A system and method for monitoring and forecasting the safety of earth-rock dam seepage

Abstract

The present invention relates to a system and method for monitoring, forecasting, and warning the safety of earth-rock dam seepage, belonging to the fields of dam safety monitoring technology and artificial intelligence technology. The system comprises a data acquisition module, a dam safety monitoring and forecasting model module, a dam safety early warning analysis module, and an early warning setting and push module. Based on a statistical model, the system collects and analyzes engineering data such as geological surveys, testing, and experiments according to rainfall conditions and dam safety monitoring data, derives the equivalent reservoir water level of the seepage channel, establishes a seepage pressure monitoring and forecasting model that takes into account the lag time of the seepage channel, and constructs a four-level monitoring and early warning indicator for seepage pressure. This system implements online early warning of dam seepage safety and allows for real-time assessment of the dam's safety status.

Description

Technical Field

[0001] The present invention relates to a system and method for monitoring and forecasting the safety of earth-rock dam seepage, and belongs to the technical field of dam safety monitoring and artificial intelligence. Background Art

[0002] As an important water conservancy project, the safety of dams directly affects the safety of people and property, social security and stability, and economic development in the region. An effective dam safety monitoring, forecasting, and early warning system can obtain real-time seepage data from the dam body and predict potential risks through model analysis, helping to promptly identify hidden dangers and take appropriate measures to ensure the safe operation of the dam. Using upstream water level, downstream water level, rainfall, and other independent variables as independent variables, and the dam seepage pressure level as the dependent variable, linear and nonlinear relationship expressions between the dependent and independent variables are established. Dam seepage safety monitoring and early warning indicators are formulated to diagnose the dam seepage safety situation in real time and predict the future dam seepage status. These are important theoretical methods and technical means to ensure the safe operation of dams. Therefore, developing efficient monitoring and early warning models and systems has important theoretical and practical significance.

[0003] Chinese invention patent publication number CN113221215A discloses a BIM-based method for visually monitoring and analyzing the dynamic seepage of earth-rockfill dams. This invention is applicable to the field of dam monitoring. The technical solution is as follows: real-time monitoring data is acquired through buried measuring points at the upstream, toe, and along the seepage lines of each section of the dam; the pressure head of the measuring point is calculated based on the seepage pressure at the measuring point, and the coordinates of the z-axis projection of the measuring point on the seepage line are obtained when the spatial coordinates (x, y, z) of the measuring point are determined; based on the monitoring data and the seepage line generation method in the previous step, the seepage line is generated and visualized on the BIM model; a particle swarm algorithm is used to optimize the inversion of the permeability coefficients of each material partition of the dam to generate the seepage field of the dam body and visualize it on the BIM model of the dam body section; a range check is performed on the real-time monitored seepage head data, the permeability coefficients of each partition obtained by inversion in the previous step, and the seepage field data obtained by forward calculation. If the range exceeds the system's preset critical value, an alarm message is generated at the corresponding problem point.

[0004] However, existing technologies struggle to account for the lag time of concentrated seepage channels within the dam body and foundation, resulting in low accuracy and insufficient prediction and early warning capabilities for nonlinear responses under complex seepage conditions. Furthermore, their low intelligence makes it difficult to implement real-time dam safety monitoring and early warning. To address this issue, a model method and system for monitoring, forecasting, and early warning of seepage safety in earth-rockfill dams that considers the lag time of seepage channels is proposed.

[0005] Therefore, there is a need for a seepage safety monitoring, forecasting and early warning system and method for earth-rock dams. Based on the automated monitoring data of dam safety, the system takes into account the influence of the lag time of concentrated leakage channels in the dam body and foundation, improves the accuracy, nonlinear response forecasting and early warning capabilities under complex seepage conditions, and the intelligence level, and realizes real-time dam safety monitoring and early warning. Summary of the Invention

[0006] The technical problem to be solved by the present invention is: in order to overcome the shortcomings of the existing technology, a seepage safety monitoring, forecasting and early warning system and method for earth-rock dams is provided, which is based on dam safety automated monitoring data, takes into account the influence of the lag time of concentrated leakage channels in the dam body and foundation, improves the accuracy, nonlinear response forecasting and early warning capabilities under complex seepage conditions, and the degree of intelligence, and realizes real-time dam safety monitoring and early warning.

[0007] The technical solution adopted by the present invention to solve the above problems is: a seepage safety monitoring, forecasting and early warning system for earth-rock dams, including a data acquisition module, a dam safety monitoring and forecasting model module, a dam safety early warning analysis module, and an early warning setting and push module;

[0008] The data acquisition module is used to collect reservoir water level, rainfall and dam seepage monitoring data;

[0009] The dam safety monitoring and prediction model module is used to construct a dam seepage safety monitoring and prediction model based on reservoir water level, rainfall and dam seepage monitoring data to predict the seepage safety status;

[0010] The dam safety early warning analysis module is used to construct seepage safety monitoring and early warning indicators;

[0011] The dam safety monitoring and forecasting model module and the dam safety early warning analysis module visualize the modeling parameters and results;

[0012] The warning setting and push module is used to set the warning mode of each seepage pressure measuring point to manual warning or automatic warning, count and display the warning results, set the warning push object, and send the warning information through the communication network.

[0013] Preferably, the dam seepage safety monitoring and prediction model includes two types: a statistical model and a machine learning model.

[0014] A method for constructing a seepage safety monitoring and prediction model for an earth-rock dam comprises the following steps:

[0015] S1: Obtain historical monitoring data on the water level, rainfall, and seepage of the earth-rock dam reservoir, and collect data on the dam's geological surveys, geophysical inspections, and grouting tests;

[0016] S2: Using monitoring, geological survey, testing and test data, calculate the equivalent reservoir water level taking into account the lag time of the leakage channel;

[0017] S3: Based on the historical monitoring data in step S1 and the equivalent reservoir water level of the leakage channel in step S2, a seepage monitoring and prediction model for the earth-rock dam is constructed, and the seepage safety status is predicted using the prediction model.

[0018] Preferably, the hysteresis effect in step S2 is described by a normal distribution function, thereby obtaining the equivalent reservoir water level expression of the leakage channel as follows:

[0019]

[0020] Where Hd is the equivalent reservoir water level considering the lag time of the leakage channel; is the normal distribution weight function; H(t) is the reservoir water level; α1 is the adjustment parameter; x1 is the number of days of water level lag; x2 is the distribution parameter of water level lag effect; is a step function.

[0021] Preferably, in the equivalent reservoir water level of the leakage channel, the hysteresis days expression derived by using Darcy's law of fractured media is:

[0022]

[0023] Where, α is the adjustment parameter; v is the flow velocity; b is the crack width; μ is the viscosity coefficient of water; L is the seepage diameter; H m ( t ) is the seepage pressure water level at the measuring point.

[0024] Preferably, the step function expression of the equivalent reservoir water level in the leakage channel is:

[0025]

[0026] Where Hc is the critical reservoir water level elevation for seepage state change, which is related to the elevation of the leakage channel.

[0027] Preferably, the earth-rock dam seepage monitoring and prediction model expression in step S3 is as follows:

[0028]

[0029] Where h is the predicted value of the seepage pressure water level; hH is the reservoir water level component, hP is the rainfall component, hθ is the time-dependent component, Hd0 is the equivalent reservoir water level function of the continuous medium, Hd is the equivalent water level function of the leakage channel, Pd is the equivalent rainfall, θ is 1 / 100 of the cumulative number of days from the start date of modeling, b0–b5 are regression coefficients, and b0 is a constant term.

[0030] Preferably, the equivalent rainfall expression is as follows:

[0031]

[0032] Where, ω 2 (t) is the normal distribution weight function; P(t) is the rainfall at time t, α2 is the adjustment parameter; x3 is the number of days of rainfall lag to be determined, x4 is the rainfall effect distribution parameter of the to-be-determined coefficient, and β is the infiltration transformation index, which is taken as β=0.4.

[0033] Preferably, the coefficients of the earth-rock dam seepage monitoring and prediction model are solved using multiple linear regression and kernel extreme learning machine respectively.

[0034] A method for monitoring and warning the safety of seepage in an earth-rock dam, comprising the following steps:

[0035] Sa: Based on the established forecast model, the predicted value of seepage pressure is generated by inputting the rainfall and reservoir water level forecast values into the forecast model;

[0036] Sb: Based on the predicted value of seepage pressure, the confidence interval method is used to construct a seepage pressure monitoring and early warning indicator;

[0037] Sc: Based on historical data and real-time monitoring data, continuously update monitoring data, forecast models and early warning indicators, regularly maintain and update models, maintain model sensitivity, achieve online early warning of dam seepage safety, and evaluate dam safety status in real time;

[0038] The seepage pressure monitoring early warning indicators in step Sb include level one, level two, level three, and level four early warning indicators, corresponding to confidence levels α=0.3%, 1%, 5%, and 10%, respectively.

[0039] Compared with the prior art, the advantages of the present invention are:

[0040] 1. This invention proposes a seepage safety monitoring, forecasting and early warning system for earth-rockfill dams that takes into account the influence of seepage channels. It integrates multiple functions such as data acquisition and processing, forecasting model establishment, early warning indicator construction, model and result visualization, and early warning information release, enabling real-time analysis and early warning of the seepage pressure safety status.

[0041] 2. The present invention constructs a leakage channel lag time function based on Darcy's law for fractured media, thereby establishing a seepage monitoring and prediction model that takes the leakage channel lag time into account, thereby improving the accuracy of seepage monitoring and prediction under complex seepage conditions;

[0042] 3. Based on the proposed seepage monitoring and prediction model, the present invention uses the confidence interval method to propose a seepage monitoring and early warning method that takes into account the lag time of the leakage channel. It can continuously update the monitoring data, prediction model and early warning indicators, realize online early warning of dam seepage safety, and evaluate the dam safety status in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a structural diagram of a seepage safety monitoring, forecasting and early warning system for earth-rock dams according to the present invention;

[0044] Figure 2 This is a flow chart of a method for monitoring and predicting earth-rock dam seepage safety according to the present invention;

[0045] Figure 3 This is a process diagram of rainfall, water level and seepage pressure of the dam in the embodiment;

[0046] Figure 4 This is a graph showing the forecast and warning results of the statistical model in the embodiment;

[0047] Figure 5 This is a cloud diagram of the kernel extreme learning machine model parameter optimization in the embodiment;

[0048] Figure 6 This is a diagram of the prediction and warning results of the nuclear extreme learning machine model in the embodiment. DETAILED DESCRIPTION

[0049] like Figure 1 As shown, a seepage safety monitoring, forecasting and early warning system for earth-rock dams includes a data acquisition module, a dam safety monitoring and forecasting model module, a dam safety early warning analysis module, and an early warning setting and push module;

[0050] The data acquisition module is used to collect reservoir water level, rainfall and dam seepage monitoring data;

[0051] The dam safety monitoring and prediction model module is used to build a dam seepage safety monitoring and prediction model based on reservoir water level, rainfall and dam seepage monitoring data to predict the seepage safety status. The dam seepage safety monitoring and prediction model includes two types: statistical model and machine learning model;

[0052] The dam safety early warning analysis module is used to construct seepage safety monitoring and early warning indicators;

[0053] The dam safety monitoring and forecasting model module and the dam safety early warning analysis module visualize the modeling parameters and results;

[0054] The warning setting and push module is used to set the warning mode of each seepage pressure measuring point to manual warning or automatic warning, count and display the warning results, set the warning push object, and send the warning information through the communication network;

[0055] like Figure 2 As shown, based on the above earth-rock dam seepage safety monitoring, forecasting and early warning system, a method for constructing an earth-rock dam seepage safety monitoring and forecasting model is provided. This model method takes into account the lag time of the leakage channel and includes the following steps:

[0056] S1: Obtain historical monitoring data on the water level, rainfall, and seepage of the earth-rock dam reservoir, and collect data on the dam's geological surveys, geophysical inspections, and grouting tests;

[0057] S2: Using monitoring, geological survey, testing and test data, calculate the equivalent reservoir water level taking into account the lag time of the leakage channel;

[0058] S3: Based on the historical monitoring data in step S1 and the equivalent reservoir water level of the leakage channel in step S2, a seepage monitoring and prediction model for the earth-rock dam is constructed, and the seepage safety status is predicted using the prediction model;

[0059] The hysteresis effect in step S2 is described by a normal distribution function, and the equivalent reservoir water level expression of the leakage channel is obtained as follows:

[0060]

[0061] Where Hd is the equivalent reservoir water level considering the lag time of the leakage channel; is the normal distribution weight function; H(t) is the reservoir water level; α1 is the adjustment parameter; x1 is the number of days of water level lag; x2 is the distribution parameter of water level lag effect; is a step function;

[0062] In the equivalent reservoir water level of the leakage channel, the hysteresis days expression is derived using Darcy's law of fractured media:

[0063]

[0064] Where, α is the adjustment parameter; v is the flow velocity; b is the crack width; μ is the viscosity coefficient of water; L is the seepage diameter; H m ( t ) is the seepage pressure water level at the measuring point;

[0065] The step function expression of the equivalent reservoir water level in the leakage channel is:

[0066]

[0067] Where Hc is the critical reservoir water level elevation for seepage state change, which is related to the elevation of the leakage channel;

[0068] The earth-rock dam seepage monitoring and prediction model expression in step S3 is as follows:

[0069]

[0070] Where h is the predicted value of the seepage pressure water level; hH is the reservoir water level component, hP is the rainfall component, hθ is the time-dependent component, Hd0 is the equivalent reservoir water level function of the continuous medium, Hd is the equivalent water level function of the leakage channel, Pd is the equivalent rainfall, θ is 1 / 100 of the cumulative number of days from the start date of modeling, b0~b5 are regression coefficients, and b0 is a constant term.

[0071] The equivalent rainfall expression is as follows:

[0072]

[0073] Where, ω 2 (t) is the normal distribution weight function; P(t) is the rainfall at time t (mm), α2 is the adjustment parameter; x3 is the number of days of rainfall lag to be determined, x4 is the rainfall effect distribution parameter of the to-be-determined coefficient, β is the infiltration transformation index, and β is taken as 0.4.

[0074] The coefficients of the earth-rock dam seepage monitoring and prediction model are solved using Multiple Linear Regression (MLR) and Kernel Extreme Learning Machine (KELM).

[0075] Based on the earth-rock dam seepage safety monitoring, forecasting and early warning system and the earth-rock dam seepage safety monitoring, forecasting and early warning model construction method, a method for earth-rock dam seepage safety monitoring, forecasting and early warning is also provided, comprising the following steps:

[0076] Sa: Based on the established forecast model, the predicted value of seepage pressure is generated by inputting the rainfall and reservoir water level forecast values into the forecast model;

[0077] Sb: Based on the predicted value of seepage pressure, the confidence interval method is used to construct a seepage pressure monitoring and early warning indicator;

[0078] Sc: Based on historical data and real-time monitoring data, continuously update monitoring data, forecast models and early warning indicators, regularly maintain and update models, maintain model sensitivity, achieve online early warning of dam seepage safety, and evaluate dam safety status in real time;

[0079] The seepage pressure monitoring early warning indicators in step Sb include level one, level two, level three, and level four early warning indicators, corresponding to confidence levels α=0.3%, 1%, 5%, and 10%, respectively.

[0080] The specific steps of embodiment, earth-rock dam seepage safety monitoring, forecasting and early warning are as follows:

[0081] Step 1: Take the historical monitoring data of reservoir water level, rainfall, and seepage of a certain earth-rock dam from October 15, 2020 to October 15, 2024 as an example to make forecasts and early warnings. The process line diagram of rainfall, water level and seepage pressure of the dam is shown in Figure 3 According to the data from the dam's previous geological surveys, geophysical inspections, and grouting tests, there are three permeable areas at 20m, 45m, and 102m below the dam crest on the right abutment.

[0082] Step 2: Pre-set parameters such as the elevation of the seepage channel, the length of the seepage path, and the width of the leakage channel, establish the equivalent reservoir water level of the leakage channel, build a seepage monitoring and prediction model for earth-rock dams, and use the prediction model to predict the seepage safety status. The model factor coefficients are solved using multiple linear regression (MLR) and kernel extreme learning machine (KELM). The constructed seepage pressure water level prediction model considering the leakage channel is expressed as follows:

[0083]

[0084] Step 3: Based on the established forecast model, the predicted water level and rainfall are manually input or the system automatically reads the predicted value of seepage pressure. The confidence interval method is used to construct the four-level monitoring and early warning indicators of seepage pressure. The predicted value, standard deviation, and level 1 to 4 early warning indicators of each measuring point are shown in Table 1. The statistical model forecast and early warning results are shown in Figure 4 , machine learning model parameter optimization cloud diagram see Figure 5 , forecast and warning results are shown in Figure 6 ;

[0085] Table 1 Unit: m

[0086]

[0087] Step 4: When the newly connected seepage pressure monitoring value exceeds the warning indicator of the corresponding level, the system automatically generates warning information and sends it to relevant personnel through the communication network, realizing online automatic warning and real-time assessment of the dam safety status.

[0088] Step 5: Complete the system database development on the SQL Server platform and enter relevant data. Use Java language to develop the information platform, use Python language to write the forecast and warning model script, use Json data format to transmit model parameters, and realize data interaction and forecast and warning system development.

[0089] In addition to the above embodiments, the present invention also includes other implementation methods. Any technical solutions formed by equivalent transformation or equivalent replacement should fall within the scope of protection of the claims of the present invention.

Claims

1. A method for constructing a seepage safety monitoring and prediction model for earth-rock dams, characterized by: The following steps are involved: S1: Obtain historical monitoring data on the water level, rainfall, and seepage of the earth-rock dam reservoir, and collect data on the dam's geological surveys, geophysical inspections, and grouting tests; S2: Using monitoring, geological survey, testing and test data, calculate the equivalent reservoir water level taking into account the lag time of the leakage channel; S3: Based on the historical monitoring data in step S1 and the equivalent reservoir water level of the leakage channel in step S2, a seepage monitoring and prediction model for the earth-rock dam is constructed, and the seepage safety status is predicted using the prediction model; The hysteresis effect in step S2 is described by a normal distribution function, and the equivalent reservoir water level expression of the leakage channel is obtained as follows: ； Where Hd is the equivalent reservoir water level considering the lag time of the leakage channel; is the normal distribution weight function; H(t) is the reservoir water level; α1 is the adjustment parameter; x1 is the number of days of water level lag; x2 is the distribution parameter of water level lag effect; Hc is the critical reservoir water level elevation for seepage state change, which is related to the elevation of the leakage channel; is a step function; In the equivalent reservoir water level of the leakage channel, the hysteresis days expression is derived using Darcy's law of fractured media: ； Where, α is the adjustment parameter; v is the flow velocity; b is the crack width; μ is the viscosity coefficient of water; L is the seepage diameter; H m ( t ) is the seepage pressure water level at the measuring point.

2. The method for constructing a seepage safety monitoring and prediction model for earth-rock dams according to claim 1, characterized in that: The step function expression of the equivalent reservoir water level in the leakage channel is: 。 3. The method for constructing a seepage safety monitoring and prediction model for earth-rock dams according to claim 1, characterized in that: The earth-rock dam seepage monitoring and prediction model expression in step S3 is as follows: ； Where h is the predicted value of the seepage pressure water level; hH is the reservoir water level component, hP is the rainfall component, hθ is the time-dependent component, Hd0 is the equivalent reservoir water level function of the continuous medium, Hd is the equivalent water level function of the leakage channel, Pd is the equivalent rainfall, θ is 1 / 100 of the cumulative number of days from the start date of modeling, b0–b5 are regression coefficients, and b0 is a constant term.

4. The method for constructing a seepage safety monitoring and prediction model for earth-rock dams according to claim 3, characterized in that: The equivalent rainfall expression is as follows: ； Where, ω 2 (t) is the normal distribution weight function; P(t) is the rainfall at time t, α2 is the adjustment parameter; x3 is the number of days of rainfall lag to be determined, x4 is the rainfall effect distribution parameter of the to-be-determined coefficient, and β is the infiltration transformation index, which is taken as β=0.

4.

5. The method for constructing a seepage safety monitoring and prediction model for earth-rock dams according to claim 1, characterized in that: The coefficients of the earth-rock dam seepage monitoring and prediction model are solved using multiple linear regression and kernel extreme learning machine respectively.

6. A system for monitoring, forecasting and warning of seepage safety in earth-rock dams, which implements the method for constructing a model for monitoring, forecasting and warning of seepage safety in earth-rock dams according to any one of claims 1 to 5, characterized in that: It includes data acquisition module, dam safety monitoring and forecasting model module, dam safety early warning analysis module, early warning setting and push module; The data acquisition module is used to collect reservoir water level, rainfall and dam seepage monitoring data; The dam safety monitoring and prediction model module is used to construct a dam seepage safety monitoring and prediction model based on reservoir water level, rainfall and dam seepage monitoring data to predict the seepage safety status; The dam safety early warning analysis module is used to construct seepage safety monitoring and early warning indicators; The dam safety monitoring and forecasting model module and the dam safety early warning analysis module visualize the modeling parameters and results; The warning setting and push module is used to set the warning mode of each seepage pressure measuring point to manual warning or automatic warning, count and display the warning results, set the warning push object, and send the warning information through the communication network.

7. The earth-rock dam seepage safety monitoring, forecasting and early warning system according to claim 6 is characterized by: The dam seepage safety monitoring and prediction model includes two types: a statistical model and a machine learning model.

8. A method for monitoring and warning the safety of seepage in earth-rock dams, using the method for constructing a model for monitoring and warning the safety of seepage in earth-rock dams according to any one of claims 1 to 5, characterized in that: The following steps are involved: Sa: Based on the established forecast model, the predicted value of seepage pressure is generated by inputting the rainfall and reservoir water level forecast values into the forecast model; Sb: Based on the predicted value of seepage pressure, the confidence interval method is used to construct a seepage pressure monitoring and early warning indicator; Sc: Based on historical data and real-time monitoring data, continuously update monitoring data, forecast models and early warning indicators, regularly maintain and update models, maintain model sensitivity, achieve online early warning of dam seepage safety, and evaluate dam safety status in real time; The seepage pressure monitoring early warning indicators in step Sb include level one, level two, level three, and level four early warning indicators, corresponding to confidence levels α=0.3%, 1%, 5%, and 10%, respectively.

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