87 results about "Spectral database" patented technology

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 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 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 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 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 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 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.

The embodiment of the application discloses a collaborative evolution multi-component spectral feature selection method based on deep reinforcement learning, which comprises collecting and collecting spectral signals and component concentration values in samples, encoding and normalizing individuals in a field spectrum database SDate, calculating feature selection rates of all individuals in the field spectrum database SDate, establishing a DQN training model, and initializing a deep neural network Net and an empty experience pool M. In the iterative process of the training set, the ratio of variables in each interval in the population to the total variables is counted, the initial main population and multiple auxiliary populations are crossed and trained and updated DQN to output the final population, and the final population constructs a mixed multi-component spectral quantitative analysis model of water, fat and protein. The deep reinforcement learning model trained in the early stage of evolution recommends the auxiliary population to evolve collaboratively with the main population, so as to quickly obtain the optimal spectral feature subset.

The invention relates to the technical field of pipeline leakage detection, in particular to a pipeline flange liquid leakage detection method and system based on hyperspectral recognition, and the method comprises the following steps: S1, initializing a pipeline flange liquid leakage detection system; s2, transmitting a monitoring light beam to establish a monitoring light path for monitoring whether the pipeline flange has liquid leakage or not, and when the pipeline flange has liquid leakage and the monitoring light path is cut off, turning to the step S2; s2, preliminary liquid leakage positioning is carried out, and a target area suspected to have liquid leakage is positioned; and S3, performing hyperspectral acquisition on the target area to obtain spectral data of the target area, performing comparative analysis according to the spectral data and a preset standard liquid spectral database, and synchronously outputting relevant information of the leaked liquid. According to the invention, all-time monitoring of the pipeline flange can be realized, and real-time accurate detection and intelligent discrimination of liquid components in a complex industrial environment are realized.

A method and system for investigating and analyzing data from a mass spectrometer, the system including a spectral database, a count service for receiving scan data generated by the mass spectrometer and identifying a number of spectra in the data, a scaling service for receiving the scan data generated by the mass spectrometer, receiving the number of spectra from the count service, and initiating a plurality of query services, each query service of the plurality of query services corresponding to at least one spectra of the number of spectra and for querying the spectral database with the corresponding at least one spectra and returning a match between the corresponding at least one spectra and at least one known spectra from the spectral database, and a results service for retrieving each match, and formatting each match into an output data structure.

The invention discloses a method for rapidly detecting petroleum hydrocarbon in soil based on visible-near infrared spectroscopy. The method comprises the following steps: preparing standard samples with the concentration gradient of 0-3000 mg / kg, and establishing a spectrum database; preprocessing the spectral data by adopting Savitzky-Golay filtering and standard normal variable transformation; using a competitive self-adaptive reweighted sampling algorithm to optimize the characteristic wavelength, and constructing a partial least square regression quantitative model; and determining the optimal performance of the CARS algorithm by comparing three feature selection methods of full spectrum, BOSS and CARS, and the like. According to the present invention, the petroleum hydrocarbon content in the soil can be accurately, rapidly and quantitatively detected, the standard detection process is established, the gas chromatography is adopted to carry out comparison verification on the standard sample, the correlation coefficient of the two methods achieves 0.9988, the average relative error is 5.28%, and the accuracy and the reliability of the spectrum detection method are verified.

The invention relates to the technical field of mixture analysis and measurement, in particular to a quantitative analysis method for aniline substances in a mixture, which comprises the following steps: constructing an information library, measuring near infrared spectrums of a mixture X sample without aniline substances, an aniline substance sample and a mixture X sample with known aniline substance concentration, and calculating the concentration of the aniline substances in the mixture X sample. Obtaining a background spectrum database B, an aniline substance spectrum database C and a sample spectrum database M of a mixture X with known aniline substance concentration. Based on the information base, constructing spectral components of the aniline substances in the mixture X sample with known aniline substance concentration, and constructing a standard curve representing the relationship between the concentration and absorbance of the aniline substances in the mixture X sample with known aniline substance concentration. And obtaining the concentration of the aniline substances in the mixture X sample with unknown aniline substance concentration according to the standard curve. The method effectively eliminates the interference of background spectrum and coexisting substances, so that the detection result is more accurate and reliable.

The invention discloses a distribution rule analysis method for characteristic pollutants of a polluted land parcel, and relates to the technical field of environmental monitoring and pollution abatement, the analysis method comprises the following specific steps: S100, sample collection and pretreatment: collecting a soil or water sample according to a collection rule. Precise identification and concentration determination of specific pollutants of polluted land parcels are realized, and the problems of tedious operation, long time consumption and insufficient sensitivity often exist in a traditional pollutant detection method, however, absorption and scattering spectrum data of soil and water body samples can be rapidly obtained by utilizing unique properties of a terahertz spectrum, so that the detection accuracy of the pollutants in the polluted land parcels is improved. The method has the advantages that the method is simple in operation, the characteristic pollutant types in the sample can be accurately identified by comparing the characteristic pollutant types with the standard spectrum database, the concentration of each pollutant in the sample can be further calculated by utilizing the spectrum concentration interaction polynomial model, and accurate data support is provided for treatment and restoration of polluted land parcels.

The invention relates to a method for predicting the content of elements in coal based on LIBS spectral feature optimization and machine learning, which comprises the following steps of: converting a preprocessed spectrum into a two-dimensional spectral matrix, matching a peak wavelength point of the two-dimensional spectral matrix with an NIST standard atomic spectrum database, identifying spectral lines belonging to target elements, and determining the content of the elements in the target elements according to the spectral lines. A characteristic wavelength set stably appearing in all coal sample spectrums is screened out to serve as candidate characteristics; further screening out a core characteristic wavelength subset which is most relevant to the content of the target element and has the minimum redundancy by applying a plurality of characteristic selection methods; and then selecting an optimal model combination by adopting a plurality of machine learning regression algorithms. According to the method, through a two-stage screening strategy combining physical spectral line identification (NIST matching) and statistical learning feature selection, irrelevant and redundant spectral information is eliminated to the greatest extent, anti-interference core features directly related to the target element content are reserved, and the quality of model input data is improved from the source.

The application discloses a temperature and gas detection system and method based on ultraviolet to near infrared spectrum, relates to the technical field of spectrum measurement, and solves the technical problem that the prior art adopts two separate devices to respectively measure temperature and monitor the environment in a smelting process, occupies space in a smelting furnace, and increases the complexity of the system; the application obtains spontaneous emission light of a target to be detected; the spontaneous emission light is dispersed and spectrally analyzed to obtain spectrum data of the full wave band of ultraviolet to near infrared light; the spectrum data are denoised to obtain the spectrum of the target to be detected; temperature data are obtained according to spectrum analysis of the target to be detected; and the concentration of gas is calculated based on the spectrum and a pre-established spectrum database; the temperature of the target to be detected can be accurately measured, and the measurement accuracy is improved.

The present application relates to camouflage recognition imaging system and camouflage recognition method, specifically relates to a kind of multispectral camouflage recognition imaging system and camouflage recognition method, solve the technical problems that existing camouflage recognition imaging system range of matter is small, application scene is limited, or camouflage recognition method cannot real-time imaging, real-time identification, or spectral database construction efficiency is low.The multispectral camouflage recognition system of the present application includes image acquisition unit, data processing unit and display storage unit, image acquisition unit uses snapshot type spectral imaging chip, can real-time exposure, real-time data processing;Data processing unit can obtain continuous spectral information by means of spectral fitting degree identification technology, according to the characteristic spectrum of different kinds of identification target selects feature database, changes identification target, greatly increases the identifiable spectral range.In the multispectral camouflage recognition method of the present application, spectral correction is included, can remove the interference of environmental light factor, work under different daylight color temperature.

The invention discloses a nuclear magnetic resonance spectrogram library construction and analysis system based on data standardization, and relates to the technical field of data analysis, the system comprises a data acquisition module, a data format conversion module, a standardization module, a spectrogram library construction module and a visual analysis module; the data acquisition module is used for acquiring original data through a nuclear magnetic instrument and marking the original data; the data format conversion module is used for preprocessing original data and converting the preprocessed data format into a data point table form data file containing chemical shift and peak intensity; the standardization module is used for respectively carrying out operation processing and cleaning processing on X-axis data representing chemical shift and Y-axis data representing peak intensity; the spectrum library construction module is used for constructing a structured nuclear magnetic spectrum database; and the visual analysis module is used for visual presentation and analysis of the spectrogram data. According to the system and the method, spectrogram library construction and data analysis of the nuclear magnetic resonance data are realized.

The invention relates to a CUDA (Compute Unified Device Architecture)-based ultrafast reconstruction method for a gas molecular absorption spectrum, belongs to the technical field of gas molecular absorption spectrum calculation, and solves the technical problem of meeting the real-time requirement of wide-spectrum high-resolution scene and non-uniform path calculation. The method comprises the following steps: acquiring spectral line data, constructing a parallel line-by-line calculation program cuLBL, calculating an absorption coefficient spectrum database, constructing a spectrum absorption coefficient fitting parameter database, realizing an absorption coefficient spectrum ultrafast reconstruction program, and reconstructing a gas molecule absorption spectrum. The method is used for realizing rapid calculation of the gas molecule absorption spectrum.

The invention discloses a tea oil variety identification method based on multi-source data, and belongs to the technical field of edible oil. The first target image, the second target image and the third target image of the to-be-analyzed tea oil are continuously collected to obtain the first target data, the second target data and the third target data, the fusion similarity is calculated according to the tea oil standard fingerprint database, and whether the tea oil is single-variety tea oil or not is judged. Furthermore, first characteristic data, second characteristic data and third characteristic data are collected, characteristic Raman shift and characteristic wavelength are screened to establish a pure oil spectrum database, and a variety identification model is established according to the pure oil spectrum database, so that the accuracy and comprehensiveness of tea oil identification are improved; a variety proportion model is established according to the second feature data and the third feature data, a verification model is established according to the first feature data, and the recognition speed and the recognition precision are improved.