Mining area groundwater environment monitoring system and method

Abstract

The invention relates to the technical field of water quality monitoring, in particular to a mining area groundwater environment monitoring system and method, and the system comprises a mining area groundwater partition recognition module, a groundwater pollution risk assessment module, a groundwater monitoring point optimization module, a groundwater pollution path analysis module and a groundwater dynamic monitoring planning module. According to the invention, through data-driven zoning and risk assessment processes, fine and dynamic management is realized in underground water environment monitoring, functional zone zoning boundaries are accurately calibrated, understanding of an underground water flowing mode is enhanced, pollution source control is more targeted, diffusion risk intensity is assessed in combination with flow velocity and pollutant release intensity, and the risk assessment accuracy is improved. The risk management strategy is optimized, the pollution diffusion range is predicted and identified more efficiently, the emergency response accuracy is improved, monitoring point arrangement is optimized through risk data, reasonable monitoring resource allocation is ensured, the sampling task weight and the time interval are dynamically adjusted, and resource optimization and monitoring efficiency improvement are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality monitoring, and in particular to a mining area groundwater environment monitoring system and method. Background Art

[0002] The field of water quality monitoring technology includes the analysis and monitoring of physical, chemical and biological components in water bodies, aiming to evaluate the quality and safety of the water environment. Its core content is to obtain and evaluate water quality information by collecting water samples or real-time online monitoring of pollutants, soluble substances and various parameters such as pH value, dissolved oxygen, conductivity and other indicators in water. This technical field is widely used in environmental protection, industrial production, agricultural irrigation, drinking water quality assurance and other aspects. It involves a series of systematic means such as sensor technology, optical analysis, chemical reagent reaction, data acquisition and processing, forming a complete technical system from water quality information acquisition to comprehensive analysis.

[0003] Among them, the mining area groundwater environment monitoring system refers to a technical system that uses professional means to conduct real-time monitoring of the water quality in the mining area groundwater environment in response to pollution or water quality changes. The system mainly detects heavy metals, acidity and alkalinity, total dissolved solids and specific pollutants in the mining area groundwater. It uses water sample analysis devices to perform optical sensing detection of groundwater components, and combines physical and chemical analysis methods to complete the collection and organization of water quality data. The system uses network transmission technology and on-site sensing equipment to achieve distributed monitoring of mining area groundwater and synchronous data analysis, so as to accurately grasp the changes in the groundwater environment.

[0004] Existing technologies are deficient in terms of real-time and dynamic performance. Existing water quality monitoring technologies focus on regular water sample collection and laboratory analysis, which limits the timeliness of monitoring data and makes it impossible to quickly respond to sudden water quality incidents. The monitoring points in existing technologies are fixedly arranged and difficult to adjust according to actual needs, which leads to monitoring blind spots in dynamically changing pollution scenarios, affecting the overall monitoring effect and data accuracy. Insufficient monitoring of heavy metals or specific chemicals leads to delayed response to pollution incidents, which in turn affects environmental protection and public health, limits the adaptability of the monitoring system to complex environmental changes, reduces the collection of key data, and thus reduces the effectiveness of early warning and pollution control. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a mining area groundwater environment monitoring system and method.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A mining area groundwater environment monitoring system comprises:

[0007] The mining area groundwater zoning identification module extracts groundwater flow direction data of mining areas, tailings pond areas, and abandoned areas based on groundwater flow direction data and permeability coefficient data, analyzes the flow direction superposition and seepage direction at the junction, and generates the boundary value of groundwater zoning in the mining area;

[0008] The groundwater pollution risk assessment module extracts the pollution source distribution data in the functional area based on the groundwater zoning boundary value of the mining area, combines the pollutant release intensity with the groundwater flow rate, quantifies the pollution diffusion intensity and determines the pollution area, and obtains the groundwater pollution risk intensity data set;

[0009] The groundwater monitoring point optimization module selects the pollution intensity value of the risk area based on the groundwater pollution risk intensity data set, analyzes the pollution diffusion range covered by the monitoring points, optimizes the number and distribution of monitoring points, adjusts the boundary position of the coverage area, and obtains the layout parameters of the mining area monitoring points;

[0010] The groundwater pollution path analysis module analyzes the pollutant diffusion path between the monitoring points based on the monitoring point layout parameters of the mining area, combines the monitoring point location and the groundwater seepage direction, calibrates the risk transmission path range, and generates the mining area pollution diffusion path distribution results;

[0011] The groundwater dynamic monitoring planning module dynamically adjusts the sampling task weights and time intervals based on the distribution results of the pollution diffusion paths in the mining area, plans the distribution parameters of the functional area monitoring tasks, and establishes a groundwater monitoring dynamic planning program.

[0012] As a further solution of the present invention, the step of obtaining the boundary value of the mining area groundwater zone is specifically as follows:

[0013] Based on the groundwater flow direction data and permeability coefficient data, the groundwater flow direction in the mining area, tailings pond area and abandoned area is analyzed, the flow direction vector data is extracted, and combined with the permeability coefficient, the groundwater flow intensity and direction in each area are analyzed, the groundwater flow direction in the intersection area is identified, and the groundwater flow direction influence coefficient is obtained;

[0014] According to the groundwater flow direction influence coefficient, the groundwater flow direction superposition at the junction is analyzed, the flow direction change data of the junction area is extracted, the change trend of the water flow direction at the junction is judged by combining the threshold, the flow direction weight of the junction between regions is identified, and the flow direction and infiltration adjustment coefficient at the junction is obtained;

[0015] According to the flow direction and infiltration adjustment coefficient of the junction, the water flow direction and infiltration situation in the area are analyzed using the formula:

[0016]

[0017] Generate groundwater zone boundary values ​​for mining areas;

[0018] Among them, B represents the boundary value of the groundwater zone in the mining area, wi is the weight coefficient of the ith region, f i is the groundwater flow direction value, c i is the permeability adjustment value, i represents the area number, and n is the total number of areas.

[0019] As a further solution of the present invention, the steps for obtaining the groundwater pollution risk intensity data set are specifically as follows:

[0020] Based on the boundary value of the groundwater zoning in the mining area, the pollution source distribution data in the mining area is extracted, the pollution source release intensity and groundwater flow rate data are called, and the impact range of the pollution source is identified by spatial analysis to obtain the pollution diffusion intensity data set;

[0021] Combining the groundwater flow characteristics with the pollution diffusion intensity dataset, the dynamic boundary of the contaminated area is derived through the interaction of flow velocity, pollutant release intensity and groundwater flow pattern, using the formula:

[0022]

[0023] Identify the spatial coordinates of the polluted area and the corresponding pollution concentration distribution, and obtain the boundary and risk level data of the polluted area;

[0024] Among them, S p represents the pollution diffusion intensity, Q represents the release intensity of the pollution source, R represents the groundwater flow rate, d represents the distance between the pollution source and the monitoring point, λ represents the pollutant attenuation factor, and D w represents the groundwater flow depth, exp is the natural exponential function;

[0025] According to the data on the boundaries and risk levels of the polluted areas, combined with the migration speed and attenuation characteristics of pollutants in groundwater, the dynamic relationship between pollution diffusion and risk assessment standards is derived. Based on the concentration and diffusion trend of pollutants in the polluted area, combined with the groundwater area risk assessment standards, the intensity level of pollution risk is judged and a groundwater pollution risk intensity data set is generated.

[0026] As a further solution of the present invention, the steps for obtaining the mining area monitoring point layout parameters are specifically as follows:

[0027] Selecting the pollution intensity value of the risk area from the groundwater pollution risk intensity data set, identifying the area with pollution risk through quantitative analysis of the pollution intensity value, and obtaining the location data of the risk pollution area;

[0028] According to the location data of the risk pollution area, the pollution diffusion range covered by the current monitoring point is analyzed, and the overlap between the area covered by the monitoring point and the pollution area is identified, using the formula:

[0029]

[0030] Calculate the coverage area of ​​monitoring points and obtain monitoring point coverage assessment data;

[0031] Among them, A e represents the coverage area of ​​monitoring points, a j represents the theoretical coverage area of ​​the jth monitoring point, d j represents the distance from the monitoring point to the pollution source, D represents the maximum distance designed for the monitoring point, e is the natural exponential function, and m represents the total number of monitoring points;

[0032] The monitoring point coverage assessment data is used to optimize the number and distribution of monitoring points, adjust the boundary position of the coverage area, re-plan the layout of monitoring points, combine the geographic information system for spatial optimization, and obtain the layout parameters of mining area monitoring points.

[0033] As a further solution of the present invention, the step of obtaining the pollutant diffusion path between the monitoring points is specifically as follows:

[0034] Based on the monitoring point layout parameters of the mining area, the geographical location relationship between the monitoring point and the pollution source is analyzed, the horizontal distance and vertical height difference between the coordinates of the monitoring point and the location of the pollution source are called, and the initial association path between the monitoring point and the pollution source is generated in combination with the groundwater flow velocity and flow direction data;

[0035] The pollutant diffusion analysis is performed on the initial association path between the monitoring point and the pollution source, and the pollutant concentration change rate of each section of the path is analyzed using the formula:

[0036]

[0037] Obtain the pollutant concentration change path;

[0038] Among them, C x is the pollutant concentration value at the end of the path, C0 is the pollutant concentration at the starting point of the path, α is the attenuation coefficient of the pollutant, B is the path length, v is the groundwater flow velocity, and R is the groundwater penetration resistance factor;

[0039] Combined with the pollutant concentration change path and the spatial layout of the monitoring points, the length, flow direction and concentration change trend of each path are summarized, the priority ranking of the paths is adjusted, and the pollutant diffusion path between the monitoring points is obtained.

[0040] As a further solution of the present invention, the steps for obtaining the distribution result of the pollution diffusion path in the mining area are specifically as follows:

[0041] Based on the pollutant diffusion path between the monitoring points, the geographical coordinates and pollutant concentration of the monitoring points are extracted, the pollutant diffusion rate is analyzed and the diffusion direction and rate data of each path are classified, and the path data is integrated with the diffusion distance, concentration gradient and time span to obtain the basic data of the pollution diffusion path;

[0042] Based on the basic data of the pollution diffusion path, the classification threshold is set according to the diffusion rate, the paths that exceed the risk classification are screened and marked, the screened path data are classified, the geographical relevance of the paths is integrated by grouping, the path concentration change data and the diffusion rate are compared, and the risk transmission path range calibration results are generated;

[0043] Based on the calibration results of the risk transmission path range, the spatial data of the calibration path and the concentration gradient are integrated, and the diffusion range map is drawn using the geometric relationship between the paths to obtain the distribution results of the pollution diffusion path in the mining area.

[0044] As a further solution of the present invention, the steps for obtaining the groundwater monitoring dynamic planning scheme are specifically as follows:

[0045] Based on the distribution results of the pollution diffusion paths in the mining area, the sampling data of the monitoring points on the diffusion paths are called, the trend of pollutant concentration changes on each path is analyzed, and the paths are divided into high priority, medium priority and low priority paths in combination with the path length and pollution intensity level to obtain the diffusion path priority distribution data;

[0046] According to the diffusion path priority distribution data and the pollution characteristics of the functional area, the weight and time interval of the sampling task are dynamically adjusted using the formula:

[0047]

[0048] Calculate the sampling time interval, optimize the sampling period by combining weight, concentration and path length, and obtain the sampling task distribution data set;

[0049] Among them, T c represents the sampling time interval, P l is the path priority weight, C m is the average pollution concentration on the path, L p is the path length, Q is the adjustment factor;

[0050] By using the sampling task distribution data set, the functional area monitoring tasks are decomposed into each priority path, and the task distribution of the monitoring points is dynamically planned according to the pollution diffusion trend of the path. By adjusting the task distribution time and path coverage, the functional area task settings of the monitoring points are optimized, and a dynamic planning scheme for groundwater monitoring is established.

[0051] A method for monitoring the environment of groundwater in a mining area, which is implemented based on the above-mentioned mining area groundwater environment monitoring system, comprises the following steps:

[0052] S1: Based on the groundwater flow direction data and permeability coefficient data, the groundwater flow direction of the mining area, tailings pond area and abandoned area is extracted, the flow direction difference of the functional area is calculated, and the seepage direction and flow trend of the intersection point are analyzed by superimposing the permeability coefficient and the intersection point to establish the boundary value of the groundwater zoning in the mining area;

[0053] S2: Based on the boundary value of the groundwater zoning in the mining area, the distribution of pollution sources in the functional area is extracted, the pollutant release intensity is summarized and then calculated, and the pollution diffusion range is analyzed in combination with the groundwater flow velocity value, and the pollution intensity in the diffusion area is segmented and divided to obtain the groundwater pollution intensity and diffusion range distribution data;

[0054] S3: Based on the groundwater pollution intensity and diffusion range distribution data, the pollution intensity area is screened, the spatial matching of monitoring points is performed, the blind areas of monitoring point coverage are analyzed, and the pollution intensity data is superimposed and analyzed to adjust the number and location of monitoring points, and generate the optimized layout parameters of monitoring points in the mining area;

[0055] S4: Based on the optimized layout parameters of the monitoring points in the mining area, the pollutant diffusion path analysis is performed on the optimized monitoring point layout positions, the monitoring point positions are associated with the groundwater seepage direction, the diffusion path range is analyzed, the risk transmission points are calibrated, the path data is classified and sorted, and the distribution results of the pollution transmission paths in the mining area are obtained;

[0056] S5: Based on the distribution results of the pollution transmission path in the mining area and combined with the transmission cycle data within the path range, the risk level within the path is analyzed for distribution, the sampling task data is called to identify the task time interval, the sampling tasks of the monitoring points are dynamically adjusted, and the dynamic planning plan for groundwater monitoring is obtained according to the zoning distribution.

[0057] Compared with the prior art, the advantages and positive effects of the present invention are:

[0058] In the present invention, by adopting data-driven zoning and risk assessment processes, refined and dynamic management is achieved in the field of groundwater environmental monitoring. By using data such as groundwater flow direction and permeability coefficient to calculate the difference value of groundwater flow in real time, the zoning boundaries of different functional areas are accurately calibrated, the understanding of groundwater flow patterns is increased, and pollution source control is made more targeted. Combined with data on groundwater flow velocity and pollutant release intensity, the risk intensity of pollution diffusion is accurately assessed, and risk management strategies are optimized. This comprehensive analysis of flow direction data and pollution source information can more effectively predict and identify the scope of pollution diffusion, improve the timeliness and accuracy of emergency response, optimize the layout of monitoring points based on risk data, ensure the effective allocation of monitoring resources, improve the coverage efficiency and data quality of the monitoring network, and dynamically adjust the sampling task weights and time intervals so that monitoring activities can be flexibly adjusted according to changes in actual pollution risks, thereby achieving resource optimization and improved monitoring efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a system flow chart of the present invention;

[0060] Figure 2 It is a flow chart of the boundary value of groundwater zoning in the mining area in the present invention;

[0061] Figure 3 It is a flow chart of the groundwater pollution risk intensity data set in the present invention;

[0062] Figure 4 It is a flow chart of the parameters for setting monitoring points in the mining area in the present invention;

[0063] Figure 5 It is a flow chart of the pollutant diffusion path between monitoring points in the present invention;

[0064] Figure 6 It is a flow chart of the distribution results of the pollution diffusion path in the mining area in the present invention;

[0065] Figure 7 It is a flow chart of the groundwater monitoring dynamic planning scheme in the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0067] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0068] See also Figure 1 The present invention provides a technical solution: a mining area groundwater environment monitoring system comprising:

[0069] The mining area groundwater zoning identification module extracts groundwater flow direction data of mining areas, tailings pond areas, and abandoned areas based on groundwater flow direction data and permeability coefficient data, analyzes the flow direction superposition and seepage direction at the junction, and generates the boundary value of groundwater zoning in the mining area;

[0070] The groundwater pollution risk assessment module extracts the pollution source distribution data in the functional area based on the groundwater zoning boundary value of the mining area, combines the pollutant release intensity with the groundwater flow rate, quantifies the pollution diffusion intensity and determines the pollution area, and obtains the groundwater pollution risk intensity data set;

[0071] The groundwater monitoring point optimization module is based on the groundwater pollution risk intensity data set, selects the pollution intensity value of the risk area, analyzes the pollution diffusion range covered by the monitoring points, optimizes the number and distribution of monitoring points, adjusts the boundary position of the coverage area, and obtains the layout parameters of the mining area monitoring points;

[0072] The groundwater pollution path analysis module is based on the mining area monitoring point layout parameters, combined with the monitoring point location and groundwater seepage direction, to analyze the pollutant diffusion path between monitoring points, calibrate the risk transmission path range, and generate the mining area pollution diffusion path distribution results;

[0073] The groundwater dynamic monitoring planning module dynamically adjusts the sampling task weights and time intervals based on the distribution results of pollution diffusion paths in mining areas, plans the distribution parameters of monitoring tasks in functional areas, and establishes a dynamic planning scheme for groundwater monitoring.

[0074] The boundary values ​​of groundwater zoning in mining areas include the boundaries of mining areas, tailings pond areas, and abandoned areas. The groundwater pollution risk intensity data set includes the pollution diffusion intensity value, the scope of the pollution area, and the pollution risk level. The monitoring point layout parameters in mining areas include the number of monitoring points, the location of monitoring points, and the boundaries of the coverage area. The distribution results of pollution diffusion paths in mining areas include pollutant transmission paths, risk diffusion scope, and diffusion path distribution data. The dynamic planning scheme for groundwater monitoring includes sampling task weights, sampling time intervals, and monitoring task distribution parameters.

[0075] See also Figure 2 , the specific steps for obtaining the boundary value of groundwater partition in the mining area are as follows:

[0076] Based on the groundwater flow direction data and permeability coefficient data, the groundwater flow direction in the mining area, tailings pond area and abandoned area is analyzed, the flow direction vector data is extracted, and combined with the permeability coefficient, the groundwater flow intensity and direction in each area are analyzed, the groundwater flow direction in the intersection area is identified, and the groundwater flow direction influence coefficient is obtained;

[0077] According to the groundwater flow direction data and permeability coefficient data, flow vectors are extracted from the mining area, tailings pond area and abandoned area respectively, and a regional database is established. The average vector is calculated using the flow vector data in each area, and the outliers of the flow direction data are eliminated during the calculation process. The maximum, minimum and mean values ​​of the permeability coefficient in the area are statistically calculated to correct the flow vector amplitude, thereby characterizing the overall flow characteristics of the groundwater in the area. Then, the intersection points between each area are analyzed, and the flow direction vector and the permeability coefficient of the intersection point are weighted to reveal the water flow characteristics of the intersection point. The permeability of significant intersection points is gradually calculated by superimposing the flow direction of the intersection points. Based on this, the intersection points with higher permeability are screened. The weight of the flow direction of each area is optimized through iterative calculation, and finally the groundwater flow direction influence coefficient is obtained, which is the result of the superposition operation of the groundwater flow direction and permeability coefficient of the entire area, and is used for the next step of intersection analysis and adjustment coefficient calculation.

[0078] According to the influence coefficient of groundwater flow direction, the groundwater flow direction superposition at the junction is analyzed, the flow direction change data of the junction area is extracted, the change trend of the water flow direction at the junction is judged by combining the threshold, the flow direction weight of the junction between regions is identified, and the flow direction and infiltration adjustment coefficient at the junction is obtained;

[0079] Firstly, the flow direction data in the junction area are extracted, and the flow direction change rate of each junction point is calculated. The change rate formula is defined as the difference ratio of the change vector length and the flow direction vector of the adjacent point. The change rate is corrected using the permeability coefficient data to more accurately describe the significance of the change rate. Then, a judgment threshold for the significance of the change is set, and the junction area with significant changes is screened by comparing the relationship between the change rate and the threshold. The weight coefficient of the junction point is further calculated based on the flow direction and permeability data of the significant change area. The product of the significance of the junction area and the weight is used to quantify the adjustment ability of the junction point. Finally, the flow direction value and permeability coefficient of each junction point are corrected by the adjustment ability to obtain the flow direction and permeability adjustment coefficient at the junction. This coefficient can be used to optimize the calculation of the partition boundary value of groundwater in the entire mining area and improve the accuracy and reliability of partition identification.

[0080] According to the flow direction and infiltration adjustment coefficient at the junction, the water flow direction and infiltration situation in the area are analyzed using the formula:

[0081]

[0082] Generate groundwater zone boundary values ​​for mining areas;

[0083] Among them, B represents the boundary value of groundwater zone in the mining area, w i is the weight coefficient of the ith region, f i is the groundwater flow direction value, c i is the permeability adjustment value, i represents the region number, and n is the total number of regions;

[0084] The benefit of the formula is that it improves the accuracy of regional boundary identification by combining the flow direction value and the permeability coefficient and dynamically adjusting the regional weight, which is more applicable in complex terrain conditions.

[0085] B is the boundary value of groundwater zone in mining area, w i is the weight coefficient of the ith region, f i is the groundwater flow direction value in the ith region, c i is the permeability adjustment value of the i-th region, i represents the region number, n is the total number of regions, and now assumes n = 3, weights w1 = 0.3, w2 = 0.5, w3 = 0.2, flow direction values ​​f1 = 1.2, f2 = 0.9, f3 = 1.5, and permeability adjustment values ​​c1 = 0.4, c2 = 0.7, c3 = 0.6;

[0086] The calculation process is as follows:

[0087] B=(0.3·(1.2+0.4))+(0.5·(0.9+0.7))+(0.2·(1.5+0.6));

[0088] B=(0.3·1.6)+(0.5·1.6)+(0.2·2.1);

[0089] B = 0.48 + 0.8 + 0.42 = 1.7;

[0090] The results show that the groundwater zoning boundary value B=1.7 in the mining area was obtained by weight and parameter superposition calculation. The results comprehensively reflect the water fluidity and permeability characteristics of the mining area, provide a reliable basis for the determination of zoning boundaries, and can be further used in the formulation of zoning planning and governance measures.

[0091] See also Figure 3 , the specific steps for obtaining the groundwater pollution risk intensity dataset are:

[0092] Based on the boundary value of groundwater zoning in the mining area, the distribution data of pollution sources in the mining area are extracted, the release intensity of pollution sources and groundwater flow rate data are called, and the impact range of pollution sources is identified by spatial analysis to obtain the pollution diffusion intensity data set;

[0093] The mining area zoning boundary file is called through the spatial analysis method, and the pollution sources in the target area are located as point features. According to the release characteristics of the pollution sources and the groundwater flow velocity data, a pollution source diffusion range calculation model is established. Combined with the regional groundwater flow distribution file, the diffusion influence range of each pollution source is calculated. The diffusion radius is combined with the water flow influence coefficient for superposition analysis. The superposition method is set according to the law that the concentration of pollutants in groundwater decays with the diffusion distance. The diffusion intensity matrix of each pollution source is generated through point-by-point calculation. The maximum value in the matrix is ​​used to normalize the entire area to ensure that the diffusion intensities of different pollution sources are comparable, and finally a pollution diffusion intensity data set is generated.

[0094] Combining the groundwater flow characteristics with the pollution diffusion intensity dataset, the dynamic boundary of the contaminated area is derived through the interaction of flow velocity, pollutant release intensity and groundwater flow pattern, using the formula:

[0095]

[0096] Identify the spatial coordinates of the polluted area and the corresponding pollution concentration distribution, and obtain the boundary and risk level data of the polluted area;

[0097] Among them, S p represents the pollution diffusion intensity, Q represents the release intensity of the pollution source, R represents the groundwater flow rate, d represents the distance between the pollution source and the monitoring point, λ represents the pollutant attenuation factor, and D w represents the groundwater flow depth, exp is the natural exponential function;

[0098] The benefit of the formula is that, by adding the pollutant attenuation factor and the comprehensive calculation of groundwater flow depth, it can more accurately reflect the attenuation trend and concentration distribution of pollutants in groundwater with distance, thereby improving the accuracy of the calculation of pollution diffusion intensity;

[0099] Q represents the release intensity of the pollution source, which is obtained through pollution source monitoring data and is expressed in mg / s. R represents the groundwater flow rate, which is obtained through hydraulic tests and is expressed in m / s. d represents the distance between the pollution source and the monitoring point, which is measured through the geographic information system and is expressed in m. λ represents the attenuation factor of the pollutant, which is obtained through laboratory tests and is expressed in D. w represents the groundwater flow depth, which is calculated through hydrological survey and regional groundwater level distribution file, and the unit is m. exp is the natural exponential function;

[0100] Assume actual data: Q = 100 mg / s, R = 0.5 m / s, d = 10 m, λ = 15, D w =10m;

[0101] The calculation process is as follows:

[0102] Compute the exponential term:

[0103] Substitute into the calculation:

[0104] The results show that the groundwater pollution diffusion intensity 10 meters away from the pollution source is 2.567 mg / m 3 , reflecting the distribution status of pollutants in groundwater and their concentration attenuation law.

[0105] Based on the pollution area boundary and risk degree data, combined with the migration speed and attenuation characteristics of pollutants in groundwater, the dynamic relationship between pollution diffusion and risk assessment standards is derived. Based on the concentration and diffusion trend of pollutants in the pollution area, combined with the groundwater regional risk assessment standards, the intensity level of pollution risk is judged and a groundwater pollution risk intensity data set is generated;

[0106] Firstly, the pollution diffusion intensity matrix is ​​extracted. By analyzing the correspondence between the pollution intensity and the spatial position in the matrix, the matrix is ​​gridded. The diffusion trend and diffusion attenuation curve of each grid unit are calculated. The concentration attenuation of each monitoring point is summarized to generate a trend graph of concentration change over time. The distribution position of the monitoring points and the groundwater flow pattern are further combined, and the gradient analysis method is used to calculate the concentration change rate of each point. The pollution diffusion risk assessment standard is established, and the concentration change rate is compared with the risk standard to judge the pollution risk intensity of each area. Finally, a groundwater pollution risk intensity data set is generated.

[0107] See also Figure 4 , the specific steps for obtaining the layout parameters of monitoring points in the mining area are as follows:

[0108] Select the pollution intensity value of the risk area from the groundwater pollution risk intensity data set, identify the pollution risk area through quantitative analysis of the pollution intensity value, and obtain the location data of the risk pollution area;

[0109] First, the pollution intensity value and geographic coordinates of each monitoring point are extracted through the geographic information system (GIS). Then, statistical software is used to perform cluster analysis on the data to find out the areas where the pollution intensity exceeds the safety threshold. The areas are marked as potential high-risk areas. By comparing the spatial distribution patterns of pollution data with historical pollution events, high-risk areas corresponding to known pollution sources are identified. Special attention is paid to using pollution data accumulated over the years and recent environmental change reports to adjust the risk assessment model to ensure that the identification of risk areas is based not only on current data but also reflects the environmental evolution trend of the region. The work at this stage provides a scientific basis for the optimal layout of subsequent monitoring points, enabling the monitoring network to more effectively cover all key high-risk pollution areas and obtain risk pollution area positioning data.

[0110] According to the location data of the risk pollution area, the pollution diffusion range covered by the current monitoring points is analyzed, and the overlap between the area covered by the monitoring points and the pollution area is identified, using the formula:

[0111]

[0112] Calculate the coverage area of ​​monitoring points and obtain monitoring point coverage assessment data;

[0113] Among them, A e represents the coverage area of ​​monitoring points, a j represents the theoretical coverage area of ​​the jth monitoring point, d j represents the distance from the monitoring point to the pollution source, D represents the maximum distance designed for the monitoring point, e is the natural exponential function, and m represents the total number of monitoring points;

[0114] The benefit of the formula is that, by introducing the distance attenuation factor, it can more accurately reflect the actual coverage efficiency of the monitoring point from the pollution source, thus avoiding overestimation of the coverage range;

[0115] A e Represents the effective coverage area of ​​the monitoring point, in square meters, a j represents the theoretical coverage area of ​​the jth monitoring point, which is calculated by the geographic information model when the monitoring point is designed, in square meters, d j represents the distance from the monitoring point to the pollution source, which is directly measured by the geographic information system and is in meters. D represents the maximum effective monitoring distance of the monitoring point, which is determined by the performance parameters of the monitoring point equipment and is in meters. e is the natural exponential function;

[0116] Assignment calculation process: Let a1 = 1000, a2 = 1200, a3 = 800, d1 = 200, d2 = 250, d3 = 300, D = 500;

[0117] Substitute into the calculation:

[0118] Calculate each index:

[0119] Substitute and continue calculating: A e =1000·(1-0.6703)+1200·(1-0.6065)+800·

[0120] (1-0.5488)=1000·0.3297+1200·0.3935+800·0.4512=329.7+

[0121] 472.2+360.96=1162.86m 2 ;

[0122] The results show that the actual effective coverage area of ​​the monitoring points is 1162.86 square meters, which reflects the monitoring efficiency under the current layout and provides a basis for the next step of optimizing the layout of monitoring points.

[0123] Using the monitoring point coverage assessment data, optimize the number and distribution of monitoring points, adjust the coverage area boundary position, re-plan the monitoring point layout, combine the geographic information system for spatial optimization, and obtain the monitoring point layout parameters in the mining area;

[0124] By calculating the coverage efficiency overlap between different monitoring points, identifying efficient coverage points and redundant monitoring points, adjusting the spacing between monitoring points during the optimization process to reduce coverage blind spots, and re-dividing the boundaries of the area covered by efficient monitoring points based on the boundary shape of the risk pollution area and the direction of groundwater flow, ensuring that the boundaries can cover the entire pollution diffusion range, and finally obtaining the monitoring point layout parameters for the mining area.

[0125] See also Figure 5 , the specific steps for obtaining the pollutant diffusion path between monitoring points are:

[0126] Based on the monitoring point layout parameters in the mining area, the geographical location relationship between the monitoring point and the pollution source is analyzed, the horizontal distance and vertical height difference between the coordinates of the monitoring point and the pollution source are called, and the initial association path between the monitoring point and the pollution source is generated by combining the groundwater flow velocity and flow direction data;

[0127] First, the coordinate information of the monitoring points is obtained from the monitoring point layout parameters and matched with the groundwater flow direction data. The initial path mapping model is established using the groundwater flow velocity and pollution source release intensity data. The path model is layered and analyzed according to the horizontal distance and vertical height difference between the monitoring point and the pollution source. At the same time, the seepage path is preliminarily divided based on the geological characteristics. The path is further segmented to calculate the attenuation characteristics of the pollutants on each path, and the initial association path between the monitoring point and the pollution source is obtained.

[0128] Conduct pollutant diffusion analysis on the initial association path between the monitoring point and the pollution source, and analyze the pollutant concentration change rate of each section of the path using the formula:

[0129]

[0130] Obtain the pollutant concentration change path;

[0131] Among them, C x is the pollutant concentration value at the end of the path, C0 is the pollutant concentration at the starting point of the path, α is the attenuation coefficient of the pollutant, B is the path length, v is the groundwater flow velocity, and R is the groundwater penetration resistance factor;

[0132] The benefit of the formula is that by introducing the attenuation coefficient, path length and penetration resistance factor, the diffusion characteristics of pollutants in groundwater flow are combined with physical barriers, which can accurately reflect the law of changes in pollutant concentrations in different paths;

[0133] C x represents the pollutant concentration at the end of the path, in mg / L; C0 represents the pollutant concentration at the starting point of the path, which is directly measured by sampling at the monitoring point, in mg / L; α is the attenuation coefficient of the pollutant, which is calculated by laboratory testing combined with the local water chemical environment, in 1 / m; B is the path length, which is calculated by path analysis and GIS system, in m; v is the groundwater velocity, which is calculated by regional hydraulic test data, in m / s; R is the groundwater penetration resistance factor, which is calculated by regional hydrogeological data and is a dimensionless coefficient;

[0134] Calculation example: Assume C0 = 100, α = 0.02, B = 500, v = 0.1, R = 1.5;

[0135] Substituting into the formula:

[0136] Compute the exponential term:

[0137] Continue to calculate: C x =100·7.2·10 -66 ≈0;

[0138] The results show that the pollutant concentration value at the end of the path approaches zero, indicating that the pollutants are almost completely attenuated in the long-distance, high-resistance path, indicating that the path has a significant purification effect on pollutants.

[0139] Combine the pollutant concentration change path with the spatial layout of the monitoring points, summarize the length, flow direction and concentration change trend of each path, adjust the priority ranking of the paths, and obtain the pollutant diffusion path between the monitoring points;

[0140] The key nodes of pollutant concentration changes on the path are extracted, and the diffusion path between each monitoring point is analyzed section by section. The path concentration change trend and flow direction characteristics are integrated, and the diffusion priority of the path is calculated and re-sorted. In the sorting process, the diffusion direction is refined by adjusting the path coverage and path length to generate the final pollutant diffusion path between monitoring points.

[0141] See also Figure 6 , the specific steps for obtaining the distribution results of the pollution diffusion path in the mining area are:

[0142] Based on the pollutant diffusion path between monitoring points, the geographical coordinates and pollutant concentration of the monitoring points are extracted, the pollutant diffusion rate is analyzed and the diffusion direction and rate data of each path are classified, and the path data is integrated with the diffusion distance, concentration gradient, and time span to obtain the basic data of the pollution diffusion path;

[0143] By recording the geographical location coordinates and pollutant concentrations of each monitoring point, analyzing the geographical location of the monitoring points one by one, converting them into digital longitude and latitude data, obtaining high-precision location coordinates through satellite positioning technology or geographic information system (GIS) tools, and combining monitoring equipment with real-time monitoring to obtain pollutant concentration data, the concentration data is recorded with a time granularity of seconds through equipment such as gas sensors and mass concentration meters, and invalid data is filtered out. Subsequently, the longitude and latitude data of multiple monitoring points and the time interval are used to construct spatial and temporal differences, and the diffusion rate is calculated through the Lagrangian model or Gaussian diffusion model. The diffusion distance between monitoring points is calculated, the distance is integrated with the concentration gradient, and the basic data of the pollution diffusion path is obtained.

[0144] Based on the basic data of pollution diffusion paths, the classification threshold is set according to the diffusion rate, the paths that exceed the risk classification are screened and marked, the screened path data are classified, the geographical relevance of the paths is integrated by grouping, the path concentration change data and the diffusion rate are compared, and the risk transmission path range calibration results are generated;

[0145] The classification threshold is set according to the numerical value of the pollution diffusion rate, and the diffusion paths that exceed the risk classification threshold are screened out. The classification threshold can be obtained through statistical methods such as the quantile method under the normal distribution or the group cumulative probability calculation to represent the risk threshold of the high-speed diffusion path. The screening process adopts a dynamic path classification algorithm. According to the path concentration change and diffusion rate parameters, the paths are classified into geographically related path groups. The formation of path groups is achieved through a clustering algorithm (such as the K-means algorithm). Subsequently, the concentration change and diffusion rate of the screened paths are compared, and the rate of change of the concentration gradient is calculated to form the annotation result of the risk transmission path range. In this process, the geographical distribution of the path, the trend of pollutant concentration change and the diffusion rate are combined to finally complete the calibration of the risk path range.

[0146] Based on the results of risk transmission path range calibration, the spatial data of the calibration path and the concentration gradient are integrated, and the diffusion range map is drawn using the geometric relationship between the paths to obtain the distribution results of the pollution diffusion path in the mining area;

[0147] The spatial data of the path, including the location of the monitoring point, the direction of the path, the concentration gradient distribution, etc., are extracted, and the data is imported into the GIS platform to construct a diffusion range visualization model. The geometric relationship analysis tool is used to generate a diffusion range map based on the geographic coordinate points of the starting and ending points of the path, combined with the polygon drawing algorithm and the concentration contour line. The diffusion range is expressed continuously by the numerical interpolation method (such as the Kriging interpolation method) to obtain the distribution results of the pollution diffusion path in the mining area, and the impact range and regional risks of the pollutant diffusion are specifically identified.

[0148] See also Figure 7 ,The specific steps for obtaining the dynamic planning scheme for groundwater monitoring are:

[0149] Based on the distribution results of the pollution diffusion paths in the mining area, the sampling data of the monitoring points on the diffusion paths are called to analyze the trend of pollutant concentration changes on each path. Combined with the path length and pollution intensity level, the paths are divided into high priority, medium priority and low priority paths to obtain the priority distribution data of the diffusion paths.

[0150] First, the historical sampling data of the monitoring points on the pollution path are called, and the concentration change trends of each path segment are counted. The path geographic information is spatially analyzed to establish a dynamic distribution model of path concentration changes. The model extracts the length, flow characteristics and concentration distribution characteristics of the path, hierarchically classifies the path data, and divides the path into different analysis units according to the length range and flow consistency. Then, the significance of the change is calculated according to the gradient value of the path concentration change, and the path concentration gradient area is marked as the key monitoring point. By comparing the path weight and concentration change trend, the path is assigned high priority, medium priority and low priority labels respectively, and the diffusion path priority distribution data is obtained. This distribution clarifies the monitoring attention level of different pollution paths, and provides a scientific basis for the subsequent dynamic adjustment of sampling weights and time intervals to ensure that the key paths of pollution diffusion in the mining area can be covered.

[0151] According to the priority distribution data of the diffusion path and the pollution characteristics of the functional area, the weight and time interval of the sampling task are dynamically adjusted using the formula:

[0152]

[0153] Calculate the sampling time interval, optimize the sampling period by combining weight, concentration and path length, and obtain the sampling task distribution data set;

[0154] Among them, T c represents the sampling time interval, P l is the path priority weight, C m is the average pollution concentration on the path, L pis the path length, Q is the adjustment factor;

[0155] The benefit of the formula is that it dynamically optimizes the sampling time interval by integrating path priority, average pollution concentration, path length and adjustment coefficient, so that the monitoring frequency matches the pollution diffusion characteristics of the path, avoiding the waste of monitoring resources and improving the monitoring accuracy of key areas;

[0156] T c Represents the sampling time interval in hours, calculated by the formula, P l Represents the path priority weight, which is determined by normalization based on the path priority distribution data and ranges from 0 to 1. m Represents the average pollution concentration on the route, which is calculated from the historical sampling data of the monitoring points on the route segment, in mg / L, L p represents the path length, which is calculated by the geographical coordinates of the path start and end points, in meters. Q represents the adjustment factor, which is used to control the flexibility of the sampling time interval and is set according to the dynamic pollution diffusion characteristics in the monitoring area.

[0157] Example of calculation: Assume P l =0.75, C m =30,L p =600, Q = 1.5;

[0158] Substituting into the formula:

[0159] Calculate the numerator: 0.75·30·600=13500;

[0160] Calculate the score value:

[0161] Calculate the square root:

[0162] The results show that the dynamic sampling time interval of the path is 94.87 hours, indicating that the path needs to be sampled and monitored approximately every 95 hours to ensure that the sampling task frequency is reasonable and consistent with the pollution diffusion characteristics, providing a quantitative reference for the time planning of subsequent dynamic monitoring plans.

[0163] Using the sampling task distribution data set, the functional area monitoring tasks are decomposed into each priority path, and the task distribution of the monitoring points is dynamically planned according to the pollution diffusion trend of the path. By adjusting the task distribution time and path coverage, the functional area task setting of the monitoring point is optimized, and a dynamic planning scheme for groundwater monitoring is established;

[0164] Firstly, the distribution weights of monitoring points are adjusted according to the path length and concentration change rate, and the task distribution of key monitoring paths and secondary monitoring paths is identified. Then, more frequent monitoring tasks are assigned to the points with significant concentration changes on the key paths to ensure that monitoring resources are focused on areas with large fluctuations in pollution concentration. At the same time, the monitoring frequency of low-priority paths is reduced to optimize resource allocation. Finally, the time intervals and monitoring density of tasks on each path in the functional area are adjusted through dynamic planning. Combined with the pollution diffusion characteristics of the monitoring paths, a dynamic planning scheme for groundwater monitoring is established. This scheme achieves a comprehensive match between monitoring tasks and dynamic changes in pollution distribution, effectively improving the accuracy and efficiency of groundwater pollution monitoring in mining areas.

[0165] The mining area groundwater environment monitoring method is based on the above mining area groundwater environment monitoring system and includes the following steps:

[0166] S1: Based on the groundwater flow direction data and permeability coefficient data, the groundwater flow direction of the mining area, tailings pond area and abandoned area is extracted, the flow direction difference of the functional area is calculated, and the seepage direction and flow trend of the intersection point are analyzed by superimposing the permeability coefficient and the intersection point to establish the boundary value of the groundwater zoning in the mining area;

[0167] S2: Based on the boundary values ​​of groundwater zones in the mining area, the distribution of pollution sources in the functional areas is extracted, the pollutant release intensity is summarized and then calculated. Combined with the groundwater flow velocity value, the pollution diffusion range is analyzed, the pollution intensity in the diffusion area is segmented and divided, and the distribution data of groundwater pollution intensity and diffusion range are obtained;

[0168] S3: Based on the groundwater pollution intensity and diffusion range distribution data, the pollution intensity area is screened, the spatial matching of monitoring points is performed, the blind areas of monitoring point coverage are analyzed, and the pollution intensity data is superimposed and analyzed to adjust the number and location of monitoring points, and generate the optimized layout parameters of monitoring points in the mining area;

[0169] S4: Based on the optimized layout parameters of the monitoring points in the mining area, the pollutant diffusion path analysis is performed on the optimized monitoring point layout positions, the monitoring point positions are associated with the groundwater seepage direction, the diffusion path range is analyzed, the risk transmission points are calibrated, the path data is classified and sorted, and the distribution results of the pollution transmission paths in the mining area are obtained;

[0170] S5: Based on the distribution results of pollution transmission paths in mining areas, combined with the transmission cycle data within the path range, the risk level within the path is analyzed for distribution, the sampling task data is called to identify the task time interval, the sampling tasks of the monitoring points are dynamically adjusted, and the dynamic planning plan for groundwater monitoring is obtained based on the zoning distribution.

[0171] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A mining area groundwater environment monitoring system, characterized in that: The system comprises: The mining area groundwater zoning identification module extracts groundwater flow direction data of mining areas, tailings pond areas, and abandoned areas based on groundwater flow direction data and permeability coefficient data, analyzes the flow direction superposition and seepage direction at the junction, and generates the boundary value of groundwater zoning in the mining area; The groundwater pollution risk assessment module extracts the pollution source distribution data in the functional area based on the groundwater zoning boundary value of the mining area, combines the pollutant release intensity with the groundwater flow rate, quantifies the pollution diffusion intensity and determines the pollution area, and obtains the groundwater pollution risk intensity data set; The groundwater monitoring point optimization module selects the pollution intensity value of the risk area based on the groundwater pollution risk intensity data set, analyzes the pollution diffusion range covered by the monitoring points, optimizes the number and distribution of monitoring points, adjusts the boundary position of the coverage area, and obtains the layout parameters of the mining area monitoring points; The groundwater pollution path analysis module analyzes the pollutant diffusion path between the monitoring points based on the monitoring point layout parameters of the mining area, combines the monitoring point location and the groundwater seepage direction, calibrates the risk transmission path range, and generates the mining area pollution diffusion path distribution results; The groundwater dynamic monitoring planning module dynamically adjusts the sampling task weights and time intervals based on the distribution results of the pollution diffusion paths in the mining area, plans the distribution parameters of the functional area monitoring tasks, and establishes a groundwater monitoring dynamic planning program.

2. The mining area groundwater environment monitoring system according to claim 1 is characterized in that: The specific steps for obtaining the boundary value of the groundwater partition in the mining area are: Based on the groundwater flow direction data and permeability coefficient data, the groundwater flow direction in the mining area, tailings pond area and abandoned area is analyzed, the flow direction vector data is extracted, and combined with the permeability coefficient, the groundwater flow intensity and direction in each area are analyzed, the groundwater flow direction in the intersection area is identified, and the groundwater flow direction influence coefficient is obtained; According to the groundwater flow direction influence coefficient, the groundwater flow direction superposition at the junction is analyzed, the flow direction change data of the junction area is extracted, the change trend of the water flow direction at the junction is judged by combining the threshold, the flow direction weight of the junction between regions is identified, and the flow direction and infiltration adjustment coefficient at the junction is obtained; According to the flow direction and infiltration adjustment coefficient of the junction, the water flow direction and infiltration situation in the area are analyzed using the formula: Generate groundwater zone boundary values ​​for mining areas; Among them, B represents the boundary value of the groundwater zone in the mining area, w i is the weight coefficient of the ith region, f i is the groundwater flow direction value, c i is the permeability adjustment value, i represents the area number, and n is the total number of areas.

3. The mining area groundwater environment monitoring system according to claim 2 is characterized in that: The steps for obtaining the groundwater pollution risk intensity dataset are as follows: Based on the boundary value of the groundwater zoning in the mining area, the pollution source distribution data in the mining area is extracted, the pollution source release intensity and groundwater flow rate data are called, and the impact range of the pollution source is identified by spatial analysis to obtain the pollution diffusion intensity data set; Combining the groundwater flow characteristics with the pollution diffusion intensity dataset, the dynamic boundary of the contaminated area is derived through the interaction of flow velocity, pollutant release intensity and groundwater flow pattern, using the formula: Identify the spatial coordinates of the polluted area and the corresponding pollution concentration distribution, and obtain the boundary and risk level data of the polluted area; Among them, S p represents the pollution diffusion intensity, Q represents the release intensity of the pollution source, R represents the groundwater flow rate, d represents the distance between the pollution source and the monitoring point, λ represents the pollutant attenuation factor, and D w represents the groundwater flow depth, exp is the natural exponential function; According to the data on the boundaries and risk levels of the polluted areas, combined with the migration speed and attenuation characteristics of pollutants in groundwater, the dynamic relationship between pollution diffusion and risk assessment standards is derived. Based on the concentration and diffusion trend of pollutants in the polluted area, combined with the groundwater area risk assessment standards, the intensity level of pollution risk is judged and a groundwater pollution risk intensity data set is generated.

4. The mining area groundwater environment monitoring system according to claim 3 is characterized in that: The steps for obtaining the mining area monitoring point layout parameters are specifically as follows: Selecting the pollution intensity value of the risk area from the groundwater pollution risk intensity data set, identifying the area with pollution risk through quantitative analysis of the pollution intensity value, and obtaining the location data of the risk pollution area; According to the location data of the risk pollution area, the pollution diffusion range covered by the current monitoring point is analyzed, and the overlap between the area covered by the monitoring point and the pollution area is identified, using the formula: Calculate the coverage area of ​​monitoring points and obtain monitoring point coverage assessment data; Among them, A e represents the coverage area of ​​monitoring points, a j represents the theoretical coverage area of ​​the jth monitoring point, d j represents the distance from the monitoring point to the pollution source, D represents the maximum distance designed for the monitoring point, e is the natural exponential function, and m represents the total number of monitoring points; The monitoring point coverage assessment data is used to optimize the number and distribution of monitoring points, adjust the boundary position of the coverage area, re-plan the layout of monitoring points, combine the geographic information system for spatial optimization, and obtain the layout parameters of mining area monitoring points.

5. The mining area groundwater environment monitoring system according to claim 4, characterized in that: The steps for obtaining the pollutant diffusion path between the monitoring points are specifically as follows: Based on the monitoring point layout parameters of the mining area, the geographical location relationship between the monitoring point and the pollution source is analyzed, the horizontal distance and vertical height difference between the coordinates of the monitoring point and the location of the pollution source are called, and the initial association path between the monitoring point and the pollution source is generated in combination with the groundwater flow velocity and flow direction data; The pollutant diffusion analysis is performed on the initial association path between the monitoring point and the pollution source, and the pollutant concentration change rate of each section of the path is analyzed using the formula: Obtain the pollutant concentration change path; Among them, C x is the pollutant concentration value at the end of the path, C0 is the pollutant concentration at the starting point of the path, α is the attenuation coefficient of the pollutant, B is the path length, v is the groundwater flow velocity, and R is the groundwater penetration resistance factor; Combined with the pollutant concentration change path and the spatial layout of the monitoring points, the length, flow direction and concentration change trend of each path are summarized, the priority ranking of the paths is adjusted, and the pollutant diffusion path between the monitoring points is obtained.

6. The mining area groundwater environment monitoring system according to claim 5, characterized in that: The steps for obtaining the distribution results of the pollution diffusion path of the mining area are specifically as follows: Based on the pollutant diffusion path between the monitoring points, the geographical coordinates and pollutant concentration of the monitoring points are extracted, the pollutant diffusion rate is analyzed and the diffusion direction and rate data of each path are classified, and the path data is integrated with the diffusion distance, concentration gradient and time span to obtain the basic data of the pollution diffusion path; Based on the basic data of the pollution diffusion path, the classification threshold is set according to the diffusion rate, the paths that exceed the risk classification are screened and marked, the screened path data are classified, the geographical relevance of the paths is integrated by grouping, the path concentration change data and the diffusion rate are compared, and the risk transmission path range calibration results are generated; Based on the calibration results of the risk transmission path range, the spatial data of the calibration path and the concentration gradient are integrated, and the diffusion range map is drawn using the geometric relationship between the paths to obtain the distribution results of the pollution diffusion path in the mining area.

7. The mining area groundwater environment monitoring system according to claim 6, characterized in that: The steps for obtaining the groundwater monitoring dynamic planning scheme are specifically as follows: Based on the distribution results of the pollution diffusion paths in the mining area, the sampling data of the monitoring points on the diffusion paths are called, the trend of pollutant concentration changes on each path is analyzed, and the paths are divided into high priority, medium priority and low priority paths in combination with the path length and pollution intensity level to obtain the diffusion path priority distribution data; According to the diffusion path priority distribution data and the pollution characteristics of the functional area, the weight and time interval of the sampling task are dynamically adjusted using the formula: Calculate the sampling time interval, optimize the sampling period by combining weight, concentration and path length, and obtain the sampling task distribution data set; Among them, T c represents the sampling time interval, P l is the path priority weight, C m is the average pollution concentration on the path, L p is the path length, Q is the adjustment factor; By using the sampling task distribution data set, the functional area monitoring tasks are decomposed into each priority path, and the task distribution of the monitoring points is dynamically planned according to the pollution diffusion trend of the path. By adjusting the task distribution time and path coverage, the functional area task settings of the monitoring points are optimized, and a dynamic planning scheme for groundwater monitoring is established.

8. A method for monitoring groundwater environment in a mining area, characterized in that: The mining area groundwater environment monitoring system according to any one of claims 1 to 7 comprises the following steps: S1: Based on the groundwater flow direction data and permeability coefficient data, the groundwater flow direction of the mining area, tailings pond area and abandoned area is extracted, the flow direction difference of the functional area is calculated, and the seepage direction and flow trend of the intersection point are analyzed by superimposing the permeability coefficient and the intersection point to establish the boundary value of the groundwater zoning in the mining area; S2: Based on the boundary value of the groundwater zoning in the mining area, the distribution of pollution sources in the functional area is extracted, the pollutant release intensity is summarized and then calculated, and the pollution diffusion range is analyzed in combination with the groundwater flow velocity value, and the pollution intensity in the diffusion area is segmented and divided to obtain the groundwater pollution intensity and diffusion range distribution data; S3: Based on the groundwater pollution intensity and diffusion range distribution data, the pollution intensity area is screened, the spatial matching of monitoring points is performed, the blind areas of monitoring point coverage are analyzed, and the pollution intensity data is superimposed and analyzed to adjust the number and location of monitoring points, and generate the optimized layout parameters of monitoring points in the mining area; S4: Based on the optimized layout parameters of the monitoring points in the mining area, the pollutant diffusion path analysis is performed on the optimized monitoring point layout positions, the monitoring point positions are associated with the groundwater seepage direction, the diffusion path range is analyzed, the risk transmission points are calibrated, the path data is classified and sorted, and the distribution results of the pollution transmission paths in the mining area are obtained; S5: Based on the distribution results of the pollution transmission path in the mining area and combined with the transmission cycle data within the path range, the risk level within the path is analyzed for distribution, the sampling task data is called to identify the task time interval, the sampling tasks of the monitoring points are dynamically adjusted, and the dynamic planning plan for groundwater monitoring is obtained according to the zoning distribution.

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