Hydraulic monitoring big data analysis method based on deep learning

Abstract

The application discloses a hydraulic monitoring big data analysis method based on deep learning, which comprises the following steps: the following steps: step 1: sensor deployment and data acquisition; step 2: data preprocessing, preprocessing the original data, including noise filtering and data standardization; step 3: time series feature extraction, using the moving average method to extract time series features; step 4: deep learning feature learning, applying a deep learning model to learn the deep features of the monitoring data; step 5: dynamic statistical model construction, constructing a dynamic statistical model to predict the structure performance in real time and determine whether it is abnormal; step 6: image data acquisition and crack analysis, acquiring image data of the surface of the hydraulic structure and extracting parameters of the cracks; step 7: risk assessment and early warning, using a risk assessment formula to analyze the crack expansion trend, assess the accident risk and complete early warning; and step 8: cloud platform data integration and deep analysis, assisting a decision maker in formulating a maintenance strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of floor flatness detection and optimization, more specifically, to a water conservancy monitoring big data analysis method based on deep learning. BACKGROUND

[0002] The safety monitoring of water conservancy structures (such as dams, reservoirs, sluices, etc.) is a complex and crucial task. In the field of modern water conservancy structure monitoring and maintenance, traditional methods usually rely on periodic physical inspections and simple sensor monitoring. These methods, while providing basic information on the health of the structure to some extent, have the following significant shortcomings:

[0003] 1. Limitations in data analysis

[0004] Traditional methods are generally simple in data processing and analysis, lacking depth and comprehensiveness. This is mainly because they rely on basic statistical methods and manual analysis, which cannot effectively handle large-scale and complex data sets;

[0005] 2. Lack of real-time monitoring

[0006] Most traditional monitoring systems cannot achieve real-time data collection and processing. This results in the inability to timely detect and respond to minor changes in the structure at critical times, increasing the risk of safety.

[0007] 3. Limited predictive ability

[0008] Traditional methods have limited ability to predict the future state of water conservancy structures. They often cannot effectively use historical data to predict potential risks and failures, thus failing to provide effective preventive maintenance recommendations.

[0009] 4. High maintenance costs

[0010] Due to the lack of accurate risk assessment and prediction, traditional methods often lead to excessive or insufficient maintenance. This not only increases maintenance costs, but also can lead to potential problems that are not discovered in time.

[0011] 5. Lack of flexibility and scalability

[0012] Traditional monitoring systems are usually designed fixedly, making it difficult to adapt to different types and sizes of water conservancy structures. This limits their application range and effectiveness.

[0013] 6. Traditional methods may not be able to timely detect potential risks such as crack propagation, structural displacement, etc. In addition, environmental factors (such as temperature, humidity) also need real-time monitoring of the impact on water conservancy structures.

[0014] The root cause of these technical problems lies in the technical limitations and outdated concepts of traditional methods. With the development of technology, especially in the fields of data processing, machine learning, and the Internet of Things, traditional methods have been unable to meet the growing demand for monitoring and maintenance. These limitations not only affect the safety and reliability of hydraulic structures, but also lead to waste of resources and low efficiency.

[0015] Therefore, it is necessary to develop a new method that can fully utilize modern technology to provide a more comprehensive, accurate, and efficient solution for monitoring and maintaining hydraulic structures. This is the purpose of the present invention, which aims to integrate advanced data processing technology to solve the problems of traditional methods and improve the safety and maintenance efficiency of hydraulic structures. SUMMARY

[0016] 1. Technical problems to be solved

[0017] In view of the problems existing in the prior art, the purpose of the present invention is to provide a deep learning-based hydraulic monitoring big data analysis method, which integrates advanced data processing, deep learning, and machine learning technologies to provide a comprehensive, efficient, and flexible solution for monitoring, early warning, and maintenance of hydraulic structures.

[0018] 2. Technical solutions

[0019] To solve the above problems, the present invention adopts the following technical solutions:

[0020] The deep learning-based hydraulic monitoring big data analysis method includes the following steps:

[0021] Step 1: Sensor deployment and data collection

[0022] Deploy sensor nodes, which are composed of microprocessors, various sensors, and communication modules; the nodes collect environmental data and structural performance data according to monitoring requirements; select temperature, displacement, noise, and other sensors according to the characteristics of the monitoring target; according to the characteristics of the hydraulic structure and the monitoring requirements, scientifically arrange the sensors to ensure that the key parts and potential risk areas are covered; select a microprocessor with sufficient computing power and low power consumption for preliminary data processing and instruction execution; deploy LoRa, Wi-Fi, or cellular network modules to ensure reliable data transmission to the cloud platform or data center; at the same time, set the data collection time interval according to the monitoring requirements, such as collecting data every hour or every day; trigger data collection when a key event (such as abnormal vibration or temperature change) is detected. Data is stored in time series, each data point contains a timestamp and the corresponding sensor reading; the data is used to monitor the health of the hydraulic structure and predict potential risks and abnormalities.

[0023] Step 2: Data preprocessing

[0024] Preprocessing of raw data, including noise filtering and data standardization; data type is time series data, including temperature, displacement, noise, etc. From the sensor network deployed in step 1.

[0025] Step 3: Time series feature extraction

[0026] Using moving average method to extract time series features;

[0027] Step 4: Deep learning feature learning

[0028] Apply deep learning model to learn deep features of monitoring data;

[0029] Step 5: Dynamic statistical model construction

[0030] Build dynamic statistical model to predict structure performance in real time and determine whether it is abnormal;

[0031] Step 6: Image data acquisition and crack analysis

[0032] Acquire image data of water structure surface, apply image recognition and crack parameter analysis formula to extract crack parameters;

[0033] Step 7: Risk assessment and early warning

[0034] Use risk assessment formula to analyze crack propagation trend, assess accident risk and complete early warning;

[0035] Step 8: Cloud platform data integration and deep analysis

[0036] Upload monitoring, warning and prediction results to cloud platform at regular intervals, and cloud platform performs deep analysis on data to assist decision makers in formulating maintenance strategies.

[0037] The step 2 uses formula (1) for data preprocessing, and the specific formula is as follows:

[0038]

[0039] Where X processed is the preprocessed data; w i is the weight of the i-th sensor; is the preprocessing parameter, used to control the shape of the Sigmoid function; is the original data of the i-th sensor; b is the bias term, used for data standardization, first collect the original data from the sensor network in step 1 Then apply the logical Sigmoid function to each data point for smoothing, reduce the influence of extreme values, and finally according to the importance w iThe weighted sum is calculated to obtain the integrated data representation. The data preprocessing in Step 2 is a critical step in the entire monitoring system, as it directly affects the accuracy and reliability of subsequent analysis. Through the use of the logistic Sigmoid function and the weighted sum method, we can effectively process and prepare the data for in-depth data analysis and model construction. The uniqueness of this step lies in its emphasis on data quality and the comprehensive processing method for different sensor data.

[0040] In Step 3, feature extraction is performed using Formula (2), as follows:

[0041]

[0042] where F ts is the extracted time series feature vector; is the size of the jth time window; is the pre-processed data point; first, determine the moving average time window size according to the characteristics of the data and the monitoring target then calculate the average value of the data points in each time window to extract the main trend of the time series, and finally combine the moving average values of different time windows into the feature vector F ts . The size of the time window should be determined according to the characteristics of the data and the monitoring target. For example, for rapidly changing data, a smaller window should be selected; for slowly changing data, a larger window should be selected.

[0043] The feature vector F ts is obtained by combining the moving average values of different time windows, which helps to capture the behavior of the data at different time scales; Step 3 effectively extracts the key features of time series data through the moving average method, providing important input for subsequent analysis and model construction. The uniqueness of this step lies in its detailed processing of time series data and comprehensive consideration of features at different time scales; through this method, we can better understand and predict the behavior of hydraulic structures, thereby providing support for decision-making.

[0044] Step 4 uses Formula (3) for deep learning feature learning, as follows:

[0045]

[0046] where F dl is the deep learning feature representation, σ is the activation function, ReLU or Sigmoid; θ dl is the weight of the neural network; γ is a parameter used for normalization, first, the time series feature vector F ts ​As input, the feature vectors are then processed through a multi-layer neural network to learn deep features of the data, and finally generate a deep learning feature representation F dl for subsequent analysis and decision-making. The training process is as follows: first, the data is divided into training and test sets, then an optimization algorithm such as gradient descent is used to train the network, then a loss function such as mean square error or cross-entropy is defined to evaluate the model performance, and finally the network weights are adjusted through multiple iterations of training.

[0047] Step 4 effectively learns the deep features of the monitoring data through the deep learning model, providing a powerful tool for understanding the complex behavior of hydraulic structures and predicting potential risks; the uniqueness of this step lies in its deep learning ability of data features and its application to complex pattern recognition; through this method, we can more accurately analyze and predict the state of hydraulic structures, thereby providing support for maintenance and risk management.

[0048] The step 5 uses formula (4) to construct a dynamic statistical model, and the specific formula is as follows:

[0049]

[0050] Where y t is the predicted structure performance; θ stat : is the coefficient of the linear regression model; F dl is the current deep learning feature; is the previous deep learning feature; λ is the smoothing parameter, first use the deep learning feature F dl learned in step 4, then use a linear regression model to analyze the feature vector to predict the structure performance, combine the current and previous feature vectors, and use the exponential smoothing method to improve the stability of the prediction.

[0051] The parameters θ stat , λ and α need to be adjusted and optimized through the training data set, first divide the data into training and test sets for cross-validation, then use optimization algorithms such as gradient descent to adjust the model parameters, and finally use appropriate evaluation indicators (such as mean square error) to evaluate the prediction performance of the model.

[0052] Step 5 effectively constructs a dynamic statistical model by combining linear regression and exponential smoothing methods for real-time monitoring and prediction of the performance of hydraulic structures. The uniqueness of this model lies in its dynamic processing of time series data and effective use of deep learning features. Through this method, we can timely discover potential structural problems and take preventive measures to ensure the safety and stability of hydraulic structures.

[0053] The step 6 uses formula (5) to obtain image data and crack analysis, and the specific formula is as follows:

[0054]

[0055] where P crack is the parameter of crack, I crack is the image of crack; θ img is the parameter of CNN model; κ is the parameter of edge detection algorithm, first, collect the image data of hydraulic structure surface, then adjust the image size, perform the normalization preprocessing step, then use the CNN model to extract the features in the image; then apply the edge detection algorithm to identify the crack, finally extract the parameters of the crack from the processed image.

[0056] Select a suitable CNN architecture for image recognition, such as AlexNet, VGG or ResNet, first, enhance the training data set by rotating, scaling and other methods, then use optimization algorithms such as Adam or SGD to train the CNN, define a suitable loss function for image recognition tasks, such as cross-entropy, finally use accuracy, recall rate and other indicators to evaluate the performance of the model.

[0057] Step 6 effectively extracts crack features from image data by combining CNN and edge detection technology, providing a powerful tool for evaluating the integrity of hydraulic structures. The uniqueness of this step lies in its in-depth analysis of image data and accurate extraction of crack features. Through this method, we can timely discover and evaluate the potential risks of hydraulic structures, and take appropriate maintenance measures.

[0058] Step 7 uses formula (6) for risk assessment and early warning, the specific formula is as follows:

[0059]

[0060] where R is the risk score; θ risk is the coefficient of logistic regression model; μ is the weighted coefficient of crack parameter and structure performance, first, use the crack parameter P crack extracted in step 6 and the structure performance prediction y t in step 5, then use the logistic regression model to analyze the influence of crack parameters and structure performance on risk, finally calculate the risk score R according to the model output; if the risk score exceeds the preset threshold, an early warning will be issued.

[0061] The parameters θ risk and μ need to be adjusted and optimized through historical data, first, collect historical crack data and structure performance data, as well as corresponding accident records; then use historical data to train the logistic regression model, optimize the parameters θ risk , finally use indicators such as AUC, accuracy and other indicators to evaluate the prediction performance of the model.

[0062] Step 7 effectively assesses the risk level of the hydraulic structure by combining a logistic regression model and a weighted average method, and can issue timely warnings; the uniqueness of this step lies in the comprehensive analysis of crack parameters and structural performance, as well as the accurate calculation of risk assessment; through this method, we can identify high-risk areas in time and take preventive measures to ensure the safety and stability of the hydraulic structure.

[0063] In step 8, the data collected and processed in steps 1 to 7 are uploaded to the cloud platform, where data from different steps are integrated to form a unified data set, and machine learning and statistical methods are applied for in-depth analysis of the data, key insights are extracted from the analysis results to assist decision-making, and finally, based on the analysis results, maintenance and prevention strategies are proposed.

[0064] Use cloud databases or data lakes to store large amounts of data; use big data processing tools such as Apache Spark or Hadoop; select appropriate machine learning models such as random forests, neural networks, etc. according to needs; establish automated processes to regularly update analysis results and insights.

[0065] Step 8 provides a comprehensive perspective and in-depth insights for decision-makers by integrating and analyzing data from the entire monitoring system on the cloud platform; the uniqueness of this step lies in its efficient processing of large data and deep analysis of complex data sets; through this method, we can better understand the overall condition of the hydraulic structure, predict potential risks, and develop effective maintenance and prevention strategies.

[0066] In step 8, formula (7) is used for cloud platform data integration and deep analysis, and the specific formula is as follows:

[0067]

[0068] where D integrated is the integrated data representation, DeepLearningModel: is the deep learning model used to extract and integrate data features; represents the data integration operation; is the i-th data set application transformation function, parameter is the analysis function applied to the integrated data, parameter θ analytics .

[0069] Data integration operation is defined as:

[0070]

[0071] DeepLearningModel is a convolutional neural network (CNN) or recurrent neural network (RNN) that extracts features from the data;

[0072] DeepLearningModel(X)=CNN(X;θ cnn )or RNN(X;θ rnn )

[0073] Analytics is a machine learning model, such as a random forest or support vector machine (SVM), that further analyzes the integrated data:

[0074] Analytics(X;θ analytics )=RandomForest(X;θ rf )or SVM(X;θ svm ).

[0075] First, the results of steps 1-7 (time series data, image data, risk scores, etc.) are integrated into a unified dataset. Then, specific transformation functions (such as normalization, encoding, etc.) are applied to standardize and optimize the data format. Next, deep learning models (such as convolutional neural networks or recurrent neural networks) are used to extract key features from the data. Finally, advanced analytics techniques (such as clustering analysis, predictive modeling, etc.) are applied to extract insights and assist decision-making.

[0076] By effectively integrating data from different steps through advanced algorithms, the efficiency of data processing is improved. Valuable insights are extracted from complex data using deep learning and advanced analytics techniques. Data-based in-depth analysis is provided to help decision-makers develop more accurate maintenance strategies and preventive measures. The prediction accuracy of future risks and trends is improved through comprehensive analysis of multiple data. This invention combines multiple advanced data processing and analysis techniques to achieve efficient integration and in-depth analysis of data, providing strong support for the monitoring and maintenance of hydraulic structures.

[0077] Step 8 provides strong support for the maintenance and risk management of hydraulic structures by efficiently integrating and analyzing data from the entire monitoring system. The innovation of this step lies in its efficient processing of multiple data types and deep analysis of complex data sets. Through this method, a better understanding of the overall condition of hydraulic structures can be achieved, potential risks can be predicted, and effective maintenance and prevention strategies can be developed.

[0078] 3. Beneficial effects

[0079] Compared with the prior art, the advantages of the present invention are:

[0080] 1. Comprehensive data analysis: By integrating data from different sensors and monitoring steps (such as temperature, displacement, noise, images, etc.), the invention provides a comprehensive perspective to monitor the state of hydraulic structures; using deep learning models to extract key features from complex data sets, enhancing the accuracy and depth of data analysis.

[0081] 2. Real-time monitoring and early warning: By processing sensor data in real time, the invention can detect small changes in hydraulic structures in a timely manner, thereby providing early warning of potential risks; combining historical data and current monitoring data, using prediction models to assess future risks, providing scientific basis for decision-making.

[0082] 3. Efficient decision support: By deeply analyzing integrated data, the invention can provide valuable insights to help decision-makers develop more effective maintenance strategies and preventive measures; combining crack analysis and structural performance prediction, the invention provides a comprehensive risk assessment framework to help identify and mitigate potential safety problems.

[0083] 4. Scalability and flexibility: Each step is designed to be modular, allowing for adjustments or replacements based on specific needs, enhancing system flexibility and scalability; the invention can adapt to different types and sizes of hydraulic structures, with broad application prospects.

[0084] 5. Cost-effective: By timely monitoring and early warning, the invention helps reduce the cost of unexpected accidents and emergency repairs; through accurate risk assessment and data analysis, maintenance resources can be allocated more effectively, improving the efficiency of maintenance work.

[0085] In summary, the invention integrates advanced data processing, deep learning and machine learning technologies to provide a comprehensive, efficient and flexible solution for monitoring, early warning and maintenance of hydraulic structures. This not only improves the accuracy and timeliness of monitoring, but also provides strong data support for decision-makers, significantly improving the safety and reliability of hydraulic structures, while reducing maintenance costs. BRIEF DESCRIPTION OF DRAWINGS

[0086] Figure 1 Flowchart of the deep learning-based hydraulic monitoring big data analysis method of the invention. DETAILED DESCRIPTION

[0087] The technical solutions in the embodiments of the invention will be described below in conjunction with the drawings; it is obvious that the described embodiments are only part of the embodiments of the invention; not all embodiments; based on the embodiments in the invention; all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the invention.

[0088] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom end" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0089] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "provided with", "sleeved / connected", "connected" should be understood broadly, for example, "connected" is fixedly connected, or detachably connected, or integrally connected; is mechanically connected, or electrically connected; is directly connected, or indirectly connected through an intermediate medium, is the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application should be understood according to the specific circumstances.

[0090] Example 1

[0091] Please refer to Figure 1 The deep learning-based hydraulic monitoring big data analysis method comprises the following steps:

[0092] Step 1: Sensor deployment and data collection

[0093] Deploy sensor nodes, which are composed of microprocessors, various sensors and communication modules; the nodes collect environmental data and structural performance data according to monitoring requirements; select temperature, displacement, noise and other sensors according to the characteristics of the monitoring target; according to the characteristics of the hydraulic structure and the monitoring requirements, arrange the sensors scientifically to ensure that the key parts and potential risk areas are covered; select a microprocessor with sufficient computing power and low power consumption to perform preliminary data processing and instructions; deploy LoRa, Wi-Fi or cellular network modules to ensure that data can be reliably transmitted to the cloud platform or data center; at the same time, set the time interval of data collection according to the monitoring requirements, such as collecting once an hour or once a day; or trigger data collection when a key event such as abnormal vibration or temperature change is detected. Data is stored in the form of time series, each data point contains a timestamp and the corresponding sensor reading; the data is used to monitor the health status of the hydraulic structure and predict potential risks and abnormalities.

[0094] Step 2: Data preprocessing

[0095] The original data is preprocessed, including noise filtering and data standardization; the data type is time series data, including temperature, displacement, noise, etc., and the data comes from the sensor network deployed in step 1.

[0096] Step 3: Time series feature extraction

[0097] Using the moving average method, time series features are extracted;

[0098] Step 4: Deep learning feature learning

[0099] Apply deep learning model to learn deep features of monitoring data;

[0100] Step 5: Dynamic statistical model construction

[0101] Build a dynamic statistical model to predict the performance of the structure in real time and determine whether it is abnormal;

[0102] Step 6: Image data acquisition and crack analysis

[0103] Obtain image data of the surface of the hydraulic structure, apply image recognition and crack parameter analysis formula, and extract crack parameters;

[0104] Step 7: Risk assessment and early warning

[0105] Use risk assessment formula to analyze crack propagation trend, assess accident risk, and complete early warning;

[0106] Step 8: Cloud platform data integration and deep analysis

[0107] Upload monitoring, warning and prediction results to cloud platform at regular intervals, and cloud platform performs deep analysis on data to assist decision makers in developing maintenance strategies.

[0108] The step 2 uses formula (1) for data preprocessing, and the specific formula is as follows:

[0109]

[0110] Where X processed is the preprocessed data; w i is the weight of the i-th sensor; is a preprocessing parameter used to control the shape of the Sigmoid function; is the original data of the i-th sensor; b is the bias term used for data standardization, which first collects the original data from the sensor network in step 1 Then apply the logical Sigmoid function to each data point for smoothing processing to reduce the influence of extreme values, and finally according to the importance w iThe weighted sum is calculated to obtain the integrated data representation. The data preprocessing in Step 2 is a critical link in the entire monitoring system, which directly affects the accuracy and reliability of subsequent analysis. Through the method of logical Sigmoid function and weighted sum, we can effectively process and prepare data, laying a solid foundation for in-depth data analysis and model construction. The uniqueness of this step lies in its emphasis on data quality and the comprehensive processing method for different sensor data.

[0111] In Step 3, feature extraction is performed using Formula (2), as follows:

[0112]

[0113] where F ts is the extracted time series feature vector; is the size of the jth time window; is the pre-processed data point; first, according to the characteristics of the data and the monitoring target, determine the size of the moving average time window Then calculate the average value of the data points in each time window to extract the main trend of the time series, and finally combine the moving average values of different time windows into the feature vector F ts . The size of the time window should be determined according to the characteristics of the data and the monitoring target. For example, for rapidly changing data, a smaller window should be selected; for slowly changing data, a larger window should be selected.

[0114] The feature vector F ts is obtained by combining the moving average values of different time windows, which helps to capture the behavior of data at different time scales; Step 3 effectively extracts the key features of time series data through the moving average method, providing important input for subsequent analysis and model construction. The uniqueness of this step lies in its detailed processing of time series data and comprehensive consideration of different time scale features; through this method, we can better understand and predict the behavior of hydraulic structures, thereby providing support for decision-making.

[0115] Step 4 uses Formula (3) for deep learning feature learning, as follows:

[0116]

[0117] where F dl is the deep learning feature representation, σ is the activation function, ReLU or Sigmoid; θ dl is the weight of the neural network; γ is the parameter used for normalization, first, the time series feature vector F ts ​As input, the feature vectors are then processed through a multi-layer neural network to learn deep features of the data, and finally generate a deep learning feature representation F dl for subsequent analysis and decision-making. The training process is as follows: first, the data is divided into training and test sets, then an optimization algorithm such as gradient descent is used to train the network, then a loss function such as mean square error or cross-entropy is defined to evaluate the model performance, and finally the network weights are adjusted through multiple iterations of training.

[0118] Step 4 effectively learns the deep features of the monitoring data through the deep learning model, providing a powerful tool for understanding the complex behavior of hydraulic structures and predicting potential risks; the uniqueness of this step lies in its deep learning ability of data features and its application to complex pattern recognition; through this method, we can more accurately analyze and predict the state of hydraulic structures, thereby providing support for maintenance and risk management.

[0119] The step 5 uses formula (4) to construct a dynamic statistical model, and the specific formula is as follows:

[0120]

[0121] Where y t is the predicted structure performance; θ stat : is the coefficient of the linear regression model; F dl is the current deep learning feature; is the previous deep learning feature; λ is the smoothing parameter, first use the deep learning feature F dl learned in step 4, then use a linear regression model to analyze the feature vector to predict the structure performance, combine the current and previous feature vectors, and use the exponential smoothing method to improve the stability of the prediction.

[0122] The parameters θ stat , λ and α need to be adjusted and optimized through the training data set, first divide the data into training and test sets for cross-validation, then use optimization algorithms such as gradient descent to adjust the model parameters, and finally use appropriate evaluation indicators (such as mean square error) to evaluate the prediction performance of the model.

[0123] Step 5 effectively constructs a dynamic statistical model by combining linear regression and exponential smoothing methods for real-time monitoring and prediction of the performance of hydraulic structures. The uniqueness of this model lies in its dynamic processing of time series data and effective use of deep learning features. Through this method, we can timely discover potential structural problems and take preventive measures to ensure the safety and stability of hydraulic structures.

[0124] The step 6 uses formula (5) to obtain image data and crack analysis, and the specific formula is as follows:

[0125]

[0126] where P crack is the parameter of the crack, I crack is the crack image; θ img is the parameter of the CNN model; k is the parameter of the edge detection algorithm. First, collect the image data of the water structure surface, then adjust the image size, perform the normalization preprocessing step, then use the CNN model to extract the features in the image; then apply the edge detection algorithm to identify the crack, and finally extract the parameters of the crack from the processed image.

[0127] Select a suitable CNN architecture for image recognition, such as AlexNet, VGG, or ResNet. First, enhance the training data set by rotating, scaling, etc., then train the CNN using optimization algorithms such as Adam or SGD, define a suitable loss function for image recognition tasks, such as cross-entropy, and finally evaluate the model performance using accuracy, recall rate, etc.

[0128] ResNet solves the training problem of deep network through residual learning, including image size adjustment, normalization, etc., to adapt to the CNN input, then enhance the image data set through rotation, scaling, flipping, etc., to improve the generalization ability of the model; use a large number of image data with crack labels to train the CNN model. The algorithm formula is as follows:

[0129] F feature = ResNet(I processed ; θ resnet )

[0130] where F feature is the extracted feature vector; I processed is the preprocessed image data; θ resnet is the parameter of the ResNet model.

[0131] The core of the ResNet model is residual learning, and its basic unit is the residual block, which can be represented as:

[0132] F out = ReLU(F in + Conv(ReLU(Conv(F in ; θ conv1 )); θ conv2 )

[0133] where F in and F out are the input and output features of the residual block, respectively; Conv represents the convolution operation, θ conv1 and θ conv2are the parameters of the convolutional layer; ReLU is the activation function; ResNet model is stacked by multiple such residual blocks.

[0134] Edge detection and crack parameter extraction, using Canny edge detection algorithm to identify the crack edges in the image, and identify the crack area in the edge image; Finally, measure the length, width, etc. of the crack area, and calculate the area, realize the extraction of crack parameters such as length, width and area from the image after edge detection.

[0135] The algorithm formula is as follows:

[0136] P crack =CannyEdgeDetect(I feature ;k)→ExtractParams(E crack )

[0137] Where P crack is the crack parameter, I feature is the feature image extracted by CNN; κ is the parameter of Canny algorithm; E crack is the crack edge image after edge detection; ExtractParams is a function of extracting crack parameters from edge image. By combining advanced CNN architecture and edge detection algorithm, this scheme can effectively extract crack features from image and accurately quantify crack parameters. This process not only improves the accuracy of crack detection, but also provides important data support for subsequent risk assessment and maintenance decision. Through continuous optimization of CNN model and adjustment of edge detection parameters, the performance and reliability of the system can be further improved.

[0138] Step 6 effectively extracts crack features from image data by combining CNN and edge detection technology, providing a powerful tool for evaluating the integrity of hydraulic structures. The uniqueness of this step lies in its in-depth analysis of image data and accurate extraction of crack features. Through this method, we can discover and evaluate the potential risks of hydraulic structures in a timely manner, so as to take appropriate maintenance measures; can effectively extract crack features from image and accurately quantify crack parameters, which is crucial for the health monitoring and maintenance of hydraulic structures, improving the accuracy and efficiency of crack detection.

[0139] Step 7 uses formula (6) for risk assessment and early warning, the specific formula is as follows:

[0140]

[0141] Where R is the risk score; θ risk is the coefficient of the logistic regression model; μ is the weighted coefficient of crack parameters and structure performance, first, use the crack parameters P crackAnd the structural performance prediction y in step 5 t Then analyze the impact of crack parameters and structural performance on risk using a logistic regression model, and finally calculate the risk score R based on the model output. If the risk score exceeds the preset threshold, issue a warning.

[0142] Parameter θ risk And μ need to be adjusted and optimized through historical data. First, collect historical crack data and structural performance data, as well as corresponding accident records. Then train the logistic regression model using historical data to optimize parameters θ risk Finally, evaluate the prediction performance of the model using indicators such as AUC, accuracy, etc.

[0143] Step 7 effectively evaluates the risk level of hydraulic structures by combining the logistic regression model and the weighted average method, and can issue timely warnings. The uniqueness of this step lies in its comprehensive analysis of crack parameters and structural performance, as well as accurate calculation of risk assessment. Through this method, we can identify high-risk areas in a timely manner and take preventive measures to ensure the safety and stability of hydraulic structures.

[0144] In step 8, the data collected and processed in steps 1 to 7 are uploaded to the cloud platform, where data from different steps are integrated to form a unified data set. Machine learning and statistical methods are applied to conduct in-depth analysis of the data, extract key insights from the analysis results, assist decision-making, and finally propose maintenance and prevention strategies based on the analysis results.

[0145] Use cloud databases or data lakes to store large amounts of data; use big data processing tools such as Apache Spark or Hadoop; select appropriate machine learning models such as random forests, neural networks, etc. as needed; establish automated processes to regularly update analysis results and insights.

[0146] Step 8 integrates and analyzes data from the entire monitoring system on the cloud platform, providing decision-makers with a comprehensive perspective and in-depth insights. The uniqueness of this step lies in its efficient processing of large data and deep analysis of complex data sets. Through this method, we can better understand the overall condition of hydraulic structures, predict potential risks, and develop effective maintenance and prevention strategies.

[0147] In step 8, formula (7) is used for cloud platform data integration and in-depth analysis, with the specific formula as follows:

[0148]

[0149] Where D integratedDeepLearningModel is a deep learning model for extracting and integrating data features for the integrated data representation; represents a data integration operation; is the i-th data set the applied transformation function with parameters Analytics is an analysis function applied to the integrated data with parameters analytics .

[0150] the data integration operation is defined as:

[0151]

[0152] DeepLearningModel is a convolutional neural network (CNN) or recurrent neural network (RNN) for extracting data features;

[0153] DeepLearningModel(X) = CNN(X; θ cnn ) or RNN(X; θ rnn )

[0154] Analytics is a machine learning model, either a random forest or support vector machine (SVM), for further analyzing the integrated data:

[0155] Analytics(X; θ analytics ) = RandomForest(X; θ rf ) or SVM(X; θ svm ).

[0156] First, the results of steps 1 to 7 (time series data, image data, risk scores, etc.) are integrated into a unified data set, then specific transformation functions (such as normalization, encoding, etc.) are applied to standardize and optimize the data format, then a deep learning model (such as a convolutional neural network or recurrent neural network) is used to extract key features of the data, and finally advanced analysis techniques (such as cluster analysis, predictive modeling, etc.) are applied to extract insights and assist decision-making.

[0157] By effectively integrating data from different steps through advanced algorithms, the efficiency of data processing is improved; valuable insights are extracted from complex data using deep learning and advanced analysis techniques; data-based in-depth analysis is provided to help decision-makers make more accurate maintenance strategies and preventive measures; by analyzing multiple data, the prediction accuracy of future risks and trends is improved; it integrates various advanced data processing and analysis techniques to achieve efficient integration and in-depth analysis of data, providing strong support for the monitoring and maintenance of hydraulic structures.

[0158] Step 8 provides strong support for the maintenance and risk management of hydraulic structures by efficiently integrating and analyzing data from the entire monitoring system. The innovation of this step lies in its efficient processing capability for multiple data types and deep analysis capability for complex data sets. Through this method, the overall condition of the hydraulic structure can be better understood, potential risks can be predicted, and effective maintenance and prevention strategies can be developed.

[0159] The above is only the preferred specific embodiment of the present application; the protection scope of the present application is not limited to this; any skilled person in the art can replace or change the technical solution and improvement concept of the present application within the technical scope disclosed by the present application; all should be covered within the protection scope of the present application.

Claims

1. A deep learning-based big data analysis method for hydraulic engineering monitoring, characterized by: Includes the following steps: Step 1: Sensor Deployment and Data Acquisition Sensor nodes are deployed, which consist of microprocessors, various sensors, and communication modules; these nodes collect environmental data and structural performance data periodically according to monitoring requirements. Step 2: Data Preprocessing Preprocess the raw data, including noise filtering and data standardization; Step 3: Time Series Feature Extraction The moving average method is used to extract time series features; Step 4: Deep Learning Feature Learning Apply deep learning models to learn the deep features of monitoring data; Step 5: Construction of Dynamic Statistical Model Build dynamic statistical models to predict structural performance in real time and determine whether there are anomalies; Step 6: Image Data Acquisition and Crack Analysis Image data of the surface of hydraulic structures are acquired, and image recognition and crack parameter analysis formulas are applied to extract crack parameters. Step 7: Risk Assessment and Early Warning Using risk assessment formulas, we analyze crack propagation trends, assess accident risks, and provide early warnings. Step 8: Cloud Platform Data Integration and In-Depth Analysis The monitoring, early warning and prediction results are uploaded to the cloud platform on a regular basis. The cloud platform performs in-depth analysis of the data to assist decision-makers in formulating maintenance strategies. Step 2 uses formula (1) for data preprocessing, and the specific formula is as follows: （1） in, This is the preprocessed data; Let be the weight of the i-th sensor; These are preprocessing parameters used to control the shape of the Sigmoid function; This represents the raw data from the i-th sensor; The bias term, used for data standardization, first collects raw data from the sensor network in step 1. Then, a logical sigmoid function is applied to each data point for smoothing to reduce the impact of extreme values. Finally, the importance of each sensor is considered. Calculate the weighted sum to obtain a comprehensive data representation; In step 3, feature extraction is performed using formula (2), and the specific formula is as follows: （2） in, This is the extracted time series feature vector; Let j be the size of the j-th time window; The data points are preprocessed; firstly, based on the characteristics of the data and the monitoring objectives, the time window size for the moving average is determined. Then, the average value is calculated for the data points within each time window to extract the main trend of the time series. Finally, the moving average values ​​from different time windows are combined into a feature vector. ; Step 4 uses formula (3) for deep learning feature learning, and the specific formula is as follows: （3） in, For deep learning feature representation, The activation function is ReLU or Sigmoid; These are the weights of the neural network; To use the parameters for normalization, firstly, the time series feature vector extracted in step 3 is... As input, the feature vectors are then processed through a multi-layer neural network to learn the deep features of the data, and finally, a deep learning feature representation is generated. This is used for subsequent analysis and decision-making. Step 5 uses formula (4) to construct a dynamic statistical model, the specific formula of which is as follows: （4） in, For predicted structural performance; These are the coefficients of the linear regression model; These are current features of deep learning; These are features from previous deep learning; To smooth the parameters, the deep learning features learned in step 4 are first used. Then, the linear regression model is used to analyze the feature vectors and predict the structural performance. The current and previous feature vectors are combined, and the exponential smoothing method is used to improve the stability of the prediction. Step 6 uses formula (5) to obtain image data and perform crack analysis. The specific formula is as follows: （5） in, For the parameters of the crack, Image of the crack; Parameters of a CNN model; To determine the parameters for the edge detection algorithm, firstly, image data of the surface of the hydraulic structure is collected, then the image size is adjusted and normalization is performed as a preprocessing step, and then a CNN model is used to extract features from the image. Then, an edge detection algorithm is applied to identify cracks, and finally, the parameters of the cracks are extracted from the processed image. Step 7 uses formula (6) for risk assessment and early warning, the specific formula is as follows: （6） Where R represents the risk score; These are the coefficients of the logistic regression model; As weighting factors for crack parameters and structural performance, firstly, the crack parameters extracted in step 6 are used. and structural performance prediction in step 5 Then, a logistic regression model is used to analyze the impact of crack parameters and structural performance on risk, and finally, a risk score is calculated based on the model output. If the risk score exceeds a preset threshold, an early warning will be issued.

2. The deep learning-based big data analysis method for hydraulic engineering monitoring according to claim 1, characterized in that: In step 8, the data collected and processed in steps 1 to 7 are uploaded to the cloud platform. The data from different steps are integrated on the cloud platform to form a unified dataset. Machine learning and statistical methods are applied to conduct in-depth analysis of the data, extract key insights from the analysis results to assist in decision-making, and finally, maintenance and prevention strategies are proposed based on the analysis results.

3. The deep learning-based big data analysis method for hydraulic engineering monitoring according to claim 2, characterized in that: In step 8, cloud platform data integration and in-depth analysis are performed using formula (7), as follows: （7） in, DeepLearningModel: a deep learning model used to extract and integrate data features, representing the integrated data. Indicates data integration operation; Transform For the i-th dataset The applied conversion function has the following parameters: Analytics For analytical functions applied to integrated data, the parameters are: .

4. The deep learning-based big data analysis method for hydraulic engineering monitoring according to claim 3, characterized in that: Data integration operations Defined as: ； A deep learning model is a convolutional neural network (CNN) or a recurrent neural network (RNN) used to extract features from data. The `Analytics` function is a machine learning model, such as a random forest or support vector machine (SVM), used for further analysis of the integrated data. 。

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