776 results about "Reflection spectrum" patented technology

The invention relates to the technical field of agricultural product quality detection, in particular to an agricultural product quality detection method and system. The method comprises the following steps: carrying out spectrum scanning on an apple through a near infrared spectrometer, and removing baseline drift to obtain an accurate reflection correction spectrum; spectral bands of sugar, acidity and cellulose are identified based on a reflection spectrum, peak shape variation structural strength analysis is carried out, furthermore, quantitative processing of background interference bands is carried out on peak shape variation structural strength data, and unnecessary interference signals are eliminated; and finally, according to the quantitative data of the background interference wave band, constructing a quality detection and identification model, and identifying the content distribution of sugar, acidity and cellulose of the apples through the model to obtain accurate component content distribution data. The process realizes efficient and accurate quality detection, and ensures the reliability and intelligence of agricultural product quality control. The agricultural product quality detection technology is optimized, so that the agricultural product quality detection technology is more perfect.

The invention relates to the technical field of filter films, and discloses a filter film thickness detection device and a detection method. The method comprises the following steps: acquiring a first parallel beam, a second parallel beam and a third parallel beam; fixing a to-be-measured filter film sample on a sample table, and measuring the to-be-measured filter film sample to obtain a reflection spectrum and an initial refractive index parameter; irradiating the to-be-measured filter film sample by using the first parallel light beam, the second parallel light beam and the third parallel light beam, dividing the measurement area into a plurality of sub-areas according to the initial refractive index parameter, and fitting by using a least square method to obtain the optimal thickness value and refractive index value of each sub-area; based on the optimal thickness value, the refractive index value and the reflection spectrum, a mapping relation between the spectral reflectivity and the thickness is established, and a target thickness distribution diagram is obtained through iterative algorithm solving and error compensation calculation. According to the invention, the problem of uneven measurement of the thickness of the large-area filter film is solved, and the precision, efficiency and reliability of thickness detection of the filter film are remarkably improved.

The invention relates to the technical field of medicine quality detection, and particularly discloses an optical detection method and system for the content of vitamin tablets. The method comprises the following steps: driving an optical fiber probe array to carry out three-dimensional multi-angle near-infrared scanning through a multi-axis mechanical arm, and collecting reflection spectrum data; a weighted spectrum is generated through derivative spectrum analysis and double evaluation, and auxiliary material noise is corrected and separated in combination with subspace projection and a dynamic kernel function; performing spectral vector projection and regression coefficient iterative optimization through an online PLS model, and synthesizing an error coefficient based on a four-level index retrieval standard library to perform dual-channel feedback; and finally, fusing the credibility weight to output a detection result of the binding confidence. The method realizes nondestructive and high-precision detection of the vitamin tablets, has the beneficial effects of multi-dimensional data fusion, dynamic error compensation and high model adaptability, and remarkably improves the detection accuracy and reliability.

The invention relates to the related technical field of semiconductor wafers, in particular to a full-automatic wafer film thickness detection method and system based on spectral reflection, and the method comprises the steps: obtaining a wafer surface reflection spectral signal, and extracting spectral feature information and wavelength parameters; identifying a polarization characteristic parameter and an interface interference characteristic parameter, introducing the wavelength parameter as an adjusting variable, determining a wavelength dependence term, and generating a global film thickness distribution map, thereby solving the problems that in a detection scene with a multi-layer film or a rough interface, the influence of an incident light polarization state on a reflection signal is not fully considered, the reflection signal characteristic extraction is distorted, and the detection accuracy is poor. According to the method, the technical problem that the film thickness obtained through reflection characteristic backstepping deviates from the actual film thickness is solved, polarization characteristic parameters and interface interference characteristic parameters are recognized, the thickness of each detection point is determined after the film refractive index is corrected, the thickness detection precision is effectively improved, and the influence of spectral signal noise and interface interference is reduced; the technical effect of accurately presenting the global film thickness distribution of the wafer film is achieved.

The intelligent cutting method comprises the steps that the temperature gradient change rate is analyzed according to temperature distribution data, a key area where an oxidation reaction is likely to happen is determined in combination with geometrical characteristics, and target monitoring area data are obtained; obtaining reflection spectrum information of the cutting area and comparison spectrum information of the adjacent non-cutting area according to the target monitoring area data, and obtaining spectrum characteristic data; performing space-time collaborative analysis on the spectral feature data, extracting a spectral feature change value, and combining the spectral feature change value with the diffusion rate of the oxidation reaction to obtain oxidation state prediction data; and according to the oxidation state prediction data, the cutting power is adjusted in different regions, the flow field parameters of the protective gas are dynamically compensated, and cutting of the titanium alloy structural part is executed. According to the technical scheme, through spectrum monitoring and space-time collaborative analysis, dynamic prediction and accurate regulation and control of the oxidation reaction are achieved, the problem that laser energy coupling is not stable due to the surface oxidation reaction is solved, and the cutting quality is guaranteed.

The present invention relates to the technical field of food quality detection, and in particular to a quality assessment system and method for Xanthoceras sorbifolia oil based on spectral analysis, comprising a spectral acquisition module, a data preprocessing module, a feature extraction module and a quality assessment module; wherein: the spectral acquisition module is used to collect spectral data of Xanthoceras sorbifolia oil samples, including reflection spectrum and transmission spectrum information; the data preprocessing module is used to preprocess the spectral data collected by the spectral acquisition module; the feature extraction module is used to extract feature data related to the quality of Xanthoceras sorbifolia oil from the preprocessed spectral data; the quality assessment module is used to construct a quality assessment model, assess the quality of Xanthoceras sorbifolia oil samples, and output the quality grade. The present invention provides an efficient, non-destructive and real-time quality assessment system and method for Xanthoceras sorbifolia oil by combining near infrared spectroscopy technology with a support vector machine regression algorithm, which significantly improves the assessment accuracy.

The invention relates to the technical field of sensor noise reduction, and particularly discloses a fiber bragg grating sensing system based on deep learning and a noise reduction method.The method comprises the steps that firstly, original reflection spectrum data of a fiber bragg grating sensor and temperature data of the environment where the fiber bragg grating sensor is located are synchronously collected, and a data processing algorithm based on deep learning is introduced; the method comprises the following steps: performing empirical mode decomposition and waveform feature extraction on original reflectance spectrum data to capture intrinsic intrinsic mode waveform features of the original reflectance spectrum data, then further combining environment temperature data, and performing implicit query interactive response coding on environment temperature features and the intrinsic mode waveform features of the original reflectance spectrum to obtain the intrinsic mode waveform features of the original reflectance spectrum; therefore, the dynamic influence mode of the environment temperature on the reflectance spectrum data can be understood, and the temperature compensation and noise reduction of the reflectance spectrum data can be realized. Through the mode, the influence of temperature fluctuation on the reflection spectrum can be adaptively identified and compensated, the noise caused by environment temperature fluctuation is effectively inhibited, and the measurement precision of the fiber grating sensor is improved.

The invention provides a wavelength deviation compensation method and system for rapid wavelength analysis of a far-end fiber bragg grating, and relates to the technical field of fiber bragg grating sensing, and the method comprises the steps: collecting grating reflection spectrum signals of different transmission distances, carrying out frequency domain transformation to separate phase components, and building a mapping relation between the transmission distance and dispersion cumulant; calculating an initial wavelength offset estimation value; identifying a refractive index section, and establishing a segmentation compensation coefficient; constructing a wavelength-strain-temperature three-dimensional space, and optimizing a compensation coefficient according to the trajectory curvature; and updating the compensation coefficient according to the residual gradient. The wavelength analysis precision of the far-end fiber bragg grating can be improved, and the influence of dispersion on measurement is reduced.

The invention discloses a cable defect detection method and system based on double-path coupling frequency domain reflection, and the method comprises the steps: enabling a signal generator to generate two groups of correlated sine frequency sweeping signals, namely a test signal S1 and a reference signal S2; s1 is injected into the head end of the cable, and a reflected signal R1 is collected; respectively extracting the spectrum characteristics of the amplitudes and the phase differences of the R1, the S1 and the S2 by adopting a double-path signal comparison device, carrying out comprehensive judgment, and determining the reflection spectrum characteristic of the R1; the reflection coefficient spectrum is optimized through a window function, and signal features are obtained; converting the optimized frequency spectrum into a time domain reflection waveform, and extracting reflection peak time information; and calculating defect positions by combining electromagnetic wave propagation velocity. According to the cable defect detection method and system based on double-path coupling frequency domain reflection, high-precision detection of a long-distance cable is realized; and two paths of coupling signals are introduced for comparison, so that the accuracy of phase measurement is improved, and an efficient nondestructive detection scheme is provided for power grid operation and maintenance and the like.

The invention relates to the technical field of image processing, in particular to a metal part defect identification method and system based on an image processing technology. The method comprises the following steps: obtaining a to-be-detected metal part; performing wide-frequency-band reflection spectrum scanning on the to-be-detected metal part to obtain a material of the to-be-detected metal part; and carrying out sinusoidal reflection judgment on the material of the to-be-detected metal part to obtain reflection data of the to-be-detected metal part. Through the data processing technology, the image processing technology and the three-dimensional modeling technology, variable light source image scanning is carried out on the metal part, image enhancement is carried out on the image of the metal part, and therefore the three-dimensional model of the metal part is constructed, defect recognition is carried out on the metal part, and the accuracy of defect detection of the metal part is improved.

The invention relates to the technical field of blueberry production, in particular to a blueberry sorting method, system and device and a storage medium. Real-time images of blueberries on a conveying line are collected through a preset period, and image segmentation is carried out to obtain subsets; analyzing the images in the subsets to obtain appearance features, and performing primary sorting based on the features to obtain a plurality of primary sorting subsets; then reflection spectrum information of the subsets is obtained, secondary sorting is carried out, and a plurality of secondary sorting subsets are obtained; recording transmission coordinates of the images in each secondary sorting subset, and generating a corresponding coordinate set; a plurality of parameters of the blueberries are calculated by utilizing the characteristics of size, color, surface defects and the like, the appearance quality is evaluated, refined grading is realized, grading is provided for deep processing or sales, the product value is improved, the subjectivity and inconsistency of manual sorting are reduced, and the efficiency and the accuracy are improved; the internal quality is detected by combining reflection spectrum information, and the diversified requirements of the market for the blueberry quality are met.

The invention provides a chirped fiber bragg grating manufacturing method based on asymmetric apodization, which is applied to the technical field of fiber bragg grating preparation, and the method comprises the steps: determining a target asymmetric envelope function corresponding to a target chirped fiber bragg grating based on the parameter requirements of the target chirped fiber bragg grating; adjusting the pulse number of laser output by the femtosecond laser based on the target asymmetric envelope function; laser is controlled to irradiate a target area in the optical fiber, a target chirped fiber grating is formed, and the pulse number of the laser changes according to a target asymmetric envelope function and is gradually reduced from the center of the grating to the two ends. According to the method, the femtosecond laser pulse number is adjusted based on the asymmetric envelope function, so that the distribution gradient of refractive index modulation is finely adjusted, better reflection spectrum and time delay characteristics are obtained, the high-precision requirements for laser energy adjustment and displacement platform movement are reduced, and the apodization machining precision of the chirped fiber grating is effectively improved.

The invention relates to an infectious bacterium detection method and system based on double effects of a reflection spectrum and autofluorescence, and belongs to the field of biomedical detection. The method comprises the following steps: acquiring hyperspectral images of different samples under different light sources; performing data preprocessing on the collected sample hyperspectral image, realizing pixel-level alignment of the dual-effect image based on feature matching and image registration, and constructing a data set; the method comprises the following steps: constructing a wound infection bacterium detection model based on double effects of a reflection spectrum and autofluorescence, and comprising a double-branch feature extraction module which comprises a reflection branch and a fluorescence branch which are respectively used for extracting reflection hyperspectral image features and fluorescence hyperspectral image features; the redundant feature screening module screens the features extracted by the reflection branch and the fluorescence branch through a channel attention mechanism; and the feature fusion module adopts a cross-branch cross attention mechanism to fuse the features of the double branches. According to the method, learning is carried out from two physicochemical characteristics, and the effectiveness and adaptability of the technology are improved.

The invention relates to the technical field of optical fiber sensing, and provides a parallel double-cavity optical fiber copper ion sensor, which comprises an optical fiber coupler, a sensing Fabry-Perot interferometer and a reference Fabry-Perot interferometer, the sensing Fabry-Perot interferometer comprises a first single-mode fiber, a first capillary glass tube and hydrogel; one end of the first single-mode optical fiber is connected with the optical fiber coupler, and the other end of the first single-mode optical fiber extends into the first capillary glass tube; hydrogel is arranged on the end surfaces of the first single-mode optical fiber and the first capillary glass tube; the reference Fabry-Perot interferometer comprises a second single-mode fiber, a second capillary glass tube and a third single-mode fiber; one end of the second single-mode optical fiber is connected with the optical fiber coupler, and the other end of the second single-mode optical fiber extends into the second capillary glass tube; the third single-mode optical fiber also extends into the second capillary glass tube; and a gap is reserved between the third single-mode optical fiber and the second single-mode optical fiber. According to the invention, two reflection spectrums with small frequency difference are superposed to generate amplified frequency change, so that the sensitivity of copper ion measurement is improved.

The invention relates to the technical field of quality detection, in particular to an agricultural product quality detection method and system. The method comprises the following steps: carrying out multi-plane spectrum scanning on an apple through a near-infrared spectrometer to obtain a reflection spectrum and carrying out baseline correction; then, analyzing the mildewing degree of the apple pulp through cellulose spectral band identification and peak intensity attenuation index deduction; thirdly, identifying a preposed abnormal spectrum risk factor based on the mildew degree data, generating a mildew preposed abnormal spectrum risk factor, and constructing a quality detection model through sequential evolution traceability association of the abnormal factor and the mildew degree; finally, the detection model is sent to a terminal to execute agricultural product quality detection. According to the invention, the quality detection technology is optimized, so that the quality detection technology is more accurate.

The invention discloses a multi-modal imaging method and system based on acoustic-thermal coupling, and relates to the field of artificial intelligence, and the method comprises the steps: firstly obtaining thermal radiation data and ultrasonic reflection data of a target equipment region, and obtaining thermal radiation embedded features and ultrasonic embedded features through feature representation learning; performing multi-stage compensation on the thermal radiation embedding characteristic to generate a thermal field compensation reference characteristic of each stage; synchronously carrying out equal-stage multi-stage compensation on the ultrasonic embedded features, coupling the ultrasonic compensation result of the previous stage with the thermal field reference features of the corresponding stage between every two continuous stages of compensation, and combining the ultrasonic embedded features and coupling output in the later stage of compensation; and finally, performing imaging by using the acoustic-thermal coupling characteristics after multi-stage compensation to obtain an acoustic-thermal coupling imaging result fusing the thermal distribution characteristics of the target equipment region and the ultrasonic reflection spectrum characteristics. According to the method, deep fusion of acoustic and thermal information is realized through multi-stage feature compensation and dynamic cross-modal coupling, and the comprehensiveness and accuracy of equipment area detection are improved.

The invention belongs to the technical field of camouflage materials, and particularly relates to a natural vegetation imitating visible light-laser-infrared compatible camouflage film. Comprising a substrate, a thermal infrared dual-band camouflage and near infrared spectrum regulation and control layer and a visible light-laser-near infrared regulation and control layer which are arranged in sequence. The upper-layer natural vegetation imitating structure is mainly used for simulating spectral characteristics of natural vegetation, and meanwhile, the extremely low reflectivity at the position of 1.06 mu m near-infrared laser wavelength is achieved by controlling the thickness of a film layer; the thermal infrared camouflage structure in the middle realizes low emissivity of medium-wave infrared and long-wave infrared; the substrate provides a bearing function. The multilayer film not only can realize refined simulation of a reflection spectrum of natural vegetation in a visible light-near infrared wavelength range, but also has the functions of adjusting different visible light spectrum colors and resisting an infrared detector and a 1.06 [mu] m laser detector; and a visible light-laser-infrared multi-band compatible camouflage performance covering a wide wavelength range of 0.4-14 microns can be realized.

The invention provides a decoupling and Kalman filtering temperature strain demodulation method and system for double fiber gratings, and relates to the technical field of optical fiber sensing. The method comprises the following steps: firstly, designing an FBG sensing module for measuring environment temperature change and strain information; the FBG sensing module comprises a two-channel sensing system composed of an FBG1 sensor and an FBG2 sensor, and is used for measuring environment temperature change and strain information at the same time. Then, aiming at optical signals which are acquired by the two FBG sensors and contain physical quantity information, a wavelength demodulation module is used for acquiring the central wavelength drift distance of reflection spectrums of the two FBG sensors; temperature sensitivity coefficients and strain sensitivity coefficients of the two FBG sensors are obtained, and a linear equation set is constructed for independent variation of temperature and strain to perform matrix decoupling so as to distinguish the influence of the temperature and the strain on the change of the central wavelength; and performing optimization estimation on the decoupled temperature and strain by using the prior information of the temperature and the strain and combining a real-time measurement value by adopting a Kalman filtering method.

The invention discloses a method for automatically identifying rock minerals based on spectral information, and particularly relates to the field of rock mineral identification, which comprises the following steps: acquiring spectral information of mixed minerals, screening mineral types matched with a static spectral feature library, and establishing feature-mineral type association. Similar mineral types are retrieved, a set number of samples are obtained, and the samples are put according to the collection coordinates; carrying out time sequence reflection spectrum data acquisition on the similar mineral samples, extracting spectral features and carrying out timestamp alignment; performing three-dimensional structuring on the spectral features, extracting dynamic spectral feature elements by adopting a sliding window, and constructing a three-dimensional matrix; distortion identification is carried out on the time window spectrum characteristic elements of the sunshine cycle, and distortion fragments are eliminated; establishing a feature library according to the effective dynamic spectrum feature element proportion, and generating a ready signal to trigger model training; and training a rock mineral identification model based on the convolutional neural network, and carrying out whole-region accurate scanning and automatic identification on the trained model.

The invention discloses a spectral polarization imaging three-dimensional reconstruction method and device based on a pixel array, and a medium, and relates to the technical field of image processing, and the method mainly comprises the steps: collecting original images, which are fixedly obtained by pixels, of corresponding spectral bands and polarization angles; extracting sub-image information under each spectral band and polarization angle according to pixel positions, and stacking to generate a four-dimensional data volume; constructing a polarization geometric model according to the four-dimensional data body, and constructing an actual expression of the linear polarization degree in combination with a Fresnel reflection theory; constructing a redundant dispersion constraint equation and a theoretical expression of the relation between the diffuse reflection spectrum polarization degree and the incident angle and the imaging target refractive index; substituting the redundant dispersion constraint equation into a combined residual function of the theoretical expression and the actual expression, and performing parameter inversion on the combined residual function; and carrying out three-dimensional shape recovery under imaging target surface normal reconstruction based on an inversion result. According to the method, time delay and space registration errors caused by time-sharing acquisition are eliminated.

The invention relates to the technical field of tailless thread sleeve detection, and provides a multispectral imaging-based tailless thread sleeve deformation detection system and method, and the method comprises the steps: S1, collecting reflection maps of tailless thread sleeve standard parts and deformation flaw parts of different models under different spectral bands through a multispectral imaging device, and constructing a data set according to model classification; s2, carrying out preprocessing and feature extraction on the obtained reflection spectrum; s3, constructing a recognition model based on a convolutional neural network (CNN); s4, verifying the accuracy of the constructed recognition model by using a verification set; s5, updating the trained recognition model to a calculation unit; s6, adding newly added detection data into the training set regularly, retraining the model and updating the model to the calculation unit; according to the invention, various deformation types of the tailless thread sleeve can be rapidly identified, the calculation amount is small, and the detection efficiency is improved.

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

The invention discloses a method for detecting the strain of a key part of a wind turbine generator based on a temperature compensation OFDR demodulation weak fiber bragg grating array, and relates to the technical field of wind turbine generator detection, and the method comprises the steps: installing a weak fiber bragg grating sensor array at the key strain part according to the structural characteristics of the wind turbine generator, a temperature reference grating is arranged in the non-strain area or the constant-temperature area so as to monitor the temperature change in real time; a tunable laser, an optical fiber coupler and a photoelectric detector are utilized to construct an optical frequency domain reflection demodulation system, and system calibration is completed; based on an optical frequency domain reflection demodulation system, reflection spectrum signals are collected, the relation between wind turbine generator strain and temperature change is analyzed through a temperature compensation mechanism, and strain information of key parts of the wind turbine generator is obtained. According to the method, the accuracy and reliability of strain detection are effectively improved, and the problem of measurement errors caused by neglecting the influence of temperature change in a traditional method is solved.

The invention discloses a saw blade tooth profile defect visual inspection system based on a deep learning model, and relates to the technical field of saw blade tooth profile defect detection.The saw blade tooth profile defect visual inspection system comprises a light source capable of providing a plurality of different specific wavelengths, and a first visual inspection device shoots a saw blade and generates a digital image; pixel information in the scanning image is subjected to edge detection, texture analysis and morphological operation, and then corresponding characteristic values are extracted; and the second visual detection device is used for receiving the light of different specific wavelengths reflected from the surface of the saw blade in a certain length range, and analyzing and generating reflection spectrum data. The method comprises the following steps: comparing a feature value extracted by a first visual detection device with a set threshold range to preliminarily identify an obvious tooth profile defect area, and taking a comparison result of the feature value and the set threshold as a starting condition for carrying out second accurate detection or not; and the secondary visual detection device performs more accurate detection on the area with the preliminarily detected potential defects, and deep defect features are identified.

The invention relates to the technical field of textile detection, in particular to a computer-aided textile color fastness prediction method, which comprises the following steps: acquiring multi-source data before and after a textile color fastness test through computer control equipment, including digital images, reflection spectrum data, process parameters and dye characteristic component data, carrying out pretreatment; extracting color difference features and spectrum similarity features, and screening by adopting a dimension reduction algorithm to form a core feature vector; a machine learning algorithm is adopted to construct a color fastness prediction model, parameters are optimized in combination with cross validation and an early stop mechanism, model fine tuning is executed, and a final optimization model is obtained; and outputting a color fastness grade prediction result, and carrying out spectrum similarity secondary verification. According to the method, objective, efficient and high-precision prediction of the color fastness grade of the textile is realized, and the problems of high subjectivity and low efficiency of traditional manual evaluation are solved.

The invention discloses a hyperspectral satellite target rock and mineral identification method and system based on spectral characteristic parameters. The method comprises the following steps: S1, collecting and preprocessing a standard rock and mineral reflection spectrum curve; s2, collecting a regional hyperspectral satellite image and carrying out image preprocessing; s3, identifying diagnostic spectral reflection and absorption characteristics of the target rock mine based on the standard curve; s4, screening and extracting a diagnostic spectral range of the hyperspectral satellite image; s5, calculating diagnostic spectral characteristic parameters of the hyperspectral image target rock mine; and S6, on the basis of the S5, processing the spectral characteristic parameters obtained by calculation, and realizing target rock and mine identification based on multi-dimensional diagnosis spectral characteristics. According to the method, the application potential of the hyperspectral satellite image in the field of remote sensing geology is effectively improved and the scientificity and the accuracy of the hyperspectral satellite image in large-range rock and ore recognition drawing application are improved through the target rock and ore standard spectral curve characteristic analysis, hyperspectral satellite image processing and spectral characteristic parameter estimation methods.

The invention relates to the technical field of exhibition lamps, in particular to an exhibition lamp and an exhibition method, and the method comprises the following steps: inputting preset exhibit database information, extracting exhibit numbers, material types, dominant tone ranges and size information, carrying out point-by-point scanning on exhibit surfaces, collecting reflection spectrum data, converting the reflection spectrum data into CIELAB values, and integrating the extracted information and the collected data. And establishing an exhibit basic information set. The comprehensive exhibit basic information set is established by combining the preset database information and the real-time scanning and acquisition of the surface reflection spectrum or the CIELAB value of the exhibit, so that the subsequent illumination design can be carried out based on the unique optical characteristics of each exhibit, the accuracy and pertinence of personalized illumination are improved, and the material details and color levels can be deeply revealed. Secondly, processing the optical data of the exhibit by using a preset weight set associated with a specific display target, and generating an ideal display illumination parameter including spectral distribution and a brightness value;

The invention belongs to the field of metamaterial design, and particularly relates to a nanometer photon metamaterial structure reverse design method based on CVAE and PSO. The method comprises the following specific steps: S1, constructing a data set; s2, segmenting the data set into 80% of a training set, 10% of a verification set and 10% of a test set; s3, constructing a forward prediction network model, and performing iterative loop training; s4, constructing a conditional variation auto-encoder network model; s5, the coding matrix and the reflection spectrum vector in the training set are used as data to be sent into a conditional variational auto-encoder network model, and iterative loop training is carried out; s6, searching an optimal potential vector through a particle swarm optimization algorithm, splicing the potential vector and a reflection spectrum vector, inputting the spliced potential vector and the spliced reflection spectrum vector into a conditional variational auto-encoder network model to obtain a candidate design structure, and performing screening by using a forward prediction network model; according to the invention, the problems of insufficient design diversity, low accuracy and difficulty in balancing in reverse design in the prior art are solved.

The invention discloses a degraded cultivated land soil organic matter three-dimensional mapping method fusing remote sensing, near-earth sensing and historical data, and relates to the technical field of digital soil mapping, the method is based on an INLA-SPDE three-dimensional model framework, remote sensing, near-earth sensing and historical data are fused, and high-precision three-dimensional mapping is achieved. The method comprises the following steps: collecting organic matter data and near-sensing visible near-infrared reflection spectrum data of different depths of a soil profile at each sampling point in a research area, then screening visible near-infrared reflection spectrum characteristic wavebands significantly related to organic matters, collecting and integrating historical data, and completing standardization processing on remote sensing and near-earth sensing data; dividing a modeling set and a verification set, calculating a range value based on a semi-variable function, determining triangular mesh parameters, and constructing an SPDE model; the near sensing data and the remote sensing data serve as covariables and are fused into the INLA-SPDE three-dimensional model, and a soil organic matter three-dimensional space distribution diagram is generated after model fitting and uncertainty is quantified. According to the method, the problem that the vertical change of the soil attribute cannot be accurately simulated by a traditional method is solved, and high-precision and high-efficiency three-dimensional mapping of the regional scale soil organic matter in the horizontal and vertical dimensions is realized.

The invention relates to the technical field of coating thickness measurement, in particular to an anti-reflection coating thickness measurement method and system based on a dual-light-path infrared reflection method, and the method comprises the steps: obtaining a reflection signal of a light beam of a measurement light path incident to the surface of a to-be-measured sample coating and a reflection signal of a light beam of a reference light path incident to the surface of a standard sample coating at each moment; performing inversion at each moment to obtain a thickness measurement value of the coating of the to-be-measured sample; collecting reflection spectrum data of the exposed substrate in real time; acquiring a spectral characteristic value and a change degree at each moment; acquiring a predicted thickness measurement error at the next moment of each moment, acquiring a base compensation factor at each moment, and evaluating whether a thickness measurement value at each moment needs to be inversely calculated again or not; if so, constructing a target function at each moment, and optimizing parameters of a Fresnel model of an inversion thickness measurement value at each moment; and carrying out inversion calculation on the thickness measurement value of the coating of the sample to be measured at each moment. The invention aims to improve the stability and precision of the thickness measurement of the anti-reflection coating.