125 results about "Spectral database" patented technology

The invention discloses an on-line detection method for gas in transformer oil based on photoacoustic spectrum self-correction, and relates to the technical field of spectrum detection and power equipment state monitoring, and the method comprises the following steps: S1, emitting modulation laser with a specific wavelength to a gas sample released by the transformer oil through a laser, and exciting gas molecules to generate photoacoustic signals; and S2, detecting the photoacoustic signal by using a photoacoustic sensor, converting the signal into an electric signal, and outputting the electric signal. According to the online detection method for the gas in the transformer oil, the correction coefficient is adjusted in real time through a self-correction algorithm, errors caused by light path changes and sensor aging are effectively compensated, the precision and stability of long-term detection are remarkably improved, and the detection accuracy is improved by combining a gas spectrum database and a mode recognition algorithm. According to the method, accurate qualitative and quantitative analysis of various gases is realized, the problem of cross sensitivity of multiple components is solved, and the method also has temperature and pressure compensation functions and can automatically adapt to environmental changes.

The invention belongs to the technical field of agricultural disease monitoring, prevention and control, and particularly relates to a rice blast monitoring and diagnosing method based on a mooring hot-air balloon and an unmanned aerial vehicle system. By constructing a dynamic observation platform of a tethered hot-air balloon and an unmanned aerial vehicle group, multi-variety, multi-temporal and multi-dimensional rice blast diseased plant multi-spectral / hyperspectral data and multi-information such as geography, soil, climate and field management are synchronously obtained, and a spectral database is constructed. An early diagnosis inversion model is constructed based on spectral characteristics of diseased rice plants and data characteristics of an unmanned aerial vehicle and a captive hot-air balloon by using self-supervised learning and deep learning technologies. According to the method, the cooperative advantages of the tethered hot-air balloon and the unmanned aerial vehicle are fully played, comprehensive acquisition and deep fusion of data are realized, the accuracy and timeliness of rice blast monitoring and diagnosis are improved, powerful technical support is provided for healthy development of the rice industry, and agricultural intelligence and sustainable development are assisted.

The invention discloses a prospecting method, system and equipment for identifying polygonum capitatum plant indication copper ore through remote sensing, and belongs to the technical field of remote sensing prospecting. Comprising the following steps: acquiring remote sensing data in a target area, and establishing a spectrum database of polygonum capitatum plants and altered minerals related to copper ore; finely identifying the Polygonum capitatum plant and the distribution characteristics thereof by using a spectral data analysis processing technology; in combination with radar, multispectral and hyperspectral remote sensing data, performing construction-alteration mineral identification and fine mapping in a target area; fusing the polygonum capitatum plant, the distribution information of the polygonum capitatum plant and the construction-alteration mineral mapping result, establishing an ore prospecting prediction model for indicating the copper ore based on the polygonum capitatum plant, delineating copper ore abnormity in a target area, and performing deep learning method verification on a prediction result; and integrating multi-source data and field investigation, further verifying the predicted target region, and optimizing the prospecting model. The method can reduce the cost, improve the efficiency and accuracy, and simplify the prospecting process.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.

The invention discloses a rice-fish symbiosis multifunctional water quality monitoring and analyzing device and a use method. The device comprises the following core modules: a data acquisition module, which is responsible for acquiring water quality data from front-end monitoring equipment in real time; the data processing module is used for deeply processing the collected water quality data and image information; the data analysis module is used for introducing a prediction model based on machine learning and accurately predicting a future water quality change trend in combination with historical water quality data and real-time monitoring data; the intelligent decision support module is responsible for automatically identifying potential risks in water quality and generating targeted intelligent decision suggestions according to the potential risks; according to the method, image recognition and spectral analysis technologies are introduced, a high-definition camera, a precise underwater spectrometer and other equipment are utilized, and a deep learning algorithm and a spectral database are combined, so that precise recognition and classification of suspended solids, algae and other biocenosis in water are realized, and more comprehensive and multi-dimensional information is provided for water quality evaluation.

The invention discloses an underground gas storage gas monitoring method and system based on optical fiber spectrum analysis. The method comprises the following steps: S1, optical fiber network deployment: arranging a corrosion-resistant optical fiber sensor network in a gas storage; s2, spectral data acquisition: transmitting light with a specific wavelength to a gas chamber at the tail end of an optical fiber through a Fourier transform infrared spectrometer, and acquiring a transmitted spectrum after interaction of gas and light; s3, spectrum decoupling and concentration calculation: based on a peak pattern fitting algorithm of the Lambert-Beer law, decoupling the infrared spectrum characteristics of the mixed gas, matching a standard spectrum database, and calculating the concentration of each gas component; s4, constructing a three-dimensional gas model: according to the position of the optical fiber sensor and the gas concentration data, establishing a gas migration model to predict a leakage position and a movement direction; according to the method, the trend, the component distribution, the concentration change and the potential leakage risk of the gas in the storage are effectively evaluated, and technical support is provided for the safety, the effectiveness and the economical efficiency of operation of the gas storage.

The invention relates to the technical field of gas detection, and provides a doped gas concentration detection method and system based on a gas analyzer. The method comprises the following steps: constructing a standard spectrum database under the same pressure and temperature as a to-be-measured site; the method comprises the following steps: introducing sample gas into an optical absorption cell, carrying out wavelength scanning through a tunable diode laser in a plurality of adjacent absorption spectral line intervals containing target gas and interference gas, superposing a high-frequency sinusoidal modulation signal on a driving current, and synchronously detecting a second harmonic component of transmission laser to generate an actual measurement spectrum; fitting the actually measured spectrum with a synthetic spectrum generated on the basis of a standard spectrum database, and outputting a target gas concentration ratio as a detection result when a residual error meets a threshold value, so as to solve the technical problem that the detection precision is reduced due to spectrum overlapping caused by interference gas, and achieve the purpose that the detection precision is reduced by constructing the standard spectrum database and second harmonic spectrum fitting analysis. And the accuracy and the anti-interference capability of gas detection are improved.

The invention relates to the technical field of component analysis, in particular to an intermediate infrared spectrometer sensor verification system which comprises the following steps: acquiring spatial distribution information of a target sample through an automatic sampling module, and generating a corresponding first feature group based on the spatial distribution information; adjusting an emission wave band and a modulation strategy corresponding to a mid-infrared light source, converting mid-infrared photons into visible light signals, and generating a corresponding second feature group; performing down-sampling processing on the original spectral data to generate a corresponding third feature group; inputting the sparse spectral coefficient into a pre-trained deep learning reconstruction model, and dynamically correcting the reconstruction process in combination with an environment temperature compensation parameter to generate reconstructed spectral data corresponding to high resolution; and analyzing the reconstructed spectrum data, matching a preset substance spectrum database, extracting a characteristic absorption peak position and an intensity ratio of the target substance, and generating a corresponding final analysis result. According to the invention, the intelligence of the sensor verification system can be improved.

The invention discloses a temperature and gas detection system and method based on an ultraviolet to near infrared spectrum, relates to the technical field of spectral measurement, and solves the technical problems that the space in a smelting furnace is occupied and the complexity of the system is increased due to the fact that two sets of independent equipment are adopted for temperature measurement and environment monitoring in the smelting process respectively in the prior art. The method comprises the following steps: acquiring spontaneous radiation light of a to-be-detected target; carrying out dispersive light splitting on the spontaneous radiation light to obtain spectral data of a full wave band from ultraviolet light to near-infrared light; performing denoising processing on the spectral data to obtain a spectrum of the to-be-detected target; obtaining temperature data according to the spectral analysis of the to-be-detected target; calculating based on the spectrum and a pre-established spectrum database to obtain gas concentration; the temperature of the to-be-measured target can be accurately measured, and the measurement accuracy is improved.

The invention relates to the technical field of salicornia europaea spectral feature extraction, and discloses a salicornia europaea lithium stress time sequence spectral feature database construction method which comprises the following steps: culturing salicornia europaea in a greenhouse by utilizing water culture vessels containing different lithium concentrations to obtain a plurality of salicornia europaea water culture vessels containing different lithium concentrations; measuring hyperspectral images of the salicornia europaea in the sampling intervals to obtain hyperspectral images of the salicornia europaea and the reference white board in each sampling interval; extracting a hyperspectral reflectivity image and hyperspectral data of the salicornia europaea in each sampling interval so as to obtain absorption characteristic parameters and red edge positions of each continuum removal spectrum under different set wavelengths, and calculating the hyperspectral reflectivity of the salicornia europaea according to the hyperspectral reflectivity image of the salicornia europaea in each sampling interval. Respectively constructing a time sequence spectrum library and a standard spectrum library of salicornia europaea at different sampling intervals under the stress of different lithium concentrations; according to the method, the spectral feature precision of salicornia europaea is improved, and meanwhile, the spectral database of salicornia europaea is enriched.

The invention discloses a remote sensing monitoring method for farmland film mulching age limit, and particularly relates to the technical field of farmland film mulching. By using multispectral and hyperspectral remote sensing technologies, combining farmland environment data such as temperature, humidity and illumination intensity, and by establishing a spectrum database, comparing current remote sensing data with mulching film spectrum characteristics in a preliminary database, and accurately distinguishing normal and abnormal spectrum characteristics; in the identification process, if the distinguishing accuracy is high, the method is directly used for farmland management decision making; if the accuracy is low, the distinguishing capability is enhanced through environment data auxiliary analysis; the accuracy of monitoring the mulching film state is improved, misjudgment is reduced, and a scientific basis is provided for replacement and management of the mulching film, so that farmland management is optimized, the service life of the mulching film is prolonged, and the purposes of reducing cost and increasing efficiency are finally achieved.

The invention discloses a fruit and vegetable disease and insect pest detection method based on a spectrum database and model confidence. Comprising the following steps: step S10, acquiring spectral data of fruit and vegetable crops; step S20, based on the disease and pest diagnosis model, outputting a disease and pest diagnosis result and a corresponding model confidence; and S30, based on a preset confidence model, judging a model confidence and a preset range corresponding to the disease and insect pest diagnosis result, and outputting the diagnosis result and the corresponding reliability. The model confidence coefficient of the diagnosis result is given by calculating and comparing the model confidence coefficient of the output diagnosis result, so that the problem of disease and insect pest detection errors caused by the fact that only disease and insect pest detection is carried out and the accuracy of disease and insect pest detection is not verified is solved.

The invention relates to the technical field of automatic driving, in particular to an automatic driving target detection method and device based on hyperspectrum, electronic equipment and a storage medium, which are used for solving the defects that the automatic driving target detection result is inaccurate and interference is generated on automatic driving under the traditional similar background interference condition. According to the method, the deep learning network is trained by using the RGB image to assist the hyperspectral data, the preprocessed hyperspectral data is synthesized into the pseudo-color image through dimension reduction, the pseudo-color image is input into the network, the final accurate target detection result is obtained through comparison and analysis of the spectral database and optimization of the output of the network, and the driving safety of the autonomous vehicle is improved.

The invention is applicable to the technical field of petroleum detection, and relates to a method for identifying the spatial maturity of a petroleum inclusion, which comprises the following steps: collecting and processing a rock sample containing the petroleum inclusion; placing the processed sample on a sample table of an O-PTIR instrument, setting surface scanning parameters, performing surface-by-surface scanning on the petroleum inclusion in the sample in the Z-axis direction, and obtaining photo-thermal infrared spectrum images and data of different Z-axis layers; preprocessing the photo-thermal infrared spectrum image and data; a three-dimensional space maturity distribution model of the petroleum inclusion is constructed, characteristic peaks related to maturity in photo-thermal infrared spectrum images of different Z-axis layers are analyzed, a petroleum inclusion standard spectrum database with known maturity is combined, a quantitative relation between characteristic parameters and the maturity is established, and the maturity of the petroleum inclusion is calculated. Therefore, maturity values of the petroleum inclusion in different spatial positions are determined; and displaying the maturity information in a visual mode. According to the invention, accurate determination of the petroleum inclusion space maturity is realized.

The invention belongs to the technical field of medical image processing, and discloses an OCT-SERS combined multi-mode gastric cancer tissue diagnosis system and method. A set of OCT-SERS combined system light path which shares a sample arm and is high in registration precision realizes high-precision registration of an OCT imaging range and an SERS detection position; the surface of a biological tissue is covered with a hydrogel film doped with silver nanoparticles, then SERS signal detection is carried out, and non-destructive SERS enhancement of the tissue is achieved; multi-modal gastric cancer diagnosis is carried out by adopting an artificial intelligence algorithm through collection of normal and cancerous gastric wall tissue OCT images and SERS spectral data and establishment of a database. According to the invention, by establishing the normal and cancer tissue OCT image and SERS spectrum database and carrying out artificial intelligence learning, real-time accurate diagnosis of pathological tissues can be realized.

The invention belongs to the technical field of agricultural environment detection and solid waste resource utilization, and provides an agricultural waste resource environment threshold identification method and system based on spectral analysis, and the method comprises the steps: collecting multispectral data and corresponding physical and chemical parameters of different types of wastes, and constructing a spectral database; an ant colony algorithm is combined with a continuous projection algorithm, and the characteristic wave bands meeting the preset correlation degree with the resource efficiency in the spectrum database are screened; establishing a quantitative relation between spectral characteristics and environmental pollution indexes by using the characteristic wave bands; and solving the Pareto frontier of the spectral characteristic parameter-environmental risk by using the quantitative relationship and adopting a Bayesian optimization algorithm, and obtaining an optimal agricultural waste recycling environmental threshold. According to the technical scheme, accurate environment management of agricultural waste recycling is achieved through the spectrum technology, and an intelligent tool is provided for circular agriculture.

The invention discloses a method and a system for detecting the content of glass beads in pavement marking, and belongs to the field of measurement data analysis, the detection method comprises the following steps: scanning glass beads with different refractive indexes by using a near infrared spectrometer, and extracting spectral data of the glass beads with different refractive indexes to construct a comparison database; the method comprises the following steps: scanning a to-be-detected sample by using a near-infrared spectrometer to obtain spectral data, constructing a spectral database, preprocessing the spectral data obtained by scanning, and performing feature extraction on the preprocessed spectral data; and analyzing the spectral peak signal characteristic data and the data in the spectral database by using a Lasso regression algorithm to obtain the final content of the glass beads. The whole detection process is convenient and rapid, and more accurate detection can be realized in combination with a data algorithm.

The invention provides a method for rapidly detecting micro-plastics based on a micro-Raman spectrum, and belongs to the technical field of environmental pollution detection. The method comprises the following steps: dispersing a single layer of a particle sample on a filter membrane, dividing a to-be-detected filter membrane into Raman spectrum detection sub-regions, and performing full-automatic splicing by using a high-precision electric loading platform to generate a Mongolia microscopic image of the detection sub-regions; checking and calibrating the coordinate positioning error of the loading platform through a real-time video window; a Raman automatic particle analysis system is adopted to directionally obtain a target particle characteristic spectrum, and a standard spectrum database is combined to carry out qualitative and quantitative analysis on substances; and calculating the micro-plastic content distribution of the whole membrane through a regional proportion quantitative extrapolation method. According to the method, an extrapolation detection algorithm based on uniform distribution of the micro-plastics is established, efficient and rapid detection of the micro-plastics with small particle sizes in the environment can be realized, meanwhile, the method also has good applicability to the micro-plastics with large particle sizes, has relatively high detection precision, is accurate and reliable, is simple to operate, and improves the intelligent level of micro-plastic detection.

The invention discloses a building attribute extraction method based on a high-resolution hyperspectral remote sensing image, and relates to the technical field of remote sensing building attribute extraction, and the method comprises the steps: obtaining large-scene high-resolution satellite image data of a town region to be measured and hyperspectral remote sensing image data in the same scene; fusing the two images to obtain a high-resolution hyperspectral fused image, inputting the high-resolution hyperspectral fused image into a Unet deep learning network model, and cutting to obtain a plurality of single building sub-images; and inputting the single building sub-images into an overall edge attention converter to obtain a roof surface edge structure line, matching the spectral data of roof surface pixels with the spectrum database of the building roof material in the to-be-detected town area to obtain building attributes, and synthesizing all the sub-images to obtain the building attributes of the to-be-detected town. According to the invention, building roof extraction, material identification and purpose determination can be conveniently and rapidly realized.

The invention discloses a qualitative and quantitative analysis method and system for explosives based on terahertz time-domain spectroscopy, sample preparation is carried out by using unified standards, and the method comprises the steps of grinding, weighing and pressing; the method comprises the following steps: measuring a terahertz time-domain spectrogram of an explosive sample by using a terahertz time-domain spectroscopy system, and obtaining a terahertz frequency-domain spectrogram through Fourier transform; data preprocessing algorithms such as data expansion, SG smoothing, multivariate scatter correction (MSC) and competitive adaptive reweighting (CARS) are used for preprocessing the data; and performing classification / regression analysis by using machine learning algorithms such as a support vector machine classification (SVC) model, a support vector machine regression (SVR) model and a one-dimensional convolutional neural network model (1D-CNN). According to the method, the problems of difficulty in constructing a spectrum database, insufficient algorithm robustness, limited practicability and the like in the prior art are solved, and a more efficient and reliable solution is provided for safety detection and prevention and control of explosives.

The invention discloses a method for rapidly determining a leaked water cooler in an ethylene device by using an online detection and identification technology, and relates to the field of water cooler leakage monitoring. Comprising the following steps: selecting a process medium component of the water cooler as a characteristic component; constructing a fingerprint spectrogram database of the characteristic components; the circulating water of the water cooler is quantitatively and qualitatively analyzed in real time by utilizing an online detection technology, and a qualitative detection spectrogram result is compared and identified by utilizing an identification technology, so that the leaked water cooler is found. According to the method, different process media in different water coolers are utilized, the leaked process media in the circulating water and the water cooler characteristic component spectrogram database are compared and analyzed through the online detection and recognition technology, so that the water cooler with the leaked process media is locked, the leakage detection speed is high, the efficiency is high, the application range is wide, accurate information is provided for timely leaking stoppage, and the method is suitable for popularization and application. Deterioration of circulating cooling water quality is avoided, and normal operation of a production device is guaranteed.

The present invention proposes a method for identifying tea varieties based on canopy reflectance spectra, which belongs to the field of detection and analysis technology. It includes: S1. Based on tea germplasm resources, collect near-infrared reflectance spectrum data of tea canopies or leaves, perform data processing, and construct an infrared reflectance spectrum database of different tea varieties; S2. Segment the spectrum database data, one part is used for identification model development, and the other part is used for model diagnosis; S3. Perform machine learning model training on tea variety identification model; S4. For different generated models, establish and verify the correction model, perform correlation analysis, and then evaluate, and finally determine the most effective model. The overall accuracy of the identification model of the present invention is high, and the accuracy of identifying varieties of closely related tea trees is slightly lower, and the application prospect is good.

The invention provides a high-throughput wide-area Raman spectrum system for bacterial colony detection and an implementation method. The high-throughput wide-area Raman spectrum system comprises a laser and light spot adjusting module, a Raman imaging module and a detection module, the spatial heterogeneity problem of a bacterial colony sample is solved by setting a light spot adjustment scheme, a bacterial colony detection scheme and a high-throughput wide-area Raman scheme, so that the spectral repeatability of the bacterial colony sample is better, and establishment of a spectral database and identification of the bacterial colony sample are facilitated; the system is different from a common wide-area Raman system, has high throughput and high resolution, is more suitable for analysis and identification of biological samples, meets the resolution requirements of analysis and identification of various biological samples, and is scientific and reasonable in overall structural design, safe and reliable in implementation process and suitable for popularization and application.

The invention provides a hyperspectral water quality detection device and a hyperspectral water quality detection method, and aims to realize water quality detection with small volume, high detection speed, high real-time performance and high precision. The device comprises a water tank, a black sealing box embedded into a transparent sample box, a hyperspectral camera, a full-spectrum light source and a processing module. A water sample is irradiated by a light source through a transparent sample box, a hyperspectral camera collects spectral data of the water sample, an embedded processing module preprocesses the data, and feature extraction and multi-parameter prediction are performed by using a water quality analysis algorithm based on a one-dimensional convolutional neural network and a multi-head attention structure. By constructing a water quality spectrum database to train the model, real-time parallel analysis and accurate prediction of spectrum data in a specific area can be realized, and predicted water quality parameters include but are not limited to indexes such as COD, turbidity, total nitrogen, total phosphorus, ammonia nitrogen and chlorophyll. The device can realize high-precision and multi-parameter water quality real-time detection, and is small in size, fast in response and wide in application range.

The application provides a kind of management and control chemical fast LIBS classification identification model construction method, medium and system, belongs to management and control chemical technical field, the application is by collecting mercury argon reference lamp spectrum to carry out wavelength calibration, according to atomic spectrum database extraction management and control element characteristic transition wavelength window and compressed to low-dimensional feature space by sparse self-encoder, with hydrogen line starker broadening and Boltzmann graph slope method real-time inversion electron density and electron temperature and to compressed characteristic matrix is physically normalized, calculates plasma drift index to adaptively adjust plasma parameter decoupling double-flow condition graph network update strategy, the normalized characteristic matrix and physical diagnostic value are input into the artificial intelligence network to output element concentration vector and class prediction probability, solve the technical problems that plasma parameter fluctuation leads to laser-induced breakdown spectroscopy classification feature instability, classification accuracy significantly decreases under the condition of cross-instrument and cross-matrix.

The present invention discloses an on-line gas detection method for transformer oil based on photoacoustic spectroscopy self-calibration, which relates to the technical fields of spectral detection and power equipment condition monitoring, and includes the following steps: S1. Emitting modulated laser with a specific wavelength to the gas sample released from the transformer oil through a laser to excite gas molecules to generate photoacoustic signals; S2. Using a photoacoustic sensor to detect the photoacoustic signals and convert the signals into electrical signals for output. The on-line gas detection method for transformer oil proposed by the present invention adjusts the calibration coefficient in real time through a self-calibration algorithm, effectively compensates for the errors caused by the change of the optical path and the aging of the sensor, significantly improves the accuracy and stability of long-term detection, combines a gas spectral database and a pattern recognition algorithm, realizes the accurate qualitative and quantitative analysis of multiple gases, overcomes the problem of multi-component cross-sensitivity, and the method also has temperature and pressure compensation functions and can automatically adapt to environmental changes.

The invention relates to the cross technical field of biological medicine and data processing, and discloses a quality detection data analysis method for a close-period liquid medicine preparation. The method comprises the following steps: acquiring a transmitted light intensity signal through a multi-wavelength optical sensing array; performing compression on the edge side to generate a 6-dimensional low-dimension spectral feature vector; and after the data is uploaded to a cloud end, performing dynamic track matching based on a hierarchical hidden Markov model constructed by a historical full-life-cycle spectrum database, calculating a degradation risk index and triggering early warning. The system comprises a sensing array, an edge computing unit and a cloud collaborative analysis platform. According to the invention, through an edge-cloud collaborative architecture, the communication and calculation overhead is significantly reduced while the early warning precision is ensured, degradation early warning in advance of not less than 48 hours is realized, and the missing of an intervention window is effectively avoided.

The application provides a cultural relic hyperspectral image reconstruction method and device, a hyperspectral area array camera and a medium, relates to the technical field of hyperspectral imaging and cultural relic digital protection, and collects the RGB image of the surface of the cultural relic under the illumination of uniform and continuous spectrum; a plurality of sampling points are selected in the RGB image, and the hyperspectral data at the plurality of sampling points is collected; the spatial registration of the RGB image and the hyperspectral data is carried out, and the corresponding relationship between the pixel coordinates and the spectral coordinates is established; the spatial information of the RGB image and the hyperspectral data are fused according to the corresponding relationship between the pixel coordinates and the spectral coordinates; the fusion result is combined with the prior constraint of the preset cultural relic pigment standard spectral database to reconstruct the complete hyperspectral image of the surface of the cultural relic, complete hyperspectral area array images are quickly generated under the condition of a small amount of sampling, have the advantages of lightweight equipment, efficient collection and reliable results, and can be widely applied to cultural relic protection scenes such as murals, calligraphy and painting, ceramics and textiles.

The invention discloses an LED lamp tube quality detection method and system based on machine vision, and the method comprises the steps: irradiating an LED lamp tube through a multispectral light source, and automatically adjusting the illumination intensity and wavelength through a self-adaptive light source control algorithm according to the surface material of the lamp tube; acquiring surface image data of the LED lamp tube after the lamp tube irradiates the light source by utilizing a structured light three-dimensional scanning technology; performing data preprocessing on the surface image data of the LED lamp tube; and identifying defects of the initial lamp tube surface image data by using a deep learning model, comparing the defect image data with a standard spectrum database based on a convolutional neural network, and if the defect image data does not conform to a target specification, positioning a defect area in the defect image data by using a target detection algorithm to obtain a detection result. And comprehensive and accurate detection of the quality of the LED lamp tube is realized.

The invention provides a cultural relic hyperspectral image reconstruction method and device, a hyperspectral area-array camera and a medium, and relates to the technical field of hyperspectral imaging and cultural relic digital protection. RGB images of the surface of a cultural relic are collected under uniform and continuous spectral illumination; selecting a plurality of sampling points in the RGB image, and collecting hyperspectral data at the plurality of sampling points; performing spatial registration on the RGB image and the hyperspectral data, and establishing a corresponding relation between pixel coordinates and spectral coordinates; and according to a corresponding relation between pixel coordinates and spectrum coordinates, carrying out fusion on spatial information of the RGB image and the hyperspectral data, combining a fusion result with prior constraints of a preset cultural relic pigment standard spectrum database to reconstruct a complete hyperspectral image of the surface of the cultural relic, and rapidly generating a complete hyperspectral area array image under the condition of a small amount of sampling. The device has the advantages of light weight, efficient collection and credible result, and can be widely applied to cultural relic protection scenes such as murals, paintings and calligraphies, ceramics and textiles.