Evaluation method and system for treatment measures based on leakage of seepage-proofing membrane in mine field

Abstract

The invention relates to the field of environmental protection, in particular to an assessment method and system for treatment measures based on leakage of an anti-seepage membrane of a mine field. The method comprises the following steps: constructing a hydrogeological model and an underground water solute transport model; determining a pollution emergency prevention and control area and a pollution spreading trend according to the hydrogeological model and the groundwater solute transport model; planning and building a monitoring well based on the pollution emergency prevention and control area and the pollution spreading trend, and detecting the monitoring well to obtain heavy metal concentration data; drawing a heavy metal pollution contour map according to the heavy metal concentration data; and through the heavy metal pollution contour map, the pollution emergency prevention and control area and the pollution spreading trend, evaluating treatment measures based on leakage of the mine field impermeable membrane. The problem that in the prior art, after leakage of an anti-seepage film in a mine field repairing area, the treatment measure effect is poor, and defects of the treatment measure cannot be detected in time, so that continuous pollution of underground water is caused is solved.

Description

Technical Field

[0001] The invention relates to the field of environmental protection, and in particular to an evaluation method and system for treatment measures based on leakage of a mine anti-seepage membrane. Background Art

[0002] After the mining activities are completed, mine ecological restoration becomes a key task. In order to prevent the residual pollutants in the mine from seeping into the groundwater and posing a threat to the surrounding ecological environment and the water safety of residents, it is a common engineering method to use an impermeable membrane to construct an isolation barrier. However, in actual applications, the leakage of the impermeable membrane occurs frequently, which brings great challenges to the protection of the groundwater environment.

[0003] Although the anti-seepage membrane can play a good anti-seepage role in theory, in actual projects, leakage problems often occur due to the influence of various factors. On the one hand, the geological conditions of the mine are complex, and the uneven settlement of the foundation may cause the anti-seepage membrane to stretch and tear, thereby forming a leakage channel. On the other hand, improper operation during the construction process, such as poor welding quality of the anti-seepage membrane and piercing by sharp objects during laying, can also cause damage to the anti-seepage membrane. In addition, long-term environmental erosion, such as ultraviolet radiation and chemical corrosion, will cause the anti-seepage membrane to age and reduce its anti-seepage performance.

[0004] In response to the leakage problem of anti-seepage membranes, a variety of control measures have been taken. However, it is difficult to judge whether these control measures are in place. The anti-seepage performance of the repaired part is difficult to accurately evaluate. Although the repair materials and processes can be tested under laboratory conditions, there are many influencing factors in the actual engineering environment, and it is difficult to simulate the real situation. Furthermore, for the groundwater pumping and treatment system, the long-term stability of its treatment effect is difficult to guarantee. The groundwater flow field is complex and changeable, and the types and concentrations of pollutants may also change over time. Existing monitoring methods are difficult to fully and real-time grasp the treatment effect. In addition, the synergistic effect between different control measures is also difficult to quantify and evaluate, resulting in a lack of scientific basis for the overall evaluation of the treatment effect.

[0005] In summary, the effectiveness evaluation of anti-seepage membrane leakage control measures in mine ecological restoration areas is an urgent problem to be solved, and further research and development of more accurate and efficient evaluation technologies and methods are needed. Summary of the invention

[0006] In view of the defects in the prior art, the present invention provides an evaluation method and system based on control measures for mine anti-seepage membrane leakage, which solves the problem in the prior art that after the anti-seepage membrane leakage in the mine restoration area, the control measures are not effective and the defects of the control measures cannot be detected in time, resulting in continuous groundwater pollution.

[0007] In order to achieve the above-mentioned purpose, one aspect of the present invention provides an evaluation method for control measures based on leakage of mine anti-seepage membranes, comprising: constructing a hydrogeological model and a groundwater solute transport model; determining pollution emergency prevention and control areas and pollution spread trends based on the hydrogeological model and the groundwater solute transport model; planning and constructing monitoring wells based on the pollution emergency prevention and control areas and the pollution spread trends, testing the monitoring wells, and obtaining heavy metal concentration data; drawing a heavy metal pollution contour map based on the heavy metal concentration data; and evaluating control measures based on leakage of mine anti-seepage membranes through the heavy metal pollution contour map, the pollution emergency prevention and control areas, and the pollution spread trends.

[0008] The present invention confirms the expected pollution range and trend of pollutants through hydrogeological models and groundwater solute transport models, and then combines comparative analysis of heavy metal pollution contour maps to compare the expected pollution level and the actual pollution level to evaluate the effectiveness of mine anti-seepage membrane leakage control measures, thereby improving the efficiency of adjustment of control measures and the efficiency of further pollution control.

[0009] Optionally, the construction of the hydrogeological model and the groundwater solute transport model includes: acquiring mine geological environment data, hydrological monitoring well water level data and an ecological restoration project design plan; preprocessing the mine geological environment data to obtain hydrogeological data; constructing a hydrogeological conceptual model based on the hydrogeological data and the hydrological monitoring well water level data; adjusting the boundaries of the hydrogeological conceptual model using the ecological restoration project design plan to construct the hydrogeological model; and constructing the groundwater solute transport model based on the hydrogeological model based on the test data of the leaching test and the immersion test.

[0010] The present invention adjusts the boundaries of the hydrogeological conceptual model through an ecological restoration engineering design scheme, thereby improving the applicability of the model, and constructs a groundwater solute migration model on this basis, thereby improving the accuracy of the model in predicting pollution evolution.

[0011] Optionally, the preprocessing of the mine geological environment data to obtain hydrogeological data includes: removing abnormal values ​​and interpolating missing values ​​from the mine geological environment data to obtain hydrogeological data.

[0012] The present invention eliminates abnormal values ​​and interpolates missing values ​​in the mine geological environment data, thereby improving the data quality.

[0013] Optionally, drawing the heavy metal pollution contour map based on the heavy metal concentration data includes: determining the monitoring area based on the pollution emergency prevention and control area and the pollution spread trend, and gridding the monitoring area to obtain multiple grid nodes located in the monitoring area; calculating the heavy metal concentrations of the grid nodes based on the heavy metal concentration data to obtain a heavy metal concentration data set of the monitoring area; and drawing the heavy metal pollution contour map based on the heavy metal concentration data set of the monitoring area.

[0014] The present invention divides the monitoring area into grids, which is convenient for the management and analysis of metal pollution data, and also provides convenience for the calculation of heavy metal concentrations in the entire monitoring area. In addition, the present invention can greatly reduce the cost of pollution control by calculating the heavy metal concentrations in the entire monitoring area through scientific means through limited monitoring well heavy metal pollutant data.

[0015] Optionally, calculating the heavy metal concentration of the grid node according to the heavy metal concentration data to obtain the heavy metal concentration data set of the monitoring area includes: constructing a heavy metal concentration correlation model for monitoring wells; solving the heavy metal concentration correlation model according to the heavy metal concentration data to obtain a heavy metal concentration difference quantization value data set; calculating the heavy metal concentration weight of the heavy metal concentration data for the grid node according to the heavy metal concentration difference quantization value data set; calculating the heavy metal concentration of the grid node according to the heavy metal concentration weight and the heavy metal concentration data to obtain the heavy metal concentration data set of the monitoring area.

[0016] The present invention calculates the heavy metal concentration correlation of each monitoring well through the existing heavy metal concentration data, and further calculates the heavy metal concentration weight of each monitoring well to the grid node, thereby calculating the heavy metal concentration of the grid node, making discrete data continuous and improving the efficiency and accuracy of data analysis.

[0017] Optionally, the heavy metal concentration correlation model of the monitoring well satisfies the following formula: , in, Quantify the difference in heavy metal concentrations between two monitoring wells. is the distance between two monitoring wells, For all distances between two monitoring wells The number of is the index variable, For the For distance The heavy metal concentration weights of the two monitoring wells, To coordinates The distance is The heavy metal concentration values ​​in the monitoring wells are is the monitoring well coordinate, The coordinates are The heavy metal concentration values ​​in the monitoring wells are To adjust the weight The weight index of the impact degree.

[0018] The monitoring well heavy metal concentration correlation model of the present invention fully considers the uncertain external factors that may be encountered during monitoring well detection. By adjusting the weights of the corresponding monitoring well heavy metal concentration point pairs, the impact of the corresponding detection well heavy metal concentration on the global situation is increased or reduced, thereby improving the fault tolerance of monitoring well detection and increasing the generality of the model.

[0019] Optionally, calculating the heavy metal concentration weight of the heavy metal concentration data for the grid node based on the heavy metal concentration difference quantization value data set includes: constructing a heavy metal concentration weight model for the heavy metal concentration data for the grid node; and calculating the weight of the heavy metal concentration of the grid node based on the heavy metal concentration weight model.

[0020] The present invention constructs a heavy metal concentration weight model through the correlation between heavy metal concentration data in limited monitoring wells, further calculates the weight of the heavy metal concentration of the detection well to the heavy metal concentration of the grid node, and improves the accuracy of the heavy metal concentration of the grid node.

[0021] Optionally, the heavy metal concentration weight model satisfies the following formula: , in, is the number of monitoring wells, is the ergodic coefficient, For the The heavy metal concentration weight of each monitoring well to the grid node, For monitoring wells and monitoring wells Quantitative value of the difference in heavy metal concentrations, For monitoring wells and monitoring wells The distance value between 1 to A positive integer, is the adjustment parameter of the conditional equation, is the heavy metal concentration of the grid node to be estimated and the monitoring well Quantitative value of the difference in heavy metal concentrations, The grid nodes and monitoring wells to be evaluated The distance value between .

[0022] The heavy metal concentration weight model of the present invention sets weight constraints, clarifies the range and physical meaning of weight values, and then constructs weight equations and sets constraint parameters through the correlation of heavy metal concentrations in various monitoring wells, thereby improving the practicality and accuracy of the model.

[0023] Optionally, the calculating the heavy metal concentration of the grid node according to the heavy metal concentration weight and the heavy metal concentration data includes: constructing a grid node heavy metal concentration model; calculating the heavy metal concentration of the grid node according to the grid node heavy metal concentration model and the heavy metal concentration data; the grid node heavy metal concentration model satisfies the following formula: , in, The coordinates are The heavy metal concentration value of the grid node, is the number of monitoring wells, is the ergodic coefficient, For the The coordinates of the monitoring wells are The heavy metal concentration weights of the grid nodes, For the Heavy metal concentrations in the monitoring wells.

[0024] The present invention calculates the heavy metal concentration of each grid node by summing the product of the weight of the heavy metal concentration of the grid node to be estimated and the heavy metal concentration through the known heavy metal concentration data of each monitoring well, thereby improving the accuracy of the calculation of the heavy metal concentration of the grid node.

[0025] Another aspect of the present invention provides an evaluation system for control measures based on mine anti-seepage membrane leakage, comprising: a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute an evaluation method for control measures based on mine anti-seepage membrane leakage as described in any one of the previous aspects of the present invention.

[0026] The present invention provides an evaluation system for control measures based on mine anti-seepage membrane leakage, which has a compact structure, stable performance, high integration and simple composition. It can stably execute the evaluation method for control measures based on mine anti-seepage membrane leakage provided in the previous aspect of the present invention, further improving the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of an evaluation method for treatment measures based on leakage of a mine anti-seepage membrane according to an embodiment of the present invention; Figure 2 The present invention is a schematic diagram of the structure of an evaluation system for treatment measures based on mine anti-seepage membrane leakage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0029] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0030] See also Figure 1 In one embodiment of the present invention, a method for evaluating treatment measures based on leakage of mine impermeable membrane is provided, which solves the problem in the prior art that the treatment measures are ineffective and the defects of the treatment measures cannot be detected in time after the leakage of the mine repair area, thus causing continuous groundwater pollution. Figure 1 As shown, the method comprises the following steps: Step S1, constructing a hydrogeological model and a groundwater solute transport model.

[0031] Among them, constructing the hydrogeological model and groundwater solute transport model specifically includes the following sub-steps: Step S101, obtaining mine geological environment data, hydrological monitoring well water level data and ecological restoration project design plan.

[0032] In this embodiment, the mine geological environment data includes meteorological and hydrological data, topographic data, stratum lithology data, geological structure data, engineering geological data and hydrogeological data.

[0033] Meteorological and hydrological data can be obtained through meteorological stations that are close to the mine recovery area. These meteorological stations are generally established and managed by the national meteorological department, and the data is highly accurate and continuous. The collected content includes basic meteorological elements such as temperature, air pressure, humidity, wind speed, wind direction, and precipitation.

[0034] In an optional embodiment, for large-scale mines, in special geological environments, the data queried by the weather station may not be very accurate. Therefore, field measurements can be carried out in the mining area for a period of time to obtain meteorological and hydrological data. This method can improve the accuracy of the data.

[0035] Topographic data can be obtained by aerial photography of the mine recovery area, and then processed by photogrammetry software to generate a high-precision digital terrain model. This method can quickly obtain terrain data over a large area and can reflect small changes in the terrain. For example, when monitoring the topography of a large open-pit mine recovery area, aerial photogrammetry can effectively detect small deformations of the slope.

[0036] The stratigraphic lithology data can be observed through rock outcrops. Look for rock outcrops in the mine recovery area and observe the color, structure, and texture of the rock. For example, for sedimentary rocks, observe whether the bedding is clear and whether there are sedimentary structures such as cross-bedding and ripples. These structures can reflect the sedimentary environment and sedimentary process. For igneous rocks, observe their mineral composition, degree of crystallization, and rock structure to determine the type and formation process of igneous rocks. Going further, core samples from deep underground are obtained through drilling. The drilling depth should be determined according to the thickness of the stratum and the needs of research. During the drilling process, the depth of the core, lithology changes, and other information should be recorded in detail. Laboratory analysis of the core samples, including rock-to-rock physical and chemical properties analysis.

[0037] Geological structural data refers to the regional geological structural map to understand the distribution of large geological structures (such as folds, faults, etc.) around the mine recovery area, and conducts a detailed geological structural survey in the mine recovery area to determine the location, direction and nature of small geological structures by observing structural signs such as fault scratches and fold hinges in rock outcrops. Then, this structural information is marked on the geological map to form a geological structural map of the mine recovery area. During the investigation, it is important to record the occurrence (strike, dip, inclination) of the structure, which is very important for analyzing the tectonic stress field and geological stability.

[0038] Engineering geological data is obtained by conducting mechanical tests such as compressive strength, tensile strength, and shear strength on the collected rock samples. These tests can be performed on equipment such as universal material testing machines. And for soil samples in the mine recovery area, soil mechanics tests such as particle analysis, liquid and plastic limit determination, and compression tests are performed. Particle analysis can determine the particle composition of the soil and thus determine the type of soil (such as sand, silt, clay, etc.). Liquid and plastic limit determination can understand the plasticity of the soil, and compression tests can obtain the compressibility parameters of the soil.

[0039] Hydrological monitoring wells are special wells set up to monitor hydrological information. Hydrogeological data are mainly obtained through aquifer characteristic surveys and groundwater flow characteristic surveys. Aquifer characteristic surveys refer to the division of aquifers based on stratum lithology and groundwater occurrence. Through the study of stratum lithology and the analysis of drilling data, it is determined which strata are the main aquifers and which are the aquicludes. Then, the hydrogeological parameters of the aquifer, such as permeability, water supply, and water storage coefficient, are determined through methods such as pumping tests and water injection tests. Groundwater flow characteristic surveys refer to the establishment of groundwater monitoring wells in and around the mine recovery area to regularly monitor groundwater levels. Based on the water level monitoring data, isowater level maps are drawn. The flow direction and hydraulic gradient of groundwater can be analyzed through the shape and density of isowater level lines.

[0040] The water level data of hydrological monitoring wells is obtained by placing liquid level meters in the hydrological monitoring wells and vibrating string piezometers around the mining pits to monitor the groundwater level. An unmanned groundwater level monitoring system and database are then built to automatically send the monitoring data wirelessly to the cloud data receiving and management platform.

[0041] The design plan for ecological restoration projects can be obtained from the website of the Ministry of Ecology and Environment. The website of the Ministry of Ecology and Environment contains policy documents and technical guidelines related to ecological restoration, as well as experience introductions and case studies of ecological restoration projects in various places, which are very helpful for understanding the macro policies and specific technical requirements of ecological restoration.

[0042] Step S102, preprocessing the mine geological environment data to obtain hydrogeological data.

[0043] The process of preprocessing the mine geological environment data to obtain hydrogeological data specifically includes the following sub-steps: The outliers are eliminated and missing values ​​are interpolated for the geological environment data of the mine to obtain hydrogeological data.

[0044] In this embodiment, by removing outliers, outliers caused by sensor failure, measurement errors or extreme events can be eliminated, thereby improving the reliability of data and the accuracy of analysis. The outlier removal algorithm is used to remove outliers from the mine geological environment data. Outliers refer to data that are significantly deviated from other data points in the data set. The outlier removal algorithm classification includes methods based on statistical distribution, methods based on distance, and methods based on model prediction.

[0045] In this embodiment, using According to the principle of eliminating outliers, the geological environment data of the mine field approximately obeys the normal distribution. According to the properties of the normal distribution, about 99.7% of the data are located in In the range, represents the mean, represents the standard deviation. The outlier range is determined as less than and greater than data.

[0046] Mean Satisfies the following formula: , In the formula, Represents data, Indicates the amount of data, Indicates the position of the data in the data sequence.

[0047] Standard Deviation Satisfies the following formula: , In the formula, Represents data, Indicates the amount of data, Indicates the position of the data in the data sequence, is the mean.

[0048] In some other embodiments, the box-and-whisker plot (IQR) method can also be used to eliminate outliers. The principle of the box-and-whisker plot is to determine outliers by the quartiles of the data. The first quartile Represents the lower 25% quantile and the third quartile of the data Indicates the upper 25% quantile and interquartile range of the data , usually less than or greater than The data points are considered as outliers.

[0049] At the same time, the K-nearest neighbor (KNN) distance method can also be used to eliminate outliers. The principle of the K-nearest neighbor distance method is to calculate the distance (such as Euclidean distance) between each data point in the mine geological environment data set and other data points. The distribution of the K nearest neighbor data points around the data point is used to determine whether it is an outlier. If the distance between a data point and its K nearest neighbor data points is significantly greater than the distance between other points, then it may be an outlier.

[0050] The Euclidean distance satisfies the following formula: , In the formula, is the Euclidean distance, and For different data points, is the position of the data point in the data sequence, is the number of data points.

[0051] The interpolation missing value algorithm is used to interpolate missing values ​​of the mine geological environment data. The interpolation missing value algorithms include: mean interpolation method, median interpolation method, mode interpolation method, regression-based interpolation method and multiple interpolation method. Interpolation missing values ​​can ensure the integrity of the data set.

[0052] In this implementation, the mean interpolation method is used to interpolate missing values. When there are missing values ​​in the data set, the mean of the variable is used to fill the missing values. This method is simple and intuitive, and is suitable for situations where the data distribution is relatively uniform and the proportion of missing values ​​is relatively small.

[0053] The mean interpolation method interpolates missing values ​​to meet the following formula: , In the formula, Indicates the pending The The data of the parameters, Indicates The The data of the parameters, Indicates The The data of the parameters, Indicates the dimension sequence number of the parameter.

[0054] In an optional implementation, the missing values ​​can be interpolated using the median interpolation method. The principle of the median interpolation method is similar to the mean interpolation method, but the median is used to fill in the missing values. The median is the value in the middle after the data is sorted. For data with skewed distribution or data with outliers, the median can better represent the center position of the data than the mean. The specific steps of this method include: sorting the non-missing values ​​of the variables with missing values; finding the median. If the number of data points is odd, the median is the middle number; if the number of data points is even, the median is the average of the two middle numbers; replace the missing values ​​with the median.

[0055] In another optional embodiment, the missing values ​​may be interpolated by using a regression-based interpolation method. The principle of the regression-based interpolation method is to predict the missing values ​​by establishing a regression model and using other related variables.

[0056] In general, removing outliers and interpolating missing values ​​in mine geological environment data can improve the quality of hydrogeological data.

[0057] Step S103: constructing a hydrogeological conceptual model based on the hydrogeological data and the water level data of the hydrological monitoring wells.

[0058] In this embodiment, the hydrogeological conceptual model is constructed using GMS (Groundwater Modeling System).

[0059] GMS is a professional groundwater simulation software that uses a unique conceptual modeling approach. Users can use familiar GIS objects such as points, arcs, and polygons to build conceptual groundwater models directly on the scanned map of the site, quickly and easily build a high-level model representation, and can easily update the model as needed. At the same time, GMS can import a variety of data formats and images, such as raster images, terrain maps, elevation data, borehole data, native MODFLOW files, ArcGIS geographic databases and shapefile files, CAD files, etc., and supports global projection systems to meet the model's needs for data from different sources. In addition, GMS supports finite difference and finite element modeling technologies such as MODFLOW, and can model various complex groundwater systems. Not only that, GMS can also simulate the movement of pollutant plumes, calculate their volumes, and generate animations to analyze and predict the changes in pollution plumes or groundwater flows over time, and supports models such as MT3DMS and RT3D.

[0060] The hydrogeological data and water level data of hydrological monitoring wells are imported into the GMS software, which automatically generates a hydrogeological conceptual model.

[0061] Step S104, using the ecological restoration project design plan to adjust the boundary of the hydrogeological conceptual model and construct the hydrogeological model.

[0062] In this embodiment, firstly, the data such as the scope of the ecological restoration project, the boundary coordinates of the restoration area, the vegetation restoration area, the soil improvement area, etc. in the ecological restoration project design plan are sorted and digitized, and these data are stored in the GIS data format. Subsequently, these GIS data are imported into the hydrogeological conceptual model to obtain the hydrogeological model.

[0063] Step S105, based on the test data of the leaching test and the immersion test, the groundwater solute migration model is constructed according to the hydrogeological model.

[0064] Leaching test is a test method used to study the migration of solutes (such as nutrients, pollutants, etc.) in soil or other porous media under leaching. Immersion test is a test method that immerses the material in a specific liquid environment to evaluate the performance changes, corrosion resistance, dissolution characteristics, etc. of the material in the environment.

[0065] Collect the test data of the leaching test, including the concentration of solutes in the leachate at different time intervals, the volume of the leachate, and other information. Organize these data into a table to ensure the accuracy and completeness of the data. For example, in a leaching test, you may get a time series of data that records the concentration of a certain pollutant in the leachate (mg / L) and the total volume of the leachate (L) at different time points after the start of the test (such as day 1, day 3, day 7, etc.). These data will reflect the rate at which the solute is released and migrated from the medium under leaching conditions.

[0066] For immersion tests, organize the release data of solutes over time during the immersion process, such as the change in solute concentration in the solution after different immersion times. Also record these data in a table, and be careful to indicate the test conditions, such as the amount of solid sample immersed, the volume of the immersion solution, temperature, etc. For example, in a soil immersion test, record the change in pollutant concentration in the solution at different immersion times (such as 0 hours, 24 hours, 48 ​​hours, etc.), which can help us understand the release characteristics of solutes under water-rock interaction.

[0067] The construction of groundwater solute transport model requires selecting appropriate solute transport equations based on the solute transport characteristics reflected by the leaching test and soaking test data, and then determining some parameters of the solute transport model through the leaching test and soaking test data. Among them, the diffusion coefficient is estimated by analyzing the changes in solute concentration over time and space in the leaching test, and the adsorption parameter is determined by fitting the adsorption isotherm data based on the changes in solute concentration in the soaking test.

[0068] Finally, the hydrogeological model and test data were imported into GMS, and the software automatically generated a groundwater solute transport model.

[0069] Step S2, determining the pollution emergency prevention and control area and the pollution spread trend according to the hydrogeological model and the groundwater solute transport model.

[0070] In this embodiment, the concentration threshold is first determined, and one or more pollutant concentration thresholds are set with reference to relevant environmental standards and the degree of harm of pollutants to human health and the ecological environment. For example, for heavy metal pollutants, the threshold can be set according to the Class III standard value in the Groundwater Quality Standard (GB / T 14848-2017). For example, the concentration threshold of mercury is set to 0.001 mg / L. When this value is exceeded, the groundwater is considered to be polluted and may pose a potential risk to human health. Then, from the results output by the groundwater solute transport model, the pollutant concentration data at different times and different spatial locations are extracted and drawn into contour maps. By comparing these concentration distributions with the set thresholds, the areas where the concentration exceeds the threshold are determined. These areas are emergency pollution prevention and control areas, because the concentration of pollutants in these areas has reached a level where emergency measures need to be taken to prevent further spread and harm.

[0071] Next, based on the hydrogeological model, the direction of water flow from high water level to low water level is determined by analyzing the groundwater level contours, and the water flow velocity is calculated using the permeability coefficient and hydraulic gradient. Pollutants will diffuse along the direction of water flow, so the area in the direction of water flow is more susceptible to pollution.

[0072] Step S3, planning and constructing monitoring wells based on the pollution emergency prevention and control area and the pollution spreading trend, testing the monitoring wells, and obtaining heavy metal concentration data.

[0073] In this embodiment, planning and constructing monitoring based on the pollution emergency prevention and control area and the pollution spreading trend means planning and constructing monitoring wells in the direction of the pollution spreading trend in the pollution emergency prevention and control area, which obviously helps to detect the concentration of heavy metal pollutants in the mining area. When testing the water quality in the heavy metal concentration monitoring well, the present invention uses a portable heavy metal detector to quickly detect the heavy metal concentration value in the water.

[0074] Portable heavy metal detectors play an important role in detecting heavy metal concentrations in water. They can quickly provide test data to meet the need for instant results. However, a single test is easily affected by environmental factors, instrument errors, etc., resulting in poor data accuracy. To solve this problem, the accuracy of the data can be increased by taking the average value of multiple tests. Multiple tests can effectively reduce accidental errors and make the data closer to the true value, thereby significantly improving the accuracy of the data. This method is easy to operate and low in cost. It can provide reliable data support for water quality testing, making it easy to grasp the status of heavy metal pollution in water in a timely and accurate manner.

[0075] Step S4, drawing a heavy metal pollution contour map based on the heavy metal concentration data.

[0076] Wherein, drawing a heavy metal pollution contour map according to the heavy metal concentration data specifically includes the following sub-steps: Step S401, determining a monitoring area according to the pollution emergency prevention and control area and the pollution spreading trend, and dividing the monitoring area into grids to obtain a plurality of grid nodes located in the monitoring area.

[0077] In this embodiment, the grid size is set to 100 meters according to the number of monitoring wells for the purpose of setting the grid according to the present invention, as well as the consideration of the comprehensive calculation amount and the consideration of calculation efficiency. It should be noted that the setting of the grid size is not a limitation of the present invention, it is only an embodiment of the present invention. When the detection wells are densely distributed, a smaller grid size can be considered. This method can improve the accuracy of data interpolation, and also means that the data calculation amount is increased. Similarly, when the monitoring wells are sparsely distributed, the size of the grid cannot be too large, which will cause the heavy metal concentration in the grid to be distorted and unable to accurately reflect the actual situation.

[0078] Step S402, calculating the heavy metal concentration of the grid node according to the heavy metal concentration data, and obtaining a monitoring area heavy metal concentration data set.

[0079] The step of calculating the heavy metal concentration of the grid node according to the heavy metal concentration data and obtaining the heavy metal concentration data set of the monitoring area specifically includes the following sub-steps: Step S40201, constructing a heavy metal concentration correlation model for monitoring wells.

[0080] Among them, the correlation model of heavy metal concentrations in monitoring wells satisfies the following formula: , in, Quantify the difference in heavy metal concentrations between two monitoring wells. is the distance between two monitoring wells, For all distances between two monitoring wells The number of is the index variable, For the For distance The heavy metal concentration weights of the two monitoring wells, To coordinates The distance is The heavy metal concentration values ​​in the monitoring wells are is the monitoring well coordinate, The coordinates are The heavy metal concentration values ​​in the monitoring wells are To adjust the weight The weight index of the impact degree.

[0081] The distance between two monitoring wells It is the basis for analyzing the correlation of quantitative values ​​of heavy metal concentration differences. The principle is that the heavy metal concentrations of two monitoring wells that are closer are more similar than those of two monitoring wells that are farther away. The heavy metal concentration differences were quantified to reveal the degree of autocorrelation of heavy metal concentrations in different monitoring wells at different locations.

[0082] Setting weights , which can reflect the importance or reliability of different monitoring well points. In the actual detection process of monitoring wells, the data of some monitoring wells are often inaccurate due to equipment detection errors or equipment damage, but they have certain value. The detection data of these heavy metal concentrations are low-quality data. In this case, the weight can be reduced. The value of can be adjusted to reduce the impact on the results. For example, the weight of high-quality monitoring well pairs is set to 2, and the weight of average-quality pairs is set to 1. In this way, when calculating the quantitative value of heavy metal concentration differences, the monitoring well pairs with high-quality data will have a greater impact on the results, so that the correlation model of heavy metal concentrations in monitoring wells can better reflect the real spatial variation characteristics of the data.

[0083] Setting weights , The role of is to adjust the weight The degree of influence on the calculation of the correlation model of heavy metal concentrations in monitoring wells, that is, by changing The value of can change the weight The importance of the whole model, such as when When The sum of the weights in the denominator will be magnified, which means that the monitoring wells with larger weights will have a relatively greater influence on the calculation of the correlation model of heavy metal concentrations in the monitoring wells, thus highlighting the impact of weight differences on the results. When the weight The role of the sum in the denominator will be reduced, which relatively reduces the impact of weight differences on the calculation of the heavy metal concentration correlation model of monitoring wells, making the contribution of monitoring well points with different weights to the results more balanced.

[0084] In addition, in some cases, the data may have some special properties or have specific requirements for the research purpose. For example, when the data quality of the monitoring wells in the monitoring area varies greatly, and it is hoped that high-quality data (i.e., data with high weights) will be highly relied on to determine the correlation model of heavy metal concentrations in the monitoring wells, a larger If the data quality is relatively uniform, or if you want to comprehensively consider the information of all data points to avoid excessive dominance of the results by individual high-weighted points, you can choose a smaller value. value.

[0085] Step S40202: solving the heavy metal concentration correlation model according to the heavy metal concentration data to obtain a heavy metal concentration difference quantitative value data set.

[0086] In this embodiment, in order to solve the heavy metal concentration correlation model, it is necessary to define the coordinates of the monitoring area and record the monitoring well data, which includes the coordinate positions of the monitoring wells, the number of monitoring wells and the heavy metal concentrations corresponding to the monitoring wells.

[0087] The difference quantification value is an indicator that uses specific numerical values ​​to clearly and accurately measure and express the degree of difference between two or more objects, phenomena, or data sets.

[0088] Substituting the heavy metal concentration data into the heavy metal concentration correlation model and solving the heavy metal concentration correlation model can obtain a data set of quantitative values ​​of heavy metal concentration differences.

[0089] In the process of solving the heavy metal concentration correlation model, when matching the heavy metal concentration point pairs of monitoring wells, it is necessary to set the distance error range of the same monitoring well point pair according to the number and location of the monitoring wells. In addition, when setting weights for the heavy metal concentration point pairs of monitoring wells, it is also necessary to fully consider the impact of geology on heavy metal pollution.

[0090] Step S40203, calculating the heavy metal concentration weight of the heavy metal concentration data to the grid node according to the heavy metal concentration difference quantization value data set.

[0091] The step of calculating the heavy metal concentration weight of the heavy metal concentration data to the grid node according to the heavy metal concentration difference quantized value data set specifically includes the following sub-steps: Step S4020301, constructing a heavy metal concentration weight model for the heavy metal concentration data to the grid nodes.

[0092] In this embodiment, the construction of the heavy metal concentration weight model of the heavy metal concentration data for the grid nodes needs to consider the physical meaning and mathematical properties of the weights and set constraints. In a physical sense, the weight can represent the proportion of each component or factor in the whole, and the sum of the weights needs to be set to 1, which means that the sum of the weights of all the difference quantization values ​​of all monitoring wells constitutes a complete distribution system without omissions or excesses. From a mathematical point of view, this constraint makes the distribution of weights standardized and comparable, facilitates mathematical operations and model solving, and ensures the uniqueness and rationality of the solution.

[0093] Step S4020302, calculating the weight of the heavy metal concentration of the grid node according to the heavy metal concentration weight model.

[0094] Wherein, the heavy metal concentration weight model satisfies the following formula: , in, is the number of monitoring wells, is the ergodic coefficient, For the The heavy metal concentration weight of each monitoring well to the grid node, For monitoring wells and monitoring wells Quantitative value of the difference in heavy metal concentrations, For monitoring wells and monitoring wells The distance value between 1 to A positive integer, is the adjustment parameter of the conditional equation, is the heavy metal concentration of the grid node to be estimated and the monitoring well Quantitative value of the difference in heavy metal concentrations, The grid nodes and monitoring wells to be evaluated The distance value between .

[0095] Step S40204, calculating the heavy metal concentration of the grid node according to the heavy metal concentration weight and the heavy metal concentration data, and obtaining the heavy metal concentration data set of the monitoring area.

[0096] Wherein, calculating the heavy metal concentration of the grid node according to the heavy metal concentration weight and the heavy metal concentration data includes: Step S4020401, constructing a grid node heavy metal concentration model.

[0097] In this embodiment, when constructing a grid node heavy metal concentration model, it is necessary to consider the factors affecting the heavy metal concentration of the grid nodes, such as the number of monitoring wells, the heavy metal concentration corresponding to each monitoring well, and the weight of the influence of the heavy metal concentration corresponding to each monitoring well on the heavy metal concentration at the grid node.

[0098] Step S4020402, calculating the heavy metal concentration of the grid node according to the grid node heavy metal concentration model and the heavy metal concentration data.

[0099] In this embodiment, the heavy metal concentration of each grid node can be calculated based on the heavy metal concentration data of each monitoring well and the weight influence of the heavy metal concentration of each monitoring well on each node. The heavy metal concentration data of each grid node is sorted to obtain the monitoring area heavy metal concentration data set.

[0100] The grid node heavy metal concentration model satisfies the following formula: , in, The coordinates are The heavy metal concentration value of the grid node, is the number of monitoring wells, is the ergodic coefficient, For the The coordinates of the monitoring wells are The heavy metal concentration weights of the grid nodes, For the Heavy metal concentrations in the monitoring wells.

[0101] Step S403: drawing the heavy metal pollution contour map according to the heavy metal concentration data set of the monitoring area.

[0102] In this embodiment, the contour map is a visualization tool widely used in the fields of science and engineering. It is drawn by connecting points with equal values ​​and is used to intuitively display the distribution of a variable in space or on a plane.

[0103] Contour maps can display the concentration of heavy metal pollutants in groundwater in an intuitive graphical manner. By drawing different contour lines, high-concentration heavy metal pollution areas and low-concentration heavy metal pollution areas can be clearly distinguished, allowing researchers, decision makers and related personnel to quickly understand the spatial distribution of heavy metal pollution. At the same time, contour lines can clearly define the boundaries of heavy metal pollution areas. This is crucial for determining the scope of land contaminated by heavy metals, and helps land planners, environmental managers, etc. determine areas that need to be monitored and managed. Draw contour maps of heavy metal pollution at different time points. By comparing these maps, you can intuitively see whether the scope of heavy metal pollution is expanding or shrinking, and whether the degree of pollution is increasing or decreasing.

[0104] Step S5, evaluating the control measures based on the leakage of the mine anti-seepage membrane through the heavy metal pollution contour map, the pollution emergency prevention and control area and the pollution spread trend.

[0105] In this embodiment, the spread of groundwater pollution in the absence of pollution control measures can be predicted based on the pollution emergency prevention and control area and the pollution spread trend, and the heavy metal pollution contour map is compared with the groundwater pollution spread. The effect of the control measures can be judged based on the difference in the comparison results. If it is determined that the previous control measures are not effective, the direction of pollution control can be further adjusted based on the effectiveness of the control measures. In addition, after the new control measures are completed, a new round of evaluation of the new control measures is still required, and the evaluation method is consistent with the present invention, and this cycle is repeated until the groundwater pollution meets the control requirements.

[0106] like Figure 2 As shown, on the other hand, the present invention also provides an evaluation system for control measures based on mine anti-seepage membrane leakage, comprising: a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute relevant steps of a relevant embodiment of an evaluation method for control measures based on mine anti-seepage membrane leakage of the present invention.

[0107] In the evaluation system for treatment measures of mine anti-seepage membrane leakage provided by the present invention, each functional component can be integrated into one processing component, or each component can exist physically separately, or two or more components can be integrated into one component. The above-mentioned integrated components can be implemented in the form of hardware or in the form of software functions.

[0108] It should be noted that in the complex environment of mining areas, groundwater pollution problems present diverse characteristics. The types of pollution are by no means a single type. In addition to the well-known heavy metal pollution, problems such as pH imbalance, excessive turbidity, abnormal hardness, and increased oxygen consumption are also common. These different types of pollution each pose a threat to the groundwater ecological environment and the water safety of surrounding residents.

[0109] However, the present invention focuses on the detection of heavy metal pollutants. This is not accidental, but based on the special status of heavy metal pollutants in groundwater in mining areas. As a typical source of pollution in groundwater in mining areas, heavy metals have the characteristics of high toxicity, difficulty in degradation, and easy enrichment. Once entering the water body, they will not only cause serious poisoning to aquatic organisms, but also may be transmitted through the food chain, ultimately endangering human health. For example, heavy metals such as lead, mercury, and cadmium, long-term contact or ingestion will damage the human nervous system, immune system, and reproductive system.

[0110] It should be made clear that the purpose of this invention is not to comprehensively evaluate the water quality, but to keenly capture the core problem of groundwater pollution in mining areas by detecting the key indicator of heavy metals. When the water in the monitoring well is found to contain a certain concentration of heavy metals, this signal strongly suggests that the current pollution control measures are not yet perfect, and it is very likely that there are still pollution sources that continue to leak into the groundwater.

[0111] The present invention chooses to reduce the detection indicators and focuses on heavy metal detection. This strategy does not ignore other pollution factors, but is based on the perspective of practical application and detection efficiency. In special environments such as mining areas, it is crucial to quickly and accurately locate key pollution sources. Reducing detection indicators can avoid the waste of time and resources caused by excessive indicator detection, greatly improve detection efficiency, and enable staff to quickly determine the effectiveness of groundwater pollution control measures based on test results, thereby laying the foundation for subsequent more comprehensive pollution control work.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for evaluating treatment measures for leakage of mine anti-seepage membranes, characterized in that: The method comprises: Construct hydrogeological models and groundwater solute transport models; Determine the pollution emergency prevention and control area and the pollution spreading trend according to the hydrogeological model and the groundwater solute transport model; Planning and constructing monitoring wells based on the pollution emergency prevention and control area and the pollution spreading trend, testing the monitoring wells, and obtaining heavy metal concentration data; Based on the heavy metal concentration data, a heavy metal pollution contour map is drawn; The control measures based on the leakage of the mine anti-seepage membrane are evaluated through the heavy metal pollution contour map, the pollution emergency prevention and control area and the pollution spread trend.

2. The evaluation method of the treatment measures based on the leakage of the mine anti-seepage membrane according to claim 1 is characterized in that: The construction of the hydrogeological model and the groundwater solute transport model includes: Obtain mining site geological environment data, hydrological monitoring well water level data and ecological restoration project design plans; Preprocessing the geological environment data of the mine to obtain hydrogeological data; Constructing a hydrogeological conceptual model based on the hydrogeological data and the water level data of the hydrological monitoring wells; Using the ecological restoration project design plan to adjust the boundaries of the hydrogeological conceptual model and construct the hydrogeological model; Based on the test data of the leaching test and the immersion test, the groundwater solute transport model is constructed according to the hydrogeological model.

3. The evaluation method of the treatment measures based on the leakage of the mine anti-seepage membrane according to claim 2 is characterized in that: The preprocessing of the mine geological environment data to obtain hydrogeological data includes: The outliers are eliminated and missing values ​​are interpolated for the geological environment data of the mine to obtain hydrogeological data.

4. The evaluation method of the treatment measures based on the leakage of the mine anti-seepage membrane according to claim 1 is characterized in that: Drawing a heavy metal pollution contour map according to the heavy metal concentration data comprises: Determine a monitoring area according to the pollution emergency prevention and control area and the pollution spreading trend, and divide the monitoring area into grids to obtain a plurality of grid nodes located in the monitoring area; Calculate the heavy metal concentration of the grid node according to the heavy metal concentration data to obtain a heavy metal concentration data set of the monitoring area; The heavy metal pollution contour map is drawn based on the heavy metal concentration data set of the monitoring area.

5. The method for evaluating treatment measures for leakage of mine anti-seepage membrane according to claim 4, characterized in that: Calculating the heavy metal concentration of the grid node according to the heavy metal concentration data to obtain a heavy metal concentration data set in the monitoring area includes: Construct a correlation model of heavy metal concentrations in monitoring wells; Solving the heavy metal concentration correlation model according to the heavy metal concentration data to obtain a heavy metal concentration difference quantitative value data set; Calculating the heavy metal concentration weight of the heavy metal concentration data to the grid node according to the heavy metal concentration difference quantization value data set; The heavy metal concentration of the grid node is calculated according to the heavy metal concentration weight and the heavy metal concentration data to obtain the heavy metal concentration data set of the monitoring area.

6. The evaluation method of the treatment measures based on the leakage of the mine anti-seepage membrane according to claim 5 is characterized in that: The correlation model of heavy metal concentrations in monitoring wells satisfies the following formula: ， in, Quantify the difference in heavy metal concentrations between two monitoring wells. is the distance between two monitoring wells, For all distances between two monitoring wells The number of is the index variable, For the For distance The heavy metal concentration weights of the two monitoring wells, To coordinates The distance is The heavy metal concentration values ​​in the monitoring wells are is the monitoring well coordinate, The coordinates are The heavy metal concentration values ​​in the monitoring wells are To adjust the weight The weight index of the impact degree.

7. The method for evaluating treatment measures for leakage of mine anti-seepage membrane according to claim 5, characterized in that: Calculating the heavy metal concentration weight of the heavy metal concentration data to the grid node according to the heavy metal concentration difference quantization value data set includes: Constructing a heavy metal concentration weight model of the heavy metal concentration data for the grid nodes; The weight of the heavy metal concentration of the grid node is calculated according to the heavy metal concentration weight model.

8. The method for evaluating treatment measures for leakage of mine anti-seepage membrane according to claim 7, characterized in that: The heavy metal concentration weight model satisfies the following formula: ， in, is the number of monitoring wells, is the ergodic coefficient, For the The heavy metal concentration weight of each monitoring well to the grid node, For monitoring wells and monitoring wells Quantitative value of the difference in heavy metal concentrations, For monitoring wells and monitoring wells The distance value between 1 to A positive integer, is the adjustment parameter of the conditional equation, is the heavy metal concentration of the grid node to be estimated and the monitoring well Quantitative value of the difference in heavy metal concentrations, The grid nodes and monitoring wells to be evaluated The distance value between .

9. The method for evaluating treatment measures for leakage of mine anti-seepage membrane according to claim 5, characterized in that: The calculating the heavy metal concentration of the grid node according to the heavy metal concentration weight and the heavy metal concentration data includes: Construct a heavy metal concentration model for grid nodes; Calculating the heavy metal concentration of the grid node according to the grid node heavy metal concentration model and the heavy metal concentration data; The grid node heavy metal concentration model satisfies the following formula: ， in, The coordinates are The heavy metal concentration value of the grid node, is the number of monitoring wells, is the ergodic coefficient, For the The coordinates of the monitoring wells are The heavy metal concentration weights of the grid nodes, For the Heavy metal concentrations in the monitoring wells.

10. An evaluation system based on the management measures of mine anti-seepage membrane leakage, characterized in that: include: A processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute an evaluation method based on control measures for mine anti-seepage membrane leakage as described in any one of claims 1 to 9.

Images