An evaluation method and system for treatment measures based on the leakage of the anti-seepage membrane in a mine

Abstract

The present invention relates to the field of environmental protection, and specifically to an evaluation method and system for treatment measures based on the leakage of anti-seepage membranes in mine sites. The method includes: constructing a hydrogeological model and a groundwater solute transport model; determining the pollution emergency prevention and control area and the pollution spread trend according to the hydrogeological model and the groundwater solute transport model; planning and building monitoring wells based on the pollution emergency prevention and control area and the pollution spread trend, detecting the monitoring wells, and obtaining heavy metal concentration data; drawing a heavy metal pollution isoconcentration map according to the heavy metal concentration data; evaluating the treatment measures based on the leakage of the anti-seepage membrane in the mine site through the heavy metal pollution isoconcentration map, the pollution emergency prevention and control area, and the pollution spread trend. The present invention solves the problem in the prior art that after the anti-seepage membrane in the mine repair area leaks, due to the poor effect of the treatment measures and the inability to detect the defects of the treatment measures in time, continuous groundwater pollution is caused.

Description

Technical Field

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

[0002] After the completion of mining activities in a mine, mine ecological restoration has become a key task. To prevent the residual pollutants in the mine from seeping into the groundwater body and threatening the surrounding ecological environment and the safety of residents' water use, using an anti-seepage membrane to build an isolation barrier is a common engineering measure. However, in actual applications, the problem of anti-seepage membrane leakage occurs frequently, bringing huge challenges to the protection of groundwater environment.

[0003] Although the anti-seepage membrane can theoretically play a good anti-seepage role, in actual engineering, due to the influence of various factors, leakage problems occur from time to time. 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 be stretched and torn, thus forming a leakage channel. On the other hand, improper operations during the construction process, such as poor welding quality of the anti-seepage membrane or being punctured by sharp objects during laying, will also cause damage to the anti-seepage membrane. In addition, long-term environmental erosion, such as ultraviolet radiation and chemical corrosion, will age the anti-seepage membrane and reduce its anti-seepage performance.

[0004] In response to the problem of anti-seepage membrane leakage, various treatment measures have been taken currently. However, it is very difficult to judge whether these treatment measures are in place. It is difficult to accurately evaluate the anti-seepage performance of the repaired area. 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. Moreover, 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 means are difficult to comprehensively and real-time grasp the treatment effect. In addition, the synergistic effect between different treatment measures is also difficult to quantitatively evaluate, resulting in a lack of scientific basis for judging the overall treatment effect.

[0005] In summary, the effectiveness evaluation of the treatment measures for the anti-seepage membrane leakage in the mine ecological restoration area is an urgent problem to be solved, and more accurate and efficient evaluation technologies and methods need to be further studied and developed. Summary of the Invention

[0006] Aiming at the defects in the prior art, the present invention provides an evaluation method and system for the treatment measures based on the leakage of the anti-seepage membrane in a mine site, which solves the problem in the prior art that the groundwater is continuously polluted due to the poor treatment effect of the anti-seepage membrane leakage in the mine restoration area and the inability to detect the defects of the treatment measures in time.

[0007] To achieve the above object, in one aspect of the present invention, an evaluation method for treatment measures based on the leakage of the anti-seepage membrane in a mine is provided, including: constructing a hydrogeological model and a groundwater solute transport model; determining a pollution emergency prevention and control area and a pollution spread trend according to the hydrogeological model and the groundwater solute transport model; planning and building monitoring wells based on the pollution emergency prevention and control area and the pollution spread trend, detecting the monitoring wells, and obtaining heavy metal concentration data; drawing a heavy metal pollution isogram according to the heavy metal concentration data; evaluating the treatment measures based on the leakage of the anti-seepage membrane in the mine through the heavy metal pollution isogram, the pollution emergency prevention and control area and the pollution spread trend.

[0008] The present invention confirms the expected pollution range and trend of pollutants through a hydrogeological model and a groundwater solute transport model, and then combines the comparative analysis of the heavy metal pollution isogram to compare the expected pollution degree and the actual pollution degree to evaluate the effect of the treatment measures for the leakage of the anti-seepage membrane in the mine, improving the efficiency of adjusting the treatment measures and the efficiency of further treating pollution.

[0009] Optionally, the constructing of the hydrogeological model and the groundwater solute transport model includes: obtaining mine geological environment data, water level data of hydrogeological monitoring wells, and an ecological restoration project design plan; preprocessing the mine geological environment data to obtain hydrogeological data; constructing a hydrogeological conceptual model according to the hydrogeological data and the water level data of the hydrogeological monitoring wells; adjusting the boundary of the hydrogeological conceptual model by using the ecological restoration project design plan to construct the hydrogeological model; constructing the groundwater solute transport model according to the hydrogeological model based on the test data of leaching tests and soaking tests.

[0010] The present invention adjusts the boundary of the hydrogeological conceptual model through the ecological restoration project design plan, improving the applicability of the model, and on this basis constructs the groundwater solute transport model, 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 outliers and interpolating missing values from the mine geological environment data to obtain hydrogeological data.

[0012] The present invention removes outliers and interpolates missing values from the mine geological environment data, improving the data quality.

[0013] Optionally, the drawing of the heavy metal pollution isopleth map according to the heavy metal concentration data includes: determining a monitoring area according to the pollution emergency prevention and control area and the pollution spread trend, and dividing the monitoring area into grids to obtain a plurality of grid nodes located in the monitoring area; calculating the heavy metal concentration of the grid nodes according to the heavy metal concentration data to obtain a heavy metal concentration data set of the monitoring area; and drawing the heavy metal pollution isopleth map according to the heavy metal concentration data set of the monitoring area.

[0014] The present invention divides the monitoring area into grids, which facilitates the management and analysis of metal pollution data, and at the same time provides convenience for calculating the heavy metal concentration of the entire monitoring area. In addition, through the heavy metal pollutant data of limited monitoring wells, the present invention can calculate the heavy metal concentration of the entire monitoring area by scientific means, which can greatly reduce the pollution control cost.

[0015] Optionally, the calculating the heavy metal concentration of the grid nodes according to the heavy metal concentration data to obtain a heavy metal concentration data set of the monitoring area includes: constructing a correlation model of the heavy metal concentration of the monitoring wells; solving the correlation model of the heavy metal concentration according to the heavy metal concentration data to obtain a data set of heavy metal concentration difference quantization values; calculating the heavy metal concentration weight of the grid nodes according to the data set of heavy metal concentration difference quantization values; and calculating the heavy metal concentration of the grid nodes 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 correlation of the heavy metal concentration of each monitoring well through the existing heavy metal concentration data, and further calculates the heavy metal concentration weight of each monitoring well for the grid nodes, thereby calculating the heavy metal concentration of the grid nodes, making the discrete data continuous, and improving the efficiency and accuracy of data analysis.

[0017] Optionally, the correlation model of the heavy metal concentration of the monitoring wells satisfies the following formula:

[0018] ,

[0019] wherein, is the heavy metal concentration difference quantization value between two monitoring wells, is the distance value between two monitoring wells, is the number of all distances of between two monitoring wells, is an index variable, is the th heavy metal concentration weight of two monitoring wells with a distance of is the distance to the coordinate is Heavy metal concentration values in the monitoring wells, is the coordinate of the monitoring well, is the coordinate of Heavy metal concentration values in the monitoring well, is to adjust the weight The weight index of the influence degree.

[0020] The correlation model of heavy metal concentration in the monitoring wells of the present invention fully considers the uncertain external factors that may be encountered during the detection of the monitoring wells. By adjusting the weights of the corresponding pairs of heavy metal concentration points in the monitoring wells, the influence of the heavy metal concentration of the corresponding detection wells on the whole is increased or decreased, the error tolerance rate of the monitoring well detection is improved, and the generality of this model is increased.

[0021] Optionally, 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 dataset includes: constructing a heavy metal concentration weight model of the heavy metal concentration data for the grid node; calculating the weight of the heavy metal concentration of the grid node according to the heavy metal concentration weight model.

[0022] The present invention constructs a heavy metal concentration weight model through the correlation between the heavy metal concentration data in a limited number of monitoring wells, and further calculates the weight of the heavy metal concentration of the detection well on the heavy metal concentration of the grid node, improving the accuracy of the heavy metal concentration of the grid node.

[0023] Optionally, the heavy metal concentration weight model satisfies the following formula:

[0024] ,

[0025] Wherein, is the number of monitoring wells, is the traversal coefficient, is the heavy metal concentration weight of the th monitoring well on the grid node, is the monitoring well and the monitoring well The quantization value of the heavy metal concentration difference, is the monitoring well and the monitoring well The distance value between, is a positive integer from 1 to , is the adjustment parameter of the conditional equation, is the quantization value of the difference between the heavy metal concentration of the grid node to be estimated and the heavy metal concentration of the monitoring well , is the distance value between the grid node to be estimated and the monitoring well .

[0026] The heavy metal concentration weight model of the present invention improves the practicability and accuracy of the model by setting the constraint conditions of the weights, clarifying the range and physical meaning of the weight values, and then constructing the weight equation and setting the constraint parameters based on the correlation of the heavy metal concentrations of each monitoring well.

[0027] Optionally, 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 heavy metal concentration model of the grid node; calculating the heavy metal concentration of the grid node according to the heavy metal concentration model of the grid node and the heavy metal concentration data; the heavy metal concentration model of the grid node satisfies the following formula:

[0028] ,

[0029] where, is the heavy metal concentration value of the grid node with coordinates , is the number of monitoring wells, is the traversal coefficient, is the th heavy metal concentration weight of the th monitoring well for the grid node with coordinates is the th heavy metal concentration value of the

[0030] The present invention calculates the heavy metal concentration of each grid node by summing the products of the weights of the heavy metal concentration of the grid node to be estimated and the heavy metal concentration based on the known heavy metal concentration data of each monitoring well, improving the accuracy of the heavy metal concentration calculation of the grid node.

[0031] Another aspect of the present invention also provides an evaluation system for the treatment measures based on the leakage of the anti-seepage membrane in the mine, including: a processor, an input device, an output device, and a memory, 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 for the treatment measures based on the leakage of the anti-seepage membrane in the mine according to any one of the previous aspects of the present invention.

[0032] The evaluation system for the treatment measures based on the leakage of the anti-seepage membrane in the mine of the present invention has a compact structure, stable performance, high integration, and simple composition, and can stably execute an evaluation method for the treatment measures based on the leakage of the anti-seepage membrane in the mine provided in the previous aspect of the present invention, further improving the overall applicability and practical application ability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1Flowchart of an evaluation method for treatment measures based on the leakage of the anti-seepage membrane in a mine

[0034] Figure 2 Schematic structural diagram of an evaluation system for treatment measures based on the leakage of the anti-seepage membrane in a mine according to an embodiment of the present invention Detailed implementation manners

[0035] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described here are only for illustrative purposes and are not used to limit the present invention. In the following description, in order to provide a thorough understanding of the present invention, a large number of specific details are set forth. However, it is obvious to those of ordinary skill in the art that the present invention does not have to employ these specific details. In other instances, well-known circuits, software, or methods have not been described in detail to avoid obscuring the present invention.

[0036] Throughout the specification, the reference to "an embodiment", "embodiment", "an example" or "example" means that the specific features, structures, or characteristics described in connection with the embodiment or example are included in at least one embodiment of the present invention. Thus, the phrases "in an embodiment", "in embodiments", "an example" or "example" appearing throughout the specification do not necessarily all refer to the same embodiment or example. In addition, the specific features, structures, or characteristics may be combined in any suitable combination and / or sub-combination in one or more embodiments or examples. Further, those of ordinary skill in the art should understand that the diagrams provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0037] Please refer to Figure 1 , in an embodiment of the present invention, an evaluation method for treatment measures based on the leakage of the anti-seepage membrane in a mine is provided, which solves the problem in the prior art that after the anti-seepage membrane in the mine repair area leaks, due to the poor effect of the treatment measures and the inability to detect the defects of the treatment measures in time, resulting in continuous pollution of groundwater, as Figure 1 shown, the method includes the following steps:

[0038] Step S1, construct a hydrogeological model and a groundwater solute transport model.

[0039] Among them, constructing the hydrogeological model and the groundwater solute transport model specifically includes the following sub-steps:

[0040] Step S101, obtain the mine geological environment data, the water level data of the hydrogeological monitoring wells, and the ecological restoration project design plan.

[0041] In this embodiment, the mine geological environment data includes meteorological and hydrological data, topographic and geomorphic data, stratigraphic lithology data, geological structure data, engineering geological data, and hydrogeological data.

[0042] Meteorological and hydrological data can be obtained by querying the observation records of meteorological stations closer to the mine restoration area through meteorological stations. These meteorological stations are generally established and managed by the national meteorological department, and the accuracy and continuity of the data are relatively high. The collected contents include basic meteorological elements such as air temperature, air pressure, humidity, wind speed, wind direction, and precipitation.

[0043] In an alternative embodiment, for large mines, in special geological environments, the data queried from meteorological stations may not be very accurate. Therefore, on-site measurements can be carried out in the mining area for a period of time to obtain meteorological and hydrological data, which can improve the accuracy of the data.

[0044] Topographic and geomorphic data can be obtained by aerial photography to acquire images of the mine restoration area, and then processed by photogrammetry software to generate a high-precision digital terrain model. This method can quickly obtain large-area topographic data and can reflect the minute changes in the terrain. For example, when monitoring the topographic and geomorphic conditions of the restoration area of a large open-pit mine, aerial photogrammetry can effectively detect minute deformations of slopes and other situations.

[0045] Stratigraphic lithology data can be obtained by observing rock outcrops. Look for rock outcrops in the mine restoration area and observe the characteristics of the rock such as color, structure, and texture. For example, for sedimentary rocks, observe whether the bedding is clear, whether there are cross-bedding, ripple marks and other sedimentary structures, which can reflect the sedimentary environment and sedimentation process. For igneous rocks, observe their mineral composition, degree of crystallization and rock structure to judge the type and formation process of igneous rocks. Further, drill cores from the deep underground can be obtained. The drilling depth should be determined according to the thickness of the strata and research needs. During the drilling process, information such as the depth of the core and lithological changes should be recorded in detail. Laboratory analysis of the core samples is carried out, including the physical and chemical properties analysis of the rocks.

[0046] Geological structure data is obtained by referring to the regional geological structure map to understand the distribution of large geological structures (such as folds, faults, etc.) around the mine restoration area, and conducting a detailed geological structure survey in the mine restoration area. By observing structural signs such as fault slickensides and fold hinges in rock outcrops, determine the location, direction and nature of small geological structures. Then mark these structural information on the geological map to form the geological structure map of the mine restoration area. During the investigation process, pay attention to recording the attitude (strike, dip, dip angle) of the structure, which is very important for analyzing the tectonic stress field and geological stability.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] Step S102, preprocessing the mine geological environment data to obtain hydrogeological data.

[0052] The process of preprocessing the mine geological environment data to obtain hydrogeological data specifically includes the following sub-steps:

[0053] The outliers are eliminated and missing values ​​are interpolated for the geological environment data of the mine to obtain hydrogeological data.

[0054] In this embodiment, by removing outliers, the outliers caused by sensor failures, measurement errors, or extreme events can be eliminated, thereby improving the reliability of the data and the accuracy of the analysis. The algorithm for removing outliers from the mine geological environment data is the outlier removal algorithm. An outlier refers to the data that significantly deviates from other data points in the dataset. The classification of the outlier removal algorithm includes methods based on statistical distributions, distance-based methods, and model prediction-based methods.

[0055] In this embodiment, the principle is used to remove outliers. The mine geological environment data approximately follows a normal distribution. According to the properties of the normal distribution, about 99.7% of the data is located within the interval, where represents the mean, and represents the standard deviation. The outlier range is determined to be the data less than and greater than .

[0056] The mean satisfies the following formula:

[0057] ,

[0058] In the formula, represents the data, represents the number of data, and represents the position of the data in the data sequence.

[0059] The standard deviation satisfies the following formula:

[0060] ,

[0061] In the formula, represents the data, represents the number of data, represents the position of the data in the data sequence, and is the mean.

[0062] In some other embodiments, the box plot (IQR) method can also be used to remove outliers. The principle of the box plot is to judge outliers through the quartiles of the data. The first quartile represents the lower 25% quantile of the data, and the third quartile represents the upper 25% quantile of the data. The interquartile range , and usually the data points less than or greater than are determined to be outliers.

[0063] Meanwhile, the K-Nearest Neighbor (KNN) distance method can also be used to eliminate outliers. The principle of the KNN distance method is that for each data point in the dataset of the mine geological environment, its distance from other data points (such as Euclidean distance) is calculated. Whether it is an outlier is judged according to the distribution of the K nearest neighbor data points around the data point. If the distance between a data point and its K nearest neighbor data points is significantly greater than the distances between other points, then it may be an outlier.

[0064] The Euclidean distance satisfies the following formula:

[0065] ,

[0066] In the formula, is the Euclidean distance, and are different data points, is the position of the data point in the data sequence, is the number of data points.

[0067] The interpolation algorithm for missing values in the mine geological environment data is used. The interpolation algorithm for missing values includes: mean imputation method, median imputation method, mode imputation method, regression-based imputation method, and multiple imputation method. Interpolating missing values can ensure the integrity of the dataset.

[0068] In this implementation, the mean imputation method is used to interpolate missing values. When there are missing values in the dataset, the mean of the variable is used to fill in the missing values. This method is simple and intuitive and is suitable for the case where the data distribution is relatively uniform and the proportion of missing values is relatively small.

[0069] The mean imputation method for interpolating missing values satisfies the following formula:

[0070] ,

[0071] In the formula, represents the data of the th parameter of the th dimension to be processed, represents the data of the th parameter of the th dimension, represents the data of the th parameter of the th dimension, represents the dimension serial number of the parameter.

[0072] In an alternative implementation, the median imputation method can be used to impute missing values. The principle of the median imputation method is similar to that of the mean imputation method, but the median is used to fill in the missing values. The median is the value at the middle position after sorting the data. For data with skewed distributions or data with outliers, the median can better represent the central position of the data than the mean. The specific steps of this method include: sorting the non-missing values of the variable containing the 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; replacing the missing values with the median.

[0073] In another alternative embodiment, the regression-based imputation method can also be used to impute missing values. The principle of the regression-based imputation method is to establish a regression model and use other relevant variables to predict the missing values.

[0074] Generally speaking, removing outliers and imputing missing values from the mine geological environment data can improve the quality of hydrogeological data.

[0075] Step S103, construct a hydrogeological conceptual model based on the hydrogeological data and the water level data of the hydrogeological monitoring wells.

[0076] In this embodiment, the Groundwater Modeling System (GMS) is used to construct the hydrogeological conceptual model.

[0077] GMS is a professional groundwater simulation software. Using a unique conceptual model method, users can directly construct a conceptual groundwater model on the scanned map of the site using familiar GIS objects such as points, arcs, and polygons, quickly and conveniently construct 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, topographic maps, elevation data, borehole data, native MODFLOW files, ArcGIS geodatabases and shapefile files, CAD files, etc., supports global projection systems, and can meet the needs of the model for different sources of data. In addition, GMS supports finite difference and finite element modeling techniques such as MODFLOW, and can model various complex groundwater systems. Moreover, GMS can also simulate the movement of pollutant plumes, calculate their volumes, and generate animations to analyze and predict the changes of pollutant plumes or groundwater flows over time, and supports models such as MT3DMS and RT3D.

[0078] Import the hydrogeological data and the water level data of the hydrogeological monitoring wells into the GMS software, and the software automatically generates a hydrogeological conceptual model.

[0079] Step S104: Adjust the boundary of the hydrogeological conceptual model using the ecological restoration engineering design plan, and construct the hydrogeological model.

[0080] In this embodiment, first, data such as the scope of the ecological restoration project, the boundary coordinates of the restoration area, the vegetation restoration area, and the soil improvement area in the ecological restoration engineering design plan are sorted out 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.

[0081] Step S105: Based on the test data of the leaching test and the soaking test, construct the groundwater solute transport model according to the hydrogeological model.

[0082] The leaching test is a test method used to study the migration law of solutes (such as nutrients, pollutants, etc.) in soil or other porous media under leaching. The soaking test is a test method that evaluates the performance changes, corrosion resistance, dissolution characteristics, etc. of materials by immersing the materials in a specific liquid environment.

[0083] Collect the test data of the leaching test, including information such as the concentration of solutes in the leachate and the volume of the leachate at different time intervals. Organize these data in tabular form to ensure the accuracy and integrity of the data. For example, in the leaching test, a time series of data may be obtained, recording the concentration (mg / L) of a certain pollutant in the leachate and the total volume (L) of the leachate at different time points (such as the 1st day, the 3rd day, the 7th day, etc.) after the start of the test. These data will reflect the release and migration rate of solutes from the medium under leaching conditions.

[0084] For the soaking test, organize the release data of solutes over time during soaking, such as the change in the concentration of solutes in the solution after soaking for different times. Record these data in tabular form as well, noting the test conditions, such as the amount of solid sample soaked, the volume of the soaking solution, the temperature, etc. For example, in a soil soaking test, record the change in the concentration of pollutants in the solution at different soaking times (such as 0 hours, 24 hours, 48 hours, etc.), which can help us understand the release characteristics of solutes under the water-rock interaction.

[0085] The construction of the groundwater solute transport model requires selecting a suitable solute transport equation according to 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 data of the leaching test and soaking test. Among them, the dispersion coefficient is estimated by analyzing the change of solute concentration with time and space in the leaching test, and the adsorption parameter is determined by fitting the data of the adsorption isotherm according to the change of solute concentration in the soaking test.

[0086] Finally, import the hydrogeological model and test data into GMS, and the software automatically generates a groundwater solute transport model.

[0087] Step S2: Determine the pollution emergency prevention and control area and the pollution spread trend according to the hydrogeological model and the groundwater solute transport model.

[0088] In this embodiment, first, determine the concentration threshold. Refer to relevant environmental standards and the harm degree of pollutants to human health and the ecological environment, and set one or more pollutant concentration thresholds. 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 it exceeds this value, it is considered that the groundwater is polluted and may pose a potential risk to human health. Then, extract the pollutant concentration data at different times and different spatial positions from the results output by the groundwater solute transport model, draw an isogram, and determine the area where the concentration exceeds the threshold by comparing these concentration distributions with the set threshold. These areas are the pollution emergency prevention and control areas because the concentration of pollutants in these areas has reached the level where emergency measures need to be taken to prevent further spread and harm.

[0089] Immediately afterwards, according to the hydrogeological model, by analyzing the groundwater level isogram, determine the direction of water flow from high water level to low water level, and calculate the water flow velocity using the permeability coefficient and hydraulic gradient. Pollutants will spread along the water flow direction, so the areas in the water flow direction are more vulnerable to pollution.

[0090] Step S3: Plan and construct monitoring wells based on the pollution emergency prevention and control area and the pollution spread trend, detect the monitoring wells, and obtain heavy metal concentration data.

[0091] In this embodiment, planning and constructing monitoring based on the pollution emergency prevention and control area and the pollution spread trend means planning and constructing monitoring wells in the direction of the pollution spread trend within the pollution emergency prevention and control area, which obviously helps to detect the concentration of heavy metal pollutants in the mining area. When detecting the water quality in the heavy metal concentration monitoring wells, the present invention uses a portable heavy metal detector to quickly detect the heavy metal concentration value in the water.

[0092] When detecting the heavy metal concentration in water, a portable heavy metal detector plays an important role. It can quickly give detection data to meet the need for immediate results. However, single detection 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 of multiple detections. Multiple detections can effectively reduce accidental errors, making the data closer to the true value, thus significantly improving the accuracy of the data. This method is easy to operate and has a low cost, which can provide reliable data support for water quality detection and facilitate timely and accurate understanding of the heavy metal pollution situation in water.

[0093] Step S4, draw an isogram of heavy metal pollution according to the heavy metal concentration data.

[0094] Among them, drawing an isogram of heavy metal pollution according to the heavy metal concentration data specifically includes the following sub-steps:

[0095] Step S401, determine the monitoring area according to the pollution emergency prevention and control area and the pollution spread trend, and divide the monitoring area into grids to obtain a plurality of grid nodes located in the monitoring area.

[0096] In this embodiment, considering the number of monitoring wells for the purpose of setting grids according to the present invention, as well as the comprehensive calculation amount and calculation efficiency, the grid size is set to 100 meters. 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 distribution of detection wells is relatively dense, a smaller grid size can be considered. This method can improve the accuracy of data interpolation and also means an increase in the data calculation amount. Similarly, when the distribution of monitoring wells is sparse, the grid size should not be too large, which will cause the heavy metal concentration in the grid to be distorted and unable to accurately reflect the actual situation.

[0097] Step S402, calculate the heavy metal concentration of the grid nodes according to the heavy metal concentration data to obtain a heavy metal concentration data set for the monitoring area.

[0098] Among them, calculating the heavy metal concentration of the grid nodes according to the heavy metal concentration data to obtain a heavy metal concentration data set for the monitoring area specifically includes the following sub-steps:

[0099] Step S40201, construct a correlation model for the heavy metal concentration of monitoring wells.

[0100] Among them, the correlation model for the heavy metal concentration of monitoring wells satisfies the following formula:

[0101] ,

[0102] Among them, is the quantification value of the difference in heavy metal concentration between two monitoring wells, is the distance value between two monitoring wells. is the number of all distances between two monitoring wells that are . is the index variable. is the th pair of monitoring wells with a distance of for the heavy metal concentration weight. is the distance to the coordinate for the monitoring well with a distance of for the heavy metal concentration value. is the monitoring well coordinate. is the heavy metal concentration value in the monitoring well with the coordinate . is the adjusted weight for the weight index of the influence degree.

[0103] The distance value between two monitoring wells is the basis for analyzing the correlation of the quantitative value of heavy metal concentration differences. The principle is that the heavy metal concentrations of two monitoring wells with a closer distance are more similar than those of two monitoring wells with a farther distance. By calculating the quantitative values of heavy metal concentration differences for different distance values between monitoring wells, the autocorrelation degree of heavy metal concentrations at different positions in different monitoring wells can be revealed.

[0104] Setting the weight , can reflect factors such as the importance or reliability of different pairs of monitoring wells. During the actual detection process of monitoring wells, due to reasons such as equipment detection errors or equipment damage, the data of some monitoring wells may be inaccurate but still have a certain value. These detected data of heavy metal concentrations are low-quality data. In this case, the value of the weight can be reduced to minimize the impact on the results. For example, the weight of a pair of monitoring wells with high quality is set to 2, and the weight of a pair of medium-quality points is set to 1. In this way, when calculating the quantitative value of heavy metal concentration differences, the pair of monitoring wells with high-quality data will have a greater impact on the results, making the correlation model of heavy metal concentrations in monitoring wells better reflect the true spatial variation characteristics of the data.

[0105] Setting the weight , the role of which is to adjust the influence degree of the weight on the calculation of the correlation model of heavy metal concentrations in monitoring wells. That is to say, by changing the value of , the importance of the weight in the whole model can be changed. For example, when , then the weight The role of the sum in the denominator will be amplified, which means that the monitoring well points with larger weights will have a relatively greater influence when calculating the correlation model of the heavy metal concentration in the monitoring wells, thus highlighting the impact of the weight difference on the results. On the contrary, when When the sum of the weights

[0106] is in the denominator, its role will be reduced, relatively reducing the impact of the weight difference on the calculation of the correlation model of the heavy metal concentration in the monitoring wells, making the contributions of the monitoring well points with different weights to the results more balanced. Moreover, in some cases, the data may have some special properties or the research purpose has specific requirements. For example, when the data quality of the monitoring wells in the monitoring area varies greatly and it is desired to highly rely on high-quality data (i.e., data with high weights) to determine the correlation model of the heavy metal concentration in the monitoring wells, a larger value can be selected; while if the data quality is relatively uniform, or it is desired to comprehensively consider the information of all data points and avoid individual high-weight points from overly dominating the results, then a smaller value can be selected.

[0107] Step S40202: According to the heavy metal concentration data, solve the heavy metal concentration correlation model to obtain a heavy metal concentration difference quantification value data set.

[0108] In this embodiment, in order to solve the heavy metal concentration correlation model, coordinates need to be defined for the monitoring area and the monitoring well data needs to be recorded. The monitoring well data includes the coordinate positions of the monitoring wells, the number of monitoring wells, and the heavy metal concentrations corresponding to the monitoring wells.

[0109] The difference quantification value is an index that uses specific numerical values to clearly and accurately measure and express the degree of difference between two or more objects, phenomena, and data sets.

[0110] Substitute the heavy metal concentration data into the heavy metal concentration correlation model, and solving the heavy metal concentration correlation model can obtain the heavy metal concentration difference quantification value data set.

[0111] During the process of solving the heavy metal concentration correlation model, when matching the heavy metal concentration point pairs of the monitoring wells, the distance error range of the same monitoring well point pair needs to be set according to the sum and position of the number of monitoring wells. In addition, when setting weights for the heavy metal concentration point pairs of the monitoring wells, the impact of geology on heavy metal pollution also needs to be fully considered.

[0112] Step S40203: According to the heavy metal concentration difference quantification value data set, calculate the heavy metal concentration weights of the heavy metal concentration data for the grid nodes.

[0113] Among them, calculating the heavy metal concentration weight of the grid node based on the heavy metal concentration difference quantization value dataset specifically includes the following sub-steps:

[0114] Step S4020301: Construct a heavy metal concentration weight model for the grid node with respect to the heavy metal concentration data.

[0115] In this embodiment, when constructing the heavy metal concentration weight model for the grid node with respect to the heavy metal concentration data, constraint conditions need to be set considering the physical meaning and mathematical properties of the weights. Physically speaking, 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 the monitoring wells constitutes a complete allocation system without omission or excess. Mathematically, this kind of constraint makes the allocation of weights have normativity and comparability, facilitating mathematical operations and model solving, and ensuring the uniqueness and rationality of the solution.

[0116] Step S4020302: Calculate the weight of the heavy metal concentration of the grid node according to the heavy metal concentration weight model.

[0117] Among them, the heavy metal concentration weight model satisfies the following formula:

[0118] ,

[0119] Among them, is the number of monitoring wells, is the traversal coefficient, is the heavy metal concentration weight of the th monitoring well for the grid node, is the heavy metal concentration difference quantization value between monitoring well and monitoring well , is the distance value between monitoring well and monitoring well , is a positive integer from 1 to , is the adjustment parameter of the conditional equation, is the heavy metal concentration difference quantization value between the heavy metal concentration of the grid node to be estimated and monitoring well , is the distance value between the grid node to be estimated and monitoring well .

[0120] Step S40204: Calculate the heavy metal concentration of the grid node according to the heavy metal concentration weight and the heavy metal concentration data, and obtain the heavy metal concentration dataset of the monitoring area.

[0121] Among them, calculating the heavy metal concentration of the grid node according to the heavy metal concentration weight and the heavy metal concentration data includes:

[0122] Step S4020401, construct a heavy metal concentration model for grid nodes.

[0123] In this embodiment, when constructing a heavy metal concentration model for grid nodes, factors affecting the heavy metal concentration of grid nodes need to be considered, such as the number of monitoring wells, the heavy metal concentrations corresponding to each monitoring well, and the weights of the heavy metal concentrations corresponding to each monitoring well on the heavy metal concentration at the grid node.

[0124] Step S4020402, calculate the heavy metal concentration of the grid node according to the heavy metal concentration model of the grid node and the heavy metal concentration data.

[0125] In this embodiment, the heavy metal concentration of each grid node can be obtained through relevant calculations 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. Organize the heavy metal concentration data of each grid node to obtain a heavy metal concentration dataset for the monitoring area.

[0126] Among them, the heavy metal concentration model of the grid node satisfies the following formula:

[0127] ,

[0128] Among them, is the heavy metal concentration value of the grid node with coordinates , is the number of monitoring wells, is the traversal coefficient, is the weight of the heavy metal concentration of the th monitoring well on the grid node with coordinates , is the th monitoring well's heavy metal concentration value.

[0129] Step S403, draw the heavy metal pollution isopleth map according to the heavy metal concentration dataset of the monitoring area.

[0130] In this embodiment, the isopleth map is a visualization tool widely used in the fields of science and engineering. It is drawn by connecting points with equal numerical values and is used to intuitively display the distribution of a certain variable in space or on a plane.

[0131] The isogram can display the concentration of heavy metal pollutants in groundwater in an intuitive graphical manner. By drawing different isograms, the high-concentration and low-concentration heavy metal pollution areas can be clearly distinguished, enabling researchers, decision-makers, and relevant personnel to quickly understand the spatial distribution of heavy metal pollution. At the same time, the isogram can clearly define the boundary range of the heavy metal pollution area. This is crucial for determining the scope of land contaminated by heavy metals and helps land planners, environmental managers, etc. determine the areas that need to be monitored and treated with priority. By drawing the heavy metal pollution isogram at different time nodes and comparing these maps, it can be intuitively seen whether the scope of heavy metal pollution is expanding or shrinking, and whether the pollution degree is increasing or decreasing.

[0132] Step S5, evaluate the treatment measures based on the leakage of the mine anti-seepage membrane through the heavy metal pollution isogram, the pollution emergency prevention and control area, and the pollution spread trend.

[0133] In this embodiment, according to the pollution emergency prevention and control area and the pollution spread trend, the situation of groundwater pollution spread without pollution treatment measures can be predicted. Compare the heavy metal pollution isogram with the situation of groundwater pollution spread, and the effectiveness of the treatment measures can be judged based on the differences in the comparison results. If it is determined that the previous treatment measures are ineffective, the direction of pollution treatment can be further adjusted according to the effectiveness of the treatment measures. In addition, after the new treatment measures are completed, a new round of evaluation of the new treatment measures is still required, and the evaluation method is the same as that of the present invention. This cycle continues until the groundwater pollution meets the treatment requirements.

[0134] As Figure 2 shown, on the other hand, the present invention also provides an evaluation system for treatment measures based on the leakage of the mine anti-seepage membrane, including: a processor, an input device, an output device, and a memory. The processor, the input device, the output device, and the memory are interconnected. Among them, the memory is used to store computer programs, and the computer programs include program instructions. The processor is configured to call the program instructions and execute the relevant steps of the relevant embodiments in an evaluation method for treatment measures based on the leakage of the mine anti-seepage membrane of the present invention.

[0135] For the evaluation system for treatment measures based on the leakage of the mine anti-seepage membrane provided by the present invention, each functional component can be integrated in a processing component, or each component can exist physically alone, or two or more components can be integrated in one component. The above-mentioned integrated components can be implemented in the form of hardware or in the form of software functions.

[0136] It should be noted that in the complex environment of the mining area, the problem of groundwater pollution presents diverse characteristics. The types of pollution are by no means limited to a single type. In addition to the well-known heavy metal pollution, problems such as imbalance of acidity and alkalinity, excessive turbidity, abnormal hardness, and increased oxygen consumption are also common. These different types of pollution pose threats to the groundwater ecological environment and the water use safety of surrounding residents respectively.

[0137] 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 the groundwater of the mining area. As typical pollution sources in the groundwater of the mining area, heavy metals have characteristics such as high toxicity, difficult degradation, and easy enrichment. Once they enter the water body, they will not only cause serious harm to aquatic organisms, but also may endanger human health through the transmission of the food chain. For example, heavy metals such as lead, mercury, and cadmium can damage the nervous system, immune system, and reproductive system of the human body when contacted or ingested for a long time.

[0138] It should be clear that the purpose of the present invention is not to comprehensively evaluate the water quality, but to aim at detecting the key index of heavy metals to keenly capture the core problem of groundwater pollution in the mining area. When a certain concentration of heavy metals is detected in the water body of the monitoring well, this signal strongly implies that the current pollution control measures are not perfect, and there is very likely still a pollution source continuously leaking into the groundwater.

[0139] The present invention chooses to reduce the detection indexes and focus on heavy metal detection. This strategy does not ignore other pollution factors, but is from the perspective of practical application and detection efficiency. In a special environment such as a mining area, it is crucial to quickly and accurately locate the key pollution sources. Reducing the detection indexes can avoid the waste of time and resources caused by detecting too many indexes, greatly improve the detection efficiency, enable the staff to quickly judge the effect of groundwater pollution control measures based on the detection results, and thus lay a foundation for subsequent more comprehensive pollution control work.

[0140] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and 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 covered by the scope of the claims and the description 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; Evaluate 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; 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.

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 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.

3. 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.

4. The evaluation method of the treatment measures based on the leakage of the mine anti-seepage membrane according to claim 3 is 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.

5. The method for evaluating treatment measures for leakage of mine anti-seepage membrane according to claim 4, characterized in that: The correlation model of heavy metal concentrations in monitoring wells satisfies the following formula: Among them, H(k) is the quantitative value of the difference in heavy metal concentration between the two monitoring wells, k is the distance between the two monitoring wells, N(k) is the number of all distances k between the two monitoring wells, i is the index variable, β i is the heavy metal concentration weight of the i-th pair of two monitoring wells with a distance k, V(p+k) is the heavy metal concentration value in the monitoring well with a distance k to the coordinate p, p is the coordinate of the monitoring well, V p is the heavy metal concentration in the monitoring well with coordinate p, α is the adjustment weight β i The weight index of the impact degree.

6. 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 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.

7. The evaluation method of the treatment measures based on the leakage of the mine anti-seepage membrane according to claim 6 is characterized in that: The heavy metal concentration weight model satisfies the following formula: Among them, m is the number of monitoring wells, i is the ergodic coefficient, and w i is the heavy metal concentration weight of the ith monitoring well to the grid node, H(m ij ) is the quantitative value of the difference in heavy metal concentration between monitoring well i and monitoring well j, m ij is the distance between monitoring well i and monitoring well j, j is a positive integer from 1 to m, μ is the adjustment parameter of the conditional equation, H(m 0j ) is the quantitative value of the difference between the heavy metal concentration of the grid node to be estimated and the heavy metal concentration of the monitoring well j, m 0j is the distance between the grid node to be estimated and the monitoring well j.

8. The method for evaluating treatment measures for leakage of mine anti-seepage membrane according to claim 4, 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: Among them, V xy is the heavy metal concentration value of the grid node with coordinates xy, m is the number of monitoring wells, i is the traversal coefficient, w i is the heavy metal concentration weight of the ith monitoring well to the grid node with coordinates xy, V i is the heavy metal concentration value of the ith monitoring well.

9. An evaluation system based on the treatment measures for leakage of mine anti-seepage membrane, 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 8.

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