A data processing method and system for hydraulic engineering

Abstract

The application discloses a kind of data processing method and system for hydraulic engineering, belong to data processing technical field, its method specifically includes: collecting the multi-source data of hydraulic engineering, and pre-processing and dynamic correction, for local water flow characteristics, establish nonlinear hydrodynamics model, the state distribution of water flow is calculated, obtain first water flow state distribution, water flow state is predicted using machine learning, obtain second water flow state distribution, combined with first water flow state distribution, obtain the final water flow state distribution, and analyze whether abnormality occurs;The application effectively improves the quality of data by combining various data preprocessing techniques, accurately predicts the state of the water flow, flood warning and other tasks, effectively analyze the water flow state information, help decision-makers obtain water flow change information in time, make quick decisions.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and specifically relates to a data processing method and system for water conservancy projects. BACKGROUND

[0002] In the design, construction and management process of water conservancy projects, a large amount of water flow data, meteorological data, geographical data and the like are involved, and these data usually have the characteristics of large scale, complexity and diversity. For example, water flow state, precipitation, temperature, flow rate and the like are crucial for the management of water resources, flood warning, irrigation scheduling and the like. With the rapid development of information technology and the Internet of Things, more and more sensors, remote sensing technologies and monitoring devices are applied to water conservancy projects, generating a large amount of real-time and historical data.

[0003] However, in the face of massive and complex data, traditional data processing methods often fail to cope with the following problems: 1. Data noise and incompleteness: data in water conservancy projects may be affected by environmental changes, equipment failures and the like, resulting in data noise or loss; 2. Data inconsistency: data from different sources, at different times and in different formats are inconsistent; 3. Efficiency and real-time performance of data processing; 4. Complex data relationships.

[0004] A water conservancy project information management method based on big data is disclosed in Chinese Patent No. CN117520752B, which comprises the following steps: based on the water level change corresponding to the initial water level data, the water level data line graph is regionally divided to confirm the high-frequency water level region and the low-frequency water level region, and then according to the wavelet decomposition layer score factor corresponding to the high-frequency water level region and the low-frequency water level region, the wavelet decomposition layer corresponding to each region in the high-frequency water level region and the low-frequency water level region is calculated, and finally based on the wavelet decomposition layer and the preset denoising algorithm, the initial water level data in each region of the high-frequency water level region and the low-frequency water level region is denoised to confirm the final water level data after denoising. By regionally dividing the water level data line graph and denoising the initial water level data in each region according to the wavelet decomposition layer of each region, the accuracy of denoising is improved compared with the traditional denoising method, thereby reducing the denoising cost.

[0005] The above-mentioned patent has the following defects: the complex relationships between data are not considered, and it is difficult to use data for prediction and the like. SUMMARY

[0006] In view of the deficiencies of the prior art, the application provides a data processing method and system for water conservancy projects, which aims to improve the accuracy, efficiency and real-time performance of data processing in water conservancy projects through multi-dimensional fusion, machine learning and prediction fusion and the like.

[0007] To achieve the above-mentioned purpose, the application provides the following technical solutions:

[0008] A data processing method for water conservancy projects, comprising the following specific steps:

[0009] Collecting multi-source data of water conservancy projects and performing preprocessing and dynamic correction;

[0010] For local water flow characteristics, a nonlinear hydrodynamic model is established to calculate the state distribution of water flow to obtain a first water flow state distribution;

[0011] The state of the water flow is predicted using machine learning to obtain a second water flow state distribution, and the final water flow state distribution is obtained by combining the first water flow state distribution, and whether an anomaly occurs is analyzed.

[0012] Specifically, the dynamic correction comprises:

[0013] A correction factor is calculated using a correction formula, and the specific formula is:

[0014] ,

[0015] Wherein, K represents the correction factor, represents the i-th preprocessed data, represents the weight factor corresponding to the i-th preprocessed data, represents the data amplification coefficient, represents the calibration coefficient, and q represents the data compression coefficient, represents an integer.

[0016] Specifically, the nonlinear hydrodynamic model is established for the local water flow characteristics, and the first water flow state distribution is calculated by calculating the state distribution of the water flow, comprising:

[0017] A nonlinear hydrodynamic model is established for the local water flow characteristics, and the specific formula is:

[0018] ;

[0019] Wherein, represents the fluid density, represents the fluid velocity vector, represents the fluid pressure, reflecting the internal mechanical state of the fluid, represents the dynamic viscosity of the fluid, represents the gradient, represents the partial derivative function, represents the turbulence correction term, represents the boundary effect correction term;

[0020] Based on the actual structure of the hydraulic engineering project, the kinematic boundary conditions are determined. At the solid boundary, a no-slip boundary condition is adopted, i.e., the flow velocity is zero. At the free water surface boundary, the effects of surface tension and atmospheric pressure are considered. Combining kinematic calculations with dynamic boundary conditions, the specific formulas for the kinematic boundary conditions are as follows:

[0021] ,

[0022] in, Indicates water surface elevation. This represents the velocity component in the x-direction within a two-dimensional plane. Represents the velocity component in the y-direction within a two-dimensional plane;

[0023] The finite volume method is used to numerically solve the nonlinear hydrodynamic model, and the state distribution of the water flow is obtained.

[0024] The calculated flow state distribution is compared and verified with the actual measurement data. The error index is calculated. Based on the comparison and verification results, the parameters of the nonlinear hydrodynamic model are adjusted and optimized, and the calculation is performed again to finally obtain the first flow state distribution.

[0025] Specifically, the process of using machine learning to predict water flow state to obtain a second water flow state distribution, combining it with the first water flow state distribution to obtain the final water flow state distribution, and analyzing whether any anomalies have occurred includes:

[0026] Select and train a machine learning model to predict future water flow conditions and obtain a second water flow state distribution.

[0027] By using a fusion strategy, the first hydraulic state distribution and the second flow state distribution are merged to obtain the final flow state distribution;

[0028] Based on the final water flow distribution, analyze whether any anomalies have occurred.

[0029] Specifically, the selection and training of the machine learning model to predict future water flow states and obtain a second water flow state distribution includes:

[0030] Collect historical water flow data to construct a time series dataset;

[0031] Based on the features extracted from the nonlinear hydrodynamic model, further flow features are extracted, including the speed of the flow, Reynolds number, and time series features.

[0032] Based on the complexity of the water flow status and the characteristics of the data in water conservancy projects, a machine learning model is selected;

[0033] Historical data is divided into training and testing sets according to a preset ratio. During training, early stopping is used to prevent overfitting. When the objective function no longer decreases within a continuous iteration cycle, training is stopped, and the parameters of the model with the best current performance are saved to obtain the trained machine learning model.

[0034] Real-time multi-source data from water conservancy projects are input into a trained machine learning model, which outputs the predicted water flow state, i.e., the second water flow state distribution.

[0035] Specifically, the method of using a fusion strategy to fuse the first hydraulic state distribution and the second flow state distribution to obtain the final flow state distribution includes:

[0036] For nonlinear hydrodynamic models, the reliability is calculated based on the accuracy of the input parameters and the boundary conditions of the nonlinear hydrodynamic model.

[0037] For machine learning models, the credibility is calculated based on the model's training performance and generalization ability.

[0038] Based on the credibility of the nonlinear hydrodynamic model and the machine learning model, fusion weights are set, and the final water flow state distribution is obtained by weighted fusion according to the fusion weights.

[0039] Specifically, the analysis of whether any anomalies have occurred based on the final water flow distribution includes:

[0040] Calculate the statistical characteristics of the final flow state distribution, including: mean and standard deviation;

[0041] A threshold range for the distribution of water flow state is set. When the statistical characteristics exceed the threshold range of the distribution of water flow state, it is judged as abnormal.

[0042] Record the time, location, and type of the anomaly, and formulate corresponding handling measures based on the cause of the anomaly.

[0043] A data processing system for water conservancy projects, used to implement the aforementioned data processing method for water conservancy projects, includes: a data processing module, a first state calculation module, and an analysis module;

[0044] The data processing module is used to collect multi-source data from water conservancy projects and perform preprocessing and dynamic correction.

[0045] The first state calculation module is used to establish a nonlinear hydrodynamic model for local water flow characteristics, calculate the state distribution of the water flow, and obtain the first water flow state distribution.

[0046] The analysis module is used to predict the water flow state using machine learning, obtain a second water flow state distribution, combine it with the first water flow state distribution to obtain the final water flow state distribution, and analyze whether any anomalies have occurred.

[0047] Specifically, the analysis module includes: a second state calculation unit and a fusion analysis unit;

[0048] The second state calculation unit is used to predict the second water flow state distribution using a machine learning model;

[0049] The fusion analysis unit is used to fuse the first and second water flow state distributions using a fusion strategy and to perform anomaly analysis.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] 1. This invention proposes a data processing method for water conservancy projects. By combining multiple data preprocessing techniques, it effectively improves the quality of data. For tasks such as accurate prediction of water flow status and flood early warning, the accuracy of data is crucial.

[0052] 2. This invention proposes a data processing method for water conservancy projects. Considering the complex relationships of water conservancy project data, it enables real-time data processing and immediate feedback, helping decision-makers to obtain information on water flow changes in a timely manner and make rapid decisions.

[0053] 3. This invention proposes a data processing method for water conservancy projects, which effectively analyzes the water flow state through physical models and machine learning models, thereby enhancing the decision support and adaptive capabilities of water conservancy projects. Attached Figure Description

[0054] Figure 1 A flowchart of a data processing method for water conservancy projects provided by the present invention;

[0055] Figure 2 This invention provides an architecture diagram of a data processing system for water conservancy projects. Detailed Implementation

[0056] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that, unless there is conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flowchart. In addition, the "

[0059] The terms "first," "second," and "third" do not limit the data or execution order; they are merely used to distinguish identical or similar items with essentially the same function and purpose.

[0060] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0061] Example 1

[0062] Please see Figure 1 The present invention provides an embodiment of a data processing method for water conservancy projects, comprising the following specific steps:

[0063] Step S1: Collect multi-source data from water conservancy projects and perform preprocessing and dynamic correction;

[0064] The multi-source data for the water conservancy project includes: sensor data, remote sensing data, meteorological data, and historical engineering data. Sensor data includes: water flow data, which uses flow sensors to measure water velocity, flow rate, and direction, often employing ultrasonic flow meters or electromagnetic flowmeters to maintain high accuracy under varying flow rates; pressure data, which uses pressure sensors to monitor water pressure and the water pressure within dams or pipelines, facilitating real-time monitoring of the physical load on the water body; temperature data, which uses temperature sensors to monitor changes in water temperature, as these changes affect water density and flow patterns; rainfall data, which uses rainfall sensors to monitor rainfall intensity and cumulative rainfall, directly impacting water replenishment and flood risk assessment; and structural stress data, which uses structural stress sensors to monitor structural stress and vibration, preventing safety issues caused by structural fatigue or abnormal vibration.

[0065] The preprocessing includes: filtering, using algorithms such as low-pass filters, median filters, or Kalman filters to reduce noise in the original data. For example, for water flow data, Kalman filtering can be used to smooth short-term fluctuations and remove random noise; signal smoothing: smoothing the data to reduce the impact of abrupt noise while retaining key dynamic change characteristics in the actual signal; data normalization: to eliminate differences in the measurement range of different sensors, the data from each sensor is normalized before data fusion to reduce the differences between different data magnitudes, facilitating subsequent multi-data fusion and model training; missing value handling: interpolation methods, when data is missing during the acquisition process, linear interpolation, spline interpolation, and other methods can be used to supplement the missing data to ensure data continuity; statistical completion, for missing data over a long period, the mean or median of historical data can be used for completion, while considering outlier detection mechanisms to prevent incorrect filling from interfering with subsequent analysis;

[0066] In the data acquisition and preprocessing stage, not only is it required that the sensors be arranged reasonably and the acquisition equipment be accurate, but also that the raw data be finely processed through a variety of preprocessing techniques (denoising, normalization, missing value completion and anomaly detection) to lay a solid foundation for subsequent data fusion, model building and intelligent prediction. Only by ensuring data quality can we achieve accurate and efficient monitoring and early warning in water conservancy projects.

[0067] The dynamic correction in step S1 includes:

[0068] The correction factor is calculated using the correction formula, which is as follows:

[0069] ,

[0070] Where K represents the correction factor. This represents the i-th preprocessed data. This represents the weight factor corresponding to the i-th preprocessed data. Indicates the data magnification factor. The calibration coefficient is represented by 'q', and the data compression coefficient is represented by 'q'. This represents an integer, to prevent division by zero.

[0071] Step S2: Based on the local flow characteristics, establish a nonlinear hydrodynamic model, calculate the flow state distribution, and obtain the first flow state distribution;

[0072] The specific steps of step S2 are as follows:

[0073] Step S201: Establish a nonlinear hydrodynamic model for the local flow characteristics. The specific formula is as follows:

[0074] ;

[0075] in, Fluid density is a quantity that typically depends on water temperature and salinity. This represents the fluid velocity vector, describing the velocity of the fluid in each direction. It represents fluid pressure and reflects the fluid's inherent mechanical state. The dynamic viscosity of a fluid reflects its internal friction characteristics. Represents the gradient. This indicates finding the partial derivative function. This represents the turbulence correction term, used to describe the energy dissipation and momentum transfer deviations caused by local turbulence. This represents the boundary effect correction term, which takes into account the additional resistance and reaction forces generated when water comes into contact with solid boundaries (such as dams and riverbeds), so that the flow state within the boundary layer can more accurately reflect the actual situation.

[0076] In this embodiment, the nonlinear convection term This describes the nonlinear acceleration effect generated by the fluid's own motion. This term is particularly crucial in high-velocity and complex flow fields. (Diffusion term) This represents the momentum diffusion effect caused by molecular viscosity, which smooths the velocity gradient in the flow and helps to describe the energy dissipation process.

[0077] Turbulence correction terms can compensate for energy and momentum disturbances caused by turbulence to some extent, making the model more sensitive to local turbulence effects. In actual hydraulic engineering, the interaction between water flow and solid boundaries is very complex. By introducing... The boundary effect correction term can better simulate the flow resistance and backflow phenomenon within the boundary layer, thereby improving the accuracy of the entire model;

[0078] The advantages of the formula are: by introducing turbulence correction and boundary effect correction terms, the model can better describe complex flow phenomena and is closer to actual working conditions. Especially in cases with high Reynolds numbers and obvious boundary layers, it can provide more accurate flow field predictions. The corrected model is not only applicable to uniform flow under ideal conditions, but can also cope with common non-uniform, nonlinear and turbulent complexities in hydraulic engineering, thus improving the applicability and robustness of the model in various practical scenarios.

[0079] Step S202: Based on the actual structure of the hydraulic engineering project, determine the precise kinematic boundary conditions. At solid boundaries (such as dam bodies, pipe walls, etc.), a no-slip boundary condition is adopted, i.e., the flow velocity is zero. At free water surface boundaries, the effects of surface tension and atmospheric pressure are considered. Combined with kinematic calculations, the dynamic boundary conditions are determined. The specific formulas for the kinematic boundary conditions are as follows:

[0080] ,

[0081] in, Indicates water surface elevation. This represents the velocity component in the x-direction within a two-dimensional plane. Represents the velocity component in the y-direction within a two-dimensional plane;

[0082] Step S203: Numerically solve the nonlinear hydrodynamic model using the finite volume method to obtain the state distribution of the water flow;

[0083] Specifically, the computational domain is divided into a series of non-overlapping control volumes. The conservation equations within the control volumes are integrated to transform the partial differential equations into algebraic equations. The surface integral is then transformed into a line integral using the Gaussian divergence theorem, and further discretized to obtain a system of linear equations.

[0084] Step S204: Compare and verify the calculated water flow state distribution with the actual measurement data, calculate the error index, adjust and optimize the parameters of the nonlinear hydrodynamic model based on the comparison and verification results, recalculate, and finally obtain the first water flow state distribution.

[0085] Step S3: Use machine learning to predict the water flow state to obtain the second water flow state distribution. Combine the second water flow state distribution with the first water flow state distribution to obtain the final water flow state distribution, and analyze whether any anomalies have occurred.

[0086] The specific steps of step S3 are as follows:

[0087] Step S301: Select and train a machine learning model to predict future water flow states and obtain the second water flow state distribution;

[0088] The specific steps of step S301 are as follows:

[0089] Step S3011: Collect historical water flow status data, integrate the flow velocity, pressure, water level and other data collected by sensors at different times and under different operating conditions, and construct a time series dataset that covers rich information;

[0090] In this embodiment, these historical data reflect the water flow characteristics of water conservancy projects under various conditions such as normal operation, seasonal changes, and different flow regulation, and are cleaned and preprocessed to ensure the quality and integrity of the data;

[0091] The quality of data directly determines the accuracy of predictions. Ensuring that the data is clear and complete helps in building an effective model. By collecting multi-dimensional data, more information can be provided to the model, increasing the accuracy of predictions.

[0092] Step S3012: Based on the turbulent kinetic energy, vorticity and other features extracted from the physical model in step S2, the feature space is further expanded to calculate the speed of the water flow, Reynolds number, time series features and other features;

[0093] In this embodiment, the speed of the water flow is crucial for determining whether the flow is in a critical state; the Reynolds number is used to measure whether the fluid flow characteristics are laminar or turbulent; differencing the time series data to obtain the rate of change of variables such as flow velocity and pressure helps to capture the dynamic trend of the water flow state. For example, first-order difference can reflect the instantaneous increase or decrease of flow velocity; dimensionality reduction techniques such as principal component analysis (PCA) are used to screen out features that contribute highly to the prediction of the water flow state, reducing data redundancy and avoiding overfitting, thereby improving the training efficiency of the machine learning model.

[0094] Feature engineering can remove irrelevant features and noise, thereby reducing interference in model training. By performing different transformations on the data, the model can be made more adaptable to the distribution characteristics of the data, thus improving its generalization ability.

[0095] Step S3013: Based on the complexity and data characteristics of the water flow state in water conservancy projects, select appropriate machine learning models. For example, Long Short-Term Memory (LSTM) networks are very effective in capturing the continuous change patterns of water flow state over time because they are good at handling long-term dependencies in time series data; Random Forest models have strong anti-overfitting capabilities, can handle high-dimensional feature data, and have advantages in multi-condition water flow state classification and prediction; Gradient Boosting Decision Tree (GBDT) builds a strong classifier by iteratively training multiple weak classifiers, and has a good fitting ability for nonlinear changes in water flow state.

[0096] Step S3014: Divide the prepared historical data into training set and test set according to a preset ratio. During the training process, use early stopping to prevent overfitting. When the objective function no longer decreases within a continuous iteration cycle, stop training, save the model parameters with the best current performance, and obtain the trained machine learning model.

[0097] In this embodiment, performance evaluation involves using a test set to evaluate the trained model and calculating various performance metrics, including root mean square error and mean absolute error.

[0098] Model tuning: Based on the evaluation results, the model is tuned. If underfitting is found (e.g., low R2, high MAE and RMSE), the model complexity can be increased, such as increasing the number of hidden layers or neurons in LSTM. If overfitting is found, regularization techniques are used, such as adding an L2 regularization term to LSTM to constrain the weight matrix and prevent the model from overlearning noise in the training data.

[0099] Step S3015: Input real-time multi-source data of water conservancy projects into the trained machine learning model and output the predicted water flow state, i.e., the second water flow state distribution.

[0100] Step S302: Using a fusion strategy, the first hydraulic state distribution and the second flow state distribution are fused to obtain the final flow state distribution;

[0101] The specific steps of step S302 are as follows:

[0102] Step S3021: For nonlinear hydrodynamic models, evaluate their reliability by considering factors such as the accuracy of model input parameters, the rationality of boundary condition settings, and the theoretical accuracy of the model itself.

[0103] For example, by analyzing the impact of sensor measurement errors on key parameters (such as fluid viscosity and density) in the hydrodynamic model, the input uncertainty of the physical model is quantified using error propagation theory. At the same time, the fitting degree between the simulation results of the physical model under different historical working conditions and the actual monitoring data is compared, and quantitative evaluation is carried out using indicators such as root mean square error (RMSE) and coefficient of determination (R²), and a confidence function based on these factors is established.

[0104] Step S3022: For the machine learning model (which yields the second water flow state distribution), the credibility is measured based on the model's training performance, generalization ability, and adaptability to new data.

[0105] On the one hand, techniques such as cross-validation are used during training to monitor overfitting or underfitting of the model to ensure that the model has good generalization performance; on the other hand, as new water flow state data are continuously collected, the deviation between the model's predicted values ​​and the actual values ​​is evaluated in real time, and a confidence function for the machine learning model is constructed using similar statistical indicators.

[0106] Step S3023: Based on the credibility of the nonlinear hydrodynamic model and the machine learning model, set the fusion weights, and obtain the final water flow state distribution by weighted fusion according to the fusion weights.

[0107] Based on the aforementioned confidence function, the confidence weight of each model at the current moment is calculated, and the sum of the weights is 1. This fusion method ensures that under any working condition, the results of the model with higher confidence are given priority, while taking into account the supplementary information of the other model, thereby improving the reliability of the final water flow state distribution.

[0108] Step S303: Analyze whether any abnormalities have occurred based on the final water flow distribution.

[0109] The specific steps of step S303 are as follows:

[0110] Step S3031: Calculate the statistical characteristics of the final flow state distribution, such as mean, standard deviation, etc.

[0111] Step S3032: Set the threshold range for the distribution of water flow state. This is usually determined based on statistical analysis of historical normal data. When the statistical characteristics exceed the threshold range of the distribution of water flow state, it is judged as abnormal.

[0112] In this embodiment, anomaly detection based on machine learning classification models is performed by training classification models, such as support vector machines (SVM) and decision trees, using historical water flow state data labeled as abnormal and normal. The current final water flow state distribution is taken as input, and the model outputs a classification judgment of whether it belongs to abnormal or normal. When training SVM, an optimal hyperplane is found to separate the two types of data and maximize the distance between them, so that the model can accurately distinguish between abnormal and normal states when faced with new data. For decision trees, decision branches are constructed according to the thresholds of different features to gradually determine the category of water flow state.

[0113] Deep learning anomaly detection: Using deep learning models such as autoencoders, unsupervised training is first performed on normal water flow state data to learn the latent feature representation of normal data. Then, in the prediction stage, the current water flow state data is input into the autoencoder to calculate the reconstruction error. If the reconstruction error exceeds a preset threshold, it indicates that the current data deviates significantly from the normal pattern and is judged as an anomaly. The autoencoder can automatically capture the complex internal structure of the data and has a good detection capability for some complex anomalies that are not easy to identify through simple statistical features.

[0114] Step S3033: Once an anomaly is detected, immediately record the time, location (based on sensor coordinate information), and type of anomaly (such as a sudden increase in flow rate or abnormal pressure fluctuations), and formulate corresponding handling measures based on the cause of the anomaly.

[0115] In-depth analysis of the causes of anomalies: This involves considering the current operational status of the water conservancy project (e.g., whether flood discharge or equipment maintenance is underway), meteorological conditions (heavy rain, strong winds, etc.), and surrounding environmental factors (e.g., riverbed siltation, landslides, etc.) to comprehensively determine the root cause of the anomaly. For example, if an abnormally low flow velocity is detected near a sluice gate, and there is a report of significant siltation upstream, it may be due to siltation obstructing the water flow channel.

[0116] Develop corresponding handling measures based on the cause of the anomaly: if the anomaly is caused by equipment failure, promptly notify maintenance personnel for emergency repairs; if it is caused by natural factors, such as flooding, activate the emergency plan, adjust the operating parameters of water conservancy facilities (such as increasing the flood discharge, closing some sluice gates, etc.) to ensure the safe and stable operation of water conservancy projects, and at the same time report the anomaly information to the management center in a timely manner for further analysis and decision-making.

[0117] By comprehensively utilizing machine learning and physical models, the final water flow distribution can be accurately obtained, and abnormal situations can be detected and handled in a timely and effective manner, providing a solid guarantee for the intelligent management and safe operation of water conservancy projects.

[0118] Example 2

[0119] Please see Figure 2 Another embodiment of the present invention provides: a data processing system for water conservancy projects, comprising: a data processing module, a first state calculation module, and an analysis module;

[0120] The data processing module is used to collect multi-source data from water conservancy projects and perform preprocessing and dynamic correction.

[0121] The first state calculation module is used to establish a nonlinear hydrodynamic model for local water flow characteristics, calculate the state distribution of the water flow, and obtain the first water flow state distribution.

[0122] The analysis module is used to predict the water flow state using machine learning, obtain a second water flow state distribution, combine it with the first water flow state distribution to obtain the final water flow state distribution, and analyze whether any anomalies have occurred.

[0123] The analysis module includes: a second state calculation unit and a fusion analysis unit;

[0124] The second state calculation unit is used to predict the second water flow state distribution using a machine learning model;

[0125] The fusion analysis unit is used to fuse the first and second water flow state distributions using a fusion strategy and to perform anomaly analysis.

[0126] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0127] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data processing method for water conservancy projects, characterized in that, The specific steps include the following: Collect multi-source data from water conservancy projects and perform preprocessing and dynamic correction; To address the local flow characteristics, a nonlinear hydrodynamic model is established to calculate the flow state distribution, yielding the first flow state distribution. Machine learning is used to predict the water flow state to obtain a second water flow state distribution. Combined with the first water flow state distribution, the final water flow state distribution is obtained, and it is analyzed whether any anomalies occur. The process involves using machine learning to predict water flow states, obtaining a second water flow state distribution, combining this with the first water flow state distribution to obtain the final water flow state distribution, and analyzing whether any anomalies have occurred, including: Select and train a machine learning model to predict future water flow conditions and obtain a second water flow state distribution. By using a fusion strategy, the first hydraulic state distribution and the second flow state distribution are merged to obtain the final flow state distribution; Based on the final water flow distribution, analyze whether any anomalies have occurred; The selection and training of a machine learning model to predict future water flow states and obtain a second water flow state distribution includes: Collect historical water flow data to construct a time series dataset; Based on the features extracted from the nonlinear hydrodynamic model, further flow features are extracted, including the speed of the flow, Reynolds number, and time series features. Based on the complexity of the water flow status and the characteristics of the data in water conservancy projects, a machine learning model is selected; Historical data is divided into training and testing sets according to a preset ratio. During training, early stopping is used to prevent overfitting. When the objective function no longer decreases within a continuous iteration cycle, training is stopped, and the parameters of the model with the best current performance are saved to obtain the trained machine learning model. Real-time multi-source data from water conservancy projects are input into a trained machine learning model, which outputs the predicted water flow state, i.e., the second water flow state distribution. The method of using a fusion strategy to fuse the first hydraulic state distribution and the second flow state distribution to obtain the final flow state distribution includes: For nonlinear hydrodynamic models, the reliability is calculated based on the accuracy of the input parameters and the boundary conditions of the nonlinear hydrodynamic model. For machine learning models, the credibility is calculated based on the model's training performance and generalization ability. Based on the credibility of the nonlinear hydrodynamic model and the machine learning model, fusion weights are set, and the final water flow state distribution is obtained by weighted fusion according to the fusion weights.

2. The data processing method for water conservancy projects as described in claim 1, characterized in that, The dynamic correction includes: The correction factor is calculated using the correction formula, which is as follows: ， Where K represents the correction factor. This represents the i-th preprocessed data. This represents the weight factor corresponding to the i-th preprocessed data. Indicates the data magnification factor. The calibration coefficient is represented by 'q', and the data compression coefficient is represented by 'q'. Represents an integer.

3. The data processing method for water conservancy projects as described in claim 2, characterized in that, The aforementioned nonlinear hydrodynamic model is established based on local flow characteristics to calculate the flow state distribution, resulting in a first flow state distribution, including: A nonlinear hydrodynamic model is established to consider the local flow characteristics. The specific formula is as follows: ； in, Indicates fluid density, Represents the fluid velocity vector. It represents fluid pressure and reflects the fluid's inherent mechanical state. Indicates the dynamic viscosity of a fluid. Represents the gradient. This indicates finding the partial derivative function. This represents the turbulence correction term. This represents the boundary effect correction term; Based on the actual structure of the hydraulic engineering project, the kinematic boundary conditions are determined. At the solid boundary, a no-slip boundary condition is adopted, i.e., the flow velocity is zero. At the free water surface boundary, the effects of surface tension and atmospheric pressure are considered. Combining kinematic calculations with dynamic boundary conditions, the specific formulas for the kinematic boundary conditions are as follows: ， in, Indicates water surface elevation. This represents the velocity component in the x-direction within a two-dimensional plane. Represents the velocity component in the y-direction within a two-dimensional plane; The finite volume method was used to numerically solve the nonlinear hydrodynamic model, and the state distribution of the water flow was obtained. The calculated flow state distribution is compared and verified with the actual measurement data. The error index is calculated. Based on the comparison and verification results, the parameters of the nonlinear hydrodynamic model are adjusted and optimized, and the calculation is performed again to finally obtain the first flow state distribution.

4. The data processing method for water conservancy projects as described in claim 1, characterized in that, The analysis of whether any anomalies have occurred based on the final water flow distribution includes: Calculate the statistical characteristics of the final flow state distribution, including: mean and standard deviation; A threshold range for the distribution of water flow state is set. When the statistical characteristics exceed the threshold range of the distribution of water flow state, it is judged as abnormal. Record the time, location, and type of the anomaly, and formulate corresponding handling measures based on the cause of the anomaly.

5. A data processing system for water conservancy projects, used to implement the data processing method for water conservancy projects according to any one of claims 1-4, characterized in that, include: Data processing module, first state calculation module, and analysis module; The data processing module is used to collect multi-source data from water conservancy projects and perform preprocessing and dynamic correction. The first state calculation module is used to establish a nonlinear hydrodynamic model for local water flow characteristics, calculate the state distribution of the water flow, and obtain the first water flow state distribution. The analysis module is used to predict the water flow state using machine learning, obtain a second water flow state distribution, combine it with the first water flow state distribution to obtain the final water flow state distribution, and analyze whether any anomalies have occurred.

6. A data processing system for water conservancy projects as described in claim 5, characterized in that, The analysis module includes: a second state calculation unit and a fusion analysis unit; The second state calculation unit is used to predict the second water flow state distribution using a machine learning model; The fusion analysis unit is used to fuse the first and second water flow state distributions using a fusion strategy and to perform anomaly analysis.

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