163 results about "Partial least squares regression" patented technology

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention provides a water quality heavy metal pollution detection method and system based on Raman spectrum. The method comprises the following steps: collecting a water sample through a water area to be detected, and scanning the water sample by adopting a Raman spectrometer to generate an original Raman spectrogram; based on the original Raman spectrogram, using a wavelet transform algorithm to perform de-noising processing, reducing background interference, using a principal component analysis technology to extract characteristic peaks, and generating a heavy metal preliminary prediction result; based on the preliminary heavy metal prediction result, constructing a heavy metal concentration prediction model by applying a partial least squares regression algorithm, performing quantitative analysis, evaluating prediction accuracy by adopting a cross validation technology, and generating a heavy metal actual concentration result; and based on the actual heavy metal concentration result, comparing with a water quality safety standard, evaluating a pollution condition, and generating a water quality heavy metal pollution detection report. The technical scheme provided by the invention has high sensitivity, can efficiently denoise and accurately predict, and provides a scientific means for monitoring and treating heavy metal pollution of water.

The invention relates to the technical field of food component analysis, in particular to a food quality component analysis method and system based on a near infrared spectrum technology, and the method comprises the following steps: obtaining a food sample set, and obtaining original near infrared spectrum data based on the food sample set, combining the original near infrared spectrum data to form an original infrared spectrum matrix; preprocessing the original infrared spectrum matrix to generate a processed spectrum matrix; and extracting the characteristic wavelength of the near infrared spectrum information based on the processed spectrum matrix, constructing a food quality quantitative analysis model according to the characteristic wavelength in combination with a partial least squares regression algorithm, and outputting a component content predicted value according to the food quality quantitative analysis model. And generating a quality component analysis report of the to-be-detected food based on the component content predicted value. The method has the advantages that food quality and ingredients can be quickly, nondestructively and accurately detected, and detection precision and accuracy are improved.

The invention relates to the technical field of civil engineering structure safety monitoring and aircraft image detection, and discloses a safety quantification nondestructive monitoring method and system based on an aircraft concrete structure, and the method comprises the steps: constructing a multi-target path optimization model, and generating a track point sequence; the aircraft collects an image and calculates an image reliability score; identifying crack and spalling defects by using a deep learning model; calculating a structure risk index SFI based on a partial least squares regression model; a monitoring report is generated and uploaded to the cloud. The technical problem that in the prior art, the safety quantitative evaluation based on the in-flight image structure cannot be realized by depending on a manual inspection or static image recognition mode, especially under the conditions of a special-shaped curved surface structure, shielding of a complex area or violent illumination change is solved. Due to the fact that three-dimensional path optimization, an image reliability scoring mechanism and AI defect recognition are fused, intelligent recognition of structure surface covering and crack peeling is achieved, and the automation degree of concrete structure monitoring is improved.

The invention relates to the technical field of mechanical performance testing of power transformer winding materials, in particular to a method for evaluating the mechanical strength of a transformer winding under a reclosing working condition, and the method comprises the steps: obtaining and preprocessing historical operation data, finite element simulation data and laboratory simulation test data; deploying a micro foil strain gauge, an MEMS acceleration sensor and a flexible piezoresistive sensor based on the preprocessed data, and synchronously collecting radial strain, axial acceleration and cushion block pressure data; after noise filtering, time alignment and feature extraction, multi-directional deformation cooperative influence is analyzed through methods of Pearson's correlation coefficients, partial least square regression and the like; and in combination with a historical damage sample training evaluation model, outputting a mechanical strength degradation level in real time. According to the method, the problems of synchronous acquisition and correlation analysis of multi-direction deformation data under reclosing impact are solved, and accurate evaluation of overall mechanical strength change caused by accumulated deformation is realized.

The invention relates to the technical field of graphics card performance monitoring, and discloses a graphics card component performance monitoring method, device and equipment, and the method comprises the steps: setting a temperature sensor array for a GPU chip, a video memory, a power management module and a radiator of a graphics card, and carrying out the temperature data collection and preprocessing, and obtaining a temperature distribution matrix; establishing a thermal network topological structure and performing finite element discretization calculation to obtain a thermal network state space equation; partial least square regression analysis is carried out to obtain m temperature control areas; the temperature state vector and the load state vector of the temperature control area are input into a mixed Q network structure, an epsilon-greedy strategy and a Q learning updating rule are executed, and a heat dissipation control parameter sequence is obtained.By means of the method, accurate calculation of a temperature coupling coefficient and design of a temperature decoupling controller are achieved, the problem of temperature coupling between assemblies is effectively solved, and the reliability of heat dissipation control is improved. And the heat dissipation control strategy can be adaptively adjusted to adapt to different working load conditions.

The invention discloses a salinization farmland crop yield prediction method, which is applied to the technical field of crop yield monitoring and prediction, and comprises the following steps: based on a vegetation index and a salinity index, inverting salinity by using a partial least square regression method; inverting the soil moisture based on an empirical relation model of the conditional vegetation temperature index and the actually measured soil moisture; inverting the leaf area index based on an empirical relation model of the normalized vegetation index and the actually measured leaf area index; the method comprises the following steps: constructing an HYDRUS-1D-EPIC coupling model considering salinity, carrying out sensitivity analysis by using an extended Fourier amplitude sensitivity test method, carrying out parameter calibration and posterior distribution inference on the coupling model by using an MCMC method, and sampling posterior distribution by using a DREAM method; and assimilating the inverted salinity, soil moisture and leaf area index to the coupling model by using an ensemble Kalman filtering method, and predicting the crop yield of the salinized farmland. According to the method, the yield prediction precision of the salinized crops is effectively improved.

The invention discloses a hybrid network method for hyperspectral image water quality inversion, and relates to the technical field of hyperspectral image analysis. The model comprises a spectral feature extraction module, an improved Transform encoder, an LSTM (Long Short Term Memory) module, a cross-time attention module and an adaptive feature fusion module. The model extracts spatial and spectral features through a deep convolutional network, enhances feature expression by using multi-scale embedding and an attention mechanism, and optimizes prediction precision in combination with a dynamic weight fusion strategy. Through end-to-end training of the model, water quality parameters (total nitrogen, dissolved oxygen and the like) in a hyperspectral image can be effectively estimated, and a spatial map of water quality distribution is generated. Compared with a traditional method (such as support vector regression, partial least square regression and the like), the hybrid network provided by the invention has the advantage that the accuracy and robustness of water quality parameter inversion are remarkably improved. Experimental results show that the method has excellent performance in a complex water body environment, and an efficient and reliable technical means is provided for accurately monitoring and evaluating water quality pollution.

Disclosed is a method and device for predicting soil organic carbon based on spectrum-guided ensemble learning. The method includes: obtaining a soil sample and a real organic carbon content and an original soil spectrum thereof, pre-processing the original soil spectrum to obtain a soil spectrum sample, constructing, based on the soil spectrum sample, a sample set, grouping the sample set into a first training set and a validation set, and using the real organic carbon content as a label; training, based on the first training set and the corresponding labels, a partial least squares regression model, a Cubist model and a random forest model to obtain carbon content predicted value sets of the three models; constructing, based on the carbon content predicted value sets of the three models and soil spectrum principal component data, a second training set, and training a second random forest model with the second training set and corresponding labels to obtain a spectrum-guided ensemble model. The method combines the advantages of different predictive models and can accurately predict the soil carbon content.

The invention belongs to the technical field of hyperspectral imaging and machine learning, and discloses a plant carotenoid rapid determination method based on hyperspectrum and machine learning. The measuring system comprises a hyperspectral imaging device, a spectrum preprocessing module, a characteristic wavelength extraction module, a machine learning modeling module and a carotenoid content prediction module which are sequentially connected through a data processing unit. According to the method, spectral data of a plant sample is obtained in combination with a hyperspectral imaging technology, key wavelengths are screened through spectrum preprocessing and a feature extraction algorithm, and a carotenoid content prediction model is established by using machine learning models such as random forest regression, partial least squares regression or a gradient elevator. According to the method, rapid and nondestructive detection of the plant carotenoid can be realized without a chemical reagent, the determination precision and efficiency are improved, and the defects of complex operation, long time consumption and strong destructiveness of a traditional detection method are overcome.

A method is provided for determining soil properties for an area of land, from soil spectrum data. In an embodiment, the method includes receiving soil spectrum data records from hyperspectral sensors that represent a mean soil spectrum of a specific geo-location of the area of land and removing interference signals from the spectrum data records to create soil spectral bands. The method also includes predicting a plurality of soil property datasets based on a partial least-square regression and the soil spectral bands and selecting specific soil property datasets from the plurality of soil property datasets to represent soil properties of the specific geo-location, wherein the specific soil property datasets include property data and spectral band data for spectral bands used to determine the property data. The specific soil property datasets may then be used to generate a crop prescription of recommended hybrid seeds or population densities for the specific geo-location.

The invention relates to the technical field of production monitoring control, and discloses a method and a system for monitoring production of eggs capable of reducing blood sugar based on fermented feed. The method comprises the following steps: detecting the hypoglycemic active components of the fermented feed through a multispectral sensor array to obtain spectral characteristic data; establishing a laying hen blood glucose metabolism correlation model by using a partial least squares regression algorithm; dynamically predicting the egg hypoglycemic index based on the incidence matrix and performing function grading; regulating and controlling formula parameters of the fermented feed in real time by adopting a particle swarm optimization algorithm; and carrying out production process quality tracing monitoring and abnormity early warning according to the optimized formula instruction. According to the method, the monitoring precision, the prediction accuracy and the quality stability of the production of the blood-sugar-reducing eggs are improved.

The invention discloses a method for analyzing a climate change rule based on remote sensing data. Multi-source remote sensing data are obtained through multiple platforms of satellites, aviation and ground, and after preprocessing of geometric correction, radiation correction and the like, features of surface temperature, humidity and the like are extracted through a split window algorithm, a polarization difference index method and the like. Principal component analysis is used for dimensionality reduction, partial least squares regression is used for determining the quantitative relation between features and climate changes, and a correlation model is established by considering human activity influences. The method comprehensively obtains data, improves the processing and analysis precision, establishes the relation between human activities and climate changes, can accurately determine the climate change rule, and provides powerful support for climate change research and response.

The invention discloses a chicken manure crude protein content detection model construction method, a chicken manure crude protein content detection system and a detection method, relates to the technical field of bioinformatics, solves the problems of high cost, serious pollution and low efficiency of an existing chicken manure crude protein content detection technology, and improves the accuracy of chicken manure resource application. The obtained sample set comprises visible-near infrared reflection spectrum and crude protein content of the chicken manure; carrying out pretreatment on the visible-near infrared reflection spectrum by adopting a plurality of pretreatment methods; screening out an optimal preprocessing method and a corresponding preprocessing spectrum by using a partial least square regression model; obtaining a characteristic wave band by using a plurality of characteristic selection methods; and based on the crude protein content and the characteristic wave band, constructing and screening an optimal chicken manure crude protein content detection model. The method provided by the invention is suitable for detecting the content of crude proteins in chicken manure by using manure resources.

The invention discloses a soil water content prediction method, a soil water content prediction device, soil water content prediction equipment and a computer readable storage medium based on a spectral analysis technology. The method comprises the following steps: acquiring target spectral data of a target soil area and standard spectral data of the target soil area after drying; performing optimal coupling spectrum calculation based on the target spectrum data and the standard spectrum data to obtain optimal coupling spectrum data; carrying out wavelength screening processing on the optimal coupling spectrum data by adopting a wavelength optimization algorithm to obtain model input data; wherein the wavelength optimization algorithm is a hybrid algorithm of a competitive adaptive reweighted sampling algorithm and a continuous projection algorithm; reasoning model input data based on the fusion prediction model to obtain a soil water content estimation value; wherein the fusion prediction model is a prediction model obtained by training and fusing an initial partial least square regression model and an initial convolutional neural network model based on spectrum training data.

The invention discloses a distillate oil property prediction method based on deep learning feature extraction and partial least squares regression. The method comprises the following steps: firstly, carrying out classification training on a near infrared spectrum through a convolution-attention double-branch fusion network, and extracting high-dimensional spectral features with local and global information; then, historical samples are retrieved from a database based on prediction categories, a plurality of most similar samples are selected by adopting cosine similarity measurement to construct a correction set, and the spectral features and property labels are subjected to standardization processing; and finally, carrying out partial least squares regression modeling on the correction set, extracting latent variables to maximize covariance between spectral features and physicochemical properties, and inputting feature vectors of an oil sample to be detected into the trained PLS model to obtain a corresponding property prediction result. According to the method, the modeling requirement and the category specificity characteristics under the small sample condition are considered while the prediction precision is guaranteed, and the method is suitable for rapid property detection and intelligent analysis in the refining process.

The invention relates to the technical field of laser cladding repair, and provides a positioning and clamping system and method for repairing the surface of a large working roll of a steel mill based on laser cladding, a three-field coupling prediction model of a temperature field, a stress field and a displacement field is established, and a partial least squares regression algorithm is combined with a Kalman filter to realize multi-step prospective prediction. According to the predicted thermal deformation, self-adaptive compensation control is implemented through a three-layer cascade compensation mechanism composed of a hydraulic drive, a piezoelectric ceramic driver and a laser head adjusting mechanism. The system adopts a time domain, frequency domain and time-frequency domain joint analysis method to extract vibration characteristics, vibration types are classified and identified through a support vector machine, and an active-passive hybrid suppression strategy is adopted for different vibration sources. High-precision positioning and stable clamping of the large working roller in the laser cladding repairing process are achieved, and the thickness uniformity and the surface quality of a cladding layer are remarkably improved.

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 plant extraction method and system based on image processing, and relates to the technical field of image processing. The method comprises the following steps: acquiring and standardizing a plant raw material image; extracting feature vectors of the images by using a neural network convolutional layer algorithm, comparing the feature vectors with a plant raw material standard feature database, and screening out qualified images of the plant raw materials; qualified images are processed through multispectral image analysis and an image segmentation algorithm, and an effective component spatial distribution diagram is obtained; constructing a partial least squares regression linear model, extracting spectral feature vectors of the effective components, inputting the spectral feature vectors into the model to obtain concentration values, and calculating quantitative data of the effective components; establishing an extraction process rule base, selecting a process parameter combination according to a spatial distribution diagram and quantitative data, and extracting to obtain a primary extracting solution and plant residues; and calculating an effective component residual error rate by matching and mapping to a qualified image coordinate, and if the effective component residual error rate is greater than a preset threshold, performing secondary extraction to obtain a secondary extracting solution. And quantitative basis is provided for extracting process parameters through the spatial distribution diagram of the effective components.

The invention discloses a wheat yield remote sensing prediction method combining phenological parameters, which comprises the following steps: S1, performing field observation in a key growth period of winter wheat, synchronously collecting canopy hyperspectral reflectivity data, leaf area index (LAI) and SPAD value of each observation sample point, and recording wheat grain yield of the corresponding sample point; s2, performing noise reduction preprocessing on the acquired canopy hyperspectral data, and extracting sensitive spectral parameters by combining principal component analysis (PCA) and correlation analysis methods; s3, taking the sensitive spectrum parameters, LAI and SPAD values as independent variables, taking the wheat grain yield as a dependent variable, and adopting partial least squares regression (PLSR) to construct a multivariable yield prediction model; and S4, performing wheat yield prediction on an independent test sample or regional scale remote sensing data by using the trained model. The method overcomes the defect that only yield sensitive spectrum parameters are used for predicting the effect, and accurate estimation of the model is achieved.

The invention relates to the field of substance moisture content detection, and particularly discloses a Xuan paper moisture content nondestructive testing method based on a near infrared reflection spectrum, which comprises the following steps: acquiring near infrared spectrum data of to-be-tested Xuan paper, inputting the near infrared spectrum data into a detection model, and then outputting a moisture content value of the to-be-tested Xuan paper, the method for constructing the detection model comprises the following steps: a, sample preparation: generating a multi-gradient water content Chinese art paper sample set through humidity control; b, spectrum acquisition: acquiring a reflection spectrum of the Chinese art paper sample in a set wavelength range; c, characteristic wavelength screening: carrying out characteristic wavelength extraction on the original spectrum data by adopting a competitive self-adaptive reweighting algorithm; and d, model training: establishing a partial least squares regression prediction model as a detection model based on the screened characteristic wavelengths. According to the method disclosed by the invention, the water content of the Chinese art paper is measured by utilizing the online nondestructive measurement characteristic of the NIRS technology, so that the rapid and nondestructive measurement of the water content of the Chinese art paper cultural relic body is effectively realized, and the preventive protection of the paper cultural relics is realized.

The invention belongs to the technical field of edible vegetable oil quality analysis, and discloses a construction method of a step-by-step temperature compensation model for thermal oxidation rapeseed oil quality detection based on an ultrasonic diagnosis technology. The invention aims to solve the problem that the environment temperature change can influence the sound wave pulse signal when the rapeseed oil quality is detected by utilizing an ultrasonic diagnosis technology. According to the method, a plurality of partial least squares (PLSR) or random forest method (RF) models are respectively established by adopting each integer temperature in a temperature range of 22-32 DEG C and correspondingly acquired ultrasonic data parameters in combination with physical and chemical data parameters acquired at the temperature; the prediction pertinence and accuracy of the temperature compensation model are improved in a mode of constructing a plurality of sub-models, and meanwhile, a training set, an internal verification set and an external verification set are introduced to perform multiple verification evaluation on the model, so that the prediction performance and stability of the model are ensured, and a quality index prediction value output by the model is closer to an actual quality index; and the accuracy and effectiveness of detection data are ensured.

The invention belongs to the technical field of physical and chemical index prediction of jerusalem artichoke, and particularly relates to a method for rapidly predicting main physical and chemical indexes in jerusalem artichoke based on a near infrared spectrum technology. The method mainly comprises the steps of pretreatment of a jerusalem artichoke sample, acquisition of physicochemical indexes and spectral data, pretreatment of the spectral data, screening of characteristic wave bands, establishment of a quantitative prediction model, evaluation and verification of model performance and the like. According to the invention, a partial least squares regression (PLSR) model of inulin, protein, total polyphenol and ash content in jerusalem artichoke is established, and an external verification result of the model shows that detection results obtained by a near infrared spectrum and a standard chemical method have no significant difference. Compared with a traditional chemical detection method, the near-infrared technology improves the analysis efficiency and reduces the detection cost on the basis of meeting basic detection requirements, and has an application prospect in rapid prediction of the physical and chemical indexes of the jerusalem artichoke in actual production.

The invention relates to the technical field of traditional Chinese medicine ingredient research, in particular to a construction method and application of a fingerprint spectrum based on ganoderma lucidum antioxidant active ingredients. Firstly, a method for ultrasonic-assisted acidolysis of the ganoderma lucidum crude polysaccharide under the guidance of free radical scavenging activity is constructed, the method is efficient and good in operability, and the free radical scavenging activity of a ganoderma lucidum crude polysaccharide depolymer can be well reflected. The method comprises the following steps: carrying out acidolysis and analysis on 52 batches of ganoderma lucidum polysaccharides from different sources by adopting hydrophilic chromatography-electrospray detector-electrospray ionization mass spectrometry, further combining grey correlation analysis and partial least squares regression analysis, screening out 12 active common peaks, and establishing the fingerprint spectrum based on the ganoderma lucidum antioxidant active components. And finally, respectively analyzing the fingerprints of the standard ganoderma lucidum, ganoderma sinense, ganoderma applanatum, ganoderma atrum and the ganoderma lucidum sample based on the ganoderma lucidum antioxidant active components by utilizing a principal component analysis method and a partial least square discriminant analysis method, so as to realize identification of the ganoderma lucidum variety.

The invention provides a full-dynamic non-invasive blood glucose detection method based on a full-spectrum peak segment optimization algorithm. The method comprises the following steps: step S1, performing blood glucose testing on a nail bed part of a human little finger by matching an infrared dual-wavelength semiconductor laser with a spectrograph, and recording blood glucose Raman spectrum data obtained in each blood glucose testing; step S2, carrying out pretreatment on the obtained blood glucose Raman spectrum data; s3, dividing the preprocessed blood glucose Raman spectrum data into a training set and a prediction set, analyzing through a principal component analysis method and a partial least square regression analysis method, and establishing a model; s4, inputting the preprocessed blood glucose Raman spectrum data as an independent variable into the model in the step S3, analyzing the blood glucose concentration of the human body, and giving a reference value; according to the full-dynamic non-invasive blood glucose detection method based on the full-spectrum peak segment optimization algorithm, the accuracy of quantitative analysis of a high-fluorescence background sample through a Raman spectrum is achieved, complex treatment on the sample is not needed, and the accuracy of human body non-invasive blood glucose detection is improved.

The invention discloses establishment and application of a white kidney bean HPLC (High Performance Liquid Chromatography) fingerprint spectrum. The HPLC fingerprint spectrum of 114 batches of white kidney bean methanol extracts from different geographical sources is established, 9 common peaks are identified, and the spectrum-effect relationship between the 9 characteristic peaks of the HPLC fingerprint spectrum and the alpha-amylase inhibition rate is established through grey correlation analysis and partial least squares regression analysis. The invention finds that the myricetin component is highly correlated with the activity index of the alpha-amylase inhibitor and is possibly a key component of the hypoglycemic activity of the white kidney beans, and provides methodological reference for evaluating the hypoglycemic activity and quality control of the white kidney beans and also provides reference for development and utilization of the white kidney beans.

The invention relates to a method for improving portable near infrared spectrum data analysis precision, which comprises the following steps: carrying out spectrum scanning on different grape samples to obtain a plurality of original near infrared spectrum data; preprocessing the original near infrared spectrum data to obtain corresponding preprocessed data, and performing dimension transformation on the preprocessed data to obtain a two-dimensional image; processing the two-dimensional image to obtain a reconstructed image, and training the initial convolutional neural network model based on the reconstructed image to obtain a target convolutional neural network model; and taking the output result of the feature extraction layer of the target convolutional neural network model as the input of a partial least square regression model, and constructing a fusion model so as to detect the soluble solid content of the to-be-detected grape through the fusion model. According to the invention, the hardware limitation of the portable near-infrared spectrometer is overcome, and the precision and efficiency of the portable near-infrared spectrum data analysis precision are improved.

The invention relates to the technical field of agricultural remote sensing and precise irrigation, in particular to a method and equipment for inverting leaf water potential of grapes before dawn based on multispectral remote sensing, and the method comprises the steps: measuring leaf water potential of representative grape plants in each planting test plot before dawn in different growth periods, and collecting canopy multispectral images in the noon period; preprocessing the remote sensing original image data; calculating a vegetation index; on the basis of the model fitting data set and the training feature set, constructing basic prediction models in different growth periods by adopting a unary linear regression algorithm, a multiple linear regression algorithm and a partial least square regression algorithm respectively; determining a PLSR inversion model according to the decision coefficient and the root-mean-square error; and carrying out pre-dawn leaf water potential prediction through the PLSR inversion model. Through the method, high-precision inversion of the water potential of the leaves before dawn based on the noon canopy multispectral data can be realized, and reliable technical support is provided for accurate management of vineyard moisture and intelligent irrigation.

The invention relates to the technical field of element detection, and discloses a rare earth element analysis method, device and equipment, a medium and a product. The spectral data is obtained by performing LIBS detection on a rock sample of trace rare earth elements at a plurality of points; inputting the spectral data into an element analysis model to obtain a quantitative analysis result of the rare earth elements of the rock sample; wherein the element analysis model is a partial least square regression model which is trained based on a sample training set and passes the verification of a sample test set. By optimizing the data acquisition mode and establishing the trained and verified regression model, the problems of non-uniform distribution of trace rare earth elements in the rock and weak signals are effectively solved, and the accuracy of quantitative analysis is improved.

The invention relates to the technical field of quality detection, and provides a method for detecting the quality of an aminobutyric acid extract in peanut sprouts, which comprises the following steps: performing specific recognition on aminobutyric acid by a molecular imprinting sensor, and calculating the impurity contribution by combining a preset peanut sprout interference database and partial least square regression; the interference of complex matrix impurities in the peanut bud extract can be specifically eliminated, and the problem that the specificity is insufficient when a traditional method is used for detecting a complex sample is solved; the molecularly imprinted sensor and the quartz crystal microbalance are connected in series through the microfluidic channel, and resonance frequency detection is triggered by using capacitance variation, so that a coherent process is formed from identification to quality analysis of sample detection, and the detection efficiency is improved; the corrected frequency offset, the phase angle and the temperature are input into the recurrent neural network, accurate output of the concentration of the aminobutyric acid is achieved by integrating multi-dimensional detection information, compared with a single parameter detection method, the real content of the aminobutyric acid in an extract can be reflected more comprehensively, and a reliable basis is provided for quality judgment.