Abandoned mine vegetation community configuration recommendation system fused with ecological knowledge graph

Abstract

This invention relates to the field of ecological restoration technology for abandoned mines, specifically disclosing a vegetation community configuration recommendation system for abandoned mines that integrates ecological knowledge graphs. The system includes a spatial data calibration module for acquiring multi-source spatial data of abandoned mines, calibrating and extracting terrain features through a two-dimensional registration evaluation model, and generating a three-dimensional basic model of the mine. This invention provides a closed-loop adaptive intelligent management and control scheme. In actual abandoned mine restoration operations, it first uses two-dimensional spatial registration and digital twin technology to accurately map the physical mine environment to virtual space, significantly reducing blind spots in the terrain data from preliminary surveys. Based on this, it utilizes knowledge graph reasoning and a multi-objective optimization model to automatically calculate the globally optimal baseline scheme that balances ecological considerations and cost constraints before commencement of work.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology for abandoned mines, specifically to a vegetation community configuration recommendation system for abandoned mines that integrates ecological knowledge graphs. Background Technology

[0002] Ecological restoration projects in abandoned mines typically rely on manual on-site surveys to obtain basic data and on expert experience for vegetation configuration and construction schedule planning. In the traditional operation mode, the construction and scheduling plan is statically fixed at the beginning of the project, and the on-site construction team simply performs mechanized land preparation and seedling planting according to the drawings.

[0003] However, the environment of abandoned mines is complex and variable, which leads to various conflicts in the practical application of existing static planning methods:

[0004] On the one hand, due to limitations in the accuracy of early data and blind spots in experience, the initial static plan is difficult to accurately match the actual terrain of the mine in physical space, which can easily lead to mismatch between vegetation configuration and local environment.

[0005] On the other hand, when sudden deviations occur at the construction site due to terrain errors or substandard work quality, strong management conflicts arise.

[0006] Forcibly pushing forward with the original plan would lead to vegetation death and waste of resources; relying on on-site personnel to make blind local modifications would disrupt the original overall balance of resource allocation and overall ecological benefits. Summary of the Invention

[0007] This invention aims to at least partially address one of the technical problems in related technologies. Therefore, the objective of this invention is to propose a vegetation community configuration recommendation system for abandoned mines that integrates ecological knowledge graphs, in order to ensure long-term ecological restoration effects.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a vegetation community configuration recommendation system for abandoned mines that integrates ecological knowledge graphs, comprising:

[0009] The spatial data calibration module is used to acquire multi-source spatial data of abandoned mines, calibrate and extract terrain features through a two-dimensional registration evaluation model, and generate a three-dimensional basic model of the mine.

[0010] The digital twin construction module is used to map and construct a full-element digital twin containing natural and engineering elements based on the three-dimensional basic model of the mine.

[0011] The collaborative scheduling recommendation module is used to generate candidate vegetation pools by performing attribute association reasoning based on the ecological engineering water resources knowledge graph, and input the data of the full-element digital twin into a multi-objective mixed integer programming model to solve and generate the initial vegetation community configuration and construction scheduling scheme.

[0012] The edge cloud management module is used to collect real-time construction status data at the edge and verify construction quality. When a construction deviation is detected, the deviation data is uploaded to the cloud.

[0013] The dynamic reconstruction module is used to evaluate the impact of the deviation data through the deviation propagation chain quantization algorithm, trigger hierarchical scheme reconstruction to update the initial vegetation community configuration and construction scheduling scheme, and issue it for execution after virtual verification is passed.

[0014] To achieve the above objectives, a second aspect of the present invention proposes a method for recommending vegetation community configurations in abandoned mines by integrating ecological knowledge graphs, comprising:

[0015] S1. Acquire multi-source spatial data of abandoned mines, calibrate and extract terrain features through a two-dimensional registration evaluation model, and generate a three-dimensional basic model of the mine.

[0016] S2. Based on the aforementioned three-dimensional basic model of the mine, a full-element digital twin containing natural and engineering elements is constructed.

[0017] S3. Generate candidate vegetation pools by performing attribute association reasoning based on the knowledge graph of ecological engineering water resources, and input the data of the full-element digital twin into a multi-objective mixed integer programming model to solve and generate the initial vegetation community configuration and construction scheduling scheme.

[0018] S4. Collect real-time construction status data at the edge and verify the construction quality. When a construction deviation is detected, upload the deviation data to the cloud.

[0019] S5. Evaluate the impact of the deviation data through the deviation propagation chain quantification algorithm, trigger hierarchical scheme reconstruction to update the initial vegetation community configuration and construction scheduling scheme, and issue it for execution after virtual verification is passed.

[0020] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the above-described method for recommending the configuration of vegetation communities in abandoned mines by integrating ecological knowledge graphs.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This invention provides a closed-loop adaptive intelligent management and control scheme. In actual restoration operations of abandoned mines, it first uses two-dimensional spatial registration and digital twin technology to accurately map the physical mine environment to the virtual space, significantly reducing the blind spots in the terrain data of the early exploration. On this basis, it uses knowledge graph reasoning and multi-objective optimization models to automatically calculate the globally optimal baseline scheme that takes into account both ecological and cost limits before construction begins. In the core on-site construction stage, it breaks through the lag of traditional post-acceptance, directly capturing the real-time construction status and intercepting quality at the edge. Once an operational deviation occurs, the cloud system no longer relies on subjective human decision-making, but uses quantitative algorithms to accurately assess the chain reaction of the deviation on the construction period, cost and ecology, and automatically triggers the corresponding level of scheduling scheme reconstruction and virtual error prevention verification.

[0023] This solution transforms static construction drawings into dynamically evolving digital life forms, effectively resolving conflicts between local on-site deviations and global scheduling plans. It ensures that mine restoration projects can guarantee long-term ecological restoration results through automated reconstruction in the face of any sudden environmental disturbances or construction errors. Attached Figure Description

[0024] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0025] Figure 1 This is a schematic diagram illustrating the implementation of the abandoned mine vegetation community configuration recommendation system that integrates ecological knowledge graphs provided by this invention.

[0026] Figure 2 This invention provides a heat map of the two-dimensional registration error conflict score of multi-source spatial data in abandoned mines, which is part of the method for recommending vegetation community configuration in abandoned mines by integrating ecological knowledge graphs.

[0027] Figure 3 This is a three-dimensional slice data map of soil heavy metal ion concentration at a depth of 0-3m in the abandoned mine vegetation community configuration recommendation method that integrates ecological knowledge graphs provided by this invention;

[0028] Figure 4 This is a Pareto front scatter plot of the collaborative scheduling bi-objective mixed integer programming method for recommending vegetation community configuration in abandoned mines, which integrates ecological knowledge graphs, as provided in this invention.

[0029] Figure 5 This is a time-series dynamic fluctuation line graph of the vegetation life cycle water vitality index in the abandoned mine vegetation community configuration recommendation method that integrates ecological knowledge graphs provided by this invention.

[0030] Figure 6 This is a schematic diagram of the spatial overlap of root system niche depth in the multi-species combination in the recommended method for configuring vegetation communities in abandoned mines by integrating ecological knowledge graphs, provided by this invention.

[0031] Figure 7 This is a curve showing the dynamic drift negative correlation between instantaneous soil moisture content and compaction tolerance threshold in the abandoned mine vegetation community configuration recommendation method that integrates ecological knowledge graphs provided by this invention.

[0032] Figure 8 This is a flowchart illustrating the method for recommending vegetation community configuration in abandoned mines by integrating ecological knowledge graphs, as provided in this invention.

[0033] Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0034] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0035] The following describes, with reference to the accompanying drawings, a method, system, and electronic device for recommending vegetation community configurations in abandoned mines using integrated ecological knowledge graphs, according to embodiments of the present invention.

[0036] Example 1:

[0037] This invention provides a vegetation community configuration recommendation system for abandoned mines that integrates ecological knowledge graphs. The system relies on a high-performance server cluster in the cloud, an edge computing gateway deployed at the mine site, and a front-end multi-source sensor network (including UAV-borne LiDAR, multispectral cameras, portable soil probes, and onboard sensors for construction machinery) to operate collaboratively in terms of hardware architecture.

[0038] Specifically, the aforementioned abandoned mine vegetation community configuration recommendation system integrating ecological knowledge graphs includes: a spatial data calibration module, a digital twin construction module, a collaborative scheduling recommendation module, an edge cloud management and control module, and a dynamic reconstruction module. These modules interact with each other via high-speed industrial Ethernet and wireless communication protocols, such as 5G or low-power wide-area networks in specific frequency bands, to support adaptive closed-loop management and control throughout the entire lifecycle of abandoned mine restoration. Specifically, the system in this embodiment includes the following:

[0039] Step 1: Acquisition of multi-source spatial data and high-precision calibration.

[0040] Abandoned mines typically feature steep terrain, highly fragmented surfaces, and strong spatial heterogeneity. Traditional single-source remote sensing mapping methods are prone to occlusion or elevation distortion. Therefore, the system's spatial data calibration module is used to acquire multi-source spatial data of abandoned mines, calibrate and extract terrain features through a two-dimensional registration evaluation model, and generate a three-dimensional basic model of the mine.

[0041] For example, the process of acquiring multi-source spatial data of an abandoned mine includes: simultaneously receiving visible light point cloud data generated by UAV oblique photography and pulse reflection elevation data acquired by airborne lidar. Due to the difference between the origin of the sensor coordinate system and the scanning viewpoint, there is an unavoidable misalignment between the two sets of data in three-dimensional space. The spatial data calibration module quantifies the error using a two-dimensional registration evaluation model.

[0042] A two-dimensional registration evaluation model is calibrated and terrain features are extracted to generate a three-dimensional basic model of the mine. This includes obtaining normalized three-dimensional surface roughness values ​​and reprojection error residual values. The three-dimensional surface roughness values ​​reflect the severity of local terrain undulations, while the reprojection error residual values ​​reflect the pixel offset of multi-viewpoint images during the three-dimensional reconstruction process.

[0043] For example, the algorithm formula for calculating the multi-source registration error conflict score is defined here:

[0044] (1)

[0045] in, This represents the conflict score for multi-source registration errors; This represents the normalized three-dimensional surface roughness value. This represents the reprojection error residual value after normalization. Represents the preset weighting coefficients corresponding to the roughness; This represents the preset weighting coefficients corresponding to the reprojection error. In practical applications, This reflects the unreliability of multi-source data fusion within the current spatial blocks.

[0046] In areas with extremely high roughness, such as the edges of mine slopes, point cloud matching is often accompanied by high reprojection errors. This formula can highlight these high-risk misalignment areas in a numerical form.

[0047] It is important to note that after obtaining the aforementioned score values, the system extracts continuous spatial regions where the score values ​​exceed a preset score threshold as potential conflict areas. Subsequently, a thin plate spline deformation control algorithm is used to fit a smooth transformation function to achieve local coordinate system transformation in these potential conflict areas. The thin plate spline deformation control algorithm is a spatial interpolation and deformation technique based on radial basis functions. Its function is to perform non-rigid spatial twisting and stretching on the potential conflict areas with the constraint of minimizing bending energy, forcing the visible light point cloud and radar point cloud to align geometrically and topologically. After calibration, a cubic spline interpolation method is used to generate an elevation correction model to output the three-dimensional foundation model of the mine, thereby ensuring that the error between the subsequent vegetation planting elevation and the actual physical surface is controlled within an extremely low range.

[0048] like Figure 2 This figure illustrates the thermal distribution of the two-dimensional registration error conflict score in multi-source spatial data of an abandoned mine. The horizontal axis represents the horizontal spatial coordinate, and the vertical axis represents the vertical spatial coordinate, both in meters, covering a local mine mapping area with dimensions of 500 meters on each side.

[0049] The legend shows the registration error conflict score, with the graphic color gradually transitioning from dark blue to dark red, corresponding to the range of score values ​​increasing from 0.1 to approximately 0.95.

[0050] The large areas in the image that are blue and green generally have scores below 0.5, reflecting low surface roughness and reprojection error in these areas, indicating good spatial positioning consistency between airborne lidar and UAV oblique photogrammetry data. However, the areas in the image where the color gradually transitions to yellow and red and forms densely closed contour lines indicate that as local topographic relief intensifies, the matching bias of the multi-source data exhibits a non-linear upward trend.

[0051] In particular, the deep red core clusters in the figure have registration error conflict scores significantly greater than the preset scoring threshold of 0.7. These high-value red areas intuitively and objectively identify multi-source data misalignment zones in steep mine slopes or severely fractured surface areas.

[0052] The system extracts these red continuous spatial regions with a score greater than 0.7 as potential conflict areas, and then triggers the thin plate spline deformation control algorithm at the corresponding location to achieve smooth correction of the local coordinate system by fitting a smooth transformation function.

[0053] The spatial distribution characteristics of the aforementioned graphic data provide a quantitative basis for identifying potential positioning misalignments, thereby effectively ensuring the high-precision generation of the mine's three-dimensional basic model at the data processing front end.

[0054] Step 2: Construction of a global digital twin and establishment of a knowledge graph.

[0055] After acquiring a high-precision 3D basic model of the mine, the system's digital twin construction module is used to map and construct a full-element digital twin containing natural and engineering elements based on the 3D basic model of the mine.

[0056] Specifically, a full-element digital twin is a high-fidelity mapping of a physical mine in cyberspace. Natural elements include a digital elevation model and a soil physicochemical property distribution matrix, such as pH value, heavy metal ion concentration grid, and hydrological runoff vector direction; engineering elements include ore transport roads, intercepting and drainage ditches, topsoil spraying surfaces, and the real-time location of construction machinery. These twins are stored in a multi-layered, spatiotemporal object database format.

[0057] Optionally, to endow the system with the ability to recognize the logic of ecological restoration, this system constructs and maintains an ecological engineering water resources knowledge graph in the cloud. The structural nodes of the ecological engineering water resources knowledge graph include at least engineering management attribute nodes and water resources attribute nodes. The engineering management attribute nodes include the real-time market price of each candidate vegetation, the reserve quantity of surrounding nurseries, the procurement and logistics cycle, and the predicted annual maintenance cost. The water resources attribute nodes include the basic water requirement at each growth stage, salt tolerance threshold, soil water holding capacity, and irrigation cost per unit area for each candidate vegetation.

[0058] like Figure 3 This diagram presents the objective structure of a three-dimensional slice map of heavy metal ion concentration in soil at a depth of 0 to 3 meters, created by a digital twin of a mine. The horizontal axis represents the horizontal spatial coordinate, and the vertical axis represents the vertical spatial coordinate, both in meters. Together, they define the local surface area for mine restoration, measuring 100 meters in length and width. The vertical axis represents the depth coordinate, also in meters, objectively mapping the three-dimensional underground spatial entity extending from 0 meters below the surface to -3 meters.

[0059] The legend on the right side of the attached figure represents the concentration of heavy metal ions, in milligrams per kilogram. The color inside the orthogonal slice graphic smoothly transitions from dark blue to dark red, accurately corresponding to the rise of soil heavy metal concentration values ​​from a relatively safe baseline level of 50 to a highly dangerous enrichment level of 250.

[0060] The image, through three-dimensional slices on different coordinate planes, reveals the gradient distribution characteristics of hidden natural elements in three-dimensional space. It clearly shows that the high-concentration areas of heavy metals, represented by red and yellow areas, exhibit significant irregular clustering and spatial heterogeneous diffusion at depths of -0.5 meters and -2 meters. This three-dimensional data visualization mapping based on real-scale surveying demonstrates that the system's full-element digital twin possesses the ability to transform complex physical underground mine environments into multi-dimensional structured data.

[0061] In actual operation, the collaborative scheduling recommendation module directly reads the three-dimensional environmental data matrix in the above-mentioned slice space and inputs it as a hard constraint parameter into the algorithm system. This enables the recommendation system to identify the deep pollution coordinate blind zone with heavy metal concentration of more than 200 mg / kg in advance when generating candidate vegetation pools. Then, it can specifically match the remediation vegetation with deep root system and extremely high heavy metal tolerance threshold for the specific location, which significantly enhances the scientific nature of the subsequent mixed integer programming model output scheduling scheme and the reliability of long-term vegetation survival.

[0062] In practical mine restoration applications, the selection of vegetation cannot only consider whether it can survive. The amount of seedling reserves in the surrounding nurseries determines whether the project can be completed on schedule, while the predicted annual maintenance cost and salt tolerance threshold determine whether the vegetation can achieve low-cost and sustainable growth in barren mining areas.

[0063] Step 3: Attribute association reasoning and multi-objective collaborative scheduling generation.

[0064] Based on the aforementioned knowledge graph and twin data, the collaborative scheduling recommendation module is used to generate candidate vegetation pools by performing attribute association reasoning based on the ecological engineering water resources knowledge graph, and inputs the data of the full-element digital twin into a multi-objective mixed integer programming model to solve and generate the initial vegetation community configuration and construction scheduling scheme.

[0065] Specifically, the collaborative scheduling recommendation module extracts the associated attributes of the aforementioned nodes and combines them with site soil data and climate environmental factors from the three-dimensional basic model of the mine to calculate filtering rules and generate the candidate vegetation pool. For example, if the soil physicochemical data of a certain mining area shows high heavy metal content, the map inference engine will automatically filter out plants with low heavy metal tolerance and retain tolerant plants in the candidate vegetation pool.

[0066] For example, the multi-objective mixed-integer programming model is a dual-objective function optimization model. In engineering practice, cost control and ecological benefits often exhibit an inverse relationship. This model aims to find the Pareto optimal solution for both, and its objective function specifically includes calculations in two dimensions:

[0067] The first dimension is minimizing the total lifecycle cost function of the project, where the total cost consists of ecological seedling costs, engineering construction costs, labor dispatch costs, and water resource consumption costs. For example, the total lifecycle cost algorithm can be expressed as follows:

[0068] (2)

[0069] in, Represents the total cost throughout the project's entire lifecycle; The market price per seedling in the candidate vegetation pool; This represents the planned planting quantity of the corresponding seedlings; This represents the construction cost, including the cost of machinery and auxiliary materials. Represents the cost of manpower allocation; Represents the projected water consumption volume over the planning lifecycle; This represents the cost of acquiring a unit volume of water resources in the local area.

[0070] The second dimension is maximizing the comprehensive ecological benefit function. This function is composed of the weighted sum of the ecological benefit scores of each selected vegetation in the candidate vegetation pool, plus a water conservation reward. The value of the water conservation reward is negatively correlated with the ratio of total water consumption to total available water resources. For example, the comprehensive ecological benefit algorithm can be expressed as follows:

[0071] (3)

[0072] in, Represents comprehensive ecological benefits; The ecological benefit score assigned to each selected vegetation in the knowledge graph represents the score. Represents the weighted proportionality coefficient related to the corresponding vegetation biomass; This represents the system's preset water-saving benchmark reward constant; This represents the total water consumption in the planning scheme. This represents the total amount of usable water resources allowed by the environmental carrying capacity of the mining area. The purpose of this formula is to guide the algorithm to prioritize recommending drought-resistant and low-water-consumption vegetation communities under the same ecological score, which is consistent with the actual working conditions of abandoned mines that are often severely short of water.

[0073] The collaborative scheduling recommendation module optimizes within the constraints of budget, inventory, construction period, and total water resources, and outputs the initial vegetation community configuration and construction scheduling scheme. The output includes a structured 3D planting point map, a material arrival schedule, and an equipment and personnel shift matrix.

[0074] like Figure 4 The diagram shows a scatter plot of the Pareto front for a collaborative scheduling bi-objective mixed-integer programming problem generated by the system during the multi-objective optimization process. The horizontal axis represents the total project lifecycle cost in ten thousand yuan, encompassing the overall capital investment including ecological seedling costs, engineering construction costs, manpower scheduling costs, and water resource consumption costs. The vertical axis represents the comprehensive ecological benefit score in points, representing the comprehensive evaluation value composed of the weighted sum of the ecological benefit scores of each selected vegetation in the candidate vegetation pool and the water conservation reward.

[0075] The attached diagram shows a large number of gray circular dots, which constitute the set of all feasible solutions obtained through massive calculations within the constraints of budget, inventory, construction period, and total water resources. The red line connecting these dots at the upper left boundary and their red circular nodes form the Pareto front curve. Each red node on this curve represents the maximum ecological benefit score achievable with the current given cost input.

[0076] As the cost value on the horizontal axis gradually increases from 1.5 million yuan to about 4.8 million yuan, the ecological benefit score on the vertical axis starts from about 65 points, showing a non-linear growth trend of first rising rapidly and then gradually leveling off. This curve objectively reflects the law of diminishing marginal benefits between capital input and ecological output in abandoned mine restoration projects.

[0077] The green pentagram in the figure represents the final recommended solution output by the recommendation system after multi-objective trade-off optimization. This solution achieved a high level of comprehensive ecological benefits of approximately 86 points with a total cost of approximately 2.7 million yuan.

[0078] The attached figure visually demonstrates that the collaborative scheduling recommendation module has the computational ability to objectively find the optimal balance between the lower limit of capital cost control and the upper limit of ecological restoration benefits in a complex array of plant community combinations and scheduling strategies, providing a highly quantitative decision-making basis for the issuance and execution of actual construction scheduling plans.

[0079] Step 4: Lightweight distribution and on-site verification based on edge cloud collaboration.

[0080] In actual construction, network signals at mining sites are generally unstable, making it impossible to rely entirely on the cloud for high-frequency quality verification. Therefore, the system's edge cloud management module is used to collect real-time construction status data at the edge and perform construction quality verification. When a construction deviation is detected, the deviation data is uploaded to the cloud.

[0081] It is important to note that, to ensure the processing efficiency of the edge computing gateway, the edge cloud management module performs the following pre-distribution steps before collecting real-time construction status data: It trims data from the full-element digital twin in the cloud according to spatial region and process dimension to generate a lightweight construction batch twin. The trimming process removes all historical records and remote spatial data irrelevant to the current process, retaining only the necessary geometric and attribute data related to the work surface.

[0082] Subsequently, the lightweight construction batch twin is distributed to the edge computing gateway deployed at the mine site. This lightweight construction batch twin contains terrain data, local vegetation configuration schemes, and construction procedure tolerance rules for the current independent construction batch. Simultaneously, the system employs an incremental differential mechanism to synchronize data characteristics of state changes between the edge computing gateway and the cloud. This incremental differential mechanism calculates changes in the hash value of the data state and only packages and transmits these changes, significantly reducing bandwidth consumption in harsh network environments.

[0083] Specifically, during on-site operations, the construction quality verification includes: acquiring actual construction characteristic parameters of core construction procedures, including land preparation, pit digging, and planting. For example, data is acquired in real time using an onboard tilt sensor and a soil compaction meter. The actual construction characteristic parameters are compared with the tolerance rules for the construction procedures to calculate the deviation value. The tolerance rules include slope thresholds, compaction ranges, and fixed-distance planting spacing requirements. Taking compaction as an example, if the topsoil compaction is too high, it will lead to oxygen deficiency in the plant roots; if it is too low, it will easily cause soil erosion under rainwater runoff.

[0084] Optionally, the system executes a tiered interception logic: deviations within a preset tolerance range are classified as minor deviations, and on-site rectification instructions are directly generated by the edge device. In this case, cloud intervention is unnecessary; the edge device directly links to the construction worker's handheld terminal to prompt for a re-sinking or re-excavation. Deviations exceeding the preset tolerance range are classified as moderate or severe deviations, and these are synchronously transmitted as deviation data to the cloud's dynamic reconstruction module.

[0085] Step 5: Deviation Quantitative Assessment and Hierarchical Scheme Dynamic Reconstruction.

[0086] After receiving deviation data of moderate or higher in the cloud, the dynamic reconstruction module is used to evaluate the impact of the deviation data through the deviation propagation chain quantification algorithm, and trigger hierarchical scheme reconstruction to update the initial vegetation community configuration and construction scheduling scheme.

[0087] For example, the impact of the deviation data is evaluated using a deviation propagation chain quantification algorithm, including: inputting the deviation data into a preset quantification analysis logic tree, and outputting quantified impact values ​​from three dimensions: project duration, cost, and ecology. The quantification analysis logic tree is a directed acyclic graph model used to trace the chain reaction of deviations at a single node on subsequent nodes.

[0088] The project duration dimension outputs the expected total project delay due to partial rework or process waiting; the cost dimension outputs the excess cost increase due to additional material losses; and the ecological dimension outputs the expected percentage decrease in vegetation survival rate after a preset assessment period due to substandard underlying processes. For example, insufficient pit depth, in addition to directly increasing re-excavation costs, will also be input to the ecological dimension node, outputting its negative quantitative expectation on the survival rate of specific deep-rooted plants.

[0089] Specifically, a hierarchical scheme reconstruction is triggered to update the initial vegetation community configuration and construction scheduling scheme. Targeted reconstruction is implemented based on the scale of the impact on the quantified values, including:

[0090] When the influence range of the quantified impact value indicates deviation does not exceed a preset local area threshold, local reconstruction is triggered, and the local solver is invoked to re-optimize the vegetation configuration of the affected plots. Local reconstruction locks in global constraints and performs local processing only at a microscale, generating remedial strategies in a short time. When the influence range of the quantified impact value indicates deviation exceeds the preset local area threshold but does not break the project's global constraints, batch reconstruction is triggered, readjusting the timing of seedling logistics allocation and water resource scheduling for the current batch. When the quantified impact value indicates deviation causes the project's global constraints, such as a hard deadline or budget ceiling, to be broken, global reconstruction is triggered, and the updated twin state is re-input into the multi-objective mixed integer programming model to generate a new initial vegetation community configuration and construction scheduling scheme. This hierarchical strategy effectively avoids the computational cost of global calculations caused by local changes by classifying and handling deviations of different ranges.

[0091] Step Six: Virtual Verification and Error-Proofing Distribution Mechanism.

[0092] To avoid secondary conflicts when the scheme generated by the system reconstruction is executed in the physical world, the dynamic reconstruction module performs virtual verification before issuing the execution.

[0093] Specifically, the reconstructed initial vegetation community configuration and construction scheduling plan are injected into the full-element digital twin for virtual simulation at preset simulation steps. The discrete event simulation engine advances the timeline based on the standard movement speed of the construction machinery and the time consumption benchmark of the work process. Subsequently, it detects whether there are resource node conflicts and timing congestion during the simulation process.

[0094] For example, the system can detect spatial interference caused by multiple excavators operating in the same narrow mine tunnel, or seedlings arriving before the land preparation is completed, leading to seedling dehydration and apparent death. If any of these exist, the system dynamically adjusts the scheme weights and re-triggers the reconstruction solution until all simulation results meet the preset verification criteria before execution.

[0095] It is important to note that after project acceptance, the system implements a post-evaluation mechanism: extracting the correlation mapping patterns between actual construction quality characteristics and actual ecological effects, and updating them in the ecological engineering water resources knowledge graph, thus completing the system's adaptive evolution. This means that with each completed mine restoration project, the system's internal knowledge graph becomes richer, and the subsequently generated recommended solutions will become more accurate.

[0096] Step 7: Adaptive closed-loop maintenance of vegetation life cycle.

[0097] Once the construction phase is complete, the system enters a long-term ecological conservation phase. It also includes an adaptive maintenance module for managing the vegetation lifecycle after the construction schedule has been executed.

[0098] Specifically, the water vitality index of the corresponding vegetation unit is calculated by integrating soil electrical conductivity and leaf water content obtained from ground-based sensor networks, as well as vegetation indices extracted from UAV remote sensing. Abandoned mines often have poor water retention, and fixed timed and quantitative watering strategies can easily lead to uneven drought and flooding. The calculation basis of the water vitality index is a multiplicative ratio function of the measured photosynthetic rate, stomatal conductance, vegetation water potential, and chlorophyll content with their corresponding standard values ​​under healthy conditions.

[0099] For example, the core ratio algorithm for the water vitality index can be expressed as follows:

[0100] (4)

[0101] in, Represents the water vitality index; This represents the ratio of the measured photosynthetic rate to the standard photosynthetic rate under healthy conditions. This represents the ratio of the measured stomatal conductance to the standard stomatal conductance under healthy conditions. It represents the ratio of the measured vegetation water potential to the standard vegetation water potential under healthy conditions; This represents the ratio of the measured chlorophyll content to the standard chlorophyll content under healthy conditions. The representative model calibrates the environmental coefficients.

[0102] In practical applications, It is a dynamic, dimensionless value that can detect early warnings of water shortage in plants earlier than visual observation. This is because when plants wilt, their internal cells are usually already damaged, while the microscopic decline in stomatal conductance and water potential is reflected in the index in advance.

[0103] Optionally, vegetation can be divided into multiple health levels based on the water vitality index, and a dynamic water demand correction factor can be generated accordingly to adaptively adjust the maintenance irrigation quota. For example, the opening time of irrigation valves in areas with severe water stress can be increased, while the quota in healthy areas can be reduced, thereby achieving precise drip irrigation scheduling of water resources.

[0104] like Figure 5 This figure illustrates the temporal dynamic fluctuations of the vegetation lifecycle water vitality index. The horizontal axis represents the time span in days, and the vertical axis represents the water vitality index, showing a data range from 0.2 to 1.0. The solid blue line in the figure represents the dynamic monitoring curve of the water vitality index. The three dashed lines from top to bottom represent the lower limit of health level at 0.8, the lower limit of mild stress at 0.6, and the lower limit of moderate stress at 0.4, respectively.

[0105] The waveform transformation characteristics of the blue curve indicate that in the early stage of the observation, the water vitality index remained above 0.8, which is within the healthy range. This means that the product ratio of the four parameters of the corresponding vegetation unit—the measured photosynthetic rate, stomatal conductance, vegetation water potential, and chlorophyll content—is close to the healthy standard benchmark.

[0106] As time progressed and soil moisture in the mine continued to dissipate, the dynamic monitoring curve showed a continuous downward trend, falling below the 0.6 value line around the tenth day, and then further below the 0.4 value line on the fourteenth day, indicating that the vegetation had entered a severe stress level. The continuous curve value changes generated by the fusion of multi-source sensor data reflected the decline in plant microphysiological indicators before the vegetation exhibited visible physical wilting.

[0107] When the system determined that the index had fallen below 0.4 and thus identified severe water stress, it automatically generated a dynamic water demand correction factor and increased the irrigation valve opening time. As a result, the dynamic monitoring curve showed a significant steep upward waveform on the fifteenth day, quickly rebounded, and stabilized again in the healthy range above 0.8.

[0108] The time-series data distribution map confirms the plant water shortage early warning function and quantitative feedback mechanism of the adaptive maintenance module, providing reliable engineering data support for the long-term precise irrigation scheduling of vegetation in abandoned mines.

[0109] Based on existing technologies, traditional vegetation restoration in abandoned mines relies heavily on static blueprint design and experience-based on-site management. This presents technical bottlenecks such as large errors in the fusion of multi-source surveying data, the inability to correlate plant physiological characteristics with engineering constraints, and the lack of quantitative response strategies for unexpected construction deviations on site.

[0110] The technical solution disclosed in this embodiment establishes a high-precision data foundation through two-dimensional calibration of spatial data and construction of digital twins; by introducing ecological engineering water resources knowledge graph and multi-objective mixed integer programming, it realizes the leap from subjective experience to objective multi-dimensional constraint optimization of plant community configuration.

[0111] More importantly, by utilizing edge cloud collaboration and deviation propagation chain quantization algorithms, this solution constructs an automated closed-loop management system from real-time deviation capture on-site to hierarchical virtual verification and reconstruction in the cloud. The overall solution effectively mitigates the long-term ecological risks caused by the harsh microenvironment of mines and unstable construction quality, significantly improving resource utilization and final vegetation survival rates throughout the project's entire lifecycle, and possesses practical industrial application value.

[0112] Example 2:

[0113] The above-mentioned Embodiment 1 mainly addresses the problem of one-way adaptive matching between individual vegetation and the physical environment of the mine, as well as the problem of multi-objective scheduling at the macro level. In contrast, Embodiment 2 delves into the sociological level of vegetation in ecology, focusing on solving the problem of interaction and feedback within the biological community when multiple vegetation species are mixed and planted in the same limited space.

[0114] The structural nodes of the ecological engineering water resources knowledge graph also include: biological interaction attribute nodes, which include the allelopathic inhibition coefficients and mycorrhizal symbiosis gain factors between each pair of vegetation in the candidate vegetation pool, as well as the root niche depth distribution range of each vegetation.

[0115] Specifically, biological interaction attribute nodes are graph structure nodes in the ecological engineering water resources knowledge graph used to represent the complex relationships between living organisms. In the actual restoration scenario of abandoned mines, the survival and succession of vegetation depends not only on soil physicochemical properties and water conditions, but also on the profound influence of interactions between associated plants. To transform this implicit ecological experience into explicit rules that can be processed by computers, this system introduces quantified attribute parameters into the knowledge graph.

[0116] For example, the allelopathic inhibition coefficient is a numerical variable used to quantify the strength of the chemical inhibition exerted by one plant on the seed germination or seedling growth of another adjacent plant through root exudates, decomposed leaf matter, or volatile gases. In nature, some plants exhibit strong exclusivity; for instance, the needles of certain pine species release specific phenolic acids during decomposition, making it difficult for certain herbaceous plants to grow beneath their canopy. In the directed graph structure of a knowledge graph, the allelopathic inhibition coefficient is defined as the weight of the directed edge connecting two vegetation entity nodes. This inhibition is usually directional; that is, a high inhibitory effect of vegetation A on vegetation B does not necessarily mean that vegetation B has the same degree of inhibitory effect on vegetation A. The system pre-extracted and labeled these directed edge weights through machine learning on a large amount of ecological literature and historical mine restoration sample data, thus providing basic data for subsequent algorithms to eliminate malicious competing combinations.

[0117] Specifically, mycorrhizal symbiotic benefit factors are numerical variables used to quantify the strength of the positive synergistic effect between different plant species through interconnected underground fungal networks or through specific nitrogen-fixing bacterial communities, thereby enhancing their survival rates and biomass accumulation. In the extremely barren soil environment of abandoned mines, symbiosis has decisive application value.

[0118] For example, leguminous plants, such as black locust and amorpha, can fix atmospheric nitrogen and convert it into available nitrogen in the soil due to the presence of rhizobia in their roots; while grasses, such as tall fescue and bermudagrass, have a high demand for nitrogen fertilizer but cannot fix nitrogen themselves. When these two types of plants are planted in the same community, there is a significant nutrient transfer and mutualistic symbiotic mechanism. The system quantifies this mutualistic mechanism as the weight values ​​of undirected or bidirectional edges and stores them in the knowledge graph. The larger the value, the more significant the positive ecological feedback effect produced when these two types of plants are planted together.

[0119] It is important to note that the root niche depth distribution range of each vegetation type is used to describe the main concentrated area of ​​active root biomass in the vertical soil profile during the mature stage of the vegetation. The topsoil or original soil layer in abandoned mines is usually thin, and resource space is relatively limited. Different plants have shallow, medium, or deep root systems based on their physiological characteristics. The system stores this attribute in the form of a one-dimensional vector interval, for example, recording the active root distribution range of a shrub as 10 cm to 40 cm below the surface. Clearly defining this interval distribution is a prerequisite for subsequent calculations of the intensity of competition for underground space resources.

[0120] Specifically, after generating the candidate vegetation pool, the collaborative scheduling recommendation module further combines the vegetation in the pool into multiple species combinations and calculates the community cohesion index of each combination.

[0121] The candidate vegetation pool generated in Example 1 only means that each individual plant in the pool meets the external constraints of the current mining block, such as climate, soil, and economic costs. However, directly randomly selecting plants from this pool for planting carries a very high risk of community collapse. Therefore, the collaborative scheduling recommendation module incorporates a combinatorial optimization algorithm. Based on preset upper and lower limits for the number of community species, such as limiting each community to consist of 3 to 5 different plant species, it uses a mathematical combinatorial traversal method to enumerate all potential multi-species combinations from the candidate vegetation pool.

[0122] To avoid computational dimensionality explosion due to an excessively large candidate pool, the collaborative scheduling recommendation module prioritizes heuristic search based on strong symbiotic edges in the graph when enumerating multi-species combinations, eliminating unrelated combinations consisting entirely of isolated nodes. For each enumerated multi-species combination, the system must evaluate its internal stability as a whole ecological unit. The core indicator for this stability assessment is the community cohesion index. The community cohesion index is a dimensionless quantitative indicator that comprehensively reflects the degree of allelopathic repulsion, symbiotic promotion, and underground niche competition among species within the combination. The higher the value, the stronger the self-sustaining capacity of the plant community, and the easier it is to form a stable climax community in abandoned mining environments lacking intensive human intervention.

[0123] Specifically, the calculation logic of the community cohesion index is as follows: the mycorrhizal symbiotic gain factors among all vegetation in the combination are accumulated, the allelopathic inhibition coefficients among all vegetation are subtracted, and the root niche complementarity value is added. The magnitude of the root niche complementarity value is negatively correlated with the spatial overlap between the depth distribution ranges of the root niches of each vegetation in the combination.

[0124] The above computational logic is divided into assessments of the spatial physical competition dimension and the biochemical interaction dimension. The collaborative scheduling recommendation module first processes the spatial physical competition dimension, that is, calculates the root niche complementarity value. The soil water storage and total nutrient content in abandoned mines are constant. If all plants in a multi-species combination concentrate in the same soil depth to absorb water and nutrients, it will inevitably lead to fierce resource competition.

[0125] For example, the root niche complementarity algorithm can be represented as follows:

[0126] (5)

[0127] in, Represents the complementary value of root niches; The preset complementary value scaling weight parameter is used to adjust the importance of spatial competition factors in the final cohesion index. This represents the total number of vegetation species included in the currently being evaluated multi-species assemblage; and Represents any two distinct vegetation species index variables within a combination; Representative plant species With vegetation species The absolute overlap thickness between the root niche depth distribution intervals is obtained by calculating the mathematical intersection length of the two depth intervals. The total depth of soil in an effective assessment of the target mine remediation area.

[0128] It's also important to note that in the application of the above formula, the numerator represents the sum of the overlap thickness of the root distribution between all pairs of species within the combination. This sum of overlap thickness, after being normalized and divided by the preset total soil depth and combination size, yields the spatial overlap. Subtracting this overlap from the constant achieves a negative correlation mapping logic. Its physical meaning is that the more the plant root distribution within the combination exhibits a three-dimensional, layered characteristic with varying heights—for example, shallow-rooted herbs paired with deep-rooted trees, without interference—the more effective the calculated... The larger the value, the more efficient the community is in utilizing underground vertical space, thus avoiding vicious competition within the same soil layer.

[0129] like Figure 6 This figure illustrates the spatial overlap and area filling status of root niches in multiple species combinations. The horizontal axis represents the relative activity of the root system as a percentage, and the vertical axis represents the soil depth in centimeters. The vertical axis is arranged in a reverse ascending order from 0 to 80 to accurately reflect the physical spatial distribution characteristics of the underground soil profile.

[0130] The figure includes the root distribution curves of two representative plants and the closed filled area formed by them and the vertical axis. The light green semi-transparent area represents the shallow root candidate plants in the legend. Their root activity is mainly concentrated in the shallow soil layer 10 cm to 25 cm below the surface, and reaches an activity peak of about 85 near a depth of 15 cm.

[0131] The dark blue semi-transparent area represents the deep-rooted candidate plants in the illustration. Their active range is mainly distributed in the medium-deep soil layer from 20 cm to 60 cm, and reaches an activity peak of about 95 near a depth of 40 cm.

[0132] Between these two main distribution areas, the system extracted the spatial overlap between them and highlighted it significantly in dark red with high opacity. This red area corresponds to the niche spatial overlap area in the legend. The waveform transformation of the curves and the overlapping shape of the graphs show that although the overall distribution depth of the two plants is staggered, there are still significant spatial overlaps and resource competition areas within the depth range of 20 cm to 30 cm.

[0133] By calculating the area intersection in this way, the system can objectively extract the root niche complementarity value between different plant combinations and use the quantitative parameters of this spatial physical competition dimension as a filtering condition for combination evaluation. This eliminates inferior competitive combinations with excessively large dark red overlapping areas, thereby ensuring that the final packaged ecological co-construction group has efficient underground three-dimensional space utilization and long-term survival stability.

[0134] After obtaining the quantified value of the space physics competition dimension, the system will further combine it with the biochemical interaction dimension to finally calculate the target parameter. For example, the community cohesion index algorithm can be expressed as follows:

[0135] (6)

[0136] in, Represents the community cohesion index; Representing vegetation species extracted from knowledge graphs With vegetation species Mycorrhizal symbiotic benefit factors between them; Representing vegetation species extracted from knowledge graphs For vegetation species The allelopathic inhibition coefficient; This represents the root niche complementarity value calculated using the first formula.

[0137] For example, equation (6) iterates through all directed interactions between species within the combination using a double-summation function. Essentially, it is a pure arithmetic logic of addition and subtraction, positively accumulating symbiotic gains that promote community prosperity, deducting allelopathic inhibition terms that lead to community degradation as a penalty function, and finally superimposing complementary reward scores from the three-dimensional root system space. In actual abandoned mine restoration projects, vegetation combinations with high scores calculated according to this formula typically possess excellent ecological structural characteristics: upper layer windbreak, drought resistance, and nitrogen fixation; lower layer water conservation and slope protection; and deep and shallow root system interweaving and anchoring.

[0138] Specifically, the collaborative scheduling recommendation module removes vegetation combinations whose community cohesion index is lower than a preset stability threshold, encapsulates the remaining high cohesion combinations into ecological co-construction groups, and uses the ecological co-construction groups as rigid selection units for generating the initial vegetation community configuration.

[0139] After the massive calculations described above, the system obtains a mapping list containing various multi-species combinations and their corresponding community cohesion indices. The collaborative scheduling recommendation module then initiates a filtering mechanism, comparing the cohesion indices in the list with pre-configured scale parameters.

[0140] For example, a high-cohesion combinatorial screening and encapsulation algorithm can be represented as follows:

[0141] (7)

[0142] in, This represents the set of ecosystem co-construction groups generated by the encapsulation, i.e., the set of rigidly selected units; Represents the first generation generated by the combinatorial algorithm A number of candidate multi-species combinations; Representing the Community cohesion index corresponding to multiple species combinations; This represents the preset stability threshold set by the system based on the specific geological hazard risk level of the mine.

[0143] It is important to note that combinations rejected using the above formula indicate severe interspecies incompatibility or resource competition. Forcing such combinations onto mines could lead to widespread mortality or loss of community diversity later on. Combinations meeting the threshold conditions are then allowed entry. The combination of sets is then logically packaged and solidified by the system, that is, encapsulated into an ecosystem co-construction group.

[0144] For example, when the ecological co-construction group is used as a rigid selection unit for generating the initial vegetation community configuration, the decision variable of the original model in the multi-objective mixed integer programming model mentioned in Embodiment 1 is a single vegetation species, such as whether to purchase plant A or whether to purchase plant B. However, under the technical mechanism of this embodiment, the decision variable of the multi-objective mixed integer programming model has been upgraded and changed to the ecological co-construction group, such as whether to purchase and configure a fixed combination of A, C, and F.

[0145] This approach of transforming independent scattered points into rigid units forces ecological constraints onto downstream operations research optimization algorithms. It effectively avoids the risk that multi-objective mixed-integer programming models, in their pursuit of minimizing engineering costs or maximizing procurement and logistics, might disorderly fragment stable plant communities and generate low-adaptability plant combinations within the algorithm. In other words, regardless of how the algorithm subsequently balances human resources, water resources, and budget, its configuration result will inevitably be constructed from cohesive groups selected through internal cohesion.

[0146] In terms of existing technology, traditional mine vegetation configuration schemes mostly remain at the preliminary stage of selecting species based on the adaptation of individual plant characteristics to environmental conditions. For example, existing seed selection software simply relies on a simple comparison and recommendation of plant tolerance ranges for temperature, humidity, and soil pH, with engineers ultimately making subjective decisions on how to mix and sow. This extensive approach completely ignores the species interaction effects at the community level. As a result, although some selected individual plants may have strong survival capabilities, after being randomly mixed and sown, they often lead to fierce interspecific competition due to strong allelopathic antagonism or complete overlap of underground root niches. The engineering results are often as follows: initially, the plants grow vigorously, but after a natural succession cycle, some highly invasive plants severely suppress other companion plants, forming a degraded community of a single species. This fails to establish a complex ecosystem network with resilience, and may even fail to provide the ecological function of deep slope stabilization and landslide prevention due to the overly singular root system in the soil layer.

[0147] The technical solutions described in this embodiment introduce complex mechanisms from ecology, such as allelopathic repulsion, mycorrhizal symbiosis, and overlapping root niches, into a digital computing framework. By constructing knowledge graph nodes with interaction attribute weights and using the community cohesion index calculation formula to accurately quantify the stability of multiple species combinations, the conflict blind spots in species mixing are effectively resolved.

[0148] Furthermore, by encapsulating high-scoring combinations into indivisible, rigid selection units, the logical gap between upstream pure ecological evaluation and downstream pure engineering mathematical programming (mixed-integer programming) is bridged. The overall scheme ensures that, in the extremely harsh and nutrient-poor habitats of abandoned mines, the recommended output is not merely a single surviving plant, but a highly resistant vegetation community with self-succession capabilities, complementary spatial utilization, and mutually promoting growth. This mechanism significantly reduces the long-term costs of water, fertilizer, and artificial maintenance after mine remediation and lowers the remediation failure rate caused by population competition at the source, possessing strong practical guiding significance for industry.

[0149] Example 3:

[0150] The aforementioned embodiments mainly address the issues of high-precision calibration of spatial data and static optimization configuration of vegetation communities at a global scale. In contrast, this third embodiment focuses on the actual physical working face, addressing the physical impact of harsh and highly uncontrollable microclimate disturbances in abandoned mines on established construction quality standards.

[0151] Specifically, before calculating the deviation value by comparing the actual construction characteristic parameters with the construction procedure tolerance rules, a microenvironment-driven tolerance rule dynamic drift calibration step is also included.

[0152] The microhabitat-driven tolerance rule dynamic drift calibration step is an adaptive algorithm operation mechanism deployed within the edge computing gateway at the mine site. In traditional engineering management, construction procedure tolerance rules issued from the cloud or design drawings, such as specified compaction degree and planting depth, are considered absolute static execution standards. However, the working environment of abandoned mines exhibits strong spatial heterogeneity and temporal abrupt changes. During specific construction periods, local microclimates and transient soil physical properties fluctuate drastically. Ignoring these environmental fluctuations and mechanically demanding that the construction site conform to static standards often leads to compliant but unreasonable destructive construction practices.

[0153] Therefore, this embodiment constructs a pre-emptive dynamic drift calibration mechanism, which allows the edge to make flexible corrections to the rules themselves within a reasonable range based on the real-time environment.

[0154] The above-mentioned microhabitat-driven tolerance rule dynamic drift calibration steps specifically include: during the execution of the core construction process, real-time acquisition of the transient microhabitat vector of the current working surface, wherein the transient microhabitat vector includes at least the instantaneous soil volumetric water content and the instantaneous surface wind speed.

[0155] For example, the process of executing core construction procedures encompasses key physical steps such as mechanized site preparation, manual or semi-automated pit digging, and planting of seedlings. Simultaneously, the system collects data at high frequency through onboard sensors deployed at the front of construction machinery or portable wireless sensor nodes pre-positioned around the work area.

[0156] It is important to note that the transient microhabitat vector is a single-column matrix or dataset used in mathematical space to characterize the multi-dimensional instantaneous environmental state of a local construction area. Instantaneous soil volumetric water content refers to the percentage of liquid water in a unit volume of soil at the physical instant of data acquisition. This parameter is typically obtained by inserting a soil moisture sensor based on time-domain reflectometry into the surface soil of the work surface. Instantaneous surface wind speed refers to the instantaneous speed of horizontal air movement at a preset height above the work surface, typically 1.5 to 2 meters; this parameter is captured in real time by an ultrasonic anemometer. The system synchronously samples these dynamically changing multi-source sensor values, digitally filters and denoises them, and then splices them in a specific dimensional order to construct a transient microhabitat vector reflecting the current environmental slice.

[0157] Optionally, the reason for choosing instantaneous soil volumetric moisture content and instantaneous surface wind speed as the basic components of transient microhabitat vectors is that these two indicators have the most direct and strongest physical and physiological interference with the basic construction quality of vegetation restoration in abandoned mines. Instantaneous soil volumetric moisture content directly determines the mechanical plasticity and compressive strength of the soil, while instantaneous surface wind speed directly affects the instantaneous evaporation rate of water on the construction surface, thereby interfering with the rate of soil water loss after excavation and the physiological survival window period when seedling roots are exposed.

[0158] After successfully acquiring and constructing the transient microhabitat vector, the edge computing gateway will perform the next step: calculate the environmental fluctuation difference between the transient microhabitat vector and the standard construction environment benchmark value, and input the environmental fluctuation difference into the preset tolerance compensation logic to calculate the tolerance drift coefficient corresponding to each core construction procedure.

[0159] Specifically, standard construction environment baseline values ​​refer to the idealized or averaged external environmental parameters assumed by the system when formulating the initial construction procedure tolerance rules in the cloud. For example, if a land preparation rule is formulated under the assumption of a soil moisture content of 15% and a windless, sunny weather, then these conditions constitute the standard construction environment baseline values.

[0160] The environmental fluctuation difference is the mathematical difference between the real-time monitoring values ​​of each item in the transient microhabitat vector and the corresponding standard construction environment benchmark value. This difference reflects the direction and severity of the deviation of the current actual physical environment from the ideal design environment. Inputting the environmental fluctuation difference into the preset tolerance compensation logic is essentially performing a spatial mapping operation of multi-dimensional features. The tolerance drift coefficient is a dimensionless multiplier factor used to indicate the proportional range by which the original static threshold needs to be amplified or reduced.

[0161] For example, the basic drift coefficient algorithm in the preset tolerance compensation logic can be expressed as follows:

[0162] (8)

[0163] in, This represents the base quantity of the calculated tolerance drift coefficient; This represents a real-time monitoring value of a specific environmental feature collected from a transient microhabitat vector. This represents the standard construction environment benchmark value corresponding to this environmental characteristic; This represents the preset environmental sensitivity weight base. represent .

[0164] It should be noted that equation (8) employs an exponentially decaying function based on the base of the natural logarithm. Its physical meaning lies in the fact that when real-time monitoring values... Infinitely close to the benchmark value When the exponential term approaches 0, the drift coefficient... A value close to 1 indicates that the current environment is consistent with the ideal state, and there is no need to drift the tolerance rules. However, as the absolute value of the environmental fluctuation difference gradually increases, the drift coefficient will be non-linearly and smoothly reduced according to the set sensitivity weight.

[0165] The drift logic is highly targeted for different core construction procedures. Specifically, for the compaction range of the land preparation procedure, the corresponding tolerance drift coefficient has a negatively correlated attenuation mapping relationship with the extent to which the instantaneous soil volumetric moisture content exceeds the standard reference moisture content.

[0166] Specifically, the compaction range is a crucial quality control indicator in land preparation, specifying the lower and upper limits of soil compaction after topsoil or hydroseeding. In abandoned mine remediation, the soil needs both sufficient compaction to resist surface runoff and water erosion, while also retaining enough capillary pores for plant root respiration and extension. However, compaction characteristics in soil mechanics indicate that soil moisture content has a decisive impact on its compaction effectiveness.

[0167] For example, when a sudden downpour hits a mining site, causing a sharp increase in the instantaneous soil volumetric moisture content, the soil is in a highly plastic state. If the edge computing gateway continues to mechanically require construction machinery to operate according to the original high compaction limit threshold, mechanical compaction will squeeze and close the originally air-filled large pores in the soil over a large area, resulting in severe soil compaction. Soil compaction forms a hard, impermeable, and air-impermeable layer, which has serious negative impacts on ecological restoration, causing large-scale suffocation and death of subsequently planted vegetation.

[0168] Therefore, the system introduces a negative correlation attenuation mapping relationship. This means that when the instantaneous soil moisture content is higher than the benchmark value, the more water exceeds the benchmark, the smaller the tolerance drift coefficient calculated by the system will be.

[0169] For example, the algorithm for calculating the negative correlation attenuation of the upper limit of compaction degree in the land preparation process can be expressed as follows:

[0170] (9)

[0171] in, This represents a specific tolerance drift coefficient calculated for the upper limit of compaction degree in land preparation processes. This represents the instantaneous volumetric water content of the soil, as collected by the sensor. This represents the standard reference water rate set by the system; This represents the preset penalty factor for exceeding the soil moisture content limit. The preset penalty factor for exceeding the soil moisture content limit... The system can forcibly lower the current acceptable compaction level.

[0172] like Figure 7 The graph illustrates the negative correlation decay curve between instantaneous soil moisture content and the dynamic drift of the compaction tolerance threshold. The horizontal axis represents instantaneous soil volumetric moisture content (in percentage), and the vertical axis represents the dynamically updated upper limit threshold for compaction (in percentage).

[0173] The solid blue line in the figure represents the dynamic decay curve of the tolerance threshold, which illustrates the adaptive adjustment process of the construction acceptance standard with changes in the microclimate environment. The red circular marker in the figure represents the inflection point of the standard reference moisture rate, located at the intersection of the horizontal axis 15 and the vertical axis 90. The waveform characteristics of the blue curve show that when the instantaneous soil volumetric moisture content is in the range of 5 to 15%, the actual moisture content does not exceed the standard reference moisture rate, the system does not trigger downward drift decay, and the upper limit threshold of compaction degree remains stably at the initial set level of 90.

[0174] When the instantaneous soil volumetric moisture content exceeds the inflection point of 15% and continues to rise, for example, when rainfall causes the soil moisture content to rise to 25% or even 35%, the upper limit threshold of compaction degree exhibits a significant downward-sloping linear waveform change, decreasing to approximately 76.5% and 63%, respectively. This negative correlation attenuation mapping mechanism, which synchronously reduces the upper limit of the acceptable compaction degree as the moisture content exceeds the standard, reflects the system's control logic of actively reducing the mechanical compaction intensity when dealing with highly plastic and moist soils.

[0175] This diagram quantifies that the edge computing gateway can reasonably adjust and compensate for the originally static construction indicators based on the micro-environmental fluctuations of the actual working surface, thereby effectively avoiding the risk of soil structure damage caused by excessive compaction and providing a flexible and scientific basis for the underlying quality execution of mine ecological restoration projects.

[0176] After calculating the correlation coefficient, the system performs the following steps: multiply the initial static threshold in the construction procedure tolerance rule by the specific tolerance drift coefficient calculated above to generate the dynamically updated real-time procedure tolerance rule.

[0177] Specifically, the initial static threshold is a fixed numerical requirement when the digital twin is initially issued, such as the initial static compaction upper limit threshold (set to...). The system multiplies and combines the initial static threshold with the corresponding specific tolerance drift coefficient in the memory of the edge computing node (i.e., ), generate the dynamic compaction upper limit threshold in the real-time process tolerance rules. For example, in scenarios where rainfall leads to excessively high moisture content, the drift coefficient is adjusted accordingly. After the decay, the original 90% upper limit of compaction may be dynamically modified to 75%.

[0178] Finally, the system uses the real-time process tolerance rule instead of the construction process tolerance rule, and compares it with the actual construction characteristic parameters to calculate the deviation value.

[0179] This step achieves closed-loop replacement of inspection standards. The edge computing gateway obtains the physical parameters achieved by the construction machinery in its current actual operation, namely the actual construction characteristic parameters, such as the current ground compaction degree transmitted by the sensor being 78%. It abandons the use of the initially issued static requirements and instead uses the real-time process tolerance rules that have just been dynamically generated in combination with environmental characteristics for threshold verification.

[0180] For example, if the original static tolerance rule (requiring 85%-90%) is used, the current 78% compaction degree would be judged as insufficient, and the system would incorrectly instruct the construction machinery to continue compaction, leading to irreversible compaction and damage to the moist soil. However, under the dynamic drift mechanism of this embodiment, the system uses a real-time process tolerance rule (the upper limit has been lowered to 75%). At this point, the 78% actual compaction degree will be accurately judged as exceeding the safe compaction limit under the current moist environment. The system will immediately generate an over-compaction deviation value and trigger an on-site rectification command at the edge, requiring the machinery to stop compaction or perform appropriate loosening operations. The deviation value calculated in this way not only reflects the error of the construction action itself but also incorporates respect for and adaptation to the carrying capacity of the current natural environment.

[0181] In terms of current technology, traditional abandoned mine remediation projects rely heavily on post-construction quality verification by project supervisors using fixed construction design specifications for random checks. This management model has two significant drawbacks:

[0182] Firstly, there is a serious time lag, often the problem is only discovered after a large area of ​​the construction surface has been damaged or the non-standard construction has been completed, resulting in high rework costs.

[0183] Secondly, the inspection standards are rather rigid, imposing absolute, inflexible indicators from construction projects onto ecological restoration projects that possess biological attributes, completely ignoring the dynamic and rapidly changing nature of micro-habitat factors such as soil and weather. For example, when faced with complex and variable weather conditions, construction teams, in order to meet rigid acceptance standards, are prone to carrying out destructive operations that, while compliant, are extremely unreasonable, such as forcefully turning over dry soil to remove moisture or forcefully compacting wet soil, ultimately leading to large-scale vegetation death after the acceptance period.

[0184] The technical solution disclosed in this embodiment deeply integrates microhabitat sensing technology with edge computing dynamic calibration algorithms. By collecting transient microhabitat vectors such as instantaneous soil volumetric moisture content and instantaneous surface wind speed in real time, the actual environmental state of the physical work surface at that moment is objectively quantified. More importantly, by introducing tolerance compensation logic and negative correlation attenuation mapping relationship, the system endows fixed construction standards with the ability to dynamically and adaptively calibrate according to the environment.

[0185] This solution has significant practical application value. First, it effectively prevents secondary physical damage to the fragile soil structure of mines caused by blindly pursuing static indicators. Through adaptive attenuation of indicators such as compaction degree, it effectively protects the large porosity and aggregate structure of the soil, reserving space for the long-term development of plant roots. Second, all environmental perception, complex formula calculations, threshold replacement, and deviation judgment actions are completed in a closed loop within the edge computing gateway at the mine site. This not only avoids the network transmission delays and disconnection risks commonly found in remote mining areas, but also achieves millisecond-level ultra-fast response and interception of erroneous construction operations. It elevates the granularity of construction quality management from traditional subjective post-event inspection to an objective micro-process control level, providing a solid and reliable underlying engineering quality guarantee for improving the long-term survival rate of vegetation communities and the stability of ecosystems in abandoned mines.

[0186] Example 4:

[0187] like Figure 8 As shown in this embodiment, a method for recommending vegetation community configuration in abandoned mines by integrating ecological knowledge graphs is provided. This method is mainly applied to the restoration of abandoned mines with fragmented terrain, extremely barren habitats, and high uncertainty. By deeply integrating physical construction equipment and sensor networks with cloud-based ecological knowledge logic, full lifecycle management is achieved from scheme design to on-site construction and dynamic correction.

[0188] Specifically, the method in this embodiment includes the following steps:

[0189] In the early stage of abandoned mine restoration, the first step S1 is to collect multi-source spatial data of the abandoned mine, calibrate and extract terrain features through a two-dimensional registration evaluation model, and generate a three-dimensional basic model of the mine.

[0190] Specifically, this embodiment uses a drone (UAV) equipped with an airborne LiDAR and a high-resolution oblique photography camera as the data acquisition terminal. The UAV scans the mining area according to a preset route to acquire multi-source spatial information, including point cloud data, orthophotos, and elevation data. Due to the presence of numerous slopes, goafs, and fractured rock surfaces on the mine surface, the data often suffers from spatial distortion.

[0191] To address this, the system utilizes a two-dimensional registration evaluation model, which verifies the consistency of the collected data from two dimensions: surface three-dimensional roughness and reprojection error. By calculating the spatial overlap of feature points, potential coordinate drift or terrain distortion areas are identified. For identified conflict areas, the system further employs a thin-plate spline interpolation algorithm for refined correction. The final result is a three-dimensional basic model of the mine, which not only possesses a high-precision geometric contour but also includes key terrain features such as slope, aspect, and catchment area, providing a fundamental geographical foundation for subsequent refined configuration of vegetation communities.

[0192] Based on the basic model, step S2 is executed: based on the three-dimensional basic model of the mine, a full-element digital twin containing natural and engineering elements is mapped and constructed.

[0193] In this embodiment, the digital twin is not only a visual mapping, but also a real-time carrier of physical attributes and logical rules. The natural element twin includes a model of the 0-3 meter underground soil layer (labeled with soil pH value, organic matter content and heavy metal concentration distribution) and local microclimate characteristics; the engineering element twin includes the dynamic coordinates of construction machinery, such as hydroseeding machines and hydraulic excavators, as well as the spatial distribution of on-site material storage areas and water sources.

[0194] The system acquires real-time parameters of the physical mine through an Internet of Things (IoT) sensor network. For example, it collects instantaneous data through soil physicochemical property sensors buried in the work area and obtains high-precision positions of construction machinery through RTK-GNSS terminals. This data is mapped in real time to a virtual twin, constructing a virtual simulation environment highly synchronized with the physical mine, thereby allowing for prior simulation of construction procedures in virtual space.

[0195] Based on the digital twin, step S3 is executed: based on the ecological engineering water resources knowledge graph, attribute association reasoning is performed to generate candidate vegetation pools, and the data of the full-element digital twin is input into a multi-objective mixed integer programming model to solve and generate an initial vegetation community configuration and construction scheduling scheme.

[0196] Specifically, the cloud server stores a hyper-converged ecosystem-engineering-water resources knowledge graph. This graph integrates plant physiological characteristics (such as salt and alkali tolerance, drought resistance, and root depth), construction procedure quotas, and water resource constraints. Based on the mine soil physicochemical indicators (such as heavy metal contamination) and slope limitations fed back by the twin, the inference engine automatically filters out a list of highly adaptable vegetation to form a candidate vegetation pool.

[0197] Subsequently, the system invokes a multi-objective mixed integer programming (MIP) model. This model has opposing objectives: maximizing ecological benefits (such as carbon sequestration capacity and biodiversity index) and minimizing life-cycle costs (such as seedling procurement, logistics, and post-construction maintenance costs). By considering the existing water resource carrying capacity of the mining area (such as irrigation radius and total water storage) and construction period constraints, the MIP model solves and generates an initial vegetation community configuration scheme (including the proportion of vegetation species and planting density in different locations) and a corresponding construction scheduling scheme (including machinery travel routes, work batches, and resource quotas).

[0198] During the construction execution phase, step S4 is executed: real-time construction status data is collected at the edge and construction quality is verified. When a construction deviation is detected, the deviation data is uploaded to the cloud.

[0199] Edge computing gateways and multi-source sensing devices are deployed at the construction site. The edge gateways trim and distribute lightweight construction batch twins from the cloud, containing only local data of the current work area to reduce processing power consumption. Construction equipment, such as intelligent planting machines, uses sensors to collect operational parameters in real time, such as digging depth, soil covering thickness, and the spread of seedling roots.

[0200] Specifically, the edge device integrates a digital twin verification engine for construction procedures, performing real-time quantitative comparisons of 12 core procedures, including clearing, land preparation, and planting. For example, when it detects that the excavation depth in a certain area does not meet the design requirements, i.e., the deviation exceeds the preset tolerance range, the edge gateway immediately determines it as a construction deviation. For minor deviations, the edge gateway issues immediate rectification instructions via a smart handheld terminal; while for moderate or severe deviations that may affect the overall effect, the deviation feature vector is synchronously uploaded to the cloud dynamic reconstruction module in real time via the 5G / LoRa network.

[0201] Once the cloud receives the deviation data, step S5 is executed: the impact of the deviation data is evaluated through the deviation propagation chain quantification algorithm, and a hierarchical scheme reconstruction is triggered to update the initial vegetation community configuration and construction scheduling scheme. After the virtual verification is passed, the scheme is sent out for execution.

[0202] The cloud system invokes a deviation propagation chain quantitative analysis algorithm to assess deviations from three dimensions: construction period, cost, and ecological impact. For example, if the compaction of a certain slope area is insufficient, the algorithm will quantify and predict the risk value of this deviation leading to soil erosion and a decline in vegetation survival rate over the next three years.

[0203] Based on the evaluation results, the system automatically triggers a hierarchical reconstruction mechanism. If the scope of the deviation does not exceed a preset local area threshold, the system performs local reconstruction to optimize the vegetation replacement scheme for the affected plots. If the scope of the deviation exceeds the preset local area threshold but has not yet broken the project's global constraints, a batch-level scheme reconstruction is triggered, dynamically adjusting the resource quotas and scheduling sequence of that batch. If the deviation causes the project's global constraints to be broken, a global-level reconstruction is triggered. The updated scheme generated by the reconstruction is first simulated in a full-process virtual construction simulation in a full-element digital twin to verify whether its progress, cost, and ecological indicators meet the project constraints. After the simulation verification is passed, the updated scheme and instruction set are again sent to the edge to guide on-site equipment and personnel to perform targeted operations, thereby forming a dynamic adaptive closed loop of perception-decision-feedback-optimization.

[0204] The method disclosed in this embodiment overcomes the bottlenecks of static schemes and delayed deviation response in traditional mine restoration by combining high-precision 3D modeling, digital twin technology, and hyper-converged knowledge graphs. In particular, by utilizing deviation propagation chains and hierarchical reconstruction mechanisms, the system acquires resilience to cope with complex construction disturbances, significantly improving the scientific nature of vegetation configuration and the long-term stability of mine restoration.

[0205] Example 5:

[0206] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0207] like Figure 9 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 100 includes a processor 101 and a memory 103. The processor 101 and the memory 103 are connected, for example, via a bus 102. Optionally, the electronic device 100 may further include a transceiver 104. It should be noted that in practical applications, the transceiver 104 is not limited to one unit, and the structure of this electronic device 100 does not constitute a limitation on the embodiments of the present invention.

[0208] Processor 101 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 101 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0209] Bus 102 may include a pathway for transmitting information between the aforementioned components. Bus 102 may be a PCI bus or an EISA bus, etc. Bus 102 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 9The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0210] The memory 103 stores a computer program corresponding to the method for recommending vegetation community configuration in abandoned mines based on the fused ecological knowledge graph of the above embodiments of the present invention. This computer program is executed under the control of the processor 101. The processor 101 executes the computer program stored in the memory 103 to implement the content shown in the aforementioned method embodiments.

[0211] Among them, electronic devices 100 include, but are not limited to: mobile terminals such as laptops and PADs (tablet computers) and fixed terminals such as desktop computers. Figure 9 The electronic device 100 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0212] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A vegetation community configuration recommendation system for abandoned mines integrating ecological knowledge graphs, characterized in that, include: The spatial data calibration module is used to acquire multi-source spatial data of abandoned mines, calibrate and extract terrain features through a two-dimensional registration evaluation model, and generate a three-dimensional basic model of the mine. The digital twin construction module is used to map and construct a full-element digital twin containing natural and engineering elements based on the three-dimensional basic model of the mine. The collaborative scheduling recommendation module is used to generate candidate vegetation pools by performing attribute association reasoning based on the ecological engineering water resources knowledge graph, and input the data of the full-element digital twin into a multi-objective mixed integer programming model to solve and generate the initial vegetation community configuration and construction scheduling scheme. The edge cloud management module is used to collect real-time construction status data at the edge and verify construction quality. When a construction deviation is detected, the deviation data is uploaded to the cloud. The dynamic reconstruction module is used to evaluate the impact of the deviation data through the deviation propagation chain quantization algorithm, trigger hierarchical scheme reconstruction to update the initial vegetation community configuration and construction scheduling scheme, and issue it for execution after virtual verification is passed.

2. The system according to claim 1, characterized in that, The process of calibrating and extracting terrain features through a two-dimensional registration evaluation model to generate a three-dimensional basic model of the mine includes: Obtain the normalized three-dimensional surface roughness value and the reprojection error residual value; The surface three-dimensional roughness value and the reprojection error residual value are multiplied by preset weighting coefficients, summed, and then square rooted to obtain the multi-source registration error conflict score. Extract continuous spatial regions where the score value is greater than a preset score threshold as potential conflict regions; The thin plate spline deformation control algorithm is used to fit a smooth transformation function to realize the local coordinate system transformation in the potential conflict area, and the cubic spline interpolation method is used to generate an elevation correction model to output the three-dimensional basic model of the mine.

3. The system according to claim 1, characterized in that, The structural nodes of the ecological engineering water resources knowledge graph include at least: The project management attribute nodes include real-time market prices for each candidate vegetation, the reserve quantity of surrounding nurseries, the procurement and logistics cycle, and the predicted annual maintenance cost. Water resource attribute nodes include the basic water requirements, salt tolerance threshold, soil water holding capacity, and irrigation cost per unit area for each candidate vegetation at different growth stages. The collaborative scheduling recommendation module extracts the associated attributes of the above nodes, combines the site soil data and climate environmental factors in the three-dimensional basic model of the mine to calculate filtering rules, and generates the candidate vegetation pool.

4. The system according to claim 1, characterized in that, The multi-objective mixed integer programming model is a dual-objective function optimization model, whose objective functions specifically include: Minimize the total cost function of the project's entire life cycle, where the total cost consists of ecological seedling cost, engineering construction cost, manpower scheduling cost, and water resource consumption cost; The comprehensive ecological benefit function is maximized. The comprehensive ecological benefit function is composed of the weighted sum of the ecological benefit scores of each selected vegetation in the candidate vegetation pool and the water saving reward item. The value of the water saving reward item is negatively correlated with the ratio of total water consumption to total available water resources. The collaborative scheduling recommendation module seeks optimization within the constraints of budget, inventory, construction period, and total water resources, and outputs the initial vegetation community configuration and construction scheduling scheme.

5. The system according to claim 1, characterized in that, Before collecting real-time construction status data, the edge cloud management module also performs the following steps: Data is trimmed from the full-element digital twin in the cloud according to spatial region and process dimension to generate a lightweight construction batch twin; The lightweight construction batch twin is distributed to the edge computing gateway deployed at the mine site. The lightweight construction batch twin includes the terrain data, local vegetation configuration scheme and construction procedure tolerance rules of the current independent construction batch. An incremental differential mechanism is used to synchronize the data characteristics of state changes between the edge computing gateway and the cloud.

6. The system according to claim 5, characterized in that, The construction quality verification includes: obtaining the actual construction characteristic parameters of the core construction process, which includes land preparation, pit digging and planting processes; The deviation value is calculated by comparing the actual construction characteristic parameters with the construction procedure tolerance rules. The tolerance rules include slope threshold, compaction range and fixed planting spacing requirements. If the deviation value is within the preset tolerance range, it is judged as a slight deviation, and an on-site rectification instruction is directly generated from the edge end; The deviation value exceeding the preset tolerance range is determined as a moderate or severe deviation, and is synchronously transmitted as deviation data to the cloud dynamic reconstruction module.

7. The system according to claim 1, characterized in that, The evaluation of the impact of the deviation data using the deviation propagation chain quantization algorithm includes: Input the deviation data into the preset quantitative analysis logic tree, and output the quantitative impact values ​​from the three dimensions of construction period, cost and ecology respectively; The project duration dimension outputs the expected total project duration delay due to partial rework or process waiting; the cost dimension outputs the excess cost increase due to additional material loss; and the ecological dimension outputs the expected percentage decrease in vegetation survival rate after the preset assessment period due to substandard underlying processes.

8. The system according to claim 7, characterized in that, The triggering hierarchical scheme reconstruction to update the initial vegetation community configuration and construction scheduling scheme specifically includes: When the influence quantification value indicates that the range of the deviation does not exceed the preset local area threshold, local reconstruction is triggered, and the local solver is called to re-optimize the vegetation configuration of the affected plots. When the influence range of the quantified value indicates deviation exceeds the preset local area threshold, and the global constraints of the project are not broken, batch reconstruction is triggered to readjust the timing of seedling logistics allocation and water resource scheduling for the current batch. When the deviation of the quantified impact value indicates that the global constraints of the project are broken, a global reconstruction is triggered. The updated twin state is then re-input into the multi-objective mixed integer programming model to generate a brand-new initial vegetation community configuration and construction scheduling scheme.

9. The system according to claim 1, characterized in that, The system also includes an adaptive maintenance module for vegetation lifecycle management after the construction scheduling plan has been completed. By integrating soil electrical conductivity and leaf water content obtained from ground sensor networks, and vegetation indices extracted by UAV remote sensing, the water vitality index of the corresponding vegetation unit is calculated. The calculation basis of the water vitality index is a multiplication ratio function of the measured photosynthetic rate, stomatal conductance, vegetation water potential and chlorophyll content with their corresponding standard values ​​under healthy conditions. The vegetation is classified into multiple health levels based on the water vitality index, and a dynamic water demand correction factor is generated accordingly to adaptively adjust the maintenance irrigation quota.

10. The system according to claim 1, characterized in that, The dynamic reconstruction module performs virtual verification before execution, specifically including: The reconstructed initial vegetation community configuration and construction scheduling scheme are injected into the full-element digital twin for virtual simulation at a preset simulation step size; The simulation process is checked for resource node conflicts and timing congestion. If any are found, the scheme weights are dynamically adjusted and the reconstructed solution is triggered again until all indicators of the simulation results meet the preset verification conditions before execution is sent out. After the project is accepted, the correlation mapping between the actual construction quality characteristics and the actual ecological effects is extracted and updated in the ecological engineering water resources knowledge graph to complete the adaptive evolution of the system.

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