1305 results about "Forest industry" patented technology

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 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 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 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 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 relates to the technical field of forestry carbon neutralization, in particular to a forest landscape afforestation full life cycle carbon emission accounting system and method.Energy consumption, material use and operation data in the whole afforestation process are collected in real time through Internet of Things equipment, the data are stored through the block chain technology, and dynamically updated regionalized emission factors are matched; and analyzing a carbon emission trend and optimizing an operation scheme in combination with a machine learning model, and finally displaying a carbon balance analysis result through a visual platform. The real-time accurate accounting of carbon emission and the dynamic evaluation of the carbon sink capability are realized, the technical defects of data acquisition lag, fixed emission factors, unlinked carbon sink and the like in the prior art are solved, and a scientific basis is provided for the carbon emission reduction decision and carbon transaction of forest landscape afforestation.

The invention relates to the technical field of forestry management, in particular to a forest and grass resource dynamic monitoring system which comprises the steps that a graph attention network is adopted to conduct high-low weight recognition processing on the structure difference value between node pairs, feature vectors are established through the canopy height difference and canopy density difference between adjacent nodes, and the canopy height difference and the canopy density difference between adjacent nodes are obtained; an edge weight scoring system is constructed in the form of segmented statistics and proportion weighting, node edge pairs with high influence relation strength are dynamically screened, ordered aggregation of spatial communication strength is realized, a graph neural network is introduced in a parameter fusion stage to construct a node parameter representation mechanism, and the spatial communication strength is improved. The tree species proportion, the grade of diameter at breast height and the community vertical structure in a regional sample plot are used as input features, unified mapping of node features of each region is completed in multiple rounds of iteration, cross-regional difference analysis is executed on model output in combination with biomass change frequency, parameter items with the difference proportion lower than a set threshold value are replaced with unified expression, and the model output is obtained. And parameter synchronization is realized to realize space nesting and feature integration.

The high-altitude picking tool for forestry comprises a telescopic rod, a picking device and a fruit basket, the bottom of the picking device is fixedly connected with the top of the telescopic rod, the left side of the fruit basket is fixedly connected with the top of the right side of the telescopic rod, and a collecting box is arranged at the bottom of the right side of the telescopic rod. By arranging the collecting box and the telescopic pipe, after fruits on trees are picked through the picking device and fall into the fruit basket, the fruits can flow into the first bent pipe, then are conveyed through the telescopic pipe and finally fall into the collecting box from the second bent pipe, finally, the fruits cannot be accumulated in the fruit basket, and the problem that the fruits in the air cannot be accumulated in the collecting box is solved. The problems that after fruits are picked by the picking device and fall into the fruit basket, the gravity center point of the telescopic rod is mainly concentrated on the fruit basket, so that when some fruits are accumulated in the fruit basket, a user cannot stably hold the telescopic rod, time and labor are wasted if the fruits are picked and taken, and the working efficiency is reduced are solved, and the effect of auxiliary picking and collecting is achieved.

The invention discloses a crown instance segmentation and maturity evaluation method based on dynamic expansion convolution. The method comprises the following steps: firstly, carrying out radiation correction, geometric correction and histogram equalization preprocessing on a forest RGB image acquired by an unmanned aerial vehicle; then extracting global contour features through an adaptive dynamic expansion convolution module, enhancing local detail capture capability in combination with an edge perception feature pyramid network, and realizing multi-level feature fusion by using a double attention mechanism; then, a density adaptive contour cross suppression algorithm is adopted to optimize the mask, and the distinguishing precision of the dense area is improved through dynamic adjustment of a suppression threshold value and ray method contour cross calculation; and finally, calculating canopy density based on a segmentation result, and constructing a maturity index model by fusing the shape, color and geometric features of the crown breadth. According to the method, the extraction capability of the sub-pixel-level details of the crown edge is remarkably improved, the problems of fuzzy irregular contour segmentation and high-density region misjudgment are effectively solved, and an efficient and accurate technical scheme is provided for forestry resource monitoring.

The invention belongs to the technical field of environment, and relates to a method for preparing a fulvic acid-rich forestry liquid fertilizer by quickly decomposing municipal sludge, which comprises the following steps: carrying out hydrodynamic cavitation treatment on municipal sludge, adding a reaction reagent into the municipal sludge to carry out Fenton-like reaction, adding an adsorbent into the municipal sludge to carry out adsorption treatment, and carrying out solid-liquid separation to obtain the fulvic acid-rich forestry liquid fertilizer. The fulvic acid type forestry liquid fertilizer is obtained. The hydraulic cavitation treatment is combined with the Fenton-like reaction and the adsorption treatment, so that the problems of high heavy metal activation risk and serious ammonia nitrogen loss in the traditional process for preparing the fulvic acid-rich fertilizer from the sludge are effectively solved, the rapid decomposition of the municipal sludge is realized, and the yield of the fertilizer is improved. The fulvic acid-rich forestry liquid fertilizer with the functions of improving soil, promoting plant growth and the like is prepared, and the purpose of efficient, environment-friendly and low-cost sludge resource utilization is achieved.

The invention relates to a high-precision single tree segmentation method based on multi-source point cloud data, and the method comprises the steps: building a high-density point cloud data set through the cooperative collection of an unmanned plane and a knapsack laser radar, carrying out the data preprocessing through the multi-site ICP registration and CSF filtering, carrying out the semantic segmentation based on an Enhanced Tree Instance Net network, and carrying out the segmentation of a single tree. Through feature contribution analysis and dynamic scene adaptive optimization segmentation data, semantic and geometric features are fused to realize individual tree instance segmentation, a physical constraint neural network is introduced to realize multi-parameter collaborative inversion of tree height, diameter at breast height, crown breadth, tree age and the like, it is ensured that the parameters conform to tree growth rules, precision evaluation and calibration are performed through a multi-level index system, and tree growth accuracy is improved. And finally realizing visual expression of a segmentation result and forest stand parameters. According to the method, the individual tree segmentation precision and the growth rule rationality are remarkably improved, and the method is suitable for urban forest carbon sink accurate measurement and intelligent forestry management.

The invention relates to the technical field of forestry intelligent monitoring, and particularly discloses an intelligent monitoring system for the growth condition of afforestation and greening nursery stocks based on deep learning, which is characterized in that multi-modal growth data of the nursery stocks are acquired through a multi-spectral imaging sensor, a three-dimensional laser scanning sensor and an environment monitoring sensor, and a standardized data set is formed through space-time alignment processing; extracting morphological structure and spectral response features through spatial domain and frequency domain parallel analysis, and generating multi-dimensional feature representation through cross-modal fusion; converting the parameters into growth state parameters by utilizing a feature recombination and space mapping technology; identifying an abnormal growth mode through time sequence dynamic analysis; and finally, adaptively adjusting the working parameters of the sensor according to the abnormal type to form closed-loop monitoring. The problem that an existing monitoring system cannot autonomously optimize a monitoring strategy according to the abnormal state is solved, and accurate monitoring and intelligent regulation and control of the nursery stock growth condition are achieved.

The utility model relates to the technical field of forestry maintenance, and discloses a tree-climbing pruning robot which comprises a supporting mechanism, the supporting mechanism comprises a machine box internally provided with a storage battery and a control module, the two sides of the machine box are each provided with a positioning connecting rod, and the whole machine box is in a clamping ring shape with a notch and is used for surrounding a tree; the tree climbing machine further comprises three tree climbing mechanisms, each tree climbing mechanism comprises a driving box with a driving shaft, the driving boxes are connected to the surface of the machine box and the ends of the two position adjusting connecting rods respectively, and the driving shafts of the driving boxes are sleeved with crawler belts attached to the surfaces of trees. The pruning device further comprises a pruning mechanism which is arranged at the top end of the driving box and used for pruning trees, the tree climbing mechanism can be attached to the surfaces of the trees and is matched with the crawler belt to move to drive the device body to move upwards, and therefore the pruning mechanism climbs the trees to the proper positions to prune branches. Manual tree climbing operation is avoided, and the safety of the pruning process is improved.

The invention discloses a forestry restoration vegetation state identification method and system based on image identification. The method comprises the following steps: obtaining a standard image sequence; segmenting the visible light image through an improved SegFormer network to obtain vegetation areas of different scales, and calculating vegetation canopy temperature distribution characteristics based on the thermal infrared image; extracting vegetation index features based on the multispectral image to obtain multi-scale feature data; according to the multi-scale feature data, utilizing an improved GAT model to process a vegetation spatial relationship graph, learning a spatial dependency relationship between vegetation areas through a multi-head attention mechanism, and generating a vegetation feature vector containing spatial information; and classifying the vegetation states by using an improved multi-scale fusion classifier in combination with the vegetation feature vectors, outputting a vegetation recovery index, and classifying the forestry restoration vegetation states according to the vegetation recovery index. The whole forestry restoration monitoring process is automatic, the monitoring efficiency is improved, and the labor cost is greatly reduced.

The invention provides a real-time accurate measurement method and system for the diameter at breast height of a forest based on a low-power-consumption wireless network, and relates to the technical field of forestry monitoring, and the method comprises the steps: deploying wireless measurement nodes in a forest region, constructing a network topology structure based on a signal intensity value, calculating a data transmission path, distributing a transmission time slot, and collecting the data of the diameter at breast height in real time; optimizing a transmission path according to the transmission delay and the congestion degree, monitoring the node electric quantity and dynamically adjusting the network; and finally, the data is transmitted to a gateway and a report is generated and uploaded to the cloud. According to the invention, real-time and accurate acquisition and reliable transmission of forest tree diameter at breast height data under the condition of low power consumption can be realized, and the efficiency and accuracy of forestry monitoring are improved.

The invention provides a forest carbon reserve prediction method, device and equipment based on forestry carbon sink monitoring, and relates to the technical field of data analysis. According to the method, firstly, a forest optical image and climate-related data are acquired, and the forest optical image at least comprises a red light band and a near-infrared band; secondly, extracting an optical global semantic vector from the forest optical image, and performing semantic constraint on the optical global semantic vector based on growth condition semantic information in the forest optical image to form an optical growth semantic vector; then, a climate global semantic vector is extracted from climate related data; thirdly, performing semantic fusion on the optical growth semantic vector and the climate global semantic vector to obtain a forest global semantic vector; and finally, performing semantic reduction based on the forest global semantic vector to form a carbon reserve prediction result. Based on the above content, the problem that the reliability of forest carbon reserve prediction is relatively low in the prior art can be improved.

The invention discloses a forestry surveying and mapping method based on remote sensing big data, and relates to the technical field of image processing, and the method comprises the following steps: S1, obtaining an initial remote sensing image of a to-be-surveyed area, and carrying out the processing through a feature extraction model, and obtaining a standard remote sensing image; s2, generating a global matrix for the to-be-mapped area according to feature row vectors extracted from the initial remote sensing image and the standard remote sensing image; and S3, determining a final vegetation coverage rate according to the global matrix of the to-be-mapped area. According to the method, the concentration ratio of the vegetation features is analyzed and quantified through the feature values of the global matrix, interference of isolated pseudo vegetation pixels is avoided, the stability of a coverage calculation result is improved, and the method is suitable for large-scale and normalized forestry surveying and mapping scenes.

The invention discloses a forest point cloud branch and leaf separation method fusing double attention and edge perception, and relates to the field of forestry environment monitoring, the method is based on a forest point cloud branch and leaf separation network CLEANet, a classical encoder-decoder architecture is adopted, an encoder layer is composed of a down-sampling module and a channel-local point attention CLPA module, and the channel-local point attention CLPA module is composed of a down-sampling module and a channel-local point attention CLPA module. The decoder layer realizes feature recovery through combination of up-sampling, an edge perception module EAM and a multi-layer perceptron MLP, the CLPA module adaptively strengthens geometric detail and semantic feature expression through a double-attention mechanism and effectively captures wood and leaf component differences, the EAM module enhances perception of a network to a local geometric structure through a neighborhood feature propagation and fusion mechanism, and the local geometric structure is effectively captured. And characteristic mutation of the wood and the leaf at the boundary is captured. The method integrates a channel-local point attention mechanism and edge perception, has excellent robustness, good generalization ability and wide practical application potential, and provides powerful technical support for forest resource investigation and ecological environment monitoring.

The invention discloses a forest tree genotype-environment interaction modeling method based on multi-modal deep learning, and relates to the technical field of forest tree breeding, the method comprises the following specific steps: multi-modal data acquisition: adopting a high-throughput phenotype platform, a whole genome sequencing technology and soil nutrient detection equipment to acquire multi-modal data; sNP locus genotype data, soil key physicochemical index environmental data and growth-related morphology and biomass parameter phenotype data of forest trees are collected respectively; forest genotype, environment and phenotype multi-modal data are collected through the system, the genotype-environment interaction algorithm and the phenotype prediction model are constructed after preprocessing and fusion, the model can accurately predict forest phenotypes, the breeding screening period is remarkably shortened, the breeding selection precision and efficiency are greatly improved, and the method is suitable for large-scale popularization and application. The method effectively solves the problem that a traditional breeding mode is short in time and efficiency, enables breeding work to respond to market demands and environmental changes more quickly and accurately, and provides powerful support for sustainable development of the forestry industry.

The invention discloses a poplar catkin flying intensity prediction method based on meteorological data, and relates to the technical field of forestry prediction, and the method comprises the steps: obtaining meteorological factor data, carrying out the preprocessing, and generating a meteorological factor sequence; carrying out change point identification based on the meteorological factor sequence, constructing Poisson prior distribution, inverse gamma prior distribution and a Gaussian likelihood function, generating posterior distribution by applying Bayesian inference, carrying out sampling to obtain a sampling set, generating credible intervals based on the sampling set, and carrying out merging to generate a credible interval set; backtracking is carried out based on the credible interval set, fitting cost values of corresponding meteorological factor data calculation intervals are obtained, iteration-updating is carried out in combination with a PELT algorithm, during iteration-updating, a hybrid optimization strategy is introduced to obtain optimal parameters of the PELT algorithm for feedback, and an optimal candidate catastrophe point position set is output; according to the method, the accuracy and stability of poplar catkin flying intensity prediction are greatly improved.

The invention discloses a forestry intelligent pest control system based on multi-sensor cooperation, and relates to the technical field of intelligent pest control. In order to solve the problems of low diagnosis precision and poor cooperation efficiency in the prior art, the invention provides a hypothesis-verification cooperation strategy: a system firstly generates a preliminary diagnosis hypothesis by using macroscopic sensor data, and adaptively regulates and controls a microscopic sensor according to the preliminary diagnosis hypothesis to carry out targeted verification so as to realize high-confidence diagnosis; the system further establishes a disease and insect pest spatio-temporal diffusion risk map for trend prediction, and formulates graded and partitioned precise prevention and control strategies. According to the invention, through prevention and treatment effect evaluation and closed-loop self-optimization learning of the model, intelligent prevention and treatment from passive response to active prediction are realized, and the diagnosis accuracy and the resource utilization efficiency are remarkably improved.

The invention relates to the technical field of forestry data processing, in particular to a landscaping maintenance system and method based on big data, and the system comprises a water potential monitoring module, a block division module, a pruning adjustment module, a man-hour scheduling module and an anomaly recognition module. According to the method, by tracking the consistency of soil moisture fluctuation in real time, accurately analyzing the dynamic influence of the root system type and the soil layer structure on water absorption and combining plant evapotranspiration physiological indexes and meteorological condition differences to finely divide water demand grades, efficient matching of water supply and plant physiological statuses is achieved; the pruning period is dynamically adjusted according to the actual change of plant photosynthesis and tissue expansion, the coordination error between maintenance working hour calculation and geographic path planning is effectively reduced, the continuity and timeliness of illumination change abnormal response recognition are improved, the accuracy and refinement level of garden plant maintenance decision making are remarkably improved, and the method is suitable for popularization and application. And the scientific decision-making capability, the space adaptive capability and the intelligent response level of greening maintenance management are improved.

The invention discloses a NeRF modeling method based on multi-scale voxel fusion and total variation regularization, and belongs to the field of image data processing, and the method comprises the steps: constructing a data set D; generating sampling points; generating L-layer spatial features of the forestry three-dimensional scene; for any sampling point x, generating a multi-scale feature of the sampling point x in each layer of spatial feature; constructing a multi-scale fusion NeRF network for generating an input vector according to the multi-scale features, and then outputting a predicted volume density and a predicted color by an MLP; constructing a total loss L; and training a multi-scale fused NeRF network to obtain a forestry three-dimensional scene model, wherein the forestry three-dimensional scene model is used for reconstructing a new view angle image in a forestry three-dimensional scene. According to the method, detail reduction and global consistency of three-dimensional reconstruction can be improved, spatial structure stability and geometric continuity in a dynamic scene are ensured, the problems of high reconstruction noise, detail loss, poor dynamic scene adaptability and the like are solved, and the method is suitable for high-precision three-dimensional scene modeling and real-time ecological monitoring.

The invention discloses a forest biomass nondestructive testing device and method in forestry carbon sink measurement, and relates to the technical field of forestry resource monitoring and carbon sink evaluation, the device comprises a multi-mode sensing unit, an underground biomass monitoring module and a data processing unit, the multi-mode sensing unit collects forest stand data through cooperation of a microwave radar, a laser radar and a hyperspectral imaging assembly. The underground biomass monitoring module collects acoustic impedance data of a root system; the data processing unit comprises an environment feature recognition module, a dynamic calibration engine, an organ recognition unit, a dynamic carbon distribution unit and a carbon sink metering module, the method comprises the following steps: cooperatively acquiring data through an air base and a foundation, analyzing environment features, dynamically calibrating, segmenting organs and generating a carbon distribution coefficient, and finally calculating the carbon sink amount of a single tree according to a formula. According to the invention, non-contact multi-parameter synchronous acquisition is realized, environmental changes are adapted through dynamic calibration, accurate metering is realized by means of organ-level carbon distribution, and the precision and reliability of forestry carbon sink metering are improved.

The invention discloses a forest three-dimensional integrated reconstruction method fusing on-forest and under-forest images, and belongs to the technical field of forestry informatization, three-dimensional reconstruction and remote sensing. According to the method, two collection tasks with complementary functions are executed by deploying the same technology; an advanced unmanned aerial vehicle-based cross surrounding route is utilized to perform canopy surveying and mapping, and a crossing unmanned aerial vehicle is innovatively adopted to perform under-forest crossing collection. And carrying out independent reconstruction and high-precision registration on the two groups of homologous image data through a motion recovery structure algorithm, and finally generating a vertical structure integrated forest three-dimensional model. The result shows that the point cloud reconstructed by the CCO route is close to the airborne laser radar in integrity, and the individual tree parameters extracted by the integrated point cloud are highly related to the foundation laser radar. Through an innovative multi-view and multi-level data acquisition and fusion strategy and a single and economic photogrammetry technology, the method has the potential of obtaining comprehensive forest three-dimensional structure information, and a new method is provided for realizing large-scale and high-precision forest dynamic monitoring.

The invention discloses an automatic forest checking method integrating point cloud precise segmentation and parameter inversion, and relates to the field of forest resource checking, and the method comprises the steps: constructing an automatic 3D forest checking frame with a double-branch point cloud segmentation network as a core, carrying out the preprocessing of forest point cloud data, extracting multi-scale features, and carrying out the segmentation of a point cloud segmentation network; performing mask scoring processing on the multi-scale features, and finally outputting a mask score, a semantic segmentation result and an instance segmentation result; dividing the point cloud into three types of semantic tags of ground, branches and leaves according to the semantic segmentation result, dividing the instance segmentation result into independent individual tree instances, performing convex hull or voxelization processing on the accurately segmented point cloud, inverting forest structure parameters, and completing automatic checking of forest resources. The automatic 3D forest checking framework is excellent in performance, efficiently and accurately draws the boundary of a single tree in a point cloud, realizes high-precision identification and separation of branches and leaves on a sample plot and a tree level, and promotes digitization, automation and intelligentization of forestry resource management.

The invention discloses an intelligent forestry monitoring system, and relates to the technical field of forestry optical monitoring, and the system comprises a multispectral data collection module which is configured to collect optical data by using the optical means of a hyperspectral camera, an infrared thermal imager and LiDAR equipment, a parameter inversion module, calculate a vegetation physiology-structure comprehensive health index, and calculate the vegetation physiology-structure comprehensive health index. The matching degree of a real-time spectrum and a health baseline is calculated in combination with a healthy vegetation spectrum fingerprint database to obtain a spectrum anomaly recognition index, and the risk assessment module is configured to couple the spectrum anomaly recognition index, a pathogenic bacterium development accumulated temperature ratio and environment humidity, calculate a pest and disease outbreak risk index through a time sequence pest and disease outbreak risk index algorithm, and evaluate the disease and disease outbreak risk index. The decision intervention module is configured to divide risk levels according to the pest and disease damage outbreak risk indexes, generate a spatial distribution diagram and output intervention measures, full-chain automation is formed from optical data collection to risk early warning-decision intervention, and then dynamic balance of high-precision early warning-low-cost monitoring is achieved.

The invention discloses a sky-ground integrated forestry resource investigation monitoring method and system, and relates to the technical field of forestry monitoring. Comprising the following steps: constructing gridding of a monitoring forest region to divide the forest region into a plurality of monitoring grids, and establishing an attribute table for each monitoring grid; attribute characteristic parameters are extracted from the attribute information, and a risk quantification portrait is realized; when a fire alarm is sensed through a satellite, an unmanned aerial vehicle, a camera or an artificial channel, a fire event is automatically generated, and the unmanned aerial vehicle is called for accurate re-checking; calculating a final risk value and determining an early warning level through a multi-level correction model by combining the fire scene situation, the meteorological data and the attribute characteristic parameters of the monitoring grid; and finally, according to the early warning level and the terrain complexity of the fire scene grid, automatically matching a preset risk-terrain strategy matrix, and generating an emergency instruction. According to the invention, fire perception, accurate positioning, intelligent research and judgment, strategy generation and scheduling automation are realized, and the emergency response speed of forest fire is improved.