464 results about "Spectral analysis" patented technology

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 discloses a temperature and gas detection system and method based on an ultraviolet to near infrared spectrum, relates to the technical field of spectral measurement, and solves the technical problems that the space in a smelting furnace is occupied and the complexity of the system is increased due to the fact that two sets of independent equipment are adopted for temperature measurement and environment monitoring in the smelting process respectively in the prior art. The method comprises the following steps: acquiring spontaneous radiation light of a to-be-detected target; carrying out dispersive light splitting on the spontaneous radiation light to obtain spectral data of a full wave band from ultraviolet light to near-infrared light; performing denoising processing on the spectral data to obtain a spectrum of the to-be-detected target; obtaining temperature data according to the spectral analysis of the to-be-detected target; calculating based on the spectrum and a pre-established spectrum database to obtain gas concentration; the temperature of the to-be-measured target can be accurately measured, and the measurement accuracy is improved.

The invention mainly relates to the technical field of spectral analysis, and provides a white spirit brewing spectral analysis method and system based on a multi-modal large language model in order to efficiently and accurately obtain white spirit brewing information according to multi-modal spectral data of all links of white spirit brewing. The method is characterized by comprising the following steps of: collecting multi-mode spectrum data including gas chromatography data, infrared spectrum data and Raman spectrum data in each link of white spirit brewing; converting the multi-modal spectral data and the natural language instruction into a feature embedding vector which can be recognized by a large language model to train the large language model which is finely adjusted based on professional knowledge in the field of white spirit, and receiving a question of a user in a natural language instruction form by the trained large language model; wine making analysis tasks such as flavor compound identification, key microorganism detection, fermentation state evaluation and base wine quality grading are carried out, and finally analysis results and wine making process optimization suggestions are output in a natural language form, so that the accuracy and efficiency of spectrum data interpretation in the white wine making process are improved.

The invention discloses a Raman spectrum joint classification and quantification method based on a prior fusion double-flow network, and belongs to the technical field of spectral analysis and chemometrics. The method comprises the following steps: acquiring Raman spectrum original data, and preprocessing to obtain standardized spectrum data; raman spectrum prior information is extracted, and a prior information double-flow network containing classification and quantitative flow sub-networks is constructed; standardized data and prior information are input, classification preheating-combined fine tuning training is adopted, conflict is relieved by combining dynamic weighting and PCGrad, and samples are expanded through a physical consistency strategy synchronously; processing unknown samples by using the trained network, and outputting categories and component contents; according to the method, weak peak information is mined through double-flow architecture and cross-flow attention, derivative noise is suppressed, classification-quantification performance is balanced through double-task optimization, and small sample overfitting is solved through physical expansion; on a polycyclic aromatic hydrocarbon data set, classification and regression indexes are superior to those of a traditional method, the low-concentration / weak-peak scene precision and robustness are better, and the engineering application prospect is good.

The invention relates to the technical field of gas monitoring, and provides a spectrum analysis-based gas leakage monitoring system, which comprises a tunable laser source, an optical curtain forming device, a scanning optical sensor, a spectrum analysis module, a three-dimensional spectrum model construction module, a leakage traceability and diffusion prediction module, an intelligent early warning module and a snapshot type multispectral imaging unit, and an open optical path and a distributed multi-node layout are adopted. According to the invention, through the steps of optical screen scanning, spectral data acquisition and processing, three-dimensional dynamic model construction, intelligent analysis leakage determination, leakage traceability visualization and adaptive learning, precise monitoring, identification, traceability and early warning of gas leakage are realized; gas identification, leakage traceability and diffusion prediction are realized based on a three-dimensional dynamic model, deep learning and fluid mechanics, the monitoring effect is optimized by adopting distributed layout and self-adaptive learning, rapid disposal is assisted by graded early warning, and the accuracy, real-time performance and safety of gas leakage monitoring are greatly improved.

The invention relates to the technical field of spectral analysis and biomedical detection, and discloses an SERS (Surface Enhanced Raman Scattering) spectrum quantitative detection method and system based on interpretable stacked ensemble learning. The method comprises the following steps: acquiring SERS spectral data of serum tumor marker standard substances with different concentration gradients; performing baseline correction and normalization preprocessing on the data, performing sparse feature selection by using an LASSO algorithm, and screening out key spectral features to construct a sample data set; constructing an interpretable stacking integration model, wherein the model adopts a base learner layer and a meta learner layer; training the model by using the training set, optimizing model hyper-parameters by using a cross validation strategy, and establishing a mapping relationship between spectral features and tumor marker concentrations; and collecting SERS spectral data of a to-be-detected serum sample, extracting key spectral features, inputting the key spectral features into the trained interpretable stacked integrated model, and outputting a concentration predicted value of the tumor marker in the to-be-detected serum sample. The method has the advantages of high precision, universality and molecular level interpretability.

The invention belongs to the technical field of optical signal collection and imaging, and particularly relates to a multi-path nonlinear optical signal collection device. The adjusting table is used for bearing a to-be-detected sample and adjusting the spatial pose of the to-be-detected sample, the laser emitting module is used for emitting laser, the to-be-detected sample is excited by the laser to obtain a nonlinear optical signal, the nonlinear optical signal is incident to the overturning mirror through the reflection transmission unit or the transmission transmission unit, and the overturning mirror is used for adjusting the spatial pose of the to-be-detected sample through angle adjustment. The nonlinear optical signal is reflected to a first signal processing module or a second signal processing module, and the first signal processing module processes the nonlinear optical signal from the reflection transmission unit or the transmission transmission unit to obtain a spectral analysis processing result; and the second signal processing module processes the nonlinear optical signal from the reflection transmission unit or the transmission transmission unit to obtain an electric signal processing result. The device is used for collecting non-linear optical signals of far-end detection optical paths of reflection, transmission, oblique incidence and the like.

The invention relates to the technical field of medical detection and spectral analysis, and discloses a spectrum detection method for microorganisms on a wound surface based on knowledge data dual-drive envelope fusion. The method comprises the following steps: firstly, acquiring a multi-band near-infrared spectrum signal of a wound surface through a fiber optic spectrometer, and carrying out normalization and smooth filtering pretreatment; analyzing optical characteristic parameters based on a diffuse reflection theory model, constructing a knowledge-driven characteristic sample space, extracting spectral high-order characteristics by using a one-dimensional deep convolutional neural network, and constructing a data-driven characteristic sample space; then generating a grain envelope sample through a double-flow feature fusion mechanism; and finally, constructing a lightweight dual-task classification model based on the particle envelope sample, and realizing rapid detection of fungal infection of the wound surface and identification of seven common bacteria. According to the method, the interpretability, the recognition precision and the robustness of characteristic pathology are considered, and an efficient and reliable technical scheme is provided for early clinical diagnosis of wound infection.

The invention provides a spectrograph, a spectrum reconstruction method and device and electronic equipment, and relates to the field of spectrum analysis. The spectrograph comprises a chip and a peripheral circuit, the chip comprises a photoelectric response layer, a gate layer and an electrode lead-out layer, and a target semiconductor is arranged in the photoelectric response layer; the photoelectric response layer is used for converting an incident spectrum into a photocurrent signal; the gate layer is used for changing the photoelectric response characteristic of the target semiconductor and covers the upper and lower surfaces of the photoelectric response layer; the electrode lead-out layer is used for outputting the photocurrent signal to external equipment, and the external equipment is used for reconstructing an incident spectrum according to the photocurrent signal and the photoelectric response characteristic of the target semiconductor; the peripheral circuit is used for maintaining the chip to work. According to the invention, the semiconductor with adjustable photoelectric response characteristics is used as the core of the photoelectric response layer, so that the spectrometer has the spectral analysis capability from infrared to terahertz wave bands, does not need to depend on a huge optical assembly and a precise mechanical displacement assembly, and can effectively solve the problems in the prior art.

The invention belongs to the technical field of circuit board detection, and particularly relates to a method for detecting, identifying and analyzing residues in a circuit board cavity, which comprises the following steps of: positioning suspected residual areas according to the gray difference of an X-ray image, generating a space coordinate set of the suspected residual areas, screening out a to-be-analyzed area set by combining chemical chromogenic reaction intensity and X-ray contour features, and analyzing the to-be-analyzed area set. The method comprises the following steps of: obtaining a plurality of regions, dividing the regions into a plurality of subsets according to spatial diffusion characteristics, obtaining element and functional group data of each region through energy spectrum and spectral analysis, analyzing candidate residue categories, and dynamically adjusting subset division based on the consistency of the candidate categories in the subsets until the consistency requirement is met. And finally, outputting a specific residue category for each region according to a division result. According to the method, dynamic subset division is carried out according to the space congruency and the contour features, systematic distinguishing and classification of diffusion type residues and attachment type residues are achieved, and the accuracy of category judgment is improved.

The invention discloses a traditional Chinese medicine cardiovascular health assessment method based on tongue picture image recognition, and the method comprises the following steps: collecting original tongue picture image data, and carrying out the image preprocessing; collecting lingual surface spectrum original data, and executing spectrum preprocessing; extracting features of the standardized tongue picture image data, and generating tongue picture image feature vectors; extracting features of the standardized lingual surface spectral data, and generating lingual surface spectral feature vectors; constructing multi-modal hidden space representation of the tongue picture image feature vector and the tongue surface spectrum feature vector, and executing feature decoupling and feature fusion processing; establishing a mapping relation between the tongue picture-spectrum fusion implicit vector and a cardiovascular health index, and generating a cardiovascular risk prediction vector; and generating a cardiovascular health risk assessment result. According to the invention, tongue image and spectral analysis are fused, cardiovascular health assessment is realized, and the method has the advantages of high accuracy and high stability.

The embodiment of the present application provides a kind of based on the method and system for detecting the quality of dairy product of spectral analysis, belong to optical detection technical field.The method comprises: the sample of the dairy product to be detected is introduced into thermostatic colorimetric cell, utilizes modulated light source to emit incident light, while collecting original spectrum signal in sample channel and reference channel;Based on original spectrum signal, generate correction spectrum, and input correction spectrum into integrating sphere to collect full direction transmission light, obtain total transmission spectrum;Based on the scattering distribution data of different angles obtained by arranging annular multi-angle detector array on the outer wall of thermostatic colorimetric cell;Based on scattering distribution data and total transmission spectrum, construct scattering compensation factor, and make scattering compensation factor act on total transmission spectrum to obtain effective absorption spectrum;Effective absorption spectrum is input into component decomposition model, and the content of each target component of the dairy product to be detected is output.The present application scheme realizes the rapid, nondestructive and high-precision detection of the content of key components of dairy product.

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. Modulation codes can optionally employ a clock divider, such as which can be configured to satisfy one or more specified constraints.

The invention provides an intelligent cashmere doping content judgment system utilizing multi-mode Raman spectrum characteristics, and relates to the technical field of intelligent spectrum analysis. The cashmere doping content intelligent judgment system utilizing the multi-mode Raman spectrum characteristics comprises a data acquisition module, a multi-wavelength laser excitation unit is used for carrying out space scanning on a sample, time sequence Raman spectrums under different polarization configurations are synchronously acquired, and the time sequence Raman spectrums are analyzed; multi-dimensional original data including space coordinates, a time sequence, spectral intensity and a polarization state is formed, and an original multi-modal data set is generated. Through multi-wavelength laser space scanning and polarization time sequence synchronous acquisition, a multi-dimensional original data set containing space coordinates, a time sequence, spectral intensity and a polarization state is constructed, the dimension limitation of a traditional spectrum is broken through, and a three-dimensional convolution kernel is adopted to perform synchronous sliding processing in space and spectrum dimensions, so that the spectrum intensity and the polarization state are obtained. And the spatial distribution rule and multi-mode spectrum correlation characteristics of the doped region are directly captured.

The application discloses a kind of microscale single particle quantitative analysis method and system based on nano-silver signal enhancement, it is related to spectral analysis technical field, the method includes: using laser-induced breakdown spectroscopy system to the carbon black particle target sample is carried out spectral data acquisition, obtains target full spectrum data and is pretreated, and then obtains target spectral data matrix;Variable importance measurement algorithm and continuous projection algorithm are used to the variable in the target spectral data matrix is screened, and target variable is obtained;Target variable is input into carbon black particle metal content prediction model, and the content of metal element in carbon black particle target sample is obtained.The application can quickly and accurately determine the content of metal element in carbon black particle target sample.

The invention belongs to the technical field of oil and gas reservoir engineering and multiphase seepage dynamic monitoring, relates to a characterization method of seepage behaviors of multiphase fluid in a porous medium, and particularly provides a dynamic construction method of a multiphase displacement capillary pressure-saturation relationship based on nuclear magnetic resonance. Comprising the following steps: S1, acquiring a low-field nuclear magnetic resonance relaxation spectrum of a porous medium in a gas-water two-phase displacement process; s2, determining two-phase fluid interfacial tension, a wetting contact angle and a rock surface relaxation rate; s3, determining the dynamic cut-off relaxation time of the displacement stage; s4, calculating equivalent capillary pressure; the residual water saturation is calculated, and the equivalent capillary pressure is the capillary pressure corresponding to the current displacement stage and is used for constructing a capillary pressure-saturation curve. According to the method, the capillary pressure change in the porous medium can be obtained only through spectral analysis without a centrifugal or mercury injection experiment.

The invention discloses a geological rock rare earth element quantitative analysis method and system based on laser-induced breakdown spectroscopy, and belongs to the technical field of element spectral analysis, and the geological rock rare earth element quantitative analysis method comprises the following steps: constructing a dual-acquisition system to obtain a spectral signal and a plasma image of a rock sample, and preprocessing the spectral signal and the plasma image; determining rock lithology through a classification model, calculating plasma temperature based on the spectral signal, calculating brightness based on the plasma image, and correcting the plasma temperature; and constructing a multi-category bimodal quantitative analysis model, and inputting the lithology, the corrected temperature and the preprocessed image to complete quantitative analysis. The system comprises a data acquisition subsystem and a quantitative analysis subsystem, and data acquisition time sequence consistency and analysis reliability are guaranteed. According to the method, a bimodal data architecture is adopted, temperature correction and an advanced feature fusion network are combined, the limitation of single spectral data is broken through, the analysis precision and stability are improved, and popularization and application in geological exploration scenes are facilitated.

The invention provides a palm vein high-security identity authentication system based on spectral analysis and deep learning, and relates to the technical field of biological recognition, the system comprises a hardware layer, a data processing layer, an algorithm layer and an application layer, through an antagonistic living body detection module in the algorithm layer, in combination with physical modeling and a GAN (Generative Adversarial Network), the high-security identity authentication of a complex imitation is realized. The method comprises the following steps: carrying out effective identification of a high-biomimetic material, carrying out physical modeling to analyze the absorption characteristics of palm veins in a hyperspectral range, such as the difference between oxyhemoglobin and oxyhemoglobin and periodic spectrum micro-interference caused by heart rate, and providing living body evidence based on physiological dynamics; meanwhile, a high-simulation template sample generated by the GAN is used for training a discriminator, the insight ability of the system to a template means is further enhanced, and compared with single traditional texture or static signal detection, the system combines physical characteristics with deep learning, and the confidence coefficient of in-vivo detection reaches up to 99.99%.

The invention relates to the technical field of spectral analysis, and particularly discloses an underwater intelligent detection machine system and method based on laser-induced breakdown spectroscopy, and the system comprises an underwater carrying platform, an orthogonal double-pulse laser-induced breakdown spectroscopy probe, a multi-sensor cooperative sensing module, a mechanical arm, a central control unit and a water surface control station. The method comprises the following steps: a first pulse vertically builds bubbles, and a second pulse parallelly enters the bubbles and is triggered according to delay time; the central control unit maintains a working distance of 1-2cm based on a distance measurement closed loop, and adaptively adjusts delay time and energy in combination with water depth pressure and cavitation feedback; and the water surface control station carries out denoising normalization on the spectrum calibration and carries out element qualitative and quantitative operation to generate an element distribution atlas. According to the method, plasma is excited in a cavitation bubble gas environment through orthogonal double pulses, and in-situ real-time high-sensitivity element detection of an underwater target is realized in combination with distance closed loop and parameter self-adaption.

The invention discloses a GIS latent defect diagnosis method and system based on multi-source signal fusion, and belongs to the field of power equipment fault diagnos.The method comprises the steps that a UWB sensor is adopted to monitor PD signals in a GIS, real-time filtering and spectral analysis are conducted on the signals in combination with an FPGA preprocessing chip, and the sampling sensitivity is dynamically adjusted according to the PD signal strength; gIS internal ultrasonic source three-dimensional imaging is realized by adopting a three-dimensional ultrasonic array; pD and ultrasonic signals are processed based on wavelet transform, PD and ultrasonic data are processed based on CNN, and a signal time sequence is processed by LSTM; constructing a GIS fault knowledge graph, setting expert rules, and calculating a GIS fault occurrence probability by adopting Bayesian reasoning; and predicting a GIS fault evolution path according to the Markov chain model, and performing dynamic adjustment in combination with reinforcement learning. According to the method, the fault state transition model of the GIS equipment is established based on the Markov chain, the evolution probability between different fault states is quantified, long-term steady-state fault risk assessment is provided, and the operation health condition of the GIS equipment can be prejudged.

The invention relates to an intelligent optimization method and system for LED light source module chip mounting in the new generation information technology field, and the method comprises the steps: collecting the initial spectrum data and position coordinates of each chip through a wafer scanning device, processing the data through a spectral analysis method, and obtaining a preliminary photoelectric parameter distribution diagram of each chip; collecting overall photoelectric response data from the mounted module substrate, analyzing color distribution by adopting an image processing technology, and if an analysis result shows a non-uniform area, marking the area to obtain a list of chips needing to be adjusted; according to the chip list needing to be adjusted, the genetic algorithm is input again for local optimization, the algorithm considers the characteristics of adjacent chips, whether the uniformity threshold value is met after optimization is judged, and a final adjustment scheme is determined; and guiding the laser lift-off equipment to remove and re-mount the specified chip through the final adjustment scheme, and collecting verification data after re-mounting to obtain the display module meeting the color consistency standard.

The invention discloses an anti-caries early warning method and system based on oral health detection, and belongs to the technical field of data processing. The method comprises the following steps: acquiring and processing target detection data to generate a clear enamel demineralization feature image and a buffer capability score; analyzing pixel-level fluorescence intensity distribution by adopting a convolutional neural network model, determining a quantized value of the enamel demineralization degree, and further determining a comprehensive dental caries risk level category; judging a risk change trend according to the historical target detection data sequence to obtain a dynamic evaluation update value; a data fusion algorithm is adopted to obtain a final decayed tooth risk assessment index, and risk early warning information is generated according to the early warning template; by integrating fluorescence imaging, an image enhancement algorithm, a convolutional neural network, spectral analysis, k-means clustering and time sequence analysis, a complete process from enamel demineralization feature extraction to comprehensive risk assessment is constructed, the accuracy and individuation level of decayed tooth risk assessment are remarkably improved, and a scientific basis is provided for early prevention and intervention.

The invention discloses a spectral analysis-based PCB chemical preparation ratio intelligent adjustment method and system, and relates to the technical field of industrial data processing. The method comprises the following steps: monitoring a cleaning solution flowing through a PCB cleaning tank in real time to obtain continuous spectrum time sequence data; analyzing in real time to obtain a copper ion concentration value sequence and a corrosion inhibitor concentration value sequence, and calculating a copper ion concentration change rate and a corrosion inhibitor concentration change rate in real time; analyzing the real-time change trend of the copper ion concentration value and the corrosion inhibitor concentration value, and analyzing the real-time numerical relationship between the copper ion concentration change rate and the corrosion inhibitor concentration change rate; judging whether a harmful side reaction which mainly consumes the corrosion inhibitor occurs or not; and when the harmful side reaction is judged to occur, inputting the copper ion concentration change rate and the corrosion inhibitor concentration change rate into a pre-trained proportioning optimization model, and outputting to obtain an acid agent adjustment amount and a corrosion inhibitor adjustment amount. According to the invention, the precise, real-time and intelligent adjustment of the proportion of the PCB chemical agent is realized.

The application discloses a kind of light field regulation detection method and device based on computing spectral reconstruction, belong to spectral analysis technical field.The method is by introducing medium-metal composite nanostructure, integrates metal nanoisland array and silicon nitride nanocolumn array on quartz substrate, realizes electric field enhancement using the local surface plasmon resonance effect excited by metal nanoisland array, simultaneously realizes wide spectral range phase coding by medium nanocolumn array;Super surface chip is integrated with single point detector, combines pre-calibrated super surface sensing matrix A and enhancement factor matrix E, and reconstructs spectrum using compressed sensing or Tikhonov regularization algorithm.The application keeps the advantages of miniaturization and no moving parts, realizes directional high sensitivity detection to weak signal, and signal-to-noise ratio is improved.

The invention discloses a quality parameter detection model construction method based on multi-source domain adaptation, and relates to the technical field of spectral analysis, the method comprises the following steps: constructing a quality parameter detection basic model, and constructing a multi-source spectral data set for training the quality parameter detection basic model; during model training, extracting source domain features and domain invariant features of the spectral data, and respectively predicting to obtain a source domain prediction result and a domain invariant prediction result; dynamically adjusting the contribution weight of each source domain training set to model training based on the performance of the domain invariant prediction result on each source domain training set; and determining a loss function of model training based on the domain invariant feature, the source domain prediction result and the domain invariant prediction result in combination with the contribution weight, and training by using the loss function to obtain a quality parameter detection model. According to the method, the problem of multi-source spectrum cross-domain regression prediction can be effectively solved under the condition of no target domain spectral data, and the robustness and accuracy of quality parameter detection are improved.

The invention relates to the technical field of food safety detection and spectral analysis, in particular to a urine prohibited substance detection method based on double-line spectrum and contrastive analysis clustering, which comprises the following steps: respectively acquiring reflection spectrum signals of a T region and a C region of a detection card under different wavebands by using two spectrum sensors; respectively carrying out temperature compensation, denoising processing, smoothing processing and normalization processing on the reflection spectrum signals of the two regions; calculating a spectrum difference and a peak value ratio according to the reflection spectrum signals of the T region and the C region under different wavebands; judging whether the spectrum difference is smaller than a set threshold and the peak value ratio is smaller than the set threshold, and if not, outputting that the detected substance does not exceed the standard; otherwise, the detected substance exceeds the standard, and the concentration corresponding to the current spectrum difference is searched according to the concentration-spectrum difference mapping table. According to the invention, a detection result is automatically judged by a threshold difference analysis method, so that high-throughput, low-cost, rapid and objective field screening capability is realized.

The invention provides an intelligent dry fish maw quality grading method and system based on spectral analysis, and the method comprises the steps: collecting the spectral data of a target dry fish maw in a specified wavelength range, processing the spectral data based on a pre-constructed prediction model, and determining the quality index of the target dry fish maw; the quality index comprises the collagen content; obtaining a visual image of the target dried fish maw, and performing feature extraction on the visual image to obtain surface feature parameters of the target dried fish maw; and based on a weighted scoring model, performing grade classification on the target dried fish maw according to the quality indexes and the surface feature parameters, and determining a target grade of the target dried fish maw. Manual participation can be reduced, and the quality grading efficiency and accuracy of the dried fish maw are improved.

The invention discloses an on-chip super-continuum spectrum light source based on a cascaded polarized ferroelectric material and a design method thereof. The pump light source is used for providing pump light; the pump light enters the super-continuum spectrum generation waveguide; the super-continuum spectrum generation waveguide meets the requirements that the group velocity dispersion is close to 0 under the wavelength of the pump light, the group velocity mismatch of the wavelength of the pump light and the wavelength of the frequency doubling light is close to 0, and the polarization period of the super-continuum spectrum generation waveguide meets the phase matching of frequency doubling. The polarization period of the super-continuum spectrum expansion waveguide changes along the propagation direction, and the super-continuum spectrum expansion waveguide outputs a super-continuum spectrum; the conical transition waveguide is used for connecting the super-continuum spectrum generation waveguide and the super-continuum spectrum expansion waveguide and eliminating mode mismatch between the super-continuum spectrum generation waveguide and the super-continuum spectrum expansion waveguide. The system has the advantages of low cost, high integration level and wide spectrum coverage, and is suitable for the fields of environmental monitoring, medical diagnosis, spectral analysis and the like.

The invention discloses a preparation method of an MF (at) NH2 (at) Ag SERS (Surface Enhanced Raman Scattering) sensor for detecting paraquat and diquat, belongs to the field of nano material synthesis and spectral analysis, and solves the problems that a traditional detection method is complicated in process and cannot detect pesticides and free radicals thereof at the same time, and a traditional SERS substrate is poor in enhancement effect and low in stability. The preparation method comprises the following steps: firstly segmenting melamine foam, cleaning and drying, soaking in hydrochloric acid, soaking in a polyethyleneimine aqueous solution to modify amino, drying, soaking in a silver nitrate solution for ultrasonic treatment, adding a sodium borohydride solution under an ice bath, stirring and reducing, and finally cleaning and drying to obtain the sensor. The sensor takes three-dimensional melamine foam as a support, silver nanoparticles are uniformly loaded on the surface of the three-dimensional melamine foam, paraquat, aquacide and free radicals thereof are enriched through electrostatic interaction based on a localized surface plasma resonance principle, and Raman signal enhancement is realized. The detection limit is 10 <-9 > mol / L, the signal stability and repeatability are excellent, the applicability to actual water sample detection is good, the detection of other pesticides can be expanded, and the method is suitable for on-site rapid trace detection.

The invention belongs to the technical field of spectral analysis, and particularly relates to a pharmaceutical volatile oil extraction interface detection method and system based on spectral analysis, and the method comprises the following steps: synchronously collecting water transmission light intensity and oil transmission light intensity at a plurality of vertical sampling heights, and combining initial calibration light intensity to obtain a phase state contrast; obtaining a scattering penalty weight in combination with the reference transmission light intensity; acquiring a compensation interface gradient based on the scattering penalty weight and the phase state contrast; performing global peak search on the compensation interface gradient to extract the interface height and the compensation interface gradient extreme value, and further obtaining the emulsification turbidity; and obtaining a safe oil collection index based on the set reference elevation, the interface height and the emulsification turbidity, and carrying out automatic liquid outlet control based on the safe oil collection index. According to the method, physical noise is effectively filtered to accurately lock a real chemical interface, a decision model of comprehensive purity and reserves is constructed, and the extraction purity and batch-to-batch stability of volatile oil are improved.