An ecological public welfare forest monitoring and intelligent management system and method

Abstract

The application 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 following steps: collecting multi-dimensional ecological data in real time and performing data fusion; based on the data fusion result, an ecological behavior evolution graph and a multi-layer state inversion network are established by adopting a graph attention mechanism, a dynamic ecological twin is formed, ecological system state prediction, historical disturbance inversion and future behavior simulation are realized; a risk tensor graph is constructed in combination with three dimensions of state abnormality, trend instability and intervention invalidity, a dynamic hierarchical heat map is generated, and a personalized regulation strategy is recommended by using reinforcement learning; finally, the actual regulation effect is perceived through a feedback mechanism, the twin model parameters are dynamically corrected by using deviation analysis, and a knowledge base is constructed to drive strategy self-optimization based on historical strategies and effects; the application forms a sensing-modeling-regulation-feedback closed loop, and the precision and adaptability of ecological management are improved.

Description

Technical Field

[0001] This invention relates to the field of forestry information technology, specifically to an ecological public welfare forest monitoring and intelligent management system and method. Background Technology

[0002] Ecological public welfare forests are important natural resources for maintaining regional ecological balance, enhancing biodiversity, and promoting carbon sequestration capacity. Their scientific monitoring and intelligent management are of great significance for achieving sustainable development of the ecological environment. With the rapid development of information technology and sensing technology, ecological data exhibits characteristics of multi-source heterogeneity and high-dimensional dynamic changes. There is an urgent need to establish efficient data fusion and intelligent analysis methods to accurately reflect the complex state and evolution trend of ecosystems.

[0003] Chinese invention patent application CN119623776A discloses a smart garden management method and system based on big data technology. This method involves collecting multi-source heterogeneous data from within the garden in real time through an environmental sensing network, integrating and comparing this data to obtain a garden ecological profile. Using this ecological profile, the current ecological health of the garden is assessed, and potential short-term ecological change trends are predicted, generating an ecological change trend prediction report. Based on the ecological change trend prediction report, an optimal solution for automatic adjustment is sought, generating a resource allocation plan and a visitor experience optimization plan. Based on the resource allocation plan and visitor experience optimization plan, a continuous learning mechanism is established, a periodic 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 enhancing overall visitor satisfaction.

[0004] Digital twin technology for ecological behavior, by constructing a virtual mapping of the ecosystem, enables the spatiotemporal dynamic simulation and prediction of ecological processes, providing solid support for scientific decision-making. Addressing the complexity and variability of ecosystems, it facilitates risk identification and intelligent regulation strategy generation, enabling early warning and precise intervention for potential ecological degradation and environmental disturbances, thus improving the effectiveness and response speed of ecological governance. Furthermore, by establishing a feedback mechanism for ecological regulation effects and a multi-cycle closed-loop adaptive optimization method, it achieves dynamic adjustment and continuous optimization of ecological governance solutions, promoting the stable and healthy development of ecosystems. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the background technology by proposing an intelligent management system and method for monitoring and managing ecological public welfare forests.

[0006] The technical solution of this invention: A method for monitoring and intelligent management of ecological public welfare forests, comprising the following specific implementation steps:

[0007] S1. Collect ecological monitoring data of ecological public welfare forests, construct a unified tensor expression of multi-source heterogeneous data, normalize, resample and align ecological semantics in the spatiotemporal of each modality of data, and generate an ecological disturbance index matrix based on the combination of multimodal ecological factors. Combine the above matrix with spatial coding, time window and plot attributes to form a four-dimensional ecological feature tensor.

[0008] S2. Based on the ecological feature tensor, construct an ecological behavior evolution map, use graph attention mechanism and time series modeling technology to extract the spatiotemporal evolution dependency of the state, and construct a multi-layer state inversion network including a forward generator and a backward restorer to complete the prediction and simulation of the ecosystem state, the backward restoration of historical disturbances, and the diversified generation of future evolution paths.

[0009] S3. Based on the multipath prediction results generated by the ecological twin simulation, risk indicators of three dimensions—state anomaly, trend instability, and intervention response failure—are extracted to construct a risk tensor map. A weighted scoring method is used to generate a risk level distribution, a dynamic heat map is generated, and reinforcement learning is combined to recommend control strategies.

[0010] S4. After implementing control measures, obtain the actual ecological feedback status and conduct deviation analysis with the twin's predicted status. Construct a control deviation distance index, perform regression evaluation based on the multi-period deviation change trend, and dynamically correct the twin model's parameters by combining the loss function constructed from deviation fitting and volatility variance. Furthermore, construct a knowledge triplet by combining the control strategy, context, and feedback results to drive the strategy's continuous self-evolution and optimization.

[0011] The preferred method for constructing the four-dimensional ecological feature tensor is as follows:

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

[0013] The Phenological Distribution Tensor Dynamic Registration (PTR) method is adopted. Based on the time series of phenological events in ecological plots, the time series of remote sensing image features are aligned by minimizing the objective function, and local observation delay compensation is introduced to correct the time error of crowdsourced data.

[0014] By weighted combination of four factors—normalized vegetation index variation, species diversity fluctuation, temperature anomaly deviation, and humidity anomaly index—an ecological disturbance index matrix (E-DIM) is constructed to dynamically quantify the disturbance intensity of each ecological unit over a specific time period.

[0015] By integrating the Ecological Disturbance Index Matrix (E-DIM) as the main feature axis, and fusing spatial plot coding, normalized multimodal ecological perception data, ecological unit plot attributes, and time node additional attributes, a four-dimensional ecological feature tensor with spatial-temporal-feature dimensions is constructed.

[0016] The preferred process for constructing an ecological behavior evolution map is as follows:

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

[0018] Edge definition: A state transition edge is constructed every two time intervals. The edge weights are combinations of perturbation factor weights:

[0019] Among them, W j This represents the edge weight, i.e., the state from t. j Evolved to t j+1 The overall intensity of the impact of the disturbance; This indicates that the k-th type of disturbance factor occurs at time t. j The value of α; k This represents the learning weight of the k-th perturbation factor; K represents the total number of perturbation factors.

[0020] EBEG spectral representation: G i =( V i,E i A i );

[0021] in, That is, the set of state nodes; E j ={e j}, that is, the state transition edge; A i G represents the set of attributes for nodes and edges. i This represents the behavioral evolution map of the i-th ecological unit.

[0022] Preferably, the multi-level state inversion network includes:

[0023] The forward generator takes the current state, perturbation embedding vector, and response sensitivity factor as input, uses Transformer to model time dependency, and Graph Convolutional Network (GCN) to model spatial structure, and achieves future state prediction.

[0024] The inversion restorer, which is structurally symmetrical with the forward generator, uses a reverse Transformer and GCN to infer the perturbation path of the ecological state. Its training is optimized by a joint loss function composed 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 takes the disturbance amount and lag time as input, outputs the ecological response amplitude, and automatically selects 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, representing state anomaly, trend instability, and intervention ineffectiveness, respectively. State anomaly is calculated by Euclidean distance from the reference steady-state interval, trend instability is measured by variance of multipath derivative, and intervention ineffectiveness is calculated by the ratio of state change before and after regulation to intervention intensity. The three indicators are integrated to form a risk score, which is then weighted and mapped to a four-level risk level, and superimposed on the ecological unit layer in the form of a heat map.

[0027] Preferably, reinforcement learning recommends using the Deep Q-learning algorithm to construct a state-action space, and combine it with the ecological risk context and simulated path to perform policy verification in a digital twin environment, quantitatively evaluate the regulation effect, select the optimal regulation scheme by maximizing the expected ecological benefits, and update the policy value function in real time.

[0028] Preferably, the control deviation distance is the Euclidean distance between the predicted ecological state of the twin and the perceived state in the field. If the deviation exceeds the threshold, linear trend regression based on multi-period deviation sequence is performed to determine the failure, convergence and insensitivity of the strategy, and then the twin model parameters are further optimized with a joint loss function.

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

[0030] The technical solution of this invention: An ecological public welfare forest monitoring and intelligent management system, used to execute the above-mentioned ecological public welfare forest monitoring and intelligent management method, comprising:

[0031] The ecological multi-source heterogeneous data acquisition module is used to collect ecological monitoring data of ecological public welfare forests in real time from ground monitoring equipment, air-space-ground remote sensing platforms, and ecological Internet of Things nodes, and to perform structured preprocessing.

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

[0033] The ecological risk identification and intelligent regulation module identifies potential degradation areas based on twin prediction results and historical trend data, using multi-scale clustering and ecological disturbance identification algorithms, and combines reinforcement learning or expert knowledge to generate site-specific ecological regulation strategies.

[0034] The ecological feedback assessment and adaptive optimization module is used to sense the actual ecological response after the implementation of regulation, construct a multi-cycle closed-loop feedback model, and dynamically correct the ecological model parameters by the deviation between the regulation effect and the twin prediction results, forming a closed-loop mechanism of ecological governance of prediction-regulation-feedback-adaptation.

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

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

[0037] By using the digital twin modeling mechanism for ecological behavior, a high-fidelity mapping model between ecosystem state and evolution process was established, which significantly enhanced the dynamic prediction capability of ecological process modeling.

[0038] Through the ecological risk identification and intelligent regulation strategy generation mechanism, ecological degradation trends can be detected in a timely manner. Combined with reinforcement learning and expert knowledge reasoning, highly adaptive governance strategies can be automatically generated, thereby improving the scientific nature and pertinence of ecological intervention measures.

[0039] By using the feedback mechanism of ecological regulation effect and the multi-cycle closed-loop adaptive optimization mechanism, a sustainable strategy optimization path was constructed, realizing the dynamic and 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 control, and significantly improved management efficiency. It is suitable for refined, intelligent and sustainable monitoring and governance scenarios of large-scale ecological public welfare forests. Attached Figure Description

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

[0042] Figure 2 This is a flowchart of a method for monitoring and intelligent management of ecological public welfare forests proposed in this invention. Detailed Implementation

[0043] Example 1, such as Figure 1 As shown, the present invention proposes an ecological public welfare forest monitoring and intelligent management system, which includes: 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 assessment and adaptive optimization module.

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

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

[0046] The ecological risk identification and intelligent regulation module identifies potential degradation areas based on twin prediction results and historical trend data using multi-scale clustering and ecological disturbance identification algorithms. It also generates site-specific ecological regulation strategies by combining reinforcement learning or expert knowledge, including but not limited to vegetation reconstruction, irrigation regulation, and species intervention.

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

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

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

[0050] S11. Data is collected synchronously from multiple sensing channels in the ecological public welfare forest, including but not limited to:

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

[0052] Aerial data: Images inspected by drones equipped with multispectral cameras, with a resolution higher than 30cm, 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, air temperature, wind speed, air humidity, and CO2 concentration;

[0054] Crowdsourced data: Images, audio recordings, and wildlife sightings uploaded by ecological volunteers, supplemented with geolocation and timestamps;

[0055] To achieve a unified representation of data from different sources, a normalization and resampling mechanism is adopted to construct a unified tensor representation:

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

[0057] S12. Using the "Phenology-Tensor Registration (PTR) mechanism," spatiotemporal and ecological semantics are co-aligned for each modality of data. Considering the temporal phase consistency of plant phenological characteristics in different data modalities, the following objective function is constructed:

[0058]

[0059] Where, φ i (t) represents the time series of phenological events (such as the time points of flowering and budding) for the i-th ecological plot; ψ i(t; θ) represents the time series of image features of the same remote sensing image; θ represents the alignment parameter of the image time feature series, characterizing the time offset / scaling factor of the remote sensing image; δ i This represents local observation delay compensation, used to correct the time error between public uploads and actual occurrences; n represents the total number of ecological units;

[0060] S13. To quantify abnormal fluctuations in the ecological state, an 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 ΔNDVI represents the intensity of ecological disturbance in the i-th ecological unit during the j-th time period. ij Indicates the degree of variation in the Normalized Difference Vegetation Index (NDVI) (rate of change); Var(S) ij ) represents the fluctuation of species diversity (variance of species distribution); TempDev ij HumAnom indicates the degree of temperature anomaly deviation, representing the difference between the temperature and the average temperature for the same period over many years. ij The humidity anomaly index measures the degree of deviation from the historical humidity distribution for 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 principal feature axis, and combining it with spatial plot coding, ecological factor groups, and time window dimensions, construct a four-dimensional ecological feature tensor:

[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 parcel attributes of the i-th ecological unit, including but not limited to slope, soil type, and irrigation method; t Additional attributes indicating time points include, but are not limited to, solar terms (such as the Beginning of Spring and Rain Water), agricultural time windows, and maintenance cycle markers.

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

[0067] S21. Construct an Ecological Behavior Evolutionary Map (EBEG) to establish a dynamic evolutionary structure of ecological states changing 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 position in each time slice t. j state vector Each node represents the ecological state at a specific moment, constructing the state vector of ecological unit i in different time slices. n is the number of time slices for ecological unit i;

[0069] Edge definition: A state transition edge is constructed every two time intervals. The edge weights are combinations of perturbation factor weights:

[0070] Among them, W j This represents the edge weight, i.e., the state from t. j Evolved to t j+1 The overall intensity of the impact of the disturbance; This indicates that the k-th type of disturbance factor occurs at time t. j The value of α; k The learning weight represents the k-th type of perturbation factor, reflecting its importance to the change in the ecosystem; K represents the total number of perturbation factors (including but not limited to meteorological, hydrological, soil, and anthropogenic dimensions);

[0071] EBEG spectral representation: G i =(V i E i A i );

[0072] in, That is, the set of state nodes; E j ={e j}, that is, the state transition edge; A i G represents the set of attributes of nodes and edges, including but not limited to perturbation factors, intervention methods, and multimodal information of biological responses; iThis represents the behavioral evolution map of the i-th ecological unit, i.e., its state transition structure over time.

[0073] S22. Construct a multi-level state inversion network (MSRN) to realize three inference paths for ecological state prediction, reconstruction, and inversion:

[0074] A1. Construct a forward generator F(·) with the current state s t , Perturbation event embedding vector e t The response sensitivity parameter γ is used as input to predict the future state:

[0075] in, This represents the predicted ecological state vector, i.e., the simulation result at time t+h; s t γ 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 disturbances (between 0 and 1, used to control the degree of model response to interventions);

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

[0077] A2. Construct an inversion restorer R(·) to inversely deduce the disturbance paths that may have caused the formation of the observed ecological state:

[0078] in, θ' represents the predicted combination of perturbations occurring at time tk, which is an inference of the cause of past state transitions; θ' represents the set of learnable inversion path parameters, used to represent the combination of perturbation types, intensities, durations, etc.; ||·|| represents the Euclidean distance, used to measure the reconstruction error between the inverted state and the actual state.

[0079] It should be noted that the inversion restorer R(·) is a reverse neural network architecture with a structure symmetrical to the forward generator F(·). Its core idea is to use a symmetric modeling mechanism to deduce the possible perturbation factors and their paths that led to the formation of the current (or future) ecological state, starting from the current (or future) ecological state. Its input-output form is a mirror image of the forward generator F(·). The network hierarchical structure is mirrored in terms of encoding dimension, number of layers, and attention mechanism, which is beneficial for collaborative training and cross-validation. The input is the current observed state vector s. t The attention location encoding of the perturbation time window (i.e., the potential impact of past time on the current state) is added. The reverse Transformer module is used to simulate the cumulative contribution of perturbation to the state. The attention mechanism is built with the goal of "interpreting the current state" by the past state. It supports multi-scale perturbation delay modeling. The reverse GCN module projects the predicted perturbation path back into the ecological graph structure to find possible historical behavior paths. The GCN runs on the candidate subgraph of perturbation path to find the most likely perturbation combination (not maximum likelihood) and finally decodes the perturbation 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 together:

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

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

[0083] Where, Δs t The value represents the magnitude of change in ecological state within time t; K represents the total number of disturbance factors; β k τ represents the response weight, used to quantify the degree of impact of the disturbance on the current ecological unit (learnable); kIndicates the lag time of the disturbance, that is, the delay 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 disturbances and lag times into ecological response values; 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 state can be dynamically updated over time, provide real-time feedback, predict behavior, and be controlled, specifically as follows:

[0085] B1. Initialize the twin:

[0086] Input: Historical graph G i Current state s t Disturbance factor d k ;

[0087] Output: Initial state of the twin

[0088] B2. Behavioral Path Generation (Multi-path): Using MSRN to simulate N perturbation paths, generate corresponding state sequences.

[0089] Among them, P n This represents the nth ecological behavior path (used to simulate development trends under different combinations of disturbances / interventions); H represents the predicted state of the ecological twin at time t+h; H represents the prediction step size, i.e., the future evolution time of the twin.

[0090] B3. State correction and synchronization: Input current real-time sensing data. Kalman filtering is used for state updates:

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

[0092] S3. The ecological risk identification and intelligent regulation module constructs an intelligent ecological risk identification mechanism based on twin behavior evolution and multi-factor response maps. Combining three perspectives—state anomaly, trend instability, and intervention ineffectiveness—it generates a dynamic hierarchical risk map and recommends personalized and regionalized regulation strategies accordingly. The specific implementation process is as follows:

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

[0094] Extract each simulated 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 This represents the aggregation of the risk state of path i at time t across three dimensions; r t (1) Indicators representing ecological state anomalies; r t (2) Indicator of ecological trend instability; r t (3) Indicators representing the failure 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 factor is defined as:

[0101] State anomaly index r t (1) To measure the degree of abrupt changes in the state of ecological variables:

[0102] in, μ represents the predicted ecological state vector of the i-th path at time t, including but not limited to NDVI, biomass, and surface temperature; ref This represents the mean value of the corresponding state during the historical steady-state period (the undisturbed seasons of the past 5 years), used to construct a reference baseline for ecological normality; σ ref This represents the standard deviation of the state variables during the aforementioned steady-state period;

[0103] Trend Instability Indicator

[0104] in, This represents the instantaneous rate of change (derivative) of the j-th path at time t; This represents the average rate of change of all paths over time t;

[0105] Ecological intervention response failure indicators Where, ρ recovery (t) represents the ecological restoration rate, which is the percentage increase in ecological condition after a certain intervention; This represents the ecological state (twin path prediction) on day δ after the intervention; Indicates the state before intervention; A t Indicators representing the intensity of intervention are externally inputted, including but not limited to ecological irrigation water volume, fertilizer volume, and enclosure area;

[0106] S32. Based on the constructed risk tensor map, it is further converted into a risk level scoring system, and spatially mapped by combining it with the geographic ecological unit layer to form a risk heat map with real-time updating capability, specifically:

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

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

[0109] A multi-level risk classification standard table is set, as shown in Table 1:

[0110] Table 1. Multi-level Risk Level Classification Standards

[0111]

[0112] Risk levels are spatially labeled onto 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 and contextualized intelligent governance recommendations. Specifically:

[0114] C1. Construct the state-action space using risk level and disturbance type as inputs:

[0115] state s t Current ecological status of the risk area;

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

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

[0118] Wherein, π(a t |s t ,r t ) indicates that the regulation strategy recommendation model is in state s t and risk context r t Take action a t The probability is a decision function learned by a reinforcement learning model (Deep Q-Learning); r t This indicates the risk context, i.e., 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, i.e., the expected benefit function when taking regulatory action a. t Then, we expect the future state of the ecosystem to be... t+1 The positive benefits it brings;

[0119] C3. Virtually run and control actions in a twin environment to verify whether the risk score S can be effectively managed. i,t Reduce to a safe range;

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

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

[0122] S41. After implementing control strategies (including but not limited to vegetation restoration, species introduction, and wetland replenishment), the ecological change status of the controlled area is monitored in real time, and the expected improvement goals are assessed. A quantitative control deviation index is generated by aligning the results with the predictions in the ecological twin and comparing their status over time. Specifically:

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

[0124] in, This represents the vector of actual ecological state values ​​observed at time point t+τ. (i.e., NDVI, temperature, humidity, carbon sequestration, etc.);

[0125] Take ecological twin prediction values That is, the expected improvement trajectory of the system;

[0126] Define the control deviation distance:

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

[0128] If δ τ >θ t If the set deviation threshold is met, it is determined that the control effect has not met expectations, and the feedback and backtracking process will begin.

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

[0130] Constructing a multi-period bias 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 bias 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] Where, δ total This represents the set of control deviation values ​​recorded over different time periods, used to construct a trend; α represents the intercept term of the control deviation trend model, reflecting the initial control response deviation level; β represents the regression slope, i.e., the speed and direction of the control deviation change over time, used to determine whether the control is "continuously improving" or "gradually failing"; ε i This represents the residual term in the i-th regression, i.e., the error term or fluctuation component;

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

[0137] Construct a model and refine the objective function:

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

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

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

[0141] S44. After revising the twin model and obtaining the actual response results, the historical control strategies, environmental context, and effect feedback are formed into knowledge triples and stored in the experience base to construct the intelligent evolution basis of the control strategies, specifically:

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

[0143] Introducing an intelligent policy evolutionary learning mechanism (reinforcement learning):

[0144]

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

[0146] For regions that are repeatedly invalid, expert rules are introduced to intervene and enhance the adaptability of the strategy;

[0147] Among them, <s t ,a t ,δ τ > represents an experiential memory unit, that is, the biased result produced by taking a certain strategy in a certain state; This represents a new recommended strategy based on current feedback and historical learning; u(s) t+τ |at ,δ t ) represents the regulation utility function, which evaluates the potential future gains of a strategy given a state and bias; argmax(·) represents selecting the strategy that maximizes utility.

[0148] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, 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. An ecological public welfare forest monitoring and intelligent management method, characterized in that, The specific implementation steps include the following: S1, collect ecological monitoring data of ecological public welfare forests, construct a unified tensor expression of multi-source heterogeneous data, normalize, resample and align the ecological semantic space-time of each modal data, and generate an ecological disturbance index matrix based on multi-modal ecological factor combination; and the above matrix and the space coding, time window and land attribute are jointly constructed to form a four-dimensional ecological feature tensor; The four-dimensional ecological feature tensor is constructed as follows: The ecological monitoring data of the ecological public welfare forests is synchronously collected through air-based remote sensing satellites, aerial unmanned aerial vehicles, ground monitoring stations and crowdsourcing, and a unified tensor expression is constructed by using normalization and resampling mechanisms; An anabasis distribution tensor dynamic registration method PTR is used to take the ecological land anabasis event time sequence as a reference, align the remote sensing image feature time sequence by minimizing the objective function, and introduce local observation delay compensation to correct the time error of the crowdsourcing data; An ecological disturbance index matrix E-DIM is constructed by weighted combination of the normalized vegetation index variation degree, species diversity fluctuation, temperature abnormal deviation degree and humidity abnormal index, and the disturbance intensity of each ecological unit in a specific time period is dynamically quantified; The ecological disturbance index matrix E-DIM is integrated as the main feature axis, the space land coding, normalized multi-modal ecological perception data, ecological unit land attribute and time node additional attribute are fused, and a four-dimensional ecological feature tensor of space-time-feature multidimension is constructed; S2, based on the ecological feature tensor, an ecological behavior evolution graph is constructed, the state space-time evolution dependence is extracted by using a graph attention mechanism and a time sequence modeling technology, a multi-layer state inversion network including a forward generator and a backward restorer is constructed, and the prediction simulation of the ecological system state, the backstepping recovery of the historical disturbance and the diversification generation of the future evolution path are completed; S3, according to the multi-path prediction results generated by the ecological twin simulation, risk index of three dimensions of state abnormality, trend instability and intervention response invalidity is extracted to construct a risk tensor graph, a risk level distribution is generated by using a weighted scoring method, a dynamic heat map is generated, and a regulation strategy is recommended combined with reinforcement learning; S4, after implementing the regulation measures, the actual ecological feedback state is obtained, the deviation analysis is performed on the twin prediction state, the regulation deviation distance index is constructed, the regression evaluation is performed based on the multi-cycle deviation change trend, the loss function constructed based on the deviation fitting and the fluctuation variance is used to dynamically correct the parameters of the twin model, and the regulation strategy, the context and the feedback result are combined to form a knowledge triple, which drives the continuous self-evolution and optimization of the strategy. 2.The ecological public welfare forest monitoring and intelligent management method according to claim 1, characterized in that, The ecological behavior evolution graph construction process is as follows: Node definition: for each ecological unit i, extract its state vector at each time slice t j Each node represents the ecological state at a specific time, and the state vector V i of ecological unit i at different time slices is constructed as follows: ; n is the number of time slices of ecological unit i.​ Edge definition: construct a state transition edge between every two time points , and the edge weight is the combination of disturbance factor weights: ; wherein, denotes the edge weight, i.e. the state transition from t j to t j+1 the combined influence strength of the disturbances; denotes the value of the kth disturbance factor at time t j ; denotes the learning weight of the kth disturbance factor; K denotes the total number of disturbance factors; The ecological behavior evolution graph (EBEG) map expresses: ; wherein, i.e., a set of state nodes; i.e., a state transition edge; A i denotes a set of attributes of nodes and edges; denotes the behavior evolution graph of the i-th ecological unit. 3.The ecological public welfare forest monitoring and intelligent management method according to claim 2, characterized in that, The multi-layer state inversion network includes: The forward generator takes the current state, disturbance embedding vector and response sensitive factor as input, uses the Transformer to model the time dependence, and uses the graph convolution network GCN to model the spatial structure, so as to realize the future state prediction; The inversion restorer is symmetrical to the forward generator, uses the reverse Transformer and GCN to backstep the disturbance path of the ecological state, and its training is optimized by a joint loss function composed of state reconstruction error and disturbance distribution KL divergence.

4. The ecological public welfare forest monitoring and intelligent management method according to claim 3, characterized in that, The ecological response kernel function is an exponential decay function, which takes 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 type of disturbance, to quantify the impact of disturbance on ecological response.

5. The ecological public welfare forest monitoring and intelligent management method according to claim 4, characterized in that, The risk tensor atlas is a three-dimensional structure representing state abnormality, trend instability, and intervention inefficiency. State abnormality is calculated by the Euclidean distance from the reference steady-state interval, trend instability is measured by the variance of multiple path derivatives, and intervention inefficiency is calculated by the ratio of state change before and after regulation to intervention intensity. The three indicators are fused to form a risk score, which is mapped to a four-level risk grade after weighting and superimposed on the ecological unit layer in the form of a heat map.

6. The ecological public welfare forest monitoring and intelligent management method according to claim 5, characterized in that, The reinforcement learning recommendation adopts the Deep Q-learning algorithm to construct a state-action space, and combines the ecological risk context and simulation path to perform strategy verification in the digital twin environment, to quantitatively evaluate the regulation effect, and select the optimal regulation scheme by maximizing the expected ecological benefit and update the strategy value function in real time.

7. The ecological public welfare forest monitoring and intelligent management method according to claim 6, characterized in that, The regulation bias distance is the Euclidean distance between the predicted ecological state of the twin and the actual perceived state. If the bias exceeds the threshold, a linear trend regression based on multiple cycle bias sequences is performed to determine the strategy failure, convergence, and insensitive state, and further optimize the twin model parameters using a joint loss function. 8.The method according to claim 7, characterized in that, The knowledge and experience triplets are stored in the knowledge base in the form of <state-action-bias>, and the strategy-bias mapping table is introduced in subsequent strategy recommendation for feedback-driven adaptive evolution, and expert rule intervention is integrated in multiple failure scenarios to form a strategy generation mechanism that combines expert knowledge and model intelligence.

9. An ecological public welfare forest monitoring and intelligent management system for performing the ecological public welfare forest monitoring and intelligent management method of any one of claims 1-8. The system includes: An ecological multi-source heterogeneous data acquisition module for real-time acquisition of ecological monitoring data of ecological public welfare forests from ground monitoring equipment, space-ground-remote sensing platforms, and ecological Internet of Things nodes, and structured preprocessing; An ecological twin construction and modeling module that builds a multi-factor ecological network relationship using a graph attention mechanism based on data fusion results, and constructs a spatio-temporal dynamic simulation model of ecological behavior to realize virtual-real mapping and contrast, and support prediction of future states of the ecological system; An ecological risk identification and intelligent regulation module that identifies potential degradation areas using multi-scale clustering and ecological disturbance identification algorithms based on twin prediction results and historical trend data, and generates site-specific ecological regulation strategies using reinforcement learning or expert knowledge; An ecological feedback evaluation and adaptive optimization module that perceives the actual ecological response after regulation implementation, constructs a multi-cycle closed-loop feedback model, and dynamically corrects ecological model parameters based on the deviation between regulation effect and twin prediction results, to form an ecological governance closed-loop mechanism of prediction-regulation-feedback-adaptation.

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