134 results about "Forest resource" patented technology

The invention provides a forest resource remote sensing data spatio-temporal change analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the ground object type classification processing of each pixel according to a spectral unmixing processing result, and generating a forest coverage distribution diagram; extracting all pixel center points on the boundary of the change region as an input point set by using the forest coverage distribution map of the plurality of time points; finding out the outermost points in the input point set through iterative calculation, and connecting the points to form a convex polygon; performing standardized integration on the shape and the range of the change area based on a convex polygon to obtain a standardized description result; calculating the annual change rate of the forest area based on the normalized description result; and calculating vegetation coverage and forest biomass ecological indexes based on the annual change rate of the forest area. According to the method, more accurate and coherent monitoring and evaluation of the space-time change of the forest resources can be realized.

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 a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention provides a multi-source remote sensing data fusion forest resource management method and system, and the method comprises the steps: extracting forest dynamic change features from cross-modal multi-source remote sensing historical data, generating a time sequence correlation curve between cross-modal data, and determining a cross-modal time sequence deviation value between different remote sensing modals through the time sequence correlation curve; calculating the characteristic coincidence degree of different remote sensing modes under the normalized spatial scale for the real-time multi-source remote sensing data, and determining the spatial scale adaptation degree of the real-time multi-source remote sensing data according to all the characteristic coincidence degrees; the fusion feature confidence of the real-time multi-source remote sensing data is calculated through the cross-modal time sequence deviation value and the spatial scale adaptation degree; and generating regionalized management parameters of forest resources of the target forest region based on the fusion feature confidence, and performing resource level-to-level management on the target forest region according to the regionalized management parameters. By adopting the scheme, precise forest resource level-to-level management can be realized based on time consistency and space consistency of multi-source remote sensing data fusion.

The invention relates to the technical field of forest resource investigation and point cloud processing, and discloses a laser radar individual tree segmentation method based on point cloud dimensionality reduction and individual tree network reforming, and the method comprises the steps: taking airborne LiDAR point cloud as a special network structure, replacing a single point with a point cloud cluster, and taking a fitting cluster circle as a hub. Dimensionality reduction processing from the three-dimensional point cloud to the two-dimensional hub is carried out, and redundant information is reduced. Three hub clustering methods (Euclidean distance, four-dimensional similarity and cosine similarity) are provided to construct individual tree hub clusters, and clustering results are mapped back to the three-dimensional point cloud to realize individual tree segmentation. The calculation pressure caused by huge point clouds is effectively relieved, and meanwhile, the segmentation precision and universality are improved; through a multi-strategy hub clustering method, the adaptability and robustness in a complex forest environment are improved; by introducing automatic precision evaluation based on convex hulls, low efficiency and subjectivity of traditional manual discrimination are avoided, objectification and automation of segmentation precision are realized, and the method has high theoretical value and application prospect.

The invention provides a forest degeneration process identification and degeneration degree division method based on a time sequence, and relates to the field of forest resource degeneration restoration. The method comprises the following steps: acquiring surface reflectance data, and calculating an NBR index after preprocessing; fitting an NBR time sequence through a LandTrendr algorithm, and extracting sample place time sequence data based on random sampling and visual interpretation; generating an adversarial network and expanding sample point fitting data into a simulation data set in combination with a self-supervised learning technology; training CNN, random forest and BOSSVS time sequence classification models, and constructing a hybrid classification model through an integrated voting strategy; and identifying a forest degeneration process by using a hybrid classification model, and generating a degeneration degree diagram through reclassification. The method can achieve the efficient recognition of the complex degradation process of the forest region, has good time sequence adaptability and regional applicability, and provides technical support for the monitoring and management of ecological restoration of regional forests.

The invention discloses a multi-source collaborative checking system and method for the real quantity of natural resource assets, and relates to the technical field of natural resource management. The system comprises a data acquisition module used for acquiring multi-source data and real-time data and preprocessing the multi-source data and the real-time data; the knowledge graph processing module is used for constructing a forest resource knowledge graph according to the multi-source data and the real-time data; and the check analysis module is used for constructing an intelligent check model based on the knowledge graph constructed by the knowledge graph processing module. By automatically collecting and preprocessing multi-source data, constructing and dynamically maintaining a knowledge graph, and intelligently checking and analyzing and visually presenting a result based on the graph, efficient data integration, accurate and intelligent checking, trend prediction and active early warning are realized, and a scientific decision basis is provided for urban forest resource management.

The invention discloses a forest resource monitoring system based on unmanned aerial vehicle remote sensing, particularly relates to the technical field of forest resource monitoring, and particularly relates to a forest resource monitoring system based on unmanned aerial vehicle remote sensing. The system comprises an unmanned aerial vehicle platform, a remote sensing sensor carried on the unmanned aerial vehicle platform, a data acquisition module, a data processing module and an output module. The core structure of the system further comprises an intelligent data fusion module and a self-adaptive path planning module. The intelligent data fusion module integrates the multi-source remote sensing data through a feature extraction and weight distribution method to generate a unified data set; and the adaptive path planning module dynamically adjusts the flight path according to the real-time environment data to realize coverage optimization and obstacle avoidance functions, so that the data processing efficiency and path planning adaptability of forest resource monitoring are improved.

The invention relates to the technical field of forest resource investigation and monitoring, and discloses a natural forest multi-source point cloud registration method and system based on point cloud segmentation optimization, and the method comprises the steps: laying a sample plot, collecting the diameter at breast height, the height and the crown breadth of a single tree, and obtaining the space coordinates of the single tree in combination with RTK; according to the method, unmanned aerial vehicle laser scanning and backpack laser scanning are adopted to collect and preprocess point cloud data of a research area, an ICP algorithm is adopted to align a source point cloud with a target point cloud through rotation and translation, point cloud registration is achieved through a minimization error function, point cloud registration precision and errors are quantified through evaluation indexes, and point cloud registration evaluation is carried out. Therefore, the point cloud registration precision is effectively improved through a segmented registration strategy, limitation caused by single sensor data can be overcome through fusion of BLS and ULS multi-source point cloud data, the extraction precision of tree structure parameters is remarkably improved, and the method is not only suitable for point cloud registration of natural forests, but also suitable for point cloud registration of natural forests. And reliable data support and technical support are provided for forest resource monitoring and ecological protection.

The invention discloses a handheld forest region sample plot calibration surveying and mapping device and method, and relates to the technical field of forest resource investigation, and the device comprises a laser radar, a visual camera and a global navigation satellite system / inertial orientation positioning navigation system integrated navigation unit; when a user carries the device to move, the processing unit constructs a three-dimensional point cloud map and calculates a six-degree-of-freedom pose; when the signal of the global navigation satellite system is unavailable, calculating a current position by combining the three-dimensional point cloud map, the six-degree-of-freedom pose and inertial data; and the processing unit calculates parameters of the stumpage and the candidate area and generates a sample plot calibration surveying and mapping report. According to the invention, the device achieves the synchronous capturing of the characteristics of the trunk base and the canopy at a handheld height, an operator only needs to click and select an initial position on a touch screen, and the system combines laser instant positioning and map construction with a global navigation satellite system / inertial orientation positioning navigation system for tight coupling positioning. And automatically generating sample plot boundaries and ecological parameters conforming to regulations.

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 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 relates to the technical field of forest resource monitoring, in particular to a forest carbon reserve calculation system and method, and the method comprises the steps: obtaining target data corresponding to a target forest; obtaining forest stand features, climate features and external influence features corresponding to the target forest based on the target data; obtaining forest types, forest stand density, forest stand age groups and soil characteristics based on forest stand characteristics; based on the forest type and the stand density, vegetation carbon reserves are obtained; based on soil characteristics and forest stand age groups, obtaining soil carbon reserves; obtaining a carbon reserve calibration coefficient based on the climate characteristics and the external influence characteristics; and obtaining the forest carbon reserve based on the vegetation carbon reserve, the soil carbon reserve and the carbon reserve calibration coefficient. According to the invention, the calculation precision of the carbon reserves can be improved.

The invention relates to the technical field of image processing, and discloses a forest resource prediction method based on remote sensing image analysis, which comprises the following steps: acquiring a multi-source remote sensing image, performing radiation and geometric correction, and acquiring a registered multi-source remote sensing image through image registration; performing wavelet decomposition on the registered multi-source remote sensing image, performing weighted fusion on low-frequency components, performing fusion on high-frequency components based on regional energy, and performing wavelet inverse transformation to generate a fused remote sensing image; extracting spectral features of the fused remote sensing image, and generating a spectral feature matrix; taking the spectral feature matrix as feature variables, taking actual observation values of forest resources as prediction target variables, calculating mutual information values between the feature variables and the prediction target variables to screen optimal spectral features, and finally realizing forest resource prediction by calculating a Gini index and an information gain and constructing an improved random forest model. According to the invention, the precision of the forest resource prediction result is improved.

The invention discloses a physical constraint deep learning forest biomass estimation method and system. The method specifically comprises the steps of satellite-borne laser radar GEDI footprint preprocessing and quality screening; sAR radiometric calibration, terrain correction and speckle noise filtering are carried out; optical data radiation correction, atmospheric correction and geometric correction; multi-source remote sensing data space-time alignment and feature variable extraction; constructing a deep learning model fused with physical constraints; feature optimization and model reconstruction are carried out based on space-time interpretability analysis (SHAP); performing comparison and precision verification on the reconstructed final model; and generating a high-resolution annual forest biomass map based on the optimal deep learning model. The estimation result obtained by the method provided by the invention has relatively high precision and accuracy, and scientific data support and decision suggestions are provided for regional forest resource management and carbon sink capability evaluation.

The invention discloses an unmanned aerial vehicle aerial photography-based forest tree measurement automatic measurement method and system. The measurement method comprises the steps of obtaining a multispectral image set of a complex forest environment through unmanned aerial vehicle aerial photography; preprocessing the multispectral image set, and obtaining an image feature set by adopting a denoising and spectral correction method; determining a tree species distribution category according to the image feature set; based on the tree species distribution category, segmenting the overlapped tree crowns through a deep convolutional neural network to obtain an independent tree crown region set; obtaining crown breadth parameters according to the independent crown region set; extracting crown height information from the crown breadth parameters to obtain a tree height parameter set; estimating the diameter at breast height by applying a regression analysis model on the basis of the tree height parameter set in combination with near-infrared characteristics of the multispectral image to obtain a diameter at breast height parameter set; and generating a comprehensive forest resource data set by adopting a data fusion technology through tree species distribution categories, crown breadth parameters, a tree height parameter set and a diameter at breast height parameter set, and determining a final measurement result.

The invention discloses a method for extracting parameters of a (subtropical) eucalyptus man-made forest based on an unmanned aerial vehicle laser radar. The method comprises the following steps: investigating a sample plot, and measuring and calculating actual forest parameters such as average diameter, average height, sectional area, stand volume and the like; acquiring and preprocessing high-density unmanned aerial vehicle laser radar point cloud data, and extracting three types of feature variables of height, density and vertical structure; deducing a model structural formula on the basis of a stock different-speed growth equation, and screening variables according to rules; constructing a power model by using a regular exhaustion method; the optimal model is screened out through model parameter estimation, inspection, significance analysis and the like. According to the method, the variable synergistic effect is mined through the exhaustion method, the multi-dimensional test system is constructed, and the exclusive model is customized, so that the problems of one-sided variable screening, insufficient model reliability and poor specific forest stand adaptability in the prior art are solved, high-precision estimation of core forest parameters is realized, and the method is suitable for large-scale popularization and application. And scientific and effective technical support is provided for refined management and sustainable operation of eucalyptus man-made forest resources.

The invention relates to a space laser radar forest structure data correction method and system, and belongs to the technical field of space laser remote sensing and forest resource monitoring. Aiming at four kinds of errors of geometric positioning offset, slow distortion, terrain broadening effect and canopy structure influence of GEDI data, UAV control points are arranged by adopting a hierarchical arrangement strategy, a homonymy point relation is established through waveform registration, and weights are calculated; performing overall geometric correction based on the regular grid control points; carrying out local distortion correction based on the encryption control point; establishing a physical compensation model to compensate the terrain broadening effect; based on the canopy structure parameters and GEDI signal quality indexes, establishing a statistical regression model to correct a canopy height residual error; and finally, obtaining a high-precision canopy height convergence result through spatial smoothing processing, and outputting corrected data. The method achieves step-by-step precision improvement from the whole domain to the local domain and from the geometry to the height, and is low in cost, high in precision and wide in application range.

The application relates to the technical field of forest resource detection, and discloses a forest resource detection method based on satellite image data, which comprises the following steps: acquiring initial satellite image data of a target forest area, pre-processing the initial satellite image data to obtain first satellite image data; performing radiation atmosphere correction on the first satellite image data through a radiation atmosphere correction model to obtain second satellite image data; performing super-resolution reconstruction on the second satellite image data according to a reconstruction enhancement model to obtain third satellite image data; identifying the third satellite image data based on a forest ground object extraction model to obtain forest ground object information; and identifying the third satellite image data and the forest ground object information through a forest resource detection model to obtain a forest resource distribution map of the target forest area. The application improves the precision and accuracy of forest resource detection, enhances the resolution of satellite image data, and realizes accurate identification of forest ground object information through the forest ground object extraction model.

The application discloses a prediction method of tree species structure of arbor forest in subtropical areas, and comprises the following steps: 1) dividing the tree species of arbor forest into three categories of pines, firs and broad-leaved trees, and establishing a prediction model according to volume proportion data of each tree species category obtained from fixed sample plots in previous forest resource surveys; 2) taking two-period survey data as modeling data, and fitting parameters by using STATA software; 3) substituting measured data of each sample plot into the prediction model for iterative calculation, obtaining volume proportion values of the future arbor forest in the two-period interval as prediction values; calculating average prediction values of the prediction values of the tree species proportions of all sample plots; and 4) comparing the prediction value of the volume proportion of the broad-leaved trees or the sum of the prediction values of the volume proportions of the pines and firs in the average prediction values of the tree species proportions with a critical value of coniferous and broad-leaved mixed forest, and determining whether artificial intervention needs to be applied in actual forestry management. The application can predict the tree species structure of arbor forest in future years, and has the function of guiding forestry production and operation activities.

This invention relates to the field of forestry resource monitoring technology, and discloses a dynamic monitoring system and method for forestry resources based on remote sensing imagery. The system includes: generating temporal remote sensing reflectance data within the same grid; generating a continuous canopy occupancy map; generating an infill set; determining the intrinsic neck scale; calculating the topological spectrum intensity of the continuous canopy infill; calculating the interlayer decoupling topological index; generating monitoring judgment results; and outputting change patches. This invention constructs an adaptive multi-scale erosion sequence based on the intrinsic neck scale, combines Eulerian features to quantify the infill topological fragmentation process, and integrates the degree of infill fragmentation with the stability state of the outer envelope through the interlayer decoupling topological index. This achieves accurate determination of change units and precise spatial patch location, avoiding the omissions or misjudgments of deep forest understory structure changes by traditional techniques, ensuring that the monitoring results accurately reflect the actual mechanisms of dynamic changes in forest resources.

The invention relates to an urban forest resource monitoring system and method based on multi-source remote sensing fusion, and the system constructs a sound-vibration cooperative control and edge intelligent processing dual-core system architecture, collects the remote sensing data of an urban complex operation environment in a high-quality manner, achieves the total-factor centimeter-level perception and robust obstacle avoidance of the urban forest complex environment, and achieves the real-time monitoring of the urban forest complex environment. And the operation safety and the data integrity are ensured. On the basis of interpretable AI and multi-modal fusion knowledge acquisition, scale pushing from a single tree to a forest stand is implemented, high-precision intelligent analysis of forest stand scale parameters is completed, and automatic and quantitative inversion of forest stand scale key parameters is realized at an airborne edge end. According to the system, key stand structure factors such as stand density, canopy density, average height and variation coefficient are automatically calculated by aggregating individual tree detection results, single-machine intelligence and group cooperation are realized, and large-scale and high-efficiency urban forest resource checking and dynamic monitoring can be realized.

The invention relates to the technical field of forest fire prevention, in particular to a large-scale forest ground surface litter load calculation method. The method comprises the steps of obtaining a forest resource partition to which a target area belongs; determining the forest type of each forest class in the target area based on the national forest resource checking data; according to the forest resource subarea to which the target area belongs and the forest type of each forest class in the target area, establishing an above-ground biomass and stock regression model of each forest class in the target area; acquiring the aboveground biomass of each forest class in the target area according to the aboveground biomass and accumulation regression model; obtaining the land surface litter carrying capacity of each forest class in the target area by inquiring the proportion of the land surface litter carrying capacity in the aboveground biomass in the forest class of each forest type; and obtaining spatial distribution of the surface litter load of the target area according to the surface litter load of all forest classes in the target area. According to the method, the efficiency and accuracy of large-scale forest surface litter load calculation can be effectively improved.

The invention provides a forest stand factor estimation method and system based on airborne laser radar data, and a terminal, and relates to the technical field of image processing, and the method comprises the steps: carrying out the normalization processing of laser radar point cloud data of a target forest region, and obtaining the standard point cloud data of the target forest region; key feature vectors in the standard point cloud data are extracted; determining the crown volume and the crown surface area of the target forest region according to the key feature vector; performing forest resource evaluation on the target forest region according to the estimated tree species in the target forest region, the crown volume and the crown surface area to obtain a forest stand factor of the target forest region; the method has the advantage that the accuracy of estimating the forest stand factor is improved.

The invention discloses a tree skeleton branch level division method based on point cloud. The method comprises the following steps: step 1, simplifying a tree skeleton; and step 2, identifying a tree skeleton branch hierarchical structure. According to the method, the point cloud scanned by the ground three-dimensional laser scanner serves as data, the method capable of simplifying the tree skeleton and accurately recognizing the tree skeleton branch hierarchical structure is constructed, the number of branches of the tree skeleton branch hierarchical structure is recognized according to the method, and the recognized number of branches has great significance in forest resource checking.

The present application provides a method for extracting short-time-span growth of eucalyptus plantations based on UAV data, and relates to the field of forest resource investigation and forestry quantitative remote sensing research. The method comprises the following steps: field investigation and UAV data collection are carried out on selected eucalyptus plantation sample plots; DEM and monthly DOM and DSM of the sample plot are obtained, and monthly CHM is obtained by subtracting monthly DSM from DEM; a training set is divided from a data set composed of 12-month CHM, each tree crown on the CHM is labeled to obtain a labeled training set, and single tree segmentation is carried out by using the training set data and deep learning algorithm to obtain a monthly single tree segmentation vector diagram; single tree height, single tree crown and DOM spectral reflectance index of the sample plot are extracted; a single tree volume inversion model of the sample plot is constructed; single tree volume of eucalyptus plantations of the sample plot is extracted every month, and the sum is obtained to obtain stand volume, so as to realize short-time-span growth extraction of single tree and stand of eucalyptus plantations.

This invention discloses a method for registering forest point clouds from UAV-LiDAR during leafy and leafless periods, relating to photogrammetry, remote sensing mapping, and forest resource monitoring. The method extracts ground points from two phases of UAV-LiDAR point clouds, constructs multi-directional radial topographic profiles, and extracts local profile segments using a sliding window. It calculates a comprehensive similarity based on trend consistency, robustness to local shape deformation, and curvature similarity, and selects stable matching segments by combining radial distance and topographic undulation amplitude consistency constraints. Three-dimensional corresponding point pairs are generated from the matching segments, and the initial rigid body transformation is solved using SVD. The registration is then optimized using ICP to obtain a multi-temporal fused point cloud. This invention can achieve reliable registration under conditions of phenological differences, missing vegetation structure, and initial biases, providing support for the reconstruction of three-dimensional forest structures and refined resource monitoring.

The invention discloses an image data acquisition method and device based on an unattended intelligent airport, and relates to the technical field of image data processing.The method comprises the steps that forest image data are acquired based on the unattended intelligent airport, the unattended intelligent airport selects a forest area meeting preset mountain forest conditions, and the selected forest area is selected as a forest area; the unattended intelligent airport is used for taking off, landing and parking a predetermined type of unmanned aerial vehicle, and the predetermined mountain forest conditions comprise that the height fall of a mountain land is greater than a preset fall threshold value, the forest vegetation coverage rate is higher than a preset coverage rate threshold value, and the number of roads in a forest is lower than a preset number threshold value; and analyzing the preprocessed forest image data by adopting a pre-trained machine learning model, identifying a forest resource state, and displaying the forest resource state on a terminal screen. The technical problems that image resource analysis is difficult to carry out on an unattended intelligent airport system in a severe environment and the operation efficiency is low in the prior art are solved.

The invention relates to the technical field of patrol robots, and provides a bionic multi-legged patrol robot suitable for forest complex terrains, which comprises a base, one end of the base is provided with an active walking mechanism, the outer edge of the outer side of the base is provided with an auxiliary walking mechanism, and the auxiliary walking mechanism and the active walking mechanism are distributed at the same angle. A rotating motor is fixedly mounted at the bottom of the mounting frame, an output shaft of the rotating motor is fixedly connected with the base, a visual monitoring mechanism for inspecting the forest area is also arranged at the top of the mounting frame, a plurality of guide mechanisms for guiding the base are arranged on the outer edge of the top of the mounting frame, and the guide mechanisms are distributed around the base at equal angles. Through the bionics principle and the intelligent control technology, stable movement, comprehensive monitoring and energy efficiency optimization of the robot under the complex terrain of the forest region are achieved, the beneficial effects are remarkable, and technical support is provided for forest resource management.