A method for evaluating coupling coordination of vegetation and soil in mine ecological restoration process

Abstract

This invention discloses a method for assessing the coupling coordination of vegetation and soil in the process of mine ecological restoration, specifically relating to the field of ecological restoration engineering management. It addresses the problem of existing assessment methods failing to quantify the interactions of multiple restoration measures, leading to biased assessment results. The method obtains a set of vegetation state parameters, a set of soil quality parameters, and a combination of restoration measures for the target restoration area. It constructs evolution vectors of the vegetation state and soil quality parameter sets relative to historical benchmarks and calculates directional synergy to obtain the coordination assessment result. It analyzes the interaction patterns between each pair of restoration measures in the combination scheme, performs simulation disturbance analysis and causal path analysis for each interaction pattern to obtain response sensitivity and intervention effect transmission intensity. Based on response sensitivity and intervention effect transmission intensity, it determines the dominant influence dimension of the interaction pattern and quantifies the comprehensive interaction influence intensity of the combination scheme on the coordination assessment result. Finally, it generates the final coordination assessment result.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration engineering management, and more specifically, to a method for assessing the coupling coordination of vegetation and soil during the ecological restoration process of mines. Background Technology

[0002] In the engineering management of mine ecological restoration, it is typically necessary to construct an assessment model for the coupling and coordination of vegetation and soil in order to scientifically evaluate the restoration effectiveness and guide subsequent measures. The general approach in existing technologies is to obtain vegetation status indicators and soil property indicators of the restoration area, and then calculate a quantitative result representing the degree of coordination between the two using a pre-set mathematical model. Based on this assessment result, the project management team judges and compares the restoration status of different technical solutions or different restoration areas, thereby making resource allocation and management decisions. This is a project management activity based on environmental data analysis.

[0003] However, existing assessment methods have inherent limitations when dealing with complex restoration engineering practices. When multiple restoration measures are implemented in the same restoration area, existing models often treat different measures as independent variables or simply add their effects together, failing to quantify the true impact of the dynamic interactions between these measures on the coordination of the vegetation-soil system. This results in assessment results that cannot accurately reflect the actual effectiveness of the combination of measures, and may overestimate or underestimate the true restoration level of the system, thereby misleading management decisions and causing misallocation of resources and deviation from restoration goals. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for assessing the coupling coordination between vegetation and soil during mine ecological restoration includes:

[0007] S1. Obtain the set of vegetation status parameters, soil quality parameters, and combination schemes of various restoration measures for the target restoration area;

[0008] S2. Construct evolution vectors of vegetation state parameter set and soil quality parameter set relative to historical baseline, and obtain the coordination degree evaluation result by calculating the directional coordination degree between evolution vectors.

[0009] S3. Analyze the interaction patterns between each pair of recovery measures in the combination scheme, including synergistic and antagonistic patterns;

[0010] S4. For each interaction mode, simulated disturbance analysis was performed on the vegetation state parameter set and soil quality parameter set, respectively. At the same time, the causal path between parameters was analyzed based on the causal discovery algorithm to obtain the response sensitivity and the transmission intensity of the intervention effect.

[0011] S5. Determine the dominant influence dimension of each interaction mode based on response sensitivity and the transmission intensity of intervention effect, and quantify the comprehensive interaction influence intensity of the combined scheme on the coordination evaluation results.

[0012] S6. Correct the coordination degree assessment results based on the intensity of comprehensive interaction effects, and generate the final coordination degree assessment results for the target recovery area.

[0013] Furthermore, the set of vegetation status parameters, soil quality parameters, and combinations of various restoration measures for the target restoration area are obtained, including:

[0014] Obtain the vegetation status parameter set of the target restoration area, which includes vegetation coverage, leaf area index and vegetation biomass.

[0015] Obtain a set of soil quality parameters for the target restoration area, including soil pH, soil organic matter content, and soil nitrogen, phosphorus, and potassium content.

[0016] Obtain a combination of various recovery measures to be implemented in the target recovery area. The combination includes the type of recovery measures, the implementation time of the recovery measures, and the intensity of the implementation of the recovery measures.

[0017] Furthermore, evolution vectors of vegetation state parameter sets and soil quality parameter sets relative to historical baselines are constructed respectively, and the coordination degree assessment results are obtained by calculating the directional coordination degree between evolution vectors, including:

[0018] Based on the vegetation state parameter set data in continuous time series, the rate of change of each parameter in the vegetation state parameter set relative to the historical baseline is calculated to form a vegetation state evolution vector.

[0019] Based on the data of the soil quality parameter set in continuous time series, the rate of change of each parameter in the soil quality parameter set relative to the historical benchmark is calculated to form a soil quality evolution vector.

[0020] The directional synergy between the evolution vectors is obtained by calculating the cosine of the angle between the vegetation state evolution vector and the soil quality evolution vector, and is used as the result of the coordination degree assessment.

[0021] Furthermore, the interaction patterns between each pair of recovery measures in the combined scheme were analyzed, including:

[0022] For each of the two restoration measures in the combined scheme, based on the data changes of the vegetation state parameter set and soil quality parameter set during the implementation time of the restoration measures, calculate the changes in the vegetation state parameter set and soil quality parameter set when each restoration measure is implemented alone, and the changes in the vegetation state parameter set and soil quality parameter set when the two restoration measures are implemented in combination.

[0023] The interaction pattern is determined by comparing the changes when two recovery measures are implemented in combination with the sum of the changes when each recovery measure is implemented alone.

[0024] When the amount of change when implemented in combination is greater than the sum of the amounts of change when implemented individually, the interaction mode is a synergistic mode.

[0025] When the amount of change when implemented in combination is less than or equal to the sum of the amounts of change when implemented individually, the interaction mode is an antagonistic mode.

[0026] Furthermore, for each interaction mode, simulated perturbation analysis was performed on the vegetation state parameter set and soil quality parameter set, respectively. Simultaneously, causal paths between parameters were analyzed based on a causal discovery algorithm to obtain response sensitivity and the transmission intensity of the intervention effect, including:

[0027] While keeping the soil quality parameter set unchanged, gradient perturbations were applied to vegetation cover, leaf area index and vegetation biomass in the vegetation state parameter set, and the changes in the coordination degree assessment results were recorded to generate a vegetation state perturbation response matrix.

[0028] While keeping the vegetation state parameter set unchanged, gradient perturbations were applied to the soil pH, soil organic matter content and soil nitrogen, phosphorus and potassium content in the soil quality parameter set, and the changes in the coordination degree assessment results were recorded to generate a soil quality perturbation response matrix.

[0029] Based on the evolution of vegetation state parameter set and soil quality parameter set in the time dimension, the causal transmission direction between vegetation state parameter set and soil quality parameter set is identified by conditional independence test.

[0030] Based on the direction of causal transmission, the influence strength of the vegetation state parameter set on the soil quality parameter set and the influence strength of the soil quality parameter set on the vegetation state parameter set are calculated respectively, and a comparison table of the intensity of the two-way intervention effect is generated.

[0031] Furthermore, the vegetation state disturbance response matrix characterizes the response sensitivity of the vegetation state parameter set to the coordination degree assessment results, the soil quality disturbance response matrix characterizes the response sensitivity of the soil quality parameter set to the coordination degree assessment results, and the two-way intervention effect intensity comparison table characterizes the transmission intensity of the intervention effect between the vegetation state parameter set and the soil quality parameter set.

[0032] Furthermore, based on the evolution of vegetation state parameter sets and soil quality parameter sets over time, the causal transmission direction between the vegetation state parameter sets and soil quality parameter sets is identified through conditional independence tests, including:

[0033] Given other parameter values ​​within a historical time window, calculate the partial correlation coefficient between the vegetation state parameter set and the soil quality parameter set in the lag period, and the partial correlation coefficient between the soil quality parameter set and the vegetation state parameter set in the lag period.

[0034] By comparing the significance levels of two partial correlation coefficients, the direction of causal transmission between the vegetation state parameter set and the soil quality parameter set can be determined.

[0035] Furthermore, based on response sensitivity and the intensity of intervention effect transmission, the dominant influence dimension of each interaction mode is determined, and the comprehensive interaction influence intensity of the combined scheme on the coordination evaluation results is quantified, including:

[0036] Based on the comparison between the vegetation state disturbance response matrix and the soil quality disturbance response matrix, the relative influence of the vegetation state parameter set and the soil quality parameter set on the coordination degree assessment results is determined.

[0037] By combining the strength of the effect of vegetation state parameter set on soil quality parameter set and the strength of the effect of soil quality parameter set on vegetation state parameter set in the comparison table of two-way intervention effect, the dominant influence dimension in the interaction mode is identified.

[0038] Based on the distribution characteristics of synergistic and antagonistic action patterns on vegetation state parameter sets and soil quality parameter sets, the intensity of the comprehensive interactive influence of the interaction of all restoration measures in the combined scheme on the coordination degree assessment results is quantified.

[0039] Furthermore, based on the comparison between the vegetation state disturbance response matrix and the soil quality disturbance response matrix, the relative influence of the vegetation state parameter set and the soil quality parameter set on the coordination degree assessment results is determined, including:

[0040] The average rate of change of the coordination degree assessment results under all disturbance conditions in the vegetation state disturbance response matrix is ​​calculated as the comprehensive sensitivity of vegetation state.

[0041] The average rate of change of the coordination degree assessment results under all disturbance conditions in the soil quality disturbance response matrix is ​​calculated as the comprehensive sensitivity of soil quality.

[0042] By comparing the numerical values ​​of the integrated sensitivity of vegetation status and the integrated sensitivity of soil quality, the relative influence of the vegetation status parameter set and the soil quality parameter set on the coordination degree assessment results is determined.

[0043] Furthermore, the coordination assessment results are revised based on the intensity of the comprehensive interaction effects to generate the final coordination assessment results for the target recovery area, including:

[0044] Based on the numerical sign and magnitude of the comprehensive interaction influence intensity, the direction and magnitude of the correction to the coordination degree assessment results are determined.

[0045] Based on the direction and magnitude of the correction, establish a correction relationship between the coordination assessment results and the intensity of the comprehensive interactive impact;

[0046] The coordination assessment results are adjusted according to the correction relationship to generate the final coordination assessment results for the target recovery area.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. By systematically integrating the interaction analysis of multiple restoration measures, the accuracy and practicality of vegetation-soil coupling coordination assessment are significantly improved. The constructed evolution vector model can dynamically capture the changing trends of vegetation status and soil quality parameters over time, while the calculation of directional synergy quantifies the degree of coordination between the two in the development process, thus providing more refined status monitoring for restoration projects. By analyzing the interaction patterns between each pair of restoration measures, including synergistic and antagonistic effects, the complex impact of measure combinations on the system can be identified, avoiding assessment bias caused by simple superposition. This ensures that the assessment results can truly reflect the actual effectiveness of the measure combination, providing reliable data support for resource allocation and measure optimization in project management, effectively reducing decision-making risks and improving the efficiency of restoration projects.

[0049] 2. Through simulation perturbation analysis and causal path discovery, the dynamic response mechanism and influence transmission path between parameters were revealed in depth. The acquisition of response sensitivity helps to identify the sensitivity of key parameters to changes in coordination, while the intensity of intervention effect transmission clarifies the direction of causal relationship between vegetation and soil systems. It can determine the dominant influence dimension of the interaction mode and quantify the intensity of comprehensive interaction influence, thereby enabling scientific correction of the coordination assessment results. This not only enhances the robustness and adaptability of the assessment results, but also ensures that the final output can fully reflect the synergistic or antagonistic effects of the combination of restoration measures. It provides accurate decision-making basis for the long-term management and dynamic adjustment of mine ecological restoration, and significantly improves the sustainability and management efficiency of the project. Attached Figure Description

[0050] Figure 1 This is a flowchart of a method for assessing the coupling coordination between vegetation and soil during mine ecological restoration, according to the present invention.

[0051] Figure 2This is a schematic diagram of the structure of a vegetation and soil coupling coordination assessment system for the ecological restoration of mines, according to the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example 1: Figure 1 This invention provides a method for assessing the coupling coordination between vegetation and soil during mine ecological restoration, comprising:

[0054] S1. Obtain the set of vegetation status parameters, soil quality parameters, and combination schemes of various restoration measures for the target restoration area;

[0055] S2. Construct evolution vectors of vegetation state parameter set and soil quality parameter set relative to historical baseline, and obtain the coordination degree evaluation result by calculating the directional coordination degree between evolution vectors.

[0056] S3. Analyze the interaction patterns between each pair of recovery measures in the combination scheme, including synergistic and antagonistic patterns;

[0057] S4. For each interaction mode, simulated disturbance analysis was performed on the vegetation state parameter set and soil quality parameter set, respectively. At the same time, the causal path between parameters was analyzed based on the causal discovery algorithm to obtain the response sensitivity and the transmission intensity of the intervention effect.

[0058] S5. Determine the dominant influence dimension of each interaction mode based on response sensitivity and the transmission intensity of intervention effect, and quantify the comprehensive interaction influence intensity of the combined scheme on the coordination evaluation results.

[0059] S6. Correct the coordination degree assessment results based on the intensity of comprehensive interaction effects, and generate the final coordination degree assessment results for the target recovery area.

[0060] In the specific implementation process, the data acquisition work in step S1 is carried out as follows: When acquiring the vegetation status parameter set of the target restoration area, data is collected through a combination of multi-source remote sensing image interpretation and ground sampling. Vegetation coverage is obtained by calculating the normalized vegetation index using a pixel-based bipartite model. Specifically, the normalized vegetation index is obtained by dividing the difference in reflectance between the near-infrared and red bands in the remote sensing image by the sum of the reflectance of the two bands. Then, a conversion model is established based on the vegetation type of the study area to obtain the percentage value of vegetation coverage. Leaf area index is obtained using a LAI-2200 plant canopy analyzer under standard measurement conditions. Measurements are taken at fixed times each day on sunny days. Data is read from each sampling point in four directions (east, west, south, and north), and the average value is taken as the leaf area index of that sampling point. Vegetation biomass is obtained through destructive sampling by setting up standard quadrats. For example, three 1-square-meter quadrats are set up within each hectare. All aboveground vegetation in the quadrats is cut at ground level and the fresh weight is immediately measured. Then, a portion of the sample is dried in a constant-temperature forced-air drying oven at 80 degrees Celsius until constant weight, and the dry matter weight is calculated. Finally, the vegetation biomass per unit area is converted based on the quadrat area.

[0061] When acquiring the soil quality parameter set for the target restoration area, sampling points were set up according to soil type and topographic features. Soil pH was determined using the potentiometric method. After the collected soil samples were air-dried, they were passed through a 2 mm sieve. A suspension was prepared at a soil-to-water mass-to-volume ratio of 1:2.5. A calibrated pH meter was inserted into the suspension and allowed to stand until stable before reading the pH. Soil organic matter content was determined using the potassium dichromate external heating method. A soil sample that had passed through a 0.15 mm sieve was accurately weighed, and potassium dichromate standard solution and concentrated sulfuric acid were added. The mixture was heated to boiling in an oil bath for 5 minutes, and then titrated with ferrous sulfate standard solution. The organic carbon content was calculated based on the volume of ferrous sulfate consumed, and then multiplied by the empirical coefficient 1.724 to obtain the soil organic matter content. Soil nitrogen, phosphorus, and potassium contents were determined using the Kjeldahl method, the molybdenum-antimony colorimetric method, and the flame photometry method, respectively. For total nitrogen determination, the sample was digested with concentrated sulfuric acid and then distilled and titrated. For total phosphorus determination, sodium hydroxide was fused and reacted with molybdenum-antimony colorimetric reagent, and then measured on a spectrophotometer. For total potassium determination, the sample was digested with hydrofluoric acid and perchloric acid and then measured using a flame photometer.

[0062] When acquiring a combination of restoration measures implemented in the target restoration area, confirmation is made by reviewing engineering supervision records, construction logs, and conducting on-site verification. Restoration measures include specific technical methods such as vegetation sowing, soil conditioner application, and terrain reshaping. Each measure's specific technical parameters are recorded; for example, vegetation sowing requires detailed information such as grass and tree species names, seed source, and sowing density. The implementation time of restoration measures is accurately recorded down to the specific year, month, and day. For measures with longer durations, both the start and end dates are recorded to ensure an accurate correspondence with subsequent parameter monitoring time series. The intensity of restoration measures is expressed using appropriate quantitative indicators based on the characteristics of the measures. For example, vegetation sowing is expressed as sowing amount per unit area, soil conditioner application as application amount per unit area, and terrain reshaping as earthwork volume. All quantitative indicators are accompanied by specific units of measurement. All acquired raw data is entered into a dedicated database, establishing complete metadata records, including auxiliary information such as data source, collection time, collection personnel, instrument model, and measurement environmental conditions, providing a reliable data foundation for subsequent analysis.

[0063] In the specific implementation process, the construction of vegetation state evolution vectors and soil quality evolution vectors in step S2, as well as the calculation of directional synergy, are performed as follows. Based on the vegetation state parameter set data in continuous time series, it is first necessary to clarify the selection principles and specific implementation methods of the historical benchmark. The historical benchmark is defined as the parameter value corresponding to the last complete monitoring cycle before the implementation of restoration measures. For example, if the restoration measures are implemented from January 15, 2024, then the monitoring data from December 31, 2023 is selected as the historical benchmark value. This benchmark value represents the initial state before restoration, and all subsequent changes are quantified relative to this benchmark. When calculating the rate of change of each parameter in the vegetation status parameter set relative to the historical baseline, the three parameters of vegetation cover, leaf area index, and vegetation biomass are processed separately for each monitoring time point. The specific formula for calculating the rate of change is: subtract the historical baseline parameter value from the current time point parameter value, divide by the historical baseline parameter value, and then multiply by 100% to obtain the rate of change value expressed as a percentage. For example, if the historical baseline vegetation cover is 50% and the current time point vegetation cover is 60%, then the rate of change is 20%. During the calculation process, it is necessary to ensure that the parameter units are consistent. Vegetation cover is expressed as a percentage, leaf area index is dimensionless, and vegetation biomass is expressed as grams per square meter. All calculations are retained to two decimal places to improve accuracy. When forming the vegetation state evolution vector, the vegetation cover change rate, leaf area index change rate, and vegetation biomass change rate at the same time point are combined into a three-dimensional vector in a fixed order. The order of vector elements always follows that the first element is the vegetation cover change rate, the second element is the leaf area index change rate, and the third element is the vegetation biomass change rate. This order remains unchanged throughout the analysis process. The vegetation state evolution vectors at all monitoring time points constitute a time series vector group for subsequent synergy analysis.

[0064] Based on continuous time series data of the soil quality parameter set, the same historical baseline definition principle is adopted, namely, the soil parameter values ​​at the last monitoring time point before the implementation of restoration measures are used as the comparison benchmark. When calculating the rate of change of each parameter in the soil quality parameter set relative to the historical baseline, for each monitoring time point, the three parameters of soil pH, soil organic matter content, and soil nitrogen, phosphorus, and potassium content are processed separately. The rate of change is also calculated as a percentage change, but attention should be paid to the differences in parameter characteristics. For example, soil pH is a dimensionless index, and its rate of change is calculated by directly multiplying the ratio of the numerical difference to the baseline value by 100%. Soil organic matter content and soil nitrogen, phosphorus, and potassium content are expressed as mass percentages, and the consistency of data units must be verified during calculation to avoid calculation errors caused by unit confusion. When forming the soil quality evolution vector, the rate of change of soil pH, the rate of change of soil organic matter content, and the rate of change of soil nitrogen, phosphorus and potassium content at the same time point are combined into another three-dimensional vector in a fixed order. The order of vector elements is always the first element: the rate of change of soil pH, the second element: the rate of change of soil organic matter content, and the third element: the rate of change of soil nitrogen, phosphorus and potassium content. This order remains constant throughout the analysis process. The soil quality evolution vectors at all monitoring time points constitute another time series vector group to ensure complete correspondence with the vegetation status evolution vector in the time dimension.

[0065] When calculating the directional coherence by taking the cosine of the angle between the vegetation state evolution vector and the soil quality evolution vector, for each monitoring time point, the vegetation state evolution vector and the soil quality evolution vector at that time point are paired for calculation. The calculation process of the cosine angle includes three core steps. The first step is to calculate the dot product of the two vectors. The dot product is the sum of the products of each element of the vegetation state evolution vector and the corresponding element of the soil quality evolution vector. For example, if the elements of the vegetation state evolution vector are a1, a2, and a3, and the elements of the soil quality evolution vector are b1, b2, and b3, then the dot product is a1×b1 + a2×b2 + a3×b3. During the calculation, it is necessary to ensure that the order of the elements is consistent and the vector dimensions match. The second step is to calculate the Euclidean norm of each vector. The norm of the vegetation state evolution vector is the square root of the sum of the squares of each element. The norm of the soil quality evolution vector is calculated in the same way. The norm calculation reflects the length or range of change of the vector. The third step involves dividing the dot product by the product of the two norms to obtain the cosine of the angle between them. This is achieved by dividing the dot product by the product of the norm of the vegetation state evolution vector and the norm of the soil quality evolution vector. This value ranges from -1 to +1 and serves as the directional coherence between the evolution vectors. A directional coherence closer to +1 indicates a more consistent evolution direction between the vegetation state parameter set and the soil quality parameter set, resulting in a higher coherence assessment. A value closer to -1 indicates opposite evolution directions, resulting in a lower coherence assessment. A value close to 0 indicates no correlation between evolution directions. All numerical processing in the calculations uses double-precision floating-point format to ensure computational accuracy. Pre-defined processing is used for special cases. For example, when the norm of a vector is zero, it indicates no change during that period; setting the directional coherence to 0 directly indicates no directional correlation, avoiding division by zero errors. Data preprocessing includes outlier detection; for example, data with a change rate exceeding 1000% requires verification of the original records. Post-processing includes range verification of the directional coherence to ensure the results are within the valid range. The entire calculation process is completed by iterating through each monitoring time point, and finally generating a time series of directional coordination degree as the coordination degree evaluation result.

[0066] In the specific implementation process, the interaction patterns between each pair of restoration measures in the analysis combination scheme of step S3 are executed as follows. For each pair of restoration measures in the combination scheme, such as restoration measure A and restoration measure B, it is first necessary to clarify the time range and method of data selection. Based on the data changes of vegetation status parameter set and soil quality parameter set during the implementation period of the restoration measures, the implementation time is defined as from the start date of the measure to a fixed monitoring cycle after the end, such as 6 months or 12 months. The specific cycle is determined according to the type of restoration measure and the vegetation growth cycle, ensuring that all comparisons are carried out within the same time length. The data source is the continuous monitoring data of vegetation status parameter set and soil quality parameter set obtained in step S1.

[0067] When calculating the changes in vegetation status parameters and soil quality parameters when each restoration measure is implemented individually, it is necessary to select historical data or experimental area data only implemented for that restoration measure. The parameter value at the start time of implementation is used as the baseline value, and the parameter value at the end time of implementation is used as the final value. For the vegetation status parameter set, which includes three parameters: vegetation cover, leaf area index, and vegetation biomass, the rate of change for each parameter is calculated separately. The formula for calculating the rate of change is: final value minus baseline value, divided by baseline value, and then multiplied by 100% to obtain the percentage rate of change. Then, the arithmetic mean of the rates of change of the three parameters is taken as the change in the vegetation status parameter set. The same method is used to process the soil quality parameter set, which includes three parameters: soil pH, soil organic matter content, and soil nitrogen, phosphorus, and potassium content. The rate of change for each parameter is calculated, and the arithmetic mean is taken as the change in the soil quality parameter set. During the calculation, it is necessary to ensure that the units of the parameters are consistent: vegetation cover is expressed as a percentage, leaf area index is dimensionless, vegetation biomass is expressed as grams per square meter, soil pH is dimensionless, soil organic matter content is expressed as a mass percentage, and soil nitrogen, phosphorus, and potassium content is expressed as a mass percentage.

[0068] When calculating the changes in vegetation status parameter set and soil quality parameter set when two restoration measures are implemented in combination, data from the period when restoration measures A and B are implemented simultaneously are selected. The parameter values ​​at the start time of the combined implementation are used as the baseline values, and the parameter values ​​at the end time of the combined implementation are used as the final values. The same rate of change calculation method and arithmetic mean aggregation method are used to obtain the changes in vegetation status parameter set and soil quality parameter set. All changes are calculated and retained to two decimal places to improve accuracy.

[0069] The interaction mode is determined by comparing the changes when two restoration measures are implemented in combination with the sum of the changes when each restoration measure is implemented alone. The comparison process is performed separately for vegetation state parameter sets and soil quality parameter sets. For the vegetation state parameter set, the difference between the change when the combination is implemented and the sum of the changes when restoration measure A is implemented alone and restoration measure B is implemented alone is calculated. The difference equals the combined change minus the sum of the individual changes. If the difference is greater than zero, the interaction mode for the vegetation state parameter set is a synergistic mode; if the difference is less than or equal to zero, the interaction mode for the vegetation state parameter set is an antagonistic mode. The same comparison method is used for the soil quality parameter set. The difference between the change when the combination is implemented and the sum of the changes when restoration measure A is implemented alone and restoration measure B is implemented alone is calculated. The synergistic or antagonistic mode is determined based on whether the difference is greater than or equal to zero. The determination of the overall interaction mode requires a combination of the results from the vegetation state parameter set and the soil quality parameter set. For example, if both are determined to be synergistic, the overall mode is synergistic; if both are determined to be antagonistic, the overall mode is antagonistic. When the two determinations are inconsistent, they can be determined according to preset rules such as the majority principle or the weighted principle. For example, the magnitude of the changes in the vegetation state parameter set and the soil quality parameter set can be used as the weight for the determination. The weight is calculated as the proportion of the absolute value of the change in each parameter set to the sum of the absolute values ​​of the two sets.

[0070] When the change resulting from the combined implementation is greater than the sum of the changes resulting from individual implementations, the interaction mode is a synergistic mode. This indicates that the combined effect of the two restoration measures on the vegetation state parameter set or soil quality parameter set is better than the sum of their individual effects. When the change resulting from the combined implementation is less than or equal to the sum of the changes resulting from individual implementations, the interaction mode is an antagonistic mode. This indicates that the combined effect of the two restoration measures on the vegetation state parameter set or soil quality parameter set is less than or equal to the sum of their individual effects. Throughout the calculations, consistent data preprocessing must be ensured. For example, checking the uniformity of parameter values ​​and verifying that the baseline value is not zero before calculating the rate of change. If the baseline value is zero, the absolute difference is used instead of the rate of change; the absolute difference is the numerical difference between the final value and the baseline value. Data post-processing includes rounding the difference calculation results to two decimal places and recording and storing the interaction mode results for subsequent analysis steps.

[0071] In the specific implementation process, the simulation perturbation analysis and causal path analysis in step S4 are performed as follows. While keeping the soil quality parameter set unchanged, gradient perturbations are applied to the vegetation cover, leaf area index, and vegetation biomass in the vegetation state parameter set. First, the perturbation amplitude and gradient setting method need to be determined. The gradient perturbation uses an equal-interval rate of change method. For example, five perturbation levels are set with rates of change of -10%, -5%, 0, +5%, and +10%, respectively. Level 0 represents no perturbation, i.e., the original parameter value. The perturbation is applied based on the current values ​​of the vegetation state parameter set obtained in step S1. For example, if the original vegetation cover value is 60%, it becomes 54% after applying a -10% perturbation and 66% after applying a +10% perturbation. At each perturbation level, the soil quality parameter set, including soil pH, soil organic matter content, and soil nitrogen, phosphorus, and potassium content, must remain unchanged, using the fixed values ​​obtained in step S1. When recording changes in the coordination assessment results, for each disturbance level, the coordination assessment results after the change in the vegetation state parameter set are recalculated. The calculation method for the coordination assessment results follows the evolution vector construction and directional coordination calculation process defined in step S2. The changes are recorded as the amount of change in the coordination assessment results relative to the undisturbed level. For example, if the coordination assessment result is 0.8 without disturbance but becomes 0.75 after disturbance, the change is -0.05. When generating the vegetation state disturbance response matrix, the matrix rows represent different disturbance levels, and the matrix columns represent different parameters in the vegetation state parameter set, including vegetation cover, leaf area index, and vegetation biomass. The matrix element values ​​are the changes in the coordination assessment results of the corresponding parameters at the corresponding disturbance level. All changes are retained to four decimal places to ensure accuracy. The vegetation state disturbance response matrix is ​​finally stored in tabular form to characterize the response sensitivity of the vegetation state parameter set to the coordination assessment results.

[0072] While keeping the vegetation status parameter set unchanged, gradient perturbations are applied to the soil pH, soil organic matter content, and soil nitrogen, phosphorus, and potassium content in the soil quality parameter set, respectively, using the same gradient perturbation setting principle. For example, the change rates of the five perturbation levels are -10%, -5%, 0%, +5%, and +10%, respectively. The perturbation is applied based on the current values ​​of the soil quality parameter set obtained in step S1. For example, if the original soil pH value is 6.5, it becomes 5.85 after applying a -10% perturbation and 7.15 after applying a +10% perturbation. Under each perturbation level, the vegetation status parameter set, including vegetation cover, leaf area index, and vegetation biomass, must be kept unchanged, and the fixed values ​​of the vegetation status parameter set obtained in step S1 are used. When recording changes in the compatibility assessment results, for each disturbance level, the compatibility assessment results after the change in the soil quality parameter set are recalculated. The calculation method for the compatibility assessment results also follows the process defined in step S2. The changes are recorded as the amount of change in the compatibility assessment results relative to the undisturbed level. For example, if the compatibility assessment result is 0.8 without disturbance and becomes 0.82 after disturbance, the change is +0.02. When generating the soil quality disturbance response matrix, the matrix rows represent different disturbance levels, and the matrix columns represent different parameters in the soil quality parameter set, including soil pH, soil organic matter content, and soil nitrogen, phosphorus, and potassium content. The matrix element values ​​are the changes in the compatibility assessment results of the corresponding parameters at the corresponding disturbance level. All changes are retained to four decimal places. The soil quality disturbance response matrix is ​​finally stored in tabular form to characterize the response sensitivity of the soil quality parameter set to the compatibility assessment results.

[0073] Based on the evolution of vegetation state parameter sets and soil quality parameter sets over time, the causal transmission direction between the vegetation state parameter sets and soil quality parameter sets can be identified through conditional independence tests. First, it is necessary to define the processing window for time series data. The historical time window is set to a fixed length, such as 12 months, which includes continuous monitoring time points. Given other parameter values ​​within a historical time window, the partial correlation coefficients between the vegetation state parameter set and the soil quality parameter set at a lag period are calculated. The lag period is set to a fixed interval, such as 1 month, indicating that the soil quality parameter set is delayed by 1 month compared to the vegetation state parameter set. The partial correlation coefficient is calculated using the partial correlation method, with the control variables being all other parameter values ​​within the historical time window except for the currently analyzed parameter. For example, when calculating the partial correlation coefficient between the vegetation state parameter set and the soil quality parameter set lagged by 1 month, the control variables include other parameter values ​​of the soil quality parameter set and the vegetation state parameter set within the historical time window. The partial correlation coefficient calculation formula is based on the correlation of linear regression residuals. Specifically, regression models of the vegetation state parameter set on the control variables and the soil quality parameter set at the lag period on the control variables are first established separately, and then the Pearson correlation coefficient of the residual sequences of the two regression models is calculated as the partial correlation coefficient. The partial correlation coefficient between the soil quality parameter set and the vegetation status parameter set at the lag period is calculated using the same method. The lag period is also set to 1 month, indicating that the vegetation status parameter set lags behind the soil quality parameter set by 1 month. The control variables are all other parameter values ​​within the historical time window except for the currently analyzed parameter. By comparing the significance levels of the two partial correlation coefficients, the causal transmission direction between the vegetation status parameter set and the soil quality parameter set is determined. The significance level is obtained through hypothesis testing, for example, using a t-test to calculate the p-value of each partial correlation coefficient. The p-value threshold is set to 0.05. If the p-value of the partial correlation coefficient between the vegetation status parameter set and the soil quality parameter set at the lag period is less than 0.05 and the p-value of the partial correlation coefficient between the soil quality parameter set and the vegetation status parameter set at the lag period is greater than or equal to 0.05, then the causal transmission direction is from the vegetation status parameter set to the soil quality parameter set. If the opposite is true, then the causal transmission direction is from the soil quality parameter set to the vegetation status parameter set. If both p-values ​​are less than 0.05, the causal transmission direction is bidirectional. If both p-values ​​are greater than or equal to 0.05, then there is no significant causal transmission direction.

[0074] Based on the direction of causal transmission, the influence strength of vegetation state parameters on soil quality parameters and the influence strength of soil quality parameters on vegetation state parameters are calculated separately. The influence strength is calculated based on regression coefficients or similar indicators. For example, if the causal transmission direction is from vegetation state parameters to soil quality parameters, a linear regression model of soil quality parameters on vegetation state parameters is established, and the standardized regression coefficients are used as the influence strength. The standardization method is to multiply the regression coefficients by the standard deviation of the vegetation state parameters and then divide by the standard deviation of the soil quality parameters to obtain standardized regression coefficients representing the influence strength. Similarly, if the causal transmission direction is from soil quality parameters to vegetation state parameters, a linear regression model of vegetation state parameters on soil quality parameters is established, and the standardized regression coefficients are calculated as the influence strength. For bidirectional causal transmission, the influence strength in both directions is calculated separately. When generating the two-way intervention effect intensity comparison table, the table contains two rows. The first row shows the effect strength of the vegetation state parameter set on the soil quality parameter set, and the second row shows the effect strength of the soil quality parameter set on the vegetation state parameter set. Effect strength values ​​are retained to four decimal places. If there is no causal transmission in a certain direction, the effect strength is recorded as zero. The two-way intervention effect intensity comparison table is used to characterize the transmission strength of the intervention effect between the vegetation state parameter set and the soil quality parameter set. Data preprocessing during all calculations includes time series alignment and missing value handling, such as using linear interpolation to complete missing monitoring data. Post-processing includes outlier removal; for example, values ​​with an absolute effect strength greater than three times the standard deviation are considered outliers and recalculated.

[0075] In the specific implementation process, the determination of the dominant influence dimension and the quantification of the comprehensive interaction influence intensity in step S5 are carried out in the following manner. Based on the comparison between the vegetation state disturbance response matrix and the soil quality disturbance response matrix, the relative influence of the vegetation state parameter set and the soil quality parameter set on the coordination degree assessment results is determined. First, all matrix element values ​​need to be extracted from the vegetation state disturbance response matrix generated in step S4. The matrix element values ​​represent the change in the coordination degree assessment results under different disturbance levels. The average value of the change rate of the coordination degree assessment results under all disturbance conditions in the vegetation state disturbance response matrix is ​​calculated as the comprehensive sensitivity of vegetation state. The specific calculation method is to sum all element values ​​in the matrix and divide by the total number of matrix elements. For example, if the vegetation state disturbance response matrix has 3 rows and 5 columns with a total of 15 elements, then the sum of the 15 element values ​​and the division by 15 are used to obtain the comprehensive sensitivity of vegetation state. Similarly, extract all matrix element values ​​from the soil quality disturbance response matrix generated in step S4, and calculate the average rate of change of the coordination degree assessment results under all disturbance conditions in the soil quality disturbance response matrix as the comprehensive sensitivity of soil quality. The calculation method is the same: sum all element values ​​of the soil quality disturbance response matrix and divide by the total number of matrix elements. By comparing the values ​​of the comprehensive sensitivity of vegetation status and the comprehensive sensitivity of soil quality, determine the relative influence of the vegetation status parameter set and the soil quality parameter set on the coordination degree assessment results. The comparison method is to calculate the ratio of the two comprehensive sensitivities. For example, if the comprehensive sensitivity of vegetation status is 0.05 and the comprehensive sensitivity of soil quality is 0.03, the ratio is 1.67, indicating that the influence of the vegetation status parameter set on the coordination degree assessment results is approximately 1.67 times that of the soil quality parameter set. The relative influence is recorded as the ratio result and retained to two decimal places.

[0076] By combining the strength of the effect of vegetation state parameter set on soil quality parameter set and the strength of the effect of soil quality parameter set on vegetation state parameter set in the comparison table of two-way intervention effect strength, the dominant influence dimension in the interaction mode needs to be identified. First, two strength values ​​need to be obtained from the comparison table of two-way intervention effect strength generated in step S4, namely the strength of the effect of vegetation state parameter set on soil quality parameter set and the strength of the effect of soil quality parameter set on vegetation state parameter set. When identifying the dominant influence dimension, the absolute values ​​of the two influence strengths are compared. A threshold of 0.1 is set for determining the dominant influence dimension; that is, the difference between the absolute values ​​of the two influence strengths must be greater than 0.1 to determine the dominant influence dimension. For example, if the absolute value of the influence strength of the vegetation state parameter set on the soil quality parameter set is 0.25, while the absolute value of the influence strength of the soil quality parameter set on the vegetation state parameter set is 0.12, the difference is 0.13, which is greater than 0.1. Therefore, the dominant influence dimension is the vegetation state parameter set. If the absolute value of the influence strength of the soil quality parameter set on the vegetation state parameter set is 0.25, while the absolute value of the influence strength of the vegetation state parameter set on the soil quality parameter set is 0.12, then the dominant influence dimension is the soil quality parameter set. If the difference between the absolute values ​​of the two influence strengths is less than or equal to 0.1, then the dominant influence dimension is a two-way equilibrium. The dominant influence dimension identification result is correlated with the interaction mode determined in step S3. For example, for a synergistic interaction mode, if the dominant influence dimension is the vegetation state parameter set, it means that the synergistic interaction is mainly achieved through changes in the vegetation state parameter set.

[0077] Based on the distribution characteristics of synergistic and antagonistic interaction modes on the vegetation state parameter set and soil quality parameter set, the comprehensive interactive influence intensity of all restoration measures in the combined scheme on the coordination degree assessment results is quantified. First, it is necessary to statistically analyze all interaction mode results obtained in step S3, including the interaction mode between each pair of restoration measures and its corresponding dominant influence dimension. The quantification process consists of three steps. The first step calculates the total intensity of the synergistic interaction mode by weighted summing of the changes in the coordination degree assessment results of restoration measure pairs with synergistic interaction modes. The weights are determined according to the dominant influence dimension. For example, if the dominant influence dimension is the vegetation state parameter set, the weight is the ratio of the comprehensive sensitivity of vegetation state to the comprehensive sensitivity of soil quality; if the dominant influence dimension is the soil quality parameter set, the weight is the ratio of the comprehensive sensitivity of soil quality to the comprehensive sensitivity of vegetation state; if the dominant influence dimension is bidirectional equilibrium, the weight is 1. The second step calculates the total intensity of the antagonistic interaction mode by weighted summing of the changes in the coordination degree assessment results of restoration measure pairs with antagonistic interaction modes. The weight determination method is the same as for the synergistic interaction mode, but negative values ​​represent negative impacts. The third step is to calculate the overall interaction intensity. The total intensity of synergistic modes is subtracted from the total intensity of antagonistic modes to obtain the overall value. This value is then normalized by dividing by the total number of recovery measure pairs, and the final result is rounded to four decimal places. The quantitative result of the overall interaction intensity characterizes the overall impact of the interactions of all recovery measures in the combined scheme on the coordination assessment result. A positive value indicates that synergy is dominant overall, while a negative value indicates that antagonism is dominant overall. The absolute value represents the magnitude of the impact.

[0078] In the specific implementation process, the correction of the coordination degree assessment results in step S6 is carried out in the following manner. Based on the sign and magnitude of the numerical value of the comprehensive interaction influence intensity, the direction and magnitude of the correction to the coordination degree assessment results are determined. First, the numerical value of the comprehensive interaction influence intensity is obtained from step S5. This value is a signed real number, and the sign indicates the overall direction of the interaction: a positive sign indicates that synergy is dominant, and a negative sign indicates that antagonism is dominant. The absolute value of the value indicates the magnitude of the influence intensity. The correction direction is determined directly based on the sign of the comprehensive interaction influence intensity. For example, if the comprehensive interaction influence intensity is positive, the correction direction is positive, i.e., increasing the coordination degree assessment result; if the comprehensive interaction influence intensity is negative, the correction direction is negative, i.e., decreasing the coordination degree assessment result. The correction magnitude is determined based on the absolute value of the comprehensive interaction intensity. It is calculated using a preset scaling factor, which is set based on historical data or experience. For example, a suitable scaling factor value is determined by analyzing the relationship between the comprehensive interaction intensity and the coordination assessment results in multiple restoration cases. The scaling factor is typically set between 0.05 and 0.2, with a commonly used value of 0.1. The correction magnitude equals the absolute value of the comprehensive interaction intensity multiplied by the scaling factor. For instance, if the comprehensive interaction intensity is +0.05 and the scaling factor is 0.1, the correction magnitude is 0.05 × 0.1 = 0.005, with a positive correction direction. If the comprehensive interaction intensity is -0.03 and the scaling factor is 0.1, the correction magnitude is 0.03 × 0.1 = 0.003, with a negative correction direction. The specific value of the scaling factor can be adjusted according to the characteristics of the restoration area and the required assessment accuracy. For example, in the early stages of vegetation restoration, the scaling factor may be set relatively small, such as 0.05, to avoid over-correction, while in the stable period, it may be set relatively large, such as 0.15, to fully reflect the impact of the interaction. The calculation of the correction magnitude also needs to take into account the magnitude of the overall interaction intensity. For example, if the absolute value of the overall interaction intensity is greater than 0.1, different scaling factors or piecewise functions may be used to ensure that the correction magnitude is not too large. For example, the maximum correction magnitude limit may be set to 0.01 to prevent individual high intensity values ​​from causing the results to be distorted.

[0079] Based on the correction direction and magnitude, a correction relationship is established between the coordination degree assessment result and the intensity of the comprehensive interaction influence. This correction relationship employs a linear adjustment model, specifically: the final coordination degree assessment result equals the original coordination degree assessment result plus the correction direction factor multiplied by the correction magnitude. The correction direction factor is set according to the correction direction; it is +1 for positive corrections and -1 for negative corrections. For example, if the original coordination degree assessment result is 0.8, the correction direction is positive, and the correction magnitude is 0.005, then the final coordination degree assessment result is 0.8 + 1 × 0.005 = 0.805. Establishing this correction relationship ensures that the quantitative impact of the intensity of the comprehensive interaction influence on the coordination degree assessment result is reasonably incorporated, while maintaining computational simplicity and interpretability. The correction relationship also needs to consider boundary condition handling. For example, if the calculated final coordination degree assessment result exceeds a reasonable range (e.g., less than -1 or greater than 1), it is truncated and forcibly set to -1 or 1 to maintain the reasonableness of the result. Furthermore, if the original harmony assessment result is close to the boundary value, such as 0.95 or -0.95, the correction magnitude may need to be adjusted to avoid overflow, for example, by using a dynamic scaling factor or limiting the maximum correction magnitude. Validation of the correction relationship is performed through backtesting with historical data, such as using data from past restoration cases. After applying the correction relationship, the final harmony assessment result is checked to see if it better reflects the actual observed ecological state, ensuring the reliability and applicability of the correction relationship. The establishment of the correction relationship also needs to consider the specificity of different combinations of restoration measures. For example, for a scheme primarily focused on vegetation restoration, the scaling factor may be biased towards the influence of the vegetation state parameter set, while for a scheme primarily focused on soil improvement, the scaling factor may be adjusted to reflect the dominant role of the soil quality parameter set.

[0080] The coordination degree assessment results are adjusted according to the correction relationship to generate the final coordination degree assessment result for the target recovery area. Specifically, the original coordination degree assessment result calculated in step S2 is obtained, and the correction direction and magnitude determined above are combined with the correction relationship for calculation. During the calculation process, data consistency must be ensured; the original coordination degree assessment result and the comprehensive interaction influence intensity must come from the same time point or assessment period. For example, if the original coordination degree assessment result is 0.75, the comprehensive interaction influence intensity is -0.02, and the scaling factor is 0.1, then the correction magnitude is 0.02 × 0.1 = 0.002, the correction direction is negative, the correction direction factor is -1, and the final coordination degree assessment result is 0.75 + (-1) × 0.002 = 0.748. Another example: if the original coordination assessment result is 0.6, the overall interaction strength is +0.08, and the scaling factor is 0.1, then the correction magnitude is 0.08 × 0.1 = 0.008, the correction direction is positive, and the final coordination assessment result is 0.6 + 1 × 0.008 = 0.608. The final coordination assessment result is rounded to four decimal places and recorded in the assessment report for subsequent decision support. The entire correction process is implemented through a standardized workflow to ensure comparability and consistency among different recovery areas and schemes. Data post-processing includes result visualization and report generation, such as generating charts showing the coordination assessment results over time to display the differences before and after correction, visually demonstrating the impact of the interaction. Furthermore, outlier checks are performed during the correction process. For example, if the overall interaction strength is an outlier exceeding three standard deviations, the calculation in step S5 needs to be reviewed or robust statistical methods used to ensure the accuracy and reliability of the final result. The final coordination assessment results also need to take into account the actual application scenarios. For example, in mine ecological restoration management, the revised results can be used to optimize the combination of restoration measures, improve restoration efficiency, and provide a benchmark reference for long-term monitoring.

[0081] Example 2: Figure 2 A schematic diagram of a vegetation and soil coupling coordination assessment system for mine ecological restoration is provided. This system, used to implement a method for assessing the coupling coordination of vegetation and soil during mine ecological restoration, includes:

[0082] The data acquisition module is used to acquire the vegetation status parameter set, soil quality parameter set, and combination scheme of various restoration measures implemented in the target restoration area;

[0083] The collaborative computing module is used to construct evolution vectors of vegetation state parameter sets and soil quality parameter sets relative to historical benchmarks, and obtain the coordination degree evaluation results by calculating the directional coordination degree between evolution vectors.

[0084] The pattern recognition module is used to analyze the interaction patterns between each pair of recovery measures in the combination scheme, including synergistic and antagonistic patterns.

[0085] The pattern analysis module is used to perform simulated perturbation analysis on the vegetation state parameter set and soil quality parameter set for each interaction mode. At the same time, it analyzes the causal path between parameters based on the causal discovery algorithm to obtain the response sensitivity and the transmission intensity of the intervention effect.

[0086] The intensity assessment module is used to determine the dominant influence dimension of each interaction mode based on response sensitivity and the transmission intensity of intervention effect, and to quantify the comprehensive interaction influence intensity of the combined scheme on the coordination assessment results.

[0087] The result correction module is used to correct the coordination degree assessment results based on the intensity of comprehensive interaction effects, and generate the final coordination degree assessment results for the target recovery area.

[0088] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0089] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0090] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0093] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0095] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0097] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the coupling coordination between vegetation and soil during mine ecological restoration, characterized in that, include: S1. Obtain the set of vegetation status parameters, soil quality parameters, and combination schemes of various restoration measures for the target restoration area; S2. Construct evolution vectors of vegetation state parameter set and soil quality parameter set relative to historical baseline, and obtain the coordination degree evaluation result by calculating the directional coordination degree between evolution vectors. S3. Analyze the interaction patterns between each pair of recovery measures in the combination scheme, including synergistic and antagonistic patterns; S4. For each interaction mode, simulated perturbation analysis is performed on the vegetation state parameter set and soil quality parameter set respectively. Simultaneously, based on the causal discovery algorithm, the causal paths between parameters are analyzed to obtain response sensitivity and the transmission strength of the intervention effect, including: While keeping the soil quality parameter set unchanged, gradient perturbations were applied to vegetation cover, leaf area index and vegetation biomass in the vegetation state parameter set, and the changes in the coordination degree assessment results were recorded to generate a vegetation state perturbation response matrix. While keeping the vegetation state parameter set unchanged, gradient perturbations were applied to the soil pH, soil organic matter content and soil nitrogen, phosphorus and potassium content in the soil quality parameter set, and the changes in the coordination degree assessment results were recorded to generate a soil quality perturbation response matrix. Based on the evolution of vegetation state parameter set and soil quality parameter set in the time dimension, the causal transmission direction between vegetation state parameter set and soil quality parameter set is identified by conditional independence test. Based on the causal transmission direction, the influence strength of the vegetation state parameter set on the soil quality parameter set and the influence strength of the soil quality parameter set on the vegetation state parameter set are calculated respectively, and a comparison table of the intensity of the two-way intervention effect is generated. S5. Determine the dominant influence dimension of each interaction mode based on response sensitivity and the transmission intensity of intervention effect, and quantify the comprehensive interaction influence intensity of the combined scheme on the coordination evaluation results. S6. The coordination assessment results are revised based on the intensity of the comprehensive interaction effects to generate the final coordination assessment results for the target recovery area, including: Based on the numerical sign and magnitude of the comprehensive interaction influence intensity, the direction and magnitude of the correction to the coordination degree assessment results are determined. Based on the direction and magnitude of the correction, establish a correction relationship between the coordination assessment results and the intensity of the comprehensive interactive impact; The coordination assessment results are adjusted according to the correction relationship to generate the final coordination assessment results for the target recovery area.

2. The method for assessing the coupling coordination of vegetation and soil during mine ecological restoration according to claim 1, characterized in that, Obtain the set of vegetation status parameters, soil quality parameters, and a combination of various restoration measures for the target restoration area, including: Obtain the vegetation status parameter set of the target restoration area, which includes vegetation coverage, leaf area index and vegetation biomass. Obtain a set of soil quality parameters for the target restoration area, including soil pH, soil organic matter content, and soil nitrogen, phosphorus, and potassium content. Obtain a combination of various recovery measures to be implemented in the target recovery area. The combination includes the type of recovery measures, the implementation time of the recovery measures, and the intensity of the implementation of the recovery measures.

3. The method for assessing the coupling coordination of vegetation and soil during mine ecological restoration according to claim 1, characterized in that, Evolution vectors of vegetation state parameter sets and soil quality parameter sets relative to historical baselines were constructed respectively. The coordination degree assessment results were obtained by calculating the directional coordination degree between the evolution vectors, including: Based on the vegetation state parameter set data in continuous time series, the rate of change of each parameter in the vegetation state parameter set relative to the historical baseline is calculated to form a vegetation state evolution vector. Based on the data of the soil quality parameter set in continuous time series, the rate of change of each parameter in the soil quality parameter set relative to the historical benchmark is calculated to form a soil quality evolution vector. The directional synergy between the evolution vectors is obtained by calculating the cosine of the angle between the vegetation state evolution vector and the soil quality evolution vector, and is used as the result of the coordination degree assessment.

4. The method for assessing the coupling coordination of vegetation and soil during mine ecological restoration according to claim 1, characterized in that, Analyze the interaction patterns between each pair of recovery measures in the combined treatment plan, including: For each pair of restoration measures in the combined scheme, based on the data changes of vegetation status parameter set and soil quality parameter set during the implementation time of the restoration measures, calculate the changes in vegetation status parameter set and soil quality parameter set when each restoration measure is implemented alone, and the changes in vegetation status parameter set and soil quality parameter set when the two restoration measures are implemented in combination; The interaction pattern is determined by comparing the changes when two recovery measures are implemented in combination with the sum of the changes when each recovery measure is implemented alone. When the amount of change when implemented in combination is greater than the sum of the amounts of change when implemented individually, the interaction mode is a synergistic mode. When the amount of change when implemented in combination is less than or equal to the sum of the amounts of change when implemented individually, the interaction mode is an antagonistic mode.

5. The method for assessing the coupling coordination of vegetation and soil during mine ecological restoration according to claim 1, characterized in that, in, The vegetation state disturbance response matrix characterizes the sensitivity of the vegetation state parameter set to the coordination degree assessment results, the soil quality disturbance response matrix characterizes the sensitivity of the soil quality parameter set to the coordination degree assessment results, and the two-way intervention effect intensity comparison table characterizes the transmission intensity of the intervention effect between the vegetation state parameter set and the soil quality parameter set.

6. The method for assessing the coupling coordination of vegetation and soil during mine ecological restoration according to claim 1, characterized in that, Based on the evolution of vegetation state parameter sets and soil quality parameter sets over time, the causal transmission direction between the vegetation state parameter sets and soil quality parameter sets can be identified through conditional independence tests, including: Given other parameter values ​​within a historical time window, calculate the partial correlation coefficient between the vegetation state parameter set and the soil quality parameter set in the lag period, and the partial correlation coefficient between the soil quality parameter set and the vegetation state parameter set in the lag period. By comparing the significance levels of two partial correlation coefficients, the direction of causal transmission between the vegetation state parameter set and the soil quality parameter set can be determined.

7. The method for assessing the coupling coordination of vegetation and soil during mine ecological restoration according to claim 1, characterized in that, The dominant influence dimensions of each interaction mode are determined based on response sensitivity and the transmission strength of the intervention effect, and the comprehensive interaction influence strength of the combined scheme on the coordination assessment results is quantified, including: Based on the comparison between the vegetation state disturbance response matrix and the soil quality disturbance response matrix, the relative influence of the vegetation state parameter set and the soil quality parameter set on the coordination degree assessment results is determined. By combining the strength of the effect of vegetation state parameter set on soil quality parameter set and the strength of the effect of soil quality parameter set on vegetation state parameter set in the comparison table of two-way intervention effect, the dominant influence dimension in the interaction mode is identified. Based on the distribution characteristics of synergistic and antagonistic action patterns on vegetation state parameter sets and soil quality parameter sets, the intensity of the comprehensive interactive influence of the interaction of all restoration measures in the combined scheme on the coordination degree assessment results is quantified.

8. The method for assessing the coupling coordination of vegetation and soil during mine ecological restoration according to claim 7, characterized in that, Based on the comparison of the vegetation state disturbance response matrix and the soil quality disturbance response matrix, the relative influence of the vegetation state parameter set and the soil quality parameter set on the coordination degree assessment results is determined, including: The average rate of change of the coordination degree assessment results under all disturbance conditions in the vegetation state disturbance response matrix is ​​calculated as the comprehensive sensitivity of vegetation state. The average rate of change of the coordination degree assessment results under all disturbance conditions in the soil quality disturbance response matrix is ​​calculated as the comprehensive sensitivity of soil quality. By comparing the numerical values ​​of the integrated sensitivity of vegetation status and the integrated sensitivity of soil quality, the relative influence of the vegetation status parameter set and the soil quality parameter set on the coordination degree assessment results is determined.

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