4380 results about "Spectral data" patented technology

The invention discloses a bridge damage detection method and system, and belongs to the technical field of bridge engineering.The method comprises the steps that image data, point cloud data and spectral data of a to-be-detected bridge are obtained; inputting the obtained image data, point cloud data and spectrum data of the to-be-detected bridge into an input module of the constructed bridge damage detection model, and determining feature vectors of the image, the point cloud and the spectrum; and a feature fusion module of the constructed bridge damage detection model fuses feature vectors of images, point clouds and spectrums, interacts and fuses obtained fused feature representations of all modals on different levels to obtain fused feature vectors, and outputs a bridge damage detection type through a classifier in a DNN classification layer. The method can improve the accuracy of bridge damage detection.

The invention discloses a ppb-grade methane gas online detection method, equipment, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring an infrared absorption spectrum signal, and obtaining original spectrum data; performing multi-scale noise separation and baseline correction on the original spectral data to obtain de-noised effective spectral features, and extracting methane specific absorption peaks from the de-noised effective spectral features; comparing the methane specific absorption peak with the interference gas spectral line through a multi-spectral line weight matching algorithm to obtain methane characteristic intensity; carrying out self-adaptive calibration on the methane characteristic intensity according to the drift compensation model to obtain calibrated characteristic intensity; obtaining a ppb-level methane concentration value through a concentration inversion model; and inputting the sorted concentration time sequence data into an anomaly detection model to detect a methane leakage event. By designing signal processing and algorithm analysis, the dependence on equipment hardware performance is reduced, and the use cost of ppb-level methane gas detection is reduced.

The invention provides a product surface printing quality detection method based on industrial vision, which comprises the following steps: acquiring paper type and ink type information used by a printed matter, and extracting material spectral characteristic parameters of a paper and ink combination from a pre-established material spectral characteristic database, comprising scattering reflection characteristics of the paper surface and absorption reflection characteristics of ink; for the area with the quality defect, analyzing the spectral data of the area, extracting refined spectral characteristics, accurately classifying defect types through a deep learning model, and analyzing the influence of paper and ink material characteristics on a defect forming mechanism; according to the defect type and the position information, similar cases are retrieved from a pre-established defect knowledge base, a defect cause analysis result and an improvement suggestion are obtained, the material spectral characteristic parameters are matched with defect characteristics, and an evaluation result of the influence degree of the material characteristics on defect formation is obtained.

The invention relates to a fermentation process abnormal state early warning method, system and device and a medium, and the early warning method comprises the steps: collecting fermentation process parameters in a fermentation tank in real time, and generating a physical parameter matrix; obtaining Raman spectrum data of the fermentation liquor and extracting metabolic feature vectors; fusing the physical parameter matrix with the metabolic feature vector to obtain a multi-modal data cube; constructing a metabolic network model, inputting the physical parameter matrix as a boundary condition, and generating calibrated metabolic flux distribution data; dynamically adjusting the monitoring weight coefficient of the fermentation process parameter, and generating a parameter sensitivity weight vector; inputting the multi-modal data cube and the parameter sensitivity weight vector into a space-time convolutional network, extracting time sequence features and spatial distribution features, and performing similarity matching with a preset abnormal mode library to obtain a matching result; and determining an abnormity early warning type according to a matching result with abnormity, and generating a corresponding compensation control instruction. The stability of the fermentation process is improved.

The present invention provides an integrated hyperspectral water quality analysis method, which belongs to the field of hyperspectral water quality analysis. First, data preprocessing is conducted by water quality data collection and water quality image collection in early stage; second, three dimensionality reduction methods are adopted to conduct dimensionality reduction processing, and fused dimensionality reduction is conducted by parameter trade-off selection; third, machine learning algorithms are adopted to train and test hyperspectral water quality inversion models on spectral data after dimensionality reduction; finally, the hyperspectral water quality inversion models are selected and optimized. The present invention adopts an innovative fusion strategy in the aspect of data dimensionality reduction processing, which can achieve a better data dimensionality reduction effect, effectively remove noise and redundant information, and provide a more accurate and reliable data basis.

The invention provides an intelligent sewage treatment process optimization method and system. The method comprises the following steps: continuously collecting the flux attenuation rate, transmembrane pressure difference and pollutant spectrum data of a ceramic membrane, carrying out time sequence analysis, constructing a pollution accumulation dynamic model, and predicting a critical threshold value; when a threshold value is reached, triggering an ultrasonic cavitation device, adjusting high-frequency vibration to generate a microbubble group, stripping pollutants by utilizing shock waves, and calculating membrane flux recovery data; fusing the spectral data and the recovery data to extract an actual pollution stripping efficiency value, and comparing a theoretical value to obtain a difference proportion; if the actual value is lower than the theoretical value, the cleaning period is shortened, the fluid pressure is increased to strengthen the stripping effect, and meanwhile, dynamic model parameters are reversely corrected based on the pressure adjustment amplitude to form closed-loop regulation and control. According to the invention, intelligent prediction, self-adaptive cleaning and closed-loop optimization of ceramic membrane pollution are realized, the sewage filtering efficiency is improved, and the energy consumption and cleaning cost are reduced.

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 crop disease recognition, in particular to a crop early disease recognition method and system based on time sequence feature fusion, and the method comprises the steps: obtaining an RGB image and a temperature feature image of a target leaf, and collecting multispectral feature data through a spectral imaging device; based on the time sequence image sequence, generating a coordinate transformation matrix of the first frame image by using a PnP algorithm, and aligning subsequent images according to the coordinate transformation matrix; multi-modal features are extracted by using a deep neural network, and the deep neural network comprises a 3D convolutional layer and an adaptive attention mechanism and is used for extracting features of space and time dimensions; realizing cross-modal feature fusion through a graph convolutional network based on the spectral data and the multi-modal features; inputting the fused features into a classifier, and generating the disease category and the disease severity of the target leaf based on the output of the classifier; outputting the disease transmission risk and the early warning result, and capturing the tiny dynamic change in the disease development process.

The invention relates to the technical field of spectral analysis, in particular to a fabric fiber component high-resolution spectral rapid analysis method which comprises the following steps: S1, acquiring three-dimensional shape data of a fabric surface through laser scanning, and identifying a fiber arrangement direction and a surface curvature characteristic based on the three-dimensional shape data; s2, dynamically adjusting the incident angle and the focusing depth of a spectrum probe according to the fiber arrangement direction and the surface curvature characteristics identified in S1, and selecting a corresponding spectrum excitation mode; s3, performing feature extraction on the multi-mode spectral data acquired in the S2, and fusing feature vectors of different spectral modes through a deep learning algorithm; s4, analyzing the fused feature vector based on a transfer learning model, and outputting a fiber component type and a proportion thereof; and S5, associating the component data output in the step S4 with space coordinates, and generating a component distribution visualization map. By dynamically adjusting the spectrum probe, fusing multi-mode spectrum data and the like, the analysis precision and speed are remarkably improved.

The invention discloses a traditional Chinese medicinal material authenticity identification platform, and relates to the technical field of traditional Chinese medicinal material authenticity identification, the traditional Chinese medicinal material authenticity identification platform comprises a data acquisition module, the data acquisition module is connected with a data analysis module, and the data analysis module is connected with a result display module; and the data acquisition module is used for acquiring appearance images, spectral data and background information of the traditional Chinese medicinal materials. According to the traditional Chinese medicine authenticity identification platform, traditional Chinese medicines are comprehensively analyzed from multiple angles, limitation of a single method is avoided, and identification accuracy is improved; the characteristics of the traditional Chinese medicinal materials are deeply analyzed to accurately judge authenticity and quality; the quality of the traditional Chinese medicinal materials is accurately evaluated by establishing a quantitative analysis model; the model is verified by adopting a cross validation method, so that the accuracy and stability are ensured, the identification accuracy is improved, and the problem that the traditional identification method is difficult to meet the requirement of quickly and accurately identifying the authenticity of the traditional Chinese medicinal materials is solved.

The invention provides an LED light source uniformity and color deviation detection method, which comprises the following steps: setting environmental parameters of a constant temperature and humidity test box, monitoring the environment in the box in real time through a temperature and humidity sensor, and triggering a temperature and humidity control system to adjust when detecting that the temperature and humidity deviate from a set value; according to the initial color characteristic database, placing an LED light source in the constant-temperature and constant-humidity test box for an accelerated aging test, and obtaining spectral data of the light source at different time points by adopting a spectrum analyzer; calculating chromaticity coordinates, color temperatures and color rendering indexes corresponding to the spectral data at different time points, calculating a color deviation value of the LED light source, and fitting a change curve of the color deviation along with time; and according to an accelerated aging test result of the LED light source, calculating the color maintenance life of the light source in a high-temperature and high-humidity environment, and completing color deviation detection through a color stability uniformity index of the LED light source.

The invention relates to the technical field of quartz sand detection, in particular to a quartz sand impurity detection method which comprises the following steps: acquiring spectral data of a quartz sand sample, and obtaining spectral line interference degrees of characteristic peaks according to energy distribution conditions of the characteristic peaks of the spectral data; based on the peak shape variation condition of each characteristic peak and the offset condition of each characteristic peak and a matrix characteristic peak, the matrix interference degree of each characteristic peak is obtained, and the interference intensity of each characteristic peak is obtained by combining the anti-interference degree of each characteristic peak so as to optimize the chaos regulation factor when the chaos mapping population is initialized; and finally, combining with an optimization algorithm to obtain a characteristic wave band with minimum interference, and detecting the purity and impurity content of the quartz sand based on the characteristic wave band. The quartz sand impurity detection precision can be improved.

The invention relates to the technical field of spectral signal processing, in particular to a high-precision spectral signal peak detection method and system. According to the technical scheme, the method comprises the following steps: denoising and smoothing spectral data; dividing a spectrum curve by adopting a dynamic partitioning strategy; generating candidate partitioning points based on various local features, and generating feature blocks through optimization processing; according to the method, the influence of feature differences of different regions on a detection algorithm is reduced through a dynamic partitioning strategy, a multi-scale morphological feature system is constructed to comprehensively describe spectral curve morphological characteristics, potential peaks are gradually mined by applying a layered iteration peak detection algorithm, and local characteristics of spectral signals are adapted by means of an adaptive parameter adjustment mechanism, so that the spectral curve morphological characteristics are accurately detected. The peak value correction and fusion mechanism is utilized to improve the quality and reliability of a detection result, spectral data with a complex structure and a high noise level can be effectively dealt with, an efficient and reliable solution is provided for the field of spectral analysis, and the accuracy and robustness of peak value detection and the conciseness and reliability of the result are improved.

The invention belongs to the technical field of image processing, and particularly relates to a mine ecological restoration effect evaluation system and method. The system comprises a multi-source remote sensing data fusion part, a data processing and analysis part and an ecological restoration effect evaluation part, the multi-source remote sensing data fusion part is used for acquiring multi-source remote sensing image data including a plurality of source images in a target mine area; the data processing and analyzing part is used for performing multi-scale fractal analysis on the fused remote sensing image data to obtain fractal dimensions related to the scale of the target mine area; and the ecological restoration effect evaluation part is used for obtaining the ecological restoration degree of the mine according to the relationship between the multispectral adaptive vegetation index in the historical data and the ecological restoration of the mine. According to the method, the accuracy and comprehensiveness of mine ecological restoration effect evaluation are remarkably improved.

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 discloses an intelligent sensing algorithm and system for crop growth vigor, computer equipment and a medium, and the algorithm comprises the following steps: 1, carrying out the image collection of a farmland, and collecting the spectral data of different wavebands in the farmland; 2, preprocessing the collected images and spectral data, and extracting multi-dimensional feature information of the crops; 3, fusing the extracted multi-dimensional feature information of the crops to obtain comprehensive and accurate crop growth feature information; 4, performing data analysis on the crop growth feature information through deep learning, and constructing an intelligent decision model; through the implementation of the invention, the characteristics and beneficial effects of multi-information fusion, high-precision perception, cost reduction, quality improvement, wide application and the like are realized through the crop growth vigor intelligent perception algorithm, and the method plays an important role in improvement of agricultural production and agricultural product quality.

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 discloses a method for rapidly and nondestructively detecting seed quality based on a near infrared spectrum technology, and relates to the technical field of modern agriculture, and the method comprises the following steps: using a deep learning model to carry out image recognition on a seed to be detected, determining a seed type and automatically adjusting spectrometer parameters; collecting spectral data by using the adjusted near-infrared spectrometer, and preprocessing the spectral data; obtaining the internal structure and density information of the seeds, and combining the internal structure and density information with the spectral data to form a multi-modal data set; constructing a seed vigor prediction model, and predicting the germination rate of the seeds; judging the growth trend of the seeds according to the prediction result; grading according to the growth trend of the seeds; according to the method, the seed vigor prediction model is constructed, so that high-precision prediction of the seed germination rate is realized; through classification and grading of growth trends, a scientific basis is provided for seed screening and planting management.

The invention relates to the technical field of chemical material detection, and discloses an AI-based chemical material detection information library retrieval and standard spectrum comparison system. The system comprises a data acquisition module, a spectrum preprocessing module, a feature extraction module, a retrieval matching module and a dynamic comparison module. The data acquisition module uses a multispectral sensor to acquire real-time spectral data covering ultraviolet, visible light and infrared bands, and the real-time spectral data is denoised by the spectrum preprocessing module. The feature extraction module adopts a deep residual convolutional network to extract spectral fingerprint feature vectors. The retrieval matching module inputs the feature vectors into a graph structure retrieval model to screen candidate standard spectrums. And the dynamic comparison module compares the candidate standard spectrums in multiple dimensions based on an improved quantum particle swarm optimization algorithm to obtain an optimal result. The system can accurately acquire and process spectral data, quickly retrieve and accurately compare standard spectrums, improve the efficiency and accuracy of chemical material detection, and have wide application prospects in the fields of chemical material research, production quality control and the like.

The invention discloses a wheat single grain appearance anomaly detection method based on a deep learning model and hyperspectral imaging. The method comprises the following steps: step 1, collecting different types of wheat grain samples; step 2, acquiring hyperspectral image data of the wheat grains by using a visible light-near infrared and short wave near infrared hyperspectral imaging system, and extracting spectral information and image information; step 3, preprocessing the spectral data, and verifying a preprocessing effect; 4, screening spectral characteristic wave bands, extracting texture characteristics and morphological characteristics in combination with a gray-level co-occurrence matrix, and constructing a middle-level data fusion model; step 5, constructing an atlas feature fusion deep learning model to realize high-level fusion of spectrum and image features; and step 6, pixel-level classification is carried out on the hyperspectral image, and a spatial distribution visualization result of the appearance abnormity of the wheat grains is generated. The method has high precision, nondestructive testing and strong generalization ability, and is suitable for wheat quality grading, processing and sorting and storage safety management.

The invention relates to the technical field of water quality detection, and discloses a water quality pollutant detection method, system and equipment based on spectral analysis, and the method comprises the following steps: carrying out real-time spectral data acquisition on a water body sample in a vehicle driving process through a multispectral sensor array in a vehicle-mounted water quality detection device; obtaining a water body spectrum three-dimensional data cube; carrying out anti-vibration wavelength calibration and ambient light source interference elimination processing to obtain a preprocessed spectrum data set; performing standard multivariate decomposition processing and horizontal local constraint optimization of a pollutant target wave band on the preprocessed spectrum data set to obtain enhanced feature fingerprint spectrums for different types of water quality pollutants; and inputting the enhanced feature fingerprint spectrum into a deep belief network model to perform pollutant feature matching analysis to obtain various pollutant types and corresponding concentration values in the water body sample, thereby effectively eliminating ambient light changes and other background interferences, not only determining the pollutant types, but also accurately quantifying the concentration values.

The invention provides a textile color consistency detection method, which comprises the following steps: acquiring spectral data of a to-be-detected product, comprehensively scanning the surface of the product through multispectral imaging, and performing noise reduction and enhancement processing on scanning data by adopting an image processing algorithm to obtain high-precision color difference detection data; according to the optimal dyeing process parameters and the pretreatment scheme, numerical simulation is adopted to simulate the dyeing process, the diffusion process of the dye in the fiber is calculated through finite element analysis, and a dyeing uniformity prediction result is obtained; and comparing the prediction result with a color fastness test standard, judging whether the dyed fabric can meet the product quality requirement or not, if so, warehousing the batch of products, and if not, feeding back the prediction result to a dyeing process control system, and carrying out the next round of optimization iteration until the product quality requirement is met.

The invention relates to the technical field of display screens, and discloses an anti-dazzle Micro LED display screen and an anti-dazzle adjusting method and system.The method comprises the steps that multi-dimensional spectral data collection is conducted on incident light, and a multi-dimensional spectral feature matrix is obtained; performing space-time light field reconstruction of tensor decomposition and spherical harmonic function expansion operation on the multi-dimensional spectral feature matrix to obtain four-dimensional light field feature data; inputting the four-dimensional light field feature data into a deep learning prediction network for dynamic modeling to obtain light field distribution prediction data; performing anti-dazzle display parameter optimization based on the light field distribution prediction data to obtain a display parameter target value; and executing a hierarchical control strategy according to the display parameter target value, and generating a multi-parameter cooperative control signal for light transmittance adjustment, polarization state modulation and microstructure actuation, the method realizes accurate conversion from the light field characteristics to the control parameters, ensures the stability and continuity of the display effect, effectively reduces the system energy consumption, and improves the operation efficiency.

The invention relates to the technical field of corn kernel quality detection, in particular to a corn kernel quality detection model construction method and system based on multi-source data fusion, and the method comprises the following steps: S1, multi-source data collection: collecting apparent morphology data of corn kernels through a multi-spectral imaging device, synchronously utilizing a near-infrared spectrometer to obtain spectral data of internal components of the corn kernels, and measuring structural density characteristic data of the corn kernels in combination with a sonic sensor; s2, multi-modal feature construction: forming a multi-modal feature matrix; s3, dynamic weighted fusion: generating a mixed feature vector; s4, constructing a dual-channel neural network: constructing a dual-channel deep neural network based on the generated mixed feature vector; and S5, model compression and deployment optimization: generating a lightweight detection model suitable for the embedded device. According to the invention, multiple requirements of an agricultural field on real-time performance, precision and deployment flexibility are met.

The invention discloses a crop disease remote sensing monitoring and early warning system based on time sequence spectral information analysis, and relates to the technical field of agricultural disease and insect pest monitoring. Computer drawing software is utilized to divide a corn planting field in equal proportion, and sensors are deployed to monitor the growth environment change in each grid in real time and generate microenvironment data information; the method comprises the following steps: calculating a microenvironment difference index Xbd according to microenvironment data, identifying a microenvironment difference aggregation area and sending a risk analysis instruction, after receiving the risk analysis instruction, capturing related spectral data information of the microenvironment difference aggregation area by using an unmanned aerial vehicle and a multispectral sensor, and constructing a multispectral fluctuation coefficient Xgp by analyzing related spectral data, and comparing the calculated rust disease risk assessment index Zpg with a preset threshold W in combination with the microenvironment difference index Xbd and the trained risk assessment model, and obtaining corresponding grade early warning according to the rust disease risk degree.

The invention relates to the field of soil spectrum analysis, in particular to a farmland soil environment detection method and system. The method comprises the following steps: firstly, analyzing fluctuation characteristics of near infrared spectrum curves of a plurality of soil samples in a preset window of a target wavelength to obtain undisturbed degrees of the near infrared spectrum curves in the target wavelength; fitting spectral data points of the near infrared spectral curve, and obtaining feature retention degrees of the extreme points on the fitted curve according to the difference of fitting absorbance of adjacent extreme points on the fitted curve, the absorbance of each wavelength and the difference of the fitting absorbance and the wavelength distribution of the extreme points on each fitted curve; filtering the spectral data points of the target curve based on the non-interference degree and the feature retention degree to obtain a filtering curve; according to the method, the interference information in the near infrared spectrum curve is removed, the existing key information is reserved as much as possible, and the precision of farmland soil environment detection is improved.

The invention discloses a method and system for improving satellite remote sensing inversion precision by fusing hyperspectral proximity, and belongs to the technical field of water environment and water ecology monitoring. Acquiring a water body hyperspectral near-sensing reflectivity spectral data set, a satellite remote sensing surface reflectance data set and a water environment actual measurement data set, and performing preprocessing; a synchronous data set containing # imgabs0 # and water quality and a synchronous data set containing # imgabs1 # and water quality are constructed; based on the synchronous data of the # imgabs2 # and the water quality, synchronous data of the # imgabs3 # and the water quality are generated, and synchronous data of the # imgabs4 # and the # imgabs5 # are obtained; the # imgabs6 # is normalized to obtain # imgabs7 #, and the quantitative conversion relation between the # imgabs7 # and the # imgabs8 # is constructed band by band to eliminate errors; generating a fused satellite-ground data set, and constructing a training water quality parameter inversion model; and updating corresponding methods and data sets in real time by repeating specified steps, and optimizing the model. According to the method, the inversion result is displayed and downloaded according to the query instruction, the satellite spectrum precision and the satellite-ground synchronous data set scale are improved, and the method is used for high-precision water environment monitoring.

The invention discloses a zinc coating shedding performance detection method and system, particularly relates to the technical field of metal surface treatment quality detection, and is used for solving the problems of high misjudgment rate and insufficient detection precision caused by coating surface pseudo defect interference in the existing method. The method comprises the following steps: synchronously acquiring a multispectral reflection image and three-dimensional morphology data of the surface of a zinc coating through an industrial vision imaging device, extracting grain boundary distribution information based on multispectral data, and identifying radial pseudo defects in combination with a curvature manifold geodesic line energy gradient direction of the three-dimensional morphology; and further performing micro-crack topology analysis and grain boundary spatial correlation verification on the candidate region, eliminating texture interference and grain boundary overlapping regions, and finally outputting a high-confidence fall-off defect detection result, so that the identification capability of real defects under a complex galvanized surface is remarkably improved, and the method is suitable for automatic detection requirements of a high-speed continuous production line.

Disclosed in the present invention is a soil organic matter prediction method based on nonlinear memory-based learning and spectroscopy, comprising: constructing a sample set on the basis of the real soil organic matter content and spectral data of each soil sample, and dividing the sample set into a training set and a validation set; from the training set, screening for k samples having spectral data most similar to that of each soil sample to be tested to construct a training subset, and fitting a random forest model on the basis of the training subset and by means of ten-fold cross validation to obtain a nonlinear memory-based learning model; verifying the prediction accuracy of the nonlinear memory-based learning model by means of the validation set, when the prediction accuracy reaches a standard, a final nonlinear memory-based learning model being obtained; and inputting the spectral data of the soil sample to be tested into the corresponding final nonlinear memory-based learning model to obtain a predicted value of soil organic matter. According to the method, corresponding soil organic matter contents can be accurately obtained on the basis of spectral data of different soil samples to be tested. Also disclosed in the present invention is a soil organic matter prediction apparatus based on nonlinear memory-based learning and spectroscopy.

The invention relates to the technical field of light source control, and discloses a multispectral LED detection light source synchronous control method, device and equipment and a storage medium. The method comprises the following steps: carrying out double-interlaced spectral data sampling on a light source, and carrying out baseline extraction and compensation correction on preset three-dimensional spectral parameters; mapping of multi-dimensional mixed domain time sequence characteristics and decomposition of working characteristic tensors are carried out on the corrected calibration spectrum data, and modulation of recursive phases and group delay compensation calculation are carried out; performing crosstalk compensation of a multi-stage LED light source channel and suppression reconstruction of a multi-harmonic component on the light source phase compensation vector subjected to compensation calculation to generate light source correction control data; and performing light source driving signal synthesis on the light source correction control data to obtain a light source driving waveform sequence, and performing light source channel distribution and synchronous adjustment on the light source driving waveform sequence to generate a light source synchronous control result of the target multispectral LED detection light source. According to the invention, high-precision synchronous control of the multispectral LED detection light source is realized.