Method and device for determining ecological restoration scheme of tailings pond and storage medium

Abstract

The application relates to a tailing pond ecological restoration scheme determination method and device and a storage medium, and relates to the technical field of ecological restoration processing. The method comprises the following steps: acquiring multi-dimensional index data of a tailing pond area; determining an ecological sensitive area in the tailing pond area; evaluating index data of each dimension in the multi-dimensional index data to obtain evaluation index results of each dimension index data; determining a plurality of candidate restoration schemes matched with each evaluation index result; determining an ecological index change amount caused by each candidate restoration scheme under each evaluation index result and a current climate environment parameter; determining a restoration scheme effect index of each candidate restoration scheme according to the ecological index change amount caused by each candidate restoration scheme; and taking the comprehensive benefits of the ecological restoration effect index, the ecological restoration cost and the land use value added benefit optimization as the target, determining a target restoration scheme of the ecological sensitive area from the plurality of candidate restoration schemes.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology, and in particular to a method, apparatus and storage medium for determining ecological restoration schemes for tailings ponds. Background Technology

[0002] Currently, tailings ponds, as key facilities in metal and non-metal mining production, are increasingly attracting widespread attention from the industry and society regarding their impact on the surrounding ecological environment. With the expansion of tailings pond scale and the increase in tailings stockpile, related technologies, relying on isolated monitoring and static models, struggle to address the spatiotemporal heterogeneity of pollutant migration, leading to remediation plans often lagging behind actual environmental evolution. Data fragmentation and dynamic complexity in mining environmental governance often result in high costs and resource consumption, hindering large-scale application. Furthermore, over-reliance on the quality of on-site data frequently reduces the accuracy of remediation plans. Complex operational procedures and a lack of unified standards further contribute to inconsistent results, while external dynamic interference makes long-term assessments more susceptible to failure, ultimately exacerbating the uncontrollable risks and sustainability challenges of ecological governance. Related technologies rarely include dedicated ecological remediation simulation and scheme development systems for tailings ponds; most focus on ecological remediation evaluation and monitoring of the entire mining area, often resulting in low simulation accuracy. Moreover, simulations of tailings, a special matrix, are scarce, as they significantly impact the overall ecological remediation effect of the mining area, making it impossible to accurately simulate the ecological remediation process of tailings pond areas and formulate optimal remediation plans. Summary of the Invention

[0003] This invention provides a method, apparatus, and storage medium for determining ecological restoration schemes for tailings ponds, aiming to at least address the problems of low accuracy, high cost, and inconsistent effectiveness of restoration schemes in related technologies. The technical solution of this invention is as follows: According to a first aspect of the present invention, a method for determining an ecological restoration scheme for a tailings dam is provided. The method includes: acquiring multidimensional indicator data of the tailings dam area; identifying ecologically sensitive areas within the tailings dam area; and determining current climate and environmental parameters of the ecologically sensitive areas. The multidimensional indicator data includes natural resource indicator parameters, ecological environment indicator parameters, and economic indicator parameters. The method evaluates the indicator data of each dimension of the multidimensional indicator data to obtain evaluation indicator results for each dimension. It determines multiple candidate restoration schemes that are compatible with each evaluation indicator result. It determines the amount of change in ecological indicators caused by each candidate restoration scheme under each evaluation indicator result and the current climate and environmental parameters. Based on the amount of change in ecological indicators caused by each candidate restoration scheme, it determines the restoration scheme effect indicator of each candidate restoration scheme. Based on the ecological restoration effect indicator of each candidate restoration scheme, the ecological restoration cost of each candidate restoration scheme, and the land use value-added benefit of each candidate restoration scheme, it determines the target restoration scheme for the ecologically sensitive area from the multiple candidate restoration schemes.

[0004] The above-mentioned scheme achieves a comprehensive assessment of the ecological status of the tailings dam area by acquiring multi-dimensional indicator data and identifying ecologically sensitive areas; it achieves accurate prediction of restoration effects by determining the changes in ecological indicators caused by candidate restoration schemes and the effect indicators of restoration schemes; and it achieves scientific selection of restoration schemes by comprehensively considering ecological restoration effect indicators, ecological restoration costs, and land use value-added benefits, thereby improving the accuracy of restoration schemes, reducing restoration costs, and ensuring restoration effects.

[0005] As a technical solution, identifying ecologically sensitive areas within a tailings dam area includes: acquiring remote sensing images of the ecological environment of the tailings dam area using remote sensing imagery equipment; and, based on remote sensing image recognition technology and a multi-scale recognition mechanism, identifying and aggregating areas in the ecological environment remote sensing images that represent ecological index parameters in the tailings dam area that are lower than preset ecological indicators to obtain ecologically sensitive areas; wherein, the ecological index parameters include vegetation coverage, soil health index parameters, and biodiversity index parameters.

[0006] The above scheme, through remote sensing image recognition technology and multi-scale recognition mechanism, can quickly and accurately identify ecologically sensitive areas, thereby improving the efficiency and accuracy of the assessment.

[0007] As a technical solution, the indicator data of each dimension in the multidimensional indicator data are evaluated to obtain the evaluation results of each dimension of the indicator data, including: determining the current complete hydrological cycle covering the dry and wet seasons based on the current water quality parameters, current water storage, current types and numbers of water pollution sources in the middle and lower reaches of the tailings dam area; determining the current land use benefits based on the current population density, current industry type and current land use type of the tailings dam area; determining the current land degradation level of the tailings dam area based on the current land damage area, current vegetation destruction amount and current soil erosion intensity of the tailings dam area; determining the target pollutant elements causing pollution based on the current soil pollution level index, current soil nutrient imbalance index and current water pollution impact index of the tailings dam area; and determining the current biodiversity index based on the current vegetation index, current plant coverage and current biomass of the tailings dam area.

[0008] The above-mentioned scheme achieves a comprehensive and quantitative assessment of the ecological status of the tailings pond area by evaluating multiple dimensions, including hydrological cycle, land use benefits, land degradation degree, target pollutants, and biodiversity index, providing a detailed data foundation for the formulation of subsequent remediation plans.

[0009] As a technical solution, multiple candidate remediation schemes adapted to the results of each assessment indicator are identified, including: based on the current complete hydrological cycle, current land use benefits, current land degradation level, target pollutants, and current biodiversity index, determining the dosage of various ecological restoration modifiers and the corresponding fertilization amount required when using different preset ecological restoration vegetation under various preset ecological restoration methods; wherein, the candidate remediation schemes include ecological restoration methods, ecological restoration vegetation types, ecological restoration modifier types, ecological restoration modifier dosages, and fertilization amounts.

[0010] Candidate restoration schemes vary depending on the ecological restoration method (topsoil replacement, chemical method, biological method, physical method, etc.), the type of ecological restoration vegetation (shrubs, herbaceous species, etc.), the type and amount of ecological restoration amendments, and the amount of fertilizer (biochar, topsoil, etc.).

[0011] The above approach achieves personalized and precise design of repair solutions by determining specific repair parameters based on the evaluation index results.

[0012] As a technical solution, this method determines the changes in ecological indicators caused by each candidate remediation scheme under various assessment indicators and current climate and environmental parameters. This includes inputting the current complete hydrological cycle, current land use efficiency, current land degradation level, current pollutants and current biodiversity index, current climate and environmental parameters, and each candidate remediation scheme into a machine learning model to obtain the changes in ecological indicators for each scheme. The machine learning model is based on historical complete hydrological cycles, historical land use efficiency, historical land degradation level, historical pollutants and historical biodiversity index, and the corresponding historical climate and environmental parameters and various historical remediation schemes. The changes in historical ecological indicators caused by the case were trained; the machine learning model represents the correlation and mapping relationship between the complete hydrological cycle, land use benefits, land degradation level, pollutant elements, biodiversity index, climate and environmental parameters, and the changes in ecological indicators of the remediation scheme; the changes in ecological indicators include changes in crop growth indicators and changes in pollutant transport indicators; the changes in crop growth indicators include the maximum photosynthetic rate, maintenance respiration coefficient, growth respiration coefficient, stomatal conductance, canopy resistance, and root growth rate of ecological vegetation; the changes in pollutant transport indicators include soil saturated water content, residual water content, porosity, bulk density, particle size composition, and convective dispersion process parameters.

[0013] The above-mentioned scheme predicts changes in ecological indicators through machine learning models, and can handle high-dimensional, multi-source, and heterogeneous data, capture nonlinear relationships between variables, and improve the accuracy and efficiency of prediction.

[0014] As a technical solution, based on the changes in ecological indicators caused by each candidate restoration scheme, the restoration effect indicators of each candidate restoration scheme are determined. This includes: inputting the changes in ecological indicators caused by each candidate restoration scheme and the current climate and environmental parameters into an ecological restoration and vegetation growth simulation model to obtain the restoration effect indicators of each candidate restoration scheme under the current climate and environmental parameters; the ecological restoration and vegetation growth simulation model characterizes the changes in ecological indicators of different restoration schemes under the climate and environmental parameters, and the resulting restoration effect indicators; wherein, the ecological restoration and vegetation growth simulation model includes a two-level coupled ecological restoration simulation model and a vegetation growth simulation model; ecological restoration and vegetation... The growth simulation model performs the following operations: It inputs changes in pollutant transport indicators and current climate and environmental parameters into the ecological restoration simulation model to simulate pollutant migration and soil restoration processes caused by changes in pollutant transport indicators under the current climate and environmental parameters, thus obtaining soil quality change indicator parameters. It then inputs current climate and environmental parameters, changes in crop growth indicators, and soil quality change parameters into the vegetation growth simulation model to simulate vegetation formation processes caused by changes in crop growth indicators under the current climate and environmental parameters, thus obtaining vegetation change parameters. Soil quality change indicator parameters include carbon sequestration potential and soil and water conservation efficiency; vegetation change parameters include biomass accumulation rate and biodiversity index.

[0015] The above scheme uses an ecological restoration and vegetation growth simulation model to achieve dynamic simulation of pollutant migration, soil restoration and vegetation growth processes. It can track and predict the progress of the restoration scheme at different time points in real time, thereby improving the rationality and practicality of the restoration goals.

[0016] As a technical solution, based on the ecological restoration effect indicators, ecological restoration costs, and land use value enhancement benefits of each candidate restoration scheme, a target restoration scheme for the ecologically sensitive area is determined from multiple candidate restoration schemes. This includes: determining the overall benefit target based on the ecological restoration effect indicators, ecological restoration costs, land use value enhancement benefits, and the first weight of the ecological restoration effect indicators, the second weight of the ecological restoration costs, and the third weight of the land use value enhancement benefits; the overall benefit target is positively correlated with the ecological restoration effect indicators, negatively correlated with the ecological restoration costs, and positively correlated with the land use value enhancement benefits; with the goal of maximizing the overall benefit target, a genetic algorithm is used to globally optimize the ecological restoration effect indicators, ecological restoration costs, and land use value enhancement benefits, so that the candidate restoration schemes corresponding to the ecological restoration effect indicators, ecological restoration costs, and land use value enhancement benefits that maximize the overall benefit target are determined as the target restoration schemes.

[0017] The above scheme uses a genetic algorithm for global optimization, taking into account ecological, economic and social benefits, and achieves multi-objective optimization of the restoration scheme. This ensures that the restoration scheme meets ecological needs while also taking into account local economic development and improving the quality of life of residents.

[0018] As a technical solution, based on the current soil pollution level indicators, current soil nutrient imbalance indicators, and current water pollution impact indicators in the tailings dam area, the target pollutant elements causing pollution are identified. Specifically, this includes: based on the current soil pollution level indicators, extracting heavy metal elements whose single pollution index or Nemerow comprehensive pollution index exceeds a preset threshold as primary soil pollutant elements; based on the current soil nutrient imbalance indicators, extracting chemical elements that cause abnormal nitrogen, phosphorus, and potassium ratio imbalance coefficients or trace element antagonism indices as nutrient stress elements; and based on the current water pollution impact indicators, extracting... Excessive dissolved heavy metal ions in surface water or groundwater are considered as water environment migrating pollutants. Pollutants that are correlated with primary soil pollutants, nutrient stressors, and water environment migrating pollutants are identified as target pollutants for tailings dam areas. Current soil pollution level indicators include individual pollution indices of heavy metal pollutants and the Nemerow comprehensive pollution index. Current soil nutrient imbalance indicators include nitrogen, phosphorus, and potassium ratio imbalance coefficients and trace element antagonism indices. Current water pollution impact indicators include surface water turbidity, chemical oxygen demand, and heavy metal leaching risk coefficients.

[0019] The above-mentioned scheme extracts target pollutant elements from three dimensions: soil, nutrients, and water environment, thereby achieving accurate identification of pollution sources and providing precise targets for the formulation of subsequent remediation plans.

[0020] According to a second aspect of the present invention, an apparatus for determining an ecological restoration scheme for a tailings dam is provided. This apparatus is capable of executing a method for determining an ecological restoration scheme for a tailings dam as described in the first aspect and any possible technical solution thereof, comprising: a data processing unit configured to acquire multi-dimensional indicator data of a tailings dam area; and to determine ecologically sensitive areas within the tailings dam area; and to determine current climate and environmental parameters of the ecologically sensitive areas; wherein the multi-dimensional indicator data includes natural resource indicator parameters, ecological environment indicator parameters, and economic indicator parameters; and an evaluation unit configured to evaluate the indicator data of each dimension in the multi-dimensional indicator data to obtain evaluation indicator results for each dimension of the indicator data; the first... The first determination unit is configured to determine multiple candidate remediation schemes that are compatible with the results of each assessment indicator; the second determination unit is configured to determine the amount of change in ecological indicators caused by each candidate remediation scheme under the results of each assessment indicator and the current climate and environmental parameters; the third determination unit is configured to determine the remediation effect indicators of each candidate remediation scheme based on the amount of change in ecological indicators caused by each candidate remediation scheme; and the remediation unit is configured to determine the target remediation scheme for the ecologically sensitive area from multiple candidate remediation schemes based on the ecological remediation effect indicators, the ecological remediation costs, and the land use value-added benefits of each candidate remediation scheme.

[0021] The above scheme, through the collaborative work of each unit, enables the automatic determination of ecological restoration plans, thereby improving the efficiency and scientific rigor of plan formulation.

[0022] According to a third aspect of the present invention, a tailings dam ecological restoration scheme determination system is provided. The tailings dam ecological restoration scheme determination system stores instructions. When the instructions in the tailings dam ecological restoration scheme determination system are executed by a controller, the controller is able to execute a tailings dam ecological restoration scheme determination method as described in the first aspect and any of its possible technical solutions.

[0023] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which instructions are stored, such that when the instructions in the computer-readable storage medium are executed by a processor of a control device, the control device is able to perform a tailings dam ecological restoration scheme determination method as described in the first aspect and any possible technical solution thereof.

[0024] According to a fifth aspect of the embodiments of this application, a computer program product is provided, the computer program product including computer instructions, which, when executed on a control device, cause the control device to execute the tailings dam ecological restoration scheme determination method described in the first aspect and any possible technical solution thereof.

[0025] The technical solutions provided by the embodiments of the present invention offer at least the following beneficial effects: This application provides a comprehensive assessment framework by integrating multi-source data from tailings dam areas, including water resource monitoring, ecological environment surveys, and socio-economic analysis. This systematic assessment not only identifies the specific degree of ecological damage and environmental pollution sources of tailings dams but also lays a solid foundation for formulating scientific and reasonable remediation measures, ensuring that no important factors are overlooked during the remediation process, thereby improving the effectiveness and sustainability of the remediation.

[0026] Secondly, advanced machine learning models and artificial intelligence technologies can process large and complex datasets and extract valuable, in-depth information. This not only identifies key parameters affecting ecological restoration but also predicts the potential effects of different restoration schemes based on historical data. This intelligent predictive capability significantly improves the efficiency of restoration scheme selection, reduces the risks of human decision-making, and makes the final selected scheme more scientific and practical.

[0027] Furthermore, by constructing a dynamic simulation model of ecological restoration and vegetation growth using tailings sand as a unique growth substrate, the progress of restoration plans at different time points can be tracked and predicted in real time. This model allows decision-makers to make dynamic adjustments when implementing restoration measures, ensuring the achievement of restoration goals. Dynamic simulation can also assess the impact of natural factors (such as climate change and seasonal changes) on restoration effectiveness, thereby setting more reasonable and realistic short-term and long-term goals and improving the flexibility and adaptability of restoration work.

[0028] Furthermore, a multi-scenario analysis approach is employed, comprehensively considering ecological, economic, and social benefits. When evaluating restoration plans, not only environmental restoration is addressed, but economic and social benefits are also taken into account. This comprehensive analysis ensures that restoration plans meet ecological needs while also contributing to local economic development and improving residents' quality of life, thereby promoting sustainable social development and achieving a win-win situation for both ecology and the economy.

[0029] Finally, by employing an accelerated genetic algorithm projection pursuit model, this invention enables in-depth evaluation of the long-term effects of ecological restoration. It can not only handle complex ecological data but also automatically generate detailed predictive reports. This long-term evaluation mechanism provides a scientific basis for subsequent policy adjustments and resource allocation, ensuring the continued effectiveness of ecological restoration efforts and providing strong support for building a healthy ecosystem.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0031] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0032] Figure 1 This is a flowchart illustrating a method for determining an ecological restoration scheme for a tailings dam according to an exemplary embodiment; Figure 2 This is a block diagram illustrating an apparatus for determining an ecological restoration scheme for a tailings dam according to an exemplary embodiment; Figure 3 This is a schematic diagram of a control device according to an exemplary embodiment. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0034] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0035] For ease of understanding, the following section, in conjunction with the accompanying drawings, provides a detailed description of the method for determining the ecological restoration scheme for tailings ponds provided in this application.

[0036] Figure 1 This is a flowchart illustrating a method for determining an ecological restoration scheme for a tailings dam according to an exemplary embodiment, such as... Figure 1 As shown, the method for determining the ecological restoration plan for the tailings dam includes the following steps: Step S100: Obtain multi-dimensional indicator data of the tailings dam area; identify ecologically sensitive areas within the tailings dam area; and determine the current climate and environmental parameters of the ecologically sensitive areas.

[0037] The multidimensional indicator data includes natural resource indicators, ecological environment indicators, and economic indicators.

[0038] This implementation step is the foundational data preparation stage for the entire remediation plan. The acquisition of multi-dimensional indicator data aims to overcome the limitations of traditional single-data environmental assessments. By introducing natural resource indicators (such as water reserves and land resource types), ecological environment indicators (such as vegetation cover and soil heavy metal content), and economic indicators (such as surrounding population density and industrial layout), a data foundation for a socio-economic-natural complex ecosystem is constructed. This multi-dimensional data collection ensures that subsequent assessments not only focus on environmental restoration but also consider local economic development and improvement of residents' quality of life. Simultaneously with data acquisition, it is necessary to identify ecologically sensitive areas within the tailings dam area. These ecologically sensitive areas refer to specific regions within the dam area where ecological indicators are below preset standards and urgently require ecological restoration. By delineating sensitive areas, the remediation space can be precisely located, avoiding indiscriminate, comprehensive operations across the entire dam area, thereby reducing remediation costs.

[0039] Furthermore, it is necessary to determine the current climate and environmental parameters of the ecologically sensitive area, such as rainfall, temperature, and sunshine duration. These parameters are key environmental boundary conditions for subsequent simulations of vegetation growth and pollutant transport, and directly affect the feasibility of the remediation plan.

[0040] Step S200: Evaluate the indicator data of each dimension in the multidimensional indicator data to obtain the evaluation indicator results of each dimension indicator data.

[0041] This implementation step involves further processing and semantic transformation of the raw data obtained in step S100. The raw multidimensional index data is usually discrete, unstructured physical quantities, which cannot be directly used to guide the formulation of repair plans.

[0042] This implementation step transforms the raw data into meaningful assessment indicators through a pre-defined evaluation model or algorithm. For example, water quality parameters and water storage capacity are converted into assessment results for a complete hydrological cycle, and land damage area and erosion intensity are converted into land degradation levels. This process achieves a leap from "data" to "information," providing standardized input for subsequent matching and restoration plans.

[0043] Step S300: Identify multiple candidate repair solutions that are compatible with the results of each evaluation metric.

[0044] Specifically, this step aims to construct a preliminary pool of remediation solutions. Different assessment indicators correspond to different ecological problems, thus requiring different remediation methods. For example, for areas with severe heavy metal pollution, suitable candidate solutions might include chemical passivation or phytoremediation techniques; while for areas with severe soil erosion, suitable candidate solutions might focus on engineering soil stabilization and vegetation restoration. This step generates multiple candidate remediation solutions by establishing a mapping relationship between assessment indicator results and remediation technologies, providing a diverse selection space for subsequent prediction and optimization, and avoiding the blindness of making decisions based on a single solution.

[0045] Step S400: Determine the changes in ecological indicators caused by each candidate remediation scheme under the results of various assessment indicators and current climate and environmental parameters.

[0046] For each candidate remediation scheme generated in step S300, this implementation step does not directly implement it, but instead predicts its implementation effect under specific environmental conditions through simulation. The change in ecological indicators refers to the expected change in ecological and environmental parameters relative to the current state after implementing a remediation scheme, such as the increase in vegetation cover or the decrease in soil heavy metal content. This prediction process fully considers the impact of current climate and environmental parameters, as factors such as rainfall and temperature significantly affect vegetation survival rate and pollutant migration rate. By quantifying the expected changes for each scheme, the remediation effect is visualized and comparable.

[0047] Step S500: Determine the remediation effect indicators of each candidate remediation scheme based on the changes in ecological indicators caused by each candidate remediation scheme.

[0048] This implementation step further integrates and evaluates the changes in ecological indicators predicted in step S400. While changes in ecological indicators reflect changes in a single dimension, the effectiveness indicators of restoration schemes are comprehensive evaluation parameters, potentially incorporating improvements in vegetation restoration, soil improvement, and biodiversity enhancement. This step transforms the predicted physicochemical changes into final restoration effectiveness indicators, providing a direct evaluation basis for the final scheme comparison.

[0049] Step S600: Based on the ecological restoration effect indicators, ecological restoration costs, and land use value-added benefits of each candidate restoration scheme, the target restoration scheme for the ecologically sensitive area is determined from multiple candidate restoration schemes.

[0050] This implementation step is the final stage of multi-objective optimization decision-making. In actual restoration projects, there is often a contradiction between high ecological benefits but high costs, or low costs but low land use value. This step no longer solely pursues maximizing ecological benefits, but introduces two key dimensions: ecological restoration costs and land use value enhancement benefits. Land use value enhancement benefits refer to the economic value that the restored land can bring as arable land, construction land, or ecological parks, etc. By comprehensively considering these three factors, an optimization algorithm (such as the genetic algorithm mentioned in subsequent embodiments) is used for global optimization to ultimately determine a target restoration scheme that achieves the best balance among ecological, economic, and social benefits. This achieves the scientific selection of the restoration scheme, avoids resource waste, and ensures the sustainability of the restoration work.

[0051] Based on the above embodiments, this embodiment details the specific process of determining ecologically sensitive areas within a tailings dam area. Accurate identification of ecologically sensitive areas is a prerequisite for developing targeted remediation plans, and the accuracy of this identification directly affects the efficiency of remediation resource allocation.

[0052] The above implementation step S100 involves identifying ecologically sensitive areas within the tailings dam area, specifically including the following steps.

[0053] Step S101: Using remote sensing imagery equipment, acquire remote sensing images of the ecological environment of the tailings dam area. This step aims to obtain highly up-to-date image data of the dam area.

[0054] Remote sensing imaging equipment may include drones equipped with high-resolution cameras, satellite remote sensing platforms (such as the Gaofen series satellites and the Landsat series satellites), or manned aerial photography systems.

[0055] Optionally, given the complex terrain and high detail requirements of tailings ponds, low-altitude photogrammetry using unmanned aerial vehicles (UAVs) can be employed to acquire centimeter-resolution orthophotos, supplemented by satellite remote sensing data to obtain wide-area spectral information. This multi-source data acquisition method effectively overcomes the limitations of single data sources in terms of spatial or spectral resolution, providing a rich data foundation for subsequent identification.

[0056] Step S102: Based on remote sensing image recognition technology and multi-scale recognition mechanism, the areas in the ecological environment remote sensing images that represent the tailings dam area with ecological index parameters lower than the preset ecological index are identified and aggregated to obtain the ecologically sensitive areas.

[0057] Among them, ecological indicators include vegetation coverage, soil health, and biodiversity.

[0058] Specifically, the multi-scale identification mechanism refers to processing remote sensing images using segmentation algorithms or sliding windows at different scales. At the macro scale, medium- and low-resolution images are used to quickly screen large areas of sparse vegetation or water pollution. At the micro scale, high-resolution images are used to perform refined identification of suspected areas and extract specific damaged patches.

[0059] For example, firstly, vegetation cover is calculated using the Normalized Difference Vegetation Index (NDVI), and a vegetation cover threshold is set in the preset ecological indicators. For instance, areas with vegetation cover below 30% are initially identified as vegetation degradation areas. At the same time, soil health index parameters (such as organic matter content and heavy metal content inversion index) are retrieved using soil spectral characteristics, and areas with soil health index parameters below the preset safety threshold are identified as soil damaged areas. Furthermore, biodiversity index parameters are analyzed in conjunction with the landscape pattern index to identify areas with biodiversity indices below the preset threshold.

[0060] After identifying regions with abnormal single indicators, an object-oriented image analysis method is used for aggregation. This method no longer operates on individual pixels but aggregates homogeneous pixels into patch objects. The system merges spatially adjacent regions with similar ecological indicator parameter characteristics, removes minor noise, and ultimately forms continuous ecologically sensitive areas with a certain spatial range.

[0061] For example, adjacent vegetation degradation patches and soil damage patches can be merged into a large ecologically sensitive area, thereby achieving precise positioning of restoration units. This identification and aggregation process effectively avoids the problems of low efficiency and strong subjectivity of traditional manual on-site surveys, significantly improving the objectivity and accuracy of ecologically sensitive area identification and providing precise spatial targets for the formulation of subsequent restoration plans.

[0062] Optionally, based on the above embodiments, this embodiment details the specific process of evaluating the indicator data of each dimension in the multidimensional indicator data in step S200. The above implementation step S200 specifically includes the following sub-steps.

[0063] The assessment process aims to transform the collected raw data into quantifiable decision-making criteria, thereby comprehensively reflecting the natural endowments, environmental pressures, and socio-economic potential of the tailings dam area.

[0064] Step S201: Based on the current water quality parameters, current water storage, current types and numbers of water pollution sources in the middle and lower reaches of the tailings dam area, determine the current complete hydrological cycle covering both the dry and wet seasons.

[0065] The hydrological cycle is a critical time boundary that determines water resource allocation and pollutant migration control in the remediation plan. In practice, the system uses a network of monitoring wells and sensors to collect real-time data on current water quality parameters (such as pH, dissolved oxygen, and heavy metal content) and current water storage in downstream water bodies. Combined with historical rainfall data provided by meteorological departments, the system analyzes the water volume fluctuation patterns during dry and wet seasons.

[0066] Simultaneously, the types and quantities of current water pollution sources (such as mineral processing wastewater and leachate) are statistically analyzed to comprehensively assess the self-purification capacity and pollution carrying capacity of water bodies in different seasons. For example, if the water volume is large during the high-water season but the number of pollution sources is high, and water quality parameters show that heavy metals exceed the standards, the determined hydrological cycle should focus on the pollutant flushing effect during the rainy season; conversely, during the dry season, attention should be paid to the toxicity enhancement effect caused by water concentration. By determining a complete hydrological cycle covering both the dry and high-water seasons, a precise time scale can be provided for the design of water treatment facilities in subsequent remediation plans, avoiding the failure of remediation projects due to seasonal water level changes.

[0067] Step S202: Determine the current land use benefits based on the current population density, current industry type, and current land use type of the tailings dam area.

[0068] Land use efficiency reflects the economic and social value potential of restored land.

[0069] In one specific implementation, GIS technology combined with census data is used to determine the current population density around the reservoir area. Areas with high population density typically indicate a greater demand for land development. The current industry type (such as agriculture, industry, and tourism) and the current land use type (such as arable land, construction land, and wasteland) determine the direction of industrial integration after restoration.

[0070] For example, in areas with high population density and predominantly agricultural industries, land use efficiency assessments will tend to favor remediation into high-standard farmland or ecological agricultural land; while in areas with low population density and industrial industries, land use efficiency assessments may favor remediation into industrial or warehousing land. The system constructs a land use efficiency assessment model, quantifying these parameters into efficiency indices, thus incorporating economic feasibility considerations from the initial planning stages to ensure that the remediation plan is not only ecologically effective but also economically reasonable.

[0071] Step S203: Determine the current land degradation level of the tailings pond area based on the current land damage area, the current amount of vegetation destruction, and the current soil erosion intensity.

[0072] The degree of land degradation is a direct basis for selecting restoration technologies (such as physical engineering, chemical remediation, or bioremediation). In practice, the system quantifies the current area of ​​land damage through a combination of remote sensing image interpretation and on-site surveys; it also calculates the current amount of vegetation damage, including the number of damaged trees and shrubs or the decrease in vegetation cover; and it uses the Universal Soil Loss Equation (USLE) or on-site monitoring data to calculate the current soil erosion intensity (e.g., mild, moderate, severe erosion). The system pre-determines degradation grading standards; for example, areas with more than 50% of the land damaged, more than 1000 damaged plants, and severe soil erosion are classified as "severely degraded," while areas with lower scores across all indicators are classified as "mildly degraded." Through quantitative assessment, the system can accurately match restoration efforts, avoiding excessive investment in engineering measures in mildly degraded areas and thus preventing resource waste.

[0073] Step S204: Based on the current soil pollution level indicators, current soil nutrient imbalance indicators, and current water pollution impact indicators of the tailings pond area, determine the target pollutant elements causing the pollution.

[0074] Identifying target pollutants is a prerequisite for precise pollution control. In practice, the system analyzes current soil pollution levels, including individual pollution indices for heavy metals and the Nemerow composite pollution index, to identify major soil pollutants. It also analyzes current soil nutrient imbalance indicators, such as nitrogen, phosphorus, and potassium ratio imbalance coefficients and trace element antagonism indices, to determine if nutrient structure problems exist. Finally, it analyzes current water pollution impact indicators, including surface water turbidity, chemical oxygen demand (COD), and heavy metal leaching risk coefficients, to assess the risk of pollutant migration into water bodies. The system then integrates these three types of indicators to extract the elements with the greatest impact on the ecological environment and the highest migration risk as target pollutants. For example, if soil pollution levels show cadmium exceeding standards and water pollution impact indicators show a high risk of cadmium leaching, then cadmium is identified as the target pollutant. This step ensures that subsequent remediation plans are targeted, allowing for the selection of appropriate passivating agents or hyperaccumulating plants for specific pollutants.

[0075] Step S205: Determine the current biodiversity index based on the current vegetation index, current plant coverage, and current biomass of the tailings dam area.

[0076] The biodiversity index reflects the self-sustaining capacity and stability of a regional ecosystem. In practice, the Normalized Difference Vegetation Index (NDVI) is calculated using satellite remote sensing data as the current vegetation index, reflecting the vegetation growth status. Current plant cover is extracted using a hybrid pixel decomposition technique. Current biomass is estimated by combining field sampling data or a light energy utilization model. The system normalizes these three parameters and performs a weighted sum to calculate the current biodiversity index. For example, areas with high vegetation index, large cover, and abundant biomass have a high biodiversity index, indicating a relatively stable ecosystem, where natural restoration or low-intervention measures can be prioritized for restoration. Conversely, areas with low biodiversity index require artificial vegetation reconstruction measures. This index provides ecological baseline data support for the selection of subsequent vegetation restoration schemes.

[0077] Through the steps S201 to S205 above, the system completes the transformation from raw multidimensional data to key assessment indicator results, and constructs a three-dimensional assessment profile including five dimensions: hydrology, land, degradation, pollution, and biology, laying a solid data foundation for the subsequent generation of suitable candidate remediation schemes.

[0078] Optionally, based on the above embodiments, this embodiment details the specific process of determining multiple candidate remediation schemes that are compatible with the results of each evaluation index in step S300. This embodiment aims to establish a precise mapping relationship between the evaluation index results and specific remediation measures, solving the problem of "one-size-fits-all" or reliance on experience-based judgment in traditional remediation scheme formulation, and achieving personalized customization of remediation schemes. Step S300 can be implemented through the following steps.

[0079] Step S301: Based on the current complete hydrological cycle, current land use benefits, current land degradation level, target pollutant elements, and current biodiversity index, determine the dosage of various ecological restoration modifiers and the corresponding fertilization amount required when using different preset ecological restoration vegetation under various preset ecological restoration methods.

[0080] The candidate restoration schemes include ecological restoration methods, types of vegetation to be restored, types of ecological restoration amendments, dosage of ecological restoration amendments, and amount of fertilizer.

[0081] This process essentially involves constructing a multi-dimensional decision matrix. The system has a pre-stored remediation knowledge base, built upon historical remediation cases, plant physiological characteristics, and soil chemical principles. The system uses the five-dimensional evaluation indicators output from step S200 as input variables, searches and matches them within the knowledge base, and outputs specific combinations of remediation parameters.

[0082] The system determines the appropriate ecological restoration method based on the current level of land degradation. Specifically, if the current land degradation level is severe, characterized by serious damage to the soil's physical structure, complete loss of topsoil, or extremely high concentrations of heavy metal pollution, conventional bioremediation or chemical remediation is unlikely to be effective in the short term. In this case, the system will determine the appropriate preset ecological restoration method as either topsoil replacement or physical tillage. Topsoil replacement directly improves the crop growth substrate by covering the land with clean external soil, making it suitable for the extremely harsh environment of tailings pond areas. If the current land degradation level is moderate, the system will determine the appropriate preset ecological restoration method as chemical stabilization, which reduces the bioavailability of heavy metals by adding passivating agents. If the current land degradation level is mild, characterized by relatively intact soil structure but nutrient deficiency, the system will determine the appropriate preset ecological restoration method as biological methods, such as applying organic fertilizer or planting green manure plants, utilizing biological metabolic activities to improve the soil microenvironment. This tiered adaptation strategy effectively avoids resource waste caused by over-remediation or secondary degradation caused by under-remediation.

[0083] The determination of vegetation species for ecological restoration should take into account both current land use benefits and current biodiversity index.

[0084] Specifically, if the current land use benefit assessment indicates that the area is planned as arable land or orchard, and the current biodiversity index is low, the system will prioritize the selection of agricultural or cash crops as the pre-set ecological restoration vegetation, such as corn, soybeans, or specific medicinal herbs, and will also provide corresponding agricultural reclamation technologies. If the current land use benefit assessment indicates that the area is planned as ecological forest or grassland, the system will select drought-resistant and barren-tolerant shrubs or herbaceous plants as the pre-set ecological restoration vegetation, such as Amorpha fruticosa, Alternanthera philoxeroides, or Leymus chinensis. These plants have well-developed root systems that can effectively stabilize the soil and retain water. In addition, for cases where the target pollutant is heavy metals, the system will also select hyperaccumulating plants, such as Centipede Grass (accumulating arsenic) and Sedum aizoon (accumulating cadmium and zinc), utilizing the extractive properties of plants to remove heavy metals from the soil.

[0085] The system primarily relies on the target pollutants and current climate and environmental parameters to determine the types and dosages of ecological restoration and improvement agents, as well as the amount of fertilizer applied.

[0086] Specifically, if the target pollutant is heavy metals such as cadmium and lead, the system will determine the appropriate ecological remediation amendment as biochar, lime, or phosphate passivating agents. Biochar has a porous structure and a large specific surface area, which can adsorb heavy metal ions and improve soil porosity; lime increases the soil pH, promoting the formation of hydroxide precipitates of heavy metals and reducing their mobility. Based on the specific values ​​of heavy metal content in the soil, the system calculates the specific dosage of the amendment using a preset dose-response model.

[0087] For example, when the soil cadmium content is 1.5 mg / kg, the system calculates a biochar application rate of 500-800 kg per acre. If the target pollutant involves nutrient imbalance, the system will determine the appropriate ecological remediation amendment type as organic fertilizer, compound fertilizer, or microbial inoculant, and adjust the fertilization rate according to current climate parameters (such as rainfall and temperature). For example, in areas with abundant rainfall, to prevent nutrient leaching, the system will appropriately increase the frequency of fertilization and reduce the amount of fertilizer applied each time; in arid areas, water-retaining amendments are recommended.

[0088] Through the above implementation steps, the system generated multiple sets of candidate remediation schemes. Each scheme is a specific combination of ecological restoration methods, vegetation types, types and amounts of amendments, and fertilization amounts. For example, Scheme A involves topsoil application, Amorpha fruticosa, biochar (800 kg / mu), and a small amount of compound fertilizer; Scheme B involves chemical stabilization, crops, lime (100 kg / mu), and conventional fertilization. These candidate schemes provide specific input parameters for subsequent effect prediction and optimization, ensuring the scientific validity and operability of the remediation schemes.

[0089] Optionally, based on the above embodiments, this embodiment details the specific process of determining the changes in ecological indicators caused by each candidate remediation scheme in step S400. This embodiment introduces a machine learning model to address the problem that traditional statistical methods struggle to handle high-dimensional, multi-source, heterogeneous data and complex nonlinear relationships between variables, thereby improving prediction accuracy.

[0090] Specifically, step S400 includes the following steps.

[0091] Step S401: Construct a machine learning model. This model is used to characterize the correlation and mapping relationships between complete hydrological cycles, land use benefits, land degradation levels, pollutants, biodiversity indices, climate and environmental parameters, remediation schemes, and changes in the ecological indicators of the remediation schemes.

[0092] The model construction process relies on a large amount of historical data. The system first collects historical complete hydrological cycles, historical land use benefits, historical land degradation levels, historical pollutant elements, and historical biodiversity indices, as well as the corresponding changes in historical ecological indicators caused by historical climate and environmental parameters and various historical restoration schemes.

[0093] These historical data originate from mine restoration archives, academic databases, and long-term field monitoring records. Through cleaning, labeling, and feature engineering of this massive amount of data, a high-quality training dataset is constructed. For model selection, the system can employ algorithms such as random forests, gradient boosting trees, or neural networks. For example, the random forest algorithm, by constructing multiple decision trees and performing ensemble voting, can effectively prevent overfitting and possesses strong nonlinear mapping capabilities; neural networks, through the connection and activation of multiple layers of neurons, can capture deep, complex patterns between data. During model training, cross-validation techniques are used to optimize model hyperparameters, ensuring the model's generalization ability.

[0094] Step S402: Input the current complete hydrological cycle, current land use benefits, current land degradation level, current pollutant elements, current biodiversity index, current climate and environmental parameters, and each candidate remediation scheme into the machine learning model to obtain the changes in ecological indicators for each candidate remediation scheme.

[0095] This process essentially involves using a trained model for inference and prediction. The system packages the evaluation results of various indicators obtained in the above embodiments, the acquired climate parameters, and the specific parameters of each candidate remediation scheme (such as the amount of amendment, vegetation type, etc.) as input feature vectors, which are then input into the machine learning model. The model outputs the predicted changes in ecological indicators through complex internal computational logic.

[0096] The changes in ecological indicators include changes in crop growth indicators and changes in pollutant transport indicators. These two types of indicators quantify the remediation effect from two dimensions: biological growth and environmental migration, respectively.

[0097] Specifically, changes in crop growth indicators include the maximum photosynthetic rate of ecological vegetation, maintenance respiration coefficient, growth respiration coefficient, stomatal conductance, canopy resistance, and root growth rate.

[0098] Maximum photosynthetic rate reflects the ability of vegetation to assimilate carbon dioxide per unit time and is a key parameter for measuring vegetation carbon sequestration potential. Maintenance respiration coefficient and growth respiration coefficient characterize the energy consumed by vegetation to maintain life activities and grow new tissues, respectively, directly affecting biomass accumulation efficiency. Stomatal conductance and canopy resistance reflect the ability of vegetation to exchange water vapor and carbon dioxide with the atmosphere, and are significantly affected by climatic parameters (such as temperature and humidity). Root growth rate determines the ability of vegetation to absorb water and nutrients from the soil, which is particularly important for infertile substrates such as tailings ponds. Machine learning models can accurately predict changes in these physiological parameters based on input amendment dosage and climatic conditions, thereby assessing the growth adaptability of vegetation under specific remediation programs.

[0099] Changes in pollutant transport indicators include soil saturated moisture content, residual moisture content, porosity, bulk density, particle size distribution, and parameters of convective dispersion processes.

[0100] Saturated and residual moisture content characterize the soil's ability to retain and release water, directly affecting the water supply to vegetation; porosity and bulk density reflect the soil's compaction and aeration, serving as important indicators for improving soil physical structure; particle size distribution determines the soil texture and influences hydraulic conductivity; and convective dispersion parameters describe the migration rate and diffusion range of pollutants in soil water, representing core parameters for evaluating the effectiveness of remediation programs in protecting groundwater. By predicting changes in these parameters using machine learning models, the system can quantify the effects of different amendments (such as biochar and topsoil) on soil physicochemical properties, thereby assessing their ability to inhibit pollutant migration.

[0101] Through the above steps, this embodiment utilizes the powerful nonlinear fitting capability of machine learning models to achieve accurate mapping from multidimensional input parameters to specific ecological indicator changes, overcoming the shortcomings of traditional mechanistic models such as difficulty in obtaining parameters and complex calculations, and providing reliable data support for subsequent simulation of restoration effects.

[0102] Based on the above embodiments, this embodiment details the specific process in step S500 of determining the remediation effect indicators of each candidate remediation scheme based on the changes in ecological indicators caused by each candidate remediation scheme. This embodiment aims to solve the problem in traditional methods where the soil remediation process and vegetation growth process are disconnected and the synergistic effect cannot be accurately predicted by constructing a simulation model with two-level coupling connections.

[0103] Specifically, step S500 includes the following steps.

[0104] Step S501: Construct an ecological restoration and vegetation growth simulation model. This model characterizes the effect of changes in ecological indicators of different restoration schemes on the effectiveness of the restoration schemes under different climatic environmental parameters.

[0105] Among them, the ecological restoration and vegetation growth simulation model includes a two-level coupled ecological restoration simulation model and a vegetation growth simulation model.

[0106] The "two-level coupling connection" referred to in this embodiment means that the output of the ecological restoration simulation model serves as the key input parameter of the vegetation growth simulation model, thereby forming a cascaded transmission relationship of "soil environment improvement - vegetation growth response". This design is based on the objective laws of tailings dam ecological restoration: vegetation growth depends on the improvement of the soil matrix environment, and vegetation growth, in turn, affects the physicochemical properties of the soil, with a strong temporal dependence and mutual feedback mechanism between the two. Traditional single models often ignore this mutual feedback, leading to large deviations in prediction results. This embodiment achieves dynamic simulation of the entire ecological restoration process through a two-level coupling mechanism.

[0107] Step S502: Input the changes in pollutant transport indicators and current climate and environmental parameters into the ecological restoration simulation model to simulate the pollutant migration and soil restoration process caused by the changes in pollutant transport indicators under the current climate and environmental parameters, and obtain soil quality change indicator parameters.

[0108] Specifically, the ecological restoration simulation model is primarily responsible for simulating changes in the soil's physicochemical environment. Input parameters include the predicted changes in pollutant transport indicators (such as saturated water content, porosity, and convective dispersion parameters) obtained in the above embodiments, as well as current climate environmental parameters (such as rainfall and evaporation). Based on porous media fluid dynamics and solute transport theory, the model simulates the process of soil structure improvement by amendments (such as biochar and imported soil) and the migration, adsorption, and desorption of pollutants such as heavy metals in soil water under specific climatic conditions. The output results are soil quality change index parameters, including carbon sequestration potential and soil and water conservation efficiency.

[0109] Among them, carbon sequestration potential refers to the ability of remediated soil to retain organic carbon, which is characterized by simulating the accumulation rate and stability of soil organic matter; soil and water conservation efficiency refers to the ability of soil to resist erosion and retain moisture, which is characterized by simulating soil infiltration rate and erosion resistance.

[0110] For example, if changes in pollutant transport indicators show a significant increase in soil porosity, the ecological restoration simulation model will calculate an improvement in soil water retention capacity, thereby outputting a higher soil and water conservation efficiency.

[0111] Step S503: Input the current climate environment parameters, crop growth index changes and soil quality change parameters into the vegetation growth simulation model, simulate the vegetation generation process caused by the changes in crop growth index under the current climate environment parameters, and obtain the vegetation change parameters.

[0112] Specifically, the vegetation growth simulation model is mainly responsible for simulating the establishment and succession of plant communities. Input parameters include the current climate environment parameters obtained in the previous embodiment, predicted changes in crop growth indicators (such as maximum photosynthetic rate and stomatal conductance), and soil quality change parameters output in step S502. Based on the principles of plant physiological ecology, the model simulates the photosynthesis, respiration, dry matter accumulation, and population competition processes of vegetation under improved soil conditions and specific climatic conditions. The output results are vegetation change parameters, including biomass accumulation rate and biodiversity index.

[0113] Among them, the biomass accumulation rate refers to the increase in vegetation dry weight per unit time, reflecting the speed of vegetation recovery; the biodiversity index refers to the richness and evenness of species in the vegetation community, reflecting the stability of the ecosystem.

[0114] For example, if soil quality change parameters show high carbon sequestration potential and good soil and water conservation efficiency, the vegetation growth simulation model will determine that the habitat is suitable for the growth of a variety of plants, thus outputting a high biodiversity index.

[0115] Through the two-stage coupled simulation in steps S502 and S503 above, the system ultimately outputs an indicator of the remediation effectiveness of each candidate remediation scheme under the current climate and environmental parameters. This indicator integrates the degree of soil environmental improvement and vegetation restoration, and can comprehensively reflect the actual ecological benefits of the remediation scheme.

[0116] For example, after two-stage coupled simulation, the candidate schemes of soil replacement and Amorpha fruticosa might output the effect indicators of "20% increase in carbon sink potential and biodiversity index reaching 1.5"; while for the scheme of "chemical stabilization and crops", the effect indicators might output the effect indicators of "15% increase in soil and water conservation efficiency and moderate biomass accumulation rate". These quantitative effect indicators provide a scientific basis for subsequent multi-objective optimization.

[0117] Based on the above embodiments, this embodiment details the specific process of determining the target restoration scheme for the ecologically sensitive area from multiple candidate restoration schemes in step S600. This embodiment aims to solve the multi-objective conflict problem in the process of selecting ecological restoration schemes, where ecological, economic, and social benefits are difficult to balance. By constructing a total benefit objective function and using a genetic algorithm for global optimization, scientific decision-making on restoration schemes can be achieved.

[0118] Specifically, step S600 includes the following steps.

[0119] Step S601: Determine the overall benefit target based on the ecological restoration effect indicators, ecological restoration costs, land use value-added benefits, and the first weight of the ecological restoration effect indicators, the second weight of the ecological restoration costs, and the third weight of the land use value-added benefits.

[0120] Among them, the overall benefit target is positively correlated with the ecological restoration effect indicator, negatively correlated with the ecological restoration cost, and positively correlated with the land use value-added benefit.

[0121] This implementation essentially involves constructing a multi-objective optimization mathematical model. Since ecological restoration effectiveness indicators (such as biodiversity enhancement and pollutant reduction) are typically maximized, ecological restoration costs (such as material and construction costs) are minimized, and land use value enhancement benefits (such as the potential revenue from reclamation into arable or construction land) are maximized, these three objectives often conflict. For example, high-ecological-effect solutions are often costly, while low-cost solutions may have low land use value. Therefore, this embodiment introduces weighting coefficients to normalize the three dimensions of indicators into a single overall benefit objective function.

[0122] Specifically, the formula for calculating the total benefit target can be expressed as: F = w1 E - w2 C + w3 V. Where F is the total benefit target value; E is the normalized ecological restoration effect index, with a value ranging from 0 to 1, where a larger value indicates a better ecological restoration effect; C is the normalized ecological restoration cost, with a value ranging from 0 to 1, where a larger value indicates a higher cost; V is the normalized land use value added benefit, with a value ranging from 0 to 1, where a larger value indicates a higher added benefit; w1 is the first weight, w2 is the second weight, and w3 is the third weight, and w1, w2, and w3 are all real numbers greater than or equal to 0, their specific values ​​reflecting the decision-maker's preference for different benefits.

[0123] Regarding the specific values ​​of the weights, this embodiment provides a flexible configuration mechanism. If the tailings dam area is located in an ecological protection zone or upstream of a water source, and its ecological sensitivity is extremely high, then the ecological restoration effect should be prioritized. In this case, the system can set the first weight w1 to a larger value, for example, w1 ranging from 0.5 to 0.7. The second weight w2 and the third weight w3 can be relatively smaller, for example, w2 ranging from 0.1 to 0.3 and w3 ranging from 0.1 to 0.3. If the tailings dam area is located within urban planning and construction land and the fiscal budget is limited, then cost control and land value enhancement should be considered. In this case, the system can set the second weight w2 and the third weight w3 to larger values, for example, w2 ranging from 0.3 to 0.5 and w3 ranging from 0.3 to 0.5. The first weight w1 can be appropriately reduced. Through this dynamic weight setting mechanism, this invention can adapt to the decision-oriented needs of different restoration scenarios and avoid the one-sidedness of single-objective decision-making in traditional methods.

[0124] Step S602: With the goal of maximizing the overall benefit, the genetic algorithm is used to perform global optimization of the ecological restoration effect indicators, ecological restoration costs, and land use value-added benefits. The candidate restoration schemes corresponding to the ecological restoration effect indicators, ecological restoration costs, and land use value-added benefits that maximize the overall benefit are then determined as the target restoration schemes.

[0125] Genetic algorithms are search algorithms that simulate natural selection and genetic mechanisms. They possess powerful global search capabilities and are particularly suitable for handling complex optimization problems involving multiple parameters and objectives, as illustrated in this embodiment. The specific implementation process is as follows.

[0126] First, encoding is performed. The system treats each candidate remediation scheme as a chromosome, encoding key parameters (such as ecological restoration method, vegetation type, amendment dosage, and fertilizer application rate) into genes. For example, using real-number encoding, "imported soil method" is encoded as 1, and "chemical method" is encoded as 2; the vegetation type "Amorpha fruticosa" is encoded as 1, and "Saprolegnia" as 2; the amendment dosage is directly mapped to a real value. Through this encoding, complex combinations of schemes are transformed into a numerical sequence that can be processed by a computer.

[0127] Secondly, fitness assessment is performed. The system calculates the fitness value of each individual in the population, which is the overall benefit objective F calculated in step S601. Individuals with higher fitness values ​​indicate that their corresponding repair scheme is more effective in terms of overall benefits, and the probability of it being passed on to the next generation is also greater.

[0128] Next, a selection process is performed. The system employs either roulette wheel selection or tournament selection, choosing superior individuals from the current population based on their fitness values. For example, in roulette wheel selection, the probability of individual i being selected is Pi = Fi / (F1, F2, ..., Fn), where Fi is the fitness value of individual i. Individuals with higher fitness have a greater probability of being selected, thus achieving "survival of the fittest."

[0129] Next, a crossover operation is performed. The system randomly pairs the selected individuals and exchanges partial gene fragments according to a preset crossover probability (e.g., 0.7 to 0.9). For example, individual A is coded as [1, 5, 800] (representing the topsoil method, Amorpha fruticosa, 800 kg of plant growth regulator), and individual B is coded as [2, 3, 500] (representing the chemical method, Alternaria alternifolia, 500 kg of plant growth regulator). After crossover, a new individual [1, 3, 800] (representing the topsoil method, Alternaria alternifolia, 800 kg of plant growth regulator) may be generated. This operation can generate new combinations of solutions, expand the search space, and avoid getting trapped in local optima.

[0130] Finally, mutation is performed. The system randomly changes the value of a gene in an individual according to a preset mutation probability (e.g., 0.01 to 0.1). For example, the amount of plant growth regulator may be mutated from 800 to 850, or the plant species may be mutated from *Amorpha fruticosa* to *Leymus chinensis*. Mutation maintains population diversity and prevents the algorithm from converging prematurely.

[0131] The system repeats the fitness assessment, selection, crossover, and mutation operations until a preset termination condition is met (e.g., the number of iterations reaches 100 generations, or the total benefit target value no longer significantly improves for 10 consecutive generations). At this point, the individual with the highest fitness in the population is the optimal solution, and the system decodes it back into specific repair scheme parameters, which are then determined as the final target repair scheme.

[0132] Through the above implementation steps, this embodiment utilizes the global optimization capability of genetic algorithms to quickly select the restoration scheme with the best comprehensive benefits from a vast solution space. This not only ensures the scientific nature of ecological restoration but also takes into account economic feasibility and social value, achieving precise decision-making for the ecological restoration of tailings dam areas.

[0133] Furthermore, based on the above embodiments, this embodiment details the specific process in step S204 of determining the target pollutant element causing pollution based on the current soil pollution level index, current soil nutrient imbalance index, and current water pollution impact index in the tailings pond area. This embodiment aims to establish a precise pollution source apportionment mechanism to identify key polluting factors from complex compound pollution scenarios, solving the problem that traditional methods only focus on single-media pollution while neglecting cross-media migration and synergistic effects.

[0134] Specifically, step S204 includes the following sub-steps.

[0135] Step S2041: Based on the current soil pollution level indicators, extract heavy metal elements whose single pollution index or Nemerow comprehensive pollution index exceeds the preset threshold as the primary soil pollution elements.

[0136] The current soil pollution level indicators include individual pollution indices for heavy metal pollutants and the Nemerow composite pollution index.

[0137] The single pollution index is used to assess the pollution level of a single heavy metal element. It is calculated as the ratio of the measured heavy metal content in the soil to the standard value for that element. The Nemerow composite pollution index, on the other hand, considers both the average and maximum pollution levels of multiple heavy metals, reflecting the overall soil pollution status. The specific values ​​of the system's preset thresholds are determined based on land use planning. For example, for areas planned for arable land, the preset threshold for the single pollution index can be set to 1.0, and the preset threshold for the Nemerow composite pollution index can be set to 0.7; for areas planned for construction land, the preset thresholds can be appropriately relaxed. If the single pollution index of a heavy metal element or its calculated Nemerow composite pollution index exceeds the corresponding preset threshold, the system identifies that heavy metal element as the primary soil pollutant. For example, if monitoring data shows that the single pollution index of cadmium in the soil is 1.5, exceeding the preset threshold of 1.0, then cadmium is identified as the primary soil pollutant. This step can quickly identify the heavy metal pollutants that contribute the most to soil environmental quality.

[0138] Step S2042: Based on the current soil nutrient imbalance indicators, extract the chemical elements that cause abnormal nitrogen, phosphorus, and potassium ratio imbalance coefficients or micronutrient antagonism indices as nutrient stress elements.

[0139] Among the current indicators of soil nutrient imbalance are the nitrogen, phosphorus, and potassium ratio imbalance coefficient and the micronutrient antagonism index. The soil in tailings pond areas often suffers from nutrient structure imbalance due to mineral processing reagent residues or weathering processes. The nitrogen, phosphorus, and potassium ratio imbalance coefficient reflects the proportional relationship between macronutrients. For example, if the nitrogen, phosphorus, and potassium ratio imbalance coefficient exceeds a preset normal range threshold (e.g., 0.8 to 1.2), it indicates nutrient stress.

[0140] The micronutrient antagonism index reflects the inhibitory effects between micronutrients such as iron, manganese, copper, and zinc. If the antagonism index exceeds a preset threshold, it indicates that the excessive presence of certain micronutrients is inhibiting the plant's absorption of other essential nutrients. The system calculates these indicators and extracts the specific chemical elements causing the abnormalities as nutrient stress elements. For example, if excessive copper is detected causing an abnormal micronutrient antagonism index, copper is identified as a nutrient stress element. This step helps identify potential limiting factors that, while not highly toxic, severely impact vegetation growth.

[0141] Step S2043: Based on the current water pollution impact indicators, extract the excessive dissolved heavy metal ions in surface water or groundwater as migrating pollutants in the water environment.

[0142] Among them, the current water pollution impact indicators include surface water turbidity, chemical oxygen demand, and heavy metal leaching risk coefficient.

[0143] The heavy metal leaching risk factor is calculated based on parameters such as soil pH, redox potential, and rainfall intensity, and is used to characterize the potential risk of heavy metals migrating from soil to water bodies. The system monitors the concentration of dissolved heavy metal ions in surface water and groundwater. If the concentration of a certain heavy metal ion exceeds the limit in the environmental quality standards for surface water or groundwater, or if the heavy metal leaching risk factor exceeds a preset safety threshold (e.g., a risk factor greater than 0.5), the corresponding waterborne pollutant element is extracted. For example, if arsenic ion concentration exceeds the standard in groundwater, and the heavy metal leaching risk factor shows that arsenic has high mobility, then arsenic is identified as a waterborne pollutant element. This step focuses on the migration and diffusion capacity of pollutants, providing a basis for blocking pollution diffusion in subsequent remediation plans.

[0144] Step S2044: Pollutants that are correlated with primary soil pollutants, nutrient stressors, and water environment migrating pollutants are identified as target pollutants for the tailings dam area.

[0145] The core of this implementation step lies in identifying the "correlationships" between polluting elements in different media. These correlations refer to the simultaneous occurrence of the same element in different media, or the geochemical characteristics of associated, symbiotic, or co-migrating relationships between different elements.

[0146] Specifically, if an element is identified as both a primary soil pollutant and a waterborne pollutant, it indicates that the element poses a threat in both soil and water bodies, exhibiting cross-media pollution characteristics, and the system identifies it as a target pollutant. For example, if cadmium is both a primary soil pollutant and a waterborne pollutant, then cadmium is identified as a target pollutant.

[0147] Furthermore, if a nutrient stress element shows a significant positive correlation with the primary soil pollutant (e.g., through the Pearson correlation coefficient test), indicating that an excess of that element has led to soil degradation accompanied by heavy metal pollution, the system will also identify it as a target pollutant. Through this correlation analysis, the system can eliminate accidental or localized pollution indicators and accurately pinpoint the key pollutants with the most widespread impact and highest risk on the entire reservoir ecosystem, thereby ensuring that subsequent remediation plans can achieve the effects of "source control, process interruption, and comprehensive treatment."

[0148] To achieve the above functions, the tailings dam ecological restoration scheme determination device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art will readily recognize that, based on the algorithmic steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0149] This disclosure also provides an embodiment such as Figure 2 The device shown is for determining the ecological restoration scheme of the tailings dam. The device includes the following units.

[0150] This embodiment provides a device for determining ecological restoration schemes for tailings ponds. This device is used to execute the tailings pond ecological restoration scheme determination method in any of the above embodiments. Through modular design, this device integrates complex data processing, evaluation, prediction, and decision-making logic, achieving automation and intelligence in determining tailings pond ecological restoration schemes. Figure 2 As shown, the device specifically includes a data processing unit 21, an evaluation unit 22, a first determination unit 23, a second determination unit 24, a third determination unit 25, and a repair unit 26.

[0151] The data processing unit 21 is configured to acquire multidimensional indicator data of the tailings dam area; and to identify ecologically sensitive areas within the tailings dam area; and to determine the current climate and environmental parameters of the ecologically sensitive areas; wherein the multidimensional indicator data includes natural resource indicator parameters, ecological environment indicator parameters, and economic indicator parameters.

[0152] Specifically, the data processing unit serves as the data entry point and fundamental support module for the entire device. This unit connects to external data sources (such as remote sensing satellites, weather stations, sensor networks, and GIS databases) via wired or wireless network interfaces to collect real-time or periodic data on natural resource indicators (such as water reserves and land resource types), ecological environment indicators (such as vegetation cover and soil heavy metal content), and economic indicators (such as surrounding population density and industrial layout) of the tailings pond area. At the hardware level, the data processing unit may include a data acquisition card, a communication module, and a preprocessing chip for format conversion, noise reduction, and standardization of the raw data. For example, the data processing unit executes step S100 in the above embodiment, using remote sensing imagery equipment to acquire images and identify ecologically sensitive areas, while simultaneously retrieving current climate and environmental parameters from the meteorological database. This unit addresses the problems of scattered data sources and inconsistent formats in traditional methods, providing a high-quality data foundation for subsequent assessments.

[0153] Evaluation unit 22 is configured to evaluate the indicator data of each dimension in the multidimensional indicator data and obtain the evaluation indicator results of each dimension indicator data.

[0154] Specifically, the assessment unit is one of the core computing modules of the device, and it has a pre-built assessment algorithm model. This unit receives standardized data output from the data processing unit and performs quantitative assessments from dimensions such as hydrology, land, degradation, pollution, and biology. At the hardware level, the assessment unit can consist of a high-performance processor (such as a CPU or GPU) and a memory storing the assessment program. For example, the assessment unit executes step S200 and its corresponding detailed steps in the above embodiment, calculating the complete hydrological cycle based on water quality parameters and water storage capacity, and calculating the degradation level based on the area of ​​land damage. This unit, through multi-dimensional comprehensive assessment, avoids the one-sidedness of single-indicator evaluation and ensures the comprehensiveness and objectivity of the assessment results.

[0155] The first determining unit 23 is configured to determine multiple candidate repair solutions that are compatible with the results of each evaluation metric.

[0156] Specifically, the first determining unit is a logical module for generating solutions, internally storing a remediation knowledge base or rule engine. This unit receives the evaluation index results output by the evaluation unit and generates multiple candidate remediation solutions by retrieving the knowledge base or running a matching algorithm. For example, the first determining unit executes step S300 and related detailed steps in the above embodiment, matching remediation methods (such as topsoil replacement or biological methods) according to the degree of degradation, and matching the type of amendment according to the target pollutant element. This unit can quickly generate diverse alternative solutions, overcoming the limitations of human experience-based decision-making and improving the efficiency and scientific rigor of solution generation.

[0157] The second determining unit 24 is configured to determine the amount of change in ecological indicators caused by each candidate remediation scheme under the results of various assessment indicators and current climate and environmental parameters.

[0158] Specifically, the second determining unit is the core module for effect prediction, integrating a pre-trained machine learning model. This unit takes the evaluation index results, climate parameters, and candidate scheme parameters as input, calls the machine learning model to perform inference and prediction, and outputs the changes in ecological indicators. At the hardware level, the second determining unit can be configured with a dedicated AI acceleration chip (such as an NPU) to improve prediction speed. For example, the second determining unit executes step S400 in the embodiment and the detailed steps further defined in related embodiments to predict changes in crop growth indicators and pollutant transport indicators. This unit utilizes artificial intelligence technology to process high-dimensional nonlinear data, achieving accurate prediction of remediation effects and solving the problems of difficult parameter acquisition and computational complexity in traditional mechanistic models.

[0159] The third determining unit 25 is configured to determine the restoration effect indicators of each candidate restoration scheme based on the changes in ecological indicators caused by each candidate restoration scheme.

[0160] Specifically, the third determining unit is a simulation module that integrates ecological restoration and vegetation growth simulation models. This unit receives the changes in ecological indicators output by the second determining unit, combines them with climate parameters, runs a two-level coupled simulation model, and outputs the effectiveness indicators of the restoration plan. For example, the third determining unit executes step S500 and the detailed steps further defined in the above embodiment to simulate pollutant migration, soil restoration, and vegetation growth processes, obtaining effectiveness indicators such as carbon sequestration potential and biodiversity index. This unit, through dynamic simulation technology, intuitively demonstrates the long-term ecological benefits of the restoration plan, providing a visualized quantitative basis for decision-making.

[0161] The restoration unit 26 is configured to determine the target restoration scheme for the ecologically sensitive area from multiple candidate restoration schemes based on the ecological restoration effect indicators, ecological restoration costs, and land use value-added benefits of each candidate restoration scheme.

[0162] Specifically, the restoration unit is the final decision-making module, which integrates optimization algorithms (such as genetic algorithms). This unit receives the effect indicators output by the third determination unit and, in conjunction with a cost database and a land use value assessment model, calculates the overall benefit objective and determines the optimal solution through global optimization. For example, in the implementation embodiment, step S600 and the detailed steps further defining step S600 utilize a genetic algorithm to perform multi-objective optimization of ecological, economic, and social benefits. This unit comprehensively considers multiple benefit indicators, achieving scientific optimization of the restoration scheme and ensuring the optimal balance between ecological feasibility, economic rationality, and social benefits in the decision-making results.

[0163] Through the collaborative work of the aforementioned units, the tailings dam ecological restoration scheme determination device provided in this embodiment achieves fully automated processing from data acquisition, current status assessment, scheme prediction to final decision-making. This device not only improves the efficiency and accuracy of ecological restoration scheme formulation but also significantly enhances the scientific rigor and foresight of decision-making by introducing machine learning and simulation technologies, providing a powerful technical support tool for the ecological restoration of tailings dam areas.

[0164] It should be understood that the "unit" described in this embodiment can be a physical module implemented by hardware circuits (such as ASIC, FPGA), a functional module implemented by a processor executing software programs, or a combination of both. This tailings dam ecological restoration scheme determination device can execute the tailings dam ecological restoration scheme determination method of any of the above embodiments or implementation methods.

[0165] Regarding the apparatus in the above embodiments, the specific manner in which each unit module performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0166] Figure 3 This is a schematic diagram of a control device provided in this application. Figure 3 The control device 50 may include at least one first processor 501 and a memory 503 for storing processor-executable instructions. The first processor 501 is configured to execute instructions in the memory 503 to implement the tailings dam ecological restoration scheme determination method in the following embodiments.

[0167] In addition, the control device 50 may also include a communication bus 502, at least one communication interface 504, an input device 506, and an output device 505.

[0168] The first processor 501 may be a processor (central processing unit, CPU), a microprocessor unit, an ASIC, or one or more integrated circuits for controlling the execution of programs according to the present application.

[0169] The communication bus 502 may include a path for transmitting information between the aforementioned components.

[0170] Communication interface 504 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0171] Input device 506 is used to receive input signals and output device 505 is used to output signals.

[0172] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed discs, laser discs, optical discs, digital universal discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory may exist independently and be connected to the processing unit via a bus. Memory may also be integrated with the processing unit.

[0173] The memory 503 stores instructions for executing the scheme of this application, and the execution is controlled by the first processor 501. The first processor 501 executes the instructions stored in the memory 503 to realize the functions of the method of this application.

[0174] In a specific implementation, as one example, the first processor 501 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 in the CPU.

[0175] In a specific implementation, as one example, the control device 50 may include multiple processors, such as... Figure 3 The first processor 501 and the second processor 507 are described. Each of these processors can be a single-core processor or a multi-core processor. A processor here can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0176] The control device, such as Figure 3 The diagram includes a first processor 501 and a memory 503 for storing executable instructions of the first processor 501. The first processor 501 is configured to execute the executable instructions to implement the tailings dam ecological restoration scheme determination method as described in any of the possible embodiments above. Since the same technical effects can be achieved, further details are omitted here to avoid repetition.

[0177] This application also provides a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by the processor of a tailings dam ecological restoration scheme determination device or control device, the tailings dam ecological restoration scheme determination device or control device can perform the tailings dam ecological restoration scheme determination method as described in any of the above possible embodiments. And it can achieve the same technical effect; to avoid repetition, it will not be described again here.

[0178] This application also provides a computer program product, including a computer program or instructions, which are executed by a processor as described in any of the possible implementations above for the tailings dam ecological restoration scheme determination method. This achieves the same technical effect, and to avoid repetition, it will not be described again here.

[0179] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0180] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for determining an ecological restoration scheme for a tailings dam, characterized in that, The method includes: The method involves acquiring multidimensional indicator data of the tailings dam area; identifying ecologically sensitive areas within the tailings dam area; and determining the current climate and environmental parameters of the ecologically sensitive areas. The multidimensional indicator data includes natural resource indicators, ecological environment indicators, and economic indicators. The indicator data of each dimension in the multidimensional indicator data are evaluated to obtain the evaluation indicator results of each dimension indicator data. Identify multiple candidate remediation schemes that fit the results of each evaluation metric; Determine the changes in ecological indicators caused by each candidate remediation scheme under the results of each assessment indicator and the current climate and environmental parameters; Based on the changes in ecological indicators caused by each candidate restoration scheme, determine the restoration scheme effectiveness indicators for each candidate restoration scheme. Based on the ecological restoration effect indicators, ecological restoration costs, and land use value enhancement benefits of each candidate restoration scheme, the target restoration scheme for the ecologically sensitive area is determined from multiple candidate restoration schemes.

2. The method according to claim 1, characterized in that, Identifying ecologically sensitive areas within the tailings dam area includes: Remote sensing images of the ecological environment of the tailings dam area were collected using remote sensing imaging equipment. Based on remote sensing image recognition technology and multi-scale recognition mechanism, the regions in the ecological environment remote sensing image that represent the tailings dam area with ecological index parameters lower than the preset ecological index are identified and aggregated to obtain the ecologically sensitive area; wherein, the ecological index parameters include vegetation coverage, soil health index parameters and biodiversity index parameters.

3. The method according to claim 1, characterized in that, The evaluation of the indicator data in each dimension of the multidimensional indicator data to obtain the evaluation indicator results for each dimension of the indicator data includes: Based on the current water quality parameters, current water storage, current types and quantities of water pollution sources in the middle and lower reaches of the tailings dam area, the current complete hydrological cycle covering both the dry and wet seasons is determined. Furthermore, the current land use benefits are determined based on the current population density, current industry type, and current land use type of the tailings dam area; Furthermore, based on the current land damage area, the current amount of vegetation destruction, and the current soil erosion intensity of the tailings pond area, the current land degradation level of the tailings pond area is determined; Furthermore, based on the current soil pollution level indicators, current soil nutrient imbalance indicators, and current water pollution impact indicators of the tailings pond area, the target pollutant elements causing the pollution are determined; Furthermore, the current biodiversity index is determined based on the current vegetation index, current plant coverage, and current biomass of the tailings pond area.

4. The method according to claim 1, characterized in that, The determination of multiple candidate repair schemes that fit the results of each evaluation metric includes: Based on the current complete hydrological cycle, the current land use benefits, the current land degradation level, the target pollutant element, and the current biodiversity index, determine the dosage of various ecological restoration modifiers and the corresponding fertilization amount required when using different preset ecological restoration vegetation under various preset ecological restoration methods. The candidate restoration schemes include ecological restoration methods, types of vegetation to be restored, types of ecological restoration amendments, dosage of ecological restoration amendments, and amount of fertilizer.

5. The method according to any one of claims 1 to 4, characterized in that, The determination of the changes in ecological indicators caused by each candidate remediation scheme under the results of various assessment indicators and the current climate and environmental parameters includes: The current complete hydrological cycle, current land use benefits, current land degradation level, current pollutant elements and current biodiversity index, current climate and environmental parameters and each candidate remediation scheme are input into the machine learning model to obtain the changes in the ecological indicators for each candidate remediation scheme. The machine learning model is trained based on historical complete hydrological cycles, historical land use benefits, historical land degradation levels, historical pollutants and historical biodiversity indices, as well as the corresponding changes in historical ecological indicators caused by historical climate and environmental parameters and various historical restoration schemes. The machine learning model represents the correlation and mapping relationship between the complete hydrological cycle, land use benefits, land degradation level, pollutants, biodiversity index, climate and environmental parameters, remediation schemes, and changes in the ecological indicators of the remediation schemes. The changes in the ecological indicators include changes in crop growth indicators and changes in pollutant transport indicators; the changes in crop growth indicators include the maximum photosynthetic rate, maintenance respiration coefficient, growth respiration coefficient, stomatal conductance, canopy resistance, and root growth rate of ecological vegetation; the changes in pollutant transport indicators include soil saturated moisture content, residual moisture content, porosity, bulk density, particle size composition, and convective dispersion parameters.

6. The method according to claim 5, characterized in that, The determination of the restoration effectiveness indicators for each candidate restoration scheme based on the changes in ecological indicators caused by each candidate restoration scheme includes: The changes in ecological indicators caused by each candidate restoration scheme and the current climate and environmental parameters are input into the ecological restoration and vegetation growth simulation model to obtain the restoration effect index of each candidate restoration scheme under the current climate and environmental parameters. The ecological restoration and vegetation growth simulation model characterizes the changes in ecological indicators of different restoration schemes under the climate and environmental parameters, and the resulting restoration effect index. The ecological restoration and vegetation growth simulation model includes a two-level coupled ecological restoration simulation model and a vegetation growth simulation model. The ecological restoration and vegetation growth simulation model performs the following operations: The changes in pollutant transport indicators and the current climate and environmental parameters are input into the ecological restoration simulation model to simulate the pollutant migration and soil restoration process that causes the changes in pollutant transport indicators under the current climate and environmental parameters, obtaining soil quality change indicator parameters; the current climate and environmental parameters, the changes in crop growth indicators, and the soil quality change parameters are input into the vegetation growth simulation model to simulate the vegetation generation process that causes the changes in crop growth indicators under the current climate and environmental parameters, obtaining vegetation change parameters; the soil quality change indicator parameters include carbon sequestration potential and soil and water conservation efficiency; the vegetation change parameters include biomass accumulation rate and biodiversity index.

7. The method according to any one of claims 1 to 4, characterized in that, Based on the ecological restoration effect indicators, ecological restoration costs, and land use value enhancement benefits of each candidate restoration scheme, a target restoration scheme for the ecologically sensitive area is determined from multiple candidate restoration schemes, including: The overall benefit target is determined based on the ecological restoration effect indicators, ecological restoration costs, land use value enhancement benefits, and the first weight of the ecological restoration effect indicators, the second weight of the ecological restoration costs, and the third weight of the land use value enhancement benefits; the overall benefit target is positively correlated with the ecological restoration effect indicators, negatively correlated with the ecological restoration costs, and positively correlated with the land use value enhancement benefits, respectively. With the goal of maximizing the overall benefit, a genetic algorithm is used to perform global optimization of ecological restoration effect indicators, ecological restoration costs, and land use value-added benefits. The candidate restoration schemes corresponding to the ecological restoration effect indicators, ecological restoration costs, and land use value-added benefits that maximize the overall benefit are then determined as the target restoration scheme.

8. The method according to any one of claims 1 to 4, characterized in that, The determination of target pollutant elements causing pollution based on the current soil pollution level, current soil nutrient imbalance, and current water pollution impact indicators in the tailings dam area specifically includes: Based on the current soil pollution level indicators, heavy metal elements whose single pollution index or Nemerow comprehensive pollution index exceeds a preset threshold are extracted as the primary soil pollution elements. Based on the current soil nutrient imbalance indicators, chemical elements that cause abnormal nitrogen, phosphorus, and potassium ratio imbalance coefficients or micronutrient antagonism indices are extracted as nutrient stress elements. Based on the current water pollution impact indicators, excess dissolved heavy metal ions in surface water or groundwater are extracted as migrating pollutants in the water environment. Pollutants that are correlated with the primary soil pollutants, the nutrient stress elements, and the water environment migration pollutants are identified as target pollutants for the tailings dam area. The current soil pollution level indicators include individual pollution indices for heavy metal pollutants and the Nemerow composite pollution index; the current soil nutrient imbalance indicators include the nitrogen, phosphorus, and potassium ratio imbalance coefficient and the trace element antagonism index; and the current water pollution impact indicators include surface water turbidity, chemical oxygen demand, and heavy metal leaching risk coefficient.

9. A device for determining an ecological restoration scheme for a tailings dam, characterized in that, The device includes: The data processing unit is configured to acquire multidimensional indicator data of the tailings dam area; and to determine the ecologically sensitive areas within the tailings dam area; and to determine the current climate and environmental parameters of the ecologically sensitive areas; wherein the multidimensional indicator data includes natural resource indicator parameters, ecological environment indicator parameters, and economic indicator parameters. The evaluation unit is configured to evaluate the indicator data of each dimension in the multidimensional indicator data to obtain the evaluation indicator results of each dimension indicator data. The first determining unit is configured to determine multiple candidate repair solutions that are compatible with the results of each evaluation metric. The second determining unit is configured to determine the amount of change in ecological indicators caused by each candidate remediation scheme under the results of each evaluation index and the current climate and environmental parameters. The third determining unit is configured to determine the restoration effect indicators of each candidate restoration scheme based on the changes in ecological indicators caused by each candidate restoration scheme. The restoration unit is configured to determine the target restoration scheme for the ecologically sensitive area from multiple candidate restoration schemes based on the ecological restoration effect indicators, ecological restoration costs, and land use value-added benefits of each candidate restoration scheme.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for determining the ecological restoration scheme of the tailings dam according to any one of claims 1 to 8.

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