2446 results about "Spectral data" patented technology

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

The invention relates to the technical field of soil pollution identification, and discloses a soil heavy metal pollution identification system. A multispectral remote sensing sensing module of the system obtains surface reflectance data and soil in-situ spectral data through a satellite load and a vehicle-mounted mobile platform respectively, and the surface reflectance data and the soil in-situ spectral data are processed by a heterogeneous data fusion gateway to generate multiband spectral response signals. In the pollution risk assessment module, a spatial distribution analysis unit outputs a heavy metal spatial distribution map, a migration risk prediction unit generates a pollution migration probability cloud map in combination with meteorological and hydrological data, and a pollution threshold defining unit outputs a soil remediation safety threshold. In the treatment decision execution module, an in-situ remediation execution unit adjusts passivator injection parameters, a pollution source management and control unit regulates pollution source blocking equipment and collects monitoring signals, and a three-dimensional dynamic early warning platform generates a comprehensive pollution risk index. According to the system, the cooperative operation of soil heavy metal pollution identification, evaluation and treatment is realized.

The invention relates to the technical field of medicine quality detection, and particularly discloses an optical detection method and system for the content of vitamin tablets. The method comprises the following steps: driving an optical fiber probe array to carry out three-dimensional multi-angle near-infrared scanning through a multi-axis mechanical arm, and collecting reflection spectrum data; a weighted spectrum is generated through derivative spectrum analysis and double evaluation, and auxiliary material noise is corrected and separated in combination with subspace projection and a dynamic kernel function; performing spectral vector projection and regression coefficient iterative optimization through an online PLS model, and synthesizing an error coefficient based on a four-level index retrieval standard library to perform dual-channel feedback; and finally, fusing the credibility weight to output a detection result of the binding confidence. The method realizes nondestructive and high-precision detection of the vitamin tablets, has the beneficial effects of multi-dimensional data fusion, dynamic error compensation and high model adaptability, and remarkably improves the detection accuracy and reliability.

The invention discloses a fire-fighting early warning system and method based on artificial intelligence, and relates to the technical field of intelligent fire-fighting early warning, and the system comprises an environment sensing module which comprises a multispectral sensor, a temperature sensor, a smoke sensor and a high-definition camera, and is used for collecting fire characteristic spectral data and transmitting the data to a data analysis module; the data analysis module comprises an edge calculation unit and a data fusion unit, and is used for performing feature extraction and anomaly detection on the fire feature spectrum data through a deep learning model to generate a fire risk assessment result; the early warning decision module comprises a risk assessment unit, an early warning grading unit and a communication unit, and is used for determining an early warning grade according to a fire risk assessment result and triggering equipment of the execution feedback module; and the execution feedback module comprises a spraying system, an emergency lighting system and an alarm and is used for executing fire emergency measures according to the instruction of the early warning decision module. According to the invention, comprehensive improvement of fire prevention and control efficiency is realized through cooperative work of multiple modules.

The invention belongs to the technical field of environmental water quality pollution monitoring, and particularly relates to a drainage basin water environment monitoring and emergency pollution rapid tracing method and system based on water quality pollution detection, and the method comprises the following steps: constructing a multi-parameter cooperative monitoring model, and carrying out the training and deployment; through distributed sampling points and sampling stations, real-time data with verification marks and time-sharing data of watershed water environment water quality pollution detection are collected; mLP and LSTM / GRU models are adopted, EEM spectrum data and environment characteristics are fused, and multi-source heterogeneous data are fused and analyzed; whether an emergency pollution event occurs or not is automatically identified according to a preset condition, the pollution types and traceability results of water environment monitoring and emergency pollution are automatically output, manual further checking is carried out, and environmental law enforcement checking is carried out. According to the invention, drainage basin water environment pollution condition monitoring and emergency pollution rapid tracking and tracing can be completed in a large-range, low-cost and high-efficiency manner so as to support environment law enforcement.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

The invention relates to the technical field of sensor demodulation, in particular to a dynamic demodulation method and system for an MEMS optical fiber multi-parameter sensor. The method comprises the following steps: deploying a high-speed parallel acquisition sensor for an MEMS optical fiber multi-parameter sensor, acquiring spectral reflection and spectral time domain data, and constructing an original multi-parameter spectral data set; performing signal separation processing on the original multi-parameter spectral data set based on a preset hybrid network to obtain a wavelength-light intensity matrix, an input layer based on the preset hybrid network being the original multi-parameter spectral data set, and an output layer being the wavelength-light intensity matrix; therefore, by constructing an integrated data flow of original spectrum data acquisition, feature decoupling, parameter fusion and performance evaluation, the problems of large coupling error, weak noise suppression and uncontrollable demodulation precision in a traditional optical fiber multi-parameter demodulation model are solved; and the demodulation accuracy and practicability of the MEMS optical fiber sensor under the multi-physical-quantity complex working condition are improved.

The invention relates to the technical field of data processing, and discloses an intelligent monitoring method and system for cyanobacterial bloom outbreak. The method comprises the following steps: collecting a water surface spectrum and underwater particle size data, carrying out atmospheric correction, calculating a normalized algae index and a blue-green wave band ratio, inputting the normalized algae index and the blue-green wave band ratio into a U-Net network to obtain a water bloom coverage area, carrying out integral interpolation on the particle size data to obtain a vertical section distribution curve, and calculating a surface layer enrichment degree and a floating trend index, and establishing a water surface-underwater association relationship through random forest regression training, and inputting the multi-dimensional features into a CNN-LSTM model to predict a water bloom outbreak probability and determine an early warning level. According to the method, the problem that the cyanobacterial bloom three-dimensional structure cannot be comprehensively described due to the lack of effective fusion of the water surface spectral data and the underwater vertical section data is solved, the problem that the early warning timeliness of cyanobacterial bloom outbreak is insufficient due to the lack of a multi-source data time sequence analysis model is solved, and the spatial integrity and early warning advance of cyanobacterial bloom monitoring are improved.

The invention relates to the technical field of spectral analysis, and discloses a method and a system for detecting the concentration of trace metal ions. The method comprises the following steps: acquiring original spectral response data of an electronic smoke sample; carrying out signal separation by utilizing a frequency domain feature extraction technology to obtain an initial spectrum data set; performing background noise filtering and baseline drift correction on the initial data to obtain a refined spectrum data set; constructing a concentration-wavelength mapping relation through absorption peak positioning and characteristic wave band screening on the basis of the refined spectrum data set; carrying out continuous time sequence sampling on the samples based on the mapping relation to generate a concentration dynamic change data set; analyzing a concentration fluctuation rule and an abnormal point in the dynamic data, and generating concentration risk assessment data; and finally outputting a time sequence detection file. According to the method, high-precision dynamic monitoring and intelligent risk assessment of trace metal ion concentration are realized, interference is effectively inhibited, and sensitivity and early warning capability are improved.

The invention discloses a liquid medicine foreign matter detection method and system based on machine vision, and the method comprises the steps: obtaining multiband spectral data of a liquid medicine sample, and carrying out the normalization processing to generate a liquid medicine background standard spectral feature template; comparing the refractive index spectral characteristics of the template and the foreign matter-containing liquid medicine, and recording the surface roughness scattering mode and polarized light reflection angle change data of a foreign matter area; calculating a spectrum peak displacement quantized value according to the polarized light data, and extracting foreign matter edge spectrum gradient information in combination with a threshold value; calculating a transparency spectral attenuation coefficient based on the edge gradient information, and obtaining spectral contrast enhancement factors of different foreign matters in combination with the spectral response characteristic data; and if the enhancement factor does not reach the standard, adjusting the spectrum correction parameter and recalculating, fusing the scattering mode and the multi-angle incident spectrum response result to carry out consistency verification, and obtaining a final identification result containing the foreign matter material type and the danger level. The method can improve the drug detection accuracy and guarantee the drug quality safety.

The invention relates to the technical field of boiler combustion control, in particular to a boiler combustion multi-target collaborative optimization control method and system. The method comprises the following steps: firstly, acquiring coal quality parameters in real time through a laser-induced breakdown spectrum analyzer, carrying out qualitative and quantitative analysis on spectral data by utilizing a machine learning algorithm, constructing a boiler combustion digital twinborn model fusing a combustion mechanism model and a data driving model, and predicting combustion characteristics, NOx generation trends and coking risks under different blending schemes; and then, dynamically generating an optimization instruction combination according to a prediction result of the digital twinborn model, establishing a safety interlocking mechanism while executing the optimization instruction, and automatically switching to a manual control mode when detecting that the working condition fluctuates severely or the model prediction deviation exceeds a preset threshold value. According to the method, the problems of coal quality information lag and untimely combustion adjustment caused by traditional manual sampling are effectively solved, and the economical efficiency, the safety and the environmental protection property of boiler operation are improved.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention relates to the field of water quality parameter detection, in particular to a full-spectrum water quality multi-parameter analysis detector which comprises a full-spectrum acquisition module, a preprocessing module, an information processing module and an application module. The full-spectrum acquisition module is used for transmitting a full-spectrum signal to a detection sample, detecting transmission light and scattered light signals and generating original spectrum data; the preprocessing module is used for preprocessing the original spectrum data by adopting an edge calculation mode to obtain a standardized spectrum matrix; the information processing module is used for calculating the actual concentration value of each impurity in the detection sample according to the standardized spectrum matrix; according to the method, the influence relation between different types of impurities is introduced, compared with a common prediction method, the prediction precision of the concentration of each impurity is higher, whether early warning is needed or not is judged according to the prediction precision, and the situation of misjudgment is avoided.

Embodiments include systems for determining one or more properties of an object. In an embodiment, a system includes a gripping element, a light source, a probe element including an optical fiber, a spectrometer, and a processor. The gripping element is configured to grasp the object. The light source is configured to illuminate the object. The probe element is operatively coupled to the gripping element. The optical fiber is configured to transmit reflected light from the object. The spectrometer configured to: (i) receive the transmitted reflected light from the optical fiber and (ii) generate spectral data based on the received transmitted reflected light. The processor is configured to: (i) receive the generated spectral data from the spectrometer and (ii) determine one or more properties of the object based on the received generated spectral data.

As the withering process of tea leaves takes a long time to reach a desired moisture level, estimating when it is time to move to the next step to reach the target tea leaf moisture is difficult and inefficient. Method and system disclosed herein provide an approach for withering schedule prediction of tea leaves. The system, by performing a spectral data analysis on an image of a plurality of tea leaves, estimates the moisture percentage in the plurality of tea leaves, for a selected time stamp. Based on the predicted moisture level, a current temperature value, a current relative humidity value, and a current time stamp, the system generates a withering schedule for the plurality of tea leaves. The generated withering schedule is fine-tuned based on a course correction of an impact of deviation in one or more ambient parameters on the prediction of the withering schedule.

According to the spectrum-to-mass concentration imaging method and device based on physical mechanism deep learning provided by the invention, the actually measured spectrum and the reference spectrum of the pollution gas smoke plume are collected, the spectrum data set is constructed after differential processing, the meteorological data and the online mass concentration label are synchronously collected, and meanwhile, the spectrum-to-mass concentration imaging method and device based on physical mechanism deep learning are provided. A high-resolution gas absorption section is obtained and is convolved into a matrix; and constructing a deep learning model fusing a feature extraction module, an expanded least square module and a full connection module, taking the spectral data set, the meteorological data and the absorption cross section matrix as input, performing training in combination with labels to obtain an optimization model, and predicting the mass concentration of the target gas. According to the method, the problems of error accumulation, low calculation efficiency and poor interpretability caused by dependence on a complex physical model in a traditional method are solved, and high-precision, high-efficiency and interpretable real-time imaging of the mass concentration of the smoke plume of the pollution gas is realized.

The invention discloses a method and a system for reflecting a growth state by a spectral curve of a plant leaf under salt mist stress and a storage medium, and relates to the field of plant growth monitoring. Processing the collected leaf image, extracting a leaf main body area, removing an interference part, and matching the filtered original spectral data to obtain target spectral data of the leaf main body area; the method comprises the following steps: combining target spectral data of a leaf main body area with leaf moisture content data, constructing a spectral analysis model by adopting a partial least square method, taking the leaf moisture content as a covariable into the spectral analysis model, peeling off the interference of leaf moisture change on the spectral data, and calculating the leaf moisture content. And finally obtaining a target spectrum curve of the plant leaf under the salt mist stress. A characteristic wave band corresponding to the target spectrum curve is extracted, a plant growth index is constructed according to the characteristic wave band, and the plant growth index is used for reflecting the growth health state of the plant. The problems that traditional monitoring is high in subjectivity, insufficient in precision and low in efficiency are solved.

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 discloses a seawater quality AI fusion intelligent monitoring system and method based on full spectrum data, and relates to the technical field of seawater quality monitoring. The system comprises a full-spectrum data acquisition module, a water quality initial evaluation and characteristic wave band identification module, an environment adaptive wave band selection module, a weight dynamic distribution and anomaly detection module, a multi-dimensional data fusion and optimization module and an abnormal event response and report module. The system collects original spectral data through a spectral sensor, establishes a basic water quality model after preprocessing, dynamically selects an effective wave band according to real-time environmental parameters, allocates weights, outputs water quality parameters after fusing data of a multi-dimensional sensor, and finally responds to abnormal events and generates a report. The system realizes intelligent and real-time monitoring of seawater quality, and can effectively deal with sudden pollution events and capture water quality fluctuation.

The invention discloses an edge real-time analysis-oriented sensing and calculation integrated spectrum camera processor system. Efficient edge calculation is realized through collaborative innovation of hardware, an algorithm and an accelerator. A hardware system adopts a three-level board card framework; a core board integrates a programmable logic unit and a processing system, and processes spectral data acquired by a CMOS board in real time; and the interface board accurately controls the power supply time sequence. In the lightweight classification network, the SSFE branch converts frequency domain filtering into cyclic matrix multiplication; carrying out normalization on the HFViT branch by using a batch normalization substitution layer; and an 8-bit full integer quantization strategy compression model is matched, and input spectrum identification characteristics are reserved. Time overlapping scheduling is designed for the accelerator, and SSFE matrix multiplication is executed in parallel in the HFViT calculation window period; the outer product pulsation architecture improves the matrix operation efficiency; and non-linear kernel multiplexing computing resources are unified. The system supports real-time classification of push-scan spectrum data, results are directly connected with sorting equipment, and the edge end embedded deployment requirement is met.

An LIBS-based bauxite component quantitative detection method and a related apparatus. The method comprises: controlling an LIBS system to collect actual spectral data of bauxite under test; preprocessing the actual spectral data to obtain preprocessed actual spectral data; on the basis of the preprocessed actual spectral data, extracting corresponding feature elements to be detected, said feature elements comprising one or more of information about feature wavelengths to be detected, information about element wavelengths to be detected, and information about remaining wavelengths to be detected; and, on the basis of said feature elements and a quantitative detection model, determining component information of said bauxite.

The invention discloses a rapid nondestructive testing method and system for the lipid content and deterioration degree of red pine nuts based on hyperspectral imaging and deep learning, and belongs to the technical field of nondestructive testing of the quality and safety of agricultural and forestry products. The method is provided for solving the problems that an existing method for detecting the lipid content and the oxidation degree in the red pine nut kernels is generally complex in operation process, high in large-batch detection cost, long in consumed time and difficult to achieve detection in the whole storage and transportation process. The method is characterized by comprising the following steps: acquiring original near infrared spectrum data of a pine nut sample through a collected hyperspectrum; determining the lipid reference content truth value and the oxidation deterioration reference degree of the pine nut samples at different sampling times, and establishing a database according to the values; the method comprises the following steps: preprocessing collected original near infrared spectrum data of red pine nuts, and dividing red pine nut sample data collected in different batches into a training set and a verification set; and designing an improved one-dimensional cavity convolutional network based on a dynamic weight distribution module to construct a deep learning model, wherein the deep learning model is used for constructing a deep learning structure suitable for spectral feature analysis of the red pine nuts. And a back propagation algorithm is adopted to train the constructed model, and reverse updating of network weight parameters is realized by minimizing a loss function. When the performance of the constructed model meets the rapid detection requirement, the method is used for efficient and lossless synchronous detection of the lipid content and the oxidative rancidity degree of the red pine nuts.

The invention provides a multi-source environment interpretation method and device based on linear frequency modulation analysis, and belongs to the field of space-based remote sensing intelligent process.The method comprises the steps that a hyperspectral image and laser radar digital surface model data of a target area are obtained, local neighborhood windows with pixels as the centers are extracted respectively, and the local neighborhood windows are obtained; generating a spatial-spectral data cube and elevation data; performing fractional domain feature extraction on the spatial spectrum data cube and the elevation data in sequence, and obtaining reconstruction features based on linear frequency modulation basis function mapping and cross-modal fusion; and identifying and classifying the reconstructed features to obtain a target area ground feature classification result. According to the method, through cascade design of fractional domain transformation, frequency modulation modeling and cross-modal attention, multi-source distribution alignment, dynamic feature enhancement and efficient fusion and generalization are realized, and an interpretable and low-dependence solution is provided for multi-source cross-regional ground feature classification.

The invention provides an intelligent paint make-up system capable of accurately identifying and matching the color of aged vehicle paint and a paint make-up method thereof, and the system comprises a color identification module which is used for extracting the color features of a to-be-repaired paint surface, thereby identifying the color of the to-be-repaired paint surface, and obtaining an image of the to-be-repaired paint surface; the data processing module is used for performing multi-angle spectral decomposition on the to-be-repaired paint surface image and predicting a paint surface fading curve so as to obtain a matching weight of color adjustment; the automatic paint mixing module comprises a precise proportioning pump unit and a multi-shaft stirring device and is used for mixing pigments according to the proportioning weight of color adjustment; and the closed-loop feedback module comprises a real-time chromatic aberration detection unit and is used for correcting errors of the pigments in real time and finally outputting the refinishing paint consistent with the spectral data of the paint surface to be repaired.

The invention relates to the technical field of spectrum detection, in particular to a method for detecting the content of trace elements in quartz sand. The method comprises the following steps: acquiring spectral data of a quartz sand solution with gradient change of concentration; in each concentration spectrum, calculating an overlapping index according to the position and peak height difference of a spectrum peak and a previous spectrum peak, and screening abnormal spectrum peaks; analyzing the cross interference of the abnormal spectrum peak and the previous spectrum peak to obtain a distortion representation degree; obtaining the deviation influence degree according to the deviation of the abnormal spectrum peaks influenced by the silicon peaks among different concentrations, obtaining the concentration change interference degree according to the deviation amplitude variation trend of the distortion representation degree in the continuous concentration spectrum data in the ordered concentration, and correcting the spectrum data of the abnormal spectrum peaks to detect the content of the trace elements. According to the method, the spectrum data deviation of the quartz sand under the gradient concentration change is dynamically analyzed and quantified, so that the interference of the silicon element on the trace elements is eliminated, and the accuracy and reliability of the detection result of the content detection of the trace elements in the quartz sand are improved.

The invention relates to a backlight module LED model selection method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to a data configuration operation in a graphical interface, and obtaining a configured reference spectrum data source, a diaphragm penetration rate frequency spectrum and a liquid crystal panel penetration rate frequency spectrum; performing spectrum modulation simulation processing on the reference spectrum data source, the diaphragm penetration rate spectrum and the liquid crystal panel penetration rate spectrum according to an optical structure hierarchical relationship of a simulation backlight module to obtain colorimetric parameters of the simulation backlight module; and determining a backlight module LED model selection result according to the colorimetric parameters. In this way, the actual assembling and measuring process of the backlight module is converted into mathematical operation, the backlight scheme of the simulated backlight module can be evaluated under the condition that the backlight module is not actually assembled, then the optimal LED model selection result of the simulated backlight module is determined, the development efficiency of the newly-assembled screen backlight module is improved, and the development cost is reduced.

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

An AI-based euryale ferox processing process quality control system of the present invention belongs to the technical field of food processing and quality control, and comprises a quality perception quantification module used for carrying out real-time nondestructive detection on a material unit entering a processing flow, and determining the quality of the material unit through a pre-established chemometrics model. The system comprises a material unit acquisition module for acquiring original spectrum data, a quality dynamic evolution prediction module for analyzing and quantifying the acquired original spectrum data into an initial starch crystallinity index, and a quality dynamic evolution prediction module for predicting a quality evolution track of the material unit in future processing time through a dynamic evolution prediction model based on the initial starch crystallinity index, a current quality state and real-time process parameters. According to the method, different processing paths can be predictively planned for materials with different characteristics, so that disturbance caused by heterogeneity of the raw materials is effectively overcome.