Tailing pond dam slope and dam foundation seepage drainage cooperation method and system

Abstract

The invention discloses a tailing pond dam slope and dam foundation seepage drainage cooperation method and system, and relates to the technical field of tailing pond intelligent seepage control, and the method comprises the steps: collecting external rainfall forecast data, combining the external rainfall forecast data with dam body-dam foundation state monitoring data, and carrying out the prediction analysis of the future seepage state of a dam slope and a dam foundation through a seepage-stress coupling numerical model, when it is predicted that an overrun risk exists in the infiltration line, a predictive drainage regulation and control instruction is generated; executing the predictive seepage drainage regulation and control instruction, and synchronously collecting real-time mechanical response data; and comparing and analyzing the seepage drainage regulation log and the predictive seepage drainage regulation instruction, and dynamically updating the seepage-stress coupling numerical model. Compared with the prior art, the seepage drainage regulation and control log and the predictive seepage drainage regulation and control instruction are compared and analyzed, and the seepage-stress coupling numerical model is dynamically updated, so that the reliability of the seepage-stress coupling numerical model is enhanced, and long-term accurate prediction and regulation and control are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent seepage control of tailings pond, and in particular to a tailings pond dam slope and dam foundation seepage coordination method and system. BACKGROUND

[0002] As a key facility of mine engineering, the stability of the dam body of the tailings pond is directly related to the environment and public safety; the current seepage control of the tailings pond is mainly based on passive seepage control engineering measures, such as setting seepage prisms on the dam slope, burying horizontal seepage pipes in the dam body, and building vertical seepage wells, collecting and draining seepage water through water-permeable materials, and actively regulating and controlling through real-time monitoring, monitoring the position of the phreatic line through the laying of seepage pressure sensor, and starting the seepage control facilities for intervention when the monitoring value exceeds the limit; the traditional method constitutes the basis of seepage control, aiming to maintain the stability of the dam seepage field through physical drainage and data feedback.

[0003] However, the traditional method still has room for optimization when dealing with complex working conditions; the control logic of the traditional method is mainly based on the lag response of the phreatic line threshold value, lacks predictability of the future evolution trend of the seepage field, and is difficult to implement proactive intervention before external load changes such as heavy rainfall; the shallow drainage of the dam slope and the deep seepage control of the dam foundation are often treated as relatively independent processing units, and the synergistic mechanism of the dam body and the dam foundation in the seepage path and mechanical response is not fully reflected, limiting the overall efficiency of the seepage control measures. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a tailings pond dam slope and dam foundation seepage coordination method to solve the problems of insufficient predictive intervention of phreatic line overrun risk and lack of dynamic coordination control of dam slope and dam foundation seepage.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a tailings pond dam slope and dam foundation seepage coordination method, which comprises collecting dam body-dam foundation state monitoring data and building a seepage-stress coupled numerical model of the tailings pond; collecting external rainfall forecast data and combining it with the dam body-dam foundation state monitoring data, predicting and analyzing the future seepage state of the dam slope and the dam foundation through the seepage-stress coupled numerical model, generating predictive seepage control instructions when predicting that the phreatic line has an overrun risk; executing the predictive seepage control instructions and synchronously collecting real-time mechanical response data; based on the real-time mechanical response data, evaluating the current stability state of the dam body according to the preset stability criterion, and dynamically adjusting the seepage control capacity of the tailings pond according to the evaluation result, generating a seepage control log; comparing and analyzing the seepage control log and the predictive seepage control instructions, and dynamically updating the seepage-stress coupled numerical model.

[0007] As a preferred scheme of the tailings dam slope and dam foundation drainage coordination method of the present application, wherein: the seepage-stress coupled numerical model of the tailings dam is constructed, and the steps are as follows, The dam body-dam foundation state monitoring data includes pore water pressure data, soil stress data and seepage flow data of the drainage channel; Collect engineering geological survey and mapping data, and establish a three-dimensional geological geometric model including the initial dam of the tailings dam, the accumulated dam body and the dam foundation rock-soil layer; Based on the dam body-dam foundation state monitoring data, the corresponding relationship between the seepage state and the mechanical state in the dam body and the dam foundation is mapped and the parameters are inverted to generate a set of physical and mechanical parameters of the tailings dam; Assign the set of physical and mechanical parameters of the tailings dam to the corresponding area in the three-dimensional geological geometric model to form a three-dimensional numerical model; In the three-dimensional numerical model, embed the drainage unit for simulating the main drainage pipe of the dam foundation and the horizontal drainage blind pipe of the dam body, and set the initial boundary conditions and hydraulic boundary conditions of the three-dimensional numerical model to generate a seepage-stress coupled initial model; The seepage-stress coupled initial model is fitted and checked by using the dam body-dam foundation state monitoring data to obtain a seepage-stress coupled numerical model.

[0008] As a preferred scheme of the tailings dam slope and dam foundation drainage coordination method of the present application, wherein: the seepage-stress coupled numerical model is used to predict and analyze the future seepage state of the dam slope and the dam foundation, and the steps are as follows, Collect external rainfall forecast data and perform time scale unification and spatial distribution arrangement to generate a rainfall prediction data set; Time synchronization and working condition association processing are performed on the rainfall prediction data set and the dam body-dam foundation state monitoring data to form a joint prediction input data set; Decouple the joint prediction input data set, set the boundary conditions and initial conditions, and drive the seepage-stress coupled numerical model to perform transient simulation calculation to obtain seepage-stress field simulation data spectrum.

[0009] As a preferred scheme of the tailings dam slope and dam foundation drainage coordination method of the present application, wherein: when it is predicted that the saturation line has an overrun risk, a predictive drainage control instruction is generated, and the steps are as follows, From the seepage-stress field simulation data spectrum, the spatial distribution and time evolution information of the saturation line in the dam body and the dam foundation are extracted to generate a saturation line prediction state parameter set; Based on the saturation line prediction state parameter set, the saturation line overrun risk of the dam slope and the dam foundation is located and quantitatively evaluated to determine the corresponding drainage control strategy and generate a predictive drainage control instruction.

[0010] As a preferred scheme of the tailings pond dam slope and dam foundation drainage coordination method, the step of executing the predictive drainage control instruction and synchronously collecting real-time mechanical response data is as follows, The predictive drainage control instruction is analyzed, the target drainage channel and the control amplitude are identified, and the drainage control parameter is generated. According to the drainage control parameter, the drainage capacity adjustment operation is performed on the corresponding drainage control component, and the drainage control working condition is formed. In the drainage control working condition, the real-time mechanical response data of the dam body and the dam foundation under the drainage control effect is collected.

[0011] As a preferred scheme of the tailings pond dam slope and dam foundation drainage coordination method, the step of executing the predictive drainage control instruction and synchronously collecting real-time mechanical response data is as follows, The stability characteristic index representing the stress state and deformation response of the dam body is extracted from the real-time mechanical response data, and is used as the stability evaluation data. The stability evaluation data is compared and analyzed with the preset stability criterion, the stability state of the dam body under the current drainage control working condition is evaluated, and the stability evaluation result is generated.

[0012] As a preferred scheme of the tailings pond dam slope and dam foundation drainage coordination method, the step of executing the predictive drainage control instruction and synchronously collecting real-time mechanical response data is as follows, According to the stability evaluation result, the preset control strategy library is matched, the dynamic drainage control decision is generated, the drainage capacity of the corresponding drainage channel of the tailings pond is adjusted, and the updated drainage control working condition is formed. The stability evaluation result and the corresponding updated drainage control working condition are recorded, and the drainage control log is generated.

[0013] As a preferred scheme of the tailings pond dam slope and dam foundation drainage coordination method, the step of executing the predictive drainage control instruction and synchronously collecting real-time mechanical response data is as follows, The drainage control log and the predictive drainage control instruction are associated and aligned in time sequence and control target, and the associated comparison data set is formed. The associated comparison data set is compared and analyzed, the difference between the expected effect of the predictive drainage control instruction and the actual control effect recorded in the drainage control log is evaluated, and the seepage-stress field control effect deviation is generated. Based on the seepage-stress field control effect deviation, the influence sources causing the inconsistency between the expected effect and the actual control effect are analyzed, and the model correction parameter is determined.

[0014] As a preferred scheme of the tailings pond dam slope and dam foundation seepage coordination method, the dynamic updating seepage-stress coupling numerical model refers to updating corresponding parameters in the seepage-stress coupling numerical model by using model correction parameters to generate an updated seepage-stress coupling numerical model.

[0015] In a second aspect, the present application provides a tailings pond dam slope and dam foundation seepage coordination system, comprising, A model construction module is configured to collect dam body-dam foundation state monitoring data and construct a seepage-stress coupling numerical model of the tailings pond. A prediction decision module is configured to collect external rainfall forecast data and combine the data with the dam body-dam foundation state monitoring data, to predict and analyze the future seepage state of the dam slope and dam foundation by using the seepage-stress coupling numerical model, and to generate a predictive seepage control instruction when it is predicted that the saturation line has an overrun risk. A control response module is configured to execute the predictive seepage control instruction and synchronously collect real-time mechanical response data. A stability evaluation module is configured to evaluate the current stability state of the dam body based on the real-time mechanical response data according to a preset stability criterion, to dynamically adjust the seepage capacity of the tailings pond according to the evaluation result, and to generate a seepage control log. A model correction module is configured to compare and analyze the seepage control log and the predictive seepage control instruction, and to dynamically update the seepage-stress coupling numerical model.

[0016] The present application has the following advantages: by predicting and analyzing the future seepage state of the dam slope and dam foundation based on the seepage-stress coupling numerical model, dynamic simulation of the seepage field and early risk identification are realized, active early warning and early intervention are achieved, and the predictability and initiative of tailings pond safety management are improved; by comparing and analyzing the seepage control log and the predictive seepage control instruction, and dynamically updating the seepage-stress coupling numerical model, the reliability of the seepage-stress coupling numerical model is enhanced, and long-term accurate prediction and control are realized. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any creative labor.

[0018] Fig. 1 The flowchart of the tailings pond dam slope and dam foundation seepage coordination method.

[0019] Fig. 2 The schematic diagram of the tailings pond dam slope and dam foundation seepage coordination system.

[0020] Fig. 3 A flow chart for generating predictive seepage control instructions.

[0021] Fig. 4 A flow chart for updating a seepage-stress coupled numerical model. DETAILED DESCRIPTION

[0022] In order to make the above objectives, features and advantages of the present application more clear and comprehensible, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0023] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details given herein, that the present application can be practiced with other than the described implementations, and that variations from the particular examples given can be made and practiced within the scope of the present application. Accordingly, the particular implementation given above is illustrative only as the present application can be practiced with a wide variety of specific implementations that are known or will be known to the skilled practitioner.

[0024] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure or characteristic that can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent or alternative to other embodiments.

[0025] Reference Figs. 1-4 For one embodiment of the present application, the embodiment provides a tailings dam slope and dam foundation seepage coordination method, comprising the following steps: S1, collecting dam body-dam foundation state monitoring data, and constructing a seepage-stress coupled numerical model of the tailings dam.

[0026] S1.1: The dam body-dam foundation state monitoring data includes pore water pressure data, soil stress data and seepage flow data of the seepage channel.

[0027] It should be noted that the pore water pressure data refers to the pressure value of water in the pores of the tailings dam body and the dam foundation rock-soil mass, which is obtained by real-time monitoring through the installation of piezometers at different elevations and positions of the dam body and the dam foundation, including static pore water pressure values of different measuring points (used to calculate the initial saturation line) and dynamic change process lines (reflecting the real-time changes of pore water pressure under the action of rainfall and seepage control); the soil stress data refers to the internal force (including skeleton stress and pore water pressure) per unit area of the tailings dam body and the dam foundation material, which is obtained by synchronous monitoring through the installation of soil pressure cells in the dam body and the dam foundation, which are arranged in conjunction with the piezometers; the seepage flow data of the seepage channel refers to the water flow through the dam foundation main seepage pipe and the dam body horizontal seepage blind pipe seepage facilities, which is obtained by continuous monitoring through the installation of flow meters (such as electromagnetic flow meters) at the outlets of the seepage pipes, including instantaneous flow values and cumulative flow values.

[0028] S1.2: Collect engineering geological survey and mapping data, and establish a three-dimensional geological geometric model including the initial dam of the tailings pond, the accumulation dam body and the dam foundation rock-soil layer.

[0029] Further, the engineering geological survey and mapping data includes the elevation point information of the initial dam crest, dam slope and the surface of the accumulation dam body of the tailings pond obtained by field mapping, and the layer position, buried depth, layer thickness distribution and interlayer interface position information of the dam foundation rock-soil layer obtained by engineering geological survey; the elevation point information and the layer position, buried depth, layer thickness distribution and interlayer interface position information are converted and corrected in the same coordinate reference and elevation reference to generate integrated spatial data in the same spatial reference system; based on the integrated spatial data, the outline of the initial dam of the tailings pond and the accumulation dam body, and the layer interface line of the dam foundation rock-soil layer are extracted, and the outline and the layer interface line are processed by layer-by-layer spatial interpolation and boundary closure to construct independent three-dimensional spatial entities corresponding to the initial dam of the tailings pond, the accumulation dam body and each layer of the dam foundation rock-soil layer; the independent three-dimensional spatial entities are spliced and topologically checked in space to establish the spatial contact relationship between the entities, and a three-dimensional geological geometric model including the initial dam of the tailings pond, the accumulation dam body and the dam foundation rock-soil layer is generated.

[0030] S1.3: Based on the dam body-dam foundation state monitoring data, the corresponding relationship between the seepage state and the mechanical state in the dam body and the dam foundation is mapped and the parameters are inverted to generate a set of physical and mechanical parameters of the tailings pond.

[0031] Further, the pore water pressure data sequence and the soil stress data sequence of each measuring point in a specific time period (such as a complete hydrological year) are extracted from the dam body-dam foundation state monitoring data, and are combined with the three-dimensional geological geometric model to perform forward calculation of the seepage field and the stress field by the finite element method to obtain the theoretical calculation sequence of the pore water pressure and the theoretical calculation sequence of the soil stress of each measuring point in the corresponding period, which together constitute the theoretical calculation data sequence; the theoretical calculation data sequence is compared with the pore water pressure data sequence and the soil stress data sequence extracted from the dam body-dam foundation state monitoring data point by point and period by period, and a multi-objective optimization function is established with the objective of minimizing the root mean square error of the theoretical calculation data sequence and the measured sequence of all measuring points and the entire period; the particle swarm optimization algorithm is used to continuously adjust the values of the to-be-inverted parameters (such as permeability coefficient, elastic modulus, cohesion and internal friction angle) of each rock-soil layer material (such as tailings sand, tailings soil and dam foundation cover layer) in the three-dimensional geological geometric model until the multi-objective optimization function converges to the error tolerance threshold; the parameter values of each rock-soil layer material determined by the particle swarm optimization algorithm that best fit the theoretical calculation and the measured data are collected to generate a set of physical and mechanical parameters of the tailings pond.

[0032] The expression for calculating the root mean square error is: ; wherein, is the root mean square error, quantifying the difference between the theoretical calculation and the measured data; is the total number of data points, including all measurement points and time steps; is the calculated value of the pore water pressure of the th data point; is the measured value of the pore water pressure of the th data point.

[0033] It should be noted that the finite element method is a numerical method for solving physical field control equations by dividing complex geometric structures into grids connected by nodes and performing mathematical approximations within each grid. In this embodiment, the finite element method is used to discretize the three-dimensional geological geometric model, and the pore water pressure distribution and stress response of the dam under external load are calculated by coupling the seepage and stress control equations on each grid. The particle swarm algorithm is a global optimization algorithm that simulates the cooperative behavior of a group. It maintains a population of candidate solutions (called "particles") and moves them in the search space to find the optimal solution based on the individual and group historical optimal solutions. In this embodiment, the particle swarm algorithm is used for parameter inversion: each particle represents a set of candidate values of the rock and soil parameters in the three-dimensional geological geometric model. The fitting degree between the finite element forward calculation results and the dam-foundation state monitoring data guides the search, and the set of physical and mechanical parameters of the tailings pond that best matches the calculated and measured values is found.

[0034] It should be noted that the error tolerance threshold is determined by determining an initial reference threshold based on the measurement accuracy of the monitoring instrument, calibrating the seepage-stress coupling initial model using a portion of the dam-foundation state monitoring data, observing the degree of agreement between the seepage-stress coupling initial model calculation results and the dam-foundation state monitoring data, verifying the prediction accuracy of the calibrated seepage-stress coupling initial model using another independent portion of the dam-foundation state monitoring data, and iteratively adjusting and confirming the initial reference threshold based on the comprehensive effects of calibration and verification, while ensuring the reliability of the seepage-stress coupling initial model and the safety of the project. The exemplary value range is 3% to 10% of the actual measured value. If it is higher than 10%, the seepage-stress coupling initial model calibration will be too loose and will not be able to capture key seepage-stress response characteristics. If it is lower than 3%, it may cause overfitting, making the seepage-stress coupling initial model overly sensitive to random fluctuations in the dam-foundation state monitoring data.

[0035] S1.4: Assign the set of physical and mechanical parameters of the tailings pond to the corresponding region in the three-dimensional geological geometric model to form a three-dimensional numerical model.

[0036] Further, based on the spatial coordinates and geometric shape data of the defined initial dam geometric part, the accumulated dam geometric part and the geometric part of each dam foundation rock layer in the three-dimensional geological geometric model, the material type attribute label corresponding to each independent geometric part (such as tailings sand, clay layer and gravel layer) is identified and extracted; according to the material type attribute label, the physical and mechanical parameter set of the tailings pond, the permeability coefficient, the elastic modulus, the cohesion and the internal friction angle parameter value corresponding to the material type are sequentially assigned to the corresponding attribute field of all geometric parts with the same material type attribute label in the three-dimensional geological geometric model; the physical property field of each geometric part in the three-dimensional geological geometric model is checked to have been filled with the corresponding parameter value, and a three-dimensional numerical model with determined material mechanical properties is formed.

[0037] S1.5: In the three-dimensional numerical model, the drainage unit for simulating the main drainage pipe of the dam foundation and the horizontal drainage blind pipe of the dam body is embedded, and the initial boundary condition and the hydraulic boundary condition of the three-dimensional numerical model are set to generate the initial model of seepage-stress coupling.

[0038] Further, according to the spatial position and geometric size 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, the corresponding long strip geometric entity with circular and square cross section is created in the three-dimensional numerical model to accurately characterize the physical existence of the main drainage pipe of the dam foundation and the horizontal drainage blind pipe of the dam body, and the geometric entity representing the drainage pipe is given a very high permeability coefficient value to simulate the rapid drainage function of the drainage pipe; according to the initial pore water pressure distribution obtained from the dam foundation-dam body state 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 the reservoir area surface of the three-dimensional numerical model are defined as the flow boundary that may accept rainfall infiltration, and 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 boundary to generate the initial model of seepage-stress coupling.

[0039] S1.6: The seepage-stress coupling initial model is fitted and checked by using the dam foundation-dam body state monitoring data to obtain the seepage-stress coupling numerical model.

[0040] Further, using the pore water pressure data and soil stress data of a specific time period in the dam body-dam foundation state monitoring data as a benchmark, the initial seepage-stress coupling model is driven to perform forward calculation, generating the pore water pressure theoretical data sequence and soil stress theoretical data sequence of the corresponding measuring points in the same time period, and comparing them with the pore water pressure data and soil stress data as the benchmark point by point and time period by time period, calculating the root mean square error, and forming a comprehensive difference index; taking the minimization of the comprehensive difference index as the goal, using the particle swarm algorithm to automatically fine-tune the material parameters (such as permeability coefficient and elastic modulus) in the initial seepage-stress coupling model, and re-performing forward calculation and difference comparison; repeating the above process until the comprehensive difference index is lower than the error tolerance threshold, determining that the initial seepage-stress coupling model has been calibrated, and obtaining the seepage-stress coupling numerical model.

[0041] S2, collect external rainfall forecast data, and combine with dam body-dam foundation state monitoring data, predict and analyze the future seepage state of the dam slope and dam foundation through the seepage-stress coupling numerical model, and generate predictive drainage control instructions when the prediction shows that the phreatic line has exceeded the limit.

[0042] S2.1: Collect external rainfall forecast data and perform time scale unification and spatial distribution arrangement to generate a rainfall prediction data set.

[0043] Further, external rainfall forecast data published in grid or station form is obtained from a meteorological data source; for the multiple time resolutions (such as 6-hourly and 12-hourly forecasts) that may exist in the external rainfall forecast data, a linear interpolation method is used to process all the data into the same fixed time step sequence that matches the calculation step size of the seepage-stress coupling numerical model; for spatial distribution, if the external rainfall forecast data is station data, a Kriging interpolation method is used to convert the data into continuous spatial grid data covering the entire tailings reservoir area, and if the external rainfall forecast data is grid data, the grid data subset corresponding to the tailings reservoir area range is directly extracted; the data that have completed time scale unification and spatial distribution arrangement are organized into a rainfall prediction data set containing time dimension, spatial location coordinate dimension, and rainfall intensity numerical field.

[0044] S2.2: Time synchronization and working condition association processing of the rainfall prediction data set and the dam body-dam foundation state monitoring data are performed to form a joint prediction input data set.

[0045] Further, the rainfall intensity data of each time step in the rainfall prediction dataset is extracted, and dam body-dam foundation state monitoring data at the same time benchmark is obtained; the time series of the rainfall prediction dataset is aligned with the time series of the dam body-dam foundation state monitoring data, so that each prediction time point has corresponding dam body-dam foundation state monitoring data representing the current dam body state; based on the aligned time series, the rainfall intensity data of each time point is paired and combined with the dam body-dam foundation state monitoring data corresponding to the time point (as the initial state or boundary condition input of the seepage-stress coupled numerical model), and a joint prediction input dataset is generated in time sequence.

[0046] S2.3: Decoupling the joint prediction input dataset, setting boundary conditions and initial conditions, and driving the seepage-stress coupled numerical model to perform transient simulation calculation to obtain seepage-stress field simulation data spectrum.

[0047] Further, the 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-dam 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, the full space-time distribution of pore water pressure and stress in the dam body and dam foundation in the future prediction period is calculated, and the seepage-stress field simulation data spectrum is integrated and generated.

[0048] S2.4: Extracting the spatial distribution and time history evolution information of the phreatic line in the dam body and dam foundation from the seepage-stress field simulation data spectrum to generate a phreatic line prediction state parameter set.

[0049] Further, the full-field pore water pressure distribution data corresponding to each time step in the seepage-stress field simulation data spectrum is read, and based on the zero pore water pressure isosurface judgment criterion, the three-dimensional coordinate set of the spatial points satisfying the judgment criterion in the dam body and dam foundation is identified and extracted as the spatial distribution discrete point set of the phreatic line; the spatial interpolation processing is performed on the phreatic line spatial distribution discrete point set of each time step to generate continuous curve and surface function expressions representing the spatial morphology of the phreatic line, and the time sequence arrangement and combination are performed to further extract the time history evolution law of the spatial position of the phreatic line with time; the phreatic line spatial distribution function expression and time history evolution law information of each time step are organized to generate a phreatic line prediction state parameter set containing spatial coordinate sequence and time-position correspondence.

[0050] It should be pointed out that the phreatic line is an important dividing line of the tailings dam, the upper part is the unsaturated zone, the soil pores are partially filled with air and water, the lower part is the saturated zone, the soil pores are completely filled with water, the height of the phreatic line determines the distribution of the seepage field and stress field inside the dam body, and is the core index for evaluating the stability of the dam slope. If the position of the phreatic line is too high, it will significantly reduce the effective stress of the dam body against sliding, increase the risk of seepage failure (such as piping), and seriously threaten the safety of the dam.

[0051] S2.5: Based on the phreatic line prediction state parameter set, the phreatic line over-limit risk of the dam slope and the dam foundation is positioned and quantitatively evaluated, the corresponding drainage control strategy is determined, and the predictive drainage control instruction is generated.

[0052] Further, the phreatic line spatial coordinate sequence in the phreatic line prediction state parameter set is compared with the safety elevation threshold point by point, the positions of all coordinate points whose elevations exceed the safety elevation threshold are identified, and are mapped and matched with the spatial coordinates of the three-dimensional geological geometry model, completing the regional positioning of the phreatic line over-limit risk in the dam slope and the dam foundation; the average depth of the phreatic line exceeding the safety elevation threshold, the total area of the over-limit region, and the maximum change rate of the over-limit depth with time are calculated; according to the results of risk positioning and quantitative evaluation, according to the preset risk level-control intensity mapping relationship, the drainage control strategy needed to be implemented for different over-limit regions is matched and determined, including target drainage channel, control action type and control amplitude; the drainage control strategy is converted into a predictive drainage control instruction that can drive the action of the execution mechanism, including specific execution time, target object (such as a certain section of the dam foundation main drainage pipe or a certain layer of the dam body horizontal drainage blind pipe) and operation parameters (such as valve target opening).

[0053] It should be pointed out that the setting process of the risk level-control intensity mapping relationship is as follows: collect historical monitoring data and seepage-stress coupling numerical simulation results, divide the dam body stability state corresponding to different phreatic line over-limit depth, over-limit area and change rate into low, medium, high and emergency risk levels, combine the design drainage capacity of the tailings dam drainage channel and engineering experience, match the preliminary control intensity benchmark value for each risk level (such as small amplitude adjustment for low risk, and start multiple drainage channels and set higher target drainage flow for high risk), simulate and verify multiple virtual working conditions in the seepage-stress coupling numerical model, and fine-tune the parameters to ensure that the control action can effectively control the risk and has no adverse effects, and the verified mapping relationship is solidified into a query mapping rule library containing clear input conditions and output control parameters.

[0054] S3, execute the predictive drainage control instruction, and synchronously collect real-time mechanical response data.

[0055] S3.1: Analyze the predictive drainage control instruction, identify the target drainage channel and the control amplitude, and generate the drainage control parameter.

[0056] Further, the data structure of the predictive drainage control instruction is analyzed, the identification information of the target drainage channel and the numerical information of the control amplitude are directly extracted from the corresponding fields, and are associated and combined to generate the drainage control parameter.

[0057] S3.2: According to the drainage control parameter, the drainage control component is implemented to adjust the drainage capacity, and the drainage control working condition is formed.

[0058] Further, according to the target drainage channel identification specified in the drainage control parameter, the corresponding drainage control component (such as a specific electric valve) is found, and the control amplitude value in the drainage control parameter is converted into a physical action instruction executable by the drainage control component (such as converting the opening percentage into a valve control signal); the physical action instruction is sent to the drainage control component to drive the drainage control component to perform the opening, closing and opening adjustment operation, change the flow capacity of the corresponding drainage channel; after confirming that the drainage control component has executed the instruction and reached the expected state, the state of all adjusted drainage control components and the corresponding channel drainage capacity at this time are recorded to form the drainage control working condition.

[0059] S3.3: In the drainage control working condition, real-time mechanical response data of the dam body and dam foundation under the drainage control action are collected.

[0060] Further, after the drainage control working condition is formed, the pore water pressure data and soil stress data of each measuring point in the drainage control working condition are continuously collected at a fixed sampling frequency (such as once per minute), and are strictly corresponding to the starting time stamp of the drainage control working condition, to form the real-time mechanical response data.

[0061] S4, based on the real-time mechanical response data, the current stability state of the dam body is evaluated according to the preset stability criterion, and the drainage capacity of the tailings pond is dynamically adjusted according to the evaluation result, and the drainage control log is generated.

[0062] S4.1: Extract the stability feature index representing the stress state and deformation response of the dam body from the real-time mechanical response data, and use it as the stability evaluation data.

[0063] Further, based on the real-time mechanical response data, the ratio of the soil stress data and the pore water pressure data of each measuring point at the same time is calculated to obtain the time series data of the excess pore water pressure ratio reflecting the effective stress state of the dam body; the change rate of the pore water pressure data of each measuring point is extracted as the seepage stability index, and the difference gradient of the soil stress data between different measuring points is calculated as the stress concentration index; the excess pore water pressure ratio time series data, the seepage stability index and the stress concentration index are integrated as the stability evaluation data.

[0064] The expression for calculating the stress concentration index is: ; wherein, is the stress concentration index; is the total number of measuring points; is the soil stress measurement value of the i-th measuring point; is the arithmetic mean of the soil stress measurement values of all measuring points; is the measuring point index.

[0065] S4.2: Compare the stability evaluation data with the preset stability criterion, evaluate the stability state of the dam under the current seepage control working condition, and generate the stability evaluation result.

[0066] Further, the excess static pore water pressure ratio time series data in the stability evaluation data is compared with the excess static pressure ratio threshold in the stability criterion: if the ratio of all measuring points is lower than the excess static pressure ratio threshold, it is recorded as meeting the standard, if there are some measuring points whose ratio is within the excess static pressure ratio threshold, it is recorded as slightly exceeding the standard, if there are some measuring points whose ratio exceeds the excess static pressure ratio threshold, it is recorded as serious exceeding the standard; the seepage stability index is compared with the seepage stability interval in the stability criterion: if the seepage stability index is in the seepage stability interval all the time, it is recorded as normal, if it temporarily exceeds the seepage stability interval but quickly recovers (such as within 3 minutes), it is recorded as temporary abnormal, if it continuously exceeds the seepage stability interval, it is recorded as continuous abnormal; the stress concentration index is compared with the required uneven coefficient (such as 0.3) in the criterion: if the stress concentration index is lower than the uneven coefficient, it is recorded as uniform distribution, if it exceeds the uneven coefficient, it is recorded as stress concentration; according to the comparison results of the three indexes, the stability state is determined— all meeting the standard is stable, only slightly exceeding the standard or temporary abnormal is basically stable, serious exceeding the standard or continuous abnormal but not simultaneously stress concentration is sub-stable, any two serious abnormalities or with stress concentration is unstable, and the stability evaluation result is generated, which clearly marks the stability state level and specific exceeding index.

[0067] ​It should be noted that the stability criterion is an index system and judgment standard for quantitatively evaluating the stability state of the tailings dam, including the excess static pressure ratio threshold, the seepage stability interval, and the non-uniformity coefficient. The excess static pressure ratio threshold is determined by drawing the effective stress path of the tailings under different consolidation pressures to determine the critical pore water pressure ratio when strength failure occurs, and analyzing the actual distribution of the critical pore water pressure ratio in a large number of stable working conditions in the long-term safety monitoring database of the tailings pond. The high statistical quantile value (such as the 95% quantile) is set, and the exemplary value range is 0.4 to 0.5. The seepage stability interval is determined by the sensor accuracy (such as ±0.5 kPa / min) to determine the measurement error band. Through numerical simulation and historical data analysis, the upper limit of the safe pore water pressure change rate that does not cause fine particle migration is determined, and finally the conservative value considering the measurement error and engineering safety is taken as the interval boundary setting. The 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 multiple working conditions to determine the allowable stress difference gradient that does not cause plastic failure under normal load and extreme load, and the measured data of the soil pressure cell installed on the dam body are used to verify and calibrate the identification results, and the critical gradient value that can effectively warn the development of the potential yield zone is set.

[0068] S4.3: According to the stability evaluation result, match the preset control strategy library, generate a dynamic drainage control decision, and adjust the drainage capacity of the corresponding drainage channel of the tailings pond to form an updated drainage control working condition.

[0069] Further, according to the stability state level clearly marked in the stability evaluation result, query the control strategy item corresponding to the stability state level in the control strategy library, and extract the target drainage channel identifier, control action type and control amplitude parameter to generate a dynamic drainage control decision containing specific execution instructions; according to the dynamic drainage control decision, drive the corresponding drainage control component to perform opening adjustment and start-stop operation, change the flow capacity of the related drainage channel; record all the adjusted drainage control component states and controlled drainage flow data to form an updated drainage control working condition.

[0070] It should be noted that the construction process of the control strategy library is as follows: based on the seepage-stress coupled numerical model, a large number of numerical simulations are performed for different stability state levels under different rainfall intensities and different saturation line heights. The drainage control scheme required to achieve the target safety state is implemented, combined with the historical operation data of the tailings pond, the drainage control scheme is verified and optimized, and the optimized drainage control scheme is classified according to the stability state level, the exceeding index type and the exceeding degree, and the control strategy library is constructed.

[0071] S4.4: Record the stability evaluation results and the corresponding updated seepage control conditions, and generate the seepage control log.

[0072] Further, the stability state level in the stability evaluation results, the specific deviation of each index, and the evaluation timestamp are recorded, and the identification of all seepage control components in the updated seepage control conditions, the state parameters before and after the adjustment action, and the working condition effective time point are recorded, and are time-correlated and bound, and the dynamic seepage control decision content triggering this control is supplemented, and the seepage control log is generated.

[0073] S5, compare and analyze the seepage control log and the predictive seepage control instruction, and dynamically update the seepage-stress coupling numerical model.

[0074] S5.1: Align the seepage control log and the predictive seepage control instruction in time sequence and on control target to form a correlation comparison data set.

[0075] Further, the seepage control log and the predictive seepage control instruction are aligned according to a unified time reference, the actual control action (such as the valve opening adjustment value) recorded in the seepage control log is compared with the expected control action specified in the predictive seepage control instruction, and the stable state after control recorded in the seepage control log is compared with the expected control target of the predictive seepage control instruction; the action execution difference, the effect difference, and the corresponding timestamp, control target identification obtained by comparison are integrated, arranged in time sequence, and a correlation comparison data set is generated.

[0076] S5.2: Compare and analyze the correlation comparison data set, evaluate the difference between the expected effect of the predictive seepage control instruction and the actual control effect recorded in the seepage control log, and generate the seepage-stress field control effect deviation.

[0077] Further, the expected control target (such as the target phreatic line depth) recorded in the correlation comparison data set and the actual stable state (such as the measured phreatic line depth) achieved after control are numerically compared, the absolute difference and the relative difference between the expected value and the actual value of the key indicators (such as the phreatic line depth, the pore water pressure change rate and the stress concentration degree) are calculated; in the time dimension, the control process is divided into three stages of start, execution and stability, the mean value and the standard deviation of the difference value in each stage are calculated respectively to evaluate the change characteristics of the deviation over time; in the spatial dimension, according to the partition of the dam body and the dam foundation, the difference value is mapped to the corresponding region, the mean value and the dispersion coefficient of the difference value in each partition are calculated to evaluate the spatial distribution characteristics of the deviation; based on the results of the time and space analysis, if the deviation only occurs in a specific stage (such as not more than 20% of the whole control process) in a certain partition (such as zone A of the dam foundation), it is determined as a local transient deviation; if the deviation exists in multiple partitions (such as three or more partitions) for a long time (such as more than 50% of the whole control process), it is determined as a whole systematic deviation; the difference value of each indicator, the time and space distribution characteristics of the deviation and the deviation type are integrated to generate the seepage-stress field control effect deviation.

[0078] S5.3: Based on the seepage-stress field control effect deviation, the influence source causing the inconsistency between the expected effect and the actual control effect is analyzed, and the model correction parameter is determined.

[0079] Further, based on the seepage-stress field control effect deviation, if the deviation type is determined as a local transient deviation, the influence source is located to a specific material partition in the seepage-stress coupling numerical model corresponding to the deviation occurrence region, and the model parameters to be corrected are the permeability coefficient and the elastic modulus of the specific material partition; if the deviation type is determined as a whole systematic deviation, the influence source is attributed to the global setting of the seepage-stress coupling numerical model, and the model parameters to be corrected are the permeability coefficient, the elastic modulus and the modified hydraulic boundary condition (such as the rainfall infiltration coefficient, the outlet pressure) of multiple material partitions; according to the specific difference value size and direction of the deviation (such as the measured value of the phreatic line is higher than the expected value, which is positive deviation, and vice versa, which is negative deviation), an optimization function with the minimum deviation as the objective is established, and a particle swarm algorithm is used to solve it, to calculate the optimal adjustment value and direction of each type of model parameter to be corrected (such as positive deviation upward adjustment, negative deviation downward adjustment), and generate the model correction parameter composed of the model correction parameter identification, the adjustment value and the adjustment direction.

[0080] S5.4: The corresponding parameters in the seepage-stress coupling numerical model are updated using the model correction parameter to generate the updated seepage-stress coupling numerical model.

[0081] Further, according to the model correction parameter identifier in the model correction parameter, the specific target parameter to be updated is located in the seepage-stress coupling numerical model, and the target parameter value is directly updated by arithmetic according to the adjustment direction (such as up or down) and the adjustment value; after completing the update of all model correction parameters, the updated seepage-stress coupling numerical model is generated.

[0082] The embodiment also provides a tailing pond dam slope and dam foundation seepage drainage coordination system, comprising: A model construction module is configured to collect dam body-dam foundation state monitoring data and construct a seepage-stress coupling numerical model of the tailing pond. A prediction decision module is configured to collect external rainfall forecast data and combine the dam body-dam foundation state monitoring data to predict and analyze the future seepage state of the dam slope and the dam foundation through the seepage-stress coupling numerical model, and generate a predictive seepage drainage control instruction when it is predicted that the saturation line has an overrun risk. A control response module is configured to execute the predictive seepage drainage control instruction and synchronously collect real-time mechanical response data. A stability evaluation module is configured to evaluate the current stability state of the dam body based on the real-time mechanical response data according to a preset stability criterion, dynamically adjust the seepage drainage capacity of the tailing pond according to the evaluation result, and generate a seepage drainage control log. A model correction module is configured to compare and analyze the seepage drainage control log and the predictive seepage drainage control instruction and dynamically update the seepage-stress coupling numerical model.

[0083] In summary, the present application realizes dynamic simulation of the seepage field and early risk identification by predicting and analyzing the future seepage state of the dam slope and the dam foundation based on the seepage-stress coupling numerical model, actively warns and implements control intervention in advance, and improves the predictability and initiative of tailing pond safety management; the reliability of the seepage-stress coupling numerical model is enhanced by comparing and analyzing the seepage drainage control log and the predictive seepage drainage control instruction and dynamically updating the seepage-stress coupling numerical model, and long-term accurate prediction and control are realized.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limiting the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all should be covered in the scope of the claims of the present application.

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. A comparative analysis was conducted on the seepage control logs and predictive seepage control instructions, and the seepage-stress coupling numerical model was dynamically updated.

2. The tailings dam slope and foundation seepage coordination method as described in claim 1, characterized in that: 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-foundation condition monitoring data to obtain a numerical model of seepage-stress coupling.

3. The tailings dam slope and foundation seepage coordination method as described in claim 1, characterized in that: 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 simulation data spectrum of the seepage-stress field.

4. The tailings dam slope and foundation seepage coordination method as described in claim 1, characterized in that: 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.

5. The method for coordinated drainage of tailings dam slope and foundation 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.

6. 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.

7. 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.

8. The tailings dam slope and foundation seepage coordination method 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.

9. 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.

10. 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 9, 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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