1341 results about "Spectral data" patented technology

The present invention belongs to the technical field of coal gangue recognition and sorting, and in particular, relates to a coal gangue recognition method based on visible-near-infrared spectrum and image multi-modal information fusion. The method includes: S1 collecting spectral information and image information about a sample to be recognized; S2 preprocessing the spectral information and the image information respectively; S3 extracting spectral features from a spectral data set by using a spectral feature extraction neural network model; and extracting image features from an image data set by using an image feature extraction neural network model; S4 inputting the spectral features and the image features obtained from feature extraction into a two-stream fusion network; S5 inputting extracted spectral features, extracted image features and a comprehensive feature into a spectral branch classifier, an image branch classifier and a fusion branch classifier, respectively; S6 calculating importance weights of a spectral branch, an image branch and an image-spectrum fusion branch; and S7 subjecting the importance weights and corresponding confidence to multiply-accumulate operation to obtain a score matrix of coal gangue, and using the score matrix to achieve coal gangue recognition.

The invention relates to the field of environmental monitoring, and particularly discloses a non-negative matrix factorization and adaptive peak recognition fluorescence feature extraction and traceability system, which comprises a spectral data preprocessing module, a spectral data non-negative matrix factorization module, a component number automatic selection module, an adaptive peak recognition module, a feature library construction module and a similarity comparison module. An improved non-negative matrix factorization model is adopted to decompose the three-dimensional fluorescence spectrum matrix of a single sample, and an optimal component number K is automatically determined through multiplicative update rule iterative optimization; the self-adaptive peak identification module carries out selective filtering, accurately extracts the position and intensity of a fluorescence peak through multiple mechanisms, and carries out peak position calibration in a neighborhood; the Hungary algorithm is adopted to carry out characteristic peak matching to calculate the comprehensive similarity between the samples, and rapid and accurate identification of the pollution source is realized. The method has the advantages of high resolution, strong anti-interference capability, low requirement on the number of samples, automation and the like, and is suitable for water quality fingerprint feature extraction of a water sample in a complex environment and real-time source tracing of sewage.

The invention provides a rapid evaluation method and system for the soil fertility level, and relates to the technical field of soil fertility detection.The rapid evaluation method comprises the steps that an original absorbance spectral data curve is obtained by scanning a soil sample, the moisture peak absorbance, the actual peak position wavelength and the moisture synthetic frequency characteristic peak width are extracted, and a spectral data basis is provided; a water-salt optical path coupling factor is calculated, water-salt correction operation is carried out in combination with a volume correction coefficient, a virtual dry soil spectrum data curve is obtained, interference of salt on a water optical path is quantified, and a water background and salt masking are eliminated; constructing a characteristic virtual baseline of the organic matter and the total nitrogen, calculating an integral area to obtain characteristic intensity of the organic matter and the total nitrogen, and providing a physical input variable for quantitative inversion; introducing a water-salt optical path coupling factor to invert the organic matter content and the total nitrogen content, and correcting signal depression caused by salt stress; the effective fertility index is calculated and graded by combining the organic matter content and the total nitrogen content of the soil, and the actual fertilizer supply capacity of the soil is comprehensively evaluated.

The invention relates to a geological disaster early warning algorithm based on hyperspectral and Internet of Things data fusion analysis, and relates to the technical field of geological disaster monitoring and early warning, the geological disaster early warning algorithm comprises the following steps: S1, preprocessing hyperspectral data and Internet of Things data, and respectively extracting spectral-spatial features and dynamic topology time sequence features; s2, fusing the spectrum-space features and the time sequence features to generate cross-modal joint features; s3, performing parameter optimization on the fusion features through a quantum-classical hybrid optimization algorithm, and calculating a disaster risk probability; and S4, based on the optimization result and the risk probability, executing an edge-cloud collaborative early warning decision. According to the method, through non-negative tensor ring decomposition of the hyperspectral data and dynamic topology modeling of the Internet of Things, the limitation of a traditional single data source in temporal-spatial resolution and physical relevance is solved, multi-dimensional joint extraction of spectrum-space-mechanical characteristics can be realized, and the characterization precision of a rock-soil body deformation evolution law is remarkably enhanced.

The invention discloses a mixed gas absorption spectrum analysis method and system based on variational mode decomposition, specific laser is injected into an optical resonant cavity unit, and a detector unit continuously monitors the light intensity change and records a light intensity attenuation signal; pre-processing the recorded light intensity attenuation signal; carrying out VMD decomposition on the preprocessed ring-down signal to obtain a plurality of IMF components, respectively introducing CO2 and CO gases with known concentrations into the optical resonance unit, and recording spectral data of each single gas component; calculating the similarity and contribution degree of each IMF component, and setting a weight combination to form a joint score; the gas with the highest joint score is selected, the score is compared with an adaptive threshold value, and when the score is higher than the adaptive threshold value, the IMF component is marked as the characteristic component of the corresponding gas; and finally, gathering and outputting the marked IMF components according to gas types to obtain characteristic signals of the gases, thereby realizing multi-component gas separation in the mixed gas absorption spectrum.

The invention relates to the technical field of shoe body detection, in particular to a shoe body quality detection method based on image visual analysis, which comprises the following steps: synchronously acquiring three-dimensional contour and surface spectral information of a to-be-detected shoe body through three-dimensional scanning and spectral imaging equipment to obtain original point cloud spectral data; performing registration and fusion processing on the original point cloud spectral data to construct a registered hyperspectral three-dimensional grid model containing space coordinates and hyperspectral reflectivity values; and calculating a normal vector field and a curvature distribution diagram of the surface based on the hyperspectral three-dimensional grid model so as to quantify geometric features of the surface of the shoe body. According to the invention, through registration and fusion processing, surface spectral information collected by a spectral imaging device is reversely projected and interpolated to each space coordinate vertex of an initial three-dimensional point cloud constructed by a three-dimensional scanning device, so that a registered hyperspectral three-dimensional grid model is constructed, and a surface chemical characteristic spectrum is endowed with three-dimensional space geometric coordinates. And the detection capability is improved.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

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 molten steel element content on-line detection system based on a laser-induced breakdown spectroscopy technology, and relates to the technical field of molten steel element content on-line detection, the molten steel element content on-line detection system comprises an excitation system, an acquisition system, a data preprocessing module and a component detection module; the excitation system is used for emitting laser to the molten steel surface to generate plasma; the acquisition system is used for collecting signal light radiated by the plasma; the data preprocessing module is used for carrying out baseline correction and noise reduction processing on the collected original spectral signals; and the component detection module comprises an element content prediction model based on a gating specific expert attention network LSEA-Net and is used for carrying out quantitative analysis on the preprocessed spectral data to obtain the element content of the molten steel. The technology has the advantages that sample preparation is not needed, online rapid analysis and multi-element synchronous detection are achieved, and the technology is suitable for the high-temperature environment and the like, and the requirement of a steelmaking site for real-time component detection is met very well.

The application discloses a kind of full automatic waste fabric identification sorting control system, it is related to resource reusing technical field, including identification analysis module, the type, color, component and fibre fineness data of waste fabric on the surface of automatic conveying module are acquired and analyzed by identification analysis module, obtain the component composition and its weight distribution of each area of waste fabric, generate data form and send to data module, and sorting action module is controlled by sorting strategy control module to carry out sorting screening;Using machine vision technology and fabric visual identification model determines the type of waste fabric and divides its area, so that different parts of fabric can be detected and identified, improve the component identification precision of fabric;By extracting the color value of different areas and correcting the color value of spectral data in the identification analysis module, the influence of the color on the surface of the fabric on light, especially visible light, is eliminated, making the spectral data closer to the truth, and the analysis result is more accurate.

The invention relates to the technical field of agricultural product safety detection, in particular to an agricultural product veterinary drug residue high-throughput detection method and system based on spectrum peak analysis. The method comprises the following steps: acquiring multiple batches of spectral data, and performing cross-batch pre-alignment according to the multiple batches of spectral data to obtain cross-batch alignment data; performing background baseline estimation according to the cross-batch alignment data to obtain background baseline data; congestion detection area extraction is carried out according to the cross-batch alignment data to obtain congestion detection area data; performing common-peak decoupling according to the congestion detection area data to obtain common-peak decoupling data; performing residual image construction on the common peak decoupling data according to the background baseline data to obtain residual image data; performing image feature extraction according to the residual image data to obtain image feature data; and performing residual judgment according to the graph feature data to obtain residual judgment data. According to the method, the identification accuracy of spectrum peak pseudo-congestion and residual abnormity in a high-throughput mixed agricultural product detection scene is improved.

The invention mainly relates to the technical field of spectral analysis, and provides a white spirit brewing spectral analysis method and system based on a multi-modal large language model in order to efficiently and accurately obtain white spirit brewing information according to multi-modal spectral data of all links of white spirit brewing. The method is characterized by comprising the following steps of: collecting multi-mode spectrum data including gas chromatography data, infrared spectrum data and Raman spectrum data in each link of white spirit brewing; converting the multi-modal spectral data and the natural language instruction into a feature embedding vector which can be recognized by a large language model to train the large language model which is finely adjusted based on professional knowledge in the field of white spirit, and receiving a question of a user in a natural language instruction form by the trained large language model; wine making analysis tasks such as flavor compound identification, key microorganism detection, fermentation state evaluation and base wine quality grading are carried out, and finally analysis results and wine making process optimization suggestions are output in a natural language form, so that the accuracy and efficiency of spectrum data interpretation in the white wine making process are improved.

The invention discloses a seed vigor prediction model construction method based on spectral indexes and a detection system, and relates to a method and a system for rapidly and nondestructively detecting seed vigor by using a visible-near infrared spectrum technology. The problem that in the prior art, the requirement for accurate and quantitative evaluation of seed vigor in modern agriculture cannot be met is solved. The method comprises the following steps: firstly, acquiring spectral data and actual germination rate data of a seed sample, and carrying out abnormal value elimination and pretreatment on the spectral data; then extracting one-dimensional, two-dimensional and three-dimensional spectral indexes from the preprocessed spectral data, and screening out an optimal spectral index by using a correlation matrix method; and finally, by taking the optimal spectral index as input and the actual germination rate as output, establishing a seed activity prediction model based on the optimal spectral index. The prediction model is embedded in the system, and seed vigor prediction is achieved. The method is suitable for the fields of seed quality detection and grading, precise sowing, agricultural production and the like.

The invention discloses a spectral data acquisition and screening method for blue carbon data inversion, and relates to the field of spectral data acquisition. The method comprises the following steps: (1) acquiring original spectral data; (2) constructing a hyperspectral unmixing model based on a spectrum modulation module and spectrum variability; (3) utilizing the constructed hyperspectral unmixing model to realize vegetation spectrum matching; (4) screening the original data based on a spectrum matching result; (5) realizing data re-verification based on dispersion estimation; and (6) according to a dispersion estimation result, obtaining a regional representative spectrum, and realizing spectral data acquisition and screening for blue carbon data inversion. The hyperspectral unmixing model based on the spectrum modulation module and the spectrum variability can adapt to heterogeneity of data obtained in different scenes, the spectrum matching precision is improved, and the comprehensive performance of spectrum data screening is improved.

The invention discloses an intelligent monitoring and evaluation method and system for newly-cultivated land, and relates to the field of intelligent agriculture. The method comprises the steps of planning an unmanned aerial vehicle route and a ground sampling grid based on newly-cultivated land boundaries and topographic data; multispectral and thermal infrared images are obtained through remote sensing of the unmanned aerial vehicle, and spectral data and soil samples are synchronously collected on the ground; performing laboratory analysis on the soil samples to obtain soil physical and chemical indexes, and associating the soil physical and chemical indexes with remote sensing spectral characteristics to construct a training sample set; based on the training sample set, remote sensing features are fused, and a soil physicochemical index spatial distribution diagram is obtained through machine learning model inversion; carrying out quality grade classification and obstacle type diagnosis by combining the soil physical and chemical indexes, the crop growth time sequence and the planting historical information; and based on classification and diagnosis results, combining an agricultural knowledge rule base to form agricultural management suggestions and generate a variable operation prescription map to guide accurate operation. Through air-ground cooperative monitoring and soil quality evaluation, the farmland quality monitoring precision is improved.

This invention discloses a fully automated, high-speed, and highly accurate three-dimensional reconstruction method and system, relating to the field of three-dimensional dynamic reconstruction. The invention introduces the construction of a spectral-point cloud fusion model and the calculation of a dynamic reflectance field by Monte Carlo ray tracing. By combining this with Fresnel's equation and dynamic ray tracing techniques, the reflectance calculation conforms to physical laws, thereby improving the accuracy of spectral mapping. Furthermore, the invention optimizes spectral data based on an error correction matrix using a spectral error compensation network, reducing the accumulation of errors caused by factors such as measurement angle and surface roughness. Moreover, in the three-dimensional reconstruction process, the invention improves the continuity and spectral consistency of the three-dimensional model by using Poisson surface reconstruction and a spectral reflectance-driven mesh optimization method based on spectrally enhanced point cloud data.

The invention discloses a hollow-core optical fiber microstructure size dynamic nondestructive measurement and control system, which is characterized in that a spectral confocal detection module acquires spectral signals reflected by each interface of the cross section of a hollow-core optical fiber from the side surface of the hollow-core optical fiber by using a dispersion confocal principle in a hollow-core optical fiber drawing process, and converts the spectral signals into spectral data; the data analysis module outputs an axial peak value calculated value by using a neural network model according to the spectral data, and converts the axial peak value calculated value into the diameter of a nested glass tube in the nested structure unit; the regulation and control module outputs an airflow regulation instruction according to the difference between the diameter of the nested glass tube and a preset value; the airflow control module adjusts the size of airflow entering the hollow-core optical fiber according to the airflow adjusting instruction, and then the diameter of the nested glass tube is adjusted. According to the invention, the diameter of the nested structure unit in the hollow-core optical fiber can be accurately measured in the production process, and the technological parameters can be adjusted in time according to the product state.

The invention provides a polarization hyperspectral water quality monitoring system and method, and the system comprises a tower footing polarization hyperspectral imaging module which is disposed on a building beside a to-be-detected water area, and carries out the area array staring scanning of the to-be-detected water area through a polarization hyperspectral imager, so as to obtain a water body polarization hyperspectral image signal of the to-be-detected water area; the image spectral data processing module is used for deducting dark current and spurious signals from the water body polarization hyperspectral image signals, and calculating the water body spectral reflectivity of the water area to be measured according to the water body polarization hyperspectral image signals by combining a gray value and reflectivity relation model constructed by using the hyperspectral image signals of the gray scale calibration plate; and the water quality parameter spatial distribution inversion module is used for performing inversion on the to-be-detected water quality parameters of the to-be-detected water area according to a pre-constructed water quality parameter inversion model and the water body spectral reflectivity. According to the technical scheme, the integration level is high, the automation degree is high, rapid deployment can be achieved, and all-day continuous unattended monitoring can be achieved.

The invention relates to the technical field of gas monitoring, and provides a spectrum analysis-based gas leakage monitoring system, which comprises a tunable laser source, an optical curtain forming device, a scanning optical sensor, a spectrum analysis module, a three-dimensional spectrum model construction module, a leakage traceability and diffusion prediction module, an intelligent early warning module and a snapshot type multispectral imaging unit, and an open optical path and a distributed multi-node layout are adopted. According to the invention, through the steps of optical screen scanning, spectral data acquisition and processing, three-dimensional dynamic model construction, intelligent analysis leakage determination, leakage traceability visualization and adaptive learning, precise monitoring, identification, traceability and early warning of gas leakage are realized; gas identification, leakage traceability and diffusion prediction are realized based on a three-dimensional dynamic model, deep learning and fluid mechanics, the monitoring effect is optimized by adopting distributed layout and self-adaptive learning, rapid disposal is assisted by graded early warning, and the accuracy, real-time performance and safety of gas leakage monitoring are greatly improved.

The invention relates to the technical field of spectral analysis and biomedical detection, and discloses an SERS (Surface Enhanced Raman Scattering) spectrum quantitative detection method and system based on interpretable stacked ensemble learning. The method comprises the following steps: acquiring SERS spectral data of serum tumor marker standard substances with different concentration gradients; performing baseline correction and normalization preprocessing on the data, performing sparse feature selection by using an LASSO algorithm, and screening out key spectral features to construct a sample data set; constructing an interpretable stacking integration model, wherein the model adopts a base learner layer and a meta learner layer; training the model by using the training set, optimizing model hyper-parameters by using a cross validation strategy, and establishing a mapping relationship between spectral features and tumor marker concentrations; and collecting SERS spectral data of a to-be-detected serum sample, extracting key spectral features, inputting the key spectral features into the trained interpretable stacked integrated model, and outputting a concentration predicted value of the tumor marker in the to-be-detected serum sample. The method has the advantages of high precision, universality and molecular level interpretability.

The invention relates to the technical field of photovoltaic panels, and discloses a photovoltaic panel dust deposition state-based power generation power prediction method, which comprises the steps of obtaining an infrared radiation image and spectral data of a photovoltaic panel, and performing primary processing on the infrared radiation image to obtain a calibration temperature matrix and a pre-processing image set; after the visible light or the ultraviolet light is converted to generate electric energy, actually measured power generation efficiency is collected for error calculation, a dynamic calibration process is triggered based on errors, and a calibrated power generation power prediction value is obtained; according to the invention, through coupling radiation attenuation, temperature loss, wind speed loss and other multi-physical field effects, the infrared spectroscopic analysis is utilized to identify dust components, so that the accuracy and operability of a physical mechanism are enhanced; besides, in combination with multi-source data such as infrared data, meteorological data and spectrum data, extreme conditions such as high humidity, strong wind speed and multi-type dust deposition can be effectively handled, the risk of single data source deviation is reduced, and high robustness and generalization ability are achieved.

The invention relates to a hyperspectral signal prediction method based on a hyperspectral remote sensing image and pseudo label guidance, and belongs to the technical field of hyperspectral signal prediction. The method comprises the following steps: collecting remote sensing spectral data and soil spectral data, and carrying out primary preprocessing on the remote sensing spectral data; based on a designed series correction algorithm, correcting the remote sensing spectral data after the primary preprocessing by using the soil spectral data, and performing secondary preprocessing; performing imaging on the remote sensing spectrum data after the secondary preprocessing; constructing a double-branch fusion model comprising a spectrum branch and an image branch; constructing a classification pseudo label, and introducing supervised contrast loss to train the double-branch fusion model; and inputting remote sensing spectral data to be predicted into the trained double-branch fusion model to obtain a hyperspectral signal prediction result so as to realize soil content prediction. The objective of the invention is to solve the technical problems of complex noise, low quality and difficulty in guaranteeing prediction accuracy when remote sensing spectral data is acquired in the prior art.

The invention provides a perfluorohexanone active fire extinguishing system applied to an energy storage power station, and belongs to the technical field of active fire extinguishing systems, the perfluorohexanone active fire extinguishing system comprises a multi-mode composite detection array composed of a thermal imaging sensor, an air pressure sensor, a visible light camera and an ultraviolet light sensor, the multi-mode composite detection array is configured to collect temperature field data, air pressure data, visible light video streams and ultraviolet spectrum data in the energy storage battery cabin by a preset timestamp synchronization mechanism, and output four-mode original data streams to the thermal runaway micro-hotspot recognition engine and the like; early discovery, accurate positioning, fine release, true suppression and self-evolution full-closed-loop active fire extinguishing of the energy storage power station in the high-altitude extreme environment are achieved, and the safety, economical efficiency and long-term maintainability of the energy storage power station are remarkably improved.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.