Dam seepage monitoring device and monitoring method

Abstract

The invention belongs to the technical field of dam monitoring, and particularly discloses a dam seepage monitoring device and method, and the method comprises the following steps: building an estimation model for the seepage state of a dam under the environmental conditions of different humidity and temperature of the dam; obtaining an influence relationship between a plurality of variables in the pre-estimation model and the seepage state, and adding a correction factor for updating the influence relationship in the pre-estimation model; creating a pressure experiment for influencing the internal moisture state of the dam through environmental conditions, and adjusting the environmental conditions in the pressure experiment to obtain a seepage state corresponding to the variable; updating the correction factor according to the variable and the corresponding seepage state so as to correct the pre-estimation model; the seepage state of the dam is obtained through the corrected pre-estimation model; the method has the advantages of integrating parameter humidity and other environmental variables, improving the comprehensiveness of dam behavior prediction, reducing misjudgment, modifying a factor model to dynamically adapt to different environments, and ensuring accurate early warning of each region.

Description

Technical Field

[0001] The present invention relates to the technical field of dam monitoring, and in particular to a dam seepage monitoring device and a monitoring method. Background Art

[0002] There is a significant problem in the current practice of dam seepage monitoring: the impact of environmental factors is often not fully considered. Although the core of seepage monitoring - piezometer technology - can provide direct measurement data on the water pressure in the dam body, it is often difficult to fully evaluate the true performance of the dam under specific environmental conditions based on these data alone. For example, factors such as rainfall, temperature changes, humidity fluctuations, and seasonal water level changes can significantly affect the behavior of water and the response of dam materials, but these factors are often not properly considered in routine monitoring.

[0003] In addition, environmental variability due to geographic and climatic differences also poses challenges to the interpretation of seepage monitoring results. For example, the same monitoring technology may show completely different effects in arid areas than in rainy areas, because differences in groundwater levels and soil moisture directly affect the distribution of pore water pressure. Such regional differences make it difficult for monitoring data to reflect and target specific environments, resulting in safety risks reflected by the data that are easily misunderstood.

[0004] Therefore, a dam seepage monitoring device and a monitoring method are proposed to solve the above-mentioned problems. Summary of the invention

[0005] In view of this, a first aspect of the present invention is to provide a dam seepage monitoring device and a monitoring method.

[0006] A second aspect of the present invention is to provide a dam seepage monitoring device.

[0007] The first aspect of the present invention provides a dam seepage monitoring method, comprising the following steps: constructing an estimation model for the seepage state of the dam under environmental conditions of different humidity and temperature; obtaining the influence relationship between multiple variables in the estimation model and the seepage state, and adding a correction factor for updating the influence relationship in the estimation model; creating a pressure experiment that affects the internal moisture state of the dam through the environmental conditions, adjusting the environmental conditions in the pressure experiment to obtain the seepage state corresponding to the variable; updating the correction factor according to the variable and the corresponding seepage state to correct the estimation model; and obtaining the seepage state of the dam through the corrected estimation model.

[0008] In any of the above technical solutions, the step of obtaining the influence relationship between multiple variables in the estimation model and the seepage state specifically includes: setting the number of variables in the estimation model according to the type of pressure that affects the seepage in the pores of the dam; judging the effect trend of each variable on the seepage state, and setting the first response parameter between the current variable and the seepage state based on the judgment result; converting the combined influence of the humidity and the temperature on the moisture state into a parameter, and assigning all the first response parameters through the parameter.

[0009] In any of the above technical solutions, the composite influence is the combined effect of the environmental conditions of the dam in changing the moisture state under cyclic changes.

[0010] In any of the above technical solutions, the parameter is assigned the first influence relationship using the following rules: under the cyclic changes of the environmental conditions, it is determined whether the temperature causes the moisture state to change between freezing and melting; if it does not, the first parameter is assigned the first response parameter as a constant value; when the temperature causes the moisture state to change between freezing and melting, the value of the first response parameter assigned by the first parameter is positively correlated with the moisture content inside the dam.

[0011] In any of the above technical solutions, the step of obtaining the influence relationship between multiple variables in the estimation model and the seepage state specifically also includes: judging the common effect trend of all the variables on the seepage state, and setting the second response parameter between the variable and the seepage state according to the judgment result; when the parameter assigns values ​​to all the first response parameters, the second response parameter follows the first response parameter and is assigned synchronously; and the first response parameter and the second response parameter are jointly used as the influence relationship.

[0012] In any of the above technical solutions, each of the first response parameter and the second response parameter is updated respectively by the correction factor.

[0013] In any of the above technical solutions, the variables are set through the following steps: the pore water pressure inside the dam and the seepage pressure that can affect the pore water pressure are used as permanent parameters, and the wave pressure and overflow pressure on the dam are used as non-resident parameters; the variables in the estimation model are set according to the permanent parameters and the non-resident parameters.

[0014] In any of the above technical solutions, the variable set according to the pore water pressure is specifically carried out through the following steps: obtaining the matrix suction of the interior of the dam to water under the current environmental conditions; and correcting the pore water pressure inside the variable through the matrix suction while considering the influence of the osmotic pressure on the pore water pressure.

[0015] In any of the above technical solutions, the matrix suction is obtained by calculating the internal moisture content of the dam and the humidity of the current environmental conditions.

[0016] A second aspect of the present invention provides a dam seepage monitoring device, comprising: a sensor for acquiring the environmental conditions and the variables and a computer storing the estimation model; wherein the sensor is connected to the computer.

[0017] Compared with the prior art, the present invention has the following beneficial effects: Traditional piezometric techniques have been expanded to include monitoring of environmental variables such as temperature and humidity, which are important factors affecting seepage behavior. By integrating these multi-dimensional data, the model can more comprehensively reflect the behavior of the dam in the actual operating environment, reducing the risk of misjudgment caused by the interpretation of isolated data.

[0018] The application of correction factors allows the prediction model to be adjusted instantly according to the latest experimental data and field monitoring data. This dynamic adaptability enables the model to effectively respond to rapidly changing environmental conditions, such as sudden climate change and seasonal impacts. It can be adjusted according to the specific characteristics of different geographical and climatic conditions to ensure the universality and local applicability of the model. For example, in rainy and arid areas, the model parameters will be optimized according to the specific environmental data of these areas, thereby providing more accurate predictions.

[0019] Additional aspects and advantages of embodiments according to the present invention will become apparent in the following description or may be learned through practice of embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 is a flow chart of the method steps of the present invention; Figure 2 It is a structural schematic diagram of the dam and the wave protection embankment of the present invention; Figure 3 This is a schematic diagram of the structure of the pressure measuring tube on the protective wave jack of the present invention. DETAILED DESCRIPTION

[0021] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0023] See also Figure 1-Figure 3 , a dam seepage monitoring device and a monitoring method according to some embodiments of the present invention are described below.

[0024] The embodiment of the first aspect of the present invention provides a method for monitoring dam seepage. In some embodiments of the present invention, Figure 1-Figure 3 As shown, the monitoring method comprises the following steps: S101, construct an estimation model for the seepage state of the dam under environmental conditions of different humidity and temperature.

[0025] Here, detailed humidity and temperature data about the environment in which the dam is located is collected. This includes real-time and historical data, which may come from meteorological observation stations installed at and around the dam. Data on the structure and material properties of the dam are collected, as well as past seepage data and any known structural problems. A seepage prediction model is constructed using the environmental data and dam properties, which is able to simulate the seepage behavior of the dam under different environmental conditions. The theory and thermodynamics of porous media flow, as well as seepage mechanics, form the basis of the model. The collected data is analyzed using statistical analysis methods and machine learning algorithms to find the relationship between humidity and temperature and seepage conditions. The model is trained using these relationships to predict the seepage state of the dam under different environmental conditions in the future. The accuracy of the model is verified using historical data. The model parameters are adjusted and optimized until the model can accurately reflect the actual observed data. The model is updated regularly to include new data and observations to ensure the timeliness and accuracy of the model. A real-time monitoring system is implemented to continuously apply the model to predict the seepage state of the dam to ensure that any potential safety risks can be responded to in a timely manner. Detailed reports and warnings are generated to notify managers and engineers to take necessary maintenance measures.

[0026] As can be seen above, humidity and temperature directly affect the physical properties of dam materials, such as expansion or contraction, which may change the seepage path and rate. The model predicts changes in the seepage state by analyzing the historical and current changes of these parameters. The seepage prediction model combines fluid dynamics, thermodynamics, and solid mechanics to form a multi-physics field coupled computational framework. This allows a more comprehensive simulation of the response of the dam under different environmental conditions. Machine learning algorithms (such as regression analysis, neural networks, etc.) are used to process large-scale data sets and learn the complex relationship between environmental conditions and seepage behavior. These algorithms are able to extract patterns from data and improve the accuracy and reliability of predictions. The model receives updated data from monitoring equipment in real time, adjusts the prediction to reflect the latest environmental changes, and ensures the real-time and accuracy of the prediction results.

[0027] S102, obtaining the influence relationship between multiple variables and the seepage state in the estimation model, and adding a correction factor for updating the influence relationship in the estimation model.

[0028] Here, data analysis is used to identify key variables that affect the seepage state of the dam, such as ambient temperature, humidity, water level, soil moisture, etc. Statistical methods and machine learning techniques are used to analyze the relationship between these variables and seepage rates and patterns. A mechanism is developed to introduce and adjust correction factors that can adjust the influence relationships in the estimation model based on new observations. The correction factors can reflect factors such as time changes, environmental changes, or improvements in monitoring technology. The correction factors are updated regularly or as needed with new data and analysis results. The effects of the correction factors are verified through real-time data monitoring and retrospective analysis, and the model is further adjusted.

[0029] As can be seen above, the variable influence relationship in the estimation model is established based on the historical data set. As new data is continuously collected, a data-driven approach is used to identify deviations and deficiencies in the model. The introduction of correction factors is based on the results of algorithmic analysis and is used to fine-tune the parameters or influence relationships in the model. This can be a coefficient, a weighted value, or a dynamic adjustment algorithm. The estimation model is continuously updated through an adaptive learning mechanism. This mechanism can be a machine learning-based method, such as an adaptive neural network or a deep learning model, which can automatically adjust the model parameters based on new input data. The adaptive learning mechanism ensures that the model is continuously optimized over time and as data accumulates, improving its prediction accuracy and reliability. The estimation model is connected to a real-time monitoring system, which can receive real-time data input and instantly adjust the prediction results based on the correction factors. The real-time feedback system allows the model to respond quickly to environmental changes and input errors, and to correct the model output by adjusting the correction factors to ensure the accuracy of the prediction.

[0030] Specifically, the steps of obtaining the influence relationship between multiple variables and seepage state in the estimation model include: The number of variables in the estimation model is set according to the type of action that affects the pressure that generates seepage through the pores in the dam.

[0031] The effect trend of each variable on the seepage state is determined, and the first response parameter between the current variable and the seepage state is set according to the result of the determination.

[0032] The combined effects of humidity and temperature on moisture status are converted into parameters, and all first response parameters are assigned through the parameters.

[0033] For the above specific description, identify and select the main variables that affect the seepage of the dam, such as water pressure, soil moisture, rainfall, moisture absorption performance of dam materials, etc. Based on the principles of engineering and geology, analyze which factors play a decisive role in the pressure of seepage in the pores of the dam. Use historical data and expert experience to select these variables. Set one or more first response parameters for each selected variable, which describe the direct relationship between the variable and the seepage state, and determine the specific impact of each variable on the seepage state through experimental data analysis or theoretical calculation, such as the increase or decrease trend of the seepage rate when the variable changes. Convert the composite effects of environmental variables such as humidity and temperature into a single parameter, which helps to simplify the model calculation and improve the prediction accuracy, and synthesize the effects of multiple environmental factors into a comprehensive parameter through physical formulas or empirical equations. For example, functions of humidity and temperature can be used to represent their joint impact on the moisture state, which in turn affects the seepage.

[0034] In this description, the model is built on a solid scientific foundation, combining principles from geology, hydrology, and materials science. At the same time, it also relies on a large amount of data analysis, using statistical methods and machine learning techniques to extract relationships between variables from historical data. Through parameterization, the model can be adjusted and optimized more flexibly to respond to new data or changing environmental conditions. The setting of the first response parameter enables the model to respond quickly to changes in variables, while parameter conversion simplifies complex environmental influences, making the model more stable and easy to calculate. The model is designed to accept real-time data input and adjust the prediction results accordingly. This design enables the model to update and correct itself when new data arrives, ensuring the accuracy and practicality of the prediction.

[0035] Specifically, the action trend is specifically manifested as a linear relationship or a nonlinear relationship on a two-dimensional graph, the nonlinear relationship is represented by an exponent, and the linear relationship is represented by a slope in a straight line.

[0036] As mentioned above, we can identify those variables in the model that have a linear relationship with the seepage state. These relationships are usually manifested as a linear increase or decrease in the seepage rate or pressure when the variable changes. The slope (coefficient) between the variable and the seepage rate is determined by statistical methods such as linear regression analysis. The slope indicates how the seepage state changes when one unit of the variable changes. Identify those variables that have nonlinear relationships with the seepage state, such as exponential relationships, which usually have more complex dynamic changes between the variable and the seepage effect. Use curve fitting techniques such as polynomial regression or exponential regression to model these relationships. Nonlinear models can capture complex dependencies and interactions between variables.

[0037] Specifically, the parameters are assigned the first influence relationship using the following rules: Under the cyclical change of environmental conditions, it is determined whether the temperature causes the water state to change between freezing and melting; if not, the first parameter is assigned a first response parameter as a constant value.

[0038] When the temperature causes the moisture state to change between freezing and melting, the value of the first response parameter assigned by the first parameter is positively correlated with the moisture content inside the dam.

[0039] For the above specific descriptions, the first response parameter is assigned a value based on whether the temperature causes the water state to change between frozen and thawed. Scenario 1: If the temperature change is not enough to cause the water state to change (i.e., the water remains in liquid or solid state), the first response parameter is set to a fixed value, indicating that under this condition, the seepage rate or pressure will not change significantly due to temperature changes. Scenario 2: If the temperature change causes the water state to change from frozen to thawed (or vice versa), the first response parameter is positively correlated with the water content inside the dam, indicating that the increase in temperature causes more water to be released, which may increase seepage. The first response parameter is dynamically adjusted according to the relationship between temperature and water state to reflect the actual seepage behavior under different environmental conditions. The change in water state directly affects the pore water pressure and seepage path. The frozen state may lead to pore closure, while melting may increase pore water pressure and seepage channels.

[0040] In this description, sensors are used to monitor the temperature of the dam and its surroundings in real time. The temperature data is updated regularly through a data collection system, and the temperature trend is analyzed. Based on the temperature data, the model automatically determines the possible changes in the moisture state and adjusts the first response parameters accordingly. When the temperature reaches near the freezing point, the system checks the moisture content inside the dam and adjusts the first response parameters accordingly to simulate the possible increase or decrease in the seepage rate. The model output is used to predict the seepage state under current and predicted environmental conditions. The prediction results are compared with the actual observed data to verify the accuracy of the model, and the model parameters or assignment logic are adjusted if necessary to optimize the prediction performance. .

[0041] Specifically, the step of obtaining the influence relationship between multiple variables and seepage state in the estimation model also includes: The common effect trend of all variables on the seepage state is determined, and the second response parameter between the variable and the seepage state is set according to the result of the determination.

[0042] When all first response parameters are assigned values, the second response parameters are assigned values ​​synchronously following the first response parameters.

[0043] The first response parameter and the second response parameter are taken together as the influence relationship.

[0044] For the above specific description, the combined effect trend between multiple variables such as temperature, humidity, water level and seepage state is analyzed and determined, and multivariate statistical analysis methods (such as multivariate regression analysis) are used to identify and quantify the joint effect of these variables on seepage rate or pressure. This analysis helps to understand that each variable not only affects seepage individually, but also that the interaction between them may produce important effects. Based on the analysis results of the joint effect of multiple variables, a comprehensive second response parameter is set for the model, which reflects the overall impact of all considered variables on the seepage state under a certain environmental combination. This parameter is a synthetic indicator and may be expressed as a weight coefficient or adjustment factor. When assigning the first response parameter, the second response parameter is also assigned to ensure that the two are kept updated synchronously in the model. By combining the first response parameter (for the impact of a single variable) and the second response parameter (reflecting the joint impact of the variable combination), the model can more comprehensively simulate the impact of environmental changes on the seepage state.

[0045] In this description, data on all relevant variables about the dam and its environment are collected and integrated. Data analysis tools and software, such as statistical packages in R or Python, are used to build and maintain a multivariate prediction model that incorporates these variables. Response parameters, including first- and second-response parameters, are updated based on real-time data or periodic data reviews. Machine learning algorithms, such as adaptive networks or deep learning frameworks, are implemented to dynamically adjust these parameters in response to new data inputs and environmental changes. The model predicts seepage conditions and compares the predictions with actual observations. Model parameters are adjusted based on the comparison results to improve the accuracy of the predictions, and the model is further optimized through feedback mechanisms.

[0046] In any of the above embodiments, each of the first response parameter and the second response parameter is updated by a correction factor respectively.

[0047] In this embodiment, a correction factor is defined for each first response parameter and second response parameter, which is used to update these parameters according to new data and information. The correction factor is set based on the latest research findings, technological advances or environmental changes, and is used to adjust the response parameters in the model to reflect the impact of these changes on the seepage state. The first response parameter and the second response parameter are systematically updated by the correction factor to ensure that the model output remains current and relevant. When new data is input into the system, the correction factor adjusts the response parameter according to the set rules, such as deviation analysis based on historical data, trend prediction or expert input.

[0048] Continuously monitor environmental data such as temperature, humidity, rainfall, etc. at and around the dam. Analyze this data regularly to assess the potential impact of changes on the seepage state of the dam. Dynamically adjust correction factors based on the latest data and analysis results. This includes using automated tools and algorithms (such as machine learning models) to predict the optimal values ​​of correction factors. The basis for adjustment includes the results of statistical analysis, assessment of prediction errors, and technical judgment of experts. Use the updated correction factors to adjust the first response and second response parameters. This step is performed regularly during the model's operation cycle to ensure that the model parameters reflect the latest data and conditions. Parameter updates are not only based on direct data changes, but also take into account the combined effects of environmental and engineering properties caused by these data changes. After updating the parameters, verify the effectiveness of the updates by comparing the model predictions with the actual observed data. Collect feedback to further fine-tune the correction factors and response parameters to optimize the overall performance and accuracy of the model.

[0049] Specifically, the variables are set by the following steps: The pore water pressure inside the dam and the seepage pressure that can affect the pore water pressure are taken as permanent parameters, and the wave pressure and overflow pressure on the left and right sides of the dam are taken as non-resident parameters.

[0050] The variables in the estimation model are set according to the resident parameters and non-resident parameters respectively.

[0051] For the above specific description, pore water pressure and seepage pressure are selected as permanent parameters because these factors persist in the daily operation of the dam and have a fundamental and continuous impact on the seepage state. Pore water pressure is directly related to the ability of water to move in the dam material, while seepage pressure affects the driving force of water through the dam pores. Wave pressure and overflow pressure are selected as non-resident parameters. These factors depend on specific climate events or temporary external conditions. Non-resident parameters do not affect the dam continuously, but they will have a significant impact on the seepage state of the dam when they occur, such as waves or overflow events under extreme weather conditions.

[0052] In this description, long-term monitoring data on pore water pressure and seepage pressure, as well as historical data on wave and overflow events, are collected. This data is analyzed to determine their specific impact on the seepage state of the dam. In the model, resident and non-resident parameters are encoded based on the data analysis results and engineering needs. Algorithms are designed to dynamically adjust the weights and influences of these parameters, especially increasing the consideration of non-resident parameters when predicting extreme events or uncommon situations. Resident parameters continuously influence the baseline prediction of the model to ensure the accuracy of predictions under normal conditions. Non-resident parameters are activated when specific trigger conditions are detected, adjusting the model output to reflect the impact of these additional pressures. A feedback mechanism is implemented to adjust parameter settings and model structure based on the comparison of actual monitoring data and prediction results. Machine learning and data mining techniques are used to continuously optimize model performance and improve the prediction ability of uncommon or extreme events.

[0053] Specifically, the variables set according to the pore water pressure are specifically set through the following steps: Obtain the matrix suction that the interior of the dam has for water under current environmental conditions.

[0054] Under the condition of considering the influence of seepage pressure on pore water pressure, the pore water pressure is corrected within the variable by matrix suction.

[0055] For the above specific description, the attraction of the dam material to water under the current environmental conditions is measured, that is, the matrix suction. This reflects the pore structure and water affinity of the material. Matrix suction is a unique physical property of the dam material and is obtained through laboratory testing or field monitoring. This force is critical in determining the movement of water in the pores. Based on the known osmotic pressure, an internal correction is made to consider the adjustment effect of matrix suction on pore water pressure. The osmotic pressure pushes water through the pores of the dam, while matrix suction resists this flow to some extent. Correcting the pore water pressure requires balancing these two forces to accurately describe the actual behavior of water in the dam.

[0056] In this description, data on the pore structure and water affinity of the dam material are collected to determine the matrix suction. Current and historical seepage pressure data are measured to understand the dynamics of water flow in the dam. Pore water pressure is calculated using a physical model that contains formulas for matrix suction and seepage pressure. The pore water pressure is dynamically corrected to adjust the relative influence of matrix suction and seepage pressure based on real-time monitoring data. Seepage prediction is performed using the corrected pore water pressure data to predict the safety and stability of the dam under different environmental conditions. Feedback adjustments to the model are made based on the prediction results and actual observations to optimize the algorithm and parameters for pressure correction.

[0057] In any of the above embodiments, the composite impact is the combined effect of the environmental conditions of the dam changing the moisture state under cyclic changes.

[0058] In this embodiment, various factors in the environment of the dam are continuously monitored, including temperature, humidity, rainfall, etc. These factors can directly or indirectly affect the water state inside the dam. Changes in these environmental variables can affect the freezing point or evaporation rate of water by affecting temperature and humidity conditions, thereby affecting the water pressure in the pores and the fluidity of water. Based on the monitored environmental data, the water state in the model is dynamically adjusted, such as the transition from liquid to solid (freezing) or from solid to liquid (melting). A decrease in ambient temperature may cause pore water to begin to freeze, increase pore pressure, and reduce water fluidity. On the contrary, an increase in temperature causes frozen water to melt, which may increase seepage.

[0059] Use a sensor network to collect data about the environment around the dam in real time, and use data analysis tools to analyze the cyclical change patterns of environmental data and predict possible impacts on water status. Based on the analysis results, adjust the water status in the seepage prediction model. For example, if a low temperature period is predicted, the model predicts an increased likelihood of water freezing, and adjusts the pore water pressure and permeability parameters to reflect the changed water status. Use the adjusted model to predict the seepage behavior and the response of the dam under specific environmental conditions, and develop response strategies, such as strengthening the dam's drainage system or adopting insulation measures, to mitigate the impact of adverse environmental conditions.

[0060] In any of the above embodiments, the matrix suction is , considering the influence of the pore water pressure inside the material on the total stress as the effective stress coefficient , considering the atmospheric pressure In the case of , the estimation model is further derived as the following formula: ; in, is the visualization dependent variable of the seepage state; and is the correction factor to be fitted; is the atmospheric pressure; is the pore water pressure; is the osmotic pressure; is the wave pressure, and It is 1 when the dam is in the wave state of water and 0 when it is not; is the overflow pressure, and When the dam is under overflow pressure, the dynamic effect of the water flow is 1 when the water level exceeds the top of the dam, and 0 when it is not; is matrix suction; is the correction factor for environmental conditions; and is the vertical range of the integration, representing the starting and ending depths of the flow; is the permeability coefficient that depends on the soil moisture content; is the reference permeability coefficient, used for normalization; is the downward unit gravity vector; is the density of water; is the acceleration due to gravity; is the gradient of the total water potential.

[0061] Further, It is a correction factor to characterize the environmental conditions, which characterizes the coupling effect of humidity and temperature on the water state. Both the first response parameter and the second response parameter are related to the freeze-thaw effect. , when there is no freeze-thaw cycle, Take 1, when there is a freeze-thaw cycle, the It decreases with the decrease of moisture content and is greatly affected by the moisture content. Its value range is (0, 1).

[0062] In any of the above embodiments, the matrix suction is obtained by calculating the internal water content of the dam and the humidity of the current environmental conditions.

[0063] In this embodiment, the attraction of the dam material to water under specific moisture content and ambient humidity conditions is evaluated. This attraction affects the movement and distribution of water inside the dam, and the matrix suction is mainly determined by the pore structure and surface characteristics of the material. When the moisture content is high, the pores inside the dam may be partially or completely filled with water, reducing the water absorption capacity of the material; changes in ambient humidity will also affect the evaporation or condensation of water inside the dam, thereby changing the matrix suction, which is specifically calculated using the following formula: =a⋅θ+b⋅H; in, is the matrix suction, a and b are coefficients related to material properties and environmental sensitivity, θ is the water content inside the dam, and H is the ambient relative humidity.

[0064] The moisture content inside the dam and the relative humidity of the external environment are continuously monitored, and the data are automatically recorded and transmitted to the central processing unit using sensors and monitoring equipment. The value of matrix suction is updated in real time based on the latest monitoring data. This includes cleaning and correcting the data to ensure the accuracy of the input data, integrating the calculated matrix suction values ​​into the seepage prediction model to predict the behavior of water in the dam material. Based on the updated matrix suction, the movement and distribution of water inside the dam are predicted, the possible seepage and leakage risks are evaluated, the prediction results are compared with the actual observations, the accuracy of the prediction is analyzed, and the coefficients a and b for calculating the matrix suction are adjusted as needed to improve the prediction ability of the model.

[0065] S103, creating a pressure experiment that affects the water state inside the dam through environmental conditions, and adjusting the environmental conditions in the pressure experiment to obtain the seepage state corresponding to the variables.

[0066] Here, the effects of different environmental factors (temperature, humidity, etc.) on the internal moisture state of the dam are simulated and controlled to evaluate the impact these variables may have on the dam structure under real conditions. The experiment changes the environmental conditions to directly observe how these changes affect the movement of moisture and pressure changes in the dam material, and how these changes affect the overall seepage behavior. In a controlled experimental environment, factors such as temperature and humidity are adjusted to simulate the impact of different climatic conditions on the seepage performance of the dam. High-precision environmental simulation equipment is used to accurately control the experimental conditions, thereby generating reliable data for analysis and model verification.

[0067] As can be seen from the above, an environmental chamber or similar facility is used, equipped with a temperature and humidity control system, as well as sensors and instruments for measuring moisture movement and pressure changes, and is set according to the specific environmental conditions that need to be simulated, such as setting a series of temperature and humidity changes to observe the response of the dam material under these changes. During the experiment, data on temperature, humidity, pore water pressure and other relevant physical parameters are continuously collected, and the data are analyzed using statistical and physical models to determine the specific effects of different environmental conditions on the seepage behavior of the dam. The mathematical model used to predict the seepage behavior of the dam is verified and adjusted using experimental data to ensure that the model can accurately reflect the phenomena observed in the experiment. According to the experimental results, the parameters in the model are adjusted or new variables and relationships are introduced to improve the predictive ability and scope of application of the model.

[0068] S104, updating the correction factor according to the variable and the corresponding seepage state to correct the estimation model; obtaining the seepage state of the dam through the corrected estimation model.

[0069] Here, based on the relationship between the variables identified in the experimental and field monitoring data and the seepage state, the correction factors are updated to accurately adjust the model's response to different environmental conditions and physical states. The correction factors are parameters or coefficients in the estimation model. They are adjusted based on actual data to ensure consistency between the model output and actual observations. The new correction factors are applied, the estimation model is adjusted, and then the model's prediction accuracy is verified using new or existing data sets. Through the continuous verification and adjustment process, the model's prediction ability is gradually enhanced and can more accurately reflect the actual seepage behavior.

[0070] As mentioned above, data is collected from sensors that monitor the environment and internal status of the dam, and these data are analyzed to determine the key variables and their changes that affect seepage. Based on the results of the data analysis, new correction factors are calculated. For example, if it is found that the model prediction deviation is large under specific humidity or temperature conditions, the corresponding correction factor will be adjusted to reduce this deviation. The new correction factor is integrated into the seepage estimation model, and the calculation rules or parameter settings of the model are updated. The output of the revised model is verified by comparing with an independent validation data set. This step is necessary to ensure that the modification improves the prediction accuracy and reliability of the model. Use the revised model for real-time or predictive simulations to calculate the current or possible future seepage status. The model prediction results provide support for dam management and help formulate maintenance plans, emergency responses, and long-term safety strategies.

[0071] The present invention provides a dam seepage monitoring method, which integrates the impact of environmental conditions (such as temperature and humidity) on dam seepage, and the estimation model can more comprehensively reflect the actual seepage state, thereby providing more accurate data. This helps to avoid misjudgment that may be caused by relying solely on pore water pressure data.

[0072] The correction factors included in the method enable the model to adjust itself according to new experimental and monitoring data, enhancing the dynamic adaptability of the prediction model. This means that the model can be effectively applied to different geographical and climatic conditions, thus ensuring that the monitoring results in different environments are highly reliable.

[0073] The use of advanced prediction models, combined with real-time environmental monitoring data, enhances the ability to provide early warning of potential risks. This helps managers take necessary maintenance and reinforcement measures before seepage problems become safety hazards, thereby avoiding large-scale safety accidents.

[0074] It provides data-based decision support, helping dam operators and decision makers make more scientific and reasonable decisions in maintenance planning, emergency response and resource allocation. In addition, the implementation of this method can record all relevant data and analysis results in detail, providing valuable information for future policy making and research.

[0075] Through accurate monitoring and timely maintenance, the cost of repairs and potential catastrophic losses due to dam failure can be significantly reduced. In addition, by optimizing maintenance plans, resources can be allocated more efficiently and unnecessary expenses can be reduced.

[0076] Ensuring the stability and safe operation of dams also means protecting downstream ecosystems and residential areas from untreated seepage problems, thereby protecting the natural environment while protecting human life and property.

[0077] The second embodiment of the present invention provides a dam seepage monitoring device. Figure 2-Figure 3 As shown, in some embodiments of the present invention, the monitoring device includes a sensor for acquiring the environmental conditions and the variables and a computer storing the estimation model; wherein the sensor is connected to the computer.

[0078] Collect real-time data about the dam and its surrounding environment, including key variables such as temperature, humidity, water level, pore water pressure, etc. Sensors are distributed at key locations of the dam structure and the surrounding environment, continuously monitoring and sending data to the central computer system. Store and run prediction models, process data from sensors, perform data analysis, and provide predictions and alerts. The computer system is loaded with advanced algorithms and prediction models, which can process and analyze the collected data in real time and output prediction results and potential risks based on the model. Various sensors automatically measure and record data at preset time intervals, and then transmit these data to the central computer system via wired or wireless networks. Data is synchronized to the computer system in real time or regularly to ensure the real-time and integrity of the data. All collected data is stored in a database for further analysis and historical comparison. The computer system uses specialized software to clean, process and analyze data. This may include removing noise, filling in data gaps, statistical analysis, etc. Based on the prediction model, the system analyzes current and historical data to predict the seepage behavior and potential risk points of the dam. If the prediction results show that the potential risk exceeds the preset threshold, the system will automatically issue an alarm to notify the maintenance team and managers. The system includes a user-friendly interface that allows users to view real-time data, historical trends, forecast results and alarm status. The system can generate regular or on-demand reports that detail monitoring results and predictive analysis to support decision making.

[0079] Specifically, the detection device includes: a water level sensor for monitoring changes in the dam reservoir and downstream water levels; a pressure sensor for measuring the pore water pressure inside the dam structure; a temperature and humidity sensor for monitoring the temperature and relative humidity of the dam and its surrounding environment, which have an important impact on seepage and structural integrity; and a displacement sensor for detecting the displacement and deformation of the dam body. These data can be used to evaluate the structural safety of the dam.

[0080] The computer system includes: the main control computer has powerful processing power and is responsible for collecting and analyzing data from various sensors. The data storage device is used to store long-term monitoring data, which is very important for trend analysis and historical comparison. The software system includes applications for data processing, calculation software for prediction models, and user interfaces. The software is able to perform necessary analysis on the collected data and perform calculations and predictions based on the prediction model. The communication equipment ensures data transmission between the sensor and the computer system. Wired networks (such as Ethernet) or wireless communication technologies (such as Wi-Fi, LTE) can be used. The data transmission line is used in the wired system to connect the sensor and the computer system to ensure real-time data transmission. The power supply system ensures the continuous operation of all equipment, which may include conventional power supply and emergency backup power supply (such as UPS or generator). The power management system is used to monitor and manage the power demand and supply status of the entire monitoring system to ensure that the system can continue to operate when the power is interrupted. The user interface and visualization tools enable operators to view data and system status in real time, perform operations and settings, and adjust settings. The analysis and reporting tools provide data analysis, report generation and early warning functions to help technicians and managers make decisions.

[0081] The present invention provides a dam seepage monitoring device that can detect any changes that may cause safety problems in real time by continuously monitoring key variables of the dam (such as water level, pressure, temperature, etc.). This real-time monitoring makes it possible to respond to possible safety threats in a timely manner, thereby preventing accidents.

[0082] Data-driven decision support, integrated sensors and computer systems provide a large amount of accurate data, which can provide managers with scientific decision support after being analyzed by predictive models. Such data analysis helps optimize the dam's operation and maintenance plans and ensure that the measures taken are based on the current and predicted dam status.

[0083] Improve prediction accuracy. The prediction model can comprehensively consider the impact of various environmental variables on dam seepage behavior, provide accurate risk assessment and future trend prediction. This advanced model analysis improves prediction accuracy and reduces resource waste and misoperation caused by prediction errors.

[0084] Reduce maintenance costs, the system can identify potential weaknesses in the dam structure, allowing maintenance work to be more targeted. This not only extends the service life of the dam, but also effectively reduces the high costs caused by frequent or unnecessary maintenance work.

[0085] Enhance public confidence and compliance. An efficient and reliable monitoring system enhances public confidence in dam safety management and helps dam operators comply with relevant safety regulations and standards. The detailed reports generated by the system can be used as evidence of compliance and review, helping to improve the transparency of dam management.

[0086] Disaster prevention: By identifying abnormal seepage or other potential structural problems at an early stage, the system helps prevent possible disaster events such as dam leakage or collapse, thereby protecting the safety and environment of downstream areas.

[0087] Furthermore, for partial data measurement of the dam, actual collection can be carried out on the protection wave embankment to reduce the inspection burden on the dam.

[0088] In the description of the present invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0089] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A dam seepage monitoring method, characterized in that: The steps include: Considering that the dam is in environmental conditions of different humidity and temperature, constructing an estimation model for the seepage state of the dam; Acquiring the influence relationship between a plurality of variables in the estimation model and the seepage state, and adding a correction factor for updating the influence relationship in the estimation model; Creating a pressure experiment that affects the water state inside the dam through the environmental conditions, and adjusting the environmental conditions in the pressure experiment to obtain the seepage state corresponding to the variable; updating the correction factor according to the variable and the corresponding seepage state to correct the estimation model; The seepage state of the dam is obtained through the modified estimation model.

2. The dam seepage monitoring method according to claim 1, characterized in that: The step of obtaining the influence relationship between the multiple variables in the estimation model and the seepage state specifically includes: According to the type of action that affects the pressure of the pores in the dam to generate seepage, the number of variables in the estimation model is set; Determine the effect trend of each variable on the seepage state, and set a first response parameter between the current variable and the seepage state according to the determination result; The combined effect of the humidity and the temperature on the moisture state is converted into a parameter, and all the first response parameters are assigned values ​​through the parameter.

3. The dam seepage monitoring method according to claim 2, characterized in that: The compound impact is the combined effect of the environmental conditions of the dam in changing the moisture state under cyclic changes.

4. The dam seepage monitoring method according to claim 3, characterized in that: The parameter is assigned the first influence relationship using the following rule: Under the cyclical change of the environmental condition, determining whether the temperature causes the water state to change between freezing and melting; if not, assigning the first parameter a value and the first response parameter a fixed value; When the temperature causes the moisture state to transition between freezing and melting, the value of the first response parameter assigned by the first parameter is positively correlated with the moisture content inside the dam.

5. The dam seepage monitoring method according to claim 2, characterized in that: The step of obtaining the influence relationship between the multiple variables in the estimation model and the seepage state specifically includes: Determine the common effect trend of all the variables on the seepage state, and set a second response parameter between the variable and the seepage state according to the determination result; When the parameter is assigned to all the first response parameters, the second response parameter is assigned synchronously with the first response parameter; The first response parameter and the second response parameter are taken together as the influence relationship.

6. The dam seepage monitoring method according to claim 5, characterized in that: Each of the first response parameter and the second response parameter is updated by the correction factor respectively.

7. The dam seepage monitoring method according to claim 2, characterized in that: The variables are set by the following steps: Taking the pore water pressure inside the dam and the seepage pressure that can affect the pore water pressure as permanent parameters, and taking the wave pressure and overflow pressure on the dam as non-permanent parameters; The variables in the estimation model are set according to the resident parameters and the non-resident parameters respectively.

8. The dam seepage monitoring method according to claim 7, characterized in that: The variables set according to the pore water pressure are specifically implemented through the following steps: Obtaining the matrix suction of the interior of the dam to water under the current environmental conditions; Under the condition that the influence of the osmotic pressure on the pore water pressure is taken into consideration, the pore water pressure is corrected within the variable by the matrix suction.

9. The dam seepage monitoring method according to claim 8, characterized in that: The matrix suction is obtained by calculating the internal water content of the dam and the humidity of the current environmental conditions.

10. A monitoring device for implementing the dam seepage monitoring method according to any one of claims 1 to 9, characterized in that: It includes a sensor for obtaining the environmental conditions and the variables and a computer storing the estimation model; wherein the sensor is connected to the computer.

Images