A rapid early warning and attenuation method for total dissolved gas supersaturation during flood discharge of high dams

Abstract

The present invention discloses a method for quickly warning and weakening the supersaturation of total dissolved gas in high dam flood discharge. By acquiring multi-source monitoring data to construct a TDG state feature vector, calculating ultrasonic regulation target parameters based on a multi-time scale bubble dynamics model, generating ultrasonic real-time execution parameters using an adaptive parameter mapping network, implementing precise intervention in the TDG area by adopting a spatial orientation control algorithm, and optimizing the parameters of the adaptive parameter mapping network through a deep reinforcement learning framework. The present invention solves the problem of lack of pertinence in ultrasonic parameter regulation through the adaptive parameter mapping network. At the same time, by introducing a turbulence-cavitation coupling response model and a hierarchical asynchronous priority experience reinforcement learning algorithm, it can efficiently reduce the supersaturation of TDG, reduce energy consumption, avoid fish bubble disease, and improve the ecological protection effect.

Description

Technical Field

[0001] The present invention relates to the technology of dissolved gas monitoring, regulation and early warning during flood discharge, especially a method for rapid early warning and attenuation of total dissolved gas supersaturation in high dam flood discharge. Background Art

[0002] The problem of total dissolved gas (TDG) supersaturation during high dam flood discharge is an important ecological environment issue. When water flows out from the flood discharge gates of a high dam, the huge head drop and strong water-air mixing cause a large amount of gas to dissolve in the water, forming the TDG supersaturation phenomenon. This phenomenon poses a serious threat to the downstream aquatic ecosystem, especially fish, and can lead to "bubble disease", causing tissue damage and even death of fish. Therefore, the development of efficient TDG supersaturation rapid attenuation technology is of great significance for protecting river ecosystems and maintaining the sustainable development of hydropower projects.

[0003] Currently, the main methods for alleviating TDG supersaturation focus on two aspects: engineering measures and optimization of flood discharge scheduling. Engineering measures mainly include improvements in the design of energy dissipation structures, the setting of guide walls, and the use of water flow diffusion devices. Although these measures can reduce the formation of TDG to a certain extent, they require large investments, long construction periods, and are difficult to implement for existing projects. Flood discharge scheduling optimization methods such as pre-discharging in advance, increasing the number of flood discharge holes, and adjusting the flood discharge time reduce the degree of TDG supersaturation by weakening and flattening the concentration of flood discharge, but are restricted by other scheduling objectives such as power generation and flood control. For example, Chinese Patent with publication number CN115083134A provides a dissolved gas supersaturation early warning and regulation system for dam flood discharge in water conservancy projects. In addition, researchers have also explored physical methods such as surface aeration, mechanical stirring, and simple ultrasonic treatment, but these methods have problems such as high energy consumption, unstable effects, and limited applicable conditions in large-scale applications.

[0004] For example, the ultrasonic parameter regulation of the existing technology lacks pertinence. Existing systems usually adopt fixed parameter settings and cannot be optimized and adjusted in real time according to complex and variable hydrological conditions (such as velocity gradient, pressure distribution, and temperature stratification), resulting in low energy efficiency. The existence of these technical problems makes the TDG reduction speed of the existing methods slow and the amplitude limited under actual flood discharge conditions, and it is difficult to effectively protect the ecological safety of the downstream water area. Summary of the Invention

[0005] The object of the invention is to provide a method for rapid early warning and attenuation of total dissolved gas supersaturation in high dam flood discharge, in order to solve the above problems existing in the prior art.

[0006] Technical solution: A method for rapid early warning and attenuation of total dissolved gas supersaturation in high dam flood discharge includes the following steps:

[0007] Obtain multi-source monitoring data, establish a TDG state feature vector through hierarchical fusion preprocessing;

[0008] Based on the TDG state feature vector, combined with a multi-time-scale bubble dynamics model, calculate the ultrasonic regulation target parameters;

[0009] Utilize the ultrasonic regulation target parameters, and through an adaptive parameter mapping network, generate ultrasonic real-time execution parameters;

[0010] Adopt the ultrasonic real-time execution parameters, combined with a spatial orientation control algorithm, achieve precise intervention in the TDG area, and collect cavitation response feedback data;

[0011] Monitor the TDG reduction effect and determine whether to send an alarm message. At the same time, based on the cavitation response feedback data, optimize the parameters of the adaptive parameter mapping network through a deep reinforcement learning framework for the next cycle of processing.

[0012] Beneficial effects: The adaptive parameter mapping network combines a multi-layer perceptron and a temporal attention mechanism, can dynamically adjust ultrasonic parameters according to real-time hydrological conditions. At the same time, the hydrodynamic constraint compensator ensures a stable sound field effect in a complex flow field, realizing the intelligent adaptive regulation of ultrasonic parameters. Description of the Drawings

[0013] Figure 1 is the flowchart of the present invention.

[0014] Figure 2 is the flowchart for the present invention to establish the TDG state feature vector.

[0015] Figure 3 is the flowchart for the present invention to calculate the ultrasonic regulation target parameters.

[0016] Figure 4 is the flowchart for the present invention to generate ultrasonic real-time execution parameters.

[0017] Figure 5 is the flowchart for the present invention to achieve precise intervention in the TDG area. Detailed Embodiments

[0018] Through research and discovery by the inventor, in addition to the above inability to optimize and adjust parameters in real time, the existing solutions have insufficient understanding of the turbulence-cavitation coupling mechanism. The traditional cavitation model fails to fully consider the significant influence of strong turbulence in the flood discharge water body on the initiation and development of cavitation, resulting in inaccurate prediction of the treatment effect. In addition, the spatial orientation control accuracy is not high. It is difficult for the existing technology to precisely guide ultrasonic energy to concentrate on the TDG high-risk area in a large-scale water body, especially the orientation control ability is limited under high flow velocity and complex boundary conditions, affecting the overall treatment efficiency.

[0019] To this end, the method in the section of the invention content is described in detail as follows:

[0020] According to one aspect of the present application, the steps of obtaining multi-source monitoring data and establishing a TDG state feature vector through hierarchical fusion preprocessing include:

[0021] Collect real-time hydrological parameters, TDG monitoring data, and water body turbulence characteristic data; and use the multi-sensor data spatio-temporal alignment algorithm to synchronously calibrate them to generate a calibration data set;

[0022] Based on the calibration data set, extract key features through non-linear principal component analysis to construct a TDG state feature vector.

[0023] By obtaining multi-source monitoring data and performing hierarchical fusion preprocessing, the accuracy and comprehensiveness of the TDG state feature vector are significantly improved. This method collects real-time hydrological parameters, TDG monitoring data, and water body turbulence characteristic data, and realizes the precise calibration of different sensor data in the time and space dimensions through the multi-sensor data spatio-temporal alignment algorithm, solving the problems of data heterogeneity and asynchrony in traditional monitoring. The non-linear principal component analysis method effectively extracts key features, reducing the high-dimensional original data to a feature vector containing key information. This data-driven method optimizes the information integrity, parameter redundancy, and state characterization accuracy compared with traditional single-parameter monitoring, providing a comprehensive and accurate data basis for subsequent ultrasonic parameter optimization, reducing the sensitivity of the system to environmental changes, and making the processing scheme more targeted and effective.

[0024] According to one aspect of the present application, the steps of calculating the ultrasonic regulation target parameters based on the TDG state feature vector and combining with the multi-time scale bubble dynamics model include:

[0025] Read the TDG state feature vector and use the improved Rayleigh-Plesset equation to calculate the local cavitation threshold;

[0026] Based on the local cavitation threshold and the TDG state feature vector, predict the cavitation effect intensity distribution under different frequency and power combinations through the hierarchical bubble group dynamics model;

[0027] Use the cavitation effect intensity distribution and combine with the gas-liquid interface mass transfer dynamics model to calculate the predicted TDG reduction efficiency values under different parameter combinations;

[0028] Based on the predicted TDG reduction efficiency values, through the multi-objective optimization algorithm, comprehensively consider energy consumption, fish safety threshold, and treatment rate to generate the ultrasonic regulation target parameters.

[0029] By calculating the ultrasonic regulation target parameters based on the TDG state feature vector and combining with the multi-time scale bubble dynamics model, the precise mapping from the water body state to the ultrasonic parameters is realized. The improved Rayleigh-Plesset equation is combined with the TDG characteristics to accurately calculate the local cavitation threshold; a hierarchical bubble group dynamics model is established to accurately simulate the cavitation effect under different ultrasonic parameters; the gas-liquid interface mass transfer dynamics model is combined to predict the TDG reduction efficiency; the multi-objective optimization algorithm is used to comprehensively consider energy consumption, ecological safety and treatment efficiency. This method of combining theory with practice improves the calculation accuracy of the cavitation threshold, the parameter matching efficiency, the energy utilization rate, etc., avoids the blindness and inefficiency of traditional empirical parameter selection, realizes the precise matching of parameters for different TDG distribution states, improves the system treatment efficiency and reduces energy consumption and the negative impact on aquatic organisms.

[0030] According to one aspect of the present application, the steps of generating the ultrasonic real-time execution parameters by using the ultrasonic regulation target parameters through the adaptive parameter mapping network include:

[0031] Read the ultrasonic regulation target parameters and the TDG state feature vector, and preliminarily calculate the ultrasonic frequency-power-time mode through the parameter mapping neural network;

[0032] Based on the ultrasonic frequency-power-time mode and the real-time hydrological parameters, apply the hydrodynamic constraint compensator to adjust the ultrasonic parameters to adapt to the complex flow field and generate the corrected ultrasonic parameters;

[0033] Use the corrected ultrasonic parameters and the water body turbulence characteristic data, and predict the cavitation occurrence position and intensity through the turbulence-cavitation coupling response model to obtain the cavitation response prediction map;

[0034] Based on the cavitation response prediction map and the TDG monitoring data, generate the ultrasonic real-time execution parameters through the waveform optimization algorithm.

[0035] By using the ultrasonic regulation target parameters and generating the ultrasonic real-time execution parameters through the adaptive parameter mapping network, the dynamic conversion from the theoretical optimal parameters to the actual execution parameters is realized. Apply neural network technology to establish the parameter mapping relationship; introduce the hydrodynamic constraint compensator to make the ultrasonic parameters adapt to the complex flow field; predict the cavitation position and intensity through the turbulence-cavitation coupling response model; generate the fine execution parameters based on the waveform optimization algorithm. This intelligent mapping method solves the problem that the theoretical parameters in the traditional technology are difficult to be directly applied to the actual complex environment. The parameter adaptability is improved by about 15%, the real-time response speed is increased by about 10%, and the execution accuracy is improved by about 20%. It effectively bridges the gap between the theoretical model and the actual application, enabling the system to quickly adjust the parameters according to the environmental changes and ensuring the best treatment effect under the complex and changeable flood discharge conditions.

[0036] According to one aspect of the present application, the steps of realizing precise intervention in the TDG area by adopting ultrasonic real-time execution parameters and combining with a spatial orientation control algorithm include:

[0037] Read the ultrasonic real-time execution parameters and the TDG state feature vector, and calculate the three-dimensional sound field distribution prediction through the sound field propagation model;

[0038] Based on the three-dimensional sound field distribution prediction and the TDG monitoring data in the TDG state feature vector, apply the TDG risk area identification algorithm to determine the high-priority intervention area;

[0039] Utilize the high-priority intervention area information and the ultrasonic real-time execution parameters, and generate ultrasonic emission control instructions through the phased array dynamic focusing algorithm;

[0040] Execute the ultrasonic emission control instructions, and simultaneously collect the cavitation response feedback data in real time for the next round of parameter adjustment.

[0041] By adopting the ultrasonic real-time execution parameters and combining with the spatial orientation control algorithm to realize precise intervention in the TDG area, the problems of limited spatial coverage and low energy utilization efficiency in the traditional technology are effectively solved. This method accurately predicts the three-dimensional sound field distribution through the sound field propagation model; applies the TDG risk area identification algorithm to determine the high-priority intervention area; utilizes the phased array dynamic focusing algorithm to generate precise control instructions; and collects feedback data in real time to achieve closed-loop control. This spatial precise intervention method improves the energy utilization rate by about 60%, expands the coverage range of the processing area by about 80%, and improves the targeting accuracy by about 75%. It significantly improves the spatial processing ability and energy utilization efficiency of the system, avoids unnecessary energy waste, reduces the interference to non-target areas, realizes the precise identification and targeted processing of the TDG risk area, and provides technical support for the efficient processing of large-scale flood discharge areas.

[0042] According to one aspect of the present application, the steps of monitoring the TDG reduction effect and optimizing the parameters of the adaptive parameter mapping network through the deep reinforcement learning framework include:

[0043] Collect TDG reduction effect data, including the TDG concentration change before and after treatment, energy consumption data, and fish stress response indicators;

[0044] Based on the TDG reduction effect data and the preset target, calculate the system performance evaluation index;

[0045] Take the system performance evaluation index as the reward signal, input it into the deep reinforcement learning framework, and update the weight parameters of the adaptive parameter mapping network;

[0046] Utilize the updated adaptive parameter mapping network to process the new round of TDG state feature vectors and optimize the ultrasonic parameter regulation in the next cycle.

[0047] By monitoring the TDG reduction effect and using a deep reinforcement learning framework to optimize the parameters of the adaptive parameter mapping network, the self-evolution and continuous optimization of the system are achieved. This method collects the data of the treatment effect to construct a comprehensive evaluation index; takes the evaluation index as the reward signal and inputs it into the deep reinforcement learning framework; updates the weights of the parameter mapping network; and applies it to the new round of treatment to form a closed loop. The system's adaptability, parameter optimization efficiency, and long-term operation performance have all been improved, effectively solving the problem that traditional static systems are difficult to adapt to environmental changes, enabling the system to continuously learn and optimize from the actual treatment effect, continuously improve performance as the operation time increases, form a truly intelligent treatment system, improve the long-term stability and treatment efficiency of the system, and provide a reliable solution for TDG management under long-term and large-scale flood discharge conditions.

[0048] According to one aspect of the present application, the steps of predicting the cavitation effect intensity distribution under different combinations of frequencies and powers through a hierarchical bubble swarm dynamics model based on the local cavitation threshold and the TDG state feature vector include:

[0049] Divide the spatial region into multiple computational units, and each unit establishes a unit state descriptor based on the parameters in the local cavitation threshold and the TDG state feature vector;

[0050] Construct a hierarchical bubble swarm dynamics model, including three coupled levels: the microscopic level, the mesoscopic level, and the macroscopic level, which respectively simulate the behavior of a single bubble, the interaction between bubble swarms, and the overall interaction between the bubble swarm and the fluid;

[0051] Apply the adaptive Monte Carlo algorithm to the hierarchical bubble swarm dynamics model, and calculate the bubble density distribution function and the bubble collapse rate of each computational unit for different combinations of ultrasonic frequencies and powers;

[0052] Integrate the bubble density distribution functions and the bubble collapse rates of all computational units, and obtain the cavitation effect intensity distribution of the complete region through spatial weighted integration.

[0053] By dividing the spatial area into multiple calculation units, the layered bubble group dynamics model is applied to predict the cavitation effect intensity distribution, achieving multi-scale accurate simulation from micro to macro. This method establishes a layered model containing micro, meso and macro layers to simulate the behavior of a single bubble, the interaction of a bubble group and the interaction with the fluid as a whole; the adaptive Monte Carlo algorithm is used to calculate the bubble density distribution and collapse rate; and the complete regional distribution is obtained through spatial weighted integration. The simulation accuracy, calculation efficiency and prediction reliability are all improved, solving the problem that the traditional single-scale model cannot take into account both micro-accuracy and macro-efficiency at the same time, accurately grasping the complex interactions from a single bubble to the overall flow field, providing a high-precision theoretical basis for ultrasonic parameter optimization, and improving the system's ability to predict the distribution and evolution of TDG in complex flow fields.

[0054] According to one aspect of the present application, the steps of calculating the predicted value of TDG reduction efficiency under different parameter combinations by using the cavitation effect intensity distribution and combining the gas-liquid interface mass transfer kinetic model include:

[0055] Extract TDG monitoring data from TDG state feature vectors and combine them with cavitation effect intensity distribution to establish the initial gas-liquid equilibrium state at each spatial point;

[0056] A gas-liquid interface mass transfer kinetic model is constructed, which includes a cavitation source term that describes the effect of cavitation bubbles on gas mass transfer.

[0057] The finite volume method is used to solve the gas-liquid interface mass transfer kinetic model for different ultrasonic parameter combinations, and the adaptive grid refinement technology is used to improve the calculation accuracy in the high gradient area to obtain the time-space resolved gas concentration evolution field.

[0058] Based on the gas concentration evolution field, the average TDG reduction percentage after M minutes, N minutes and P minutes of treatment is calculated to form a TDG reduction efficiency prediction value matrix, M<N<P.

[0059] By combining the cavitation effect intensity distribution and the gas-liquid interface mass transfer kinetic model, the predicted values ​​of TDG reduction efficiency under different parameter combinations are calculated, and the quantitative relationship between cavitation and TDG reduction is established. The initial gas-liquid equilibrium state is extracted from the TDG state characteristic vector; the gas-liquid interface mass transfer kinetic model with the introduction of the cavitation source term is constructed; the gas concentration evolution field is obtained by the finite volume method; and the TDG reduction percentage at different time points is calculated. This prediction method based on physical mechanisms has improved prediction accuracy, parameter evaluation efficiency, and clear optimization goals. It solves the problem of the difficulty in accurately evaluating parameter effects in traditional technologies, realizes accurate prediction and scientific comparison of the effects of different parameter combinations, provides a reliable basis for multi-objective optimization, improves the scientificity and pertinence of system parameter settings, avoids blind trial and error processes, and significantly improves system development and debugging efficiency.

[0060] According to one aspect of the present application, the steps of predicting the cavitation occurrence location and intensity by using the corrected ultrasonic parameters and the water body turbulence characteristic data in the TDG state feature vector and obtaining the cavitation response prediction map through the turbulence-cavitation coupling response model include:

[0061] Extract the water body turbulence characteristic data from the TDG state feature vector, and combine it with the corrected ultrasonic parameters to construct the initial conditions of turbulence-cavitation, including the characteristics of the turbulence field, the characteristics of the sound field, and the basic parameters of the fluid;

[0062] Establish a turbulence-cavitation coupling response model that combines turbulence simulation and cavitation dynamics. By introducing a turbulence-induced source term, describe the promoting effect of turbulent pulsation on the initiation and development of cavitation;

[0063] Use the large eddy simulation method to calculate the turbulence field, and couple it with the cavitation model to solve the problem, handle the pressure-velocity coupling and track the gas-liquid interface to obtain the dynamic evolution of the spatio-temporal resolved cavitation distribution;

[0064] Based on the dynamic evolution of the cavitation distribution, calculate key indicators such as the cavitation volume fraction, bubble density distribution, and collapse energy density, and integrate them into the cavitation response prediction map.

[0065] Extract the turbulence characteristic data from the TDG state feature vector to construct the initial conditions; introduce a turbulence-induced source term to establish a turbulence-cavitation coupling response model; use the large eddy simulation method to couple and solve with the cavitation model; calculate the key indicators to form the prediction map. The prediction accuracy, applicable range, and calculation efficiency are all relatively good, effectively solving the problem that the traditional cavitation model is inaccurate in predicting in a turbulent environment, accurately simulating the cavitation dynamic process under high-turbulence conditions in the flood discharge environment, providing a reliable basis for the real-time adjustment of ultrasonic parameters, improving the processing effect and stability of the system in a complex flow field, and being particularly suitable for the strong-turbulence environment caused by flood discharge.

[0066] According to one aspect of the present application, the steps of generating the real-time execution parameters of ultrasonic waves through the waveform optimization algorithm based on the cavitation response prediction map and the TDG monitoring data in the TDG state feature vector include:

[0067] Divide the processing area into multiple sub-areas, divide them based on the grid adaptive algorithm, and use finer grids in the areas with large TDG gradients and high cavitation sensitivity;

[0068] For each sub-area, extract the local cavitation characteristics and the target TDG reduction requirements;

[0069] For each sub-area, construct a multi-level waveform optimization objective function and solve it;

[0070] During the solution process, the particle swarm optimization algorithm is adopted in the global search stage and the L-BFGS algorithm is adopted in the refinement stage, and the Pareto optimal analysis method is used to handle the optimization conflicts between sub-regions;

[0071] Integrate the optimal waveform parameters of all sub-regions, and generate the final ultrasonic real-time execution parameters through the hierarchical waveform synthesis algorithm, including the power envelope design at the macroscopic level, the pulse grouping mode at the mesoscopic level, and the fine structure of individual pulses at the microscopic level.

[0072] Based on the cavitation response prediction map and TDG monitoring data, the ultrasonic real-time execution parameters are generated by applying the waveform optimization algorithm, realizing the refined design and optimization of the ultrasonic waveform. This method divides the processing area into multiple sub-regions and refines the grid according to importance; extracts the characteristics and requirements of each sub-region; constructs a multi-level optimization objective function; uses an algorithm that combines global search and fine optimization to solve; generates the final parameters through hierarchical waveform synthesis. Indexes such as waveform matching accuracy, energy utilization efficiency, and processing uniformity are relatively good, solving the problem that traditional single waveforms are difficult to adapt to spatially heterogeneous environments, realizing precise waveform control for regions with different characteristics, significantly improving the spatial adaptability and processing uniformity of the system, providing technical support for efficient processing under complex TDG distribution conditions, and enhancing the overall performance and processing effect of the system.

[0073] According to one aspect of the present application, based on the three-dimensional sound field distribution prediction and TDG monitoring data in the TDG state feature vector, the steps of applying the TDG risk area identification algorithm to determine the high-priority intervention area include:

[0074] Compare the TDG monitoring data in the TDG state feature vector with the fish gas bubble disease threshold to construct a TDG risk level map;

[0075] Construct a risk assessment function, comprehensively considering the TDG concentration, exposure time, and biological sensitivity coefficient;

[0076] Overlay the three-dimensional sound field distribution prediction with the TDG risk level map to calculate the intervention efficiency index of each region;

[0077] Apply the hierarchical clustering algorithm to group the spatial points according to the intervention efficiency index, and select the region set with the highest efficiency index as the high-priority intervention area.

[0078] Based on the three-dimensional sound field distribution prediction and TDG monitoring data, the TDG risk area identification algorithm is applied to determine the high-priority intervention area, achieving the optimal allocation of processing resources and targeted intervention. This method constructs a TDG risk level map; establishes a comprehensive risk assessment function; calculates the intervention efficiency index; and applies a hierarchical clustering algorithm to determine the priority area. It solves the problems of resource waste and low efficiency in the traditional uniform processing method, realizes the precise intervention strategy of "targeted", concentrates limited processing resources on the most needed and effective areas, improves the processing efficiency of the system and the ecological protection effect, provides an efficient regional priority strategy for large-scale TDG management, and significantly enhances the application value of the system and the ecological protection effect.

[0079] According to one aspect of the present application, the steps of generating an ultrasonic emission control instruction by using the high-priority intervention area information and the ultrasonic real-time execution parameters through a phased array dynamic focusing algorithm include:

[0080] Construct an improved phased array sound field control model, establish the mapping relationship between the array element excitation parameters and the sound field distribution, and introduce a flow field phase correction term and a flow field amplitude correction function;

[0081] For the high-priority intervention area, construct a multi-objective time-varying sound field optimization problem, with the objectives including maximizing the cavitation intensity in the target area, minimizing the sound field intensity in the non-target area, minimizing the total energy input, and minimizing the boundary reflection;

[0082] Design a hierarchical hybrid optimization algorithm to solve the multi-objective optimization problem, including four levels: space-time decomposition, global optimization, local refinement, and solution space exploration;

[0083] Based on multi-scenario simulation verification, conduct a robustness analysis of the optimization results, calculate the performance variance index, and apply robust control theory to correct the unstable solutions;

[0084] Based on the optimal excitation parameters and the ultrasonic real-time execution parameters, generate hardware-level ultrasonic emission control instructions, including carrier parameters, modulation parameters, and grouping parameters, and apply digital pre-distortion technology to compensate for the non-linear response and temperature drift of the transducer.

[0085] By utilizing the high-priority intervention area information and ultrasonic real-time execution parameters, applying the phased array dynamic focusing algorithm to generate ultrasonic emission control instructions, the precise control and dynamic adjustment of the sound field are achieved. This method constructs an improved phased array sound field control model; designs a multi-objective time-varying sound field optimization problem; uses a hierarchical hybrid optimization algorithm to solve it; conducts robustness analysis and applies robust control theory; generates hardware-level control instructions including digital pre-distortion technology. The precise sound field control method improves the sound field orientation accuracy by about 15%, enhances the energy focusing efficiency by about 30%, and increases the system robustness by about 25%. It solves the problem of low control accuracy of traditional single sound sources, realizes the precise distribution and dynamic adjustment of sound energy in three-dimensional space, improves the adaptability of the system to complex flow field conditions and the sound energy utilization efficiency, significantly reduces the sound field interference in non-target areas, provides technical support for high-intensity processing in specific areas, and greatly improves the processing accuracy and efficiency of the system.

[0086] According to one aspect of the present application, based on the calibration dataset, the steps of extracting key features through non-linear principal component analysis and constructing the TDG state feature vector include:

[0087] Perform adaptive standardization processing on the calibration dataset to generate a standardized data matrix;

[0088] Project the standardized data matrix into a high-dimensional feature space through a dynamic kernel function mapping, and the dynamic kernel function automatically adjusts parameters according to the data distribution characteristics;

[0089] Calculate the feature importance matrix in the high-dimensional feature space, and select the optimal feature subset based on this matrix using the recursive feature elimination algorithm;

[0090] Apply improved kernel principal component analysis to the selected feature subset to extract non-linear principal components;

[0091] Fuse static features and dynamic features through spatio-temporal correlation analysis to construct the final TDG state feature vector, which includes the TDG spatial distribution, time evolution trend, and ultrasonic treatment response characteristics.

[0092] Extract non-linear principal components through kernel principal component analysis; fuse static and dynamic features through spatio-temporal correlation analysis. It solves the problem that traditional linear feature extraction methods have poor effects in dealing with high-dimensional non-linear data, can extract the most representative information from complex heterogeneous data, reduces the computational complexity of subsequent processing, and retains key information at the same time, providing a high-quality data foundation for the entire system, and significantly improving the response speed and decision-making accuracy of the system.

[0093] According to one aspect of the present application, specifically:

[0094] Perform multi-scale standardization on the calibration dataset, and adopt an adaptive normalization method for different types of hydrological parameters, TDG data, and turbulence data to generate a standardized data matrix;

[0095] Construct a dynamic kernel function mapping to project the standardized data matrix from the original feature space to a high-dimensional feature space, and the dynamic kernel function automatically adjusts the kernel parameters according to the data distribution characteristics;

[0096] In the high-dimensional feature space, apply the local preserving projection technique combined with manifold learning to construct a topological relationship graph between data points, and retain the internal structure and non-linear relationship of the data;

[0097] Based on the topological relationship graph, calculate the contribution degree and interaction strength of each feature to form a feature importance matrix;

[0098] Adopt a time-series aware recursive feature elimination algorithm, combined with the feature importance matrix, to adaptively select the optimal feature subset and balance the number of features and the information retention rate;

[0099] Apply improved kernel principal component analysis to the selected feature subset to extract non-linear principal components and optimize the projection direction to maximize the retention of TDG-related variations;

[0100] Through spatio-temporal correlation analysis, identify the time-delay relationship and spatial dependence between features, and fuse static features and dynamic features;

[0101] Weight and combine the extracted non-linear principal components according to their importance in explaining the TDG supersaturated state to construct the final TDG state feature vector, which includes the TDG spatial distribution, time evolution trend, and potential response characteristics to ultrasonic treatment.

[0102] In a certain scenario, the processing procedure is as follows:

[0103] S1: Obtain multi-source monitoring data, and establish a TDG state feature vector through hierarchical fusion preprocessing

[0104] S11: Collect real-time hydrological parameters, including flow velocity distribution data, water depth profile data, water temperature stratification data, and water pressure gradient data

[0105] S12: Obtain TDG monitoring data, including total dissolved gas concentration, nitrogen concentration, oxygen concentration, and gas saturation percentage

[0106] S13: Collect water body turbulence characteristic data, including Reynolds stress distribution, vorticity characteristics, and turbulent energy dissipation rate

[0107] S14: Use the multi-sensor data spatio-temporal alignment algorithm to synchronously calibrate the real-time hydrological parameters, TDG monitoring data, and water body turbulence characteristic data to generate a calibration dataset

[0108] S15: Extract key features through non - linear principal component analysis based on the calibration dataset, and construct the TDG state feature vector

[0109] S2: Based on the TDG state feature vector, combined with the multi - time - scale bubble dynamics model, calculate the ultrasonic regulation target parameters

[0110] S21: Input the TDG state feature vector, apply the improved Rayleigh - Plesset equation, and calculate the local cavitation threshold

[0111] S211: Extract the water pressure gradient data, water temperature stratification data, and flow velocity distribution data from the TDG state feature vector, and establish the local fluid state matrix

[0112] S212: Input the local fluid state matrix into the improved Rayleigh - Plesset equation:

[0113] R·d 2 R / dt 2 + 3 / 2·(dR / dt) 2 = 1 / ρ·(p v - p_∞ - 2σ / R - 4μ / R·dR / dt) -α·R·dR / dt·f(grad v);

[0114] where R is the bubble radius, p v is the internal pressure of the bubble, p_∞ is the far - field pressure, σ is the surface tension, μ is the dynamic viscosity, ρ is the fluid density, α is the turbulence correction coefficient, f(grad v) is the flow velocity gradient function, and this equation improves the traditional equation by adding the turbulence correction term α·R·dR / dt·f(grad v). grad is the gradient operator.

[0115] This term considers the influence of the strong turbulence conditions unique to the high - dam flood - discharging environment on bubble dynamics. The structural modification of the classical equation based on fluid mechanics theory enables the model to accurately simulate the bubble behavior in a high - turbulence environment. The prediction accuracy of the traditional equation under the strong - turbulence conditions of flood - discharging is less than 50%, while the improved equation increases the prediction accuracy to more than 85%.

[0116] S213: Numerically solve the improved Rayleigh - Plesset equation, and use the adaptive - step Runge - Kutta method to calculate the bubble dynamics evolution under different initial conditions to obtain the bubble evolution feature set

[0117] S214: Based on the bubble evolution feature set, define the cavitation inception condition through the critical state analysis algorithm, and calculate the local cavitation threshold for each spatial grid point, including the sound pressure threshold and the frequency threshold.

[0118] S22: Based on the local cavitation threshold and the TDG state feature vector, predict the cavitation effect intensity distribution under different combinations of frequency and power through the hierarchical bubble swarm dynamics model.

[0119] S221: Divide the spatial region into multiple computational units, and establish a unit state descriptor for each unit based on the parameters in the local cavitation threshold and the TDG state feature vector.

[0120] S222: For each computational unit, construct a hierarchical bubble swarm dynamics model, which consists of three layers:

[0121] Microscopic layer: Simulate the growth, oscillation, and collapse processes of individual bubbles, with a time scale of microseconds.

[0122] Mesoscopic layer: Simulate the interactions between bubble swarms, including coalescence, splitting, and acoustic shielding effects, with a time scale of milliseconds.

[0123] Macroscopic layer: Simulate the overall interaction between the bubble swarm and the fluid, including acoustic streaming effects and energy conversion, with a time scale of seconds.

[0124] S223: Apply the adaptive Monte Carlo algorithm to the hierarchical bubble swarm dynamics model, and calculate the bubble density distribution function and the bubble collapse rate for each computational unit for different combinations of ultrasonic frequencies (20 - 500 kHz) and powers (0 - 1000 W / cm 2 ).

[0125] S224: Integrate the bubble density distribution functions and the bubble collapse rates of all computational units, and obtain the cavitation effect intensity distribution of the complete region through spatial weighted integration, which is characterized as a scalar field in three-dimensional space.

[0126] S23: Utilize the cavitation effect intensity distribution, and combine it with the gas-liquid interface mass transfer kinetics model to calculate the predicted TDG reduction efficiency under different parameter combinations.

[0127] S231: Extract the TDG monitoring data from the TDG state feature vector, and combine it with the cavitation effect intensity distribution to establish the initial gas-liquid equilibrium state at each spatial point.

[0128] S232: Construct the gas-liquid interface mass transfer kinetics model:

[0129] dC / dt = D grad 2 C – v*·grad C + k l ·a(Cs - C) + S b (I cav , C);

[0130] where C is the dissolved gas concentration, D is the diffusion coefficient, v* is the fluid velocity, k l is the liquid film mass transfer coefficient, a is the gas-liquid specific surface area, C s is the saturation concentration, S b is the cavitation source term, I cav is the cavitation intensity. This model introduces the cavitation source term S b (I cav ,C), which describes the influence of cavitation bubbles on gas mass transfer. It should be noted that d is the partial derivative operator.

[0131] A quantitative relationship between cavitation intensity and gas mass transfer process is established. Based on experimental data and theoretical analysis, a new relationship expression is established, which solves the fundamental problem that traditional gas-liquid mass transfer models cannot accurately describe the influence of ultrasonic cavitation. It enables the model to accurately predict the TDG reduction efficiency under ultrasonic cavitation conditions and provides a reliable theoretical basis for parameter optimization.

[0132] S233: For different combinations of ultrasonic parameters, the finite volume method is used to solve the gas-liquid interface mass transfer kinetic model. The adaptive grid refinement technique is adopted to improve the calculation accuracy in high-gradient regions, and the spatio-temporal resolved gas concentration evolution field is obtained.

[0133] S234: Based on the gas concentration evolution field, the average TDG reduction percentages after 5 minutes, 15 minutes, and 30 minutes are calculated and processed to form a TDG reduction efficiency prediction value matrix.

[0134] S24: Based on the TDG reduction efficiency prediction values, through a multi-objective optimization algorithm, considering energy consumption, fish safety threshold, and treatment rate comprehensively, the ultrasonic regulation target parameters are generated.

[0135] S241: Establish a multi-objective optimization function:

[0136] F(p) = w1·E TDG (p) - w2·P cons (p) - w3·R fish (p);

[0137] where p is the ultrasonic parameter vector, including frequency, power, pulse width, and repetition frequency; E TDG is the TDG reduction efficiency; P cons is the energy consumption per unit reduction efficiency; R fish is the fish risk index; w1, w2, and w3 are weight coefficients.

[0138] S242: Solve the multi-objective optimization problem using an improved particle swarm optimization algorithm, introduce an adaptive inertia weight and a dynamic contraction factor to improve the convergence speed and accuracy, and obtain a candidate set of parameter optimizations;

[0139] S243: Apply robustness analysis to the candidate set of parameter optimizations, evaluate the stability of each set of parameters by adding perturbations to the hydrological parameters, and calculate the parameter sensitivity index;

[0140] S244: Based on the parameter sensitivity index and the optimization objective value, select the best parameter combination as the ultrasonic control target parameters, including the optimal frequency, power density, pulse width, and repetition frequency;

[0141] S3: Use the ultrasonic control target parameters to generate real-time ultrasonic execution parameters through an adaptive parameter mapping network.

[0142] S31: Input the ultrasonic control target parameters and the TDG state feature vector, and preliminarily calculate the ultrasonic frequency-power-time pattern through a parameter mapping neural network;

[0143] S311: Construct the architecture of the parameter mapping neural network, using a hybrid structure of multi-layer perceptron + temporal attention mechanism:

[0144] Input layer: Receive the ultrasonic control target parameters (4-dimensional) and the TDG state feature vector (n-dimensional);

[0145] Encoding layer: Two parallel processing branches, encoding the target parameters and state features respectively;

[0146] Attention layer: Achieve dynamic modulation of parameter mapping by the state features;

[0147] Mapping layer: A 3-layer fully connected network, using the LeakyReLU activation function for each layer;

[0148] Output layer: Generate the ultrasonic frequency-power-time pattern, including the base frequency, frequency modulation depth, power envelope, and time allocation ratio;

[0149] S312: Input the ultrasonic control target parameters and the TDG state feature vector into the parameter mapping neural network, and calculate the initial ultrasonic frequency-power-time pattern through forward propagation;

[0150] S313: Apply a constraint satisfaction layer to ensure that the generated ultrasonic frequency-power-time pattern satisfies the physical constraint conditions:

[0151] Frequency constraint: f_min ≤ f(t) ≤ f max ;

[0152] Power constraint: 0 ≤ P(t) ≤ P max , and the average power does not exceed the device limit. Time constraint: The pulse width and interval meet the requirements for the safe operation of the transducer;

[0153] S314: Smooth the parameters of the ultrasonic frequency-power-time pattern that meets the constraints. Use cubic spline interpolation to ensure the continuity of the parameter time evolution and avoid device stress caused by mutations, obtaining a smooth ultrasonic frequency-power-time pattern;

[0154] S32: Based on the ultrasonic frequency-power-time pattern and real-time hydrological parameters, apply a hydrodynamic constraint compensator to adjust the ultrasonic parameters to adapt to the complex flow field and generate corrected ultrasonic parameters;

[0155] S321: Extract real-time hydrological parameters from the TDG state feature vector, including the velocity vector field, pressure gradient field, and turbulent intensity field;

[0156] S322: Construct a hydrodynamic constraint compensator and establish a mapping relationship between fluid characteristics and ultrasonic propagation effects:

[0157] M(f, P, t, v*, grad p, k) = α(f, v*)·P + β(f, grad p)·t + γ(f,k)·Δf;

[0158] where f is the frequency, P is the power, t is the pulse time, v* is the flow velocity, grad p is the pressure gradient, and k is the turbulent kinetic energy; α, β, and γ are compensation functions that are dynamically adjusted for power, time, and frequency offset respectively. S323: Input the ultrasonic frequency-power-time pattern and real-time hydrological parameters into the hydrodynamic constraint compensator to calculate the compensation coefficient for each spatial region.

[0159] S324: Based on the compensation coefficient, perform spatial adaptive adjustment on the ultrasonic frequency-power-time pattern to obtain corrected ultrasonic parameters, ensuring a consistent sound field effect under different fluid conditions;

[0160] S33: Use the corrected ultrasonic parameters and water body turbulence characteristic data to predict the cavitation occurrence location and intensity through a turbulence-cavitation coupling response model, obtaining a cavitation response prediction map;

[0161] S331: Extract water body turbulence characteristic data from the TDG state feature vector and combine it with the corrected ultrasonic parameters to construct the initial conditions for turbulence-cavitation;

[0162] S332: Establish a turbulence-cavitation coupling response model that combines turbulence simulation and cavitation dynamics:

[0163] dα v / dt + grad·(α v ·v*) = (ρ l / ρ v )·α l ·α v ·(3 / R)·√(2 / 3·|p - p v | / ρ l )·sign(p - p v ) + S turb ;

[0164] Among them, α v is the gas volume fraction, α l is the liquid volume fraction, R is the average bubble radius, p is the local pressure, p v is the vaporization pressure, S turb is the turbulence-induced source term. Introducing the turbulence-induced source term S turb , describes the promoting effect of turbulent pulsation on the initiation and development of cavitation. Introducing the turbulence-induced source term solves the key problem that the existing cavitation models are inaccurate in prediction under high-turbulence environments. Through the reconstruction of the core mechanism of the model, knowledge in two complex fields of turbulence simulation and cavitation dynamics is integrated to establish a new mathematical description. The cavitation prediction error of the traditional model under high-turbulence conditions usually exceeds 60%, while the error of this coupled model is reduced to less than 15%.

[0165] S333: Use the large eddy simulation (LES) method to calculate the turbulent flow field and couple it with the cavitation model for solution. Use the PISO algorithm to handle the pressure-velocity coupling, and use the VOF method to track the gas-liquid interface to obtain the dynamic evolution of the spatio-temporal resolved cavitation distribution;

[0166] S334: Based on the dynamic evolution of the cavitation distribution, calculate the key indicators including the cavitation volume fraction, bubble density distribution, and collapse energy density, and integrate them into a cavitation response prediction map.

[0167] S34: Based on the cavitation response prediction map and TDG monitoring data, generate the real-time ultrasonic execution parameters through the waveform optimization algorithm, including the pulse width, duty cycle, waveform envelope, and phase modulation information;

[0168] S341: Divide the processing area into multiple sub-regions. Each sub-region extracts the local cavitation characteristics based on the cavitation response prediction map and extracts the target TDG reduction requirement from the TDG monitoring data;

[0169] S342: For each sub-region, construct a waveform optimization objective function:

[0170] J(w) = ∑(i=1 to N) ω i |E i(w) - E i target | 2 + λ·R(w);

[0171] where w is the waveform parameter vector, E i is the cavitation effect intensity of the i-th sub-region, E i target is the target intensity, ω i is the weight coefficient, R(w) is the regularization term, and λ is the regularization coefficient.

[0172] S343: Apply the gradient descent method combined with the simulated annealing algorithm to solve the waveform optimization problem, avoid falling into local optimal solutions, and obtain the optimal waveform parameters for each sub-region;

[0173] S344: Integrate the optimal waveform parameters of all sub-regions, and generate the final real-time ultrasonic execution parameters through the waveform synthesis algorithm, including:

[0174] The fundamental frequency and its modulation mode (single frequency, swept frequency or multi-frequency combination);

[0175] The power output curve (including the ramp-up, hold and decay phases);

[0176] The pulse sequence design (pulse width, interval and grouping mode);

[0177] The phase modulation information (for spatial orientation control);

[0178] S4: Adopt the real-time ultrasonic execution parameters and combine with the spatial orientation control algorithm to achieve precise intervention in the TDG region

[0179] S41: Input the real-time ultrasonic execution parameters and the TDG state feature vector, and calculate the three-dimensional sound field distribution prediction through the sound field propagation model

[0180] S411: Extract the flow field information from the TDG state feature vector and construct the characteristic matrix of the sound propagation medium, including the density distribution, sound speed distribution and attenuation coefficient distribution;

[0181] S412: Based on the real-time ultrasonic execution parameters, set the sound source characteristics of the transducer, including the radiation pattern, frequency response and transmission power;

[0182] S413: Construct the non-uniform flow field sound propagation model grad 2 p - (1 / c 2 )·d 2 p / dt 2 = -ρ0·grad·(a*·grad v*) - grad·(Q·grad p) + d 2 / dt 2 (η·p) + S(x,t);

[0183] where p is the sound pressure, c is the speed of sound, ρ0 is the average density, a* is the fluid acceleration, v* is the flow velocity, Q is the sound attenuation coefficient caused by the flow field, η is the medium inhomogeneity parameter, and S(x,t) is the sound source term. By introducing the flow velocity gradient term and the medium inhomogeneity term, it is applicable to the flood discharge environment with high turbulence and strong gradients.

[0184] S414: Use the pseudo-spectral method combined with the adaptive perfectly matched layer (PML) technique to solve the sound field propagation model and obtain the prediction of the complete three-dimensional sound field distribution, including the sound pressure amplitude, phase, and intensity distribution;

[0185] S42: Based on the three-dimensional sound field distribution prediction and TDG monitoring data, apply the TDG risk area identification algorithm to determine the high-priority intervention areas;

[0186] S421: Compare the TDG monitoring data with the fish bubble disease threshold, construct a TDG risk level map, and divide the risks into four levels: low (100% - 110%), medium (110% - 120%), high (120% - 130%), and extremely high (> 130%);

[0187] S422: Construct a risk assessment function:

[0188] R(x) = α·[TDG(x) - TDG_threshold] + + β·t_exp(x) + γ·B(x);

[0189] where TDG(x) is the TDG concentration at position x, TDG_threshold is the safety threshold, t_exp is the exposure time, B(x) is the biological sensitivity coefficient (related to the fish activity area), and α, β, and γ are weight coefficients, [...] + represents the positive part

[0190] S423: Overlay the three-dimensional sound field distribution prediction with the TDG risk level map and calculate the intervention efficiency index for each area:

[0191] E(x) = I cav (x)·R(x) / P input (x);

[0192] where I cav is the predicted cavitation intensity, R is the risk assessment value, and P input is the input power;

[0193] S424: Group the spatial points according to the intervention efficiency index using the hierarchical clustering algorithm, and select the set of regions with the highest efficiency index as the high-priority intervention regions;

[0194] S43: Utilize the high-priority intervention region information and the ultrasonic real-time execution parameters, and generate ultrasonic emission control instructions through the phased array dynamic focusing algorithm;

[0195] S431: Construct a phased array sound field control model and establish the mapping relationship between the array element excitation parameters and the sound field distribution:

[0196] p(x,t) = ∑ i=1 to N A i ·g i (t-τ i )·D i (x,ω)·e (-j(ωt - k|x-xi|) ) / |x-x i |;

[0197] Among them, p is the sound pressure field, A i is the amplitude of the i-th array element, g i is the pulse function, τ i is the delay, D i is the directivity function, ω is the angular frequency, k is the wave number, and x i is the array element position.

[0198] S432: For the high-priority intervention regions, construct a multi-objective sound field optimization problem:

[0199] Objective 1: Maximize the cavitation intensity within the intervention region;

[0200] Objective 2: Minimize the sound field intensity in the non-target regions;

[0201] Objective 3: Minimize the total energy input;

[0202] S433: Use the genetic algorithm combined with the gradient descent method to solve the multi-objective optimization problem, and obtain the optimal excitation parameters for each array element, including amplitude, phase, and delay;

[0203] S434: Based on the optimal excitation parameters and the ultrasonic real-time execution parameters, generate hardware-level ultrasonic emission control instructions, including the specific drive signals for each transducer;

[0204] The phased array dynamic focusing algorithm not only considers the phase control in traditional phased array systems, but also introduces a flow field phase correction term, a flow field amplitude correction function, as well as time-varying multi-objective optimization and robust control theory, achieving precise sound field control in complex flow environments. The design for the special environment of high dam flood discharge solves the problem of low control accuracy of traditional phased array systems in high flow velocity and high turbulence environments. This algorithm has greatly improved both the sound field orientation accuracy and the energy focusing efficiency, and this performance improvement exceeds the level that can be achieved by conventional technical improvements.

[0205] S44: Execute the ultrasonic emission control instruction and simultaneously collect cavitation response feedback data in real time for the next round of parameter adjustment;

[0206] S441: Send the ultrasonic emission control instruction to the ultrasonic drive system to start the sound field action process;

[0207] S442: Real-time collect cavitation response feedback data through the sensor network, including acoustic emission signals, cavitation noise spectra, and water body TDG change rates;

[0208] S443: Apply fast Fourier transform and wavelet analysis to process the cavitation response feedback data and extract cavitation characteristic indicators, including cavitation noise energy, main frequency distribution, and pulsation period;

[0209] S444: Compare the cavitation characteristic indicators with the expected targets, calculate the sound field control deviation, and pass it to the parameter adjustment link of the next control cycle;

[0210] S5: Monitor the TDG reduction effect and optimize the parameters of the adaptive parameter mapping network through the deep reinforcement learning framework;

[0211] S51: Collect TDG reduction effect data, including TDG concentration changes before and after treatment, energy consumption data, and fish stress response indicators;

[0212] S511: Collect TDG concentration data before and after treatment at different water depths and distances through a distributed TDG sensor network, and calculate the spatio-temporal reduction curve of TDG;

[0213] S512: Record the energy consumption data of the ultrasonic system through the power monitoring module, including instantaneous power, cumulative energy consumption, and energy conversion efficiency;

[0214] S513: Monitor the behavioral changes of fish in the water body through an underwater camera and sonar system, and calculate fish stress response indicators, including the frequency of escape behavior, changes in swimming patterns, and the dissolution of schools;

[0215] S514: Integrate the above data to generate a complete TDG reduction effect dataset;

[0216] S52: Calculate the system performance evaluation index based on the TDG reduction effect data and the preset target;

[0217] S521: Define the TDG reduction efficiency index:

[0218] η TDG = ∑(i = 1 to N) w i ·[TDG i ^before - TDG i after / ∑(i = 1 to N) w i ·[TDG i before - TDG safe + ;

[0219] Wherein, TDG i before and TDG i after are the TDG concentrations before and after treatment respectively, TDG safe is the safety threshold, w i is the weight coefficient, [...] + represents the positive part;

[0220] S522: Define the energy efficiency index:

[0221] η energy = ΔTDG avg ·V water / E consumed ;

[0222] Wherein, ΔTDG avg is the average TDG reduction percentage, V water is the volume of the treated water body, E consumed is the consumed energy;

[0223] S523: Define the eco - friendly index:

[0224] η eco = 1 - ∑(i = 1 to M) S i / (M·S max )

[0225] Wherein, S i is the value of the i - th fish stress response index, S max is the maximum stress threshold, and M is the number of indexes;

[0226] S524: Integrate the above indexes to calculate the comprehensive system performance evaluation index:

[0227] J​s System = α·η TDG + β·η energy + γ·η eco ;

[0228] Where α, β, and γ are weight coefficients, which are adjusted according to the specific requirements of the application scenario;

[0229] S53: Use the system performance evaluation index as the reward signal, input it into the deep reinforcement learning framework, and update the weight parameters of the adaptive parameter mapping network;

[0230] S531: Build a deep reinforcement learning environment:

[0231] State space: The combination of TDG state feature vectors and current ultrasonic parameters;

[0232] Action space: The adjustment direction and amplitude of the adaptive parameter mapping network parameters;

[0233] Reward function: The comprehensive evaluation index of system performance;

[0234] State transition: Apply the adjusted parameters to perform TDG processing in the next cycle and observe the system changes;

[0235] S532: Implement the Double Deep Q-Network (DDQN) algorithm:

[0236] Main network: Used to select actions, input the current state, and output the Q-values of different actions;

[0237] Target network: Used to evaluate the action value, and its parameters are copied from the main network periodically;

[0238] Experience replay buffer: Store the quadruple <state, action, reward, next state>;

[0239] ε-greedy policy: Balance exploration and exploitation, and gradually reduce the exploration probability as the learning progresses;

[0240] S533: Execute the batch learning process:

[0241] Randomly sample batch data from the experience replay buffer;

[0242] Calculate the mean squared error loss between the target Q-value and the current Q-value;

[0243] Use the Adam optimizer to minimize the loss function and update the main network parameters;

[0244] Update the target network parameters every N steps;

[0245] S534: Extract the optimized network parameters and update the weight parameters of the adaptive parameter mapping network;

[0246] S54: Use the updated adaptive parameter mapping network to process the TDG state feature vectors of the new round and optimize the ultrasonic parameter regulation for the next cycle;

[0247] S541: Input the current TDG state feature vectors into the updated adaptive parameter mapping network to generate new ultrasonic regulation target parameters;

[0248] S542: Evaluate the difference between the new parameters and the previous parameters, calculate the parameter adjustment amount, and ensure that the parameter change is smooth and not drastic;

[0249] S543: Apply a parameter smoothing filter to process the parameter adjustment amount, generate a smooth parameter adjustment curve, and avoid system instability caused by parameter mutations;

[0250] S544: Apply the smooth parameter adjustment curve to the current parameter settings to achieve a new round of ultrasonic parameter optimization regulation and complete the closed-loop control.

[0251] Specifically designed for the TDG control in a specific field, including a specific network structure, loss function, and learning strategy. This system can continuously learn and optimize from the actual processing effects.

[0252] In some scenarios, the process of real-time monitoring and risk positioning of TDG during the flood discharge process is as follows: Deploy TDG sensors in key downstream areas during flood discharge (such as fish migration channels and spawning areas) to monitor the dissolved gas saturation in real time (such as triggering an alarm when TDG ≥ 110%). Combine the flood discharge flow, water pressure, and water temperature data to predict the TDG supersaturation risk areas (such as flood discharge energy dissipation areas and deep water vortex areas).

[0253] In some scenarios, the process of starting and parameter adaptation of the directional ultrasonic treatment system is as follows: Select ultrasonic parameters according to the flood discharge conditions (flow rate, head height): Low-frequency ultrasonic waves (20 - 50 kHz): For large-scale waters, enhance the cavitation effect and promote gas precipitation; High-frequency ultrasonic waves (100 - 500 kHz): For local high-TDG areas, refine bubbles and accelerate the dissolution equilibrium. Adjust the ultrasonic power and action time to match the water depth and flow rate (such as shortening the action time in the rapid flow area).

[0254] In some scenarios, the process of ultrasonic waves generating cavitation bubbles in the target water area is as follows: Microjet impact: Destroy the gas-liquid interface and promote the escape of supersaturated gases (such as N2, O2) from the water; Local high temperature and high pressure: Break up gas clusters in the water and inhibit the stabilization of the supersaturated state; Enhanced turbulence: Enhance the gas-liquid mass transfer rate and accelerate the diffusion of dissolved gases into the atmosphere. Directly reduce the TDG concentration in the water and relieve the tissue damage or death of fish caused by bubble disease.

[0255] Based on the data of TDG sensors and fish behavior monitoring (such as sonar or cameras), the ultrasonic parameters are dynamically adjusted through a closed-loop control system; combined with the opening of flood discharge gates and the water flow model, the changing trend of TDG is predicted, and the processing system is started or shut down in advance; edge computing is used to optimize energy consumption in real time to ensure the continuous operation of the system during the flood season.

[0256] The problem of lack of pertinence in ultrasonic parameter regulation is solved by an adaptive parameter mapping network. This network combines a multi-layer perceptron and a temporal attention mechanism, which can dynamically adjust ultrasonic parameters according to real-time hydrological conditions. At the same time, a hydrodynamic constraint compensator ensures a stable sound field effect in a complex flow field, realizing the intelligent adaptive regulation of ultrasonic parameters.

[0257] The interference of ultrasonic waves on fish behavior is reduced through directional processing, and low-frequency ultrasonic waves avoid damaging sensitive fish species. The cavitation effect reduces the TDG saturation by 20%-40% (reference experimental data), which is significantly lower than the fish lethal threshold (usually TDG≥130%).

[0258] First, real-time hydrological parameters, TDG monitoring data, and water body turbulence characteristic data are collected. The multi-sensor data spatio-temporal alignment algorithm and nonlinear principal component analysis are used to extract key features, and a TDG state feature vector is constructed to provide comprehensive environmental information for parameter regulation. Then, a hybrid structure integrating a multi-layer perceptron and a temporal attention mechanism is designed, which can capture the time series characteristics of the TDG state feature vector and realize the dynamic modulation of the state features on parameter mapping through the attention mechanism. Next, the designed hydrodynamic constraint compensator can dynamically adjust ultrasonic parameters according to the characteristics of complex flow fields, establish the mapping relationship between fluid characteristics and ultrasonic propagation effects, and ensure consistent sound field effects in areas with different flow velocities, pressure gradients, and turbulence intensities. Finally, the multi-level waveform optimization objective function and the hierarchical waveform synthesis algorithm can accurately control the ultrasonic waveform at the macro, meso, and micro levels, realizing customized ultrasonic intervention for different TDG distribution characteristics.

[0259] The problem of insufficient understanding of the turbulence-cavitation coupling mechanism is solved by a turbulence-cavitation coupling response model. Specifically, by introducing a turbulence-induced source term, combining the improved Rayleigh-Plesset equation and the hierarchical bubble population dynamics model, the influence of turbulence on the cavitation process is accurately described, significantly improving the cavitation prediction accuracy.

[0260] Among them, the improved Rayleigh-Plesset equation with a turbulent correction term can accurately describe the bubble dynamics under turbulent conditions, solving the problem of low prediction accuracy of traditional equations in the high-turbulence environment of flood discharge. The three-layer coupling model (microscopic layer, mesoscopic layer, macroscopic layer) can comprehensively simulate the complete process from the behavior of a single bubble to the overall interaction between the bubble swarm and the fluid, covering multiple time scales from microseconds to seconds. By introducing a turbulent induction source term, which describes the three main turbulent induction cavitation mechanisms of pressure pulsation, interfacial shear, and vortex pumping, the applicability of the cavitation model in the high-turbulence flood discharge environment is significantly improved. Finally, the gas-liquid interface mass transfer kinetic model with a cavitation source term can accurately describe the impact of cavitation bubbles on gas mass transfer, achieving an accurate prediction of the TDG reduction process.

[0261] The problem of low spatial orientation control accuracy is solved by the phased array dynamic focusing algorithm. This algorithm combines an improved phased array sound field control model and a TDG risk area identification algorithm, introduces a flow field phase correction term to compensate for the influence of flow velocity on sound wave propagation, and realizes precise orientation processing of high-risk TDG areas in the high-flow-velocity flood discharge environment. By introducing a flow field phase correction term and a flow field amplitude correction function to compensate for the influence of flow velocity on sound wave propagation, it is particularly suitable for sound field control in the high-flow-velocity flood discharge environment. Combining TDG monitoring data and the fish bubble disease threshold to construct a risk assessment function can accurately identify high-risk areas that need to be processed preferentially, achieving precise allocation of resources. The four-layer optimization strategy (space-time decomposition, global optimization, local refinement, and solution space exploration) can efficiently solve the multi-objective time-varying sound field optimization problem, achieving precise control of the sound field while ensuring computational efficiency. Robustness analysis ensures the stability of the optimization results under various flow velocity distributions and TDG distribution scenarios, significantly improving the adaptability of the system in complex and changing environments.

[0262] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for rapidly warning and weakening the total dissolved gas supersaturation of a high dam flood discharge, characterized in that: The following steps are involved: Acquire multi-source monitoring data, and establish TDG state feature vectors through layered fusion preprocessing; the multi-source monitoring data includes real-time hydrological parameters, TDG monitoring data, and water turbulence characteristic data; Based on the TDG state eigenvector and combined with the multi-time scale bubble dynamics model, the ultrasonic control target parameters are calculated; Using ultrasonic control target parameters, the ultrasonic real-time execution parameters are generated through an adaptive parameter mapping network; including: reading ultrasonic control target parameters and TDG state characteristic vectors, and preliminarily calculating ultrasonic frequency-power-time mode through parameter mapping neural network; based on ultrasonic frequency-power-time mode and real-time hydrological parameters, applying fluid mechanics constraint compensators, adjusting ultrasonic parameters to adapt to complex flow fields, and generating corrected ultrasonic parameters; using corrected ultrasonic parameters and water turbulence characteristic data, through a turbulence-cavitation coupling response model, predicting the location and intensity of cavitation, and obtaining a cavitation response prediction diagram; based on the cavitation response prediction diagram and TDG monitoring data, generating ultrasonic real-time execution parameters through a waveform optimization algorithm; Adopt ultrasonic real-time execution parameters, combined with spatial directional control algorithm, to achieve precise intervention in TDG area and collect cavitation response feedback data; including: reading ultrasonic real-time execution parameters and TDG state characteristic vector, calculating three-dimensional sound field distribution prediction through sound field propagation model; applying TDG risk area identification algorithm based on three-dimensional sound field distribution prediction and TDG monitoring data in TDG state characteristic vector, to determine high-priority intervention area; using high-priority intervention area information and ultrasonic real-time execution parameters, through phased array dynamic focusing algorithm, to generate ultrasonic emission control instructions; executing ultrasonic emission control instructions, and collecting cavitation response feedback data in real time for the next round of parameter adjustment; The steps of determining the high-priority intervention area include: comparing the TDG monitoring data in the TDG state feature vector with the fish gas bubble disease threshold to construct a TDG risk level map; constructing a risk assessment function, comprehensively considering the TDG concentration, exposure time and biological sensitivity coefficient; superimposing the three-dimensional sound field distribution prediction with the TDG risk level map to calculate the intervention efficiency index of each area; applying a hierarchical clustering algorithm to group the spatial points according to the intervention efficiency index, and selecting the set of areas with the highest efficiency index as the high-priority intervention area; Monitor the TDG mitigation effect and determine whether to send an alarm message. At the same time, based on the cavitation response feedback data, optimize the parameters of the adaptive parameter mapping network through the deep reinforcement learning framework for the next cycle processing.

2. The method according to claim 1, characterized in that The steps of acquiring multi-source monitoring data and establishing the TDG state feature vector through layered fusion preprocessing include: Collect real-time hydrological parameters, TDG monitoring data and water turbulence characteristics data; and use the multi-sensor data spatiotemporal alignment algorithm to synchronize and calibrate them to generate a calibration data set; Based on the calibration data set, key features are extracted through nonlinear principal component analysis to construct the TDG state feature vector.

3. The method according to claim 1, characterized in that Based on the TDG state eigenvector and combined with the multi-time scale bubble dynamics model, the steps of calculating the ultrasonic control target parameters include: Read the TDG state eigenvector, improve the Rayleigh-Plesset equation by adding turbulence correction terms, and calculate the local cavitation threshold; Based on the local cavitation threshold and TDG state eigenvector, the cavitation effect intensity distribution under different frequency and power combinations is predicted through the layered bubble group dynamics model. Using the cavitation effect intensity distribution and the gas-liquid interface mass transfer kinetic model, the predicted values ​​of TDG reduction efficiency under different parameter combinations are calculated. Based on the predicted value of TDG reduction efficiency, the ultrasonic control target parameters were generated through a multi-objective optimization algorithm, taking into account energy consumption, fish safety threshold and processing rate.

4. The method according to claim 1, characterized in that: The steps to monitor the TDG reduction effect and optimize the parameters of the adaptive parameter mapping network through the deep reinforcement learning framework include: Collect TDG reduction effect data, including TDG concentration changes before and after treatment, energy consumption data and fish stress response indicators; Calculate system performance evaluation indicators based on TDG reduction effect data and preset goals; The system performance evaluation index is used as a reward signal and input into the deep reinforcement learning framework to update the weight parameters of the adaptive parameter mapping network; The updated adaptive parameter mapping network is used to process the new round of TDG state feature vectors to optimize the ultrasonic parameter control of the next cycle.

5. The method according to claim 3, characterized in that: Based on the local cavitation threshold and TDG state eigenvector, the steps of predicting the cavitation effect intensity distribution under different frequency and power combinations through the layered bubble group dynamics model include: The spatial region is divided into multiple computational units, and each unit establishes a unit state descriptor based on the local cavitation threshold and the parameters in the TDG state feature vector; Construct a layered bubble group dynamics model, which includes three coupled levels: micro, meso and macro, to simulate the behavior of a single bubble, the interaction between a bubble group and the overall interaction between a bubble group and the fluid. An adaptive Monte Carlo algorithm is applied to the stratified bubble group dynamics model to calculate the bubble density distribution function and bubble collapse rate of each calculation unit for different combinations of ultrasonic frequency and power. The bubble density distribution function and bubble collapse rate of all calculation units are integrated, and the cavitation effect intensity distribution of the entire area is obtained through spatial weighted integration.

6. The method according to claim 3, characterized in that Using the cavitation effect intensity distribution and the gas-liquid interface mass transfer kinetic model, the steps for calculating the predicted value of TDG reduction efficiency under different parameter combinations include: Extract TDG monitoring data from TDG state feature vectors and combine them with cavitation effect intensity distribution to establish the initial gas-liquid equilibrium state at each spatial point; Construct a gas-liquid interface mass transfer kinetic model, including a cavitation source term that describes the effect of cavitation bubbles on gas mass transfer; The finite volume method is used to solve the gas-liquid interface mass transfer kinetic model for different ultrasonic parameter combinations, and the adaptive grid refinement technology is used to improve the calculation accuracy in the high gradient area to obtain the time-space resolved gas concentration evolution field. Based on the gas concentration evolution field, the average TDG reduction percentage after M minutes, N minutes and P minutes of treatment is calculated to form a TDG reduction efficiency prediction value matrix, M<N<P.

7. The method according to claim 1, characterized in that The steps of predicting the location and intensity of cavitation by using the modified ultrasonic parameters and the water turbulence characteristic data in the TDG state eigenvector through the turbulence-cavitation coupling response model and obtaining the cavitation response prediction map include: Extract water turbulence characteristic data from TDG state feature vectors, and combine with corrected ultrasonic parameters to construct turbulence-cavitation initial conditions, including turbulence field characteristics, acoustic field characteristics and basic fluid parameters; A turbulence-cavitation coupling response model combining turbulence simulation and cavitation dynamics is established. By introducing turbulence-induced source terms, the effect of turbulence pulsation on the initiation and development of cavitation is described. The turbulence field is calculated using the large eddy simulation method and coupled with the cavitation model to solve the pressure-velocity coupling and track the gas-liquid interface to obtain the dynamic evolution of the cavitation distribution with time and space resolution. Based on the dynamic evolution of cavitation distribution, the cavitation volume fraction, bubble density distribution and collapse energy density are calculated and integrated into the cavitation response prediction map.

8. The method according to claim 1, characterized in that Based on the cavitation response prediction diagram and the TDG monitoring data in the TDG state characteristic vector, the steps of generating ultrasonic real-time execution parameters through the waveform optimization algorithm include: The treatment area is divided into multiple sub-areas based on the grid adaptive algorithm, and finer grids are used in areas with large TDG gradients and high cavitation sensitivity; For each sub-region, local cavitation characteristics and target TDG abatement requirements are extracted; For each sub-region, a multi-level waveform optimization objective function is constructed and solved; When solving, the particle swarm optimization algorithm is used in the global search stage and the L-BFGS algorithm is used in the fine stage, and the Pareto optimal analytical method is used to deal with the optimization conflicts between sub-regions; The optimal waveform parameters of all sub-regions are integrated, and the final ultrasonic real-time execution parameters are generated through a hierarchical waveform synthesis algorithm, including the power envelope design at the macro level, the pulse grouping mode at the meso level, and the fine structure of a single pulse at the micro level.

9. The method according to claim 1, characterized in that: The steps of generating ultrasonic emission control instructions by using high priority intervention area information and ultrasonic real-time execution parameters through phased array dynamic focusing algorithm include: An improved phased array acoustic field control model is constructed, the mapping relationship between array element excitation parameters and acoustic field distribution is established, and flow field phase correction terms and flow field amplitude correction functions are introduced; For high-priority intervention areas, a multi-objective time-varying acoustic field optimization problem is constructed, with objectives including maximizing the cavitation intensity in the target area, minimizing the acoustic field intensity in the non-target area, minimizing the total energy input, and minimizing the boundary reflection. Design a hierarchical hybrid optimization algorithm to solve multi-objective optimization problems, including four levels: spatiotemporal decomposition, global optimization, local refinement, and solution space exploration; Based on multi-scenario simulation verification, the optimization results are analyzed for robustness, the performance variance index is calculated, and the robust control theory is applied to correct unstable solutions; Based on the optimal excitation parameters and ultrasonic real-time execution parameters, hardware-level ultrasonic emission control instructions are generated, including carrier parameters, modulation parameters and grouping parameters, and digital pre-distortion technology is applied to compensate for the nonlinear response and temperature drift of the transducer.

10. The method according to claim 2, characterized in that Based on the calibration data set, the key features are extracted by nonlinear principal component analysis, and the steps of constructing the TDG state feature vector include: Adaptively normalize the calibration data set to generate a standardized data matrix; The standardized data matrix is ​​projected into the high-dimensional feature space through dynamic kernel function mapping, and the dynamic kernel function automatically adjusts the parameters according to the data distribution characteristics; Calculate the feature importance matrix in the high-dimensional feature space, and select the optimal feature subset based on the matrix using a recursive feature elimination algorithm; Applying improved kernel principal component analysis to the selected feature subset to extract nonlinear principal components; The static and dynamic features are fused through spatiotemporal correlation analysis to construct the final TDG state feature vector, which includes the TDG spatial distribution, time evolution trend and ultrasonic treatment response characteristics.

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