449 results about "Spectrum imaging" patented technology

The invention provides an intelligent ecological restoration system, method and device for a high and steep slope of a strip mine in an arid region. Comprising a data acquisition layer which realizes real-time acquisition of multi-dimensional environmental data through InSAR satellite remote sensing, a ground sensor network and unmanned aerial vehicle multispectral imaging; the transmission layer adopts LoRa and 5G hybrid networking; the platform layer is used for constructing a slope stability prediction and restoration scheme optimization platform based on a digital twinborn model and a deep learning algorithm; and the application layer is used for remotely controlling the repairing device through a mobile terminal and a Web terminal and monitoring the repairing progress in real time. The problems that a traditional restoration technology is poor in adaptability, low in vegetation survival rate, high in ecological restoration cost, insufficient in monitoring technology application, insufficient in monitoring feedback mechanism, insufficient in intelligence, long in ecological restoration period and the like are solved.

The invention relates to the technical field of unmanned aerial vehicle dynamic dust suppression intelligent decision making based on multi-sensor data fusion, in particular to a loess photovoltaic construction dust suppression unmanned aerial vehicle spraying dynamic regulation and control device and method, and the method comprises the steps: building a dynamically updated four-dimensional concentration field through a sensor cooperation unit in combination with a turbulence diffusion model of a dust field reconstruction engine; and the strategy knowledge base is optimized to shorten the flight path planning decision-making period from the minute level of the existing offline planning to the second level response. And the anti-interference execution unit realizes accurate spray trajectory tracking under a complex wind field condition. And a real-time evaluation closed loop of the dust suppression effect is constructed by a dual-spectrum imaging and deep learning analysis technology of the efficiency feedback unit, so that a dust field model can dynamically correct boundary condition parameters. The response lag time of an existing dust suppression system is shortened, meanwhile, the spray coverage rate is increased, energy consumption is reduced on the premise that the dust suppression effect is guaranteed, and an intelligent solution is provided for photovoltaic construction flying dust treatment.

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

The invention belongs to the technical field of hydraulic engineering detection, and particularly provides a hydraulic metal structure surface coating defect detection method and a hydraulic metal structure surface coating defect detection device based on image processing. In a wet and dry alternating environment of a hydraulic metal structure, a multispectral imaging device is used for acquiring an image of a metal structure surface coating, and the acquired multispectral image is preprocessed; a complete and clear image to be detected is obtained; extracting features related to coating defects from the preprocessed to-be-detected image, wherein the features comprise spectral features, texture features and color features; and inputting the extracted features related to the coating defects into a pre-trained classification model, and classifying the coating defects through the classification model. According to the method and the device, the state of the coating can be comprehensively described, and the type and the degree of the coating defect can be accurately identified.

The invention relates to an emotion accompanying robot system based on multi-dimensional perception and an interaction method, and belongs to the technical field of intelligent robots. The system comprises a sensing module which captures micro-expression, voice, body temperature, touch and other multi-source data through a biological radar array, multispectral imaging and a touch sensing fabric; the decision-making module quantifies the emotion intensity by using an emotion state calculation engine, constructs a user personalized emotion file in combination with the dynamic knowledge graph, and generates a dynamic emotion graph; and the execution module realizes anthropomorphic emotion expression through bionic face driving, joint compliance control and thermal feedback. The interaction method comprises the steps of data capture, emotion file construction, dynamic graph generation, interactive execution and feedback adjustment, definition of an emotion intensity quantification formula, a graph edge weight model and the like. According to the method, physiological signals, environment situations and bionic expression are fused, multi-modal precise emotion perception and safe interaction are achieved, man-machine naturalness is improved, the method is suitable for elderly accompanying, psychological counseling and other scenes, and the emotion accompanying effect is enhanced.

According to the BIM-based construction process monitoring and early warning system provided by the invention, the data acquisition dimension and precision are remarkably improved through multi-source sensing fusion of millimeter-wave radar, multispectral imaging and voiceprint recognition; feature vector voxel units carrying material characteristics and process constraints are adopted, so that risk early warning has space-time relevance and process interpretability; through a composite risk calculation model containing environmental interference correction and time-varying gradient, the problem that a traditional threshold value method is poor in adaptability to complex working conditions is solved; the holographic early warning mechanism realizes upgrading from sound-light alarm to touch sense-three-dimensional projection cooperative interaction; the double-circulation self-optimization system not only guarantees real-time control, but also realizes block chain evidence storage of empirical data, and finally forms a construction monitoring closed-loop system with space-time perception, intelligent decision, accurate early warning and sustainable evolution capabilities.

A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.

The invention relates to a leather material cutting robot and a cutting method thereof, and belongs to the technical field of material cutting, the leather material cutting robot comprises a human-computer interaction interface, a CAD / CAM system, a multi-mode visual perception unit, a central processing system, a dynamic compensation cutting unit and a quality closed-loop system, and the multi-mode visual perception unit comprises a high-resolution multispectral imaging module and a 3D scanning module. According to the method, the problems that a traditional leather material cutting robot visual recognition system cannot recognize leather thickness changes, material waste is easily caused by subcutaneous defects, natural curling deformation of leather is difficult to adapt, and the edge precision error is large are solved, the rejection rate can be further reduced through multispectral and 3D defect recognition, and the production efficiency is improved. Three-dimensional dynamic compensation enables edge errors to be further reduced, and real-time force control further reduces the occurrence rate of crush damage.

The invention discloses a polarization spectrum modulation type multispectral stereo imaging method which solves the problem that in the prior art, an existing polarization spectrum imaging scheme does not achieve three-dimensional reconstruction of a target and achieves the purpose that the three-dimensional reconstruction of the target is not achieved under the condition that active illumination or special marks are not needed. Synchronous high-precision reconstruction of physical attributes and three-dimensional geometry of the surface of the target is realized; the method comprises the following steps: uniformly modulating polarization information and spectral information of incident light of a target through a three-way optical filter and a polaroid to obtain modulated incident light information; performing aliasing acquisition of a plurality of polarization spectrum components on the modulated incident light information through a multi-color detector combination to obtain aliasing light intensity; performing linear solution on the aliasing light intensity to obtain each polarization spectrum component vector, calculating the polarization degree and the polarization angle of each channel, and further deriving a surface zenith angle and an azimuth angle; reconstructing the three-dimensional morphology of each spectrum channel through normal gradient field frequency domain integration; and finally, fusing the multi-spectral three-dimensional data to obtain a multi-spectral three-dimensional imaging result.

The invention relates to the technical field of shoe body detection, in particular to a shoe body quality detection method based on image visual analysis, which comprises the following steps: synchronously acquiring three-dimensional contour and surface spectral information of a to-be-detected shoe body through three-dimensional scanning and spectral imaging equipment to obtain original point cloud spectral data; performing registration and fusion processing on the original point cloud spectral data to construct a registered hyperspectral three-dimensional grid model containing space coordinates and hyperspectral reflectivity values; and calculating a normal vector field and a curvature distribution diagram of the surface based on the hyperspectral three-dimensional grid model so as to quantify geometric features of the surface of the shoe body. According to the invention, through registration and fusion processing, surface spectral information collected by a spectral imaging device is reversely projected and interpolated to each space coordinate vertex of an initial three-dimensional point cloud constructed by a three-dimensional scanning device, so that a registered hyperspectral three-dimensional grid model is constructed, and a surface chemical characteristic spectrum is endowed with three-dimensional space geometric coordinates. And the detection capability is improved.

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

The invention discloses a food waste detection method and system based on image processing, belongs to the field of image recognition, and aims to realize efficient and automatic recognition and quantification of kitchen waste. According to the method, residual food images are collected at multiple periods and multiple angles in a kitchen garbage can or a dinner plate recovery area through high-resolution and multi-spectral imaging equipment, preprocessing is carried out in combination with an improved Retinex algorithm and a space self-adaptive denoising technology, and the image quality is improved. Afterwards, fine segmentation of a food area is achieved through a multi-scale super-pixel segmentation and graph segmentation algorithm, and multi-category intelligent recognition is conducted on remaining food through a recognition network fused with multi-modal features. The system further combines stereoscopic vision and Monte Carlo sampling to dynamically and accurately count the volume or weight of various residual foods. The method has the advantages of high adaptability and accurate statistical result, and can provide data support for catering management, resource recovery, nutrition evaluation and the like.

The invention relates to a multispectral imaging PCBA (Printed Circuit Board Assembly) welding spot three-dimensional defect detection method, which comprises the following steps of: acquiring image data of a welding spot under different wave bands through multispectral imaging equipment, and generating a preliminary image data set; preprocessing the image data set, extracting strength features and texture features of the welding spots, and constructing a response feature set; analyzing a feature change trend according to the response feature set, and screening a key waveband combination; multi-dimensional feature vectors are extracted through the key wave band combination, and a feature vector set representing welding spot characteristics is generated; constructing a classification detection feature matrix according to the feature vector set, and generating feature distribution data for distinguishing welding spot categories; classifying the feature distribution data through a classification algorithm to generate a classification label set of welding spot types; and performing optimization processing on the classification label set, generating a simplified feature matrix, completing fine classification, and outputting a final welding spot classification result.

The invention relates to the technical field of unmanned aerial vehicle measurement, in particular to an unmanned aerial vehicle measurement system based on low-altitude meteorological high-precision positioning, which comprises a low-altitude meteorological sensing unit, a positioning fusion calculation unit, a cooperative calibration control unit and a self-adaptive measurement unit. The positioning fusion calculation unit uses an atmospheric delay correction model and fuzzy adaptive Kalman filtering to compensate an atmospheric delay error and adjust a positioning noise matrix to output accurate track coordinates, and cooperates with the calibration control unit to establish a relation between a humidity gradient and a positioning height error, obtain an optimal compensation coefficient and convert the optimal compensation coefficient into a flight control instruction, and finally control the flight control instruction. And the adaptive measurement unit controls the multispectral imager to switch wavebands and adjust the scanning angle of the laser radar according to the flight path and the meteorological parameters, so that the aerosol vertical profile synchronous inversion is realized, and the positioning accuracy of the unmanned aerial vehicle and the reliability of measurement data under the complex meteorological condition are improved.

The invention relates to the technical field of hyperspectral imaging, in particular to a skin cosmetic residue grade dynamic monitoring method based on a hyperspectral imaging technology, which comprises the following steps: step 1, acquiring skin surface hyperspectral image data and preprocessing to eliminate environmental interference and noise; 2, compensating and correcting the preprocessed hyperspectral data based on the established skin heterogeneity correction model; 3, extracting multi-dimensional spectral features in the corrected data, and vectorizing correlation model input to predict the residual level; and step 4, outputting a dynamic monitoring result of the cosmetic residue grade for guiding safe use evaluation, and compared with the traditional and prior art, the method solves the problems of long time consumption, high cost, strong subjectivity, low detection precision and the like. By optimizing instrument channels, compensating spectral errors and constructing a quantitative model, rapid nondestructive testing is achieved, the detection accuracy and real-time performance are improved, and reliable technical support is provided for cosmetic safety assessment.

Aiming at the problems of low efficiency, strong subjectivity, insufficient modeling ability, complex hyperspectral data noise, weak waxiness spectrum difference and the like of a traditional rice grain waxiness discrimination method, the invention provides a hyperspectral rice grain waxiness discrimination method based on multi-branch collaborative modeling. The method comprises the following steps: S1, acquiring glutinous and non-glutinous rice grain images by using a hyperspectral imaging system to obtain spectral data; s2, preprocessing spectral data through a combined method of SG smoothing, an asymmetric weighted penalty least square method and multivariate scatter correction to reduce noise and interference; s3, constructing a multi-branch modeling architecture which comprises a CNN local feature extraction module, an SRU spectrum sequence modeling module and a HorNet global high-order modeling module, and outputting a discrimination result through a classifier after multi-branch features are subjected to fusion and pooling attention weighting; and S4, evaluating the performance. According to the method, high-precision lossless discrimination of the waxiness is realized, and a technical support is provided for germplasm screening and quality evaluation in rice breeding.

The invention relates to the technical field of aerial target detection, in particular to an aerial target detection method based on image recognition. Comprising the steps of image acquisition and preprocessing, target detection, target classification, target tracking, data storage, alarm and feedback and the like. According to the invention, technologies of deep learning, multispectral imaging and the like are combined, the defects of a traditional detection technology are effectively overcome, and the detection precision of low-altitude, low-speed and small targets is improved; the requirement of real-time monitoring is met while the detection precision is ensured; effective detection can be carried out at night and in a complex environment, and the adaptability of the system under different illumination conditions is improved; a target tracking algorithm is optimized, continuous and stable tracking of multiple targets can be realized, and target motion trails can be accurately output; the system integrates detection, identification, storage and alarm, and is suitable for various application scenes such as airport security, important facility protection, city management and the like.

The invention discloses a coarse cereal aflatoxin detection method combining hyperspectral imaging and deep learning, and belongs to the field of agricultural product quality safety detection. The method comprises the following steps: firstly, acquiring hyperspectral imaging data of coarse cereal grains, extracting space and spectral information, forming basic data, and performing preprocessing, space-time registration and feature alignment; building a hyperspectral space-spectrum double-branch feature fusion convolutional neural network deep learning model, and importing the processed data to complete iterative training; and finally, performing same-standard data acquisition and processing on to-be-detected coarse cereals, importing the trained model, and realizing aflatoxin detection and pollution area positioning through pixel-level analysis. According to the method, deep fusion of space and spectral features is realized, the detection accuracy and generalization ability are effectively improved, the detection result is stable, reliable and traceable, and the fine detection requirement of aflatoxin in coarse cereals is met.

The invention discloses a meteorological satellite multispectral imaging observation-based subtropical high voltage parameter extraction method, which comprises the following steps of: S1, projecting target data into a static meteorological satellite observation view field to calculate a subtropical high voltage influence area, and performing space-time matching on the subtropical high voltage influence area, historical observation data and sea-land template data; s2, randomly sampling the space-time matching data set, selecting time or space discrete points, and dividing a plurality of sampling areas according to different sky states; s3, sample sets of the multiple sampling areas are divided into a training set and a test set, an XGBoost classification decision tree is adopted to establish an identification inversion model, and Bayesian iterative optimization is adopted to establish an identification optimal model; s4, inputting real-time static observation data, and identifying the subtropical high-voltage influence area by using the model to obtain the influence probability of each point; s5, dividing according to a probability threshold value; s6, performing projection conversion on a division result, and applying Canny edge detection on a specified range to extract a subtropical high-voltage outer boundary; and S7, calculating the area index or the strength index of the subtropical high pressure.

The invention relates to the technical field of material detection, in particular to a cross-linked polyethylene insulation material impurity full-detection device and system for a high-voltage cable and application, the detection device carries out detection based on single-row continuous flowing insulation material particles, and the detection device comprises a near infrared spectrum imaging unit, a visible light imaging unit and an image processor, and identifying black spot impurities and heterochromatic particles. The full-detection system provides a detection channel for installing full-detection equipment and comprises a current limiting pipe body and a camera obscura structure, and reflection spectrum and surface color information are collected in the camera obscura structure; the full-inspection system further comprises a buffer stock bin, a finished product stock bin, a pneumatic sorting module, a cyclone separator, a cooling fluidized bed and a metal separator. Impurity identification reliability is guaranteed through double optical detection, full-process online detection of insulation material impurities can be achieved, limitation of traditional sampling detection is broken through, full-detection coverage of insulation material particles is achieved, the impurity missing detection risk is eliminated, and the strict requirement of an ultrahigh-voltage cable for insulation material cleanliness is fundamentally met.

The invention relates to the technical field of industrial vision multispectral imaging detection, in particular to a defect detection method and system based on multispectral fusion imaging, and the method comprises the steps: synchronously collecting a multi-channel spectral image of the surface of an object to be detected, and generating an enhanced feature map fusing multispectral information; identifying and preliminarily marking a suspected defect region by applying a region segmentation algorithm based on anomaly detection; extracting a multi-dimensional spectral response curve of each region in each original spectral channel, and constructing a spectral feature vector; matching the vector with a standard template of a pre-established defect spectral feature database, and classifying and confirming defect types; and carrying out contour refined analysis, calculating the geometric dimension and position of the defect, and generating a structured detection report. According to the method, the sensitivity of defect detection and the classification accuracy are improved through accurate matching of multispectral fusion and spectral features, and automatic and high-precision defect identification is realized.

The invention discloses a household traditional Chinese medicine robot which comprises a robot body, a face diagnosis acquisition module, a tongue diagnosis analysis module, an intelligent inquiry module, a pulse diagnosis detection module and a multi-modal data fusion center module. The system has the advantages that firstly, multi-mode traditional Chinese medicine diagnosis integration is achieved, intelligent fusion of four diagnosis modes of traditional Chinese medicine is achieved through collaborative analysis of face diagnosis, tongue diagnosis, pulse diagnosis and intelligent inquiry diagnosis, and the limitation of a traditional single diagnosis mode is broken through; 2, an accurate data acquisition technology: adopting advanced technologies such as five-color spectral imaging, tongue body dynamic tracking and a three-dimensional pulse pressure sensor to improve objectivity and accuracy of traditional Chinese medicine sign detection; and 3, dynamic personalized inquiry: based on the Bayesian network and the symptom knowledge graph, realizing real-time optimization of an inquiry path, and improving the dialectical efficiency. The method is suitable for household scenes, and the threshold of professional traditional Chinese medicine diagnosis is lowered.

The invention discloses a shelf life kiwi fruit comprehensive quality quantitative evaluation method based on hyperspectral imaging and a convolutional neural network, and the method comprises the steps: 1, obtaining spectral images of kiwi fruits of different shelf lives through a hyperspectral imaging system, and extracting the original spectral data of a region-of-interest sample; step 2, measuring the soluble solid content, hardness and color parameters of the sample, normalizing the parameters with correlation, extracting a dominant factor combination with an accumulative contribution rate greater than or equal to 80% by using a factor analysis method, and constructing a KCQI (Kiwi berry Comprehensive Quality Index) based on factor load weight; step 3, preferably selecting a characteristic wave band related to the KCQI, and performing sample set division; 4, taking the spectral data of the characteristic wave band as input, and adopting a one-dimensional convolutional neural network to construct a quantitative prediction model of KCQI; and step 5, applying the model to a spectral image of a sample, generating a KCQI spatial distribution visual image pixel by pixel, and visually displaying the comprehensive quality change.

The invention discloses a handheld laser radar scanner and an application method of the handheld laser radar scanner in rural house surveying and mapping, and aims to solve the problems of low efficiency, poor precision and insufficient environmental adaptability of traditional surveying and mapping. The equipment integrates a multi-line laser radar, an inertial navigation system (INS), a multispectral imaging unit and an edge calculation module, and realizes three-dimensional reconstruction through a dynamic scanning strategy and an intelligent point cloud fusion algorithm. The core innovation comprises: 1, a multi-modal data fusion technology: laser point cloud and a multi-spectral image cooperate to identify physical characteristics of rural house building materials (such as rammed earth and wood); the narrow space is optimally designed, the size of the device is smaller than or equal to 15 cm * 8 cm * 5 cm, one-hand operation is supported, and indoor collision is avoided through a built-in anti-collision sensor; and 3, a farm house special algorithm library which comprises a roof inclination angle calculation module, a crack detection module and a structure safety evaluation module. Actual measurement shows that the equipment can still realize + / -2cm absolute precision in a GNSS signal-free environment, the surveying and mapping of 200m < 2 > rural houses can be completed in only 20 minutes through one-time operation, and the efficiency is improved by 5 times compared with that of a traditional total station.

A digital-physical twin system and method for environmental process modeling and forecasting are disclosed. The system includes a digital twin server configured to receive environmental data from a real-world environment, including hyperspectral and spectroscopic imaging, simulate environmental process transitions using a predictive model based on the environmental data, and generate parameters for a physical experiment designed to validate or refine the predictive model. A physical twin device, comprising a scaled and instrumented representation of the real-world environment, is configured to execute the physical experiment under controlled conditions. Experimental data is returned to the digital twin to iteratively refine the predictive model in a closed-loop learning cycle using self-supervised and reinforcement learning. The system supports spatially-spectrally selective experimentation, including fluorescence spectroscopy, to enhance environmental sensing. This architecture enables scalable, sample-efficient modeling of processes such as post-wildfire hydrology, vegetation regrowth, and soil change.

The invention discloses an intelligent dynamic regulation and control method for compaction quality of a high-liquid-limit fly ash roadbed, which comprises the following steps of: establishing a mapping relation among high-spectral characteristics of high-liquid-limit fly ash, a specific surface area index, a total hydroxyl content and a water content through a first neural network model, and establishing an optimal water content prediction model through a second neural network model; the third to fifth neural network models construct the correlation of the compactness, the dielectric constant, the rigidity and the water content; two-stage water content regulation and control of a storage yard and a paving layer are realized through hyperspectral imaging of an optical fiber probe and an unmanned aerial vehicle, and pre-humidification and secondary precise regulation and control are performed in combination with an intelligent spraying system; in the compaction stage, data are collected in real time through a multi-frequency ground penetrating radar and a vibration sensor, compaction degree distribution is inverted, layered evaluation is conducted, and rolling parameters are dynamically adjusted. According to the invention, a hyperspectral technology, multi-source sensing and deep learning are fused, intelligent management of compaction quality is realized, and construction uniformity and quality stability of the fly ash roadbed are significantly improved.

The invention provides a machine vision-based soft package battery tab accurate positioning and cutting method and system, and relates to the technical field of battery manufacturing, and the method comprises the steps: collecting front and side images of a soft package battery, and constructing a tab three-dimensional grid model; using a deep neural network to extract connection interface morphology features, and analyzing stress distribution; identifying a stress concentration area by adopting spectral imaging, and determining a cutting safety area; and finally planning a tool path and generating machining parameters. The optimal cutting position of the tab can be accurately identified, the cutting precision is improved, the deformation and damage risks of the tab are reduced, and the service life of the battery is prolonged.

The invention relates to the technical field of optical remote sensing hyperspectral imaging, in particular to a hyperspectral image classification method based on distance separability waveband optimization. Firstly, an original image is preprocessed and then divided into a training set and a test set which are not overlapped in content; secondly, pairing ground feature categories needing to be identified in pairs to form a ground feature category combination, and calculating the wavebands in the image to obtain an optimal separable waveband set; then, according to the transformation wave band in the optimal separable wave band set, the training set and the test set are simplified, and only data consistent with the transformation wave band in the optimal separable wave band set are reserved; and finally, using the optimized training set to train a support vector machine, and using the trained support vector machine to complete classification labeling of ground feature categories on the optimized test set. According to the method, the Hughes phenomenon under limited samples can be effectively solved, the classification complexity and cost are greatly reduced, the classification speed is high, and the classification precision is high.

The invention discloses a road intelligent diagnosis method based on an AI large model, and relates to the technical field of road engineering detection, and the method comprises the following steps: building a unified time base line, constructing a phase reference field, injecting a calibration pulse, separating dynamic shadow time sequence texture and crack initial features in a road image sequence, and obtaining a road image sequence; generating prior information of dynamic shadows and cracks; under the constraint of prior information, polarization spectrum three-dimensional coding imaging is carried out, polarization angle distribution and spectrum gradient are extracted, a feature dictionary of dynamic shadows and cracks is constructed, and phase drift in the prior information is calibrated. According to the method, a unified time base line and a phase reference field are constructed, and accurate distinguishing of dynamic shadows and cracks is realized by combining polarization spectrum imaging and anti-fact distillation; the point cloud and acceleration data are fused for geometric reconstruction, and through phase conjugate extinguishing and closed-loop feedback, the continuity and accuracy of diagnosis are significantly improved, and maintenance error decisions caused by misjudgment are avoided.

The invention discloses a full-automatic high-flux space-time resolution fluorescence spectrum imaging system and method, and relates to the field of fluorescence signal spectrum. Comprising a working table, a sample table, an objective lens, a mechanical arm sample injection module used for grabbing the sample table loaded with a sample in advance and placing the sample table on the working table, and a picosecond pulse laser output module used for outputting a laser beam to the objective lens, the fluorescence signal detection module and the control module are used for simultaneously collecting spectral data and dynamic data of a fluorescence signal generated after the sample is focused by the laser beam; the sample table and the objective lens are sequentially arranged on the workbench at intervals in the vertical direction from top to bottom; the mechanical arm sampling module, the picosecond pulse laser output module and the fluorescence signal detection module are respectively in communication connection with the control module. According to the invention, simultaneous acquisition of spectrum data and dynamic data of fluorescence signals can be fully automatically realized.