A biomimetic ecological restoration method and system for open-pit limestone mines

Abstract

This invention provides a biomimetic ecological restoration method and system for open-pit limestone mines, belonging to the field of ecological restoration technology. The method includes: acquiring mining area data; sequentially performing soil and microbial synergistic restoration, biomimetic configuration of plant communities, coordinated regulation of animals and microorganisms, and near-natural topography and hydrology reshaping; and dynamically monitoring and adaptively regulating the restoration area. This invention, through the integrated application of a systematic biomimetic restoration process and multiple intelligent algorithms, achieves synergistic optimization and dynamic management of multiple ecological elements such as soil, vegetation, animals, microorganisms, and hydrogeomorphology. It effectively solves the technical problems of isolated elements, lack of systematic synergy, and lack of dynamic regulation capabilities in traditional restoration methods, and can efficiently construct a stable and self-sustaining near-natural mining area ecosystem.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology, and in particular to a biomimetic ecological restoration method and system for open-pit limestone mines. Background Technology

[0002] Open-pit limestone mining activities are often accompanied by severe ecological damage, leading to vegetation loss, soil degradation, water and soil erosion, and biodiversity loss. To address these problems, traditional mine ecological restoration methods have emerged, typically involving soil improvement, afforestation, and terrain reshaping. These methods can improve the appearance of mining areas to some extent and restore some vegetation cover.

[0003] However, existing restoration schemes fail to systematically integrate and simulate the complex synergistic relationships and dynamic succession processes among various elements (soil, plants, animals, microorganisms, topography, and hydrology) in natural ecosystems. This results in restoration ecosystems with simple structures, fragile functions, and poor stability, making long-term self-sustainability difficult. Specifically, this manifests in the following ways: soil improvement relies on chemical methods, neglecting the crucial role of microbial communities; vegetation configuration lacks scientific planning for inter-species synergistic succession; the restoration process lacks proactive introduction and coordinated regulation of biological elements such as animals and microorganisms; topographic reshaping and hydrological design are divorced from natural landform characteristics; and the entire restoration process emphasizes construction over monitoring, failing to dynamically adjust and optimize based on real-time ecological feedback. Summary of the Invention

[0004] To address the technical problem of lack of systematic coordination and dynamic regulation in ecosystem restoration in the aforementioned background technologies, this invention provides a biomimetic ecological restoration method and system for open-pit limestone mines by integrating multi-level biomimetic configuration, multi-agent collaborative optimization, and information-based dynamic monitoring and regulation. The aim is to construct a near-natural ecosystem that is structurally complete, functionally stable, and capable of self-evolution and maintenance.

[0005] To achieve the above objectives, the first aspect of the present invention provides a biomimetic ecological restoration method for open-pit limestone mines, comprising: Obtain soil, topography, and ecological data of the mine to be restored; Based on the data, the soil in the mining area was reconstructed and treated with microorganisms for co-remediation to obtain the first remediation area; In the first restoration area, a biomimetic configuration of plant communities is performed to obtain a second restoration area; Within the second remediation area, a combined animal introduction and microbial regulation treatment was implemented to obtain the third remediation area; The third restoration area was subjected to near-natural terrain reshaping and hydrological regulation treatment to obtain the fourth restoration area; The fourth restoration area is dynamically monitored and adaptively adjusted based on the monitoring results to complete the ecological restoration of the mining area.

[0006] Furthermore, the aforementioned soil reconstruction and co-remediation treatment with microorganisms in the mining area includes: Grid sampling was conducted on the soil in the mining area to obtain soil property data; The soil property data is input into an LSTM-based soil amendment prediction model for processing to obtain an optimized soil amendment ratio. Soil improvement was carried out according to the specified amendment ratio, and the combination of microbial agents was optimized based on the NSGA-II multi-objective optimization algorithm. The optimized microbial inoculant was applied to the improved soil.

[0007] Furthermore, the process of performing biomimetic configuration of plant communities includes: Obtain data on the topographic conditions of the mining area and the growth characteristics of pioneer and successor plants; The terrain conditions and growth characteristics data are input into the vegetation succession simulation algorithm to simulate the changes in vegetation coverage under different plant configuration schemes. The simulation results are processed using a genetic algorithm to optimize the plant community structure and planting density with the goals of maximizing biomass, minimizing maintenance costs, and maximizing carbon sequestration.

[0008] Furthermore, the implementation of the animal introduction and microbial coordinated regulation treatment includes: Acquire data on the behavior and habitat preferences of local beneficial insects and soil animals; The behavioral and habitat preference data are processed using the Transformer model to predict the migration routes and habitat preferences of the target animals and optimize the release plan. Construct a regional food web energy flow model and identify key species based on graph neural networks; Based on the identification results of the key species, the spraying and inoculation strategies of the microbial inoculum are dynamically adjusted.

[0009] Furthermore, the near-natural terrain reshaping and hydrological regulation treatment includes: Obtain digital elevation model data for the mining area; The digital elevation model data is input into DEM-GAN for processing to generate a terrain design scheme that conforms to the natural landform characteristics. Based on the aforementioned terrain design scheme, the layout path of vegetated swales and artificial wetlands is optimized using the ant colony algorithm to improve runoff interception efficiency.

[0010] Furthermore, the dynamic monitoring and adaptive adjustment based on the monitoring results include: The sensor network acquires real-time monitoring data of soil moisture, temperature, and pH value in the fourth remediation area, and the camera acquires image data of vegetation coverage and wildlife activity. The real-time monitoring data and image data are transmitted to the central control system, and a dynamic model of the restoration area is constructed using digital twin technology. Based on the model, the risk of geological disasters is predicted. Based on multi-agent reinforcement learning technology, the collaborative management strategy for soil, plants, animals, microorganisms and hydrology is dynamically adjusted according to the monitoring data and risk prediction results. Blockchain technology is used to record key data and carbon sequestration information during the restoration process.

[0011] The second aspect of the present invention provides a biomimetic ecological restoration system for open-pit limestone mines, comprising: a data acquisition module for acquiring soil, topography and ecological data of the mine to be restored; The soil reconstruction and microbial remediation module is used to reconstruct the soil in the mining area based on the data and to perform co-remediation treatment with microorganisms to obtain a first remediation area; The plant community biomimetic configuration module is used to perform plant community biomimetic configuration processing on the first restoration area to obtain a second restoration area; The animal-microbe linkage regulation module is used to implement animal introduction and microbe linkage regulation treatment in the second repair area to obtain the third repair area; The near-natural terrain reshaping and hydrological regulation module is used to perform near-natural terrain reshaping and hydrological regulation on the third restoration area to obtain the fourth restoration area. The dynamic monitoring and adaptive control module is used to dynamically monitor the fourth restoration area and adaptively control it based on the monitoring results to complete the ecological restoration of the mining area.

[0012] A third aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the biomimetic ecological restoration method for open-pit limestone mines as described in the first aspect of the present invention.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the biomimetic ecological restoration method for open-pit limestone mines as described in the first aspect of the present invention.

[0014] A fifth aspect of the present invention provides a computer program product comprising software code, wherein the program in the software code performs the steps of the biomimetic ecological restoration method for open-pit limestone mines as described in the first aspect of the present invention.

[0015] Compared with existing technologies, the biomimetic ecological restoration method and system for open-pit limestone mines provided by this invention have the following beneficial effects: (1) This invention simulates the construction and succession process of a natural ecosystem by sequentially carrying out soil-microbe synergistic restoration, biomimetic configuration of plant communities, animal-microbe linkage regulation, near-natural topographic-hydrological reshaping and dynamic monitoring feedback, realizing the systematic integration and synergistic effect of multiple ecological elements, and significantly improving the structural complexity and functional stability of the restored ecosystem.

[0016] (2) The various artificial intelligence algorithms introduced in this invention, such as LSTM model, NSGA-II algorithm, genetic algorithm, Transformer model, graph neural network (GNN), generative adversarial network (DEM-GAN), ant colony algorithm (ACO) and multi-agent reinforcement learning (MARL), respectively conduct data-driven modeling and optimization of soil improvement, fungicide combination, plant configuration, animal behavior, terrain design, water conservancy layout and system coordination strategy, realize the precision, personalization and optimization of the remediation scheme, effectively reduce costs and improve the remediation efficiency of each link.

[0017] (3) This invention constructs a sensing, digital twin early warning system based on sensor networks, digital twin early warning system and feedback layer. A closed-loop control mechanism encompassing analysis, decision-making, execution, and feedback. By real-time monitoring of the environment and biological elements in the remediation area, utilizing digital twin models for risk prediction, and leveraging MARL technology to dynamically adjust management strategies, dynamic and adaptive management of the remediation process is achieved. This enables timely response to unforeseen issues and ensures the long-term sustainability of remediation results.

[0018] (4) This invention utilizes the characteristics of blockchain technology to record key data and carbon sink information in the restoration process, realizing the tamper-proof storage and traceability of restoration data, and providing reliable technical support for the quantitative assessment and market-based trading of ecological benefits (such as carbon sinks). Attached Figure Description

[0019] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0020] Figure 1 The flowchart illustrates the biomimetic ecological restoration method for open-pit limestone mines provided in Embodiment 1 of the present invention.

[0021] Figure 2 This is a flowchart of soil remodeling provided in Embodiment 1 of the present invention.

[0022] Figure 3 This is a flowchart of the microbial implementation process provided in Embodiment 1 of the present invention.

[0023] Figure 4 This is a flowchart of animal introduction provided in Embodiment 1 of the present invention.

[0024] Figure 5 This is a flowchart of microbial regulation provided in Embodiment 1 of the present invention.

[0025] Figure 6 This is a schematic diagram of the plant configuration provided in Embodiment 1 of the present invention.

[0026] Figure 7 This is a diagram illustrating the architecture of a biomimetic ecological restoration system for open-pit limestone mines, as provided in Embodiment 2 of the present invention. Detailed Implementation

[0027] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0028] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0030] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0031] Example 1 like Figure 1 This embodiment provides a biomimetic ecological restoration method for open-pit limestone mines, including: S1: Obtain soil, topography, and ecological data of the mine to be restored; Among them, soil data focuses on detecting typical characteristics caused by the weathering of limestone parent material and mining activities, such as high pH value (usually >8.0), high calcium ion concentration, low organic matter content (<1%), and potential heavy metal (such as lead and cadmium) pollution; topographic data uses drone aerial photography or lidar scanning to generate a high-precision digital elevation model (DEM), paying particular attention to the geometry and stability of steep slopes, platforms, mine pits and waste rock piles formed by mining; ecological data includes a list of native plants remaining in and around the mining area (especially alkali-tolerant and drought-tolerant species), local insect and soil animal population surveys, and historical climate data (average annual temperature, precipitation, evaporation), etc.

[0032] S2: Based on the data, the soil in the mining area is reconstructed and treated with microorganisms for remediation to obtain the first remediation area; Specifically, the soil in the mining area is reconstructed and treated in synergistic microbial remediation, including: Grid sampling was conducted on the soil in the mining area to obtain soil property data; The soil property data is input into an LSTM-based soil improvement prediction model for processing to obtain an optimized soil amendment ratio. Based on the degree of degradation of the limestone mine, three stages are defined for targeted improvement. The recommended soil amendment ratio, calculated per acre, includes: (1) Severe degradation stage: Acidity adjustment: 120-150 kg of sulfur powder + 300-400 kg of humic acid; Organic matter supplementation: 3000-4000 kg of well-rotted cow manure + 80-100 kg of biochar; Nutrient balance: 100-120 kg superphosphate + 30-40 kg urea + dolomite powder; Structural improvement: 50-60 kg perlite + 200-300 kg weathered sand; Biological aid: 5-8 kg of phosphorus and potassium solubilizing bacterial agent.

[0033] (2) Moderate improvement period: Acidity adjustment: Approximately 300 kg of peat moss; Organic matter supplementation: approximately 1500 kg of organic fertilizer + approximately 800 kg of well-rotted straw; Nutrient balance: 80-100kg of compound fertilizer + 50-60kg of superphosphate; Structural improvement: Vermiculite 40-50kg; Biological aid: 3-5 kg ​​of rhizobium inoculant + 5-6 kg of humus inoculant; Other: 300-400 kg of crushed coal gangue can be added to help adjust the pH and utilize mine waste.

[0034] (3) Near-natural stable period: Organic matter supplementation: 1500-2000 kg / mu of local green manure + 50-80 kg / mu of humic acid; Nutrient balance: 30-50 kg / mu of slow-release compound fertilizer; Biological aid: 3-5 kg / mu of compound microbial inoculant; Other: 50-100 kg / mu of wood ash to supplement potassium and regulate the microbial community.

[0035] Soil was improved according to the ratio of the amendment, and the combination of microbial agents was optimized based on the NSGA-II multi-objective optimization algorithm. The optimization objectives were set as follows: survival rate and functional activity of the agents, tolerance to calcium ions, and efficiency in promoting phosphorus release under the high pH environment of limestone. The optimized microbial inoculant was applied to the improved soil.

[0036] This approach intelligently couples soil physicochemical improvement with microbial bioremediation. Grid-based sampling enables refined zoning management of the remediation process. The LSTM model handles the temporal and spatial heterogeneity of soil property data, predicting the most effective amendment ratios and avoiding indiscriminate application. The NSGA-II multi-objective optimization algorithm finds optimal solutions for complex microbial agent combinations under constraints such as cost, efficiency, and safety. Applying the optimized agents to the improved soil creates favorable conditions for physicochemical environmental optimization and functional microbial community reconstruction, synergistically accelerating soil health recovery and addressing the problems of traditional methods neglecting microorganisms and having short-lasting improvement effects.

[0037] S3: On the first restoration area, a biomimetic configuration of plant communities is performed to obtain a second restoration area; Specifically, the biomimetic configuration of plant communities includes: Obtain data on the topographic conditions of the mining area and the growth characteristics of pioneer and successor plants; The terrain conditions and growth characteristics data are input into the vegetation succession simulation algorithm to simulate the changes in vegetation coverage under different plant configuration schemes. The simulation results are processed using a genetic algorithm to optimize the plant community structure and planting density with the goals of maximizing biomass, minimizing maintenance costs, and maximizing carbon sequestration.

[0038] Vegetation succession simulation algorithms can predict the growth and cover of different plant combinations at different time scales, avoiding consideration of only short-term greening effects. Genetic algorithms, on the other hand, use multiple ecological and economic indicators such as biomass, carbon sequestration, and cost as optimization objectives to automatically search for the optimal plant species, proportions, and spatial layout schemes. This method transforms plant community configuration from empirical and static to scientific and dynamic, enabling the construction of structurally rational, functionally diverse, and positively successive plant communities, thus solving the problems of green desertification and community instability.

[0039] S4: In the second remediation area, implement animal introduction and microbial linkage regulation treatment to obtain the third remediation area; Specifically, the implementation of animal introduction and microbial synergistic regulation includes: Acquire data on the behavior and habitat preferences of local beneficial insects and soil animals; The behavioral and habitat preference data are processed using the Transformer model to predict the migration routes and habitat preferences of the target animals and optimize the release plan. Construct a regional food web energy flow model and identify key species based on graph neural networks; Based on the identification results of the key species, the spraying and inoculation strategies of the microbial inoculum are dynamically adjusted.

[0040] Introducing and managing high-trophic-level organisms can activate and stabilize ecosystems. Transformer models excel at processing sequence data, analyzing animal behavioral patterns, and accurately predicting their activity patterns to develop scientific release (introduction) plans and improve colonization success rates. Graph Neural Networks (GNNs) are adept at processing relational data, constructing and analyzing food webs, and identifying species (such as certain pollinating insects and soil animals) that play a crucial role in energy flow and structural stability. Based on this identification, microbial inoculation strategies can be adjusted in a targeted manner (e.g., providing support for symbiotic microorganisms of key species), thereby achieving coordinated regulation between animals and microorganisms and enhancing the complexity and resilience of the ecosystem.

[0041] S5: The third restoration area is subjected to near-natural terrain reshaping and hydrological regulation to obtain the fourth restoration area; Specifically, near-natural terrain reshaping and hydrological regulation are carried out, including: Obtain digital elevation model data for the mining area; The digital elevation model data is input into DEM-GAN for processing to generate a terrain design scheme that conforms to the natural landform characteristics. Based on the aforementioned terrain design scheme, the layout path of vegetated swales and artificial wetlands is optimized using the ant colony algorithm to improve runoff interception efficiency.

[0042] This study utilizes generative AI and biomimetic optimization algorithms for near-natural engineering design. Based on DEM-GAN, it learns from a large number of natural landform features to generate terrain design schemes similar to the target functional characteristics, reducing landscape abruptness and geological hazard risks. Ant Colony Optimization (ACO) simulates the foraging path optimization behavior of ants to solve the optimal spatial layout problem of linear facilities such as vegetated swales and wetlands, maximizing material utilization efficiency or minimizing construction interference while satisfying runoff interception functions. This solves the problems of traditional terrain reshaping being rigid, energy-intensive, and lacking in ecological function.

[0043] S6: Dynamically monitor the fourth restoration area and adaptively adjust it according to the monitoring results to complete the ecological restoration of the mining area.

[0044] The method provided in this embodiment mimics the construction sequence of a natural ecosystem, defining a logically rigorous and interconnected restoration process. First, the soil substrate and microbial base are treated (S1) to create prerequisites for life activities. Based on this, producers (plant communities, S2) are configured. Next, consumers and decomposers (animals and microorganisms, S3) are introduced to form a preliminary food web. Then, the abiotic environment (topography and hydrology, S4) is modified to provide a stable habitat. Finally, dynamic monitoring and regulation (S5) are used to maintain and optimize the balance of the entire system. This sequential logic, from substrate, producers, consumers / decomposers, environment to final regulation, ensures that ecological restoration is not a chaotic accumulation but an orderly and coordinated systems engineering project, fundamentally solving the technical problems of isolated restoration elements and poor synergy.

[0045] Specifically, dynamic monitoring and adaptive adjustment based on monitoring results include: The sensor network acquires real-time monitoring data of soil moisture, temperature, and pH value in the fourth remediation area, and the camera acquires image data of vegetation coverage and wildlife activity. The real-time monitoring data and image data are transmitted to the central control system, and a dynamic model of the restoration area is constructed using digital twin technology. Based on the model, the risk of geological disasters is predicted. Based on multi-agent reinforcement learning technology, the collaborative management strategy for soil, plants, animals, microorganisms and hydrology is dynamically adjusted according to the monitoring data and risk prediction results. Blockchain technology is used to record key data and carbon sequestration information during the restoration process.

[0046] A digital twin-driven intelligent operation and maintenance system for ecological restoration is constructed. Sensor networks and cameras constitute the system's senses, collecting multi-dimensional data in real time. Digital twin technology mirrors the physical restoration area as a dynamic digital model and integrates risk prediction algorithms to achieve early warning of disasters such as landslides. Multi-agent reinforcement learning (MARL) treats soil, plants, and animals as intelligent agents in the environment, finding the optimal overall management strategy through collaborative learning. Blockchain provides a trusted record of all key operations and data. This solution transforms restoration management from static, passive response to dynamic, proactive intervention and continuous optimization, solving the problem of long-term lack of post-restoration maintenance.

[0047] In one specific embodiment, the biomimetic ecological restoration method for the open-pit limestone mine, such as... Figure 2 and Figure 3 The process begins with soil reconstruction and co-remediation with microorganisms. Grid sampling is conducted in the mining area to analyze soil properties, including pH and organic matter content. Based on the analysis results, organic materials such as straw and compost (20-30 tons per hectare) are added to infertile areas, while high-quality topsoil (20-30 cm thick per hectare) is applied to severely degraded areas. An LSTM-based soil amendment prediction model is used to optimize the amendment ratio, ensuring the best remediation plan. Local dominant microbial strains are propagated in the laboratory as biocatalysts and mixed into seeds or directly applied to the root zone (5-10 ml high-concentration microbial agent per hectare). The NSGA-II multi-objective optimization algorithm is used to balance the cost, degradation efficiency, and ecological safety of the microbial agent, improving soil health, reducing unnecessary chemical use, lowering remediation costs, ensuring the optimal combination of microbial agents, and enhancing the effectiveness of microbial remediation.

[0048] Then, as Figure 4 This involves ecological regulation based on the interaction between animals and microorganisms. Beneficial local insects such as bees and beetles are released (50-100 insect habitats per hectare, each habitat housing approximately 20-30 insects, totaling approximately 1000-3000 insects per hectare). Insect habitats such as piles of dead wood and stones are also established within the area. Soil animals such as earthworms are introduced to improve soil structure (50-100 earthworms per square meter, requiring 500,000-1,000,000 earthworms per hectare). Organic matter is also regularly introduced as a food source.

[0049] like Figure 5 Regularly spray EM bacterial solution, inoculate arbuscular mycorrhizal fungi, dynamically track insect behavior, predict their migration routes and habitat preferences using Transformer models, construct a food web energy flow model based on graph neural networks, identify key species, accurately identify and track animal behavior, optimize release plans, understand and regulate species interactions, and enhance ecosystem stability.

[0050] Secondly, such as Figure 6 A biomimetic plant community configuration was selected. Pioneer plants included drought-tolerant and barren-soil-tolerant herbs such as Bermuda grass and alkali grass (20-30 kg seeds per hectare) and shrubs such as sea buckthorn and Amorpha fruticosa (500-800 plants per hectare). In the mid-term, leguminous shrubs such as Caragana korshinskii and trees such as Black locust and Platycladus orientalis (300-500 trees and 500-800 shrubs per hectare) were introduced. Planting density and methods were rationally arranged according to terrain conditions. Support frames were provided for trees to prevent lodging, and shrubs were regularly pruned to promote branching and root development. A vegetation succession simulation algorithm was used to simulate vegetation cover changes under different configuration schemes. A genetic algorithm was used to optimize the plant community structure, maximizing biomass, minimizing maintenance costs, and maximizing carbon sequestration. This scientifically planned plant community configuration ensured long-term vegetation cover, identified the optimal plant combination and density, and improved ecosystem stability.

[0051] Furthermore, near-natural terrain reshaping design and hydrological regulation were implemented. Stepped platforms were excavated in steep slope areas, and retaining walls were constructed. Small reservoirs were excavated in low-lying areas. DEM-GAN was used to generate design schemes that conform to natural landform characteristics. V-shaped vegetated swales were excavated on the slopes, and moisture-tolerant herbaceous plants were planted within the swales (20-30 kg of seeds per hectare). Artificial wetlands were constructed, and benthic animals were introduced (1000-2000 benthic animals per hectare). The Ant Colony Algorithm (ACO) was used to optimize the layout of the vegetated swales and wetlands, improving runoff interception efficiency, generating realistic terrain design schemes, enhancing visual harmony and stability, finding optimal paths or layouts, and improving resource utilization.

[0052] Finally, the system enables dynamic monitoring and adaptive regulation. V-shaped vegetated trenches are excavated to plant moisture-tolerant herbaceous plants, and artificial wetlands are constructed to release benthic animals. The Ant Colony Algorithm (ACO) is used to optimize the layout of the vegetated trenches and wetlands to improve runoff interception efficiency. Multiple sensors are deployed to monitor parameters such as soil moisture, temperature, and pH, and cameras are installed to track vegetation cover and wildlife activity in real time. Data is transmitted to the central control system via a wireless network. Based on data analysis results, remediation strategies are dynamically adjusted. MARL (Marginal Analysis and Responsiveness) is used to drive synergy among various elements, and a digital twin early warning system is used to predict landslide risks in advance. Blockchain technology is integrated to achieve transparency in carbon trading, support public participation in decision-making, achieve optimal overall results, identify potential risks in advance, take preventative measures, ensure the safety of personnel and facilities, improve the transparency and public trust in the remediation process, and provide a more comprehensive large-scale ecological impact assessment.

[0053] Example 2 like Figure 7 This embodiment provides a biomimetic ecological restoration system for open-pit limestone mines, including: The data acquisition module is used to acquire soil, topography, and ecological data of the mine to be restored. The soil reconstruction and microbial remediation module is used to reconstruct the soil in the mining area based on the data and to perform co-remediation treatment with microorganisms to obtain a first remediation area; The plant community biomimetic configuration module is used to perform plant community biomimetic configuration processing on the first restoration area to obtain a second restoration area; The animal-microbe linkage regulation module is used to implement animal introduction and microbe linkage regulation treatment in the second repair area to obtain the third repair area; The near-natural terrain reshaping and hydrological regulation module is used to perform near-natural terrain reshaping and hydrological regulation on the third restoration area to obtain the fourth restoration area. The dynamic monitoring and adaptive control module is used to dynamically monitor the fourth restoration area and adaptively control it based on the monitoring results to complete the ecological restoration of the mining area.

[0054] In one specific embodiment, the biomimetic ecological restoration system for the open-pit limestone mine includes: a perception layer, an algorithm layer, an execution layer, and a feedback layer; The sensing layer deploys a sensor network to monitor soil moisture and temperature parameters; The algorithm layer integrates multiple intelligent algorithms, including an LSTM-based soil improvement prediction model, the NSGA-II multi-objective optimization algorithm, the vegetation succession simulation algorithm VESA, genetic algorithms, Transformer models, graph neural networks (GNN), DEM terrain generative adversarial networks (DEM-GAN), ant colony algorithms (ACO), multi-agent reinforcement learning (MARL), and a digital twin early warning system. The implementation layer carries out specific operations such as soil improvement, planting, animal introduction, and terrain reshaping; The feedback layer establishes a data processing and feedback mechanism to adjust the repair strategy in real time.

[0055] Example 3 Embodiment 3 of the present invention provides an electronic device.

[0056] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the biomimetic ecological restoration method for open-pit limestone mines as described in Embodiment 1 of the present invention.

[0057] The detailed steps are the same as those of the biomimetic ecological restoration method for open-pit limestone mines provided in Example 1, and will not be repeated here.

[0058] Example 4 Embodiment 4 of the present invention provides a computer-readable storage medium.

[0059] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the biomimetic ecological restoration method for open-pit limestone mines as described in Embodiment 1 of the present invention.

[0060] The detailed steps are the same as those of the biomimetic ecological restoration method for open-pit limestone mines provided in Example 1, and will not be repeated here.

[0061] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0062] A computer program product includes software code, wherein the program in the software code performs the steps of the biomimetic ecological restoration method for open-pit limestone mines as described in Embodiment 1 of the present invention.

[0063] The detailed steps are the same as those of the biomimetic ecological restoration method for open-pit limestone mines provided in Example 1, and will not be repeated here.

[0064] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0065] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A biomimetic ecological restoration method for open-pit limestone mines, characterized in that, include: Obtain soil, topography, and ecological data of the mine to be restored; Based on the data, the soil in the mining area was reconstructed and treated with microorganisms for co-remediation to obtain the first remediation area; In the first restoration area, a biomimetic configuration of plant communities is performed to obtain a second restoration area; Within the second remediation area, a combined animal introduction and microbial regulation treatment was implemented to obtain the third remediation area; The third restoration area was subjected to near-natural terrain reshaping and hydrological regulation treatment to obtain the fourth restoration area; The fourth restoration area is dynamically monitored and adaptively adjusted based on the monitoring results to complete the ecological restoration of the mining area.

2. The method as described in claim 1, characterized in that, The aforementioned soil reconstruction and co-remediation treatment with microorganisms in the mining area includes: Grid sampling was conducted on the soil in the mining area to obtain soil property data; The soil property data is input into an LSTM-based soil amendment prediction model for processing to obtain an optimized soil amendment ratio. Soil improvement was carried out according to the specified amendment ratio, and the combination of microbial agents was optimized based on the NSGA-II multi-objective optimization algorithm. The optimized microbial inoculant was applied to the improved soil.

3. The method as described in claim 1, characterized in that, The aforementioned biomimetic configuration treatment of plant communities includes: Obtain data on the topographic conditions of the mining area and the growth characteristics of pioneer and successor plants; The terrain conditions and growth characteristics data are input into the vegetation succession simulation algorithm to simulate the changes in vegetation coverage under different plant configuration schemes. The simulation results are processed using a genetic algorithm to optimize the plant community structure and planting density with the goals of maximizing biomass, minimizing maintenance costs, and maximizing carbon sequestration.

4. The method as described in claim 1, characterized in that, The implementation of the animal introduction and microbial linkage regulation treatment includes: Acquire data on the behavior and habitat preferences of local beneficial insects and soil animals; The behavioral and habitat preference data are processed using the Transformer model to predict the migration routes and habitat preferences of the target animals and optimize the release plan. Construct a regional food web energy flow model and identify key species based on graph neural networks; Based on the identification results of the key species, the spraying and inoculation strategies of the microbial inoculum are dynamically adjusted.

5. The method as described in claim 1, characterized in that, The near-natural terrain reshaping and hydrological regulation treatment includes: Obtain digital elevation model data for the mining area; The digital elevation model data is input into DEM-GAN for processing to generate a terrain design scheme that conforms to the natural landform characteristics. Based on the aforementioned terrain design scheme, the layout path of vegetated swales and artificial wetlands is optimized using the ant colony algorithm to improve runoff interception efficiency.

6. The method as described in claim 1, characterized in that, The dynamic monitoring and adaptive adjustment based on the monitoring results include: The sensor network acquires real-time monitoring data of soil moisture, temperature, and pH value in the fourth remediation area, and the camera acquires image data of vegetation coverage and wildlife activity. The real-time monitoring data and image data are transmitted to the central control system, and a dynamic model of the restoration area is constructed using digital twin technology. Based on the model, the risk of geological disasters is predicted. Based on multi-agent reinforcement learning technology, the collaborative management strategy for soil, plants, animals, microorganisms and hydrology is dynamically adjusted according to the monitoring data and risk prediction results. Blockchain technology is used to record key data and carbon sequestration information during the restoration process.

7. A biomimetic ecological restoration system for open-pit limestone mines, characterized in that, include: The data acquisition module is used to acquire soil, topography, and ecological data of the mine to be restored. The soil reconstruction and microbial remediation module is used to reconstruct the soil in the mining area based on the data and to perform co-remediation treatment with microorganisms to obtain a first remediation area; The plant community biomimetic configuration module is used to perform plant community biomimetic configuration processing on the first restoration area to obtain a second restoration area; The animal-microbe linkage regulation module is used to implement animal introduction and microbe linkage regulation treatment in the second repair area to obtain the third repair area; The near-natural terrain reshaping and hydrological regulation module is used to perform near-natural terrain reshaping and hydrological regulation on the third restoration area to obtain the fourth restoration area. The dynamic monitoring and adaptive control module is used to dynamically monitor the fourth restoration area and adaptively control it based on the monitoring results to complete the ecological restoration of the mining area.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the biomimetic ecological restoration method for open-pit limestone mines as described in any one of claims 1 to 6.

9. 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 steps of the biomimetic ecological restoration method for open-pit limestone mines as described in any one of claims 1 to 6.

10. A computer program product, comprising software code, characterized in that, The program in the software code performs the steps of the biomimetic ecological restoration method for open-pit limestone mines as described in any one of claims 1 to 6.

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