Hydraulic engineering seepage intelligent monitoring system

Abstract

The invention relates to the technical field of hydraulic engineering management, and discloses a hydraulic engineering seepage intelligent monitoring system. Comprising a multi-sensor sensing module, a data acquisition module, a data fusion and processing module, a machine learning prediction module, an intelligent decision module, an adaptive monitoring scheduling module, an automatic inspection and response module, a digital twinning and simulation analysis module and a communication module. The multi-sensor sensing module is responsible for collecting multi-dimensional data related to seepage in real time. A machine learning algorithm is adopted to intelligently predict the seepage trend, and risk assessment is performed in combination with real-time data, so that compared with a traditional monitoring method based on a preset threshold value, the method can automatically identify a complex seepage mode penguin through learning historical data and real-time data to predict the future risk in advance, and the risk assessment efficiency is improved. The prospective management of the seepage risk of the water conservancy project is realized, and the prediction precision and the response speed of the system are remarkably improved.

Description

Technical Field

[0001] The 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 water conservancy projects is one of the important means to ensure the safety of dam structures. Traditional seepage monitoring systems usually rely on fixed threshold warning and sensor monitoring methods. However, existing technologies are still insufficient in dealing with complex and dynamic seepage in water conservancy projects and cannot meet the needs of modern water conservancy projects for real-time monitoring, intelligent prediction and automated response. The following specific issues are explained by comparing existing technologies.

[0003] Traditional seepage monitoring methods are mostly based on preset thresholds. Sensors are used to 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 are difficult to foresee potential risks before the threshold is exceeded, resulting in the inability to issue early warnings. The monitoring system in the comparative document, a water conservancy project seepage intelligent monitoring system and monitoring method, mainly relies on traditional threshold monitoring methods, lacks learning of historical data and trend analysis based on time series, and cannot predict future seepage risks through intelligent means.

[0004] Inspection work in traditional seepage monitoring systems usually relies on manual work or equipment, especially manual inspections need to be arranged after early warning signals are detected. 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, a water conservancy project seepage intelligent monitoring system and monitoring method, relies on manual operation and lacks the automated inspection and emergency response capabilities of drones and ground robots. In emergencies, the lag of manual inspections limits the system's emergency response capabilities and the efficiency of handling seepage events.

[0005] The existing water conservancy project monitoring system cannot provide intuitive three-dimensional visualization monitoring methods, especially for complex seepage behaviors. Traditional monitoring data and two-dimensional graphic displays are difficult to help managers accurately judge the diffusion path of seepage and future potential risk areas. The technology in the comparative document, a water conservancy project seepage intelligent monitoring system and monitoring method, 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 in decision-making and cannot provide intuitive decision support methods.

[0006] Therefore, those skilled in the art provide a water conservancy project seepage intelligent monitoring system to solve the problems raised in the above background technology. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an intelligent seepage monitoring system for water conservancy projects, 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 seepage monitoring systems for water conservancy projects.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent monitoring system for seepage in a water conservancy project, 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;

[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 predicts the seepage trend using a machine learning algorithm 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 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 body and surrounding environment through real-time monitoring data, and performs simulation analysis of seepage and soil erosion;

[0017] The communication module is responsible for 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 the 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 predicts the trends of seepage, soil erosion, and groundwater level changes using a trained model based on 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 the real-time data and the prediction results of the machine learning model;

[0024] The early warning unit automatically generates an early warning signal according to the result of the risk assessment unit;

[0025] The emergency response unit automatically starts the emergency response procedure when it is in a high risk or 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 the ground robot;

[0033] The image recognition unit uses a convolutional neural network to analyze images collected by the drone and the ground robot 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 the 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, and managers can view the real-time seepage status of the dam body 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 data set 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 Overall Prediction: The final prediction result of the random forest is the average or voting result of all decision tree prediction results:

[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, 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 the level of seepage risk is judged based on the 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 factor, and α1, α2, and α3 are weight coefficients;

[0052] Calculation formula for water body 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 soil moisture content, T threshold is the preset threshold of soil moisture content, 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 is the preset risk threshold. If the threshold is exceeded, an early warning signal will be triggered and 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 is: 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 finite element model: The geometric model of the digital twin is discretized by 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 hydraulic 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, which include:

[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 boundary for hydraulic head and the solid boundary for water flux.

[0073] Preferably, the virtual reality integration unit integrates the digital twin model and the 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] Three-dimensional 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, ΔP(t) is the position or attribute change value brought by the real-time monitoring data;

[0078] VR user interaction formula:

[0079]

[0080] Among them, d interact is the distance between the user and the interactive 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 monitoring system for water conservancy project seepage, which has the following beneficial effects:

[0083] 1. The present invention adopts machine learning algorithm to make intelligent prediction of seepage trend, and combines real-time data for risk assessment. Compared with the traditional monitoring method 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, realize forward-looking management of seepage risks of water conservancy projects, and significantly improve the prediction accuracy and response speed of the system.

[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 simulates the seepage behavior of the dam body, and combines virtual reality technology to provide a visualized three-dimensional scene. Managers can view the seepage conditions 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 A schematic diagram of a machine learning prediction module of the present invention;

[0088] Figure 3 It is a schematic diagram of the intelligent decision-making module of the present invention;

[0089] Figure 4 It is a schematic diagram of the adaptive monitoring scheduling module of the present invention;

[0090] Figure 5 It 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] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

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

[0094] Example:

[0095] Please refer to the attached Figure 1 -Attached Figure 6 , an embodiment of the present invention provides a water conservancy project seepage intelligent monitoring system, 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 collection 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 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 automatic inspection and emergency response;

[0103] The digital twin and simulation analysis module builds 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;

[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 multi-sensor perception modules ensure the comprehensiveness and real-time nature of data collection, improve the accuracy of system monitoring, and ensure coverage without blind spots;

[0106] The benefits of the data acquisition module ensure that the sensor data can be quickly and efficiently collected into the system, ensuring the continuity and timeliness of the monitoring data, helping to detect seepage risks in a timely manner 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, which is helpful for comprehensive analysis of seepage conditions;

[0108] The benefits of the machine learning prediction module can identify potential risks in advance, avoid the limitations of relying on fixed threshold monitoring methods, improve the prediction accuracy of the system, and help 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] Benefits of the adaptive monitoring and scheduling module: Optimize resource utilization, save energy in low-risk situations, and ensure that key areas are monitored in high-risk situations;

[0111] Benefits of the automatic inspection and response module: Improve inspection efficiency, especially in high-risk and hard-to-reach areas. Automated inspection reduces reliance on manual labor and can respond quickly to emergencies, ensuring comprehensiveness and timeliness of inspections.

[0112] Benefits of digital twin and simulation analysis modules help managers to more intuitively understand the seepage dynamics and future diffusion trends, realize three-dimensional visualization 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 the 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 high risk or warning status;

[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 builds a digital twin model of the dam and its surrounding environment based on real-time monitoring data, and dynamically simulates the seepage and soil erosion of the dam;

[0127] The seepage simulation unit uses simulation technology to analyze and predict the dynamic process of seepage diffusion path, 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 devices.

[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 benefits of the model determination unit are 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] Benefits of the real-time prediction unit: It can identify trends in seepage, soil erosion and groundwater changes in advance, ensuring that the system can respond quickly to prevent potential risks from evolving 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, reduce the lag of manual intervention, and ensure that potential safety issues are handled in a timely manner;

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

[0135] The benefits of monitoring frequency control units 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 are inspected and monitored in a timely and accurate manner;

[0137] Benefits of UAV inspection units: Reduce the time and cost of manual inspections. At the same time, it can obtain comprehensive information about the dam from a high-altitude perspective, which is particularly effective in dangerous areas or difficult-to-reach places.

[0138] The benefits of the ground robot inspection unit ensure the comprehensiveness and accuracy of the inspection process, greatly 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] Benefits of the digital twin modeling unit: 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 the monitoring system and improve the decision support effect of managers.

[0144] 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:

[0145] Decision tree training: Assume that the data set 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 Overall Prediction: The final prediction result of the random forest is the average or voting result of all decision tree prediction results:

[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, where 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, 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 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, by integrating the prediction results of multiple trees It can reduce the deviation 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 function of the mean square error formula is to measure the deviation between the model prediction value and the true value. By calculating the square of the error between the prediction value and the true value and taking the average value, it reflects the accuracy of the model prediction. The square error in the formula makes the model more sensitive to large deviations and is more helpful in optimizing the performance of the model. A small mean square error means that the model's prediction result is 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 current comprehensive risk index R of the system. t , and the level of seepage risk is judged based on the 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 factor, and α1, α2, and α3 are weight coefficients;

[0162] Calculation formula for water body 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, 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 is the preset risk threshold. If the threshold is exceeded, an early warning signal will be triggered and 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 function of the comprehensive risk index formula is to evaluate 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 according to different environmental parameters. The weight coefficient adjusts the degree of influence of each factor on the overall risk. It can effectively integrate various influencing factors and generate a unified risk assessment result to judge the seepage risk level of the current system.

[0174] The function of the water safety factor calculation formula is to calculate the impact of the current water body on the stability of the dam body through 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 threat degree of the water body to the project;

[0175] The erosion safety factor calculation formula is used to evaluate 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, thereby reflecting the degree of soil erosion to the dam body.

[0176] The role of the weather condition coefficient calculation formula The weather condition coefficient formula combines the current rainfall and light intensity to evaluate the impact of weather factors on seepage. Rainfall reflects the direct impact on water level and seepage, while light intensity affects soil evaporation and water loss. The 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 determine 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 boundary values, the warnings are divided into low, medium and high levels. Different levels of warnings correspond to different response measures, ensuring that the system can take appropriate prevention or emergency response actions according to the severity of the risk.

[0179] The digital twin modeling unit builds 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;

[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 is: 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 finite element model: The geometric model of the digital twin is discretized by 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 hydraulic 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, which include:

[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 boundary for hydraulic head and the solid boundary for water flux.

[0189] The role of the governing equations of the seepage problem The governing equations are based on Darcy's law, which describes the linear relationship between the seepage velocity and the hydraulic gradient. The permeability coefficient is a characteristic parameter of the medium material, which characterizes the permeability of the soil or other medium to water. It is used to calculate the seepage velocity through the dam or soil under the action of the 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 situation inside the project.

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

[0192] The Fixed Flow boundary condition is used to describe boundary conditions where the flow is fixed, i.e., the flow is constant at a particular boundary. This boundary condition is used to simulate situations where the flow is fixed, such as flow into or out of an area, and ensures that the model takes into account 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 three-dimensional 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, ΔP(t) is the position or attribute change value brought by the real-time monitoring data;

[0198] VR user interaction formula:

[0199]

[0200] Among them, d interact is the distance between the user and the interactive 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 function of the 3D scene generation formula is to convert the 3D coordinates of the physical world into the display coordinates in the virtual reality. The projection matrix is ​​responsible for mapping the points in the 3D space to the 2D screen of the virtual reality environment. The process ensures that the seepage conditions of the dam in the physical world can be accurately displayed in 3D in the virtual reality system, providing a basis for user visualization interaction.

[0203] The function of the real-time data update formula is to dynamically update the display coordinates in the virtual reality scene. Through the position or attribute change values ​​brought by real-time monitoring data, the system can update the scene in the virtual reality 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 the virtual reality can keep pace with the actual environment, and the management personnel can observe the latest seepage situation in real time through VR equipment;

[0204] The function of the VR user interaction formula is to calculate 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, thereby enhancing the user's immersion and operating experience.

[0205] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A water conservancy project seepage intelligent monitoring system, 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 predicts the seepage trend using a machine learning algorithm 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 adaptive monitoring 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 body and surrounding environment through real-time monitoring data, and performs simulation analysis of seepage and soil erosion; The communication module is responsible for 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 is 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 the 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 predicts the trends of seepage, soil erosion, and groundwater level changes using a trained model based on current real-time monitoring data.

3. The intelligent monitoring system for water conservancy project seepage according to claim 1 is characterized in that: 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 the real-time data and the prediction results of the machine learning model; The early warning unit automatically generates an early warning signal according to the result of the risk assessment unit; The emergency response unit automatically starts the emergency response procedure when it is in a high risk or warning state.

4. The intelligent monitoring system for water conservancy project seepage according to claim 1 is 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.

5. The intelligent monitoring system for water conservancy project seepage according to claim 1 is 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 the ground robot; The image recognition unit uses a convolutional neural network to analyze images collected by the drone and the ground robot to automatically detect structural abnormalities of the dam body.

6. The water conservancy project seepage intelligent monitoring system according to claim 1 is characterized in that: 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 the 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 integrated unit combines the digital twin model with virtual reality technology, and managers can view the real-time seepage status of the dam body through VR equipment.

7. The intelligent monitoring system for water conservancy project seepage according to claim 2 is 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 data set 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: L=∑ (xi,yi)∈D (y i -T i (x)) 2 , 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 Overall Prediction: The final prediction result of the random forest is the average or voting result of all decision tree prediction results: 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, 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.

8. The water conservancy project seepage intelligent monitoring system according to claim 3 is characterized in that: The risk assessment unit combines real-time data and forecast data to calculate the current comprehensive risk index R of the system. t , and the level of seepage risk is judged based on the 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 factor, and α1, α2, and α3 are weight coefficients; Calculation formula for water body 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, 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 is the preset risk threshold. If the threshold is exceeded, an early warning signal will be triggered and marked as 1, otherwise it is 0; Warning level formula: Among them, R low , R medium , R high It is the risk index cutoff value of the warning level.

9. The water conservancy project seepage intelligent monitoring system according to claim 6, characterized in that: 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 is: 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 finite element model: The geometric model of the digital twin is discretized by 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 hydraulic head gradient, φ is the shape function, and q is the water source term; Boundary conditions: The digital twin modeling unit must meet boundary conditions, which include: h(x)=h0, at the boundary Γ D , q(x)=q0, at the boundary Γ N Among them, Γ D and Γ N are the fixed boundary for hydraulic head and the solid boundary for water flux.

10. The water conservancy project seepage intelligent monitoring system according to claim 6, characterized in that: 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; 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, ΔP(t) is the position or attribute change value brought by the real-time monitoring data; VR user interaction formula: Among them, d interact is the distance between the user and the interactive 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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