1251 results about "Spectral analysis" patented technology

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention relates to the technical field of fault positioning, and discloses a power distribution network grounding fault positioning method and system. The method comprises the following steps: acquiring a fault initial transient section signal, an arcing continuous section signal and an arc quenching recovery section signal of the power distribution network, and executing spectral analysis to obtain a fault feature matrix; performing zero-sequence transient frequency spectrum reconstruction on the fault feature matrix to obtain reconstructed frequency spectrum features and time-varying frequency spectrum features; calculating a transient characteristic index and a fault type based on the reconstructed spectrum characteristic and the time-varying spectrum characteristic; performing fault positioning calculation on the power distribution network according to the transient characteristic index and the fault type to obtain an initial fault point position; and performing transient fingerprint matching and probability density accumulation compensation on the initial fault point position to obtain a target fault point position. According to the method, high-precision characterization of the rapid change signal in the short time window is realized, interference of the compensation state on fault positioning is eliminated, and the grounding fault positioning accuracy of the power distribution network is improved.

The invention provides a meteorological element three-dimensional analysis method combining remote sensing and ground observation, and relates to the technical field of meteorological monitoring, and the method comprises the steps: obtaining remote sensing and ground observation data, and obtaining meteorological element data of a unified space-time reference; dividing the data into space-time grid units and performing quality evaluation to obtain a quality evaluation index; calculating a fusion weight coefficient according to the quality evaluation index based on an adaptive fusion algorithm of a dynamic weight, and performing weighted fusion on the meteorological element data in the grid units to generate an initial three-dimensional meteorological field; performing scale decomposition and reconstruction by adopting a spectrum analysis method to obtain an optimal three-dimensional meteorological field, constructing a self-adaptive tree-shaped composite analysis grid, analyzing evolution characteristics of a weather system in a multi-layer progressive mode, determining topological evolution parameters, and outputting a three-dimensional analysis result of meteorological elements.

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 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 multi-source data fusion modeling method and system in an aeration process, and is applied to the field of intelligent aeration control in sewage treatment. The method comprises the steps that multi-source time sequence data such as dissolved oxygen, turbidity, flow, temperature, power and pool bottom pressure pulsation signals are collected, dissolved oxygen response lag is calculated through cross-correlation analysis with power change as the reference, time sequence alignment is carried out, and a dissolved oxygen reference interval is predicted by utilizing calibration data in combination with a physical constraint LSTM model; performing spectral analysis on the pressure pulsation signal to extract a gas-liquid coupling characteristic value, and generating a cooperative regulation instruction of the frequency of the blower and the rotating speed of the stirrer based on the information; by means of the scheme, control oscillation caused by lag of the dissolved oxygen sensor can be effectively overcome, online monitoring of bubble form distribution is achieved, the gas-liquid mass transfer efficiency is improved, invalid aeration is avoided, and system energy consumption is remarkably reduced on the premise that stable effluent quality is guaranteed.

The invention provides a farmland disease and pest monitoring method and device and a storage medium, and relates to the technical field of agriculture, the method comprises the steps: dividing a farmland into a plurality of sub-regions, obtaining an infrared spectrum image, and segmenting the infrared spectrum image into corresponding sub-images; and calculating a vegetation stress index based on the reflectivity of the insect pest related characteristic wave band, and screening high-risk sub-regions. Risk plants are randomly extracted in a high-risk area, physiological parameters of leaves are collected, physiological health indexes of the leaves are constructed, and dynamic correction is carried out through an environment regulation coefficient according to environment data. Using the trained network model to identify the risk sample leaf scab, and calculating the scab area index. And finally, fusing the corrected leaf physiological health index and the scab area index, calculating a normalized risk value through a risk assessment function, and dividing the normalized risk value into four risk levels. Through multi-stage fusion of spectral analysis, physiological parameter correction and deep learning scab detection, accurate early warning of diseases and insect pests is realized, and the monitoring efficiency is improved.

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 an unmanned aerial vehicle positioning method based on a Bayesian network, and the method comprises the steps: collecting unmanned aerial vehicle data, carrying out the preprocessing, obtaining sound wave data, motion data, infrared image data and environment data, carrying out the spectrum analysis based on the sound wave data, obtaining sound spectrum time sequence data, and carrying out the positioning of the unmanned aerial vehicle. Constructing a probability density thermodynamic diagram and extracting a first range through a sparse variational Gaussian process according to time, frequency and energy three-dimensional information of the sound spectrum time sequence data and environment data, and obtaining first positioning data through a Bayesian network according to the first range and motion data, and performing adaptive wavelet down-sampling on the infrared image data and the sound spectrum time sequence data in the lateral direction to obtain lateral positioning data, and performing deviation correction on the first positioning data by using the lateral positioning data to obtain second positioning. According to the method, through space-time synchronization, adaptive processing and probabilistic reasoning of sound spectrum time sequence data, the positioning precision and stability in a complex environment are improved.

The invention relates to the technical field of big data service, in particular to a chemical fiber raw material impurity spectrum intelligent analysis system which comprises a spectrum feature deconstruction module, an impurity feature modeling module, a periodic drift analysis module and a component quantification output module. According to the method, noise is suppressed through moving window scanning in combination with Savitzky-Golay filtering, peak shape features are reserved, the signal-to-noise ratio is increased, feature distortion caused by mean filtering is avoided, segments are divided through extreme value density, weak impurity recognition is enhanced in combination with a dynamic threshold value, and a multi-dimensional fusion model is established through peak width change rate and baseline offset convolution. Linear dimension reduction limitation is broken through to improve discrimination precision, dynamic time warping is used for aligning peak height difference and symmetry degree time sequence, drift and interference influences are eliminated, periodic error accumulation is solved, Kalman filtering is used for carrying out recursive optimization on compensated concentration, model lag is reduced, and a closed-loop optimization system is constructed through multi-stage feature decoupling and dynamic compensation. The sensitivity is improved; and the misjudgment rate is reduced.

The invention discloses a laser processing method and device for a special shell, and the method comprises the steps: S1, obtaining the structural feature data of the shell through three-dimensional scanning, planning a laser processing path according to the structural feature data, and generating a processing path file; s2, starting a dust removal assembly, emitting positioning laser at the same time, measuring the optical path offset through a photoelectric detection assembly, and generating calibration parameters; s3, the light transmittance change of the light blocking assembly is controlled based on the calibration parameters, and a stable laser beam is formed by adopting a closed-loop control strategy; s4, a stable laser beam irradiates the surface of the shell along the machining path, and the irradiation position is dynamically corrected in combination with the deformation prediction model; s5, local laser strengthening is conducted on the machined key area, and meanwhile the machining path file of the next period is optimized; a light blocking assembly with adjustable light transmittance is combined with a closed-loop control strategy to dynamically adjust laser energy, deformation prediction and spectral analysis provide real-time quality guarantee, the process is continuously optimized through iterative optimization of a digital model, and a high-quality shell finished product is obtained.

The invention relates to the technical field of metal structure detection, in particular to an intelligent special-shaped aluminum plate structure parameter detection method and system based on spectral analysis, and solves the problems of multi-parameter splitting, poor complex structure adaptability and inaccurate risk assessment in traditional detection. According to the method, data are synchronously collected through multi-spectral imaging and line laser scanning, surface component parameters such as the thickness gradient of an oxide film are inverted, the local curvature is calculated based on three-dimensional point cloud, a spiral encryption or grid sparse scanning track is dynamically switched, differential detection of a complex structure and a plane area is achieved, and the detection precision is improved. And then a space correlation model of the oxidation film thickness and the hole deformation quantity is constructed, a structure failure risk area is accurately recognized by combining a three-level risk judgment rule and cross-regional influence analysis, and the system effectively improves the accuracy and detection efficiency of defect recognition of the special-shaped aluminum plate through cooperation of dynamic scanning control, component deformation correlation and a composite structure decision unit.

The invention discloses a compensation method based on ultraviolet-visible spectrum interaction interference, which relates to the technical field of spectral analysis, and comprises the following steps: acquiring ultraviolet-visible spectrum data of turbidity, COD, nitrate single gradient solution and mixed solution of the turbidity, COD and nitrate single gradient solution, and preprocessing the data; constructing a mapping relation model of the turbidity and the spectral data; for spectral data within the wavelength range of 260-320nm, calculating interactive interference data, establishing a relationship between the turbidity and the interactive interference data, and performing interactive interference compensation and spectral analysis; for the spectral data in the wavelength range of 220-260nm, constructing a COD and spectral data relation model, and calculating interactive interference data; and based on the interactive interference data characteristics, establishing a regression model, analyzing the relationship between the interference spectrum characteristics and other substances, and performing interactive interference compensation and spectral analysis. According to the method, the resolution of the spectral signal is remarkably improved, the reliability of data analysis is improved, and the method has a wide application prospect.

The invention relates to the technical field of spectral analysis, in particular to an intelligent spectral pesticide residue detection system which comprises a spectral data acquisition module, a spectral feature analysis module, a pesticide spectral feature classification module, a spectral matching weight optimization module and a pesticide residue concentration analysis module. In the invention, by collecting spectral signals of a pesticide sample in different wavelength ranges and combining time, space and frequency dimension information, the comprehensiveness of data collection is ensured, spectral data is analyzed in multiple dimensions, mutation features are extracted, associated frequency bands are identified, angle deviation is eliminated, and the accuracy and reliability of spectral features are improved; classification is performed according to spectral distribution differences and change rates, detection resolution is improved, spectrum matching rules are optimized, high-contribution wavebands are screened, detection accuracy is improved, concentration normalization analysis is combined, key feature contribution values are adjusted, misjudgment risks are reduced, optical paths are optimized, optical loss is reduced, signal acquisition stability is improved, and adaptability of a detection system is enhanced.

An electronic interface device is configured for use with associated optics for employing coded light for illuminating a liquid, gaseous, or other analyte or target, such as for measuring or determining a composition or other property of the analyte or target. The arrangement can help enable a far higher level of performance, such as for spectral analysis, imaging, or both. A shared common clock on both the illumination and receive sides of the system can be included, such as together with one or more of reference detection, per-channel wavelength-dependent modulation, multiple concurrent feedback loops at different levels, orthogonal and circulant codes, phase-scrambling to better utilize ADC dynamic range, oversampling, and other signal processing can be provided.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.

The invention provides a high and low temperature alternating environment temperature and pressure mixed signal decoupling method and system based on a multi-core optical fiber. According to the method, temperature and pressure mixed signals are synchronously acquired through phase difference data of a plurality of fiber cores, and multi-channel mixed sensing signals are generated; performing mechanical stress suppression on the multi-core optical fiber by using a thermal expansion adaptive packaging structure to form an anti-deformation optical fiber assembly; carrying out adaptive time slot distribution on the mixed sensing signal based on a time division multiplexing time sequence to generate a synchronous calibration signal; spectral features of the synchronous calibration signals are separated through the spectral analysis technology, scanning parameters are dynamically adjusted in combination with the temperature change rate and the deformation quantity, and temperature and pressure feature data are extracted; and performing decoupling operation on the feature data through the coupling model, and dynamically correcting the model parameter weight based on the deformation quantity feedback data to obtain a temperature parameter and a pressure parameter. According to the invention, the accuracy of synchronous decoupling measurement of temperature and pressure in a high-low temperature alternating environment is realized.

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 provides an efficient and accurate frequency modulation screen printing color management color matching auxiliary system which comprises a collector, the collector comprises an image collection module, the image collection module can capture a multi-channel filter array of a printing manuscript through a multispectral imaging device, and an analyzer comprises a spectral analysis module and a color matching prediction module. The spectral analysis module is in communication connection with the image acquisition module, after low-dimensional feature vectors are generated, candidate schemes are listed, and the color matching prediction module predicts an ink matching scheme based on the low-dimensional feature vectors and a deep convolutional neural network; relates to the technical field of frequency modulation screen printing toning. According to the method, the limitation of traditional single-factor color matching is broken through by introducing a multi-modal data fusion (spectrum + base material + ambient light) and Bayesian optimization feedback mechanism, the nonlinear mapping problem of high-dimensional spectral data is solved through cooperation of manifold learning dimension reduction and deep neural network prediction, and efficient and accurate frequency modulation network color matching adjustment is realized.

The invention belongs to the technical field of spectral measurement, and particularly discloses a non-contact high-temperature detector based on a visible light spectrometer and a full-spectrum method, which comprises a full-spectrum collection system and a spectral analysis system. The output end of the full-spectrum collection system is connected with the input end of the spectral analysis system; the light splitting module is used for acquiring spontaneous radiation light of a to-be-detected target and carrying out dispersion light splitting on the radiation light to obtain visible light full-band spectral data; and the spectral analysis system is used for analyzing and processing the visible light full-band spectral data to obtain the spectrum of the to-be-detected target, and obtaining the temperature based on the spectrum of the to-be-detected target, so that the precision of temperature detection can be remarkably enhanced.

The invention discloses a territorial space dynamic remote sensing monitoring system and method based on spectral analysis, and relates to the technical field of remote sensing monitoring. A characteristic wave band extraction module; the image fusion and ground object monitoring module is used for fusing the feature data of each wave band by using an image fusion algorithm, identifying the fused ground object type and monitoring the change of the ground object type along with time; and a change monitoring and visualization module. According to the method, the remote sensing images of different spectrum sections are acquired and corrected, so that the accuracy and consistency of data are remarkably improved, the accuracy of a monitoring result is ensured, and a solid data basis is provided for subsequent analysis.

The invention discloses a coal quality online detection multispectral analysis system and analysis method, and belongs to the technical field of coal quality detection. Comprising a detection table, a crushing barrel, a supporting frame, a spectrum detector and a conveying belt are installed at the top end of the detection table, the conveying belt is driven by a second motor, and a flattening assembly, a collecting guide plate, a collecting box and a smoothing assembly are arranged at the top of the conveying belt. Through cooperation of the crushing cylinder and the flattening assembly, coal samples can be conveyed to the conveying belt after being crushed, the coal samples are automatically flattened and concentrated so that the coal samples can be conveyed into a spectrum detector to be subjected to spectrum analysis through a laser head, automatic operation is achieved in the whole process, rapidness and convenience are achieved, the detection efficiency is high, manual participation is not needed, and the labor intensity of workers is reduced. Meanwhile, through cooperation of the collecting box, the collecting guide plate and the smoothing assembly, the detected coal samples can be collected and stored in a targeted mode, and waste of coal resources is avoided.

The invention discloses a rice-fish symbiosis multifunctional water quality monitoring and analyzing device and a use method. The device comprises the following core modules: a data acquisition module, which is responsible for acquiring water quality data from front-end monitoring equipment in real time; the data processing module is used for deeply processing the collected water quality data and image information; the data analysis module is used for introducing a prediction model based on machine learning and accurately predicting a future water quality change trend in combination with historical water quality data and real-time monitoring data; the intelligent decision support module is responsible for automatically identifying potential risks in water quality and generating targeted intelligent decision suggestions according to the potential risks; according to the method, image recognition and spectral analysis technologies are introduced, a high-definition camera, a precise underwater spectrometer and other equipment are utilized, and a deep learning algorithm and a spectral database are combined, so that precise recognition and classification of suspended solids, algae and other biocenosis in water are realized, and more comprehensive and multi-dimensional information is provided for water quality evaluation.

The invention belongs to the field of industrial automation, and discloses an industrial cleaning system based on multi-mode sensing and intelligent regulation and control. By integrating various sensors, accurate stain identification is realized by applying multi-modal sensing and spectral analysis; cleaning path intelligent planning is carried out by means of topological optimization and reinforcement learning; intelligent regulation and control of cleaning parameters are achieved through self-adaptive control and model prediction; a multi-robot collaborative cleaning system is constructed to process large-scale complex objects; and an intelligent monitoring and management platform is built. The invention aims to solve the problems of inaccurate stain identification, unreasonable path planning, unintelligent parameter regulation and control and the like in industrial cleaning, improve the efficiency, quality and adaptability of industrial cleaning and meet the requirements of modern industrial production.

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 invention relates to the technical field of mathematical modeling, in particular to a mathematical model modeling method based on data analysis operation processing. The method comprises the following steps: collecting metal pollution data of a target water area by using a multi-parameter water quality sensor array; performing time sequence alignment on the metal pollution data to generate a standardized pollution data set; constructing a metal migration model based on fluid dynamics and chemical adsorption coupling; inputting the standardized pollution data set into the metal migration model for three-dimensional space interpolation calculation, and generating a metal pollutant diffusion cloud picture; pollution source reverse analysis is carried out on the metal pollutant diffusion cloud picture, and a potential pollution source coordinate set and pollution contribution degree sorting are generated. According to the method, by integrating multi-parameter water quality sensor data, fluid dynamics and chemical models and multispectral analysis, the modeling precision of the water pollution diffusion mathematical model is improved.

The invention relates to the technical field of natural language processing, and discloses a generated text quality processing method and system based on a large language model, and the method comprises the steps: constructing a logic topological graph and a Laplacian matrix of an original generated text, and extracting a feature value sequence; recognizing a logic bearing wall based on the attention gradient and generating an anti-fact contrast text; calculating a logic collapse index by combining the difference between the map characteristic values of the original text and the contrast text and the logic polarity overturning condition; and judging the quality of the generated text according to the logic collapse index, blocking the text which does not pass the judgment, and shaping and resampling the output probability value of the error position by utilizing the spectrum difference information to generate a new text. The method does not need to depend on an external knowledge base, quantifies the stability of text logic through anti-fact interference and spectrum analysis, and identifies a high-risk illusion text; and a wrong logic path is automatically corrected through Logits shaping, so that the logic self-consistency of the generated content is improved on the premise of ensuring the semantic smoothness of the text language.

The invention relates to the technical field of spectral analysis and concentration quantitative detection, and discloses an LIBS quantitative analysis method, device and system based on a thin plate spline regression algorithm. The method comprises the steps that multiple sets of sample data are obtained, and each set of sample data comprises corresponding target sample concentration, laser energy and spectral intensity; constructing a concentration quantitative model about the concentration, the laser energy and the spectral intensity based on a spline function; determining an optimal penalty parameter by adopting a cross validation method; based on the optimal penalty parameter, solving parameters of the concentration quantitative model through a minimization objective function to obtain a constructed concentration quantitative model; and performing quantitative analysis on a sample with unknown concentration by using the constructed concentration quantitative model. Thus, the output energy of the laser is incorporated into the core model, and an I-E-C nonlinear coupling relationship is constructed, so that the problem of calibration deviation caused by laser energy fluctuation is reduced, and the precision improvement of concentration quantification in a complex scene is realized.

The invention relates to the technical field of infrared spectroscopic analysis, in particular to a spectroscopic analysis-based corn kernel nutrient abundance and deficiency state evaluation method, which comprises the following steps of: irradiating corn kernels by using near-infrared and mid-infrared light sources, collecting reflected spectral signals, and recording spectral signal data; and based on the spectral signal data, extracting key wavelength region data to obtain key spectral data. According to the invention, the near-infrared light source and the mid-infrared light source are combined, so that the capability of acquiring nutrient information of the corn kernels is enhanced, the reflection characteristic of spectral signals is utilized, the interference of environmental factors on data acquisition is reduced, and the stability of spectral data is improved. The screening of key wavelength regions reduces irrelevant information, so that the spectral data is more concentrated on the characteristic wave bands influencing the nutrient state. The spectral signals are processed through wavelet transform, the resolution ratio of the characteristic peak value is improved, the local change trend of the spectral signals is enhanced, and characteristic extraction is more accurate.

The invention provides a bearing fault diagnosis method based on a one-dimensional local binary pattern and a Hankel matrix. The bearing fault diagnosis method comprises the following steps: acquiring a discrete vibration signal; performing first-order differential operation on the discrete vibration signal to obtain a differential signal; performing inherent time scale decomposition on the differential signal to obtain an inherent rotation component signal; performing quantization and signal reconstruction on each inherent rotation component signal by taking a root mean square as a quantization criterion of a one-dimensional local binary mode method to obtain a decimal feature signal; constructing a Hankel matrix of the decimal characteristic signal and performing signal reconstruction according to a covariance matrix of the Hankel matrix; performing spectral analysis on the reconstructed signal, calculating the fault characteristic frequency of the bearing, and then judging the state and the fault type of the bearing through a frequency component obtained through spectral analysis and the fault characteristic frequency of the bearing obtained through calculation. According to the bearing fault diagnosis method, noise can be effectively suppressed, the bearing fault feature information can be effectively extracted, and the bearing state and the fault type can be accurately identified.