Tailings dam slope and dam foundation drainage coordination method and system

Abstract

The application discloses a tailing pond dam slope and dam foundation seepage coordination method and system, relates to the intelligent seepage control technical field of tailing pond, and comprises the following steps: collecting external rainfall forecast data, combining with dam body-dam foundation state monitoring data, predicting and analyzing future seepage states of the dam slope and the dam foundation through a seepage-stress coupling numerical model, generating a predictive seepage drainage regulation instruction when it is predicted that the saturation line has an out-of-limit risk, executing the predictive seepage drainage regulation instruction, synchronously collecting real-time mechanical response data, comparing and analyzing the seepage drainage regulation log and the predictive seepage drainage regulation instruction, and dynamically updating the seepage-stress coupling numerical model. The application compares and analyzes the seepage drainage regulation log and the predictive seepage drainage regulation instruction, and dynamically updates the seepage-stress coupling numerical model, thereby enhancing the reliability of the seepage-stress coupling numerical model and realizing long-term accurate prediction and regulation.

Description

Technical Field

[0001] This invention relates to the field of intelligent seepage control technology for tailings ponds, and in particular to a method and system for coordinated drainage of tailings dam slope and dam foundation. Background Technology

[0002] As a key facility in mining engineering, the stability of tailings dams directly affects environmental and public safety. Current tailings dam seepage control mainly relies on passive drainage engineering measures, such as setting drainage prisms on the dam slope, burying horizontal drainage pipes inside the dam body, and constructing vertical drainage wells. These measures collect and guide seepage water using permeable materials, as well as active control methods based on real-time monitoring. This involves deploying piezometer sensors to monitor the position of the phreatic line, and activating drainage facilities to intervene when the monitored value exceeds the limit. Traditional methods form the basis of seepage control, aiming to maintain the stability of the seepage field in the dam body through physical drainage and data feedback.

[0003] However, traditional methods still have room for optimization when dealing with complex working conditions; the control logic of traditional methods is mainly based on the hysteresis response of the phreatic line threshold, which lacks the foresight to predict the future evolution trend of the seepage field and makes it difficult to implement forward-looking intervention before changes in external loads such as heavy rainfall; shallow drainage of the dam slope and deep seepage control of the dam foundation are often treated as relatively independent treatment units, which fails to fully reflect the synergistic mechanism between the dam body and the dam foundation in terms of seepage path and mechanical response, thus limiting the overall efficiency of seepage control measures. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for coordinated drainage of tailings dam slope and foundation to address the problems of insufficient predictive intervention for risks of exceeding the seepage line limit and lack of dynamic coordinated control of drainage between dam slope and foundation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for coordinated seepage control between tailings dam slope and foundation, comprising: collecting dam-foundation status monitoring data and constructing a seepage-stress coupling numerical model of the tailings dam; collecting external rainfall forecast data and combining it with dam-foundation status monitoring data, using the seepage-stress coupling numerical model to predict and analyze the future seepage state of the dam slope and foundation, and generating a predictive seepage control command when the risk of exceeding the seepage line is predicted; executing the predictive seepage control command and simultaneously collecting real-time mechanical response data; evaluating the current stability state of the dam body based on the real-time mechanical response data and a preset stability criterion, and dynamically adjusting the seepage control capacity of the tailings dam according to the evaluation results, generating a seepage control log; comparing and analyzing the seepage control log and the predictive seepage control command, and dynamically updating the seepage-stress coupling numerical model.

[0008] As a preferred embodiment of the tailings dam slope and foundation seepage coordination method described in this invention, the steps for constructing the seepage-stress coupling numerical model of the tailings dam are as follows:

[0009] The dam body-foundation condition monitoring data includes pore water pressure data, soil stress data, and seepage flow data of drainage channels;

[0010] Collect engineering geological survey and mapping data, and establish a three-dimensional geological geometric model including the initial tailings dam, the accumulation dam body and the foundation soil and rock layers;

[0011] Based on the dam body-dam foundation state monitoring data, state mapping and parameter inversion are performed on the correspondence between seepage state and mechanical state in the dam body and dam foundation to generate a set of physical and mechanical parameters of the tailings dam.

[0012] The physical and mechanical parameters of the tailings dam are assigned to the corresponding region in the three-dimensional geological geometric model to form a three-dimensional numerical model.

[0013] In the three-dimensional numerical model, drainage units are embedded to simulate the main drainage pipe of the dam foundation and the horizontal drainage blind pipe of the dam body, and the initial boundary conditions and hydraulic boundary conditions of the three-dimensional numerical model are set to generate the seepage-stress coupling initial model.

[0014] The initial model of seepage-stress coupling was fitted and verified using dam body-foundation condition monitoring data to obtain a numerical model of seepage-stress coupling.

[0015] As a preferred embodiment of the tailings dam slope and foundation seepage coordination method described in this invention, the steps for predicting and analyzing the future seepage state of the dam slope and foundation using a seepage-stress coupled numerical model are as follows:

[0016] Collect external rainfall forecast data, and unify the time scale and spatial distribution to generate a rainfall forecast dataset;

[0017] The rainfall prediction dataset and the dam body-dam foundation condition monitoring data are synchronized in time and correlated with operating conditions to form a joint prediction input dataset.

[0018] The joint prediction input dataset is decoupled, boundary conditions and initial conditions are set, and the seepage-stress coupled numerical model is driven to perform transient simulation calculations to obtain the simulation data spectrum of the seepage-stress field.

[0019] As a preferred embodiment of the tailings dam slope and foundation seepage coordination method described in this invention, the step of generating a predictive seepage control command when the risk of exceeding the seepage line is predicted includes the following steps:

[0020] The spatial distribution and time history evolution information of the seepage lines in the dam body and dam foundation are extracted from the seepage-stress field simulation data spectrum to generate a set of predicted state parameters for the seepage lines.

[0021] Based on the predicted state parameter set of the phreatic line, the risk of exceeding the phreatic line limit on the dam slope and dam foundation is located and quantitatively assessed, the corresponding drainage control strategy is determined, and predictive drainage control instructions are generated.

[0022] As a preferred embodiment of the tailings dam slope and foundation seepage coordination method described in this invention, the steps of executing predictive seepage control commands and simultaneously collecting real-time mechanical response data are as follows:

[0023] The predictive drainage control commands are analyzed to identify the target drainage channels and control amplitudes, and drainage control parameters are generated.

[0024] Based on the drainage and seepage control parameters, the drainage capacity of the corresponding drainage and seepage control components is adjusted to form drainage and seepage control conditions.

[0025] Under seepage control conditions, real-time mechanical response data of the dam body and dam foundation under seepage control were collected.

[0026] As a preferred embodiment of the tailings dam slope and foundation seepage coordination method described in this invention, the step of evaluating the current stability state of the dam body according to a preset stability criterion includes the following steps.

[0027] Stability characteristic indicators that characterize the stress state and deformation response of the dam body are extracted from real-time mechanical response data and used as stability assessment data.

[0028] The stability assessment data is compared and analyzed with the preset stability criteria to evaluate the stability of the dam body under the current seepage control conditions and generate stability assessment results.

[0029] As a preferred embodiment of the tailings dam slope and foundation seepage coordination method described in this invention, the steps of dynamically adjusting the seepage capacity of the tailings dam based on the evaluation results and generating a seepage control log are as follows:

[0030] Based on the stability assessment results, a preset control strategy library is matched to generate dynamic drainage control decisions, and the drainage capacity of the corresponding drainage channel of the tailings dam is adjusted to form an updated drainage control condition.

[0031] Record the stability assessment results and the corresponding updated drainage and seepage control conditions, and generate a drainage and seepage control log.

[0032] As a preferred embodiment of the tailings dam slope and foundation seepage coordination method described in this invention, the steps for comparing and analyzing the seepage control log and predictive seepage control instructions are as follows:

[0033] The drainage control logs and predictive drainage control instructions are correlated and aligned in terms of time series and control targets to form a correlation comparison dataset;

[0034] Comparative analysis of the associated comparison dataset is performed to evaluate the difference between the expected effect of the predictive drainage control command and the actual control effect recorded in the drainage control log, and to generate the seepage-stress field control effect bias.

[0035] Based on the deviation of the seepage-stress field control effect, the sources of the inconsistency between the expected and actual control effects are analyzed, and the model correction parameters are determined.

[0036] As a preferred embodiment of the tailings dam slope and dam foundation seepage coordination method described in this invention, the dynamic updating of the seepage-stress coupling numerical model refers to updating the corresponding parameters in the seepage-stress coupling numerical model using model correction parameters to generate an updated seepage-stress coupling numerical model.

[0037] Secondly, this invention provides a tailings dam slope and foundation seepage coordination system, comprising,

[0038] The model building module is used to collect dam body-dam foundation condition monitoring data and build a seepage-stress coupling numerical model of the tailings dam.

[0039] The prediction and decision-making module is used to collect external rainfall forecast data and combine it with dam body-dam foundation status monitoring data. It uses a seepage-stress coupled numerical model to predict and analyze the future seepage status of the dam slope and dam foundation. When the risk of exceeding the limit of the seepage line is predicted, a predictive drainage control instruction is generated.

[0040] The control and response module is used to execute predictive drainage control commands and simultaneously collect real-time mechanical response data.

[0041] The stability assessment module is used to assess the current stability of the dam body based on real-time mechanical response data and preset stability criteria, and dynamically adjust the seepage discharge capacity of the tailings dam according to the assessment results, generating a seepage control log.

[0042] The model correction module is used to compare and analyze the seepage control logs and predictive seepage control instructions, and to dynamically update the seepage-stress coupling numerical model.

[0043] The beneficial effects of this invention are as follows: By predicting and analyzing the future seepage state of the dam slope and foundation based on the seepage-stress coupling numerical model, dynamic simulation of the seepage field and advanced risk identification are realized, enabling proactive early warning and early intervention, thus improving the predictability and initiative of tailings dam safety management; by comparing and analyzing the seepage control log and predictive seepage control instructions, and dynamically updating the seepage-stress coupling numerical model, the reliability of the seepage-stress coupling numerical model is enhanced, achieving long-term accurate prediction and control. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of a method for coordinated drainage of tailings dam slope and dam foundation.

[0046] Figure 2 This is a schematic diagram of the coordinated drainage system of the tailings dam slope and dam foundation.

[0047] Figure 3 A flowchart for generating predictive drainage control instructions.

[0048] Figure 4 A flowchart for updating the seepage-stress coupling numerical model. Detailed Implementation

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0050] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0052] Reference Figures 1-4As one embodiment of the present invention, this embodiment provides a method for coordinated drainage of tailings dam slope and dam foundation, comprising the following steps:

[0053] S1. Collect monitoring data on the condition of the dam body and foundation, and construct a numerical model of the seepage-stress coupling of the tailings dam.

[0054] S1.1: Dam body-foundation condition monitoring data includes pore water pressure data, soil stress data, and seepage flow data of drainage channels.

[0055] It should be noted that pore water pressure data refers to the pressure value borne by water in the pores of the tailings dam body and foundation soil, which is obtained through real-time monitoring by piezometers buried at different elevations and locations in the dam body and foundation. It includes static pore water pressure values ​​at different measuring points (used to calculate the initial phreatic line) and dynamic change process lines (reflecting the real-time changes in pore water pressure under the effects of rainfall and drainage regulation). Soil stress data refers to the internal forces (including skeletal stress and pore water pressure) borne per unit area within the tailings dam body and foundation materials, which is obtained through synchronous monitoring by earth pressure cells buried in the dam body and foundation and arranged in conjunction with the piezometers. The seepage flow rate data of the drainage channel refers to the water flow rate through the main drainage pipe of the dam foundation and the horizontal drainage blind pipe drainage facilities of the dam body, which is obtained through continuous monitoring by installing flow meters (such as electromagnetic flow meters) at the outlet of the drainage pipe, including instantaneous flow rate values ​​and cumulative flow rate values.

[0056] S1.2: Collect engineering geological survey and mapping data, and establish a three-dimensional geological geometric model including the initial tailings dam, the accumulation dam body, and the foundation soil and rock layers.

[0057] Furthermore, the engineering geological survey and mapping data includes elevation point information of the initial tailings dam crest, slope, and surface of the accumulation dam body obtained through field surveying, as well as information on the stratum depth, layer thickness distribution, and interlayer interface location of the foundation soil and rock layers obtained through engineering geological surveys. The elevation point information and the stratum depth, layer thickness distribution, and interlayer interface location information are converted and corrected using a unified coordinate reference and elevation reference to generate integrated spatial data under the same spatial reference system. Based on the integrated spatial data, the outline of the initial tailings dam and the accumulation dam body, as well as the layer interface lines of the foundation soil and rock layers, are extracted. Layer-by-layer spatial interpolation and boundary closure processing are performed on the outline and layer interface lines to construct independent three-dimensional spatial entities corresponding to the initial tailings dam, the accumulation dam body, and each layer of foundation soil and rock layers. These independent three-dimensional spatial entities are then spatially stitched together and their topological relationships are checked to establish spatial contact relationships between the entities, generating a three-dimensional geological geometric model containing the initial tailings dam, the accumulation dam body, and the foundation soil and rock layers.

[0058] S1.3: Based on the dam body-dam foundation state monitoring data, perform state mapping and parameter inversion on the correspondence between the seepage state and mechanical state in the dam body and dam foundation to generate a set of physical and mechanical parameters for the tailings dam.

[0059] Furthermore, pore water pressure and soil stress data sequences for each measuring point within a specific time period (such as a complete hydrological year) are extracted from the dam body-foundation condition monitoring data. These sequences are then combined with a three-dimensional geological geometric model, and forward modeling of the seepage and stress fields is performed using the finite element method. This yields theoretical calculation sequences of pore water pressure and soil stress for each measuring point within the corresponding time period, collectively forming a theoretical calculation data sequence. This theoretical calculation data sequence is then compared point-by-point and time-by-time with the pore water pressure and soil stress data sequences extracted from the dam body-foundation condition monitoring data. With the goal of minimizing the root mean square error between the theoretical calculation data sequence and the measured sequence over the entire time period at all measuring points, a multi-objective optimization function is established. The values ​​of the parameters to be inverted (such as permeability coefficient, elastic modulus, cohesion, and internal friction angle) of each soil and rock layer material (such as tailings silt, tailings soil, and dam foundation overburden) in the three-dimensional geological geometric model are continuously adjusted using the particle swarm optimization algorithm until the multi-objective optimization function converges to the error tolerance threshold. The parameter values ​​of each soil and rock layer material that are finally optimized by the particle swarm optimization algorithm and have the highest agreement between the theoretical calculation and the measured data are summarized to generate a set of physical and mechanical parameters of the tailings dam.

[0060] The expression for calculating the root mean square error is:

[0061] ;

[0062] in, It is the root mean square error, used to quantify the difference between theoretical calculations and measured data; This is the total number of data points, including all measurement points and time steps; It is the first Calculated pore water pressure values ​​for each data point; It is the first Measured values ​​of pore water pressure at each data point.

[0063] It should be noted that the finite element method (FEM) is a numerical method for solving the physical field control equations by dividing a complex geometric structure into a grid connected by nodes and making mathematical approximations within each grid. In this embodiment, the FEM is used to discretize the three-dimensional geological geometric model. By coupling the solution of seepage and stress control equations on each grid, the pore water pressure distribution and stress response of the dam body under external loads are simulated and calculated. The particle swarm optimization (PSO) algorithm is a global optimization algorithm that simulates the cooperative behavior of a group. It maintains a population of candidate solutions (called "particles") and allows the particles to move collaboratively in the search space based on the historical best solutions of the individual and the group to find the optimal solution. In this embodiment, the PSO algorithm is used for parameter inversion: each particle represents a set of candidate values ​​of soil and rock layer parameters in the three-dimensional geological geometric model. The search is guided by the degree of fit between the finite element forward modeling results and the dam body-foundation state monitoring data to find the set of physical and mechanical parameters of the tailings dam that best matches the calculated values ​​with the measured values.

[0064] It should be noted that the error tolerance threshold is determined by setting an initial reference threshold based on the measurement accuracy of the monitoring instrument. A portion of the dam-foundation condition monitoring data is used to calibrate the initial seepage-stress coupling model. The degree of agreement between the calculation results of the initial seepage-stress coupling model and the dam-foundation condition monitoring data is observed. Another portion of independent dam-foundation condition monitoring data is used to verify the predictive accuracy of the calibrated initial seepage-stress coupling model. Based on the combined effect of calibration and verification, and under the premise of ensuring the reliability of the initial seepage-stress coupling model and engineering safety, the initial reference threshold is iteratively adjusted and confirmed. An exemplary value range is 3% to 10% of the actual measured value. If it exceeds 10%, the initial seepage-stress coupling model calibration will be too lenient, failing to capture key seepage-stress response characteristics. If it is below 3%, it may cause overfitting, making the initial seepage-stress coupling model overly sensitive to random fluctuations in the dam-foundation condition monitoring data.

[0065] S1.4: Assign the set of physical and mechanical parameters of the tailings dam to the corresponding region in the three-dimensional geological geometric model to form a three-dimensional numerical model.

[0066] Furthermore, based on the spatial coordinates and geometric morphology data of the initial dam geometry, the accumulation dam geometry, and the foundation soil and rock layers of the tailings dam as defined in the three-dimensional geological geometric model, the material type attribute labels (e.g., tailings sand, clay layer, and gravel layer) corresponding to each independent geometric part are identified and extracted. According to the material type attribute labels, the permeability coefficient, elastic modulus, cohesion, and internal friction angle parameters corresponding to the material type in the tailings dam physical and mechanical parameters are assigned to the corresponding attribute fields of all geometric parts with the same material type attribute labels in the three-dimensional geological geometric model. The physical attribute fields of each geometric part in the three-dimensional geological geometric model are verified to have been filled with the corresponding parameter values, forming a three-dimensional numerical model with definite material mechanical properties.

[0067] S1.5: In the three-dimensional numerical model, a drainage unit is embedded to simulate the main drainage pipe of the dam foundation and the horizontal drainage blind pipe of the dam body, and the initial boundary conditions and hydraulic boundary conditions of the three-dimensional numerical model are set to generate the seepage-stress coupling initial model.

[0068] Furthermore, based on the spatial location and geometric dimensions determined by the actual design drawings of the main drainage pipe of the dam foundation and the horizontal drainage blind pipe of the dam body, corresponding elongated geometric entities with circular and square cross sections are created in the three-dimensional numerical model to accurately represent the physical existence of the main drainage pipe of the dam foundation and the horizontal drainage blind pipe of the dam body. The geometric entities representing the drainage pipes are assigned extremely high permeability coefficient values ​​to simulate the rapid drainage function of the drainage pipes. Based on the initial pore water pressure distribution obtained from the dam body-dam foundation condition monitoring data, the initial pore water pressure field of each part of the three-dimensional numerical model is set to the corresponding measured value. The upstream dam slope surface and reservoir surface of the three-dimensional numerical model are defined as the flow boundaries that may accept rainfall infiltration. The outlet of the main drainage pipe of the dam foundation and the downstream escape surface of the three-dimensional numerical model are defined as the pore water pressure boundaries, generating a seepage-stress coupling initial model.

[0069] S1.6: The initial model of seepage-stress coupling is fitted and verified using the dam body-dam foundation condition monitoring data to obtain the numerical model of seepage-stress coupling.

[0070] Furthermore, using pore water pressure data and soil stress data from specific time periods in the dam body-foundation condition monitoring data as benchmarks, the seepage-stress coupling initial model is driven to perform forward modeling calculations. This generates theoretical data sequences of pore water pressure and soil stress at corresponding measuring points within the same time period. These sequences are then compared point-by-point and time-by-time with the benchmark pore water pressure and soil stress data, respectively, to calculate the root mean square error and form a comprehensive difference index. With the goal of minimizing the comprehensive difference index, the particle swarm optimization algorithm is used to automatically fine-tune the material parameters (such as permeability coefficient and elastic modulus) in the seepage-stress coupling initial model, and the forward modeling calculation and difference comparison are performed again. This process is repeated until the comprehensive difference index is lower than the error tolerance threshold, at which point the seepage-stress coupling initial model is considered to have been calibrated, and the seepage-stress coupling numerical model is obtained.

[0071] S2. Collect external rainfall forecast data and combine it with dam body-dam foundation status monitoring data. Use a seepage-stress coupled numerical model to predict and analyze the future seepage status of the dam slope and dam foundation. When the risk of exceeding the limit of the phreatic line is predicted, generate predictive drainage control instructions.

[0072] S2.1: Collect external rainfall forecast data, unify the time scale and spatial distribution, and generate a rainfall forecast dataset.

[0073] Furthermore, external rainfall forecast data, published in grid or station format, is obtained from meteorological data sources. For the various time resolutions (e.g., 6-hourly, 12-hourly forecasts) that may exist in the external rainfall forecast data, linear interpolation is used to unify all data into a fixed time step sequence that matches the calculation step size of the seepage-stress coupled numerical model. For spatial distribution, if the external rainfall forecast data is station data, Kriging interpolation is used to convert the data into continuous spatial grid data covering the entire tailings dam area; if the external rainfall forecast data is grid data, a subset of grid data corresponding to the tailings dam area is directly extracted. The data, after time scale unification and spatial distribution processing, is organized into a rainfall prediction dataset containing time dimension, spatial location coordinate dimension, and rainfall intensity numerical fields.

[0074] S2.2: Synchronize the rainfall prediction dataset with the dam body-dam foundation condition monitoring data in time and correlate them with the operating conditions to form a joint prediction input dataset.

[0075] Furthermore, rainfall intensity data for each time step in the rainfall prediction dataset is extracted, and dam body-foundation condition monitoring data under the same time reference is obtained. The time series of the rainfall prediction dataset is aligned with the time series of the dam body-foundation condition monitoring data to ensure that each prediction time point has corresponding dam body-foundation condition monitoring data that characterizes the current dam body state. Based on the aligned time series, the rainfall intensity data at each time point is paired and combined with the corresponding dam body-foundation condition monitoring data (as the initial state or boundary condition input of the seepage-stress coupled numerical model), and arranged in chronological order to generate a joint prediction input dataset.

[0076] S2.3: Decouple the joint prediction input dataset, set boundary and initial conditions, and drive the seepage-stress coupled numerical model to perform transient simulation calculations to obtain the seepage-stress field simulation data spectrum.

[0077] Furthermore, rainfall prediction data is extracted from the joint prediction input dataset and applied as time-varying hydraulic boundary conditions to the upstream dam slope surface and reservoir surface boundary of the seepage-stress coupled numerical model. At the same time, the latest dam body-foundation state monitoring data is extracted as the initial pore water pressure field and initial stress field of the seepage-stress coupled numerical model. After setting the boundary conditions and initial conditions, the transient solution of the seepage-stress coupled numerical model is executed to calculate the full spatiotemporal distribution of pore water pressure and stress in the dam body and foundation during the future prediction period, and integrate them to generate the seepage-stress field simulation data spectrum.

[0078] S2.4: Extract the spatial distribution and time history evolution information of the seepage line in the dam body and dam foundation from the seepage-stress field simulation data spectrum, and generate a set of predicted state parameters for the seepage line.

[0079] Furthermore, the full-field pore water pressure distribution data corresponding to each time step in the seepage-stress field simulation data spectrum is read. Based on the isosurface judgment criterion of zero pore water pressure, the three-dimensional coordinate set of spatial points in the dam body and dam foundation that meet the judgment criterion is identified and extracted as the spatial distribution discrete point set of the seepage line. Spatial interpolation processing is performed on the spatial distribution discrete point set of the seepage line at each time step to generate continuous curves and surface function expressions that characterize the spatial morphology of the seepage line. These are arranged and combined in chronological order to extract the time history evolution law of the spatial position of the seepage line changing with time. The spatial distribution function expression and time history evolution law information of the seepage line at each time step are organized to generate a set of predicted state parameters of the seepage line containing the spatial coordinate sequence and the time-position correspondence.

[0080] It should be noted that the phreatic line is an important dividing line in a tailings dam. Above it is the unsaturated zone, where the soil pores are partially filled with air and water, while below it is the saturated zone, where the soil pores are completely filled with water. The position of the phreatic line determines the distribution of the seepage field and stress field inside the dam body and is a core indicator for assessing the stability of the dam slope. If the phreatic line is too high, it will significantly reduce the effective stress for the dam body to resist sliding, increase the risk of seepage failure (such as piping), and seriously threaten the safety of the dam.

[0081] S2.5: Based on the predicted state parameter set of the phreatic line, locate and quantify the risk of the phreatic line exceeding the limit of the dam slope and dam foundation, determine the corresponding drainage control strategy, and generate predictive drainage control instructions.

[0082] Furthermore, the spatial coordinate sequence of the seepage line in the predicted state parameter set is compared point by point with the safe elevation threshold to identify the locations where the elevation exceeds the safe elevation threshold among all coordinate points. This is then mapped and matched with the spatial coordinates of the three-dimensional geological geometric model to complete the regional location of the seepage line exceeding the limit risk in the dam slope and dam foundation. The average depth of the seepage line exceeding the safe elevation threshold, the total area of ​​the exceeding area, and the maximum rate of change of the exceeding depth over time are calculated for each region. Based on the results of risk location and quantitative assessment, and according to the preset risk level-control intensity mapping relationship, the seepage control strategies to be implemented for different exceeding areas are matched and determined, including the target seepage channel, control action type, and control amplitude. The seepage control strategy is transformed into a predictive seepage control instruction that includes specific execution time, target object (such as a section of the dam foundation main seepage pipe or a horizontal seepage blind pipe of a dam body layer) and operating parameters (such as the target valve opening degree) that can drive the actuator.

[0083] It should be noted that the process of setting the risk level-control intensity mapping relationship is as follows: Historical monitoring data and seepage-stress coupling numerical simulation results are collected. The stability state of the dam body corresponding to different combinations of exceeding the limit depth, exceeding the limit area and change rate of the seepage line is divided into multiple risk levels: low, medium, high and emergency. Combined with the design drainage capacity of the tailings dam seepage channels and engineering experience, a preliminary control intensity benchmark value is matched for each risk level (e.g., low risk corresponds to small-amplitude adjustment, and high risk corresponds to starting multiple seepage channels and setting a higher target drainage flow). The preliminary mapping relationship is simulated and verified in multiple sets of virtual working conditions and the parameters are fine-tuned in the seepage-stress coupling numerical model to ensure that the control action can effectively control the risk without adverse effects. The verified mapping relationship is solidified into a query mapping rule library containing clear input conditions and output control parameters.

[0084] S3. Execute predictive drainage control commands and simultaneously collect real-time mechanical response data.

[0085] S3.1: Analyze the predictive drainage control instructions, identify the target drainage channels and control amplitude, and generate drainage control parameters.

[0086] Furthermore, the data structure of the predictive drainage control command is analyzed, and the identification information of the target drainage channel and the numerical information of the control amplitude are directly extracted from the corresponding fields. These are then correlated and combined to generate drainage control parameters.

[0087] S3.2: Adjust the drainage capacity of the corresponding drainage control components according to the drainage control parameters to form drainage control conditions.

[0088] Furthermore, based on the target drainage channel identifier specified in the drainage control parameters, the corresponding drainage control component (such as a specific electric valve) is located. The control amplitude value in the drainage control parameters is converted into a physical action command that the drainage control component can execute (such as converting the opening percentage into a valve control signal). The physical action command is sent to the drainage control component to drive it to perform opening, closing, and opening adjustment operations, thereby changing the flow capacity of the corresponding drainage channel. After confirming that the drainage control component has executed the command and reached the expected state, the status of all adjusted drainage control components and the corresponding channel drainage capacity are recorded to form the drainage control conditions.

[0089] S3.3: Under the seepage control condition, collect real-time mechanical response data of the dam body and dam foundation under the seepage control action.

[0090] Furthermore, after the drainage and seepage control conditions are established, pore water pressure data and soil stress data at each measuring point under the drainage and seepage control conditions are continuously collected at a fixed sampling frequency (e.g., once per minute), and strictly correlated with the start timestamp of the drainage and seepage control conditions to form real-time mechanical response data.

[0091] S4. Based on real-time mechanical response data, assess the current stability of the dam body according to preset stability criteria, and dynamically adjust the seepage discharge capacity of the tailings dam according to the assessment results, generating a seepage control log.

[0092] S4.1: Extract stability characteristic indicators that characterize the stress state and deformation response of the dam body from real-time mechanical response data, and use them as stability assessment data.

[0093] Furthermore, based on real-time mechanical response data, the ratio of soil stress data to pore water pressure data at each measuring point at the same time is calculated to obtain time-series data of excess pore water pressure ratio reflecting the effective stress state of the dam body; the rate of change of pore water pressure data at each measuring point is extracted as a seepage stability index, and the gradient of difference in soil stress data between different measuring points is calculated as a stress concentration index; the time-series data of excess pore water pressure ratio, seepage stability index, and stress concentration index are integrated as stability assessment data.

[0094] The expression for calculating the stress concentration index is:

[0095] ;

[0096] in, It is a stress concentration index; This is the total number of measuring points; It is the first The measured values ​​of soil stress at each measuring point; It is the arithmetic mean of the soil stress measurements at all measuring points; It is the measurement point index.

[0097] S4.2: Compare and analyze the stability assessment data with the preset stability criteria to evaluate the stability of the dam body under the current seepage control conditions and generate stability assessment results.

[0098] Furthermore, the time-series data of excess pore water pressure ratio in the stability assessment data are compared with the excess pressure ratio threshold in the stability criterion: if the ratio of all measuring points is lower than the excess pressure ratio threshold, it is considered acceptable; if the ratio of some measuring points is within the excess pressure ratio threshold, it is considered slightly exceeding the standard; if the ratio of some measuring points exceeds the excess pressure ratio threshold, it is considered severely exceeding the standard. The seepage stability index is compared with the seepage stability range in the stability criterion: if the seepage stability index is within the seepage stability range throughout, it is considered normal; if it momentarily exceeds the seepage stability range but quickly recovers (e.g., falls back within 3 minutes), it is considered momentary abnormal. If the seepage continues to exceed the stable seepage range, it is considered a continuous anomaly. The stress concentration index is compared with the non-uniformity coefficient (e.g., 0.3) required by the criterion: if the stress concentration index is lower than the non-uniformity coefficient, it is considered uniformly distributed; if it exceeds the non-uniformity coefficient, it is considered stress concentration. The stable state is determined by combining the comparison results of the three types of indicators: all meet the standard, it is stable; only slightly exceed the standard or instantaneous anomaly, it is basically stable; serious exceed the standard or continuous anomaly but no stress concentration occurs at the same time, it is understability; any two serious anomalies or stress concentration are present, it is unstable. A stability assessment result with a clear label of the stability state level and specific exceedance indicators is generated.

[0099] It should be noted that the stability criterion is an index system and judgment standard used to quantitatively assess the stability state of tailings dams, including the excess static pressure ratio threshold, seepage stability interval, and non-uniformity coefficient. The excess static pressure ratio threshold is determined by plotting the effective stress paths of tailings material under different consolidation pressures, identifying the critical pore water pressure ratio corresponding to the occurrence of strength failure, and analyzing the actual distribution of critical pore water pressure ratios under a large number of stable operating conditions in the long-term safety monitoring database of tailings dams, taking a high statistical quantile value (such as the 95th percentile). An exemplary value range is 0.4 to 0.5. The seepage stability interval is determined based on the sensor accuracy (such as ±0.5 kPa / min) to identify the measurement error zone, and is determined by... Through simulation and historical data analysis, a safe upper limit for the rate of change of pore water pressure that will not cause the migration of fine particles is determined. Finally, a conservative value that takes into account measurement error and engineering safety and leaves room for error is set as the boundary of the interval. An exemplary value range is ±1 to ±3 kPa / min. The non-uniformity coefficient is calculated by performing finite element analysis on the dam body under various working conditions, calculating the stress distribution in the dam body under normal load and extreme load, identifying the allowable stress difference gradient that will not cause plastic failure, and using the measured data of the earth pressure cells installed on the dam body to verify and calibrate the identification results. The critical gradient value that can effectively warn of the development of potential yield zones is set.

[0100] S4.3: Based on the stability assessment results, match the preset control strategy library, generate dynamic drainage control decisions, and adjust the drainage capacity of the corresponding drainage channels of the tailings dam to form updated drainage control conditions.

[0101] Furthermore, based on the stability level clearly marked in the stability assessment results, the control strategy entries corresponding to the stability level are queried in the control strategy library, and the target drainage channel identifier, control action type, and control amplitude parameters are extracted to generate a dynamic drainage control decision containing specific execution instructions. The dynamic drainage control decision drives the corresponding drainage control components to perform opening and closing operations, changing the flow capacity of the relevant drainage channels. The status of all adjusted drainage control components and the controlled drainage flow data are recorded to form the updated drainage control conditions.

[0102] It should be noted that the construction process of the control strategy library is as follows: Based on the seepage-stress coupling numerical model, a large number of numerical simulations were carried out for different stability levels. The simulations simulated the drainage and seepage control schemes required to achieve the target safety state under different rainfall intensities and different phreatic line heights. Combined with the historical operation data of the tailings dam, the drainage and seepage control schemes were verified and optimized. The optimized drainage and seepage control schemes were then classified according to the stability level, the type of exceeding index, and the degree of exceeding the standard to construct the control strategy library.

[0103] S4.4: Record the stability assessment results and the corresponding updated drainage control conditions, and generate a drainage control log.

[0104] Furthermore, the stability assessment results are recorded, including the stability level, specific deviations of each indicator, and assessment timestamps. At the same time, the identifiers of all regulated drainage control components in the updated drainage control operation are recorded, along with the status parameters before and after the adjustment action and the effective time of the operation. Time correlation binding is performed, and the dynamic drainage control decision content that triggered this control is supplemented and recorded to generate a drainage control log.

[0105] S5. Compare and analyze the drainage control log and predictive drainage control instructions, and dynamically update the seepage-stress coupling numerical model.

[0106] S5.1: Correlate and align the drainage control logs with the predictive drainage control instructions in terms of time series and control targets to form a correlation comparison dataset.

[0107] Furthermore, the seepage control logs and predictive seepage control instructions are aligned with a unified time base. The actual control actions recorded in the seepage control logs (such as valve opening adjustment values) are extracted and compared item by item with the expected control actions specified in the predictive seepage control instructions. At the same time, the stable state after control recorded in the seepage control logs is compared with the expected control targets of the predictive seepage control instructions. The differences in action execution and effect achievement obtained from the comparison are integrated with the corresponding timestamps and control target identifiers, arranged in chronological order, and a correlated comparison dataset is generated.

[0108] S5.2: Perform comparative analysis on the associated comparison dataset to evaluate the difference between the expected effect of the predictive drainage control command and the actual control effect recorded in the drainage control log, and generate the seepage-stress field control effect bias.

[0109] Furthermore, the expected control targets (such as the target phreatic line depth) recorded in the correlation comparison dataset are extracted and numerically compared with the stable state achieved after actual control (such as the measured phreatic line depth). The absolute and relative differences between the expected and actual values ​​of key indicators (such as phreatic line depth, pore water pressure change rate, and stress concentration) are calculated. In the time dimension, the control process is divided into three stages: initiation, execution, and stabilization. The mean and standard deviation of the differences within each stage are calculated to assess the characteristics of deviation changes over time. In the spatial dimension, based on the dam body and dam foundation zoning, the differences are... The values ​​are mapped to the corresponding regions, and the mean and coefficient of variation of the difference values ​​within each zone are calculated to assess the spatial distribution characteristics of the deviation. Based on the spatiotemporal analysis results, if the deviation only occurs in a specific stage of an individual zone (such as zone A of the dam foundation) (such as not exceeding 20% ​​of the entire control process), it is determined to be a local transient deviation. If the deviation persists in multiple zones (such as three or more zones) (such as covering more than 50% of the entire control process), it is determined to be an overall systematic deviation. The difference values ​​of various indicators, the spatiotemporal distribution characteristics of the deviation, and the type of deviation are integrated to generate the seepage-stress field control effect deviation.

[0110] S5.3: Based on the deviation of the seepage-stress field control effect, analyze the sources of the inconsistency between the expected effect and the actual control effect, and determine the model correction parameters.

[0111] Furthermore, based on the deviation of the seepage-stress field regulation effect, if the deviation type is determined to be a local instantaneous deviation, the source of influence is located to a specific material partition in the seepage-stress coupled numerical model corresponding to the region where the deviation occurs, and the model parameters that need to be corrected are the permeability coefficient and elastic modulus of the material in the specific material partition. If the deviation type is determined to be a global systemic deviation, the source of influence is attributed to the global settings of the seepage-stress coupled numerical model, and the permeability coefficient, elastic modulus, and hydraulic boundary conditions (such as rainfall infiltration coefficient and outlet pressure) of multiple material partitions in the model need to be corrected simultaneously. According to the specific difference value and direction of the deviation (such as a positive deviation if the measured value of the wetting line is higher than the expected value, and a negative deviation if it is lower), an optimization function with the goal of minimizing the deviation is established and solved using the particle swarm optimization algorithm. The optimal adjustment value and direction of various model parameters that need to be corrected are calculated by inversion (such as adjusting upward for positive deviation and downward for negative deviation), and model correction parameters consisting of model correction parameter identifiers, adjustment values, and adjustment directions are generated.

[0112] S5.4: Update the corresponding parameters in the seepage-stress coupling numerical model using the model correction parameters to generate the updated seepage-stress coupling numerical model.

[0113] Furthermore, based on the model correction parameter identifier in the model correction parameters, the specific target parameter that needs to be updated is located in the seepage-stress coupling numerical model. According to the adjustment direction (such as upward or downward adjustment) and adjustment amount, the target parameter value is directly updated arithmetically. After all the model correction parameters are updated, the updated seepage-stress coupling numerical model is generated.

[0114] This embodiment also provides a tailings dam slope and dam foundation seepage coordination system, including:

[0115] The model building module is used to collect dam body-dam foundation condition monitoring data and build a seepage-stress coupling numerical model of the tailings dam.

[0116] The prediction and decision-making module is used to collect external rainfall forecast data and combine it with dam body-dam foundation status monitoring data. It uses a seepage-stress coupled numerical model to predict and analyze the future seepage status of the dam slope and dam foundation. When the risk of exceeding the limit of the seepage line is predicted, a predictive drainage control instruction is generated.

[0117] The control and response module is used to execute predictive drainage control commands and simultaneously collect real-time mechanical response data.

[0118] The stability assessment module is used to assess the current stability of the dam body based on real-time mechanical response data and preset stability criteria, and dynamically adjust the seepage discharge capacity of the tailings dam according to the assessment results, generating a seepage control log.

[0119] The model correction module is used to compare and analyze the seepage control logs and predictive seepage control instructions, and to dynamically update the seepage-stress coupling numerical model.

[0120] In summary, this invention achieves dynamic simulation of the seepage field and advanced risk identification by using a seepage-stress coupled numerical model to predict and analyze the future seepage state of the dam slope and foundation. This enables proactive early warning and timely intervention, improving the predictability and initiative of tailings dam safety management. Furthermore, by comparing and analyzing the seepage control logs and predictive seepage control instructions, and dynamically updating the seepage-stress coupled numerical model, the reliability of the seepage-stress coupled numerical model is enhanced, enabling long-term accurate prediction and control.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated drainage of tailings dam slope and dam foundation, characterized in that: include, Collect monitoring data on the condition of the dam body and foundation, and construct a seepage-stress coupling numerical model for the tailings dam. External rainfall forecast data is collected and combined with dam body-foundation status monitoring data. The future seepage state of the dam slope and dam foundation is predicted and analyzed through a seepage-stress coupled numerical model. When the risk of exceeding the limit of the seepage line is predicted, a predictive drainage control instruction is generated. Execute predictive drainage control commands and simultaneously collect real-time mechanical response data; Based on real-time mechanical response data, the current stability state of the dam body is evaluated according to the preset stability criteria, and the drainage capacity of the tailings dam is dynamically adjusted according to the evaluation results to generate a drainage control log. Comparative analysis was conducted on the drainage control logs and predictive drainage control instructions, and the seepage-stress coupling numerical model was dynamically updated. The steps for constructing the seepage-stress coupling numerical model of the tailings dam are as follows: The dam body-foundation condition monitoring data includes pore water pressure data, soil stress data, and seepage flow data of drainage channels; Collect engineering geological survey and mapping data, and establish a three-dimensional geological geometric model including the initial tailings dam, the accumulation dam body and the foundation soil and rock layers; Based on the dam body-dam foundation state monitoring data, state mapping and parameter inversion are performed on the correspondence between seepage state and mechanical state in the dam body and dam foundation to generate a set of physical and mechanical parameters of the tailings dam. The physical and mechanical parameters of the tailings dam are assigned to the corresponding region in the three-dimensional geological geometric model to form a three-dimensional numerical model. In the three-dimensional numerical model, drainage units are embedded to simulate the main drainage pipe of the dam foundation and the horizontal drainage blind pipe of the dam body, and the initial boundary conditions and hydraulic boundary conditions of the three-dimensional numerical model are set to generate the seepage-stress coupling initial model. The initial model of seepage-stress coupling was fitted and verified using dam body-dam foundation condition monitoring data to obtain a numerical model of seepage-stress coupling. The steps for predicting and analyzing the future seepage state of the dam slope and dam foundation using a seepage-stress coupled numerical model are as follows. Collect external rainfall forecast data, and unify the time scale and spatial distribution to generate a rainfall forecast dataset; The rainfall prediction dataset and the dam body-dam foundation condition monitoring data are synchronized in time and correlated with operating conditions to form a joint prediction input dataset. The joint prediction input dataset is decoupled, boundary conditions and initial conditions are set, and the seepage-stress coupled numerical model is driven to perform transient simulation calculations to obtain the seepage-stress field simulation data spectrum. When a risk of exceeding the infiltration line is predicted, a predictive drainage control instruction is generated, and the steps are as follows: The spatial distribution and time history evolution information of the seepage lines in the dam body and dam foundation are extracted from the seepage-stress field simulation data spectrum to generate a set of predicted state parameters for the seepage lines. Based on the predicted state parameter set of the phreatic line, the risk of exceeding the phreatic line limit on the dam slope and dam foundation is located and quantitatively assessed, the corresponding drainage control strategy is determined, and predictive drainage control instructions are generated.

2. The tailings dam slope and foundation seepage coordination method as described in claim 1, characterized in that: The steps for executing predictive drainage control commands and simultaneously collecting real-time mechanical response data are as follows. The predictive drainage control commands are analyzed to identify the target drainage channels and control amplitudes, and drainage control parameters are generated. Based on the drainage and seepage control parameters, the drainage capacity of the corresponding drainage and seepage control components is adjusted to form drainage and seepage control conditions. Under seepage control conditions, real-time mechanical response data of the dam body and dam foundation under seepage control were collected.

3. The tailings dam slope and foundation seepage coordination method as described in claim 1, characterized in that: The steps for evaluating the current stability state of the dam body based on preset stability criteria are as follows: Stability characteristic indicators that characterize the stress state and deformation response of the dam body are extracted from real-time mechanical response data and used as stability assessment data. The stability assessment data is compared and analyzed with the preset stability criteria to evaluate the stability of the dam body under the current seepage control conditions and generate stability assessment results.

4. The tailings dam slope and foundation seepage coordination method as described in claim 1, characterized in that: The steps for dynamically adjusting the tailings dam's drainage capacity based on the assessment results and generating a drainage control log are as follows: Based on the stability assessment results, a preset control strategy library is matched to generate dynamic drainage control decisions, and the drainage capacity of the corresponding drainage channel of the tailings dam is adjusted to form an updated drainage control condition. Record the stability assessment results and the corresponding updated drainage and seepage control conditions, and generate a drainage and seepage control log.

5. The method for coordinated drainage of tailings dam slope and foundation as described in claim 1, characterized in that: The comparative analysis of the seepage control log and predictive seepage control instructions follows these steps. The drainage control logs and predictive drainage control instructions are correlated and aligned in terms of time series and control targets to form a correlation comparison dataset; Comparative analysis of the associated comparison dataset is performed to evaluate the difference between the expected effect of the predictive drainage control command and the actual control effect recorded in the drainage control log, and to generate the seepage-stress field control effect bias. Based on the deviation of the seepage-stress field control effect, the sources of the inconsistency between the expected and actual control effects are analyzed, and the model correction parameters are determined.

6. The method for coordinated drainage of tailings dam slope and foundation as described in claim 1, characterized in that: The dynamic update of the seepage-stress coupling numerical model refers to updating the corresponding parameters in the seepage-stress coupling numerical model using model correction parameters to generate an updated seepage-stress coupling numerical model.

7. A tailings dam slope and foundation seepage coordination system, based on the tailings dam slope and foundation seepage coordination method according to any one of claims 1 to 6, characterized in that: include, The model building module is used to collect dam body-dam foundation condition monitoring data and build a seepage-stress coupling numerical model of the tailings dam. The prediction and decision-making module is used to collect external rainfall forecast data and combine it with dam body-dam foundation status monitoring data. It uses a seepage-stress coupled numerical model to predict and analyze the future seepage status of the dam slope and dam foundation. When the risk of exceeding the limit of the seepage line is predicted, a predictive drainage control instruction is generated. The control and response module is used to execute predictive drainage control commands and simultaneously collect real-time mechanical response data. The stability assessment module is used to assess the current stability of the dam body based on real-time mechanical response data and preset stability criteria, and dynamically adjust the seepage discharge capacity of the tailings dam according to the assessment results, generating a seepage control log. The model correction module is used to compare and analyze the seepage control logs and predictive seepage control instructions, and to dynamically update the seepage-stress coupling numerical model.

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