Method and storage medium for dynamic optimization and adaptive learning of large water system models

Abstract

This invention proposes a dynamic self-optimization and adaptive learning method for large-scale water conservancy system models, comprising steps such as data acquisition and preprocessing, model construction and initialization, real-time monitoring and model evaluation, difference analysis and adaptive adjustment strategy generation, model updating and optimization, and continuous learning and knowledge accumulation. This method, through dynamic data-driven approaches, intelligent difference analysis, and adaptive optimization, achieves a transformation of water conservancy system models from "static and passive" to "dynamic and proactive." Compared to existing technologies, it offers greater accuracy, real-time performance, economy, and sustainability, providing a feasible technical path for the intelligent upgrading of water conservancy systems and possessing broad application potential in fields such as flood control and disaster reduction, agricultural irrigation, and clean energy production.

Description

Technical Field

[0001] This invention relates to the field of water conservancy technology, and in particular to a dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model. Background Technology

[0002] The stable operation of water conservancy systems plays a crucial role in the rational utilization of water resources, flood control and disaster reduction, agricultural irrigation, and energy production. Traditional water conservancy system models are typically built based on fixed parameters and structures, making it difficult to cope with the complex and ever-changing operating conditions faced by water conservancy systems in actual operation. These conditions include changes in water flow patterns and fluctuations in water demand caused by factors such as climate change, the impact of human activities, and the aging of water conservancy facilities. These changes reduce the predictive accuracy of the models, failing to provide accurate and reliable support for the management and decision-making of water conservancy systems. Furthermore, existing model update methods rely heavily on manual experience and periodic data updates, which are not only inefficient but also fail to capture the dynamic characteristics of the system in real time, thus limiting the intelligent development of water conservancy systems.

[0003] Therefore, the applicant provides a dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model to overcome the problems of poor adaptability, untimely updates, and low intelligence level of existing water conservancy system models, thereby achieving efficient and accurate operation and management of water conservancy systems. Summary of the Invention

[0004] This invention proposes a dynamic self-optimization and adaptive learning method for large-scale water conservancy system models. It addresses the problems of low data standardization and governance efficiency in existing technologies, as well as the inability to comprehensively cover the entire process from data governance to intelligent analysis. It achieves a transformation of water conservancy system models from "static and passive" to "dynamic and proactive." Compared to existing technologies, it offers greater accuracy, real-time performance, economy, and sustainability, providing a feasible technical path for the intelligent upgrading of water conservancy systems. It has broad application potential in fields such as flood control and disaster reduction, agricultural irrigation, and clean energy production. The technical solution of this invention is implemented as follows:

[0005] A dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model includes the following steps: real-time collection of operational data of the water conservancy system; construction of a large-scale water conservancy system model based on a deep learning framework and employing a hybrid model structure; initial training of the model using historical data; inputting the real-time collected data into the trained large-scale water conservancy system model; the model performs calculations and inferences based on the input data, outputting predictions including water level predictions, flow allocation schemes, and water conservancy facility failure probability predictions; simultaneously, acquiring corresponding real values ​​through actual on-site monitoring equipment; comparing and evaluating the model output results with the real values ​​using multiple evaluation indicators to determine the model's accuracy and reliability; when the model evaluation indicators show that the error between the model output and the real value exceeds a preset threshold, initiating a difference analysis process to analyze the root causes of the error from dimensions such as physical processes, time trends, and spatial distribution, and automatically generating adjustment strategies; adjusting model parameters or structure according to the strategies, and retraining using real-time data to ensure the model adapts to new operating conditions; storing the experience gained during the optimization process in a knowledge base, and reusing historical experience through data mining and case reasoning to improve the model's ability to cope with future complex operating conditions.

[0006] As a preferred technical solution, 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 to capture dynamic changes in the time series, and a fully connected neural network (FCN) to integrate and process various feature information, thereby achieving a comprehensive simulation and prediction of the operating status of the water conservancy system. The model training adopts a mini-batch stochastic gradient descent algorithm to optimize the model parameters, combined with an 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 operating laws and characteristics of the water conservancy system.

[0007] As a preferred technical solution, the output includes predicted water level values, flow distribution schemes, and predicted failure probabilities of water conservancy facilities. 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:

[0008] Step S1: Data synchronization and alignment to ensure that the timestamps of the model prediction results are consistent with those of the actual monitoring data; match the predicted values ​​output by the model with the actual sensor values ​​at the corresponding geographical locations to avoid misjudgments due to data misalignment.

[0009] Step S2: Error index calculation, using mean squared error (MSE), mean absolute error (MAE), and Nash efficiency coefficient (NSE) to quantify the deviation between the model's predicted values ​​and the actual values, and pre-determining thresholds to trigger further analysis;

[0010] Step S3: Error distribution analysis, draw an error distribution histogram or probability density curve, observe whether the error is centered at zero and follows a normal distribution, calculate skewness and kurtosis, and identify systematic biases;

[0011] Step S4: Trend consistency assessment. The cross-correlation function (CCF) is used to analyze the synchronicity of the predicted and actual values ​​over time. The long-term trend is fitted linearly or nonlinearly, and the slope and intercept of the predicted trend and the actual trend are compared to see if they are consistent.

[0012] Step S5: Outlier detection and attribution. Use the Grubbs criterion or Dixon criterion to identify outliers and distinguish whether the source of the outlier is a system 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 in 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 between these energy components, analyze the differences between actual operating data and model predictions, and check the coordinated operation of water conservancy facilities.

[0015] Based on time series decomposition and trend analysis: Using time series decomposition methods, the model's performance is compared with that of reality in terms of seasonality and periodicity, long-term trends and abrupt changes are detected, and the model's response to these changes is analyzed.

[0016] Based on spatial correlation and regional difference analysis: Combining geographic information, we 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, eliminate outliers, and use Monte Carlo simulation to analyze the impact of parameter uncertainty on model output.

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

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

[0020] Step 1) Utilize multimodal data fusion technology to deeply fuse data collected by different types of sensors in the water conservancy system, constructing a high-dimensional feature space containing comprehensive operational information of the water conservancy system.

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

[0022] Step 3) Using graph theory-based complex network analysis, the various components of the water conservancy system and their interconnections are abstracted into a complex network model, where nodes represent water conservancy facilities and edges represent water flow or information transmission relationships; by calculating the topological characteristic parameters of the network, the network structure changes of the water conservancy system under different operating states are analyzed, and the impact of key nodes and key paths on model errors is determined.

[0023] Step 4) Use time series decomposition techniques, 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. Analyze the differences between the model predictions and actual values ​​of each component to determine the error performance of the model at 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 adaptive adjustment strategy for updating the large-scale water conservancy system model is as follows:

[0026] For parameter tuning: a gradient-based optimization algorithm is used to update relevant parameters by backpropagation based on the error between the model output and the true value. The adjustment step size is adaptively determined according to the error magnitude and trend to ensure the stability and effectiveness of parameter updates. In order to avoid excessive parameter updates that lead to model performance degradation, upper and lower limits are set for parameter updates to restrict the parameters from changing within a reasonable range.

[0027] For model structure adjustment: First, the structure is expanded based on the original model. Then, the useful features and parameters learned in the original model are transferred to the new structure using transfer learning technology. The newly added parts are randomly initialized, and the entire model is fine-tuned by combining the current real-time data and historical data. A small learning rate and a large number of training rounds are used 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 updates include the following steps:

[0029] Step a): After the model is updated, store all data and operation records from this update process in the knowledge base, including the model status before the update, the difference analysis results, the adaptive adjustment strategy, the updated model parameters, and the corresponding actual running data information.

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

[0031] Step c): Using knowledge rules, the large-scale model of the water conservancy system was further optimized and improved, and a method based on case reasoning and model fusion was developed to realize the reuse of empirical knowledge.

[0032] As a preferred technical solution, step c) specifically includes the following steps: For newly emerging water conservancy system operating conditions, firstly, search for similar historical cases in the knowledge base, and quickly obtain the corresponding model adjustment scheme and parameter settings as initial references based on CBR technology; then, through model fusion technology, merge the initial model based on historical cases with the current real-time data-driven online model. 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 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.

[0033] A non-transitory storage medium for storing a program for executing the aforementioned dynamic self-optimization and adaptive learning method for a large-scale hydraulic system model.

[0034] Compared with existing technologies, this solution has the following advantages:

[0035] (1) Intelligent difference analysis and strategy generation: Through advanced feature importance analysis and cluster analysis algorithms, the root causes and patterns of model errors are deeply explored. It can automatically generate accurate adaptive adjustment strategies for different types of error situations, overcome the limitations and subjectivity of manual experience judgment in traditional methods, greatly improve the pertinence and efficiency of model optimization, and enable the model to quickly adapt to the complex and ever-changing operating conditions of the water conservancy system.

[0036] (2) Efficient model update mechanism: The model is updated by combining gradient-based optimization algorithm and transfer learning technology. This ensures the efficiency and accuracy of parameter updates, makes full use of the existing knowledge and experience of the original model, reduces the consumption of computing resources and data requirements during the model update process, and ensures that the model can maintain good performance stability and continuity after the update, avoiding the problem of model overfitting or underfitting caused by frequent retraining.

[0037] (3) Continuous learning and knowledge reuse: A comprehensive knowledge base and knowledge mining mechanism have been established, which can transform data and experience in the model update process into reusable knowledge rules, enabling the model to continuously learn and self-evolve. Through knowledge reuse, the model can quickly make optimization adjustments when facing similar working conditions, improving the emergency response capability and long-term operation and management efficiency of the water conservancy system, and providing solid technical support for the intelligent development of the water conservancy system. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[0040] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0041] Reference Figure 1This invention provides a dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model, comprising the following steps: real-time collection of operational data of the water conservancy system; construction of a large-scale water conservancy system model based on a deep learning framework and employing a hybrid model structure; initial training of the model using historical data; inputting the real-time collected data into the trained large-scale water conservancy system model; the model performs calculations and inferences based on the input data, outputting predictions including water level predictions, flow allocation schemes, and water conservancy facility failure probability predictions; simultaneously, acquiring corresponding real values ​​through actual on-site monitoring equipment; comparing and evaluating the model output results with the real values ​​using multiple evaluation indicators to determine the model's accuracy and reliability; when the model evaluation indicators show that the error between the model output and the real value exceeds a preset threshold, initiating a difference analysis process to analyze the root causes of the error from dimensions such as physical processes, time trends, and spatial distribution, and automatically generating adjustment strategies; adjusting model parameters or structure according to the strategies, and retraining in conjunction with real-time data to ensure the model adapts to new operating conditions; storing the experience gained during the optimization process in a knowledge base, and reusing historical experience through data mining and case reasoning to improve the model's ability to cope with future complex operating conditions.

[0042] Specifically, it includes the following steps:

[0043] Step 1: Data Acquisition and Preprocessing

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

[0045] 2. Preprocess the collected data, including noise removal, missing value imputation, and data normalization, to ensure data quality and consistency. Simultaneously, classify and label the data, constructing a structured dataset according to time series and spatial location to provide a foundation for subsequent model training and analysis.

[0046] Step 2: Model Building and Initialization

[0047] 1. A large-scale model of the water conservancy system is constructed based on a deep learning framework. The model architecture adopts a hybrid model structure, combining convolutional neural networks (CNN) to extract the spatial features of the water conservancy system, recurrent neural networks (RNN) and their variants (such as long short-term memory networks LSTM and gated recurrent units GRU) to capture dynamic changes in time series, and fully connected neural networks (FCN) to integrate and process various feature information, so as to realize the comprehensive simulation and prediction of the operating status of the water conservancy system.

[0048] 2. Use historical data to initialize and train the model, use the Mini-Batch Stochastic Gradient Descent algorithm to optimize the model parameters, combine early stopping to prevent overfitting, determine the initial weights and biases of the model, so that the model can initially reflect the basic operating rules 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-scale water conservancy system model. The model performs calculations and inferences based on the input data and outputs prediction results including water level predictions, flow distribution schemes, and water conservancy facility failure probabilities.

[0051] 2. Simultaneously, actual values ​​are obtained through on-site monitoring equipment. Multiple evaluation indicators, such as mean squared error (MSE), mean absolute error (MAE), and Nash efficiency coefficient (NSE), are used to compare and evaluate the model output results with the actual values ​​to determine the model's accuracy and reliability. Detailed steps for determining the model's accuracy and reliability:

[0052] 1) Data synchronization and alignment

[0053] When acquiring model outputs and corresponding real values, the first step is to ensure precise synchronization and alignment between the two in terms of timestamps and spatial locations. For data with time-series characteristics, such as water level changes over time or dynamic monitoring values ​​of flow, data should be collected and recorded at the same time intervals (e.g., per hour, per minute) to ensure a one-to-one correspondence between the model output and the actual monitoring data in the time dimension. Spatially, for sensor data from different locations (e.g., flow measurements at different cross-sections of rivers, water level data in different areas of reservoirs), the corresponding spatial location identifiers of the water conservancy system should be clearly defined to ensure that the model's predicted values ​​for the corresponding locations can accurately match the real values, avoiding evaluation errors caused by data misalignment.

[0054] 2) Error distribution analysis

[0055] Various common error metrics, such as mean squared error (MSE), mean absolute error (MAE), and Nash efficiency coefficient (NSE), are calculated to comprehensively measure the model's accuracy. Then, frequency distribution histograms or probability density function curves of the errors are plotted to visually demonstrate the distribution of model errors across different value ranges. The errors between the model output values ​​and the true values ​​are grouped and statistically analyzed according to certain intervals (e.g., -5 to -4, -4 to -3, ..., 3 to 4, 4 to 5, etc.), calculating the frequency or probability density of errors within each interval. By observing the shape, central location, and dispersion of the error distribution, the central tendency and dispersion of model errors can be understood. For example, if the error distribution exhibits an approximately normal distribution centered at 0 with low dispersion, it indicates that the model's prediction errors are relatively stable and concentrated within a small range, suggesting high model accuracy. Conversely, if the error distribution is relatively dispersed, exhibiting obvious long tails or skewness, it may mean that the model has significant prediction bias under certain specific conditions, requiring further analysis of the causes of these abnormal errors, such as the influence of extreme weather or sudden failures of water conservancy facilities.

[0056] Calculating skewness and kurtosis, among other statistical measures, further quantifies the characteristics of the error distribution. Skewness measures the degree of asymmetry in the error distribution. A skewness of 0 indicates a symmetrical distribution; a skewness greater than 0 indicates a positively skewed distribution (long tail on the right); a skewness less than 0 indicates a negatively skewed distribution (long tail on the left). Kurtosis reflects the sharpness or flatness of the error distribution. Compared to a normal distribution, a kurtosis greater than 3 indicates a sharper distribution with thicker tails; a kurtosis less than 3 indicates a relatively flat distribution. Analyzing skewness and kurtosis provides insight into the morphological characteristics of the error distribution, aiding in the identification of potential systematic errors or abnormal fluctuations in the model. For example, a positively skewed error distribution with high kurtosis may suggest a tendency for the model to underestimate larger values, requiring focused examination and optimization of the model's predictive capabilities under high flow or high water level conditions.

[0057] 3) Trend Consistency Assessment

[0058] Time series analysis methods, such as calculating the cross-correlation function (CCF) between the model output and the actual values, are used to assess the consistency of their temporal trends. The CCF measures the correlation between two time series at different lag orders. By observing the CCF value in and around the lag order of 0, it can be determined whether the model output and the actual values ​​have a synchronous trend. If the CCF value at lag order 0 is close to 1 and maintains a high correlation within a certain lag range, it indicates that the model can follow the temporal trend of the actual values ​​well and has good dynamic response capability. Conversely, if the CCF value is low or fluctuates greatly, it may indicate that the model has a delay or distortion in capturing the dynamic changes of the water conservancy system, requiring optimization of the model's dynamic characteristics. For example, adjusting the parameters of the time series processing module in the model (such as the RNN or LSTM layer) is necessary to improve the model's ability to track time trends.

[0059] Perform trend fitting on both the model output and the actual values, for example, using linear regression or multinomial regression to fit the trend lines of their changes over time. Compare parameters such as the slope, intercept, and goodness of fit of the two trend lines to evaluate the model's accuracy in long-term trend prediction. If the trend line of the model output and the trend line of the actual values ​​are similar in slope and intercept, and the goodness of fit is high, it indicates that the model can accurately grasp the long-term changing trend of the water system's operating parameters, such as accurately predicting the long-term downward trend of reservoir water levels or the seasonal increase trend of river flow. Otherwise, it is necessary to analyze the reasons for the inconsistency in trends, which may be due to the model not considering certain long-term influencing factors (such as changes in precipitation patterns caused by climate change, long-term growth trends in regional water demand, etc.). Targeted improvements to the model should then be made, such as introducing new trend variables or adjusting the model's long-term prediction module.

[0060] 4) Outlier Detection and Analysis

[0061] Statistical hypothesis testing methods, such as the Grubbs criterion or the Dixon criterion, are used to detect outliers between the model output and the actual values. These criteria are based on the statistical distribution characteristics of the data. By calculating the sample mean, standard deviation, and specific statistics, they determine whether data points significantly deviate from other data, thereby identifying potential outliers. For detected outliers, further analysis is conducted on 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 due to floods or earthquakes, sudden changes in water quality due to short-term heavy rainfall or industrial wastewater discharge), or by defects in the model itself (such as insufficient predictive ability of the model under extreme conditions, or abnormal model calculations due to data input errors).

[0062] When outliers are determined to be caused by the model, the sample data corresponding to these outliers are analyzed separately. For example, by comparing the differences in model input features under normal and abnormal operating conditions, feature importance analysis methods (such as feature importance assessment in random forests) can be used to identify key feature factors that may lead to abnormal model outputs. Simultaneously, these outlier samples are stored as special cases in a knowledge base so that the model structure or parameters can be adjusted in a targeted manner during subsequent model optimization and improvement. This enhances the model's predictive ability and robustness against similar abnormal operating conditions. For example, monitoring and early warning mechanisms for abnormal events can be added to the model, or the feature variables related to outliers can be modeled and processed more meticulously to reduce the interference of outliers on the model's accuracy and reliability assessment, and improve the model's stability and adaptability in complex and variable water conservancy system environments.

[0063] Through the above comprehensive and systematic comparative evaluation steps, we can deeply analyze the differences between the model output results and the actual values ​​from multiple perspectives, accurately judge the accuracy and reliability of the model, and provide a solid data foundation and scientific basis for subsequent difference analysis and adaptive adjustment strategy generation, thereby achieving efficient optimization and precise operation of the large-scale water conservancy system model.

[0064] Step 4: Difference Analysis and Adaptive Adjustment Strategy Generation

[0065] 1. When the model evaluation indicators show that the error between the model output and the true value exceeds the preset threshold, the difference analysis process is initiated.

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

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

[0068] a) Hydraulic factor decomposition

[0069] To address errors in water flow simulation, the total energy of the flow is decomposed into potential energy, kinetic energy, and pressure energy. The differences between the model output and actual values ​​in these energy components are analyzed. For example, the energy values ​​at each point are calculated using Bernoulli's equation to determine whether the large error is due to water level difference (potential energy), flow velocity changes (kinetic energy), or local pressure changes (pressure energy). If a significant deviation is found between the calculated potential energy and the actual situation in a certain area, it may indicate a problem with the model's processing of terrain or water level data, or inaccurate simulation of the gravity effect on the water flow.

[0070] The turbulent characteristics of the water flow are analyzed, and parameters such as turbulent kinetic energy and turbulent dissipation rate are calculated. The model output is compared with the actual measured turbulence-related indicators to determine whether the model can accurately capture the turbulence of the water flow. Turbulent characteristics are crucial for many hydraulic engineering applications (such as river scouring and pollutant diffusion); if the model has biases in turbulence simulation, it may lead to significant errors in the prediction of these processes.

[0071] b) Analysis of Water Conservancy Facility Operation Modes

[0072] For the operational status of water conservancy facilities (such as sluice gates, pumping stations, and dams), a detailed analysis is conducted to examine the differences between parameters such as opening degree, flow regulation method, and power generation and the settings and predictions in the model. For example, for sluice gates, the inconsistency between the actual opening degree change curve and the opening degree curve predicted by the model is studied, and how this difference affects the water level and flow relationship between upstream and downstream areas. Through actual operating data and equipment operation logs, it is determined whether there are any unforeseen operating modes or malfunctions of the water conservancy facilities that were not considered by the model, such as partial valve blockage or decreased pumping station efficiency. These factors may lead to significant errors in the model's predictions in the area near the water conservancy facilities.

[0073] This study analyzes the collaborative operation of water conservancy facilities. For example, in a water conservancy system containing multiple sluice gates and pumping stations, it examines the differences between the model's representation of joint scheduling strategies and actual operation. If specific joint scheduling methods are adopted in actual operation to meet flood control or irrigation needs, but the model fails to accurately simulate this synergistic effect, it may lead to deviations in the prediction of water level and flow rate for the overall system. By analyzing the flow distribution ratio and water level control relationships among the facilities, the study identifies shortcomings in the model regarding the collaborative operation mechanism of the facilities.

[0074] 2) Time series decomposition and trend analysis

[0075] a) Extraction of seasonal and periodic components

[0076] Time series decomposition methods (such as classic additive or multiplicative decomposition models) are employed to decompose the time series data of key parameters of the water system (such as water level and flow rate) into seasonal, periodic, and residual components. The performance of model predictions and actual values ​​across each component is compared to determine whether the model accurately captures the seasonal and periodic variations of the water system. For example, in areas with significant seasonal precipitation and water demand changes, the model's predictions of seasonal fluctuations in water level and flow rate are examined to ensure consistency with actual conditions. If the model consistently overestimates or underestimates water level predictions for a given season, further analysis of parameter settings related to seasonal factors (such as evapotranspiration coefficient and seasonal precipitation distribution patterns) may be necessary.

[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 estuarine water systems and the weekly or daily variations in industrial or irrigation water use. Through techniques such as spectral analysis, the model's responsiveness to these periodic components is determined, as well as whether any periodic components are omitted or incorrectly simulated, leading to significant prediction errors within the corresponding time periods.

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

[0079] Trend analysis methods (such as linear regression, nonlinear regression, or trend filtering algorithms) are used to detect long-term trends in water system parameters, such as long-term rising or falling water levels and long-term flow variation trends. The long-term trends predicted by the model are compared with the actual observed trends to determine whether the model accurately reflects the long-term evolution characteristics of the water system. If there is a significant deviation between the model's predicted long-term trends and the actual trends, it may be necessary to consider whether the model has not adequately considered some long-term influencing factors, such as long-term changes in precipitation patterns due to climate change, long-term adjustments in regional water use structures (such as changes in agricultural irrigation methods and increases or decreases in industrial water use), or long-term aging and performance degradation of water conservancy facilities.

[0080] Abrupt change detection algorithms (such as the Pettitt test and BayeScan method) are employed to identify abrupt changes in the time series data of the water conservancy system. These abrupt changes may correspond to major water conservancy projects, natural disasters (such as floods and droughts), or policy and regulatory changes (such as adjustments to water resource management policies). The predictive performance of the model before and after these abrupt changes is analyzed to determine whether the model can adapt to these sudden changes in a timely manner, or whether structural adjustments or parameter updates are needed to better handle abrupt changes in the data and reduce prediction errors caused by abrupt events.

[0081] 3) Spatial correlation and regional difference analysis

[0082] a) Geographic Information System (GIS) Spatial Analysis

[0083] This approach combines geographic information about the water conservancy system (such as topography, river distribution, and the location of water conservancy facilities) with model output and actual monitoring data, utilizing GIS technology for spatial analysis. By creating various thematic maps (such as water level contour maps, flow vector maps, and error distribution maps), the spatial operational status of the water conservancy system and the distribution of model errors are visually displayed. For example, areas with higher or lower water levels predicted by the model are marked on the map and compared with actual measured water level data. This allows for analysis of the topographic features, flow paths, and relationships with surrounding water conservancy facilities in these areas, determining whether there are uneven spatial error distributions caused by inaccurate model simulations of topography or flow boundary conditions.

[0084] Spatial autocorrelation analysis methods (such as Moran's I index and Geary's C coefficient) are used to study the spatial correlation and dependence of water system parameters (such as water level and water quality). The accuracy of the model in simulating this spatial autocorrelation structure is assessed. If significant differences are found between the model's output spatial autocorrelation characteristics and the actual situation, it may indicate deficiencies in the model's consideration of water flow exchange, diffusion processes, or mutual influences between adjacent areas. Optimization of the model's spatial coupling mechanism is necessary to improve its ability to simulate the spatial distribution characteristics of the water system and reduce spatial errors.

[0085] b) Regional Classification and Comparative Analysis

[0086] Based on the natural geographical characteristics of the water conservancy system (such as mountainous areas, plains, and estuaries), functional zoning (such as drinking water source protection areas, agricultural irrigation areas, and industrial water use areas), or administrative divisions, the water conservancy system is divided into different regions. The model output values ​​and actual values ​​are compared and analyzed for each region to determine the differences in the model's prediction accuracy across different areas. For example, in mountainous areas, due to complex terrain and drastic changes in water flow, the model's prediction error may be relatively large; while in plains, due to relatively stable water flow, the model may perform better. Through this regional comparative analysis, specific problems with the model in different regions can be identified. For instance, in mountainous areas, it may be necessary to improve terrain modeling methods and increase consideration of stormwater runoff formation mechanisms; in industrial water use areas, it may be necessary to more accurately simulate the impact of wastewater discharge and treatment processes on water quality and flow. This allows for targeted model optimization, improving the model's applicability and accuracy throughout the entire water conservancy system.

[0087] 4) Data quality and uncertainty analysis

[0088] a) Sensor error assessment and correction

[0089] The measurement errors of various sensors (water level sensors, flow sensors, water quality sensors, etc.) in the water conservancy system are evaluated. This is achieved through comparative calibration with high-precision standard measuring equipment, or by cross-validating data using redundant sensor configurations (multiple sensors measuring the same parameter), to determine the measurement error range and distribution characteristics of each sensor. The relationship between model prediction errors and sensor measurement errors is analyzed. If a correlation is found between the model's errors in certain regions or time periods and the sensor error distribution, it may be necessary to correct the sensor data or adjust the model's processing method for sensor data to reduce model errors caused by sensor errors.

[0090] Consider sensor malfunctions and outlier detection. Employ statistical methods (such as the Grubbs criterion and box plots) and outlier identification methods based on physical models (such as judging the reasonableness of flow and water level data based on the flow continuity equation and energy equation) to identify outliers and potential fault points in sensor measurement data. For these outliers, special handling is performed during the difference analysis, such as excluding them from the initial analysis or using data repair techniques (such as time series smoothing algorithms and spatial interpolation algorithms) to correct them. This ensures the quality of data used for difference analysis and avoids incorrect assessments of model performance and inaccurate difference analysis results due to sensor malfunction data.

[0091] b) Data uncertainty propagation analysis

[0092] Because many parameters in a water conservancy system (such as soil permeability, roughness coefficient, and water demand) are subject to uncertainty, this study analyzes the propagation process of these uncertainties in the model and their impact on the model output. Using the Monte Carlo simulation method, the uncertain parameters are randomly sampled, and the model is run multiple times to obtain the probability distribution of the model output. By comparing the uncertainty range of the model output with the distribution of actual measured values, it is determined whether the model can reasonably reflect the impact of data uncertainty on the prediction results. If the uncertainty range of the model output is found to be too narrow or too wide, it may be necessary to adjust the way uncertain parameters are handled in the model, such as improving the probability distribution assumptions of the parameters or increasing the consideration of the correlation between uncertain factors, to improve the reliability and accuracy of the model under uncertain conditions, so that the model prediction results can better match the uncertainty of the actual situation, thereby reducing the error between the model and the actual values ​​caused by uncertain factors.

[0093] By comprehensively applying the above methods, we can analyze the differences between the model output and the actual values ​​in a thorough and in-depth manner. We can identify the causes of errors from multiple perspectives, such as physical processes, time series, spatial distribution, data quality, and uncertainty. This provides a solid foundation for formulating targeted adaptive adjustment strategies, thereby achieving effective optimization and adaptive improvement of the large-scale water conservancy system model.

[0094] 2. First, multimodal data fusion technology is used to deeply fuse data collected from different types of sensors in the water conservancy system (such as water level data, flow data, meteorological data, and water conservancy facility operation status data) to construct a high-dimensional feature space containing comprehensive operational information of the water conservancy system. Within this feature space, a combination of principal component analysis (PCA) and independent component analysis (ICA) is employed to reduce the dimensionality of the high-dimensional data, while simultaneously extracting the main feature components and independent latent factors. This more clearly reveals the essential characteristics and potential change patterns of the water conservancy system's operational status, thereby more accurately identifying the sources of factors that may lead to model errors.

[0095] 3. Next, variational autoencoder (VAE) technology is introduced to perform unsupervised learning on the fused water conservancy system data, learning the potential distribution patterns of the data, and measuring the model's ability to capture data features through reconstruction error. The reconstruction error of actual operating data is compared and analyzed with the reconstruction error of data under normal operating conditions to identify areas or time periods where the reconstruction error increases significantly. These areas and time periods often correspond to parts of the water conservancy system that have experienced abnormal changes or are difficult for the model to accurately describe.

[0096] 4. Then, using graph theory-based complex network analysis, the various components of the water conservancy system (such as reservoirs, rivers, sluices, pumping stations, etc.) and their interconnections are abstracted into a complex network model, where nodes represent water conservancy facilities and edges represent water flow or information transmission relationships. By calculating the network's topological characteristic parameters (such as node degree, clustering coefficient, betweenness centrality, etc.), the network structure changes of the water conservancy system under different operating states are analyzed to determine the impact of key nodes and critical paths on model errors. For example, if the betweenness centrality of a node containing a sluice gate changes significantly under conditions of large errors, it indicates that the operating state of the sluice gate may have a significant impact on the water flow distribution of the entire water conservancy system that has not been accurately considered by the model, thus making it a key object of analysis.

[0097] 5. Furthermore, time series decomposition techniques, such as Empirical Mode Decomposition (EMD) or wavelet decomposition, are employed to decompose the time series data of key operating parameters of the water conservancy system (such as water level and flow rate) into components of different frequencies, including long-term trends, seasonal fluctuations, and short-term random fluctuations. The differences between the model's predicted and actual values ​​for each component are analyzed separately to determine the model's error performance at different time scales. For example, if a large error is found in the short-term random fluctuation component, it may indicate that the model's response to sudden changes in water flow or temporary water conservancy facility operations is insufficient, requiring targeted optimization of the model's dynamic response mechanism.

[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 model's expected range, such as a surge in rainfall under extreme weather conditions, the adjustment strategy includes adding a sensitivity adjustment parameter for that variable to the model, or introducing new features related to that variable (such as the rate of change of rainfall intensity, duration, and rainfall pattern distribution). At the same time, generative adversarial networks (GANs) are used to model and learn the complex relationship between the new features and other related features to enhance the model's adaptability 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 rapid water flow switching, the adjustment strategy is to increase the depth and complexity of convolutional or recurrent layers in the corresponding parts of the model (such as the water flow simulation module) based on the key nodes and paths determined by complex network analysis, or introduce a spatiotemporal attention module based on the attention mechanism, so that the model can focus more on key water flow change areas, time periods, and changes in the state of water conservancy facilities, thereby improving the model's ability to simulate complex dynamic processes.

[0099] Step 5: Model Update and Optimization

[0100] 1. Update the large model of the water conservancy system based on the generated adaptive adjustment strategy.

[0101] 2. For parameter tuning, gradient-based optimization algorithms, such as the Adaptive Moments Estimator (Adam), are used. Relevant parameters are updated via backpropagation based on the error between the model output and the true value. The adjustment step size is adaptively determined based on the magnitude and trend of the error to ensure the stability and effectiveness of parameter updates. Simultaneously, to prevent excessive parameter updates from degrading model performance, upper and lower bounds are set to limit parameter changes 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 based on the original model, and then use transfer learning technology to transfer the useful features and parameters already learned in the original model to the new structure. Randomly initialize the newly added parts, and fine-tune the entire model by combining the current real-time data and historical data. Use a small learning rate and a large number of training rounds 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, all data and operation records during the update process are stored in the knowledge base, including the model status before the update, the difference analysis results, the adaptive adjustment strategy, the updated model parameters, and the corresponding actual running data.

[0105] 2. Regularly organize and analyze the knowledge base, employing advanced data mining and machine learning techniques to deeply uncover the complex patterns and rules it contains. For example, use deep neural networks to construct a knowledge graph, visualizing and structuring various elements in the operation of the water conservancy system (such as meteorological conditions, hydrological characteristics, water conservancy facility status, water resource demand, etc.) and their interrelationships. Based on this, through association rule mining algorithms (such as the improved Apriori algorithm) and sequence pattern mining algorithms (such as the PrefixSpan algorithm), accurately discover the potential correlations between model parameter adjustments, structural changes, and the improvement of water conservancy system operation performance under different operating conditions, and extract universal and guiding knowledge rules.

[0106] 3. Utilizing these knowledge rules, the large-scale water conservancy system model was further optimized and improved. An innovative method based on Case-Based Reasoning (CBR) and model fusion was developed to reuse experiential knowledge. For newly emerging water conservancy system operating conditions, similar historical cases were first searched in the knowledge base. Based on CBR technology, corresponding model adjustment schemes and parameter settings were quickly obtained as initial references. Then, through model fusion technology, the initial model based on historical cases was merged with the current real-time data-driven online model. A dynamic weighting strategy was adopted during the fusion process, assigning different weights to the two models based on the similarity between the current operating conditions and historical cases, as well as the reliability of the real-time data. This allows the merged model to fully utilize historical experience and current practical information, quickly adapt to new operating conditions, and provide more accurate predictions and decision support. For example, when a new type of meteorological disaster affects the water conservancy system, the system finds historical cases of similar meteorological changes with slightly different degrees in the knowledge base, extracts the corresponding successful response model adjustment methods, and combines them with the detailed data of the water conservancy system monitored in real time. Through dynamic weighted model fusion, it quickly generates an optimized model for this new type of disaster, effectively guiding the regulation of water conservancy facilities and the rational allocation of water resources, improving the emergency response capability and long-term operation and management efficiency of the water conservancy system, and providing solid technical support for the intelligent development of the water conservancy system.

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

[0108] Based on the above innovations, this patent achieves innovation and breakthroughs in ultrasound image data processing and intelligent analysis methods by integrating automated data governance, intelligent analysis technology, and closed-loop management throughout the entire process. By introducing multimodal AI models and automated data governance technology, the system not only significantly improves data processing efficiency and analysis accuracy but also enhances the interpretability and clinical applicability of the analysis results. This provides a more efficient and precise intelligent tool for clinical diagnosis and research applications, contributing to the intelligent development of the field of ultrasound medical imaging.

[0109] The following detailed description is based on 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 irrigation water, power generation, and flood control for the surrounding area. Its operation is affected by a variety of factors, such as seasonal precipitation changes, fluctuations in agricultural water demand, and the supply and demand of the electricity market.

[0111] 1. Data Acquisition and Preprocessing

[0112] High-precision water level sensors, flow sensors, and water quality sensors were installed at various water level monitoring points in the reservoir, key cross-sections of the canals, and the inlet and outlet of the hydropower station. Meanwhile, meteorological data such as precipitation, temperature, and wind speed were collected at the meteorological station, and condition monitoring equipment was installed on each water conservancy facility to obtain real-time operating parameters (such as pump speed and sluice gate opening).

[0113] The collected data is wirelessly transmitted and aggregated at the data processing center, where it undergoes preprocessing. First, obvious noise, such as outliers caused by transient sensor malfunctions or communication interference, is removed. For any missing data, linear interpolation is used to fill in the gaps and ensure data continuity. Then, all data is normalized to a value range between 0 and 1, facilitating subsequent model training and computation. After preprocessing, the data is structured and stored according to time series and spatial location, forming a spatiotemporal dataset encompassing comprehensive operational information of the water conservancy system.

[0114] 2. Model Construction and Initialization

[0115] A large-scale water conservancy system model was constructed based on a deep learning framework. The overall architecture of the model adopts a hybrid structure combining convolutional neural networks (CNN) and long short-term memory networks (LSTM). The CNN part is used to extract spatial features of the water conservancy system, such as identifying water convergence and diffusion areas and key hydraulic conduction paths through convolutional operations on topographic data and the layout of water conservancy facilities. The LSTM is used to capture dynamic changes in time series, such as the trends of water level and flow over time, as well as the impact of seasonal fluctuations in meteorological conditions and water demand on the water conservancy system.

[0116] The model was initialized and trained using historical data from the past three years. During training, the dataset was divided into training, validation, and test sets. The model parameters were optimized using a mini-batch stochastic gradient descent algorithm, combined with early stopping to prevent overfitting. After multiple iterations of training, the initial weights and biases of the model were determined, enabling the model to initially simulate the basic behavior and patterns of a water conservancy system under normal operating conditions, such as accurately predicting daily changes in reservoir water levels, flow distribution in irrigation channels, and power generation of hydropower stations.

[0117] 3. Real-time monitoring and model evaluation

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

[0119] Meanwhile, actual values ​​were obtained through on-site monitoring equipment, and the model output was compared with the actual values ​​using evaluation indicators such as mean squared error (MSE), mean absolute error (MAE), and Nash efficiency coefficient (NSE). For example, within a certain period, the MSE of the predicted reservoir water level was found to be 0.2, the MAE to be 0.4, and the NSE to be 0.6, while the preset MSE threshold was 0.1. This indicated a significant error between the model output and the actual value, necessitating a discrepancy analysis process to identify the cause and optimize the model.

[0120] 4. Difference Analysis and Adaptive Adjustment Strategy Generation

[0121] 1) Feature analysis based on physical process understanding

[0122] Hydraulic factor decomposition: A detailed analysis of the reservoir's flow conditions was conducted, and the potential energy, kinetic energy, and pressure energy of the flow were calculated using Bernoulli's equation. It was found that the calculated kinetic energy near the reservoir outlet deviated significantly from the actual measured values. Further investigation revealed that this was because the model used a simplified empirical formula to account for local head losses at the outlet, failing to accurately account for the impact of the actual outlet structure (such as roughness and irregular shape) on the flow energy.

[0123] Analysis of Water Conservancy Facility Operation Mode: Analysis of the operation data of various hydropower stations revealed that the actual power generation of one hydropower station did not match the model prediction. Upon examining the hydropower station's operation logs, it was found that the turbine blades experienced partial wear during that period, leading to a decrease in efficiency; however, this change in equipment performance was not accounted for in the model.

[0124] 2) Time series decomposition and trend analysis

[0125] Seasonal and periodic component extraction: Time series decomposition of irrigation canal flow data revealed that the model failed to accurately capture the periodic peaks in flow caused by the significant increase in crop water demand when predicting flow during the summer peak irrigation period. Further analysis showed that the model's modeling of the relationship between crop growth cycle and water demand was not accurate enough, and it did not fully consider the changes in water demand of different crops at different growth stages.

[0126] Long-term trend and abrupt change detection: Trend analysis revealed a slow, declining water level over the past year. However, the model's predicted decline was relatively gradual, deviating from the actual situation. Investigations showed this was due to prolonged drought in the basin leading to increased infiltration and decreased precipitation, factors the model failed to adequately account for. Furthermore, a minor earthquake nearby caused slight leakage in the dam, resulting in a sudden change in water level, which the model failed to respond to in a timely manner.

[0127] 3) Spatial correlation and regional difference analysis

[0128] Geographic Information System (GIS) Spatial Analysis: Using GIS technology to create water level contour maps and flow vector maps, it was found that the model's prediction of flow rate for a certain irrigation canal branch differed significantly from actual measurements in terms of spatial distribution. Analysis of the area's topographic data revealed that the model overestimated the soil permeability, leading to inaccurate calculations of water infiltration losses underground, thus affecting the prediction of canal flow rate.

[0129] Regional Classification and Comparative Analysis: The water conservancy system was divided into three regions for comparative analysis: reservoir area, irrigation area, and power generation area. In the irrigation area, significant differences in the model's irrigation water prediction accuracy were found among farmland areas with different soil types. For sandy soil areas, the model's predicted irrigation water volume was significantly insufficient, while for areas with high clay content, the model showed over-irrigation. This is because the model did not adequately consider the different soil hydraulic characteristics and did not employ appropriate soil moisture movement models for different regions.

[0130] 4) Data quality and uncertainty analysis

[0131] Sensor Error Assessment and Correction: During the calibration and inspection of the water level sensors, it was found that one of the sensors had a certain degree of zero-point drift, causing its measured water level value to be higher than the actual value. This deviation is also reflected in the model's prediction results, especially in the water level prediction for the area where this sensor is located, where the error is relatively large.

[0132] Data Uncertainty Propagation Analysis: Analysis of water demand data revealed significant uncertainty due to adjustments in agricultural planting structures and fluctuations in industrial production. Monte Carlo simulation analysis of this uncertainty showed that the model failed to adequately consider the interrelationships between different water-using sectors, leading to decreased reliability of predictions under conditions of high water demand uncertainty.

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

[0134] Model parameter adjustment: In response to performance changes of water conservancy facilities (such as turbine blade wear) and errors in water flow energy calculation, the parameters related to the operating efficiency of water conservancy facilities and water flow resistance in the model are adjusted. Gradient-based optimization algorithms (such as the Adam optimizer) are used to backpropagate the errors between actual data and model output to update these parameters, so as to improve the model's simulation accuracy of the current operating status of water conservancy facilities and water flow physical processes.

[0135] Model Structure Improvements: To better capture the seasonal and cyclical variations in irrigation water demand, an attention-based time-series processing layer was added to the model's water demand prediction module. This allows the model to pay closer attention to the impact of crop growth cycles and meteorological conditions on water demand. Simultaneously, in the soil moisture movement model, regional adaptive parameters were introduced based on the characteristics of different soil types to improve the accuracy of irrigation water predictions in various regions.

[0136] Data processing optimization: Water level sensors exhibiting zero-point drift issues are calibrated and their data corrected. The corrected data is then re-inputted into the model for training and validation. Simultaneously, to address the uncertainty in water demand data, a probability distribution-based input representation method is employed. This method incorporates 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] Based on the generated adaptive adjustment strategy, the large-scale water conservancy system model is updated. First, the original model is expanded and modified according to the adjusted model structure, and the newly added layers and parameters are randomly initialized. Then, the model is retrained using current real-time data and historical data over a certain period (including corrected data). During training, a small learning rate and a large number of training epochs are used to ensure that the model can quickly adapt to new operating conditions and data characteristics while retaining its original knowledge.

[0139] After multiple iterations of training, the model's predictive performance was significantly improved. Real-time monitoring and evaluation of the model again revealed that the MSE for reservoir water level prediction decreased to 0.08, the MAE to 0.3, and the NSE increased to 0.85. The accuracy of flow prediction for each irrigation canal and the accuracy of hydropower station power generation prediction also showed significant improvement, achieving the expected optimization results.

[0140] 6. Continuous learning and knowledge accumulation

[0141] All data and operation records during this model update process will be stored in a knowledge base, including the model status before the update, the results of the difference analysis, the adaptive adjustment strategy, the updated model parameters, and the corresponding actual operating data. The knowledge base will be regularly organized and analyzed, and data mining techniques (such as association rule mining and cluster analysis) will be used to summarize the experience and patterns of model optimization under different operating conditions.

[0142] For example, analysis reveals that under specific meteorological conditions (such as continuous high temperatures and low rainfall) and agricultural planting patterns (such as large-scale planting of water-intensive crops), employing specific model parameter adjustments and structural optimization methods (such as increasing consideration of soil moisture evaporation and adjusting the weight of the irrigation water prediction module) can effectively improve the operational efficiency of water conservancy systems and water resource utilization. Summarizing and organizing these knowledge rules into reusable empirical knowledge allows models to quickly apply this experience for self-optimization and adjustment when encountering similar operating conditions in the future, achieving continuous learning and performance improvement, and further enhancing the intelligent management level of water conservancy systems and their ability to cope with complex and changing operating conditions.

[0143] The above examples demonstrate the application process and effectiveness of dynamic self-optimization and adaptive learning methods for large-scale water conservancy system models in the actual operation and management of water conservancy systems. Through continuous monitoring, analysis, adjustment, and learning, the model can maintain high accuracy and reliability, providing strong support and guarantee for the efficient and stable operation of water conservancy systems. This achieves a transformation of water conservancy system models from "static and passive" to "dynamic and proactive." Compared with existing technologies, it offers greater accuracy, real-time performance, economy, and sustainability, providing a feasible technical path for the intelligent upgrading of water conservancy systems and possessing broad application potential in areas such as flood control and disaster reduction, agricultural irrigation, and clean energy production.

[0144] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic self-optimization and adaptive learning method for a large-scale hydraulic system model, characterized in that, Includes the following steps: Real-time data collection of the water conservancy system's operation is used to construct a large-scale water conservancy system model based on a deep learning framework and employing a hybrid model structure. Historical data is used to initialize and train the model. The real-time collected data is then input into the trained large-scale water conservancy system model, which performs calculations and inferences based on the input data, outputting predicted water levels, flow distribution schemes, and predicted failure probabilities of water conservancy facilities. Simultaneously, actual values ​​are obtained through on-site monitoring equipment, and various evaluation indicators are used to compare and evaluate the model's output with the actual values ​​to determine the model's accuracy and reliability. When the model evaluation indicators show that the error between the model's output and the actual value exceeds a preset threshold, a difference analysis process is initiated to analyze the root causes of the error from multiple dimensions, including physical processes, time trends, and spatial distribution, and automatically generate adjustment strategies. Adjust the model parameters or structure according to the strategy, and retrain the model with real-time data to ensure that the model adapts to the new working conditions. The experience gained during the optimization process is stored in a knowledge base, and historical experience is reused through data mining and case reasoning to improve the model's ability to cope with complex future working conditions; The large-scale water conservancy system model 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 to capture dynamic changes in the time series, and a fully connected neural network (FCN) to integrate and process various feature information, so as to achieve comprehensive simulation and prediction of the operating status of the water conservancy system. The model training uses mini-batch stochastic gradient descent algorithm to optimize model parameters, combined with early stopping method to prevent overfitting, and determines the initial weights and biases of the model so that the model reflects the operation law and characteristics of the water conservancy system.

2. The dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model as described in claim 1, characterized in that, The output includes predicted water level values, flow allocation schemes, and predicted failure probabilities of water conservancy facilities. 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 timestamps of the model prediction results are consistent with those of the actual monitoring data; match the predicted values ​​output by the model with the actual sensor values ​​at the corresponding geographical locations to avoid misjudgments due to data misalignment. Step S2: Error index calculation, using mean squared error (MSE), mean absolute error (MAE), and Nash efficiency coefficient (NSE) to quantify the deviation between the model's predicted values ​​and the actual values, and pre-determining 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 follows a normal distribution, calculate skewness and kurtosis, and identify systematic biases; Step S4: Trend consistency assessment. The cross-correlation function (CCF) is used to analyze the synchronicity of the predicted and actual values ​​over time. The long-term trend is fitted linearly or nonlinearly, and the slope and intercept of the predicted trend and the actual trend are compared to see if they are consistent. Step S5: Outlier detection and attribution. Use the Grubbs criterion or Dixon criterion to identify outliers and distinguish whether the source of the outlier is a system anomaly or a model defect. If it is a model defect, analyze the differences in input features and optimize the model logic.

3. The dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model as described in claim 1, characterized in that, The methods used in the difference analysis include: Feature analysis based on physical process understanding: decompose the energy of water flow into potential energy, kinetic energy and pressure energy, analyze the differences between these energy components, analyze the differences between actual operating data and model predictions, and check the coordinated operation of water conservancy facilities. Based on time series decomposition and trend analysis: Using time series decomposition methods, the model's performance is compared with that of reality in terms of seasonality and periodicity, long-term trends and abrupt changes are detected, and the model's response to these changes is analyzed. Based on spatial correlation and regional difference analysis: Combining geographic information, we 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, eliminate outliers, and use Monte Carlo simulation to analyze the impact of parameter uncertainty on model output.

4. The dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model as described in claim 1, characterized in that, The long-term trend and mutation detection process uses linear regression and mutation point detection algorithms to analyze long-term trend deviations and model responses to mutation events; the sensor calibration and anomaly handling process uses redundant data cross-validation and statistical methods to detect sensor errors and outliers, and corrects data inputs; 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.

5. The dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model as described in claim 1, characterized in that, The difference analysis specifically includes the following steps: Step 1) Using multimodal data fusion technology, data collected by different types of sensors in the water conservancy system are deeply fused to construct a high-dimensional feature space containing comprehensive operational 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 pattern of the data, and measure the model's ability to capture data features by the reconstruction error; Step 3) Using graph theory-based complex network analysis, the various components of the water conservancy system and their interconnections are abstracted into a complex network model, where nodes represent water conservancy facilities and edges represent water flow or information transmission relationships; by calculating the topological characteristic parameters of the network, the network structure changes of the water conservancy system under different operating states are analyzed, and the impact of key nodes and key paths on model errors is determined. Step 4) Use time series decomposition techniques, 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. Analyze the differences between the model predictions and actual values ​​of each component to determine the error performance of the model at different time scales. Step 5) Automatically generate adaptive adjustment strategies based on difference analysis.

6. The dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model as described in claim 1, characterized in that, The adaptive adjustment strategy for updating the large-scale model of the water conservancy system is as follows: For parameter tuning: a gradient-based optimization algorithm is used to update relevant parameters by backpropagation based on the error between the model output and the true value. The adjustment step size is adaptively determined according to the error magnitude and trend to ensure the stability and effectiveness of parameter updates. In order to avoid excessive parameter updates that lead to model performance degradation, upper and lower limits are set for parameter updates to restrict the parameters from changing within a reasonable range. For model structure adjustment: First, the structure is expanded based on the original model. Then, using transfer learning technology, the useful features and parameters learned in the original model are transferred to the new structure. The newly added parts are randomly initialized, and the entire model is trained by combining the current real-time data and historical data. The learning rate and number of training rounds are determined, and the parameters of the new structure are gradually optimized so that the model can adapt to the new operating conditions while maintaining the original knowledge.

7. The dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model as described in claim 1, characterized in that, Continuous updates include the following steps: Step a): After the model is updated, store all data and operation records from this update process in the knowledge base, including the model status before the update, the difference analysis results, the adaptive adjustment strategy, the updated model parameters, and the corresponding actual running data information. Step b): Regularly organize and analyze the knowledge base, using a combination of advanced data mining and machine learning techniques to deeply explore the complex patterns and rules contained within it; Step c): Using knowledge rules, the large-scale model of the water conservancy system was further optimized and improved, and a method based on case reasoning and model fusion was developed to realize the reuse of empirical knowledge.

8. The dynamic self-optimization and adaptive learning method for a large-scale water conservancy system model as described in claim 7, characterized in that, Step c) specifically includes the following steps: For newly emerging water conservancy system operating conditions, firstly, search for similar historical cases in the knowledge base, and quickly obtain the corresponding model adjustment schemes and parameter settings as initial references based on CBR technology; then, through model fusion technology, merge the initial model based on historical cases with the current real-time data-driven online model. 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 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.

9. A non-transitory 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-scale hydraulic system model as described in any one of claims 1 to 8 above.

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