Ecological element collaborative characteristic-based abandoned mine natural recovery evaluation method

Abstract

The invention relates to the technical field of abandoned mine recovery evaluation, in particular to an abandoned mine natural recovery evaluation method based on ecological element collaborative characteristics, and the method comprises the steps: obtaining data; performing primary judgment; performing secondary judgment; and finally carrying out collaborative judgment. And adjusting a threshold value and generating an evaluation report. According to the method, the vegetation coverage is rapidly and preliminarily identified; secondly, evaluating the internal recovery potential of the starting area from the dimensions of provenance, microorganism, hydrological connectivity and the like; and finally, for the potential-sufficient region, accurately judging a system which is still fragile in function and a health system which has formed stable self-maintenance capability by quantifying water and soil vegetation process coupling, a biological interaction network and anti-interference capability, and dynamically adjusting an initial threshold value based on the time sequence stability of a classification result of each region. The whole evaluation system has a data-driven adaptive capability, and the problems of low evaluation result accuracy and response speed lag caused by single evaluation dimension are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of abandoned mine restoration assessment technology, and in particular to an evaluation method for the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements. Background Technology

[0002] Abandoned mine remediation is a major area of ​​national ecological protection and restoration. Current mine restoration and evaluation technologies are undergoing profound evolution, shifting from early land reclamation and vegetation reconstruction to a greater emphasis on ecological restoration and systemic repair. However, many practices remain at the level of morphological revegetation, focusing only on increasing vegetation cover while neglecting the integrity of the ecosystem's structure and function. Current mainstream effectiveness assessments often rely on single indicators or weighted sums of social, ecological, and economic benefits, failing to measure the inherent synergistic relationships and self-organizing capabilities of the ecosystem, and unable to effectively diagnose key bottlenecks hindering the restoration process.

[0003] Chinese Patent Publication No. CN117314221A discloses a method and system for evaluating the effectiveness of ecological restoration in open-pit mines. The method includes: determining the ecological restoration area of ​​the mine; acquiring measurement data within the ecological restoration area using UAV oblique photography; constructing a three-dimensional solid model and a mine surface model based on preset UAV parameter data and measurement data; extracting ecological restoration evaluation parameters based on the three-dimensional solid model and the mine surface model; obtaining quantity growth indicators and quality improvement indicators based on the ecological restoration evaluation parameters; calculating the total evaluation value of the ecological restoration effectiveness of the open-pit mine using the quantity growth indicators and quality improvement indicators, and determining the ecological restoration level of the open-pit mine.

[0004] Therefore, the existing technology has the following problems: the data dimension is limited, only assessing morphological parameters that can be captured by optical images, such as topography, surface cover, and three-dimensional vegetation structure, making it difficult to effectively assess the implicit key factors that determine the success or failure of ecological restoration; the evaluation indicators are superficial, and the ecological restoration evaluation parameters and the derived quantitative growth indicators and quality improvement indicators all belong to the static and isolated state of the ecosystem in terms of quantity and apparent quality; the evaluation logic is static, and the model is built and calculations are performed based on UAV data at a certain moment, which is essentially a one-time snapshot-style acceptance assessment of the state at a certain point in time after the restoration project is completed. Summary of the Invention

[0005] To address this, the present invention provides a method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements. This method overcomes the problems of low accuracy and slow response speed in existing technologies due to the single evaluation dimension by acquiring multi-dimensional data characteristics, conducting synergistic analysis of ecological elements, and implementing dynamic adjustment mechanisms.

[0006] To achieve the above objectives, this invention provides a method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements, comprising: Step S1: Obtain soil data, hydrological data, vegetation data, and animal behavior data for each area to be tested in the abandoned mine; Step S2: Based on the comparison results between the vegetation coverage and the preset coverage threshold, determine several primary problem areas and several primary areas to be investigated, wherein the vegetation coverage is obtained based on the vegetation data; Step S3: Determine several secondary problem areas and several secondary investigation areas based on the numerical characteristics of the ecological memory index, wherein the ecological memory index is calculated based on the soil data, hydrological data and animal behavior data of each of the primary investigation areas; Step S4: Determine several third-level problem areas and several well-restored areas based on the numerical characteristics of the self-organizing collaborative efficiency index. The self-organizing collaborative efficiency index is calculated based on the soil data, hydrological data, vegetation data, and animal behavior data of each of the second-level areas to be investigated. Step S5: Adjust the preset coverage threshold based on the temporal stability of all first-level problem areas, all second-level problem areas, all third-level problem areas, and all recovered good areas within the preset inspection time. Step S6: Generate an abandoned mine natural restoration report based on all the first-level problem areas, all the second-level problem areas, all the third-level problem areas, and all the well-restored areas obtained after adjusting the preset coverage threshold.

[0007] Furthermore, when the vegetation coverage is greater than or equal to the preset coverage threshold, the area to be tested is determined to be the first-level area to be investigated; when the vegetation coverage is less than the preset coverage threshold, the area to be tested is determined to be the first-level problem area.

[0008] Furthermore, when the ecological memory index is greater than or equal to a preset ecological memory threshold, the first-level area to be investigated is determined to be the second-level area to be investigated; when the ecological memory index is less than the preset ecological memory threshold, the first-level area to be investigated is determined to be the second-level problem area.

[0009] Furthermore, the ecological memory index is calculated based on the life reservoir vitality value, water-soil coupling coefficient, ecological connectivity, preset life reservoir weight, preset water-soil weight, and preset connectivity weight.

[0010] Furthermore, the life bank vitality value is determined based on the matching results of the soil seed bank and the soil microbial bank, the water-soil coupling coefficient is calculated based on the water storage points and germination demand points, and the ecological connectivity is calculated based on the seed dispersal potential value and the pollination service effectiveness value.

[0011] Furthermore, when the self-organizing collaborative efficiency index is greater than or equal to a preset index threshold, the secondary investigation area is determined to be the recovery-excellent area; when the self-organizing collaborative efficiency index is less than the preset index threshold, the secondary investigation area is determined to be the tertiary problem area.

[0012] Furthermore, the self-organizing collaborative efficiency index is calculated based on the coupling efficiency of water, soil and vegetation processes, the complexity of biological interaction networks, the anti-interference reconstruction force, the preset process coupling weight, the preset network complexity weight, and the preset reconstruction weight.

[0013] Furthermore, the coupling efficiency of the water, soil and vegetation process is calculated based on the peak value of the cross-correlation function between the increase in soil moisture and the increase in vegetation greenness. The complexity of the biological interaction network is based on the ecological network constructed from plant species composition and abundance, soil microbial community composition and animal access records, and is determined according to the number of connections and modularity index of the ecological network. The anti-interference reconstruction capability is determined based on the change range of all key indicators before and after the interference event within the preset detection period, the time required to recover to the baseline level, and the completeness of the recovery trajectory.

[0014] Further, step S5 includes: Step S51: Calculate the overlap stability based on all the first-level problem regions, all the second-level problem regions, all the third-level problem regions, and all the recovered good regions from the initial time to each time within the preset inspection time. Step S52: When the overlap stability is less than the preset stability threshold, adjust the preset coverage threshold according to the overlap stability and the preset stability threshold.

[0015] Further, step S1 includes: Step S11: Based on the on-site stratified soil analysis report, obtain soil data including the soil seed bank, the soil microbial bank, and the soil microbial community. Step S12: Based on terrain analysis and hydrological spatial modeling, obtain hydrological data including the water storage points, the soil moisture increment, and the germination demand points; Step S13: Based on the remote sensing image interpretation and ground quadrat survey report, obtain vegetation data including the vegetation coverage, vegetation greenness increment, and plant species composition and abundance; Step S14: Based on landscape resistance modeling, animal behavior parameter analysis, and field monitoring records, obtain animal behavior data including the seed dispersal potential value, the pollination service effectiveness value, and the animal visit records.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: By following the natural succession law of ecosystems from structural recovery to functional maturity, it first uses the most readily available vegetation cover data to quickly identify stagnant and initiating recovery zones; then, for initiating zones, it integrates soil, hydrological, and animal behavior data to calculate an ecological memory index, assessing their inherent recovery potential from dimensions such as seed source, microorganisms, and hydrological connectivity, distinguishing between areas with insufficient potential and areas with sufficient potential; finally, for areas with sufficient potential, it integrates all four types of data to calculate a self-organizing synergistic efficiency index, quantifying the coupling of water, soil, and vegetation processes, biological interaction networks, and anti-interference capabilities, accurately identifying systems that are still functionally fragile and healthy systems that have formed stable self-sustaining capabilities. Based on the temporal stability of the classification results for each region, the initial threshold is dynamically adjusted, enabling the entire evaluation system to possess data-driven adaptive capabilities. This allows for accurate characterization and adaptation to complex and ever-changing natural recovery processes, ultimately providing a scientific basis for precise governance by zone classification that surpasses static evaluation, effectively solving the problems of low accuracy and slow response speed caused by a single evaluation dimension.

[0017] Furthermore, by designing a comprehensive and three-dimensional data collection framework that encompasses everything from underground to above ground, from static structure to dynamic function, and from individual organisms to landscape processes, a solid and logically consistent data foundation is provided for subsequent progressive evaluation. On-site stratified soil analysis directly obtains the material carriers of ecological memory; quantifies the spatial matching relationship between water, a key limiting factor, and the emergence of life; achieves verification complementarity between macro-vegetation cover and micro-species composition; and dynamically assesses the execution efficiency of key ecological processes such as biological dispersal and pollination. These data are not simply parallel but constitute a causal evidence chain: soil / hydrology provides basic conditions - vegetation reflects the state after utilization conditions - animal behavior confirms the realization of system functions. This ensures the scientificity and accuracy of subsequent index calculations and judgments, overcoming the one-sided evaluation problem caused by traditional methods due to single data sources or missing dimensions.

[0018] Furthermore, by using vegetation cover—the most intuitive and readily available core indicator—as a primary screening filter, a rapid and efficient classification of the restoration status of abandoned mines across a wide area was achieved. Following the principle of optimizing resource allocation from easy to difficult and from superficial to in-depth, the area to be tested was divided into "Level 1 Problem Areas" and "Level 1 Areas to be Investigated." This critical triage was completed at the outset of the evaluation. For Level 1 Problem Areas, it can be directly determined that there are obvious obstacles to vegetation reconstruction, requiring priority engineering intervention without immediately investing in in-depth soil and animal surveys, thus saving resources. For Level 1 Areas to be Investigated, it means that they have passed the first hurdle of restoration and are qualified to undergo more refined and costly ecological potential and system function diagnosis. This defines the target scope for subsequent steps, allowing the entire evaluation system to focus on areas that appear to have restored but whose underlying health status is unknown, thereby improving the overall efficiency and relevance of the evaluation.

[0019] Furthermore, by calculating the ecological memory index using multidimensional data such as seed stock reserves, decomposition and nutrient cycling, water security for germination and growth, and seed dispersal and gene flow, the intrinsic capital of the ecosystem for self-sustaining and natural succession was accurately assessed. When the index meets the standard, it indicates that the region possesses strong intrinsic recovery momentum and can be upgraded to undergo a higher level of system function testing; when the index is insufficient, it exposes the potential crisis beneath its apparent prosperity, namely, the system lacks the foundation for sustainable recovery and needs to be marked as a potential risk area requiring foundation consolidation. This achieves a leap from evaluating the current situation to predicting the future, enabling management measures to shift from simple vegetation reconstruction to fundamental interventions such as targeted cultivation of soil vitality, optimization of hydrological patterns, or construction of biological corridors.

[0020] Furthermore, through a weighted summation model, the three core pillars constituting the ecosystem's restoration potential are accurately characterized and integrated: the life pool (seed source and microbial base), soil and water conditions (germination and growth medium), and ecological connectivity (channels for the flow of matter and genes). Max-min normalization is applied to these three sub-indices, solving the problem of direct comparison and fusion of data with different dimensions. Introducing preset weights reflects the method's adaptive management approach to the dominant limiting factors in different regions and restoration stages. For example, the weight of "soil and water coupling" can be increased in arid areas, and the weight of "ecological connectivity" can be increased in isolated habitats, ensuring that the final index more accurately reflects the true restoration potential of a specific region. This provides a reliable quantitative criterion for scientifically selecting secondary regions with inherent development momentum from primary regions to be investigated.

[0021] Furthermore, by decomposing the abstract ecological memory into three quantifiable, operational, and ecologically meaningful core dimensions, a complete logical chain of existence-matching-flow is constructed. True restoration potential not only requires the system to possess restoration materials, namely the life bank vitality value reflecting the richness and activity of the soil seed bank and microbial bank, but also requires these materials to encounter a suitable spatiotemporal window for germination and growth, namely the water-soil coupling coefficient reflecting the spatial matching degree between water storage sites and plant germination needs, and the ability to maintain population renewal and gene flow through key ecological processes, namely the ecological connectivity reflecting the efficiency of animal-mediated seed dispersal and pollination services. These three sub-indicators answer the three fundamental questions of "whether there are seeds," "whether the water conditions support it," and "whether it can spread and reproduce." Their data logic is interconnected, jointly ensuring that the ecological memory index can transcend the static assessment of a single resource and dynamically and systematically diagnose the comprehensive potential of a region to achieve self-sustaining through its inherent vitality and processes.

[0022] Furthermore, by identifying whether an ecosystem has transcended the restoration stage requiring external intervention and entered a self-organizing state with self-sustaining, self-regulating, and disturbance-resistant capabilities, a self-organizing synergy efficiency index is calculated by integrating all four types of data. This index comprehensively quantifies the coupling efficiency of abiotic and biotic processes (soil and vegetation synergy), the network complexity of interbiotic interactions (food webs, symbiotic relationships, etc.), and the system's ability to reconstruct after disturbance. When the index meets the standard, it indicates that the area not only has complete elements, but more importantly, a positive and stable synergistic network and feedback mechanism have been formed among the elements, enabling it to autonomously respond to external changes. Therefore, it can be ultimately certified as a "highly restored area" and recommended for inclusion in the scope of nature conservation. When the index is insufficient, it reveals that its internal processes are still uncoordinated or the network is fragile. Although it has potential, the system's functions are incomplete, and there is a risk of collapse. It needs to be marked as a "Level 3 problem area" requiring adaptive management, providing the most direct and authoritative completion certification for the ultimate goal of ecological restoration: the formation of a self-sustaining natural ecosystem.

[0023] Furthermore, by deconstructing and quantifying the self-organization of an ecosystem into three measurable, comparable, and ecologically significant core functional dimensions, and then integrating them through weighted averages to form a comprehensive diagnostic index, a truly healthy and self-sustaining ecosystem must simultaneously possess efficient internal process coupling—that is, close synergy between soil and water conditions and vegetation growth response, reflecting the system's material and energy utilization efficiency; a complex network of biological interactions—that is, diverse interspecific relationships forming a stable and redundant structure, reflecting the system's biological organization level; and strong resistance to disturbance and reconstruction capabilities—that is, the resilience to recover to its original state or trajectory after being disturbed, reflecting the system's dynamic stability. Normalizing these three indicators ensures that process data of different dimensions can be fairly integrated; while introducing preset weights for weighted summation allows for flexible adjustments to the evaluation criteria based on regional characteristics, such as focusing more on process coupling in arid regions and more on resistance to disturbance in fragile habitats. This enables the final self-organization synergy efficiency index to accurately and scientifically determine whether a seemingly healthy ecosystem has formed a robust self-organizing state or remains in a stage of factor accumulation and functionally fragile dependence on other organizations.

[0024] Furthermore, by calculating the peak cross-correlation between soil moisture and vegetation greenness, the sensitivity and synchronicity of the feedback loop between the abiotic environment and the biological response were quantified at the dynamic process level, revealing the system's internal operational efficiency. By integrating plant, microbial, and animal data to construct an ecological network and calculating the connection number and modularity index, the organizational level and stability of the system's biodiversity and interspecific relationships were quantified at the static structural level. By monitoring the amplitude, time, and trajectory changes of key indicators before and after disturbance, the system's resilience in resisting shocks, maintaining its state, or restoring its original state was quantified at the behavioral level. These three aspects, from the three indispensable and mutually corroborating perspectives of process, structure, and behavior, jointly answer the ultimate question of whether the system is operating efficiently, is stably organized, and is resilient and reliable. The logical closed loop of the data ensures the comprehensiveness and scientific nature of the final judgment.

[0025] Furthermore, by monitoring the temporal stability of the evaluation results themselves, the rationality of the initial preset standard can be verified and calibrated in reverse. Under a stable and reliable evaluation standard, the regional classification results should maintain high temporal consistency within a continuous monitoring period. Overlap stability essentially transforms the results of the static judgments in the previous steps into a meta-indicator measuring the overall discriminative consistency of the system. When this stability is insufficient, it indicates that the initial preset coverage threshold, a key classification standard, may not match the actual local restoration dynamics, leading to drastic changes in classification results with seasonal or interannual fluctuations. In this case, the threshold is quantitatively lowered based on the stability deviation, reducing the entry barrier for vegetation cover and allowing more areas to enter the subsequent in-depth diagnostic process of ecological memory and self-organization. This allows for the use of richer and more stable intrinsic functional indicators to arbitrate complex or marginal cases, ultimately freeing the output of the entire evaluation system from excessive reliance on single, volatile superficial indicators, making it more robust and reliable. Attached Figure Description

[0026] Figure 1 This is a flowchart of the evaluation method for the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements in this embodiment; Figure 2 This is a flowchart of step S1 in this embodiment; Figure 3 This is the logic diagram for determining the primary problem area and the primary area to be investigated in this embodiment; Figure 4 This is a logic diagram for determining the secondary problem area and the secondary investigation area in this embodiment. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Please see Figure 1 The flowchart shown is a process for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements in this embodiment. This embodiment provides a method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements, including: Step S1: Obtain soil data, hydrological data, vegetation data, and animal behavior data for each area to be tested in the abandoned mine; Step S2: Based on the comparison results between the vegetation coverage and the preset coverage threshold, determine several primary problem areas and several primary areas to be investigated, wherein the vegetation coverage is obtained based on the vegetation data; Step S3: Determine several secondary problem areas and several secondary investigation areas based on the numerical characteristics of the ecological memory index, wherein the ecological memory index is calculated based on the soil data, hydrological data and animal behavior data of each of the primary investigation areas; Step S4: Determine several third-level problem areas and several well-restored areas based on the numerical characteristics of the self-organizing collaborative efficiency index. The self-organizing collaborative efficiency index is calculated based on the soil data, hydrological data, vegetation data, and animal behavior data of each of the second-level areas to be investigated. Step S5: Adjust the preset coverage threshold based on the temporal stability of all first-level problem areas, all second-level problem areas, all third-level problem areas, and all recovered good areas within the preset inspection time. Step S6: Generate an abandoned mine natural restoration report based on all the first-level problem areas, all the second-level problem areas, all the third-level problem areas, and all the well-restored areas obtained after adjusting the preset coverage threshold.

[0030] In this embodiment, when evaluating abandoned mines, the "test areas" are not artificially defined regular grids, but rather relatively homogeneous assessment units with consistent internal ecological characteristics, based on the natural differentiation of mine landforms and ecological conditions, combined with survey accessibility. The division mainly follows two principles: first, using landforms and micro-topography as the framework, such as using slope tops, slope surfaces, slope toes, valleys, platforms, and mine pits as basic units, because these units have significant differences in hydrological, soil, and light conditions; second, superimposing key ecological characteristics, further subdividing within the same landform unit based on boundaries of soil parent material, existing vegetation types, or traces of human disturbance, thereby ensuring that each "test area" has similar recovery starting points and limiting factors. This ensures that the subsequently collected soil, hydrological, vegetation, and animal behavior data have spatial representativeness and ecological comparability, providing an accurate spatiotemporal analysis basis for the three-level progressive evaluation.

[0031] In this embodiment, the abandoned mine natural restoration report is the final decision support document generated based on the three-level progressive evaluation method. Its core content is the integration of a map, a table, and a prescription. The report visually displays the spatial distribution of the first, second, and third-level problem areas and the restoration-excellent areas after dynamic calibration in map form, and is supplemented by a data table that details the key diagnostic indicator values ​​and level determination criteria for each area. At the same time, the report conducts a dominant limiting factor attribution analysis for each type of problem area, such as indicating whether it is due to seed bank deficiency, soil-water mismatch, or weak collaborative network, and proposes differentiated and actionable hierarchical management strategy recommendations, such as engineering treatment for first-level areas and ecological assistance for third-level areas, ultimately forming a comprehensive conclusion that integrates current status diagnosis, spatial planning, and management actions.

[0032] The preset coverage threshold is a critical value used to determine whether the vegetation cover of a region is significant. It depends on the climate zone, native vegetation type, and specific management objectives of the evaluation area. It is usually set between 15% and 30%. In this embodiment, it is set to 20%, which can effectively and initially identify key areas with extremely scarce vegetation, extremely weak ecological foundation, and which must be given priority for artificial intervention or detailed investigation in the vast abandoned mine area.

[0033] By following the natural succession pattern of ecosystems from structural recovery to functional maturity, this approach first uses readily available vegetation cover data to quickly identify stagnant and initiating recovery zones. Next, for initiating zones, an ecological memory index is calculated by integrating soil, hydrological, and animal behavior data to assess their inherent recovery potential from dimensions such as seed provenance, microorganisms, and hydrological connectivity, distinguishing between zones with insufficient and sufficient potential. Finally, for zones with sufficient potential, a self-organizing synergistic efficiency index is calculated by integrating all four types of data. By quantifying the coupling of water, soil, and vegetation processes, biological interaction networks, and resistance to interference, it accurately identifies systems that are still functionally fragile and healthy systems that have formed stable self-sustaining capabilities. The initial threshold is dynamically adjusted based on the temporal stability of the classification results for each region, giving the entire evaluation system data-driven adaptive capabilities. This allows for precise characterization and adaptation to complex and ever-changing natural recovery processes, ultimately providing a scientific basis for precise regional and categorized governance that surpasses static evaluations. This effectively solves the problems of low accuracy and slow response speed caused by a single evaluation dimension.

[0034] Please see Figure 2 The diagram shows a flowchart of step S1 in this embodiment. In this embodiment, step S1 includes: Step S11: Based on the on-site stratified soil analysis report, obtain soil data including the soil seed bank, the soil microbial bank, and the soil microbial community. Step S12: Based on terrain analysis and hydrological spatial modeling, obtain hydrological data including the water storage points, the soil moisture increment, and the germination demand points; Step S13: Based on the remote sensing image interpretation and ground quadrat survey report, obtain vegetation data including the vegetation coverage, vegetation greenness increment, and plant species composition and abundance; Step S14: Based on landscape resistance modeling, animal behavior parameter analysis, and field monitoring records, obtain animal behavior data including the seed dispersal potential value, the pollination service effectiveness value, and the animal visit records.

[0035] In this embodiment, through on-site stratified soil sampling combined with laboratory germination experiments and molecular biology techniques, a soil seed bank reflecting the stock of plant propagules in the soil, a soil microbial bank reflecting the total amount of microbial resources, and the specific composition of the soil microbial community are obtained. Through UAV or lidar topographic scanning combined with hydrological model simulation and soil moisture sensor network monitoring, water storage points identifying natural water catchment areas, soil moisture increment reflecting water dynamics, and germination demand points generated by combining seed location and water conditions are obtained. Through pixel interpretation and inversion of multispectral / hyperspectral remote sensing images, combined with field standard quadrat survey records, vegetation coverage representing the proportion of green vegetation area, vegetation greenness increment reflecting growth vitality, and plant species composition and abundance reflecting community structure are obtained. Through GIS-based landscape resistance model simulation, animal activity range parameters obtained from literature or observation, and direct field monitoring such as infrared cameras, seed dispersal potential values ​​quantifying the difficulty of external seed source input, pollination service effectiveness values ​​quantifying pollen transfer service levels, and animal access records as direct evidence are obtained.

[0036] In this embodiment, the boundary of the undisturbed natural ecosystem (such as natural forest and meadow) surrounding the evaluation area is defined as the seed source area, and the current area to be tested is defined as the target area. Based on the land use / cover map, each land type is assigned a resistance value reflecting its degree of obstruction to the spread of target animals (such as birds and rodents) (e.g., forest = 1, shrubland = 10, bare land = 100, road = 500). The resistance value setting refers to relevant animal dispersal literature or expert experience. Using a circuit theory model, the seed source area is set as the power source and the target area is set as the ground. The model simulates multiple paths of random walkers spreading from the seed source to the target, and finally outputs a cumulative current density map. The average current density in the area to be tested is extracted as the original propagation potential value. The original propagation potential values ​​calculated for all areas to be tested in the waste gas mine study area are normalized by max-min to obtain the seed propagation potential value of each area, which ranges from 0 to 1.

[0037] In this embodiment, known key habitats for pollinating insects (such as flowering grasslands and woodland edges) existing within or around the evaluation area are defined as source areas, and the area to be tested is defined as the target area. A resistance surface is constructed for pollinating insects (such as bees), considering their preference for different habitats (e.g., flowering fields = 1, urban land = 100). A minimum cost path model is used to calculate the minimum cumulative resistance from each source area to the target area. Then, a negative exponential function is used to convert the resistance into a connectivity value. The connectivity values ​​of all source areas are summed to obtain the original effective pollination service value of the target area. The original effective pollination service values ​​calculated for all test areas within the abandoned mine study area are then subjected to max-min normalization to obtain the effective pollination service value for each area, which ranges from 0 to 1.

[0038] By designing a comprehensive and three-dimensional data acquisition framework that spans from underground to above ground, from static structure to dynamic function, and from individual organisms to landscape processes, a solid and logically consistent data foundation is provided for subsequent progressive evaluation. On-site stratified soil analysis directly obtains the material carriers of ecological memory; quantifies the spatial matching relationship between water, a key limiting factor, and the emergence of life; achieves verification complementarity between macro-vegetation cover and micro-species composition; and dynamically assesses the execution efficiency of key ecological processes such as biological dispersal and pollination. These data are not simply parallel but constitute a causal evidence chain: soil / hydrology provides basic conditions - vegetation reflects the state after utilization conditions - animal behavior confirms the realization of system functions. This ensures the scientificity and accuracy of subsequent index calculations and judgments, and overcomes the one-sided evaluation problem caused by traditional methods due to single data sources or missing dimensions.

[0039] Please see Figure 3 As shown, this is the logic diagram for determining the first-level problem area and the first-level investigation area in this embodiment. In this embodiment, when the vegetation coverage is greater than or equal to the preset coverage threshold, the area to be tested is determined to be the first-level investigation area. When the vegetation coverage is less than the preset coverage threshold, the area to be tested is determined to be the first-level problem area.

[0040] By using vegetation cover, the most intuitive and readily available core indicator, as a primary screening filter, a rapid and efficient classification of the restoration status of abandoned mines across a wide area was achieved. Following the principle of optimizing resource allocation from easy to difficult and from superficial to in-depth, the assessment area was divided into "Level 1 Problem Areas" and "Level 1 Investigation Areas," completing a critical triage at the outset. For Level 1 Problem Areas, it can be directly determined that there are obvious obstacles to vegetation reconstruction, requiring priority engineering intervention without immediately investing in in-depth soil and animal surveys, thus saving resources. For Level 1 Investigation Areas, it means that they have passed the first hurdle of restoration and are qualified to undergo more refined and costly ecological potential and system function diagnosis, defining the target scope for subsequent steps. This allows the entire assessment system to focus on areas that appear to have restored but whose underlying health status is unknown, thereby improving the overall efficiency and relevance of the assessment.

[0041] Please see Figure 4 As shown, this is the logic diagram for determining the secondary problem area and the secondary investigation area in this embodiment. In this embodiment, when the ecological memory index is greater than or equal to the preset ecological memory threshold, the primary investigation area is determined to be the secondary investigation area. When the ecological memory index is less than the preset ecological memory threshold, the primary investigation area is determined to be the secondary problem area.

[0042] The preset ecological memory threshold is a benchmark value used to determine whether a region has sufficient inherent ecological restoration potential. It depends on the definition standard of ecological restoration potential, that is, how much life capital needs to be reserved to support its subsequent natural restoration process. It is usually set between 0.4 and 0.6. In this embodiment, it is set to 0.5, which can effectively identify areas with weak soil seed banks, mismatched water and soil conditions, or those that are isolated from the outside world and lack the inherent capital for continuous restoration.

[0043] The ecological memory index is calculated using multidimensional data including seed stock reserves, decomposition and nutrient cycling, water security for germination and growth, and seed dispersal and gene flow. This accurately assesses the intrinsic capital of an ecosystem for self-sustaining and natural succession. When the index meets the standard, it indicates that the region possesses strong intrinsic recovery momentum and can proceed to a higher level of system function testing. When the index is insufficient, it exposes the potential crisis beneath its apparent prosperity, namely, the lack of a foundation for sustainable recovery. This area needs to be marked as a potential risk zone requiring foundational strengthening. This represents a leap from evaluating the current state to predicting the future, enabling management measures to shift from simple vegetation restoration to fundamental interventions such as targeted cultivation of soil vitality, optimization of hydrological patterns, or construction of biological corridors.

[0044] Specifically, the ecological memory index is calculated by weighted summation of the life reservoir vitality value, water-soil coupling coefficient, ecological connectivity, preset life reservoir weight, preset water-soil weight, and preset connectivity weight. The life reservoir vitality value, water-soil coupling coefficient, and ecological connectivity are values ​​obtained after maximum-minimum normalization.

[0045] The preset life pool weight, preset soil and water weight, and preset connectivity weight are importance coefficients assigned to the three core indicators of life pool vitality, soil and water coupling coefficient, and ecological connectivity, respectively, when calculating the ecological memory index. The sum of the three is 1, depending on the core evaluation objective and theoretical emphasis: if the emphasis is on the system's internal reproductive bodies and microbial driving force, the life pool weight is higher; if the basic matching of water and seeds is emphasized, the soil and water weight is higher; if the key role of external seed sources and gene flow is considered, the connectivity weight is higher. The value of each weight is usually between 0.2 and 0.5. In this embodiment, the preset life pool weight is set to 0.4, the preset soil and water weight is set to 0.4, and the preset connectivity weight is set to 0.2, emphasizing the scientific principle of prioritizing internal factors and fundamentals in ecological restoration, giving equal importance to the system's internal life capital and key habitat conditions, while considering external connectivity as an important auxiliary factor, so that the calculation of the ecological memory index can reflect the core components of restoration potential in a balanced and prominent manner.

[0046] Through a weighted summation model, the three core pillars constituting the ecosystem's restoration potential are accurately characterized and integrated: the life pool (seed source and microbial base), soil and water conditions (germination and growth medium), and ecological connectivity (channels for the flow of matter and genes). Max-min normalization is applied to the three sub-indices, solving the problem of direct comparison and integration of data with different dimensions. The introduction of preset weights reflects the method's adaptive management approach to the dominant limiting factors in different regions and restoration stages. For example, the weight of "soil and water coupling" can be increased in arid areas, and the weight of "ecological connectivity" can be increased in isolated habitats, ensuring that the final index more accurately reflects the true restoration potential of a specific region. This provides a reliable quantitative criterion for scientifically selecting secondary regions with inherent development momentum from primary regions to be investigated.

[0047] Specifically, the life bank vitality value is determined based on the matching results of the soil seed bank and the soil microbial bank, the water-soil coupling coefficient is calculated based on the water storage points and germination demand points, and the ecological connectivity is calculated based on the seed dispersal potential value and the pollination service effectiveness value.

[0048] In this embodiment, the life bank vitality value is calculated by quantifying the matching degree between the number of active seeds in the soil seed bank and the abundance of key functional groups in the soil microbial bank, reflecting the functional synergy between seed source reserves and the germination environment; the water-soil coupling coefficient is calculated by GIS spatial analysis to determine the spatial overlap or distance decay function value between water storage points and plant germination demand points, assessing the spatiotemporal matching efficiency between water conditions and life germination; the ecological connectivity value is a weighted average of seed dispersal potential value, pollination service effectiveness value, preset dispersal weight, and preset pollination weight, reflecting the biological media efficacy of gene flow and population renewal. All the above calculations convert the original observation data into dimensionless values ​​in the 0-1 range through normalization and other standardization processes, ensuring that they can be weighted and integrated into the final ecological memory index.

[0049] In this embodiment, the life bank vitality value characterizes the synergistic level between propagules and the microbial environment in the soil, and the calculation formula is as follows: Where V is the life bank vitality value; S is the soil seed bank vitality index after logarithmic transformation, obtained by germination experiments to obtain the number of germinating active seeds per unit area of ​​soil and then logarithmically transforming it; M is the soil microbial function index after logarithmic transformation, obtained by high-throughput sequencing to obtain the number of key functional microorganisms (such as nitrogen-fixing bacteria, phosphate-solubilizing bacteria, and arbuscular mycorrhizal fungi) in the soil and then logarithmically transforming it; Smax and Mmax are the maximum values ​​of S and M in all test areas within the abandoned mine study area, respectively, used for subsequent normalization.

[0050] In this embodiment, the soil-water coupling coefficient represents the spatial matching degree between water conditions and seed germination requirements, calculated through GIS spatial analysis. Where C is the soil-water coupling coefficient; A1 is the total area of ​​the potential germination zone formed by connecting all the 'germination demand points' identified based on topography, illumination and soil texture analysis within the area to be measured; A2 is the spatial overlap area between the above potential germination zone and the 'water storage points'. The 'water storage points' are expanded into surface data of catchment areas or soil moisture dominance areas through runoff simulation, and then the overlap ratio between them and the total area of ​​the potential germination zone is calculated.

[0051] In this embodiment, ecological connectivity characterizes the potential for bio-mediated material and gene flow, and the calculation formula is as follows: Where F is the ecological connectivity; u is the preset propagation weight; P is the seed propagation potential value; b is the preset pollination weight; and D is the effective value of pollination service.

[0052] The preset dispersal weight and preset pollination weight are coefficients used to balance the relative importance of the two external biological vector functions of seed dispersal and pollination service when calculating ecological connectivity. The sum of the two is 1, which depends on the recovery stage and core objectives being evaluated. In the early stage of recovery or when the focus is on community reconstruction, the weight of seed dispersal is greater; in the later stage of recovery or when the focus is on maintaining genetic diversity and reproductive success, the weight of pollination service is greater. Each weight is usually set between 0.3 and 0.7. In this embodiment, the preset dispersal weight is set to 0.6 and the preset pollination weight is set to 0.4, which conforms to the basic temporal logic of ecological restoration of planting first and then reproduction. It emphasizes the decisive role of seed dispersal in plant community reconstruction and population renewal in the early stage of recovery, while reasonably incorporating the supporting efficacy of pollination service for plant reproduction and genetic exchange, so that the ecological connectivity index can more realistically reflect the biological vector functions in the key stages of recovery.

[0053] By decomposing the abstract concept of ecological memory into three quantifiable, operational, and ecologically meaningful core dimensions, a complete logical chain of existence-matching-flow is constructed. True restoration potential requires not only that the system possess restoration materials, i.e., the vitality value of the life bank reflecting the richness and activity of the soil seed bank and microbial bank, but also that these materials encounter a suitable spatiotemporal window for germination and growth, i.e., the water-soil coupling coefficient reflecting the spatial matching degree between water storage sites and plant germination needs, and the ability to maintain population renewal and gene flow through key ecological processes, i.e., the ecological connectivity reflecting the efficiency of animal-mediated seed dispersal and pollination services. These three sub-indicators answer the three fundamental questions of "whether there are seeds," "whether the water conditions support it," and "whether it can spread and reproduce." Their data logic is interconnected, ensuring that the ecological memory index can transcend the static assessment of a single resource and dynamically and systematically diagnose the comprehensive potential of a region to achieve self-sustaining through its inherent vitality and processes.

[0054] Specifically, when the self-organizing collaborative efficiency index is greater than or equal to a preset index threshold, the secondary investigation area is determined to be the recovery-excellent area; when the self-organizing collaborative efficiency index is less than the preset index threshold, the secondary investigation area is determined to be the tertiary problem area.

[0055] The preset index threshold is a benchmark value used to determine whether an ecosystem has formed a healthy, efficient, and stable self-organizing function. It depends on how strong the system’s internal collaborative operation capability needs to be to be considered as having entered a benign self-sustaining state. It is usually set between 0.6 and 0.8. In this embodiment, it is set to 0.7, which can accurately identify those sub-healthy areas that, although the basic conditions are acceptable, have low internal process collaboration efficiency, simple biological networks, or insufficient resilience.

[0056] By identifying whether an ecosystem has moved beyond the restoration stage requiring external intervention and entered a self-organizing state with self-sustaining, self-regulating, and disturbance-resistant capabilities, a self-organizing synergy efficiency index is calculated by integrating all four types of data. This index comprehensively quantifies the coupling efficiency of abiotic and biotic processes (soil and vegetation synergy), the network complexity of interbiotic interactions (food webs, symbiotic relationships, etc.), and the system's ability to reconstruct after disturbance. When the index meets the standard, it indicates that the area not only has complete elements, but more importantly, a positive and stable synergistic network and feedback mechanism have been formed among the elements, enabling it to autonomously respond to external changes. Therefore, it can be ultimately certified as a "highly restored area" and recommended for inclusion in the scope of nature conservation. When the index is insufficient, it reveals that its internal processes are still uncoordinated or the network is fragile. Although it has potential, the system's functions are incomplete, and there is a risk of collapse. It needs to be marked as a "Level 3 problem area" requiring adaptive management. This provides the most direct and authoritative completion certification for the ultimate goal of ecological restoration: the formation of a self-sustaining natural ecosystem.

[0057] Specifically, the self-organizing collaborative efficiency index is calculated based on the coupling efficiency of water, soil and vegetation processes, the complexity of biological interaction networks, the anti-interference reconstruction force, the preset process coupling weight, the preset network complexity weight, and the preset reconstruction weight. Among them, the coupling efficiency of water, soil and vegetation processes, the complexity of biological interaction networks, and the anti-interference reconstruction force are values ​​obtained after maximum-minimum normalization.

[0058] The preset process coupling weight, preset network complexity weight, and preset reconstruction weight are importance coefficients assigned to three functional indicators—water and soil vegetation process coupling efficiency, biological interaction network complexity, and anti-interference reconstruction capability—when calculating the self-organizing synergistic effectiveness index. The sum of these three is 1, depending on the different emphases the evaluation places on the three dimensions of ecosystem current operational efficiency, structural stability, and long-term resilience. If the immediate efficiency of material and energy flow is emphasized, the process coupling weight is higher; if the maturity and stability of interspecies relationships are considered, the network complexity weight is higher; and if the system's ability to cope with future uncertainties is valued, the reconstruction weight is higher. The value of each weight typically ranges from 0.2 to 0.5. In this embodiment, the preset process coupling weight is set to 0.4, the preset network complexity weight to 0.3, and the preset reconstruction weight to 0.3, emphasizing that the efficiency of internal system processes is the most direct reflection of health in the mid-to-late stages of natural recovery, thus assigning the highest weight. Simultaneously, indicators characterizing the maturity of the system's organizational structure and its ability to resist risks are given equal importance, ensuring that the evaluation results reflect both the current level of synergy and the stability and sustainability of the system.

[0059] By deconstructing and quantifying the self-organization of an ecosystem into three measurable, comparable, and ecologically significant core functional dimensions, and then integrating them through weighted averages to form a comprehensive diagnostic index, a truly healthy and self-sustaining ecosystem must simultaneously possess efficient internal process coupling—that is, close synergy between soil and water conditions and vegetation growth response, reflecting the system's material and energy utilization efficiency; a complex network of biological interactions—that is, diverse interspecific relationships forming a stable and redundant structure, reflecting the system's biological organization level; and strong resistance to disturbance and reconstruction capabilities—that is, the resilience to recover to its original state or trajectory after being disturbed, reflecting the system's dynamic stability. Normalizing these three indicators ensures that process data of different dimensions can be fairly integrated. Introducing preset weights for weighted summation allows for flexible adjustments to the evaluation criteria based on regional characteristics, such as focusing more on process coupling in arid regions and more on resistance to disturbance in fragile habitats. This enables the final self-organization synergy efficiency index to accurately and scientifically determine whether a seemingly healthy ecosystem has achieved a robust self-organized state or remains in a stage of factor accumulation and functionally fragile dependence on other organizations.

[0060] Specifically, the coupling efficiency of the water, soil and vegetation process is calculated based on the peak value of the cross-correlation function between the increase in soil moisture and the increase in vegetation greenness. The complexity of the biological interaction network is based on the ecological network constructed from plant species composition and abundance, soil microbial community composition and animal access records, and is determined according to the number of connections and modularity index of the ecological network. The anti-interference reconstruction capability is determined based on the change range of all key indicators before and after the interference event within the preset detection period, the time required to recover to the baseline level, and the completeness of the recovery trajectory.

[0061] The preset monitoring period is a continuous monitoring time necessary for quantitatively assessing the ecosystem's resilience to disturbance and remodeling. It depends on the occurrence cycle of typical disturbance events in the target area (such as seasonal drought, heavy rain, and frost) and the time scale required for the ecosystem to recover from this type of disturbance to the baseline or a new steady state, and is set between 2 and 5 years. In this embodiment, it is set to 3 years, which can reliably cover at least one natural disturbance event of moderate to severe intensity (such as drought) and allows the system to exhibit a complete "response-recovery" trajectory. This ensures that the calculation of "resilience to disturbance and remodeling" is based on sufficient and stable time-series observation data, avoiding misjudgment of system resilience due to an excessively short period or serious lag in evaluation adjustments due to an excessively long period.

[0062] In this embodiment, the coupling efficiency of the soil-water-vegetation process is obtained by calculating the cross-correlation function between the soil moisture increment time series and the vegetation greenness increment time series. This function calculates the correlation coefficient between the two series at different time lags, and its peak value is extracted as the coupling efficiency value. It quantifies the sensitivity and synchronicity of the vegetation response driven by water changes. The complexity of the biological interaction network first integrates plant species composition and abundance, soil microbial community composition (such as OTU table), and animal access records. Each species or functional group is defined as a network node, and connection edges are constructed based on empirical or probabilistic relationships such as nutrition, symbiosis, pollination, or dispersal, forming a multi-trophic-level ecosystem. The complexity of a dynamic network is determined by the total number of connections and the modularity index Q. The modularity index Q, calculated using a community detection algorithm, reflects the separation and tightness of its internal substructures. The anti-interference reconstruction capability is determined by identifying specific interference events occurring within a preset detection period, such as drought or fire. This involves quantifying the average relative change of all key indicators before and after the event, the time required to recover from the deviation point to the baseline level, and the fit or area overlap between the recovery trajectory and the ideal index recovery curve. Finally, these three dimensions are combined into a single reconstruction capability index through weighted or comprehensive scoring. All key indicators refer to the aforementioned coupling efficiency, network connection count, and other data. All original calculation results are standardized for subsequent weighted integration.

[0063] In this embodiment, the construction rules of the ecological network are as follows: (1) Define nodes: All plant species, dominant soil microbial groups (such as the top 20 OTUs in relative abundance), and animal species with visit records are listed as nodes; (2) Define and quantify connection edges: Plant-animal (pollination / spread): If an animal species' visit record is clearly associated with a plant species (such as photographing flower visits or fruit consumption), a connection is established between the two, and the connection weight can be set as the frequency of the visit event record; Plant-plant (competition / promotion): The association can be calculated based on the spatial co-occurrence rate of plant species in the quadrat. For species pairs with significant positive or negative correlations (e.g., Spearman correlation coefficient), the system establishes connections between species pairs with significant positive or negative correlations. For plant-microbe pairs, the system queries public databases (e.g., the Functional Traits database or literature) to determine which types of arbuscular mycorrhizal fungi (AMF) or ectomycorrhizal fungi (ECM) typically coexist with the target plant species, and then connects the plant nodes with their known symbiotic fungal groups (corresponding groups in the OTU table). For microbe-microbe pairs, the system calculates the time-series correlation of abundance between OTUs and connects OTU pairs with significant correlations (e.g., correlation > 0.7).

[0064] In this embodiment, the calculation of the anti-interference reconstruction force includes: (1) determining the set of key indicators: including vegetation coverage, ecological memory index, water-soil-vegetation process coupling efficiency and number of biological interaction network connections, a total of four items; (2) quantifying three dimensions: the average relative change amplitude is the simple arithmetic mean of the change amplitudes of all key indicators; recovery time, that is, the time required to recover from the deviation point to the baseline level is the time taken to return to the average value before the interference; trajectory integrity, that is, the fit or area overlap between the recovery trajectory and the ideal index recovery curve, is obtained by using the S-shaped logistic growth curve as the ideal recovery model, calculating the determination coefficient between the actual recovery time series of each key indicator and the fitted S-curve, and taking the average value. (3) calculating the anti-interference reconstruction force. Where Y is the anti-interference reconstruction force; E is the average relative change amplitude; G is the recovery time; H is the trajectory integrity; w1, w2 and w3 are the preset interference weight, preset recovery weight and preset trajectory weight, respectively, and w1+w2+w3=1. In this embodiment, w1=0.4, w2=0.3 and w3=0.3.

[0065] By calculating the peak cross-correlation between soil moisture and vegetation greenness, the sensitivity and synchronicity of the feedback loop between the abiotic environment and biological response were quantified at the dynamic process level, revealing the system's internal operational efficiency. By integrating plant, microbial, and animal data to construct an ecological network and calculating the connection number and modularity index, the organization level and stability of the system's biodiversity and interspecific relationships were quantified at the static structural level. By monitoring the amplitude, time, and trajectory changes of key indicators before and after disturbance, the system's resilience to shocks, maintaining its state, or restoring its original state was quantified at the behavioral level. These three aspects, from the perspectives of process, structure, and behavior—three indispensable and mutually corroborating perspectives—jointly answer the ultimate question of whether the system is operating efficiently, is stably organized, and is resilient and reliable. The logical closed loop of the data ensures the comprehensiveness and scientific nature of the final judgment.

[0066] Specifically, step S5 includes: Step S51: Calculate the overlap stability based on all the first-level problem regions, all the second-level problem regions, all the third-level problem regions, and all the recovered good regions from the initial time to each time within the preset inspection time. Step S52: When the overlap stability is less than a preset stability threshold, the preset coverage threshold is reduced based on the relative deviation between the overlap stability and the preset stability threshold and a preset adjustment coefficient, wherein, , R is the adjusted preset coverage threshold, a is the preset adjustment coefficient, and N is the overlap stability. This is a preset stability threshold.

[0067] In this embodiment, steps S2-S4 are executed every six months within a preset testing period to obtain a regional classification map for each six-month period (including level 1, 2, and 3 problem areas and areas with good recovery). Overlap stability is defined as: the percentage of the area of ​​the same region that is consistently classified into the same level (e.g., always a level 1 problem area) over many consecutive years, relative to the maximum area of ​​that level in any given year. This percentage is then averaged over all levels, using the following formula: Where Q is the overlap stability; k is the number of grades (4); T is the area of ​​the region that has been classified as grade k for many consecutive years; and Tmax is the maximum area of ​​grade k in any single year.

[0068] The preset testing duration is the length of time used to assess the stability of the evaluation system itself and to perform dynamic threshold calibration. It depends on the complete response cycle of the ecosystem to external disturbances and the minimum time scale at which significant recovery dynamics or reasonable changes in evaluation results can be observed. It is usually set between 2 and 5 years. In this embodiment, it is set to 3 years, which can cover enough seasonal fluctuations and occasional climate events to make the calculation of overlap stability statistically significant, while avoiding the evaluation system calibration lag caused by an excessively long period. Thus, under the premise of ensuring the reliability of the evaluation, timely and appropriate dynamic optimization of the evaluation standard is achieved.

[0069] The preset stability threshold is a benchmark value used to judge whether the evaluation results are sufficiently stable and reliable over time. It depends on the stringent requirements for the repeatability of the evaluation system results and is usually set between 0.75 and 0.9. In this embodiment, it is set to 0.8, which can effectively identify and trigger frequent fluctuations in evaluation results caused by environmental changes or unreasonable initial thresholds, while avoiding unnecessary threshold adjustments caused by normal and reasonable ecological succession changes, thus ensuring the necessity and rigor of the calibration action.

[0070] The preset adjustment coefficient is a proportional coefficient used to control the adjustment range. It depends on the aggressiveness or conservatism of the threshold adjustment strategy and is usually set between 0.05 and 0.2. In this embodiment, it is set to 0.1, which can not only make a clear response to unstable signals and make the evaluation criteria adapt to changes in the environment or repair process, but also effectively avoid drastic fluctuations in the evaluation criteria due to excessive adjustment in a single instance, thus ensuring the convergence of the entire evaluation system and the stability of the final result.

[0071] By monitoring the temporal stability of the evaluation results themselves, the rationality of the initial preset standard can be verified and calibrated. Under a stable and reliable evaluation standard, the regional classification results should maintain high temporal consistency within a continuous monitoring period. Overlap stability essentially transforms the results of the static judgments in the previous steps into a meta-indicator measuring the overall consistency of the system's discrimination. When this stability is insufficient, it indicates that the initial preset coverage threshold, a key classification standard, may not match the actual local restoration dynamics, causing the classification results to fluctuate drastically with seasonal or interannual variations. In this case, the threshold is quantitatively lowered based on the stability deviation, reducing the entry barrier for vegetation cover and allowing more areas to enter the subsequent in-depth diagnostic process of ecological memory and self-organization. This allows for the use of richer and more stable intrinsic functional indicators to arbitrate complex or marginal cases, ultimately freeing the output of the entire evaluation system from excessive reliance on single, volatile superficial indicators, making it more robust and reliable.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements, characterized in that, include: Step S1: Obtain soil data, hydrological data, vegetation data, and animal behavior data for each area to be tested in the abandoned mine; Step S2: Based on the comparison results between the vegetation coverage and the preset coverage threshold, determine a number of first-level problem areas and a number of first-level areas to be investigated. The vegetation coverage is obtained based on the vegetation data. The first-level problem areas refer to the areas to be tested where the vegetation coverage is less than the preset coverage threshold, and the first-level areas to be investigated refer to the areas to be tested where the vegetation coverage is greater than or equal to the preset coverage threshold. Step S3: Determine several secondary problem areas and several secondary investigation areas based on the numerical characteristics of the ecological memory index. The ecological memory index is calculated based on the soil data, hydrological data and animal behavior data of each of the primary investigation areas. The ecological memory index is an index used to characterize the internal recovery potential of the area. Secondary problem areas refer to primary investigation areas with an ecological memory index less than a preset ecological memory threshold, and secondary investigation areas refer to primary investigation areas with an ecological memory index greater than or equal to the preset ecological memory threshold. Step S4: Determine several level-3 problem areas and several recovery-excellent areas based on the numerical characteristics of the self-organization and synergy effectiveness index. The self-organization and synergy effectiveness index is calculated based on the soil data, hydrological data, vegetation data, and animal behavior data of each level-2 area to be investigated. The self-organization and synergy effectiveness index is an index used to characterize the self-organization capacity of an ecosystem. Level-3 problem areas refer to level-2 areas to be investigated where the self-organization and synergy effectiveness index is less than a preset index threshold, and recovery-excellent areas refer to level-2 areas to be investigated where the self-organization and synergy effectiveness index is greater than or equal to the preset index threshold. Step S5: Adjust the preset coverage threshold according to the temporal stability of all the first-level problem areas, all the second-level problem areas, all the third-level problem areas, and all the recovered good areas within the preset inspection time. The preset inspection time is the length of time used to evaluate the stability of the evaluation system itself and to perform dynamic threshold calibration. Step S6: Generate an abandoned mine natural restoration report based on all the first-level problem areas, all the second-level problem areas, all the third-level problem areas, and all the well-restored areas obtained after adjusting the preset coverage threshold.

2. The evaluation method for the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 1, characterized in that, When the vegetation coverage is greater than or equal to the preset coverage threshold, the area to be tested is determined to be the first-level area to be investigated; when the vegetation coverage is less than the preset coverage threshold, the area to be tested is determined to be the first-level problem area.

3. The method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 2, characterized in that, When the ecological memory index is greater than or equal to a preset ecological memory threshold, the first-level investigation area is determined to be the second-level investigation area; when the ecological memory index is less than the preset ecological memory threshold, the first-level investigation area is determined to be the second-level problem area.

4. The evaluation method for the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 3, characterized in that, The ecological memory index is calculated based on the life reservoir vitality value, water and soil coupling coefficient, ecological connectivity, preset life reservoir weight, preset water and soil weight, and preset connectivity weight.

5. The evaluation method for the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 4, characterized in that, The life bank vitality value is determined based on the matching results of the soil seed bank and the soil microbial bank; the water-soil coupling coefficient is calculated based on the water storage points and germination demand points; and the ecological connectivity is calculated based on the seed dispersal potential value and the pollination service effectiveness value.

6. The method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 5, characterized in that, When the self-organizing collaborative efficiency index is greater than or equal to a preset index threshold, the secondary investigation area is determined to be the recovery-excellent area; when the self-organizing collaborative efficiency index is less than the preset index threshold, the secondary investigation area is determined to be the tertiary problem area.

7. The method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 6, characterized in that, The self-organizing collaborative efficiency index is calculated based on the coupling efficiency of water, soil and vegetation processes, the complexity of biological interaction networks, the anti-interference reconstruction force, the preset process coupling weight, the preset network complexity weight, and the preset reconstruction weight.

8. The method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 7, characterized in that, The coupling efficiency of the water, soil and vegetation process is calculated based on the peak value of the cross-correlation function between the increase in soil moisture and the increase in vegetation greenness. The complexity of the biological interaction network is based on the ecological network constructed from plant species composition and abundance, soil microbial community composition and animal access records, and is determined according to the number of connections and modularity index of the ecological network. The anti-interference reconstruction capability is determined based on the change range of all key indicators before and after the interference event within the preset detection period, the time required to recover to the baseline level, and the completeness of the recovery trajectory.

9. The method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 8, characterized in that, Step S5 includes: Step S51: Calculate the overlap stability based on all the first-level problem regions, all the second-level problem regions, all the third-level problem regions, and all the recovered good regions from the initial time to each time within the preset inspection time. Step S52: When the overlap stability is less than the preset stability threshold, adjust the preset coverage threshold according to the overlap stability and the preset stability threshold.

10. The method for evaluating the natural restoration of abandoned mines based on the synergistic characteristics of ecological elements according to claim 9, characterized in that, Step S1 includes: Step S11: Based on the on-site stratified soil analysis report, obtain soil data including the soil seed bank, the soil microbial bank, and the soil microbial community. Step S12: Based on terrain analysis and hydrological spatial modeling, obtain hydrological data including the water storage points, the soil moisture increment, and the germination demand points; Step S13: Based on the remote sensing image interpretation and ground quadrat survey report, obtain vegetation data including the vegetation coverage, vegetation greenness increment, and plant species composition and abundance; Step S14: Based on landscape resistance modeling, animal behavior parameter analysis, and field monitoring records, obtain animal behavior data including the seed dispersal potential value, the pollination service effectiveness value, and the animal visit records.

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