geometric mean is calculated to obtain the turbulence characteristic index. The flow stability coefficient and turbulence characteristic index are weighted and fused to obtain the flow uniformity index, which characterizes the uniformity of flow motion.

[0063] The cross-section stability analysis module utilizes lidar or side-scan sonar to collect real-time morphological change data of the target river channel control section, extracting riverbed elevation changes and scour / deposition rate characteristic parameters (maximum scour rate, average deposition rate). A riverbed scour / deposition trend prediction model (such as an ARIMA-based time series model) is constructed. The riverbed elevation changes are input into the model for smoothing, and the probability of riverbed imbalance in future periods is output. The logarithm of the ratio of the maximum scour rate to the average deposition rate is calculated to obtain the scour / deposition dynamic factor. The riverbed imbalance probability and the scour / deposition dynamic factor are linearly combined to generate the cross-section stability assessment value.

[0064] The multi-source fusion judgment module receives the flow uniformity index and cross-sectional stability assessment value, while simultaneously retrieving water level change anomaly data from the hydrological parameter acquisition module. It sets the influence weights and calculates the water level impact compensation value. The flow uniformity index, cross-sectional stability assessment value, and water level impact compensation value are normalized to generate a flow adaptability fusion index. A preset flow adaptability judgment threshold is set; if the fusion index is below the threshold, a gate regulation signal is generated; if it is above the threshold, a diversion optimization signal is generated.

[0065] Control parameter generation module: After receiving the gate regulation signal or the flow optimization signal, it performs flow control strategy matching: If it is the gate regulation signal, it dynamically sets parameters such as the gate opening amplitude and regulation rate to generate gate opening regulation parameters; if it is the flow optimization signal, it optimizes the configuration of parameters such as the deflection angle and duration of the flow guiding facility to generate water flow guidance correction parameters.

[0066] Example 1:

[0067] The system's flow imbalance risk assessment and analysis specifically includes the collection and processing of parameters such as the target river channel water level change rate, the mainstream flow velocity and sidebank flow velocity in the cross-sectional flow velocity distribution parameters, and the flow fluctuation amplitude and sediment content peak value in the upstream inflow parameters. It also includes the logic for generating flow anomaly signals based on the comparison of each parameter with preset thresholds. The specific implementation method is as follows:

[0068] This system focuses on collecting and calculating the anomaly of the target river channel's water level change rate parameter. Water level sensors (such as ultrasonic or pressure level gauges) deployed at key locations in the river channel acquire water level data in real time at a set sampling frequency (e.g., once per minute), calculating the water level change rate per unit time (in m / h or cm / h). The system has pre-set threshold values ​​for the water level change rate, determined based on historical river hydrological data, flood control standards, and ecological flow requirements. These threshold values ​​can be dynamically adjusted according to different seasons, river conditions (e.g., flood season, non-flood season), or control objectives. For example, a higher threshold might be set during the flood season to allow for larger water level fluctuations, while a lower threshold is set during the non-flood season to maintain a relatively stable water level. The real-time collected water level change rate parameter is compared with this set threshold, and the percentage deviation is calculated using the following formula:

[0069]

[0070] The calculation result characterizes the degree to which the current water level change deviates from the normal range; the larger the value, the more significant the abnormality of the water level change.

[0071] Secondly, regarding the processing of cross-sectional velocity distribution parameters, the velocity distribution of the target river cross-section is measured using an ultrasonic current meter or an acoustic Doppler current profiler (ADCP) to obtain data on the main flow velocity and the side-bank velocity. The main flow velocity is typically extracted from the central region of the river cross-section, for example, the average velocity within the middle 30% of the cross-sectional width. In this region, the flow is less affected by boundary conditions and can more accurately reflect the movement characteristics of the main flow. The side-bank velocity is selected from the area near the riverbank, such as the average velocity within 10% of the width on either side of the bank. In this region, the flow is significantly affected by riverbed topography and vegetation, resulting in relatively lower and more complex velocities. After obtaining the velocity data for both, the absolute value of the difference between the main flow velocity and the side-bank velocity is calculated to obtain the velocity distribution characteristic value, using the following formula:

[0072] Velocity distribution characteristic value = |Main flow velocity - Sideline flow velocity|

[0073] This characteristic value reflects the velocity gradient between the main stream and the side bar areas on the river cross section. The larger the gradient, the more uneven the velocity distribution, which may indicate instability of the water flow structure or the risk of local scouring and siltation.

[0074] Third, the processing of upstream inflow parameters involves the collection and normalized weighted calculation of flow fluctuation amplitude and peak sediment content. Upstream inflow parameters are obtained through hydrological monitoring stations located in the upstream river channel. Flow fluctuation amplitude is defined as the difference between the maximum and minimum upstream inflow flow per unit time (e.g., 1 hour), reflecting the instability of the upstream inflow. Peak sediment content is the highest sediment content in the water body within the same time interval, reflecting the upstream inflow's ability to carry sediment and its potential scouring and deposition effects. To comprehensively assess the impact of upstream inflow on the target river channel's flow balance, these two parameters need to be normalized and weighted. The specific steps are as follows:

[0075] Determine the threshold values ​​for each parameter: the threshold value for flow fluctuation is determined based on the design flood discharge capacity of the target river channel and the upstream reservoir scheduling rules, while the threshold value for sediment content is determined based on the allowable sediment deposition rate of the river channel and the ecological sediment requirement.

[0076] Normalization: Divide the real-time collected flow fluctuation amplitude and sediment content peak value by the corresponding set threshold to obtain the dimensionless normalized value, as shown in the formula:

[0077]

[0078] Weighted calculation: Based on the degree of influence of upstream inflow and sediment on the risk of river flow imbalance, weighting coefficients are set (e.g., the weight of flow fluctuation amplitude is 0.6, and the weight of peak sediment content is 0.4). The comprehensive upstream inflow index is obtained through linear combination, as shown in the formula:

[0079] Upstream inflow comprehensive index = 0.6 × normalized value of flow fluctuation + 0.4 × normalized value of sediment content

[0080] This index comprehensively reflects the instability of upstream water flow and the impact of sediment transport on the target river channel. The higher the value, the greater the potential threat of upstream water conditions to the river channel flow balance.

[0081] After calculating the three parameters (water level anomaly, velocity distribution characteristic value, and upstream inflow comprehensive index), the system compares each parameter with its corresponding preset threshold. The preset thresholds are determined based on historical river hydrological data, hydraulic model simulation results, and engineering practice experience. For example, the water level anomaly threshold can be set to 30%, meaning that a water level change rate exceeding 30% of the set threshold is considered abnormal; the velocity distribution characteristic value threshold can be set to 0.5 m / s, indicating that a velocity distribution anomaly is considered to occur when the velocity gradient between the main flow area and the side banks exceeds this value; and the upstream inflow comprehensive index threshold can be set to 0.8, indicating that upstream inflow conditions pose a significant threat to the river's flow balance.

[0082] When any parameter exceeds its corresponding preset threshold, the system determines that there is a risk of flow imbalance and generates a flow anomaly signal. This signal serves as an instruction to trigger subsequent collaborative monitoring processes, initiating the flow characteristic analysis module and the cross-sectional stability analysis module to conduct in-depth analysis of the river's flow turbulence characteristics and cross-sectional morphological changes, in order to further assess the adaptability and potential risks of the river's flow. If all parameters do not exceed their corresponding thresholds, the system maintains its current monitoring status and continues to collect and analyze hydrological parameters according to the regular cycle.

[0083] It should be noted that the collection frequency, threshold settings, and weighting coefficients of the above parameters can be flexibly adjusted according to the specific geographical characteristics, hydrological conditions, and control objectives of the river. For example, for rivers with high sediment content, the weight of peak sediment content in the calculation of the upstream inflow comprehensive index can be appropriately increased; for mountainous rivers with drastic flow velocity changes, the time interval for flow velocity monitoring can be shortened to improve data real-time performance. In addition, the system also has a parameter self-learning function, which can optimize threshold settings and weight allocation through historical data accumulation and machine learning algorithms, thereby improving the accuracy and adaptability of flow imbalance risk assessment.

[0084] Example 2:

[0085] The specific implementation method for the quantitative evaluation of water flow uniformity is as follows:

[0086] Acoustic Doppler current profiler (ADCP) is used to collect turbulence data of key sections of a target river channel. ADCP equipment is typically installed on fixed supports or bridges along the riverbanks. It uses ultrasonic technology to transmit and receive sound signals, measuring the velocity of particles in the water through the Doppler effect, thereby obtaining information on water flow velocity at different depths and locations within the profile. During the acquisition process, the equipment continuously transmits sound waves at set time intervals (e.g., every second or every few seconds) and receives the echo signals reflected by suspended particles in the water. After signal processing, a continuous sequence of water flow velocity data is generated. This data covers the velocity distribution in different areas of the profile, such as the main flow zone and the side banks, as well as the characteristics of water flow changes over time.

[0087] The collected turbulence data is transmitted to the system's flow characteristic analysis module, which first generates a velocity time series graph and an eddy energy spectrum distribution graph. The velocity time series graph uses time as the horizontal axis and the average velocity at each monitoring point or cross-section as the vertical axis, displaying the fluctuations in flow velocity over time through continuous curves or scatter plots. This visually reflects the stability and periodicity of the flow, such as whether there are periodic increases or decreases in velocity caused by tides or floods. The eddy energy spectrum distribution graph, through Fourier transform and other spectral analysis processing of the velocity time series data, decomposes the kinetic energy of the flow into eddy components of different frequencies, displaying the distribution of eddy energy at each frequency. High-frequency eddies (such as short-period, high-frequency flow fluctuations) are usually related to the intensity of turbulence, while low-frequency eddies may be related to the overall flow trend of the river channel.

[0088] The standard deviation of flow velocity and the turbulence intensity coefficient are extracted from the flow velocity time series plot. The standard deviation of flow velocity is a statistic that measures the dispersion of flow velocity data. It is calculated as the square root of the average of the squares of the differences between the flow velocity values ​​at each time point and the average flow velocity value. Its magnitude reflects the fluctuation range of water flow velocity over time; a larger standard deviation indicates more drastic changes in flow velocity and poorer uniformity. The turbulence intensity coefficient is a parameter characterizing the degree of turbulence in water flow, usually defined as the ratio of turbulent kinetic energy to average kinetic energy. Turbulent kinetic energy is calculated from the mean square value of fluctuating flow velocity, while average kinetic energy is calculated based on the cross-sectional average flow velocity. A larger turbulence intensity coefficient indicates a higher proportion of irregular turbulent motion energy in the flow and lower flow uniformity.

[0089] After obtaining the standard deviation of the flow velocity and the turbulence intensity coefficient, the two are multiplied and their reciprocal is taken to obtain the flow stability coefficient. The logic of this calculation process is as follows: the larger the product of the standard deviation of the flow velocity and the turbulence intensity coefficient, the larger the fluctuation range of the flow velocity and the higher the proportion of turbulent energy, and the less stable the flow motion; after taking the reciprocal, the value of the flow stability coefficient is positively correlated with the flow stability, that is, the larger the coefficient value, the more stable and uniform the flow.

[0090] The high-frequency eddy energy proportion and energy spectrum decay rate are extracted from the eddy energy spectrum distribution map. The high-frequency eddy energy proportion refers to the percentage of eddy energy with frequencies above a set critical value (e.g., 5 Hz) in the total energy. A higher proportion indicates a more significant high-frequency turbulent component in the flow, a more complex flow structure, and potentially poorer uniformity. The energy spectrum decay rate reflects how quickly the energy in the eddy energy spectrum decays with increasing frequency. It is usually determined by fitting the slope of the energy spectrum curve or calculating the energy decay per unit frequency interval. A faster decay rate indicates faster energy dissipation of the high-frequency eddies, and a greater tendency for the scale distribution of turbulence in the flow to be smaller-scale eddies, potentially suggesting improved uniformity of the flow motion.

[0091] The geometric mean of the high-frequency eddy energy proportion and the energy spectrum decay rate is calculated and denoted as the turbulence characteristic index. The geometric mean calculation method comprehensively considers the influence of both parameters, avoiding the bias of a single parameter. For example, if the high-frequency eddy energy proportion is high but the energy spectrum decay rate is fast, the geometric mean of both may be at a moderate level, reflecting the overall state of the water flow turbulence characteristics.

[0092] The flow uniformity index is obtained by weighted fusion of the flow stability coefficient and the turbulence characteristic index. The weighted fusion process requires setting reasonable weight coefficients for the two parameters, based on the degree of influence of flow stability and turbulence characteristics on flow uniformity. For example, the flow stability coefficient may be assigned a higher weight (e.g., 0.7) because it directly reflects the fluctuation characteristics of flow velocity over time and is an important intuitive indicator of flow uniformity; the turbulence characteristic index is assigned a lower weight (e.g., 0.3) to supplement the reflection of the potential influence of the frequency structure of flow turbulence on uniformity. Through weighted summation or other fusion algorithms, the final flow uniformity index is a quantitative indicator that comprehensively reflects the temporal stability of flow and the frequency characteristics of turbulence. Its numerical range can be set according to the calculation method (e.g., 0-100), with higher values ​​indicating better flow uniformity.

[0093] In practical applications, the installation location and acquisition parameters of ADCP need to be adjusted according to the characteristics of the river channel. For example, for wide and shallow rivers, it may be necessary to increase the density of lateral monitoring points to accurately capture the cross-sectional velocity distribution; for deep and narrow rivers, it is necessary to ensure the accuracy of vertical stratified monitoring to reflect the velocity differences at different water depths. In addition, the system can dynamically adjust the data acquisition frequency according to the real-time water flow status of the river channel. For example, the sampling frequency can be increased when the water flow conditions are complex (such as during flood season or ice season) to obtain denser velocity data and improve the real-time performance and accuracy of uniformity assessment.

[0094] The entire process of quantitatively assessing the uniformity of water flow involves acquiring multidimensional flow data using advanced monitoring equipment. Combined with statistical and spectral analysis methods, the uniformity of flow is quantitatively characterized from both time series and frequency characteristics, providing crucial foundational data for river flow adaptability analysis. This assessment method avoids the subjectivity and limitations of traditional manual observation, achieving an objective and dynamic evaluation of flow uniformity. It helps in the timely detection of flow structure anomalies, providing a scientific basis for subsequent gate regulation or diversion optimization.

[0095] Example 3:

[0096] The implementation method for predicting the trend of scour and deposition balance state of the riverbed structure is as follows:

[0097] Real-time monitoring of riverbed morphology changes is achieved using topographic surveying equipment (such as 3D laser scanners, multibeam echo sounders, or UAV aerial surveying equipment) permanently deployed at the target river channel control sections. This equipment can periodically collect 3D topographic data of the control sections, for example, once per hour or half-day, with the specific frequency adjusted according to the intensity of riverbed scour and deposition changes. The collected data includes elevation values ​​at various measuring points on the riverbed, riverbed surface morphology (such as the location and outline of deep channels and shallows), etc. By comparing with historical topographic data, the changes in riverbed elevation and scour and deposition rate characteristics are extracted. The changes in riverbed elevation are the elevation difference (in centimeters) between the current measurement time and the previous measurement time at the same measuring point, reflecting the magnitude of scour or deposition at that measuring point over a given time period. The scour and deposition rate characteristics include the maximum scour rate and the average deposition rate. The maximum scour rate is the highest scour rate among all measuring points during the monitoring period (in centimeters per hour), and the average deposition rate is the arithmetic mean of the deposition rates at all measuring points where deposition has occurred (in centimeters per hour).

[0098] A model for predicting riverbed scour and deposition trends is constructed. This model is based on time series analysis methods, such as Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), or seasonal decomposition models (e.g., STL), to train and fit historical riverbed elevation change data. The model's input is a sequence of riverbed elevation changes over a past time period (e.g., 24 hours), and the output is the probability of riverbed imbalance within a future time interval (e.g., 12 hours). The probability of riverbed imbalance is a value between 0 and 1; a higher value indicates a higher likelihood of significant scour or deposition in the riverbed in the future, leading to structural imbalance. The model training process utilizes historical riverbed scour and deposition data. By adjusting model parameters (e.g., the number of layers and neurons in the LSTM, the order of the ARIMA), the error between the model's predictions and actual observations is minimized, thereby improving the accuracy of the predictions.

[0099] After extracting the characteristic parameters of scour and sedimentation rates, the maximum scour rate and average sedimentation rate need to be processed to obtain the scour and sedimentation dynamic factor. The specific calculation method is as follows: take the ratio of the maximum scour rate to the average sedimentation rate, and then take the natural logarithm of this ratio to obtain the scour and sedimentation dynamic factor. The formula is expressed as:

[0100]

[0101] Where F is the scouring and deposition dynamic factor, v s This indicates the maximum scouring rate (unit: cm / hour), v dThis represents the average sedimentation rate (unit: cm / hour). In this formula, if the maximum scour rate is greater than the average sedimentation rate, the ratio is greater than 1, and the natural logarithm is positive, indicating that scour is dominant. If the maximum scour rate is less than the average sedimentation rate, the ratio is less than 1, and the natural logarithm is negative, indicating that sedimentation is dominant. If the two are equal, the ratio is 1, and the natural logarithm is 0, indicating that scour and sedimentation are in dynamic equilibrium. The larger the absolute value of the scour-sedimentation dynamic factor, the more significant the dominance of scour or sedimentation, and the more easily the stability of the riverbed structure is threatened.

[0102] After obtaining the probability of riverbed imbalance and the scour-deposition dynamic factor, they need to be linearly combined to generate a cross-sectional stability assessment value. During the linear combination process, different parameters are assigned corresponding weight coefficients, which are set based on the degree of influence of the probability of riverbed imbalance and the scour-deposition dynamic factor on cross-sectional stability. For example, the probability of riverbed imbalance directly reflects the possibility of future scour-deposition imbalance and can be assigned a higher weight (e.g., 0.8); the scour-deposition dynamic factor reflects the relative intensity of current scour and deposition and can be assigned a lower weight (e.g., 0.2). The calculation formula is as follows:

[0103] S = w1 × P + w2 × F

[0104] Where S is the cross-sectional stability assessment value, P is the riverbed imbalance probability, F is the scour-deposition dynamic factor, and w1 and w2 are the weighting coefficients of the corresponding parameters (w1+w2=1). The numerical range of the cross-sectional stability assessment value depends on the parameter values ​​and weight settings, and is usually mapped to the interval of 0 to 1 through normalization. The lower the value, the higher the stability of the riverbed structure and the better the scour-deposition balance; the higher the value, the lower the stability and the risk of scour-deposition imbalance.

[0105] In practical applications, the accuracy and coverage of topographic surveying equipment are crucial. For example, 3D laser scanners need to ensure a sufficiently high density of survey points (e.g., one survey point per square meter) to capture subtle topographic changes in riverbeds; UAV aerial surveys must be conducted in good weather conditions to avoid cloud cover or wind speed affecting data quality. Furthermore, the prediction duration of the model must match the data acquisition frequency. If the data acquisition interval is one hour, when the model predicts trends over the next 12 hours, it must ensure that the time span of the input data is sufficient to support long-term predictions, avoiding prediction bias due to insufficient data samples.

[0106] In the calculation of the dynamic factor of scouring and silting, if the average silting rate v dIf the value is 0 (meaning no siltation occurred during the monitoring period), the formula needs special processing. For example, a minimum value (such as 0.1 cm / hour) can be added to the denominator to avoid division by zero and ensure the stability of the calculation process. This processing method can be adapted to different river conditions, and is especially suitable for scouring and sedimentation analysis in strongly scour rivers or under clear water discharge conditions.

[0107] The entire trend prediction process, through real-time topographic data acquisition, model prediction, and parameter calculation, achieves dynamic assessment of riverbed scour and deposition status and prediction of future risks. This method combines the advantages of measured data and numerical models, reflecting both current scour and deposition characteristics and predicting development trends, providing crucial information on riverbed stability for river flow regulation. By regularly updating data and optimizing the model, the accuracy and reliability of predictions can be continuously improved, ensuring that the regulation system can respond proactively to changes in riverbed scour and deposition, avoiding safety issues such as river flow interruption and levee collapse caused by structural imbalances.

[0108] Example 4:

[0109] The implementation method of the adaptive collaborative analysis of river flow is as follows:

[0110] After receiving the flow uniformity index output by the flow characteristic analysis module and the cross-sectional stability assessment value output by the cross-sectional stability analysis module, the multi-source fusion judgment module needs to further combine the water level change anomaly data from the hydrological parameter acquisition module to conduct multi-dimensional collaborative data analysis. Taking a plain river as an example, assuming that the water level change anomaly of the river in a certain period is 40% (i.e., the real-time water level change rate exceeds the set threshold by 40%), the system first sets an influence weight for this parameter. The weight value is determined according to the river characteristics. For example, in water level-sensitive rivers (such as urban landscape rivers or ecological base flow protection sections), the influence weight of water level change anomaly can be set to 0.2. Through weight calculation processing, the water level influence compensation value is obtained. The calculation logic of this value is: the higher the water level change anomaly, the greater the negative impact on the river's flow adaptability. Therefore, the compensation value decreases as the anomaly increases. For example, when the water level change anomaly is 40%, the compensation value is converted to 0.6 (assuming a full score of 1) through a linear mapping method, indicating that the compensation effect of water level change on flow adaptability is weak.

[0111] The system normalizes the flow uniformity index, cross-sectional stability assessment value, and water level impact compensation value. The purpose of normalization is to map parameters with different dimensions and value ranges to the same numerical interval (e.g., 0-1) for direct comparison and fusion. For example, if the original flow uniformity index ranges from 0 to 100, and the calculated index value at a certain moment is 75, then the normalized value is 0.75. If the original range of the cross-sectional stability assessment value is 0-1, and the value at a certain moment is 0.3, then the normalized value remains unchanged. If the water level impact compensation value is 0.6 according to the above calculation, then it is directly used as the normalized value. The normalization process usually uses a linear transformation method. For example, for a parameter X with a value range of a, b, the normalization formula is: However, in practical applications, different normalization methods can be selected according to the characteristics of the parameters, such as logarithmic transformation or standardization.

[0112] After normalization, the system merges the normalized values ​​of the three parameters to generate a flow adaptability fusion index. The fusion method can be an arithmetic mean, weighted average, or other statistical methods, depending on the contribution of each parameter to flow adaptability. For example, in rivers where flood control is the primary objective, the flow uniformity index (reflecting the stability of the flow structure) may be assigned a higher weight (e.g., 0.5), the cross-sectional stability assessment value (reflecting the risk of riverbed scouring and deposition) a weight of 0.3, and the water level impact compensation value a weight of 0.2. The fusion index is obtained by weighted summation. Assuming that at a certain moment the normalized flow uniformity index is 0.7, the cross-sectional stability assessment value is 0.4, and the water level impact compensation value is 0.6, the fusion index calculated according to the above weights is 0.7 × 0.5 + 0.4 × 0.3 + 0.6 × 0.2 = 0.61.

[0113] The system presets a flow adaptability threshold, which is determined based on the river's functional positioning and control objectives. For example, for rivers serving navigation purposes, the threshold can be set to 0.6 to avoid excessively rapid or slow flow affecting navigation safety; for rivers primarily used for flood drainage, the threshold can be appropriately lowered to 0.5 to allow for greater flow fluctuations. If the calculated fusion index is lower than the threshold (e.g., 0.61 in the example above is lower than the threshold of 0.6; assuming the threshold is 0.65), the current river's flow adaptability is deemed insufficient, and gate regulation is needed to improve stability. At this point, a gate regulation signal is generated. The gate regulation signal contains specific instructions for gate operation, such as the direction of adjustment for parameters like opening amplitude and regulation rate. For example, if the main reason for a low fusion index is a low flow uniformity index (indicating severe turbulence and uneven velocity distribution), the system may instruct to increase the gate opening to increase the discharge flow and alleviate the turbulence caused by water compression; if the cross-sectional stability assessment value is low (indicating a high risk of riverbed scour), the system may instruct to decrease the gate opening to reduce the flow velocity and alleviate the scour pressure.

[0114] If the fusion index is higher than the threshold, it is determined that the current river flow adaptability is good, but there is still room for optimization. At this time, a flow optimization signal is generated. The flow optimization signal targets the flow guidance facilities in the river channel (such as groynes, longitudinal dikes, and guide walls), and the instructions include the optimized configuration of parameters such as the deflection angle and duration of the flow guidance facilities. For example, when the flow uniformity index is high but the water level impact compensation value is low (indicating that the water level changes significantly but the flow structure is stable), the system may instruct the adjustment of the deflection angle of the guide wall to guide the flow to diffuse towards the side beach area to balance the impact of water level fluctuations on both banks of the river. If the cross-sectional stability assessment value is high (indicating a significant risk of siltation), it may instruct the initiation of a periodic flow scouring procedure to enhance the scouring effect of the main stream on the riverbed by adjusting the angle of the flow guidance facilities, thereby inhibiting siltation.

[0115] In practical applications, parameter weighting and threshold settings need to be calibrated using historical data. For example, if a river experienced three flooding events due to sudden water level rises in the past year, with each event corresponding to a water level anomaly exceeding 30%, then the weight of the water level impact compensation value can be appropriately increased to 0.3 to enhance the system's sensitivity to water level anomalies. Furthermore, the system can automatically adjust the judgment threshold according to seasonal changes. For instance, during the flood season, the threshold can be increased to 0.7 to strictly control flow adaptability and ensure flood control safety; during the non-flood season, the threshold can be decreased to 0.5 to allow for a certain degree of flow fluctuation to meet ecological water replenishment needs.

[0116] The entire collaborative analysis process, through multi-parameter fusion and threshold determination, achieves a comprehensive assessment of river flow adaptability and generates targeted control commands based on the assessment results. This analytical method avoids the limitations of single-parameter judgment, comprehensively reflecting the synergistic effects of river flow conditions, riverbed stability, and water level changes, providing a scientific and flexible decision-making basis for gate regulation and diversion optimization. Through real-time data updates and dynamic parameter adjustments, the system can continuously adapt to changes in river conditions, ensuring the rationality and effectiveness of flow control strategies.

[0117] Example 5:

[0118] The implementation methods of the riverbed coupling analysis module and the dynamic feedback execution module of the system are as follows:

[0119] Taking a mountainous river as an example, the riverbed coupling analysis module continuously monitors the coordination between the evolution of the main channel path and the riverbed morphology using side-scan sonar equipment deployed on both banks. The side-scan sonar emits fan-shaped sound waves covering the river cross-section and receives the echo signals reflected from the riverbed surface. After processing, it generates data on the main channel centerline position and riverbed topography. For example, during a certain monitoring period, the system detects that the main channel centerline has shifted 2 meters to the right relative to its design position, while the riverbed topography change (volume change per unit length of river channel) in that section is 150 cubic meters. The absolute value of the difference in the rates of change between these two factors is calculated to obtain the main channel sway amplitude. The calculation of the rate of change difference is based on the amount of change per unit time. For example, if the rate of shift of the main channel centerline is 0.5 m / h and the rate of change of the riverbed topography is 30 m / h, the absolute value of the difference between the two is 29.5 (the unit needs to be uniformly processed according to the actual dimensions). This value reflects the synchronicity between the main channel path and the riverbed topography change: if the difference is small, it indicates that the main channel swing and the riverbed scouring and deposition response are relatively coordinated; if the difference is large, it may indicate that the main channel swing has failed to trigger the riverbed adjustment in time, or that the riverbed scouring and deposition lags behind the water flow change, resulting in a decrease in the coordination between the two.

[0120] Next, the system extracts data on the adjustment of riverbed topography after changes in the main channel path. For example, when the main channel centerline shifts, the riverbed may gradually form new deep channels or shoals under the new main channel path. Topographic adjustment data includes changes in the location of deep channels and the expansion of siltation areas in shoals. The time interval from the moment the main channel shift occurs to the point where significant adjustments in riverbed topography begin (e.g., a change in elevation of more than 5 cm at a certain measuring point) is recorded as the topographic response time. Simultaneously, the average period of main channel path change is calculated (e.g., the average interval of main channel oscillation over the past week is 12 hours). The ratio of these two values ​​is the riverbed response delay time. If the topographic response time is 6 hours and the change period is 12 hours, the ratio is 0.5, indicating that the riverbed's response to the main channel oscillation lags behind by half a period. A longer response delay time may reduce the coordination between the main channel path and the riverbed morphology, increasing the risk of localized scour or siltation.

[0121] The mainstream swing amplitude and the riverbed response delay time are weighted and summed to generate a riverbed coupling assessment value. The weighting settings must consider river characteristics. For example, in wandering rivers, the mainstream swing amplitude has a significant impact on riverbed evolution; therefore, a weight of 0.6 can be assigned to the mainstream swing amplitude, and a weight of 0.4 to the riverbed response delay time. Assuming that at a certain moment the calculated mainstream swing amplitude is 30 (dimensionless) and the calculated response delay time is 20 (dimensionless), the coupling assessment value is 30 × 0.6 + 20 × 0.4 = 26. A lower value indicates better coordination between the mainstream path and the riverbed morphology, and vice versa. After receiving this assessment value, the multi-source fusion judgment module performs a secondary correction on the flow adaptability fusion index. For example, if the original fusion index is 0.65 and the riverbed coupling assessment value shows poor coordination (e.g., a value higher than the threshold of 25), the corrected fusion index may be lowered to 0.62 to reflect the additional impact of the interaction between the mainstream and the riverbed on flow adaptability.

[0122] The dynamic feedback execution module generates gate opening adjustment parameters and flow guidance correction parameters based on the control parameters, adjusting the system's data acquisition cycle in real time to form a closed-loop control. For example, when the control parameters involve adjusting the gate opening range (such as increasing the gate opening from 50% to 70% to cope with floods), the dynamic feedback execution module simultaneously triggers the water level sensor's sampling frequency adjustment mechanism, increasing the sampling frequency from once every 10 minutes to once every 1 minute. This is because changes in gate opening directly affect river water level fluctuations; high-frequency sampling can promptly capture transient water level changes, avoiding control lag due to data delays. The water level sensor may be a pressure-type or radar-type device. After adjusting the sampling frequency, the device transmits real-time water level data to the hydrological parameter acquisition module at shorter time intervals, providing more intensive monitoring samples for subsequent flow imbalance risk assessment.

[0123] If the control parameters involve the deflection of the flow guiding structure (e.g., adjusting the flow guiding angle of a groyne from 30 degrees to 45 degrees to improve flow distribution), the dynamic feedback execution module increases the density of cross-sectional velocity monitoring points. For example, if the original velocity monitoring points were one every 50 meters, the density can be increased to one every 20 meters after adjustment, achieved by adding temporary current meters or using mobile monitoring equipment (such as unmanned vessels equipped with current meters). Increased monitoring points allow for more precise capture of lateral velocity distribution changes after the flow guiding structure adjustment, such as whether the velocity in the sidebar area decreases due to the change in the flow guiding angle, and whether the velocity in the main flow area becomes more uniform. This real-time velocity data is fed back to the hydrological parameter acquisition module to calculate velocity distribution characteristic values ​​and flow uniformity index, and to evaluate the flow guiding optimization effect.

[0124] After the adjusted parameters are input into the hydrological parameter acquisition module, the system restarts the monitoring process. Taking gate opening adjustment as an example, after the new water level data enters the hydrological parameter acquisition module, the water level change rate and water level change anomaly degree are first calculated. If the anomaly degree is found to exceed the threshold (e.g., 30%), the flow anomaly signal is triggered again, initiating flow characteristic analysis and cross-sectional stability analysis, forming a closed loop of "monitoring-analysis-control-remonitoring". This closed-loop control mechanism ensures that the effect of control measures is tracked in real time. For example, if the water level continues to rise abnormally after the gate is opened, the system can adjust the gate adjustment parameters in a timely manner, avoiding the limitations of a single control command.

[0125] In practical applications, the response time of the feedback mechanism needs to match the timeliness of the control measures. For example, the impact of gate opening adjustment on water level usually becomes apparent within minutes to tens of minutes; therefore, the increase in the sampling frequency of the water level sensor needs to take effect immediately after the gate action. The impact of flow velocity distribution on the deflection of the flow guide structure may require several hours or even days of water flow scouring to stabilize; therefore, the density of flow velocity monitoring points can be set to automatically return to normal after a period of time (e.g., 24 hours) to save system resources. In addition, the dynamic feedback execution module can integrate priority management functions. When both gate adjustment and flow guide optimization commands exist simultaneously, the parameters with more urgent data acquisition needs are prioritized (e.g., gate adjustment prioritizes increasing the water level sampling frequency) to ensure the real-time nature of critical data.

[0126] The entire implementation process utilizes a riverbed coupling analysis module for quantitative monitoring of the interaction between water flow and the riverbed, and a dynamic feedback execution module for intelligent scheduling of monitoring resources, achieving adaptive optimization of the control system. This design not only considers the direct needs of river flow control but also deeply analyzes the potential impact of the water flow-riverbed coupling effect. Through a closed-loop control link, it enhances the system's dynamic response capability to complex river conditions, ensuring the effectiveness and stability of the flow control strategy under multiple constraints.

[0127] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0128] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A river flow adaptive regulation system, characterized in that, include: The hydrological parameter acquisition module is used to dynamically collect the water level change rate, cross-sectional flow velocity distribution and upstream water parameters of the target river channel to obtain the river channel hydrological state parameter set. Based on the river channel hydrological state parameter set, the risk of flow imbalance is judged and analyzed, and an abnormal flow signal is generated. The generated abnormal flow signal triggers the collaborative monitoring command, and the water flow characteristic analysis module and cross-sectional stability analysis module are executed according to the triggered collaborative monitoring command. The water flow characteristic analysis module is used to extract the water flow turbulence characteristic parameters of key sections of the target river channel, quantify the uniformity of water flow, and obtain the water flow uniformity index. The cross-section stability analysis module is used to extract parameters of the morphological changes of the target river channel control section, predict the trend of the scour and deposition balance state of the riverbed structure, and obtain the cross-section stability assessment value. The multi-source fusion judgment module is used to receive the water flow uniformity index and cross-sectional stability assessment value, perform collaborative analysis on the river flow adaptability, and generate gate regulation signals and diversion optimization signals. The control parameter generation module is used to receive gate adjustment signals and flow optimization signals, perform flow control strategy matching, and generate gate opening adjustment parameters and flow guidance correction parameters. The quantitative evaluation of the uniformity of water flow includes: Water flow turbulence data were collected using an acoustic Doppler current profiler to generate flow velocity time series plots and eddy energy spectrum distribution plots. Extract the velocity standard deviation and turbulence intensity coefficient from the velocity time series plot, calculate their product and take the reciprocal to obtain the flow stability coefficient; The high-frequency eddy energy ratio and energy spectrum decay rate are extracted from the eddy energy spectrum distribution map, and the geometric mean of the two is calculated and marked as the turbulence characteristic index. The flow uniformity index is obtained by weighted fusion of the flow stability coefficient and the turbulence characteristic index. The trend prediction of the scour-deposition balance state of the riverbed structure includes: Real-time data collection of morphological changes at the target river channel control section; extraction of characteristic parameters of riverbed elevation change and scouring / deposition rate. A riverbed scouring and deposition trend prediction model is constructed. The riverbed elevation change is input into the model and smoothed over time. The model outputs the probability of riverbed imbalance in the future time interval. Extract the maximum scouring rate and average sedimentation rate from the characteristic parameters of scouring and sedimentation rate, and calculate the logarithmic value of their ratio to obtain the dynamic factor of scouring and sedimentation. The stability assessment value of the cross section is obtained by linearly combining the probability of riverbed imbalance with the dynamic factors of scouring and deposition.

2. The river flow adaptive regulation system according to claim 1, characterized in that, The analysis and assessment of traffic imbalance risk includes: By collecting the target river channel water level change rate parameter in real time, calculating its percentage deviation from the set change threshold, and marking it as the water level change anomaly; Extract the mainstream velocity and sidebank velocity from the velocity distribution parameters of the target river section, calculate the absolute value of the velocity gradient of the two, and mark them as the velocity distribution characteristic value; The upstream inflow parameters, including the flow fluctuation amplitude and peak sediment content, are collected, and normalized weighted calculations are performed to obtain the comprehensive upstream inflow index. The abnormality of water level change, the characteristic value of flow velocity distribution, and the comprehensive index of upstream water inflow are compared with preset thresholds. When any parameter exceeds the corresponding threshold, an abnormal flow signal is generated.

3. The river flow adaptive regulation system according to claim 1, characterized in that, The collaborative analysis of river flow adaptability includes: Retrieve water level change anomaly data, set its impact weight, and obtain the water level impact compensation value through calculation and processing; The values ​​of flow uniformity index, cross-sectional stability assessment value and water level impact compensation value are normalized and calculated to generate flow adaptability fusion index. Set a flow adaptability judgment threshold. If the fusion index is lower than the threshold, a gate adjustment signal is generated. If it is higher than the threshold, a flow optimization signal is generated.

4. The river flow adaptive regulation system according to claim 1, characterized in that, The process of matching traffic control strategies includes: If a gate adjustment signal is detected, a gate control command is triggered. Based on the command, the gate opening amplitude and adjustment rate parameters are dynamically set to generate gate opening adjustment parameters. If a flow guidance optimization signal is detected, a flow guidance structure command is triggered. Based on the command, the deflection angle and duration parameters of the flow guidance facility are optimized and configured to generate flow guidance correction parameters.

5. The river flow adaptive regulation system according to claim 1, characterized in that, Also includes: The riverbed coupling analysis module is used to monitor the coordination between the evolution of the main channel path and the riverbed morphology, extract the main channel swing amplitude and the riverbed response delay time, and generate riverbed coupling evaluation values. The multi-source fusion judgment module further combines the riverbed coupling evaluation value to perform a secondary correction on the flow adaptability fusion index.

6. The river flow adaptive regulation system according to claim 5, characterized in that, The monitoring of the coordination between the evolution of the main river channel path and the riverbed morphology includes: The offset of the main channel centerline and the change in riverbed topography were collected by side-scan sonar. The difference in the rate of change between the two was calculated and the absolute value was taken as the amplitude of the main channel swing. Extract the adjustment data of riverbed topography after the change of the main path, and calculate the ratio of topographic response time to change period, which is marked as riverbed response delay time; The mainstream swing amplitude and the riverbed response delay time are weighted and summed to generate the riverbed coupling assessment value.

7. The river flow adaptive regulation system according to claim 1, characterized in that, Also includes: The dynamic feedback execution module is used to adjust the data acquisition cycle of the control system in real time based on the generated gate opening adjustment parameters and water flow guidance correction parameters, and to feed the execution results back to the hydrological parameter acquisition module to form a closed-loop control link.

8. The river flow adaptive regulation system according to claim 7, characterized in that, The specific execution process of the dynamic feedback execution module includes: If the gate opening adjustment parameters involve adjusting the opening amplitude, the sampling frequency of the water level sensor should be increased simultaneously. If the water flow guidance correction parameters involve the deflection of the guiding structure, the density of cross-sectional velocity monitoring points should be increased simultaneously. Input the adjusted parameters into the hydrological parameter acquisition module and restart the monitoring process.

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