5318 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.

A method and device for inverting ocean-atmosphere optical parameters and storage medium. The method includes: obtaining a vector apparent reflectance ρl, ρQ, ρU of water-leaving radiance at top of atmosphere and observation geometry of a multi-angle polarized satellite, and inputting the vector apparent reflectance ρl, ρQ, ρU and the observation geometry into a trained ocean-atmosphere optical parameter model, to output a group of ocean-atmosphere optical parameters, the output ocean-atmosphere optical parameters comprise: PBOAw, FAOD / CAOD, FRm / CRm, W, CHL and SPM, the ocean-atmosphere optical parameter model is an XGBoost machine training model. In this way, the ocean-atmosphere optical parameters can be quickly and stably inverted.

The invention provides a deep learning-based rapid ore sorting method under adaptive illumination intensity, and relates to the technical field of ore sorting, the deep learning-based rapid ore sorting method comprises the following steps: obtaining illumination intensity distribution information and ore three-dimensional point cloud data, and calculating ore space orientation parameters based on quaternion attitude estimation; inputting the obtained data into a layered illumination compensation network, and obtaining an illumination normalized enhanced image through adaptive brightness mapping and local light spot correction in combination with reflectivity recovery and non-local mean filtering; inputting the illumination normalized enhanced image and the ore three-dimensional point cloud data into a multi-branch feature extraction network, constructing feature high-order association, and adopting adaptive feature reconstruction and conditional random field optimization features; and inputting the reconstructed features into a classification model based on active learning, optimizing a feature manifold space by using a self-organizing mapping network, carrying out sample labeling in combination with active sampling of adaptive entropy threshold screening and uncertainty perception, and realizing ore classification and sorting through random forest integration.

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.

Provided is a low-illumination image adaptive enhancement method, an apparatus thereof, a device and a storage medium. The method includes: inputting a low-illumination image to be enhanced into a pre-trained low-illumination image adaptive enhancement model to obtain an enhanced image. A method of training the low-illumination image adaptive enhancement model includes: acquiring a training set containing the low-illumination image and the corresponding reference image; constructing the low-illumination image adaptive enhancement model based on a Retinex algorithm. The low-illumination image adaptive enhancement model includes a projection module, an illumination component module, a reflectance component module and an enhancement module. The training set is used to train the low-illumination image adaptive enhancement model, and the trained low-illumination image adaptive enhancement model is obtained. The brightness and the contrast of the image are enhanced, and at the same time, the color and structure information of the image is restored effectively.

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 relates to the technical field of image processing, in particular to a road area intelligent extraction system for unmanned aerial vehicle aerial images, and the system comprises an image noise filtering module which analyzes and inputs frequency domain information of the unmanned aerial vehicle aerial images, recognizes a target noise mode, and filters the unmanned aerial vehicle aerial images. According to the method, frequency domain noise analysis and notch filtering are combined, the sharpness of the road edge is reserved, meanwhile, high-frequency noise and periodic interference are restrained, and the visibility of a low-contrast road area is improved; local pixel spectrum dispersion calculation is introduced and is dynamically fused with morphological parameters, and direction consistency and length-width ratio constraints are combined, so that the connectivity perception capability of the fragmented non-paved road is enhanced, and the fracture misjudgment under the form-changeable terrain is reduced; a normalized vegetation index and multi-band reflectivity are combined for modeling, water reflection interference is eliminated through a blue light band standard deviation, and a space mask of a non-road surface feature interference area is constructed.

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 discloses a method and system for improving satellite remote sensing inversion precision by fusing hyperspectral proximity, and belongs to the technical field of water environment and water ecology monitoring. Acquiring a water body hyperspectral near-sensing reflectivity spectral data set, a satellite remote sensing surface reflectance data set and a water environment actual measurement data set, and performing preprocessing; a synchronous data set containing # imgabs0 # and water quality and a synchronous data set containing # imgabs1 # and water quality are constructed; based on the synchronous data of the # imgabs2 # and the water quality, synchronous data of the # imgabs3 # and the water quality are generated, and synchronous data of the # imgabs4 # and the # imgabs5 # are obtained; the # imgabs6 # is normalized to obtain # imgabs7 #, and the quantitative conversion relation between the # imgabs7 # and the # imgabs8 # is constructed band by band to eliminate errors; generating a fused satellite-ground data set, and constructing a training water quality parameter inversion model; and updating corresponding methods and data sets in real time by repeating specified steps, and optimizing the model. According to the method, the inversion result is displayed and downloaded according to the query instruction, the satellite spectrum precision and the satellite-ground synchronous data set scale are improved, and the method is used for high-precision water environment monitoring.

This invention relates to an optical wireless communication (OWC) system that is configured to make use of observations of straylight reflections as generated by an OWC transmitter in order to support contention-based channel access including that of multiple basic or extended service sets (BSS, ESS) with overlapping coverage areas. It is proposed to have an additional optical signal or signal component generated in the OWC transmitter which allows for simple detection without having to do heavy digital demodulation processing. A learning phase may allow to adjust for environmental conditions like reflectivity or transmitter characteristics.

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 water and soil conservation monitoring, and discloses a remote sensing image-based water and soil conservation monitoring method, which comprises the following steps of: 1, integrating optical, infrared and microwave remote sensing data, and constructing a soil information atlas; step 2, capturing soil humidity change in each season by using long-time-sequence data according to the soil information, and determining the influence of rainfall, snow melting and evaporation on the reflectivity; step 3, training data of each wave band through a deep neural network according to the influence information of the reflectivity; through an adaptive learning algorithm and a model parameter regular updating mechanism, the problem that in the prior art, characteristic spectrums of wet soil, vegetation and bare soil are overlapped due to seasonal changes is effectively solved, after the seasonal modulation factors are introduced, the system can accurately capture influences of natural factors such as rainfall, snow melting and evaporation on the soil reflectivity, and the soil reflectivity is improved. And model parameters are adaptively adjusted, so that the interference of reflectivity fluctuation on a monitoring result in wet and drought states is eliminated, and the stability of long-term monitoring is ensured.

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 relates to the technical field of image processing, in particular to an image data processing method and system, computer equipment and a storage medium, and the method comprises the following steps: obtaining image target boundary pixel data, calculating boundary gradient change data and local pixel density change data, judging the form offset direction and amplitude, and extracting an offset maximum region. And setting a local form adjustment weight, calculating a direction compensation value, adjusting an image target contour, and generating a form compensation data set. According to the invention, through boundary pixel extraction and gradient change calculation, local pixel density fluctuation is captured, target form adjustment is more accurate, illumination intensity gradient and spectral reflectivity extraction are combined, the optical matching degree of a target area is improved, the target contour separation precision is optimized in trajectory analysis, and based on a separation boundary value and form compensation data, the target form is more accurately adjusted. According to the overall scheme, the target contour precision, the optical adaptability and the classification stability are enhanced, and high recognition reliability is kept in a complex environment.

The invention relates to the technical field of optical line protection, and discloses a river water quality monitoring method based on an unmanned aerial vehicle hyperspectral remote sensing image and machine learning, which combines the unmanned aerial vehicle hyperspectral remote sensing image with a machine learning algorithm to construct a river water quality parameter inversion model. Comprising the following steps: collecting a water sample, obtaining an unmanned aerial vehicle hyperspectral image and preprocessing; extracting an image of the river part and selecting the reflectivity of each wave band of the image; performing spectrum transformation on the original spectrum of the image, establishing an inversion data set through feature screening, and constructing a water quality parameter inversion model by using a machine learning algorithm; and simulating the water quality parameters of the river through the water quality parameter inversion model, and visualizing the simulation result. According to the method, urban inland small river water quality monitoring can be rapidly achieved at low cost, the dual problem that the time resolution and the space resolution are limited in urban small river monitoring based on satellite remote sensing images is solved, and a scientific basis is provided for urban river intelligent inversion.

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 present invention proposes an electronic component appearance defect detection method and system based on multispectral imaging, which belongs to the field of optical measurement technology, including: obtaining an appearance image dataset of an electronic component to be detected, performing image preprocessing, extracting an area of ​​interest of the appearance of the electronic component to be detected, and obtaining a multispectral image of the area of ​​interest; extracting the spectral reflectance characteristics and spatial characteristics of the multispectral image of the area of ​​interest through a spectral reflectance reconstruction method combining a genetic algorithm and a neural network, and performing feature fusion to form a spatial-spectral joint feature dataset of the area of ​​interest, which is input into a trained appearance defect detection model to obtain the defect type and size of the appearance of the electronic component to be detected; and displaying the appearance defect detection results of the electronic component to be detected. The present invention improves the accuracy and efficiency of appearance defect detection of electronic components.

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 discloses a reflective method and a reflective device for real-time in-situ measurement of film transmittance. By adding the reflecting surface, the transmittance of the thin film can be directly calculated through a formula only by respectively measuring the light intensity reflectivity R of the thin film and the light intensity reflectivity R'of the thin film-reflecting surface, and a precise optical model and complex fitting do not need to be established; reflected light of the thin film and light reflected back by the reflecting surface do not interfere with each other, amplitude superposition is not involved, only light intensity superposition is achieved, the transmittance can be obtained only by obtaining the light intensity, and the transmittance of the thin film is measured through the method of measuring the reflectivity twice. In the process of measuring the transmittance by using the reflection-type method, the thickness and the optical property of the film do not need to be known in advance, so that the method is very suitable for measuring and monitoring the growth process of the film in real time in situ; according to the reflection type method, all optical elements are arranged on one side of the thin film, the available angle range of incident light is far larger than the angle range of an ellipsometer, and the ellipsometer can be easily integrated with other film manufacturing equipment.

The invention discloses a PCB production line defect detection analysis method and system based on an identification model, particularly relates to the technical field of PCB quality detection, and is used for solving the problems of low PCB defect detection precision and high omission ratio in the prior art. The method comprises the following steps: scanning the whole surface of a PCB to generate a thickness distribution diagram, identifying a thickness non-uniform region by adopting a threshold segmentation algorithm, and marking a key detection region; potential defects irrelevant to process fluctuation are identified through historical production data analysis; and adjusting the polarization angle of the light source to analyze the reflectivity difference, and identifying the hidden defect area in combination with the microstructure of the material. The union set of the potential defect area and the hidden defect area is marked as the suspected incomplete area, and the influence of the suspected incomplete area on the performance of the PCB is analyzed according to the quality standard, so that the detection precision and comprehensiveness are improved, and the high-quality control of the PCB is realized.

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 discloses a video image color correction method and system based on artificial intelligence, and relates to the technical field of color correction, and the method comprises the steps: collecting video data, carrying out the image size normalization and denoising processing, and obtaining the preprocessed video frame sequence data; and extracting reflectivity data and illumination data by using a Retinex-Net deep learning model, calculating an illumination adjustment coefficient, carrying out pixel correction, and carrying out color temperature correction on the pixel data of the video frame subjected to pixel correction according to the illumination adjustment coefficient. According to the method, the preliminary color mapping matrix is generated through the color histogram matching method, the color distribution of the image can be optimized, the image is enabled to better conform to the color features of the target reference frame, the color temperature adjustment is performed on the image according to the illumination adjustment coefficient and the illumination threshold, the cold and warm tones of the video are enabled to be accurately adjusted, and the image quality is improved. Through successively carrying out pixel correction, color correction and local area correction, a stable color adjustment frame is provided, so that the video styles are consistent.

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.

An optical combiner is employed to reflect a first part and a second part of a synthetic light field towards a first eye and a second eye of user(s), respectively, whilst optically combining the first part and the second part with a real-world light field. Portion(s) of the optical combiner from which a part of the synthetic light field is reflecting towards a given eye of a given user is determined, based on a relative location of the given eye with respect to the optical combiner. Portion(s) of active optical device(s) that corresponds to the portion(s) of the optical combiner are activated, to decrease transmittance of the real-world light field passing through the portion(s) of the optical combiner towards the given eye, and / or to increase reflectance of the synthetic light field being reflected by the portion(s) of the optical combiner towards the given eye.

The invention relates to the technical field of computer vision, in particular to a station platform limit intelligent detection method and system based on computer vision, and the method comprises the following steps: obtaining platform region spectral data, extracting spectral features according to the reflectivity value of each pixel point in a spectral range, comparing the spectral features with the features of various known materials, and determining the position of the station platform limit. And identifying material types of the plurality of positions, detecting obstacles, and generating material classification feature values. According to the method, the spectral data is acquired, the reflectivity value is compared to identify the material type, the texture feature and the gradient change rate are utilized to analyze and identify the fuzzy area, misjudgment on the fuzzy identification is reduced, point cloud data and image information are adopted for time-space synchronous registration, and multi-source sensing information is fused to enhance the time-space consistency of three-dimensional reconstruction. The size accuracy of the platform model is improved, the space offset is calculated by comparing engineering design drawing data, a structure deviation quantitative evaluation system is established, a deformation area is positioned, and the foreseeability of safety early warning is 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.