Big data-based regional ecological protection intelligent decision support and dynamic management system

Abstract

This invention belongs to the field of ecological and environmental protection technology. It discloses a regional ecological protection intelligent decision support and dynamic management system based on big data. The system acquires multi-temporal ecological monitoring data, constructs an ecological factor correlation network through time-delay mutual information analysis, uses virtual intervention experiments and counterfactual comparisons to perform causal inference, and identifies real ecological action paths. It calculates lag propagation time and signal attenuation rate to establish a multi-scale time anchor sequence; identifies feedback loops and calculates loop gain and stability domain boundary parameters; constructs a dynamic evolution model containing a system of time-delay differential equations, and simulates and generates a hierarchical set of decision schemes; and performs residual analysis through real-time monitoring of ecological responses to trigger adaptive correction of model parameters and dynamic adjustment of schemes. This invention achieves a leap from correlation analysis to causal identification, and from static planning to dynamic management, improving the scientific rigor and accuracy of ecological protection decision-making.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental protection technology, and more specifically, to a regional ecological protection intelligent decision support and dynamic management system based on big data. Background Technology

[0002] While existing big data-based ecological protection decision-making systems demonstrate powerful capabilities in massive data correlation analysis, they generally fall into the trap of "being good at finding correlations but struggling to identify the true causal chains" when dealing with complex, time-varying ecosystems. This is particularly evident in their severe modeling deficiencies when handling long-term lag effects and complex feedback loops. For example, in wetland protection practices, decision-makers observe a statistically high correlation between vegetation cover and water quality indicators, concluding that increasing vegetation planting can quickly improve water quality. However, the reality is that it takes 2-3 years for vegetation roots to modify soil structure and form an effective filtration layer. This lag process is accompanied by intermediate stages such as soil microbial community succession and nutrient redistribution, each with a response delay ranging from several weeks to several months. Furthermore, improved water quality can promote the proliferation of certain algae, whose decomposition consumes dissolved oxygen, thus inhibiting vegetation growth and creating a negative feedback loop. Conversely, when nutrients accumulate to a threshold, they can trigger a positive feedback loop of algal blooms. This dynamic process, characterized by intertwined positive and negative feedback and multi-scale time lags, is fundamentally beyond the accurate depiction of traditional big data models.

[0003] Existing methods often calculate the correlation of data within the same time window, misjudging causal effects that only appear after three months as irrelevant, or mistaking symbiotic phenomena driven by a third factor as direct causation. This leads decision-makers to either be confused by the lack of "effects" during the lag period and abandon correct measures, or to invest large amounts of resources in ineffective interventions due to misjudgments of causality. Furthermore, due to the lack of quantitative identification of feedback loop gain coefficients and stability domain boundaries, decision-making systems cannot predict the chain reactions that interventions may trigger. A moderate intervention intended to promote species diversity may lead to population imbalance due to unrecognized positive feedback amplification; while strong restoration measures for degraded ecosystems may result in over-intervention and resource waste by ignoring the self-stabilizing effect of negative feedback. Ecological processes often involve diurnal fluctuations in photosynthesis, monthly hydrological cycles, seasonal phenological changes, and interannual climate impacts. These driving factors at different time scales are coupled together through complex time-delay transmission, forming a cross-scale causal network. However, existing models lack multi-scale decomposition and consistency testing mechanisms to separate the effects of different time dimensions, and also lack dynamic modeling tools such as time-delay differential equations to describe delayed responses and memory effects. They can only use static regression or simple time series models for fitting, and their prediction results frequently fail when faced with nonlinear cumulative effects and threshold mutations, making protection schemes based on model outputs lack scientific rigor and foresight.

[0004] In view of this, the present invention proposes a regional ecological protection intelligent decision support and dynamic management system based on big data to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned shortcomings of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a regional ecological protection intelligent decision support and dynamic management system based on big data, comprising:

[0006] The acquisition module is used to acquire multi-temporal ecological monitoring data and environmental driving factor data of the target area;

[0007] The correlation network construction module is used to construct a time-delay coupling correlation network between ecological factors based on multi-temporal ecological monitoring data and environmental driving factor data;

[0008] The causal inference module is used to screen the causal inference paths in the time-delay coupled correlation network. By constructing virtual intervention experiments and comparing them with counterfactual data, it identifies ecological action paths with causal transmission characteristics.

[0009] The time anchor construction module is used to obtain historical evolution data of factors at each node along the ecological action path, calculate the hysteresis transmission time and signal attenuation rate between adjacent nodes, and establish a multi-scale time anchor sequence.

[0010] The feedback loop analysis module is used to identify closed-loop feedback loops based on multi-scale time anchor sequence, and to calculate the loop gain and stability domain boundary parameters of the feedback loop.

[0011] The dynamic model building module is used to construct an ecosystem dynamic evolution model containing a set of time-delay differential equations based on the loop gain and the stability domain boundary parameters.

[0012] The decision-making scheme generation module is used to simulate and extrapolate various conservation intervention schemes based on the dynamic evolution model of the ecosystem, and generate a hierarchical set of intelligent decision-making schemes.

[0013] The ecological response monitoring module is used to collect ecological response data in real time after the implementation of decisions through a sensor network, and to extract transient change characteristics and trend evolution characteristics from the ecological response data.

[0014] The residual analysis module is used to perform residual analysis on transient change characteristics and trend evolution characteristics with the prediction output of the ecosystem dynamic evolution model, and to identify the structural and stochastic characteristics of prediction bias.

[0015] The adaptive adjustment module is used to trigger adaptive correction of model parameters based on structural features and to adjust the execution intensity and timing of the intelligent decision-making scheme set based on random features.

[0016] The modules are connected via wired and / or wireless means to enable data transmission between them.

[0017] The technical effects and advantages of this invention, a regional ecological protection intelligent decision support and dynamic management system based on big data, are as follows:

[0018] This invention, through virtual intervention experiments and counterfactual comparative analysis, achieves a leap from statistical correlation to a true causal chain, enabling ecological protection decisions to accurately pinpoint key driving factors and avoid investing resources in statistically relevant but actually causally ineffective intervention targets. In modeling long-term lag effects, this invention can accurately characterize delayed response processes on a weekly, monthly, quarterly, and yearly basis, quantifying the time delay and effect decay of each transmission link. This allows decision-makers to clearly understand when protection measures show effects and when they reach their peak, enhancing the sustainability of ecological protection policies. Regarding feedback mechanism identification, the system can automatically discover hidden positive and negative feedback loops and quantitatively assess their amplification or inhibition strength and stability boundaries. This allows decision-makers to anticipate potential chain reactions before implementing measures, setting safe upper limits for intervention intensity in advance for vulnerable systems and avoiding excessive intervention and resource waste in robust systems. This invention establishes a complete adaptive closed-loop management mechanism that can continuously monitor ecological responses, intelligently identify the underlying causes of deviations between actual and expected effects, and achieve dynamic management. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the big data-based intelligent decision support and dynamic management system for regional ecological protection according to the present invention. Detailed Implementation

[0020] 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.

[0021] This application provides an example of a regional ecological protection intelligent decision support and dynamic management system based on big data. The main entities executing this system include, but are not limited to, the following: ecological monitoring devices, data processing servers, causal inference engines, dynamic model builders, decision support platforms, and sensor networks, which can be considered as general computing nodes in this application.

[0022] Please see Figure 1 This invention provides a regional ecological protection intelligent decision support and dynamic management system based on big data, including:

[0023] The acquisition module is used to acquire multi-temporal ecological monitoring data and environmental driving factor data of the target area;

[0024] The correlation network construction module is used to construct a time-delay coupling correlation network between ecological factors based on multi-temporal ecological monitoring data and environmental driving factor data;

[0025] The causal inference module is used to screen the causal inference paths in the time-delay coupled correlation network. By constructing virtual intervention experiments and comparing them with counterfactual data, it identifies ecological action paths with causal transmission characteristics.

[0026] The time anchor construction module is used to obtain historical evolution data of factors at each node along the ecological action path, calculate the hysteresis transmission time and signal attenuation rate between adjacent nodes, and establish a multi-scale time anchor sequence.

[0027] The feedback loop analysis module is used to identify closed-loop feedback loops based on multi-scale time anchor sequence, and to calculate the loop gain and stability domain boundary parameters of the feedback loop.

[0028] The dynamic model building module is used to construct an ecosystem dynamic evolution model containing a set of time-delay differential equations based on the loop gain and the stability domain boundary parameters.

[0029] The decision-making scheme generation module is used to simulate and extrapolate various conservation intervention schemes based on the dynamic evolution model of the ecosystem, and generate a hierarchical set of intelligent decision-making schemes.

[0030] The ecological response monitoring module is used to collect ecological response data in real time after the implementation of decisions through a sensor network, and to extract transient change characteristics and trend evolution characteristics from the ecological response data.

[0031] The residual analysis module is used to perform residual analysis on transient change characteristics and trend evolution characteristics with the prediction output of the ecosystem dynamic evolution model, and to identify the structural and stochastic characteristics of prediction bias.

[0032] The adaptive adjustment module is used to trigger adaptive correction of model parameters based on structural features and to adjust the execution intensity and timing of the intelligent decision-making scheme set based on random features.

[0033] The modules are connected via wired and / or wireless means to enable data transmission between them.

[0034] This invention achieves accurate identification of causal relationships in ecosystems through multi-temporal ecological data acquisition and time-delay coupling analysis. It innovatively introduces virtual intervention experiments and counterfactual analysis methods to verify causal relationships, constructs multi-scale time anchor sequence to characterize the time-delay transmission characteristics of ecological processes, identifies key feedback loops and their stability parameters, and builds a high-precision ecosystem dynamic evolution model based on a set of time-delay differential equations. This enables intelligent deduction and evaluation of intervention strategies, and forms closed-loop control through residual analysis and adaptive adjustment, ultimately achieving precise decision support and dynamic management for regional ecological protection.

[0035] In this embodiment of the invention, the method for constructing a time-delay coupled correlation network among ecological factors includes:

[0036] Multi-scale decomposition was performed on the time series of various ecological factors in multi-temporal ecological monitoring data to extract the component series at daily, monthly, seasonal and interannual scales.

[0037] The environmental driving factor data is decomposed into multi-scale components at the same scale to obtain the driving factor component sequences.

[0038] At each time scale, the time-delay mutual information matrix between the ecological factor component sequence and the driving factor component sequence is calculated. The row and column indices of the time-delay mutual information matrix correspond to the factor number and the time lag step, respectively.

[0039] Threshold screening is performed on the time-delay mutual information matrix, and factor pairs with mutual information values ​​greater than the preset information threshold and their corresponding optimal lag step sizes are retained and denoted as candidate coupling relationships.

[0040] Perform cross-scale consistency tests on candidate coupling relationships, calculate the lag step ratio of the same factor pair at different time scales, and mark candidate coupling relationships whose ratios conform to the scale multiple law as stable coupling node pairs.

[0041] Based on stable coupled node pairs and their optimal lag step size, a time-delay coupled correlation network is constructed, and the edge weights of the network are normalized time-delay mutual information values.

[0042] In this embodiment, multi-temporal ecological monitoring data and environmental driving factor data are first received from the acquisition module. These data typically include time series data such as vegetation indices (e.g., NDVI), precipitation, temperature, soil moisture, and surface albedo.

[0043] Wavelet transform or empirical mode decomposition (EMD) methods are used to decompose the time series of each ecological factor into multiple scales. For the daily scale component, high-frequency filtering is used to extract the diurnal variation characteristics. For the monthly scale component, monthly moving average and detrending processing are applied. For the seasonal scale component, seasonal decomposition algorithm is used to extract seasonal fluctuations. For the interannual scale component, low-frequency filtering or annual cumulative statistics are used to extract the long-term trend.

[0044] Simultaneously, the same multi-scale decomposition process is performed on the environmental driving factor data to ensure the consistency of ecological factors and driving factor components across time scales. At each time scale, the time-delay mutual information between the ecological factor component sequence and the driving factor component sequence is calculated to form a time-delay mutual information matrix, where the rows of the matrix represent factor i, the columns represent factor j, and the third dimension represents different lag steps τ. Each matrix element MI(i,j,τ) represents the mutual information value between factor i and factor j at lag step τ. The mutual information is calculated using a nonparametric method based on kernel density estimation, which avoids prior assumptions about the data distribution and improves the ability to capture nonlinear relationships.

[0045] Based on different ecosystem types and research objectives, an appropriate mutual information threshold is set (usually the upper limit of the 95% confidence interval after random permutation testing), and factor pairs with mutual information values ​​greater than the threshold are screened. For each factor pair that passes the threshold screening, the mutual information values ​​are compared at different lag steps to determine the lag step when the mutual information reaches its maximum value, which is recorded as the optimal lag step for that factor pair. Factor pairs that meet the mutual information threshold condition and their optimal lag steps are recorded as candidate coupling relationships. Further cross-scale consistency testing is performed on the candidate coupling relationships, which is the innovation of this system. Calculate the optimal lag step ratio for the same factor pair across four time scales: daily, monthly, seasonal, and interannual, and test whether the ratio conforms to the scale multiple law. For example, the optimal lag step ratio between the monthly and daily scales should be close to 30, and the ratio between the seasonal and monthly scales should be close to 3. When the lag step ratio of a factor pair conforms to the scale multiple law (with an allowable error range of ±20%) across at least three consecutive time scales, the factor pair is marked as a stable coupled node pair. This cross-scale consistency test effectively filters out spurious associations caused by accidental statistical correlations.

[0046] Based on the identified stable coupled node pairs and their optimal lag steps, a time-delay coupled correlation network is constructed. The nodes in the network represent ecological factors or environmental driving factors, and the directed edges represent the time-delay coupling relationship between factors. The direction of the edges points from the factor with a positive lag step to another factor. The edge weights of the network are set to the normalized time-delay mutual information values. The normalization process ensures that the mutual information values ​​at different scales are comparable. Z-score normalization or Min-Max normalization methods are usually used. To enhance the interpretability of the network, the thickness of the edges is visualized to represent the mutual information intensity, and the labels on the edges represent the optimal lag step.

[0047] This method effectively identifies stable and reliable time-delay coupling relationships among ecological factors through multi-scale decomposition and cross-scale consistency tests, overcoming the shortcomings of traditional correlation analysis which is susceptible to noise and random correlation interference. In particular, the introduction of cross-scale consistency tests ensures that the captured associations have physical meaning at different time scales, significantly improving the reliability of the association network. Compared with traditional methods, the time-delay coupled association network constructed by this method not only reveals "which factors are correlated" but also quantifies "with what time difference they are correlated," providing a foundation for subsequent causal inference and dynamic model construction.

[0048] In this embodiment of the invention, a method for identifying ecological action pathways with causal transmission characteristics by constructing virtual intervention experiments and counterfactual comparative analysis includes:

[0049] Select a predetermined number of factor pairs with the highest connection strength from the time-delay coupled correlation network, and denote them as candidate causal pairs. Each candidate causal pair contains a potential cause factor and a potential result factor.

[0050] For each candidate causal pair, a pulse-type perturbation signal of preset amplitude is superimposed on a specific time node of the historical time series of the potential causal factor to generate a virtual intervention sequence;

[0051] Based on the optimal lag step size corresponding to the candidate causal pair, a conditional transition probability matrix is ​​constructed. The virtual intervention sequence is used as input, and the state probability distribution of the potential outcome factors is predicted after time alignment according to the optimal lag step size. An intervention prediction sequence is then generated from the sample.

[0052] Meanwhile, keeping the original historical sequence of the potential cause factors unchanged, the evolution trajectory of the potential outcome factors in the same time period is predicted using the same conditional transition probability matrix, which is denoted as the counterfactual prediction sequence.

[0053] Calculate the pointwise difference between the intervention prediction sequence and the counterfactual prediction sequence, and integrate the difference sequence over time to obtain the cumulative intervention effect size;

[0054] Extract segments from the true historical observation sequence of potential outcome factors that are aligned with the intervention time window, and calculate the actual change of that segment relative to the pre-intervention baseline level;

[0055] Calculate the correlation coefficient between the cumulative intervention effect and the actual change. When the correlation coefficient is greater than the preset causal threshold and passes the significance test, mark the connection path corresponding to the candidate causal pair as an ecological action path.

[0056] In this embodiment, the factor pairs with the highest mutual information values ​​are first selected from the time-delay coupled correlation network as candidate causal pairs. Typically, the top 10% or top 50 pairs are selected (depending on the network size). Each candidate causal pair contains a potential causal factor and a potential outcome factor. The potential causal factor corresponds to a factor with a positive optimal lag step. To ensure the robustness of the analysis, a pre-screening process can be applied to the candidate factor pairs to eliminate correlations that are obviously affected by common factors or pairs that are physically impossible to have a causal relationship.

[0057] For each candidate causal pair, a virtual intervention experiment is designed, which is the core method for determining causal relationships. Appropriate time points are selected from the historical time series of potential causal factors for virtual intervention. The selection strategy for intervention points includes typical periods such as normal fluctuation periods, after extreme events, and seasonal transition periods. The intervention uses pulse-type perturbation signals, with amplitudes typically set to 1-3 times the standard deviation of the factor's historical fluctuations. These perturbation signals can take the form of a single pulse, a step change, or multiple consecutive pulses. Based on the optimal lag step size corresponding to the candidate causal pair, a conditional transition probability matrix is ​​constructed. This matrix represents the probability distribution of the system transitioning to each possible future state given the current state and the lagged input. The construction of the conditional transition probability matrix uses nonparametric methods such as kernel density estimation or Gaussian process regression, which can capture complex nonlinear state transition relationships. The virtual intervention sequence is used as input and time-aligned according to the optimal lag step size τ, meaning the outcome state at time t is influenced by the causal state at time t-τ. The conditional transition probability matrix is ​​applied to predict the state probability distribution of potential outcome factors. Multiple possible evolutionary trajectories are generated from the distribution through Monte Carlo sampling, and the average value is taken as the intervention prediction sequence.

[0058] Simultaneously, keeping the original historical sequence of the causal factors unchanged (i.e., the situation without intervention), the same conditional transition probability matrix is ​​used to predict the evolution trajectory of potential outcome factors over the same time period, representing the counterfactual scenario of "what would have happened if the intervention had not occurred"; the point-by-point difference between the intervention prediction sequence and the counterfactual prediction sequence is calculated to form a difference sequence; the difference sequence is integrated over time (e.g., by calculating the cumulative sum or weighted integral) to obtain the cumulative intervention effect representing the total intervention effect; segments in the true historical observation sequence of potential outcome factors that correspond to the intervention time window are found, and the actual change of this segment relative to the pre-intervention baseline level is calculated; if the candidate causal relationship is established, a virtual intervention is performed. The pre-effect should be consistent with the actual changes under similar conditions in history; calculate the correlation coefficient or consistency index between the cumulative intervention effect and the actual change, and apply methods such as Bootstrap or permutation test to assess statistical significance; set a preset causal threshold (usually 0.7 or determined according to domain knowledge), and when the correlation coefficient is greater than the threshold and passes the significance test (p<0.05), the candidate causal pair is confirmed to have a causal relationship, and the corresponding connection path is marked as an ecological action path with causal transmission characteristics; optionally, sensitivity analysis is performed on the identified ecological action path, and the robustness of the causal relationship is assessed by changing the intervention intensity, timing and duration.

[0059] This method addresses the challenge of identifying causal relationships in ecosystems through virtual intervention experiments and counterfactual analysis. Unlike traditional statistical correlation-based methods, this approach draws upon the core concept of causal inference—"intervention"—and effectively distinguishes between true causal relationships and statistical correlations by comparing the differences in system responses under intervention and non-intervention scenarios. In particular, comparing the results of virtual intervention with historical actual changes further enhances the reliability of causal judgments. This method provides a feasible pathway for causal discovery when ecological monitoring data is limited and actual controlled experiments are not feasible, offering a scientific basis for ecological protection decision-making.

[0060] In this embodiment of the invention, a method for calculating the hysteresis propagation time and signal attenuation rate between adjacent nodes and establishing a multi-scale time anchor sequence includes:

[0061] In the ecological action path, the node factors are topologically sorted according to the causal transmission direction to obtain the directed path sequence;

[0062] For adjacent node pairs in a directed path sequence, calculate their cross-correlation function, identify the time offset corresponding to the maximum value of the cross-correlation function, and denot it as the lag propagation time;

[0063] Within the time window corresponding to the lag propagation time, the ratio of the change amplitude of the predecessor node factor to the response amplitude of the successor node factor is calculated and denoted as the initial propagation efficiency.

[0064] The initial transmission efficiency of all adjacent node pairs in the directed path sequence is calculated by path accumulation. The cumulative attenuation coefficient from the starting point of the path to each node is obtained. The inverse of the cumulative attenuation coefficient is normalized and used as the signal attenuation rate.

[0065] The lag time is accumulated on the directed path sequence, and the accumulated lag time from the starting point is marked at each node position to form a time anchor point sequence for a single path.

[0066] The time anchor sequences of all ecological action pathways are integrated and divided into rapid response layer, medium-term transmission layer and long-term accumulation layer according to the magnitude of the lag time, to construct a multi-scale time anchor sequence.

[0067] In this embodiment, the identified ecological action paths are obtained from the causal inference module, and the node factors on each ecological action path are topologically sorted according to the causal transmission direction. The topological sorting ensures the consistency of causal flow, solves the possible loop problem, and obtains a linearized directed path sequence. For each pair of adjacent nodes (predecessor node → successor node) in the directed path sequence, the cross-correlation function (CCF) of its time series is calculated. The cross-correlation function calculation adopts standardization processing to eliminate the influence of amplitude difference and focus on time pattern matching.

[0068] Identify the time offset corresponding to the maximum value of the cross-correlation function. This offset represents the time required for the signal to propagate from the predecessor node to the successor node, denoted as the lag propagation time. To enhance accuracy, interpolation can be applied to the cross-correlation function to achieve time delay estimation of the subsampling interval. Set an analysis window of appropriate width (usually ±30% of the lag time) before and after the identified lag propagation time. Within this window, extract the change events of the predecessor node factors (such as significant fluctuations, abrupt changes, or trend reversals) and analyze the response characteristics of the successor node. Calculate the ratio of the change amplitude of the predecessor node (such as standard deviation or range) to the response amplitude of the successor node. This ratio reflects the energy transfer efficiency during signal propagation and is denoted as the initial propagation efficiency. Repeat the above calculation for all adjacent node pairs on the same ecological action path to obtain the initial propagation efficiency of each connection segment on the path.

[0069] The initial transmission efficiency is calculated by accumulating the values ​​of each segment from the starting point of the path. This involves multiplying the values ​​of each segment to obtain the cumulative attenuation coefficient from the starting point to each node. The cumulative attenuation coefficient reflects the degree of signal strength attenuation during transmission; a smaller coefficient indicates more severe attenuation. The reciprocal of the cumulative attenuation coefficient is then normalized using Min-Max to obtain a standardized signal attenuation rate, with a value range of [0,1]. A signal attenuation rate of 0 indicates complete attenuation, while 1 indicates no attenuation. Simultaneously, based on the calculated lag transmission time, the cumulative lag time from the starting point is accumulated on the directed path sequence, and the cumulative lag time from the starting point is marked at each node, forming a time anchor sequence for a single path. The above analysis was repeated for all identified ecological pathways to obtain a series of time anchor sequences. The time anchor sequences of all pathways were integrated, and the nodes were divided into three levels according to the magnitude of the cumulative lag time: the rapid response layer (hourly to daily scale, such as meteorological factor response), the medium-term transmission layer (weekly to monthly scale, such as vegetation growth response), and the long-term accumulation layer (seasonal to annual scale, such as ecosystem structure change). Nodes within the same level may come from different ecological pathways but have similar time response characteristics. The final multi-scale time anchor sequence is a hierarchical time network that describes the time scale and signal transmission characteristics of different processes in the ecosystem.

[0070] This method constructs a multi-scale anchor sequence characterizing the temporal dynamics of an ecosystem by quantitatively analyzing the time delay and intensity attenuation of signal transmission. This approach analyzes the interactions of ecological factors within a time frame, revealing the temporal characteristics and energy transfer efficiency of ecological processes. In particular, the introduction of a hierarchical structure of rapid response, medium-term transmission, and long-term accumulation layers enables the system to distinguish ecological processes at different time scales and formulate targeted conservation strategies. Compared to traditional static network analysis, this time-anchor sequence better captures the dynamic characteristics of the ecosystem, providing an important temporal reference for subsequent feedback loop analysis and dynamic model construction.

[0071] In this embodiment of the invention, the method for identifying feedback loops forming closed loops and calculating the loop gain and stability domain boundary parameters of the feedback loops includes:

[0072] Nodes of the fast response layer, medium-term transmission layer and long-term accumulation layer in the multi-scale time anchor sequence are extracted. Within each time scale layer, the depth-first search algorithm is used to traverse the ecological action path and identify closed paths with the same starting and ending node, which are denoted as candidate loops within the scale.

[0073] A time consistency test is performed on candidate loops within the scale. The sum of the lag propagation times between all adjacent nodes in the loop is calculated as the loop cycle period. When the deviation between the loop cycle period and the characteristic cycle of the time scale layer is less than the preset cycle threshold, it is confirmed as a candidate feedback loop.

[0074] For each candidate feedback loop, calculate the inverse product of the signal attenuation rates of all node pairs on the path, and use it as the initial value of the loop gain;

[0075] The number of negative suppression effects in candidate feedback loops is counted; if the number is even, it is marked as a positive feedback loop, and if the number is odd, it is marked as a negative feedback loop.

[0076] For a positive feedback loop, a discrete iterative model is constructed based on the initial value of the loop gain and the loop cycle period. Multi-step iterative iteration is performed, and the initial value of the loop gain is corrected according to the divergence rate of the iterative output value to obtain an effective loop gain.

[0077] For a negative feedback loop, a step disturbance is applied at the loop start point. The relaxation time and oscillation damping coefficient required for the system to recover to equilibrium are calculated. The boundary parameters of the stability domain are determined based on the ratio of the oscillation damping coefficient to the loop cycle period.

[0078] In this embodiment, node sets of the rapid response layer, medium-term transmission layer and long-term accumulation layer are extracted based on multi-scale time anchor sequence. Within each time scale layer, the ecological action path is transformed into a directed graph structure, where nodes represent ecological factors and edges represent causal relationships. An improved depth-first search (DFS) algorithm is used to traverse the directed graph and identify closed paths with the same starting and ending node. To improve efficiency, a maximum search depth (usually 5-7, considering the length of feedback loops commonly found in ecosystems) and a minimum loop length (usually 2, excluding self-loops) are set.

[0079] The identified closed paths are initially screened, eliminating redundant loops in the graph structure (such as large loops formed by combining multiple small loops). For candidate loops within each scale, the sum of the lag propagation times between all adjacent nodes in the loop is calculated, i.e., the sum of the time delays of all edges, as the loop cycle period. The calculated loop cycle period is compared with the characteristic period of the time scale layer, which is typically: fast response layer (1-7 days), medium propagation layer (1-3 months), and long-term accumulation layer (1-5 years). A preset period threshold is set (e.g., ±50% of the characteristic period). When the relative deviation between the loop cycle period and the characteristic period is less than the threshold, the closed path is confirmed as a physically feasible candidate feedback loop. This step ensures the rationality of the identified feedback loops in terms of time scale and avoids the physical irrationality that may result from identification based solely on the graph structure. For candidate feedback loops that pass the time consistency test, the initial value of the loop gain is calculated.

[0080] Reviewing the signal attenuation rate calculated in the previous steps, the reciprocal of the signal attenuation rate of all adjacent node pairs in the loop is multiplied to obtain the initial value of the loop gain. The reciprocal of the signal attenuation rate represents the signal amplification factor, and their product reflects the total amplification or attenuation of the signal after completing one loop cycle. Analyze the causal relationship properties in the loop to distinguish between positive promoting effects and negative inhibiting effects. Promoting effects indicate that an increase in the precursor factor leads to an increase in the successor factor, while inhibiting effects indicate that an increase in the precursor factor leads to a decrease in the successor factor. Count the number of negative inhibiting effects in the loop and determine the feedback type based on the parity of the number of negative effects: an even number of negative effects form a positive feedback loop, and an odd number of negative effects form a negative feedback loop.

[0081] For the identified positive feedback loop, a discrete iterative model is constructed based on the initial value of the loop gain and the loop cycle period; the iterative model adopts the form of time-delay difference equations, such as... ,in The cycle period of the loop. This is the initial value of the loop gain. For small perturbation terms, perform multi-step iterative cycles (usually 10-50 cycles) and observe the changing trend of the output value; calculate the divergence rate of the iterative sequence (such as the exponential growth rate or Lyapunov exponent), and correct the initial value of the loop gain based on the difference between the actual divergence rate and the theoretical expectation (based on the initial loop gain) to obtain the effective loop gain that can accurately predict the system dynamics; for negative feedback loops, use different analysis methods to simulate applying a step perturbation at the loop start point and observe the system's response characteristics; calculate the relaxation time required for the system to recover to the equilibrium state, i.e., the time required for the perturbation response to decay to 10% of the original amplitude; analyze the oscillation characteristics during the response process and calculate the oscillation decay coefficient, which is usually defined as the natural logarithm of the ratio of adjacent oscillation peaks; determine the stability domain boundary parameters based on the ratio of the oscillation decay coefficient to the loop cycle period; the stability domain boundary parameters describe the range of the system's ability to remain stable under perturbation and are key parameters for subsequent construction of dynamic models.

[0082] This method achieves accurate identification of key feedback loops in ecosystems by combining depth-first search with time consistency checks. A mechanism for verifying the matching degree between loop cycle period and time scale is introduced to ensure that the identified feedback loops have practical ecological significance. Targeted analysis methods are employed for positive and negative feedback loops, using discrete iterative models and step response analysis to quantitatively assess the dynamic characteristics and stability parameters of the feedback loops. These parameters not only reveal the self-regulation mechanisms of the ecosystem but also provide a scientific basis for subsequent dynamic model construction and intervention strategy formulation. Compared with traditional static network analysis, this method focuses more on the dynamic behavior of feedback loops and can more accurately predict the system's response to intervention measures.

[0083] In this embodiment of the invention, a method for constructing an ecosystem dynamic evolution model containing a system of time-delay differential equations includes:

[0084] State variables are established for each ecological factor node based on the number of nodes in the time-delay coupled network, and a state vector space is constructed.

[0085] For each causal connection in the ecological action path, extract its lag propagation time and signal attenuation rate, construct the basic time-delay differential term, and multiply the state value of the predecessor node before the lag time by the propagation coefficient as the driving term of the differential equation of the successor node.

[0086] Traverse the state vector space to identify nodes participating in the feedback loop. For nodes participating in the positive feedback loop, calculate the feedback enhancement factor based on the effective loop gain and the loop cycle period, and add a self-enhancing term to the differential equation of that node.

[0087] For nodes participating in the negative feedback loop, the equilibrium point and maximum offset threshold of the state variables are determined according to the stability domain boundary parameters, and a self-inhibition term of Logistic type with saturation parameters determined by the stability domain boundary parameters is added to the differential equation of the node.

[0088] By superimposing the basic time-delay differential terms with self-enhancing or self-inhibiting terms, and introducing environmental driving factors as time-varying forcing terms, a complete set of time-delay differential equations is constructed.

[0089] The parameters of the time-delay differential equation system were calibrated using historical ecological monitoring data, and a numerical solver was constructed using a dedicated algorithm for delayed differential equations. The time-delay differential equation system after parameter calibration was integrated with the numerical solver to generate an ecosystem dynamic evolution model. The ecosystem dynamic evolution model can simulate the dynamic evolution process of ecological factors under time-delay effects and feedback.

[0090] In this embodiment, firstly, based on the number N of nodes in the time-delay coupled correlation network, a corresponding state variable xi (i=1,2,...,N) is established for each ecological factor node, constructing an N-dimensional state vector space; the state variables represent the quantitative indicators of ecological factors, such as vegetation cover, species diversity index, water quality parameters, etc.; for each causal connection (node ​​i→node j) on the identified ecological action path, the lag propagation time calculated in the previous step is extracted. and signal attenuation rate Based on these parameters, a fundamental time-delay differential term is constructed, expressed as: ,in The conduction coefficient is usually set as a function of the signal attenuation rate, such as... (k is the scaling parameter); this form of differential term describes the predecessor node i in How does the state before time affect the current rate of change of the successor node j?

[0091] Traverse all nodes in the state vector space to identify the set of nodes participating in the feedback loop; for node i participating in the positive feedback loop, calculate the feedback enhancement factor based on the effective loop gain Gi and loop cycle period Ti calculated in the previous steps. Typically, exponential or power functions are used; self-enhancing terms are added to the differential equation at node i. This indicates the self-reinforcing effect brought about by positive feedback; for node j participating in the negative feedback loop, the equilibrium point of the state variable is determined based on the stability domain boundary parameters. and maximum offset threshold Add a Logistic-type self-suppression term to the differential equation at node j. ,in The self-inhibition strength is determined by the boundary parameters of the stability region; this form of self-inhibition term can simulate the equilibrium recovery characteristics caused by negative feedback, and the inhibition effect is stronger as the state deviates further from the equilibrium point; the basic time-delay differential term, self-reinforcing term, or self-inhibition term are superimposed to form the prototype of the differential equation for each node; at the same time, environmental driving factors are introduced. As a time-varying forcing term, it is usually expressed as ,in The weight of the influence of the driving factor on node i. Index of environmental driving factors ( Integrating the above, a complete system of time-delay differential equations is constructed, in general form:

[0092] ;

[0093] in The random noise term represents the influence of unknown factors. Historical ecological monitoring data is used to calibrate the parameters of the time-delay differential equation system. Global optimization methods such as evolutionary algorithms or particle swarm optimization are employed to minimize the error between model predictions and observed values. Considering the complexity of solving time-delay differential equations, a dedicated numerical solver for the delay differential equations is developed or adopted. Common methods include the improved Runge-Kutta method, linear multistep methods, or spectral methods. The model is validated and its stability is analyzed to ensure numerical stability within the expected parameter range. The parameter-calibrated time-delay differential equation system is integrated with the numerical solver to construct a complete ecosystem dynamic evolution model. This model can accept initial conditions and environmental driving factor time series as inputs and output the dynamic evolution trajectory of each ecological factor.

[0094] This method achieves a precise description of the complex dynamics of ecosystems by constructing a mathematical model containing a system of time-delay differential equations. It innovatively integrates time-delay effects, positive and negative feedback mechanisms, and environmental driving factors into a unified mathematical framework, overcoming the limitations of traditional ecological models that neglect time delays or feedback complexity. In particular, it employs differentiated mathematical expressions (self-reinforcing terms and logistic self-inhibition terms) for nodes participating in different types of feedback loops, enabling the model to accurately capture the nonlinear dynamic behavior of the system. This mechanism-based dynamic model can not only interpret historical observation data but also predict the system's response to future interventions, providing a powerful tool for scientific decision-making. Compared with traditional statistical prediction models, this model has stronger interpretability and generalizability, and is particularly suitable for simulating common lag effects and threshold behaviors in ecosystems.

[0095] In this embodiment of the invention, a method for simulating and extrapolating multiple conservation intervention schemes based on an ecosystem dynamic evolution model to generate a hierarchical set of intelligent decision-making schemes includes:

[0096] Based on the regional ecological protection goals, identify the core ecological indicators that need to be prioritized for improvement and mark the corresponding nodes as target nodes;

[0097] In time-delay coupled correlation networks, a reverse tracing algorithm is used to start from the target node and search backward along the causal path to identify key driving nodes that have a significant impact on the target node.

[0098] For each key driving node, design multiple sets of intervention plans, including combinations of intervention timing, intervention intensity, and intervention duration;

[0099] Each intervention scheme is transformed into an external input signal for the corresponding state variable in the dynamic evolution model of the ecosystem. The model is run to perform forward simulation to obtain the evolution trajectory of the target node after the intervention.

[0100] The evolutionary trajectory is evaluated for convergence, and intervention schemes with short arrival times and small fluctuations are marked as efficient schemes;

[0101] The robustness index of the efficient scheme is calculated by superimposing random environmental fluctuations during the simulation process to test the disturbance resistance capability of the scheme.

[0102] All efficient solutions are ranked based on a comprehensive score of robustness index and intervention cost. The preferred solutions are then divided into three levels—gradual, moderate, and aggressive—according to the intensity and timing of intervention, thus constructing an intelligent decision-making solution set.

[0103] In this embodiment, firstly, based on the specific goals and policy requirements of regional ecological protection, core ecological indicators that need to be prioritized for improvement are identified, such as biodiversity index, carbon storage, water quality indicators, or ecosystem health index. The nodes corresponding to these indicators are then marked as target nodes in the dynamic evolution model. In the time-delay coupled correlation network, a reverse tracing algorithm is used to search backwards along the causal path from the target nodes; essentially, this is an improved reverse breadth-first search (BFS). A maximum tracing depth (usually 3-5 layers) and an impact threshold are set, retaining only paths whose impact exceeds the threshold. The cumulative impact strength of each upstream node on the target node is calculated; this strength is determined by the mutual information value and signal attenuation rate of all connections along the path. Nodes ranked according to cumulative impact strength are selected as key driving nodes (usually top 5-10), representing the potential entry points with the highest intervention efficiency.

[0104] For each key driving node, a multi-dimensional combination of intervention schemes is designed. Key parameters include: intervention timing (e.g., early season, before drought, species breeding season, etc.), intervention intensity (e.g., weak, moderate, and high intensity, corresponding to 10%, 30%, and 50% of the target change), intervention duration (e.g., pulse intervention, short-term intervention, and continuous intervention, corresponding to 1-3 days, 1-3 months, and more than 1 year, respectively), and intervention form (e.g., single intervention, periodic intervention, or progressively enhanced intervention). Combining these parameter dimensions, 8-12 typical intervention schemes are designed for each key driving node. Each intervention scheme is transformed into external input signals for the corresponding state variables in the dynamic evolution model, such as mathematical expressions like step functions, impulse functions, or slope functions. The ecosystem dynamic evolution model after parameter calibration is run, and forward time integration is performed to simulate the dynamic evolution process of all state variables after the intervention is implemented. Special attention is paid to the response trajectory of the target node, and key features such as response amplitude, response delay, stability, and persistence are extracted.

[0105] The convergence of the evolution trajectory of the target node is evaluated by calculating the following indicators: arrival time (the time required for the target node to reach the expected improvement target), fluctuation amplitude (the standard deviation of the fluctuation after reaching the target), and steady-state deviation (the difference between the final steady-state value and the target value); a convergence scoring function is designed based on these indicators. ,in For arrival time, For fluctuation range, For steady-state deviation, , , These are the weighting coefficients.

[0106] The top-ranked solutions in terms of convergence score (typically 30% of all solutions) are marked as efficient solutions. These efficient solutions undergo perturbation resistance testing by superimposing random environmental fluctuations (such as random precipitation changes, temperature anomalies, etc.) into the simulation. Multiple sets (e.g., 100-500 sets) of random perturbation scenarios are typically generated using the Monte Carlo method. The model is rerun under each perturbation scenario to calculate the degree to which the target node deviates from the original expected trajectory. Based on the statistical distribution characteristics of the deviation degree (such as mean, variance, and extreme values), the robustness index of the solution is calculated, typically defined as... ,in The average degree of deviation, The goal is to improve the quantity of intervention. Finally, a comprehensive evaluation function is established, combining robustness index, intervention cost (considering funding, human resources and technological complexity), and implementation difficulty (considering policy barriers and social acceptance) to rank the efficient solutions. The preferred solutions are divided into three levels according to intervention intensity and implementation sequence: gradual (low intensity, long cycle, multiple stages), moderate (medium intensity, medium cycle, few stages), and aggressive (high intensity, short cycle, single stage), forming a hierarchical intelligent decision-making solution set. For decision-making scenarios with different risk preferences and resource conditions, the system can recommend the most suitable intervention strategy from the solution set.

[0107] By identifying key driving nodes through reverse tracing, designing multi-dimensional intervention schemes, and conducting systematic evaluations, a crucial transformation from ecosystem understanding to concrete decision support was achieved. A scientifically sound evaluation system for decision-making schemes was constructed, comprehensively considering intervention effectiveness, resilience, and implementation costs. In particular, robustness testing under random environmental fluctuations was introduced to ensure the feasibility of the schemes in complex and ever-changing ecological environments. The hierarchical scheme organization structure adapts to the needs of different decision-making scenarios, supporting both gradual long-term protection strategies and addressing ecological crises requiring rapid response. Unlike traditional experience-based decision-making, this system uses quantitative extrapolation based on mechanism models, improving the scientific rigor and predictability of decision-making.

[0108] In this embodiment of the invention, the method for extracting transient change features and trend evolution features from ecological response data includes:

[0109] Seasonal trend decomposition was performed on the ecological response data, separating the data into trend components, periodic components, and residual components.

[0110] The first difference of the trend component is used to obtain the rate of change sequence, and the sliding window variance of the residual component is used to obtain the fluctuation intensity sequence. The two are normalized and multiplied to obtain the transient anomaly index sequence.

[0111] In the transient anomaly index sequence, peak points are identified as candidate points of transient changes. An equal-length comparison window is set before and after each candidate point, and the Kolmogorov-Smirnov test statistic of the ecological response data within the comparison window is calculated.

[0112] Candidate points with statistics greater than the preset mutation threshold are identified as transient change points. The change amplitude of the trend component, the phase jump variable of the periodic component, and the variance mutation of the residual component are extracted at each transient change point. The three are combined to form transient change characteristics.

[0113] The trend component is segmented with the transient change point as the dividing point. Linear fitting is performed in each segment between adjacent transient change points, and the segment slope is extracted as the evolution rate of that period.

[0114] Calculate the difference in evolution rate between adjacent segments. When the absolute value of the difference is greater than the preset trend threshold, mark the boundary point as a trend inflection point. Calculate the curvature change of the trend component at each trend inflection point.

[0115] Within the segments between each trend inflection point, the monotonicity coefficient of the trend component, the amplitude stability of the periodic component, and the stationarity index of the residual component are calculated. The evolution rate, curvature change, and the three stability indices are combined to form the trend evolution characteristics.

[0116] In this embodiment, ecological response data collected in real time by the sensor network is received from the ecological response monitoring module. This data represents the actual response of the ecosystem after the implementation of intervention measures. Advanced time series decomposition methods such as STL (Seasonal-Trend Decomposition using Loess) or EEMD (Ensemble Empirical Mode Decomposition) are applied to separate the ecological response data into three components: a trend component representing long-term change trends, a periodic component representing periodic change patterns (such as seasonality or daily cycles), and a residual component representing random fluctuations and short-term changes. The trend component is subjected to first-order differencing (i.e., calculating the difference between adjacent time points) to obtain a rate of change sequence representing the rate of trend change. The local variance is calculated using a sliding window method on the residual component, with the window length typically chosen to be 5-10 times the data acquisition frequency, to obtain a fluctuation intensity sequence representing the intensity of short-term fluctuations. The rate of change sequence and the fluctuation intensity sequence are respectively subjected to Z-score standardization, and then their dot product is calculated to obtain a transient anomaly index sequence. This index considers both the rate of trend change and the intensity of short-term fluctuations, and can effectively identify sudden changes in the system state.

[0117] In transient anomaly index sequences, peak detection algorithms are applied to identify peak points where the anomaly index is significantly higher than the background level, marking them as candidate points for transient changes. To avoid false detections, a minimum peak spacing (typically 3-5 times the data frequency) and a minimum peak height threshold (typically the mean plus 2 standard deviations) are set. Contrast windows of equal length (typically 10-20 times the data frequency) are set before and after each candidate point to compare the data distribution characteristics within the windows. The Kolmogorov-Smirnov (KS) test is applied to compare the probability distribution differences of the data within the windows before and after. The KS test is independent of the data distribution pattern and is applicable to various ecological data. The KS test statistic D value is calculated, representing the maximum difference in the cumulative distribution function between the windows before and after. A preset mutation threshold is set (typically based on a significance level of p < 0.01 or calibrated using historical data), and candidate points whose KS statistic exceeds the threshold are confirmed as transient change points. These points represent significant jumps in the state of the ecosystem, which may be a direct response to intervention measures or a result of changes in environmental conditions.

[0118] For each confirmed transient change point, its multidimensional features are extracted: the magnitude of the trend component change (the difference between the average values ​​of 10 time steps before and after the change point), the phase jump variable of the periodic component (the phase change calculated using Hilbert transform), and the variance mutation of the residual component (the logarithm of the ratio of the variances of the preceding and following windows). These three features are combined into a vector form to form a complete description of the transient change features. Next, the trend evolution features are analyzed. First, the trend component is segmented using the transient change point as the dividing point. Within each segment between adjacent transient change points, the following is applied: Linear regression is used to extract the slope of the regression line as the evolution rate for that period. The difference in evolution rates between adjacent segments is calculated, and when the absolute value of the difference exceeds a preset trend threshold (usually twice the standard deviation of the historical evolution rate), the boundary point is marked as a trend inflection point. At each trend inflection point, the curvature change of the trend component is calculated, typically by fitting a quadratic function near the inflection point and calculating the second derivative. Within the segments between each trend inflection point, three stability indices are further extracted: the monotonicity coefficient of the trend component (Kendall's tau coefficient, measuring trend consistency), the amplitude stability of the periodic component (the ratio of the amplitude standard deviation to the mean, measuring the stability of periodic fluctuations), and the stationarity index of the residual component (calculated through the ADF test or Hurst exponent, measuring the structural characteristics of random fluctuations). The segmented evolution rates, the curvature changes at inflection points, and the three stability indices are combined into structured data to form a complete trend evolution characteristic.

[0119] This system utilizes advanced time series analysis techniques to accurately extract two key features from ecological response data: transient changes and trend evolution. By combining transient anomaly indices and the KS test, the system can objectively and robustly identify key change points in the ecosystem. Simultaneously, through segmented analysis and multi-dimensional feature extraction, it comprehensively captures the rate, turning points, and stability characteristics of trend evolution. This multi-level feature extraction method enables the system to distinguish between the immediate effects and long-term impacts of interventions, providing a data foundation for subsequent model adjustments and decision optimization. Unlike traditional single-indicator monitoring, this system characterizes the dynamic response features of the ecosystem from multiple dimensions, significantly improving our understanding of complex ecological processes.

[0120] In this embodiment of the invention, the method for identifying the structural and stochastic characteristics of prediction bias includes:

[0121] The predicted output of the ecosystem dynamic evolution model is time-aligned with the ecological response data, and the difference between the predicted and measured values ​​is calculated to generate the original residual sequence.

[0122] Extract the time position of the transient change point in the transient change feature, calculate the jump amplitude of the original residual within the window before and after the corresponding time, and mark the residual segment of that time period as a mutation model mismatch when the jump amplitude exceeds the preset mutation threshold.

[0123] Extract the evolution rate of each segment between trend inflection points in the trend evolution features, calculate the linear trend slope of the original residuals in the corresponding time period of each segment, and mark the residual of the segment as monotonic drift error when the residual slope continuously deviates from zero and the absolute value is greater than the preset drift threshold.

[0124] Spectral analysis is performed on the original residual sequence to identify periodic components with significant amplitude in the frequency domain. When the energy proportion of the periodic component exceeds a preset period threshold, the frequency and phase of the periodic component are extracted and marked as a periodic systematic error.

[0125] The structural characteristics of prediction bias are formed by combining mutational model mismatch, monotonic drift error, and periodic systematic error.

[0126] The destructured residuals are obtained by subtracting the bias components corresponding to the structural features from the original residual sequence. The destructured residuals are then subjected to a white noise test. The destructured residuals that pass the test are marked as random features. Their mean, variance, and skewness coefficient are statistically described as random features.

[0127] In this embodiment, the predicted output of the ecosystem dynamic evolution model is first time-aligned with the actually observed ecological response data to ensure that the state at the same moment is being compared. Data interpolation or resampling techniques are applied to handle potential sampling frequency inconsistencies. The difference between the predicted and measured values ​​is calculated to generate the original residual sequence. The characteristics of the residual sequence are initially evaluated through visualization analysis and basic statistical tests. The time location set {t1,t2,...,tk} of transient change points is obtained from the transient change features extracted in the previous step. For each transient change point ti, an analysis window [ti-w,ti+w] is set before and after it, with the window width w typically being 5-10 times the data sampling frequency. The jump amplitude of the original residual within the window is calculated. At the same time, the significance statistics of the jump, such as the t-test or Mann-Whitney test, are calculated. The p-value of the U-test; set a preset mutation threshold (usually 3 times the standard deviation of historical residuals or p<0.01). When the jump amplitude exceeds the threshold, the residual segment of that period is marked as a mutation model mismatch. Mutation mismatch usually indicates that the model fails to accurately capture the system's rapid response to external intervention or sensitive response to environmental mutations.

[0128] Extract the time intervals {[s1,e1],[s2,e2],...,[sn,en]} and corresponding evolution rates {v1,v2,...,vn} for each segment between trend inflection points from the trend evolution characteristics; apply linear regression to the original residual sequence within each time interval [si,ei] to extract the linear trend slope Slope(i) of the residual sequence; calculate the t-test p-value between the residual slope and zero to evaluate the significance of the slope; set a preset drift threshold (usually 0.1-0.3 or p<0.05), when the residual slope continuously deviates from zero (i.e., the slope has the same sign for multiple consecutive time periods) and the absolute value is greater than the threshold, the residual segment is marked as monotonic drift. Shift error; monotonic drift usually indicates a systematic bias in the model parameters or failure to capture the influence of some slowly changing environmental factors; perform spectral analysis on the entire original residual sequence, applying Fast Fourier Transform (FFT) or Lomb-Scargle periodogram methods (suitable for unequal interval data); identify periodic components with amplitudes significantly higher than the background level in the frequency domain, typically using 95% or 99% confidence intervals as significance criteria; calculate the energy proportion of significant periodic components (i.e., the ratio of the energy of that frequency component to the total energy); set a preset periodic threshold (usually 10%-20%), and extract the frequency of that periodic component when its energy proportion exceeds the threshold. and phase Represent it as a periodic function The periodic systematic error is marked as such. Periodic errors usually indicate that the model fails to capture certain periodic processes in the system, such as potential seasonal drivers. The three types of structural features (abrupt model mismatch, monotonic drift error and periodic systematic error) are superimposed and merged over time to construct a complete structural residual model. The structural residual represents the part of the prediction bias that can be explained by the deterministic model. The destructured residual is obtained by subtracting the structural residual from the original residual.

[0129] The destructured residuals are subjected to white noise tests, such as the Ljung-Box test, ACF (autocorrelation function) analysis, or BDS test, to verify whether they have random properties. Destructured residuals that pass the tests are marked as random features, indicating prediction biases that cannot be explained by deterministic models. The statistical characteristics of the random residuals are calculated, including the mean (to test whether it is zero mean), variance (to quantify the intensity of random fluctuations), and skewness coefficient (to detect the asymmetry of the distribution). These statistics constitute a complete description of the random features.

[0130] This system achieves precise decomposition and feature identification of prediction bias by systematically analyzing the discrepancies between model predictions and actual observations. Bias is categorized into interpretable structural features and uninterpretable stochastic features, enabling the system to specifically improve the model and adjust decisions. The identification of abrupt mismatches, monotonic drift, and periodic errors provides concrete clues to understanding model limitations, while statistical analysis of stochastic features quantifies the system's uncertainty level. This meticulous residual analysis method significantly improves the model's interpretability and reliability, providing a scientific basis for adaptive management. Compared to traditional methods that simply assess prediction errors, this system focuses more on the structure and sources of errors, guiding more precise model improvements and decision optimization.

[0131] In this embodiment of the invention, the method for adaptively correcting model parameters based on structural features and adjusting the execution intensity and timing of the intelligent decision-making scheme set based on stochastic features includes:

[0132] For the identified periodic systematic errors, their period and phase parameters are extracted, and periodic compensation terms with the same period are introduced into the dynamic evolution model of the ecosystem.

[0133] For the identified monotonic drift error, its drift rate is calculated, and the Kalman filter algorithm is used to correct the state variables and transmission coefficients of the ecosystem dynamic evolution model online;

[0134] For the identified mutation-type model mismatch, the mutation time is associated with the transient change point in the transient change feature, and the existence of unidentified causal path in the time-delay coupling correlation network is checked. The structure of the time-delay differential equation system is updated according to the matching result.

[0135] The model, after compensation, correction, and structural update, is solved numerically again to generate a corrected ecosystem dynamic evolution model.

[0136] A risk assessment is performed on the statistical description of the randomness characteristics. When the variance exceeds the preset risk threshold, the failure probability of each scheme in the intelligent decision scheme set is calculated. The execution intensity of the decision scheme is adjusted according to the failure probability, and the original intervention intensity is multiplied by a safety factor that is negatively correlated with the failure probability.

[0137] Analyze the temporal distribution of transient change characteristics and trend evolution characteristics, identify sensitive time windows and passive time windows of the ecosystem, re-plan the implementation sequence of the intelligent decision-making scheme set according to the characteristics of the time windows, and arrange high-intensity intervention measures within the sensitive time windows.

[0138] In this embodiment, the precise period of the periodic component is first extracted from the structural features for the identified periodic systematic error. and phase These parameters describe the missing periodic patterns in the model.

[0139] Introduce corresponding periodic compensation terms into the dynamic evolution model of the ecosystem. The amplitude of the compensation term The compensation term is determined by minimizing historical residuals; it can be added to the differential equations of relevant nodes as an additional driving factor or as a periodic correction term for state variables; for monotonic drift errors, the drift rate (i.e., the slope of the linear trend of residuals) of each drift interval is calculated; the Kalman filter framework is implemented, using the discrete form of the ecosystem dynamic evolution model as the state equation and the observation data as the measurement equation.

[0140] The state vector of a Kalman filter contains the model's state variables and key conduction coefficients. Based on the magnitude and direction of the drift rate, the process noise covariance matrix in the Kalman filter is adjusted to increase the uncertainty of the corresponding parameters. The state variables and conduction coefficients are recursively corrected online using the Kalman filter algorithm to automatically track slow changes in system parameters. For abrupt model mismatches, the identified residual abrupt change moments are matched with transient change points in the transient change features. Time distance and feature similarity are calculated to identify highly correlated point pairs.

[0141] For strongly correlated point pairs, check whether specific external events or interventions occur simultaneously; re-examine the time-delay coupled correlation network, paying particular attention to weakly correlated paths that were filtered out by thresholds in the original analysis, and determine whether there are any uncaptured causal relationships; based on the analysis results, modify the structure of the time-delay differential equation system, possible modifications include: adding new interaction terms, adjusting time-delay parameters, or introducing conditional response functions (such as threshold response); for abrupt mismatches that cannot be explained by structural modifications, consider introducing event response functions to simulate the system's response to specific external shocks; integrate the above three types of corrections (periodic compensation, online parameter correction, and structural update) into the original model to form an enhanced dynamic evolution model; use historical data to validate the enhanced model to ensure that the correction measures effectively reduce prediction bias.

[0142] The corrected model is then numerically solved again to generate a corrected ecosystem dynamic evolution model. Next, the execution of decision-making schemes is adjusted based on stochastic characteristics. First, a risk assessment is performed on the statistical descriptions of the stochastic characteristics (mean, variance, and skewness coefficient). A preset risk threshold is set (usually determined based on historical observations or expert knowledge). When the variance of the stochastic residuals exceeds the threshold, the system is considered to have high uncertainty. Based on the statistical characteristics of the stochastic residuals and combined with the Monte Carlo simulation method, the failure probability of each decision-making scheme under uncertainty conditions is calculated. Failure is defined as a situation where the target indicator fails to reach the expected improvement threshold after the implementation of the plan; the execution intensity of the decision-making plan is adjusted according to the failure probability, and the specific formula is as follows: ,in, The adjusted intervention intensity, The original intervention intensity and safety factor , This is a risk adjustment parameter (typically set to 0.5-0.8). This represents the probability of failure.

[0143] This adjustment ensures a more conservative intervention strategy under high uncertainty. Simultaneously, it analyzes the temporal distribution characteristics of transient changes and trend evolution to extract system response characteristics across different time periods. Through statistical analysis, it identifies sensitive time windows (periods when the system responds rapidly and significantly to interventions) and passive time windows (periods when the system responds slowly or weakly to interventions). Sensitive window identification can be based on the temporal distribution of historical transient change points, seasonal patterns of trend evolution rates, or prior knowledge related to biological rhythms. Based on the identified time window characteristics, the implementation sequence of the intelligent decision-making scheme set is re-planned. High-intensity interventions are placed within sensitive time windows to maximize intervention effectiveness, while maintenance or preventative interventions are placed within passive time windows to maintain system stability. The revised decision-making scheme set undergoes a consistency check to ensure synergy and temporal rationality among different interventions. Ultimately, this results in an adaptively adjusted intelligent decision-making scheme set with a temporal arrangement, providing precise implementation guidance for regional ecological protection.

[0144] This system achieves adaptive correction of model parameters and dynamic adjustment of decision-making schemes by differentially processing structural and stochastic features. Targeted correction strategies are employed for different types of prediction bias: periodic errors are addressed by introducing compensation terms, monotonic drift is tracked in real-time using Kalman filtering, and abrupt mismatches are handled through structural updates or event response functions. Simultaneously, based on uncertainty assessment of stochastic features, risk adjustment of the execution intensity of decision-making schemes is achieved, and the timing of intervention measures is optimized through sensitive time window analysis. This comprehensive adaptive adjustment method significantly improves the accuracy and robustness of ecological protection decisions, enabling continuous optimization of protection effects in complex and ever-changing ecological environments. Compared with traditional static decision-making methods, this system possesses a closed-loop feedback capability of "learning-adjustment-optimization," making it particularly suitable for long-term ecosystem management.

[0145] Through the detailed implementation methods described above, intelligent decision support and dynamic management for regional ecological protection based on big data were achieved. Starting from multi-temporal ecological monitoring data, the system identified causal relationships in the ecosystem through time-delay coupled correlation network analysis and virtual intervention experiments. Based on multi-scale time anchor sequence and feedback loop analysis, a dynamic evolution model incorporating time-delay effects and nonlinear feedback was constructed. Through simulation and multi-dimensional evaluation, a hierarchical set of intelligent decision-making schemes was generated. Residual analysis and adaptive adjustment were used to achieve continuous model optimization and dynamic adjustment of decisions. Compared with traditional methods, this system demonstrates significant advantages in understanding ecosystem mechanisms, predicting dynamic evolution, and providing decision support, offering scientific, precise, and dynamic technical support for regional ecological protection.

[0146] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0147] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0148] Although embodiments of the invention have been shown and described, those skilled in the art will understand 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 claims and their equivalents.

Claims

1. A regional ecological protection intelligent decision support and dynamic management system based on big data, characterized in that, include: The acquisition module is used to acquire multi-temporal ecological monitoring data and environmental driving factor data of the target area; The correlation network construction module is used to construct a time-delay coupling correlation network between ecological factors based on the multi-temporal ecological monitoring data and the environmental driving factor data; The causal inference module is used to perform causal inference screening on the association paths in the time-delay coupled association network. By constructing virtual intervention experiments and comparing them with counterfactual data, it identifies ecological action paths with causal transmission characteristics. The time anchor construction module is used to obtain historical evolution data of factors at each node in the ecological action path, calculate the hysteresis transmission time and signal attenuation rate between adjacent nodes, and establish a multi-scale time anchor sequence. The feedback loop analysis module is used to identify closed-loop feedback loops based on the multi-scale time anchor sequence and to calculate the loop gain and stability domain boundary parameters of the feedback loop. The dynamic model construction module is used to construct an ecosystem dynamic evolution model containing a set of time-delay differential equations based on the loop gain and the stability domain boundary parameters. The decision-making scheme generation module is used to simulate and extrapolate various conservation intervention schemes based on the dynamic evolution model of the ecosystem, and generate a hierarchical set of intelligent decision-making schemes. The ecological response monitoring module is used to collect ecological response data in real time after the implementation of decisions through a sensor network, and to extract transient change characteristics and trend evolution characteristics from the ecological response data. The residual analysis module is used to perform residual analysis on the transient change characteristics, the trend evolution characteristics, and the prediction output of the ecosystem dynamic evolution model to identify the structural and stochastic characteristics of the prediction bias. The adaptive adjustment module is used to trigger adaptive correction of model parameters based on the structural features, and to adjust the execution intensity and timing of the intelligent decision-making scheme set based on the randomness features.

2. The system according to claim 1, characterized in that, The construction of the time-delay coupling network among ecological factors includes: The time series of each ecological factor in the multi-temporal ecological monitoring data are decomposed into multiple scales to extract the component series at daily, monthly, seasonal and interannual scales. The environmental driving factor data is decomposed into multi-scale components at the same scale to obtain the driving factor component sequence. At each time scale, calculate the time-delay mutual information matrix between the ecological factor component sequence and the driving factor component sequence; The time-delay mutual information matrix is ​​subjected to threshold screening, and factor pairs with mutual information values ​​greater than a preset information threshold and their corresponding optimal lag step sizes are retained and denoted as candidate coupling relationships. The candidate coupling relationships are subjected to cross-scale consistency test. The ratio of the lag step size of the same factor pair at different time scales is calculated. Candidate coupling relationships whose ratios conform to the scale multiple law are marked as stable coupling node pairs. Based on the stable coupled node pairs and their optimal lag step size, the time-delay coupled correlation network is constructed.

3. The system according to claim 1, characterized in that, The method of identifying ecological action pathways with causal transmission characteristics through the construction of virtual intervention experiments and counterfactual comparative analysis includes: Select a predetermined number of factor pairs with the highest connection strength from the time-delay coupled correlation network, and denot them as candidate causal pairs. Each candidate causal pair contains a potential cause factor and a potential result factor. For each candidate causal pair, a pulse-type perturbation signal of preset amplitude is superimposed on a specific time node of the historical time series of the potential causal factor to generate a virtual intervention sequence; Based on the optimal lag step length corresponding to the candidate causal pair, a conditional transition probability matrix is ​​constructed. The virtual intervention sequence is used as input, and the state probability distribution of the potential outcome factor is predicted after time alignment according to the optimal lag step length. An intervention prediction sequence is then generated by sampling from this distribution. Meanwhile, keeping the original historical sequence of the potential causal factors unchanged, the evolution trajectory of the potential outcome factors in the same time period is predicted using the same conditional transition probability matrix, which is denoted as the counterfactual prediction sequence. Calculate the pointwise difference between the intervention prediction sequence and the counterfactual prediction sequence, and integrate the difference sequence over time to obtain the cumulative intervention effect size; Extract segments from the true historical observation sequences of the potential outcome factors that are aligned with the intervention time window, and calculate the actual change of these segments relative to the pre-intervention baseline level; Calculate the correlation coefficient between the cumulative intervention effect and the actual change. When the correlation coefficient is greater than a preset causal threshold and passes the significance test, mark the connection path corresponding to the candidate causal pair as the ecological action path.

4. The system according to claim 1, characterized in that, The calculation of the hysteresis propagation time and signal attenuation rate between adjacent nodes, and the establishment of a multi-scale time anchor sequence, includes: Along the ecological action path, the node factors are topologically sorted according to the causal transmission direction to obtain a directed path sequence; For adjacent node pairs in the directed path sequence, calculate their cross-correlation function, identify the time offset corresponding to the maximum value of the cross-correlation function, and record it as the hysteresis propagation time; Within the time window corresponding to the hysteresis propagation time, the ratio of the change amplitude of the predecessor node factor to the response amplitude of the successor node factor is calculated and denoted as the initial propagation efficiency. The initial conduction efficiency of all adjacent node pairs in the directed path sequence is calculated by path accumulation to obtain the cumulative attenuation coefficient from the path start point to each node. The inverse of the cumulative attenuation coefficient is normalized and used as the signal attenuation rate. The lag propagation time is accumulated on the directed path sequence, and the accumulated lag time from the starting point is marked at each node position to form a time anchor point sequence for a single path. The time anchor sequences of all the ecological action pathways are integrated and divided into a rapid response layer, a medium-term transmission layer and a long-term accumulation layer according to the magnitude of the lag time, to construct the multi-scale time anchor sequence.

5. The system according to claim 1, characterized in that, The process of identifying the feedback loop that forms a closed loop and calculating the loop gain and stability domain boundary parameters of the feedback loop includes: Nodes of the fast response layer, medium-term conduction layer and long-term accumulation layer in the multi-scale time anchor sequence are extracted. Within each time scale layer, the ecological action path is traversed using a depth-first search algorithm to identify closed paths with the same starting and ending node, which are denoted as candidate loops within the scale. A time consistency test is performed on the candidate loops within the scale. The sum of the lag propagation times between all adjacent nodes on the loop is calculated as the loop cycle period. When the deviation between the loop cycle period and the characteristic cycle of the time scale layer is less than a preset cycle threshold, it is confirmed as a candidate feedback loop. For each candidate feedback loop, calculate the inverse product of the signal attenuation rates of all node pairs on the path, and use it as the initial value of the loop gain; The number of negative suppression effects in the candidate feedback loops is counted. If the number is even, it is marked as a positive feedback loop; if the number is odd, it is marked as a negative feedback loop. For the positive feedback loop, a discrete iterative model is constructed according to the initial value of the loop gain and the loop cycle period, and multi-step cyclic iteration is performed. The initial value of the loop gain is corrected according to the divergence rate of the iterative output value to obtain an effective loop gain. For the negative feedback loop, a step disturbance is applied at the loop start point. The relaxation time and oscillation decay coefficient required for the system to recover to the equilibrium state are calculated. The stability domain boundary parameters are determined based on the ratio of the oscillation decay coefficient to the loop cycle period.

6. The system according to claim 1, characterized in that, The construction of the ecosystem dynamic evolution model, which includes a system of time-delay differential equations, includes: Based on the number of nodes in the time-delay coupled correlation network, state variables are established for each ecological factor node, and a state vector space is constructed. For each causal connection in the ecological action path, extract its lag propagation time and signal attenuation rate, construct a basic time-delay differential term, and multiply the state value of the predecessor node before the lag time by the propagation coefficient as the driving term of the differential equation of the successor node. The nodes participating in the feedback loop are identified by traversing the state vector space. For nodes participating in the positive feedback loop, the feedback enhancement factor is calculated based on the effective loop gain and the loop cycle period, and a self-enhancing term is added to the differential equation of that node. For nodes participating in the negative feedback loop, the equilibrium point and maximum offset threshold of the state variables are determined according to the stability domain boundary parameters, and a Logistic type self-inhibition term with saturation parameters determined by the stability domain boundary parameters is added to the differential equation of the node. The basic time-delay differential term is superimposed with the self-enhancing term or the self-inhibiting term, and an environmental driving factor is introduced as a time-varying forcing term to construct a complete set of time-delay differential equations. The time-delay differential equation system is calibrated using historical ecological monitoring data, and a numerical solver is constructed using a dedicated algorithm for delayed differential equations. The calibrated time-delay differential equation system is then integrated with the numerical solver to generate the dynamic evolution model of the ecosystem.

7. The system according to claim 1, characterized in that, The simulation and deduction based on the dynamic evolution model to generate a hierarchical set of intelligent decision-making schemes includes: Based on the regional ecological protection goals, identify the core ecological indicators that need to be prioritized for improvement and mark the corresponding nodes as target nodes; In the time-delay coupled correlation network, a reverse tracing algorithm is used to start from the target node and search backward along the causal path to identify key driving nodes; For each of the aforementioned key driving nodes, multiple intervention schemes are designed, including combinations of intervention timing, intervention intensity, and intervention duration. Each set of intervention schemes is transformed into external input signals for the corresponding state variables in the dynamic evolution model of the ecosystem. The model is run to perform forward simulation to obtain the evolution trajectory of the target node after the intervention. The evolutionary trajectory is evaluated for convergence, and intervention schemes with short arrival times and small fluctuations are marked as efficient schemes. The robustness index of the efficient solution was calculated by superimposing random environmental fluctuations during the simulation. All efficient solutions are ranked based on the comprehensive score of the robustness index and intervention cost. The preferred solutions are then divided into three levels—gradual, moderate, and aggressive—according to the intensity of intervention and the timing of implementation, thus constructing the intelligent decision-making solution set.

8. The system according to claim 1, characterized in that, The extraction of transient change features and trend evolution features from the ecological response data includes: The ecological response data is subjected to seasonal trend decomposition, separating the data into trend components, periodic components and residual components. The trend component is subjected to first-order difference to obtain the rate of change sequence, the residual component is subjected to sliding window variance calculation to obtain the fluctuation intensity sequence, and the two are normalized and multiplied to obtain the transient anomaly index sequence. In the transient anomaly index sequence, peak points are identified as candidate points for transient changes. An equal-length comparison window is set before and after each candidate point, and the Kolmogorov-Smirnov test statistic of the ecological response data within the comparison window is calculated. Candidate points whose statistics exceed a preset mutation threshold are identified as transient change points. The change amplitude of the trend component, the phase jump variable of the periodic component, and the variance mutation amount of the residual component are extracted at each transient change point. The three are combined to form the transient change feature. The trend component is segmented using the transient change point as the dividing point. Linear fitting is performed in each segment between adjacent transient change points, and the segment slope is extracted as the evolution rate of that time period. Calculate the difference in evolution rate between adjacent segments. When the absolute value of the difference is greater than a preset trend threshold, mark the boundary point as a trend inflection point. Calculate the curvature change of the trend component at each trend inflection point. Within the segments between each trend inflection point, the monotonicity coefficient of the trend component, the amplitude stability of the periodic component, and the stationarity index of the residual component are calculated. The evolution rate, curvature change, and the three stability indices are combined to form the trend evolution feature.

9. The system according to claim 1, characterized in that, The structural and stochastic characteristics for identifying prediction bias include: The predicted output of the ecosystem dynamic evolution model is time-aligned with the ecological response data, and the difference between the predicted and measured values ​​is calculated to generate the original residual sequence. Extract the time position of the transient change point in the transient change feature, calculate the jump amplitude of the original residual within the window before and after the corresponding time, and mark the residual segment of that time period as a mutation model mismatch when the jump amplitude exceeds the preset mutation threshold. Extract the evolution rate of each segment between trend inflection points in the trend evolution features, calculate the linear trend slope of the original residual within the corresponding time period of each segment, and mark the residual of the segment as monotonic drift error when the residual slope continuously deviates from zero and the absolute value is greater than the preset drift threshold. Spectral analysis is performed on the original residual sequence to identify periodic components with significant amplitude in the frequency domain. When the energy proportion of the periodic component exceeds a preset period threshold, the frequency and phase of the periodic component are extracted and marked as a periodic system error. The mutational model mismatch, the monotonic drift error, and the periodic systematic error are combined to form the structural characteristics of the prediction bias; The destructured residual is obtained by subtracting the bias component corresponding to the structural feature from the original residual sequence. The destructured residual is then subjected to a white noise test, and the destructured residual that passes the test is marked as a random feature.

10. The system according to claim 9, characterized in that, The adaptive correction of model parameters triggered based on the structural features, and the adjustment of the execution intensity and timing of the decision-making scheme based on the stochastic features, include: For the identified periodic systematic errors, their period and phase parameters are extracted, and periodic compensation terms with the same period are introduced into the dynamic evolution model of the ecosystem. For the identified monotonic drift error, its drift rate is calculated, and the Kalman filter algorithm is used to correct the state variables and transmission coefficients of the ecosystem dynamic evolution model online; For the identified mutation model mismatch, the mutation time is associated with the transient change point in the transient change feature, and the existence of unidentified causal path in the time-delay coupling association network is checked. The structure of the time-delay differential equation system is updated according to the matching result. The model, after compensation, correction, and structural update, is then numerically solved again to generate the corrected ecosystem dynamic evolution model. A risk assessment is performed on the statistical description of the randomness characteristics. When the variance exceeds a preset risk threshold, the failure probability of each scheme in the intelligent decision scheme set is calculated. The execution intensity of the decision scheme is adjusted according to the failure probability, and the original intervention intensity is multiplied by a safety factor that is negatively correlated with the failure probability. Analyze the temporal distribution of the transient change characteristics and the trend evolution characteristics, identify the sensitive time windows and passivation time windows of the ecosystem, and re-plan the implementation sequence of the intelligent decision-making scheme set based on the characteristics of the time windows.

Images