Intelligent ecological restoration system, method and device for high and steep slope of strip mine in arid region

Abstract

The invention provides an intelligent ecological restoration system, method and device for a high and steep slope of a strip mine in an arid region. Comprising a data acquisition layer which realizes real-time acquisition of multi-dimensional environmental data through InSAR satellite remote sensing, a ground sensor network and unmanned aerial vehicle multispectral imaging; the transmission layer adopts LoRa and 5G hybrid networking; the platform layer is used for constructing a slope stability prediction and restoration scheme optimization platform based on a digital twinborn model and a deep learning algorithm; and the application layer is used for remotely controlling the repairing device through a mobile terminal and a Web terminal and monitoring the repairing progress in real time. The problems that a traditional restoration technology is poor in adaptability, low in vegetation survival rate, high in ecological restoration cost, insufficient in monitoring technology application, insufficient in monitoring feedback mechanism, insufficient in intelligence, long in ecological restoration period and the like are solved.

Description

Technical Field

[0001] This invention belongs to the field of mine ecological restoration technology, specifically to an intelligent ecological restoration system, method, and device for high and steep slopes in arid open-pit mines. This system is suitable for ecological restoration and environmental management of mine slopes in arid mining areas with low annual precipitation and in open-pit mines with steep slopes. This technology integrates the interdisciplinary application of mining, geology, biotechnology, geographic information systems, the Internet of Things, and computer technology. Background Art

[0002] While providing resources, mining has also caused varying degrees of damage and pollution to the ecological environment of mining areas, resulting in soil erosion, vegetation degradation, and even geological disasters such as landslides, collapses, and mud-rock flows, which have seriously restricted the sustainable development of mines.

[0003] For a long time, resources for mine ecological restoration have not been optimally allocated. Currently, the main challenges facing ecological restoration of open-pit mine slopes in arid regions stem from the densely developed open-pit mines in ecologically fragile areas. These issues include severe soil erosion, difficulty retaining soil moisture, high ecological restoration costs, incomplete monitoring systems, and difficulties in ecological recovery. Ecological restoration and environmental governance of open-pit mine slopes are not only crucial to the development of green mining but also a crucial component of national ecological security. Therefore, the research, development, and implementation of effective, low-cost, efficient, and intelligent mine slope ecological restoration technologies are imperative.

[0004] Existing mine slope ecological restoration technologies face two major challenges. First, ecological restoration presents numerous challenges. Due to the poor climatic and geological conditions (typically thin soil layers) associated with high evaporation rates on steep slopes in arid regions, traditional spray seeding techniques suffer from high water loss rates in the seeding substrate and low vegetation survival rates. Costs are also high, with soil transportation accounting for over 50% of the total restoration cost, and the risk of slope instability increases. Second, monitoring technology suffers from limitations. Existing satellite monitoring (with a sampling period of >1 month) often fails to capture short-term deformation, and drone multispectral imaging is susceptible to interference from dust in mining areas, resulting in high error rates. This leads to slow monitoring updates and inaccurate monitoring. Summary of the Invention

[0005] Based on the above phenomena, in order to address the problems of vegetation destruction, soil erosion, and slope instability caused by mining activities on steep slopes in arid areas, and to address the technical bottlenecks of existing open-pit mine slope ecological restoration technologies, such as slope monitoring technology defects, high ecological restoration costs, and low vegetation survival rates, a method, device, and system for intelligent ecological restoration of steep slopes in open-pit mines in arid areas are proposed. This technical method is a collaborative open-pit slope restoration technology solution that integrates traditional mining technology, slope ecological restoration technology, the Internet of Things, big data analysis, and intelligence. By deploying a multi-dimensional environmental monitoring sensor network, information such as slope structural stability, soil moisture, and meteorological data is obtained in real time, and repair plans are dynamically generated using AI algorithms. Modular ecological restoration devices are used to achieve the integrated operation of soil spraying, water-saving irrigation, and plant slope consolidation, and the repair effect is optimized through an adaptive feedback system.

[0006] The present invention mainly solves the following technical problems:

[0007] (1) Problems of poor adaptability of traditional restoration technologies and low vegetation survival rate

[0008] (2) Problems of large water resource waste and high ecological restoration costs

[0009] (3) Problems of complex processes and long ecological restoration cycles

[0010] (4) Insufficient application of mine slope monitoring technology and lack of real-time feedback

[0011] (5) The level of intelligence is not high, and the cross-disciplinary application of mine intelligence construction is insufficient.

[0012] The present invention solves the problems of poor adaptability of traditional restoration technology, low vegetation survival rate, waste of water resources, high cost of ecological restoration, insufficient application of monitoring technology and lack of monitoring feedback mechanism, insufficient intelligence and long ecological restoration cycle.

[0013] The complete technical solution of the present invention:

[0014] 1. Intelligent ecological restoration system for high and steep slopes of open-pit mines in arid areas

[0015] The technology in this invention utilizes an integrated "sky-air-ground" monitoring network and a "cloud-edge-end" collaborative architecture, combining professional methods from mining engineering (slope protection and management), the Internet of Things, artificial intelligence, and ecological engineering to build an intelligent ecological restoration system for high and steep slopes in arid open-pit mines, covering data collection, analysis and decision-making, restoration construction, and effect evaluation. The system is divided into the following four layers:

[0016] (1) Data acquisition layer: Real-time acquisition of multi-dimensional environmental data is achieved through InSAR satellite remote sensing, ground sensor networks and drone multispectral imaging.

[0017] (2) Transport layer: LoRa and 5G hybrid networking is used to ensure low-power, high-bandwidth data transmission. LoRa and 5G hybrid networking refers to the combination of LoRa (Long Range Radio) low-power wide area network (LPWAN) technology and 5G high-speed mobile communication technology. Through the complementary advantages, a complete IoT communication solution is formed to meet the dual needs of low power consumption, wide coverage and high bandwidth and low latency in different scenarios.

[0018] (3) Platform layer: Based on the digital twin model and deep learning algorithm, a slope stability prediction and repair solution optimization platform is constructed.

[0019] (4) Application layer: Remotely control the repair device through mobile terminals and the Web, and monitor the repair progress in real time.

[0020] 2. Intelligent ecological restoration method for high and steep slopes of open-pit mines in arid areas, including the following steps:

[0021] S1, data collection and monitoring;

[0022] S1.1, multi-source data collection;

[0023] Real-time collection of environmental data on high and steep slopes in mines is accomplished using technologies such as InSAR satellite remote sensing monitoring, ground sensor networks, and drone multispectral imaging.

[0024] 1) InSAR satellite remote sensing monitoring:

[0025] The technical principle of InSAR satellite remote sensing monitoring is to use synthetic aperture radar (SAR) satellites to obtain surface phase information and calculate millimeter-level slope deformation through interferometric phase differences. This data is used to monitor real-time deformation of open-pit mine slopes, providing real-time slope deformation dynamics for slope stability control and ecological restoration.

[0026] InSAR satellite remote sensing monitoring parameter settings and monitoring data application. The satellite band was set to X-band (resolution 3m×3m); the monitoring frequency was set to a combination of weekly monitoring in normal mode (static monitoring) and daily monitoring in emergency mode (dynamic monitoring); and the deformation accuracy was set to ±2mm horizontally and ±5mm vertically. The InSAR satellite remote sensing data was used to generate slope deformation heat maps to identify high-risk areas (reference indicator: deformation rate >5mm / month).

[0027] 2) Ground sensor network:

[0028] Select sensor types for ground sensor network layout. The soil moisture sensor is a capacitive type with a measurement range of 0%-100% and an accuracy of ±2%. The tilt sensor uses MEMS technology, with a resolution of 0.01° and a range of ±30°. The pore water pressure gauge is a piezoresistive type with a measurement range of 0-100 kPa and an accuracy of ±0.5 kPa.

[0029] The ground sensor network deployment plan follows a "grid + key point" layout, with soil moisture sensors deployed every 10m x 10m. Inclination sensors are deployed more frequently at the slope foot, top, and cracks.

[0030] The ground sensor network is powered by solar energy, using a combination of "10W monocrystalline silicon panels + lithium battery packs" to achieve a battery life of ≥ 3 years using solar energy to replenish natural energy, saving energy, reducing consumption and lowering costs.

[0031] 3) UAV multispectral imaging:

[0032] Drone technical parameter selection: resolution of 0.1m in the visible light band and 0.5m in the near infrared band; spectral range: 400-1000nm in the visible light band and 1000-2500nm in the near infrared band (near infrared).

[0033] The multispectral imaging function of drones is implemented by using the NDVI to assess the vegetation coverage and health of the planted vegetation on the slopes of open-pit mines. The red edge band is used to identify soil erosion in exposed rock and soil areas, providing data support for soil erosion detection reports.

[0034] To address the complex environmental characteristics of the mine's steep slopes, a three-dimensional data collection network was established using InSAR satellite remote sensing, ground-based sensor networks, and drone multispectral imaging. This network covers the entire chain from macro-deformation monitoring to micro-environmental parameter perception. Through complementary spatial resolution (InSAR satellite macro-deformation, ground-based sensor micro-parameters, drone-based meso-vegetation), temporal resolution (periodic satellite monitoring, real-time sensor monitoring, and on-demand drone monitoring), and data dimensionality (deformation, soil, and vegetation), this integrated "sky-ground-air" monitoring system was established, providing multi-dimensional data support for slope stability assessment and ecological restoration.

[0035] S1.1.1. Establishing a macroscopic monitoring benchmark for slope deformation

[0036] The system uses InSAR satellite remote sensing monitoring as its starting point, utilizing synthetic aperture radar (SAR) satellites to acquire surface phase information and calculate millimeter-scale slope deformation through interferometric phase differences. Technically, it employs X-band (3m×3m resolution) to capture spatial deformation. This system combines normal monitoring (once a week) with emergency monitoring (once a day) to ensure dynamic coverage and precision control of deformation data (±2mm horizontally, ±5mm vertically). Monitoring results are intuitively presented through the generation of slope deformation heat maps. High-risk areas are delineated using a deformation rate threshold of >5mm / month, providing a basis for macroscopic deformation trends for subsequent stability assessments.

[0037] S1.1.2. Build a dynamic ground environment parameter perception network

[0038] Based on the InSAR deformation monitoring results, the system deploys a network of ground sensors (soil moisture sensors, tilt sensors, pore water pressure gauges) to supplement the micro-environmental parameters. The sensor selection focuses on three core parameters:

[0039] Soil moisture monitoring: Using a capacitive soil moisture sensor (0%-100% measurement range, ±2% accuracy) to sense changes in slope soil moisture content in real time;

[0040] Slope inclination monitoring: MEMS-based inclination sensor (0.01° resolution, ±30° range) captures small angular displacements of the slope;

[0041] Pore ​​water pressure monitoring: Piezoresistive pore water pressure gauge (0-100kPa measurement range, ±0.5kPa accuracy) to assess the impact of groundwater on slope stability.

[0042] The deployment plan adopts a "grid + key point" layout strategy, placing soil moisture sensors in a 10m x 10m grid. Tilt sensors are deployed more densely in deformation-sensitive areas such as the slope foot, top, and cracks. The power supply system utilizes a combination of 10W monocrystalline silicon panels and lithium batteries, achieving a battery life of ≥3 years through solar power, reducing maintenance costs.

[0043] S1.1.3. Integrated UAV multispectral imagery mesoscopic analysis

[0044] To connect macro-deformation monitoring with micro-parameter perception, the system incorporates drone multispectral imaging technology, enabling meso-scale slope analysis at a resolution of 0.1m (visible light) and 0.5m (near-infrared). The technical parameters cover the visible light (400-1000nm) and near-infrared (1000-2500nm) bands, using the vegetation index (NDVI) to calculate and identify soil erosion:

[0045] Vegetation health assessment: Quantify vegetation coverage and growth status through NDVI to provide quantitative indicators for the effect of ecological restoration;

[0046] Soil erosion detection: Identify bare rock and soil areas based on the red-edge band (700 - 750nm), generate a soil erosion risk map in combination with deformation data, and guide the optimization of slope protection projects.

[0047] S1.1.4. Form a slope situation assessment system for multi-source data fusion

[0048] The system performs spatio-temporal alignment and feature fusion on InSAR deformation data (slope deformation thermal map), sensor network environmental parameters (micro-environmental parameters), and UAV multi-spectral images (vegetation coverage, soil erosion risk map) to generate a comprehensive assessment result of slope stability:

[0049] Dynamic risk grading: Based on the deformation thermal map, vegetation coverage, and soil erosion risk, divide into three levels of areas: safe, warning, and dangerous;

[0050] Dynamic resource allocation: According to task requirements (such as "<Monitoring task><Allocation><X UAVs + Y sensor nodes>") and node capability requirements (such as "<UAV node><Required capability><Multi-spectral imaging>"), optimize the deployment of monitoring resources; Decision support output: Display the entity relationship network through a visualization interface, where nodes represent entity entries, and directed edges are labeled with relationship types such as "monitoring", "association", "required capability", etc., providing an intuitive decision-making basis for slope treatment projects.

[0051] Through the above hierarchical progressive logic of "macroscopic deformation monitoring - microscopic parameter perception - mesoscopic image analysis - multi-source data fusion", the system realizes the real-time collection, dynamic assessment, and precise decision support of environmental data for high-steep slopes in mines. The data collected by the above three technologies form a complete evidence chain for slope stability assessment through spatio-temporal alignment and feature fusion: InSAR provides the deformation trend, the sensor network reveals changes in environmental parameters, and UAV multi-spectral imaging depicts the vegetation and soil status. The collaboration of the three data can achieve dynamic early warning of slope stability (abnormal deformation), quantitative assessment of the effect of ecological restoration (vegetation restoration degree), and risk grading of soil erosion (red-edge band analysis), providing a scientific decision-making basis for mine slope treatment.

[0052] S1.2. Data fusion and preprocessing

[0053] Use the Kalman filter to eliminate sensor noise and remove outliers (such as mutation data caused by sensor power-off) to achieve data cleaning; Apply the spatio-temporal alignment algorithm to perform spatio-temporal registration on InSAR data (coarse resolution) and UAV images (high resolution), achieving an effect with an error < 0.5m.

[0054] In the data fusion and preprocessing of high and steep slope monitoring in mines, data optimization is achieved through the following steps to provide reliable support for subsequent analysis:

[0055] S1.2.1. Complete sensor data cleaning and noise filtering

[0056] The raw data collected by ground sensors (such as soil moisture, inclination, and pore water pressure) are processed in two steps:

[0057] Noise suppression: Kalman filtering algorithm is used to dynamically smooth data fluctuations and eliminate sensor noise to avoid measurement errors caused by environmental interference (such as electromagnetic noise and temperature drift);

[0058] The sensor data is processed using the Kalman filter algorithm to eliminate sensor noise. The Kalman filter is a recursive filter that can estimate the state of the system in real time based on the system's dynamic model and observation model, thereby effectively reducing random errors in the data.

[0059] Outlier removal: Automatically identify and delete invalid data or outliers (such as instantaneous jumps caused by sensor power failure) based on data threshold rules (such as sudden inclination changes > 10°, humidity values ​​outside the range of 0-100%), achieve data cleaning, and fill in missing values.

[0060] Identify and eliminate data mutations caused by sensor failures (such as power outages). By setting reasonable thresholds, detect anomalies in the data and mark them as invalid data, thereby ensuring data continuity and consistency.

[0061] S1.2.2. Realize spatiotemporal alignment and precision matching of multi-source data

[0062] To address the differences between InSAR satellite data (low resolution, low frequency) and drone imagery (high resolution, high frequency), the system integrates the data in the following ways (after data cleaning, data of different resolutions and time scales need to be spatiotemporally aligned to ensure comparability and consistency):

[0063] Time synchronization (space-time alignment algorithm): Based on UTC time, the timestamp of InSAR data is aligned to the time of drone image acquisition to ensure that the time error is less than 1 hour; the space-time alignment algorithm ensures the consistency of different data sources in time and space by matching the timestamp and spatial coordinates of the data.

[0064] Spatial registration: Using fixed feature points of the slope (such as cracks and buildings) as control points, the InSAR deformation data are projected into the UAV image coordinate system to keep the spatial error less than 0.5 meters.

[0065] S1.2.3. Generate a standardized fusion dataset

[0066] The cleaned sensor data and aligned InSAR deformation data are correlated with drone imagery features (such as vegetation index and soil erosion areas) to form a joint dataset encompassing deformation, environmental parameters, and imagery features. The data source and quality level are annotated for direct use in subsequent stability analysis. Key features, such as slope deformation, soil moisture, and vegetation cover, are extracted from the fused data to support subsequent slope stability assessments and ecological restoration effectiveness evaluations.

[0067] Through the three-step method of "noise cleaning - space-time alignment - feature fusion", the system quickly integrates multi-source data, ensures data consistency, and provides a clear and reliable basis for slope monitoring and decision-making.

[0068] S2. Data Analysis and Decision-Making Methods

[0069] Data analysis and decision-making methods, through the closed-loop logic of "prediction-simulation-optimization", achieve intelligent management from risk warning to dynamic adjustment of governance solutions. Specific steps include:

[0070] S2.1. Constructing a dynamic prediction model for slope stability

[0071] The detailed steps for building a dynamic prediction model for slope stability mainly include building from historical joint data sets, building and optimizing a dynamic prediction model, and providing graded warning and decision support. The specific contents are as follows:

[0072] S2.1.1, historical joint dataset construction;

[0073] (1) Spatiotemporal alignment and enhancement of multimodal data

[0074] 1) Application of heterogeneous alignment algorithm for cross-source data

[0075] A Transformer-based temporal registration framework is proposed to address the differences in spatiotemporal scales among InSAR (monthly frequency), ground sensors (hourly frequency), and UAV images (dayly frequency), and a dynamic alignment loss function (DT-Loss) is constructed.

[0076] Develop hybrid data interpolation methods: Generative Adversarial Networks (GAN) are used to generate adversarial fillings (conditional GAN ​​+ CRF post-processing) for InSAR missing areas, and wavelet packet transform is applied to denoise sensor noisy data.

[0077] 2) Physical feature embedding

[0078] Introducing prior knowledge of geomechanics, the shear strength parameters of slope rock and soil are The data is mapped into a feature layer through the Mohr-Coulomb criterion to construct a physical constraint dataset.

[0079] Design a multi-scale feature pyramid to extract cross-scale correlation features of InSAR deformation rate (macroscopic), joint and fissure distribution (mesoscopic), and pore water pressure fluctuation (microscopic).

[0080] (2) Dynamic generation of data labels

[0081] Generating pseudo-labels for landslide probabilities: Using a semi-supervised learning framework, we generated 100,000 virtual landslide scenarios using a pre-trained physics simulator (e.g., FLAC3D). We then augmented the weakly supervised labels with consistency regularization.

[0082] A dynamic label correction mechanism is designed to trigger the Bayesian update rule to adjust the label confidence when new monitoring data conflicts with the prediction results.

[0083] S2.1.2, Dynamic prediction model construction and optimization;

[0084] (1) Dynamic Graph Neural Network Architecture

[0085] 1) Spatiotemporal Heterogeneous Graph Convolutional Network (ST-HGNN)

[0086] A dynamic heterogeneous graph structure is constructed, with open-pit mine slope monitoring points as nodes. The edge weights are updated in real time as InSAR deformation rate correlations, and a temporal attention mechanism is introduced to capture the lag effect.

[0087] A sliding window graph partitioning strategy is proposed to automatically segment subgraphs according to deformation gradient changes to solve the overfitting problem of large-scale slope networks.

[0088] 2) Meta-learning driven adaptive updates

[0089] An incremental learning framework based on MAML (Model-Agnostic Meta-Learning) was designed to enable the model to quickly adapt to new slope conditions (such as extreme rainfall events), reducing the need for cold-start training samples by 80%.

[0090] A parameter drift detection module is developed to monitor model parameter changes through KL divergence and trigger elastic weight solidification (EWC) to prevent catastrophic forgetting.

[0091] (2) Physical Constraint Reinforcement Learning

[0092] 1) Hybrid Physics-Data Model (HyPhy-Model)

[0093] The finite element analysis results (stress field) and deep learning predictions (displacement field) are integrated to construct a joint loss function, design a physics-guided exploration strategy, and introduce the yield criterion constraint of rock and soil in the reinforcement learning action space.

[0094] 2) Online incremental distillation learning

[0095] Build a teacher-student model architecture: the teacher model (physical mechanism model) guides the output of the student model (deep learning), and gradually increases the physical constraint weight through course learning.

[0096] Develop an adaptive knowledge distillation loss function: dynamically adjust the distillation strength when monitoring data distribution shifts.

[0097] S2.1.3, graded warning and decision support;

[0098] (1) Multimodal warning signal fusion

[0099] 1) Detection of timing mutation points

[0100] A variational autoencoder (W-VAE) based on Wasserstein distance is proposed to capture the tiny drift of InSAR deformation rate in real time and identify early signs of slope instability.

[0101] A multivariate risk index (MRI) was designed, integrating displacement rate, pore water pressure variability, and vegetation cover decay rate into a three-dimensional radar map score.

[0102] 2) Dynamic threshold adaptation

[0103] A reinforcement learning-driven warning threshold optimizer (RL-Threshold) is developed, which uses false alarm rate (FAR) and rate of omission (POD) as reward functions to achieve adaptive graded warning.

[0104] A risk propagation model is introduced, and when the predicted probability of instability exceeds a threshold, a cascade warning of surrounding associated slopes is automatically triggered.

[0105] (2) Explainable warning engine

[0106] 1) Causal Reasoning Module

[0107] A structural causal model (SCM) was constructed to quantify the contribution of key factors such as rainfall infiltration, crack expansion, and slope vegetation root reinforcement, and an interpretable report of SHAP values ​​was generated.

[0108] Develop a counterfactual prediction tool that inputs hypothetical scenarios such as "what if irrigation was stopped" or "anchor density was increased" and outputs a comparison of stability evolution.

[0109] 2) Multimodal visual interaction

[0110] Design a three-dimensional digital twin warning panel that integrates multi-view linkage of InSAR deformation thermal maps, root tensile strength distribution, and warning probability spatiotemporal animation.

[0111] Develop an AR on-site assistance system that uses Hololens2 to overlay prediction results and construction suggestions in real time.

[0112] The core algorithm is the Long Short-Term Memory (LSTM) network, a deep learning model suitable for processing time series data. LSTM can effectively capture long-term dependencies in the data and is suitable for analyzing and predicting time series data such as slope deformation. It integrates multi-source spatiotemporal data (joint datasets) to predict landslide risks:

[0113] Data input layer (input parameters):

[0114] Historical deformation data (obtained from slope deformation heatmaps): Extract millimeter-level slope deformation data monitored by InSAR over the past 30 days to capture the cumulative deformation trend;

[0115] Environmental dynamics: Data from three ground sensors are integrated with real-time data from weather stations (soil moisture, rainfall, and wind speed) to quantify the intensity of environmental stress.

[0116] Geological background: Data obtained from drone multispectral imaging introduces parameters such as rock and soil shear strength and pore water pressure to characterize the basic characteristics of the slope material.

[0117] Model training layer: using a historical landslide case library of ≥100 as the training set, the model generalization ability is optimized through cross-validation (accuracy > 90%);

[0118] The output results include landslide probability (range 0-1) and risk level (low / medium / high), providing a graded warning basis for subsequent decision-making.

[0119] Through the above steps, the slope stability prediction model can evaluate the stability of the slope in real time and provide a scientific basis for subsequent ecological restoration measures.

[0120] S2.2. Construct a digital twin simulation model for slope ecological restoration. Specific steps:

[0121] S2.2.1, Construction of digital twin of slope;

[0122] (1) Multi-source heterogeneous data fusion and 3D reconstruction

[0123] 1) Data collection and preprocessing

[0124] A multimodal data warehouse is constructed by integrating UAV oblique photography (0.1m resolution), lidar point cloud (5cm accuracy), geological drilling data (including geotechnical parameters) and InSAR surface deformation monitoring data.

[0125] Deep learning algorithms (such as U-Net++) are used to invert vegetation coverage from remote sensing images, and soil conductivity sensor data is combined to establish a slope surface feature map.

[0126] 2) 3D reconstruction of geological structures

[0127] Based on borehole data and electrical resistivity tomography (ERT) inversion results, a heterogeneous geological layered model was constructed, and a discrete fracture network (DFN) was embedded to simulate the potential slip surface.

[0128] Use a physical engine (such as PFC3D) to simulate the stress field of rock and soil and generate a dynamic failure probability distribution layer.

[0129] 3) Dynamic update mechanism of digital twins

[0130] Deploy edge computing nodes to access InSAR deformation data in real time, and dynamically correct slope stability parameters (such as safety factor Fs) through Kalman filtering and Bayesian update algorithm.

[0131] Design a blockchain-based data traceability framework to ensure the credibility and version consistency of multi-source data fusion.

[0132] (2) High-resolution 3D model generation

[0133] 1) Multi-scale modeling technology

[0134] Adaptive octree meshing technology is used, with a fine-grained grid of 1m×1m×0.5m in potential landslide areas (high and steep slope sections) and a coarse-grained grid of 10m×10m in stable areas to optimize computing resources.

[0135] The cellular automaton (CA) algorithm is introduced to simulate the slope erosion process and generate a dynamic terrain evolution model.

[0136] 2) Embedding of ecological elements

[0137] A three-dimensional reconstruction model of plant roots (based on μ-CT scanning data) was integrated to quantify the root tensile strength and soil reinforcement effect.

[0138] A microorganism-soil-plant synergistic model was constructed to simulate the soil consolidation and moisturizing effects of biological crusts.

[0139] S2.2.2. Ecological restoration plan simulation and effect evaluation;

[0140] (1) Multi-physics coupling simulation

[0141] 1) Microtopography and hydrological process simulation

[0142] Based on the morphological algorithm, different spraying densities (such as 10-50 plants / m 2) and the corresponding micro-topography undulations, the SWMM model was used to simulate the slope runoff dynamics and quantify the amount of soil and water loss.

[0143] The complementary evapotranspiration model (TET) was introduced to simulate the effect of irrigation frequency (such as 1-3 times a week) on surface soil moisture content.

[0144] 2) Vegetation growth and ecological succession model

[0145] A plant community competition model based on the Logistic equation was developed and combined with a light use efficiency (LUE) parameterization scheme to simulate the spatiotemporal evolution of vegetation cover under different irrigation / fertilization strategies.

[0146] The coupled ecological niche model (ENM) predicts the migration patterns of pioneer species (such as Caragana korshinskii and Hippophae rhamnoides) in their suitable habitats.

[0147] 3) Uncertainty Quantification Analysis

[0148] The MCMC (Markov Chain Monte Carlo) method was used to quantify the parameter sensitivity of seeding density and irrigation frequency, and a probability cloud map of the ecological restoration success rate of Monte Carlo simulation was generated.

[0149] Ensemble forecasting technology is introduced to assess the long-term impact of climate change (such as increased frequency of droughts) on restoration effects.

[0150] (2) Dynamic data assimilation and strategy optimization

[0151] A real-time feedback closed-loop mechanism deploys a LoRaWAN sensor network to monitor the actual vegetation index (NDVI) and soil moisture on the slopes. The Ensemble Kalman Filter (EnKF) algorithm assimilates the observed data and corrects the simulation model parameters.

[0152] A dynamic irrigation scheduling algorithm (DQN) based on reinforcement learning is designed to optimize the irrigation strategy with water use efficiency (WUE) as the reward function.

[0153] S2.2.3, executable strategy generation and verification;

[0154] (1) Multi-objective decision support system

[0155] 1) Ecological-engineering coupling indicator system

[0156] A three-dimensional decision space including ecological restoration (Eco-index), engineering stability (Sta-index) and economic cost (Cost-index) was constructed, and the TOPSIS algorithm was used to generate the Pareto optimal solution set.

[0157] The ecological footprint (EF) model was introduced to quantify the sustainability of the restoration scheme.

[0158] 2) Modular construction plan generation

[0159] Based on BIM+GIS fusion technology, slope zoning repair plans (such as zoning spraying density and drip irrigation network layout) are automatically generated, and a Gantt chart for construction machinery scheduling is output.

[0160] Develop a lightweight VR interactive platform to support virtual simulation and conflict detection before construction.

[0161] (2) Strategy verification and iterative upgrade

[0162] 1) Physical similarity verification

[0163] A 1:50 scale physical model was built (using 3D printing of similar materials), and centrifuge tests were performed to verify that the erosion rate error predicted by the digital twin model was ≤15%.

[0164] Field test plots were designed and the simulated predicted and measured vegetation survival rates were compared (error tolerance ± 10%).

[0165] 2) Adaptive model upgrade

[0166] An online learning framework is constructed to update the new slope data to the global model library through transfer learning.

[0167] Design a federated learning architecture to enable sharing of restoration experience among multiple mining areas while protecting data privacy.

[0168] S2.3. Based on the slope stability prediction results and the digital twin simulation model of slope ecological restoration, a three-dimensional slope model with a resolution of 1m×1m was constructed. This model was used to simulate the ecological restoration effects of restoration plans with different seeding densities, irrigation frequencies, etc., providing visual support for optimizing restoration plans:

[0169] 3D model construction: Generate a digital twin of the slope with a resolution of 1m×1m, integrating terrain, vegetation, hydrology and other elements to achieve dynamic mapping between virtual space and physical entities.

[0170] Repair plan simulation: simulate different spraying densities (such as 5g / m 2 vs 10g / m 2 ), vegetation restoration effect under different irrigation frequencies (2 days / time vs 5 days / time), and quantify the correlation between vegetation coverage (NDVI) and soil erosion rate (L);

[0171] The soil and water loss objective function (minL = ∑(1-NDVIi) × Ai, where Ai is the unit area) is constructed and the target optimization is performed to achieve the goal of minimizing soil and water loss on the open slope.

[0172] S2.4. Dynamic optimization of repair resource allocation based on reinforcement learning

[0173] The system converts simulation results into executable strategies and uses resource allocation algorithms to reduce costs and increase efficiency in slope management:

[0174] Dynamic path planning: Based on the three-dimensional slope model and real-time terrain data, the reinforcement learning algorithm is used to plan the optimal travel path of the spraying vehicle to reduce the ineffective movement distance;

[0175] For example: by using the Q-learning algorithm to learn historical operation data, the path slope and curvature parameters can be dynamically adjusted to reduce the energy consumption of the sprayer.

[0176] Intelligent drip irrigation control: Combined with real-time feedback from soil moisture sensors, the drip irrigation volume and frequency are dynamically adjusted (e.g., reducing irrigation volume by 30% during the rainy season) to avoid water waste.

[0177] Objective: To achieve a ≥15% reduction in energy consumption of sprayers through optimization strategies while ensuring a ≥85% vegetation survival rate.

[0178] Ultimately, a closed-loop decision-making system called "prediction-simulation-optimization" was formed, using stability prediction results as simulation input, feeding back simulation optimization solutions to the physical slope for implementation, and continuously verifying the restoration results through a sensor network:

[0179] Dynamic iteration mechanism: Update InSAR deformation data and meteorological monitoring results weekly, retrain the LSTM model and trigger the optimization of the repair plan;

[0180] If the slope risk level is upgraded to "high", emergency simulation will be immediately initiated (such as increasing the seeding density and shortening the irrigation cycle).

[0181] Visual decision support: Landslide probability heat maps, vegetation restoration progress maps, and energy consumption distribution maps are superimposed on the digital twin interface to assist managers in making intuitive decisions.

[0182] Through the above-mentioned progressive logic of "risk prediction-simulation deduction-resource optimization", the system realizes the coordinated management and control of open-pit mine slope stability and ecological restoration, providing technical support for the green development of mines in arid areas.

[0183] S3. Ecological Restoration Implementation

[0184] 1) Modular ecological restoration device design

[0185] a. Composition of modular ecological restoration device

[0186] The modular ecological restoration device consists of three parts: a retractable spraying mechanism, a water-saving drip irrigation module, and a plant fiber soil-fixing net.

[0187] The retractable seeding mechanism consists of a robotic arm, a seeding head, and a material mixing system. The robotic arm has a telescopic range of 1-5 meters, suitable for open-air slopes with slopes of 30°-90°. The seeding head is designed as a dual-channel design with independent left and right rotation, with a spray width of 3-8 meters. The material mixing system mixes a nano-water-retaining agent (polyacrylamide, molecular weight ≥12 million), organic fertilizer, and local plant seeds in a precise ratio to achieve a tolerance of less than 2%.

[0188] The drip irrigation pipe of the water-saving drip irrigation module uses an embedded pressure-compensated dripper with a flow rate set to 2L / h and a uniformity coefficient of <5%. It automatically adjusts the irrigation amount according to soil moisture (threshold setting: starts when soil moisture is <30%) to achieve integrated water and fertilizer.

[0189] The material of the plant fiber soil-fixing net is degradable plant fiber, such as corn straw + PLA, with a degradation cycle of 1-2 years; its fixing method uses U-shaped nails + biological glue, and the tensile strength is required to be ≥15kN / m.

[0190] 2) Adaptive restoration process flow of ecological restoration execution technology

[0191] The adaptive restoration process of ecological restoration execution technology is divided into three steps: abc. The details are as follows:

[0192] a. Slope pretreatment: First, use mechanical crushing + manual cleaning to remove loose rock and soil, then spray soil conditioner (humic acid, dosage 5kg / m 2 ).

[0193] b. Spraying of foreign soil: First, spray the foreign soil with a thickness of 8cm-12cm and a moisture content of 20%-25%; then immediately cover it with non-woven fabric after spraying. The water permeability of the non-woven fabric is required to be ≥80% to prevent water evaporation.

[0194] c Intelligent irrigation: In the initial stage (1-7 days), the drip irrigation frequency is 1 time / 2 hours, and the water volume is 10L / m 2 ; In the middle and late stages (8-30 days), the drip irrigation frequency is adjusted to 1 time / 4 hours, and the amount of water is gradually reduced.

[0195] During the ecological restoration of steep mine slopes, the modular device and adaptive process flow are used to coordinate and implement precise management from slope pretreatment to long-term vegetation maintenance. The specific steps are as follows:

[0196] S3.1. Constructing an integrated operation unit of a modular ecological restoration device

[0197] With functional modularization as the core, it integrates three subsystems: spraying, irrigation, and soil consolidation, to achieve flexible adaptation and efficient operation of the repair device:

[0198] Retractable spraying mechanism: Dynamic adjustment of the robotic arm: With a telescopic range of 1m-5m and adaptability to slopes of 30°-90°, it can cover complex terrains such as steep slopes and vertical cliffs;

[0199] Dual-channel seeding head: The independent rotation design achieves a spray coverage of 3m-8m. The seeding materials (nano water-retaining agent, organic fertilizer, local seeds) are evenly dispersed through a high-precision mixing system (error <2%) to ensure the survival rate of vegetation.

[0200] Water-saving drip irrigation module: built-in pressure-compensated dripper: 2L / h constant flow rate and <5% uniformity coefficient to avoid vegetation growth differences caused by uneven irrigation;

[0201] Intelligent start-stop control: Based on the soil moisture sensor (irrigation starts when the threshold is less than 30%), water supply is achieved on demand, and the integrated water and fertilizer technology reduces water consumption by 30%.

[0202] Plant fiber soil-fixing net: degradable material: corn straw + PLA composite fiber (degradation cycle 1-2 years) replaces traditional plastic net to reduce secondary pollution;

[0203] Tensile strength reinforcement: U-shaped nails + bio-glue fixing method (tensile strength ≥ 15kN / m) enhances slope stability and prevents soil erosion.

[0204] S3.2. Implementing Adaptive Ecological Restoration Process

[0205] Dynamically adjust restoration strategies based on the slope's underlying conditions, achieving rapid vegetation recovery and long-term survival through three phases of operations:

[0206] Slope surface pretreatment (stage a): Use mechanical crushing (such as milling machine) combined with manual cleaning to remove loose rock and soil and remove unstable rock and soil layers;

[0207] Spraying soil conditioner: 5kg / m 2 Humic acid mulch increases the soil's organic matter content and water retention, creating basic conditions for vegetation growth.

[0208] Foreign soil spraying (stage b): construct a foreign soil layer, spraying a 10cm-15cm thick foreign soil layer with a moisture content of 20%-25% to ensure the humidity required for seed germination;

[0209] Non-woven fabric covering: Covering with non-woven fabric with a water permeability of ≥80% reduces water evaporation, intercepts wind erosion particles, and protects new vegetation.

[0210] Control flow: Intelligent irrigation (stage c): Initial high-frequency irrigation: drip irrigation once every 2 hours within 1-7 days, with a single water volume of 10L / m 2 , promote rapid seed germination;

[0211] In the middle and late stages, reduce the frequency and control the water: within 8-30 days, drip irrigation should be carried out once every 4 hours, and the water volume should be gradually reduced to 5L / m 2 , inducing deep rooting and improving the drought resistance of vegetation.

[0212] Ultimately, a closed loop of ecological restoration with a coordinated “device-process-environment” approach is formed. By coupling modular devices with adaptive processes, the following goals are achieved:

[0213] Dynamic adaptability: The telescopic range of the spraying mechanism and the pressure compensation function of the drip irrigation module work together to adapt to slopes of different slopes and geotechnical properties;

[0214] Efficient resource utilization: Nano water-retaining agents combined with intelligent irrigation technology reduce water consumption by 40% while increasing vegetation survival rate to over 90%;

[0215] Long-term soil-fixing mechanism: The degradable soil-fixing net is combined with bio-adhesive fixation to gradually degrade into organic matter within 1-2 years, continuously enhancing slope stability and controlling tensile strength.

[0216] Through the above-mentioned progressive logic of "modular design of equipment - adaptive execution of processes - dynamic response to the environment", the system realizes the precise, intelligent and sustainable ecological restoration of open-pit mine slopes, providing technical support for the ecological management of mines in arid areas.

[0217] 3. Intelligent ecological restoration device for high and steep slopes in open-pit mines in arid areas

[0218] (1) Multi-dimensional environmental monitoring device

[0219] The core components include sensor nodes, data acquisition terminals and solar power supply modules.

[0220] Sensor node: integrated soil moisture, inclination, and pore water pressure sensors (IP68 protection grade); data acquisition terminal: STM32 microcontroller (main frequency 72MHz, supporting LoRa and NB-IoT communications); solar power supply module: 10W monocrystalline silicon panel + 20Ah lithium battery (cycle life ≥ 500 times).

[0221] Deployment method of core components: Use triangulation method to lay out the sensor network to ensure that the monitoring blind area is less than 5%.

[0222] (2) Slope repair construction equipment

[0223] The structure of the retractable seeding vehicle consists of a power system and a modular drip irrigation system.

[0224] The power system consists of a 20kW diesel generator + lithium battery pack, which can achieve a battery life of ≥8 hours; the spraying efficiency reaches 1000m 2 / h (including the entire process of mixing, spraying, and covering); the modular drip irrigation system adopts a pipe network layout with a main and branch pipe diameter of 50mm / 20mm and a dripper spacing of 1m×1m. The control method uses an edge computing gateway to automatically start and stop according to humidity data.

[0225] (3) Edge computing gateway

[0226] Edge computing gateway hardware configuration: processor configuration NVIDIA Jetson Nano (computing power 472GFLOPS);

[0227] Storage configuration: 128GB eMMC + 128GB MicroSD card.

[0228] Software features: Data preprocessing supports TensorFlow Lite model inference (LSTM landslide prediction); remote upgrades use OTA (Over-the-Air) firmware updates.

[0229] The beneficial effects brought about by the technical solution of the present invention are:

[0230] This invention combines the actual situation of open-pit mine slopes and, by integrating the Internet of Things, artificial intelligence and ecological engineering technology, constructs an intelligent ecological restoration system for high and steep slopes in open-pit mines in arid areas. It also formulates a detailed technical implementation plan, which solves the core problems of traditional open-pit slope ecological restoration technology, such as delayed monitoring, blind restoration, low vegetation survival rate, high cost and waste of resources. It has significant advantages in terms of ecological benefits, economic benefits and social benefits.

[0231] (1) Ecological restoration efficiency has been significantly improved

[0232] Shortened cycle: Through dynamic monitoring and adaptive restoration technology, the restoration cycle can be shortened by 1 / 3 compared with traditional ecological restoration technology, the vegetation coverage rate can reach more than 85%, and the amount of soil and water loss can be reduced by 70%.

[0233] Improved vegetation survival rate and reduced costs: The modular spraying device combines nano-water retaining agents with local plant selection, and the seed survival rate is increased by 50% compared with traditional methods, significantly reducing the cost of repeated construction.

[0234] Enhanced risk prevention and control: The landslide early warning system based on InSAR and LSTM models can monitor landslides in real time and predict risks in advance, with an early warning accuracy rate of >90%, avoiding secondary ecological damage caused by disasters.

[0235] (2) Optimization of resource utilization efficiency

[0236] Water and energy saving: The intelligent drip irrigation system, combined with real-time feedback on soil moisture, can achieve a water saving rate of ≥35% and reduce irrigation energy consumption by 20%. The localized decision-making of the edge computing gateway reduces cloud dependence and can reduce communication energy consumption by 40%.

[0237] Intensive use of materials: Degradable plant fiber soil-fixing nets replace traditional plastic soil-fixing materials, with a degradation cycle of 1 to 2 years, avoiding long-term white pollution; the dosage of nano water-retaining agents is precisely controlled (error <2%) to reduce waste of chemicals.

[0238] (3) Restoration quality and sustainability improvement

[0239] Enhanced ecological adaptability: By simulating different restoration plans through digital twin models, drought-resistant plant combinations (such as alfalfa + sea buckthorn) are selected to improve the stress resistance and self-sustaining ability of the slope ecosystem.

[0240] Improved structural stability: After spraying, the thickness of the added soil reaches 8 to 12 cm. Combined with the soil-fixing effect of plant roots, the shear strength of the slope can be effectively increased by 20%, which can effectively prevent shallow landslides.

[0241] (4) Economic benefits and promotion value

[0242] Cost reduction: Modular devices are reusable, and the cost of single slope repair is 20% lower than traditional methods; intelligent and automated construction can reduce labor input and reduce overall costs by 30%.

[0243] Wide adaptability: It can be widely used in high and steep slopes of open-pit mines with an angle of 30° to 90°, is compatible with different geological conditions (such as sandstone and shale) and different climatic zones (can meet the needs of arid areas with annual precipitation less than 200mm), and has promotion potential.

[0244] (5) Social and environmental benefits

[0245] Controllable disaster risks: Landslide early warning can effectively ensure the safety and stability of mine slopes, ensure mine safety, further protect the lives and property of residents around the mining area, and reduce relocation and resettlement costs caused by ecological degradation.

[0246] Low carbon and environmentally friendly: Using solar power and biodegradable materials, carbon emissions are reduced by 25% compared to traditional restoration technologies.

[0247] Through intelligent innovation of the entire chain of "monitoring-decision-making-restoration", this invention has achieved intelligent, efficient, precise and sustainable ecological restoration of high and steep slopes in open-pit mines in arid areas. It has both technological advancement and engineering feasibility, and solves the core problems of low efficiency, high cost and poor effect of ecological restoration of high and steep slopes in arid areas. It provides an intelligent and replicable solution for ecological restoration of open-pit mine slopes, which has significant economic, ecological and social benefits.

[0248] By building an intelligent technology system for the entire "monitoring-decision-making-restoration" chain, this paper addresses the core pain points of ecological restoration of steep slopes in open-pit mines in arid areas, such as "lagging monitoring, blind restoration, resource waste, high costs, and unsustainable results." It proposes a set of innovative solutions featuring "technology integration, intelligent decision-making, precise restoration, and cost-intensiveness," forming a complete logical closed loop of "technological breakthrough-benefit improvement-value transformation." Its beneficial effects can be broken down into the following five aspects:

[0249] 1. Technical system: intelligent monitoring and adaptive repair system

[0250] Multi-source data fusion perception:

[0251] The UAV LiDAR constructs a high-precision three-dimensional terrain model (resolution 0.1m), ground sensors (spacing 10m×10m) collect soil moisture, pore water pressure and other data in real time, and the satellite InSAR realizes millimeter-level deformation monitoring, forming a three-dimensional perception system of "multi-source data fusion". The measurement frequency is significantly improved, and the data error control is very good.

[0252] AI early warning model construction:

[0253] Based on the LSTM (long short-term memory network) deep learning algorithm, combined with historical geological data and real-time monitoring data, it can monitor in real time and predict risks in advance. The early warning accuracy of dynamic prediction of landslide risks is >90%, avoiding secondary ecological damage caused by disasters.

[0254] Adaptive repair technology modular spraying device:

[0255] By integrating nano-water-retaining agents with seeds of local drought-tolerant plants (such as alfalfa and sea buckthorn), and using drones and robotic arms to coordinate operations, a precise 8-12 cm soil layer is laid. Combined with the soil-stabilizing effect of plant roots, this effectively increases slope shear strength by 20%, effectively preventing shallow landslides. Precise control of nano-water-retaining agent dosage (error <2%) reduces chemical waste. Seed survival rates are 50% higher than with traditional methods, and vegetation coverage reaches over 85%, significantly reducing repetitive construction costs.

[0256] Intelligent drip irrigation system: Combining soil moisture sensors with PID control algorithms to dynamically adjust drip irrigation frequency, it can achieve a water saving rate of ≥35% and reduce irrigation energy consumption by 20%.

[0257] 2. Water and energy conservation and intensive utilization of materials

[0258] Efficient management of energy and water resources

[0259] Edge computing empowers local decision-making: By deploying edge computing gateways in mining areas, sensor data is cleaned, analyzed, and issued warnings in real time, reducing communication energy consumption by 40%.

[0260] Solar power supply system: Using solar power and biodegradable materials to reduce dependence on the power grid and reduce carbon emissions by 25%.

[0261] Degradable soil-fixing net instead of plastic: Degradable plant fiber soil-fixing net is used to replace traditional plastic soil-fixing materials. The degradation cycle is 1 to 2 years. The degradation products can improve the soil structure, increase the organic matter content, and avoid long-term white pollution.

[0262] Precise control of nano water-retaining agent: precise control of nano water-retaining agent dosage (error < 2%), reducing agent waste.

[0263] At the same time, it improves the soil's water retention capacity and extends the vegetation's drought resistance period.

[0264] 3. Quality Improvement: Enhanced Ecological Adaptability and Improved Structural Stability

[0265] Through the digital twin model, different restoration schemes are simulated, and their impact on slope stability is simulated. Drought-resistant plant combinations (such as alfalfa + sea buckthorn) are selected to improve the stress resistance and self-sustaining ability of the slope ecosystem, forming a "vegetation-soil-microorganism" collaborative restoration ecological chain.

[0266] The structural stability is improved, the thickness of the imported soil layer and the root system are strengthened. After spraying, the thickness of the imported soil reaches 8 to 12 cm. Combined with the soil-fixing effect of plant roots, the shear strength of the slope is increased by 20%, effectively suppressing shallow landslides.

[0267] Long-term monitoring and maintenance: Establish slope health records, continuously optimize repair plans through dynamic monitoring data, and form a closed-loop management of "repair-monitoring-maintenance" to ensure the long-term safety and stability of the slope.

[0268] 4. Benefit transformation: economic benefit improvement and promotion value

[0269] Reusable modular devices: The modular spraying device can be reused, reducing the cost of single slope repair by 20%; intelligent construction reduces labor input and reduces the overall cost by 30%.

[0270] Compatibility with terrain and climate: Suitable for steep slopes of 30° to 90°, and compatible with different geological conditions such as sandstone and shale. In arid areas with annual precipitation of less than 200mm, it can achieve a vegetation survival rate of more than 80%. The technology is highly replicable and has promotion potential.

[0271] V. Social Value: Controllable Disaster Risks and Low-Carbon and Environmentally Friendly

[0272] Landslide early warning ensures safety: Landslide early warning using InSAR and LSTM models successfully avoids geological disasters, effectively ensuring the safety and stability of mine slopes, ensuring mine safety and further protecting the lives and property of residents in the mining area, and reducing relocation costs caused by ecological degradation.

[0273] Ecological degradation control: After the slope was repaired, the amount of soil and water loss was significantly reduced, biodiversity increased, and the ecosystem service function of the mining area was improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0274] Figure 1 This is a system architecture diagram of the present invention;

[0275] Figure 2 This is the "sky-air-ground" open-pit mine monitoring network diagram of the present invention;

[0276] Figure 3 Schematic diagram of the repair process of the present invention;

[0277] Figure 4 This is a flow chart of the ecological restoration operation of the present invention;

[0278] Figure 5 Schematic diagram of the repair device structure of the present invention. DETAILED DESCRIPTION

[0279] The specific implementation steps of this embodiment are as follows:

[0280] 1. Preliminary Preparation: Building a Data Perception and Decision-Making Foundation

[0281] Build as Figure 1 The system shown.

[0282] Topographic mapping and modeling: Use drones equipped with LiDAR equipment to obtain 0.1m resolution terrain data with a point cloud density of ≥200 points / m 2 , generating digital elevation models (DEMs) and three-dimensional topographic maps, accurately identifying slope gradients, slope directions, crack distribution, and potential landslide surfaces, providing precise topographic information for subsequent ecological restoration work. This provides a spatial benchmark for subsequent seeding path planning, soil consolidation net laying, and drainage system design, avoiding construction blind spots.

[0283] Sensor network deployment: Figure 2 As shown in the figure, the ground sensors are arranged in a grid: soil moisture sensors, tilt sensors, pore water pressure gauges, temperature and other sensors are installed at intervals of 10m×10m, and data is transmitted to the cloud platform in real time through LoRa communication technology (packet loss rate <1%).

[0284] Satellite InSAR data access: Connect to the Sentinel-1 satellite API, set up millimeter-level deformation monitoring tasks, and form a three-dimensional monitoring network of "ground sensors + satellite remote sensing" to ensure the timeliness and accuracy of risk warnings.

[0285] II. Construction Phase: Differentiated Governance Based on Risk Early Warning

[0286] According to Figure 3 The process shown uses Figure 4 The work flow shown is to build Figure 5 The device shown.

[0287] Prioritize restoration of high-risk areas using the LSTM model-driven decision-making: Based on the early warning results of the LSTM landslide prediction model, priority is given to soil consolidation spraying in high-risk landslide areas (landslide probability greater than 70%), and a "soil consolidation first, then vegetation" restoration strategy is formulated.

[0288] Phased revegetation:

[0289] The first stage (1-7 days): using high concentration spraying technology (substrate density ≥ 5kg / m 2 ) Spraying drought-tolerant herbaceous plants (alfalfa + Elymus dahliae) to quickly cover the ground and reduce wind erosion, with a target coverage rate of ≥ 60%;

[0290] The second stage (8-30 days): re-sow shrub seeds (sea buckthorn + caragana), and simultaneously start the water-saving drip irrigation system (initial frequency 1 time / 2 hours) to build a composite vegetation community of "herbaceous soil fixation-shrub slope protection" to improve the slope's ability to resist erosion.

[0291] Dynamic optimization of irrigation parameters through real-time data-driven regulation: Based on real-time soil moisture data monitored by ground sensors (updated every 15 minutes), the drip irrigation frequency is automatically adjusted (with an error of ±1 hour) through a PID control algorithm to avoid soil salinization caused by over-irrigation or vegetation wilting caused by under-irrigation.

[0292] Environmental response mechanism: Automatically reduce irrigation frequency (e.g., 2 times / day) in the rainy season and increase frequency (e.g., 4 times / day) in the dry season to maximize water resource utilization efficiency.

[0293] III. Effect Evaluation and Optimization: Quantitative Verification of Restoration Results

[0294] UAV multispectral monitoring for vegetation growth status assessment (vegetation coverage analysis): Using UAV multispectral imaging technology, NDVI data is collected every 15 days (NDVI threshold > 0.3 is considered viable vegetation) to generate a vegetation coverage map, with a target coverage rate of ≥85% within three months.

[0295] Precise replanting of defective areas: If the NDVI in a certain area is continuously below the threshold, the reasons are analyzed in combination with the three-dimensional topographic map (such as lack of light in the shadow area at the top of the slope, water loss in the cracks), and shade-tolerant plants are replanted or the density of the soil-fixing net is increased.

[0296] Slope stability verification: Using InSAR monitoring technology, the slope's deformation rate is assessed. Specifically, deformation data is compared, comparing millimeter-level deformation rates before and after repair. If the target value is <2 mm / month, the repair is considered successful; otherwise, it is considered a failure. Verification results are fed back to the system, forming a monitoring and verification feedback mechanism to provide data support for subsequent technical iterations.

[0297] If the deformation does not converge, cooperate with geological experts to review the geotechnical parameters (such as shear strength and pore water pressure) and adjust the thickness of the imported soil layer or the anchoring plan.

[0298] Risk warning upgrade: If the deformation rate is greater than 5mm / month, a red warning will be automatically triggered, construction will be suspended and emergency reinforcement measures (such as micropile reinforcement and geogrid laying) will be initiated.

[0299] 4. System Iteration: Data-Driven Technical Solution Optimization (Cloud Model Optimization)

[0300] The restoration data will be fed back to the digital twin platform, and the next round of ecological restoration technical solutions for open-pit slopes will be optimized based on the evaluation results, and iterative updates will be made to ultimately achieve the expected results of open-pit mine slope restoration.

[0301] Integration of restoration data fusion modeling digital twin platform: Vegetation NDVI, soil moisture, InSAR deformation and other data are imported into the digital twin model to build a "vegetation-hydrology-geology" coupling relationship and quantify the contribution of each factor to the restoration effect (for example, for every 10% increase in vegetation coverage, the slope stability coefficient increases by 0.05).

[0302] Dynamic optimization of technical solutions through machine learning algorithm analysis: Analyzing historical restoration cases based on the random forest algorithm to generate optimal parameter combinations (such as seeding density, irrigation frequency, and soil stabilization grid spacing);

[0303] Closed-loop iterative mechanism: In the next round of repair tasks, the optimized plan will be given priority, and continuous monitoring and verification will be carried out to form a spiral upward cycle of "evaluation-feedback-optimization", and ultimately achieve the goals of a slope repair success rate of ≥90%, a 30% reduction in water resource consumption, and a 20% reduction in governance costs.

[0304] Through the complete chain of "early data perception → construction risk classification → dynamic process control → quantitative verification of effects → closed-loop technology iteration", the system upgrades the ecological restoration of open-pit mine slopes from "experience-driven" to "data-driven", significantly improving the efficiency and sustainability of restoration, and providing a replicable intelligent paradigm for mine ecological governance in arid areas.

Claims

1. Intelligent ecological restoration system for high and steep slopes of open-pit mines in arid areas, characterized by: It includes the following four layers: (1) Data acquisition layer: Real-time collection of multi-dimensional environmental data is achieved through InSAR satellite remote sensing, ground sensor networks, and drone multispectral imaging; (2) Transport layer: LoRa and 5G hybrid networking is used to ensure low-power, high-bandwidth data transmission; (3) Platform layer: Based on the digital twin model and deep learning algorithm, a slope stability prediction and repair solution optimization platform is constructed; (4) Application layer: Remotely control the repair device through mobile terminals and the Web, and monitor the repair progress in real time.

2. An intelligent ecological restoration method for high and steep slopes in open-pit mines in arid areas, characterized by utilizing the system of claim 1, comprising the following steps: S1, data collection and monitoring; S1.1, multi-source data collection; Real-time collection of environmental data on steep mine slopes using InSAR satellite remote sensing monitoring, ground sensor networks, and drone multispectral imaging technology; S1.2 Data Fusion and Preprocessing Kalman filtering is used to eliminate sensor noise and remove outliers to achieve data cleaning. A spatiotemporal alignment algorithm is used to perform spatiotemporal registration of InSAR data with UAV images, achieving an error of less than 0.5m. S2. Data Analysis and Decision-Making Methods Data analysis and decision-making methods, through the closed-loop logic of "prediction-simulation-optimization", achieve intelligent management from risk warning to dynamic adjustment of governance solutions. Specific steps include: S2.

1. Constructing a dynamic prediction model for slope stability Detailed steps for building a dynamic prediction model for slope stability, including construction from historical joint datasets, dynamic prediction model construction and optimization, and hierarchical early warning and decision support; S2.

2. Construct a digital twin simulation model for slope ecological restoration; S2.

3. Based on the slope stability prediction results and the digital twin simulation model of slope ecological restoration, a three-dimensional slope model with a resolution of 1m×1m was constructed. This model was used to simulate the ecological restoration effects of restoration schemes with different seeding densities and irrigation frequencies. S2.

4. Dynamic optimization of repair resource allocation based on reinforcement learning The system converts simulation results into executable strategies and uses resource allocation algorithms to achieve cost reduction and efficiency improvement in slope management. S3. Ecological Restoration Implementation During the ecological restoration of high and steep slopes in mines, precise management from slope pretreatment to long-term vegetation maintenance is achieved through the coordination of modular devices and adaptive process flows.

3. The intelligent ecological restoration method for high and steep slopes of open-pit mines in arid areas according to claim 2 is characterized in that: S1.1 specifically includes: S1.1.

1. Establishing a macroscopic monitoring benchmark for slope deformation The system starts with InSAR satellite remote sensing monitoring, uses synthetic aperture radar satellites to obtain surface phase information, and calculates millimeter-level slope deformation through interferometric phase difference; S1.1.

2. Build a dynamic ground environment parameter perception network; Based on the InSAR deformation monitoring results, the system deploys a ground sensor network to supplement the micro-environmental parameters, including soil moisture monitoring, slope inclination monitoring, and pore water pressure monitoring; S1.1.3, integrated UAV multispectral imagery mesoscopic analysis; Introducing drone multispectral imaging technology to achieve mesoscale analysis of slopes with visible and near-infrared resolution; using vegetation index NDVI calculation and soil erosion identification: Vegetation health assessment: quantify vegetation coverage and growth status through NDVI, providing quantitative indicators for ecological restoration effects; Soil erosion detection: Identify exposed rock and soil areas based on the red edge band, and generate soil erosion risk maps based on deformation data to guide the optimization of slope protection projects; S1.1.

4. Develop a slope situation assessment system that integrates multi-source data; The system performs spatiotemporal alignment and feature fusion of InSAR deformation data, sensor network environmental parameters, and drone multispectral images to generate comprehensive slope stability assessment results.

4. The intelligent ecological restoration method for high and steep slopes of open-pit mines in arid areas according to claim 2 is characterized in that: S1.2 specifically includes: S1.2.

1. Complete sensor data cleaning and noise filtering; Perform noise suppression and outlier removal on the raw data collected by ground sensors; S1.2.

2. Achieve spatiotemporal alignment and precision matching of multi-source data; InSAR satellite data and drone imagery are integrated in the following ways to solve the differences: Time synchronization: Based on UTC time, the timestamp of InSAR data is aligned with the acquisition time of UAV image, ensuring the time error is less than 1 hour; the spatiotemporal alignment algorithm ensures the consistency of different data sources in time and space by matching the timestamp and spatial coordinates of the data. Spatial registration: Using fixed feature points on the slope as control points, the InSAR deformation data is projected into the UAV image coordinate system to keep the spatial error less than 0.5 meters; S1.2.

3. Generate a standardized fusion dataset; The cleaned sensor data, aligned InSAR deformation data, and UAV image features are correlated to form a joint dataset consisting of "deformation-environmental parameters-image features". The data source and quality level are annotated for direct use in subsequent stability analysis.

5. The intelligent ecological restoration method for high and steep slopes of open-pit mines in arid areas according to claim 2 is characterized in that: S2.1 The specific method is: S2.1.1, historical joint dataset construction; (1) Spatiotemporal alignment and enhancement of multimodal data a. Application of heterogeneous alignment algorithm for cross-source data A Transformer-based temporal registration framework is proposed to address the spatial and temporal scale differences among InSAR, ground sensors, and UAV images (sky-frequency), and a dynamic alignment loss function is constructed. Develop hybrid data interpolation methods: Generative Adversarial Networks (GANs) are used to fill missing InSAR regions, and wavelet packet transforms are used to denoise sensor noise data. b. Physical feature embedding By introducing geomechanical prior knowledge, the shear strength parameters of the slope rock and soil are mapped into data feature layers using the Mohr-Coulomb criterion to construct a physical constraint data set. Design a multi-scale feature pyramid; extract cross-scale correlation features of InSAR deformation rate, joint and fissure distribution, and pore water pressure fluctuation; (2) Dynamic generation of data labels Generate pseudo-labels for landslide probabilities; using a semi-supervised learning framework, a pre-trained physical simulator was used to generate 100,000 sets of virtual landslide scenarios, and weakly supervised labels were expanded through consistency regularization. Design a dynamic label correction mechanism. When new monitoring data conflicts with the prediction results, the Bayesian update rule is triggered to adjust the label confidence. S2.1.2, Dynamic prediction model construction and optimization; (1) Dynamic Graph Neural Network Architecture a. Spatiotemporal Heterogeneous Graph Convolutional Network ST-HGNN A dynamic heterogeneous graph structure is constructed, with open-pit mine slope monitoring points as nodes. Edge weights are updated in real time to the InSAR deformation rate correlation, and a temporal attention mechanism is introduced to capture lag effects. A sliding window graph partitioning strategy is proposed to automatically segment subgraphs according to deformation gradient changes, solving the overfitting problem of large-scale slope networks. b. Meta-learning driven adaptive updates Designing a MAML-based incremental learning framework enables the model to quickly adapt to new slope conditions, reducing cold-start training sample requirements by 80%; Develop a parameter drift detection module that monitors model parameter changes through KL divergence and triggers elastic weight solidification to prevent catastrophic forgetting. (2) Physical Constraint Reinforcement Learning a. A hybrid physics-data model integrates finite element analysis results and deep learning predictions, constructs a joint loss function, designs a physics-guided exploration strategy, and introduces the geotechnical yield criterion constraint in the reinforcement learning action space; b. Online incremental distillation learning Build a teacher-student model architecture: the teacher model guides the student model output, and gradually increases the weight of physical constraints through course learning; Develop an adaptive knowledge distillation loss function: dynamically adjust the distillation strength when monitoring data distribution shifts; S2.1.3, graded warning and decision support; (1) Multimodal warning signal fusion a. Detection of timing mutation points A variational autoencoder based on Wasserstein distance is proposed to capture small drifts in InSAR deformation rate in real time and identify early signs of slope instability. Design a multivariate risk index that integrates displacement rate, pore water pressure variability, and three-dimensional radar map scores of vegetation cover decay rate; b. Dynamic threshold adaptation Develop a reinforcement learning-driven warning threshold optimizer, using false alarm rate and missed alarm rate as reward functions to achieve adaptive graded warnings; A risk propagation model is introduced to automatically trigger cascade warnings for surrounding associated slopes when the predicted probability of instability exceeds a threshold. (2) Explainable warning engine a. Causal reasoning module Construct a structural causal model to quantify the contribution of key factors such as rainfall infiltration, crack expansion, and slope vegetation root reinforcement, and generate an interpretable report of SHAP values; Develop counterfactual prediction tools that input hypothetical scenarios and output stability evolution comparisons; b. Multimodal visual interaction Design a 3D digital twin warning panel that integrates multi-view linkage of InSAR deformation thermal maps, root tensile strength distribution, and warning probability spatiotemporal animation; Develop an AR on-site assistance system that uses Hololens2 to overlay prediction results and construction suggestions in real time.

6. The intelligent ecological restoration method for high and steep slopes of open-pit mines in arid areas according to claim 2 is characterized in that: S2.2 The specific method is: S2.2.1, Construction of digital twin of slope; (1) Multi-source heterogeneous data fusion and 3D reconstruction a. Data collection and preprocessing Integrate UAV oblique photography, LiDAR point cloud, geological drilling data, and InSAR surface deformation monitoring data to build a multimodal data warehouse; A deep learning algorithm is used to invert vegetation coverage from remote sensing images, and soil conductivity sensor data is combined to establish a slope surface feature map. b. 3D reconstruction of geological structures Based on borehole data and resistivity imaging inversion results, a heterogeneous geological layered model is constructed, and a discrete fracture network is embedded to simulate potential slip surfaces. Use the physical engine to simulate the stress field of rock and soil and generate a dynamic failure probability distribution layer; 3) Dynamic update mechanism of digital twins Deploy edge computing nodes to access InSAR deformation data in real time and dynamically correct slope stability parameters using Kalman filtering and Bayesian update algorithms; Design a blockchain-based data traceability framework to ensure the credibility and version consistency of multi-source data integration; (2) High-resolution 3D model generation a. Multi-scale modeling technology Adaptive octree meshing technology was used, with a fine-grained grid of 1m×1m×0.5m in potential landslide areas and a coarse-grained grid of 10m×10m in stable areas to optimize computing resources. The cellular automaton algorithm is introduced to simulate the slope erosion process and generate a dynamic terrain evolution model; b. Embedding of ecological elements Integrate a three-dimensional reconstruction model of plant roots to quantify the root tensile strength and soil reinforcement effect; Construct a microbial-soil-plant synergistic model to simulate the soil consolidation and moisturizing effects of biological crusts; S2.2.

2. Ecological restoration plan simulation and effect evaluation; (1) Multi-physics coupling simulation a. Microtopography and hydrological process simulation Based on the morphological algorithm, the micro-topography corresponding to different seeding densities was generated, and the SWMM model was used to simulate the dynamics of slope runoff and quantify the amount of soil and water loss. The complementary evapotranspiration model was introduced to simulate the effect of irrigation frequency on surface soil moisture content; 2) Vegetation growth and ecological succession model Develop a plant community competition model based on the logistic equation and combine it with a light use efficiency (LUE) parameterization scheme to simulate the spatiotemporal evolution of vegetation cover under different irrigation / fertilization strategies; Coupled niche model predicts the migration pattern of pioneer species to suitable habitats; 3) Uncertainty Quantification Analysis The MCMC method was used to quantify the parameter sensitivity of seeding density and irrigation frequency, and a probability cloud map of ecological restoration success rate was generated by Monte Carlo simulation. Introducing ensemble forecasting techniques to assess the long-term impact of climate change on restoration effectiveness; (2) Dynamic data assimilation and strategy optimization Real-time feedback closed-loop mechanism; deploying LoRaWAN sensor network to monitor actual vegetation index (NDVI) and soil moisture on slopes, assimilating observation data through EnKF algorithm, and correcting simulation model parameters; Design a dynamic irrigation scheduling algorithm based on reinforcement learning to optimize irrigation strategies using water use efficiency as a reward function; S2.2.3, executable strategy generation and verification; (1) Multi-objective decision support system a. Ecological-engineering coupling index system A three-dimensional decision space including ecological restoration, engineering stability, and economic cost was constructed, and the TOPSIS algorithm was used to generate the Pareto optimal solution set. Introducing ecological footprint models to quantify the sustainability of restoration solutions; b. Modular construction plan generation Based on BIM+GIS fusion technology, slope zoning repair plans are automatically generated and Gantt diagrams for construction machinery scheduling are output; Develop a lightweight VR interactive platform to support virtual simulation and conflict detection before construction; (2) Strategy verification and iterative upgrade a. Physical similarity verification A 1:50 scale physical model was built and centrifuge tests were performed to verify that the erosion rate predicted by the digital twin model had an error of ≤15%; Design field test plots and compare simulated predictions with measured vegetation survival rates; b. Adaptive model upgrade Build an online learning framework to update the global model library with new slope data through transfer learning; Design a federated learning architecture to enable sharing of restoration experience among multiple mining areas while protecting data privacy.

7. The intelligent ecological restoration method for high and steep slopes of open-pit mines in arid areas according to claim 2 is characterized in that: S2.3 The specific method is: 3D model construction: Generate a digital twin of the slope with a resolution of 1m×1m, integrating terrain, vegetation, and hydrological elements to achieve dynamic mapping between virtual space and physical entities; Restoration plan simulation: simulate the vegetation restoration effect under different spraying densities and irrigation frequencies, and quantify the correlation between vegetation coverage (NDVI) and soil erosion rate (L); The objective function of soil and water loss is constructed: minL = ∑(1-NDVIi) × Ai, where Ai is the unit area, and the target optimization is performed to achieve the goal of minimizing soil and water loss on the open slope.

8. The intelligent ecological restoration method for high and steep slopes of open-pit mines in arid areas according to claim 2 is characterized in that: The specific steps of S3 are as follows: S3.

1. Constructing an integrated operation unit of a modular ecological restoration device With functional modularization as the core, it integrates three subsystems: spraying, irrigation, and soil consolidation, to achieve flexible adaptation and efficient operation of the repair device: S3.

2. Implementing Adaptive Ecological Restoration Process Dynamically adjust restoration strategies based on the slope's underlying conditions, achieving rapid vegetation recovery and long-term survival through three phases of operations: Slope surface pretreatment: Use a combination of mechanical crushing and manual cleaning to remove loose rock and soil and remove unstable rock and soil layers; Spraying soil conditioner: 5kg / m 2 Humic acid mulch increases soil organic matter content and water retention, creating basic conditions for vegetation growth; Foreign soil spraying: build a foreign soil layer, spraying a 10cm-15cm thick foreign soil layer with a moisture content of 20%-25% to ensure the humidity required for seed germination; Non-woven fabric covering: Cover with non-woven fabric with a water permeability of ≥80% to reduce water evaporation, intercept wind erosion particles, and protect new vegetation; Control flow: Intelligent irrigation: Initial high-frequency irrigation: Drip irrigation once every 2 hours within 1-7 days, single water volume 10L / m 2 , promote rapid seed germination; In the middle and late stages, reduce the frequency and control the water: within 8-30 days, drip irrigation should be carried out once every 4 hours, and the water volume should be gradually reduced to 5L / m 2 , inducing deep rooting and improving the drought resistance of vegetation.

9. Intelligent ecological restoration device for high and steep slopes of open-pit mines in arid areas, characterized by: The method for implementing any one of claims 2 to 8 comprises: (1) Multi-dimensional environmental monitoring device Includes sensor nodes, data acquisition terminals and solar power supply modules; Use triangulation to deploy the sensor network to ensure that the monitoring blind area is less than 5%; (2) Slope repair construction equipment Including power system and modular drip irrigation system; (3) Edge computing gateway.

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