606 results about "Qualitative analysis" patented technology

A system and method for automated qualitative analysis of computerized conversational agent (CCA) conversational data. In an embodiment, the system comprises a system configuration manager for establishing CCA characteristics and factors to be included in analyses, a data ingestion engine for ingesting conversations from a CCA, a conversation indicator extractor for identifying and classifying key aspects of conversations, a fallback analyzer for identifying the type and frequency of CCA failures, a topic modeling engine for identifying trends in conversations, and a CCA score generator for generating score assessments for qualitative aspects of the CCA's performance.

The invention relates to a hydrogen energy system risk factor identification and accident severity prediction method fusing XGBoost-SHAP and LSTM, and belongs to the technical field of hydrogen energy safety. The method comprises the following steps: marking a plurality of hydrogen energy accident cause factors and hydrogen energy accident consequence grades of a hydrogen energy accident report data set; an XGBoost algorithm is adopted to construct a risk factor importance evaluation model, and a core risk factor is obtained in combination with an SHAP interpretability analysis method; an LSTM hydrogen energy accident risk severity prediction model is established, and rolling prediction of a hydrogen energy system risk level is realized according to core risk factor time sequence data; and judging whether the predicted risk level of the hydrogen energy system exceeds a threshold value or not, and if so, triggering graded early warning and outputting a key cause factor interpretation report. According to the method, the subjective deviation of traditional qualitative analysis is remarkably reduced, the capability of capturing the risk factor time sequence dependency relationship is enhanced, and technical support is provided for real-time risk early warning.

The invention discloses a quantitative analysis method for an artificial rain and snow enhancement effect based on fine calculation of an influence zone, which comprises the following steps: acquiring the influence zone in an artificial spreading catalyst diffusion zone according to artificial ice crystal concentration or silver iodide concentration; carrying out extraction and qualitative analysis on the radar products in the influence area; performing space-time matching on the influence area and the radar product, and calculating a mean value and distribution of radar parameters in the influence area; performing spatial distribution and time sequence change analysis on the radar parameters, and calculating parameter change and change rate of the radar product in the influence area; and quantitatively analyzing the parameter change and the change rate, and giving a rainfall enhancement operation effect. The method not only can improve the precision of quantitative analysis of the artificial rain and snow enhancement effect based on fine calculation of the influence area, but also has good interpretability, and can be directly applied to a quantitative analysis system of the artificial rain and snow enhancement effect.

The invention belongs to the technical field of medical informatization, and particularly relates to a medical insurance medication rationality interception system and method based on deep reinforcement learning and an FP-Growth algorithm, and the method comprises the following steps: S1, receiving electronic prescription data transmitted by a hospital HIS system in real time; s2, according to original diagnosis information in the prescription, performing standardization processing to obtain a diagnosis set; according to the original medicine information in the prescription, cleaning and integrating to obtain a medicine set; s3, based on an FP-Growth association rule algorithm, performing diagnosis-drug association analysis and drug-drug association analysis on the diagnosis set and the drug set, and identifying a low-association prescription according to a preset condition and marking the low-association prescription as a suspicious prescription; s4, starting deep reinforcement learning analysis on the suspicious prescription through DRL decision optimization, and outputting a prescription reinforcement learning feature set; and S5, outputting a prescription qualitative analysis result and a corresponding confidence score according to the prescription reinforcement learning feature set.

The invention discloses an intelligent on-line monitoring method and system for oil abrasion particles of hydraulic forcible entry equipment. The method comprises an on-line monitoring method and an on-line monitoring system. According to the online monitoring method, an electrostatic, inductive and optical multi-source sensor data acquisition module, a feature engineering processing module, a multi-modal feature fusion module, an abrasive particle quantitative and qualitative analysis module and an abrasion analysis module are included, and the online monitoring system comprises the online monitoring method, a monitoring sensor installation module and an abrasion intelligent decision-making module. According to the method, quantitative monitoring and abrasion mode analysis of metal abrasive particles, non-metal abrasive particles, ferromagnetic metal abrasive particles and non-ferromagnetic metal abrasive particles in oil of the hydraulic type forcible entry equipment can be synchronously completed through multi-mode fusion monitoring, online monitoring of the abrasion particles of the hydraulic type forcible entry equipment is achieved, and an intelligent maintenance decision is given.

The invention relates to the technical field of agriculture, in particular to a Raman spectrum and deep learning-based pesticide residue detection method and electronic equipment. The method comprises the following steps: acquiring Raman spectrum data of a sample to be detected; inputting the Raman spectrum data into a pre-trained qualitative analysis model, and outputting the category probability of the pesticide components in the to-be-detected sample; inputting the Raman spectrum data into a pre-trained quantitative analysis model, and outputting the content proportion of the pesticide components in the to-be-detected sample; wherein the qualitative analysis model realizes conjoint analysis of spectral global features and local features based on a multi-modal fusion deep learning network; the quantitative analysis model realizes spectral decomposition and concentration regression based on a deep learning architecture of multi-scale feature fusion. The detection defects of an enzyme inhibition method and a chromatography method are overcome, the problem that the potential mode of the Raman spectrum cannot be accurately and stably found through machine learning is solved, and higher detection precision and higher detection speed are achieved.

The invention discloses a new energy equipment sequence impedance multi-level frequency domain identification method, and relates to the field of power electronic equipment parameter and model frequency domain identification. Complex vector fitting is carried out on frequency domain response data of impedance measurement, an analytical model or an equivalent zero-pole model of power electronic equipment is obtained, and a basis is provided for grid-connected resonance stability analysis and impedance remodeling; comprising the following steps: S1, carrying out sequence impedance frequency scanning of single harmonic disturbance injection to obtain impedance frequency domain response data; s2, performing complex vector fitting to obtain a pole-residue type port frequency domain model; s3, evaluating'grey / black box 'characteristics according to the transparency of the control architecture information; s4, deducing a theoretical sequence impedance model of the grey box equipment, and identifying parameters based on a pole-residue expression; deducing an equivalent zero pole model for the black box equipment or the new energy cluster; compared with traditional qualitative analysis, accurate quantitative analysis and impedance remodeling are achieved, and analysis accuracy and engineering applicability are improved.

The invention discloses a building facade model monitoring method and system based on three-dimensional scanning, and relates to the technical field of building monitoring, and the method comprises the steps: collecting the three-dimensional data of a building facade, calculating the minimum sampling spacing between points, and screening a sparse point cloud set; and constructing a point cloud feature vector based on the sparse point cloud set, and constructing a label distribution model. According to the method, the point cloud data set with the label has the characteristics of complete semantic information and high classification accuracy, a reliable data basis is provided for subsequent building facade analysis and decision making, qualitative analysis can be carried out on the crack through point cloud segmentation and crack label identification, and the accuracy of classification is improved. In addition, quantitative evaluation of the crack can be assisted through a segmentation result, subsequent maintenance and decision making are facilitated, introduction of a local detail loss function is combined with a high-curvature point characteristic optimization model, and the feature extraction capacity of the local optimization model for the crack area is remarkably enhanced.

The invention belongs to the field of automatic testing, and particularly relates to an automatic testing system and method for aviation fly-by-wire flight control equipment, and the system comprises a system self-inspection unit, a system resource unit, a system interface adaption unit, and a system testing control unit. And the system self-checking unit controls the automatic test system to execute a self-checking function through operation software. The test efficiency and the test coverage rate can be remarkably improved, the problem of function test omission can be effectively solved, the fault of the tested airborne avionics equipment is analyzed by adding the YMECA fault analysis module and combining the definition event and the structure function of the fault tree, qualitative analysis is carried out on the fault, a professional basis is provided for fault diagnosis, the maintenance difficulty is reduced, and the test efficiency is improved. The aircraft maintenance guarantee efficiency is improved, and joint test of a plurality of airborne avionics devices and parallel test of a plurality of airborne avionics devices of the same model in a fly-by-wire flight control system of a certain type of fighter can be realized.

The invention discloses a law enforcement case qualitative analysis method based on a large language model, and belongs to the technical field of law enforcement informatization. The system mainly comprises a data preprocessing module, a law article and case retrieval module, a prompt template generation module, a large language model reasoning module, a consistency checking module and a manual auditing and writing-back module. The method comprises the following steps: S1, data preprocessing; s2, retrieving law provisions and cases; s3, a prompt template is constructed; s4, large language model reasoning; s5, checking the consistency; and S6, performing manual auditing and write-back. In the step S1, sensitive information such as a natural person name, an identity card number, a contact way, a family address and the like in a case text is subjected to anonymization replacement or hash processing. The law enforcement case qualitative analysis method based on the large language model achieves the effects of improving accuracy, consistency and interpretability.

The invention provides unfavorable geology real-time early warning equipment in a tunnel construction process and a use method thereof, and the unfavorable geology real-time early warning equipment in the tunnel construction process comprises a data collection module, a data processing and analysis module and a risk level establishment and early warning information issuing module, according to the unfavorable geology real-time early warning equipment in the tunnel construction process and the use method thereof, the data acquisition module and the data processing and analysis module are integrated, the analytic hierarchy process and the multi-stage fuzzy comprehensive evaluation method are combined, the limitation that a traditional evaluation method depends on experience judgment and qualitative analysis is overcome, and the early warning efficiency is improved. The influence of various risk factors on tunnel construction safety can be quantified more accurately, so that the accuracy of the evaluation result is improved, and the problems that the accuracy of the evaluation result is insufficient, early warning information is difficult to provide timely and effectively and the like due to the fact that the influence of various risk factors on the tunnel construction safety is difficult to quantify accurately in a traditional evaluation method are solved.

The invention discloses a method for determining volatile organic compounds in surface water by purging and trapping and gas chromatography-mass spectrometry, which comprises the following steps: acquiring mass spectrometry data of volatile organic compounds in a surface water sample, and separating and detecting a multi-component mixture in a complex matrix by gas chromatography-mass spectrometry, obtaining an original data set containing retention time and mass spectrum information; according to an output result of the classification model, carrying out qualitative analysis on unknown compounds in a to-be-detected sample, and if the classification probability is greater than a preset threshold value, determining the types of the compounds to obtain an accurate qualitative recognition result; and establishing a concentration standard curve by adopting a corresponding quantitative ion peak area according to a target compound confirmed in the qualitative identification result, and calculating a linear relation parameter between the peak area and the concentration to obtain a regression equation for quantitative analysis.

The invention relates to the technical field of high and steep slope dangerous rock mass exploration, in particular to a dangerous rock risk rapid quantitative evaluation method based on probability calculation. Comprising the following steps: S1, constructing a multi-level evaluation factor system of dangerous rock dangerousness, including three factor types of a basic geological environment factor, a dangerous rock falling rock collapse development factor and an induction factor; s2, performing quantitative analysis or qualitative analysis on the multi-level evaluation factor indexes according to the evaluation factor types, establishing a comprehensive evaluation system with subjective and objective fusion based on a system interaction analysis method, and calculating the weight of each evaluation factor; s3, constructing a dangerous rock danger calculation model based on a risk triangle theory, and inputting the weight of each evaluation factor into the dangerous rock danger calculation model to calculate a danger value; and S4, dividing danger levels of the dangerous rock mass according to the danger values, and outputting a visual evaluation result. According to the quantitative evaluation method provided by the invention, the danger of the high and steep slope can be quantified more accurately, and a basis is provided for engineering construction safety.

The invention discloses a test method for simulating rock hydraulic fracturing in a true triaxial stress state, and relates to the technical field of hydraulic fracturing, the test method comprises the following steps: S1, a sample preparation stage: processing a rock block into a standard square rock sample, and processing a plurality of hydraulic fracturing holes according to a test target size; s2, a sample mounting stage; s3, an equipment inspection stage; s4, a pre-load applying stage; s5, a hydraulic pressure applying stage of the hydraulic fracturing model: injecting hydraulic fracturing fluid with different flow rates into the hydraulic fracturing holes at different flow rates by utilizing a constant-flow constant-pressure pump through the hydraulic fracturing hydraulic pressure applying pipeline, and recording data and closing the constant-flow constant-pressure pump after the square rock sample is damaged; s6, an unloading stage; and S7, ending the test. According to the invention, a single hydraulic fracturing hole or a plurality of hydraulic fracturing holes are processed on a square rock sample to simulate single well and multi-well model samples, and different hydraulic pressurization tests are realized, so that the qualitative analysis of the reservoir rock crack evolution law under the true triaxial stress condition is completed.

The invention discloses an ancient site flood disaster risk assessment method coupling quantitative simulation and qualitative analysis, and the method comprises the steps: collecting related data of an ancient site flood disaster, and carrying out the preprocessing of the related data; risk analysis and vulnerability analysis are carried out on the related data, and the risk analysis comprises the steps that according to the fusion environment data and the rainfall data, a hydrodynamic model is adopted to set a rainfall scene, numerical simulation is carried out to obtain rainfall data, and a disaster is evaluated to obtain a quantitative risk level; the vulnerability analysis comprises the following steps: carrying out vulnerability grade division according to the stability, value loss and repairable ability of cultural relics in a disaster scene to obtain a qualitative vulnerability grade; and organically coupling the quantitative risk level and the qualitative vulnerability level to obtain an ancient site flood disaster risk level map. The method not only improves the accuracy of ancient site flood disaster risk assessment, but also has good interpretability, and can be directly used in an ancient site flood disaster risk assessment system.

The invention relates to the technical field of building design, in particular to an ancient building visual color matching method, which comprises the following steps of: 1, acquiring multi-source data; step 2, color feature extraction; 3, constructing a historical color knowledge base; step 4, constructing a color matching model; and step 5, performing three-dimensional visual rendering. According to the historic building visual color matching method, qualitative analysis of'regional culture-natural environment 'is converted into quantitative collection of multispectral images and three-dimensional point clouds, and spatial accurate positioning of color features is achieved; a standardized knowledge base containing historical periods and technologies is constructed, and the fragmentation problem of traditional color matching experience is solved; through physical realistic rendering, the method is expanded to color simulation of the ancient building under different climate conditions, and the recovery credibility is improved.

The invention discloses a deep learning-based drainage basin abnormal rainfall event identification monitoring and weather behavior analysis method and system, and the method comprises the steps: obtaining the summer rainfall original data of a drainage basin, and carrying out the preprocessing; the abnormal rainfall event is divided into different subspace modes through a self-organizing mapping machine learning method; qualitatively analyzing a space-time evolution rule and an interaction mechanism of water vapor conveying characteristics and atmospheric circulation configuration in abnormal rainfall according to each subspace mode; evaluating the influence of different meteorological factors on extreme weather conditions through a Bayesian method, and quantitatively exploring abnormal rainfall event change factors; the system comprises a data preprocessing module, a self-organizing mapping mode classification module, a water vapor conveying and atmospheric circulation influence mechanism module and a Bayesian method meteorological factor influence analysis module. According to the method, accurate identification and monitoring of the drainage basin abnormal rainfall event are realized, the internal characteristics of rainfall data can be effectively learned, and the identification capability of the abnormal rainfall mode is enhanced.

The invention relates to the technical field of FPC (Flexible Printed Circuit) laminated structure optimization, in particular to a module FPC laminated structure optimization system based on signal integrity, which comprises a laminated parameter sensitivity extraction module used for defining the thickness of a dielectric layer, the thickness of a copper foil and a dielectric constant as closed interval values with upper and lower boundaries according to FPC design specifications. According to the method, the accumulated deviation area and the normalized confidence score are obtained in combination with integral operation, conversion from a frequency domain to a time domain and from qualitative analysis to quantitative evaluation is achieved, tiny flaws of impedance continuity are captured, inferior paths are screened based on the confidence score, and a physical junction area is positioned. The geometric gravity center of the deviation area is calculated to be matched with the slope and the length of the gradient line, a smoothly-transited geometric figure is constructed, structural compensation can be achieved in areas where impedance is prone to sudden change such as a reinforcing plate junction and covering film pressing, and signal reflection caused by physical sudden change of a stacked structure is eliminated.

The invention discloses a coronary artery lesion risk prediction method, a coronary artery lesion risk processing device, a coronary artery lesion risk prediction system and a computer readable storage medium. Obtaining a point sequence according to high-frequency QRS wave group data obtained by the exercise electrocardiogram data; aiming at a point sequence corresponding to any electrocardiogram lead, taking any sampling point in a plurality of sampling points in the point sequence as a current reference sampling point in a traversal cycle, and traversing sampling points in a preset time interval from the current reference sampling point, determining whether a first sampling point and a second sampling point meeting a preset condition exist in the preset time interval, wherein the first sampling point is earlier than the second sampling point in time sequence; and if the preset condition is met, determining that the high-frequency QRS wave group data has waveform characteristics indicating the coronary artery lesion, thereby determining the possibility of the coronary artery lesion. Compared with a previous scheme, the scheme has the advantage that the qualitative accuracy of coronary artery lesion is improved.

The invention relates to the technical field of electrical instrument management, in particular to a petroleum drilling machine electrical instrument management system and method based on SIL analysis, and the management system comprises a drilling machine electrical instrument function management module and an electrical instrument safety management module. The drilling machine electrical instrument function management module is used for completing periodic management screening and qualitative analysis of a drilling machine electrical instrument, function identification of an electrical instrument system and safety integrity level determination of the electrical instrument system; the electrical instrument safety management module is used for determining the working range of a drilling machine and the management range of electrical instruments of the drilling machine, screening to-be-evaluated scenes, selecting initial events, identifying independent protection layers, determining the average failure probability of all the protection layers, calculating the frequency of all the scenes and determining risk reduction factors. And confirming the security integrity level of the target. Through the management system and method, the problems that the accuracy of risk assessment of an electrical instrument system of a petroleum drilling machine in a scene event is poor and safety assessment is not facilitated can be solved.

The invention discloses a high-precision multi-physics field reconstruction method based on a nested neural network architecture. The method comprises the following steps: S1, acquiring open-source multi-physics field data; s2, a multi-scale Fourier operator-driven U-Net physical field reconstruction model is constructed, the physical field reconstruction model comprises an embedding module, a coding module and a decoding module, the coding module comprises at least one coding layer, each coding layer is formed by sequentially connecting a multi-scale Fourier neural operator layer and a down-sampling layer, and the multi-scale Fourier neural operator layer and the down-sampling layer are embedded into the embedding module; the decoding module comprises at least one decoding layer, and each decoding layer is formed by sequentially connecting a multi-scale Fourier neural operator layer and an up-sampling layer; s3, training the physical field reconstruction model based on a training set; and S4, performing quantitative analysis and qualitative analysis on the reconstructed physical field by using the test set.

The invention relates to the technical field of medicine analysis and detection, in particular to a method for qualitatively analyzing small molecule compounds in Xinnaoning capsules by using a UPLC-MS (Ultra Performance Liquid Chromatography-Mass Spectrometry) technology. The small molecule compounds comprise polar small molecule compounds and weak polar small molecule compounds. The method comprises the following steps: preparing a test solution; carrying out UPLC / Q-TOF MS detection; and performing qualitative analysis. Through qualitative analysis of polar and weak polar small molecule compounds in the Xinnaoning capsule, a material basic component library is established, and a theoretical basis and technical support are provided for more comprehensive and effective control of product quality.

The invention relates to the technical field of ultrasonic detection, in particular to a glue pouring bubble detection device and method for a water immersion ultrasonic transducer, and the device comprises a signal generator, a power amplifier, an oscilloscope, a to-be-detected ultrasonic transducer, a clamp, a fixing device, an infrared camera and a computer. In order to solve the problems that a traditional infrared thermal imaging technology is sensitive to material characteristics, high in excitation condition requirement and difficult to achieve defect quantitative analysis, an excitation electric signal drives an ultrasonic transducer to work to generate heat after power amplification, an infrared camera is adopted to capture a surface temperature distribution image of the ultrasonic transducer, and a convolutional neural network model is constructed; the volume fractions of the bubble defects of the glue pouring layer of the ultrasonic transducer in the infrared image are subjected to hierarchical identification (including four hierarchical intervals of less than 10%, 10%-30%, 30%-60% and more than 60%). The method solves the limitation of defect qualitative analysis of a traditional method, realizes quantitative evaluation of the bubble content of the glue pouring layer, and provides data support for quality detection of the ultrasonic transducer.

The invention belongs to the field of plant insect-resistant characteristic analysis, and particularly discloses a real-time in-situ detection method and system for the insect-resistant level of a plant, and the method specifically comprises the following steps: after a to-be-detected plant is irradiated by ultraviolet light of different doses, a hyperspectral stimulated Raman scattering imaging subsystem is adopted to carry out stomata imaging and spectral signal acquisition on leaves of the to-be-detected plant; performing qualitative analysis on different metabolites in the leaves by adopting spectral signals of different wavebands, and performing quantitative detection on the metabolites in combination with characteristic peak areas and peak intensity characteristics; acquiring pore structure change based on pore imaging; and calculating the correlation between the pore structure and metabolite change of the plant caused by different irradiation and the insect resistance, thereby realizing in-situ detection of the insect resistance of the plant. According to the method provided by the invention, the complex process of plant disease and insect pest resistance can be comprehensively analyzed, and the non-invasiveness, high specificity and qualitative and quantitative capability can provide important theoretical basis and practical guidance for developing a green disease and insect pest control technology.

The invention discloses a silkworm cocoon source discrimination and quality evaluation method and system based on molecular marker analysis, and the method comprises the steps: carrying out the alkaline degumming treatment of a silkworm cocoon sample, and achieving the preliminary discrimination of silkworm cocoons with different feeding sources based on the degumming rate difference; the method comprises the following steps: extracting a silkworm cocoon sample through an organic solvent to prepare a to-be-detected sample solution for liquid chromatography-mass spectrometry; carrying out separation detection on the sample solution to be detected by using a liquid chromatography-mass spectrometry technology to obtain chromatography-mass spectrometry data of the silkworm cocoon sample; performing feature extraction and similarity learning on the chromatography-mass spectrometry data by adopting a deep learning model to finish qualitative analysis of the molecular marker; and based on the qualitative analysis result, establishing a standard curve through an external standard method, and calculating the content of the target compound in the silkworm cocoon according to the peak area of the target compound. According to the embodiment of the invention, accurate tracing of silkworm cocoon sources and objective and quantitative evaluation of internal quality can be realized.

The invention belongs to the technical field of gas detection, and particularly relates to a device and a method for detecting decomposition products of insulating gas perfluoroalkyl nitrile. The device comprises a gas collection module, a pretreatment module, a multi-technology combination module, a detection gas chamber and an intelligent data processing and control system, wherein the gas collection module collects and concentrates a gas sample through an equipment gas chamber sampling unit and a low-temperature enrichment unit; the pretreatment module purifies gas and converts complex components through a high-efficiency filter, a dryer and a catalytic conversion unit; the multi-technology combination module is used for separating gas by using a micro gas chromatography unit and carrying out qualitative analysis by using an infrared spectrum unit; a gas sensitive sensor array of the detection gas chamber realizes multi-dimensional detection; a storage module of the intelligent data processing and control system stores various databases, an analysis module calls the databases to analyze multi-module data, and sampling, detection and intelligent analysis of decomposition products are achieved. The problems that existing detection has a leak detection condition and lacks pertinence and high efficiency are solved.

The invention discloses a measuring result stability analysis method and system of a metering instrument, and relates to the technical field of time sequence data analysis. The method comprises the steps of measurement data flow alignment, measurement result stability baseline feature construction, three-state feature decomposition and measurement result stability analysis of the metering instrument. Extracting repeatability features, trend features and structural features of the measurement result, and constructing a measurement result stability baseline feature vector; a three-state feature decomposition method based on baseline constraint is adopted to decompose the stability change of the measurement result into a drift feature, a noise feature and a jump feature; comprehensively analyzing the stable state and the change trend of the measurement result of the metering instrument based on the baseline feature and the tri-state feature decomposition result; according to the method, the stability of the measurement result of the metering instrument can be subjected to refined and cause-distinguishable analysis under the complex working condition and the multi-state operation condition, and a reliable basis is provided for the evaluation of the operation state of the instrument and the judgment of the credibility of the measurement result.

The invention provides a power system load prediction method, device and equipment, and the method comprises the steps: determining a multi-input-output table of policy text data based on a policy consistency index model; determining a first matrix by adopting a word frequency-inverse document frequency statistical method according to the multi-input-output table; determining a second matrix by adopting an analytic hierarchy process according to the multi-input-output table; inputting the first matrix and the second matrix into a policy data processing unit for feature extraction; inputting the geographic area data into a geographic area data processing unit; inputting the economic data into an economic data processing unit; and obtaining prediction data of the power system load. According to the method, the policy text data is subjected to quantitative analysis and qualitative analysis, so that the analysis matrix of the policy text data is obtained. And by combining geographic area data and economic data and adopting an advanced neural network architecture, stable and accurate future power system load trend prediction is provided, and the decision-making process of the power industry is assisted.

The invention belongs to the technical field of Raman spectrum detection, and relates to a mixture qualitative and quantitative analysis method and system based on Raman spectrum. The method comprises the following steps: taking spectral information of a mixture and a qualitative dictionary as input, adopting a sparseness-based adaptive step-by-step orthogonal matching pursuit method for analysis, preliminarily determining various pure substance components in the mixture and corresponding spectrums and weights thereof, and adding constraint conditions to the pure substance components to obtain a final qualitative analysis result. And finally, taking the mixture spectrum, the qualitative component list and the quantitative dictionary as input of an orthogonal matching pursuit algorithm, and carrying out iterative loop according to qualitative analysis steps to obtain a quantitative analysis result. The components and the concentration of the mixture can be quickly and accurately judged through an orthogonal matching pursuit algorithm; meanwhile, by adding constraint conditions, the problem that analysis results are affected by factors such as baseline drift and noise interference due to the reasons of spectrums is effectively avoided, and the accuracy and efficiency of the analysis results are effectively improved.

The invention discloses a method for researching a polyolefin resin odor compound based on a headspace-gas chromatography-ion mobility spectrometry technology. The method is specifically implemented according to the following steps: step 1, carrying out headspace-gas chromatography-ion mobility spectrometry detection on polyolefin resin; step 2, extracting signal peaks of volatile compounds of different brands of resin through software LAV to construct a fingerprint spectrum; step 3, carrying out qualitative analysis processing on the signal peak of the area selected in the step 2 through GC-IMS Library Search Software qualitative software, and carrying out qualitative analysis processing on the signal peak of the area selected in the step 2 through GC-IMS Library Search Software qualitative software; step 4, taking the signal intensity of each compound in the odor compound fingerprint as a basis for comparing odor substances of resins with different brands, the GC-IMS technology is applied to odor analysis of related materials such as polyolefin resins with various brands, sample pretreatment is simple, complex pretreatment is not needed, the detection period is short, and the detection accuracy is high. Compared with the prior art, the method has the advantages of high sensitivity, resolution and resolution, and can compare and analyze the difference of odor compounds of different samples through the obtained fingerprint spectrum, carry out visual monitoring, and reduce the difficulty of difference analysis of different samples.