276 results about "Spectrum imaging" patented technology

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.

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.

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 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.

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 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 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 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.

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 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.

The application relates to a hyperspectral remote sensing image quality grade evaluation method, which comprises the following steps: dividing an image into fixed-size blocks, then constructing a quality evaluation model, obtaining a feature map with spectral weight through a spatial-spectral attention module, and inputting the feature map into a frequency spectrum integration embedding module to generate an embedding feature map of local adjacent spectral bands; after linear projection, the embedding feature map is input into an encoder, and a skip connection mechanism is set to fuse the outputs of different encoders; the output of the last encoder is input into a linear layer and an activation function layer to obtain a quality evaluation result of the image; the quality evaluation model is trained, and the trained model is used for image quality grade evaluation. The application can perceive the good and bad degrees of a hyperspectral remote sensing image, and can dynamically detect and adjust the image output by an image processing system in a hyperspectral imager according to the quality grade of the image, thereby providing a more effective basis for parameter optimization of a real-time hyperspectral imager system.

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 discloses an intelligent monitoring system for camellia oleifera processing based on artificial intelligence, and relates to the technical field of camellia oleifera fruit processing, and the system comprises a temperature monitoring module which comprises a multi-point temperature sensing array which is arranged along the axial direction of a presser and is installed at the front end, the middle part and an oil outlet of a barrel, the temperature sensor is used for collecting temperature and forming temperature distribution data in the barrel; the color detection module is arranged at an outlet of the oil outlet pipeline, obtains oil chromaticity parameters and a yellow-red ratio through spectral imaging, and extracts color feature data; and the volatile matter detection module is arranged above the discharged oil collection cavity and is used for detecting the concentration of volatile organic compounds through a gas sensing array and generating flavor characteristic data. By arranging the temperature monitoring module, the color detection module and the volatile matter detection module, real-time collection of multi-dimensional information in the squeezing process is achieved, and based on an asynchronous triggering and weighted fusion mechanism, space-time alignment and conjoint analysis are conducted, a joint feature vector is constructed, and a real-time prediction result is output.

The application discloses a hyperspectral imaging chip, device and system based on an adjustable reconfigurable metasurface, and the chip comprises a metasurface unit array, a metasurface reconfiguration module, a metasurface adjustment module, a CMOS image sensor and a spectrum reconstruction and identification module, the metasurface unit array comprises a plurality of metasurface units arranged on a silicon layer; the metasurface reconfiguration module is used for adjusting the number of metasurface units included in the reconfigurable metasurface unit array; the metasurface adjustment module is used for connecting the silicon layer of the metasurface unit array through wires, and is used for changing the spectral response of the metasurface unit by adjusting the voltage of the wires; the CMOS image sensor is used for detecting light signals from the reconfigurable metasurface unit array after modulation; and the spectrum reconstruction and identification module is used for performing hyperspectral reconstruction and imaging according to the light signals detected by the CMOS image sensor. The application has adjustable spectral response, ultrahigh spectral resolution, real-time dynamic monitoring capability and self-adaptive imaging capability.

The invention provides a sparse knowledge graph entity alignment method and system. The method comprises the following steps: acquiring structure data of chemical molecules, and identifying a minimum ring structure based on topological connection and bond energy distribution; clustering molecules with the same ring structure and the bond energy deviation smaller than a threshold value into a molecular group, extracting a shared minimum ring structure as a substructure node, creating a molecular node, and constructing a bidirectional correlation map of the molecules and the substructure node; extracting absorption peak data of the compound sample by utilizing spectral imaging, and generating an energy level spectrum; searching a substructure node combination corresponding to the homonymous molecule based on a map, and obtaining an embedded vector; and segmenting the energy level atlas into energy fragments according to a substructure path, extracting a distribution vector, performing multi-scale fusion on the distribution vector, a path feature and an embedded vector, and generating a composite feature vector to identify an equivalent entity and establish aligned connection. According to the method, high-precision alignment of the functional equivalent entities in the chemical sparse knowledge graph is realized.

The present disclosure relates to an X-ray system for multi-spectrum imaging. The system comprises an X-ray source configured to be sequentially operable in a plurality of spectral modes in dependence of at least one synchronization signal, the plurality of modes including a first mode, in which the X-ray source is configured to emit first X-rays having a first spectrum, and a second mode, in which the X-ray source is configured to emit second X-rays having a second spectrum different from the first spectrum; and an X-ray detector comprising a pixel controller, a readout unit, and an active pixel including a first storage element and a second storage element. The pixel controller is configured to control the active pixel in dependence of the at least one synchronization signal to record a first signal associated with the first X-rays in the first storage element and record a second signal associated with the second X-rays in the second storage element. The readout unit is configured to read out the recorded first signal during or after the recording of the second signals by the active pixel.

The invention relates to the field of optical cable detection systems, in particular to a communication optical cable surface flaw detection system based on visual inspection, which comprises the following modules: a multi-modal data acquisition module, a data fusion processing module and a process linkage feedback module, according to the invention, through a multi-modal data acquisition architecture of ''event camera + micro X-ray + multi-spectral imaging'', the detection range is expanded from the traditional ''surface morphology'' to ''internal structure'' and ''material characteristic'' for the first time: the event camera can accurately capture micron-sized surface transient flaws under high-speed production, and the problem of motion blurring of the traditional camera is solved.

The invention relates to the technical field of coating quality monitoring, in particular to an intelligent monitoring method and system for electrophoresis powder spraying coating quality, and the method comprises the steps: firstly obtaining the global geometric information of a workpiece through three-dimensional scanning with submillimeter precision, then obtaining the optical characteristics of a coating through combining with a multispectral imaging module, achieving the multi-source data fusion through a time-space registration module, and carrying out the monitoring of the coating quality. A deep neural network model is adopted to invert and generate a three-dimensional film thickness distribution field, the coating thickness uniformity is accurately quantified, meanwhile, a three-dimensional convolutional neural network is utilized to intelligently recognize defects such as pinholes, orange peels and sagging, qualitative and quantitative comprehensive analysis is achieved, and finally spraying process parameters are adjusted in real time through a closed-loop feedback control module. According to the technical scheme, fundamental transformation from offline sampling inspection to online full inspection, from manual judgment to intelligent diagnosis and from post inspection to process intervention is achieved, and the strict requirements of modern intelligent manufacturing for coating quality full-process management and control are comprehensively met.

The invention discloses a vegetation coverage index algorithm and system based on an unmanned aerial vehicle. The algorithm comprises the following steps: S1, data acquisition and multi-source data preprocessing; s2, dynamic environment correction and multi-source data fusion; s3, red edge enhanced vegetation index calculation and terrain correction; s4, vegetation coverage intelligent prediction and precision verification; and S5, result visualization and decision support: generating a vegetation coverage spatial distribution map and a statistical report, and supporting a resource management decision. By integrating the unmanned aerial vehicle, the multispectral imaging sensor, the dynamic environment correction model and the data fusion algorithm, efficient and high-precision technical support is provided for precision agriculture, ecological resource management and disaster monitoring.

The application discloses a kind of full-automatic high-throughput space-time resolution fluorescence spectrum imaging system and method, it is related to fluorescence signal spectrum field, including: workbench, sample stage, objective, for preloading sample stage with sample and placing it on workbench on mechanical arm sample inlet module, for laser beam to the output module of picosecond pulse laser of objective, for simultaneously collecting the spectral data and dynamic data of fluorescence signal generated after sample is focused by laser beam fluorescence signal detection module and control module;Sample stage and objective are sequentially spaced apart on workbench along the vertical direction from top to bottom;Mechanical arm sample inlet module, picosecond pulse laser output module and fluorescence signal detection module are respectively connected with control module communication.This application can realize the simultaneous acquisition of spectral data and dynamic data of fluorescence signal automatically.

The invention provides an adaptive reconfigurable metasurface array multiplexing method based on a detection signal-to-noise ratio, and relates to the technical field of spectral imaging, and the method comprises the steps: for each superpixel unit, calculating the detection signal-to-noise ratio of the superpixel unit based on a signal inputted to the superpixel unit; based on a comparison result of the detection signal-to-noise ratio of the super-pixel unit and at least one preset signal-to-noise ratio threshold value, determining a scale adjustment mode for the super-pixel unit; based on the scale adjustment mode for each superpixel unit, generating a reconstruction instruction, and distributing the reconstruction instruction to each metasurface; the reconstruction instruction is used for indicating the adjustment of the resonance characteristic of the super-surface so as to realize the scale adjustment of the super-pixel unit. According to the method, the superpixel scale can be automatically adjusted according to different signal-to-noise ratios of input signals, so that the optimal distribution of the spectral resolution and the spatial resolution under different input signal-to-noise ratios is realized.

The invention relates to the technical field of petroleum component detection, in particular to a drilling waste component identification and flocculation state detection method based on hyperspectral imaging. According to the method, firstly, data are acquired through the polarization hyperspectral imaging system, specular reflection noise can be effectively suppressed, and the detection precision is improved; secondly, a multi-task deep learning model is adopted to synchronously analyze components and flocculation states, so that efficient and accurate multi-parameter detection is realized; and finally, by outputting a detection result and recommending processing process parameters, a closed-loop solution is provided for practical application, and the intelligent level of drilling waste processing is remarkably improved.

The invention provides a sludge organic matter distribution visualization method and device. The device comprises a multi-mode imaging device, a data processing unit and a display terminal. The multi-mode imaging device is used for acquiring spectrum, fluorescence and heat distribution data of the sludge sample through a near infrared spectrum imaging module, a fluorescence microscopic imaging module and a heat imaging module; the data processing unit performs fusion analysis on the multi-modal data by using a deep learning algorithm to generate a three-dimensional visual model; the display terminal displays the result. According to the method, high-precision characterization and real-time monitoring of sludge organic matter distribution are realized through multi-modal cooperation and an intelligent algorithm, the problems of difficulty in visual display and low detection precision in the prior art are solved, and technical support is provided for optimization of a sludge treatment process.

The invention provides a micro-nano plastic detection method and system, and relates to the technical field of micro-nano plastic detection.The method comprises the steps that a to-be-detected sample is obtained, and the positions of all suspicious micro-nano plastics in the sample are determined based on hyperspectral imaging; eliminating a biological matrix on the surface of the suspicious micro-nano plastic by utilizing laser with stepped power based on a photo-thermal ablation pretreatment technology; the magnitude of the stepped power is determined according to a biological matrix; and collecting the Raman spectrum of the suspicious micro-nano plastic after photo-thermal ablation pretreatment, and outputting a detection result according to the Raman spectrum. The positions of all suspicious micro-nano plastics in a sample are determined based on hyperspectral imaging, and the sample is detected by using Raman spectrum, so that the efficient positioning capability of nanoscale hyperspectral imaging and the accurate identification capability of laser confocal Raman spectrum are organically combined, and a photo-thermal ablation pretreatment technology is introduced, so that the biological matrix interference is effectively overcome, and the detection accuracy is improved. The high-flux, high-resolution and high-accuracy detection on the micro-nano plastic is realized.

A phase analyzer includes a data acquisition unit that acquires a plurality of pieces of spectrum imaging data in which positions on a sample are associated with spectra which are based on signals from the sample; a first acquisition unit that acquires a first representative spectrum group for each piece of spectrum imaging data by performing multivariate analysis on each of the plurality of pieces of spectrum imaging data; a second acquisition unit that acquires a second representative spectrum group by performing multivariate analysis on the plurality of first representative spectrum groups acquired by the first acquisition unit; and a phase analysis unit that performs phase analysis on each of the plurality of pieces of spectrum imaging data by using the second representative spectrum group.

The application belongs to the technical field of multispectral imaging system, and discloses a filter wheel control method for multispectral arbitrary spectrum imaging. The method compares the actually detected current position with the previously calibrated position, obtains the shortest path planning, avoids the detected blind area during the movement, drives the filter wheel to rotate according to the corresponding control curve stroke mode, so that the required filter aligns the optical path for imaging. The application has simple system structure requirement and simple control method implementation, can effectively and quickly complete the control of any filter in the filter wheel within half a cycle, and realizes multispectral time-sharing resident imaging.

This invention discloses an on-board calibration method for a long-wave infrared hyperspectral imager based on an adaptive composite model. The method includes: step 1, constructing a radiometric calibration dataset; step 2, constructing a physically constrained composite calibration model; step 3, adaptive global optimization of the model order and parameters; step 4, generating the blackbody radiometric calibration equation within the half-optical path; step 5, generating the external blackbody calibration equation within the full-optical path; step 6, establishing an internal-external blackbody conversion model; and step 7, on-board calibration of the long-wave infrared hyperspectral imager. By establishing the conversion relationship between the internal and external blackbody radiance across the entire dynamic range, the method effectively compensates for systematic deviations caused by thermal radiation and transmittance attenuation of the front optical path components. This ensures that during on-orbit operation, high-precision equivalent entrance pupil radiance can be obtained by relying on the on-board internal blackbody, solving the calibration reference drift problem caused by optical path differences.