851 results about "Spectral analysis" patented technology

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 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 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.

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

The invention discloses a 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 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.

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

The invention belongs to the technical field of navigation satellite clock correction forecasting, and discloses a navigation satellite clock correction forecasting method based on inter-satellite measurement and a related device. The navigation satellite clock error forecasting method comprises the following steps: acquiring a navigation satellite clock error; based on the navigation satellite clock error, establishing a clock error forecast combination model; based on the clock correction prediction combination model, obtaining a navigation satellite clock correction prediction result of a selected time period in the future; wherein in the process of establishing the clock error forecasting combination model, trend term modeling is firstly carried out by using a quadratic polynomial, then periodic term modeling is carried out on residual data by using a Lomb-Scargle spectrum analysis method, and finally, trend term modeling and periodic term modeling results are linearly superposed to obtain the final clock error forecasting combination model. According to the technical scheme disclosed by the invention, satellite clock correction prediction can be supported when the navigation satellite operates autonomously or the ground operation and control service is interrupted, and meanwhile, the clock correction performance of the system in an autonomous operation mode can be improved.

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 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, and solves the problems that an existing chicken manure crude protein content detection technology is serious in pollution in the detection process, and a spectral analysis technology is serious in information redundancy during detection, high in cost and poor in model stability. The sample set comprises visible-near infrared reflection spectrum and crude protein content of chicken manure; obtaining three spectral indexes corresponding to the visible-near infrared reflection spectrum, the MSC pretreatment spectrum and the SNV pretreatment spectrum, and screening out the optimal wavelength position and the corresponding three spectral indexes; constructing a data set by using the three spectral indexes and the crude protein content, and performing fitting through multiple fitting modes; evaluating the plurality of fitting equations, and screening out an optimal fitting equation and a corresponding pretreatment method and wavelength position as a 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 relates to a soybean detection method and device based on a near infrared spectrum and a storage medium, and belongs to the technical field of spectral analysis. Index values of various classification indexes are extracted based on a sample near infrared spectrum of a soybean sample; determining the type of the soybean sample based on the index values of the various classification indexes; preprocessing the near infrared spectrum of the sample based on a preprocessing model corresponding to the type of the soybean sample to obtain a processed sample spectrum; detecting the processed sample spectrum based on a soybean detection model corresponding to the type of the soybean sample to obtain soybean detection parameters; the prediction accuracy and stability can be improved, the experience requirements on operators are reduced, and industrial application is easier.

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 belongs to the technical field of spectral analysis, and particularly discloses a Raman spectrum baseline correction method, device and system, a medium and equipment, and the method comprises the steps: obtaining a to-be-processed original Raman spectrum, recognizing trough points of the original Raman spectrum, and screening the recognized trough points to obtain baseline trough points; fitting the base line wave trough points to obtain a Raman spectrum base line; and correcting the Raman spectrum baseline to obtain a corrected Raman spectrum. Through multi-stage trough point screening and adaptive fitting, background interference can be effectively eliminated, and the signal-to-noise ratio and the characteristic peak fidelity of the Raman spectrum can be improved.

The invention discloses an online quality monitoring method and system for fine dried noodle production and a storage medium. The method comprises the following steps: acquiring initial optical characteristic data of surface and internal tissues of the fine dried noodles, acquiring multiband reflection and transmission light signals through spectral analysis equipment, and determining initial characteristic distribution of surface dispersion and internal pore structures of the fine dried noodles; light source parameters are adjusted according to the initial optical characteristic data, and optimized light source parameter configuration is obtained; applying the optimized light source parameter configuration to a detection area through an optical fiber light guide technology to obtain scattering spectrum data; processing the scattered spectrum data to obtain an enhanced spectrum signal; analyzing the enhanced spectral signal through a spectral decomposition algorithm, and determining the tissue uniformity distribution of the fine dried noodles; according to the fine dried noodle tissue uniformity distribution, judging fine dried noodle quality indexes by adopting a classification algorithm to obtain a quality evaluation result; and generating real-time feedback data of the fine dried noodle production line according to a quality evaluation result, integrating quality indexes of multiple batches of fine dried noodles, and determining production optimization parameters.

The invention relates to the technical field of microbiological detection, in particular to a public area microbiological pollution risk assessment system based on spectral analysis, which comprises a spectrum acquisition module used for acquiring a Raman spectrum curve of microorganisms contained in the air of a public area; the spectrum correction module is used for carrying out baseline correction on the Raman spectrum curve, specifically, baseline values of the Raman spectrum curve at all wavelengths are determined, and the corrected Raman spectrum curve is obtained; the feature analysis module is used for identifying the types of the microorganisms, specifically, the feature evaluation value of each wave crest is calculated, all the wave crests are distinguished, and a feature main peak and a feature shoulder peak are obtained; extracting characteristic peaks contained in the acromion; identifying the types of microorganisms in the air; and the risk assessment module assesses the microbial pollution risk of the public area. According to the method, misjudgment and missing detection caused by characteristic peak overlapping are reduced, and the accuracy of microorganism variety identification is improved, so that the pollution risk of microorganisms in a public area is accurately evaluated.

The invention provides an oil monitoring control method, device and equipment fused with machine learning and a medium. According to the method, a polarized light scattering image of oil is collected through a sensor, and an original data set with time aligned with voiceprint signals is generated; optical features are extracted for the polarized light image, and acoustic features are extracted for the voiceprint signal; optical and acoustic features are associated and fused, and a spatial-temporal feature vector is constructed; based on the fusion feature vector, the double-branch neural network outputs a bubble interference probability and a metal particle existence probability; outputting a real particle count based on the bubble interference probability and the metal particle existence probability; and verifying the real particle count through physical sorting and spectral analysis to obtain a verification result, and optimizing the double-branch neural network according to the verification result. The invention further discloses a corresponding device, equipment and medium. According to the invention, the industrial problem that bubbles are misreported as dangerous particles in oil circulation is solved, and the identification specificity and alarm reliability of wear particles under complex working conditions are improved.

The invention provides a vital sign detection method and device based on a millimeter wave radar, electronic equipment and a storage medium. The method comprises the following steps: obtaining a distance frequency domain signal based on a radar echo signal; performing spectral analysis on the distance frequency domain signal, positioning the position of the to-be-detected object, and extracting phase information at the position; filtering through a band-pass filter to obtain a breathing frequency band interval signal and an initial heartbeat frequency band interval signal; performing real-time respiration rate estimation based on the respiration frequency band interval signal to obtain a respiration frequency, and eliminating respiration harmonics in the initial heartbeat frequency band interval signal by using an adaptive wave trap based on the respiration frequency to obtain a heartbeat frequency band interval signal; and with the minimum fuzzy entropy as an optimization target, decomposing the heartbeat frequency band interval signal by using a variational mode decomposition algorithm to obtain a heartbeat signal, and carrying out real-time heart rate estimation based on the heartbeat signal to obtain a real-time heart rate. According to the method, the device, the electronic equipment and the storage medium provided by the invention, the accuracy of heart rate measurement can be improved.

The invention discloses a PVC film coating uniformity detection method based on spectral analysis, and relates to the technical field of optical detection, and the method comprises the following steps: S001, adopting a controllable incident illumination system with a multi-angle rotation function to carry out continuous angle scanning on the surface of a PVC film, collecting reflection spectrum intensity data in a corresponding wave band range according to different incident angles, and generating a spectrum data matrix containing interference characteristics; and S002, carrying out continuous wavelet transform processing on the spectral data matrix, carrying out multi-scale local decomposition on a spectral response signal, extracting and separating a periodic interference characteristic signal, and obtaining a purified spectral matrix after interference reduction. According to the method, through multi-angle spectrum collection and interference feature stripping, accurate identification of a coating thickness abnormal area and combination of strength indexes, spraying parameters are regulated and controlled, a detection-compensation-reinspection closed-loop mechanism is constructed, interference misjudgment is effectively avoided, and the accuracy and regulation and control efficiency of uniformity detection of the PVC film coating are improved.

The invention discloses a multi-station synchronous plastic cup spoon full-surface visual defect detection device and method, and relates to the technical field of visual detection.The multi-station synchronous plastic cup spoon full-surface visual defect detection device comprises a multi-station rotating platform, multiple sets of flexible jigs used for clamping parts to be detected, a multi-station detection module and a detection controller; the four detection modules sequentially cover surface texture, three-dimensional morphology, material characteristics and mechanical property full-dimensional defects of a to-be-detected part; 2D characteristics such as surface smudginess, scratches and printing defects are captured through a multispectral camera; the 3D point cloud reconstruction module identifies three-dimensional shape defects such as pits and deformation through laser or structured light scanning; the hyperspectral material detection module is used for identifying material variation and wear degree through spectral analysis; the dynamic stress test module detects hidden mechanical defects such as stress whitening and microcracks through a loading test.

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 provides a soil sampling device for soil analysis, and relates to the technical field of soil analysis. A depth sensing module, an intelligent sampling rod, an analysis and feedback module and a reinforcement learning module are integrated. The optimal sampling depth is predicted through the ultrasonic detection and near infrared scanning technology and the deep learning model. The intelligent sampling rod controls the sampling depth, the sampling amount, the vertical rotating force and the vertical rotating direction according to set parameters. The analysis and feedback module integrates various types of microsensors, monitors the pH value, conductivity and moisture of soil in real time, pretreats a sample, and obtains a soil component overview through spectral analysis. And the reinforcement learning module continuously updates and optimizes a sampling strategy through real-time data feedback and cloud computing. The display module visually displays real-time data, supports mode switching, planning and recording, and is simple and convenient to operate. According to the method, multiple methods such as deep learning and reinforcement learning are combined, the accuracy and efficiency of soil sampling are improved, and powerful technical support is provided for soil analysis.

The invention discloses a CAN bus fault diagnosis method and device, a medium and equipment in the technical field of CAN bus communication in the engineering machinery industry. The method comprises the following steps: acquiring operation data of a CAN bus of target engineering machinery; extracting multi-dimensional features including electrical layer features and protocol layer features based on the operation data of the CAN bus of the target engineering machinery; and inputting the multi-dimensional features into a pre-trained neural network model to enable the neural network model to generate a fault type probability value and a reconstruction error according to the multi-dimensional features, and determining the fault type and the specific position of the fault according to the fault type probability value and the reconstruction error. According to the method, various fault modes can be accurately identified, the accurate position and reason of the fault can be given, spectrum analysis is carried out in combination with a correlation analysis technology, the accurate position and reason of the fault can be finally given, and the operation reliability and stability of the CAN bus are improved.

The invention relates to the technical field of metal materials, and discloses a spectral analysis monitoring method for aluminum alloy component detection, and the method comprises the following steps: carrying out standardized surface pretreatment on an aluminum alloy sample; collecting spectral data of the sample by adopting a laser-induced breakdown spectroscopy technology; performing noise filtering and background correction on the spectral data through a wavelet transform algorithm; establishing a spectral feature database for element qualitative identification; carrying out element quantitative analysis by adopting an internal standard method, and calculating the content of each element through a calibration curve; comparing a detection result with a standard mark component range to generate component conformity evaluation; performing deviation analysis and performance prediction on samples which do not meet the standard; and finally, generating a quality grading report based on the multi-dimensional evaluation indexes. According to the method, the accuracy of component qualitative identification is ensured, the detection error is reduced, the repeatability and efficiency of detection are improved, and the effectiveness of risk management and control and the overall reliability of a detection result are improved.

The invention discloses a method for detecting pollutant concentration based on deep ultraviolet Raman spectrum of deep learning, and belongs to the technical field of spectral analysis. According to the method, aiming at the problems of spectral line overlapping interference, weak trace signals, strong background noise, nonlinear response and the like when a traditional deep ultraviolet Raman spectrum technology is used for detecting pollutants, high-precision quantitative analysis is realized by constructing an SSA-CNN-LSTM-Attention fusion model and combining a deep ultraviolet Raman spectrum pretreatment technology. The model adopts 1D-CNN to extract spectral local features, LSTM to capture sequence long-range dependence, an Attention mechanism to enhance feature peak response weight, SSA to optimize hyper-parameters, and a concentration prediction value is output through a weighted loss function optimization model. And meanwhile, by combining a synthetic data expansion strategy and environment matrix standard sample library training, the adaptability of the model to a complex scene is improved. According to the method, the anti-interference capability and generalization of new pollutant detection are effectively improved, and the method has important application value in the fields of environmental monitoring, industrial safety and the like.

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.