Tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning

Abstract

The invention relates to the technical field of biomass and carbon sink collaboration, in particular to a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning. According to the technical scheme, the system comprises a multi-modal data acquisition system, a space-time coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic evaluation system, a multi-objective decision optimization engine and an intelligent interaction execution platform. Data are comprehensively and safely acquired by means of a multi-modal data acquisition system, the data quality is optimized through a space-time coupling preprocessing module, biomass and carbon sink are accurately analyzed by means of an ecological coupling deep learning model and a carbon sink dynamic evaluation system, a scientific decision is made through a multi-objective decision optimization engine, execution is guaranteed, and the method is suitable for large-scale popularization and application. Convenient interaction and efficient execution are realized by means of the intelligent interaction execution platform, so that the accuracy, scientificity and execution efficiency of tropical rainforest biomass and carbon sink collaborative optimization decision making are remarkably improved, and ecological protection and sustainable development of tropical rainforests are powerfully promoted.

Description

Technical Field

[0001] The present invention relates to the field of biomass and carbon sink synergy technology, and in particular to a tropical rainforest biomass and carbon sink synergy optimization decision-making system based on deep learning. Background Art

[0002] In the field of tropical rainforest ecosystem research and management, it is crucial to achieve coordinated optimization of biomass and carbon sinks, but it is currently facing comprehensive technical difficulties. Existing technologies have a series of problems in data acquisition, processing and analysis, model construction, decision-making and execution, making it difficult to fully and accurately grasp the dynamic changes of tropical rainforest biomass and carbon sinks, and it is impossible to formulate scientific and effective coordinated optimization decision-making plans.

[0003] From a data perspective, the tropical rainforest environment is complex, and traditional data collection methods are difficult to fully obtain canopy-surface-underground three-dimensional ecological data. Different data sources (such as satellite images, drone data, and ground sensor data) have format differences and mismatched spatiotemporal resolutions, making data fusion difficult and difficult to form a unified, high-quality data set, which cannot provide a solid foundation for subsequent analysis.

[0004] In terms of data processing and analysis, the spatial registration and temporal alignment of multi-source data are not accurate enough to accurately reflect the spatiotemporal variation characteristics of ecosystems. Moreover, in the face of special situations such as extreme climate, the lack of training data leads to poor generalization of the model and the inability to accurately predict changes in biomass and carbon sinks.

[0005] In terms of model construction, the existing models are not accurate enough in extracting the heterogeneous characteristics of tropical rainforest vegetation, making it difficult to quantify the ecological coupling effects of vegetation communities and failing to fully consider the mechanisms of ecological processes, resulting in large errors in biomass and carbon sink prediction results and being unable to meet actual decision-making needs.

[0006] There are also defects in the decision-making and execution links. The lack of a scientific and reasonable multi-objective decision-making optimization model makes it difficult to balance the relationship between ecological protection and economic development. The ecological impact rehearsal of the decision-making plan is not good, and the verification and implementation of the carbon sink plan lack an effective supervision and guarantee mechanism. In addition, the lack of an efficient intelligent interactive execution platform makes it impossible to achieve real-time decision-making of mobile terminals in the field, and it is difficult to trace key influencing factors, which is not conducive to timely adjustment of decision-making strategies.

[0007] In summary, this application proposes a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning. Summary of the invention

[0008] The purpose of the present invention is to address the problem in the background technology that it is difficult to comprehensively and accurately grasp the dynamic changes of tropical rainforest biomass and carbon sinks, and to propose a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning.

[0009] Technical solution of the present invention: A collaborative optimization decision-making system for tropical rainforest biomass and carbon sink based on deep learning, comprising:

[0010] Multi-modal data acquisition system: Configure a cross-platform heterogeneous data integration unit to integrate sub-meter hyperspectral satellite images, unmanned aerial vehicle light detection and ranging (LiDAR) point clouds, and ground Internet of Things sensor networks, and securely acquire three-dimensional ecological data of the canopy-surface-underground through a quantum encryption channel;

[0011] Spatio-temporal coupling preprocessing module: Use an improved cycle generative adversarial network (CycleGAN) network to achieve spatial registration of multi-source data, combine a dynamic time warping long short-term memory network-attention mechanism (LSTM-Attention) model to solve the problem of temporal alignment, and integrate an adversarial generative network to expand extreme climate training data;

[0012] Ecological coupling deep learning model: Construct a three-dimensional convolutional graph attention network, extract vegetation heterogeneity features through deformable convolution, combine the graph attention mechanism to quantify the ecological coupling effect of vegetation communities, and design a two-stream gating fusion mechanism to integrate spatio-temporal feature streams;

[0013] Carbon sink dynamic assessment system: Based on the Bayesian neural network framework, achieve biomass probability prediction, fuse δ13C isotope tracer data to analyze the carbon source contribution ratio, and embed a physical constraint layer to couple the ecological process mechanism equation;

[0014] Multi-objective decision-making optimization engine: Use the framework of the third-generation non-dominated sorting genetic algorithm (NSGA-III) to construct an ecological-economic Pareto front model, integrate a digital twin simulator to pre-demonstrate the ecological impact of decisions, and automatically verify and execute carbon sink solutions through blockchain smart contracts;

[0015] Intelligent interaction execution platform: Deploy a mixed reality three-dimensional visualization interface to support holographic terrain interaction, establish an edge-cloud collaborative architecture to achieve real-time decision-making for field mobile terminals, and trace the spatial distribution of key influencing factors in combination with a feature inversion network.

[0016] Optionally, the ground Internet of Things sensor network includes:

[0017] Canopy vertical profile monitoring array: Composed of liftable nodes, each node integrates a chlorophyll fluorescence sensor, a multi-spectral reflectometer, and a microenvironment recorder, and is deployed on a 30m high tower at an interval of 0.5m;

[0018] Underground biomass monitoring unit: Use a carbon fiber reinforced probe, implant it to a depth of 2m, and be equipped with an impedance type fine root detector and a micro-CT scanning module;

[0019] Dynamic Ad Hoc Network Protocol: It adopts the Time-Synchronized Channel Hopping (TSCH) protocol, completes the network topology reconstruction of 200 nodes within 1.5 seconds, and realizes ultra-long-distance transmission of 10 km level based on the Long Range Wide Area Network (LoRaWAN) architecture.

[0020] Optionally, the multi-source data fusion adopts a hierarchical feature alignment method:

[0021] First-level alignment: Through the algorithm of the third-generation real-time visual SLAM system based on ORB features (ORB-SLAM3), the LiDAR point cloud of the UAV is sub-pixel registered with the satellite image, and a feature matching model based on Scale-Invariant Feature Transform - Generalized Matching Strategy (SIFT-GMS) is established;

[0022] Second-level alignment: Develop an adaptive kernel function transformer:

[0023]

[0024] Among them, K(x, y): kernel function value, used to measure the similarity between data points x and y;

[0025] ‖x - y‖: Euclidean distance between data points x and y;

[0026] σ: bandwidth parameter of the kernel function, dynamically adjusted by Bayesian optimization;

[0027] Map the heterogeneous data collected by the ground sensor network to a unified feature space, and the kernel function parameters are dynamically adjusted by Bayesian optimization;

[0028] Third-level alignment: Construct a three-dimensional spatio-temporal cube, adopt the Trilinear interpolation algorithm to fuse multi-scale data, and embed a differentiable rendering layer to realize cross-modal feature visualization.

[0029] Optionally, the improved Cycle Generative Adversarial Network (CycleGAN) includes:

[0030] Adaptive attention mechanism: Embed a coordinate attention module in the generator U-Net architecture to capture spatially position-sensitive features through 2D global pooling;

[0031] Multi-scale discriminator: Adopt a cascade structure composed of 3 Patch Generative Adversarial Network (PatchGAN) discriminators, and introduce spectral normalization constraints;

[0032] Dynamic cycle consistency loss: Design a cycle loss function with adaptive weights, and the formula is:

[0033] L cyc =λ1·‖G AB (G BA(x)) - x‖1 + λ2·SSIM(G AB (G BA (x)), x)

[0034] Among them, G AB and G BA : respectively represent the generators from domain A to domain B and from domain B to domain A; x: input data sample; ‖·‖1: L1 norm; SSIM(·): structural similarity index, and λ1 and λ2 are learnable weight parameters used to balance the importance of L1 loss and SSIM loss.

[0035] Optionally, the three-dimensional graph convolutional attention network (3D-CGAT) specifically includes:

[0036] Vegetation patch influence domain division: Based on the Voronoi diagram algorithm, a dynamic influence domain is generated with the dominant tree species as the center, and the radius R = 5 × tree height + 3 × crown width;

[0037] Heterogeneous graph convolutional layer: Defines three types of nodes and five types of edges, and uses the second-generation graph attention network (GATv2) convolutional kernel to achieve feature propagation. The three types of nodes include the tree layer, the shrub layer, and the surface layer, and the five types of edges include light competition, root interaction, nutrient transfer, seed diffusion, and microbial association;

[0038] Dynamic graph structure update: Update the graph topology through transfer learning every six months, use the Gromov-Wasserstein distance to measure the graph structure similarity, and set the update threshold to 0.35, Gromov-Wasserstein distance:

[0039]

[0040] Among them, G1 and G2: two graph structures, respectively representing the vegetation patch influence domains at different time points;

[0041] γ: joint probability distribution, and respectively represent the distance metrics between node pairs in graphs G1 and G2; Γ(u1, u2): the set of all possible joint probability distributions, where u1 and u2 are the node distributions of graphs G1 and G2 respectively.

[0042] Optionally, the implementation method of the physical constraint layer is:

[0043] Embed the CENTURY model mechanism equation: Use the soil organic carbon decomposition rate equation as a hard constraint, formula:

[0044]

[0045] Among them, Rate of change of soil organic carbon content over time; k: decomposition rate constant; C: current soil organic carbon content; f(T): temperature correction function, in the form of the Arrhenius equation: where E a is the activation energy, R is the gas constant, and T is the absolute temperature; f(θ): moisture correction function, usually expressed as a non-linear function of soil water content; f(pH): pH correction function, reflecting the effect of pH on microbial activity;

[0046] Construct a hybrid loss function:

[0047] L total = α·L data + β·L physics + γ·L reg

[0048] where, L data : data-driven loss, using mean squared error, used to measure the difference between the model prediction value and the true value; L p g ysics : physical constraint loss; L reg : regularization loss, used to prevent model overfitting, using L2 regularization or weight decay; α, β, γ: weight coefficients, respectively controlling the relative importance of data-driven loss, physical constraint loss, and regularization loss; α = 0.6, β = 0.3, γ = 0.1, L p h ysics contains 21 ecological process conservation equations;

[0049] Develop a differentiable solver: Based on the PyTorch framework, implement automatic differentiation of the mechanism equation, supporting GPU-accelerated computing.

[0050] Optionally, the digital twin simulator includes:

[0051] Multi-resolution modeling framework: At a scale of 1 km 2 scale, use a process-based Biome-Biogeochemical Cycle model (Biome-BGC), at a scale of 1 ha, use an individual tree model, and at a scale of 1 m 2 scale, use a microenvironment fluid model based on the Lattice Boltzmann Method (LBM);

[0052] Cross-scale coupling interface: Develop a dynamic downscaling converter, and achieve parameter transfer through a Wavelet Generative Adversarial Network (Wavelet-GAN) network to ensure the conservation of energy and material fluxes;

[0053] Reinforcement learning training environment: Design a Markov decision process with a 12-dimensional state space and a 7-dimensional action space, and use a Soft Actor-Critic algorithm (SAC) training strategy network.

[0054] Optionally, the blockchain smart contract implementation method includes:

[0055] Carbon sink tokenization protocol: Divide the carbon sink amount per hectare into 1000 Ethereum fungible and non-fungible token standard - number 1155 (ERC-1155) tokens, with each token corresponding to 1 kg CO2 equivalent, and store the satellite image verification data of the corresponding area through IPFS;

[0056] Dynamic Oracle mechanism: Deploy an off-chain oracle based on a trusted execution environment to obtain real-time meteorological data from the National Oceanic and Atmospheric Administration (NOAA) of the United States and remote sensing data from Landsat-9 as the trigger conditions for the smart contract;

[0057] Zero-knowledge verification layer: Use zero-knowledge succinct non-interactive knowledge argument (zk-SNARKs) technology to generate carbon sink amount calculation proofs, with verification time < 2 seconds, proof size < 1 KB, and support for verification in a million-node network.

[0058] Optionally, the edge-cloud collaborative architecture includes:

[0059] Model dynamic slicing technology: Divide the 3D-CGAT network into 12 independently operable sub-modules, and automatically select cloud inference or edge computing according to the network bandwidth;

[0060] Incremental federated learning framework: Each edge node maintains a local model copy, updates the global model every 24 hours through a secure aggregation protocol, and uses differential privacy technology to protect data privacy;

[0061] Disaster recovery mechanism: When a network interruption is detected for more than 5 minutes, automatically switch to an emergency communication mode based on Beidou short messages, and optimize the transmission efficiency to 140 bytes per time.

[0062] Optionally, the feature inversion network adopts:

[0063] Deep Taylor decomposition algorithm: Calculate the contribution degree of each input pixel to the carbon sink prediction result, and generate a heat map with a resolution of 0.5 m;

[0064] Spatio-temporal attention tracing module: Identify key time nodes and spatial regions through a multi-head attention mechanism;

[0065] Uncertainty visualization engine: Use the Marching Cubes algorithm to render a three-dimensional probability distribution cloud map, support dynamic adjustment of the transparency and color mapping of the 95% confidence interval, and probability distribution rendering in the Marching Cubes algorithm

[0066]

[0067] Where: P(x, y, z): the probability density value at the three-dimensional space point (x, y, z); E(x, y, z): the energy function, representing the deviation between the ecological parameters (such as carbon storage, biomass) at this point and the predicted value; T: the temperature parameter, controlling the smoothness of the probability distribution; Z: the normalization constant, ensuring that the integral of the probability density function is 1.

[0068] Compared with the prior art, the present application includes at least one of the following beneficial technical effects:

[0069] The multi-modal data acquisition system comprehensively acquires three-dimensional ecological data and ensures secure transmission. The spatio-temporal coupling preprocessing module effectively integrates multi-source data, solves the spatio-temporal alignment problem, and expands the training data, laying a solid foundation for subsequent analysis.

[0070] The ecological coupling deep learning model accurately extracts vegetation features and quantifies the ecological coupling effect. The carbon sink dynamic assessment system combines physical constraints to improve the prediction accuracy and interpretability, and can more accurately evaluate biomass and carbon sinks.

[0071] The multi-objective decision-making optimization engine balances ecological and economic objectives. The digital twin simulator previews the decision impact. The blockchain smart contract ensures the execution of the carbon sink plan, ensuring that the decision is scientific and feasible.

[0072] The visualization interface of the intelligent interaction execution platform is convenient for operation. The edge-cloud collaborative architecture enables real-time decision-making. The feature inversion network traces key factors, improving the timeliness and accuracy of decision-making.

[0073] The present invention comprehensively and securely obtains data through the multi-modal data acquisition system, optimizes the data quality through the spatio-temporal coupling preprocessing module, accurately analyzes biomass and carbon sinks using the ecological coupling deep learning model and the carbon sink dynamic assessment system, formulates scientific decisions through the multi-objective decision-making optimization engine and ensures their execution, and realizes convenient interaction and efficient execution relying on the intelligent interaction execution platform. Thus, it significantly improves the accuracy, scientificity, and execution efficiency of the collaborative optimization decision-making of tropical rainforest biomass and carbon sinks, and strongly promotes the ecological protection and sustainable development of tropical rainforests. Brief Description of the Drawings

[0074] Figure 1 It is a schematic structural diagram of a collaborative optimization decision-making system for tropical rainforest biomass and carbon sinks based on deep learning. Detailed Embodiments

[0075] The technical solutions of the present invention will be further described below in conjunction with the drawings and specific embodiments.

[0076] Embodiment 1

[0077] As Figure 1As shown in the figure, a collaborative optimization decision-making system for tropical rainforest biomass and carbon sink based on deep learning proposed by the present invention includes a multi-modal data acquisition system, a spatio-temporal coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic assessment system, a multi-objective decision-making optimization engine, and an intelligent interaction execution platform. The following will explain each part in detail.

[0078] In this embodiment, the multi-modal data acquisition system is the foundation of the entire collaborative optimization decision-making system for tropical rainforest biomass and carbon sink. It integrates a variety of advanced devices and technologies to achieve all-round and multi-level data acquisition of the tropical rainforest. A cross-platform heterogeneous data integration unit is configured to integrate sub-meter hyperspectral satellite images, UAV LiDAR point clouds, and ground Internet of Things sensor networks, and securely obtain three-dimensional ecological data of the canopy-surface-underground through a quantum encryption channel. By integrating sub-meter hyperspectral satellite images, UAV LiDAR point clouds, and ground Internet of Things sensor network data, tropical rainforest information is obtained comprehensively from macro to micro and from air to ground. Satellite images can cover large areas and provide information on the overall vegetation distribution and ecological pattern; UAV LiDAR point clouds can accurately obtain the three-dimensional structure of vegetation, such as tree height and crown width; the ground Internet of Things sensor network monitors microenvironment parameters in real time, enabling the data to cover multiple levels of the tropical rainforest ecosystem and providing a rich and comprehensive data basis for subsequent analysis.

[0079] Data from different data sources are complementary. The spectral information of hyperspectral satellite images can assist in identifying vegetation species and health status. The three-dimensional structure data of LiDAR point clouds can be combined with spectral information to more accurately evaluate biomass; the real-time environmental data provided by the ground sensor network can be used to explain the reasons for vegetation growth and carbon sink changes, enhancing the comprehensive analysis value of the data.

[0080] Among them, the ground Internet of Things sensor network includes:

[0081] Canopy vertical profile monitoring array: composed of liftable nodes, each node integrating a chlorophyll fluorescence sensor (measurement wavelength 400 - 750nm), a multi-spectral reflectometer (8 bands, including red edge bands), and a microenvironment recorder, deployed on a 30m high tower at 0.5m intervals;

[0082] Underground biomass monitoring unit: using carbon fiber reinforced probes implanted to a depth of 2m, equipped with an impedance-based fine root detector (resolution 0.1mm) and a micro-CT scanning module (scanning frequency 3 times a day);

[0083] Dynamic Ad Hoc Network Protocol: It adopts the Time Slotted Channel Hopping (TSCH) protocol, completes the network topology reconstruction of 200 nodes within 1.5 seconds, and realizes ultra-long-distance transmission of up to 10 km based on the LoraWAN architecture. The canopy vertical profile monitoring array deploys nodes at intervals of 0.5 m, which can finely monitor the physiological and environmental parameters at different heights of the canopy, such as chlorophyll fluorescence and multispectral reflectance, helping to deeply study the photosynthesis and growth conditions of vegetation. The carbon fiber-reinforced probe of the underground biomass monitoring unit is implanted to a depth of 2 m and is equipped with advanced detection equipment, which can accurately obtain the underground root structure and biomass distribution, filling the gap in the monitoring of underground ecological information; the dynamic self-organizing network of the Time Slotted Channel Hopping (TSCH) protocol can quickly complete the network topology reconstruction to ensure smooth communication between sensor nodes; the ultra-long-distance transmission of up to 10 km achieved by the Long Range Wide Area Network (LoraWAN) architecture can adapt to the complex geographical environment of the tropical rainforest and ensure that data can be transmitted to the data center in a timely manner. The quantum encryption channel ensures the security of data transmission and prevents data from being stolen or tampered with during transmission.

[0084] Spatio-temporal coupling preprocessing module. The spatio-temporal coupling preprocessing module preprocesses the multi-source data collected to improve the quality and usability of the data. It uses an improved Cycle Generative Adversarial Network (CycleGAN) network to achieve spatial registration of multi-source data, combines a Dynamic Time Warping Long Short-Term Memory Network-Attention mechanism (LSTM-Attention) model to solve the time series alignment problem, and integrates an adversarial generative network to expand the training data for extreme climates; the multi-source data fusion adopts a hierarchical feature alignment method:

[0085] First-level alignment: Through a real-time visual SLAM system based on ORB features (the third generation), the full English name is Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping 3, abbreviated as the ORB-SLAM3 algorithm, the LiDAR point cloud of the unmanned aerial vehicle (density ≥ 200 points / m 2 ) and satellite images (resolution 0.3 m) are sub-pixel registered, and a feature matching model based on Scale-Invariant Feature Transform-Generalized Matching Strategy (SIFT-GMS) (Grid-based Motion Statistics) is established;

[0086] Second-level alignment: Develop an adaptive kernel function transformer:

[0087]

[0088] Among them, K(x, y): the value of the kernel function, which is used to measure the similarity between data points x and y;

[0089] ‖x - y‖: the Euclidean distance between data points x and y;

[0090] σ: the bandwidth parameter of the kernel function, which is dynamically adjusted by Bayesian optimization;

[0091] Map the heterogeneous data (including leaf area index time series data and soil CO2 flux pulse data) collected by the ground sensor network to a unified feature space, and the kernel function parameters are dynamically adjusted by Bayesian optimization;

[0092] Third-level alignment: Construct a three-dimensional spatio-temporal cube (resolution 1m×1m×0.1m), use the Trilinear interpolation algorithm to fuse multi-scale data, and embed a differentiable rendering layer to achieve cross-modal feature visualization. Through the three-level alignment, data from different sources and scales are unified into the same feature space and spatio-temporal framework. Sub-pixel registration and feature matching improve the spatial consistency of the data, and the adaptive kernel function transformer solves the problem of heterogeneous data fusion. The three-dimensional spatio-temporal cube and the interpolation algorithm enhance the continuity and integrity of the data, enabling multi-source data to complement and verify each other, providing high-quality input for subsequent deep learning models. The differentiable rendering layer realizes cross-modal feature visualization, which helps researchers intuitively observe and analyze the relationships between different modal data, mine potential features hidden in the data, improve the feature expression ability of the data, and provide richer information for the training of the ecological coupling deep learning model.

[0093] It should be noted that the improved CycleGAN network includes:

[0094] Adaptive attention mechanism: Embed a Coordinate Attention module in the generator U-Net architecture to capture spatially position-sensitive features through 2D global pooling;

[0095] Multi-scale discriminator: Use 3 PatchGAN discriminators (with receptive fields of 16×16, 32×32, and 64×64 pixels respectively) to form a cascaded structure, and introduce spectral normalization constraints;

[0096] Dynamic cycle consistency loss: Design a cycle loss function with adaptive weights, and the formula is:

[0097] L cyc =λ1·‖G AB (G BA (x)) - x‖1 + λ2·SSIM(G AB (G BA (x)), x)

[0098] Among them, G AB and G BA respectively represent the generators from domain A to domain B and from domain B to domain A;

[0099] x: input data sample;

[0100] ‖·‖1: L1 norm, used to measure the absolute difference between the generated data and the original data;

[0101] SSIM(·): Structural Similarity Index, used to evaluate the similarity of the generated data and the original data in terms of structure, brightness, and contrast. The improved CycleGAN network combined with the adaptive attention mechanism can more accurately achieve the spatial registration of multi-source data, solving the problem of spatial inconsistency of different data sources. The dynamic time warping LSTM-Attention model effectively processes the mismatch problem of time series data, ensuring the accurate correspondence of data in time and space and improving the utilization efficiency of the model for spatio-temporal information.

[0102] Through accurate registration and alignment, the model can better learn the true patterns and laws in the data, reduce the errors caused by data inconsistency, thereby improving the generalization ability of the model and enabling it to have better performance in different datasets and scenarios. The integrated adversarial generation network expands the extreme climate training data, increasing the diversity of the training data. In practical applications, the impact of extreme climate events on the biomass and carbon sink of tropical rainforests is crucial, but such data is often scarce. The extreme climate data generated by the generative adversarial network can enable the model to better learn the ecological response patterns in extreme situations, improving the prediction accuracy and robustness of the model when facing various complex climate conditions.

[0103] Ecological coupling deep learning model: Construct a three-dimensional convolutional graph attention network (3D-CGAT), extract vegetation heterogeneity features through deformable convolution, quantify the ecological coupling effect of the vegetation community by combining the graph attention mechanism, and design a two-stream gating fusion mechanism to integrate spatio-temporal feature streams; the 3D-CGAT network specifically includes:

[0104] Vegetation patch influence domain division: Based on the Voronoi diagram algorithm, generate a dynamic influence domain centered on the dominant tree species, with a radius R = 5 × tree height + 3 × crown width;

[0105] Heterogeneous graph convolutional layer: Define three types of nodes (arbor layer, shrub layer, ground layer) and five types of edges (light competition, root interaction, nutrient transfer, seed dispersal, microbial association), and use the second-generation graph attention network (GATv2) convolutional kernel to achieve feature propagation;

[0106] Dynamic graph structure update: Update the graph topology every six months through transfer learning. Use the Gromov-Wasserstein distance to measure the similarity of graph structures, and set the update threshold to 0.35. Gromov-Wasserstein distance:

[0107]

[0108] where G1 and G2 are two graph structures, representing the influence domains of vegetation patches at different time points respectively;

[0109] γ is the joint probability distribution used to align the nodes of the two graph structures;

[0110] and represent the distance metrics between node pairs in graphs G1 and G2 respectively;

[0111] Γ(u1,u2) is the set of all possible joint probability distributions, where u1 and u2 are the node distributions of graphs G1 and G2 respectively, and λ1 and λ2 are learnable weight parameters used to balance the importance of L1 loss and SSIM loss. 3D-CGAT network:

[0112] Accurate ecological modeling: Divide the influence domain of vegetation patches based on the Voronoi diagram algorithm, which can dynamically adjust the influence range according to the actual growth of vegetation, and more accurately simulate the interactions between vegetation. The heterogeneous graph convolutional layer defines various nodes and edges, considering the relationships between different levels and ecological processes in the vegetation community, such as light competition, root interactions, etc., enabling the model to capture the complexity of the ecosystem more comprehensively.

[0113] Adapting to the dynamic changes of the ecosystem: Update the graph topology every six months through transfer learning, which can timely reflect the dynamic changes of the tropical rainforest ecosystem. When the ecological environment changes, the model can quickly adjust its own structure, maintain accurate predictions of biomass and carbon sinks, and improve the adaptability and timeliness of the model. Deformable convolution can adaptively adjust the sampling position of the convolution kernel, better capture the complex shapes and texture information of vegetation, and extract more representative heterogeneous features. These features are of great significance for accurately evaluating the biomass and carbon sinks of the tropical rainforest, because the heterogeneity of different vegetation types and growth states will significantly affect their ecological functions.

[0114] The graph attention mechanism can assign different attention weights according to the relationships between nodes, highlighting important ecological coupling relationships, enabling the model to more accurately quantify the ecological coupling effect of the vegetation community. The dual-stream gated fusion mechanism integrates spatio-temporal feature streams, fully utilizes the information in the time and space dimensions, and improves the model's understanding and prediction ability of the dynamic changes of the ecosystem.

[0115] Carbon Sink Dynamic Assessment System: Based on the Bayesian neural network framework, it realizes the prediction of biomass probability, integrates δ13C isotope tracer data to analyze the carbon source contribution ratio, and embeds a physical constraint layer to couple the ecological process mechanism equation; the implementation method of the physical constraint layer is as follows:

[0116] Embed the CENTURY model mechanism equation: Take the soil organic carbon decomposition rate equation as a hard constraint, the formula:

[0117]

[0118] where The change rate of soil organic carbon content over time;

[0119] k: Decomposition rate constant, related to soil type and microbial activity;

[0120] C: Current soil organic carbon content;

[0121] f(T): Temperature correction function, in the form of the Arrhenius equation: where E a is the activation energy, R is the gas constant, and T is the absolute temperature;

[0122] f(θ): Moisture correction function, usually expressed as a non-linear function of soil water content;

[0123] f(ph): pH correction function, reflecting the influence of pH value on microbial activity;

[0124] Construct a mixed loss function:

[0125] L total = α·L data + β·L physics + γ·L reg

[0126] where, L data : Data-driven loss, using mean square error, used to measure the difference between the model prediction value and the true value;

[0127] L physics : Physical constraint loss;

[0128] L reg : Regularization loss, used to prevent model overfitting, using L2 regularization or weight decay;

[0129] α, β, γ: Weight coefficients, respectively controlling the relative importance of data-driven loss, physical constraint loss, and regularization loss;

[0130] α = 0.6, β = 0.3, γ = 0.1, L physics contains 21 ecological process conservation equations;

[0131] Develop a differentiable solver: Implement automatic differentiation of mechanism equations based on the PyTorch framework, supporting GPU-accelerated computing.

[0132] In this embodiment, biomass probability prediction is realized based on the Bayesian neural network framework, considering the uncertainty of the model. In actual ecosystems, there are many factors that are difficult to accurately measure and predict, such as the uncertainty of climate change and the randomness of biological individuals. By outputting the probability distribution of biomass, decision-makers can more comprehensively understand the reliability of the prediction results and make more reasonable decisions. Integrating δ13C isotope tracer data to analyze the carbon source contribution ratio can more accurately analyze the contributions of different carbon sources to the carbon sink. This helps to deeply understand the mechanism of the carbon cycle in tropical rainforests and provides a scientific basis for formulating targeted carbon sink management strategies, such as determining which carbon sources are the main contributors and which carbon sources need to be controlled or protected. Embedding the CENTURY model mechanism equation and taking the soil organic carbon decomposition rate equation as a hard constraint to construct a hybrid loss function. This way integrates the physical mechanism of ecological processes into the model, improving the interpretability of the model. At the same time, the prediction results of the model are ensured to conform to the basic laws of the ecosystem through the constraint conditions, improving the accuracy and reliability of the prediction. The differentiable solver developed based on the PyTorch framework supports GPU-accelerated computing and can efficiently solve mechanism equations. When dealing with large-scale ecological data and complex models, GPU acceleration can significantly shorten the calculation time, improve the training and inference efficiency of the model, and enable the system to provide carbon sink assessment results more timely.

[0133] Multi-objective decision-making optimization engine: Use the NSGA-III framework to construct an ecological-economic Pareto front model, integrate the digital twin simulator to pre-demonstrate the ecological impact of decisions, and realize the automatic verification and execution of carbon sink solutions through blockchain smart contracts; the digital twin simulator includes:

[0134] Multi-resolution modeling framework: At the 1km 2 scale, use the process-based Biome-BGC model, at the 1ha scale, use the individual tree model (including 26 morphological parameters), and at the 1m 2 scale, use the microenvironment fluid model based on the lattice Boltzmann method (LBM);

[0135] Cross-scale coupling interface: Develop a dynamic downscaling converter to realize parameter transfer through the Wavelet-GAN network, ensuring the conservation of energy and material fluxes;

[0136] Reinforcement learning training environment: Design a Markov decision process with a 12-dimensional state space (carbon storage, species diversity) and a 7-dimensional action space (thinning intensity, replanting density, etc.), and use the SAC algorithm to train the policy network.

[0137] In addition, the method for implementing the blockchain smart contract includes:

[0138] Carbon sink tokenization protocol: Divide the carbon sink per hectare into 1000 ERC-1155 tokens, with each token corresponding to 1 kg CO2 equivalent, and store the satellite image verification data of the corresponding area through the InterPlanetary File System (IPFS);

[0139] Dynamic Oracle mechanism: Deploy an off-chain oracle based on TEE (Trusted Execution Environment) to obtain NOAA meteorological data and Landsat-9 remote sensing data in real time as the trigger conditions for the smart contract;

[0140] Zero-knowledge verification layer: Use zk-SNARKs technology to generate carbon sink calculation proofs, with verification time < 2 seconds, proof size < 1 KB, and support for million-level node network verification.

[0141] In this embodiment, the Pareto front model: Construct an ecological-economic Pareto front model using the framework of the third-generation non-dominated sorting genetic algorithm (NSGA-III), which can find the optimal balance point between ecological protection and economic development. A series of non-dominated solutions generated by this model provide decision-makers with multiple choices, enabling decision-makers to weigh between ecological and economic benefits according to different policy goals and resource constraints, and formulate decision-making plans that better meet actual needs.

[0142] Among them, the multi-resolution modeling framework uses models at different scales, from the macroscopic regional scale to the microscopic individual scale, and can comprehensively and accurately describe the ecosystem of the tropical rainforest. The dynamic downscaling converter between different scale models ensures cross-scale coupling of energy and material flux conservation, improves the accuracy and consistency of simulation, and provides a more reliable basis for decision-making. The reinforcement learning training environment trains the policy network through a Markov decision process and the (Soft Actor-Critic algorithm) SAC algorithm, can simulate the dynamic changes of the tropical rainforest, and continuously optimize the decision-making strategy. In practical applications, the system can automatically adjust the management strategy according to real-time ecological data and decision-making goals, improving the scientificity and effectiveness of decision-making.

[0143] In addition, the carbon sink tokenization protocol converts carbon sinks into tradable assets, promoting the development of the carbon sink market. In the form of fungible and non-fungible tokens (ERC-1155) on Ethereum, it makes carbon sink transactions more convenient and transparent, attracting more investors to participate in the carbon sink market and providing financial support for the protection and management of tropical rainforests. The dynamic Oracle mechanism ensures that smart contracts are automatically executed based on real-time environmental data, improving the timeliness and accuracy of decision-making. The zero-knowledge verification layer uses zero-knowledge succinct non-interactive knowledge argument (zk-SNARKs) technology to verify the calculation results of carbon sink amounts without revealing data privacy, ensuring the security and credibility of carbon sink transactions, and reducing transaction risks and disputes.

[0144] Intelligent interaction execution platform: Deploy a mixed reality (MR) three-dimensional visualization interface to support holographic terrain interaction, establish an edge-cloud collaborative architecture to achieve real-time decision-making for field mobile terminals, and combine a feature inversion network to trace the spatial distribution of key influencing factors. The edge-cloud collaborative architecture includes:

[0145] Model dynamic slicing technology: Divide the 3D-CGAT network into 12 independently operable sub-modules, and automatically select cloud inference or edge computing according to the network bandwidth (threshold set to 10Mbps);

[0146] Incremental federated learning framework: Each edge node maintains a local model copy, updates the global model every 24 hours through the Secure Aggregation protocol, and uses differential privacy technology (ε = 0.5, δ = 1e -5 ) to protect data privacy;

[0147] Disaster recovery mechanism: When a network interruption is detected for more than 5 minutes, automatically switch to the emergency communication mode based on Beidou short messages, and optimize the transmission efficiency to 140 bytes per time.

[0148] Among them, the feature inversion network adopts:

[0149] Deep Taylor Decomposition algorithm: Calculate the contribution degree of each input pixel to the carbon sink prediction result, and generate a heat map with a resolution of 0.5m;

[0150] Spatio-temporal attention tracing module: Identify key time nodes (30 days before the occurrence of drought events) and spatial regions (such as sensitive areas with a slope > 25°) through the multi-head attention mechanism;

[0151] Uncertainty visualization engine: Use the Marching Cubes algorithm to render a three-dimensional probability distribution cloud map, support dynamic adjustment of the transparency and color mapping of the 95% confidence interval, and the probability distribution rendering in the Marching Cubes algorithm

[0152]

[0153] Where: P(x, y, z): the probability density value at the three-dimensional space point (x, y, z); E(x, y, z): the energy function, representing the deviation between the ecological parameters (such as carbon storage, biomass) at this point and the predicted values; T: the temperature parameter, controlling the smoothness of the probability distribution; Z: the normalization constant, ensuring that the integral of the probability density function is 1.

[0154] Intelligent Interaction Execution Platform, Mixed Reality 3D Visualization Interface: Provides a mixed reality 3D visualization interface for holographic terrain interaction, enabling users to intuitively observe the 3D terrain and ecological information of the tropical rainforest. This immersive interaction experience helps users better understand the ecological system structure and function of the tropical rainforest, conduct virtual field investigations and decision-making simulations, and improve the intuitiveness and accuracy of decision-making.

[0155] Edge-Cloud Collaborative Architecture: The model dynamic slicing technology automatically selects cloud inference or edge computing according to the network bandwidth, and can flexibly adjust the allocation of computing resources. When the network bandwidth is sufficient, the powerful computing power of the cloud is used for large-scale model inference; when the network bandwidth is limited, part of the computing tasks are assigned to edge nodes, improving the operation efficiency of the system and reducing the data transmission cost. The incremental federated learning framework maintains local model copies at each edge node, updates the global model through a secure aggregation protocol, and uses differential privacy technology to protect data privacy. This method not only ensures the security of data, but also can make full use of the data at edge nodes for model training, improving the accuracy and generalization ability of the model. The disaster recovery mechanism ensures that when the network is interrupted, the system can automatically switch to the emergency communication mode, ensuring data transmission and decision execution, and improving the reliability and stability of the system.

[0156] The heat map generated by the Deep Taylor decomposition algorithm can intuitively display the contribution degree of different regions to carbon sinks, helping decision-makers quickly identify key regions and influencing factors. The spatio-temporal attention tracing module identifies key time nodes and spatial regions through the multi-head attention mechanism, which helps to deeply understand the dynamic change laws of the tropical rainforest ecosystem and provides a basis for formulating targeted management strategies. The uncertainty visualization engine presents the uncertainty of carbon sink prediction in the form of a 3D probability distribution cloud map, supporting dynamic adjustment of the transparency and color mapping of the confidence interval. This enables decision-makers to more intuitively understand the reliability of the prediction results, fully consider uncertainty factors in the decision-making process, and formulate more robust decision-making plans.

[0157] The above specific embodiments are only several alternative embodiments of the present invention. Based on the technical solution of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning, characterized in that: include: Multimodal data collection system: Configure a cross-platform heterogeneous data integration unit, integrate sub-meter-level hyperspectral satellite images, drone light detection and ranging point clouds, and ground IoT sensor networks, and achieve secure acquisition of canopy-surface-underground three-dimensional ecological data through quantum encryption channels; Spatiotemporal coupling preprocessing module: an improved recurrent generative adversarial network is used to achieve spatial registration of multi-source data, a dynamic time-warped long short-term memory network-attention mechanism model is combined to solve the problem of temporal alignment, and an integrated adversarial generative network is used to expand extreme climate training data; Ecological coupling deep learning model: construct a three-dimensional convolutional graph attention network, extract vegetation heterogeneity features through deformable convolution, combine the graph attention mechanism to quantify the ecological coupling effect of vegetation communities, and design a dual-stream gated fusion mechanism to integrate spatiotemporal feature streams; Carbon sink dynamic assessment system: Based on the Bayesian neural network framework, biomass probability prediction is realized, δ13C isotope tracer data is integrated to analyze the carbon source contribution ratio, and the physical constraint layer is embedded to couple the ecological process mechanism equation; Multi-objective decision optimization engine: The third-generation non-dominated sorting genetic algorithm framework is used to build an ecological and economic Pareto frontier model, the digital twin simulator is integrated to preview the ecological impact of decisions, and the automatic verification and execution of carbon sink solutions are realized through blockchain smart contracts; Intelligent interactive execution platform: deploy a mixed reality 3D visualization interface to support holographic terrain interaction, establish an edge-cloud collaborative architecture to realize real-time decision-making of mobile terminals in the field, and combine the feature inversion network to trace the spatial distribution of key influencing factors.

2. According to claim 1, a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning is characterized in that: The ground IoT sensor network comprises: Canopy vertical profile monitoring array: It is composed of liftable nodes, each of which integrates chlorophyll fluorescence sensors, multi-spectral reflectometers and micro-environment recorders, and is deployed on a 30m high tower at 0.5m intervals; Underground biomass monitoring unit: uses a carbon fiber reinforced probe, implanted at a depth of 2m, equipped with an impedance-type fine root detector and a micro CT scanning module; Dynamic self-organizing network protocol: Using time-synchronized channel hopping protocol, the network topology reconstruction of 200 nodes can be completed within 1.5 seconds, and 10km ultra-long-distance transmission can be achieved based on the long-distance wide area network architecture.

3. The tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning according to claim 2 is characterized in that: The multi-source data fusion adopts a hierarchical feature alignment method: First-level alignment: The third-generation real-time visual SLAM system algorithm based on ORB features is used to perform sub-pixel registration of the UAV LiDAR point cloud and satellite images, and a feature matching model based on scale-invariant feature transformation-generalized matching strategy is established; Second level alignment: Developing adaptive kernel transformers: Among them, K(x,y): kernel function value, used to measure the similarity between data points x and y; ||xy||: Euclidean distance between data points x and y; σ: bandwidth parameter of the kernel function, dynamically adjusted through Bayesian optimization; The heterogeneous data collected by the ground sensor network are mapped into a unified feature space, and the kernel function parameters are dynamically adjusted through Bayesian optimization; The third level of alignment: construct a three-dimensional space-time cube, use the trilinear interpolation algorithm to fuse multi-scale data, and embed a differentiable rendering layer to achieve cross-modal feature visualization.

4. The tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning according to claim 1 is characterized in that: The improved cyclic generative adversarial network comprises: Adaptive attention mechanism: embedding the coordinate attention module in the generator U-Net architecture to capture spatial position sensitive features through 2D global pooling; Multi-scale discriminator: Three PatchGAN discriminators are used to form a cascade structure, and spectral normalization constraints are introduced; Dynamic cycle consistency loss: Design a weight-adaptive cycle loss function, the formula is: L cyc =λ1·||G AB (G BA (x))-x||1+λ2·SSIM(G AB (G BA (x)),x) Among them, G AB and G BA : represents the generator from domain A to domain B and from domain B to domain A respectively; x: input data sample; ||·||1: L1 norm; SSIM(·): structural similarity index, λ1 and λ2 are learnable weight parameters used to balance the importance of L1 loss and SSIM loss.

5. The tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning according to claim 1 is characterized in that: The three-dimensional graph convolutional attention network specifically includes: Division of vegetation patch influence domain: Based on the Voronoi diagram algorithm, a dynamic influence domain is generated with the dominant tree species as the center, with a radius of R = 5 × tree height + 3 × crown width; Heterogeneous graph convolution layer: defines three types of nodes and five types of edges, and uses the second-generation graph attention network convolution kernel to achieve feature propagation. The three types of nodes include tree layer, shrub layer, and surface layer. The five types of edges include light competition, root interaction, nutrient transfer, seed diffusion, and microbial association. Dynamic graph structure update: The graph topology is updated every 6 months through transfer learning. The Gromov-Wasserstein distance is used to measure the graph structure similarity. The update threshold is set to 0.

35. The Gromov-Wasserstein distance: Among them, G1 and G2 are two graph structures, representing the influence domain of vegetation patches at different time points respectively; γ: joint probability distribution, and Represent the distance measure between node pairs in graphs G1 and g2 respectively; Γ(u1,u2): the set of all possible joint probability distributions, where u1 and u2 are the node distributions of graphs G1 and G2 respectively.

6. The tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning according to claim 1 is characterized in that: The physical constraint layer is implemented as follows: Embedding the CENTURY model mechanism equation: Taking the soil organic carbon decomposition rate equation as a hard constraint, the formula is: in, The rate of change of soil organic carbon content over time; k: decomposition rate constant; C: current soil organic carbon content; f(T): temperature correction function, in the form of Arrhenius equation: Where E a is the activation energy, R is the gas constant, and T is the absolute temperature; f(θ): moisture correction function, usually expressed as a nonlinear function of soil moisture content; f(pH): pH correction function, reflecting the effect of pH on microbial activity; Construct a hybrid loss function: L total =α·L data +β·L physics +γ·L reg Among them, L data : Data-driven loss, using mean square error, to measure the difference between the model prediction value and the true value; L physics : physical constraint loss; L reg : Regularization loss, used to prevent model overfitting, using L2 regularization or weight decay; α, β, γ: weight coefficients, respectively controlling the relative importance of data-driven loss, physical constraint loss, and regularization loss; α = 0.6, β = 0.3, γ = 0.1, L physics Contains 21 conservation equations for ecological processes; Develop a differentiable solver: Implement automatic differentiation of mechanism equations based on the PyTorch framework, and support GPU accelerated computing.

7. The tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning according to claim 1 is characterized in that: The digital twin simulator comprises: Multi-resolution modeling framework: at 1km 2 The process-based biome-biogeochemical cycle model was used at the 1ha scale, the individual tree model was used at the 1m scale, and the 2 The scale adopts a microenvironment fluid model based on the lattice Boltzmann method; Cross-scale coupling interface: Develop a dynamic downscaling converter to achieve parameter transfer through a wavelet generative adversarial network to ensure conservation of energy and material flux; Reinforcement learning training environment: Design a Markov decision process with a 12-dimensional state space and a 7-dimensional action space, and use the soft actor-critic algorithm to train the policy network.

8. The tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning according to claim 1 is characterized in that: The blockchain smart contract implementation method includes: Carbon sink tokenization agreement: divide each hectare of carbon sink into 1,000 Ethereum fungible and non-fungible tokens standard-numbered 1155 tokens, each token corresponds to 1kgCO2 equivalent, and store satellite image verification data of the corresponding area through IPFS; Dynamic Oracle Mechanism: Deploy off-chain oracles based on a trusted execution environment to obtain NOAA weather data and Landsat-9 remote sensing data in real time as trigger conditions for smart contracts; Zero-knowledge verification layer: zk-SNARKs technology is used to generate carbon sink calculation proof, with verification time <2 seconds, proof size <1KB, and support for million-level node network verification.

9. The tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning according to claim 1, characterized in that: The edge-cloud collaborative architecture includes: Model dynamic slicing technology: divides the 3D graph convolutional attention network into 12 independently run sub-modules, and automatically selects cloud inference or edge computing based on network bandwidth; Incremental Federated Learning Framework: Each edge node maintains a local model copy, updates the global model every 24 hours through a secure aggregation protocol, and uses differential privacy technology to protect data privacy; Disaster recovery mechanism: When a network interruption is detected for more than 5 minutes, it automatically switches to the emergency communication mode based on Beidou short messages, and the transmission efficiency is optimized to 140 bytes per time.

10. The tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning according to claim 1, characterized in that: The feature inversion network adopts: Deep Taylor decomposition algorithm: calculates the contribution of each input pixel to the carbon sink prediction result and generates a heat map with a resolution of 0.5m; Spatiotemporal attention tracing module: identifies key time nodes and spatial regions through a multi-head attention mechanism; Uncertainty visualization engine: uses the marching cube algorithm to render 3D probability distribution cloud maps, supports dynamic adjustment of transparency and color mapping of 95% confidence intervals, and probability distribution rendering in the MarchingCubes algorithm Where: P(x,y,z): probability density value at the three-dimensional space point (x,y,z); E(x,y,z): energy function, which represents the deviation of the ecological parameter at this point from the predicted value; T: temperature parameter, which controls the smoothness of the probability distribution; Z: normalization constant, which ensures that the integral of the probability density function is 1.

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