An intelligent seepage monitoring system for water conservancy projects

Abstract

The present invention relates to the technical field of water conservancy project management, and discloses an intelligent monitoring system for water conservancy project seepage, comprising a multi-sensor perception module, a data acquisition module, a data fusion and processing module, a machine learning prediction module, an intelligent decision-making module, an adaptive monitoring and scheduling module, an automatic inspection and response module, a digital twin and simulation analysis module, and a communication module; the multi-sensor perception module is responsible for collecting multi-dimensional data related to seepage in real time. A machine learning algorithm is used to intelligently predict seepage trends, and risk assessment is performed in combination with real-time data. Compared with traditional monitoring methods based on preset thresholds, the present invention can automatically identify complex seepage patterns by learning historical data and real-time data, predict future risks in advance, and achieve forward-looking management of water conservancy project seepage risks, significantly improving the system's prediction accuracy and response speed.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy project management, and in particular to an intelligent seepage monitoring system for water conservancy projects. Background Art

[0002] Seepage monitoring in hydraulic projects is a crucial means of ensuring the safety of dam structures. Traditional seepage monitoring systems typically rely on fixed threshold warnings and sensor-based monitoring. However, existing technologies remain inadequate for addressing the complex and dynamic seepage conditions of hydraulic projects, failing to meet the demands of modern hydraulic projects for real-time monitoring, intelligent prediction, and automated response. The following specific issues are illustrated by comparing existing technologies.

[0003] Traditional seepage monitoring methods are mostly based on preset thresholds. Sensors monitor parameters such as soil moisture, groundwater level, and water pressure in real time. When certain parameters exceed the threshold, the system issues an early warning. In addition, systems based on fixed thresholds have difficulty foreseeing potential risks before the threshold is exceeded, resulting in the inability to provide early warnings. The monitoring system in the comparative document "An Intelligent Seepage Monitoring System and Monitoring Method for Water Conservancy Projects" mainly relies on traditional threshold monitoring methods, lacks the learning of historical data and time series-based trend analysis, and is unable to predict future seepage risks through intelligent means.

[0004] Inspection work in traditional seepage monitoring systems usually relies on manual labor or equipment, especially the need to arrange manual inspections after detecting early warning signals. Other inspection methods are inefficient, especially in sudden seepage events, where manual inspections cannot quickly cover all high-risk areas. The inspection system in the comparative document, an intelligent monitoring system and monitoring method for seepage in a water conservancy project, relies on manual operation and lacks the automated inspection and emergency response capabilities of drones and ground robots. In emergency situations, the lag of manual inspections limits the system's emergency response capabilities and the efficiency of handling seepage events.

[0005] Existing water conservancy project monitoring systems cannot provide intuitive three-dimensional visualization monitoring methods, especially for complex seepage behaviors. Traditional monitoring data and two-dimensional graphical displays make it difficult for managers to accurately judge the diffusion path of seepage and future potential risk areas. The technology in the comparative document, an intelligent monitoring system and monitoring method for seepage in a water conservancy project, does not use digital twin or virtual reality technology to perform three-dimensional simulation analysis of seepage behavior, which limits the comprehensiveness and accuracy of managers' decision-making and cannot provide intuitive decision support methods.

[0006] Therefore, those skilled in the art provide an intelligent seepage monitoring system for water conservancy projects to solve the problems raised in the above background technology. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the present invention provides an intelligent monitoring system for water conservancy project seepage, which solves the problems of low prediction accuracy, low inspection efficiency, delayed emergency response and lack of dynamic simulation and three-dimensional visualization support in the existing water conservancy project seepage monitoring system.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent monitoring system for seepage in water conservancy projects, including a multi-sensor perception module, a data acquisition module, a data fusion and processing module, a machine learning prediction module, an intelligent decision-making module, an adaptive monitoring and scheduling module, an automatic inspection and response module, a digital twin and simulation analysis module, and a communication module;

[0009] The multi-sensor perception module is responsible for collecting multi-dimensional data related to seepage in real time;

[0010] The data acquisition module is responsible for collecting real-time monitoring data into the system;

[0011] The data fusion and processing module fuses, filters and preprocesses multi-source data collected by different sensors;

[0012] The machine learning prediction module uses a machine learning algorithm to predict the seepage trend based on historical monitoring data and real-time collected data;

[0013] The intelligent decision-making module combines machine learning prediction results and real-time monitoring data to calculate the current seepage risk level;

[0014] The adaptive monitoring and scheduling module dynamically adjusts the system's monitoring frequency, sensor acquisition cycle, and inspection scheduling to generate risk assessment results;

[0015] The automatic inspection and response module is responsible for automatic inspection and emergency response;

[0016] The digital twin and simulation analysis module constructs a digital twin model of the dam and its surrounding environment through real-time monitoring data, and performs simulation analysis of seepage and soil erosion;

[0017] The communication module is responsible for the smooth data transmission between each sensor node, automatic inspection equipment and the control center.

[0018] Preferably, the machine learning prediction module includes a training unit, a model determination unit, and a real-time prediction unit;

[0019] The training unit trains a machine learning model based on historical monitoring data and real-time data;

[0020] After the training is completed, the model determination unit determines the optimal model for real-time prediction;

[0021] The real-time prediction unit uses the trained model to predict the trends of seepage, soil erosion, and groundwater level changes based on the current real-time monitoring data.

[0022] Preferably, the intelligent decision-making module includes a risk assessment unit, an early warning unit, and an emergency response unit;

[0023] The risk assessment unit calculates the current seepage risk based on real-time data and prediction results of the machine learning model;

[0024] The early warning unit automatically generates an early warning signal based on the results of the risk assessment unit;

[0025] The emergency response unit automatically starts the emergency response procedure when it is in a high-risk or early warning state.

[0026] Preferably, the adaptive monitoring and scheduling module includes a monitoring frequency control unit and a resource scheduling unit;

[0027] The monitoring frequency control unit dynamically adjusts the monitoring frequency of the sensor based on the risk assessment result;

[0028] The resource scheduling unit manages resource allocation in the system.

[0029] Preferably, the automatic inspection and response module includes a drone inspection unit, a ground robot inspection unit, a path planning unit, and an image recognition unit;

[0030] The drone inspection unit is responsible for managing drone inspection tasks;

[0031] The ground robot inspection unit is responsible for ground robot inspection tasks and monitors the dam body;

[0032] The path planning unit uses a path optimization algorithm to intelligently plan the inspection routes of the drone and ground robot;

[0033] The image recognition unit uses a convolutional neural network to analyze images collected by drones and ground robots to automatically detect structural abnormalities of the dam body.

[0034] Preferably, the digital twin and simulation analysis module includes a digital twin modeling unit, a seepage simulation unit, and a virtual reality integration unit;

[0035] The digital twin modeling unit constructs a digital twin model of the dam body and surrounding environment based on real-time monitoring data, and dynamically simulates the seepage and soil erosion of the dam body;

[0036] The seepage simulation unit uses simulation technology to analyze and predict the dynamic process of seepage diffusion path, soil erosion and groundwater infiltration;

[0037] The virtual reality integrated unit combines the digital twin model with virtual reality technology, allowing managers to view the real-time seepage status of the dam through VR equipment.

[0038] Preferably, in the training unit, the random forest training algorithm optimizes the performance of the model by constructing multiple decision trees and combining the prediction results of each tree during the training process, wherein the random forest training algorithm is as follows:

[0039] Decision tree training: Assume that the dataset is (x, y), where x is the input feature vector and y is the output target value. Each decision tree is based on minimizing the following loss function L:

[0040]

[0041] Among them, T i (x) represents the prediction result of the i-th tree for input x, y i is the actual target value;

[0042] Random Forest Ensemble Prediction: The final prediction of the random forest is the average or voting result of all the decision tree predictions:

[0043] Where N is the number of decision trees, is the combined predicted value, T i (x) is the output of each tree;

[0044] In the model determination unit, the optimal model is determined for real-time prediction by evaluating the performance of multiple training models, wherein the model evaluation indicators include mean square error and mean absolute error;

[0045] Mean square error formula:

[0046] Among them, y i is the true value, is the predicted value of the model, and n is the number of samples;

[0047] Mean absolute error formula:

[0048] Among them, y i is the true value, is the model prediction value, and n is the number of samples.

[0049] Preferably, the risk assessment unit combines real-time data and forecast data to calculate the current comprehensive risk index R of the system. t , and judge the level of seepage risk based on this index;

[0050] The risk assessment formula is: R t=α1Sxs+α2Qxs+α3Txs,

[0051] Among them, Sxs is the water safety factor, Qxs is the erosion safety factor, Txs is the weather condition coefficient, and α1, α2, and α3 are weight coefficients;

[0052] Calculation formula for water safety factor Sxs:

[0053] Among them, Sy is the monitored water pressure value, S threshold is the preset water pressure safety threshold, β1 is the adjustment parameter;

[0054] Calculation formula for erosion safety factor Qxs:

[0055] Where, Ts is the soil moisture content, T threshold is the preset threshold of soil moisture content, and β2 is the adjustment parameter;

[0056] Weather condition coefficient Txs calculation formula: Txs = α r R t +α g G q ,

[0057] Among them, R t is the current rainfall, G q is the current light intensity, α r and α g To adjust the parameters;

[0058] The early warning unit calculates the comprehensive risk index R according to the risk assessment unit. t , determine whether to trigger an early warning signal and determine the level of the early warning;

[0059] Early warning judgment formula:

[0060] Among them, R t is the currently calculated comprehensive risk index, R threshold The preset risk threshold, exceeding which triggers an early warning signal, is marked as 1, otherwise it is 0;

[0061] Warning level formula:

[0062] Among them, R low 、R medium 、R high It is the risk index cutoff value of the warning level.

[0063] Preferably, the digital twin modeling unit constructs a digital model corresponding to the physical world by combining real-time monitoring data, historical data and the geometric structure of the water conservancy project;

[0064] The digital twin model uses a three-dimensional finite element model to describe the stress, strain, and permeation behavior in the physical system by integrating geometric information, physical state, and real-time monitoring data;

[0065] The governing equation of the seepage problem: Based on Darcy's law in the seepage process, the seepage velocity v s The relationship with the hydraulic gradient i is: s =-k·i,

[0066] Among them, v s is the seepage velocity, k is the permeability coefficient, and i is the hydraulic gradient;

[0067] Discretization of the finite element model: The geometric model of the digital twin is discretized using the finite element method. For any spatial domain V, the weak form of the finite element method is used to express the stress-strain relationship. According to the physical mechanics equation, the weak form of seepage is expressed as:

[0068]

[0069] Where h is the water head height, is the head gradient, φ is the shape function, and q is the water source term;

[0070] Boundary conditions: The digital twin modeling unit must meet boundary conditions, including:

[0071] h(x)=h0, at the boundary Γ D , q(x)=q0, at the boundary Γ N

[0072] Among them, Γ D and Γ N are the fixed boundaries for head and water flux.

[0073] Preferably, the virtual reality integration unit integrates the digital twin model and simulation results into the virtual reality platform to provide a three-dimensional visualization of the dam body seepage situation and simulation effects of future risks;

[0074] 3D scene generation formula: P VR =M·P world ,

[0075] Among them, P VR is the display coordinate in virtual reality, P world is the three-dimensional coordinate of the point in the world coordinate system, and M is the projection matrix of the scene;

[0076] Real-time data update formula: PVR (t) = P VR (t-1)+ΔP(t),

[0077] Among them, P VR (t) is the display coordinate at the current moment, P VR (t-1) is the display coordinate at the previous moment, and ΔP(t) is the position or attribute change value brought about by real-time monitoring data;

[0078] VR user interaction formula:

[0079]

[0080] Among them, d interact is the distance between the user and the interaction object, (x u ,y u , z u ) is the user's virtual location coordinate, (x o ,y o , z o ) are the coordinates of the interactive objects in the virtual reality scene;

[0081] If the distance d interact When the value is less than the preset interaction threshold, the system allows the user to interact with the virtual object.

[0082] The present invention provides an intelligent seepage monitoring system for water conservancy projects. It has the following beneficial effects:

[0083] 1. The present invention uses a machine learning algorithm to intelligently predict seepage trends and combines real-time data for risk assessment. Compared with traditional monitoring methods based on preset thresholds, the present invention can automatically identify complex seepage patterns by learning historical data and real-time data, predict future risks in advance, and achieve forward-looking management of seepage risks in water conservancy projects, significantly improving the system's prediction accuracy and response speed.

[0084] 2. The present invention uses drones and ground robots to work together to achieve automated inspections and emergency responses. Its unmanned inspection mode improves monitoring efficiency and significantly reduces human intervention, ensuring the comprehensiveness and timeliness of inspections. Especially in sudden seepage events, drones and robots can respond quickly and conduct key inspections of high-risk areas through automatic path planning.

[0085] 3. The present invention combines digital twin technology with real-time seepage simulation to construct a three-dimensional digital twin model, dynamically simulate the seepage behavior of the dam body, and combine virtual reality technology to provide a visualized three-dimensional scene. Managers can view the seepage situation of the dam body in real time in a virtual environment, predict future risk diffusion paths, and provide intuitive support for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a system framework diagram of the present invention;

[0087] Figure 2 This is a schematic diagram of the machine learning prediction module of the present invention;

[0088] Figure 3 Schematic diagram of the intelligent decision-making module of the present invention;

[0089] Figure 4 Schematic diagram of the adaptive monitoring and scheduling module of the present invention;

[0090] Figure 5 This is a schematic diagram of the automatic inspection and response module of the present invention;

[0091] Figure 6 Schematic diagram of the digital twin and simulation analysis module of the present invention. DETAILED DESCRIPTION

[0092] To help those skilled in the art understand the present invention, the following will provide a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only partial embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0093] The present invention is described in detail below with reference to the accompanying drawings:

[0094] Example:

[0095] Please see the attached Figure 1 -Attached Figure 6 , an embodiment of the present invention provides an intelligent monitoring system for seepage in a water conservancy project, including a multi-sensor perception module, a data acquisition module, a data fusion and processing module, a machine learning prediction module, an intelligent decision-making module, an adaptive monitoring and scheduling module, an automatic inspection and response module, a digital twin and simulation analysis module, and a communication module;

[0096] The multi-sensor perception module is responsible for collecting multi-dimensional data related to seepage in real time;

[0097] The data acquisition module is responsible for collecting real-time monitoring data into the system;

[0098] The data fusion and processing module fuses, filters and preprocesses the multi-source data collected by different sensors;

[0099] The machine learning prediction module uses machine learning algorithms to predict seepage trends based on historical monitoring data and real-time collected data;

[0100] The intelligent decision-making module combines machine learning prediction results and real-time monitoring data to calculate the current seepage risk level;

[0101] The adaptive monitoring and scheduling module dynamically adjusts the system's monitoring frequency, sensor acquisition cycle, and inspection scheduling to generate risk assessment results;

[0102] The automatic inspection and response module is responsible for automated inspection and emergency response;

[0103] The digital twin and simulation analysis module uses real-time monitoring data to build a digital twin model of the dam and its surrounding environment, and conducts simulation analysis of seepage and soil erosion.

[0104] The communication module is responsible for the smooth data transmission between each sensor node, automatic inspection equipment and the control center.

[0105] The benefits of the multi-sensor perception module ensure comprehensive and real-time data collection, improve the accuracy of system monitoring, and ensure no blind spot coverage;

[0106] The benefits of the data acquisition module are that it ensures that sensor data can be quickly and efficiently collected into the system, ensuring the continuity and timeliness of monitoring data, helping to promptly detect seepage risks and achieve rapid response to the status of water conservancy projects;

[0107] The benefits of the data fusion and processing module are that it can eliminate data noise, improve data accuracy and consistency, and at the same time, the fusion of multi-dimensional data can provide more accurate monitoring information and help to comprehensively analyze the seepage conditions;

[0108] The benefits of the machine learning prediction module include identifying potential risks in advance, avoiding the limitations of relying on fixed threshold monitoring methods, improving the system's prediction accuracy, and helping managers make forward-looking seepage management decisions;

[0109] The benefits of the intelligent decision-making module are that it can dynamically calculate the current seepage risk level and automatically generate response measures, reducing the delay of human decision-making and improving the speed and accuracy of emergency response;

[0110] The benefits of the adaptive monitoring and scheduling module are to optimize resource utilization, save energy in low-risk situations, and ensure that key areas are monitored in high-risk situations;

[0111] The benefits of the automatic inspection and response module are to improve inspection efficiency, especially in high-risk and hard-to-reach areas. Automated inspection reduces reliance on manual labor and can quickly respond to emergencies, ensuring comprehensive and timely inspections.

[0112] The benefits of the digital twin and simulation analysis module help managers more intuitively understand seepage dynamics and future diffusion trends, achieve three-dimensional visual analysis of seepage behavior, and improve the intuitiveness and accuracy of decision-making;

[0113] The benefits of the communication module help managers get the latest monitoring information at any time.

[0114] The training unit trains the machine learning model based on historical monitoring data and real-time data;

[0115] After the training is completed, the model determination unit determines the optimal model for real-time prediction;

[0116] The real-time prediction unit uses the trained model to predict the trends of seepage, soil erosion, and groundwater level changes based on the current real-time monitoring data;

[0117] The risk assessment unit calculates the current seepage risk based on real-time data and prediction results of the machine learning model;

[0118] The early warning unit automatically generates early warning signals based on the results of the risk assessment unit;

[0119] The emergency response unit automatically initiates the emergency response procedure when in a high-risk or warning state;

[0120] The monitoring frequency control unit dynamically adjusts the monitoring frequency of the sensor based on the risk assessment results;

[0121] Resource allocation in the resource scheduling unit management system;

[0122] The drone inspection unit is responsible for managing drone inspection tasks;

[0123] The ground robot inspection unit is responsible for ground robot inspection tasks and monitors the dam body;

[0124] The path planning unit uses a path optimization algorithm to intelligently plan the inspection routes of drones and ground robots;

[0125] The image recognition unit uses convolutional neural networks to analyze images collected by drones and ground robots to automatically detect structural anomalies in the dam body;

[0126] The digital twin modeling unit constructs a digital twin model of the dam and its surrounding environment based on real-time monitoring data, dynamically simulating seepage and soil erosion conditions within the dam.

[0127] The seepage simulation unit uses simulation technology to analyze and predict the dynamic processes of seepage diffusion paths, soil erosion and groundwater infiltration;

[0128] The virtual reality integration unit combines the digital twin model with virtual reality technology, allowing managers to view the real-time seepage status of the dam through VR equipment.

[0129] Benefits of the training unit: Learning the laws under different seepage patterns and environmental conditions provides a solid foundation for subsequent accurate predictions and improves the accuracy of seepage risk prediction;

[0130] The benefit of the model determination unit is that the best prediction model can be used in actual monitoring to achieve high-precision prediction of seepage, soil erosion and groundwater level change trends;

[0131] The benefits of the real-time prediction unit include the ability to identify trends in seepage, soil erosion, and groundwater changes in advance, ensuring the system can respond quickly and prevent potential risks from developing into actual accidents.

[0132] Benefits of the risk assessment unit: It can calculate the seepage risk level in real time, provide accurate risk assessment, and prevent risks in advance;

[0133] The benefits of the early warning unit are that it can promptly notify relevant personnel when there is a seepage risk or abnormal situation, reducing the lag of manual intervention and ensuring that potential safety issues are handled in a timely manner;

[0134] Benefits of the emergency response unit: It can automatically initiate emergency response procedures, quickly implement preventive or remedial measures, ensure safety, reduce the possibility of accidents, and improve the efficiency of emergency response;

[0135] The benefits of monitoring the frequency control unit are to save energy when the risk is low and to enhance monitoring efforts when the risk is high, thus ensuring the energy efficiency of the system and the flexibility of monitoring;

[0136] Benefits of the Resource Scheduling Unit: Ensure that monitoring resources are deployed preferentially in high-risk areas, optimize the utilization efficiency of monitoring resources, and ensure that key areas receive timely and accurate inspections and monitoring;

[0137] Benefits of UAV inspection units: They reduce the time and cost of manual inspections and, at the same time, provide comprehensive information about the dam from a high-altitude perspective, particularly effective in hazardous areas or hard-to-reach places.

[0138] The benefits of the ground robot inspection unit ensure the comprehensiveness and accuracy of the inspection process, significantly reducing the workload of manual inspections;

[0139] The benefits of the path planning unit ensure that high-risk areas are monitored and the efficiency of inspection tasks is optimized;

[0140] Benefits of the image recognition unit: Improve recognition accuracy, reduce subjective errors and time costs of manual analysis, and ensure real-time early warning of structural problems;

[0141] The benefits of the digital twin modeling unit are to provide managers with a realistic virtual mapping model to help them better understand and manage the operation of water conservancy projects;

[0142] The benefits of the seepage simulation unit enable the system to simulate future seepage trends in advance, helping managers assess potential risks and take appropriate countermeasures;

[0143] Benefits of Virtual Reality Integration Unit Enhance the visualization capability of monitoring system and improve the decision support effect of managers.

[0144] In the training unit, the random forest training algorithm optimizes the model's performance by constructing multiple decision trees and combining the prediction results of each tree during the training process. The random forest training algorithm is as follows:

[0145] Decision tree training: Assume that the dataset is (x, y), where x is the input feature vector and y is the output target value. Each decision tree is based on minimizing the following loss function L:

[0146]

[0147] Among them, T i (x) represents the prediction result of the i-th tree for input x, y i is the actual target value;

[0148] Random Forest Ensemble Prediction: The final prediction of the random forest is the average or voting result of all the decision tree predictions:

[0149] Where N is the number of decision trees, is the combined predicted value, T i (x) is the output of each tree;

[0150] In the model determination unit, the performance of multiple training models is evaluated to determine the optimal model for real-time prediction. The model evaluation indicators include mean square error and mean absolute error.

[0151] Mean square error formula:

[0152] Among them, y i is the true value, is the predicted value of the model, and n is the number of samples;

[0153] Mean absolute error formula:

[0154] Among them, y i is the true value, is the model prediction value, and n is the number of samples.

[0155] The role of decision tree training describes the training process of a single decision tree. By minimizing the loss function, the decision tree can learn the mapping relationship between input features and output targets. Specifically, the loss function Used to measure the decision tree T i The purpose of minimizing the loss function for the fitting effect of the data set D is to make the model's prediction result T i (x) is close to the true target value y i , and improve the prediction performance of a single tree;

[0156] The role of random forest overall prediction means that random forest makes the final prediction through a collection of multiple decision trees, and integrates the prediction results of multiple trees. It can reduce the bias caused by a single tree and improve the stability and accuracy of the model. The final prediction of the random forest is obtained by averaging or voting multiple trees. This mechanism enhances the model's ability to resist overfitting and improves the overall prediction performance.

[0157] The mean square error formula measures the deviation between the model's predicted value and the true value. By calculating the square of the error between the predicted value and the true value and taking the average, it reflects the accuracy of the model's prediction. The square error in the formula makes the model more sensitive to large deviations and is more helpful in optimizing the model's performance. A small mean square error indicates that the model's prediction results are close to the true value, which is an important criterion for evaluating regression models.

[0158] The function of the mean absolute error formula is to measure the average absolute deviation between the model prediction value and the true value. Unlike the mean square error, the mean absolute error does not have a square term in its error processing. Therefore, it can reflect the robustness of the model to the overall error.

[0159] The risk assessment unit combines real-time data and forecast data to calculate the system's current comprehensive risk index R t , and judge the level of seepage risk based on this index;

[0160] The risk assessment formula is: R t =α1Sxs+α2Qxs+α3Txs,

[0161] Among them, Sxs is the water safety factor, Qxs is the erosion safety factor, Txs is the weather condition coefficient, and α1, α2, and α3 are weight coefficients;

[0162] Calculation formula for water safety factor Sxs:

[0163] Among them, Sy is the monitored water pressure value, S threshold is the preset water pressure safety threshold, β1 is the adjustment parameter;

[0164] Calculation formula for erosion safety factor Qxs:

[0165] Where, Ts is the soil moisture content, T threshold is the preset threshold of soil moisture content, and β2 is the adjustment parameter;

[0166] Weather condition coefficient Txs calculation formula: Txs = α r R t +α g G q ,

[0167] Among them, R t is the current rainfall, G q is the current light intensity, α r and α g To adjust the parameters;

[0168] The early warning unit calculates the comprehensive risk index R based on the risk assessment unit. t , determine whether to trigger an early warning signal and determine the level of the early warning;

[0169] Early warning judgment formula:

[0170] Among them, R t is the currently calculated comprehensive risk index, R threshold The preset risk threshold, exceeding which triggers an early warning signal, is marked as 1, otherwise it is 0;

[0171] Warning level formula:

[0172] Among them, R low 、R medium 、R high It is the risk index cutoff value of the warning level.

[0173] The comprehensive risk index formula assesses the seepage risk of the current water conservancy project by integrating the water safety factor, erosion safety factor, and weather condition factor. Each coefficient reflects different seepage influencing factors based on different environmental parameters, and the weight coefficient adjusts the degree of influence of each factor on the overall risk. It can effectively integrate various influencing factors to generate a unified risk assessment result, which is used to determine the seepage risk level of the current system.

[0174] The water safety factor calculation formula calculates the impact of the current water body on the stability of the dam body by using the monitored water pressure value and the preset water pressure threshold. The function form used in this formula makes the water safety factor increase significantly when the water pressure value approaches or exceeds the threshold, which can sensitively reflect the degree of threat posed by the water body to the project.

[0175] The erosion safety factor calculation formula is used to assess the erosion risk of soil moisture to the dam structure. By comparing the monitored moisture content with the set safety threshold, the formula can quickly increase the coefficient value when the soil moisture exceeds the threshold, reflecting the degree of soil erosion on the dam body.

[0176] The weather condition coefficient calculation formula combines current rainfall and sunlight intensity to assess the impact of weather factors on seepage. Rainfall reflects the direct impact on water levels and seepage, while sunlight intensity affects soil evaporation and water loss. This formula comprehensively considers the impact of weather factors on risk and adjusts the weight in risk assessment by adjusting parameters.

[0177] The function of the early warning judgment formula is to judge whether the early warning signal is triggered by comparing the currently calculated comprehensive risk index with the preset risk threshold. t If the threshold is exceeded, an early warning signal is triggered and marked as 1, otherwise it is marked as 0. This formula ensures that the system can automatically issue an early warning signal when it detects that the risk threshold is exceeded, helping managers to respond to potential risks in a timely manner;

[0178] The function of the warning level formula is to determine the severity level of the warning according to the size of the comprehensive risk index. t According to the preset level thresholds, warnings are divided into low, medium and high levels. Different levels of warnings correspond to different response measures to ensure that the system can take appropriate preventive or emergency response actions according to the severity of the risk.

[0179] The digital twin modeling unit combines real-time monitoring data, historical data, and the geometric structure of the water conservancy project to build a digital model corresponding to the physical world;

[0180] The digital twin model uses a three-dimensional finite element model to describe the stress, strain, and permeation behavior in the physical system by integrating geometric information, physical state, and real-time monitoring data;

[0181] The governing equation of the seepage problem: Based on Darcy's law in the seepage process, the seepage velocity v s The relationship with the hydraulic gradient i is: s =-k·i,

[0182] Among them, v s is the seepage velocity, k is the permeability coefficient, and i is the hydraulic gradient;

[0183] Discretization of the finite element model: The geometric model of the digital twin is discretized using the finite element method. For any spatial domain V, the weak form of the finite element method is used to express the stress-strain relationship. According to the physical mechanics equation, the weak form of seepage is expressed as:

[0184]

[0185] Where h is the water head height, is the head gradient, φ is the shape function, and q is the water source term;

[0186] Boundary conditions: The digital twin modeling unit must meet boundary conditions, including:

[0187] h(x)=h0, at the boundary Γ D , q(x)=q0, at the boundary Γ N

[0188] Among them, Γ D and Γ N are the fixed boundaries for head and water flux.

[0189] The governing equations for seepage problems are based on Darcy's law, which describes the linear relationship between seepage velocity and hydraulic gradient. The permeability coefficient is a characteristic parameter of the medium material, indicating the permeability of soil or other media to water. It is used to calculate the seepage velocity through the dam or soil under the influence of hydraulic gradient, ensuring that the model can accurately reflect the physical behavior of the seepage process.

[0190] The discretization of the finite element model expresses the weak form of the seepage problem based on the finite element method. By discretizing the spatial domain, the finite element method converts the continuous seepage equation into a discrete algebraic equation, which is convenient for numerical solution in the digital twin model and ensures that the numerical simulation of seepage can accurately describe the seepage conditions within the project.

[0191] The fixed head boundary condition is used to define a fixed head situation, that is, the head is constant in a specific boundary area. This condition is used to simulate fixed head boundaries in actual projects, such as water level control at the bottom of a dam, to ensure that the model can accurately reflect the actual boundary conditions.

[0192] The Fixed Flow boundary condition describes a boundary condition where the flow is fixed, meaning that the flow is constant at a specific boundary. This boundary condition is used to simulate situations where the flow is fixed, such as flow into or out of a region, ensuring that the model accounts for specific flow conditions at the flow boundary.

[0193] The virtual reality integration unit integrates the digital twin model and simulation results into the virtual reality platform, providing 3D visualization of the dam body seepage conditions and simulation effects of future risks;

[0194] Three-dimensional scene generation formula: P VR =M·P world ,

[0195] Among them, P VR is the display coordinate in virtual reality, P worldis the three-dimensional coordinate of the point in the world coordinate system, and M is the projection matrix of the scene;

[0196] Real-time data update formula: P VR (t) = P VR (t-1)+ΔP(t),

[0197] Among them, P VR (t) is the display coordinate at the current moment, P VR (t-1) is the display coordinate at the previous moment, and ΔP(t) is the position or attribute change value brought about by real-time monitoring data;

[0198] VR user interaction formula:

[0199]

[0200] Among them, d interact is the distance between the user and the interaction object, (x u ,y u , z u ) is the user's virtual location coordinate, (x o ,y o , z o ) are the coordinates of the interactive objects in the virtual reality scene;

[0201] If the distance d interact When the value is less than the preset interaction threshold, the system allows the user to interact with the virtual object.

[0202] The 3D scene generation formula converts the 3D coordinates of the physical world into display coordinates in virtual reality. The projection matrix is ​​responsible for mapping points in 3D space onto the 2D screen of the virtual reality environment. This process ensures that the seepage conditions of the dam body in the physical world can be accurately displayed in 3D in the virtual reality system, providing a basis for user visual interaction.

[0203] The real-time data update formula dynamically updates the display coordinates in the virtual reality scene. By using the position or attribute change values ​​brought by real-time monitoring data, the system can update the virtual reality scene according to the current monitoring data changes, so that the virtual environment can reflect the dynamic changes of the physical system, ensuring that the dam seepage simulation in virtual reality can keep pace with the actual environment. Managers can observe the latest seepage conditions in real time through VR equipment;

[0204] The VR user interaction formula calculates the distance between the user and the interactive object in the virtual reality environment. By calculating the three-dimensional distance between the user's virtual position and the interactive object's position, if the distance is less than the set interaction threshold, the system allows the user to interact with the virtual object. This formula ensures that the user can interact with the virtual objects in the scene in a natural way in the virtual reality environment, enhancing the user's immersion and operating experience.

[0205] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring system for water conservancy project seepage, characterized in that: It includes multi-sensor perception module, data acquisition module, data fusion and processing module, machine learning prediction module, intelligent decision-making module, adaptive monitoring and scheduling module, automatic inspection and response module, digital twin and simulation analysis module, and communication module; The multi-sensor perception module is responsible for collecting multi-dimensional data related to seepage in real time; The data acquisition module is responsible for collecting real-time monitoring data into the system; The data fusion and processing module fuses, filters and preprocesses multi-source data collected by different sensors; The machine learning prediction module uses a machine learning algorithm to predict the seepage trend based on historical monitoring data and real-time collected data; The intelligent decision-making module combines machine learning prediction results and real-time monitoring data to calculate the current seepage risk level; The intelligent decision-making module includes a risk assessment unit, an early warning unit, and an emergency response unit; The risk assessment unit calculates the current seepage risk based on real-time data and prediction results of the machine learning model; The early warning unit automatically generates an early warning signal based on the results of the risk assessment unit; The emergency response unit automatically initiates the emergency response procedure when in a high-risk or warning state; The risk assessment unit combines real-time data and forecast data to calculate the current comprehensive risk index R of the system. t , and judge the level of seepage risk based on this index; The risk assessment formula is: R t =α1Sxs+α2Qxs+α3Txs, Among them, Sxs is the water safety factor, Qxs is the erosion safety factor, Txs is the weather condition coefficient, and α1, α2, and α3 are weight coefficients; Calculation formula for water safety factor Sxs: Among them, Sy is the monitored water pressure value, S threshold is the preset water pressure safety threshold, β1 is the adjustment parameter; Calculation formula for erosion safety factor Qxs: Where, Ts is the soil moisture content, T threshold is the preset threshold of soil moisture content, and β2 is the adjustment parameter; Weather condition coefficient Txs calculation formula: Txs = α r R t +α g G q , Among them, R t is the current rainfall, G q is the current light intensity, α r and α g To adjust the parameters; The early warning unit calculates the comprehensive risk index R according to the risk assessment unit. t , determine whether to trigger an early warning signal and determine the level of the early warning; Early warning judgment formula: Among them, R t is the currently calculated comprehensive risk index, R threshold The preset risk threshold value, exceeding the threshold value will trigger an early warning signal, marked as 1, otherwise it is 0; Warning level formula: Among them, R low 、R medium 、R high The risk index cutoff value for the warning level; The adaptive monitoring and scheduling module dynamically adjusts the system's monitoring frequency, sensor acquisition cycle, and inspection scheduling to generate risk assessment results; The automatic inspection and response module is responsible for automatic inspection and emergency response; The digital twin and simulation analysis module constructs a digital twin model of the dam and its surrounding environment through real-time monitoring data, and performs simulation analysis of seepage and soil erosion; The digital twin and simulation analysis module includes a digital twin modeling unit, a seepage simulation unit, and a virtual reality integration unit; The digital twin modeling unit constructs a digital twin model of the dam body and surrounding environment based on real-time monitoring data, and dynamically simulates the seepage and soil erosion of the dam body; The seepage simulation unit uses simulation technology to analyze and predict the dynamic process of seepage diffusion path, soil erosion and groundwater infiltration; The virtual reality integration unit combines the digital twin model with virtual reality technology, allowing managers to view the real-time seepage status of the dam body through VR equipment; The digital twin modeling unit constructs a digital model corresponding to the physical world by combining real-time monitoring data, historical data and the geometric structure of the water conservancy project; The digital twin model uses a three-dimensional finite element model to describe the stress, strain, and permeation behavior in the physical system by integrating geometric information, physical state, and real-time monitoring data; The governing equation of the seepage problem: Based on Darcy's law in the seepage process, the seepage velocity v s The relationship with the hydraulic gradient i is: s =-k·i, Among them, v s is the seepage velocity, k is the permeability coefficient, and i is the hydraulic gradient; Discretization of the finite element model: The geometric model of the digital twin is discretized using the finite element method. For any spatial domain V, the weak form of the finite element method is used to express the stress-strain relationship. According to the physical mechanics equation, the weak form of seepage is expressed as: Where h is the water head height, is the head gradient, φ is the shape function, q is the water source term, and dV is the differential integral; Boundary conditions: The digital twin modeling unit must meet boundary conditions, including: h(x)=h0, at the boundary Γ D , q(x)=q0, at the boundary Γ N Where h(x) is the water head height at a specific position x on the boundary, h0 is the initial water head height, Γ D and Γ N is the fixed boundary of water head and water flux, q(x) ... N The flow rate value at a specific position x on the graph, q0 is the initial flow rate value; The communication module is responsible for the smooth data transmission between each sensor node, automatic inspection equipment and the control center.

2. The intelligent monitoring system for water conservancy project seepage according to claim 1, characterized in that: The machine learning prediction module includes a training unit, a model determination unit, and a real-time prediction unit; The training unit trains a machine learning model based on historical monitoring data and real-time data; After the training is completed, the model determination unit determines the optimal model for real-time prediction; The real-time prediction unit uses the trained model to predict the trends of seepage, soil erosion, and groundwater level changes based on the current real-time monitoring data.

3. The intelligent monitoring system for water conservancy project seepage according to claim 1, characterized in that: The adaptive monitoring and scheduling module includes a monitoring frequency control unit and a resource scheduling unit; The monitoring frequency control unit dynamically adjusts the monitoring frequency of the sensor based on the risk assessment result; The resource scheduling unit manages resource allocation in the system.

4. The intelligent monitoring system for water conservancy project seepage according to claim 1, characterized in that: The automatic inspection and response module includes a drone inspection unit, a ground robot inspection unit, a path planning unit, and an image recognition unit; The drone inspection unit is responsible for managing drone inspection tasks; The ground robot inspection unit is responsible for ground robot inspection tasks and monitors the dam body; The path planning unit uses a path optimization algorithm to intelligently plan the inspection routes of the drone and ground robot; The image recognition unit uses a convolutional neural network to analyze images collected by drones and ground robots to automatically detect structural abnormalities of the dam body.

5. The intelligent monitoring system for water conservancy project seepage according to claim 2, characterized in that: In the training unit, the random forest training algorithm optimizes the performance of the model by constructing multiple decision trees and combining the prediction results of each tree during the training process. The random forest training algorithm is as follows: Decision tree training: Assume that the dataset is (x, y), where x is the input feature vector and y is the output target value. Each decision tree is based on minimizing the following loss function L: Among them, T i (x) represents the prediction result of the i-th tree for input x, y i is the actual target value; Random Forest Ensemble Prediction: The final prediction of the random forest is the average or voting result of all the decision tree predictions: Where N is the number of decision trees, is the combined predicted value, T i (x) is the output of each tree; In the model determination unit, the optimal model is determined for real-time prediction by evaluating the performance of multiple training models, wherein the model evaluation indicators include mean square error and mean absolute error; Mean square error formula: Among them, y i is the true value, is the predicted value of the model, and n is the number of samples; Mean absolute error formula: Among them, y i is the true value, is the model prediction value, and n is the number of samples.

6. The intelligent monitoring system for water conservancy project seepage according to claim 1, characterized in that: The virtual reality integration unit integrates the digital twin model and simulation results into the virtual reality platform, providing a three-dimensional visualization of the dam body seepage situation and simulation effects of future risks; Three-dimensional scene generation formula: P VR =M·P world , Among them, P VR is the display coordinate in virtual reality, P world is the three-dimensional coordinate of the point in the world coordinate system, and M is the projection matrix of the scene; Real-time data update formula: P VR (t) = P VR (t-1)+ΔP(t), Among them, P VR (t) is the display coordinate at the current moment, P VR (t-1) is the display coordinate at the previous moment, and ΔP(t) is the position or attribute change value brought about by real-time monitoring data; VR user interaction formula: Among them, d interact is the distance between the user and the interaction object, (x u ,y u , z u ) is the user's virtual location coordinate, (x o ,y o , z o ) are the coordinates of the interactive objects in the virtual reality scene; If the distance d interact When the value is less than the preset interaction threshold, the system allows the user to interact with the virtual object.

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