525 results about "Infrared spectroscopy" patented technology

The present application pertains to the technical field of neural stimulation. Disclosed are a temporal interference-based closed-loop multimodal neural stimulation system and method. The system comprises a temporal interference stimulation system, an electroencephalography-functional near-infrared spectroscopy sampling system, and an upper-level control system. The temporal interference stimulation system utilizes a beat-frequency electric field generated by two sets of electrodes to precisely stimulate a specified brain region. The electroencephalography-functional near-infrared spectroscopy sampling system is a bimodal collector coupling electroencephalography and functional near-infrared spectroscopy, including two parts: signal extraction and correlation analysis, and analyzes stimulation effects and adjusts stimulation schemes by integrating unified brain signal data that combines the temporal precision of EEG and the spatial precision of fNIRS. The upper-level control system includes bimodal fusion model computation, graph convolutional neural network prediction, and stimulation scheme formulation. The present application addresses the problems that traditional stimulation methods lack a closed-loop regulation system, have no means for calibration and optimization, and require a long adaptation period between the stimulation scheme and the user, thus being disadvantageous for applications.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention relates to a motor imagery recognition method combining EEG (electroencephalogram) and functional near infrared spectroscopy, which comprises the following steps: synchronously acquiring EEG data and functional near infrared spectroscopy signal fNIRS data under a motor imagery task through a multi-channel acquisition system, and preprocessing and enhancing the acquired two modal data. Aiming at the characteristics of the two signals, respectively extracting the time-frequency characteristics and the space-domain characteristics of the EEG and the space-domain characteristics of the fNIRS; designing a mask auto-encoder for each modal feature to carry out pre-training; and finally, carrying out feature fusion through a cross-modal cross attention mechanism in combination with parameters obtained by pre-training, and carrying out training through cross entropy loss. According to the method, the accuracy of motor imagery recognition is improved, and independent information and complementary information of EEG and fNIRS are effectively combined.

The invention relates to the crossing field of precision agriculture and artificial intelligence technology, in particular to a soil component detection system based on machine learning and infrared spectroscopy, which is characterized in that a soil spectrum is acquired on site through a portable Fourier infrared spectrometer, and after pretreatment, feature vectors are constructed by fusing climate, soil and crop data; the deep learning decision-making module extracts spectral features by using one-dimensional convolution, fuses multi-dimensional information through an attention mechanism, synchronously outputs a fertilization scheme, crop suitability scores and soil improvement measures by a multi-task learning sub-network, and finally, verifies by combining an agronomic knowledge base so as to obtain a fertilization result. According to the method, a comprehensive decision report containing a quantitative fertilization formula, a crop suitability sequence and a soil improvement scheme is generated, precise agricultural guidance is realized, spectral features are automatically extracted by adopting a one-dimensional convolutional neural network, and a multi-modal data fusion and multi-task learning framework is combined, so that the system achieves relatively high precision in the aspect of soil nutrient prediction.

A method for monitoring temperature-induced deformation of components in an intelligent moxibustion robot based on infrared spectroscopy includes the following steps. Infrared spectral images and frequency spectra of a target component of the intelligent moxibustion robot at different detection points are acquired. Motion influence confidence factors for each detection position are constructed based on frequency differences between peaks and troughs in the frequency spectrum. The box-counting method is used to obtain scale-relationship graphs of infrared spectra for all detection positions. The overall light absorption difference index of the target component is determined according to the scale-relationship graphs. By combining the overall light absorption difference index with motion influence confidence factors, local outlier factors (LOF) for each detection position in the thermal data sequence are calculated using a LOF anomaly detection algorithm. Finally, a temperature deformation risk of the target component is evaluated based on a thermal alarm threshold.

The invention relates to the technical field of rehabilitation data prediction, and discloses a motion function rehabilitation prediction method and system based on multi-modal data, a terminal and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal, an electromyographic signal and a functional near infrared spectrum signal of a target user, pre-processing to obtain a target electroencephalogram signal, a target electromyographic signal and a target functional near infrared spectrum signal; respectively carrying out feature extraction to obtain three time-frequency features, and carrying out source positioning processing on the target electroencephalogram signal and the target functional near infrared spectrum signal to obtain brain source intensity; and fusing the three time-frequency features to obtain a target time-frequency feature, inputting the target time-frequency feature and the brain source intensity into a multi-modal data rehabilitation prediction model, and outputting a motor function rehabilitation prediction result. According to the method, the relationship between the three signals of the target user and the motion function of the target user is modeled in the time sequence, so that the long-term evolution of the motion function in the time sequence is quickly evaluated, and the accuracy of a prediction result is improved.

The invention relates to the technical field of garbage recognition, and particularly discloses an intelligent garbage recognition system and method based on multi-modal fusion, a hardware sensing unit is composed of a three-dimensional vision module, a touch sensing module and a near infrared spectrum module, and point cloud data, a pressure distribution matrix and spectrum data of garbage are obtained respectively; and an edge computing unit is equipped to carry an NPU acceleration chip for data processing. And the software module realizes multi-modal feature fusion by using ResNet-50, LSTM (Long Short Term Memory), 1D-CNN (Convolutional Neural Network) and Transform Encoder, and is also provided with a multi-sensor timestamp synchronous controller. And the dynamic feature database starts online learning to update the material feature library when the garbage recognition confidence coefficient is lower than 85%. During feature fusion, a cross-modal feature mapping relation is established, the weight is adjusted through an attention mechanism, and dynamic learning adopts a model lightweight method based on knowledge distillation. And starting an arbitration mechanism to correct the classification result when different modal identification results conflict. According to the method, the advantages of different modal data are fully utilized, and the recognition capability in a complex environment is improved.

The invention discloses a rice straw decomposition process full life cycle monitoring system based on a cloud platform, and relates to the field of agricultural informatization, and the system comprises a field data collection terminal which is used for collecting near infrared spectrum data, image data and operation and environment data of a straw decomposition process; the layered identification and modeling unit is used for identifying initial mass proportions and decomposition progress of soluble components, cellulose components, hemicellulose components and lignin components of the straws, and establishing a layered progress bar; the fusion progress calculation unit is used for weighting each layered progress bar based on the initial mass ratio to generate an overall decomposition progress bar; the progress output unit is used for simultaneously outputting a layered progress bar and an overall decomposition progress bar; and the mode mining unit is used for clustering the historical layered progress bars, forming a progress category prototype library and outputting potential blocking reasons. The problems that the field straw decomposition process is difficult to monitor in real time and the blocking reason is difficult to identify are solved.

The invention discloses an automatic quality detection system for a food production line, and relates to the technical field of food detection.The system collects food surface images in real time through an industrial camera, and an appearance feature data set is generated after preprocessing; using a deep learning model constructed by a convolutional neural network to extract appearance features, forming a food defect data set, calculating an appearance score through an image processing algorithm, and evaluating the appearance score and a surface flaw score threshold value; component content and microbial pollution data are acquired and processed in real time through near infrared spectrum analysis and multi-sensor detection, and component content scores and pollution scores are generated; performing quality judgment by calculating a comprehensive quality score and evaluating the comprehensive quality score with a set comprehensive quality score threshold value; in an abnormal condition, the system triggers an alarm and informs a worker through wireless communication; the whole system realizes automatic, precise and multi-dimensional detection of food quality, and the production detection efficiency and the food safety control capability are remarkably improved.

The invention belongs to the technical field of agricultural pest detection, and particularly discloses a multi-modal citrus pest detection method based on a near infrared spectrum and a visible light image, and the method comprises the steps: obtaining citrus sampling data which comprises an original near infrared spectrum of a citrus and a plurality of visible light images of the citrus, different visible light images correspond to different shooting angles; preprocessing the original near infrared spectrum to eliminate scattering influence, and based on the preprocessed near infrared spectrum, screening out a key characteristic wavelength to construct a key characteristic near infrared spectrum; and based on the key feature near infrared spectrum and the plurality of visible light images, through a multi-modal network model, obtaining an insect pest detection result of the citrus, the multi-modal network model being used for fusing image features corresponding to the visible light images and spectral features corresponding to the key feature near infrared spectrum, and outputting the insect pest detection result. According to the method and the device, the detection precision can be improved from multiple dimensions, and the citrus diseased insects can be accurately detected.

The invention relates to the technical field of grease detection, in particular to a grease detection data analysis system and method based on multi-source data. The method comprises the following steps: acquiring grease multi-source detection data, and performing near infrared spectrum detection feature extraction according to the grease multi-source detection data so as to obtain infrared spectrum detection data; performing signal enhancement processing on the infrared spectrum detection data to generate enhanced infrared spectrum data; carrying out physicochemical index feature extraction based on the grease multi-source detection data so as to obtain physicochemical index data; performing detection anomaly analysis according to the physicochemical index data and the enhanced infrared spectrum data so as to obtain grease anomaly detection data; and performing type division on the grease anomaly detection data so as to obtain grease chemical component anomaly data and grease microorganism anomaly data. Based on the grease detection technology, the abnormal recognition rate and the quality evaluation accuracy of grease detection are improved.

The invention provides an intelligent precise baking method and system for improving the tobacco leaf quality, and belongs to the technical field of tobacco leaf baking. The method comprises the steps that a basic process parameter set is established, and domain division is conducted according to the tobacco leaf maturity; acquiring a basic process parameter set in real time; constructing a first optimization vector, and determining a weight contribution rate through an intelligent process adaptation model; constructing a second optimization vector through a machine vision and near infrared spectrum fusion technology; establishing a stable change threshold judgment mechanism to start parameter adjustment; performing internal iterative optimization processing on the first optimization vector; calculating a process effect contribution rate; and intelligent and accurate parameter adjustment is realized through a gating weight function of the intelligent process adaptation model. The system comprises a curing barn structure unit, an environment sensing unit, a tobacco leaf information acquisition unit, a data processing unit and a control execution unit, all the units are integrated into a complete system through an industrial bus, and whole-process closed-loop management from environment sensing, tobacco leaf state monitoring to intelligent control is achieved.

Optical patient monitoring systems are disclosed. The system may comprise an optical coupling system configured to transmit to and receive light signals from one or more locations on a subject; an optical processing system configured to generate optical data using the received light signals; and a computer programmed to receive the optical data; determine, using the optical data, at least one indicator of blood pressure; estimate an estimated blood pressure using the at least one indicator of blood pressure; and generate a report indicative of the estimated blood pressure. The at least one indicator of blood pressure comprises one or more of near-infrared spectroscopy (NIRS) data; photoplethysmography (PPG) data, diffuse correlation spectroscopy (DCS) data, speckle contrast optical spectroscopy (SCOS) data, speckleplethysmography (SPG) data, first derivative PPG data, second derivative PPG data, first derivative SPG data, second derivative SPG data, inflow (Fin) data, outflow (Fout) data, heart rate data, physiological data, and combinations thereof. Methods for estimating blood pressure are also disclosed.

The invention provides a micro-nano optical fiber probe for biochemical component detection based on infrared spectroscopy and detection equipment, and a preparation method of the micro-nano optical fiber probe comprises the following steps: S1, selecting one section of a multimode quartz optical fiber as a sensing area, and then removing a cladding layer and a coating layer of the sensing area to expose a bare fiber; s2, dripping a mixed acid solution prepared from hydrofluoric acid and citric acid to the bare fiber to corrode the bare fiber, and then cleaning and blow-drying; immersing the treated multimode quartz optical fiber into a silane coupling agent solution, reacting for 0.5-1 hour, and then drying; s3, immersing the multimode quartz optical fiber treated in the step S2 into a solution containing silver nanoparticles, and reacting for 10-20 minutes to obtain an optical fiber modified by the silver nanoparticles; and S4, sputtering a gold layer on the surface of the silver-modified optical fiber by adopting a vacuum magnetron sputtering technology to obtain the micro-nano optical fiber probe with the silver-gold film layer. After the surface of the silver nanostructure is covered with the gold film, oxygen, water, corrosive molecules and the like are effectively prevented from making direct contact with silver. In addition, both the gold and the silver can support a surface plasmon resonance (SPR) effect, and particularly, the prepared detection equipment can ensure the sensitivity of the SPR.

The invention relates to the technical field of peanut plant growth state intelligent monitoring, in particular to a peanut plant growth state intelligent monitoring method and system and electronic equipment. The method comprises the following steps: respectively acquiring visible light and near-infrared images, laser radar point cloud data, near-infrared spectra and microscopic images of peanut plants by using a multispectral camera, a laser radar, a near-infrared camera and a microscopic imager carried by a field robot; carrying out de-atomization and color correction on the image by adopting a self-adaptive illumination compensation algorithm; a laser radar point cloud is fused to construct a plant three-dimensional point cloud model; segmenting an overlapped leaf area based on a graph neural network, and extracting morphological characteristics of a single plant; the nitrogen content and the moisture content of the leaves are analyzed through near infrared spectroscopy, and early scabs are recognized in combination with microscopic imaging; and inputting the morphological characteristics and the physiological parameters into a growth state evaluation model, and outputting growth indexes and disease and pest early warning. According to the invention, automatic and refined monitoring and growth state comprehensive evaluation of peanut plant morphology and physiological parameters are realized.

The invention discloses a method for detecting the nitrogen content of organ tissues of different varieties of oilseed rapes based on visible near-infrared spectroscopy, which comprises the following steps: firstly, collecting oilseed rape leaf, shell, stalk and root system samples under the conditions of multiple varieties and multiple nitrogen fertilizer levels, and acquiring spectral data within the range of 430-2500nm by using a visible near-infrared spectroscopy; and a Kjeldahl method is synchronously adopted to measure the real nitrogen content as a reference value. Preprocessing the spectral data, including de-noising, standard normal variable transformation, multivariate scatter correction and derivative processing, so as to weaken the influence of scattering and baseline drift; characteristic wavelengths related to the nitrogen content are screened through stepwise regression, variable projection importance, competitive self-adaptive reweighted sampling, a continuous projection algorithm and other methods, and a random forest model, a support vector machine model, a partial least squares discriminant analysis model, a partial least squares regression model, a support vector regression model, an XGBoost model and other models are combined. And respectively constructing a classification identification model and a regression prediction model.

The invention discloses a cordyceps sinensis intelligent bionic cultivation control system and method based on the Internet of Things, and the method comprises the steps: collecting the temperature and humidity, gas concentration, illumination intensity and larva behavior data of a cordyceps sinensis matrix through a sensor network, and carrying out the fusion through an edge calculation gateway, and generating a dynamic environment map; constructing a plateau ecological digital twinborn model based on the dynamic environment map in combination with historical meteorological data and a host-strain interaction rule, and generating a staged control time sequence; based on a staged control time sequence, temperature and humidity coupling compensation is carried out through a feedforward-feedback composite algorithm, and actuator linkage is adjusted through fuzzy PID; aI vision is used for identifying a larva infection window period and accurate spraying is carried out, and hypha infection is monitored in combination with a near infrared spectrum to dynamically adjust the CO2 concentration; and constructing an LSTM (Long Short Term Memory) model to predict the growth risk in combination with a dynamic environment map and insect body monitoring data, and actively intervening in an abnormal environment. According to the invention, high-precision, dynamic and intelligent regulation and control of the growth environment of cordyceps sinensis are realized.

The invention provides an intelligent matching method for recycling purposes of industrial solid wastes, and belongs to the technical field of industrial solid waste resources, and the method comprises the following steps: firstly, carrying out comprehensive feature extraction and fusion on solid waste samples by adopting multi-mode sensing technologies such as near infrared spectroscopy and X-ray fluorescence to generate solid waste multi-dimensional feature fingerprints; then, utilizing a solid waste physical and chemical reaction mechanism equation to analyze the recycling potential of the solid waste; meanwhile, a pre-trained WRIM model is adopted to classify the solid waste, and the model combines a convolutional neural network and a multi-head attention mechanism; then, quantifying the matching degree between the solid waste characteristics and the resource application demand by using a tensor kernel similarity calculation function; and finally, through a multi-level priority evaluation engine, the comprehensive similarity scoring matrix, the resource potential index and the basic attribute category, generating an optimal resource scheme recommendation, and realizing accurate matching of solid waste resource purposes.

The invention relates to the technical field of attention deficit hyperactivity disorder detection, and discloses a device for detecting cognitive and developmental conditions of children with hyperactivity disorder, which comprises a handle, a head-mounted device, a bracelet, a hierarchical adaptive algorithm module, a multi-dimensional execution module and a data fusion and analysis module, a multi-mode sensing module is arranged in each of the handle, the head-mounted device and the bracelet; a motion capture unit, a high-sensitivity key array, a tactile feedback module and a wireless transmission module are integrated in the handle, and a wireless electroencephalogram acquisition module, a miniature near infrared spectrum module and an eye movement tracking module are integrated in the head-mounted device. According to the invention, through deep fusion of a multi-dimensional cognitive evaluation system and gamepad hardware, the device not only breaks through the limitation of the traditional neuropsychological test in methodology, but also realizes refined capture of ADHD heterogeneity characteristics in the technical level, and provides a hardware basis for establishing an objective and quantitative diagnostic tool with high ecological efficiency.

The invention relates to a multi-quality parameter cooperative detection method and system based on near infrared spectrum. The method comprises the following steps: providing to-be-detected near infrared spectrum data of a to-be-detected substance, loading the to-be-detected near infrared spectrum data into a constructed quality parameter collaborative detection model, carrying out collaborative detection processing by utilizing the quality parameter collaborative detection model, and generating multi-quality parameter information of the to-be-detected substance, during cooperative detection processing, at least performing feature dimension expansion processing, feature extraction processing and feature fusion prediction processing on the near infrared spectrum data to be detected, and generating multi-quality parameter information of the substance to be detected after the feature fusion prediction processing. According to the invention, cooperative detection of multiple quality parameters can be effectively realized, the precision and reliability of multi-quality parameter detection are improved, and the cost and complexity of multi-quality parameter cooperative detection are reduced.

The invention relates to a cognitive state classification method based on EEG-fNIRS spatio-temporal fusion features, which comprises the following steps of: setting an experiment according to a mental calculation experiment normal form, and synchronously acquiring EEG data and fNIRS data of a tested mental calculation task; preprocessing the two collected data to obtain electroencephalogram signal data and hemoglobin concentration change data; eEG signal data and hemoglobin concentration change data are taken, data enhancement is carried out through a time window, then the data are sent to a space-time fusion network, EEG features, oxyhemoglobin HBO features and deoxyhemoglobin HBR features are obtained, fusion features are obtained through fusion, and an EEG classification result, an fNIRS classification result and a fusion feature classification result are obtained. According to the method, the characteristics of the EEG signal and the fNIRS signal are effectively combined, the overfitting problem in modal deep learning is relieved, and cognitive state classification can be accurately carried out.

A method and apparatus for classifying water-rich weak surrounding rock based on in-situ scribing are provided. The method includes placing a scribing device on tunnel cross-sections of varying lengths; performing in-situ scribing on the tunnel cross-sections to obtain the mechanical parameters of the surrounding rock and determining the surrounding rock strength at different locations; while performing in-situ scribing, employing near-infrared spectroscopy to simultaneously measure the in-situ spectrum of the tunnel surrounding rock, thereby determining the surface water content at different locations; based on the scribing mechanical parameters of the tunnel surrounding rock, establishing a scribing depth curve, and calculating a joint development degree of the tunnel surrounding rock at different locations from the scribing depth curve; and based on the surrounding rock strength, surface water content, and joint development degree of the tunnel surrounding rock at different locations, classifying the surrounding rock grades of the tunnel cross-section.

The invention provides a surgical process management method and system, and relates to the technical field of surgical process management.The method comprises the steps that DTI, fMRI and 3D-T1WI image data are fused through a non-rigid registration algorithm, a three-dimensional brain network atlas is generated, then an LSTM deep learning model is constructed, a brain surface displacement field monitored in real time in an operation is used as input, and an LSTM deep learning model is constructed; a deep tissue displacement value is used as a label for training to obtain a deep tissue displacement predicted value, and a dynamic relation between brain neural element electrical activity and oxyhemoglobin saturation is analyzed by combining data monitored by high-frequency EEG and near infrared spectrum and utilizing a wavelet time-frequency coherence algorithm to obtain a dynamic influence coefficient; and finally, combining the deep tissue displacement predicted value with the dynamic influence coefficient, calculating a comprehensive evaluation index, and comparing the comprehensive evaluation index with a preset threshold value, so that a surgeon can quickly adjust an operation strategy and timely deal with possible risks.

The invention relates to the technical field of gas detection, and discloses an underground pipe gallery leakage gas identification system based on infrared spectroscopic analysis, comprising an acoustic sensor unit passively monitoring a pipe gallery acoustic signal and generating a wake-up instruction and azimuth information, the infrared spectrum analysis module emits infrared beams with specific wavelength combination to a pre-estimated leakage sector according to the azimuth information and receives a returned light signal to generate spectral data; and the control processing unit analyzes the spectral data and judges and confirms a leakage event in cooperation with the azimuth information. According to the method, energy efficiency mode innovation from continuous scanning to event driving is realized through a cooperative mechanism of acoustic triggering and infrared directional detection, and the accuracy and response speed of trace leakage identification in a complex environment are remarkably improved by combining a differential spectrum technology and spatial coding logic verification.

The invention discloses a component detection system for unmanned automatic production of an extracting solution, and relates to the technical field of automatic production and quality control. The system integrates a plurality of detection technologies, including infrared spectroscopic analysis, OD value detection, pH value monitoring and absorbance detection, so as to realize real-time monitoring and automatic production management of the components of the extracting solution. By establishing a standard product fingerprint spectrum database, the system can automatically compare real-time detection data with a standard spectrum, and immediately triggers an alarm or stops production once an abnormality is found, so that the stability of product quality and the high efficiency of a production process are ensured. The system not only improves the automation degree of a production line, but also predicts and optimizes bioactive components through a whole genome nutrition group analysis technology in combination with species genome, transcriptome data and metabolome information, realizes precise formula and efficient production, and provides powerful support for an unattended automatic production system.

The invention relates to a wide-temperature-range stable dual-band infrared optical filter for an extreme environment and a preparation method thereof. The wide-temperature-range stable dual-band infrared optical filter comprises a front film system, a substrate and a back film system, the front film system and the back film system are formed by alternately overlapping germanium layers and zinc sulfide film layers; the drift distance of the central wavelength of the passband of the infrared optical filter is less than 0.05% within the temperature range of-120 DEG C to + 85 DEG C. According to the invention, the internal stress of the film layer is obviously reduced through the optimized film system design, so that the excellent stability of the optical performance in an extreme temperature environment is realized, the technical problem that some special infrared spectrum detection systems work in a wide temperature range is solved, and a reliable solution is provided for high-end aerospace infrared application.

Provided are a coal testing system and method, and a storage medium. The system comprises: a detection unit, which is used for collecting spectral data of a coal sample under test; a training unit, which is used for constructing, by means of the spectral data, neural networks across neural network dimensions on the basis of a greedy search, and obtaining a coal sample test model by means of performing training on the basis of simulated samples which have been subjected to sample augmentation; and an analysis unit, which is used for executing fusion processing on the spectral data to obtain target spectral data, and on the basis of the coal sample test model, executing target spectral data inference to obtain a coal test result, wherein the detection unit comprises a near-infrared spectroscopy collection module and an X-ray fluorescence spectroscopy collection module.

The invention discloses a honey adulteration detection method, which belongs to the technical field of food detection, and comprises the following steps: collecting an absorbance spectrum of a honey sample by using a near infrared spectrometer; extracting characteristic absorbance values A < lambda > 1 and A < lambda > 2 at a 1450 nm water molecule absorption peak and a 1490 nm saccharide molecule absorption peak; the ratio R is equal to A lambda 1 / A lambda 2; and comparing the R with a threshold range of 0.85-1.15 of pure honey so as to judge whether the honey is true or false. The portable device comprises a sample bin, a near infrared spectrum module, a control unit and a human-computer interaction interface, wherein a rotatable sample table is arranged in the sample bin to accommodate a plurality of disposable sample cups. The method abandons a complex black box model, adopts a characteristic wavelength ratio method with clear physical significance, has the outstanding advantages of simple principle, rapid detection (less than 30 seconds), high accuracy, low cost, simplicity and convenience in operation and the like, perfectly solves the problems of expensive equipment, long time consumption, limited identification capability and dependence on professionals in the prior art, and has a wide application prospect. The method is especially suitable for market on-site rapid screening.

The invention discloses a near infrared spectrum modeling method and system based on merge matrix PCA, and relates to the technical field of near infrared spectrum and the field of chemometrics. The method comprises the following steps: acquiring original near infrared spectrum data and reference data of a to-be-detected sample as independent variable data and corresponding dependent variable data respectively; respectively performing data set division on the independent variable data and the dependent variable data by adopting a sample set division method; and constructing a regression prediction model based on a least square regression method, training and verifying the regression prediction model by using the preprocessed data set, performing correlation analysis on the data set and a prediction target based on a principal component analysis method in the training process of the regression prediction model, and performing principal component extraction. According to the near infrared spectrum modeling method and system based on merging matrix PCA, dimensionality reduction is carried out on high-dimensional data possibly existing in a sample, then a robust prediction model is established, and rapid and accurate quantitative analysis is achieved.

The invention discloses a traditional Chinese medicine composition for preventing and treating cardiovascular and cerebrovascular diseases and a traditional Chinese medicine detection system, and relates to the technical field of traditional Chinese medicine detection. Five functional modules of raw material component identification, component uniformity detection, stability prediction, data processing feedback and visual monitoring are integrated; accurate identification of raw material components of the traditional Chinese medicine extract, real-time evaluation of mixing uniformity and predictive analysis of component stability under a high-temperature and high-pressure process are realized; the system carries out normalization and trend modeling on multi-batch data, automatically identifies abnormal fluctuation and feeds back a regulation and control signal to a production control system, and assists an operator in fast decision making in combination with a visual interface, so that the problems of component interference, poor uniformity, insufficient stability, large quality fluctuation and the like in traditional Chinese medicine tablet production are effectively solved, and the production efficiency is improved. The consistency, the safety and the intelligent level of the preparation are obviously improved.