228 results about "Multispectral data" patented technology

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 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 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 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.

The invention relates to a real-time monitoring method for soil remediation by utilizing multi-spectrum of an unmanned aerial vehicle and sensing coupling of the Internet of Things, and belongs to the technical field of soil environment monitoring and remediation. The method comprises the following steps: constructing a space grid model of a monitoring area; a space-air-ground integrated monitoring network is arranged, image data are obtained through multi-spectral remote sensing of an unmanned aerial vehicle, and soil environment parameters are collected through a ground Internet of Things sensor; preprocessing and fusing the multi-source spatio-temporal data, and establishing a spatio-temporal matching model; constructing an inversion model of the soil heavy metal content, the organic matter content and the pollutant degradation degree based on the fusion data; and the repair efficiency is dynamically calculated and visualized, and real-time evaluation and early warning of the repair process are realized. According to the invention, space-air-ground data collaboration is realized, the real-time performance, accuracy and space coverage of monitoring are remarkably improved, the defects of high cost, low efficiency and limited data dimension of a traditional method are overcome, and whole-process and intelligent decision support is provided for soil remediation.

The invention belongs to the field of crop yield estimation, and discloses a corn yield remote sensing estimation method and system based on multispectral data coupling radiation transmission and a crop growth model, and the method comprises the steps: obtaining and preprocessing a Sentinel-2 multispectral remote sensing image, and obtaining the reflectivity data of a corn planting region; constructing a PROSAIL forward simulation spectrum library, and performing domain correction on the simulation spectrum by using an auto-encoder and a residual error alignment network; establishing a machine learning inversion model based on the corrected simulated spectrum and the actually measured spectrum, and generating regional leaf area index (LAI) distribution; key agronomic parameters are obtained, a localized WOFOST crop growth model is constructed, the LAI state of the model is assimilated by adopting ensemble Kalman filtering at a remote sensing observation moment, and the crop growth process is dynamically corrected; and advancing the model to a mature period, outputting the dry weight of the corn kernels, and realizing remote sensing estimation of the regional corn yield. The method can effectively improve the LAI inversion precision and yield prediction reliability, and is suitable for the fields of agricultural monitoring, grain evaluation, agricultural condition management and the like.

The invention provides a method and system for on-line determination of molten steel carbon content in converter steelmaking, and relates to the technical field of converter steelmaking, the method comprises the following steps: obtaining multi-source process data, the multi-source process data comprising exhaust gas components, furnace mouth flame multispectrum and process parameters; performing time synchronization and feature extraction on the multi-source process data to generate a feature vector; inputting the feature vector into a carbon content prediction model to obtain a carbon content prediction value and an uncertainty estimation value thereof; the molten pool temperature is obtained, and based on the molten pool temperature, the waste gas components and the technological parameters, a theoretical carbon content value is calculated through a thermodynamic carbon content calculation model; and carrying out weighted fusion on the carbon content predicted value and the theoretical carbon content value to generate a fused carbon content as a target carbon content. According to the method and system for online determination of the carbon content of the molten steel in converter steelmaking, the precision and reliability of online determination of the carbon content of the molten steel can be effectively improved, and powerful support is provided for intelligent production of converter steelmaking.

The invention relates to the technical field of structural safety detection of steel structural members, in particular to a steel structural damage identification and repair method, system and device and a medium, and the method comprises the steps: obtaining a multi-source image data set and a standard corrosion test block database of a steel structural member through an unmanned aerial vehicle, the multi-source image data set comprises a polarized visible light image, a long-wave infrared temperature matrix and hyperspectral reflectivity data, and the standard corrosion test block database comprises spectral feature vectors of different corrosion grades and corresponding physical parameters; and carrying out sub-pixel level registration and feature extraction on the multi-source image data set. According to the method, the unmanned aerial vehicle synchronously carries the three cameras to carry out real-time multi-dimensional data acquisition, the problem of data splitting in traditional single-mode detection is solved, the multi-spectral data acquisition efficiency and the sub-millimeter level hidden damage identification precision are remarkably improved, and dynamic environment interference is inhibited in real time through an intelligent parameter inversion mechanism.

The invention discloses a graphite equipment weld defect intelligent detection and positioning system based on multispectral imaging, and particularly relates to the field of welding, and the system is characterized in that a multiband image acquisition unit acquires spectral image data of multiple bands and transmits the spectral image data to a multispectral data storage library; the spectral parameter analysis unit extracts characteristic parameters of the weld joint area and establishes a spectral characteristic parameter library; a spectrum abnormity identification unit locates a suspected defect area of spectrum abnormity; the weld defect positioning unit marks defect boundaries and ranges; the defect identification and grading unit calculates a defect severity index; the element component analysis unit is used for detecting the suspected defect position sample and analyzing the types and contents of trace elements in the suspected defect position sample; the defect and component data are integrated through the weld quality comprehensive evaluation unit, the quality grade and the rectification suggestion are generated, the problem that the defect cause cannot be positioned in the prior art is solved, data support is provided for follow-up maintenance and process optimization, and potential safety hazards of equipment operation and maintenance are reduced.

The invention provides a strip mine area automatic extraction method based on a convolutional neural network and an attention mechanism, and the method comprises the steps: carrying out the data preprocessing, and constructing multispectral data containing a vegetation index and a plurality of wavebands; a strip mining area extraction model is set, a double-branch encoder structure is constructed in the strip mining area extraction model, a main encoder in the double-branch encoder structure inputs the visible light wave band of the remote sensing image to extract geometric and texture features, and an auxiliary encoder fuses the near-infrared wave band and the vegetation index to extract vegetation features; high-level features output by the double-branch encoder are input into a cavity space convolution pooling pyramid module, and multi-scale context information is integrated; constructing a decoder containing a depth supervision mechanism, and setting an attention segmentation head at the tail end of the decoder; and training the strip mining area extraction model by adopting the dynamic weighted mixed loss function, and outputting an automatic extraction result of the strip mining area based on the trained model. According to the invention, rapid and accurate extraction of the strip mine area can be realized.

The invention discloses an element stripping method and device based on multispectral data, equipment and a medium, and relates to the field of remote sensing image processing, and the method comprises the steps: collecting a multispectral image of a target region; a self-adaptive window filtering algorithm is adopted to pre-process the multispectral image to obtain a multispectral enhanced image, and the self-adaptive window filtering algorithm calculates a window adjustment coefficient based on noise density, gray variance and gradient magnitude; spectral features and texture features are extracted from the multispectral enhanced image; fusing the spectral features and the texture features, and constructing fusion features of each pixel in the multispectral enhanced image; determining a ground feature category of each pixel based on the fusion features to strip a plurality of vectorized layers containing different elements; according to the method, the three indexes of the noise density, the gray variance and the gradient magnitude can be fused, the window adjustment coefficient is obtained, dynamic self-adaptive change of the filtering window size is achieved, and higher-precision data support is provided for subsequent element extraction.

The invention discloses an intelligent optical sensing system based on multispectral fusion, and relates to the technical field of optical sensing, and the system comprises a multispectral image collection module which is used for synchronously collecting original image data of a target scene under three spectral channels of visible light, near-infrared and short-wave infrared; the space-time registration and preprocessing module is used for carrying out high-precision space-time registration and radiation correction on the images of different spectrum channels; the self-adaptive feature extraction and fusion module is used for extracting multi-scale spectral features from the registered multi-spectral image and dynamically selecting and weighting a fusion strategy according to scene content; and the lightweight decision network module outputs a final target identification and state discrimination result. According to the technical scheme, the time-space consistency of multispectral data acquisition can be realized, the cross-band registration precision is improved, the robustness under the conditions of low contrast, shielding and severe weather is enhanced, meanwhile, the calculation complexity and memory occupation are greatly reduced, and the method is suitable for edge calculation equipment with limited resources.

The invention discloses a method for monitoring the heat dissipation performance of a module in a salt mist environment, and belongs to the technical field of material performance monitoring, and the method comprises the steps: carrying out the continuous spectrum data collection of the heat dissipation surface of the module in the salt mist environment, obtaining original multispectral data, carrying out the background correction, generating multispectral imaging data, and recognizing an action region; analyzing the spectral response attribute of the action area, obtaining a characteristic spectral fingerprint, carrying out physical parameter inversion, obtaining heat dissipation related physical parameters, and combining heat flow distribution data during the operation of the synchronous acquisition module to establish a related mapping relationship; and carrying out quantitative evaluation on the influence degree and the degradation trend of the salt mist environment on the heat dissipation performance of the module by combining the related mapping relationship to obtain a performance evaluation report. According to the method, the technical means of combining multispectral imaging analysis and heat flow data modeling is adopted, and quantitative diagnosis and trend prediction of heat dissipation performance degradation can be achieved.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

The invention belongs to the field of guidance of crop planting through image data processing, and particularly relates to a crop nutrient abundance and deficiency diagnosis model based on unmanned aerial vehicle image data and application thereof. The invention firstly provides a method for constructing a crop nutrient abundance and deficiency diagnosis model based on unmanned aerial vehicle image data, and the method comprises the following steps: S1, collecting canopy multispectral data of crops with different fertilizer application amount gradients in different growth periods, and collecting soil samples; s2; processing the multispectral data to obtain a vegetation index of crop growth, and measuring a soil nutrient index; and S3, obtaining a regression equation for calculating the soil nutrient index by using the key vegetation index, and judging whether the fertilizer needs to be supplemented or not by using the full-amount fertilization contrast as a threshold value. The invention further comprises the model and application of the model in guiding fertilization. Through unmanned aerial vehicle multispectral monitoring and dynamic threshold analysis, nutrient demand changes of crops in different growth periods are accurately captured in combination with an intelligent algorithm, and a field phosphate fertilizer deficient area is effectively identified to realize accurate fertilization.

The invention discloses a forsythia suspensa growth vigor monitoring method based on unmanned aerial vehicle remote sensing, and particularly relates to the technical field of unmanned aerial vehicle remote sensing monitoring. S2, calculating a vegetation index; s3, obstacle recognition; s4, evaluating the growth vigor; and S5, grade drawing. According to the method, a structured data set is constructed through integration of multispectral data and space coordinates, a high-resolution vegetation index map is generated in a pixel-by-pixel calculation mode, non-vegetation obstacles are accurately recognized by combining band ratio analysis and threshold judgment, vegetation index change trends are analyzed through time series data, and phenological period standard interval deviation is calculated. Spatial growth trend grade distribution is generated through mapping evaluation coefficients, the processing logic realizes closed-loop linkage of data acquisition and analysis, eliminates interference of non-target ground features, improves the accuracy of monitoring results, establishes a dynamic evaluation mechanism, and provides reliable basis for precise agricultural management.

The invention relates to the technical field of agricultural remote sensing and precise irrigation, in particular to a method and equipment for inverting leaf water potential of grapes before dawn based on multispectral remote sensing, and the method comprises the steps: measuring leaf water potential of representative grape plants in each planting test plot before dawn in different growth periods, and collecting canopy multispectral images in the noon period; preprocessing the remote sensing original image data; calculating a vegetation index; on the basis of the model fitting data set and the training feature set, constructing basic prediction models in different growth periods by adopting a unary linear regression algorithm, a multiple linear regression algorithm and a partial least square regression algorithm respectively; determining a PLSR inversion model according to the decision coefficient and the root-mean-square error; and carrying out pre-dawn leaf water potential prediction through the PLSR inversion model. Through the method, high-precision inversion of the water potential of the leaves before dawn based on the noon canopy multispectral data can be realized, and reliable technical support is provided for accurate management of vineyard moisture and intelligent irrigation.

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 relates to the technical field of power transmission line monitoring, in particular to a power transmission line visual multi-dimensional monitoring system based on an AI model, which comprises an image acquisition module, an edge calculation module, a deep learning analysis module, an intelligent diagnosis module and a time sequence correlation analysis module. The image acquisition module acquires multispectral data through a visible light camera, an infrared thermal imager and an unmanned aerial vehicle; the edge calculation module adopts an FPGA (Field Programmable Gate Array) chip to realize data preprocessing; the deep learning analysis module comprises a feature fusion sub-module, an anomaly detection sub-module and a three-dimensional reconstruction sub-module which are respectively used for extracting multi-scale features, identifying equipment defects and constructing digital twin bodies; the intelligent diagnosis module integrates the knowledge graph to provide decision support; and the time sequence correlation analysis module is combined with an LSTM-GRU model to realize state prediction. According to the invention, through multi-source data fusion and intelligent analysis, the problems of many blind areas, low identification precision and the like in traditional monitoring are solved, and all-weather and intelligent monitoring, operation and maintenance of the power transmission line are realized.

The invention provides a water body pollution spatial-temporal distribution dynamic monitoring system based on multi-source remote sensing data fusion and deep learning, and relates to the technical field of data processing, and the system comprises the steps: obtaining a satellite hyperspectral image, unmanned aerial vehicle multispectral data, and water quality parameter data collected in real time through ground Internet of Things equipment; the inversion module is used for performing pollutant concentration inversion on the satellite hyperspectral image to obtain pollutant concentration data; performing local pollution source positioning on the unmanned aerial vehicle multispectral data to obtain pollution source position data; acquiring real-time water quality parameter data from ground Internet of Things equipment; the spatial discretization module is used for carrying out spatial discretization processing on pollutant concentration data, pollution source position data and real-time water quality parameter data, establishing a quadtree index and carrying out recursion division on the quadtree index into sub-regions; and generating a dynamic space correction parameter according to the statistical characteristics of the data points in each sub-region. The method can effectively improve the water body pollution time-space distribution monitoring precision and the pollution early warning reliability.

The invention discloses an outdoor box video monitoring method, device and system based on multi-dimensional fusion. The system has the advantages that the visible light camera adopted by the main lens collects the physical state of the surface of the box body and the peripheral visible dynamic state, and the short-wave infrared equipment is adopted by the auxiliary lens to collect invisible characteristics and temperature distribution; feature points are extracted to calculate similarity, pixel-level matching is realized through a constructed perspective transformation matrix, and a correlation database is constructed; based on the multispectral data, three types of interaction events of biological interference, natural action and environmental transaction are identified in combination with deep learning, a fault and event association data set is constructed, a weight is assigned, a baseline threshold is set, and early warning is triggered when the threshold is exceeded; after early warning, the starting time and position of an event are positioned, a risk network is constructed to calculate the contribution degree of the event to determine a core risk source, three levels of risks are divided, a traceability map is generated, a differentiated operation and maintenance scheme is generated, monitoring is optimized, and the accuracy and operation and maintenance efficiency of outdoor box monitoring are improved.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a three-dimensional terrain reconstruction method and system based on a remote sensing image, and the method comprises the following steps: collecting visible light and near-infrared three-dimensional multispectral data, carrying out pixel-level registration based on geometric calibration, constructing a geometric registration multispectral image matrix, generating waveband difference, and extracting a structure edge; the method comprises the following steps of: constructing a spectral signal diffusion impedance field, forming a terrain self-adaptive matching support domain, calculating a multispectral matching parallax error, and resolving a three-dimensional terrain reconstruction model based on space forward intersection, in the method, the terrain boundary recognition capability is enhanced through multispectral geometric registration and waveband difference analysis, and stable edge features are extracted in combination with gradient amplitudes; the matching process adapts to topographic fluctuation changes, signal diffusion impedance constructed based on spectral difference restrains a matching support domain form, cross-ground object interference is suppressed, parallax calculation stability is improved, and three-dimensional elevation and structure expression reliability under complex earth surface conditions is enhanced.

The invention discloses an expressway pit pond early warning method and system based on time sequence feature fusion, and relates to the technical field of intelligent traffic monitoring and road maintenance. The method comprises the following steps: S1, collecting multispectral image information of a road surface, synchronously obtaining external information, and respectively generating multispectral and external data sets; s2, correcting and fusing the multispectral data, extracting multidimensional features, and constructing a time sequence optimization feature vector in combination with external data; s3, inverting states of a bonding layer, a base layer and a surface layer in the pavement according to a mapping relation between spectral characteristics and structure parameters, and generating an implicit structure health index; s4, taking the time sequence optimization feature vector, the implicit structure health index and the external data set as input, and utilizing an LSTM model to predict a pit-pond formation probability and a development trend; and S5, calculating a dynamic risk level according to a prediction result. Through fusion analysis of multispectral time sequence characteristics and structural health states, full-period early warning from early damage to forming damage of a pit and a pond is realized, and support is provided for accurate maintenance.

The invention discloses a multi-spectral fusion high-voltage equipment hidden defect detection method and system, and relates to the technical field of power systems. The method comprises the following steps: collecting multispectral data, and carrying out time alignment and space mapping processing to form a unified spectral feature set; cross-spectrum conflict relation analysis is carried out on the abnormal features, conflict types are identified, and conflict credibility indexes are calculated; constructing a defect energy closed-loop verification model, and calculating an energy closed-loop integrity index; setting a risk budget limit for the target high-voltage equipment, and calculating a risk consumption value to obtain a risk budget balance; determining a current defect stage, switching a current decision dominant spectrum, and generating a dominant right identifier; and when the conflict credibility index, the energy closed-loop integrity index and the dominant right identifier meet corresponding conditions, judging that the target high-voltage equipment has a structural hidden defect, and outputting a corresponding defect type and a risk level. According to the method, the accuracy, the reliability and the mechanism interpretation capability of hidden defect detection are improved.

The invention discloses a forest tree DBH (diameter at breast height) inversion method based on a multispectral remote sensing image, which relates to the technical field of remote sensing information inversion, and comprises the following steps of: performing radiometric calibration and geometric correction processing on original multispectral observation data, generating a standardized multispectral data set, and performing single tree identification and canopy segmentation processing to obtain a tree DBH (diameter at breast height); generating single-plant tree crown image data; combining the illumination direction features with the red-edge wave band and the near-infrared wave band, performing multi-scale feature analysis and fusion processing, and outputting canopy feature data; performing nonlinear processing and illumination correction on the canopy feature data to generate breast diameter prediction data; and carrying out space mapping and visual mapping processing on the diameter at breast height prediction data to generate a diameter at breast height distribution diagram result, carrying out precision evaluation and quality inspection processing, and outputting a forest tree diameter at breast height inversion result. The stability, the precision and the environmental adaptability of the breast diameter inversion are improved, and high-reliability technical support is provided for forest structure monitoring and resource evaluation.

The invention provides a farmland data acquisition and analysis system and method based on an unmanned aerial vehicle, and belongs to the technical field of agricultural information. According to the system, multi-dimensional data, including high-resolution visible light images, multispectral data, thermal infrared data and environmental parameters, of a farmland are synchronously acquired through a multi-sensor fusion unmanned aerial vehicle acquisition module; performing standardization processing on the original data through the data preprocessing module; a three-level fusion architecture is adopted to realize deep fusion of multi-source data; crop state real-time analysis and growth trend prediction are realized based on a lightweight deep learning model; an accurate farming operation suggestion is generated and displayed through a visual platform; and data storage and management are carried out by adopting an edge-cloud hybrid architecture. The system realizes closed-loop management from data acquisition to intelligent decision, has the characteristics of comprehensive data dimension, strong analysis real-time performance, high decision precision and the like, can significantly improve the farmland management efficiency and the resource utilization rate, and provides powerful support for the development of intelligent agriculture.

The invention discloses a fruit and vegetable pesticide residue rapid screening method based on an enzyme inhibition ratio method, and relates to the technical field of pesticide detection.The method comprises the following steps that S100, visible light, near-infrared and hyperspectral data of fruit and vegetable samples are synchronously collected through a multispectral sensing module, and a pH adjusting module is dynamically triggered based on spectral characteristic peak intensity; s200, executing dynamic pH regulation and enzyme reaction monitoring based on the output of the step S100; s300, based on the output of the S200, fusing the multispectral data and the dynamic curve through an intelligent analysis platform, outputting a pH sensitivity response curved surface through a transfer learning module, and dynamically updating proportional-integral control parameters; and S400, establishing a feedback verification closed loop based on a feedback module, automatically generating a unique identification code and triggering a GC-MS laboratory for confirmation when a positive sample of which the inhibition ratio is greater than a preset value is detected, and finally, synchronously and reversely calibrating the spectrum fusion weight in S300 and the pH adjustment parameter in S200 based on the deviation value.

The invention discloses a method, system, equipment and medium for predicting regional soil moisture, and relates to the technical field of soil moisture diagnosis and prediction.The method comprises the steps that a regional surface soil moisture model driven by unmanned aerial vehicle data is established through a random forest algorithm on the basis of unmanned aerial vehicle multispectral data and in-situ data synchronously collected on the ground; dividing a research area plane into a plurality of grids, regarding each grid as a one-dimensional vertical soil column, and predicting the surface soil moisture content of each grid by using the trained area surface soil moisture model; and for one-dimensional soil columns of all grids, by utilizing an ensemble Kalman filtering method, the predicted surface soil moisture of the grids is regarded as observation and independently and synchronously assimilated into a one-dimensional vertical soil moisture physical model of the grids, so that a regional quasi-three-dimensional soil moisture prediction model jointly driven by the multi-spectral data of the unmanned aerial vehicle and the physical model is formed. Therefore, accurate prediction of soil moisture at any space-time node in the research area is realized.

The invention provides an asphalt pavement segregation evaluation method, which comprises the following steps of: synchronously acquiring a spectral reflection image and a temperature distribution diagram of a paved pavement through a multispectral camera and an infrared thermal imager, associating spatial coordinate data, calculating a normalized difference segregation index based on multispectral data, quantifying distribution difference of asphalt and aggregate, and evaluating the segregation of the asphalt pavement. Constructing a three-dimensional temperature gradient model based on thermal infrared data, identifying a temperature segregation region, inputting a spectral feature map and temperature field data into a two-channel convolutional neural network, outputting a segregation probability map and a segregation type classification result through feature fusion, and calculating a segregation type classification result according to a segregation region area proportion, a normalized difference segregation index mean value and a temperature deviation value. Calculating a segregation comprehensive index, dividing evaluation grades, and generating a segregation distribution electronic map with superimposed thermodynamic diagrams. The problems of real-time monitoring of the asphalt pavement construction process and quantitative evaluation of the segregation degree are solved.