Self-adaptive regulation and control system for river flow

Abstract

The invention relates to the technical field of river flow regulation and control, and discloses a river flow self-adaptive regulation and control system which comprises a hydrological parameter acquisition module, a water flow characteristic analysis module, a section stability analysis module, a multi-source fusion judgment module, a regulation and control parameter generation module and an additional riverbed coupling analysis and dynamic feedback execution module. The hydrological parameter acquisition module acquires parameters to judge a flow unbalance risk and triggers subsequent analysis; the water flow characteristic analysis module extracts turbulence parameters to evaluate water flow uniformity; the section stability analysis module extracts morphological parameters to predict the erosion and deposition trend; the multi-source fusion judgment module fuses the data to generate a regulation and control signal; the regulation and control parameter generation module generates regulation and control parameters according to the matching strategy; a riverbed coupling analysis module monitors a main stream and riverbed coordination correction fusion index; and the dynamic feedback execution module adjusts the acquisition period and feeds back to form a closed loop. The system realizes the real-time monitoring, risk assessment and self-adaptive regulation and control of the river flow, and improves the scientificity and accuracy of regulation and control.

Description

Technical Field

[0001] The present invention relates to the technical field of river flow control, and in particular to a river flow adaptive control system. Background Art

[0002] In the fields of water conservancy and water resources management, the rational regulation of river flow is crucial for flood control and drought relief, optimal water resource allocation, water ecological protection, and the safe and stable operation of rivers. Traditional methods of river flow regulation rely primarily on manual monitoring and empirical control, which have many insurmountable flaws.

[0003] From the monitoring perspective, traditional methods are often unable to achieve real-time, dynamic, and comprehensive collection of river hydrological parameters. For example, for key data such as the rate of change of water level, cross-sectional velocity distribution, and upstream water parameters (such as flow fluctuation amplitude and sediment content peak), manual monitoring is not only inefficient, but also difficult to ensure the real-time and accuracy of the data, resulting in the inability to detect the risk of flow imbalance in a timely manner. At the same time, traditional monitoring methods are insufficient in the collection and analysis of characteristic parameters of water flow turbulence (such as velocity standard deviation, turbulence intensity coefficient, high-frequency eddy energy ratio, etc.) and riverbed scouring and deposition change parameters (such as riverbed elevation change, scouring and deposition rate characteristic parameters), making it difficult to deeply evaluate the uniformity of water flow movement and the stability of riverbed structure.

[0004] In terms of control strategies, traditional systems lack the ability to integrate and analyze multi-source data, and are unable to comprehensively consider the impact of multiple 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 adjustment or diversion optimization can easily lead to a one-sided control strategy and fail to achieve adaptive and precise flow control. Furthermore, traditional control systems are typically open-loop control systems that lack dynamic feedback mechanisms. This makes it impossible to adjust monitoring and control parameters in real time based on control execution results, making it difficult to form a closed-loop control chain. This results in poor control effectiveness and an inability to effectively respond to the complex changes in river hydrological conditions.

[0005] With global climate change and intensified human activities, river hydrological environments are becoming increasingly complex, with increased flow fluctuations and frequent changes in riverbed erosion and deposition. Traditional river flow control technologies are no longer able to meet the needs of modern water management. Therefore, there is an urgent need to develop a new river flow control system that can dynamically collect multi-source hydrological parameters in real time, accurately assess flow imbalance risks, comprehensively analyze flow characteristics and riverbed stability, and implement adaptive control and dynamic feedback. This system can improve the scientificity, 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 aquatic ecosystems. Summary of the Invention

[0006] The purpose of the present invention is to provide a river flow adaptive control system to solve the problems raised in the above background technology.

[0007] To achieve the above-mentioned object, the present invention provides the following technical solution: a river flow adaptive control 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 a set of river channel hydrological state parameters. Based on the set of river channel hydrological state parameters, the module determines and analyzes the flow imbalance risk, generates a flow anomaly signal, triggers a collaborative monitoring instruction based on the generated flow anomaly signal, and executes the water flow characteristic analysis module and the cross-sectional stability analysis module based on the triggered collaborative monitoring instruction.

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

[0010] The cross-section stability analysis module is used to extract the morphological change parameters of the target river channel control section, predict the trend of the erosion 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 section stability assessment value, conduct collaborative analysis of river flow adaptability, and generate gate adjustment signals and diversion optimization signals;

[0012] The control parameter generation module is used to receive the gate adjustment signal and the diversion optimization signal, match the flow control strategy, and generate the gate opening adjustment parameters and the water flow diversion correction parameters.

[0013] Preferably, the determination and analysis of the flow imbalance risk includes:

[0014] By collecting the target river water level change rate parameters in real time, the percentage of deviation from the set change threshold is calculated and marked as the water level change anomaly degree;

[0015] Extract the mainstream area velocity and the beach 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] Collect the flow fluctuation amplitude and sediment content peak value of upstream water parameters, perform normalized weighted calculation, and obtain the upstream water comprehensive index;

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

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

[0019] Acoustic Doppler current profiler is used to collect water flow turbulence data and generate flow velocity time series graphs and eddy kinetic energy spectrum distribution graphs;

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

[0021] Extract the high-frequency eddy energy proportion and energy spectrum attenuation rate from the eddy kinetic energy spectrum distribution diagram, calculate the geometric mean of the two, and mark it as the turbulence characteristic index;

[0022] The water flow uniformity index is obtained by weighted fusion of the water flow stability coefficient and the turbulence characteristic index.

[0023] Preferably, the trend prediction of the scouring and silting balance state of the riverbed structure includes:

[0024] Collect the morphological change data of the target river channel control section in real time, and extract the riverbed elevation change and scouring and deposition rate characteristic parameters;

[0025] Construct a riverbed erosion and deposition trend prediction model, input the riverbed elevation change into the model for time series smoothing, and output 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, calculate the logarithm of the ratio between the two and obtain the scouring and sedimentation dynamic factor;

[0027] The probability of riverbed imbalance and the dynamic factor of scouring and deposition are linearly combined to obtain the section stability assessment value.

[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 flow uniformity index, section stability assessment value and water level impact compensation value are normalized and calculated to generate the flow adaptability fusion index;

[0031] Set the 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 diversion optimization signal is generated.

[0032] Preferably, the traffic control strategy matching includes:

[0033] If the gate adjustment signal is captured, the gate control instruction is triggered, and the gate opening amplitude and adjustment rate parameters are dynamically set according to the instruction to generate the gate opening adjustment parameters;

[0034] If the diversion optimization signal is captured, the diversion structure instruction is triggered, and the deflection angle and action duration parameters of the diversion facility are optimized according to the instruction to generate water flow guidance correction parameters.

[0035] Preferably, the system further comprises:

[0036] The riverbed coupling analysis module is used to monitor the evolution coordination between the mainstream path of the river and the riverbed morphology, extract the mainstream swing amplitude and riverbed response delay time, and generate a riverbed coupling assessment value;

[0037] The multi-source fusion determination 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 evolution coordination between the main river path and the riverbed morphology includes:

[0039] The side-scan sonar is used to collect the mainstream centerline offset and riverbed topography changes, and the difference in their rates of change is calculated and taken as the absolute value, which is marked as the mainstream swing amplitude.

[0040] Extract the adjustment data of riverbed terrain after the mainstream path changes, calculate the ratio of terrain response time to change period, and mark it 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 comprises:

[0043] The dynamic feedback execution module is used to adjust the data acquisition cycle of the control system in real time according to the generated gate opening adjustment parameters and water flow guidance correction parameters, and feed back the execution results 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 parameter involves 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 diversion structure, the density of the cross-section flow 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 present invention has the following beneficial effects:

[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 the water level change rate, cross-sectional velocity distribution, and upstream water parameters in real time. By calculating the degree of water level change anomaly, the characteristic value of the velocity distribution, and the comprehensive index of upstream water inflow, and comparing them with preset thresholds, it can promptly and accurately identify abnormal flow signals, 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 monitoring sensitivity and accuracy of flow imbalance risks, and can provide early warning of disaster risks such as floods and droughts, buying valuable time for emergency response.

[0050] In terms of water flow characteristics and riverbed stability analysis, the water flow characteristics analysis module uses an acoustic Doppler flow profiler to collect water turbulence data. By extracting parameters such as the standard deviation of flow velocity, turbulence intensity coefficient, and the proportion of high-frequency eddy energy, it constructs a water flow uniformity assessment model. This can quantitatively assess the uniformity of water flow movement and provide a scientific basis for determining whether the river flow state is stable. The cross-sectional stability analysis module constructs a riverbed scouring and silting trend prediction model by collecting riverbed elevation changes and scouring and silting rate characteristic parameters in real time. Combined with the probability of riverbed imbalance and scouring and silting dynamic factors, it realizes the trend prediction of the riverbed scouring and silting balance state. The collaborative work of these two modules enables the system to gain an in-depth understanding of the dynamic changes in river flow and riverbed, providing key basic data support for the formulation of flow control strategies, and helping to prevent the deterioration of river channel morphology and embankment safety hazards caused by excessive scouring or silting of the riverbed.

[0051] Regarding multi-source data fusion and control strategy generation, the multi-source fusion judgment module normalizes and fuses multiple data sources, including flow uniformity index, cross-section stability assessment, and water level impact compensation, to generate a flow adaptability fusion index. Based on the comparison of the fusion index with a threshold, it intelligently generates gate adjustment signals or diversion optimization signals. This multi-source data collaborative analysis mechanism comprehensively considers the impact of multiple factors on flow adaptability, such as river flow, riverbed morphology, and water level fluctuations. This avoids the one-sidedness and empirical nature of traditional control strategies, making control decisions more scientific and rational. The control parameter generation module dynamically matches flow control strategies based on different control signals, generating precise gate opening adjustment parameters and flow diversion correction parameters, enabling refined control of river flow. For example, gate adjustment triggered by flow anomaly signals can dynamically set the gate opening range and adjustment rate to ensure timely and effective flow control. For diversion optimization signals, the module can optimize the deflection angle and duration of diversion facilities to improve river flow patterns, enhance flow uniformity, and improve riverbed stability.

[0052] In terms of system closed-loop control and dynamic feedback, the dynamic feedback execution module adjusts the data acquisition cycle in real time based on 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, synchronously increasing the sampling frequency of the water level sensor can monitor water level changes more promptly, providing real-time data support for further control; when water flow diversion correction involves deflection of the diversion structure, increasing the density of cross-sectional flow velocity monitoring points can more accurately grasp changes in water flow velocity distribution and evaluate the diversion optimization effect. Through this closed-loop control, the system can continuously adapt to changes in river hydrological conditions, continuously optimize the control strategy, and improve the stability and reliability of the control effect.

[0053] Furthermore, the introduction of a riverbed coupling analysis module, by monitoring the amplitude of mainstream flow swings and the delay time of riverbed response, assesses the evolving coordination between the mainstream path and riverbed morphology, generates a riverbed coupling assessment value, and performs a secondary correction to the flow adaptability fusion index, further improving the system's adaptability to the overall evolution of the river channel and the comprehensiveness of its regulatory decisions. This multi-module collaborative and multi-dimensional analysis design concept enables the system to deeply understand the hydrodynamic characteristics of the river channel and the evolution of the riverbed from multiple levels, achieving all-round, three-dimensional regulation of river flow, and providing strong technical support for ensuring the safe and stable operation of the river channel, optimizing water resource allocation, and protecting the aquatic ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a working principle diagram of the river flow adaptive control system of the present invention;

[0055] Figure 2 Design drawings for flow imbalance risk assessment and analysis;

[0056] Figure 3 Design diagram for water flow uniformity assessment;

[0057] Figure 4 Design drawings for collaborative analysis of river flow adaptability;

[0058] Figure 5 Design diagram for dynamic feedback implementation. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] See also Figure 1-Figure 5 The present invention relates to a river flow adaptive control system, which includes a hydrological parameter acquisition module, a water flow characteristic analysis module, a cross-section stability analysis module, a multi-source fusion determination module, and a control parameter generation module. The specific implementation scheme is as follows:

[0061] Hydrological parameter acquisition module: Through the sensor array deployed in the target river (such as water level gauges, flow meters, sediment content monitors, etc.), the water level change rate, cross-sectional flow velocity distribution (including mainstream area flow velocity, side beach flow velocity) and upstream water parameters (flow fluctuation amplitude, sediment content peak) of the river are dynamically collected to form a river hydrological state parameter set containing multi-dimensional data. Based on this parameter set, the system performs flow imbalance risk determination analysis: real-time calculation of the percentage deviation between the water level change rate and the set threshold (i.e., the degree of water level change anomaly), extraction of the mainstream area and side beach flow velocity in the cross-sectional flow velocity distribution and calculation of the absolute value of the velocity gradient of the two (i.e., the velocity distribution characteristic value), and normalization and weighted calculation of the upstream water parameters to obtain the upstream water comprehensive index. When any of the above parameters exceeds the preset threshold, a flow anomaly signal is generated, and the coordinated monitoring instruction is triggered to start the water flow characteristic analysis module and the cross-sectional stability analysis module.

[0062] Water Flow Characteristic Analysis Module: An acoustic Doppler current profiler (ADCP) collects water turbulence data from key sections of the target river channel, generating velocity time series and eddy energy spectrum distribution diagrams. The velocity standard deviation and turbulence intensity coefficient are extracted from the velocity time series diagram, and the inverse of their product is calculated to obtain the flow stability coefficient. The high-frequency eddy energy fraction and energy spectrum decay rate are extracted from the eddy energy spectrum distribution diagram, and the geometric mean of the two 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 represents the uniformity of water flow motion.

[0063] Section Stability Analysis Module: Utilizing lidar or side-scan sonar, this module collects real-time data on morphological changes in the target river channel's control sections, extracting riverbed elevation changes and characteristic parameters of scour and sedimentation rates (maximum scour rate, average sedimentation rate). A riverbed scour and sedimentation 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 output is the probability of riverbed imbalance in the future. The logarithm of the ratio of the maximum scour rate to the average sedimentation rate is calculated to obtain the scour and sedimentation dynamic factor. The riverbed imbalance probability and the scour and sedimentation dynamic factor are linearly combined to generate a section stability assessment value.

[0064] The Multi-Source Fusion Determination Module receives the flow uniformity index and cross-section stability assessment values, while simultaneously accessing water level anomaly data from the Hydrological Parameter Acquisition Module. It sets impact weights and calculates water level impact compensation values. It normalizes the flow uniformity index, cross-section stability assessment values, and water level impact compensation values ​​to generate a flow adaptability fusion index. A preset flow adaptability determination threshold is set. If the fusion index falls below the threshold, a gate adjustment signal is generated; if it exceeds the threshold, a diversion optimization signal is generated.

[0065] Control parameter generation module: After receiving the gate adjustment signal or diversion optimization signal, it executes flow control strategy matching: if it is a gate adjustment signal, it dynamically sets the gate opening amplitude, adjustment rate and other parameters to generate the gate opening adjustment parameters; if it is a diversion optimization signal, it optimizes the configuration of the diversion facility's deflection angle, action duration and other parameters to generate the 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 target river channel water level change rate parameters, mainstream and sideshore flow velocities in cross-sectional flow velocity distribution parameters, and upstream inflow parameters, including flow fluctuation amplitude and sediment content peak values. The system also generates flow anomaly signals based on the comparison of these parameters with preset thresholds. The specific implementation is as follows:

[0068] Collection and anomaly calculation of water level change rate parameters for the target river. Through water level sensors (such as ultrasonic water level gauges or pressure water level gauges) deployed at key locations in the river, water level data is obtained in real time at a set sampling frequency (for example, once per minute), and the water level change rate per unit time is calculated (in m / h or cm / h). The system has a preset water level change rate threshold value based on the historical hydrological data of the river, flood control standards and ecological flow requirements. The threshold can be dynamically adjusted according to different seasons, river conditions (such as flood season, non-flood season) or control targets. For example, a higher threshold may be set during the flood season to allow larger water level fluctuations, while a lower threshold may be set during the non-flood season to maintain a relatively stable water level state. The real-time collected water level change rate parameter is compared with the set threshold to calculate its deviation percentage. The formula is:

[0069]

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

[0071] Secondly, regarding the processing of cross-sectional velocity distribution parameters. The velocity distribution of the target river section is measured by an ultrasonic flow meter or an acoustic Doppler current profiler (ADCP) to obtain the mainstream area velocity and the beach velocity data. Among them, the extraction of the mainstream area velocity usually selects the central area of ​​the river section, such as the average velocity within the middle 30% of the transverse width of the section. The water flow in this area is less affected by the boundary conditions and can more truly reflect the movement characteristics of the mainstream of the river; the beach velocity selects the beach area close to the river bank, such as the average velocity within 10% of the width on both sides of the river bank. The water flow in this area is greatly affected by factors such as riverbed topography and vegetation, and the flow velocity is relatively low and complex. After obtaining the velocity data of the two, the absolute value of the difference between the mainstream area velocity and the beach velocity is calculated to obtain the velocity distribution characteristic value. The formula is:

[0072] Velocity distribution characteristic value = |mainstream area velocity - beach velocity|

[0073] This characteristic value reflects the flow velocity gradient between the mainstream and the beach area on the river section. The larger the gradient, the more uneven the flow velocity distribution, which may indicate the instability of the water flow structure or the risk of local scouring and siltation.

[0074] Third, the processing of upstream water parameters involves the collection and normalized weighted calculation of flow fluctuation amplitude and sediment content peak value. Upstream water parameters are obtained through hydrological monitoring stations arranged in the upstream river. The flow fluctuation amplitude is defined as the difference between the maximum and minimum upstream water flow in a unit time (such as 1 hour), reflecting the instability of upstream water; the sediment content peak value is the highest value of sediment content in the water body within the same time interval, reflecting the ability of upstream water to carry sediment and potential scouring and siltation effects. In order to comprehensively evaluate the impact of upstream water on the flow balance of the target river, these two parameters need to be normalized and weighted. The specific steps are as follows:

[0075] Determine the setting thresholds for each parameter: the flow fluctuation setting threshold is based on the designed flood discharge capacity of the target river channel and the upstream reservoir scheduling rules, and the sediment content setting threshold is determined according to the allowable sedimentation rate of the river channel and the ecological sediment demand requirements.

[0076] Normalization processing: Divide the real-time flow fluctuation amplitude and sediment content peak by the corresponding set threshold to obtain a dimensionless normalized value. The formula is:

[0077]

[0078] Weighted calculation: Based on the impact of upstream water flow and sediment on the risk of river flow imbalance, weight coefficients are set (for example, the flow fluctuation amplitude weight is 0.6, and the sediment content peak weight is 0.4). The upstream water comprehensive index is obtained through linear combination. The formula is:

[0079] Comprehensive index of upstream water inflow = 0.6 × normalized value of flow fluctuation + 0.4 × normalized value of sediment content

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

[0081] After completing the calculation of the above three parameters (water level change anomaly, velocity distribution characteristic value, upstream water comprehensive index), the system will compare each parameter with the corresponding preset threshold value. The preset threshold value is determined based on the historical hydrological data of the river, the results of hydraulic model simulation and engineering practice experience. For example: the water level change anomaly threshold can be set to 30%, which means that when the water level change rate exceeds 30% of the set threshold, it is considered abnormal; the velocity distribution characteristic value threshold can be set to 0.5m / s. When the velocity gradient between the mainstream area and the side beach exceeds this value, the velocity distribution is considered abnormal; the upstream water comprehensive index threshold can be set to 0.8. When the comprehensive index exceeds this value, it indicates that the upstream water conditions pose a greater threat to the flow balance of the river.

[0082] When any parameter exceeds its corresponding preset threshold, the system determines a flow imbalance risk and generates a flow anomaly signal. This signal triggers the subsequent collaborative monitoring process, activating the flow characteristics analysis module and the cross-section stability analysis module. This module conducts in-depth analysis of the turbulent flow characteristics and cross-section morphological changes in the river channel to further assess the adaptability of the river flow and potential risks. If all parameters remain within their corresponding thresholds, the system maintains the 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 factors for each of the above parameters can be flexibly adjusted based on the specific river's geographic characteristics, hydrological conditions, and control objectives. For example, for rivers with high sediment content, the weight of the peak sediment content in the calculation of the upstream water inflow comprehensive index can be appropriately increased; for mountain rivers with drastic flow rate fluctuations, the flow rate monitoring interval can be shortened to improve data real-time performance. Furthermore, the system features a parameter self-learning function that optimizes threshold settings and weighting through historical data accumulation and machine learning algorithms, improving the accuracy and adaptability of flow imbalance risk assessment.

[0084] Example 2:

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

[0086] The water turbulence data of key sections of the target river are collected by using an acoustic Doppler current profiler (ADCP). ADCP equipment is usually installed on fixed supports or bridges on both sides of the river. It uses ultrasonic technology to transmit and receive sound wave signals, and measures the movement speed of particles in the water body through the Doppler effect, thereby obtaining water flow velocity information at different depths and different positions in the section. During the collection process, the equipment continuously emits sound waves at a set time interval (for example, every second or every few seconds), and receives echo signals reflected by suspended particles in the water body. After signal processing, a continuous water flow velocity data sequence is generated. These data cover the flow velocity distribution in different areas such as the mainstream area and the side beach area within the section, as well as the changing characteristics of the water flow over time.

[0087] The collected water turbulence data is transmitted to the water flow characteristic analysis module of the system, which first generates a flow velocity time series diagram and an eddy kinetic energy spectrum distribution diagram. The flow velocity time series diagram uses time as the horizontal axis and the average flow velocity of each monitoring point or section as the vertical axis. It displays the fluctuation of water flow velocity over time through continuous curves or scattered points, which can intuitively reflect the stability and periodic change characteristics of the water flow, such as whether there are periodic increases and decreases in flow velocity caused by tides, flood processes, etc. The eddy kinetic energy spectrum distribution diagram decomposes the kinetic energy of the water flow into eddy components of different frequencies by performing spectral analysis such as Fourier transform on the flow velocity time series data, and displays the distribution of eddy energy at each frequency. High-frequency eddies (such as short-period, high-frequency water flow fluctuations) are usually related to the turbulence intensity of the water flow, while low-frequency eddies may be related to the overall water flow movement trend of the river.

[0088] The velocity standard deviation and turbulence intensity coefficient are extracted from the velocity time series graph. The velocity standard deviation is a statistic that measures the degree of dispersion of velocity data. It is calculated as the square root of the average of the squares of the differences between the velocity values ​​at each moment and the average velocity value. Its numerical value reflects the fluctuation amplitude of the water velocity in the time dimension. The larger the standard deviation, the more drastic the change in the water velocity and the worse the uniformity. The turbulence intensity coefficient is a parameter that characterizes the degree of turbulence in the water flow. It is usually defined as the ratio of turbulent kinetic energy to average kinetic energy, where turbulent kinetic energy is calculated by the mean square value of the pulsating velocity, and the average kinetic energy is calculated based on the average velocity of the section. The larger the turbulence intensity coefficient, the higher the proportion of irregular turbulent motion energy in the water flow and the lower the uniformity of the water flow.

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

[0090] The proportion of high-frequency eddy energy and the energy spectrum attenuation rate are extracted from the eddy energy spectrum distribution diagram. The proportion of high-frequency eddy energy refers to the proportion of eddy energy with a frequency higher than the set critical value (such as 5Hz) in the total energy. The higher the proportion, the more significant the high-frequency turbulent component in the water flow, the more complex the water flow structure, and the worse the uniformity may be. The energy spectrum attenuation rate reflects the speed at which 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 attenuation within a unit frequency interval. The faster the decay rate, the faster the energy dissipation of the high-frequency eddy, and the more the scale distribution of water flow turbulence tends to small-scale eddies, which may indicate that the uniformity of water flow movement has improved.

[0091] The geometric mean of the high-frequency eddy energy fraction and the energy spectrum decay rate is calculated and labeled as the turbulence characteristic index. This geometric mean comprehensively considers the influence of both parameters, avoiding the one-sidedness of a single parameter. For example, if the high-frequency eddy energy fraction is high but the energy spectrum decay rate is fast, the geometric mean of the two values ​​may be moderate, reflecting the overall state of the water flow turbulence.

[0092] The water flow smoothness coefficient and the turbulence characteristic index are weighted and fused to obtain the water flow uniformity index. The weighted fusion process requires setting reasonable weight coefficients for the two parameters. The weights are determined based on the degree of influence of water flow smoothness and turbulence characteristics on water flow uniformity. For example, the water flow smoothness coefficient may be given a higher weight (such as 0.7) because it directly reflects the fluctuation characteristics of flow velocity over time and is an important intuitive indicator of water flow uniformity; the turbulence characteristic index is given a lower weight (such as 0.3) to supplement the potential impact of the frequency structure of water flow turbulence on uniformity. Through weighted summation or other fusion algorithms, the final water flow uniformity index is a quantitative indicator that can comprehensively reflect the time stability and turbulence frequency characteristics of water flow. Its numerical range can be set according to the calculation method (such as 0-100). The higher the value, the better the uniformity of water flow movement.

[0093] In actual applications, the installation location and acquisition parameters of the 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 layered 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, when the water flow conditions are complex (such as flood season and ice season), the sampling frequency can be increased to obtain more intensive velocity data, thereby improving the real-time and accuracy of uniformity assessment.

[0094] The entire quantitative assessment process for flow uniformity utilizes advanced monitoring equipment to acquire multidimensional flow data. Combined with statistical and spectral analysis methods, this quantitatively characterizes flow uniformity from both a time series and frequency perspective, providing critical foundational data for river flow adaptability analysis. This approach avoids the subjectivity and limitations of traditional manual observations, enabling an objective and dynamic assessment of flow uniformity. This helps identify flow structural anomalies promptly and provides a scientific basis for subsequent gate adjustments or diversion optimization.

[0095] Example 3:

[0096] The implementation method of the scouring and silting balance state trend prediction of the riverbed structure is as follows:

[0097] Real-time monitoring of riverbed morphology changes is achieved through topographic survey equipment (such as 3D laser scanners, multi-beam bathymetry systems, or drone-based aerial survey equipment) deployed at target river channel control sections. Such equipment periodically collects 3D topographic data from the control sections, for example, every hour or half a day, with the frequency adjusted based on the severity of erosion and sedimentation changes in the river channel. The collected data includes the elevation values ​​of each measuring point on the riverbed and the riverbed surface morphology (such as the location and contours of deep grooves and shallows). By comparing it with historical topographic data, the riverbed elevation change and erosion and sedimentation rate characteristic parameters are extracted. The riverbed elevation change is the difference (in centimeters) in elevation between the current and previous measurement points at the same measuring point, reflecting the extent of erosion or sedimentation at that measuring point during the time period. The erosion and sedimentation rate characteristic parameters include the maximum erosion rate and the average sedimentation rate. The maximum erosion rate is the maximum erosion rate (in centimeters per hour) among all measuring points during the monitoring period, while the average sedimentation rate is the arithmetic mean of the sedimentation rates (in centimeters per hour) at all measuring points experiencing sedimentation.

[0098] A riverbed erosion and siltation trend prediction model 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 (such as STL), and is trained and fitted on historical riverbed elevation change data. The model input is a sequence of riverbed elevation changes within a certain time period in the past (such as 24 hours), and the output is the probability of riverbed imbalance within a future time interval (such as 12 hours). The riverbed imbalance probability is a value between 0 and 1, with a larger value indicating a higher probability of significant erosion or siltation of the riverbed in the future, leading to structural imbalance. The model training process requires the use of historical riverbed erosion and siltation data. By adjusting model parameters (such as the number of layers and neurons in the LSTM, and the order of the ARIMA), the error between the model prediction and the actual observation is minimized, thereby improving the accuracy of the prediction.

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

[0100]

[0101] Among them, F is the dynamic factor of erosion and deposition, v s Indicates the maximum scouring rate (unit: cm / h), v drepresents 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-sediment dynamic factor, the more significant the dominance of scour or sedimentation, and the more vulnerable the stability of the riverbed structure is to threats.

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

[0103] S=w1×P+w2×F

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

[0105] In practical applications, the accuracy and coverage of topographic surveying equipment are crucial. For example, 3D laser scanners must ensure a high enough density of measurement points (e.g., one measurement point per square meter) to capture subtle topographic changes in the riverbed; drone aerial surveys must be conducted in good weather conditions to avoid cloud cover or wind speed affecting data quality. Furthermore, the model's prediction duration must match the frequency of data collection. For example, if the data collection interval is 1 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 to avoid prediction bias due to insufficient data samples.

[0106] In the calculation of the dynamic factor of scouring and deposition, if the average deposition rate v dIf the rate is 0 (i.e., no riverbed sedimentation occurred during the monitoring period), the formula requires special processing, such as adding a minimum value (such as 0.1 cm / hour) to the denominator to avoid division by zero and ensure the stability of the calculation process. This processing method can adapt to different river conditions and is particularly suitable for erosion and sedimentation analysis in highly scour rivers or clear water discharge conditions.

[0107] The entire trend prediction process, through real-time terrain data collection, model prediction, and parameter calculation, enables dynamic assessment of riverbed erosion and deposition conditions and prediction of future risks. This method combines the strengths of measured data and numerical models, reflecting both current erosion and deposition characteristics and predicting future trends. This provides a key basis for riverbed stability control and regulation. Regular data updates and model optimization continuously improve the accuracy and reliability of predictions, ensuring that the control system can proactively respond to changes in riverbed erosion and deposition, thereby preventing safety issues such as river interruption and embankment collapse caused by structural imbalances.

[0108] Example 4:

[0109] The implementation method of the collaborative analysis of river flow adaptability 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 further combines the water level anomaly data from the hydrological parameter acquisition module to perform a collaborative analysis of the multi-dimensional data. For example, for a plain river channel, assuming the water level anomaly for a certain period is 40% (i.e., the real-time water level change rate exceeds the set threshold by 40%), the system first assigns an impact weight to this parameter. The weight value is determined based on the channel characteristics. For example, in water-level-sensitive channels (such as urban landscape channels or sections that ensure ecological baseflow), the impact weight of water level anomaly can be set to 0.2. This weighted calculation process generates a water level impact compensation value. The calculation logic for this value is that higher water level anomaly has a greater negative impact on the flow adaptability of the channel. Therefore, the compensation value decreases with increasing anomaly. For example, when the water level anomaly is 40%, the compensation value is linearly mapped to 0.6 (assuming a full score of 1), indicating that the water level change has a weaker compensating effect on flow adaptability.

[0111] The system performs normalization calculations on the water flow uniformity index, section stability assessment value, and water level impact compensation value. The purpose of normalization is to uniformly map parameters of different dimensions and different value ranges to the same numerical interval (such as the 0-1 interval) to facilitate direct comparison and fusion. Taking the water flow uniformity index as an example, if its original value range is 0-100, and the index value calculated at a certain moment is 75, it will be 0.75 after normalization; if the original range of the section stability assessment value is 0-1, and the value at a certain moment is 0.3, it will remain unchanged after normalization; if the water level impact compensation value is 0.6 through the above calculation, it will be directly used as the normalized value. The normalization process usually uses a linear transformation method. For example, for the 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 parameter characteristics, such as logarithmic transformation or standardization processing.

[0112] After normalization, the system fuses the normalized values ​​of the three parameters to generate a fused flow adaptability index. This fusion method can be arithmetic averaging, weighted averaging, or other statistical methods, depending on the contribution of each parameter to flow adaptability. For example, in a river channel where flood control and safety are the primary goals, the flow uniformity index (reflecting the stability of the flow structure) may be assigned a higher weight (e.g., 0.5), the cross-section stability assessment value (reflecting the risk of riverbed erosion and deposition) may be weighted 0.3, and the water level impact compensation value may be weighted 0.2. The fused index is then calculated by weighted summation. Assuming that at a certain moment in time, the normalized flow uniformity index is 0.7, the cross-section stability assessment value is 0.4, and the water level impact compensation value is 0.6, the fused index calculated using these weights is 0.7 × 0.5 + 0.4 × 0.3 + 0.6 × 0.2 = 0.61.

[0113] The system presets a flow adaptability judgment threshold, which is determined according to the functional positioning and regulation objectives of the river. For example, for rivers that undertake navigation functions, in order to avoid the water flow being too fast or too slow and affecting navigation safety, the threshold can be set to 0.6; for rivers that are mainly used for drainage, the threshold can be appropriately lowered to 0.5 to allow for a larger flow fluctuation space. If the calculated fusion index is lower than the threshold (such as 0.61 in the above example is lower than the threshold of 0.6, assuming the threshold is 0.65), it is determined that the current river flow adaptability is insufficient and stability needs to be improved through gate adjustment. At this time, a gate adjustment signal is generated. The gate adjustment signal contains specific instructions for gate operation, such as the adjustment direction of parameters such as the opening amplitude and the adjustment rate. For example, if the main reason for the low fusion index is the low water flow uniformity index (indicating severe water turbulence and uneven flow velocity distribution), the system may instruct to increase the gate opening to increase the downstream flow and alleviate the turbulence caused by water flow squeezing; if the section stability assessment value is low (indicating a high risk of riverbed scour), it may instruct to reduce the gate opening to reduce the flow velocity and reduce the scour pressure.

[0114] If the fusion index is higher than the threshold, it is determined that the current river flow has good adaptability, but there is still room for optimization. At this time, a diversion optimization signal is generated. The diversion optimization signal is aimed at the diversion facilities in the river (such as spur dikes, spur dikes, diversion walls, etc.). The instructions include the optimization configuration of parameters such as the deflection angle and action duration of the diversion facilities. For example, when the water flow uniformity index is high but the water level impact compensation value is low (indicating that the water level changes greatly but the water flow structure is stable), the system may instruct to adjust the deflection angle of the diversion wall to guide the water flow to the beach area to balance the impact of water level fluctuations on both sides of the river; if the section stability assessment value is high (indicating that the risk of siltation is significant), it may instruct to start a periodic diversion flushing program to enhance the scouring effect of the mainstream on the riverbed by adjusting the angle of the diversion facility to inhibit siltation.

[0115] In practical applications, parameter weighting and threshold settings must be calibrated using historical data. For example, a river experienced three flooding events due to sudden water level rises in the past year, with the corresponding water level fluctuation anomaly exceeding 30%. Therefore, when setting the weights, the water level impact compensation value can be appropriately increased to 0.3 to enhance the system's sensitivity to water level anomalies. In addition, the system can automatically adjust the judgment threshold based on seasonal changes. For example, 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 lowered to 0.5 to allow a certain degree of flow fluctuation to meet ecological water replenishment needs.

[0116] The collaborative analysis process, through multi-parameter fusion and threshold determination, achieves a comprehensive assessment of river flow adaptability and generates targeted control instructions based on the assessment results. This analysis method avoids the limitations of single-parameter judgment and comprehensively reflects 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 continuously adapts to changing river conditions, ensuring the rationality and effectiveness of flow control strategies.

[0117] Example 5:

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

[0119] Taking a mountain river as an example, the riverbed coupling analysis module continuously monitors the evolution and coordination of the main stream path and riverbed morphology through side-scan sonar equipment deployed on both sides of the river. The side-scan sonar emits fan-shaped sound waves covering the cross-section of the river channel, receives the echo signal reflected by the riverbed surface, and generates the main stream centerline position and riverbed topography data after processing. For example, during a certain monitoring period, the system detected that the main stream centerline was offset to the right by 2 meters relative to the designed position. At the same time, the change in the riverbed topography in this section (the volume change per unit length of the river channel) was 150 cubic meters. The absolute value of the difference in the rate of change of the two was calculated to obtain the main stream swing amplitude. The calculation of the rate of change difference is based on the amount of change per unit time. For example, if the mainstream centerline deviation rate is 0.5 m / h and the riverbed topography change rate is 30 cubic meters / h, the absolute value of the difference between the two is 29.5 (the unit needs to be uniformly processed according to the actual dimension). This value reflects the synchronization of the mainstream path and the riverbed topography changes: if the difference is small, it indicates that the mainstream swing and the riverbed scouring and deposition response are relatively coordinated; if the difference is large, it may indicate that the mainstream swing fails to trigger riverbed adjustment in time, or that riverbed scouring and deposition lag behind water flow changes, resulting in a decrease in coordination between the two.

[0120] Next, the system extracts the adjustment data of the riverbed terrain after the mainstream path changes. For example, when the centerline of the mainstream shifts, the riverbed may gradually form new deep grooves or shallows under the new mainstream path. The terrain adjustment data includes changes in the position of deep grooves and the expansion of the shallow siltation range. The time interval from the moment the mainstream shift occurs to the time when the riverbed terrain begins to show significant adjustments (such as the elevation change of a certain measuring point exceeds 5 cm) is counted and recorded as the terrain response time. At the same time, the average period of the mainstream path change is calculated (such as the average interval of the mainstream swing in the past week is 12 hours). The ratio of the two is the riverbed response delay time. If the terrain 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 mainstream swing lags behind by half a period. A long response delay may lead to a decrease in the coordination between the mainstream path and the riverbed morphology, increasing the risk of local scouring or siltation.

[0121] The riverbed coupling assessment value is generated by weighting the mainstream oscillation amplitude and the riverbed response delay time. The weighting should take into account the characteristics of the river channel. For example, in a wandering river channel, where mainstream oscillation significantly influences riverbed evolution, a weight of 0.6 could be assigned to the mainstream oscillation amplitude and a weight of 0.4 to the riverbed response delay time. Assuming that the mainstream oscillation amplitude calculated at a certain moment is 30 (after dimensionless conversion) and the response delay time calculated is 20 (after dimensionless conversion), the coupling assessment value is 30 × 0.6 + 20 × 0.4 = 26. Lower values ​​indicate better coordination between the mainstream path and riverbed morphology, while lower values ​​indicate poorer coordination. 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 indicates poor coordination (e.g., a value above a threshold of 25), the revised fusion index may be lowered to 0.62 to reflect the additional impact of the interaction between the mainstream and riverbed on flow adaptability.

[0122] The dynamic feedback execution module adjusts the system's data acquisition cycle in real time and forms a closed-loop control loop based on the gate opening adjustment parameters and water flow correction parameters output by the control parameter generation module. 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 minute. This is because changes in gate opening directly affect river water level fluctuations, and high-frequency sampling can capture water level transients in a timely manner, avoiding control delays caused by data lags. The water level sensor may use a pressure or radar device. After adjusting the sampling frequency, the device transmits real-time water level data to the hydrological parameter acquisition module at shorter intervals, providing more intensive monitoring samples for subsequent flow imbalance risk assessment.

[0123] If the control parameters involve the deflection of the diversion structure (such as adjusting the diversion angle of a certain dike from 30 degrees to 45 degrees to improve the water flow distribution), the dynamic feedback execution module will increase the density of the cross-sectional flow velocity monitoring points. For example, the original flow velocity monitoring point is one section every 50 meters, and after adjustment, it is encrypted to one section every 20 meters, which is achieved by adding temporary flow meters or activating mobile monitoring equipment (such as unmanned boats equipped with flow meters). Encrypted monitoring points can more accurately capture the changes in the lateral distribution of water flow velocity after the diversion structure is adjusted, such as whether the flow velocity in the beach area is reduced due to the change in the diversion angle, and whether the flow velocity in the mainstream area is uniform. These real-time flow velocity data are fed back to the hydrological parameter acquisition module to calculate the flow velocity distribution characteristic value and the water flow uniformity index, and to evaluate the diversion optimization effect.

[0124] After the adjusted parameters are input into the hydrological parameter acquisition module, the system restarts the monitoring process. Taking the gate opening adjustment as an example, after the new water level data enters the hydrological parameter acquisition module, the water level change rate and the degree of water level change anomaly are first calculated. If the anomaly is found to exceed the threshold (such as 30%), the flow anomaly signal is triggered again, and the water flow characteristic analysis and cross-section stability analysis are initiated, forming a closed loop of "monitoring-analysis-control-re-monitoring". This closed-loop control mechanism ensures that the effects of control measures are 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 instruction.

[0125] In practical applications, the response time of the feedback mechanism must match the effectiveness of the control measures. For example, the impact of gate opening adjustment on water level usually appears within minutes to tens of minutes, so the increase in the sampling frequency of the water level sensor needs to take effect immediately after the gate is actuated; the impact of the deflection of the diversion structure on the flow velocity distribution may require hours or even days of water flushing to stabilize, so the density of flow velocity monitoring points can be set to automatically restore the normal density after a period of time (such as 24 hours) to save system resources. In addition, the dynamic feedback execution module can integrate priority management functions. When gate adjustment and diversion optimization instructions exist at the same time, the parameters with more urgent data collection needs are adjusted first (such as gate adjustment gives priority to increasing the water level sampling frequency) to ensure the real-time nature of key data.

[0126] The entire implementation process achieved adaptive optimization of the control system through quantitative monitoring of flow-riverbed interactions using the Riverbed Coupling Analysis Module and intelligent scheduling of monitoring resources using the Dynamic Feedback Execution Module. This design not only considers the direct needs of river flow regulation but also deeply analyzes the potential impact of flow-riverbed coupling. Through a closed-loop control link, the system's dynamic response to complex river conditions is enhanced, 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, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

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

Claims

1. A river flow adaptive control 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 a set of river channel hydrological state parameters. Based on the set of river channel hydrological state parameters, the module determines and analyzes the flow imbalance risk, generates a flow anomaly signal, triggers a collaborative monitoring instruction based on the generated flow anomaly signal, and executes the water flow characteristic analysis module and the cross-sectional stability analysis module based on the triggered collaborative monitoring instruction. The water flow characteristic analysis module is used to extract the water flow turbulence characteristic parameters of the key sections of the target river channel, quantitatively evaluate the uniformity of the water flow movement, and obtain the water flow uniformity index; The cross-section stability analysis module is used to extract the morphological change parameters of the target river channel control section, predict the trend of the erosion 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 section stability assessment value, conduct collaborative analysis of river flow adaptability, and generate gate adjustment signals and diversion optimization signals; The control parameter generation module is used to receive the gate adjustment signal and the diversion optimization signal, match the flow control strategy, and generate the gate opening adjustment parameters and the water flow diversion correction parameters.

2. A river flow adaptive control system according to claim 1, characterized in that: The determination and analysis of the flow imbalance risk includes: By collecting the target river water level change rate parameters in real time, the percentage of deviation from the set change threshold is calculated and marked as the water level change anomaly degree; Extract the mainstream area velocity and the beach 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; Collect the flow fluctuation amplitude and sediment content peak value of upstream water parameters, perform normalized weighted calculation, and obtain the upstream water comprehensive index; The water level change anomaly, flow velocity distribution characteristic value and upstream water comprehensive index are compared with the preset thresholds respectively. When any parameter exceeds the corresponding threshold, a flow anomaly signal is generated.

3. A river flow adaptive control system according to claim 1, characterized in that: The quantitative evaluation of the uniformity of water flow movement includes: Acoustic Doppler current profiler is used to collect water flow turbulence data and generate flow velocity time series graphs and eddy kinetic energy spectrum distribution graphs; Extract the velocity standard deviation and turbulence intensity coefficient from the velocity time series diagram, calculate the product of the two and take the reciprocal to obtain the flow stability coefficient; Extract the high-frequency eddy energy proportion and energy spectrum attenuation rate from the eddy kinetic energy spectrum distribution diagram, calculate the geometric mean of the two, and mark it as the turbulence characteristic index; The water flow uniformity index is obtained by weighted fusion of the water flow stability coefficient and the turbulence characteristic index.

4. The river flow adaptive control system according to claim 1, characterized in that: The trend prediction of the scouring and silting balance state of the riverbed structure includes: Collect the morphological change data of the target river channel control section in real time, and extract the riverbed elevation change and scouring and deposition rate characteristic parameters; Construct a riverbed erosion and deposition trend prediction model, input the riverbed elevation change into the model for time series smoothing, and output 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, calculate the logarithm of the ratio between the two and obtain the scouring and sedimentation dynamic factor; The probability of riverbed imbalance and the dynamic factor of scouring and deposition are linearly combined to obtain the section stability assessment value.

5. The river flow adaptive control 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 flow uniformity index, section stability assessment value and water level impact compensation value are normalized and calculated to generate the flow adaptability fusion index; Set the 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 diversion optimization signal is generated.

6. The river flow adaptive control system according to claim 1, characterized in that: The traffic control strategy matching includes: If the gate adjustment signal is captured, the gate control instruction is triggered, and the gate opening amplitude and adjustment rate parameters are dynamically set according to the instruction to generate the gate opening adjustment parameters; If the diversion optimization signal is captured, the diversion structure instruction is triggered, and the deflection angle and action duration parameters of the diversion facility are optimized according to the instruction to generate water flow guidance correction parameters.

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

8. A river flow adaptive control system according to claim 7, characterized in that: The monitoring of the coordination between the evolution of the main river path and the riverbed morphology includes: The side-scan sonar is used to collect the mainstream centerline offset and riverbed topography changes, and the difference in their rates of change is calculated and taken as the absolute value, which is marked as the mainstream swing amplitude. Extract the adjustment data of riverbed terrain after the mainstream path changes, calculate the ratio of terrain response time to change period, and mark it 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.

9. The river flow adaptive control 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 according to the generated gate opening adjustment parameters and water flow guidance correction parameters, and feed back the execution results to the hydrological parameter acquisition module to form a closed-loop control link.

10. A river flow adaptive control system according to claim 9, characterized in that: The specific execution process of the dynamic feedback execution module includes: If the gate opening adjustment parameter involves 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 diversion structure, the density of the cross-section flow 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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