Dynamic optimization and adaptive learning method of water conservancy system large model and storage medium

Abstract

The invention provides a dynamic self-optimization and adaptive learning method for a large model of a water conservancy system, and the method comprises the steps: data collection and preprocessing, model construction and initialization, real-time monitoring and model evaluation, difference analysis and adaptive adjustment strategy generation, model updating and optimization, continuous learning and knowledge accumulation, etc. According to the method, through dynamic data driving, intelligent difference analysis and self-adaptive optimization, conversion of a water conservancy system model from static passive to dynamic active is realized. Compared with the prior art, the method has accuracy, real-time performance, economical efficiency and sustainability, provides a landing technical path for intelligent upgrading of a water conservancy system, and has wide application potential in the fields of flood control and disaster reduction, agricultural irrigation, clean energy production and the like.

Description

Technical Field

[0001] The present invention relates to the field of water conservancy technologies, and particularly to a method for dynamic self-optimization and adaptive learning of a large water conservancy system model. Background Art

[0002] The stable operation of a water conservancy system plays a crucial role in aspects such as the rational utilization of water resources, flood control and disaster reduction, agricultural irrigation, and energy production. Traditional water conservancy system models are usually constructed based on fixed parameters and structures, and it is difficult to cope with the complex and changeable operating conditions faced by the water conservancy system during actual operation, such as changes in water flow states and fluctuations in water use demands caused by factors such as climate change, human activity impacts, and aging of water conservancy facilities. These changes lead to a decline in the prediction accuracy of the model and cannot provide accurate and reliable support for the management and decision-making of the water conservancy system. In addition, existing model update methods mostly rely on manual experience and regular data updates, which are not only inefficient but also difficult to capture the dynamic characteristics of the system in real time, restricting the intelligent development of the water conservancy system.

[0003] Therefore, the applicant provides a method for dynamic self-optimization and adaptive learning of a large water conservancy system model to overcome problems such as poor adaptability, untimely updates, and low intelligence level of the water conservancy system model in the prior art, and to achieve the efficient, accurate operation and management of the water conservancy system. Summary of the Invention

[0004] The present invention proposes a method for dynamic self-optimization and adaptive learning of a large water conservancy system model, which solves the problems of low data standardization and governance efficiency and the inability to comprehensively cover the entire process from data governance to intelligent analysis in the prior art, and realizes the transformation of the water conservancy system model from "static and passive" to "dynamic and active". Compared with the prior art, it has accuracy, real-time performance, economy, and sustainability, provides a feasible technical path for the intelligent upgrade of the water conservancy system, and has broad application potential in fields such as flood control and disaster reduction, agricultural irrigation, and clean energy production. The technical solution of the present invention is realized as follows:

[0005] A dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model, comprising the following steps: collecting the operation data of the water conservancy system in real time, constructing a large-scale water conservancy system model with a hybrid model structure based on a deep learning framework, and initializing and training the model using historical data; inputting the real-time collected data into the trained large-scale water conservancy system model, and the model performs calculations and inferences according to the input data, and outputs including water level prediction values, flow distribution schemes, and water conservancy facility failure probability prediction results; at the same time, obtaining the corresponding real values through on-site actual monitoring devices, and using a variety of evaluation indicators to compare and evaluate the model output results with the real values to judge the accuracy and reliability of the model; when the model evaluation indicators show that the error between the model output and the real value exceeds the preset threshold, start the difference analysis process, analyze the error sources from dimensions such as physical processes, time trends, and spatial distributions, and automatically generate adjustment strategies; adjust the model parameters or structure according to the strategies, and retrain in combination with real-time data to ensure that the model adapts to the new working conditions; store the experience in the optimization process in the knowledge base, and reuse historical experience through data mining and case reasoning to improve the model's ability to handle future complex working conditions.

[0006] As a preferred technical solution, the model architecture adopts a hybrid model structure, combining a convolutional neural network CNN for extracting the spatial features of the water conservancy system, a recurrent neural network RNN and its variants for capturing the dynamic changes in the time series, and a fully connected neural network FCN for integrating and processing various feature information to achieve a comprehensive simulation and prediction of the operation state of the water conservancy system; the model training uses the mini-batch stochastic gradient descent algorithm to optimize the model parameters, combines the early stopping method to prevent overfitting, and determines the initial weights and biases of the model, so that the model can initially reflect the basic operation laws and characteristics of the water conservancy system.

[0007] As a preferred technical solution, the output includes water level prediction values, flow distribution schemes, and water conservancy facility failure probability prediction results, and a variety of evaluation indicators include mean square error MSE, mean absolute error MAE, and Nash efficiency coefficient NSE. The specific steps for judging the accuracy and reliability of the model are as follows:

[0008] Step S1: Data synchronization and alignment to ensure that the time stamps of the model prediction results are consistent with the actual monitoring data; match the predicted values output by the model with the measured values of the sensors at the corresponding geographical locations to avoid misjudgment of errors caused by data misalignment;

[0009] Step S2: Error index calculation, using the mean square error MSE, mean absolute error MAE, and Nash efficiency coefficient NSE indicators to quantify the deviation between the model predicted values and the real values, and a predetermined threshold to trigger further analysis;

[0010] Step S3: Error distribution analysis. Plot an error distribution histogram or probability density curve to observe whether the errors are centered around zero and follow a normal distribution. Calculate skewness and kurtosis to identify systematic biases.

[0011] Step S4: Trend consistency assessment. Analyze the synchrony of predicted values and true values in terms of time trend through the cross - correlation function CCF. Perform linear or non - linear fitting on the long - term trend and compare whether the slopes and intercepts of the model - predicted trend and the actual trend are consistent.

[0012] Step S5: Outlier detection and attribution. Use the Grubbs' criterion or Dixon's criterion to identify outliers, and distinguish whether the source of outliers is a systematic anomaly or a model defect. If it is a model defect, analyze the differences in input features and optimize the model logic.

[0013] As a preferred technical solution, the methods used for difference analysis include:

[0014] Feature analysis based on physical process understanding: Decompose the energy of water flow into potential energy, kinetic energy, and pressure energy, analyze the differences in these energy components, analyze the differences between actual operation data and model predictions, and check the collaborative operation of water conservancy facilities.

[0015] Based on time - series decomposition and trend analysis: Use time - series decomposition methods to compare the performance of the model and the actual situation in terms of seasonality and periodicity, detect long - term trends and mutation points, and analyze the model's response to these changes.

[0016] Based on spatial correlation and regional difference analysis: Combine geographical information to analyze the spatial error distribution, divide the system into different regions, and compare the model performance of each region.

[0017] Data quality and uncertainty analysis: Check the accuracy of sensor data, exclude outliers, and use Monte Carlo simulation to analyze the impact of parameter uncertainty on model output.

[0018] As a preferred technical solution, in the long - term trend and mutation detection process, linear regression and mutation - point detection algorithms are used to analyze the long - term trend deviation and the model response to mutation events; in the sensor calibration and anomaly handling process, redundant data cross - validation and statistical methods are used to detect sensor errors and outliers and correct data input; in the uncertainty propagation analysis, Monte Carlo simulation is used to quantify the impact of parameter uncertainty on model output and optimize the probability distribution assumption; in the time - series decomposition method, EMD / wavelet decomposition is used to analyze the error characteristics of the model at different time scales.

[0019] As a preferred technical solution, the difference analysis specifically includes the following steps:

[0020] Step 1) Apply multi-modal data fusion technology to deeply fuse the data collected by different types of sensors in the water conservancy system, and construct a high-dimensional feature space containing comprehensive operation information of the water conservancy system.

[0021] Step 2) Introduce the variational autoencoder (VAE) technology to perform unsupervised learning on the fused water conservancy system data, learn the potential distribution law of the data, and measure the ability of the model to capture data features through the reconstruction error.

[0022] Step 3) Use the complex network analysis method based on graph theory to abstract each component and its interconnection relationship in the water conservancy system into a complex network model, where nodes represent water conservancy facilities and edges represent the transmission relationship of water flow or information; by calculating the topological feature parameters of the network, analyze the changes in the network structure of the water conservancy system under different operating states, and determine the influence of key nodes and key paths on the model error.

[0023] Step 4) Adopt time series decomposition technology, such as empirical mode decomposition (EMD) or wavelet decomposition, to decompose the time series data of the key operating parameters of the water conservancy system into components with different frequencies, including long-term trends, seasonal fluctuations, and short-term random fluctuations, and respectively analyze the differences between each component in the model prediction value and the actual value to determine the error performance of the model on different time scales.

[0024] Step 5) Based on the above multi-dimensional and in-depth difference analysis results, automatically generate an adaptive adjustment strategy.

[0025] As a preferred technical solution, the strategy for updating the large model of the water conservancy system by the adaptive adjustment strategy is as follows:

[0026] For parameter adjustment: Adopt a gradient-based optimization algorithm to update relevant parameters according to the error backpropagation between the model output and the true value. The adjustment step size is adaptively determined according to the error magnitude and change trend to ensure the stability and effectiveness of parameter update. To avoid model performance degradation caused by excessive parameter update, set upper and lower limits for parameter update to restrict the parameters to change within a reasonable range.

[0027] For model structure adjustment: First, perform structure expansion on the basis of the original model, and then use transfer learning technology to transfer the useful features and parameters learned in the original model to the new structure. Randomly initialize the newly added part, and fine-tune and train the entire model in combination with the current real-time data and historical data. Adopt a smaller learning rate and a larger number of training epochs to gradually optimize the parameters of the new structure, so that the model can adapt to the new operating conditions while maintaining the original knowledge.

[0028] As a preferred technical solution, continuous update includes the following steps:

[0029] Step a): After the model is updated, store all the data and operation records in the current update process in the knowledge base, including the model state before the update, the result of difference analysis, the adaptive adjustment strategy, the model parameters after the update, and the corresponding actual operation data information;

[0030] Step b): Regularly organize and analyze the knowledge base, and adopt a combination of advanced data mining and machine learning techniques to deeply explore the complex patterns and rules contained therein;

[0031] Step c): Use knowledge rules to further optimize and improve the large model of the water conservancy system, and develop a method based on case-based reasoning and model fusion to realize the reuse of empirical knowledge.

[0032] As a preferred technical solution, step c) specifically includes the following steps: For the newly emerged operating conditions of the water conservancy system, first search for similar historical cases in the knowledge base, and quickly obtain the corresponding model adjustment plan and parameter settings as the initial reference based on CBR technology; Then, through model fusion technology, fuse the initial model based on historical cases with the online model driven by current real-time data. During the fusion process, a dynamic weighting strategy is adopted, and different weights are assigned to the two models according to the similarity between the current working conditions and historical cases and the reliability of real-time data, so that the fused model can make full use of historical experience and current actual information, quickly adapt to the new working conditions and provide more accurate prediction and decision-making support.

[0033] A non-transitory storage medium is used to store a program for executing the dynamic self-optimization and adaptive learning method of a large model of a water conservancy system.

[0034] Compared with the prior art, the present solution has the following beneficial effects:

[0035] (1) Intelligent difference analysis and strategy generation: Through advanced feature importance analysis and clustering analysis algorithms, deeply explore the root causes and rules of model errors, and can automatically generate accurate adaptive adjustment strategies for different types of error situations, overcoming the limitations and subjectivity of manual experience judgment in traditional methods, greatly improving the pertinence and efficiency of model optimization, and enabling the model to quickly adapt to the complex and changeable operating conditions of the water conservancy system.

[0036] (2) Efficient model update mechanism: The model is updated by combining gradient-based optimization algorithms and transfer learning techniques. This not only ensures the efficiency and accuracy of parameter updates but also makes full use of the existing knowledge and experience of the original model, reducing the computational resource consumption and data requirements during the model update process. At the same time, it ensures that the model can maintain good performance stability and continuity after the update, avoiding problems of overfitting or underfitting caused by frequent retraining.

[0037] (3) Continuous learning and knowledge reuse: A complete knowledge base and knowledge mining mechanism are established, which can transform the data and experience in the model update process into reusable knowledge rules, realizing the continuous learning and self-evolution of the model. Through knowledge reuse, the model can quickly make optimization adjustments when facing similar working conditions, improving the emergency response ability and long-term operation management efficiency of the water conservancy system, providing a solid technical support for the intelligent development of the water conservancy system. Brief Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is a flowchart of a dynamic self-optimization and adaptive learning method for a large model of a water conservancy system according to the present invention. Detailed Embodiments

[0040] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0041] Refer to Figure 1, the present invention provides a dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model, including the following steps: Real-time collect the operation data of the water conservancy system, construct a large-scale water conservancy system model with a hybrid model structure based on a deep learning framework, and use historical data to initialize the training of the model; Input the real-time collected data into the trained large-scale water conservancy system model, and the model performs calculations and inferences based on the input data, and outputs including water level prediction values, flow distribution plans, and water conservancy facility failure probability prediction results; At the same time, obtain the corresponding real values through on-site actual monitoring equipment, and use a variety of evaluation indicators to compare and evaluate the model output results with the real values to judge the accuracy and reliability of the model; When the model evaluation indicators show that the error between the model output and the real value exceeds the preset threshold, start the difference analysis process, analyze the root cause of the error from dimensions such as physical processes, time trends, and spatial distributions, and automatically generate adjustment strategies; Adjust the model parameters or structure according to the strategy, and retrain in combination with real-time data to ensure that the model adapts to the new working conditions; Store the experience in the optimization process in the knowledge base, reuse historical experience through data mining and case reasoning, and improve the model's ability to handle future complex working conditions.

[0042] Specifically, it includes the following steps:

[0043] Step 1: Data collection and preprocessing

[0044] 1. Deploy a variety of high-precision sensors at key positions in the water conservancy system, such as reservoirs, rivers, sluices, pumping stations, etc., including water level sensors, flow sensors, water quality sensors, meteorological sensors, and water conservancy facility status monitoring sensors, etc., to real-time collect the operation data of the water conservancy system.

[0045] 2. Preprocess the collected data, including operations such as removing noise, filling missing values, and data normalization, to ensure the quality and consistency of the data. At the same time, classify and label the data, and construct a structured data set according to time series and spatial positions to provide a basis for subsequent model training and analysis.

[0046] Step 2: Model construction and initialization

[0047] 1. Construct a large-scale water conservancy system model based on a deep learning framework. The model architecture adopts a hybrid model structure, combining a convolutional neural network (CNN) to extract the spatial features of the water conservancy system, a recurrent neural network (RNN) and its variants (such as long short-term memory network LSTM, gated recurrent unit GRU) to capture the dynamic changes in time series, and a fully connected neural network (FCN) to integrate and process various feature information to achieve a comprehensive simulation and prediction of the operation state of the water conservancy system.

[0048] 2. Initialize and train the model using historical data, optimize the model parameters using the Mini-Batch Stochastic Gradient Descent algorithm, and combine with the Early Stopping method to prevent overfitting, determining the initial weights and biases of the model so that the model can initially reflect the basic operation laws and characteristics of the water conservancy system.

[0049] Step 3: Real-time monitoring and model evaluation

[0050] 1. Input the real-time collected data into the trained large water conservancy system model. The model performs calculations and inferences based on the input data, and outputs prediction results including water level prediction values, flow distribution plans, water conservancy facility failure probabilities, etc.

[0051] 2. At the same time, obtain the corresponding real values through on-site actual monitoring devices, and use various evaluation indicators such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), etc. to compare and evaluate the model output results with the real values, and judge the accuracy and reliability of the model. Detailed steps for judging the accuracy and reliability of the model:

[0052] 1) Data synchronization and alignment

[0053] When obtaining the model output results and the corresponding real values, first ensure the precise synchronization and alignment of the two in terms of timestamp and spatial location. For data with time series characteristics, such as the change of water level over time, dynamic monitoring values of flow, etc., collect and record data at the same time interval (such as every hour, every minute, etc.) to ensure that the model output corresponds one-to-one with the actual monitoring data in the time dimension. Spatially, for sensor data at different positions (such as flow measurement values at different cross-sections of the river channel, water level data in different areas of the reservoir, etc.), clarify the corresponding spatial location identifiers of the water conservancy system, so that the predicted values at the corresponding positions of the model output can be accurately matched with the real values, avoiding evaluation errors caused by data misalignment.

[0054] 2) Error distribution analysis

[0055] Calculate various common error metrics, such as mean squared error (MSE), mean absolute error (MAE), Nash-Sutcliffe efficiency coefficient (NSE), etc., to comprehensively measure the accuracy of the model. Then, plot the frequency distribution histogram or probability density function curve of the errors to visually display the distribution of model errors within different value ranges. Group and statistically analyze the errors between the model output values and the true values according to a certain interval (such as -5 to -4, -4 to -3, …, 3 to 4, 4 to 5, etc.), and calculate the frequency or probability density of the errors occurring within each interval. By observing the shape, central position, and dispersion degree of the error distribution, the central tendency and dispersion of the model errors can be understood. For example, if the error distribution shows an approximately normal distribution centered around 0 and has a small dispersion degree, it indicates that the prediction errors of the model are relatively stable and concentrated within a small range, and the accuracy of the model is relatively high; on the contrary, if the error distribution is relatively dispersed, with obvious long tails or skewness, it may mean that there are large prediction deviations in the model under certain specific working conditions, and it is necessary to further analyze the reasons for these abnormal errors, such as whether they are affected by extreme weather, sudden failures of water conservancy facilities, etc.

[0056] Calculate statistics such as skewness and kurtosis of the errors to further quantify the characteristics of the error distribution. Skewness is used to measure the asymmetry degree of the error distribution. If the skewness is 0, the error distribution is symmetric; if the skewness is greater than 0, it indicates that the error distribution shows positive skewness, that is, a long tail on the right side; a skewness less than 0 is negative skewness, with a long tail on the left side. Kurtosis reflects the sharpness or flatness of the error distribution. Compared with the normal distribution, a kurtosis greater than 3 indicates that the error distribution is sharper, with thicker tails; a kurtosis less than 3 indicates that the distribution is relatively flat. By analyzing skewness and kurtosis, the morphological characteristics of the error distribution can be deeply understood, and it can assist in judging possible systematic errors or abnormal fluctuations in the model. For example, when the error distribution shows positive skewness and a large kurtosis, it may imply that the model has a tendency to underestimate when predicting larger values, and it is necessary to focus on checking and optimizing the prediction ability of the model under high flow or high water level conditions.

[0057] 3) Trend consistency assessment

[0058] The time series analysis method is adopted, such as calculating the cross - correlation function (CCF) between the model output value and the true value, to evaluate the consistency of the two in terms of time trend. CCF can measure the correlation between two time series at different lag orders. By observing the values of CCF at lag order 0 and the nearby region, it can be judged whether the model output value and the true value have a synchronous change trend. If the value of CCF at lag order 0 is close to 1 and maintains a high correlation within a certain lag range, it indicates that the model can better follow the time - varying trend of the true value and has good dynamic response ability; on the contrary, if the CCF value is low or fluctuates greatly, it may indicate that there are delays or distortions in the model when capturing the dynamic changes of the water conservancy system, and it is necessary to optimize the dynamic characteristics of the model, such as adjusting the time series processing module in the model (such as the parameters of the RNN or LSTM layer), to improve the model's ability to track the time trend.

[0059] Trend fitting is performed on the model output value and the true value respectively. For example, linear regression or polynomial regression methods are used to fit the change trend lines of the two over time. Compare parameters such as the slopes, intercepts, and goodness - of - fit of the two trend lines to evaluate the accuracy of the model in long - term trend prediction. If the trend lines of the model output value and the true value are similar in slope and intercept and have a high goodness - of - fit, it indicates that the model can accurately capture the long - term change trend of the operating parameters of the water conservancy system. For example, the prediction of the long - term decline trend of the reservoir water level or the seasonal growth trend of river flow is relatively accurate; otherwise, it is necessary to analyze the reasons for the inconsistent trends, which may be due to the model not considering some long - term influencing factors (such as changes in precipitation patterns caused by climate change, long - term growth trends in regional water use demand, etc.), and then improve the model accordingly, such as introducing new trend variables or adjusting the long - term prediction module of the model.

[0060] 4) Outlier detection and analysis

[0061] Statistical hypothesis testing methods, such as Grubbs' criterion or Dixon's criterion, are used to detect outliers in the model output value and the true value. These criteria are based on the statistical distribution characteristics of the data. By calculating the sample mean, standard deviation, and specific statistics, it is judged whether the data points deviate significantly from other data, so as to identify possible outliers. For the detected outliers, further analyze the corresponding operating conditions and environmental conditions of the water conservancy system to determine whether the outliers are caused by sudden abnormal events in the actual system (such as damage to water conservancy facilities caused by floods, earthquakes, sudden heavy rainfall or water quality mutations caused by industrial wastewater discharge in a short period of time, etc.) or due to defects in the model itself (such as insufficient prediction ability of the model under extreme conditions, abnormal model calculations caused by incorrect data input, etc.).

[0062] When it is determined that the outliers are caused by model reasons, the sample data corresponding to these outliers are analyzed separately. For example, by comparing the differences in model input features under normal and abnormal conditions, and using feature importance analysis methods (such as feature importance evaluation in random forests), the key feature factors that may cause abnormal model output are identified. At the same time, these outlier samples are stored in the knowledge base as special cases, so that in the subsequent model optimization and improvement process, the model structure or parameters can be adjusted specifically to improve the model's prediction ability and robustness for similar abnormal conditions. For example, adding a monitoring and early warning mechanism for abnormal events to the model, or conducting more detailed modeling and processing of the feature variables related to the outliers to reduce the interference of outliers on the evaluation of model accuracy and reliability, and improve the stability and adaptability of the model in the complex and changeable water conservancy system environment.

[0063] Through the above comprehensive and systematic comparison and evaluation steps, the differences between the model output results and the true values can be analyzed in depth from multiple perspectives, accurately judging the accuracy and reliability of the model, providing a solid data basis and scientific basis for subsequent difference analysis and the generation of adaptive adjustment strategies, so as to achieve the efficient optimization and precise operation of the large water conservancy system model.

[0064] Step 4: Difference analysis and generation of adaptive adjustment strategies

[0065] 1. When the model evaluation index shows that the error between the model output and the true value exceeds the preset threshold, start the difference analysis process.

[0066] The methods adopted in the difference analysis process are as follows:

[0067] 1) Feature analysis based on physical process understanding

[0068] a) Hydraulic factor decomposition

[0069] For the errors related to water flow simulation, the total energy of the water flow is decomposed into components such as potential energy, kinetic energy, and pressure energy. Analyze the differences between the model output and the actual values in these energy components. For example, calculate the energy values at each point through the Bernoulli equation to determine whether the large errors are caused by water level differences (potential energy), changes in flow velocity (kinetic energy), or local pressure changes (pressure energy). If it is found that the calculation of potential energy in a certain area deviates significantly from the actual situation, it may imply problems in the model's processing of terrain or water level data, or inaccurate simulation of the gravitational effect of water flow.

[0070] Analyze the turbulence characteristics of the water flow, and calculate parameters such as turbulent kinetic energy and turbulent dissipation rate. Compare the turbulence-related indicators output by the model with the actual measurements to determine whether the model can accurately capture the turbulence of the water flow. Turbulence characteristics are crucial for many water conservancy engineering applications (such as river channel erosion, pollutant diffusion, etc.). If there are deviations in the turbulence simulation of the model, it may lead to large errors in the prediction of these processes.

[0071] b) Analysis of the operation mode of water conservancy facilities

[0072] For the operating status of water conservancy facilities (such as sluice gates, pumping stations, dams, etc.), analyze in detail the differences between parameters such as their opening degree, flow regulation method, and power generation power and the settings and predicted values in the model. For example, for a sluice gate, study the inconsistency between the actual opening degree change curve and the predicted opening degree curve by the model, and how this difference affects the water level and flow relationship upstream and downstream. Through actual operation data and equipment operation logs, determine whether there are operating modes or fault conditions not considered by the model in water conservancy facilities, such as partial blockage of valves, reduction in the efficiency of pumping stations, etc. These factors may lead to large errors in the model's prediction in the area near water conservancy facilities.

[0073] Analyze the collaborative work situation among water conservancy facilities. For example, in a water conservancy system containing multiple sluice gates and pumping stations, study the differences between the manifestation of their joint dispatching strategies in the model and the actual operation situation. If a specific joint dispatching method is adopted in actual operation to meet flood control or irrigation requirements, but the model fails to accurately simulate this collaborative effect, it may lead to deviations in the water level and flow prediction of the overall system. By analyzing the flow distribution ratio, water level control relationship, etc. among various facilities, find out the deficiencies in the model regarding the collaborative work mechanism of facilities.

[0074] 2) Time series decomposition and trend analysis

[0075] a) Extraction of seasonal and periodic components

[0076] Adopt time series decomposition methods (such as the classical additive or multiplicative decomposition model) to decompose the time series of key parameters (such as water level, flow, etc.) of the water conservancy system into seasonal components, periodic components, and residual components. Compare the performance of the model prediction values and the actual values in each component to determine whether the model can accurately capture the seasonal and periodic change laws of the water conservancy system. For example, in areas with obvious seasonal precipitation and water use demand changes, check whether the model's prediction of the seasonal fluctuations of water level and flow is consistent with the actual situation. If it is found that the model's water level prediction in a certain season is always too high or too low, it may be necessary to further analyze the parameter settings related to seasonal factors in the model (such as evapotranspiration coefficient, seasonal distribution pattern of precipitation, etc.).

[0077] For periodic components, in addition to common annual and monthly cycles, some special periodic phenomena are also analyzed, such as the impact of tides on the water conservancy system in estuary areas, and the weekly or daily cycle changes of industrial or irrigation water use. Through techniques such as spectral analysis, determine the response ability of the model to these periodic components, as well as whether there are omissions or incorrect simulations of certain periodic components, which may lead to large prediction errors during the corresponding time periods.

[0078] b) Detection of long-term trends and change points

[0079] Use trend analysis methods (such as linear regression, non-linear regression or trend filtering algorithms) to detect the long-term trends of water conservancy system parameters, such as the long-term rising or falling trends of water levels, and the long-term change trends of flow rates. Compare the long-term trends predicted by the model with the actually observed trends to determine whether the model can accurately reflect the long-term evolution characteristics of the water conservancy system. If there are obvious deviations between the long-term trends predicted by the model and the actual trends, it may be necessary to consider whether some long-term influencing factors are not fully considered in the model, such as long-term changes in precipitation patterns caused by climate change, long-term adjustments in regional water use structures (such as changes in agricultural irrigation methods, increases or decreases in industrial water use), or long-term aging and performance degradation of water conservancy facilities.

[0080] Adopt change point detection algorithms (such as Pettitt test, BayeScan method, etc.) to identify change points in the time series data of the water conservancy system. These change points may correspond to events such as major water conservancy project construction, natural disasters (such as floods, droughts), and policy and regulation changes (such as adjustments in water resource management policies). Analyze the prediction performance of the model before and after these change points to determine whether the model can adapt to these sudden changes in a timely manner, or whether the model structure needs to be adjusted or parameters updated to better handle the change situations in the data and reduce prediction errors caused by change events.

[0081] 3) Spatial correlation and regional difference analysis

[0082] a) Spatial analysis of geographic information system (GIS)

[0083] Combine the geographic information of the water conservancy system (such as terrain, water system distribution, location of water conservancy facilities, etc.) with the model output and actual monitoring data, and use GIS technology for spatial analysis. By drawing various thematic maps (such as water level contour maps, flow vector maps, error distribution maps, etc.), visually display the operating status of the water conservancy system in space and the model error distribution. For example, mark the areas with higher or lower water levels predicted by the model on the map, compare them with the actually measured water level data, analyze the terrain features, water flow paths in these areas, and their relationships with surrounding water conservancy facilities, and determine whether there are uneven spatial error distributions caused by inaccurate simulations of terrain or water flow boundary conditions in the model.

[0084] Using spatial autocorrelation analysis methods (such as Moran's I index, Geary's C coefficient, etc.), study the spatial correlation and dependence of water conservancy system parameters (such as water level, water quality, etc.). Judge whether the model can accurately simulate this spatial autocorrelation structure. If significant differences are found between the spatial autocorrelation characteristics output by the model and the actual situation, it may mean that the model has deficiencies in considering the water flow exchange, diffusion process, or mutual influence relationship between adjacent regions, and it is necessary to optimize the spatial coupling mechanism of the model to improve the model's simulation ability of the spatial distribution characteristics of the water conservancy system and reduce spatial errors.

[0085] b) Regional classification and comparative analysis

[0086] According to the natural geographical characteristics of the water conservancy system (such as mountainous areas, plains, estuary areas, etc.), functional area division (such as drinking water source protection areas, agricultural irrigation areas, industrial water use areas, etc.), or administrative area division, divide the water conservancy system into different regions. Conduct a comparative analysis of the model output values and actual values within each region respectively to determine the differences in the prediction accuracy of the model in different regions. For example, in mountainous areas, due to complex terrain and drastic water flow changes, the prediction error of the model may be relatively large; while in plain areas, due to relatively stable water flow, the model may perform better. Through this regional comparative analysis, identify specific problems existing in the model in different regions. For example, in mountainous areas, it may be necessary to improve the terrain modeling method and increase the consideration of the formation mechanism of storm runoff; in industrial water use areas, it may be necessary to more accurately simulate the impact of sewage discharge and treatment processes on water quality and water flow, etc., so as to optimize the model targeted and improve the applicability and accuracy of the model in the entire water conservancy system.

[0087] 4) Data quality and uncertainty analysis

[0088] a) Sensor error assessment and correction

[0089] Evaluate the measurement errors of various sensors (water level sensors, flow sensors, water quality sensors, etc.) in the water conservancy system. Determine the measurement error range and distribution characteristics of each sensor through comparison and calibration with high-precision standard measurement equipment, or by using the redundant configuration of sensors (multiple sensors measure the same parameter) for data cross-validation. Analyze the relationship between the model prediction error and the sensor measurement error. If it is found that the error of the model in certain regions or time periods is correlated with the error distribution of the sensors, it may be necessary to correct the sensor data or adjust the model's processing method for the sensor data to reduce the model error caused by sensor errors.

[0090] Consider the fault conditions and outlier detection of sensors. Use statistical methods (such as Grubbs' criterion, box plot method, etc.) and outlier judgment methods based on physical models (such as judging the rationality of flow rate and water level data according to the water flow continuity equation and energy equation) to identify outliers and possible fault points in the sensor measurement data. For these abnormal data, special processing is carried out in the difference analysis, such as excluding them from the preliminary analysis, or using data repair techniques (such as time series smoothing algorithm, spatial interpolation algorithm, etc.) to correct the outliers to ensure the data quality for difference analysis and avoid false evaluation of model performance and inaccurate difference analysis results caused by sensor fault data.

[0091] b) Analysis of data uncertainty propagation

[0092] Since there are certain uncertainties in many parameters in the water conservancy system (such as soil permeability, roughness coefficient, water use demand, etc.), analyze the propagation process of these uncertainties in the model and their impact on the model output. Use the Monte Carlo simulation method to randomly sample the uncertain parameters, run the model multiple times, and obtain the probability distribution of the model output. Compare the uncertainty range of the model output with the distribution of actual measurement values to determine whether the model can reasonably reflect the impact of data uncertainty on the prediction results. If it is found that the uncertainty range of the model output is too narrow or too wide, it may be necessary to adjust the processing method of uncertain parameters in the model, such as improving the probability distribution assumption of parameters, increasing the consideration of the correlation between uncertainty factors, etc., to improve the reliability and accuracy of the model under uncertain conditions, make the model prediction results better match the uncertainty of the actual situation, and thus reduce the error between the model and the actual value caused by uncertainty factors.

[0093] Through the comprehensive application of the above various methods, it is possible to comprehensively and deeply analyze the differences between the model output and the actual values, find out the causes of errors from multiple perspectives such as physical processes, time series, spatial distribution, data quality, and uncertainty, provide a solid foundation for formulating targeted adaptive adjustment strategies in the follow-up, and thus achieve the effective optimization and adaptive improvement of the large water conservancy system model.

[0094] 2. First, apply the multi-modal data fusion technology to deeply fuse the data collected by different types of sensors in the water conservancy system (such as water level data, flow data, meteorological data, operation status data of water conservancy facilities, etc.) to construct a high-dimensional feature space containing comprehensive operation information of the water conservancy system. In this feature space, a method combining principal component analysis (PCA) and independent component analysis (ICA) is used to reduce the dimension of the high-dimensional data, and at the same time extract the main feature components and independent potential factors in the data to more clearly show the essential characteristics and potential change patterns of the operation status of the water conservancy system, so as to more accurately locate the sources of factors that may cause model errors.

[0095] 3. Then, introduce the variational autoencoder (VAE) technology to perform unsupervised learning on the fused water conservancy system data, learn the potential distribution law of the data, and measure the ability of the model to capture data features through the reconstruction error. Compare and analyze the reconstruction error of the actual operation data with the reconstruction error of the data under normal working conditions to find out those regions or time periods where the reconstruction error increases significantly. These regions and time periods often correspond to parts of the water conservancy system where abnormal changes occur or the model is difficult to accurately describe.

[0096] 4. Next, use the complex network analysis method based on graph theory to abstract the various components of the water conservancy system (such as reservoirs, rivers, sluices, pumping stations, etc.) and their interconnection relationships into a complex network model, where nodes represent water conservancy facilities and edges represent the transmission relationships of water flow or information. By calculating the topological characteristic parameters of the network (such as node degree, clustering coefficient, betweenness centrality, etc.), analyze the changes in the network structure of the water conservancy system under different operation states, and determine the influence of key nodes and key paths on the model error. For example, if it is found that the betweenness centrality of the node where a certain sluice is located changes significantly under the condition of large error, it indicates that the operation state of this sluice may have an important impact on the water flow distribution of the entire water conservancy system that is not accurately considered by the model, so it is taken as the key analysis object.

[0097] 5. Further, adopt time series decomposition techniques, such as empirical mode decomposition (EMD) or wavelet decomposition, to decompose the time series data of the key operation parameters of the water conservancy system (such as water level, flow, etc.) into components of different frequencies, including long-term trends, seasonal fluctuations, short-term random fluctuations, etc. Analyze the differences between the model prediction values and the actual values of each component respectively to determine the error performance of the model on different time scales. For example, if it is found that the model has a large error in the short-term random fluctuation component, it may mean that the model lacks sufficient response to sudden water flow changes or temporary water conservancy facility operations, and it is necessary to optimize the dynamic response mechanism of the model accordingly.

[0098] 6. Based on the above multi-dimensional and in-depth difference analysis results, an adaptive adjustment strategy is automatically generated. If the error is caused by the change of a certain type of feature variable exceeding the expected range of the model, such as a sharp increase in rainfall under extreme weather conditions, the adjustment strategy includes increasing the sensitivity adjustment parameter of the variable in the model, or introducing new features related to the variable (such as the intensity change rate, duration, and rainfall pattern distribution of rainfall), and at the same time using the generative adversarial network (GAN) technology to model and learn the complex relationship between the new features and other related features to enhance the adaptability of the model to special working conditions; if the model structure cannot effectively capture the dynamic changes of the water conservancy system under specific working conditions, such as the hydraulic transient phenomenon during the rapid water flow switching process, the adjustment strategy is to increase the depth and complexity of the convolutional layer or recurrent layer in the corresponding part of the model (such as the water flow simulation module) according to the key nodes and paths determined by complex network analysis, or introduce a spatio-temporal attention module based on the attention mechanism, so that the model can focus more on the key water flow change areas, time periods, and the state changes of water conservancy facilities, and improve the model's simulation ability for complex dynamic processes.

[0099] Step Five: Model Update and Optimization

[0100] 1. Update the large water conservancy system model according to the generated adaptive adjustment strategy.

[0101] 2. If it is a parameter adjustment, use a gradient-based optimization algorithm, such as the Adaptive Moment Estimation (Adam) optimizer, to update the relevant parameters by backpropagating the error between the model output and the true value. The adjustment step size is adaptively determined according to the error magnitude and change trend to ensure the stability and effectiveness of parameter update. At the same time, to avoid the degradation of model performance caused by excessive parameter update, set the upper and lower limit constraints for parameter update to limit the parameter change within a reasonable range.

[0102] 3. If it is a model structure adjustment, such as adding neural network layers or modifying the inter-layer connection method, first expand the structure on the basis of the original model, then use transfer learning technology to transfer the useful features and parameters learned in the original model to the new structure, randomly initialize the newly added part, and fine-tune and train the entire model in combination with the current real-time data and historical data. Adopt a smaller learning rate and a larger number of training epochs to gradually optimize the parameters of the new structure, so that the model can adapt to the new operating conditions while maintaining the original knowledge.

[0103] Step Six: Continuous Learning and Knowledge Accumulation

[0104] 1. After the model is updated, store all the data and operation records during this update process in the knowledge base, including information such as the model state before the update, the result of differential analysis, the adaptive adjustment strategy, the model parameters after the update, and the corresponding actual operation data.

[0105] 2. Regularly organize and analyze the knowledge base, and adopt a combination of advanced data mining and machine learning techniques to deeply explore the complex patterns and rules contained therein. For example, use a deep neural network to construct a knowledge graph, visually and structurally represent various elements (such as meteorological conditions, hydrological characteristics, the state of water conservancy facilities, water resource demands, etc.) and their interrelationships during the operation of the water conservancy system. On this basis, through association rule mining algorithms (such as the improved Apriori algorithm) and sequential pattern mining algorithms (such as the PrefixSpan algorithm), accurately discover the potential correlation relationships between model parameter adjustments, structural changes, and the improvement of the operation performance of the water conservancy system under different working conditions, and extract general and guiding knowledge rules.

[0106] 3. Utilize these knowledge rules to further optimize and improve the large water conservancy system model, and innovatively develop a method based on case-based reasoning (CBR) and model fusion to realize the reuse of empirical knowledge. For newly emerging operation conditions of the water conservancy system, first search for similar historical cases in the knowledge base, and quickly obtain the corresponding model adjustment plan and parameter settings as the initial reference based on CBR technology. Then, through model fusion technology, fuse the initial model based on historical cases with the current real-time data-driven online model. During the fusion process, adopt a dynamic weighting strategy, and assign different weights to the two models according to the similarity between the current working condition and the historical case and the reliability of the real-time data, so that the fused model can make full use of historical experience and current actual information, quickly adapt to the new working condition, and provide more accurate prediction and decision-making support. For example, when encountering a new type of meteorological disaster event affecting the water conservancy system, the system finds a historical case with similar meteorological changes but slightly different degrees in the knowledge base, extracts the corresponding successful model adjustment method for coping, combines it with the detailed data of the current real-time monitored water conservancy system, and quickly generates an optimized model for this new type of disaster through dynamic weighted model fusion, effectively guiding the regulation of water conservancy facilities and the rational allocation of water resources, improving the emergency response ability and long-term operation management efficiency of the water conservancy system, and providing a solid technical support for the intelligent development of the water conservancy system.

[0107] Through the above steps, the large-scale model of the water conservancy system of the present invention can real-time sense the changes in the system operation state, automatically perform self-optimization and adaptive learning, continuously improve the accuracy and reliability of the model, provide strong support for the scientific management and decision-making of the water conservancy system, and realize the efficient utilization of water conservancy resources and the safe and stable operation of water conservancy facilities.

[0108] Based on the above innovation points, this patent realizes the innovation and breakthrough of ultrasonic image data processing and intelligent analysis methods by integrating automated data governance, intelligent analysis technology, and full-process closed-loop management. By introducing multi-modal AI models and automated data governance technology, the system not only significantly improves the data processing efficiency and analysis accuracy, but also enhances the interpretability and clinical applicability of the analysis results, providing a more efficient and accurate intelligent tool for clinical diagnosis and scientific research applications, and helping to promote the intelligent development of the field of ultrasonic medical imaging.

[0109] The following is a detailed description in combination with a specific embodiment.

[0110] Consider a comprehensive water conservancy system that includes a large reservoir, an irrigation canal network, and multiple small hydropower stations. This system provides functions such as irrigation water supply, power generation, and flood control for the surrounding areas, and its operation is affected by various factors such as seasonal precipitation changes, fluctuations in agricultural water demand, and the supply and demand situation in the electricity market.

[0111] 1. Data collection and preprocessing

[0112] High-precision water level sensors, flow sensors, and water quality sensors are installed at different water level monitoring points of the reservoir, key sections of the canal, and the inlets and outlets of the hydropower stations. At the same time, meteorological data such as precipitation, temperature, and wind speed are collected at the meteorological station, and status monitoring equipment is equipped on each water conservancy facility to obtain the operation parameters of the equipment in real time (such as the rotation speed of the water pump, the opening of the sluice gate, etc.).

[0113] The collected data is aggregated to the data processing center through wireless transmission, and the data is preprocessed in the center. First, obvious noise data, such as outliers generated by short-term sensor failures or communication interference, is removed. For a small amount of missing data, linear interpolation is used for filling to ensure the continuity of the data. Then, all the data is normalized so that its numerical range is between 0 and 1, which is convenient for subsequent model training and calculation. After preprocessing, the data is structured and stored according to time series and spatial position, forming a spatio-temporal dataset covering the comprehensive operation information of the water conservancy system.

[0114] 2. Model construction and initialization

[0115] Build a large - scale model of the water conservancy system based on a deep - learning framework. The overall architecture of the model adopts a hybrid structure that combines a Convolutional Neural Network (CNN) and a Long Short - Term Memory Network (LSTM). The CNN part is used to extract spatial features of the water conservancy system. For example, through convolutional operations on terrain data and the layout of water conservancy facilities, it can identify areas of water flow convergence and diffusion, key hydraulic conduction paths, etc.; the LSTM is used to capture dynamic changes in the time series, such as the changing trends of water levels and flows over time, and the impacts of seasonal fluctuations in meteorological conditions and water use demands on the water conservancy system.

[0116] Initialize and train the model using historical data from the past three years. During the training process, divide the dataset into a training set, a validation set, and a test set. Use the mini - batch stochastic gradient descent algorithm to optimize the model's parameters, and combine early stopping to prevent overfitting. After multiple iterative trainings, determine the initial weights and biases of the model, enabling the model to initially simulate the basic behaviors and laws of the water conservancy system under normal operating conditions. For example, it can relatively accurately predict the daily changes in reservoir water levels, the flow distribution in irrigation channels, and the power generation of hydropower stations.

[0117] 3. Real - time Monitoring and Model Evaluation

[0118] During the actual operation process, input the real - time collected data at fixed time intervals (such as every hour) into the trained large - scale model of the water conservancy system. The model performs calculations and inferences based on the input data and outputs predicted values of reservoir water levels, flow distribution plans for each irrigation channel, and predicted values of power generation for each hydropower station within a future period (such as the next 24 hours).

[0119] At the same time, obtain the corresponding true values through on - site installed monitoring devices, and use evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Nash - Sutcliffe Efficiency (NSE) to compare and evaluate the model output results with the true values. For example, within a certain period, it is found that the MSE between the predicted reservoir water level value and the actual water level value reaches 0.2, the MAE is 0.4, and the NSE is 0.6, while the preset MSE threshold is 0.1. This indicates that there is a large error between the model output and the true value, and it is necessary to initiate a difference analysis process to find the reasons and optimize the model.

[0120] 4. Difference Analysis and Generation of Adaptive Adjustment Strategies

[0121] 1) Feature Analysis Based on Understanding of Physical Processes

[0122] Hydraulic factor decomposition: Conduct a detailed analysis of the water flow in the reservoir. Calculate the potential energy, kinetic energy, and pressure energy of the water flow through the Bernoulli equation. It is found that there is a large deviation between the calculated value and the actual measured value of the water flow kinetic energy near the reservoir outlet in the model. Further inspection reveals that this is because the model uses a simplified empirical formula when dealing with the local head loss at the outlet and fails to accurately consider the influence of the actual outlet structure (such as certain roughness and irregular shape) on the water flow energy.

[0123] Analysis of the operation mode of water conservancy facilities: Analyze the operation data of each hydropower station and find that the actual power generation of one hydropower station does not match the model prediction value. By checking the operation log of the hydropower station, it is found that the blades of the water turbine have suffered partial wear during this period, resulting in a decrease in its efficiency, but this change in equipment performance is not considered in the model.

[0124] 2) Time series decomposition and trend analysis

[0125] Extraction of seasonal and periodic components: Conduct time series decomposition on the flow data of the irrigation canal and find that the model fails to accurately capture the periodic peak of the flow caused by the substantial increase in the water demand of crops during the peak irrigation period in summer. Further analysis reveals that the relationship between the crop growth cycle and water demand in the model is not accurately modeled, and the changing water demand of different crops at different growth stages is not fully considered.

[0126] Detection of long-term trends and mutation points: Through trend analysis, it is found that the water level of the reservoir has shown a long-term trend of slow decline in the past year, but the predicted water level decline trend in the model is relatively gentle, showing a deviation from the actual situation. After investigation, it is due to the long-term drought in the basin, which leads to an increase in infiltration and a decrease in precipitation, and these long-term climate factor changes are not adequately considered in the model. At the same time, due to a small earthquake occurring nearby at a certain moment, there is a slight leakage in the reservoir dam, resulting in a mutation in the water level, but the model fails to respond to this mutation in a timely manner.

[0127] 3) Spatial correlation and regional difference analysis

[0128] Spatial analysis of Geographic Information System (GIS): Use GIS technology to draw water level contour maps and flow vector maps and find that there is a large spatial difference between the model prediction and the actual measured value of the flow in a branch of an irrigation canal. By analyzing the topographic data of this area, it is found that the model overestimates the soil permeability of this area, resulting in inaccurate calculation of the infiltration loss of water flow underground, thus affecting the prediction of the canal flow.

[0129] Regional Classification and Comparative Analysis: The water conservancy system is divided into three regions, namely the reservoir area, the irrigation area, and the power generation area, for comparative analysis. In the irrigation area, significant differences in the prediction accuracy of irrigation water use by the model were found in farmland areas with different soil types. For sandy soil areas, the model significantly underestimated the irrigation water volume, while for areas with a higher clay content, the model over-irrigated, which was due to the model not being detailed enough in considering the different soil hydraulic characteristics and not using appropriate soil water movement models for different regions.

[0130] 4) Data Quality and Uncertainty Analysis

[0131] Sensor Error Assessment and Calibration: When calibrating and checking the water level sensors, it was found that one of the sensors had a certain zero drift problem, resulting in the measured water level value being higher than the actual value. This deviation was also reflected in the model's prediction results, especially in the water level prediction in the area where the sensor was located, with relatively large errors.

[0132] Analysis of Data Uncertainty Propagation: When analyzing the water use demand data, it was found that due to the adjustment of the agricultural planting structure and the fluctuations in industrial production, there was a large uncertainty in water use demand. By analyzing the uncertainty of water use demand through Monte Carlo simulation, it was found that the model failed to fully consider the mutual influence relationship between different water use sectors when dealing with this uncertainty, resulting in a decrease in the reliability of the model's prediction results under high water use demand uncertainty.

[0133] Based on the above comprehensive difference analysis results, the following adaptive adjustment strategies were automatically generated:

[0134] Model Parameter Adjustment: In response to the performance changes of water conservancy facilities (such as the wear of water turbine blades) and the calculation errors of water flow energy, the parameters related to the operation efficiency of water conservancy facilities and water flow resistance in the model were adjusted. A gradient-based optimization algorithm (such as the Adam optimizer) was used to update these parameters in reverse propagation according to the error between the actual data and the model output, so as to improve the simulation accuracy of the model for the current operation state of water conservancy facilities and the physical process of water flow.

[0135] Model Structure Improvement: In order to better capture the seasonal and periodic changes in irrigation water use demand, a time series processing layer based on the attention mechanism was added to the water use demand prediction module of the model, enabling the model to pay more attention to the impact of crop growth cycles and meteorological condition changes on water use demand. At the same time, in the soil water movement model, regional adaptive parameters were introduced according to the characteristics of different soil type regions to improve the prediction accuracy of irrigation water use by the model in different regions.

[0136] Data processing optimization: Calibrate and correct the data of the water level sensor with zero drift problem, and re-enter the corrected data into the model for training and verification. At the same time, for the uncertainty of water demand data, a probability distribution-based input representation method is adopted to incorporate the uncertainty range and probability distribution information of water demand into the model input, enabling the model to better handle data uncertainty and improve the reliability of prediction results.

[0137] 5. Model update and optimization

[0138] Update the large model of the water conservancy system according to the generated adaptive adjustment strategy. First, expand and modify the original model according to the adjusted model structure, and randomly initialize the newly added layers and parameters. Then, retrain the model using the current real-time data and historical data for a certain period of time (including the corrected data). During the training process, adopt a smaller learning rate and a larger number of training epochs to ensure that the model can quickly adapt to the new operating conditions and data characteristics while maintaining the original knowledge.

[0139] After multiple iterative trainings, the prediction performance of the model has been significantly improved. Monitor and evaluate the model in real time again, and it is found that the MSE of the reservoir water level prediction has decreased to 0.08, the MAE is 0.3, and the NSE has increased to 0.85. The prediction accuracies of the flow rates of each irrigation channel and the power generation power of the hydropower station have also been significantly improved, achieving the expected optimization effect.

[0140] 6. Continuous learning and knowledge accumulation

[0141] Store all the data and operation records in the knowledge base during the model update process, including the model state before update, the result of difference analysis, the adaptive adjustment strategy, the updated model parameters, and the corresponding actual operation data, etc. Regularly organize and analyze the knowledge base, and use data mining techniques (such as association rule mining and clustering analysis) to summarize the experience and rules of model optimization under different operating conditions.

[0142] For example, through analysis, it is found that under the combination of specific meteorological conditions (such as continuous high temperature and little rain) and agricultural planting patterns (such as large-scale planting of crops with high water demand), adopting specific model parameter adjustment and structure optimization methods (such as increasing the consideration of soil water evaporation and adjusting the weight of the irrigation water demand prediction module) can effectively improve the operation efficiency of the water conservancy system and the water resource utilization rate. Induce and organize these knowledge rules to form reusable empirical knowledge, so that when encountering similar operating conditions in the future, the model can quickly apply these experiences for self-optimization and adjustment, achieve continuous learning and performance improvement, and further improve the intelligent management level of the water conservancy system and the ability to cope with complex and changeable operating conditions.

[0143] Through the above examples, the application process and effects of the dynamic self-optimization and adaptive learning method of the large model of the water conservancy system in the actual operation and management of the water conservancy system are demonstrated. Through continuous monitoring, analysis, adjustment and learning, the model can always maintain high accuracy and reliability, providing strong support and guarantee for the efficient and stable operation of the water conservancy system. The transformation of the water conservancy system model from "static and passive" to "dynamic and active" is realized. Compared with the existing technologies, it has accuracy, real-time performance, economy and sustainability, providing a feasible technical path for the intelligent upgrade of the water conservancy system, and having broad application potential in the fields of flood control and disaster reduction, agricultural irrigation, clean energy production, etc.

[0144] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic self-optimization and adaptive learning method for a large water conservancy system model, characterized in that: The following steps are involved: Collect the operation data of the water conservancy system in real time, build a large model of the water conservancy system based on the deep learning framework and a hybrid model structure, and use historical data to initialize and train the model; input the real-time collected data into the trained large model of the water conservancy system, and the model will calculate and infer based on the input data, and the output includes water level prediction values, flow distribution plans, and water conservancy facility failure probability prediction results; at the same time, obtain the corresponding real values ​​through the actual on-site monitoring equipment, and use a variety of evaluation indicators to compare and evaluate the model output results with the real values ​​to determine the accuracy and reliability of the model; when the model evaluation indicators show that the error between the model output and the real value exceeds the preset threshold, start the difference analysis process, analyze the root cause of the error from the dimensions of physical process, time trend, spatial distribution, etc., and automatically generate adjustment strategies; Adjust model parameters or structure according to the strategy and retrain with real-time data to ensure that the model adapts to new working conditions. Store the experience gained during the optimization process in the knowledge base, reuse historical experience through data mining and case reasoning, and enhance the model's ability to cope with complex working conditions in the future.

2. A method for dynamic self-optimization and adaptive learning of a large water conservancy system model as claimed in claim 1, characterized in that: The model architecture adopts a hybrid model structure, combining convolutional neural network (CNN) to extract spatial features of the water conservancy system, recurrent neural network (RNN) and its variants to capture dynamic changes in time series, and fully connected neural network (FCN) to integrate and process various feature information, to achieve comprehensive simulation and prediction of the operating status of the water conservancy system; The model training uses a small batch stochastic gradient descent algorithm to optimize the model parameters, combined with the early stopping method to prevent overfitting, and determine the initial weights and biases of the model, so that the model can preliminarily reflect the basic operating laws and characteristics of the water conservancy system.

3. The method for dynamic self-optimization and adaptive learning of a large water conservancy system model as claimed in claim 1, characterized in that: The output includes water level prediction value, flow distribution plan, and water conservancy facility failure probability prediction result. Multiple evaluation indicators include mean square error (MSE), mean absolute error (MAE), and Nash efficiency coefficient (NSE). The specific steps for judging the accuracy and reliability of the model are as follows: Step S1: Data synchronization and alignment to ensure that the model prediction results are consistent with the timestamps of the actual monitoring data; match the predicted values ​​output by the model with the actual values ​​measured by the sensors at the corresponding geographical locations to avoid errors and misjudgments caused by data misalignment; Step S2: Error index calculation, using mean square error MSE, mean absolute error MAE and Nash efficiency coefficient NSE indicators and quantitative model prediction values ​​and true values, and presetting thresholds to trigger further analysis; Step S3: Error distribution analysis, draw an error distribution histogram or probability density curve, observe whether the error is centered at zero and normally distributed, calculate the skewness and kurtosis, and identify systematic deviations; Step S4: Trend consistency assessment, using the cross-correlation function CCF to analyze the synchronization of the predicted value and the true value in the time trend, performing linear or nonlinear fitting on the long-term trend, and comparing whether the slope and intercept of the model predicted trend are consistent with the actual trend; Step S5: Outlier detection and attribution. Use the Grubbs criterion or the Dixon criterion to identify outliers and distinguish whether the source of the anomaly is a system anomaly or a model defect. If it is a model defect, analyze the input feature differences and optimize the model logic.

4. The method for dynamic self-optimization and adaptive learning of a large water conservancy system model as claimed in claim 1, characterized in that: The method used in the difference analysis includes: Characteristic analysis based on understanding of physical processes: decomposing the energy of water flow into potential energy, kinetic energy and pressure energy, analyzing the differences between these energy components, analyzing the differences between actual operation data and model predictions, and checking the coordinated work of water conservancy facilities; Based on time series decomposition and trend analysis: Use time series decomposition methods to compare the model and actual performance in seasonality and periodicity, detect long-term trends and mutation points, and analyze the model's response to these changes; Based on spatial correlation and regional difference analysis: Combine geographic information to analyze the spatial error distribution, divide the system into different regions, and compare the model performance of each region; Data quality and uncertainty analysis: Check the accuracy of sensor data, exclude outliers, and use Monte Carlo simulation to analyze the impact of parameter uncertainty on model output.

5. The method for dynamic self-optimization and adaptive learning of a large water conservancy system model as claimed in claim 1, characterized in that: The long-term trend and mutation detection process uses linear regression and mutation point detection algorithms to analyze the model response of long-term trend deviations and mutation events; the sensor calibration and anomaly handling process detects sensor errors and outliers through redundant data cross-validation and statistical methods, and corrects data input; the uncertainty propagation analysis uses Monte Carlo simulation to quantify the impact of parameter uncertainty on model output and optimize probability distribution assumptions; the time series decomposition method uses EMD / wavelet decomposition to analyze the error characteristics of the model at different time scales.

6. The method for dynamic self-optimization and adaptive learning of a large water conservancy system model as claimed in claim 1, characterized in that: The difference analysis specifically comprises the following steps: Step 1) Use multimodal data fusion technology to deeply fuse the data collected by different types of sensors in the water conservancy system to construct a high-dimensional feature space containing comprehensive operation information of the water conservancy system. Step 2) Introduce variational autoencoder (VAE) technology to perform unsupervised learning on the fused water conservancy system data, learn the potential distribution law of the data, and measure the model's ability to capture data features through reconstruction error; Step 3) Using the complex network analysis method based on graph theory, the various components of the water conservancy system and their interconnected relationships are abstracted into a complex network model, in which nodes represent water conservancy facilities and edges represent water flow or information transmission relationships; by calculating the topological characteristic parameters of the network, the changes in the network structure of the water conservancy system under different operating conditions are analyzed to determine the impact of key nodes and key paths on the model error; Step 4) Use time series decomposition technology, such as empirical mode decomposition (EMD) or wavelet decomposition, to decompose the time series data of key operating parameters of the water conservancy system into components of different frequencies, including long-term trends, seasonal fluctuations, and short-term random fluctuations, and analyze the differences between the model prediction values ​​and the actual values ​​of each component to determine the error performance of the model at different time scales; Step 5) Based on the above multi-dimensional and in-depth difference analysis results, an adaptive adjustment strategy is automatically generated.

7. The method for dynamic self-optimization and adaptive learning of a large water conservancy system model as claimed in claim 1, characterized in that: The adaptive adjustment strategy updates the large model of the water conservancy system as follows: For parameter adjustment: Adopt a gradient-based optimization algorithm to update related parameters based on the back propagation of the error between the model output and the true value. The adjustment step size is adaptively determined according to the error size and change trend to ensure the stability and effectiveness of parameter updates. In order to avoid excessive parameter updates leading to model performance degradation, set upper and lower limits for parameter updates to limit parameter changes within a reasonable range. For model structure adjustment: first, expand the structure based on the original model, then use transfer learning technology to migrate the useful features and parameters learned in the original model to the new structure, randomly initialize the newly added parts, and fine-tune the entire model based on the current real-time data and historical data. Use a smaller learning rate and a larger number of training rounds to gradually optimize the parameters of the new structure so that the model can adapt to new operating conditions while maintaining the original knowledge.

8. The method for dynamic self-optimization and adaptive learning of a large water conservancy system model as claimed in claim 1, characterized in that: Continuous updating includes the following steps: Step a): After the model is updated, all data and operation records in the update process are stored in the knowledge base, including the model state before the update, the difference analysis results, the adaptive adjustment strategy, the updated model parameters and the corresponding actual operation data information; Step b): Regularly organize and analyze the knowledge base, using advanced data mining and machine learning techniques to deeply explore the complex patterns and laws contained therein; Step c): Using knowledge rules, the large model of the water conservancy system is further optimized and improved, and a method based on case reasoning and model fusion is developed to realize the reuse of empirical knowledge.

9. A method for dynamic self-optimization and adaptive learning of a large water conservancy system model as claimed in claim 8, characterized in that: Step c) specifically includes the following steps: for newly emerged water conservancy system operating conditions, first search for similar historical cases in the knowledge base, and quickly obtain corresponding model adjustment plans and parameter settings as initial references based on CBR technology; then, through model fusion technology, the initial model based on historical cases is fused with the current real-time data-driven online model. A dynamic weighting strategy is adopted in the fusion process. Different weights are assigned to the two models according to the similarity between the current operating conditions and historical cases and the reliability of real-time data, so that the fused model can make full use of historical experience and current actual information, quickly adapt to new operating conditions and provide more accurate prediction and decision support.

10. A non-temporary storage medium, characterized in that: It is used to store a program for executing a dynamic self-optimization and adaptive learning method for a large water conservancy system model as described in any one of claims 1 to 9 above.

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