3168 results about "Reflectivity" patented technology

The invention provides a photovoltaic power station photovoltaic power prediction method and system. A cloud layer motion time sequence library is constructed based on a historical cloud picture, and a nonlinear model of cloud speed and output attenuation is established. The light sensor array monitors the cloud layer thickness and the aerosol density in real time, calculates the light intensity attenuation gradient of the shadow area in combination with the model, and predicts the remaining time of the cloud cluster reaching the core area. Dividing a power prediction confidence interval according to remaining time, inputting satellite data into a multi-scale meteorological fusion architecture, and adjusting a meteorological feature extraction path of an attention mechanism based on cloud density. And fusing the surface reflectance correction value calculated by the scattering data of the light sensor to generate a short-term power prediction sequence of the sudden change cloud layer scene. And inputting the sequence into an energy management system, and outputting an active and reactive double-target scheduling instruction in combination with a grid-connected point voltage deviation and a reactive compensation parameter constrained by a power grid rule. According to the technical scheme provided by the invention, the cooperative scheduling precision of the photovoltaic power station in the cloud abrupt change scene is improved.

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The invention relates to the technical field of control and regulation systems, in particular to a photovoltaic module anomaly recognition method based on laser detection, which comprises the following steps: generating a clock signal synchronous with a laser scanning time sequence through a hardware synchronous trigger, and driving a temperature and humidity sensor, a salt mist sensor and an illumination intensity sensor to collect environmental parameters; and constructing a multi-source data set containing the salt crystallization trend and the environmental interference level. And based on a temperature and humidity segmented compensation reflectivity base line, a salt mist mode switching optimization dust retention judgment threshold value, and illumination correlation sensitivity adjustment hot spot detection parameters, dynamic model correction is realized. And a fault database is matched to trigger a grading alarm mechanism, and a decision tree classifier is combined to verify time continuity to generate a diagnosis report. A dynamic detection path is constructed through spiral path planning, a multi-machine contract network protocol and wind disturbance trajectory correction, data feedback is executed to form closed-loop control logic, and the problems of misjudgment and missing detection in high salt mist, temperature change and illumination fluctuation environments are effectively solved.

The invention discloses a construction method and system for a coal mine geological transparent working face, and relates to the technical field of intelligent mine and geological information, and the method comprises the following steps: collecting and preprocessing visible light, near-infrared images and point cloud data, extracting texture, reflectivity, curvature and density features, and calculating the texture, reflectivity, curvature and density features; the method comprises the following steps: constructing a three-dimensional structure reconstruction model, a rock stratum physical property inversion model and a geological stress prediction model, generating a three-dimensional geometric structure coordinate set, a rock stratum moisture content matrix and a vertical stress distribution matrix of each region, setting an anomaly judgment condition, outputting three-dimensional coordinates of an abnormal region, carrying out graded early warning, synthesizing a three-dimensional visual image, and predicting the geological stress. And holographic transparent expression of the working face geological state is realized. According to the method, centimeter-level three-dimensional reconstruction of the geological transparent working face of the coal mine, precise positioning of a hidden water-containing area and a stress abnormal area and construction of a full-attribute dynamic visual scene are achieved, the timeliness and accuracy of early warning of disasters such as water inrush and rockburst are remarkably improved, real-time geological decision support is provided for intelligent mining, and safe and efficient production is guaranteed.

The invention provides a modified metal surface defect detection method and device and a medium, and the method comprises the steps: obtaining a multi-angle reflection image sequence of a modified metal surface under the irradiation of a multi-spectral light source, and generating a defect sensitive parameter set based on a preset modified metal material characteristic database and in combination with the spectral reflectivity distribution information of the multi-angle reflection image sequence; performing cooperative feature enhancement on the multi-angle reflection image sequence and the defect sensitive parameter set, and enhancing the feature contrast of a defect area and a normal area through weight distribution to obtain an enhanced defect feature set; joint anomaly detection of a spatial domain and a spectral domain is carried out on the enhanced defect feature set, a potential defect region of the modified metal surface is obtained through identification, and a defect region feature descriptor is generated; and performing defect morphological quantitative analysis according to the defect region feature descriptors, and determining the type, position and severity level of the modified metal surface defect. According to the invention, the comprehensiveness and reliability of a defect detection result can be improved.

The invention provides a solar cell, which comprises a silicon substrate having a backlight surface and a light facing surface which are opposite, the backlight surface comprises first areas and second areas which are alternately arranged, the first areas of the backlight surface are provided with first pyramid structures, the light facing surface is provided with second pyramid structures, the first pyramid structures are provided with first passivation contact layers, and the second pyramid structures are provided with second passivation contact layers; the first pyramid structure is provided with a first passivation contact layer, the reflectivity of the first area is 5%-14%, the second pyramid structure is provided with a passivation antireflection layer, the reflectivity of the light facing face is 5%-14%, the second area is provided with a second passivation contact layer, the reflectivity of the second area is 25%-50%, and the ratio of the area of the first area to the total area of the backlight face is 40%-70%. The light trapping effect of the light-facing surface is further improved by setting different shapes in different areas of the light-facing surface and the backlight surface, and the passivation effect and the carrier transport effect of the surface of the back pyramid structure are balanced while the light trapping effect of the backlight surface is improved; the photoelectric conversion efficiency and the double-sided rate of the solar cell are improved; and the appearance color difference of the receiving surface is improved.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention relates to the technical field of optical measurement, in particular to a laser-based building engineering flatness measurement method and system, and the method comprises the following steps: obtaining point cloud data through three-dimensional laser scanning, dividing grids, removing low-density units, carrying out the statistics of a reflectivity mean value and a variable coefficient, extracting a normal main direction, and generating optical features; and constructing a weighted linear model to correct a classification threshold, performing density clustering analysis based on the corrected threshold, calculating height range and normal deviation distribution, outputting flatness and gradient indexes, and generating a construction surface flatness evaluation result. According to the method, through combination of three-dimensional laser scanning and point cloud density screening, a multi-dimensional optical feature vector is constructed, a classification threshold value is dynamically corrected through weight distribution, a normal and a height feature are integrated through density clustering, a regional collaborative index is constructed, matching performance and evaluation precision are enhanced, and construction surface flatness continuity, data relevance and deformation capture precision are improved.

The invention discloses a mobile phone silica gel shell appearance detection system based on an optical image sensor, and relates to the technical field of appearance defect detection and intelligent visual identification. An image acquisition module acquires a high-quality multi-channel image through multi-angle synchronous acquisition; the dynamic light field regulation and control module realizes self-adaptive regulation of a regional light source based on reflectivity perception and a gray gradient change rate; the image processing and defect identification module fuses edge features, texture changes and a deep learning model to realize pixel-level defect classification; the material identification and shielding module identifies a non-silica gel area through multi-dimensional spectral features and eliminates interference; a time sequence defect evolution analysis module models a defect evolution track; the trend prediction module is used for predicting the extension trend of potential cracks and fatigue areas; the control feedback module realizes intelligent judgment and response linked with the manufacturing system; the system can realize high-precision and high-robustness automatic defect detection and risk prediction, and is suitable for online quality monitoring of large-batch flexible products.

The invention belongs to the technical field of prefabricated box girder crack detection, and particularly relates to a prefabricated box girder crack dynamic detection system and a prefabricated box girder crack dynamic detection method. The crack initiation position is efficiently identified from a single-source spectral image by utilizing spectral reflectivity difference, so that the crack detection efficiency is remarkably improved, and meanwhile, crack information and stress data are subjected to correlation analysis to determine a critical stress distribution state causing crack initiation, so that effective prediction of potential cracks is realized, and the accuracy of crack detection is improved. And meanwhile, after the crack is initiated, the crack propagation speed and the crack propagation direction are continuously monitored, so that the real-time tracking of the crack dynamic evolution process is realized, and targeted crack propagation response measures are taken by utilizing the risk orientation obtained by performing risk assessment in combination with a crack propagation monitoring result, so that the further propagation of the crack can be effectively prevented.

The invention discloses an image enhancement method for brightness gain self-adaptive regulation and control under low illumination, and relates to the technical field of image data processing. The image enhancement method for brightness gain adaptive regulation and control under low illumination comprises the following steps: S1, acquiring original image data and auxiliary processing data under a low illumination environment for preprocessing, and constructing an image processing database after storing the original image data and the auxiliary processing data; s2, performing illumination distribution optimization and brightness correction operation through brightness deviation analysis, and outputting an illumination enhancement component; s3, performing de-noising and detail enhancement processing according to a noise color deviation result, and outputting a corrected reflectivity component; s4, through image quality enhancement analysis, using a loss function to guide a double U-Net architecture to carry out detail reconstruction and color optimization on the image; and S5, based on the quality feedback and the difficult sample, adjusting processing parameters and driving algorithm closed-loop optimization. The problems of insufficient image brightness, noise interference, color distortion and detail missing multiple degradation in a low-light environment are solved.

The invention relates to the technical field of optical detection, in particular to a glass bottle surface spraying quality detection method which comprises the following steps: under a specified wavelength, acquiring target incident light by adjusting a polarization angle, irradiating a glass bottle spraying layer, collecting a transmission spectrum signal to calculate parallel and vertical polarization transmissivity and ratio of the spraying layer, and generating polarization transmissivity. According to the method, the light with the polarization angle irradiates the spraying layer, the polarization component of the transmission light is obtained, the thickness uniformity and the internal structure are analyzed, weak areas, particle flaws and excessive spraying of the spraying layer are recognized, the defect detection precision is improved, multi-angle transmissivity data are collected, the change rate is calculated, and color deviation and thickness abnormity are accurately positioned; through cross analysis of multiple optical parameters, evaluation of adhesive force, curing quality and color consistency is more detailed, spraying abnormity is classified, multiple detection means act synergistically, interference such as environment illumination and material reflectivity is reduced, and the detection accuracy of spraying quality is improved.

The invention relates to the technical field of cloth dyeing uniformity detection, in particular to a cloth dyeing uniformity detection method and system based on image analysis, and the method comprises the steps: building a three-dimensional optical response model according to the optical characteristics of a fluff cloth fiber material; calculating to obtain a villus shadow intensity distribution diagram according to the linear polarization degree image data, and calculating to obtain a theoretical artifact reflectivity distribution diagram through a three-dimensional optical response model according to the villus shadow intensity distribution diagram and the surface normal vector mapping data; extracting an actual measurement reflectivity distribution diagram from the hyperspectral image data, and performing differential operation on the actual measurement reflectivity distribution diagram and the theoretical artifact reflectivity distribution diagram to obtain a real dyeing component diagram; dividing villus unit grids on the real dyeing component graph, and calculating a spectral fingerprint variance value of pixels in each villus unit grid; and when the spectral fingerprint variance value exceeds a preset process tolerance threshold value, judging that the corresponding fluff unit grid has a real dyeing defect. And the identification precision of the dyeing defects of the fluff cloth is effectively improved.

The invention relates to the technical field of assembly production lines, and particularly discloses an assembly production line workpiece defect detection method and system based on machine vision. The method aims at accurately detecting the workpiece defects of the assembly production line, firstly, position information of a lens, a workpiece and a light source of a machine vision device is obtained, a basis is provided for subsequent calculation, the detection accuracy is ensured, and then the incident angle of the mirror surface is determined according to the characteristic information of the lens, influences reflected light and imaging and is a key parameter. Then obtaining imaging influence factors according to workpiece characteristics, measuring interference of workpiece surface characteristics on imaging, providing a basis for optimizing reflectivity, calculating specular reflectance through light source position information, reflecting light reflection conditions, combining the specular reflectance and the reflection conditions to obtain collaborative optimization specular reflectance, more accurately evaluating the influence of reflection on imaging, and finally, determining the influence of reflection on imaging. If the reflectivity is greater than a preset threshold value, correction is carried out, a correction angle is obtained to adjust a lens detection angle, misjudgment caused by mirror reflection is reduced, and the detection precision is improved.

The invention provides an HCG and LH test paper identification method and system based on optical characteristics and a storage medium. The method comprises the following steps: firstly, collecting and generating a reflectivity curve of a test paper detection area and a quality control area; secondly, extracting a reflection characteristic value of a curve in a detection area for test paper validity verification, and entering a core identification stage if the test paper is qualified; preliminary judgment is carried out by calculating a similarity deviation value of a quality control area curve and an HCG / LH standard curve, when the deviation is significant, a preliminary recognition result is generated and enters a cross validation link, and a result is confirmed by comparing a reflection characteristic value with a dynamic threshold range; when the similarity deviation is not obvious or the cross validation is not passed, starting a pre-trained discrimination model to perform final arbitration; by constructing a quadruple judgment mechanism of validity verification, preliminary recognition, cross validation and model arbitration, the operation complexity and misjudgment risk of manual test paper type selection are overcome, and the automation degree of the detection process and the accuracy and reliability of the recognition result are improved.

The invention provides a data detection method and system, a detection device, computer equipment and a storage medium, and the method comprises the steps: obtaining a detection signal of each wafer surface in a planetary reaction cavity, and cutting the detection signal based on a trigger signal to obtain a first signal sequence and a second signal sequence; processing the first signal sequence by adopting a classification model to obtain a material type on each wafer; querying a corresponding optical model based on the material type; and performing iterative optimization on the optical model, the first signal sequence and the second signal sequence by adopting a decoupling algorithm to obtain a temperature detection value and a reflectivity detection value. According to the method, the original detection signal is segmented into the independent signal sequences based on the trigger signal, then the material type is determined according to the independent signal sequences, the corresponding optical model is selected according to the material type, and finally the temperature detection value and the reflectivity detection value with high accuracy are obtained through decoupling according to the optical model. Accurate analysis of a single wafer signal is realized, and process repeatability and stability are greatly improved.

The invention discloses a film material surface defect detection method and system based on machine vision, and relates to the technical field of machine vision. Joint modeling is carried out by utilizing a local reflectivity difference value Ploc (x, y) of a pixel coordinate (x, y), a local stacking disturbance index S (x, y) of the pixel coordinate (x, y) and a local gray variance Wvar (x, y) of the pixel coordinate (x, y) in a film material image irradiated by a dual-angle light source, and the detection sensitivity of asymmetric optical disturbance caused by trace salt mist deposition is enhanced. Compared with the current situation that a traditional visual detection method is insufficient in response to early corrosion spots, the method has the advantages that a light reflection subsurface corrosion map can be effectively formed through quantitative combination of two factors of reflection anomaly and structural disturbance in the image, and rapid positioning and early warning of the membrane material salt spray corrosion initial stage are achieved.

The invention discloses an ultra-weak light detection system and method based on an SPAD array and time correlation photon counting, and belongs to the technical field of ultra-weak light detection. A multi-channel SPAD array detection unit; a time-to-digital conversion module; the photon arrival time analysis unit comprises a TCSPC model, a noise modeling and suppression module and a self-adaptive convolution filter; a data compression and fitting module; a spatial denoising filter; an output unit; and a master control processing unit. Based on a multi-channel SPAD array detection unit, a time-to-digital conversion module, a TCSPC model, a noise modeling and suppression module, a self-adaptive convolution filter and the like, high-sensitivity imaging and quantitative detection of materials (such as black silicon) with extremely low reflectivity are realized. The system has the advantages of being good in real-time performance, high in time resolution, high in signal-to-noise ratio, high in on-chip processing capacity, high in integration and the like, and meanwhile the bottleneck of a traditional TCSPC method for large-scale photon data processing is solved.

The invention relates to the technical field of machine vision image analysis, in particular to a lubricating oil abrasive particle quality analysis method and system based on machine vision, and the method comprises the steps: obtaining a thickness distribution diagram of an oil residual layer on the surface of an abrasive particle through a gray ratio relation between a first transmission image and a second transmission image; performing optical compensation on the first transmission image to obtain a final compensation image, and performing phase consistency edge detection to obtain an abrasive particle edge enhanced image; calculating the polarization reflectivity ratio of each abrasive particle area; extracting the mirror reflection intensity difference of each abrasive particle in the abrasive particle edge enhanced image; and when the abrasive particles are analyzed as metal attributes according to the polarization reflectivity ratio and the specular reflection intensity difference, identifying the morphological characteristic parameters of the abrasive particles, matching the morphological characteristic parameters with a pre-stored abrasive particle wear characteristic library, and outputting a quality evaluation report of the lubricating oil according to a matching result. The influence of oil film interference is effectively inhibited, the metal particles are accurately distinguished, and high-precision analysis on the lubricating oil abrasive particles is realized.

The invention relates to the technical field of remote sensing monitoring, solves the technical problems that an existing soil salinity inversion method is insufficient in precision and poor in generalization ability, and particularly relates to a bare soil salinity inversion method fusing partial least squares and a random forest. Comprising the following steps: acquiring spectral reflectivity data and salinity spectral index of a multiband range and spatial resolution, and preprocessing to establish a digital orthoimage; removing a water body and a vegetation coverage area in the digital orthoimage by using a normalized vegetation index to obtain bare soil data containing 24-dimensional feature variables; establishing a fusion model and carrying out training evaluation; and performing prediction by taking Sentinel-2 satellite remote sensing data as input of the fusion model to obtain the salt content of the bare soil. According to the method, the precision, reliability and generalization ability of soil salinity inversion can be effectively improved, compared with a traditional single model, the nonlinear relation and noise in remote sensing data can be better processed, and the generalization ability of salinity inversion is remarkably improved.

The invention provides a fruit tree canopy three-dimensional model reconstruction method and a fruit tree canopy three-dimensional model reconstruction system based on multi-modal data, and relates to the technical field of fruit tree canopy three-dimensional model reconstruction based on multi-modal data. Sub-pixel-level space registration is achieved by means of ground control points, voxelization division is carried out based on the range of a point cloud bounding box, and the number of point clouds is screened to construct an initial seed set; and adopting a structure-spectrum collaborative growth strategy, taking the adjacent voxels which are not accessed as candidate voxels, calculating a spectrum similarity mean value of the candidate voxels and directly adjacent voxels in the seed set, stopping growth when no new voxels are added in continuous three times of iteration, and outputting a final seed set. Meanwhile, the set is converted into a three-dimensional canopy model fusing space coordinates and full-wave-band spectral reflectivity, voxel-level pathological labeling is achieved based on the chlorophyll sensitivity index and the blue-red slope, and the whole process of reconstruction and analysis is completed.

The invention relates to the technical field of satellite remote sensing application, and discloses a crop classification method based on a multi-source satellite image, and the method comprises the steps: firstly obtaining multi-temporal satellite image data, extracting spectral reflectivity characteristics, generating a matrix, and constructing a classification model set; setting a feature parameter category set, and establishing a feature fusion correlation model based on the obtained texture, vegetation index parameters and spatial resolution data of the historical image; extracting spectrum and texture feature parameters of a crop area in the current image, and generating an optimized feature set in combination with model optimization; updating classification logic based on the optimized feature set and the classification model, generating a target classification scheme and calibrating a spatial relationship; and finally, acquiring crop growth cycle data, and establishing a time sequence feature association rule to optimize a crop type spatial distribution map. According to the method, through multi-source feature fusion and dynamic optimization, the crop classification precision and reliability are improved, and the method is suitable for a precision agricultural management scene.

The invention discloses a large-range high-frequency all-sky earth surface solar irradiance machine learning estimation method and system suitable for a cloudy area, and relates to the technical field of irradiance estimation, and the method comprises the steps: obtaining earth surface radiation observation data, generating a station position table, and constructing a training data set; constructing a ground solar radiation degree model, and dividing the training data set into a training set and a test set according to a proportion of 80% to 20%; determining hyper-parameters of the Xgboost algorithm through parameter search, training the model on the training set, and checking the model by using the test set; new satellite reflectivity is used for observing data and meteorological elements, data cleaning is carried out, and the model is input to estimate the space-time distribution of the total solar radiation irradiance of the earth surface. According to the method, the space and time precision of radiation inversion is improved, the complex nonlinear characteristics and data interaction relation are accurately captured, efficient inversion of the earth surface solar radiation degree is achieved, and the prediction performance and generalization ability of the model are remarkably improved.

The invention relates to the technical field of forest image processing, in particular to a forest information extraction method based on a remote sensing image. The method comprises the following steps: obtaining remote sensing image data of a forest area and a GPS positioning sample place area, and carrying out sample area remote sensing image processing and vegetation index analysis to obtain vegetation index data corresponding to each forest type in the sample area; performing end member component reflectivity analysis on the remote sensing image data of the sample area to obtain spectral reflectivity values corresponding to all end member components of the forest in the sample area; performing mixed pixel decomposition on a corresponding sample area remote sensing image in the sample area remote sensing image data in combination with a linear mixed pixel decomposition model so as to generate an abundance map of each ground feature type of the sample area; and carrying out pixel forest information classification extraction on each ground feature type abundance map of the sample region to obtain each forest type distribution map of the sample region. According to the invention, different forest information in the remote sensing image can be obtained by integrating the surface reflectance and the vegetation index.

The invention belongs to the technical field of radar wave-absorbing materials, and discloses a double-sided wave-absorbing broadband stealth composite material and a preparation method thereof. The broadband stealth composite material with the double-sided wave absorbing function comprises a non-metal-based composite material body and at least five metamaterial layers arranged in the thickness direction of the non-metal-based composite material body at intervals. The number of the metamaterial layers is an odd number, a middle metamaterial layer is arranged in the middle of the metamaterial layers in the thickness direction, and the rest metamaterial layers are symmetrically distributed on the two sides of the middle metamaterial layer; the metamaterial layer comprises conductive pattern units which are periodically arranged; the relative dielectric constant of the nonmetal-based composite material body is not greater than 10; the double-sided wave-absorbing broadband stealth composite material does not contain a metal reflecting layer. When electromagnetic waves with the frequency of 1-18 GHz vertically enter the material, the transmissivity of the material is small (smaller than-13 dB, namely smaller than 5%), and meanwhile the material has low reflectivity in a broadband. And the preparation method is simple to operate and easy to realize large-scale production.

The invention relates to the technical field of germ detection, in particular to a method and a device for detecting response of corn to setosphaeria turcica infection. The method comprises the following steps: acquiring initial spectral reflectivity of corn leaves and environmental parameters of an environment where the corn leaves are located; the environment parameters comprise temperature, humidity and illumination; according to the growth characteristics of the corn leaves in different growth stages, extracting the initial spectral reflectance to obtain a key spectral reflectance; extracting the spatial-spectral characteristics of the key spectral reflectivity, and extracting the time sequence correlation characteristics of the environmental parameters and the key spectral reflectivity; generating a disease sensitive feature vector according to the space-spectrum feature and the time sequence correlation feature; according to the disease sensitive feature vector, determining the infection detection result of the corn leaves responding to the setosphaeria turcica.

The invention relates to the field of remote sensing image processing, and provides a remote sensing image terrain radiation correction method, device and equipment, and the method comprises the steps: obtaining a to-be-corrected remote sensing image, calculating the terrain parameter of each pixel, dividing the to-be-corrected remote sensing image into a shadow region and a non-shadow region, and determining the direction weighting function of each pixel; calculating a topographic modulation spherical harmonic primary function corresponding to each pixel; training samples are selected in the shadow area and the non-shadow area respectively, a linear model is constructed, and spherical harmonic illumination coefficients representing overall three-dimensional illumination distribution of the shadow area and the non-shadow area are solved; and based on the topographic modulation spherical harmonic primary function, combining the spherical harmonic illumination coefficient corresponding to the area to which each pixel belongs, reconstructing the average incident radiance of each pixel, and then performing inversion to obtain the real reflectivity of the earth surface. According to the method, the problem of inaccurate radiation distortion correction of the remote sensing image under the complex terrain in the prior art is solved, and accurate inversion of the real reflectivity of the earth surface is realized.

The invention discloses a calibration correction method and a test method of a single-point DTOF laser radar, and the correction method comprises the steps: carrying out the temperature drift correction of a collected conventional channel distance value TOF 1 through a mapping relation between original TOF data and original temperature data TS by employing the single-point DTOF laser radar; and carrying out one-time global fitting calibration correction and two-time segmented calibration linear correction on acquired data of a black target, a white target, a high-reflection target and the like under different calibration point distances. The calibration correction method and the test method of the single-point DTOF laser radar provided by the invention can be applied to the single-point DTOF laser radar, and effectively improve ranging result drift caused by temperature drift; the method can overcome the problems that SPAD saturation and black target cannot cover a lower reflectivity scene in different high reflectivity scenes, and improves the ranging error caused by different pileup degrees in different reflectivity scenes. Distance measurement errors caused by different light spot energy distribution at different distances can be improved.

The invention provides a wafer film thickness measuring method and device and chemical mechanical polishing equipment, the wafer film thickness measuring method comprises the following steps: a measuring spectrum and an interference spectrum are obtained, the measuring spectrum is formed by measuring light, and the interference spectrum is formed by reflected light after the measuring light is reflected by a wafer; calculating a correction coefficient according to the measured spectrum, and correcting the interference spectrum according to the correction coefficient; calculating a reflectivity curve of the wafer according to the corrected interference spectrum; and determining the film thickness of the wafer according to the reflectivity curve. According to the wafer film thickness measurement method, the influence of measurement noise on an interference spectrum is eliminated, the control precision of the wafer surface quality is improved, and the chip manufacturing yield is further improved.

The present invention provides a display device and a method for manufacturing the display device. A metal layer is formed on a highly light-transmissive substrate; a resist mask having an opening pattern is formed on the metal layer; exposed portions of the metal layer is etched away in this state to form openings; then the resist mask is removed; and a surface of the metal layer and an inner side wall of each of the openings are oxidized to form a metal oxide layer. Thus, a front surface and a rear surface of the aperture plate are caused to have different reflectances. The oxide layer is formed at the same time as when the resist mask is ashed to remove resist.