389 results about "Multispectral data" patented technology

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention discloses an online detection system and method based on ink-jet printing, and relates to the technical field of ink-jet printing, and the method comprises the steps: collecting the operation state data of ink-jet printing in real time, and transmitting the data to a deep learning model; predicting the track, drop point and shape change of the ink droplet through a deep learning model in combination with a fluid dynamics principle, generating a prediction result, and providing positioning information of an ink jet area; inputting the multispectral data into a convolutional neural network model, identifying color deviation and defect types in combination with a prediction result, and generating corresponding correction parameters; the correction parameters are transmitted to ink jet control equipment, and the jet parameters of the nozzle are adjusted in real time; according to the invention, the information of the boundary, the central point, the area, the coverage rate and the offset of the ink jet area is generated in real time, and a high-precision positioning basis is provided for subsequent multispectral detection and defect correction, so that efficient and accurate online detection and ink jet quality control are realized.

The invention relates to the technical field of hydraulic engineering intelligent operation and maintenance, and discloses an AI unmanned aerial vehicle-based auxiliary decision-making platform for hydraulic engineering operation and maintenance, and the platform comprises a data collection module which is used for obtaining multi-modal data of a water conservancy facility through an AI unmanned aerial vehicle, the multi-modal data comprises a visible light image, thermal imaging data, multispectral data and laser radar data; the data processing and fusion module is used for carrying out formatting processing, space-time alignment and feature fusion on the multi-modal data; the health assessment and prediction module is used for assessing the health state of the water conservancy facility based on the processed data and predicting the future change trend of the facility state; and the auxiliary decision-making module is used for generating inspection priority planning of the water conservancy facilities. According to the invention, through multi-source data fusion, dynamic risk assessment and intelligent optimization distribution, the effects of accurate monitoring, efficient operation and maintenance and risk visual management of the whole life cycle of the water conservancy facilities are realized.

The invention discloses an amphibious unmanned aerial vehicle riverway sediment concentration real-time monitoring method based on underwater light field imaging and cross-modal fusion, and relates to the technical field of riverway sediment monitoring. Comprising the following steps: acquiring aerial multispectral data and preprocessing the aerial multispectral data; an integrated polarized light field camera is carried on an amphibious unmanned aerial vehicle to capture underwater light field data, and underwater three-dimensional light field information is reconstructed and sediment scattering characteristics are extracted through an optical model and a lightweight deep learning network; the air multispectral data and sediment scattering characteristics are fused through space-time alignment and an attention mechanism, a sediment concentration prediction model is generated, and then a spatial distribution diagram of the river sediment concentration is output; and carrying out real-time monitoring and early warning on the river sediment concentration according to the spatial distribution diagram of the river sediment concentration through edge calculation and cloud collaboration architecture. The method is combined with an advanced deep learning algorithm, high-resolution silt concentration inversion can be realized, and the method has real-time performance and adapts to a complex water body environment.

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention discloses an intelligent management method for liquidation receipts based on multi-modal data fusion, and particularly relates to the field of data analysis. Comprising the steps of S1, multispectral data acquisition in a limited illumination environment, S2, cross-modal feature decoupling and recombination, S3, space-time heterograph neural network analysis, S4, multi-scale attention decision fusion, S5, resistance enhancement verification, and S6, incremental management based on knowledge distillation. According to the method, the physical anti-counterfeiting capability is remarkably improved, the paper material, the ink components and the surface structure are deeply analyzed through a multispectral sequence acquisition mechanism, and hidden tampering behaviors such as color fading and chemical altering of the thermo-sensitive paper are accurately identified. Cross-modal deep correlation analysis is achieved in a breakthrough mode, a physical-semantic decoupling technology and a space-time heterogeneous graph network are adopted for modeling, and non-dominant laws such as commodity position offset and tax rate anomaly are effectively captured.

The invention discloses a zinc coating shedding performance detection method and system, particularly relates to the technical field of metal surface treatment quality detection, and is used for solving the problems of high misjudgment rate and insufficient detection precision caused by coating surface pseudo defect interference in the existing method. The method comprises the following steps: synchronously acquiring a multispectral reflection image and three-dimensional morphology data of the surface of a zinc coating through an industrial vision imaging device, extracting grain boundary distribution information based on multispectral data, and identifying radial pseudo defects in combination with a curvature manifold geodesic line energy gradient direction of the three-dimensional morphology; and further performing micro-crack topology analysis and grain boundary spatial correlation verification on the candidate region, eliminating texture interference and grain boundary overlapping regions, and finally outputting a high-confidence fall-off defect detection result, so that the identification capability of real defects under a complex galvanized surface is remarkably improved, and the method is suitable for automatic detection requirements of a high-speed continuous production line.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention provides an automatic intelligent defect analysis method and system based on power transmission and distribution line unmanned aerial vehicle image acquisition. The method comprises the following steps: acquiring infrared thermal imaging time sequence data and multispectral imaging data of the surface of a wire through an unmanned aerial vehicle carrying an infrared thermal imager and a multispectral camera; calculating a temperature gradient direction of a conductor main body area, screening an abnormal hot spot area whose axial included angle with the conductor is smaller than a preset angle, and positioning an oxidation area boundary by using a spectral absorption attenuation curve of an oxidation layer in the multispectral data; performing space matching on the hot spot migration direction and the oxidation boundary to generate a visual map; establishing a dynamic association rule according to an included angle variable quantity of a hot spot path in the map and a spectrum abrupt change point in the oxidation region; according to the critical angle threshold value and the sudden change point number threshold value, the defect grade is judged, and a defect analysis report is generated. According to the technical scheme provided by the invention, the automation degree and accuracy of defect detection are improved.

The invention discloses a shoal plastic garbage unmanned aerial vehicle monitoring method based on map characteristics. The method is characterized by comprising the steps of carrying out aerial photography modeling on a multispectral unmanned aerial vehicle, preprocessing visible light and multispectral data, training an instance segmentation model to detect garbage targets and generate masks at the same time, and extracting image and spectral features of each garbage target; and classifying and identifying each garbage in combination with the image and spectral features, and establishing a mass-occupied area model of each garbage for stock estimation, and the like. According to the method, the image features reflecting the morphology difference of the plastic products and the spectral features reflecting the material difference are extracted based on the instance segmentation model, the recognition dimension of the plastic garbage is increased, and compared with the prior art, multi-dimensional high-precision plastic garbage type recognition is achieved by combining the image features and the spectral features; and the plastic garbage stock estimation corresponding to the category is realized based on the identification result, and the method has good application prospects and commercial development values.

The invention relates to the technical field of vegetation remote sensing recognition, in particular to a multispectral and visible light remote sensing image fusion segmentation method and system and a medium. The method comprises the following steps: constructing a double-branch model comprising a visible light branch encoder, a multispectral branch encoder, a multi-scale feature fusion module and a shared decoder; inputting the visible light data into a visible light branch encoder to obtain a spatial feature map; inputting the multispectral data into a multispectral branch encoder to obtain a spectral feature map; the multi-scale feature fusion module applies a channel-space joint attention weight to an input feature map, then performs channel splicing according to scales, compresses the feature map to an original channel number by adopting 1 * 1 convolution, and then applies a channel attention weight to obtain a multi-scale fusion feature sequence; and the shared decoder analyzes the multi-scale fusion feature sequence and outputs an invasive plant segmentation mask image of the target detection area, so that high-robustness invasive plant identification under a complex background is realized.

The invention relates to the technical field of information, in particular to an intelligent agricultural information big data intelligent acquisition management system and method. The method comprises the following steps: acquiring farm sensing data, and performing crop growth analysis according to the farm sensing data so as to obtain crop growth data; performing crop health state feature extraction and crop physiological feature extraction according to the crop growth data so as to obtain crop health state data and crop physiological data; performing disease and pest detection according to the crop health state data and the crop physiological data to obtain crop disease and pest data; the method comprises the following steps: acquiring farm remote sensing data, performing crop multispectral analysis according to the farm remote sensing data so as to obtain crop multispectral data, and performing crop moisture state evaluation according to the crop multispectral data so as to obtain crop moisture state data. The pesticide spraying strategy is optimized based on the information technology, and the agricultural production management efficiency is improved.

The invention provides an ocean red tide early warning method based on remote sensing inversion and a time-space map convolutional network. The ocean red tide early warning method comprises the following steps: collecting hyperspectral data and multispectral data; respectively carrying out data quality inspection on the collected hyperspectral data and multispectral data; preprocessing the hyperspectral data and the multispectral data after the quality inspection is qualified to respectively obtain a hyperspectral result image and a digital orthoimage; analyzing the hyperspectral result image and the digital orthoimage to obtain remote sensing inversion data of water temperature, salinity, total nitrogen, total phosphorus and chlorophyll a, and generating a gradient distribution thematic map of each index; according to water temperature, salinity, total nitrogen, total phosphorus, chlorophyll a and historical monitoring data, in combination with a change trend of key environmental parameters, introducing a space-time diagram convolutional network model, simulating a red tide diffusion situation, setting a risk threshold, and performing risk prediction on red tide occurrence through a red tide occurrence risk prediction model; and updating the red tide occurrence risk prediction model based on buoy monitoring and field monitoring data.

The invention relates to the technical field of robot reconnaissance, and discloses a tracked robot intelligent reconnaissance system based on panoramic vision and multispectral analysis, which comprises the following steps: dynamically adjusting sensor parameters based on environmental spectral characteristics to adapt to different light conditions, and collecting high-quality panoramic vision and multispectral data; performing panoramic vision fusion processing on the acquired data to compensate light change in a complex environment and enhance image quality; performing multispectral data analysis according to the fused data, and classifying target materials based on an analysis result to improve reconnaissance accuracy; and a relevance optimization algorithm of visual data and spectral information is combined to filter interference in multi-target reconnaissance and feed back and correct path planning of the robot in real time. Target materials are classified according to a multispectral data analysis result to solve the problem of insufficient target recognition accuracy in the reconnaissance process, and the success rate and high efficiency of a reconnaissance task are ensured by combining machine learning with a prediction algorithm.

According to the unmanned aerial vehicle multi-source quantitative remote sensing rape growth state evaluation method, rape is divided into a seedling stage, a flowering stage and a legume stage according to growth nodes, ground hyperspectral and multi-spectral data are synchronously obtained, resolution is increased and decreased, the data are converted into all-dimensional, multi-scale and multi-temporal data, a growth state factor inversion model of the rape is established, and the rape growth state is evaluated. Evaluating the contribution of different growth state factors to the growth vigor of the rape, and evaluating the growth state of the rape based on comprehensive evaluation indexes of leaf area index, overground biomass and chlorophyll content. The method comprises the following steps: establishing a model based on multi-layer linear regression and partial least square optimization models of three types of growth factors LAI, AGB and CC, multi-mode prediction based on entropy aggregation and hierarchical analysis of entropy loading expert knowledge in combination with expert knowledge and agronomic knowledge, and setting a model with an optimal verification index in different growth periods as a growth state evaluation model in the period. The rape growth state evaluation period is short, the pertinence is strong, and the accuracy is good.

This invention relates to a pair of intelligent, multimodal smart eyeglasses designed for adaptive vision, biometric ocular monitoring, and predictive emergency response. The system integrates multilayer lenses filled with electrochromic gel and surrounded by elastic structures to enable dynamic focal length modulation in response to user visual behavior. An array of sensors—including visible light, ultraviolet, micro-cameras, infrared emitters, gyroscopes, and ultrasonic transceivers—collects multispectral data to evaluate environmental and physiological parameters. Real-time data processing is performed by an embedded quantum microcontroller, which manages light transmittance, detects eye fatigue, maps ocular surfaces, and identifies hemodynamic abnormalities such as fainting, seizure, or ocular stroke. Advanced imaging techniques, such as ultrasonic ptychography and wave diffraction analysis, enable non-invasive 3D mapping of ocular layers and internal eye motion. The system also supports wireless communication with external platforms including smartphones, vehicles, and brain-machine interfaces, enabling proactive safety interventions. These features work in tandem to support early diagnosis of critical conditions, protect vision, and personalize user experiences through machine learning and adaptive feedback mechanisms.

The invention relates to the technical field of satellite-borne hyperspectral image processing and analysis, and solves the technical problem that the huge spectral and spatial resolution difference between low-resolution hyperspectral data and high-resolution multispectral data cannot be fully considered in the existing method. The forest hyperspectral satellite image-oriented deep and shallow double-branch super-resolution method comprises the steps of constructing a double-branch network architecture, performing feature fusion reconstruction on output features of the double-branch network architecture, and obtaining high-resolution hyperspectral data with low spectral variation characteristics of forest vegetation in spaceborne hyperspectral image data. According to the method, the problem of modal difference between low-resolution hyperspectral data and high-resolution multispectral data is effectively solved by learning on different feature levels and scales, and the model is enabled to pay more attention to low-spectral variation characteristics of different forest vegetation in a satellite image through a plurality of feature attention mechanisms. And the requirements of subsequent fine monitoring tasks of various forest resources can be met.

The invention provides an automatic online detection method and system for the surface smoothness of a precise injection mold, and relates to the field of precise manufacturing, the method comprises the following steps: S1, synchronously collecting visible light and near-infrared reflection images of the surface of the mold through multispectral coaxial imaging; s2, calculating a dynamic texture parameter based on a multi-directional gradient field weighted variance, and generating a composite reflection parameter in combination with wavelet domain fusion; s3, fusing the features by adopting a dual-path neural network, and comparing the standard library through a modified Stereer ratio model to output the degree of finish; and S4, adjusting the period in real time according to the detected dynamic standard deviation and linking closed-loop control. According to the method, the restriction of single-band characterization is broken through through multispectral data fusion, and the processing speed is increased by utilizing gradient field parallel computing hardware; in combination with a self-adaptive threshold value and a process parameter dynamic correction mechanism, the problems of poor adaptability of a fixed period, high hardware delay and single feature dimension of a traditional method are solved, and the online detection precision and the process control efficiency are remarkably optimized.

The invention belongs to the technical field of crop growth prediction, and particularly relates to a crop growth prediction method and system based on multispectral unmanned aerial vehicle monitoring. The method comprises the following steps: acquiring multi-dimensional data of real crops in different growth stages under different soil water contents and disease and insect pest states, determining the contribution degree of each vegetation index to crop growth through a factor analysis algorithm, carrying out dimension reduction on hyperspectral data by using a principal component analysis algorithm, fusing with the multi-spectral data, constructing a multi-spectral resolution characteristic space, and carrying out multi-spectral analysis on the hyperspectral data. The method comprises the following steps: firstly, obtaining a real plant height growth fitting function of crops through analogue simulation by combining LiDAR data and vegetation indexes, thirdly, calculating a real growth vegetation index space and obtaining a real growth state space of a standard staged growth period, and finally, inputting the calculated space and function into a model constructed by a reinforcement learning algorithm for training, and accurate prediction of the crop growth state is realized.

The invention discloses a heparin production process optimization method and system based on environmental analysis, and aims to improve the purity and quality of heparin production. The method comprises the following steps: acquiring historical environmental monitoring data and multispectral data of an enzymolysis reaction kettle, and determining substance generation paths under different environmental conditions; establishing a nonlinear mapping model of environmental parameters and production purity based on a random forest algorithm, and calculating the influence weight of each parameter; generating an environment-adaptive enzymolysis reaction optimization scheme according to the weight; the method comprises the following steps: detecting molecular weight distribution data of a heparin crude product, and dynamically adjusting enzymolysis reaction parameters in combination with vibration spectrum and molecular conformation change characteristics; and finally, carrying out multi-target matching on the environment optimization scheme and the reaction parameters to generate a production optimization scheme containing an environment-process parameter mapping relation. The method can effectively improve the heparin production efficiency and quality, and is suitable for industrial production optimization.

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

The invention discloses an unmanned aerial vehicle target segmentation and detection method based on large model visual perception, and the method comprises the steps: obtaining a to-be-detected data stream in real time through a multi-region spectral imaging module carried by an unmanned aerial vehicle, effectively supplementing the deficiency of single sensor information through the fusion of multi-spectral data, and improving the multi-dimensional information expression of an image; the image of the image quality evaluation network is used for noise evaluation and image optimization processing, the quality of the image is further improved, and the accuracy of a subsequent detection result is ensured; through an adaptive enhancement algorithm and weighted fusion, multi-system feature maps can be fused more accurately, so that multi-level and multi-scale feature information can be fully integrated, and a comprehensive feature map with cross-topology information is obtained. Based on a target segmentation detection model of a deformable structure, an accurate pixel-level segmentation mask and a bounding box can be generated for the problems of target deformation and posture change.

The invention relates to a data analysis technology, and discloses a spectral feature and artificial intelligence fused grain heavy metal detection method and system.The method comprises the steps that a near-infrared diffuse reflection spectrum, a laser-induced breakdown spectrum and a surface enhanced Raman spectrum of a grain sample are obtained, a matrix accumulation area thermodynamic diagram is constructed according to the near-infrared diffuse reflection spectrum, and the matrix accumulation area thermodynamic diagram is obtained; the method comprises the following steps: optimizing a laser-induced breakdown spectrum based on a thermodynamic diagram of a matrix accumulation area, carrying out dual-channel spatial-temporal feature extraction on a surface enhanced Raman spectrum and the optimized laser-induced breakdown spectrum to obtain a fusion feature vector, carrying out weighted fusion optimization on the fusion feature vector based on a signal-to-noise ratio of multispectral data to obtain an optimized fusion feature vector, and finally obtaining a fusion feature vector. And carrying out migration adaptation on a pre-trained grain detection model by using a pre-marked new grain sample, and carrying out heavy metal content identification on the grain sample by using the adaptive grain detection model according to the optimized fusion feature vector to obtain a heavy metal detection result. The precision of grain heavy metal detection can be improved.

The invention belongs to the technical field of lake remote sensing monitoring, discloses a lake water level and water reserve dynamic monitoring method based on satellite-borne active and passive remote sensing information fusion, and effectively solves the problem that lake information in remote areas is difficult to efficiently and accurately obtain. Comprising the following steps: S1, preprocessing laser data; s2, preprocessing the multispectral data; s3, taking the ICESat-2 data as a core, combining with a multispectral image covering a corresponding region, and constructing a lake bottom DEM inversion data set based on neighborhood features; s4, constructing and training an elevation inversion convolutional neural network model; s5, predicting and generating a lake bottom DEM by using the trained elevation inversion convolutional neural network model; and S6, calculating the absolute water volume of the lake according to the DEM at the bottom of the lake, and carrying out lake water level-water reserve monitoring. According to the invention, long-time-sequence hydrological observation of remote region lakes can be effectively realized with low cost.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention discloses a multi-modal fusion identification method and system for rice leaf type diseases and insect pests. The method comprises the following steps: respectively carrying out feature extraction on image data and non-imaging multispectral data of a detected rice area to obtain image features and spectral features; performing cross attention feature extraction on the image features and the spectral features, and performing adaptive gating feature fusion on the obtained features to obtain modal fusion features; according to the gating feature fusion structure provided by the invention, the fusion weight is dynamically adjusted according to different input features so as to improve the flexibility of model information fusion, so that the fusion features containing different modal effective information are obtained, and the accuracy of rice leaf type disease and pest identification is improved. In addition, the spectral reflectivity data and the spectral vegetation index are subjected to feature extraction at the same time, feature encoders corresponding to the spectral reflectivity data and the spectral vegetation index are stacked for multiple times, and full extraction of spectral features is achieved.

The invention provides a small-region forest carbon sink accurate estimation method and system based on multiple constraints, and the method comprises the steps: respectively generating a canopy height model, a leaf area index and a chlorophyll content through obtaining unmanned plane laser radar data and multispectral data; generating a digital elevation model by adopting a lowest point filtering technology, realizing individual tree segmentation in combination with a gradient map and a watershed algorithm, and extracting individual tree geometric parameters; and constructing a carbon sink estimation model by using the geometric parameters and the physical and chemical parameters, and comprehensively calculating the carbon sink amount of the small region. The problems that a traditional carbon sink estimation method is insufficient in precision and low in automation level are solved, the single-tree segmentation precision and the carbon sink amount calculation precision are improved through deep fusion of geometric information and spectral information, full-process automation of data acquisition, data processing and carbon sink estimation is achieved, and the method is suitable for large-scale popularization and application. The method is suitable for carbon sink monitoring requirements of various forest types and complex terrain scenes.

The invention discloses a plant protection unmanned aerial vehicle operation method fusing weed recognition and real-time variable spraying, and the method comprises the following steps: employing a plant protection unmanned aerial vehicle to carry a multispectral sensor, collecting multispectral data in real time, and generating a high-resolution multispectral image; converting the radiation intensity value into an actual reflectivity value by using a spectral reflectivity standard plate; according to the generated multispectral image, an NDVI value is calculated in real time through an airborne calculation module; in combination with flight path data, mapping the NDVI index and the geographic position of the flight operation; the classification standard of the NDVI value is adjusted in combination with a machine learning algorithm, and a spraying operation prescription map is generated; the electronic control spraying device adjusts the spraying flow in real time; and establishing a spraying model, and calculating an adjustment coefficient alpha to obtain the average pesticide application amount Y per mu. According to the plant protection unmanned aerial vehicle operation method, the spraying amount can be adjusted according to field weed distribution and crop growth vigor, the precision of plant protection operation is improved, the pesticide usage amount is reduced, and environmental pollution is reduced.

The invention discloses a ground wire adaptive visual trajectory tracking control method for unmanned aerial vehicle inspection, relates to the technical field of unmanned aerial vehicle inspection, and solves the problem that it is difficult to effectively fuse geometric, texture and multispectral features to construct a ground wire lightweight model. And a three-dimensional coupling constraint system is difficult to be combined to adapt to a dynamic scene of ground wire inspection. Comprising the following steps: collecting an initial visual image and multispectral data of a ground wire, extracting and splicing geometric, texture and state features, and constructing a ground wire lightweight model based on knowledge distillation to output a state evaluation result; the flight controller combines the model and geographic information to construct a three-dimensional coupling constraint system, calls a dynamic programming algorithm to generate an initial trajectory and sets a constraint deviation real-time monitoring mechanism; multi-dimensional deviation is calculated, and comprehensive deviation is obtained through weighting of the incidence matrix; and if the deviation exceeds a threshold value, starting a lightweight prediction controller to solve an optimal control problem and adjust flight parameters to generate a new trajectory, otherwise, flying along an initial trajectory.

The invention relates to the technical field of ecological environment protection, in particular to a mangrove forest carbon sink monitoring and metering method based on an unmanned aerial vehicle, a radar and an AI technology, and the method comprises the steps: 1, obtaining the laser radar data of a mangrove forest through a laser radar carried by the unmanned aerial vehicle; step 2, acquiring elevation data in a mangrove forest vegetation layer area, and obtaining point cloud data after topographic error correction; 3, obtaining a multispectral image of the mangrove forest, and obtaining a multispectral data matrix; 4, identifying forest growth data features, constructing a mangrove forest carbon sink prediction model, and predicting the mangrove forest carbon sink amount; step 5, marking the image region with the NDVI value higher than a preset NDVI threshold value as a blade over-dense region; and according to the area of the overdense leaf region and the multispectral data matrix, calculating a light depression factor by using a photosynthetic depression factor formula, determining the carbon sink deviation of the overdense leaf region by using a regional carbon sink deviation formula, and obtaining a real carbon sink value of the mangrove forest according to a carbon sink calculated value obtained by prediction.