30 results about "Environmental modeling" patented technology

The invention discloses a reinforcement learning-based virtual power plant response optimization scheduling system and method, and relates to the technical field of virtual power plant intelligent scheduling. The system comprises an environment modeling module, an intelligent agent module, a multi-agent coordination module and a self-adaptive optimization module which are respectively used for constructing a multi-dimensional state space and a layered action space, generating and optimizing an action strategy based on an Actor-Critic network, executing a scheduling instruction through a layered multi-agent structure and realizing conflict consensus, and dynamically adapting to state space change in combination with incremental learning and meta-learning mechanisms. The system and the method have the advantages of fine state modeling, efficient action response, adaptive strategy updating, stable agent coordination and the like, and can keep the continuity, the stability and the optimality of a scheduling strategy in an operation environment in which multi-source heterogeneous power resources participate in scheduling cooperatively, market rules change frequently and load fluctuation is violent.

The invention discloses a dynamic planning method for an air route of an urban low-altitude unmanned aerial vehicle. The method comprises the following steps: firstly, constructing an urban low-altitude environment model based on an airspace rasterization technology, fusing multi-dimensional constraint factors such as geographic data, meteorological conditions and airspace control information through multi-source environment information, and accurately calibrating the position of a take-off and landing point; secondly, a dynamic grid availability evaluation model is established in combination with environmental constraints and a real-time airspace state, and the navigation feasibility of each grid unit is quantitatively analyzed; then, an improved A * algorithm is combined with a grid availability evaluation result to generate a global optimal initial route; finally, a rolling time domain optimization strategy is introduced, and uncertain factors such as sudden obstacles and airspace dynamic limitation are responded in real time through periodic non-flight route re-planning after the unmanned aerial vehicle takes off. Through collaborative fusion of static environment modeling and a dynamic optimization mechanism, the problem of real-time planning of the air route of the unmanned aerial vehicle in the urban low-altitude complex environment is effectively solved, and the environmental adaptability and task reliability of the system are remarkably improved.

The invention relates to the technical field of safety production management, and discloses a safety production hidden danger closed-loop management and control supervision system and a risk assessment early warning method, and the system comprises an environment modeling module which is used for building a digital model of a production unit and calculating a systematic instability coefficient based on historical data; the risk assessment module is used for quantifying the initial risk potential energy in combination with the coefficient and the hidden danger inherent attribute; the risk evolution module is used for dynamically updating risk potential energy according to a time and space coupling rule; the closed-loop verification module is used for judging the actual effectiveness of the intervention measure by comparing the management entropy before and after the intervention after the intervention measure is implemented; and the self-adaptive feedback module is used for automatically updating the systematic instability coefficient in the environment model according to the judgment result. According to the method, risk quantification, dynamic early warning, effect verification and model self-optimization are integrated, a complete closed-loop management process is formed, and the scientificity and foresight of safety production management are remarkably improved.

A navigation system and method are disclosed for operation in GPS-denied environments using quantum-inspired sensor fusion, dynamic virtual anchor points (VAPs), and predictive environmental modeling. The system represents multiple position hypothesis using wavefunction-like expansions and integrates VAP-based triangulation for drift correction. A predictive modeling module ingests solar, geomagnetic, and environmental data to proactively adjust sensor weighting. A cybersecurity module employs quantum-algebraic key generation and location-derived ephemeral keys to secure inter-device communication. The system includes an augmented reality (AR) interface to visualize and edit anchor references, and a neurofeedback module that adapts the AR interface based on real-time physiological signals from the user. The method further enables anchor optimization via AI-driven repositioning and supports low-power edge execution using approximate amplitude filtering. Additional modules may include fractal antennas, neuromorphic processors, and adaptive forecasting layers to maintain positional accuracy and user experience in subterranean, multi-floor, or magnetically complex environments.

The invention discloses a wind power plant fire intelligent sensing and early warning method based on multi-sensor fusion, and belongs to the technical field of fire sensing and early warning, and the method comprises the steps: obtaining data information collected by a plurality of sensors in real time through the plurality of sensors disposed in a wind power plant, and carrying out the data preprocessing, according to the method, the importance weights of the environmental parameters are dynamically adjusted, the importance degrees of the parameters are reflected in real time according to environmental changes, the accuracy of environmental condition monitoring and modeling is improved, the space-time correlation model is formed in combination with the multi-dimensional time sequence matrix and the space correlation matrix, the uncertainty of the environmental state is comprehensively evaluated, and the accuracy of environmental condition monitoring and modeling is improved. The dynamic environment modeling and prediction are carried out, the time and space factors are fully considered, the fire key features are extracted by adaptively adjusting the weight according to the change of the dynamic environment model and the environment parameters to the fire sensitivity, the information related to the fire can be captured in a more targeted manner, and the accuracy and effectiveness of fire feature extraction are improved.

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

The invention relates to the technical field of unmanned aerial vehicle automatic inspection, provides an unmanned aerial vehicle take-off and landing platform rapid deployment and path planning method for a building curtain wall, constructs a gradient risk field model fusing geometric structures, environmental risks, visual perception and task regulations, and realizes unified quantitative evaluation of operation environmental risks. And based on the risk field, generating a global safe energy-saving path on the curved surface of the curtain wall through mixed search and path smoothing optimization guided by a risk navigation potential function. Through real-time updating of a local risk field and model prediction control, autonomous obstacle avoidance and smooth return flight of the unmanned aerial vehicle to sudden obstacles are realized. Meanwhile, a dynamic scheduling mechanism considering multi-target cost is introduced, and collaborative optimization of multiple unmanned aerial vehicles and take-off and landing platform resources is achieved. According to the method, full-link closed loop from environment modeling, global planning, real-time control to system scheduling is realized, and the safety, continuity and overall operation efficiency of automatic routing inspection of the super high-rise building curtain wall are improved.

The invention discloses a hierarchical semantic map dynamic construction method and system deployed on the end side of a robot, and the method comprises the steps: obtaining multi-modal environment sensing data of an external environment where the robot is located through a sensing unit disposed on the robot, and transmitting the multi-modal environment sensing data to an end-side processing unit; the end-side processing unit performs space-time alignment and fusion according to the multi-modal environment perception data to obtain fusion data, generates an object instance according to the fusion data and performs vectorization to obtain a vectorized object instance, and performs dynamic duplicate removal and updating on the basis of semantic similarity and spatial distance dual judgment. And dynamically constructing a hierarchical semantic map according to the vectorized object instance. According to the method, the data redundancy is effectively inhibited, the storage burden of the end side equipment is remarkably reduced, and the environment modeling efficiency of the robot system and the compactness of the map are improved.

This invention discloses a multi-objective path planning method and system for unmanned surface vessels (USVs) in complex marine environments, belonging to the field of USV path planning technology. The method includes the following steps: environmental modeling is performed using a voxelized raster method based on a terrain grayscale map; distance transformation is used to calculate the Euclidean distance from each free-space grid to the nearest obstacle, providing a quantitative basis for path safety assessment; an improved multi-objective particle swarm optimization method is used to complete a global path search under safety constraints; a three-dimensional constraint dual-level processing mechanism is designed to process the initial path solution set, generating a set of paths to be selected; based on the designed cost function and according to the Pareto dominance principle, the generated set of paths to be selected is filtered to obtain the Pareto front. This invention considers both path length and path safety, and provides a wider range of random paths for multi-objective path planning, offering more and more reasonable path selections.

This invention belongs to the technical field of agricultural environmental control and UAV applications, and relates to a UAV-based microclimate precision control system for forest understory cultivation areas. The system includes: an environmental modeling and initialization module, a prediction and decision-making module, a command execution and observation module, a data verification and extraction module, an error calculation and correction source term generation module, a correction source term propagation and field update module, and a closed-loop iterative scheduling module. By constructing a three-dimensional voxel grid and generating an initial microclimate prediction field, identifying target voxels based on prediction trends and generating predictive control frames, the UAV executes intervention actions and collects observation data. After verification and error calculation, correction source terms are generated. The microclimate prediction field is updated based on predicted airflow propagation, achieving closed-loop iterative control. This invention solves the problems of lagging control and insufficient intervention targeting caused by the lack of dynamic prediction of three-dimensional microclimate fields and digital twin linkage capabilities in existing fixed sensor networks and traditional UAV operation methods.

The present invention relates to the field of rotary probe detection, and in particular to a method for detecting a rotating probe path in an expansion tube area. The method comprises the following steps: environmental modeling and initialization processing; determining the relative position of the robot's starting center point S and the target center point E0; if S and E0 are in the same row or column, calculating the absolute value of the column difference, determining the moving direction, generating path points through loop iteration, and generating a full pre-path matrix; if S and E0 are in different rows and columns, constructing a rectangle with S and E0 as diagonal points, obtaining the other two diagonal points of the rectangle, and selecting the diagonal point closest to the center of the panel as the inflection point; calculating the segmented pre-path matrix from S to the inflection point, and the segmented pre-path matrix from the inflection point to E0 respectively; connecting the two segmented pre-path matrices together to obtain the full pre-path matrix; and calculating the full pre-path distance. The present invention satisfies the requirements of the shortest Manhattan distance and the least inflection points, thereby improving efficiency.

This invention discloses an adaptive compliant force control method for robotic arms based on reinforcement learning, belonging to the fields of robotics and automation. This method is based on an integrated framework of "reinforcement learning-adaptive compliant force control" for a robotic arm-dexterous hand-six-dimensional force / torque sensor system. It aims to completely eliminate existing problems such as the need for manual parameter tuning during model changes, difficulties in contact dynamics modeling, model parameter estimation, and the frequent need to prove the stability of adaptive laws through intelligent control strategies. It achieves self-learning and self-adaptation of upper-level task planning and lower-level compliant force / position control. After deployment, no repeated manual parameter tuning is required for different working conditions. The same control strategy maintains a high assembly success rate and low peak contact force for workpieces with small gaps, easy deformation, and multiple materials, eliminating bottlenecks in manual calibration, environmental modeling, and online parameter estimation, and enabling rapid "zero-downtime" switching in flexible production lines.

This invention provides a multi-machine collaborative scheduling system for a human-machine controlled container, belonging to the field of multi-machine collaborative scheduling and control technology. The system includes: a task semantic understanding and planning module; an environmental feature intelligent classification and surface modeling module connected to the task semantic understanding and planning module; a task semantic enhancement and risk assessment module connected to the environmental feature intelligent classification and surface modeling module; and a multi-machine collaborative decision-making module connected to the task semantic enhancement and risk assessment module. This invention achieves this by closely cooperating through task semantic understanding, environmental modeling, risk assessment, and decision-making functions to form an organic whole. Under the unified scheduling of the system, different types of machines can efficiently and collaboratively complete complex tasks, breaking the limitations of isolated module operation in traditional systems and meeting the stringent requirements for multi-machine collaborative operations in multiple fields and scenarios.

This patent application introduces a novel and comprehensive method for assessing novelty in quantum physics and related technical fields using the McGinty Equation (MEQ). The McGinty Equation, represented as Ψ(xt)=ΨQFT(xt)+ΨFractal(xtDmqs)+ΨGravity(xtG), uniquely integrates quantum field theory (QFT), fractal geometry, and gravitational theory into a unified mathematical framework. This integration enables the equation to capture the complex interactions and behaviors of particles and fields at quantum scales, offering a versatile tool for a broad spectrum of scientific and technological applications. Key applications of the MEQ include advancements in quantum computing, material science, environmental modeling, energy generation, and medical imaging. The invention's novelty lies in its unique approach to amalgamating disparate physical theories, providing significant insights into the quantum realm and facilitating technological innovations.

The invention relates to a coastal area composite network stability and carbon neutralization capability cooperative improvement method, and belongs to the field of ecological informatics and environment modeling. According to the method, firstly, metabolism subjects are divided based on multi-stage land utilization data, artificial carbon emission and natural carbon sink of each metabolism subject are evaluated, a carbon metabolism path is determined according to the area difference of land types before and after land utilization change and the carbon income and expenditure level difference of unit area, a carbon metabolism network is constructed, and key regulation and control nodes are identified; and by simulating different development scenes, dynamically evaluating the change rule of the stability and the carbon neutralization capability of the composite network, and finally forming a space planning and policy intervention scheme for synergistically improving the stability and the carbon neutralization capability of the composite network. According to the method, the crossing from static evaluation to dynamic optimization regulation is realized, the perspectiveness and scientificity of management decision are effectively improved, and the method is of great significance to guarantee the sustainable development of the coastal area.

This invention relates to the field of robotics technology and discloses a large-model-driven method and system for navigation and autonomous path planning of a composite robot. The method includes: collecting environmental data through multimodal sensors; inputting the data into a visual-language large model (VLM) to construct a holistic environmental model containing geometric, semantic, and dynamic object information; parsing high-level natural language navigation instructions using the VLM and generating navigation target points based on the holistic environmental model; and finally, coordinating the robot's body movement trajectory and end effector action sequence based on the model and target points, and driving their execution. This invention, by introducing a large model for holistic environmental modeling and task parsing, solves the problems of weak environmental understanding and unnatural human-computer interaction in traditional robot navigation technologies. It achieves deep perception of complex dynamic environments and autonomous planning of high-level instructions, significantly improving the intelligence level and operational efficiency of composite robots in real-world scenarios.

This invention discloses a robot movement path planning method based on ant colony optimization algorithm. It includes the following steps: environmental modeling of the robot's movement space using a grid method to obtain a grid map, with grid coordinates representing the robot's starting and ending positions; generating an initial path on the grid map using a jump-point search algorithm, and calculating the non-uniform initial pheromone concentration distribution along the initial path; searching for the optimal path on the grid map using the ant colony optimization algorithm; eliminating redundant nodes on the optimal path and performing smoothing processing to obtain the robot's movement path. This invention improves convergence speed and search efficiency by using a non-uniform distribution of the initial pheromone concentration in the grid map, and shortens the planned path length by eliminating redundant nodes on the optimal path and performing smoothing processing.

The invention relates to the technical field of risk early warning, in particular to an in-field mobile machinery real-time risk early warning system based on AI large model capability and a production scheduling method. The method comprises the following steps: a data acquisition and processing unit acquires operation state parameters and operation environment parameters of the in-field flow machinery, and preprocesses the operation state parameters and the operation environment parameters; the AI large model analysis and risk identification unit is used for extracting feature vectors of the operation state parameters and the operation environment parameters, modeling the interaction relation between the operation track, the behavior mode and the environment of the mobile machinery by utilizing a BERT model and a long-short-term memory network model, and generating a risk index; and the risk early warning and response unit generates alarm information based on the risk index. According to the method, a sensing neural radiation field (F-NeRF) environment modeling method is introduced, and discrete sensor data is converted into continuous space-time representation of working environments (such as temperature and humidity, visibility and ground conditions).

The invention discloses a time-varying wind field environment unmanned aerial vehicle full-coverage flight path planning method and system based on reinforcement learning, and mainly solves the problem that an existing unmanned aerial vehicle coverage flight path planning method cannot plan a full-coverage flight path in a dynamic wind field environment. According to the implementation scheme, the method comprises the steps that two-dimensional environment modeling is conducted on an unmanned aerial vehicle task area through a grid method, and the task area is discretely segmented into grids of the same size; according to the two-dimensional environment model, constructing a Markov decision model comprising an action space, a state transfer function and a reward function through a reinforcement learning algorithm; according to the time-varying characteristics of the wind field, designing an initialized time-sharing Q table capable of planning the full-coverage flight path of the time-varying wind field environment, and training the initialized time-sharing Q table; according to the trained time-sharing Q table and the Markov decision model, the short-endurance full-coverage flight path of the unmanned aerial vehicle in the time-varying wind field environment is obtained.For the dynamic time-varying wind field environment, an effective coverage path completely covering a task area can be planned, complete reconnaissance of the unmanned aerial vehicle on the task area is guaranteed, and the method can be used for unmanned aerial vehicle reconnaissance tasks.

The application discloses a multi-spatial resolution remote sensing simulation and error influence stripping method, and relates to the technical fields of remote sensing image processing, quantitative remote sensing and environmental modeling. Firstly, a multi-scale remote sensing image is simulated by combining a modulation transfer function (MTF) and mean value interpolation; secondly, a multiple regression model is constructed to explain model errors by image quality indexes, so that the interference of image quality on analysis results is stripped, and normalized residual errors caused by changes in spatial resolution are obtained; finally, a sliding window-based RSI and "Median+IQR" scoring method is proposed to identify stable resolution intervals and perform next-step analysis, thereby guiding the selection of actual modeling scales. The method has high universality and scale adaptability, and is suitable for pre-scale analysis and optimized modeling in various remote sensing quantitative analysis tasks such as land cover, vegetation parameters, urban heat island, water body monitoring and the like.

This invention discloses a UAV gas inspection system and method based on dynamic route planning, relating to the field of gas inspection technology. The environmental modeling module acquires basic spatial data and building structure data within the inspection area, constructs a three-dimensional spatial environment model, identifies flyable spaces, and generates a flight space constraint model. The dynamic route planning module acquires a list of inspection targets, filters inspection targets based on the list, and generates a set of candidate inspection waypoints. Based on the candidate waypoint set and the flight space constraint model, a UAV inspection path is generated through coverage optimization and path cost optimization. The gas detection module collects and analyzes gas concentration data in real time, identifies abnormal concentrations, and triggers an alarm mechanism. The privacy protection module performs real-time analysis of the collected inspection images, identifies sensitive areas in the images, and automatically occludes or blurs these sensitive areas, achieving both gas safety detection and user privacy protection.

The application discloses an environment modeling method and device based on a hyperspectral laser radar, and the method comprises the following steps: acquiring point cloud information of a to-be-detected scene and original spectrum of a detection target in the to-be-detected scene through the hyperspectral laser radar; positioning and mapping are performed by using the point cloud information of the to-be-detected scene, and an initial environment three-dimensional model is constructed; surface attribute parameters of the detection target are determined; the original spectrum of the detection target is inversely processed according to the surface attribute parameters of the detection target and an output response model of the hyperspectral laser radar, so that reflectivity spectrum of the detection target is obtained; and the initial environment three-dimensional model is mapped again by using the reflectivity spectrum of the detection target, so that a target environment multi-dimensional model is obtained. According to the scheme, the original spectrum output by the hyperspectral laser radar is finely inversely processed, so that the real spectrum of the detection target is obtained, the accuracy of the spectrum is effectively improved, and the spatial geometric structure information and the spectrum information are fully matched, so that accurate environment modeling is realized.

The invention discloses an AI-supported environment-friendly robot. According to the technical scheme, the AI-supported environment-friendly robot is characterized by comprising a data sensing module, an AI intelligent processing module, a task decision module, an execution control module, a remote interaction and cooperation module and a data storage and iteration module; the data sensing module is used for collecting multi-source environment data and robot operation state data; the AI intelligent processing module carries out fusion analysis, intelligent identification and environment modeling on the collected data; the task decision module generates a dynamic operation strategy based on an AI analysis result; the execution control module executes the operation strategy and corrects the action deviation; the remote interaction and cooperation module realizes man-machine interaction and multi-machine cooperation; the data storage and iteration module stores data and updates an algorithm model; according to the invention, the recognition accuracy is improved through AI-driven multi-source fusion.

A multi-modal tight coupling SLAM method based on gradient descent optimization, comprising: collecting environmental feature data in the motion process of the intelligent mobile device in real time, and determining the spatial position of the intelligent mobile device by detecting the reflective marker, to construct a time-synchronized multi-modal observation set. From the multi-modal observation set, multi-modal features are identified and extracted, and the multi-modal features are fused to obtain a unified coded joint feature set. Based on the joint feature set, an initial pose graph is constructed with multi-modal feature constraints and marker anchor constraints. An error function is called to model the multi-modal observation residual and the marker residual, and the initial pose graph is optimized by iteratively adjusting the error of the modeled estimate and the pose. The optimized initial pose graph is globally consistent, and the environmental modeling result and the pose state of the intelligent mobile device are output, solving the problem of SLAM convergence difficulty caused by narrow space and metal interference in complex scenes.

This invention discloses a method and device for incremental dynamic updating of point cloud maps in 3D environmental modeling, comprising the following steps: acquiring front-end data and constructing an environmental map; extracting keyframe features from the environmental map, performing loop closure detection, and acquiring a sensor pose map; optimizing the sensor pose map and correcting the keyframe poses; classifying the updated keyframes into historical keyframes and newly created keyframes, and sequentially performing local point cloud transient update, global point cloud transient update, and redundant historical keyframe deletion processing, finally outputting historical keyframe data with outdated point clouds and redundant keyframes removed. The beneficial effects of this invention are: improved system efficiency, reduced storage space, ensuring the map always reflects the latest state, and wide applicability.

This invention relates to the field of environmental modeling and employs a multi-layered, multi-scale marine environmental field modeling method for marine robots. The aim is to address the low accuracy of environmental field models for marine robots operating in complex and dynamic marine environments. The invention generates a grid map of the target sea area and obtains predicted environmental field data, including sea breezes, waves, and currents, from an international weather forecasting model, storing this data in the grid area. Subsequently, an environmental perception module collects current and historical environmental field observation data. Based on the characteristics of marine robots, the spatiotemporal model of the environmental field is divided into three categories: large-scale, medium-scale, and small-scale models. A large-scale spatiotemporal model is constructed using the predicted environmental field data; a medium-scale model is further constructed based on the large-scale model; and a small-scale model is generated by combining observation data. Finally, the environmental field data of the medium-scale model is updated using the observation data from the small-scale model.

This invention discloses a real-time wireless communication system for mine shafts, belonging to the field of wireless communication technology. It includes a distributed wireless communication node set, a sparse observation node set, an interference reference acquisition node, and a controller. The distributed wireless communication node set is deployed along the shaft depth to form a multi-hop wireless communication network and carries communication services including at least alarm services. By setting up a sparse observation node set within the shaft and introducing a structured electromagnetic interference reference sequence output by the interference reference acquisition node, the controller can construct and continuously update a shaft propagation parameter field that varies with depth. This allows key propagation characteristics such as propagation attenuation, delay spread, and interference intensity to have quantifiable, alignable, and reproducible expressions. Therefore, compared to schemes relying solely on fixed configurations or single-point empirical measurements, this system can more accurately reflect the differentiated propagation environment along the shaft depth and improve the effectiveness of environmental modeling.

In various examples, an environment visualization pipeline may determine whether to generate or otherwise enable a visualization using an environmental modeling pipeline that models an environment as a 3D bowl or using an environmental modeling pipeline that models the environment using some other 3D representation, such as a detected 3D surface topology. The determination may made based on various factors, such as ego-machine state, (e.g., one or more detected features indicative of a designated operational scenario, proximity to a detected object, speed of ego-machine, etc.), estimated image quality of a corresponding environment visualization, and / or other factors. Accordingly, an environment around an ego-machine, such as a vehicle, robot, and / or other type of object, may be visualized in systems such as parking visualization systems, Surround View Systems, and / or others.

This application discloses a method for analyzing and retrieving the situation of water pollution incidents, relating to the fields of remote sensing technology and environmental modeling technology. This method fuses features from acquired infrared and ultraviolet fluorescence images, establishes an oil film thickness calculation model based on the fused comprehensive features, uses this model to obtain the observed concentration in the detection area, establishes a convection-diffusion model for the target water area, and introduces time- and velocity-related loss terms. The convection-diffusion model is then used to estimate the predicted concentration in the detection area. Based on the observed and predicted concentrations, an inversion model is established, and the leak location and time in the target water area are inferred through optimization iterations. The technical solution in this application fuses features from infrared and ultraviolet fluorescence images, enabling accurate calculation of the oil concentration in the detection area. The introduction of time- and velocity-related loss terms into the convection-diffusion model improves the accuracy of retrieving the leak location and time.

The present invention discloses a modeling method, system and storage medium based on a semantic segmentation model, which belongs to the field of three-dimensional modeling technology. The method includes: S1: remote sensing data preprocessing stage; S2: semantic segmentation model training and tuning stage; S3: forest area semantic segmentation recognition stage, using the trained semantic segmentation model to perform pixel-level classification on the multispectral radar data of the target area to obtain semantic segmentation results, and performing vectorization conversion to obtain the Thiessen polygon area of ​​the effective dense forest area; S4: modeling scene construction stage; S5: scene asset generation and output stage, based on the digital elevation model and the vegetation spatial coordinate point set, a dense forest environment model is generated; based on the CGA rule, the vegetation monomer model is geometrically randomized to obtain a digital twin scene asset and output. The spatial distribution characteristics of trees based on satellite observations are restored more accurately, so that the accuracy of environmental modeling is improved within a low cost range.