Ecological restoration effect prediction system based on space-time sequence analysis

Abstract

The invention relates to the technical field of ecological monitoring and prediction, and discloses an ecological restoration effect prediction system based on space-time sequence analysis. According to the system, a prediction process is realized through cooperative processing of an ecological data reconstruction unit, a spatial adaptation judgment unit, a time sequence change mapping unit, an effect anomaly screening unit and a risk prediction output unit. The system firstly collects and processes ecological data, and generates a restoration point ecological suitability data set; identifying spatial consistency and difference sections through spatial proximity sorting; evaluating the ecological response intensity in combination with environment time sequence data; abnormal point locations are screened based on composite conditions of response value anomalies and spatial difference sections; and finally generating risk early warning. The method has the technical effects that through coupling space-time analysis, the defect that the space context is neglected when a traditional method depicts spatial heterogeneity and performs anomaly recognition is overcome, and more accurate prediction and risk positioning on the ecological restoration effect are realized.

Description

Technical Field

[0001] This invention relates to the field of ecological monitoring and prediction technology, specifically to an ecological restoration effect prediction system based on spatiotemporal sequence analysis. Background Technology

[0002] Currently, the assessment of ecological restoration effectiveness largely relies on static comparisons of specific indicators before and after restoration, or independent time-series trend analyses of single points. These methods focus on describing macro-trends or tracking historical changes at local locations, lacking coupled analysis of the spatial correlation and temporal dynamics of restoration effects. Conventional spatial analysis techniques are typically used to generate continuous spatial surfaces of ecological parameters. These methods struggle to effectively identify and quantify abrupt boundaries or local discontinuities in ecological conditions in space, resulting in insufficient characterization of spatial heterogeneity in responses to restoration measures and an inability to accurately identify the spatial consistency and disparities in the distribution patterns of ecological restoration effects.

[0003] In identifying anomalies, existing technologies primarily rely on statistical process control or outlier detection algorithms based on global thresholds. These methods judge whether data points are abnormal solely from the perspective of numerical distribution, completely ignoring the spatial context of the anomalies. A point that appears numerically abnormal may be located in a stable region with uniform ecological attributes, or it may be a natural manifestation of an ecological transition zone. This purely numerical anomaly screening, detached from spatial location, generates a large amount of "statistical noise" unrelated to restoration effects, or misses key anomaly points located at ecologically fragile boundaries with potential for spread, significantly reducing the targetedness and accuracy of risk warnings. Summary of the Invention

[0004] The purpose of this invention is to provide an ecological restoration effect prediction system based on spatiotemporal sequence analysis to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides an ecological restoration effect prediction system based on spatiotemporal sequence analysis, the system comprising: The ecological data reconstruction unit receives the geographic coordinates and time information of the ecological restoration points, collects the vegetation coverage and soil properties of the corresponding locations, associates the restoration points and derives the ecological suitability estimate, and outputs the ecological suitability dataset of the restoration points. The spatial adaptation determination unit extracts the ecological suitability estimate and corresponding coordinates from the ecological suitability dataset of the restoration point, sorts adjacent point pairs according to spatial proximity, identifies the consistent and different segments in the adjacent point pairs, and generates spatial consistency partitioning labeling results. The temporal change mapping unit selects the repair points located in the consistency segment from the spatial consistency partitioning labeling results, obtains the time series data of temperature and precipitation, compares the change range with the repair measures, evaluates the intensity of ecological response under environmental variation, and forms ecological response superposition analysis data. The abnormal effect screening unit identifies abnormal points in the ecological response superposition analysis data where the ecological response value is higher than the average response benchmark and is located in the differential segment, and constructs a set of abnormal points for restoration. The risk prediction output unit obtains detailed attributes of all points from the set of abnormal restoration points, marks points with potential spread risks, and generates ecological restoration effect prediction and risk warning output.

[0006] Preferably, the ecological suitability dataset for restoration points includes ecological suitability estimates, spatial coordinates of restoration points, and standardized ecological indicators. The spatial consistency partitioning labeling results specifically include consistency segment identifiers, difference segment identifiers, and the difference in suitability estimates between adjacent restoration points. The ecological response overlay analysis data covers the impact of temperature change rate on restoration, the impact of precipitation change rate on restoration, and the comparison of response measures under various environmental variation scenarios. The set of abnormal restoration points includes spatial information of abnormal points, wind direction and precipitation variation characteristics of abnormal points, and the ratio of measures to area changes at abnormal points. The ecological restoration effect prediction and risk warning output includes detailed predictions of abnormal points and integrated multi-parameter judgment labels for abnormal points.

[0007] Preferably, the ecological data reconstruction unit includes: a restoration information collection subunit that acquires the coordinates and restoration time of the ecological restoration point, collects vegetation index and soil quality data corresponding to the coordinates, and stores the collection results as two ecological parameters: vegetation index and soil index, to obtain a restoration point ecological parameter data set; a data verification subunit that performs integrity checks and anomaly detection on the vegetation index and soil index in the restoration point ecological parameter data set to ensure data quality and generate a verified ecological parameter data set; and an ecological index standardization subunit that performs standardization transformation on the vegetation index and soil index data in the verified ecological parameter data set, associates the standardized transformation results with the coordinates of the restoration point, calculates the comprehensive mean of the standardized vegetation value and the standardized soil value as an ecological suitability estimate, and outputs a restoration point ecological suitability dataset.

[0008] Preferably, the spatial suitability determination unit includes: a suitability estimation extraction subunit extracts ecological suitability estimates and corresponding coordinate data from the ecological suitability dataset of the restoration points, identifies the spatial distribution pattern of all restoration points based on the coordinate information, calls the set of restoration point coordinates, and performs distance measurement and sorting of the restoration points in space based on the proximity distance threshold to generate a distance sorting list of adjacent restoration points; a difference calculation subunit calculates the ecological suitability estimation difference between each pair of adjacent restoration points based on the distance sorting list of adjacent restoration points, and obtains a suitability estimation difference sequence by comparing the estimation difference with the average estimation and introducing vegetation cover difference and soil attribute difference for weighted adjustment; a spatial consistency identification subunit extracts the vegetation change direction and soil change direction between adjacent restoration points from the suitability estimation difference sequence, classifies and marks each pair of restoration points according to the consistency of the change trend in the two directions, records and groups segments with consistent trends and conflicting trends respectively, and generates spatial consistency partitioning labeling results.

[0009] Preferably, the time-series change mapping unit includes: an environmental sequence acquisition subunit that filters segments marked as consistent from the spatial consistency partitioning labeling results, detects temperature and precipitation data within the restoration period of each restoration point, arranges them in chronological order to form temperature and precipitation time series, and generates an environmental variation time series set; a sequence processing subunit performs missing value imputation and noise filtering on the temperature and precipitation time series in the environmental variation time series set to improve data consistency and obtain a processed time series set; and an ecological response overlay calculation subunit that, based on the processed time series set, calculates the rate of temperature change and the rate of precipitation change within consecutive time intervals in the time series of each restoration point, performs parallel analysis of the rate of temperature change and the rate of precipitation change under the same measures, identifies the correlation between the two types of change rate indicators by integrating and evaluating them, aggregates the response value sequence of each restoration point, and forms ecological response overlay analysis data.

[0010] Preferably, the abnormal effect screening unit includes: a record acquisition subunit that filters restoration points with ecological response values ​​higher than the average response benchmark and restoration points in differential segments from the ecological response overlay analysis data, acquires continuous restoration records of restoration points in chronological order, collects restoration measures and restoration area data corresponding to each time point, and generates a continuous restoration record set; a measure-area ratio calculation subunit calls the continuous restoration record set, acquires the measure values ​​and areas of restoration points at two consecutive time points, calculates the measure change ratio and area change ratio respectively, integrates them into a measure change ratio sequence and an area change ratio sequence, and establishes a restoration change dataset; an anomaly detection subunit, based on the restoration change dataset, extracts wind direction variation data and precipitation fluctuation data for the corresponding time period, determines whether the measure change ratio and area change ratio both exceed a preset change detection threshold, determines whether wind direction variation and precipitation fluctuation both exceed anomaly identification thresholds, marks time points that meet the conditions as abnormal points, and constructs a set of restoration abnormal points.

[0011] Preferably, the risk prediction output unit includes: a parameter integration judgment subunit that obtains all points and their corresponding coordinates and identification information from the set of abnormal restoration points, calculates the integrated risk judgment value for each point, and performs a synthesis operation based on the deviation of the ecological response value and the ecological suitability estimate from the risk threshold to establish an integrated risk judgment value sequence; and an anomaly output sorting subunit that, based on the integrated risk judgment value sequence, filters points whose ecological response value is higher than the ecological response risk threshold, whose ecological suitability estimate is lower than the suitability benchmark value, and whose spatial consistency label is a differential segment, extracts the corresponding point code, location description, and partition, marks them as predicted anomalies with diffusion risk, and outputs points that meet the integration conditions in a structured layout by partitioning, generating ecological restoration effect prediction and risk warning output.

[0012] Preferably, the ecological data reconstruction unit further includes: a data fusion subunit that acquires multi-source ecological parameter data, performs data integration and correction, improves data representativeness, and generates a fused ecological parameter dataset; and a dynamic update subunit that dynamically adjusts the ecological suitability dataset of the restoration points according to the progress of time to ensure data timeliness.

[0013] Preferably, the spatial adaptation determination unit further includes: a spatial interpolation subunit performing spatial interpolation on sparsely distributed restoration points to estimate the ecological suitability of unmeasured points and complete spatial coverage; and a partition optimization subunit optimizing the partition boundaries of the spatial consistency partition labeling results based on the interpolation results to enhance the partition logic.

[0014] Preferably, the risk prediction output unit further includes: a visualization generation subunit that converts the ecological restoration effect prediction and risk warning output into a spatial distribution map; and a report generation subunit that automatically generates a structured warning report document based on the spatial distribution map.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By constructing adjacent point pairs through spatial proximity ranking and identifying areas of consistency and difference, continuous geographic space is divided into units with relatively homogeneous ecological attributes and transitional zones with significant variations. This method breaks through the pursuit of overall smoothness in traditional spatial interpolation, and can accurately capture the abrupt boundaries and local discontinuities of ecological parameters in space. The resulting refined spatial zoning framework provides an accurate spatial context for temporal analysis, enabling subsequent ecological response assessments to differentiate between stable regions and sensitive edges, thereby profoundly revealing the spatial differentiation patterns and intrinsic structure of the effects of restoration measures.

[0016] Anomaly screening is performed based on a combination of ecological response values ​​and spatial zoning attributes, identifying locations whose response values ​​deviate from the norm and are located in areas of disparity. This mechanism integrates purely statistical anomaly detection with spatial location attributes, eliminating numerical anomalies caused by random fluctuations or measurement noise within stable areas and reducing false alarms. It ensures that the identified anomaly locations possess both numerical significance and spatial sensitivity; these locations are often associated with the boundary effects of remediation measures or the emergence of ecological risk sources, improving the relevance and reliability of risk warning signals in relation to actual ecological processes. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the working principle of the ecological restoration effect prediction system based on spatiotemporal sequence analysis described in this invention. Figure 2 Flowchart for ecological data reconstruction unit; Figure 3 Flowchart for the spatial adaptation determination unit; Figure 4 Spatial adaptation determination analysis diagram; Figure 5 This is a graph showing the temporal changes and anomaly detection analysis of the environment. Detailed Implementation

[0018] 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.

[0019] Please see Figure 1 This invention provides an ecological restoration effect prediction system based on spatiotemporal sequence analysis. The system includes: an ecological data reconstruction unit that receives the geographic coordinates and time information of ecological restoration points, collects vegetation cover and soil attribute data at the corresponding locations, derives ecological suitability estimates by associating restoration points, and outputs a restoration point ecological suitability dataset; a spatial adaptation determination unit that extracts ecological suitability estimates and corresponding coordinates from the restoration point ecological suitability dataset, sorts adjacent point pairs according to spatial proximity, identifies consistent and dissimilar segments in adjacent point pairs, and generates spatial consistency partitioning results; a temporal change mapping unit that selects restoration points located in consistent segments from the spatial consistency partitioning results, acquires time series data of temperature and precipitation, compares the range of changes with restoration measures, assesses the intensity of ecological response under environmental variation, and forms ecological response overlay analysis data; and an effect anomaly screening unit that identifies anomalous points in the ecological response overlay analysis data whose ecological response values ​​are higher than the average response benchmark and are located in dissimilar segments, and constructs a set of restoration anomaly points. The risk prediction output unit obtains detailed attributes of all points from the set of abnormal points, marks points with potential spread risks, and generates ecological restoration effect prediction and risk warning output.

[0020] Example 1: In practical implementation, the ecological suitability dataset for restoration sites serves as the foundational data set for subsequent spatial and temporal analyses. This dataset, generated and output by the ecological data reconstruction unit, comprises three core components: ecological suitability estimate, spatial coordinates of restoration sites, and standardized ecological indicators. The ecological suitability estimate is a comprehensive numerical indicator, calculated by averaging standardized vegetation and soil values, reflecting the potential suitability of a specific geographical location for ecological restoration at a specific time. The spatial coordinates of restoration sites accurately record the location of each ecological restoration site in geographic space using latitude, longitude, or projected coordinates. The standardized ecological indicators include normalized raw ecological parameters. For example, the normalized differential vegetation index characterizes vegetation cover, while soil quality parameters such as soil organic matter content and soil moisture are incorporated into the dataset after standardization. The standardization process eliminates the influence of different dimensions and numerical ranges, ensuring comparability of ecological indicators from different sources and of different types.

[0021] In practice, the spatial consistency zoning labeling result is the product of the spatial adaptation judgment unit's spatial relationship analysis of the ecological suitability dataset of restoration points. Specifically, it includes consistency segment identifiers, difference segment identifiers, and the difference in suitability estimates between adjacent restoration points. Consistency segment identifiers are used to mark sets of spatially adjacent restoration points with consistent ecological suitability estimate trends. In practice, when the vegetation change direction and soil change direction of a pair of adjacent restoration points are both consistent, the segment is assigned a consistency identifier. Difference segment identifiers are used to indicate the areas where pairs of spatially adjacent restoration points have conflicting or significantly different ecological suitability estimate trends. The difference in suitability estimates between adjacent restoration points is a quantitative indicator, calculated by comparing the absolute difference in ecological suitability estimates between each pair of adjacent restoration points with the average ecological suitability estimate of the area. Sometimes, differences in vegetation cover and soil properties are also introduced for weighted adjustments to more accurately characterize local spatial heterogeneity.

[0022] In practice, the ecological response overlay analysis data is generated by a time-series change mapping unit. This data focuses on the superimposed impact of environmental factors' time-series changes on the ecological restoration process. Its content includes the measurement of the impact of temperature change rate on restoration, the measurement of the impact of precipitation change rate on restoration, and a comparison of responses to measures under various environmental variation scenarios. The measurement of the impact of temperature change rate on restoration is obtained by calculating the slope or percentage change of temperature at the restoration point over different consecutive time intervals, used to quantify the direct impact of temperature fluctuations on vegetation restoration or soil improvement. The measurement of the impact of precipitation change rate on restoration uses a similar method to calculate the rate of change of precipitation series, assessing the contribution of changes in water conditions to the ecological restoration process. The comparison of responses to measures under various environmental variation scenarios places different restoration measures under the same temperature and precipitation change background, analyzing the differences in the response of the same measure under different environmental variation intensities, or comparing the performance of different measures under the same environmental variation scenario. This parallel analysis helps to identify the effectiveness and stability of measures.

[0023] In practice, the set of abnormal sites for restoration is a compilation of abnormal sites formed after the anomaly screening unit filters and judges the ecological response overlay analysis data. The set includes spatial information of the abnormal sites, wind direction and precipitation variation characteristics of the abnormal sites, and the ratio of measures taken to area change for each abnormal site. The spatial information of the abnormal sites details their unique identifiers, precise geographic coordinates, and the spatial consistency zone to which they belong. The wind direction and precipitation variation characteristics of the abnormal sites describe the wind direction changes and precipitation fluctuations experienced by the site within the time period it was marked, such as the stability or fluctuation range of wind direction and abnormally high or low precipitation. The ratio of measures taken to area change for the abnormal sites quantifies the dynamic changes in the restoration activities themselves. The measure variation ratio reflects the degree of change in the type or intensity of restoration measures taken between consecutive time points, while the area variation ratio reflects the magnitude of change in the coverage area of ​​the restoration project.

[0024] In practical implementation, the ecological restoration effect prediction and risk warning output are the final deliverables of the system, generated by the risk prediction output unit. These mainly include a detailed list of predicted anomaly locations and a multi-parameter integrated judgment label for these locations. The detailed list of predicted anomaly locations is a structured list or database record that details all anomaly locations identified by the system as having potential risks. The list includes the location's complete code, standardized geographical location description, associated ecological suitability estimate, ecological response value, and the identifier of its corresponding consistency or difference zone. The multi-parameter integrated judgment label for anomaly locations is a comprehensive risk classification label. In practical implementation, this label is generated based on a multi-parameter integrated judgment algorithm. The algorithm comprehensively considers multiple parameters, such as whether the location's ecological response value exceeds the ecological response risk threshold, whether the ecological suitability estimate is lower than the suitability benchmark value, and whether the location is located in a difference zone. Through weighted synthesis or logical judgment rules, it assigns an integrated label representing the risk level or risk type to each anomaly location, such as "High Risk - Possible Spread" or "Medium Risk - Requires Continuous Monitoring."

[0025] In some embodiments, the standardized ecological indicators of the remediation site ecological suitability dataset can be further subdivided into vegetation indicator subsets and soil indicator subsets. The vegetation indicator subset may include standardized data such as normalized vegetation index and leaf area index; the soil indicator subset may include standardized parameters such as soil pH and soil nitrogen, phosphorus, and potassium content. The calculation of the difference in suitability estimates between adjacent remediation sites in the spatial consistency zoning labeling results can employ a dynamic weight adjustment method. In areas with high vegetation cover, the weight of vegetation cover difference in the difference calculation can be appropriately increased; in areas where soil properties play a dominant role in ecological restoration, the weight of soil property differences will be increased accordingly, making the difference calculation more reflective of the influence of the dominant ecological factors in the region.

[0026] Example 2: See Figure 2 In practical implementation, the ecological data reconstruction unit is the system's fundamental data preprocessing module. Its core function is to integrate raw ecological observation data and generate a standardized dataset of ecological suitability for restoration sites. The ecological data reconstruction unit comprises a restoration information collection subunit, a data verification subunit, and an ecological indicator standardization subunit. The restoration information collection subunit is responsible for receiving ecological restoration site information from external systems or manual input. This information includes the geographical coordinates of the restoration site and the year and month in which the restoration project began. The geographical coordinates are expressed in latitude and longitude using the WGS84 coordinate system. The restoration information collection subunit then collects vegetation index and soil quality data for the corresponding locations from connected remote sensing image databases and soil survey databases, based on the received coordinates. The vegetation index is typically the Normalized Difference Vegetation Index (NDVI), while the soil quality data includes key parameters such as soil moisture, soil organic matter content, and soil pH. The collected data is categorized and stored, forming two sets of ecological parameters—vegetation indicators and soil indicators—based on the restoration site. This initial set is called the restoration site ecological parameter data set. The restoration point ecological parameter data set is a structured data table, with each row representing a restoration point and columns containing coordinates, time, and original values ​​of multiple vegetation and soil parameters.

[0027] The data validation subunit performs quality control on the ecological parameter dataset of the remediation sites. Integrity checking is the first step; this process scans every cell in the data table to identify any missing data. For missing vegetation index values, the data validation subunit marks the record as incomplete. Anomaly detection is performed using statistical methods. For example, for the soil organic matter content field, the data validation subunit calculates the mean and standard deviation of this parameter for all remediation sites, identifying values ​​that deviate from the mean by more than three standard deviations as outliers. The data validation subunit marks the identified missing and outlier values ​​and performs simple processing according to preset rules. For example, for small-scale, consecutive missing vegetation indices, neighbor interpolation may be used to fill in the gaps, while obvious outliers are removed and marked as invalid. Finally, a cleaned and marked validated ecological parameter dataset is output. The validated ecological parameter dataset ensures the reliability and consistency of the data upon which subsequent calculations rely.

[0028] The ecological indicator standardization subunit is responsible for converting heterogeneous data in the validated ecological parameter dataset into comparable dimensionless values. The ecological indicator standardization subunit reads the validated ecological parameter dataset and performs standardization transformation on each vegetation and soil indicator. Taking soil organic matter content as an example, the ecological indicator standardization subunit uses the min-max normalization method, as shown in the following formula:

[0029] in: This represents the standardized soil organic matter content value. This represents the original soil organic matter content measurement value at a specific remediation site within the validated ecological parameter data set. and These represent the maximum and minimum values ​​of soil organic matter content at all remediation sites, respectively. The Normalized Difference Vegetation Index (NDVI) is also standardized using the same linear transformation method to obtain standardized vegetation values. The ecological index standardization subunit calculates the arithmetic mean of the standardized vegetation and standardized soil values ​​for each remediation site; the resulting composite mean is defined as the ecological suitability estimate for that remediation site. The ecological index standardization subunit combines the remediation site coordinates, remediation time, standardized ecological indicators, and the calculated ecological suitability estimate to output the final remediation site ecological suitability dataset.

[0030] In its implementation, the ecological data reconstruction unit also integrates a data fusion subunit and a dynamic update subunit to enhance the robustness and timeliness of data processing. The data fusion subunit is initiated after the restoration information collection subunit. This subunit can receive ecological parameter data from different data sources; for example, vegetation indices may originate from both Landsat and Sentinel satellites, and soil data may come from soil surveys in different years. The data fusion subunit first integrates the data, aligning the multi-source data on spatial and temporal scales. For multiple vegetation index observations at the same restoration point within the same time period, the subunit calculates their average as a representative value. Subsequently, the subunit performs data correction. For example, to address potential systematic errors from different satellite sensors, the subunit applies an empirical correction coefficient to eliminate biases, thereby improving the representativeness and accuracy of the fused ecological parameter dataset. The fused ecological parameter dataset then serves as input to the data validation subunit, participating in subsequent processes.

[0031] The dynamic update subunit runs as a background process, periodically checking for new ecological restoration point data additions or updates to the time-series data of existing restoration points. When new data input is detected, the dynamic update subunit triggers some or all subunits of the ecological data reconstruction unit to re-execute the data processing flow. For example, if a restoration point adds vegetation index data from the latest quarter, the dynamic update subunit will guide the ecological indicator standardization subunit to recalculate the ecological suitability estimate for that point and update the corresponding record in the restoration point's ecological suitability dataset. This mechanism ensures that the restoration point's ecological suitability dataset reflects the latest ecological conditions, maintaining data timeliness.

[0032] Example 3: See Figure 3In practical implementation, the spatial suitability determination unit is responsible for spatial relationship modeling and partitioning of the ecological suitability dataset of restoration points. This unit includes a suitability estimation extraction subunit, a difference calculation subunit, and a spatial consistency identification subunit. The suitability estimation extraction subunit reads each record from the ecological suitability dataset of restoration points, extracting the ecological suitability estimation field and the corresponding spatial coordinate field, represented by decimal latitude and longitude values. Based on the coordinate set of all restoration points, the suitability estimation extraction subunit uses nearest neighbor analysis in spatial statistics to identify the spatial distribution pattern of the restoration points, determining whether the distribution is clustered, random, or uniform. Subsequently, the suitability estimation extraction subunit calculates the pairwise Euclidean distance between all restoration points according to a preset proximity threshold. This proximity threshold is typically dynamically set based on the size of the study area and the density of restoration points, for example, 5 kilometers. The suitability estimation extraction sub-unit sorts the calculated point-to-point distances in ascending order, generating a sorted list of adjacent repair point distances. Each row in the list contains a unique identifier for a pair of adjacent repair points and the distance between them.

[0033] The difference calculation subunit takes a sorted list of distances between adjacent restoration points as input. It iterates through each pair of adjacent restoration points in the list, retrieving their respective ecological suitability estimates from the restoration point ecological suitability dataset. The subunit calculates the absolute difference between these ecological suitability estimates and queries the average ecological suitability estimate for all restoration points in the region. The subunit introduces vegetation cover difference and soil property difference for weighted adjustment. Vegetation cover difference refers to the absolute difference in standardized vegetation values ​​between the pair of points, and soil property difference refers to the absolute difference in standardized soil values. Weighting coefficients are assigned to vegetation cover difference and soil property difference during the weighting process; these coefficients can be configured based on the region's ecological characteristics. The subunit calculates the final suitability estimate difference using a composite formula, as follows:

[0034] in: This represents the difference in suitability estimates between repair point i and repair point j. and These represent the ecological suitability estimates for restoration point i and restoration point j, respectively. The absolute difference representing the ecological suitability assessment. This represents the average ecological suitability estimate for all restoration sites. The difference in vegetation cover represents the absolute difference between the standardized vegetation values ​​of restoration point i and restoration point j. The difference in soil properties represents the absolute difference between the standardized soil values ​​at remediation point i and remediation point j. and These are the pre-defined weights for vegetation cover difference and soil property difference. The difference calculation subunit calculates a difference for each pair of adjacent restoration points. The values ​​form a sequence of suitability assessment differences.

[0035] The spatial consistency identification subunit processes the suitability estimation difference sequence. The spatial consistency identification subunit not only focuses on... The numerical values ​​focus more on analyzing the direction of change of ecological elements between adjacent restoration points. The spatial consistency identification subunit extracts the standardized vegetation and standardized soil values ​​corresponding to each pair of adjacent restoration points, and determines the direction of vegetation change and soil change, respectively. The direction of vegetation change is determined by comparing the standardized vegetation values ​​of restoration point i and restoration point j. If the standardized vegetation value of restoration point i is greater than that of restoration point j, it is recorded as a direction of vegetation increase; otherwise, it is recorded as a direction of vegetation decrease. The direction of soil change is determined in a similar way by comparing standardized soil values. The spatial consistency identification subunit classifies and labels each pair of restoration points based on whether the directions of vegetation change and soil change are consistent. If the directions of vegetation change and soil change of a pair of restoration points are the same, for example, both pointing to increase or both pointing to decrease, then the pair of restoration points is marked as having a consistent trend; if the directions of vegetation change and soil change are opposite, for example, one increasing and one decreasing, then the pair of restoration points is marked as having a conflicting trend. The spatial consistency identification subunit identifies the regions connected by all adjacent repair point pairs marked as having the same trend as consistent regions as consistent regions, and identifies the regions connected by all adjacent repair point pairs marked as having conflicting trends as dissimilar regions. Finally, it outputs the spatial consistency partitioning labeling results containing these labeling information.

[0036] In its implementation, the spatial adaptation determination unit also integrates a spatial interpolation subunit and a partitioning optimization subunit to handle spatial data incompleteness and optimize partition boundaries. The spatial interpolation subunit operates after the suitability estimation extraction subunit. It detects regions with sparse spatial distribution of ecological suitability data for restoration points, i.e., areas with large gaps and adjacent restoration points far exceeding the average distance. For these sparse regions, the spatial interpolation subunit uses Kriging spatial interpolation, based on the ecological suitability estimates and spatial relationships of surrounding known restoration points, to establish a semi-variogram model to estimate the ecological suitability estimates of unmeasured points. The spatial interpolation subunit performs interpolation calculations on spatial grid nodes, generating a continuous ecological suitability estimation surface to complete the spatial cover. These interpolation points are labeled to distinguish them from the actually measured restoration points.

[0037] The partitioning optimization subunit operates after the spatial consistency identification subunit generates preliminary partitioning results. It reads the interpolation results generated by the spatial interpolation subunit and incorporates the interpolated points as virtual repair points into the analysis. Based on the complete dataset containing both actual measurement points and virtual interpolated points, the partitioning optimization subunit re-runs the spatial consistency identification logic, calculating the direction of change and consistency of adjacent point pairs, including the interpolated points. The subunit employs boundary optimization algorithms, such as morphological closing operations, to fill small holes caused by individual differences within consistent regions, or uses Gaussian filtering to smooth partition boundaries and eliminate jagged edges. The optimized partition boundaries are more continuous and reasonable, enhancing the logic and practicality of the spatial consistency partitioning annotation results.

[0038] See Figure 4 This figure illustrates the spatial distribution of remediation sites within the study area. Different colored dots represent different types of spatial consistency: green dots indicate consistent zones where remediation sites exhibit high spatial autocorrelation in vegetation and soil change trends; red dots indicate dissimilar zones where adjacent remediation sites show significant variations in ecological characteristics. The size of the dot is directly proportional to the ecological suitability estimate; larger dots indicate higher ecological suitability. The gradient contour lines in the background illustrate the spatial distribution pattern of ecological suitability, with greener lines indicating higher suitability. Low-suitability areas marked with shading are ecologically vulnerable areas requiring close attention. These analysis results provide a spatial basis for subsequent remediation optimization and resource allocation decisions.

[0039] Example 4: In specific implementation, the time series change mapping unit and the effect anomaly screening unit work together to complete the entire process from environmental time series analysis to anomaly point identification. The time series change mapping unit first processes the spatial consistency partitioning labeling results, focuses on the remediation points in the consistency section, acquires and analyzes the time series data of temperature and precipitation, and generates ecological response superimposed analysis data after data cleaning and change rate calculation. The effect anomaly screening unit then conducts in-depth mining of the ecological response superimposed analysis data to identify those remediation points with abnormally high ecological response values ​​and located in the differential section. By analyzing the changes in remediation measures and area as well as environmental fluctuation characteristics, a set of remediation anomaly points is finally constructed. The temporal variation mapping unit comprises an environmental sequence acquisition subunit, a sequence processing subunit, and an ecological response overlay calculation subunit. The environmental sequence acquisition subunit reads the partition identification information of all restoration points from the spatial consistency partitioning labeling results and filters out the list of restoration points marked as consistency segments. The environmental sequence acquisition subunit connects to an external meteorological database and queries and downloads the historical temperature data and historical precipitation data of the corresponding location based on the geographical coordinates and restoration period of each consistency restoration point. The temperature data is usually the daily average temperature value, and the precipitation data is the daily cumulative precipitation value. The environmental sequence acquisition subunit arranges these data in chronological order to form an independent temperature time series and precipitation time series for each restoration point. The sequence data of all restoration points are combined into an environmental variation time series set. The sequence processing subunit receives a set of environmental variation time series. It performs data quality improvement operations on the temperature and precipitation time series for each repair point. Missing value imputation uses a linear interpolation method. For continuous missing data points in the sequence caused by sensor failure or data transmission interruption, the sequence processing subunit estimates and fills the missing data points using the linear relationship between known data points before and after the missing point. Noise filtering uses a moving average filter to average the data within a time window to smooth random fluctuations. For example, a 7-day moving window is used to smooth the temperature time series. The processed time series data has better continuity and reduced random errors, forming a processed time series set.

[0040] The ecological response overlay calculation subunit performs rate of change calculation and response analysis based on the processed time series dataset. For each restoration point, the subunit defines continuous time intervals, such as months, and calculates the rate of change in temperature and precipitation within each interval. The rate of change in temperature is obtained by dividing the difference in temperature values ​​between adjacent time points by the time interval length. The rate of change in precipitation is calculated using a similar method to determine the relative change in precipitation. The subunit performs parallel analysis of temperature and precipitation rates of change under the same restoration measures. For example, for restoration points that have all implemented tree planting, the subunit compares the combined impact of temperature and precipitation rates of change on vegetation restoration within the same time period. By integrating and evaluating these two types of rate of change indicators, the subunit quantifies the response intensity of each restoration point to environmental variability.

[0041] The measure-area ratio calculation subunit analyzes the dynamic changes in the continuous restoration record set. For each restoration point's continuous restoration record, the subunit extracts the measure value and area value between two adjacent time points. The measure value can be a numerical indicator, such as fertilizer application rate or tree planting density, and the area value is the number of hectares covered by the restoration project. The measure-area ratio calculation subunit calculates the measure change ratio and area change ratio separately. The measure change ratio is the difference between the later measure value and the earlier measure value divided by the earlier measure value, and the area change ratio is calculated using a similar method. The measure-area ratio calculation subunit integrates the change ratios of each restoration point across the time series into a measure change ratio sequence and an area change ratio sequence, establishing a restoration change dataset. The anomaly detection subunit integrates multi-source information to perform anomaly judgment. The anomaly detection subunit accesses the restoration change dataset and simultaneously obtains wind direction variation data and precipitation fluctuation data for each restoration point within the corresponding time period from the external environment database. Wind direction variation data is represented by the standard deviation of wind direction angle, and precipitation fluctuation data is represented by the coefficient of variation of precipitation. The anomaly detection subunit sets a change detection threshold to determine whether changes in measures and area are significant, and an anomaly identification threshold to determine whether wind direction and precipitation fluctuations are abnormal. The anomaly detection subunit checks each repair point at each time point. If both the change rate of measures and the change rate of area exceed the change detection threshold, and both wind direction variation and precipitation fluctuation exceed the anomaly identification threshold, then the repair point at that time point is marked as an anomaly. The anomaly detection subunit collects information on all anomaly points, including point coordinates, anomaly occurrence time, relevant environmental and repair parameters, and constructs a set of repair anomaly points. Table 1 shows a temperature time series of a repair point, illustrating the data format processed by the environmental sequence acquisition subunit.

[0042] Table 1: Time Series Table of Temperature at Repair Points

[0043] See Figure 5 This chart illustrates the environmental changes and anomaly detection results at a specific location over a two-year period. The blue curve represents the temporal trend of temperature, with the thick line being the moving average and the thin line representing the raw daily temperature data. The blue filled area shows the range of temperature variation. The green bar chart represents precipitation, reflecting the seasonal and interannual variations of hydrological conditions. Anomalies marked with red circles indicate detected abnormal ecological responses at these time points. Each anomaly is clearly labeled, including the anomaly type (e.g., temperature anomaly, precipitation anomaly) and the diffusion risk level. The chart also marks the implementation dates of key remediation measures, indicated by orange dashed lines, demonstrating the correlation between human intervention and environmental change. The statistical information box below the chart provides key statistical indicators, offering quantitative evidence for anomaly identification. This time-series analysis effectively identifies potential problems during the remediation process, providing time-dimensional decision support for risk warning and measure adjustment.

[0044] Example 5: In specific implementation, the risk prediction output unit is the terminal module for generating the final early warning product. This unit is responsible for in-depth processing and comprehensive analysis of the set of abnormal restoration points, outputting ecological restoration effect predictions and risk warnings with direct guiding significance. The risk prediction output unit includes a parameter integration and judgment subunit and an abnormal output processing subunit. The parameter integration and judgment subunit first reads all points marked as abnormal from the set of abnormal restoration points. This information includes the point's unique identifier, spatial coordinates, ecological response value, ecological suitability estimate, and partition identifier inherited from the spatial consistency partitioning results. The parameter integration and judgment subunit calculates an integrated risk judgment value for each abnormal point. The calculation process is based on the degree of deviation between the ecological response value and the ecological suitability estimate from a preset risk threshold. The ecological response risk threshold is a key parameter, representing the threshold at which the ecological response value is considered high-risk, while the suitability benchmark value is the low threshold at which the ecological suitability estimate is considered unsuitable. The parameter integration and judgment subunit integrates the deviations of the above parameters into a comprehensive index using a mathematical formula, as follows:

[0045] in: The integrated risk assessment value represents the anomaly point m. This represents the ecological response value at the anomaly location m. This represents a preset ecological response risk threshold. This represents the ecological suitability estimate for an anomaly location m. This represents the preset suitability benchmark value. This is an adjustment coefficient used to balance the relative importance of deviations in ecological response values ​​and deviations in ecological suitability estimates in the overall risk assessment. The parameter integration and judgment subunit calculates a [parameter value] for each anomalous point in the set of anomalous points to be restored. This allows for the creation of an integrated risk assessment value sequence indexed by location, which quantifies the overall risk level of each anomalous location.

[0046] The anomaly output processing subunit performs final filtering and structured output based on the integrated risk assessment value sequence. This subunit sets logical conditions to filter locations requiring high-level early warning. The subunit iterates through each anomaly location in the integrated risk assessment value sequence, checking if three conditions are met simultaneously: the location's ecological response value... Exceeding the ecological response risk threshold Ecological suitability assessment of the site Below the suitability benchmark The spatial consistency label for each location is a dissimilar segment. For locations that simultaneously meet all three integration conditions, the anomaly output processing subunit marks them as "predicted anomalies with diffusion risk." Subsequently, the anomaly output processing subunit extracts detailed information about these high-risk locations, including location code, standardized geographic location description, name of the consistency or dissimilar partition to which it belongs, ecological response value, ecological suitability estimate, and calculated integration risk judgment value. The abnormal output processing sub-unit follows a preset structured layout, such as according to risk level. The data points are sorted from highest to lowest quality or grouped by geographical region, and their detailed information is organized and arranged to generate an ecological restoration effect prediction and risk warning output. This output is typically a detailed list or database table that clearly identifies anomaly points requiring close monitoring and their core risk parameters.

[0047] In its implementation, the risk prediction output unit also integrates a visualization generation subunit and a report generation subunit to enhance the intuitiveness and practicality of the output results. The visualization generation subunit starts working after the anomaly output processing subunit generates the ecological restoration effect prediction and risk warning output in text or tabular form. The visualization generation subunit reads the data from the ecological restoration effect prediction and risk warning output, especially the spatial coordinates and risk labels of each location. The visualization generation subunit calls upon geographic information system components to map the spatial coordinates of the locations onto a digital map and determines the risk based on the integrated risk assessment value of each location. Alternatively, a "Predicted anomalies with potential for spread" label could be used, differentiated by different colors, symbols, or sizes. For example, an integrated risk assessment value could be employed. Extremely high-risk locations are represented by large red triangles, while medium-risk locations are represented by orange circles, thus generating a spatial distribution map that visually displays the spatial distribution and risk levels of abnormal locations. This spatial distribution map makes the risk pattern immediately apparent.

[0048] The report generation subunit further integrates textual information with visualization results. Based on structured data from spatial distribution maps and ecological restoration effect predictions and risk warning outputs, the subunit automatically fills in a preset report template. The report generation subunit generates a structured warning report document, which typically includes section titles, generation date, overview, a detailed list of high-risk locations, spatial distribution maps as illustrations, risk analysis, and recommendations. The report generation subunit ensures the accuracy of the document content and maintains a standardized format, making it easy for ecological management personnel to use for decision-making reference or archiving, thus productizing the warning information.

[0049] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for predicting the effects of ecological restoration based on spatiotemporal series analysis, characterized in that, The system implements the prediction process through the following processing units: The ecological data reconstruction unit receives the geographic coordinates and time information of the ecological restoration points, collects the vegetation coverage and soil properties of the corresponding locations, associates the restoration points and derives the ecological suitability estimate, and outputs the ecological suitability dataset of the restoration points. The spatial adaptation determination unit extracts the ecological suitability estimate and corresponding coordinates from the ecological suitability dataset of the restoration point, sorts adjacent point pairs according to spatial proximity, identifies the consistent and different segments in the adjacent point pairs, and generates spatial consistency partitioning labeling results. The temporal change mapping unit selects the repair points located in the consistency segment from the spatial consistency partitioning labeling results, obtains the time series data of temperature and precipitation, compares the change range with the repair measures, evaluates the intensity of ecological response under environmental variation, and forms ecological response superposition analysis data. The abnormal effect screening unit identifies abnormal points in the ecological response superposition analysis data where the ecological response value is higher than the average response benchmark and is located in the differential segment, and constructs a set of abnormal points for restoration. The risk prediction output unit obtains detailed attributes of all points from the set of abnormal restoration points, marks points with potential spread risks, and generates ecological restoration effect prediction and risk warning output.

2. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 1, characterized in that, The ecological suitability dataset for restoration points includes ecological suitability estimates, spatial coordinates of restoration points, and standardized ecological indicators. The spatial consistency zoning labeling results specifically include consistency segment identifiers, difference segment identifiers, and the difference in suitability estimates between adjacent restoration points. The ecological response overlay analysis data covers the impact of temperature change rate on restoration, the impact of precipitation change rate on restoration, and the comparison of response measures under various environmental variation scenarios. The set of abnormal restoration points includes spatial information of abnormal points, wind direction and precipitation variation characteristics of abnormal points, and the ratio of measures to area changes at abnormal points. The ecological restoration effect prediction and risk warning output includes detailed predictions of abnormal points and integrated multi-parameter judgment labels for abnormal points.

3. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 1, characterized in that, The ecological data reconstruction unit includes: a restoration information collection subunit that acquires the coordinates and restoration time of the ecological restoration point, collects vegetation index and soil quality data corresponding to the coordinates, and stores the collection results as two ecological parameters: vegetation index and soil index, to obtain a restoration point ecological parameter data set; a data verification subunit that performs integrity checks and anomaly detection on the vegetation index and soil index in the restoration point ecological parameter data set to ensure data quality and generate a verified ecological parameter data set; and an ecological index standardization subunit that performs standardization transformation on the vegetation index and soil index data in the verified ecological parameter data set, associates the standardized transformation results with the coordinates of the restoration point, calculates the comprehensive mean of the standardized vegetation value and the standardized soil value as an ecological suitability estimate, and outputs a restoration point ecological suitability dataset.

4. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 1, characterized in that, The spatial suitability determination unit includes: a suitability estimation extraction subunit, which extracts ecological suitability estimates and corresponding coordinate data from the ecological suitability dataset of the restoration points, identifies the spatial distribution pattern of all restoration points based on the coordinate information, calls the set of restoration point coordinates, and performs distance measurement and sorting of the restoration points in space based on the proximity distance threshold, generating a distance sorting list of adjacent restoration points; a difference calculation subunit, based on the distance sorting list of adjacent restoration points, calculates the ecological suitability estimation difference between each pair of adjacent restoration points, and obtains a suitability estimation difference sequence by comparing the estimation difference with the average estimation and introducing vegetation cover difference and soil attribute difference for weighted adjustment; and a spatial consistency identification subunit, which extracts the vegetation change direction and soil change direction between adjacent restoration points from the suitability estimation difference sequence, classifies and marks each pair of restoration points according to the consistency of the change trends in the two directions, records and groups segments with consistent trends and conflicting trends respectively, and generates spatial consistency partitioning labeling results.

5. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 1, characterized in that, The temporal variation mapping unit includes: an environmental sequence acquisition subunit that filters segments marked as consistent from the spatial consistency partitioning labeling results, detects temperature and precipitation data within the restoration period of each restoration point, arranges them in chronological order to form temperature and precipitation time series, and generates an environmental variation time series set; a sequence processing subunit performs missing value imputation and noise filtering on the temperature and precipitation time series in the environmental variation time series set to improve data consistency and obtain a processed time series set; and an ecological response overlay calculation subunit that, based on the processed time series set, calculates the rate of temperature change and the rate of precipitation change within consecutive time intervals in the time series of each restoration point, performs parallel analysis of the rate of temperature change and the rate of precipitation change under the same measures, identifies the correlation between the two types of change rate indicators by integrating and evaluating them, aggregates the response value sequence of each restoration point, and forms ecological response overlay analysis data.

6. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 1, characterized in that, The abnormal effect screening unit includes: a record acquisition subunit that filters restoration points with ecological response values ​​higher than the average response benchmark and restoration points in differential segments from the ecological response superposition analysis data, acquires continuous restoration records of restoration points in chronological order, collects restoration measures and restoration area data corresponding to each time point, and generates a continuous restoration record set; a measure-area ratio calculation subunit that calls the continuous restoration record set, acquires the measure values ​​and areas of restoration points at two consecutive time points, calculates the measure change ratio and area change ratio respectively, integrates them into a measure change ratio sequence and an area change ratio sequence, and establishes a restoration change dataset; an anomaly detection subunit that, based on the restoration change dataset, extracts wind direction variation data and precipitation fluctuation data for the corresponding time period, determines whether the measure change ratio and area change ratio both exceed a preset change detection threshold, determines whether wind direction variation and precipitation fluctuation both exceed anomaly identification thresholds, marks time points that meet the conditions as abnormal points, and constructs a restoration anomaly point set.

7. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 1, characterized in that, The risk prediction output unit includes: a parameter integration judgment subunit that obtains all points and their corresponding coordinates and identification information from the set of abnormal points for restoration, calculates the integrated risk judgment value for each point, and performs a synthesis operation based on the deviation of the ecological response value and the ecological suitability estimate from the risk threshold to establish an integrated risk judgment value sequence; and an anomaly output sorting subunit that, based on the integrated risk judgment value sequence, filters points whose ecological response value is higher than the ecological response risk threshold, whose ecological suitability estimate is lower than the suitability benchmark value, and whose spatial consistency label is a differential segment, extracts the corresponding point code, location description, and partition, marks them as predicted anomalies with diffusion risk, and outputs points that meet the integration conditions in a structured layout by partition, generating ecological restoration effect prediction and risk warning output.

8. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 3, characterized in that, The ecological data reconstruction unit further includes: a data fusion subunit that acquires multi-source ecological parameter data, performs data integration and correction, improves data representativeness, and generates a fused ecological parameter dataset; and a dynamic update subunit that dynamically adjusts the ecological suitability dataset of the restoration points according to the progress of time to ensure data timeliness.

9. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 4, characterized in that, The spatial adaptation determination unit further includes: a spatial interpolation subunit that performs spatial interpolation on sparsely distributed restoration points, estimates the ecological suitability of unmeasured points, and completes spatial coverage; and a zoning optimization subunit that optimizes the zoning boundaries of the spatial consistency zoning labeling results based on the interpolation results, thereby enhancing the zoning logic.

10. The ecological restoration effect prediction system based on spatiotemporal sequence analysis according to claim 7, characterized in that, The risk prediction output unit further includes: a visualization generation subunit that converts the ecological restoration effect prediction and risk warning output into a spatial distribution map; and a report generation subunit that automatically generates a structured warning report document based on the spatial distribution map.

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