An ecological restoration evaluation method and system based on evolution resilience and a storage medium

Abstract

The present application relates to the technical field of ecological environment data management, and more particularly to an ecological restoration evaluation method and system based on evolution resilience and a storage medium. The method comprises the following steps: collecting vegetation coverage data and surface soil porosity data of an ecological restoration area to be evaluated year by year; constructing a vegetation coverage time sequence node sequence and a surface soil porosity time sequence node sequence respectively; binding the vegetation coverage time sequence node and the surface soil porosity time sequence node corresponding to the same restoration year, to generate a resilience coupling node of ecological restoration in each year. The present application realizes segmented quantification of ecological time sequence evolution and identification of vegetation-soil coupling state by constructing a benchmark follow-up node time sequence pairing deduction structure and a double-index difference trend polymorphism discrimination, thereby improving the refinement degree and discrimination accuracy of ecological restoration resilience time sequence evaluation.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental data management technology, and in particular to an ecological restoration evaluation method, system and storage medium based on evolutionary resilience. Background Technology

[0002] Ecological restoration resilience assessment is a core technical means to measure the stability and sustainable evolution capacity of regional ecosystem restoration. Currently, the industry mostly uses core ecological indicators such as vegetation and soil to evaluate the temporal restoration effect. By monitoring the changes in ecological indicators over multiple years, the overall restoration and evolution trend of the ecosystem is analyzed to complete the overall assessment of ecological restoration resilience. Existing assessment methods mostly rely on overall temporal data to conduct macro-trend analysis, which can only obtain the long-term overall change pattern of the ecosystem and lacks a refined decomposition and segmented quantitative analysis system for the annual temporal evolution process. At the same time, existing assessment schemes mostly focus on the fluctuation changes of single-type indicator values, without conducting coupled matching analysis on the change trends of the two core indicators of vegetation and soil, and cannot effectively distinguish the differentiated evolution states of ecosystem co-evolution, single-dimensional fluctuations, and mutual checks and balances. These technical limitations result in low recognition and differentiation of the dynamic evolution characteristics within the ecosystem by existing assessment methods, making it difficult to accurately characterize the stage-by-stage resilience fluctuation characteristics in the ecological restoration process, and failing to achieve standardized and refined quantitative assessment of ecological restoration resilience. Summary of the Invention

[0003] Therefore, it is necessary to provide an ecological restoration assessment method, system, and storage medium based on evolutionary resilience to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an ecological restoration assessment method based on evolutionary resilience is proposed, the method comprising the following steps:

[0005] Step S1: Collect vegetation cover data and topsoil porosity data of the ecological restoration area to be evaluated year by year; construct time-series node sequences of vegetation cover and topsoil porosity respectively.

[0006] Step S2: Bind the vegetation cover time series node and the surface soil porosity time series node corresponding to the same restoration year to generate the resilience coupling node for ecological restoration in each year.

[0007] Step S3: According to the preset repair time sequence, perform state progression deduction on the resilient coupling nodes of adjacent years in turn, and determine the temporal evolution trend between adjacent resilient coupling nodes.

[0008] Step S4: Accumulate and record the temporal evolution differences of adjacent resilient coupling nodes within the entire time series to form temporal evolution characteristics; classify the ecological restoration resilience evolution level according to the temporal evolution characteristics to complete the quantitative evaluation of ecological restoration evolution resilience.

[0009] This invention also provides an ecological restoration assessment system based on evolutionary resilience, used to execute the above-described ecological restoration assessment method based on evolutionary resilience. The ecological restoration assessment system based on evolutionary resilience includes:

[0010] The time-series data construction module is used to collect vegetation cover data and topsoil porosity data of the ecological restoration area to be evaluated year by year; and to construct time-series node sequences of vegetation cover and topsoil porosity respectively.

[0011] The resilience node coupling module is used to bind the vegetation cover time series node and the surface soil porosity time series node corresponding to the same restoration year, and generate the resilience coupling node for ecological restoration in each year.

[0012] The temporal situation simulation module is used to perform state progression simulation of resilient coupling nodes in adjacent years in accordance with the preset repair time sequence, and to determine the temporal evolution of adjacent resilient coupling nodes.

[0013] The resilience level evaluation module is used to accumulate and record the temporal evolution differences of adjacent resilience coupling nodes within the entire time series, forming temporal evolution characteristics; based on the temporal evolution characteristics, the ecological restoration resilience evolution level is divided to complete the quantitative evaluation of ecological restoration evolution resilience.

[0014] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed, implements the above-described method for evaluating ecological restoration based on evolutionary resilience.

[0015] The beneficial effects of the present invention are as follows:

[0016] On the one hand, this invention, targeting multi-year continuous ecological time-series data, uniquely employs a pairing and extrapolation structure based on annual time-series benchmark nodes and follow-up nodes. By grouping resilient coupling nodes into adjacent groups for each year, and strictly defining the preceding benchmark node and subsequent follow-up node for each extrapolation unit, a progressively segmented comparison method replaces the traditional overall time-series analysis model. This limiting method clearly defines the time reference benchmark for ecological evolution, enabling directional and quantitative differential calculation of vegetation and soil index changes within each annual interval, accurately pinpointing the actual ecological evolution increment differences between adjacent years, and achieving refined segmented quantification of the time-series evolution process.

[0017] On the other hand, this invention establishes a comprehensive situational assessment system based on a three-tiered situational discrimination mechanism that compares the trends of dual-indicator differences. This system relies on the unidirectional, unidirectional, and reverse offset characteristics of the differences in vegetation mean variation and soil porosity mean variation to identify synchronous, unidirectional, and bidirectional balancing changes. Unlike single-indicator numerical evaluation, this invention accurately captures the internal dynamics of the synergistic evolution, individual fluctuations, and mutual balancing of vegetation and soil ecological elements through the coupled matching of dual-indicator change trends. This achieves an essential characterization of the resilience fluctuation characteristics of the ecological restoration system, effectively improving the accuracy and technical distinguishability of identifying the temporal evolution characteristics of ecological resilience. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the steps of an ecological restoration evaluation method based on evolutionary resilience.

[0019] Figure 2 This is a remote sensing image of the ecological restoration area to be evaluated in one embodiment;

[0020] Figure 3 This is a flowchart illustrating the quantitative evaluation of the resilience of ecological restoration evolution in one embodiment.

[0021] Figure 4 This is a block diagram of the ecological restoration evaluation system based on evolutionary resilience of the present invention;

[0022] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0024] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0025] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0026] To achieve the above objectives, please refer to Figures 1 to 4 An ecological restoration evaluation method based on evolutionary resilience, the method comprising the following steps:

[0027] Preferably, step S1: collect vegetation cover data and topsoil porosity data of the ecological restoration area to be evaluated year by year; construct time-series node sequences of vegetation cover and topsoil porosity respectively.

[0028] Optionally, the specific steps in step S1 for collecting vegetation cover data of the ecological restoration area to be evaluated year by year are as follows:

[0029] Delineate the fixed outer boundary of the ecological restoration area, and within the boundary area, complete the layout of square grid points at equal intervals;

[0030] The actual area occupied by vegetation within each grid point is defined by iterating through each grid point in turn, and the percentage of the total grid space occupied by the actual area occupied by vegetation within each grid point is calculated one by one.

[0031] The percentage values ​​obtained from all grid points within the region are aggregated and averaged to obtain the original vegetation cover data for each year that can be included in the time series compilation.

[0032] In one embodiment, remote sensing mapping technology is used to establish a fixed sampling benchmark for the ecological restoration area to be evaluated, and a closed outer boundary is delineated for the target ecological restoration area, maintaining a constant boundary range throughout the process. The delineated area is then divided into uniformly spaced grids, with a standard side length of 10 meters for each square grid cell. Seamless, full-coverage grid coverage is achieved through orthogonal arrangement, ensuring no overlap or gaps in grid cells. Each grid cell is assigned an independent spatial code. The grid layout specifications, spacing parameters, and arrangement methods remain completely uniform across years, constructing a long-term stable spatial sampling system and providing a standardized and unified carrier for the quantitative statistical analysis of continuous vegetation data over multiple years.

[0033] In another embodiment, a grid-by-grid detailed identification operation is carried out on the entire area grid units using 0.5-meter resolution remote sensing image interpretation technology. Each individual grid unit is treated as an independent statistical subject, distinguishing between vegetation-covered areas and non-vegetated blank areas within the unit to accurately define the actual vegetation coverage within each grid. During the entire grid statistical process, typical grid units are selected as calculation examples. Forest grids with good vegetation growth have a vegetation coverage area of ​​over 85 square meters, corresponding to a vegetation ratio higher than 0.85. Transitional grids with average vegetation growth have a vegetation coverage area between 60 and 80 square meters, corresponding to a vegetation ratio between 0.60 and 0.80. Sparsely vegetated edge grids have a vegetation coverage area of ​​less than 60 square meters, corresponding to a vegetation ratio less than 0.60. Following a unified calculation standard, the area and ratio of all grid units in the entire area are calculated one by one, and the original data of vegetation ratio for all individual grids are completely preserved.

[0034] It should be noted that the vegetation percentage values ​​of all grid units in the entire region for the current year are summarized to form an annual vegetation grid statistical data set. A unified data cleaning operation is carried out on the dataset to remove abnormal deviation data caused by image occlusion and terrain interference, and retain all valid statistical samples. The arithmetic mean of the selected valid vegetation percentage values ​​is calculated for the entire region. The calculation result is used as the average vegetation grid percentage for the corresponding year, and this average value is determined as the original data of annual vegetation coverage.

[0035] In another embodiment, see Figure 2 This is a remote sensing image of the ecological restoration area to be evaluated. The image delineates a closed outer boundary and within the boundary, a sampling system composed of equally spaced grids is deployed, with standardized sampling points set at the grid intersections. The area includes plots at different restoration stages, such as vegetation restoration and bare soil, and the grid units can correspond to sampling areas of different restoration states. This image demonstrates the deployment method of the unified spatial grid benchmark in this invention, providing a clear spatial reference for the synchronous collection of vegetation cover and topsoil porosity data, and ensuring the consistency and comparability of multi-year time-series data collection.

[0036] Optionally, the specific steps in step S1, which involve collecting surface soil porosity data for the ecological restoration area to be evaluated year by year, are as follows:

[0037] Using the outer boundary of the area and all square grid layout points defined when collecting vegetation cover data, the soil pore penetration area and soil solid filling area are divided within each same grid point.

[0038] The percentage of the soil pore penetration area within each individual grid point is statistically analyzed. The percentages obtained from all grid points in the region are then aggregated and averaged to obtain the original surface soil porosity data for each year that can be included in the time series compilation.

[0039] In one embodiment, when collecting surface soil porosity data, the complete outer boundary of the area defined during the vegetation cover data collection phase is used throughout the entire process. Simultaneously, all pre-deployed square grid points within the area are also used. The grid layout specifications, unit division methods, and spatial coding order remain consistent, without readjusting the spatial sampling range and unit division structure during the soil data collection phase. This ensures that both types of basic ecological data are collected synchronously using the same spatial sampling framework, unifying the spatial statistical unit ranges for both types of data and guaranteeing a unified spatial benchmark for subsequent data fusion processing. Field sampling is limited to the surface soil layer up to 20 centimeters below the surface. Using a predetermined square grid as the fixed operational unit, the soil structure zoning of all points is completed sequentially according to the grid layout order.

[0040] In another embodiment, soil profile real-scene detection technology combined with soil structure identification is used to classify soil regions within each predetermined grid point, accurately separating the soil pore penetration area and the soil solid filling area within the grid. The boundary delineation is strictly based on the actual internal structure of the soil, and the boundary delineation closely matches the natural structure of the soil. A unified measurement standard is set in the whole-domain grid statistical process, using the surface projection space range corresponding to a single square grid as the overall measurement benchmark. Numerical statistics are completed for grid units in different ecological areas. In areas with loose soil, the proportion of soil pore penetration area in the grid can reach above 0.75, while in areas with compact soil, the proportion is between 0.45 and 0.65. In areas with frequent human disturbance, the proportion remains in a lower range. Numerical statistics are completed for all grid points according to the same measurement rules, and the soil pore space proportion value of each grid is recorded and generated one by one.

[0041] The data collection process gathers soil pore space ratio values ​​for all square grid points within the current year, forming an annual grid soil porosity statistical data set. Following a unified data filtering rule, the data within the dataset are standardized, removing values ​​that deviate from the normal range due to sudden soil disturbances or local geological anomalies, retaining only valid statistical values ​​that conform to the normal structural characteristics of surface soil. A unified arithmetic mean is then calculated on all filtered valid soil pore space ratio values, and the resulting annual average soil grid ratio is directly defined as the original surface soil porosity data for that year.

[0042] Preferably, step S2: bind the vegetation cover time series node and the surface soil porosity time series node corresponding to the same restoration year to generate the resilience coupling node of ecological restoration for each year.

[0043] Of particular importance, step S2 includes the following steps:

[0044] Retrieve the average vegetation grid ratio and average soil pore grid ratio data obtained by statistically integrating the data from all grid points within the same restoration year;

[0045] Select vegetation dimension statistical data and soil structure dimension statistical data generated in the same year, and assign annual exclusive time-series codes with consistent specifications to the vegetation dimension statistical data and soil structure dimension statistical data.

[0046] Based on the completed temporal coding, the vegetation dimension statistical data and the soil structure dimension statistical data are aligned and arranged in the temporal dimension.

[0047] Based on the predetermined arrangement of time-series coding, the average data of vegetation grid proportion and the average data of soil pore grid proportion are accurately aligned and merged, so that each time-series node simultaneously includes the quantitative statistics of vegetation spatial distribution and the quantitative statistics of surface soil structure within the corresponding year, forming ecological restoration resilience coupling nodes for each year.

[0048] In one embodiment, after completing the grid statistics and mean integration of annual vegetation and soil data, the annual ecological data collection and resilience coupling node construction operation is initiated. This involves retrieving the average vegetation grid ratio and average soil porosity grid ratio data generated for the current restoration year through global grid point statistics, outlier removal, and arithmetic mean calculation. Both sets of data are generated using a unified 10-meter side rectangular grid sampling system, possessing a completely consistent spatial statistical benchmark. The data collection process only matches the two types of statistical data for the same restoration year, avoiding cross-year and cross-spatial data retrieval. The screening criteria and data source standards for single-year data collection are fixed, ensuring accurate extraction and locking of the single-year dual-dimensional basic data.

[0049] In another embodiment, standardized time-series coding is performed on the collected vegetation and soil structure statistical data. A fixed coding rule of "restoration start year + annual sequence" is used to generate a unique annual time-series code. Taking the processing of multi-year ecological restoration time-series data as an example, the first year of restoration is uniformly coded as TY01, the second year as TY02, and so on, sequentially completing the coding configuration for each year of the entire restoration cycle. This ensures that each set of annual dual-dimensional statistical data corresponds to a unique and uniform time-series code. All coding character lengths, arrangement rules, and naming formats are consistent throughout the process, achieving the same coding binding between the average vegetation grid proportion data and the average soil porosity grid proportion data, thus completing the unified processing of the time-series identity of these two independent ecological data types.

[0050] Based on the standardized arrangement rules of the annual time-series codes, the time-series alignment and arrangement of vegetation dimension statistical data and soil structure dimension statistical data are carried out. Using the annual progression of the time-series codes as the arrangement benchmark, data with consistent codes in both dimensions are grouped into the same time-series level, eliminating time-series misalignment issues caused by differences in statistical dimensions. Strictly following the predetermined time-series level positions of the codes, the average proportion data of vegetation grids and the average proportion data of soil porosity grids with the same code are merged point-to-point, ensuring that a single time-series unit simultaneously includes both the quantitative statistical content of vegetation spatial distribution and the quantitative statistical content of surface soil structure for the current year.

[0051] Preferably, step S3: according to the preset repair time sequence, the state progression of the resilient coupling nodes in adjacent years is sequentially deduced to determine the temporal evolution trend between adjacent resilient coupling nodes.

[0052] Optionally, in step S3, the state progression deduction of the resilient coupling nodes in adjacent years is performed sequentially according to the preset repair time sequence, specifically as follows:

[0053] Identify the annual sequence in which resilient coupling nodes for ecological restoration are generated;

[0054] Based on the annual order, two resilient coupling nodes with adjacent sorting positions are selected to form a set of deduction combinations;

[0055] Following the chronological order of the years, the combination and division of all adjacent nodes within the entire cycle are completed sequentially;

[0056] The node ranked first in each simulation combination is determined as the timing reference node, and the other nodes after the first are determined as timing follow-up nodes, so as to complete the arrangement setting of all simulation combinations.

[0057] In one embodiment, a time-series sorting and calibration operation is first performed on all annual ecological restoration resilience coupling nodes generated throughout the entire restoration cycle. The sorting is based on the annual-specific time-series code corresponding to each resilience coupling node, and the temporal dimension positioning of all nodes is completed using time-series regularization technology. Following the natural timeline of the ecological restoration project implementation, all resilience coupling nodes from TY01, TY02, TY03 to the final year of the entire cycle are linearly sorted, strictly matching the actual annual time series of node generation. This prevents node sorting errors, time series reversals, and node omissions, completing the standardized time-series construction of the entire domain's resilience coupling nodes, fixing the arrangement order of nodes throughout the entire cycle, and providing a unified time-series benchmark for subsequent deduction and combination division.

[0058] Based on the temporal linear sorting of resilient coupling nodes, a combination grouping operation of adjacent nodes throughout the entire cycle is performed. Using the temporal sorting result as the sole reference standard, two resilient coupling nodes with adjacent positions are selected as a single inference unit to form a complete inference combination. For the temporal node sequence containing 5 consecutive repair years, 4 inference combinations are divided sequentially: TY01-TY02, TY02-TY03, TY03-TY04, and TY04-TY05. Following the grouping rules of successive adjacent pairing, no cross-year pairing, and no repeated pairing, the division of all nodes throughout the entire cycle is completed in the direction of temporal progression. This ensures that the node change relationship of any adjacent year within the entire repair cycle corresponds to an independent inference combination, completing the full coverage splitting of the inference unit across the entire domain.

[0059] In another embodiment, a node attribute calibration operation is performed on each group of completed deduction combinations. The annual resilience coupling node with the highest time sequence within the combination is uniformly set as the time sequence baseline node, and the annual resilience coupling node with the lowest time sequence is set as the time sequence follow-up node. For the TY01-TY02 deduction combination, the resilience coupling node corresponding to TY01 is calibrated as the time sequence baseline node, and the resilience coupling node corresponding to TY02 is calibrated as the time sequence follow-up node; for the TY02-TY03 deduction combination, the resilience coupling node corresponding to TY02 is calibrated as the time sequence baseline node, and the resilience coupling node corresponding to TY03 is calibrated as the time sequence follow-up node. All other deduction combinations are distinguished according to this unified calibration rule.

[0060] Optionally, a set of inference combinations can be formed by selecting two resiliently coupled nodes with adjacent sorting positions based on the annual order, including:

[0061] For each set of simulation combinations that have been arranged, the average proportion of vegetation grid and the average proportion of soil pore grid stored in the time-series baseline node are extracted separately, and the average proportion of vegetation grid and the average proportion of soil pore grid stored in the time-series follow-up node are extracted separately.

[0062] Calculate the difference in vegetation mean at each time-series follow-up node relative to the vegetation mean at the baseline node, and simultaneously calculate the difference in soil porosity mean at each time-series follow-up node relative to the soil porosity mean at the baseline node.

[0063] In one embodiment, after completing the full-cycle simulation combination layout setting and node attribute calibration, the precise extraction of two-dimensional basic data within the simulation combination is initiated. Each simulation combination that has completed time-series positioning is treated as an independent processing unit. Based on the attribute identifiers of the time-series baseline node and the time-series follow-up node, standardized statistical data stored within each resilient coupling node is retrieved. The data types are strictly limited to the average proportion of vegetation grid and the average proportion of soil pore grid. All extracted data originates from the original statistical values ​​integrated during the annual resilient coupling node construction phase. The data extraction process is strictly executed independently according to the time-series combination unit, ensuring data isolation between different simulation combinations and preventing cross-retrieval and mixing, thus maintaining the independence and integrity of each group's data.

[0064] In another embodiment, a hierarchical data parsing technique is used to complete the dual-index data splitting of a single set of extrapolation combinations. This separates the two core data categories: the average vegetation grid ratio and the average soil pore grid ratio carried by the time-series baseline node. Simultaneously, it separates the two core data categories: the average vegetation grid ratio and the average soil pore grid ratio carried by the time-series follow-up nodes. Taking the continuous year extrapolation combination as an example, the TY01-TY02 extrapolation combination separately extracts the average vegetation grid ratio and the average soil pore grid ratio of the TY01 baseline year, and simultaneously extracts the average vegetation grid ratio and the average soil pore grid ratio of the TY02 follow-up year. All other time-series extrapolation combinations complete the independent extraction and classification of the four basic data categories according to the same splitting standard.

[0065] In another embodiment, difference quantization calculation technology is used to solve the time-series variation difference of the two-dimensional indicators. A fixed difference calculation formula is uniformly set, with the time-series follow-up node indicator value as the minuend and the time-series baseline node indicator value as the subtrahend, to complete the standardized difference calculation. For vegetation dimension indicators, the difference in vegetation mean value between the time-series follow-up node vegetation grid proportion and the time-series baseline node vegetation grid proportion is calculated; for soil structure dimension indicators, the difference in soil porosity mean value between the time-series follow-up node soil porosity grid proportion and the time-series baseline node soil porosity grid proportion is calculated.

[0066] Optionally, the variation difference of the vegetation mean at the time-series follow-up nodes relative to the vegetation mean at the baseline node is calculated separately, and the variation difference of the soil porosity mean at the time-series follow-up nodes relative to the soil porosity mean at the baseline node is also calculated, including:

[0067] Extract the difference between the calculated vegetation mean of the time-series follow-up nodes and the vegetation mean of the baseline node, as well as the difference between the soil porosity mean of the time-series follow-up nodes and the soil porosity mean of the baseline node.

[0068] The overall trend of the variation in vegetation mean and the overall trend of the variation in soil porosity mean were identified separately.

[0069] In one embodiment, after calculating the differences in vegetation mean and soil porosity mean for each group of simulation combinations, the collection and standardization of time-series difference data is initiated. The differences in vegetation mean relative to the baseline vegetation mean and soil porosity mean relative to the baseline soil porosity mean for each simulation combination with fixed precision throughout the entire cycle are retrieved uniformly. All difference data retain four decimal places and are generated strictly using a unified calculation formula and a unified data benchmark. The two types of difference data are sequentially collected according to the time-series arrangement of each simulation combination. Data is organized and archived according to the progressive order of annual time-series coding, ensuring that the time-series arrangement of the collected difference data completely corresponds to the arrangement order of the simulation combinations of the resilient coupling nodes. This prevents time-series errors, group misalignments, and data omissions, constructing a time-ordered, group-corresponding global difference data set.

[0070] Using time-series trend identification technology, the collected vegetation mean variation differences are independently identified. Each set of derived vegetation mean variation differences is used as an independent identification unit, and trend determination is completed by combining the continuous difference sequence over the entire period. When multiple consecutive sets of vegetation mean variation differences consistently show positive values, and the values ​​remain stably increasing or fluctuating slightly, the vegetation mean variation difference is defined as showing a positive upward trend. When multiple consecutive sets of vegetation mean variation differences consistently show negative values, and the values ​​remain stably decreasing, the vegetation mean variation difference is defined as showing a negative downward trend. All identification operations use quantified difference values ​​as the sole criterion, relying entirely on the continuous change pattern of the values ​​to complete trend calibration and fix the trend determination standard for vegetation dimension differences.

[0071] In another embodiment, the same temporal trend identification technology is used to independently identify the direction of the variation difference in soil pore mean, maintaining judgment rules and temporal reference standards completely consistent with those for vegetation dimension direction identification. The soil pore mean variation difference corresponding to each set of temporal projection combinations is used as the identification basis. The direction is classified based on the fluctuation pattern of continuous values ​​throughout the entire period. According to the positive and negative attributes and continuous increase / decrease states of the soil pore mean variation difference, the positive upward trend and negative downward trend of the soil pore mean variation difference are respectively labeled.

[0072] The difference trend identification for both types of indicators uses the annual time-series progression direction as the sole inference direction. Each group of difference trend results is bound and stored with its corresponding inference combination, generating separate data for the difference trend of vegetation mean change and soil porosity mean change for each inference combination. The trend identification parameters, judgment criteria, and time-series reference system for all inference combinations across the entire domain remain consistent, forming a two-dimensional, time-series-based, and standardized difference trend dataset. This provides a standardized quantitative trend basis for subsequent comparison and judgment of the time-series evolution of adjacent resilient coupling nodes.

[0073] Optionally, step S3, determining the temporal evolution of adjacent resilient coupling nodes, includes:

[0074] Compare the trend of the variation of the vegetation mean value of the time-series follow-up nodes relative to the vegetation mean value of the baseline node within the same simulation unit.

[0075] Compare the trend of the variation of the mean soil porosity at the time-following nodes within the same simulation unit relative to the mean soil porosity at the baseline node.

[0076] When the trends of the variation difference of vegetation mean and the variation difference of soil porosity mean are completely overlapping, it is determined that the current adjacent toughness coupling nodes exhibit a temporal evolution trend of synchronous iteration of dual indicators.

[0077] Configure the corresponding simulation unit with a synchronous iteration identifier to complete the calibration of the synchronous time-series evolution situation.

[0078] In one embodiment, after obtaining the trend identification results of each group of independent simulation units, a precise alignment verification of the temporal change trends of dual indicators within the same unit is carried out. All verification work takes the simulation unit composed of adjacent annual resilience coupling nodes as the sole processing object. The temporal change trend data of the difference between the vegetation mean and the soil porosity mean of the time-following node and the time-following difference of the soil porosity mean are retrieved. Both sets of trend data are generated based on the difference sequence statistics with 4 decimal places precision and correspond to the same time-progression interval. The annual temporal change trajectories of two core indicators, namely the average proportion of vegetation grid and the average proportion of soil porosity grid, are locked. The trend attributes of the vegetation dimension and the soil structure dimension are fixed respectively. All change trends are uniformly divided into two standardized trend forms: positive rise and negative fall, which serve as the unified technical benchmark for subsequent matching and judgment.

[0079] In another embodiment, a refined matching and verification operation of the dual-indicator trends is performed, strictly defining the technical judgment conditions for the synchronous iteration of the two indicators. When the continuous time series value of the vegetation mean change difference shows an increasing pattern and the overall trend is positive, and the soil porosity mean change difference within the same projection unit also shows a continuous increasing pattern and the overall trend is also positive, the time series change trajectories of the two types of indicators completely overlap. When the continuous time series value of the vegetation mean change difference shows a decreasing pattern and the overall trend is negative, and the soil porosity mean change difference within the same projection unit also shows a continuous decreasing pattern and the overall trend is also negative, the time series change trajectories of the two types of indicators also meet the standard of complete overlap. Only when the trend patterns of the two types of indicators are consistent, the time series fluctuation directions are synchronized, and the overall evolution trajectories are completely aligned, can the synchronization status judgment stage be entered, excluding asynchronous conditions such as single indicator fluctuations, bidirectional reverse fluctuations, and indicator trend misalignments.

[0080] It should be noted that after completing the trend matching verification, situational characterization and labeling operations are performed on the simulation units that meet the overlap criteria. Adjacent resilient coupling nodes that meet the above technical judgment conditions are defined as time-series evolutionary trends with synchronous changes in two indicators. This trend represents the synchronous and unidirectional temporal evolution of the regional vegetation spatial distribution and surface soil structure within a single time-series simulation interval, with the temporal evolution rhythms of the two types of ecological indicators maintaining a high degree of consistency. A pre-set synchronous change-specific label is uniformly added to this type of simulation unit, and the label information is bound and stored with the simulation unit's time-series code, dual-indicator difference data, and trend matching results, completing the accurate labeling of synchronous time-series evolutionary trends. All simulation units throughout the entire cycle adopt this set of refined trend matching standards and situational characterization rules, unifying the technical definition boundaries of synchronous change trends and ensuring the standardization and uniqueness of the time-series trend judgment results across the entire region.

[0081] Optionally, step S3, in determining the temporal evolution of adjacent resilient coupling nodes, further includes:

[0082] When only one type of difference in the vegetation mean change or soil porosity mean change of the time-series tracking node shows a trend of trend shift, while the other type of difference remains constant, it is determined that the current adjacent resilient coupling node shows a time-series evolution trend of single index change and a unidirectional change indicator is configured.

[0083] When both the difference in vegetation mean and the difference in soil porosity mean show a reverse shift, it is determined that the current adjacent resilient coupling nodes exhibit a temporal evolution trend of bidirectional checks and balances and a reverse check and balance indicator is configured to complete the calibration of the full temporal evolution trend.

[0084] In this embodiment, after completing the determination process for the synchronous change of dual indicators, a refined identification of the remaining types of temporal evolution trends is carried out on the remaining unlabeled temporal simulation units. All identification operations are based on the retained data of the variation difference of vegetation mean and soil porosity mean with 4 decimal places and their corresponding temporal change trends. The simulation unit composed of a single group of adjacent toughness coupling nodes is used as an independent identification carrier. The three standardized difference state definitions of positive rise, negative fall, and constant value are used as a unified technical benchmark for the determination of asynchronous trends. The constant value state is defined as the absolute value of the indicator variation difference being within the range of 0.0000±0.0005. This parameter standard is applicable to the steady-state determination of indicators in all simulation units across the entire domain.

[0085] Quantitative determination of single-indicator iteration trends is carried out based on dual-indicator state differentiation comparison technology. The temporal changes of two types of differences within each group of simulation units are independently verified and classified. When the difference in vegetation mean change between the time-following node and the time-reference node within a simulation unit shows a positive upward or negative downward trend, while the difference in soil pore mean change remains constant during the same period, the simulation unit meets the single-indicator iteration determination criteria. Similarly, when the difference in soil pore mean change within a simulation unit shows a positive upward or negative downward trend, while the difference in vegetation mean change remains constant during the same period, the simulation unit also meets the single-indicator iteration determination criteria. All simulation units meeting these criteria are classified, defining the temporal evolution trend of single-indicator iteration at corresponding adjacent resilient coupling nodes. A unidirectional iteration identifier is uniformly assigned to this type of simulation unit, completing the standardized calibration and archiving of single-indicator iteration trends.

[0086] Quantitative determination of the bidirectional balance and equilibrium shift is carried out based on dual-indicator reverse trend comparison technology. Final state verification is performed on the remaining simulation units that have not completed state calibration. Within the same simulation unit, both the difference in vegetation mean and the difference in soil porosity mean show directional shifts, and the shift directions of the two types of differences are completely opposite. Specifically, the difference in vegetation mean shows a positive upward trend while the difference in soil porosity mean shows a negative downward trend, or vice versa. Simulation units that meet this reverse shift condition are defined as exhibiting a temporal evolution of bidirectional balance and equilibrium shifts at their corresponding adjacent resilient coupling nodes, and a unified reverse balance and equilibrium identifier is assigned to these simulation units.

[0087] The global simulation unit sequentially completes the hierarchical screening and labeling configuration of synchronous iterative situation, single-indicator iterative situation, and two-way check and balance iterative situation, with the screening order, judgment parameters, and state definition standards remaining consistent throughout the process. All time-series simulation units complete the binding and storage of dedicated situation labels, achieving full coverage and omission-free labeling of all time-series evolution situations of adjacent resilience coupling nodes throughout the entire repair cycle. This forms a global situation labeling dataset with clear categories, unified parameters, and time-series traceability, providing complete quantitative foundational data for subsequent time-series evolution feature summarization and resilience level classification.

[0088] Preferably, step S4: cumulatively record the temporal evolution differences of adjacent resilient coupling nodes within the entire time interval to form temporal evolution characteristics; classify the ecological restoration resilience evolution level according to the temporal evolution characteristics to complete the quantitative evaluation of ecological restoration evolution resilience.

[0089] Of particular importance, step S4 includes the following steps:

[0090] Arrange them in chronological order according to the year, retrieve the deduction combinations that have completed all the annotations in turn, and read the time sequence annotation content carried by the two adjacent deduction combinations in turn.

[0091] Following the chronological progression, record the changes in the label content between the previous and subsequent combinations, and collect all label change-related content within the entire restoration cycle year by year and complete a unified summary;

[0092] The statistical summary includes the duration of consecutive occurrences of various time-series markers and the frequency of switching between them within the completed marker replacement content.

[0093] Based on the preset time sequence identifier hierarchy classification standard, the resilience evolution levels are divided into different levels according to the overall time sequence arrangement formed by the identifier arrangement.

[0094] In one embodiment, using the annual timeline progression of ecological restoration as a fixed sorting benchmark, all deduced combinations with completed status marker annotations throughout the entire restoration cycle are linearly ordered. Each deduced combination is retrieved sequentially according to the annual timeline coding arrangement, and the three types of fixed timeline markers bound and stored within each combination—synchronous iteration markers, unidirectional iteration markers, and reverse balancing iteration markers—are read one by one. The marker retrieval operation strictly follows the chronological logic, with each group of marker information extracted and archived separately, ensuring the retrieval order fully matches the actual progress of ecological restoration, thus completing the orderly collection of all timeline marker data throughout the entire cycle.

[0095] By comparing the marker categories of adjacent combinations along the timeline using time-series trajectory tracking technology, the actual state of markers remaining unchanged or undergoing category replacement between successive time-series units is accurately recorded. All marker continuation and replacement information throughout the entire restoration cycle is uniformly collected and integrated to form a complete summary of time-series marker evolution data. The data fully records the stable existence intervals of each marker and the time-series nodes of marker transitions. All records correspond to specific restoration year nodes, forming a complete original record of marker evolution with a complete timeline, determining the actual existence period and transition nodes of various markers throughout the entire cycle.

[0096] Quantitative accounting was conducted using the natural restoration year as the basic statistical unit. The duration of continuous occupation of synchronous, unidirectional, and reverse-balancing replacement markers within their respective time series intervals was statistically analyzed. The actual number of pairwise transitions between these three types of markers was also recorded. The minimum unit of measurement for duration was uniformly set as one complete restoration year, and frequency statistics used adjacent combinations of marker changes as the basis for single-event counting. The resulting statistical summary yielded the duration of each type of marker across the entire region, as well as the overall marker switching frequency, forming a definitive quantitative statistical result of the time series characteristics.

[0097] Based on the established time-series identifier hierarchy classification standards, specific resilience levels were determined, resulting in four fixed evaluation levels: Level 1 (High Resilience), Level 2 (Medium Stable Resilience), Level 3 (Fluctuating Resilience), and Level 4 (Weak Resilience). Level 1 (High Resilience) was defined as follows: synchronously changing identifiers with a continuous existence duration of 75% or more across the entire time-series, and an identifier switching frequency of less than 2 times per year; synchronously changing identifiers with a existence duration between 50% and 75%, and an identifier switching frequency between 2 and 4 times; unidirectionally changing identifiers with a large distribution and an overall identifier switching frequency between 4 and 6 times; and reverse-balancing changing identifiers with a high proportion and an overall identifier switching frequency exceeding 6 times. Based on the overall pattern of the full-cycle identifier distribution and corresponding quantitative thresholds, the precise level classification of the ecological restoration evolution resilience of all evaluation areas was completed, determining the final quantitative evaluation results.

[0098] In another embodiment, see Figure 3 This flowchart illustrates the complete process of quantitatively evaluating the resilience of ecological restoration evolution, from the collection of temporal evolution characteristics to the classification of resilience levels. The process begins with "temporal identifier collection," sequentially executing temporal trajectory recording, quantitative parameter statistics, and finally outputting the evaluation results through the "resilience level determination" stage. The arrows in the diagram clearly indicate the direction of data flow, forming a closed-loop evaluation logic, reflecting the resilience grading mechanism based on identifier replacement statistics and threshold matching in this invention.

[0099] The present invention also provides an ecological restoration assessment system based on evolutionary resilience, used to execute the above-described ecological restoration assessment method based on evolutionary resilience. The ecological restoration assessment system 10 based on evolutionary resilience includes:

[0100] The time-series data construction module 101 is used to collect vegetation coverage data and topsoil porosity data of the ecological restoration area to be evaluated year by year; and to construct time-series node sequences of vegetation coverage and topsoil porosity respectively.

[0101] The resilience node coupling module 102 is used to bind the vegetation cover time sequence node and the surface soil porosity time sequence node corresponding to the same restoration year to generate resilience coupling nodes for ecological restoration in each year.

[0102] The temporal situation simulation module 103 is used to perform state progression simulation of the resilient coupling nodes in adjacent years in accordance with the preset repair time sequence, and to determine the temporal evolution of adjacent resilient coupling nodes.

[0103] The resilience level evaluation module 104 is used to accumulate and record the temporal evolution differences of adjacent resilience coupling nodes within the entire time series, forming temporal evolution characteristics; based on the temporal evolution characteristics, the ecological restoration resilience evolution level is divided to complete the quantitative evaluation of ecological restoration evolution resilience.

[0104] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed, implements the above-described method for evaluating ecological restoration based on evolutionary resilience.

[0105] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0106] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An ecological restoration evaluation method based on evolutionary resilience, characterized in that, Includes the following steps: Step S1: Collect vegetation cover data and topsoil porosity data of the ecological restoration area to be evaluated year by year; Construct time-series node sequences for vegetation cover and topsoil porosity, respectively. Step S2: Bind the vegetation cover time series node and the surface soil porosity time series node corresponding to the same restoration year to generate the resilience coupling node for ecological restoration in each year. Step S3: According to the preset repair time sequence, perform state progression deduction on the resilient coupling nodes of adjacent years in turn, and determine the temporal evolution trend between adjacent resilient coupling nodes. Step S4: Accumulate and record the temporal evolution differences of adjacent resilient coupling nodes within the entire time series to form temporal evolution characteristics; The resilience evolution levels of ecological restoration are classified according to the characteristics of temporal evolution in order to complete the quantitative evaluation of the resilience evolution of ecological restoration.

2. The ecological restoration evaluation method based on evolutionary resilience according to claim 1, characterized in that, The specific steps in step S1, which involve collecting vegetation cover data for the ecological restoration area to be evaluated year by year, are as follows: Delineate the fixed outer boundary of the ecological restoration area, and within the boundary area, complete the layout of square grid points at equal intervals; The actual area occupied by vegetation within each grid point is defined by iterating through each grid point in turn, and the percentage of the total grid space occupied by the actual area occupied by vegetation within each grid point is calculated one by one. The percentage values ​​obtained from all grid points within the region are aggregated and averaged to obtain the original vegetation cover data for each year that can be included in the time series compilation.

3. The ecological restoration evaluation method based on evolutionary resilience according to claim 2, characterized in that, The specific steps in step S1, which involve collecting surface soil porosity data for the ecological restoration area to be evaluated year by year, are as follows: Using the outer boundary of the area and all square grid layout points defined when collecting vegetation cover data, the soil pore penetration area and soil solid filling area are divided within each same grid point. The percentage of the soil pore penetration area within each individual grid point is statistically analyzed. The percentages obtained from all grid points in the region are then aggregated and averaged to obtain the original surface soil porosity data for each year that can be included in the time series compilation.

4. The ecological restoration evaluation method based on evolutionary resilience according to claim 1, characterized in that, In step S3, the state progression deduction of the resilient coupling nodes in adjacent years is performed sequentially according to the preset repair time sequence, specifically as follows: Identify the annual sequence in which resilient coupling nodes for ecological restoration are generated; Based on the annual order, two resilient coupling nodes with adjacent sorting positions are selected to form a set of deduction combinations; Following the chronological order of the years, the combination and division of all adjacent nodes within the entire cycle are completed sequentially; The node ranked first in each simulation combination is determined as the timing reference node, and the other nodes after the first are determined as timing follow-up nodes, so as to complete the arrangement setting of all simulation combinations.

5. The ecological restoration evaluation method based on evolutionary resilience according to claim 4, characterized in that, Based on the chronological order of the years, two resiliently coupled nodes with adjacent sorting positions are selected to form a set of inference combinations, including: For each set of simulation combinations that have been arranged, the average proportion of vegetation grid and the average proportion of soil pore grid stored in the time-series baseline node are extracted separately, and the average proportion of vegetation grid and the average proportion of soil pore grid stored in the time-series follow-up node are extracted separately. Calculate the difference in vegetation mean at each time-series follow-up node relative to the vegetation mean at the baseline node, and simultaneously calculate the difference in soil porosity mean at each time-series follow-up node relative to the soil porosity mean at the baseline node.

6. The ecological restoration evaluation method based on evolutionary resilience according to claim 5, characterized in that, Calculate the variation of the vegetation mean at each time-series follow-up node relative to the vegetation mean at the baseline node, and simultaneously calculate the variation of the soil porosity mean at each time-series follow-up node relative to the soil porosity mean at the baseline node, including: Extract the difference between the calculated vegetation mean of the time-series follow-up nodes and the vegetation mean of the baseline node, as well as the difference between the soil porosity mean of the time-series follow-up nodes and the soil porosity mean of the baseline node. The overall trend of the variation in vegetation mean and the overall trend of the variation in soil porosity mean were identified separately.

7. The ecological restoration evaluation method based on evolutionary resilience according to claim 6, characterized in that, Step S3 involves determining the temporal evolution of adjacent resiliently coupled nodes, including: Compare the trend of the variation of the vegetation mean value of the time-series follow-up nodes relative to the vegetation mean value of the baseline node within the same simulation unit. Compare the trend of the variation of the mean soil porosity at the time-following nodes within the same simulation unit relative to the mean soil porosity at the baseline node. When the trends of the variation difference of vegetation mean and the variation difference of soil porosity mean are completely overlapping, it is determined that the current adjacent toughness coupling nodes exhibit a temporal evolution trend of synchronous iteration of dual indicators. Configure the corresponding simulation unit with a synchronous iteration identifier to complete the calibration of the synchronous time-series evolution situation.

8. The ecological restoration evaluation method based on evolutionary resilience according to claim 7, characterized in that, Step S3, determining the temporal evolution of adjacent resilient coupling nodes, also includes: When only one type of difference in the vegetation mean change or soil porosity mean change of the time-series tracking node shows a trend of trend shift, while the other type of difference remains constant, it is determined that the current adjacent resilient coupling node shows a time-series evolution trend of single index change and a unidirectional change indicator is configured. When both the difference in vegetation mean and the difference in soil porosity mean show a reverse shift, it is determined that the current adjacent resilient coupling nodes exhibit a temporal evolution trend of bidirectional checks and balances and a reverse check and balance indicator is configured to complete the calibration of the full temporal evolution trend.

9. An ecological restoration evaluation system based on evolutionary resilience, characterized in that, For executing the evolutionary resilience-based ecological restoration assessment method as described in claim 1, the evolutionary resilience-based ecological restoration assessment system comprises: The time-series data construction module is used to collect vegetation cover data and topsoil porosity data of the ecological restoration area to be evaluated year by year; and to construct time-series node sequences of vegetation cover and topsoil porosity respectively. The resilience node coupling module is used to bind the vegetation cover time series node and the surface soil porosity time series node corresponding to the same restoration year, and generate the resilience coupling node for ecological restoration in each year. The temporal situation simulation module is used to perform state progression simulation of resilient coupling nodes in adjacent years in accordance with the preset repair time sequence, and to determine the temporal evolution of adjacent resilient coupling nodes. The resilience level evaluation module is used to accumulate and record the temporal evolution differences of adjacent resilience coupling nodes within the entire time series, forming temporal evolution characteristics; based on the temporal evolution characteristics, the ecological restoration resilience evolution level is divided to complete the quantitative evaluation of ecological restoration evolution resilience.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the ecological restoration evaluation method based on evolutionary resilience as described in any one of claims 1 to 8.

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