1953 results about "Forest industry" patented technology

The invention provides a forestry pest and disease prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a dynamic monitoring data set of a target forest region, covering an environment and vegetation physiological parameter sequence collected by a plurality of monitoring nodes, and a space coordinate region corresponding to each node; extracting features of the dynamic monitoring data set to obtain a forestry environment feature set containing vegetation growth, environment fluctuation and potential identification features of plant diseases and insect pests, and analyzing the forestry environment feature set by using a pre-trained plant disease and insect pest prediction model to generate a prediction parameter set representing the occurrence probability and influence range grade of the plant diseases and insect pests; executing dynamic prediction matching, determining a disease and insect pest distribution thermodynamic map and generating a control priority strategy, and finally fusing the disease and insect pest distribution thermodynamic map and the disease and insect pest control priority strategy to generate a forest region control optimization instruction set, and feeding back the instruction set to a forestry monitoring platform to trigger resource scheduling so as to realize efficient disease and insect pest control.

The invention relates to the technical field of data security management, and discloses a blockchain-based forestry data security management system and method, and the system comprises a data collection unit which is used for comprehensively obtaining a multi-dimensional information flow of forest ecology and resource states; the data processing unit is used for carrying out deep cleaning, standardized regulation and feature value extraction on the originally collected mass heterogeneous data; the block chain storage unit is used for constructing a permanent, tamper-proof and distributed secure storage infrastructure platform of the forestry core data; a verification unit; the safety control unit is used for constructing a covering data transmission, storage and access full-chain depth defense system structure model; a user interface unit; an auditing unit; and a network communication unit. The method is reasonable in design, and the intelligent analysis module is used for processing to form a forest interannual growth trend prediction function distribution map state visual output result set.

The present invention relates to a method and system for estimating forest carbon storage that combines artificial intelligence algorithms and multimodal remote sensing data. This approach comprehensively utilizes laser radar satellites, multi- / hyperspectral satellites, radar satellites, high-resolution optical imagery, etc. A hybrid technical system is employed for different forest coverage areas, resulting in high-precision forest carbon storage mapping with a resolution of 10 meters and area coverage. This provides technical support and assurance for assessing global forest carbon storage and supporting forestry carbon sequestration transactions.

The invention discloses a forest prevention and control method based on disease and pest monitoring, and belongs to the technical field of forestry prevention and control, and the method comprises the steps: dividing a target forest into regions according to vegetation types, collecting historical multi-source data, filling the missing data, analyzing a time sequence and causal relationship to construct a time sequence causal diagram, and simulating a disease and pest spatio-temporal dynamic state in combination with current data. And determining a prevention and control risk level and a key induction factor of each region, executing a prevention and control strategy according to the level, and updating a causal diagram through reinforcement learning according to forest health response data. According to the method, a closed loop is formed from data processing to model construction, accurate prevention and control are achieved, efficiency and scientificity are improved, forest ecological changes can be dynamically optimized and adapted, and the prevention and control effect is guaranteed.

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

The invention discloses a forestry ecological environment real-time monitoring and management method based on big data, and belongs to the technical field of forestry ecological environment management, and the method comprises the following steps: S1, multi-source heterogeneous data collection and sensor network deployment; s2, multi-modal data fusion and intelligent transmission: developing an adaptive communication protocol dynamic switching module, and automatically switching to a satellite communication link in a 4G / 5G network blind area; s3, mass data storage and distributed calculation: constructing a hybrid cloud storage architecture, and storing real-time monitoring data into a Redis cache queue; s4, intelligently extracting ecological environment indexes; s5, dynamically evaluating the ecological bearing capacity; s6, performing multi-level early warning and emergency response; s7, making a precise forestry management decision; and S8, carrying out system self-optimization and closed-loop management. According to the method, the air-space-ground integrated monitoring network is constructed, so that the comprehensiveness and the real-time performance of forestry ecological environment perception are remarkably improved; multi-source data of the unmanned aerial vehicle, the satellite remote sensing and the ground sensor are complementary.

The invention discloses a dynamic three-dimensional reconstruction method based on incremental updating, which belongs to the field of image data processing, and comprises the following steps: acquiring a forestry scene multi-view image to be reconstructed to form an initial data set D0; constructing an initial three-dimensional scene model of the forestry scene based on D0 and 3DGS methods; monitoring a forestry scene to construct an incremental data set Dt of the tth change; during each incremental learning, the previous incremental model is divided into a change area and a non-change area, and the incremental model of the tth incremental learning is obtained by optimizing the change area and the non-change area and constructing a new scene model and is used for image rendering. According to the method, full-scene reconstruction is avoided, and the accuracy of the three-dimensional model can be ensured while the efficiency of three-dimensional reconstruction in a dynamic environment is remarkably improved. And the scene can be updated only by adding a small number of view angle images, so that the cost and time of data acquisition are greatly reduced, and the method is particularly suitable for large-scale monitoring of forestry and the like and scenes needing long-term monitoring and real-time updating.

The invention discloses an unmanned aerial vehicle real-time vegetation classification system and method based on a lightweight AI model, and belongs to the technical field of vegetation monitoring and analysis. On the basis of an existing processor of the unmanned aerial vehicle, vegetation real-time classification is achieved by optimizing the deep learning model, and the flight path is dynamically adjusted according to the classification result. The method comprises the steps of system configuration and initialization, lightweight AI model development and optimization, real-time data acquisition and processing, adaptive flight path adjustment and feedback optimization. The method has the following advantages: (1) real-time classification of vegetation and adaptive flight path adjustment are realized, and the acquisition efficiency is optimized; (2) the sampling density is adaptively adjusted for different value regions, and the precision and coverage efficiency of vegetation classification are improved; (3) the acquisition strategy is optimized in real time in combination with the battery state, and unnecessary flight time and energy consumption are reduced; and (4) the method is suitable for application fields such as agricultural monitoring, forestry management and ecological protection.

The invention provides an intelligent processing method and system for multi-modal data of an industrial chain of the forest industry, and relates to the technical field of data processing. According to the method, acquisition nodes are arranged in links of seedling raising, planting, tending, felling, transportation, processing, storage and sales, and three types of original data of images, sensors and texts are obtained; forming a time-space aligned image feature, sensor feature and text feature set through unified time correction, space coordinate conversion, anomaly elimination, deletion interpolation and normalization; and further mapping the three types of features to the same feature space by adopting a unified coding mechanism, and constructing a knowledge graph by combining space-time diagram modeling and a forest ontology library to obtain multi-modal fusion feature representation. According to the invention, a high-consistency and extensible data basis is provided for forestry full-life-cycle digital management.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The invention relates to a forest cultivation dynamic monitoring method and system based on a remote sensing technology, and the method comprises the steps: collecting point cloud and multi-source topographic data of a complex topographic region through the fusion of an airborne laser radar and a satellite radar technology, and generating a high-precision point cloud and topographic parameter matrix through denoising and registration; a segmentation threshold value is dynamically optimized in combination with topographic features, the canopy point cloud penetration rate of abrupt slope and valley areas is improved, and the problem of canopy missegmentation caused by uneven point cloud density under a complex terrain is solved; constructing a penetration rate compensation model driven by terrain influence factors, correcting laser radar point cloud deviation and inverting high-precision tree height, crown breadth and biomass parameters; and based on fusion analysis of the multi-temporal vegetation index and a machine learning model, early warning of diseases and insect pests and fire hazards and dynamic evaluation of a man-made forest cultivation effect are realized. According to the method, the bottleneck of complex terrain monitoring is broken through, the forest parameter inversion precision and the real-time response capability are remarkably improved, and technical support is provided for precise management of forestry resources.

The invention discloses a forestry fireproof early warning method and system based on the Internet of Things, and relates to the technical field of forestry monitoring, prevention and control, a heterogeneous sensing network is constructed through millimeter wave radar and multispectral imaging, and omnibearing monitoring in a complex environment is realized by combining terrain shielding coefficient Dmxs and signal coverage intensity Xgql analysis. The thermal radiation intensity Rsqd, the combustible drying index Gzzs and the airflow disturbance coefficient Qlys are fused to improve the hidden fire source identification precision; a LoRa relay and a Mesh network are adopted to ensure communication stability of a weak signal area, data transmission is optimized through edge calculation, and high-risk area resources are preferentially scheduled based on hierarchical response of a joint early warning index Lyzb; the risk assessment module calculates a fire danger index Hxzs in real time to predict fire spreading, the intelligent decision-making module comprehensively assesses hidden fire source probability Yhgl and a positioning error Wcsp to generate an early warning strategy, and the emergency linkage module automatically executes hierarchical response to form an intelligent monitoring-early warning-disposal closed-loop management system.

The invention relates to the technical field of forestry investigation, and discloses a forestry investigation system for carbon sink forest management, which comprises a data acquisition module, a data processing module, a data integration module, a model construction and analysis module, a decision and early warning module, an output module and an implementation monitoring module. Through multi-dimensional index investigation, information can be obtained from multiple levels of an ecological system, the carbon cycle process and ecological functions of the carbon sink forest are comprehensively known, more comprehensive data support is provided for scientific management, in addition, more variables influencing carbon sink are introduced, so that the model better conforms to the actual situation, the accuracy and generalization ability of the model are enhanced, and the method is suitable for popularization and application. According to the method, carbon reserve changes of the carbon sink forest under different conditions can be better predicted, finally, effect prediction of multiple management schemes is provided for managers through scene simulation and model analysis, scientific and reasonable management decision making is assisted, and meanwhile, an early warning mechanism can find potential risks in time and guarantee stable development of the carbon sink forest.

The invention relates to the technical field of carbon sink calculation, in particular to a forestry carbon sink calculation model, a forestry carbon credit calculation method and a storage medium. The model comprises an overground biological carbon sink amount estimation module used for calculating overground biological carbon sink data of a target area, an underground soil carbon sink amount estimation module used for calculating soil carbon sink data of the target area, and a litter carbon reserve estimation module used for calculating litter carbon sink data of the target area. The combined loss calculation module is used for calculating carbon loss data; and the final carbon sink total quantity fusion module is used for calculating the carbon sink quantity according to the aboveground biological carbon sink data, the soil carbon sink data, the litter carbon sink data and the carbon loss data. A forestry carbon sink calculation model based on multiple dimensions is established through multiple types of carbon sink estimation modules, and the problem that the requirement for rapid authentication of the global carbon market cannot be met due to high manual dependency is solved; the problems of low calculation precision, high cost and the like caused by insufficient model dynamics, single model calculation dimension and the like are solved.

The present invention belongs to the technical field of tree transplanting support and protection devices, and discloses a tree transplanting support and protection device for forestry, which includes two fixed rings hinged to each other. The free ends of the two fixed rings are both fixedly provided with mounting frames. The mounting frames are U-shaped. An installation block is rotatably arranged inside the mounting frame. A rod hole is formed in the installation block. A fastening screw is movably arranged between the through holes on the two installation blocks. A fastening nut is threadedly arranged at one end of the fastening screw; A protective pad is fixedly arranged together on the inner sides of the two fixed rings; In the present invention, through the cooperation of the protective pad, the arc-shaped cavity, the driving member and the fastening nut, etc., the air pressure in the protective pad can be changed by the growth of the tree. Using the air pressure as the power, the driving member drives the fastening nut to move, so as to achieve the purpose of adjusting the space between the two fixed rings and play a role in protecting the tree.

The invention relates to the technical field of forestry environment monitoring, and discloses a forestry environment monitoring method and system based on big data analysis. The method comprises the following steps: acquiring multi-dimensional environmental parameters such as soil moisture content, vegetation coverage and meteorological factors through a distributed sensor network, and generating a real-time weight coefficient through a dynamic weight distribution engine; and completing anomaly detection by using the space-time correlation analysis model, and triggering a self-adaptive sampling strategy to perform high-density acquisition on an abnormal region. Real-time data and satellite remote sensing data are integrated through a multi-source data fusion algorithm, a forestry environment state matrix is generated, and a reference threshold is dynamically updated in combination with an incremental learning mechanism. And generating a regulation and control instruction set for soil improvement, vegetation maintenance and disaster early warning according to the updated threshold value, and executing and collecting feedback data by the edge computing node. And comparing feedback data with an expected index through a bidirectional verification mechanism, generating system optimization parameters, and returning the system optimization parameters to a dynamic weight distribution engine, thereby realizing accurate monitoring and efficient regulation and control of a forestry environment.

The invention discloses a forest fire risk assessment method based on interpretable deep learning, and relates to the technical field of forestry disaster prevention, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and generating a multi-dimensional data cube; based on the multi-dimensional data cube, extracting and forming a logical relationship of the forest fire danger, and based on the logical relationship of the forest fire danger, constructing a forest fire danger domain knowledge graph; based on a multi-dimensional data cube as a training sample, a concept bottleneck interpretable deep learning model is constructed and trained, and the multi-dimensional data cube input into the concept bottleneck interpretable deep learning model is mapped to a semantic concept layer defined by a forest fire danger domain knowledge graph. The method comprises the following steps: collecting multi-source heterogeneous data to generate a multi-dimensional data cube, constructing a forest fire danger domain knowledge graph, training a concept bottleneck interpretable deep learning model, mapping input data to a semantic concept layer, and outputting a fire danger grade and a concept activation result.

The invention relates to the field of data fusion processing, and particularly discloses a data fusion method of a forestry data management system, which is used for processing real-time acquired data of the forestry data management system. Dividing the forest resource data of the target area into different independent areas, and establishing a special forest resource growth quantitative model for the forest resource data of each independent area; detecting whether a forest resource data conflict exists or not; when the forest resource data of a certain independent region conflicts, the conflict data is corrected through the forest resource growth quantification model of the independent region; when the forest resource data of a plurality of independent regions conflicts, the conflict data is corrected through the combination of the forest resource growth quantification models of the plurality of independent regions; and correcting the conflict data and then completing data fusion.

The invention relates to the field of forestry monitoring data processing, in particular to a forestry pest and disease damage intelligent monitoring system and method based on unmanned aerial vehicle inspection, and the system comprises a data interaction unit which is used for building a real-time communication channel between an unmanned aerial vehicle and a server through employing a WebSocket protocol; the multi-scale feature extraction unit is used for constructing a three-stage pyramid convolution structure, and respectively extracting microscopic, medium and macroscopic scale features of the blade through convolution kernels of different specifications and cavity convolution; the time sequence parameter adjusting unit is used for fusing Transform and GRU, analyzing a feature map through a multi-head self-attention mechanism, learning a tree phenological law through the GRU, performing condition normalization adjustment on convolutional layer parameters based on the tree phenological law, and distinguishing physiological and disease features; and the decision fusion unit is used for splicing and fusing the composite feature map and the time sequence feature vector, carrying out parallel processing on an SVM and a Softmax classifier, and outputting a disease and pest recognition conclusion according to a dynamic threshold decision.

The invention discloses a forest dynamic growth model construction method and system, and belongs to the technical field of forestry digital twinning. According to the invention, through constructing the live-action three-dimensional model and carrying out refined modeling on the single plant, refined simulation and multi-dimensional prediction of the forest growth process are realized; a digital twinborn platform is utilized to integrate a forest simulation model and basic forest information, forest stand structure parameters are extracted, an arbor layer growth dynamic model is established, and then the species diversity and natural update density of a shrub layer are dynamically simulated. According to the method, the limitation of single plant scale is broken through, co-evolution of the arbor layer, the shrub layer and the natural updating layer is integrated for the first time, multi-dimensional prediction and visualization are supported, and scientific support is provided for forestry digital twinning.

The invention relates to the technical field of forestry monitoring, in particular to a forestry seedling raising monitoring method and system based on the Internet of Things, and the method comprises the following steps: obtaining soil humidity, temperature and illumination data of a monitoring point, positioning an abnormal fluctuation range, generating a fluctuation boundary diagram, recognizing an illumination and humidity collaborative change vector, and counting the frequency; the collaborative distribution information is extracted to generate a synchronism index, synchronism deviation is analyzed, a deviation distribution diagram is generated, the sampling density is subjected to clustering analysis, and a dynamic change data set is generated through normalization. According to the method, a fluctuation extreme point is positioned through adjacent time sequence difference operation, abnormal fluctuation characteristics of seedling environment parameters are captured, the real-time performance and accuracy of data anomaly detection are improved, illumination and humidity collaborative change direction vectors are identified, collaborative frequencies are subjected to aggregation statistics according to segments, and dynamic relevance of environment factors is quantified. The coupling relation between the monitoring points is accurately identified, the monitoring points with synchronism deviating from the threshold value are screened, the space-time heterogeneity characteristics are revealed, and the monitoring point layout is optimized.

The invention discloses a forest pest and disease development trend prediction method and system combined with remote sensing monitoring, and relates to the related field of forestry monitoring data processing, and the method comprises the steps: carrying out the remote sensing monitoring of a target forest, building a remote sensing image data set, calling ground monitoring data, and carrying out the rasterization based on a time step after the interpolation; performing multi-feature joint extraction on the remote sensing image data set, and superposing extraction results to a unified grid to form a multi-layer feature map; constructing a joint feature matrix by using the rasterized data and the multi-layer feature map, and performing pest distribution identification through a classification model; performing pest development trend prediction by taking the pest distribution diagram as a basic feature and taking the combined feature matrix as an additional feature; and according to a development trend prediction result, carrying out pest abnormity early warning. The technical problem of insufficient prediction accuracy caused by insufficient monitoring efficiency and accuracy in existing forest pest development trend prediction is solved, and the technical effects of improving the monitoring efficiency and accuracy and improving the pest prediction accuracy are achieved.

The invention relates to the technical field of forestry intelligent management and control, and discloses a forestry optimization management and control method and system based on artificial intelligence. The method comprises the following steps: collecting multi-source environment monitoring data such as soil humidity, illumination intensity and vegetation growth indexes in a forestry area, aligning a time sequence through a dynamic time warping algorithm, and separating steady-state and transient components; detecting anomalies through an isolated forest algorithm and generating a thermodynamic diagram, and obtaining a potential disease and pest outbreak area through causal reasoning in combination with historical records; constructing a dynamic growth reference curve, and identifying a disturbance sensitive area by using a generative adversarial network and a convolutional neural network; and integrating and generating a management and control priority partition map, optimizing resource allocation through deep reinforcement learning, outputting a task sequence, and driving equipment to execute irrigation, fertilization or pest control operation. According to the method, multiple artificial intelligence algorithms are fused, accurate control over the whole forestry process is achieved, and the method adapts to the dynamically-changing forestry environment.

The invention discloses a large language model construction method for the vertical field of forestry, which belongs to the technical field of computer models and comprises the following steps: constructing a first data set of forestry management business questions and answers; constructing a multi-modal coding model; freezing a text stream of the multi-modal coding model, forming the multi-modal coding model and an LLM decoder into a large language model, and training the large language model for generating a target text related to user input during intelligent question answering; constructing an RAG knowledge base; and performing intelligent question and answer based on RAG retrieval. Aiming at the characteristic that forestry management business images and texts have high matching, innovative technologies of multi-modal joint coding, weighted similarity calculation and LLM training by a frozen text encoder are introduced, and the quality and application value of forestry intelligent question answering and document generation are greatly improved. And the problem that the existing image and text matching precision is insufficient is solved, and more flexible and accurate retrieval and intelligent question answering can be realized in combination with an RAG technology.

The invention discloses a double-platform point cloud-oriented bidirectional hierarchical clustering single tree segmentation method, which belongs to the technical field of forestry monitoring, and comprises the following steps of: firstly, preprocessing and filtering an original point cloud, and then vertically layering along a Z axis according to a set slice thickness; in each layer, a clustering radius is adaptively determined by using a local point density estimation result, point cloud clustering is carried out by using a region growing method based on a search radius, and an initial single wood structure unit is extracted; and then, space connection operation is executed between the upper layer and the lower layer, points with continuous structures are assigned to the same target based on triple constraints of adjacent distance, lateral offset and height continuity, and finally accurate segmentation of the complete single-tree point cloud is realized. The BLS method supports a top-down segmentation strategy and a bottom-up segmentation strategy, automatic adaptation can be carried out according to platform types, trunk point clouds are preferentially segmented in TLS data, and a canopy structure is preferentially constructed in ULS data.

The invention discloses a forestry scene target detection method and system based on multi-modal continuous learning, and the method comprises the five steps: building a multi-modal data set, preprocessing data, constructing an incremental hybrid expert model, training a model, and carrying out the detection and reasoning of a target. In combination with an adaptive expert extension module, a frequency domain router module and a weight fusion method based on spherical linear interpolation, efficient and accurate detection of a multi-modal forestry scene target is realized. According to the method, the expert module can be dynamically generated and adjusted in the process of continuously introducing new tasks so as to adapt to target detection requirements of different fields and categories, the disastrous forgetting phenomenon is effectively prevented, and robust representation of the model on the previous tasks is kept.

The invention relates to the technical field of digital asset management, in particular to a block chain-based carbon asset full-life-cycle traceability and digital asset management platform. According to the platform, multiple data modeling and intelligent contract mechanisms are fused, and credible management of carbon assets in the whole process from generation, accounting, registration, transaction to auditing is achieved. The method comprises the following steps: collecting operation data of a forestry carbon sink project, constructing a structured Hash fingerprint, generating index root Hash by adopting a Merkle tree, and calculating a carbon sink amount as a basic data source of carbon assets; accounting the carbon sink credibility based on a Bayesian network model and eliminating abnormal data; carrying out anti-fact intervention analysis by utilizing a causal map, and screening abnormal assets with low causal consistency; on-chain matchmaking transaction of the carbon assets and the users is completed through a clustering and greedy matching mechanism; a life cycle auditing module is combined to evaluate responsibility contribution of key behavior variables to ESG indexes, and quantifiable, traceable and verifiable green asset full life cycle management is achieved.

The invention discloses a forest resource map modeling method and system, and belongs to the technical field of forestry information. The method comprises the following steps: capturing multi-source heterogeneous original observation data through a sensing unit group deployed in a forest region; performing space-time alignment and quality evaluation on the data by a map generation engine to generate an original observation sequence; calling and analyzing auxiliary geographic information in the environment context library to set prior configuration parameters of the atlas reckoning device; and finally, a map reckoning device is driven to perform fusion reckoning on the observation sequence, and a structured forest resource semantic network is output. The system correspondingly comprises a sensing unit group, an environment context library, an atlas generation engine and an atlas reckoning device. According to the method, full-chain intelligent management of forest resources from precise perception and intelligent cognition to prospective planning is realized through space-based collaborative intelligent perception, a depth generation model of historical knowledge injection and operation simulation based on space-time prediction, and the precision, efficiency and decision support capability of forest resource monitoring are greatly improved.

The invention discloses a forest management data processing method driven by a large language model, and belongs to the technical field of forest resource management and artificial intelligence data processing crossing. The method comprises the steps of multi-source data acquisition and preprocessing, forest management knowledge graph construction, large language model fine adjustment and retrieval enhancement generation, data semantic fusion and understanding and management strategy reasoning, wherein the large language model performs automatic reasoning based on fused data, management intention and industry knowledge to form a management strategy conforming to a forest growth law and multi-target balance; operation plan text generation: on the basis of completing strategy and space matching, compiling a forest operation plan text meeting forestry industry specifications and management requirements, and man-machine interaction and continuous optimization: collecting feedback information of forestry workers on forest operation plans through a man-machine interaction interface, and systematic evaluation is carried out on the generated operation scheme from forest resource sustainability, ecological function improvement, operation goal achievement degree, risk controllability and policy compliance.

The invention discloses forestry land preparation equipment, and relates to the technical field of forestry protection, the forestry land preparation equipment comprises a cockpit, an obstacle removing assembly and a land preparation assembly, the horizontal longitudinal two sides of the bottom end of the cockpit are fixedly connected with first cross beams, and the bottom ends of the first cross beams are fixedly connected with second cross beams; the two horizontal transverse sides of the second cross beams penetrate through and are jointly and rotationally connected with a driving shaft, advancing wheels are arranged at the two ends of the driving shaft, and limiting grooves facing the outer side of the device are formed in one ends of the two first cross beams. By arranging the obstacle clearing assembly, obstacle clearing operation is conducted on the ground before soil preparation operation, the obstacle clearing capacity is adaptive to complex conditions of the ground, and then the working efficiency of the device is effectively improved; by arranging the adjusting mechanism, the inclination angle of the lifting mechanism can be freely adjusted, and then horizontal soil preparation operation on the ground with different gradients is achieved; a lifting mechanism is arranged, soil preparation operation is synchronously completed along with advancing of the device, and the soil preparation effect can be freely controlled according to the ground condition.