43 results about "Multispectral data" patented technology

This invention proposes a method for continuously optimizing the efficiency of garden maintenance processes. The method includes: real-time data acquisition via handheld ground-based data acquisition devices and drones equipped with various data acquisition devices, including: hyperspectral data, multispectral data, laser point cloud data, and ground orthophoto maps of plants; preprocessing the real-time acquired data and then fusing it into multidimensional data, constructing a garden maintenance process intelligent analysis model based on the fused data; optimizing the scheduling and resource allocation of garden maintenance plans based on the intelligent analysis model; optimizing the intelligent analysis model based on the real-time data collected during the garden maintenance process according to the garden maintenance plan in step S3, combined with the data continuously collected in real-time in step S1; automatically optimizing the garden maintenance plan based on the optimized intelligent analysis model, thereby improving garden maintenance efficiency, reducing maintenance costs, and improving garden maintenance quality.

The present application relates to the technical field of water and soil monitoring, in particular to a water and soil loss data intelligent analysis system based on unmanned aerial vehicle multi-spectral monitoring, which aims to solve the problems of inconsistent brightness between multi-spectral image frames, pixel saturation failure and inability to intervene in flight behavior in real time caused by environmental light fluctuation; the technical scheme comprises a data acquisition module, which ensures that the skyward light sensor and the groundward multi-spectral camera are triggered along the same direction through a hardware synchronization circuit, and outputs original multi-spectral data frames; an analysis and processing module, which divides the data into a steady-state queue and an abnormal queue by gradient energy comparison, and interpolates and repairs the saturated pixels in the abnormal queue along the texture direction; and an early warning output module, which splices the repaired frames and the steady-state frames, calculates the vegetation index to identify the water and soil loss area, converts the area coordinates into flight control instructions and issues the unmanned aerial vehicle; the present application can eliminate light interference, repair failed pixels, realize perception and flight control closed loop, and improve the accuracy and real-time performance of water and soil loss monitoring.

This invention discloses a method for predicting soil organic carbon (SOC) content based on UAV multispectral data, belonging to the field of soil remote sensing monitoring technology. The method constructs a multi-source heterogeneous fusion dataset by collecting soil visible-near-infrared spectral data and environmental data such as topography and climate. The SG-FD algorithm is used to preprocess the spectra to enhance feature signals. A dynamic deep separable convolutional network (DS-DCNN) is constructed, and a scale pyramid dynamic weight module is used to adaptively extract key spectral bands. Environmental driving factors are deeply fused to achieve high-precision prediction of forest soil SOC content. Finally, the SHAP framework is introduced to analyze the contribution of each factor. This invention overcomes the limitations of traditional single-spectral modeling and effectively solves the problems of low SOC inversion accuracy and poor model interpretability in complex forest environments, providing support for forest carbon storage estimation.

This invention discloses a wetland landscape pattern classification method based on optimized feature parameters. First, multispectral and topographic data of the target wetland are preprocessed to extract feature parameters from the preprocessed data. Wetland landscape categories are defined, identifying 13 land cover types. Training samples are determined by referencing field survey data, original multispectral image data, principal component analysis (PCA1) of original remote sensing data, and the tasseled cap transformation brightness component of original remote sensing data. For the preprocessed multispectral data, wetland information extraction is performed using both maximum likelihood classification and decision tree classification. The classification results from the two extractions are combined with the extracted feature parameters to form fused data. Information extraction based on maximum likelihood classification is then performed on the fused data to obtain dynamic change analysis results of wetland landscape classification. Combined with optimized feature parameters, multiple feature parameters are fused to achieve information extraction from remote sensing images and refined classification of wetland landscapes.

The present application relates to a kind of tree species identification method based on spectral orthogonal transformation and multivariate filtering, comprising the following steps: S1, the high spatial resolution remote sensing image of forest area is collected, and pretreatment is carried out;S2, preliminary interpretation is carried out to the image after pretreatment, and tree species sample is obtained in combination with field investigation, tree species sample is marked on the image after pretreatment, and is divided into training sample and verification sample;S3, the spectral band of remote sensing image is orthogonally transformed, and a set of two two orthogonal feature layers are generated;S4, multivariate filtering processing is carried out to the layer after orthogonal transformation, and filter feature is extracted, and hybrid feature set is constructed;S5, based on hybrid feature set, tree species is identified using machine learning classification method, and classification result and precision statistics are output.The present application reduces the correlation between layers by orthogonal transformation of multispectral data, then highlights the difference of tree species by four filters of texture, low-pass, median and enhanced Lee, to promote the identification of tree species.

The application provides a soil organic carbon estimation method based on a Sentinel-2 image, and steps include sample collection, remote sensing data processing, feature screening, model building, training and testing, and optimal model inversion. The application aims to solve the problems of insufficient precision and interpretability of existing estimation methods, and constructs a high-precision estimation technology system that fuses multispectral data and stacking model algorithms. Only small-scale sample collection is required to train the model, without the need for repeated field sampling and laboratory analysis, which greatly reduces the cost, has small external constraints and is environmentally friendly. The application can efficiently realize large-scale continuous estimation, and the estimation result has high precision and strong stability, which can accurately depict the spatial distribution of carbon content and provide reliable technical support and data guarantee for related work.

The present application is suitable for the field of intelligent agricultural technology, and provides a tea garden precision fertilization closed-loop system based on multispectral imaging and environmental response, comprising: a data acquisition subsystem that acquires multispectral data, environmental sensor data and weather forecast data of the tea garden in real time; a data processing and analysis subsystem that inverts the nutrient content and calculates the influence coefficient of temperature on the fertilizer decomposition rate, the influence coefficient of humidity on the root absorption efficiency and the predicted loss amount; a fertilization decision subsystem that comprehensively considers the nutrient gap, the influence coefficient and the predicted loss amount, and calculates the corrected fertilization amount and the target fertilization time window in combination with the weather forecast, generates a variable fertilization prescription map; a fertilization execution and feedback subsystem that executes zoned variable fertilization and re-measures the multispectral data to evaluate the effect; and a cloud management platform that realizes data storage, visualization and alarm. The present application effectively improves the accuracy of fertilization decision and the fertilizer utilization rate, and realizes the precision and intelligence of tea garden fertilization.

The application discloses a kind of wheat scab monitoring methods based on satellite remote sensing, belong to remote sensing monitoring technical field, first obtain the remote sensing data and meteorological precipitation data of wheat planting area in predicted year and N years before predicted year, the remote sensing data include multispectral data and thermal infrared remote sensing data;Then the relative vigor index is calculated using the multispectral data;Using the multispectral data and thermal infrared remote sensing data obtains normalized surface temperature index;Using the meteorological precipitation data obtains normalized precipitation index;Finally, the relative vigor index, normalized surface temperature index and normalized precipitation index are used to calculate the wheat scab incidence index of predicted year;The application can better reduce the interference caused by the difference of wheat vigor in different regions in large-scale monitoring on scab monitoring model, improve the objectivity and practicability of scab remote sensing monitoring.

The application provides a kind of intelligent livestock animal positioning method and system based on big dipper positioning, it is related to intelligent livestock and satellite positioning technical field, the original positioning data reported on big dipper positioning terminal worn on livestock animal is obtained in the application;According to the speed change mode of livestock animal, the continuous trajectory points in the original positioning data are classified, and the moving point set and the stationary point set are obtained;The moving point set is input into the motion constraint model for position correction;The trajectory points in the stationary point set are combined with vegetation distribution data and analyzed, and the candidate trajectory point subset is screened out;Fusion multispectral data compensates the propagation deviation of big dipper signal in vegetation cover environment, and the accurate calibration of candidate trajectory point position is completed;Finally, the corrected trajectory points and the calibrated candidate trajectory points are fused to generate behavior space-time trajectory, which can effectively improve the positioning accuracy of livestock animals in pasture environment and provide reliable data support for precise livestock management.

This invention discloses a multispectral polarization imaging system and data processing method based on a filter wheel. The system includes a four-channel polarization imaging system, a filter wheel system, and a main control unit. The four-channel aperture-differentiated polarization imaging system is arranged in a 2×2 array, containing three linear polarization channels and one circular polarization channel, achieving full Stokes vector imaging. The filter wheel system is located between the imaging lens and the detector, and uses a motor to switch between single-channel and three-channel bandpass filters, combined with a color focal plane detector to achieve multispectral scanning. The main control unit controls synchronous exposure and processes data. The data processing method includes raw data preprocessing, multispectral data recovery (processing separately for single-channel and three-channel bandpass filters), channel registration, and polarization information extraction. This invention can simultaneously acquire multispectral and full polarization information, and its modular design makes it easy to operate and flexible to use.

The application discloses a kind of high sediment concentration remote sensing inversion method and device of suspended load based on Sentinel-2 image, first, the satellite multispectral data and measured sediment concentration data of target water area are collected, and are preprocessed;Then the initial spectral feature set including the band feature combination of visible light-short wave infrared range is constructed, then the cross-validation recursive feature elimination algorithm (RFECV) is used to carry out preliminary screening from initial feature library, then according to spectral sensitivity analysis, the optimal feature subset is obtained by fine screening;Again, using random forest (RF) model is based on optimal spectral feature and sediment concentration data set training, constructs suspended load sediment concentration inversion model;Finally, the model is applied to realize the remote sensing inversion of high sediment concentration of suspended load, generates the sediment concentration time series of target water area.The present application effectively overcomes the common spectral saturation problem under high sediment concentration condition, can realize high-precision inversion in 0-45 kg / m³ concentration range, significantly improves the robustness and accuracy of suspended load high sediment concentration remote sensing inversion.

This invention discloses a multispectral anomaly detection method and system based on a physically constrained spatial-spectral decoupling GAN, belonging to the field of spectral detection technology. The method includes: acquiring and preprocessing a multispectral image to obtain a standardized tensor block; inputting this block into a dual-stream generator to output an ideal background reconstruction image; inputting this reconstruction image and a real background image into a multi-scale block discriminator for adversarial discrimination, and sending the discrimination result into a model training and optimization module; calculating the gradient using a composite loss function integrating physical constraints, synchronously updating the parameters of the dual-stream generator and the multi-scale block discriminator to obtain a multi-scale dual-stream GAN model; inputting the multispectral image to be tested into the trained model to obtain the ideal background reconstruction image, calculating the anomaly score map between the reconstruction image and the original image, and outputting the anomaly detection binarized result after threshold segmentation. This application solves the problem of difficulty in simultaneously considering texture and spectral features in multispectral data, significantly improves the model's sensitivity to anomalous targets, and reduces the false alarm rate.

This invention discloses a full-cycle digital management and control system for multiple varieties of rare Chinese medicinal herbs, belonging to the field of digital technology for Chinese medicinal herbs. The system includes the following steps: collecting hyperspectral cubic data of plants and soil water potential time series data; generating a non-uniform pulsed micro-stress control matrix based on a twin model; collecting acoustic emission events of xylem vessel cavitation, and re-watering via a drip irrigation solenoid valve driven by a dry contact when the event rate exceeds a limit; performing joint scanning of medicinal herb samples, extracting biochemical topology, and generating biological intrinsic hash values ​​for on-chain processing; collecting two-dimensional near-infrared and multispectral data of medicinal herb fragments, then retrieving moisture content and performing inverse expansion registration, and verifying the results with the on-chain biochemical topology through tomographic matching. This method achieves closed-loop management and control of Chinese medicinal herbs from induction, boundary control, fresh-state database construction to end-point ownership confirmation, enhancing the consistency between source information and physical samples, and thus improving the accuracy of authenticity verification in dried sliced ​​state.

This application discloses a harvesting system, method, and medium based on electrical signals and multispectral imaging in the field of computer technology. This application establishes a fully automated harvesting system with dual-dimensional detection of multispectral imaging and plant electrical signals through the collaborative operation of a control terminal, a drone, an edge computing node, a server, and a robot. The drone utilizes a multispectral camera to quickly scan the harvesting area, while the edge computing node performs image denoising and stitching, resulting in fewer and more concise images. For image processing, the server determines the first maturity level using multispectral data, then introduces plant electrical signals to detect the second maturity level, thereby continuously improving the accuracy of plant maturity determination. Finally, the multispectral data and plant electrical signals are fused to confirm whether harvesting is necessary. After harvesting confirmation, the server automatically issues execution instructions, enabling the robot to perform precise harvesting, realizing a complete process from area inspection and maturity detection to automated harvesting.

This invention provides a method, apparatus, and equipment for monitoring vegetation in power plants based on unmanned aerial vehicle (UAV) remote sensing. The method includes: acquiring distribution data of power generation equipment in a target area; dividing the target area into monitoring zones based on the power generation equipment distribution data; collecting data from the monitoring zones via UAV remote sensing according to a preset planned flight path to obtain initial multi-source data, which includes multispectral data and visible light data; preprocessing the initial multi-source data to obtain target multi-source data; obtaining vegetation growth indicators based on the target multi-source data, which include normalized vegetation index (NVI), vegetation cover, and vegetation height; and evaluating the vegetation in the power plant based on the vegetation growth indicators to obtain the power plant vegetation monitoring results. This invention can improve the accuracy, efficiency, and timeliness of power plant vegetation monitoring.

The application discloses a single crystal turbine blade surface crack identification method based on multispectral imaging, and belongs to the field of nondestructive testing of an aero-engine. The method first collects ultraviolet, visible light and short-wave infrared multi-narrow-band original spectral images, generates a multispectral data cube through sub-pixel level spatial registration and reflectivity normalization; then extracts a pixel spectral response curve, calculates a spectral index and combines a spatial gradient feature to construct a multi-dimensional fusion feature map; a deep learning model embedded with a double attention mechanism is used to identify a crack candidate area, false defects are removed by using cooling hole coordinates and crystal orientation information extracted from a three-dimensional numerical model of the blade, and finally, accurate identification results are output, and crack geometric measurement and three-dimensional positioning report generation can be optionally realized. The method effectively suppresses high light reflection and structural interference, significantly improves the detection rate of micron-level hidden cracks, reduces the false detection rate, and adapts to the needs of automatic batch detection.

This invention discloses a rapid detection method for capsanthin content based on multispectral data, belonging to the field of non-destructive rapid detection and spectral analysis technology of crop components. It solves the problems of traditional chemical detection methods, such as sample destruction, time-consuming and labor-intensive methods, and inability to achieve high-throughput rapid analysis. This invention acquires spectral reflectance data of chili samples through multispectral imaging. After standardization, a two-step method based on multivariate statistical indicators and model residual analysis is used to accurately remove outlier samples to construct a high-quality modeling set. Then, cross-validation is used to train various machine learning regression models to select the optimal prediction model. The spectral data of the sample to be tested is then input into this model after the same preprocessing, and the reliability of the prediction results is evaluated using a weighted neighborhood consistency index. Ultimately, this achieves rapid, accurate, and non-destructive quantitative detection of capsanthin content, providing efficient and reliable technical support for high-throughput intelligent sorting of chili quality.

The application provides an ARHUD-based perception and display method, system, medium and equipment, and belongs to the technical field of automobile intelligent driving auxiliary display. The method comprises the following steps: acquiring collected multispectral sensor data; adopting a multisensor data fusion algorithm based on distribution transformation to fuse the multispectral sensor data to obtain fused multispectral data; constructing a deep learning model based on a convolutional neural network to perform target identification and classification on the fused multispectral data; adopting a Kalman filtering algorithm to combine deep learning feature matching to perform target tracking; and adopting a differentiated display mode and color division for different types of targets to optimize ARHUD display content and effect. The above method can solve the problem of insufficient perception ability of a single sensor in a harsh environment, realize more accurate target identification and tracking, optimize ARHUD display content and effect, provide comprehensive and accurate environmental situation awareness, and assist the driver in safe driving.

The utility model discloses unmanned aerial vehicle multispectral data acquisition device, include: acquisition unmanned aerial vehicle is used for carrying integral device movement, fixed upper tube is installed in the bottom end of acquisition unmanned aerial vehicle, and the bottom end fixed mounting has fixed lower tube, the bottom end fixed mounting of fixed lower tube has stable frame, and the top fixed connection of stable frame has rubber pad layer, the installation shell body that installation upper shell and installation lower shell constitute, and the outer wall fixed sleeve of installation shell body has flexible rubber cover. The utility model discloses through multilayer shock attenuation design reduces flight vibration influence: the shock attenuation pad of installation disc bottom directly buffers unmanned aerial vehicle flight vibration, and the flexible rubber cover in fixed lower tube and hard rubber cover double -deck shock attenuation structure, further absorb high frequency / low frequency vibration, cooperate stable frame, rubber pad layer and stable connecting rod + stable spring's combination, all -round restraint flight and in the mechanical shaking of collection process, the accuracy of spectral data acquisition and equipment durability are improved significantly.

The application discloses a regional soil moisture prediction method, system, device and medium, and relates to the technical field of soil moisture diagnosis and prediction, and comprises the following steps: based on unmanned aerial vehicle multispectral data and in-situ data synchronously collected on the ground, a regional surface soil moisture model driven by unmanned aerial vehicle data is established by using a random forest algorithm; a plane of a research region is divided into a plurality of grids, each grid is regarded as a one-dimensional vertical soil column, and the trained regional surface soil moisture model is used to predict surface soil moisture of each grid; for one-dimensional soil columns of all grids, the predicted surface soil moisture of the grid is regarded as observation, and is independently and synchronously assimilated into a one-dimensional vertical soil moisture physical model of the grid by using an ensemble Kalman filtering method, thereby forming a regional quasi-three-dimensional soil water prediction model driven by unmanned aerial vehicle multispectral data and a physical model, and accurate prediction of soil moisture at any spatiotemporal node in the research region is realized.

The present application relates to the technical field of refined cotton impurity online detection, and specifically discloses a refined cotton production process impurity online detection system and method, which integrates a visible light and near-infrared dual-mode imaging unit, synchronously collects cotton net images and generates a multispectral data set, extracts color distribution and spectral reflection characteristics, and compares impurity and interference characteristics by using a dynamic feature fusion model; at the same time, a space-time mapping algorithm is combined with a production line speed parameter to convert two-dimensional image coordinates into three-dimensional physical positions, supporting an automatic removal device to accurately execute; finally, a built-in feedback self-learning mechanism dynamically updates a feature library and decision logic through downstream station verification results, and adapts to material batch changes and equipment aging and other working conditions. The present application solves the problems of high false positives, missed detection and fuzzy positioning of traditional detection schemes by using multispectral fusion technology, and significantly improves the refined cotton product quality stability and the production line automation level.

This invention relates to the field of quantitative detection technology, and in particular to a rapid quantitative detection system for solid powder samples based on combined spectroscopy, comprising a sample acquisition module and a detection module. During sample preparation, the raw materials are first sieved using a raw material sieving device, then transported manually or mechanically to a drying device for drying. After drying, the dried raw materials are pulverized using a pulverizing device, and finally, the pulverized raw materials are compressed into tablets / suspended for sample preparation. During detection, the prepared sample is placed at a designated station, and the sample surface is alternately excited using a spectral excitation device. The spectrum is then captured by a signal acquisition device, and after aligning the multispectral data using a data processing device, a pre-trained model is called using an analysis device to output the component concentrations and physical properties.

This utility model discloses a multispectral data acquisition instrument for historical building surveying, including a support assembly, a limiting assembly fixedly installed on the top of the support assembly, and an acquisition assembly disposed within the limiting assembly. The acquisition assembly includes a placement body with a semi-circular groove at the center of its top. A multispectral data acquisition device is fixedly installed on the top of the semi-circular block, and strips are fixedly installed on both sides of the multispectral data acquisition device. An electric actuator is fixedly installed downwards on one side of the strip of the multispectral data acquisition device, and a multi-directional suction cup is installed at the bottom of the electric actuator. This utility model, by placing the multispectral data acquisition device on the top of the semi-circular block, and allowing the semi-circular block to rotate smoothly within the semi-circular groove, enables the multispectral data acquisition device to be rotated to a horizontal position at all times during use. When the multispectral data acquisition device is rotated to a horizontal position, the user can use the electric actuator to move the multi-directional suction cup downwards to fix the horizontally oriented multispectral data acquisition device.

This invention discloses a method and system for remote sensing classification and diagnosis of soil nutrients based on multi-source data fusion, belonging to the field of agricultural remote sensing monitoring technology. The method includes acquiring multispectral data, hyperspectral data, and soil data from sample plots; constructing a soil nutrient classification standard system by classifying the data; fusing the multispectral and hyperspectral data of the sample plots using optimized multi-source data fusion technology to obtain TIFF image data of the sample plots; inputting this data into a trained soil nutrient spectral remote sensing inversion model for inversion to obtain the soil nutrient distribution results of the sample plots; and performing classification processing based on the soil nutrient classification standard system to obtain a spatial distribution thematic map of the soil nutrient classification and diagnosis results. This invention achieves multi-source fusion of spectral data and soil nutrient classification and diagnosis, ensures stable acquisition and processing of spectral data, simplifies the soil nutrient diagnosis process, and improves the detection accuracy and efficiency of soil nutrient diagnosis.

The present application relates to the technical field of spectral data acquisition, and provides a three-dimensional mural multi-spectral data acquisition method and device, wherein the acquisition range of the mural is established in a plane coordinate system according to the actual boundary of the mural; the dense point cloud data of the mural is obtained by scanning according to the acquisition range of the mural; the two-dimensional depth map of the mural is obtained according to the dense point cloud data and the acquisition range of the mural; the surface of the mural is segmented into multiple quasi-plane regions by using a clustering algorithm according to the two-dimensional depth map of the mural; the acquisition path and acquisition parameters are planned according to the multiple quasi-plane regions obtained by segmentation; the multi-spectral data of the mural in the corresponding quasi-plane region is respectively acquired according to the acquisition path and acquisition parameters, and the position information when the multi-spectral data is acquired is recorded; the multi-spectral data of the mural obtained by acquisition is spliced according to the position information when the multi-spectral data is acquired, and the complete mural multi-spectral data is obtained. The preset active focusing is realized, and the acquisition efficiency is improved.

This invention discloses a method, device, storage medium, and program product for monitoring the concentration of galvanized baths. The method includes: acquiring multispectral data collected by an optical fiber probe; calculating a quality assessment index vector and extracting feature vectors; calculating the reliability weights of each spectrum based on current measurement conditions using an adaptive attention module; weighting and fusing the features of each spectrum according to the reliability weights; inputting the fused features into a neural network and adding random perturbations to perform multiple forward propagations to obtain multiple sets of predicted values; statistically calculating the mean as the final predicted value and the standard deviation as the uncertainty; calculating the deviation between the predicted temperature and the measured probe temperature and adjusting the reliability score accordingly; and assigning a reliability level based on the uncertainty and reliability score. This invention avoids contamination by inferior data through adaptive weight adjustment and provides a reliability assessment by combining uncertainty quantification and temperature cross-validation, thereby improving monitoring reliability and accuracy.

The utility model discloses a periscope formula spectrum colorimeter, including shell, color wheel disc, intelligent camera and camera lens, and color wheel disc rotation is connected in the front side of shell, and intelligent camera is coaxial with camera lens and is arranged, and intelligent camera is in the back side of shell, and periscope one end is equipped with the light entrance mirror, and the other end is equipped with the light exit mirror, periscope is integrated on color wheel disc, and effective utilization space has solved the shortcoming that traditional imaging colorimeter has no spectrum diagram. The imaging colorimeter of integrated spectrometer can carry out data calibration to the measured sample at the test site, so that the data measured by the imaging colorimeter is more accurate, and convenient to use. The spectrum diagram is obtained through the operation analysis in the spectrometer, the spectrum data and the image are fused, the multispectral data cube or color analysis report is generated, and the spectral characteristics (such as reflectivity, radiation intensity) of the target are obtained.

The application discloses an intelligent identification method and device for distinguishing dry sand from wet sand by using multispectral data, and belongs to the technical field of remote sensing monitoring, and comprises the following steps: acquiring a geometric distortion coefficient based on an instantaneous field of view angle of imaging, resolution and ground surface wind speed; acquiring a background heterogeneity coefficient based on the bulk density of sand surface, porosity and salinity; acquiring a surface pollution coefficient based on the coverage rate of algal and microbial film and the coverage degree of coexisting impurities; further combining atmospheric aerosol optical thickness, sand particle size median value and volume percentage of main mineral components to construct an atmosphere-particle-component matching model and acquire matching parameters; and finally, acquiring the target effective spectral channel number by a channel optimization model based on the matching parameters, the current effective spectral channel number and the background heterogeneity coefficient. The application realizes the fusion evaluation of multi-source influencing factors through multi-level system modeling, and significantly improves the precision and environmental adaptability of dry and wet sand identification.

The application relates to the technical field of fire management, in particular to a fire safety management method and system for a charging place, which performs multispectral data collection on the charging place, obtains safety monitoring data of the charging place, performs fire risk prediction on the charging place according to the safety monitoring data, generates fire safety supervision information of the charging place, analyzes the fire safety supervision information, and executes corresponding preset management countermeasures.

The application discloses a graphite equipment weld defect intelligent detection and positioning system based on multispectral imaging, and particularly relates to the field of welding, and comprises a multi-band image acquisition unit, a spectral parameter analysis unit, a spectral anomaly identification unit, a weld defect positioning unit, a defect identification and grading unit and an element composition analysis unit.The multi-band image acquisition unit collects 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 a weld area, and establishes a spectral characteristic parameter library.The spectral anomaly identification unit locates suspected defect areas of spectral anomalies.The weld defect positioning unit marks defect boundaries and ranges.The defect identification and grading unit calculates a defect severity index.The element composition analysis unit detects suspected defect position samples, and analyzes types and contents of trace elements in the suspected defect position samples.The weld quality comprehensive evaluation unit integrates defect and composition data, generates a quality grade and rectification suggestions, solves the problem that existing technologies cannot locate defect causes, provides data support for subsequent maintenance and process optimization, and reduces equipment operation and maintenance safety hazards.