Comprehensive treatment construction method for waste iron ore tailings pond

Abstract

The invention provides a waste iron ore tailing pond comprehensive treatment construction method which comprises the following steps: S1, landform measurement: measuring and detecting landform by using an unmanned aerial vehicle so as to form a digital model of a landform area range, S2, landform trimming simulation: trimming the generated digital model so as to form a landform area range, and S3, carrying out comprehensive treatment on the landform area range. The volume of earthwork needing to be carried and moved in the correction process can be calculated through a digital model, the carrying directions of different earthwork volumes are determined, and aiming at the problems of low actual construction efficiency and the like, protective measures such as an impermeable layer and a slope masonry grid are arranged on a dam body, vegetation greening and the like are adopted to eliminate potential safety hazards of the dam body; a percolation facility composed of a water storage layer, a percolation body, an anti-seepage film, a percolation pipe, a drainage pipe and the like, a plant buffer zone and the like are arranged in the reservoir area beach face range, and therefore a comprehensive integrated system combining interception, drainage, buffering and percolation of surface runoff is formed in the reservoir area range.

Description

Technical Field

[0001] The present invention relates to the technical field of comprehensive management, and particularly relates to a construction method for comprehensive management of abandoned iron ore tailing ponds. Background Art

[0002] The problem of environmental pollution by tailing ponds has become an obstacle to ecological environment construction. Especially for tailing ponds within the river basins of the Loess Plateau, they not only pollute the environment but also cause corresponding soil erosion and water resource pollution. This has a huge impact on the ecological environment of the river basins and threatens the lives and property safety of the nearby people.

[0003] Chinese Patent with publication number CN112647518A discloses an ecological restoration method for comprehensive management of magnesite mining areas to reduce the impact of heavy metals on the surrounding ecological environment.

[0004] However, the above-mentioned disclosed solutions have the following deficiencies: In the actual use process of the above solutions, there are problems such as inaccurate measurement during the construction process, resulting in the need for repeated adjustment and modification during the comprehensive management process, which reduces the management efficiency.

[0005] The present invention proposes a construction method for comprehensive management of abandoned iron ore tailing ponds to solve this problem. Summary of the Invention

[0006] The purpose of the present invention is to generate a digital model through drone measurement to simulate the treatment of the polluted environment, thereby improving the efficiency in subsequent actual construction, and thus overcoming the problems in the above-mentioned background art.

[0007] Based on the above technical ideas, the technical solution adopted by the present invention is as follows: A construction method for comprehensive management of abandoned iron ore tailing ponds, comprising the following steps: S1 Geomorphic measurement step: In this step, a drone is used to measure and detect the geomorphology, thereby forming a digital model of the geomorphic area range, and calculating various size data within the current geomorphic area through the digital model; S2 Geomorphic trimming simulation step: In this step, the above-generated digital model is trimmed, and the volume of soil to be transported and moved during the correction process can be calculated through the digital model, and the transportation directions of different volumes of soil are determined; S3 Inspection and trimming step: In this step, various parameters of the digital model after correction are analyzed, thereby realizing automatic judgment of the trimmed digital model. By adding different constraint conditions, the corrected model can be judged and corresponding automatic adjustment steps can be generated, and the automatic adjustment steps are merged and added to the above trimming step; Step S4 for generating the drainage system: After the earthwork and its various parameters are trimmed in the digital model, add the drainage system model to the digital model. During the addition process, the space occupied by the drainage system model will generate the corresponding volume of earthwork to be transported. Confirm the appropriate installation position by moving the drainage system in the digital model; Step S5 for actual construction: After the above digital model operations are completed, perform actual construction operations based on the calculated volume of transported earthwork, excavation depth, and other data; Step S6 for constructing drainage supporting facilities: After the actual construction is completed, perform supporting processing around the drainage system during actual construction. Set slope protection structures on both sides of the drainage ditch and build a controllable soil and water dam structure at one end of the drainage ditch to achieve the drainage control effect.

[0008] Further limitation of the above technical solution: In the step S1 of geomorphic measurement, this step further includes a data preprocessing link. The data preprocessing link includes geometric correction and registration. Geometric registration and radiometric calibration are performed on the UAV images to eliminate image distortion, and the coordinate systems of different data sources are unified using field control points (GPS measured); data cleaning and denoising, eliminating invalid points (such as measurement error points, interference signals), removing noise through filtering algorithms, and retaining real surface features; interpolation and completion, interpolating sparse or missing areas, and common methods include Kriging interpolation, inverse distance weighting method.

[0009] Further limitation of the above technical solution: In the step S2 of geomorphic trimming simulation, this step includes different trimming methods, including trimming tools based on SketchUp and 3D point cloud and DEM data processing. Trimming tools based on SketchUp include the Push / Pull tool, which directly adjusts the height of terrain vertices or faces and is suitable for fine-tuning of simple terrains; terrain tools, which provide functions such as carving, flattening, stretching, etc., and support complex terrain modeling; plugin assistance, such as plugins like TopoShaper, Artisan, etc., which can generate mesh surfaces and optimize terrain details to improve accuracy; 3D point cloud and DEM data processing include open-source DEM fusion, combining open-source elevation data such as AW3D, SRTM, etc., and using models such as SE, C to correct terrain reflectance errors, which is suitable for correcting terrain effects of remote sensing images; multi-source data fusion modeling, integrating lidar, satellite images, and UAV data to construct a high-precision 3D terrain model.

[0010] For further limitation of the above technical solution, in the S3 inspection and trimming step, while inspecting and trimming the digital model, this step also includes a mathematical accuracy evaluation and a geometric and topological consistency check. The mathematical accuracy evaluation includes the checkpoint method, where measured control points or high-precision reference data are selected, and the mean absolute error (MAE) and root mean square error (RMSE) between the DEM elevation value and the actual value are calculated. For example, in ASTER GDEM and TanDEM-X data, the MAE can be controlled within 30 meters. Contour overlay analysis is performed by overlaying the contours generated from the DEM with the original topographic map, and the consistency of the contour morphology is checked through visual or automatic matching algorithms to identify areas with elevation mutations or distortions. Profile line comparison is carried out by extracting the DEM elevation sequence along a specific terrain profile and comparing it with the measured profile data to evaluate the restoration accuracy of local terrain features (such as ridges and valleys).

[0011] For further limitation of the above technical solution, the geometric and topological consistency check includes topological relationship verification to ensure that there are no topological errors such as hanging nodes and self-intersecting edges in the triangulated irregular network (TIN) or regular grid, and an automated detection is achieved using the topological check tool of GIS software (such as ArcGIS). Terrain feature line matching is performed by extracting ridge lines and valley lines through edge detection algorithms (such as the Canny operator), comparing the feature lines generated from the DEM with the actual terrain, and evaluating the accuracy of the geometric morphology. Adjacent edge smoothness detection is carried out by checking whether the boundary elevations of adjacent DEM tiles are continuous, and a weighted smoothing algorithm is used to eliminate the jump error at the seam.

[0012] For further limitation of the above technical solution, the S3 inspection and trimming step also includes a verification link. The verification link includes dynamic and multi-scale verification. Dynamic and multi-scale verification includes temporal change detection, where differential analysis is performed on multi-temporal DEM data to identify surface deformation or erosion / deposition processes, and the terrain evolution trend is predicted by combining with the LSTM network. Multi-resolution consistency analysis is carried out through the pyramid model or LOD technology to verify the logical consistency between DEMs of different resolutions and ensure the reliability of cross-scale applications. Semantic enhancement verification is performed by fusing semantic information such as geology and vegetation to construct a multi-attribute DEM model, and the random forest algorithm is used to evaluate the matching degree between semantic labels and terrain features.

[0013] For further limitation of the above technical solution, in the S4 step of generating the drainage system, this step includes determining the model framework and scope. Determining the model framework and scope includes classifying according to the drainage system into sewage, rainwater, and combined sewer models. The combined sewer model needs to simulate both sewage and rainwater elements simultaneously. According to the generalization scale, there are primary models (main trunk pipelines), secondary models (municipal pipelines), and tertiary models (source facilities and neighborhood pipelines). According to the construction stage, there are current situation models (based on measured data) and planning models (based on design conditions).

[0014] For further limitation of the above technical solution, the step of generating the drainage system in S4 further includes model testing and parameter calibration. The model testing and parameter calibration include stability testing, running extreme working conditions (such as a rainstorm recurrence period of 100 years) to verify the model convergence. The node water volume continuity error should be ≤10%, and the total system error should be ≤5%. Parameter calibration and verification are carried out. Independent data sets are used for calibration, and sensitivity parameters (such as Manning coefficient, permeability) are adjusted by comparing measured flow / water level data. The rainy-day data should include different rainfall intensity sessions. During verification, historical waterlogging records are reproduced, and the waterlogging depth and recession time are matched.

[0015] For further limitation of the above technical solution, the step of actual construction in S5 further includes dynamic progress plan and node control and multi-source resource optimization allocation. The dynamic progress plan and node control include segmented plan management. The overall project is decomposed into sub-tasks such as dredging, revetment, and greening. Phased milestones are set (such as 3.8 km of river dredging needs to be completed within 2 months). The progress deviation is monitored through Gantt charts or the Critical Path Method (CPM). The "list management + node promotion" mode is adopted. For example, the renovation of Xing'an Road requires completion before the Tomb-Sweeping Festival, and the completion rate needs to be tracked through daily scheduling meetings. The resource and progress linkage mechanism is established. Construction logs are used to record data such as the usage rate of machinery and the consumption of materials, and the resource allocation is adjusted in real time. For example, the river dredging project needs to dynamically dispatch transport vehicles according to the capacity of the silt drying yard.

[0016] For further limitation of the above technical solution, the multi-source resource optimization allocation includes the coordinated scheduling of manpower and equipment. Skilled personnel are matched according to the process requirements. For example, during the dredging stage, excavator operators and slurry pump technicians are preferentially allocated, and gardeners are transferred during the greening stage. For mechanical equipment, the "centralized + decentralized" strategy is adopted. Large equipment (such as 20t rollers) is used for the main projects, and small machinery is configured for the corner areas. Dynamic material reserves and emergency responses are carried out. The material usage is predicted through the BIM model. For example, ecological bricks and concrete need to be reserved in advance for the revetment project. At the same time, an emergency material warehouse is established to cope with work stoppages caused by sudden weather.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. Low usage cost. The present invention uses a digital model to make an accurate plan for construction before actual construction, thereby improving the efficiency during the actual construction process. 2. Improved construction accuracy. The present invention accurately simulates the construction on the digital model, thereby ensuring that the construction finished product is the same as the simulation model during actual construction, improving the construction accuracy. 3. Algorithm optimization. The verification link of the present invention includes dynamic and multi-scale verification algorithms, which can ensure the accuracy of the digital model terrain collected, reducing the usage error of the present invention. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic diagram of the overall structure of the flow chart of the method of the present invention; Figure 2 It is a schematic diagram of the survey grid and earthwork quantity of the UAV reservoir area mapping of the present invention; Figure 3 It is a schematic diagram of the plane layout of the drainage (interception) water, percolation pipe, and vegetation buffer zone in the reservoir area; Figure 4 It is a large-scale drawing of the structure of the percolation pipe in the reservoir area; Figure 5 It is a large-scale drawing of the structure of the percolation layer in the reservoir area; Figure 6 It is a schematic diagram of the cross-sectional structure of the dam body; Figure 7 It is a schematic diagram of the grid slope protection structure of the dam body slope.

[0020] Among them, 1. Drainage (interception) ditch in the reservoir area; 2. Vegetation buffer zone; 3. Percolation pipe system; 4. Percolation pipe (perforated PE pipe); 5. 20 - 30 gravel filling layer; 6. Permeable geotextile; 7. Cover soil layer; 8. Anti-seepage geomembrane; 9. Lower water storage layer; 10. Percolation body; 11. Upper water storage layer; 12. Soil matrix layer; 13. Dam body; 14. Tailings pond reservoir area; 15. 1.0 m thick covering soil; 16. Masonry protection on the top surface of the dam body; 17. Masonry grid; 18. Turf on the dam body slope. Specific implementation manner

[0021] The following will further elaborate on the present invention in conjunction with the attached Figures 1-4 For a more detailed description of the present invention.

[0022] Embodiment 1: This embodiment provides a comprehensive treatment construction method for abandoned iron ore tailings ponds, as Figures 1-4 shown, including the following steps: S1 Geomorphological measurement step, in this step, the geomorphology is measured and detected by using a UAV, and then a digital model of the geomorphological area range is formed. The respective dimensional data within the current geomorphological area are calculated through the digital model; S2 Geomorphological trimming simulation step, in this step, the above-generated digital model is trimmed. Through the digital model, the volume of earthwork that needs to be transported and moved during the correction process can be calculated, and the transportation directions of different volumes of earthwork can be determined; S3 Inspection and trimming step, which analyzes the parameters after the digital model is corrected, and then realizes the automatic judgment of the trimmed digital model. By adding different constraint conditions, the corrected model can be judged and corresponding automatic adjustment steps can be generated, so as to merge and add the automatic adjustment steps into the above trimming step; S4 Drainage system generation step. After the earthwork and its parameters are trimmed in the digital model, a drainage system model is added to the digital model. During the addition process, the space occupied by the drainage system model will generate the corresponding earthwork volume to be transported, and the appropriate installation position is confirmed by moving the drainage system position in the digital model; S5 Actual construction step, which performs actual construction operations according to the calculated earthwork volume to be transported, excavation depth and other data after the above digital model operations are completed; S6 Construction of drainage supporting facilities. After the actual construction is completed, the surrounding of the drainage system in the actual construction is processed in a supporting manner. A slope protection structure is set on both sides of the drainage ditch and a controllable soil and water dam structure is built at one end of the drainage ditch to achieve the drainage control effect.

[0023] The S1 landform measurement step also includes a data preprocessing link. The data preprocessing link includes geometric correction and registration, geometric registration and radiometric calibration of UAV images to eliminate image distortion, and unifying the coordinate systems of different data sources using field control points (GPS measured); data cleaning and denoising, removing invalid points (such as measurement error points, interference signals), removing noise through filtering algorithms, and retaining real surface features; interpolation and completion, interpolating sparse or missing areas, and common methods include Kriging interpolation, inverse distance weighting method.

[0024] The S2 landform trimming and simulation step includes different trimming methods, including trimming tools based on SketchUp and 3D point cloud and DEM data processing. Trimming tools based on SketchUp include push-pull tools, which directly adjust the height of terrain vertices or faces and are suitable for simple terrain fine-tuning, terrain tools, which provide functions such as carving, leveling, stretching, etc., and support complex terrain modeling, and plug-in assistance, such as plug-ins like TopoShaper and Artisan can generate mesh surfaces and optimize terrain details to improve accuracy; 3D point cloud and DEM data processing includes open-source DEM fusion, combining open-source elevation data such as AW3D and SRTM, and using models such as SE and C to correct terrain reflectance errors, which is suitable for remote sensing image terrain effect correction, and multi-source data fusion modeling, integrating lidar, satellite images and UAV data to build a high-precision 3D terrain model.

[0025] The S3 inspection and trimming step includes, while inspecting and trimming the digital model, mathematical precision evaluation and geometric and topological consistency checking. Mathematical precision evaluation includes the checkpoint method, where measured control points or high-precision reference data are selected to calculate the mean absolute error (MAE) and root mean square error (RMSE) between the DEM elevation value and the actual value. For example, in ASTER GDEM and TanDEM-X data, the MAE can be controlled within 30 meters. Contour overlay analysis is also carried out, where the contours generated from the DEM are overlaid with the original topographic map, and the consistency of the contour morphology is checked through visual or automatic matching algorithms to identify areas with elevation mutations or distortions. Profile line comparison is done by extracting the DEM elevation sequence along a specific terrain profile and comparing it with the measured profile data to evaluate the restoration accuracy of local terrain features (such as ridges and valleys).

[0026] The geometric and topological consistency checking includes topological relationship verification to ensure that there are no topological errors such as hanging nodes and self-intersecting edges in the triangulated irregular network (TIN) or regular grid, and automated detection is achieved using the topological checking tools of GIS software (such as ArcGIS). Terrain feature line matching is carried out by extracting ridge lines and valley lines through edge detection algorithms (such as the Canny operator) and comparing the feature lines generated from the DEM with the actual terrain to evaluate the accuracy of the geometric morphology. Adjacent edge smoothness detection is performed to check whether the boundary elevations of adjacent DEM tiles are continuous, and a weighted smoothing algorithm is used to eliminate the jump error at the seam.

[0027] The S3 inspection and trimming step also includes a verification link. The verification link includes dynamic and multi-scale verification. Dynamic and multi-scale verification includes time-series change detection, where differential analysis is carried out on multi-temporal DEM data to identify surface deformation or erosion / deposition processes, and the LSTM network is combined to predict the terrain evolution trend. Multi-resolution consistency analysis is performed through the pyramid model or LOD technology to verify the logical consistency between DEMs of different resolutions and ensure the reliability of cross-scale applications. Semantic enhancement verification is carried out by fusing semantic information such as geology and vegetation to construct a multi-attribute DEM model, and the random forest algorithm is used to evaluate the matching degree between semantic labels and terrain features.

[0028] The S4 step of generating the drainage system includes determining the model framework and scope. Determining the model framework and scope includes classifying according to the drainage system into sewage, rainwater, and combined sewer models. The combined sewer model needs to simulate both sewage and rainwater elements simultaneously. According to the generalization scale, there are first-level models (main trunk pipe networks), second-level models (municipal pipe networks), and third-level models (source facilities and neighborhood pipes). According to the construction stage, there are current situation models (based on measured data) and planning models (based on design conditions).

[0029] The S4 step of generating a drainage system also includes model testing and parameter correction. The model testing and parameter correction include stability testing, running extreme conditions (such as a 100-year rainstorm return period) to verify model convergence, node water volume continuity error must be ≤10%, and the total system error must be ≤5%. Parameter calibration and verification use independent data sets for calibration, and compare measured flow / water level data to adjust sensitivity parameters (such as Manning coefficient, permeability). Rainy day data must include different rainfall intensities. During verification, historical waterlogging records must be repeated to match waterlogging depth and water withdrawal time.

[0030] The actual construction steps of S5 also include dynamic progress planning and node control and multi-source resource optimization configuration. Dynamic progress planning and node control include segmented planning management, which decomposes the overall project into subtasks such as dredging, bank protection, and greening, sets phased milestones (such as 3.8km of river dredging must be completed within 2 months), monitors progress deviations through Gantt charts or critical path method (CPM), and adopts the "checklist management + node-based promotion" model. For example, the renovation of Xing'an Road requires completion before Qingming Festival, and daily scheduling meetings are required to track the completion rate; resource and progress linkage mechanism, establish a construction log to record data such as machine utilization rate and material consumption, and adjust resource allocation in real time. For example, the river dredging project needs to dynamically dispatch transportation vehicles according to the capacity of the sludge drying yard.

[0031] The multi-source resource optimization configuration includes coordinated scheduling of manpower and equipment, matching skilled personnel according to process requirements, such as giving priority to excavator operators and mud pump technicians in the dredging stage, transferring gardeners in the greening stage, adopting a "centralized + decentralized" strategy for mechanical equipment, using large equipment (such as 20t rollers) for trunk projects, and configuring small machinery in corner areas, dynamic material storage and emergency response, and predicting material usage through BIM models, such as the need to reserve ecological bricks and concrete in advance for bank protection projects. At the same time, an emergency material warehouse is established to deal with shutdowns caused by sudden weather.

[0032] The dam body is equipped with an anti-seepage layer, a mortar-stone grid on the slope, and protective measures such as vegetation greening to eliminate the potential safety hazards of the dam body; infiltration facilities and plant buffer zones consisting of aquifers, infiltration bodies, anti-seepage membranes, infiltration pipes, and drainage pipes are set up within the reservoir area, thereby forming a comprehensive integrated system combining interception, drainage, caching, and infiltration of surface runoff within the reservoir area. Reduce soil erosion in the reservoir area and control soil and water pollution. At the same time, it can reduce material and labor input and construction costs. Promote the ecological protection and high-quality development of river basins.

[0033] Example 2: This example provides a comprehensive treatment construction method for an abandoned iron ore tailings pond, such as Figures 1-4As shown in the figure, Step 1: Use a drone to conduct an overall topographic survey of the reservoir area. The overall topographic survey of the reservoir area is carried out by using a survey drone to conduct an overall survey of the reservoir area, establishing a field model by measuring and analyzing data, determining the earthwork volume of the reservoir area and the overall leveling construction plan.

[0034] Step 2: Level, compact and cover the tailings beach surface 14 in the reservoir area. When leveling the tailings beach surface 14 in the reservoir area, the slope needs to be controlled at 2%, with the middle high and both sides low, and the slope direction is towards the drainage (intercepting) ditches 1 on both sides. The covering soil 15 needs to be constructed synchronously with the seepage filtration facilities in the reservoir area.

[0035] Step 3: Set up seepage filtration facilities composed of a water storage layer 9, a seepage filter 10, an anti-seepage geomembrane 8, a seepage pipe 4, etc. within the range of the reservoir area beach surface 14, and connect the seepage pipe 4 with the drainage (intercepting) ditches 1 around the reservoir area to form a drainage system. Thus, effectively control the moisture content of the tailings in the reservoir area and timely drain the excess water in the reservoir area.

[0036] As Figure 5 shown, after the tailings reservoir 14 beach surface in the reservoir area is leveled and compacted, the following processes are carried out in sequence: laying an anti-seepage geomembrane 8, a 20 - 30 cm gravel filling layer 5, a lower water storage layer 9, a seepage filter 10, an upper water storage layer 11, a soil matrix layer 12, a covering soil layer 7, etc.

[0037] Step 4: As Figure 3 shown, set up a 2 - m - wide plant buffer zone 2 on the side of the drainage (intercepting) ditch 1 around the reservoir area close to the reservoir area, and its slope is controlled at about 2%. Through the interception of vegetation and the infiltration of the reservoir area soil, slow down the flow velocity of surface runoff, maintain soil and water and remove some pollutants in the runoff. The vegetation species should be selected as native plant varieties with developed roots, strong growth, salt tolerance, drought tolerance and waterlogging tolerance.

[0038] Step 5: Dam body 13 renovation: As Figure 6 shown, set up a waterproof geomembrane 7 at a depth of 500 mm below the surface of the dam body 13. After covering the soil, set up a masonry grid 17 with a spacing of 1500 mm on the surface layer of the dam body for slope protection (see Figure 7 shown), and plant lawn 18 within the grid.

[0039] The drainage (intercepting) ditches 1 around the reservoir area can adopt a masonry structure or a reinforced concrete structure; the thickness of the covering soil layer 7 in the reservoir area is 1 m; the seepage filtration facilities are located in the covering soil layer 7 of the reservoir area beach surface; the dam body 13 is an earth dam body, and its outer slope ratio is 1:2; the dam body grid 17 is formed by masonry, with a cross - section size of 300 mm × 300 mm, buried 250 mm into the soil of the dam body 13 and exposed 50 mm. Masonry border structures with a cross - section of 300 mm × 300 mm are set at the top, bottom and both sides of the dam body slope. In addition to the vegetation buffer zone 2 in the reservoir area range in Step 4, the reservoir area also needs to be closed - field greened in accordance with the principle of 50% shrubs and 50% turf.

[0040] The above content is a further detailed description of the present invention in combination with specific preferred implementation schemes, facilitating those skilled in the art of this technology to understand and apply the present invention. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions.

Claims

1. A comprehensive treatment construction method for abandoned iron ore tailings pond, characterized in that: The following steps are involved: S1 landform measurement step, in which the landform is measured and detected by using a drone, thereby forming a digital model of the landform area, and calculating various size data within the current landform area through the digital model; S2: a landform modification simulation step, in which the digital model generated above is modified, and the amount of earthwork that needs to be moved during the modification process can be calculated through the digital model and the direction of movement of different earthwork amounts can be determined; S3 inspection and trimming step, which analyzes various parameters of the digital model after trimming, and then automatically judges the trimmed digital model. By adding different constraints, the corrected model can be judged and the corresponding automatic adjustment steps can be generated, so that the automatic adjustment steps are merged and added to the above trimming steps; S4 is a step of generating a drainage system. After the earthwork and its various parameters are modified in the digital model, the drainage system model is added to the digital model. During the adding process, the space occupied by the drainage system model will generate the corresponding earthwork volume to be transported. The appropriate installation position is confirmed by moving the drainage system position in the digital model. S5 is an actual construction step, in which after the above digital model operation is completed, actual construction operation is performed according to the calculated data such as the transport earthwork volume and excavation depth; S6 builds drainage supporting facilities. After the actual construction is completed, this step is to carry out supporting processing around the drainage system during the actual construction. Slope protection structures are set up on both sides of the drainage ditch and a controllable water and soil dam structure is built at one end of the drainage ditch to achieve drainage control effects.

2. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 1, characterized in that: The S1 geomorphic measurement step also includes a data preprocessing step, which includes geometric correction and registration, geometric registration and radiation calibration of drone images, elimination of image distortion, and use of field control points (GPS measurements) to unify the coordinate systems of different data sources; data cleaning and denoising, eliminating invalid points (such as measurement error points, interference signals), removing noise through filtering algorithms, and retaining real surface features; interpolation and completion, interpolation of sparse or missing areas, and commonly used methods include Kriging interpolation and inverse distance weighted method.

3. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 2, characterized in that: The S2 terrain modification simulation step includes different modification methods, including SketchUp-based modification tools and three-dimensional point cloud and DEM data processing. The SketchUp-based modification tools include push-pull tools, which directly adjust the height of terrain vertices or faces, and are suitable for simple terrain fine-tuning. Terrain tools provide carving, leveling, stretching and other functions, support complex terrain modeling, and plug-in assistance, such as TopoShaper, Artisan and other plug-ins can generate mesh surfaces and optimize terrain details to improve accuracy; three-dimensional point cloud and DEM data processing includes open source DEM fusion, combined with open source elevation data such as AW3D and SRTM, and uses SE, C models to correct terrain reflectivity errors, which is suitable for remote sensing image terrain effect correction, multi-source data fusion modeling, integration of lidar, satellite images and drone data, and construction of high-precision three-dimensional terrain models.

4. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 3, characterized in that: The S3 inspection and trimming step inspects and trims the digital model and also includes mathematical accuracy assessment and geometric and topological consistency inspection. The mathematical accuracy assessment includes the checkpoint method, selecting measured control points or high-precision reference data, and calculating the mean absolute error (MAE) and root mean square error (RMSE) between the DEM elevation value and the actual value. For example, in ASTER GDEM and TanDEM-X data, the MAE can be controlled within 30 meters. Contour fitting analysis is performed to overlay the contour lines generated by the DEM with the original topographic map, and the contour line morphology consistency is checked visually or by automatic matching algorithms. The elevation mutation or distortion area is identified, and the profile line comparison is performed to extract the DEM elevation sequence along a specific terrain profile, compare it with the measured profile data, and evaluate the restoration accuracy of local terrain features (such as ridges and valleys).

5. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 4, characterized in that: The geometric and topological consistency check process includes topological relationship verification to ensure that there are no topological errors such as hanging nodes and self-intersecting edges in the triangulated network (TIN) or regular grid, automatic detection using the topological check tool of GIS software (such as ArcGIS), terrain feature line matching, extraction of ridge lines and valley lines through edge detection algorithms (such as the Canny operator), comparison of feature lines generated by the DEM with the actual terrain, evaluation of geometric accuracy, edge smoothness detection, checking whether the boundary elevations of adjacent DEM blocks are continuous, and using a weighted smoothing algorithm to eliminate jump errors at seams.

6. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 5, characterized in that: The S3 inspection and trimming step also includes a verification step, which includes dynamic and multi-scale verification. The dynamic and multi-scale verification includes time series change detection, differential analysis of multi-phase DEM data, identification of surface deformation or erosion / sedimentation processes, prediction of terrain evolution trends in combination with LSTM networks, multi-resolution consistency analysis, verification of logical consistency between DEMs of different resolutions through pyramid models or LOD technology, ensuring the reliability of cross-scale applications, semantic enhancement verification, integration of semantic information such as geology and vegetation, construction of a multi-attribute DEM model, and use of a random forest algorithm to evaluate the matching degree between semantic labels and terrain features.

7. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 6, characterized in that: The S4 step of generating a drainage system includes determining the model framework and scope. The model framework and scope determination include dividing the model into sewage, rainwater, and combined system models according to the drainage system. The combined system needs to simulate sewage and rainwater elements at the same time. According to the generalized scale, there are primary models (trunk pipe network), secondary models (municipal pipe network), and tertiary models (source facilities and neighborhood pipes). According to the construction stage, there are current models (based on measured data) and planning models (based on design conditions).

8. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 7, characterized in that: The S4 step of generating a drainage system also includes model testing and parameter correction. The model testing and parameter correction include stability testing, running extreme conditions (such as a 100-year rainstorm return period) to verify model convergence, node water volume continuity error must be ≤10%, and the total system error must be ≤5%. Parameter calibration and verification use independent data sets for calibration, and compare measured flow / water level data to adjust sensitivity parameters (such as Manning coefficient, permeability). Rainy day data must include different rainfall intensities. During verification, historical waterlogging records must be repeated to match waterlogging depth and water withdrawal time.

9. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 8, characterized in that: The actual construction steps of S5 also include dynamic progress planning and node control and multi-source resource optimization configuration. Dynamic progress planning and node control include segmented planning management, which decomposes the overall project into subtasks such as dredging, bank protection, and greening, sets phased milestones (such as 3.8km of river dredging must be completed within 2 months), monitors progress deviations through Gantt charts or critical path method (CPM), and adopts the "checklist management + node-based promotion" model. For example, the renovation of Xing'an Road requires completion before Qingming Festival, and daily scheduling meetings are required to track the completion rate; resource and progress linkage mechanism, establish a construction log to record data such as machine utilization rate and material consumption, and adjust resource allocation in real time. For example, the river dredging project needs to dynamically dispatch transportation vehicles according to the capacity of the sludge drying yard.

10. A method for comprehensive treatment of abandoned iron ore tailings pond according to claim 9, characterized in that: The multi-source resource optimization configuration includes coordinated scheduling of manpower and equipment, matching skilled personnel according to process requirements, such as giving priority to excavator operators and mud pump technicians in the dredging stage, transferring gardeners in the greening stage, adopting a "centralized + decentralized" strategy for mechanical equipment, using large equipment (such as 20t rollers) for trunk projects, and configuring small machinery in corner areas, dynamic material storage and emergency response, and predicting material usage through BIM models, such as the need to reserve ecological bricks and concrete in advance for bank protection projects. At the same time, an emergency material warehouse is established to deal with shutdowns caused by sudden weather.

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