Ecological public welfare forest monitoring and intelligent management system and method

Abstract

The invention relates to the technical field of forestry informatization, in particular to an ecological public welfare forest monitoring and intelligent management system and method. The method comprises the steps of collecting multi-dimensional ecological data in real time and performing data fusion; based on a data fusion result, establishing an ecological behavior evolution graph and a multi-layer state inversion network by adopting a graph attention mechanism, forming a dynamic ecological twinborn body, and realizing ecological system state prediction, historical disturbance inversion and future behavior simulation; a risk tensor map is constructed in combination with state abnormality, trend instability and intervention ineffectiveness in a three-dimensional mode, a dynamic grading thermodynamic diagram is generated, and a personalized regulation and control strategy is recommended through reinforcement learning; and finally, sensing an actual regulation and control effect through a feedback mechanism, dynamically correcting twin model parameters by using deviation analysis, and constructing a knowledge base based on historical strategies and effects to drive strategy self-optimization. According to the method, a sensing-modeling-regulation-feedback closed loop is formed, and the accuracy and adaptability of ecological management are improved.

Description

Technical Field

[0001] The present invention relates to the field of forestry information technology, and in particular to an ecological public welfare forest monitoring and intelligent management system and method. Background Art

[0002] As an important natural resource for maintaining regional ecological balance, enhancing biodiversity, and promoting carbon sequestration, the scientific monitoring and intelligent management of ecological public welfare forests are of great significance for achieving sustainable development of the ecological environment. With the rapid development of information technology and sensing technology, ecological data has become multi-source, heterogeneous, and high-dimensional, with dynamic changes. There is an urgent need to establish efficient data fusion and intelligent analysis methods to accurately reflect the complex state and evolutionary trends of ecosystems.

[0003] The Chinese invention patent application with announcement number CN119623776A discloses a smart garden management method and system based on big data technology. Multi-source heterogeneous data in the garden are collected in real time through an environmental perception network, and the multi-source heterogeneous data are integrated and compared to obtain a garden ecological portrait; the garden ecological portrait is used to evaluate the current ecological health status of the garden, predict the ecological change trends that may occur in the short term, and generate an ecological change trend forecast report; according to the ecological change trend forecast report, the optimal solution for automatic adjustment is found, and a resource allocation plan and a tour experience optimization plan are generated; based on the resource allocation plan and the tour experience optimization plan, a continuous learning mechanism is established, a regular review process is implemented, and a management system configuration is generated; the technical solution provided by this application significantly improves the efficiency of garden management and resource utilization, while also improving the overall satisfaction of tourists.

[0004] Digital twin technology of ecological behavior, by constructing a virtual mapping of the ecosystem, can simulate and predict the spatiotemporal dynamics of ecological processes, providing solid support for scientific decision-making. In response to the complexity and variability of ecosystems, it can carry out risk identification and generate intelligent control strategies, enabling early warning and precise intervention of potential ecological degradation and environmental disturbances, improving the effectiveness and response speed of ecological governance. By establishing a feedback mechanism for the effects of ecological regulation and a multi-cycle closed-loop adaptive optimization method, it can achieve dynamic adjustment and continuous optimization of ecological governance plans, promoting the stable and healthy development of ecosystems. Summary of the Invention

[0005] The purpose of the present invention is to address the problems existing in the background technology and propose an ecological public welfare forest monitoring and intelligent management system and method.

[0006] The technical solution of the present invention is a method for monitoring and intelligently managing ecological public welfare forests, including the following specific implementation steps:

[0007] S1. Collect ecological monitoring data from public welfare forests, construct a unified tensor representation of multi-source heterogeneous data, normalize, resample, and align ecological semantics in space and time for each modal data, and generate an ecological disturbance index matrix based on a combination of multimodal ecological factors. This matrix is ​​combined with spatial encoding, time window, and plot attributes to form a four-dimensional ecological characteristic tensor.

[0008] S2. Construct an ecological behavior evolution graph based on ecological feature tensors. Leveraging graph attention mechanisms and time series modeling techniques to extract spatiotemporal state evolution dependencies, we construct a multi-layer state inversion network consisting of a forward generator and a reverse restorer. This allows for predictive simulation of ecosystem states, inverse recovery from historical disturbances, and the generation of diverse future evolutionary pathways.

[0009] S3. Based on the multi-path prediction results generated by the ecological twin simulation, we extract risk indicators in three dimensions: state abnormality, trend instability, and intervention response failure. We then construct a risk tensor map, use a weighted scoring method to generate a risk level distribution, and generate a dynamic heat map. We then combine this with reinforcement learning to recommend control strategies.

[0010] S4. After implementing regulatory measures, the actual ecological feedback state is obtained. Deviation analysis is performed against the twin model's predicted state to construct a regulatory deviation distance indicator. Regression evaluation is performed based on multi-period deviation trend. The twin model's parameters are dynamically modified using a loss function constructed by combining deviation fitting and fluctuation variance. The regulatory strategy, context, and feedback results are then combined into a knowledge triplet to drive continuous self-evolution and optimization of the strategy.

[0011] Preferably, the four-dimensional ecological characteristic tensor construction process is as follows:

[0012] Ecological monitoring data of public welfare forests are collected simultaneously through airborne remote sensing satellites, aerial drones, ground monitoring stations, and crowdsourcing. A unified tensor representation is constructed using normalization and resampling mechanisms.

[0013] The phenological distribution tensor dynamic registration method (PTR) was used to align the time series of remote sensing image features by minimizing the objective function, taking the time series of phenological events of ecological plots as the benchmark. Local observation delay compensation was also introduced to correct the time error of crowdsourced data.

[0014] By weighting and combining four factors—normalized vegetation index variability, species diversity fluctuation, temperature anomaly deviation, and humidity anomaly index—the ecological disturbance index matrix (E-DIM) was constructed to dynamically quantify the disturbance intensity of each ecological unit in a specific time period.

[0015] The ecological disturbance index matrix E-DIM is integrated as the main characteristic axis, and spatial plot coding, normalized multimodal ecological perception data, ecological unit plot attributes and time node additional attributes are integrated to construct a four-dimensional ecological characteristic tensor with space-time-feature multidimensionality.

[0016] Preferably, the process of constructing the ecological behavior evolution map is:

[0017] Node definition: For each ecological unit i, extract its j The state vector Each node represents the ecological state at a specific moment, and the state vector of ecological unit i at different time slices is constructed. n is the number of time slices in ecological unit i;

[0018] Edge definition: Construct a state transition edge every two moments The edge weight is a combination of perturbation factor weights:

[0019] Among them, W j Represents the edge weight, that is, the state from t j Evolved to t j+1 The combined impact intensity of the disturbance; Indicates the k-th type of disturbance factor at time t j The value of α k represents the learning weight of the k-th type of disturbance factor; K represents the total number of disturbance factors;

[0020] EBEG map expression: G i =( V i,E i ,A i );

[0021] in, That is, the state node set; E j ={e j}, that is, the state transfer edge; A i Represents the attribute set of nodes and edges; G i Represents the behavioral evolution map of the i-th ecological unit.

[0022] Preferably, the multi-layer state inversion network comprises:

[0023] The forward generator takes the current state, perturbation embedding vector, and response sensitivity factor as input, uses Transformer to model temporal dependencies and graph convolutional network (GCN) to model spatial structure, thus achieving future state prediction.

[0024] The inversion restorer is symmetrical in structure with the forward generator. It uses the reverse Transformer and GCN to reverse the perturbation path of the ecological state. Its training is optimized by a joint loss function consisting of the state reconstruction error and the KL divergence of the perturbation distribution.

[0025] Preferably, the ecological response kernel function is in the form of an exponential decay function, which receives the disturbance amount and the lag time as input, outputs the ecological response amplitude, and automatically selects the weight and decay rate parameters according to the disturbance type to quantify the impact of the disturbance on the ecological response.

[0026] Preferably, the risk tensor map is a three-dimensional structure, which respectively represents state abnormality, trend instability and intervention ineffectiveness. State abnormality is calculated by the Euclidean distance from the reference steady-state interval, trend instability is measured by the variance of multi-path derivatives, and intervention ineffectiveness is calculated by the ratio of the state change before and after regulation to the intervention intensity. The three indicators are fused to form a risk score, which is mapped into four risk levels after weighting and superimposed on the ecological unit layer in the form of a heat map.

[0027] Preferably, reinforcement learning recommends the use of the Deep Q-learning algorithm to construct a state-action space, and perform strategy verification in a digital twin environment in combination with the ecological risk context and simulation path, quantitatively evaluate the control effect, select the optimal control plan by maximizing the expected ecological benefits, and update the strategy value function in real time.

[0028] Preferably, the regulated deviation distance is the Euclidean distance between the twin's predicted ecological state and the field perception state. If the deviation exceeds the threshold, a linear trend regression based on a multi-period deviation sequence is performed to determine strategy failure, convergence, and insensitive states, and further optimize the twin model parameters using a joint loss function.

[0029] Preferably, the knowledge-experience triples are stored in the knowledge base in the form of <state-action-bias>. A strategy-bias mapping table is introduced in the subsequent strategy recommendation to perform feedback-driven adaptive evolution. In addition, expert rule intervention is integrated in multiple failure scenarios to form a strategy generation mechanism that integrates expert knowledge and model intelligence.

[0030] The technical solution of the present invention is: an ecological public welfare forest monitoring and intelligent management system, which is used to implement the above-mentioned ecological public welfare forest monitoring and intelligent management method, including:

[0031] An ecological multi-source heterogeneous data acquisition module, which collects real-time public welfare forest ecological monitoring data from ground monitoring equipment, air-space remote sensing platforms, and ecological Internet of Things nodes, and performs structured preprocessing.

[0032] The ecological twin construction and modeling module uses a graph attention mechanism to model multi-factor ecological network relationships based on data fusion results. It also constructs a spatiotemporal dynamic simulation model of ecological behavior, achieving virtual-reality mapping and comparison, and supporting the prediction of the future state of the ecosystem.

[0033] The ecological risk identification and intelligent regulation module uses multi-scale clustering and ecological disturbance identification algorithms to identify potential degradation areas based on twin prediction results and historical trend data. It also combines reinforcement learning or expert knowledge to generate ecological regulation strategies tailored to local conditions.

[0034] The ecological feedback evaluation and adaptive optimization module is used to perceive the actual ecological response after the implementation of regulation, build a multi-period closed-loop feedback model, and dynamically correct the ecological model parameters based on the deviation between the regulation effect and the twin prediction results, forming an ecological governance closed-loop mechanism of prediction-regulation-feedback-adaptation.

[0035] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0036] This paper designs an ecological public welfare forest monitoring and intelligent management system and method. By constructing a multi-source heterogeneous ecological data fusion model, it achieves a deep integration of meteorological, remote sensing, ground sensing, and ecological environment data, effectively improving the comprehensiveness and timeliness of data perception.

[0037] Through the ecological behavior digital twin modeling mechanism, a high-fidelity mapping model of ecosystem status and evolution process was established, significantly enhancing the dynamic prediction capability of ecological process modeling;

[0038] Through the ecological risk identification and intelligent control strategy generation mechanism, ecological degradation trends can be detected in a timely manner. Combining reinforcement learning with expert knowledge reasoning, it can automatically generate highly adaptable governance strategies, improving the scientific nature and targetedness of ecological intervention measures.

[0039] Through ecological regulation effect feedback and a multi-cycle closed-loop adaptive optimization mechanism, a sustainable strategy optimization path was established, achieving dynamic coordinated adjustment between regulation strategy and ecological response, and enhancing the system's adaptability to long-term ecological changes.

[0040] The overall solution forms an intelligent closed-loop ecological governance system from data perception, behavior modeling, risk assessment, strategy generation to effect feedback. It has technical advantages such as fast response speed, high degree of intelligent regulation, and significantly improved management efficiency. It is suitable for the refined, intelligent, and continuous monitoring and governance scenarios of large-scale ecological public welfare forests. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1This is a system architecture diagram of an ecological public welfare forest monitoring and intelligent management system proposed by the present invention;

[0042] Figure 2 This is a flow chart of the method for monitoring and intelligent management of ecological public welfare forests proposed by the present invention. DETAILED DESCRIPTION

[0043] Example 1, as Figure 1 As shown, the present invention proposes an ecological public welfare forest monitoring and intelligent management system, including: an ecological multi-source heterogeneous data acquisition module, an ecological twin construction and modeling module, an ecological risk identification and intelligent regulation module, and an ecological feedback evaluation and adaptive optimization module.

[0044] The ecological multi-source heterogeneous data acquisition module is used to collect real-time ecological monitoring data of public welfare forests from ground monitoring equipment (including but not limited to weather stations, soil moisture sensors, and high-definition cameras), air-space remote sensing platforms (including but not limited to drones and multispectral satellites), and ecological Internet of Things nodes, including but not limited to NDVI, temperature and humidity, soil moisture, vegetation coverage, and carbon sink dynamic multi-dimensional indicator data, and perform structured preprocessing;

[0045] The ecological twin construction and modeling module uses a graph attention mechanism to model multi-factor ecological network relationships based on data fusion results. It also constructs a spatiotemporal dynamic simulation model of ecological behavior, achieving virtual-reality mapping and comparison to support the prediction of the future state of the ecosystem.

[0046] The ecological risk identification and intelligent regulation module uses multi-scale clustering and ecological disturbance identification algorithms to identify potential degradation areas based on twin prediction results and historical trend data. It also combines reinforcement learning or expert knowledge to generate locally appropriate ecological regulation strategies, including but not limited to vegetation reconstruction, irrigation regulation, and species intervention.

[0047] The ecological feedback evaluation and adaptive optimization module is used to perceive the actual ecological response after the implementation of regulation, build a multi-period closed-loop feedback model, dynamically correct the ecological model parameters through the deviation between the regulation effect and the twin prediction results, and realize the self-learning and evolution of the governance strategy, thereby continuously improving the accuracy, flexibility and adaptability of ecological governance.

[0048] Example 2, as Figure 2 As shown, the present invention proposes an ecological public welfare forest monitoring and intelligent management method, which is used to implement the ecological public welfare forest monitoring and intelligent management system proposed in Example 1. The specific implementation steps are as follows:

[0049] S1. The ecological multi-source heterogeneous data acquisition module constructs a multimodal perception map construction method based on ecological semantics. Through multi-step preprocessing, feature mapping, spatiotemporal registration, and ecological disturbance index modeling, it achieves high-resolution perception and modeling of the ecological status of the forest area. Specifically:

[0050] S11. Synchronously collect data from multiple sensing channels of the ecological public welfare forest, including but not limited to:

[0051] Airborne data: Multi-temporal remote sensing satellite images (including but not limited to Sentinel-2 and Landsat-8) to obtain NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), surface temperature, etc.

[0052] Aerial data: UAV inspection images equipped with multispectral cameras, with a resolution greater than 30 cm, are mainly used for terrain details and local disturbances;

[0053] Ground data: ecological monitoring data collected by ecological monitoring stations, including but not limited to soil moisture, temperature, wind speed, air humidity, and CO2 concentration;

[0054] Crowdsourced data: images, voices, and biological sightings uploaded by ecological volunteers, supplemented by geolocation and timestamps;

[0055] In order to achieve a unified representation of data from different sources, a normalization and resampling mechanism is used to construct a unified tensor expression:

[0056] Among them, D k (x, y, t) represents the original observation value of the k-th data source (including but not limited to NDVI, humidity, etc.) at the spatial location (x, y) and time point t; Represents unified format data after spatial resampling and temporal interpolation;

[0057] S12. Use the Phenology-Tensor Registration (PTR) mechanism to coordinate the spatiotemporal and ecological semantics of each modality data, consider the temporal phase consistency of plant phenological characteristics in different data modalities, and construct the following objective function:

[0058]

[0059] Among them, φ i (t) represents the time series of phenological events in the ith ecological plot (such as the time points of flowering and germination); ψ i(t; θ) represents the image feature time series of the remote sensing image of the same plot; θ represents the alignment parameter of the image time feature series, which represents the time offset / scaling coefficient of the remote sensing image; δ i represents the local observation delay compensation, which is used to correct the time error between public upload and actual occurrence; n represents the total number of ecological units;

[0060] S13. To quantify the abnormal fluctuation of ecological status, the ecological disturbance index matrix E-DIM is constructed:

[0061] E ij =ω1·ΔNDVI ij +ω2·Var(S ij )+ω3·TempDev ij +ω4·HumAnom ij ;

[0062] Among them, E ij Indicates the ecological disturbance intensity of the i-th ecological unit in the j-th time period; ΔNDVI ij Indicates the degree of variation of the Normalized Difference Vegetation Index (NDVI change rate); Var(S ij ) represents species diversity fluctuation (variance of species distribution); TempDev ij Indicates the temperature anomaly deviation, the difference from the multi-year average temperature; HumAnom ij represents the humidity anomaly index, which measures the degree of deviation from the historical humidity distribution of the season; ω1, ω2, ω3, and ω4 represent the weights of various disturbance factors, which are automatically adjusted through Bayesian optimization to maximize the performance of the prediction model;

[0063] S14. Using the above E-DIM matrix as the main characteristic axis, and combining the spatial plot coding, ecological factor group and time window dimension, a four-dimensional ecological characteristic tensor is constructed:

[0064]

[0065] Where T(x,y,t,f) represents the ecological multidimensional tensor in space (x,y), time t, and feature dimension f; Represents ecological perception data of different modalities (after normalization); C i L represents the land attributes of the i-th ecological unit, including but not limited to slope, soil type, and irrigation method; t Additional attributes representing a time node, including but not limited to solar terms (such as Lichun and Yushui), agricultural time windows, and maintenance cycle markers.

[0066] S2, the ecological risk identification and intelligent regulation module, builds a spatiotemporal twin modeling framework for ecological behavior processes based on the multi-source fusion ecological multidimensional tensor T(x, y, t, f) output from step S1. It introduces the Eco-behavior Evolution Graph (EBEG) and the Multi-Layer State Reversal Network (MSRN) to construct a predictable, reversible, and simulatable ecological behavior process model. The specific implementation process is as follows:

[0067] S21. Construct an Ecological Behavioral Evolutionary Graph (EBEG) to establish a dynamic evolutionary structure of ecological states over time and under the influence of disturbances, capturing the complex dependencies of “state-event-intervention-response”. Specifically:

[0068] Node definition: For each ecological unit i, extract its j The state vector Each node represents the ecological state at a specific moment, and the state vector of ecological unit i at different time slices is constructed. n is the number of time slices in ecological unit i;

[0069] Edge definition: Construct a state transition edge every two moments The edge weight is a combination of perturbation factor weights:

[0070] Among them, W j Represents the edge weight, that is, the state from t j Evolved to t j+1 The combined impact intensity of the disturbance; Indicates the k-th type of disturbance factor at time t j The value of α k represents the learning weight of the k-th disturbance factor, reflecting its importance to the ecosystem change; K represents the total number of disturbance factors (including but not limited to meteorological, hydrological, soil, and human dimensions);

[0071] EBEG map expression: G i =(V i ,E i ,A i );

[0072] in, That is, the state node set; E j ={e j}, that is, the state transfer edge; A i Represents a set of attributes of nodes and edges, including but not limited to disturbance factors, intervention methods, and multimodal information of biological responses; G iRepresents the behavioral evolution graph of the i-th ecological unit, that is, its state change structure in the time series;

[0073] S22. Construct a multi-layer state inversion network (MSRN) to implement three reasoning paths: prediction, reconstruction, and inference of ecological status:

[0074] A1. Construct a forward generator F(·) with the current state s t , disturbance event embedding vector e t , response sensitivity parameter γ is input and the future state is predicted:

[0075] in, represents the predicted ecological state vector, i.e. the simulation result at time t+h; s t represents the currently observed ecological state vector (including but not limited to: NDVI, species density, and multidimensional indicators of meteorological conditions); γ represents the sensitivity parameter of the ecosystem to disturbance (between 0 and 1, used to control the degree of response of the model to intervention);

[0076] It should be noted that the forward generator F(·) is a network composed of a Transformer (for modeling long-term dependencies) and a GCN (for graph structure state transfer): the multi-dimensional ecological state tensor output in step S1 is converted into a time series input sequence, and a time series encoder (Transformer Block) is used to process the temporal dependencies in the ecological sequence, modeling the potential evolution pattern of the ecosystem state over time. A multi-head attention mechanism is used to capture ecological responses at different time scales, supporting global dependency modeling across time periods (for example, drought may delay ecological state changes by 2 to 3 weeks), and a spatial propagation module (GCNBlock) is used to process spatial dependencies and structured propagation between ecological units. Based on the ecological behavior map constructed in step S2.1, the updated state information is transmitted on the graph structure. After processing the time series input, an enhanced state representation is output, and the final state prediction result is output.

[0077] A2. Construct an inverse restorer R(·) to infer the disturbance path that may have caused the formation of the observed ecological state:

[0078] in, represents the predicted perturbation combination occurring at time tk, which is an inference of the cause of the past state transition; θ' represents a set of learnable inversion path parameters, which is used to represent the type, intensity, duration, and other combinations of perturbations; ||·|| represents the Euclidean distance, which is used to measure the reconstruction error between the inverted state and the actual state;

[0079] It should be noted that the inverse restorer R(·) is a reverse neural network architecture with a structure symmetrical to the forward generator F(·). Its core idea is to use the symmetrical modeling mechanism to infer the possible disturbance factors and their paths that lead to the formation of the current (or future) ecological state from the current (or future) ecological state. Its input and output forms are mirror images of each other with those of the forward generator F(·). The network hierarchy is mirrored in reverse in terms of encoding dimension, number of layers, and attention mechanism, which is conducive to collaborative training and cross-validation: the input is the current observation state vector s t , adding attention position encoding of the disturbance time window (i.e., the potential impact of past time on the current state), using the reversed transformer module to simulate the cumulative contribution of disturbances to the state. The attention mechanism is built with the goal of "explaining the current state" from the past state, supporting multi-scale disturbance delay modeling, and using the reverse GCN module (Reverse GCN) to project the predicted disturbance path back into the ecological graph structure to find possible historical behavior paths. GCN runs on the disturbance path candidate subgraph to find the most likely disturbance combination (not maximum likelihood), and finally decodes the disturbance vector;

[0080] It should be noted that by constructing a closed-loop reconstruction loss, the inversion restorer R(·) and the forward generator F(·) are trained collaboratively:

[0081] Among them, s t represents the ecological state vector at the current time t; L represents the training objective function, the total loss; Represents the state reconstruction error loss, i.e., from the predicted disturbance When the state is restored, the mean square error with the original state is used to constrain the rationality of the inversion disturbance; It represents the KL divergence between the predicted disturbance vector and the true disturbance distribution egt, which measures the accuracy and matching degree of the predicted disturbance; λ represents the weight balance coefficient of each item in the loss function, which is used to adjust the importance of matching reconstruction and disturbance. It is a hyperparameter set by cross-validation;

[0082] S23. Introducing the ecological response kernel function κ eco , represents the disturbance factor d k Ecological response Δs t Nonlinear mapping:

[0083] Where Δs t represents the change amplitude of ecological state within time t; K represents the total number of disturbance factors; β k Represents the response weight, which is used to quantify the impact of the disturbance on the current ecological unit (learnable); τ krepresents the disturbance lag time, that is, the delay period between the occurrence of the disturbance and the ecological response; κ eco (d k ,τ k ) represents the ecological response kernel function, which is used to convert the disturbance amount and lag time into the ecological response value; μ represents the decay coefficient (used in the exponential kernel function), which controls the decay rate of the response over time;

[0084] S24. Construct an ecological mirror body whose status can be dynamically updated over time, with real-time feedback, behavior prediction, and control modeling. Specifically:

[0085] B1. Initialize the twin:

[0086] Input: Historical graph G i , current status s t , disturbance factor d k ;

[0087] Output: Initial state of twins

[0088] B2. Behavior path generation (multi-path): Use MSRN to simulate N disturbance paths and generate corresponding state sequences:

[0089] Among them, P n represents the nth ecological behavior path (used to simulate development trends under different disturbance / intervention combinations); represents the predicted state value of the ecological twin at time t+h; H represents the prediction step length, that is, the future evolution time of the twin;

[0090] B3. State correction and synchronization, input current real-time perception data Use Kalman filter to update the state:

[0091] in, represents the original twin prediction state; Indicates real-time observation status; represents the twin state after real-time observation correction; K(·) represents the Kalman filter gain matrix, which is used to balance the correction weight between the observation and prediction values.

[0092] S3, Ecological Risk Identification and Intelligent Control Module: This module constructs an intelligent ecological risk identification mechanism based on twin behavior evolution and multi-factor response maps. It generates a dynamic hierarchical risk map based on three perspectives: state abnormality, trend instability, and intervention ineffectiveness. Based on this, it recommends personalized and regionalized control strategies. The specific implementation process is as follows:

[0093] S31. Extract multi-path evolution trends from the ecological twin prediction results. By modeling the three dimensions of state anomaly, trend instability, and intervention ineffectiveness, a multi-factor identification map of ecological risk is constructed. Specifically:

[0094] Extract each simulation trajectory from the twin path set generated in step S2:

[0095]

[0096] Define three core risk factors and construct them into a unified tensor structure: R i,t =[r t (1) ,r t (2) ,r t (3) ];

[0097] Among them, R i,t represents the aggregation of the risk status of path i at time t in three dimensions; r t (1) Represents the abnormality index of ecological status; r t (2) Represents an indicator of ecological trend instability; r t (3) It represents the failure index of ecological intervention response;

[0098] Construct the risk tensor matrix R t :R t =[R 1,t ,...,R i,t ,...,R N,t ];

[0099] Where N represents the number of twin paths;

[0100] It should be noted that, in this embodiment, the core risk factors are defined as:

[0101] Status abnormality index r t (1) , which measures the degree of state mutation of ecological variables:

[0102] in, represents the predicted ecological state vector of the i-th path at time t, including but not limited to NDVI, biomass, and surface temperature; μ ref represents the state mean corresponding to the historical steady-state period (no interference season in the past five years), which is used to construct the ecological normal reference benchmark; σ ref represents the standard deviation of the state variable in the above steady-state period;

[0103] Trend Instability Indicator

[0104] in, represents the instantaneous rate of change (derivative) of the jth path at time t; represents the average rate of change of all paths at time t;

[0105] Ecological intervention response failure index Among them, ρ recovery (t) represents the ecological recovery rate, that is, the improvement ratio of the ecological status after a certain intervention; represents the ecological state on the δth day after the intervention (twin path prediction value); Indicates the state before the intervention is performed; A t It represents the intervention intensity index, which is input from the outside, including but not limited to the amount of ecological irrigation water, amount of fertilizer applied, and closed area;

[0106] S32. Based on the constructed risk tensor map, it is further converted into a risk grade scoring system and spatially mapped in combination with the geographic ecological unit layer to form a risk thermal zoning map with real-time update capabilities. Specifically:

[0107] The three factors are combined into a risk score using a weighting function:

[0108] Among them, S i,t represents the comprehensive ecological risk score of the i-th path at time t; w k represents the weighted coefficient of the k-th risk factor, that is, the importance of this type of factor in the current environment;

[0109] Set up a multi-level risk classification standard table, as shown in Table 1:

[0110] Table 1 Multi-level risk classification standards

[0111]

[0112] The risk level space is annotated to the ecological unit layer to generate a spatiotemporal dynamic heat map;

[0113] S33. After identifying high-risk areas, a reinforcement learning framework combined with an expert knowledge base is used to conduct virtual strategy evaluation within the twin prediction space to select the optimal control path and achieve personalized, contextualized intelligent governance recommendations. Specifically:

[0114] C1. Using risk level and disturbance type as input, construct the state-action space:

[0115] Status t : Current ecological status of the risk area;

[0116] Action at : Strategic options (including but not limited to fencing, ecological water replenishment, and artificial vegetation restoration);

[0117] C2. Establish a strategy function: π(a t |s t ,r t )→maxE[u(s t+1 ,a t )];

[0118] Among them, π(a t |s t ,r t ) represents the control strategy recommendation model in state s t and risk context t Next take action a t The probability of , the decision function learned by the reinforcement learning model (Deep Q-Learning); r t Represents the risk context, that is, the risk assessment background of the system at the current moment, including but not limited to risk level and dominant factors; E[u(s t+1 ,a t )] represents the expected benefit function, that is, when taking control action a t After that, the expected future state of the ecosystem t+1 Positive benefits brought about;

[0119] C3. Virtually run the control action in the twin environment to verify whether the risk score S i,t Fall to a safe range;

[0120] C4. If effective, output the control report and add it to the knowledge base; otherwise, iterate the strategy optimization.

[0121] S4, the Ecological Feedback Assessment and Adaptive Optimization Module, comprehensively improves the scientific nature and sustainability of ecosystem regulation by building an intelligent governance closed-loop mechanism based on "feedback perception - trend regression - twin correction - strategy re-evolution". The specific implementation process is as follows:

[0122] S41. After implementing control strategies (including but not limited to vegetation restoration, species introduction, and wetland water replenishment), real-time perception of ecological changes in the control area is conducted, and an assessment is made as to whether the expected improvement goals have been achieved. By performing time alignment and status comparison with the predicted results in the ecological twin, a quantitative control deviation indicator is formed, specifically:

[0123] Construct the actual ecological state vector after regulation:

[0124] in, Represents the actual value vector of the ecological state observed at time point t+τ (i.e. NDVI, temperature, humidity, carbon sink, ...);

[0125] Take the ecological twin prediction value That is, the system's expected improvement trajectory;

[0126] Define the control deviation distance:

[0127] in, represents the ecological state vector at time t+τ predicted by the ecological digital twin model; δ τ It represents the Euclidean distance between the actual state and the twin predicted state, which is used to measure the regulation deviation;

[0128] If δ τ >θ t (set deviation threshold), it is determined that the control effect does not meet expectations and enters the feedback and retrospective process;

[0129] S42. Construct response trend lines across multiple time periods to identify the direction of change and stability of regulatory effects within different cycles and determine whether there are areas of strategy failure or ecological resistance. Specifically:

[0130] Constructing a multi-period deviation sequence: δ total ={δ τ1 ,δ τ2 ,...,δ τn};

[0131] Perform linear regression modeling: δ τi =α+β·τ i +ε i ;

[0132] If β>0: the deviation increases and the strategy fails;

[0133] If β<0: the strategy is effective and continues to converge;

[0134] If β = 0: the intervention is insensitive and the strategy needs to be redesigned;

[0135] Among them, δ total represents the set of control deviation values ​​recorded in different time periods, which is used to construct the change trend; α represents the intercept term of the control deviation trend model, which reflects the initial control response deviation level; β represents the regression slope, that is, the speed and direction of the change of the control deviation over time, which is used to judge whether the control is "continuously improving" or "gradually failing"; ε i represents the residual term in the i-th regression, that is, the error term or volatility component;

[0136] S43. Introduce a feedback-driven twin model correction mechanism to update the understanding of ecological mechanisms through error-contribution weight analysis, and improve the accuracy and sensitivity of simulation predictions. Specifically:

[0137] Construct the model correction objective function:

[0138] Based on the loss function, use incremental training to optimize the twin model parameters (including but not limited to GRU neuron weights and GAT graph edge weights);

[0139] Generate an updated set of ecological path predictions:

[0140] Among them, L update represents the updated loss function of the ecological twin model, which comprehensively considers the bias fitting and error stability; λ1 and λ2 represent weighting factors, which respectively control the relative importance of the prediction error and the bias fluctuation variance in the optimization; Var(δ τ ) represents the variance of deviation fluctuation, which is used to measure the instability of the control effect; represents the new prediction path generated by the revised ecological twin model; Represents the updated state value at time t+h; H represents the length of the future prediction time window;

[0141] S44. After modifying the twin model and obtaining the real response results, the historical control strategy, environmental context, and effect feedback are formed into a knowledge triple and stored in the experience library to build the intelligent evolution foundation of the control strategy. Specifically:

[0142] Constructing experience memory items: <s t ,a t ,δ τ >;

[0143] Introducing intelligent strategy evolution learning mechanism (reinforcement learning):

[0144]

[0145] Establish a mapping table between effects and strategies to achieve effect-oriented strategy recommendation evolution;

[0146] For multiple invalid areas, expert rules are introduced to intervene and enhance the adaptability of the strategy;

[0147] Among them, <s t ,a t ,δ τ > represents an experience memory unit, that is, the deviation result produced by adopting a certain strategy under a certain state; represents the new recommendation strategy based on current feedback and historical learning; u(s t+τ |at ,δ t ) represents the control utility function, which evaluates the possible future benefits of a strategy under given states and deviations; argmax(·) represents the strategy that maximizes the utility.

[0148] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for monitoring and intelligent management of ecological public welfare forests, characterized in that: The specific implementation steps include the following: S1. Collect ecological monitoring data of public welfare forests, construct a unified tensor representation of multi-source heterogeneous data, normalize, resample, and align ecological semantics in space and time for each modal data, and generate an ecological disturbance index matrix based on a combination of multimodal ecological factors. Combine this matrix with spatial coding, time window, and plot attributes to form a four-dimensional ecological characteristic tensor. S2. Construct an ecological behavior evolution map based on ecological feature tensors, extract the spatiotemporal evolution dependencies of states using graph attention mechanisms and time series modeling techniques, and build a multi-layer state inversion network including forward generators and reverse restorers to complete the prediction and simulation of ecosystem states, reversely recover historical disturbances, and generate diversified future evolutionary paths; S3. Based on the multi-path prediction results generated by the ecological twin simulation, we extract risk indicators in three dimensions: state abnormality, trend instability, and intervention response failure. We then construct a risk tensor map, use a weighted scoring method to generate a risk level distribution, generate a dynamic heat map, and combine reinforcement learning to recommend control strategies. S4. After implementing regulatory measures, the actual ecological feedback status is obtained, and deviation analysis is performed with the twin model's predicted status. A regulatory deviation distance index is constructed, and regression evaluation is performed based on the multi-period deviation change trend. The loss function constructed by combining deviation fitting and fluctuation variance is used to dynamically correct the twin model's parameters. The regulatory strategy, context, and feedback results are then combined into a knowledge triplet to drive continuous self-evolution and optimization of the strategy.

2. The method for monitoring and intelligent management of ecological public welfare forests according to claim 1, characterized in that: The construction process of the four-dimensional ecological characteristic tensor is as follows: The ecological monitoring data of public welfare forests are collected synchronously through air-based remote sensing satellites, aerial drones, ground monitoring stations and crowdsourcing, and a unified tensor expression is constructed using normalization and resampling mechanisms; The phenological distribution tensor dynamic registration method (PTR) is used to align the time series of phenological events in ecological plots by minimizing the objective function, and local observation delay compensation is introduced to correct the time error of crowdsourced data. By weightedly combining four factors, namely the degree of variation of the normalized vegetation index, species diversity fluctuation, temperature anomaly deviation, and humidity anomaly index, an ecological disturbance index matrix (E-DIM) was constructed to dynamically quantify the disturbance intensity of each ecological unit in a specific time period. The ecological disturbance index matrix E-DIM is integrated as the main characteristic axis, and spatial plot coding, normalized multimodal ecological perception data, ecological unit plot attributes and time node additional attributes are integrated to construct a four-dimensional ecological characteristic tensor with space-time-feature multidimensionality.

3. The method for monitoring and intelligent management of ecological public welfare forests according to claim 2, characterized in that: The process of constructing the ecological behavior evolution map is as follows: Node definition: For each ecological unit i, extract its j The state vector Each node represents the ecological state at a specific moment, and the state vector V of ecological unit i at different time slices is constructed i : n is the number of time slices in ecological unit i; Edge definition: Construct a state transition edge every two moments The edge weight is a combination of perturbation factor weights: Among them, W j Represents the edge weight, that is, the state from t j Evolved to t j+1 The combined impact intensity of the disturbance; Indicates the k-th type of disturbance factor at time t j The value of α k represents the learning weight of the k-th type of disturbance factor; K represents the total number of disturbance factors; EBEG map expression: G i =(V i ,E i ,A i ); in, That is, the state node set; E j ={e j }, that is, the state transfer edge; A i Represents the attribute set of nodes and edges; G i Represents the behavioral evolution map of the i-th ecological unit.

4. The method for monitoring and intelligent management of ecological public welfare forests according to claim 3, characterized in that: The multi-layer state inversion network includes: The forward generator takes the current state, perturbation embedding vector, and response sensitivity factor as input, uses Transformer to model temporal dependencies, and graph convolutional network (GCN) to model spatial structure to achieve future state prediction; The inversion restorer is symmetrical in structure with the forward generator. It uses the reverse Transformer and GCN to reverse the perturbation path of the ecological state. Its training is optimized by a joint loss function consisting of the state reconstruction error and the KL divergence of the perturbation distribution.

5. The method for monitoring and intelligent management of ecological public welfare forests according to claim 4, characterized in that: The ecological response kernel function is in the form of an exponential decay function, which receives the disturbance amount and lag time as input, outputs the ecological response amplitude, and automatically selects the weight and decay rate parameters according to the disturbance type to quantify the impact of the disturbance on the ecological response.

6. The method for monitoring and intelligent management of ecological public welfare forests according to claim 5, characterized in that: The risk tensor map is a three-dimensional structure, representing state abnormality, trend instability and intervention ineffectiveness respectively. State abnormality is calculated by the Euclidean distance from the reference steady-state interval, trend instability is measured by the variance of multi-path derivatives, and intervention ineffectiveness is calculated by the ratio of the state change before and after regulation to the intervention intensity. The three indicators are fused to form a risk score, which is mapped into four risk levels after weighting and superimposed on the ecological unit layer in the form of a heat map.

7. The method for monitoring and intelligently managing ecological public welfare forests according to claim 6, characterized in that: Reinforcement learning recommends using the Deep Q-learning algorithm to construct a state-action space, and combine the ecological risk context and simulation path to perform strategy verification in a digital twin environment, quantitatively evaluate the control effect, select the optimal control plan by maximizing the expected ecological benefits, and update the strategy value function in real time.

8. The method for monitoring and intelligently managing ecological public welfare forests according to claim 7, characterized in that: The regulation deviation distance is the Euclidean distance between the twin's predicted ecological state and the field perception state. If the deviation exceeds the threshold, a linear trend regression based on a multi-period deviation sequence is performed to determine strategy failure, convergence, and insensitivity, and further optimize the twin model parameters using a joint loss function.

9. The method for monitoring and intelligently managing ecological public welfare forests according to claim 8, characterized in that: The knowledge-experience triples are stored in the knowledge base in the form of <state-action-bias>. A strategy-bias mapping table is introduced in the subsequent strategy recommendation to perform feedback-driven adaptive evolution. Expert rule intervention is integrated in multiple failure scenarios to form a strategy generation mechanism that collaborates expert knowledge with model intelligence.

10. An ecological public welfare forest monitoring and intelligent management system, which is used to implement the ecological public welfare forest monitoring and intelligent management method according to any one of claims 1 to 9, characterized in that: include: The ecological multi-source heterogeneous data acquisition module is used to collect ecological monitoring data of public welfare forests in real time from ground monitoring equipment, air-space remote sensing platforms, and ecological Internet of Things nodes, and perform structured preprocessing; The ecological twin construction and modeling module uses a graph attention mechanism to model multi-factor ecological network relationships based on data fusion results. It also constructs a spatiotemporal dynamic simulation model of ecological behavior, achieving virtual-reality mapping and comparison, and supporting the prediction of the future state of the ecosystem. The ecological risk identification and intelligent regulation module uses multi-scale clustering and ecological disturbance identification algorithms to identify potential degradation areas based on twin prediction results and historical trend data, and combines reinforcement learning or expert knowledge to generate ecological regulation strategies tailored to local conditions; The ecological feedback evaluation and adaptive optimization module is used to perceive the actual ecological response after the implementation of regulation, build a multi-period closed-loop feedback model, and dynamically correct the ecological model parameters through the deviation between the regulation effect and the twin prediction results, forming a prediction-regulation-feedback-adaptive ecological governance closed-loop mechanism.

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