57 results about "Spectral database" patented technology

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 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 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 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.

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 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 application relates to the technical field of biological medicine and data processing, and discloses a quality detection data analysis method for near-efficiency liquid medicine preparation. The method comprises the following steps: collecting a transmission light intensity signal through a multi-wavelength optical sensing array; generating a 6-dimensional low-dimensional spectral feature vector through edge side compression; after being uploaded to the cloud, performing dynamic trajectory matching based on a hierarchical hidden Markov model constructed based on a historical full-life-cycle spectral database, calculating a deterioration risk index, and triggering an early warning. The system comprises a sensing array, an edge computing unit and a cloud collaborative analysis platform. Through the edge-cloud collaborative architecture, the application can guarantee early warning accuracy while significantly reducing communication and calculation overheads, realize deterioration early warning for not less than 48 hours, and effectively avoid missing the intervention window.

The invention relates to a method for synchronously and rapidly detecting multiple parameters of respirable dust and silicon dioxide on a membrane, and belongs to the field of dust monitoring. The method comprises the following steps: S1, carrying out on-membrane sampling and Fourier infrared spectrum scanning on the quartz respirable dust, taking the quartz respirable dust as a spectrum library of a standard sample, and establishing a standard silicon dioxide and quartz respirable dust quality prediction model based on a spectrum absorption peak of silicon dioxide at 800cm <-1 >; s2, selecting different types of dust, carrying out on-film sampling and spectrum comparative analysis, and establishing spectrum databases and dynamic correction models of different types of dust; and S3, aiming at concentration calculation deviation caused by sampling flow fluctuation, establishing a flow compensation and concentration calculation model, and finally realizing synchronous detection of multiple parameters such as respirable dust mass concentration, silicon dioxide mass concentration and silicon dioxide mass percent.

The method comprises the following steps: establishing a near infrared spectrum database of key indexes of the salt-making mother liquor, and carrying out chemical component calibration by combining a laboratory standard method; introducing the mother liquor into an optical detection pool through a bypass circulation system, and collecting near infrared spectrum data; constructing a semi-supervised regression model between the spectral data and the target parameters, and calculating the sulfate radical content, the chloride ion content and the solid-to-liquid ratio in the mother liquor by utilizing the established semi-supervised regression model; a detection result is transmitted to a control system, and crystallization process parameters are adjusted. According to the scheme provided by the invention, the continuous and real-time monitoring of the salt-making mother liquor is realized through the combination of the bypass circulation system and the near infrared spectrum technology; the semi-supervised regression model is utilized, the flow model depth reversible network and the maximum mutual information coefficient are combined, overlapping signals and complex background interference in spectral data are effectively decoupled, and the accuracy and stability of multi-component quantitative analysis are improved.

The application provides a hyperspectral image segmentation method and system based on end member spectral classification results, comprising reading basic data of hyperspectral original data, generating position labeling data based on data size, merging the position labeling data and the hyperspectral original data to become labeled hyperspectral data, performing slice processing on the labeled hyperspectral data to form an end member labeled spectral data set, inputting all the end member labeled spectral data into a spectral database for identification and classification, generating an end member type result map corresponding to the end member labeled spectral data, performing image segmentation on all the end member type result maps and outputting all the mask results, and obtaining a target and a corresponding target type according to all the mask results. The application fully utilizes one-dimensional spectral information and two-dimensional spatial position information in the hyperspectral data, does not require manual intervention in the whole process, and completely avoids the uncertainty of results caused by processes such as setting a threshold, screening a specific waveband or image labeling.

The present application relates to pipeline leakage detection technical field, specifically to a kind of pipeline flange liquid leakage detection method and system based on hyperspectral identification, and one kind of pipeline flange liquid leakage detection method based on hyperspectral identification includes the following steps: S1: pipeline flange liquid leakage detection system initialization, emit monitoring light beam to establish the monitoring light path for monitoring whether pipeline flange has liquid leakage, when pipeline flange occurs liquid leakage and causes monitoring light path to be cut off, turn into step S2;S2: preliminary liquid leakage positioning is carried out, and the target area of suspected existence liquid leakage is positioned;S3: target area is carried out hyperspectral collection, obtains the spectral data of target area, according to the spectral data and the comparison analysis of pre-set standard liquid spectral database, the relevant information of leakage liquid is output simultaneously.The present application can realize the full-time monitoring of pipeline flange, realizes real-time accurate detection and liquid component intelligent discrimination under complex industrial environment.

The invention belongs to the technical field of coal quality detection, and particularly discloses a coal mineral component proportion estimation method and system, and the method comprises the steps: collecting an original coal sample hyperspectral image, and carrying out the preprocessing of the original coal sample hyperspectral image; the method comprises the following steps: determining types and proportions of minerals in a coal sample through a coal quality test method, collecting spectral data of each mineral in the coal sample by adopting a hyperspectral imager, and further establishing a spectral database of the minerals in the coal; constructing a 3D convolutional neural network model based on an encoder-decoder structure on the basis of the preprocessed coal sample hyperspectral image and the spectral database of the minerals in the coal; and inputting a to-be-detected original coal sample hyperspectral image into the 3D convolutional neural network model based on the encoder-decoder structure to carry out space-spectral feature fusion, introducing physical prior constraints, and estimating to obtain mineral proportion data in the coal sample. The problems that an existing coal quality detection method is low in precision and poor in applicability, and online real-time detection cannot be achieved are solved.

Provided is an analysis system 1 comprising an analysis device, wherein the analysis device 10 is provided with: a determination unit 105 that calculates the degree of difference between the shapes of a plurality of spectra measured at multiple points within a minute range of the surface of a first measurement object 100a, and determines whether the degree of difference between the shapes is equal to or less than a threshold value; an estimation unit 106 that, if the degree of difference between the respective shapes is equal to or less than a threshold value, estimates the plurality of spectra as being pure-substance spectra derived from a compound having a single composition; a registration unit 107 that registers, in a pure-substance spectrum DB 40, one or more types of pure-substance spectrum data estimated from one or more first measurement objects; an extraction unit 109 that extracts, from a spectrum group measured at multiple points on the surface of a second measurement object 100b, spectrum data that matches the shape of a pure-substance spectrum registered in the pure-substance spectrum DB and / or spectrum data that does not match said shape; and an output unit 110 that outputs the extracted spectrum data as a component of the second measurement object.

The utility model relates to the technical field of gas spectrum analysis, in particular to an automatic calibration and storage device of a gas spectrum database, which comprises a gas chamber, a sealing cover is arranged on the upper side of the gas chamber, and an adjustable light source module, a gas spectrum calibration module and an integral processor are sequentially arranged on the upper side of the sealing cover from left to right. A heat dissipation type storage module is arranged at the right end of the lower side of the gas chamber, two gas path assemblies are arranged in the gas chamber and can be divided into a calibration gas path assembly and a cleaning gas path assembly, the calibration gas path assembly is used for normal gas calibration and conveying, and the cleaning gas path assembly is used for cleaning the calibration gas path assembly. According to the utility model, a highly integrated modular structural design is adopted, and the adjustable light source module, the gas spectrum calibration module, the overall processor and the heat dissipation type storage module are organically integrated around the gas chamber, so that the integrated functions of automatic calibration, analysis and storage of gas spectrum data are realized; and independent maintenance and upgrading of each functional module are facilitated.

The invention belongs to the technical field of biological detection, and particularly discloses a rapid microbiological detection system and method. The method comprises the following steps: acquiring a morphology image of a microbial sample by adopting the laser scanning subsystem; identifying a microbial target from the morphology image of the microbial sample by adopting an image segmentation method, and extracting position coordinates of the microbial target; based on the position coordinates of the identified microbial target, calculating the shortest path starting from the initial position to access all microbial target points as a scanning track; controlling a laser scanning subsystem and a spectrum detection subsystem to perform spectrum detection on each target position according to the scanning track; and carrying out feature extraction on spectral data including but not limited to Raman spectrum, comparing the spectral data with a standard spectral database, identifying the category of a microbial target, and judging the activity and other characteristics of microorganisms. According to the invention, the species, activity and other characteristics of microorganisms can be rapidly identified.

The application discloses a Raman spectrum discrimination method and system, and relates to the field of Raman spectrum analysis. The method comprises the following steps: pre-processing a to-be-detected sample signal to obtain a first Raman scattering signal of the to-be-detected sample signal; calculating a correlation coefficient according to the first Raman scattering signal and a preset spectrum database; and obtaining a matching spectrum type of the to-be-detected sample according to the correlation coefficient. According to the method, the correlation coefficient is calculated according to the first Raman scattering signal and the preset spectrum database, and the matching spectrum type of the to-be-detected sample is obtained according to the correlation coefficient. Compared with the traditional method, the calculation amount is greatly reduced, the algorithm running time is shortened, the algorithm time consumption is greatly reduced, and the operation is simple.