127 results about "Multivariate statistical" patented technology

The invention relates to the technical field of data analysis, and discloses a precise targeted early warning method and device for meteorological disasters of kiwifruit canker and a medium. The method comprises the following steps: performing standardization processing on historical occurrence time, morbidity degree and distribution range data of kiwifruit canker as well as meteorological observation data and cultivation management data of corresponding time periods to obtain a historical disease database; performing multivariate statistical analysis processing on the three-dimensional meteorological monitoring network data and the historical disease database to obtain a disease risk assessment index; performing feature extraction processing on the disease risk assessment indexes through a deep learning algorithm to obtain disease early warning parameters; performing gridding partition processing on the disease early warning parameters to obtain partition early warning data; and performing dynamic optimization processing on the accuracy, the advance and the coverage rate of the partition early warning data to obtain accurate targeted early warning information of the meteorological disaster of the kiwifruit canker. According to the application, the accuracy and timeliness of the existing kiwi fruit canker meteorological disaster early warning method are improved.

The invention discloses a method and a system for analyzing a pollution source of a rainwater pipe network. The method comprises the following steps of: 1, obtaining a first key fluorescence characteristic parameter combination according to basic physicochemical indexes by adopting a fluorescence characteristic parameter prediction model; 2, three-dimensional fluorescence characteristic parameters of the pollution source are obtained, and a pollution source three-dimensional fluorescence characteristic data set is obtained; screening by adopting a multivariate statistical method to obtain a second key fluorescence characteristic parameter combination; 3, determining a fluorescence characteristic parameter combination according to the first key fluorescence characteristic parameter combination and the second key fluorescence characteristic parameter combination; 4, inputting the fluorescence characteristic parameter combination into the Bayesian mixture model to obtain the contribution rate of the pollution source to the receptor water quality, and completing the analysis of the pollution source of the rainwater pipe network; according to the method, the DOM fluorescent fingerprint information is predicted by adopting basic data; and based on the three-dimensional fluorescence characteristic parameters and in combination with a Bayesian mixture model, precise quantitative analysis of the pipe network pollution source is realized.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention belongs to the field of engineering technology and environmental protection, and discloses a sustainable tunnel construction water inflow prediction method based on a machine learning technology, and the method comprises the following steps: data collection and preprocessing; identifying a main control factor of the water inflow by using multivariate statistics and a machine learning method; and establishing efficient water inflow prediction models by utilizing a Python programming language according to the key influence factors of the water inflow and based on a random forest, XGBoost and a deep neural network model algorithm, and evaluating and comparing the performances of different efficient water inflow prediction models according to evaluation parameters of the output performance of the initial water inflow prediction model. Determining the efficient water inflow prediction model with the closest output performance of each efficient water inflow prediction model training set and test set as an optimal water inflow prediction model; and a sustainable tunnel construction water inflow intelligent prediction system is constructed. According to the method, a traditional water inflow prediction method is optimized by combining various machine learning technologies.

The invention relates to a multi-mode-based pit mud quality detection method, device, equipment and medium, and the method comprises the following steps: acquiring near infrared spectrum data NIR and mid-infrared spectrum data MIR of a pit mud sample to obtain original spectrum data of the pit mud sample, and preprocessing the original spectrum data to obtain standard spectrum data; dynamically adjusting a data fusion mode of NIR and MIR by adopting a multivariate statistical analysis algorithm based on the standard spectral data, and further constructing an NIR-MIR fusion model; carrying out spectral analysis on the pit mud sample based on an NIR-MIR fusion model so as to extract key component information in the pit mud sample; and based on the key component information, according to a preset multi-dimensional evaluation strategy, carrying out comprehensive evaluation on the pit mud sample, and generating a quality evaluation result. The pit mud detection device has the effect of improving pit mud detection efficiency.

The invention relates to a multilevel ecological risk assessment method and device for soil pollution, and belongs to the technical field of soil pollution risk assessment, and the method comprises the following steps: carrying out hazard identification: collecting and analyzing source-path-sink information of soil pollution, and carrying out risk screening when the risk exists through assessment; performing risk probability quantification on the optimal control pollutants, quantifying the risk probability by establishing a joint probability curve, and performing risk causal relationship judgment on the optimal control pollutants by evaluating the existence of the risk; and determining statistical causal association of pollution-ecological effect, obtaining a risk conclusion and other risk results of each level, and ending evaluation. The invention provides a multi-level progressive ecological risk assessment method, a soil pollution ecological risk conclusion with local representativeness and ecological robustness is formed, and safe utilization of a site soil ecological environment is guaranteed.

The invention provides a comprehensive evaluation method for dissemination characteristics of minerals in coal of different particle sizes, and relates to the field of coal quality analysis. The method comprises the following steps: collecting coal analysis samples of different size fractions, and preparing coal polished sections of all the size fractions; the method comprises the following steps: collecting microscopic images, and constructing a representative microscopic image sample database of each size fraction; carrying out size fraction judgment, generating a component segmentation image on a single particle and a component segmentation image on a microscopic image, and extracting dissemination characteristic parameters of mineral substances in coal of each size fraction; key parameters are screened out, and comprehensive evaluation is carried out on the dissemination characteristics of the minerals in the coal of different size fractions. According to the method, an image processing technology and multivariate statistical analysis are combined, and qualitative and quantitative analysis of the dissemination characteristics of the minerals in the coal is achieved. Compared with an existing microscopic image analysis method, the method has the advantages that the human subjective influence and the labor intensity are remarkably reduced, and the analysis result has higher objectivity and reproducibility.

The invention relates to the technical field of traditional Chinese medicine detection, in particular to a UPLC-Q / TOF-MS identification method for pinellia ternate and rhizoma typhonii flagelliformis. According to the method, on the basis of pinellia ternate, ginger processed pinellia, rhizoma typhonii flagelliformis and ginger processed rhizoma typhonii flagelliformis component information collected by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry, characteristic substances are screened in combination with a multivariate statistics method, OPLS-DA analysis is carried out on pinellia ternate and rhizoma typhonii flagelliformis, differential compounds of the pinellia ternate and the rhizoma typhonii flagelliformis are found, and The method comprises the following steps: determining five key compounds for identifying pinellia ternate, rhizoma typhonii flagelliformis and processed products thereof by combining a plurality of analysis methods such as box plot analysis, thermogram analysis and correlation analysis, then determining the relative content of the five key compounds, and establishing a pinellia ternate and rhizoma typhonii flagelliformis identification model by utilizing OPLS-DA analysis. The detection of rhizoma typhonii flagelliformis doped in the rhizoma pinelliae and processed products thereof can be realized.

The invention relates to the technical field of electrochemical detection and food quality safety, and particularly discloses an edible oil rancidity degree rapid detection system based on an electrochemical sensor, comprising: a data acquisition module for receiving an original electrochemical response signal and performing format standardization, abnormal data rejection and integrity verification; the data preprocessing module adopts a statistical optimization algorithm to reduce noise, correct baseline drift, generate a unified dimension data set and complete stability verification; the feature analysis module is used for extracting rancidity related feature parameters through statistical correlation analysis, and carrying out fusion calculation after significance verification to obtain feature values representing rancidity; and the evaluation and judgment module inputs the characteristic value into a pre-training model, outputs a quantitative index through multivariate statistical calculation, dynamically sets a safety threshold in combination with the edible oil type, and outputs a rancidity judgment conclusion after comparison. The method depends on statistical data processing and analysis, the detection reliability and accuracy are improved, and the method is suitable for edible oil quality safety rapid screening and quality monitoring.

The invention relates to a wind power plant state monitoring technology, in particular to a multi-unit joint monitoring model based on spatial-temporal correlation, which comprises the following steps: S1, performing cross reference on multiple units to eliminate spatial correlation; s2, vector autoregression prediction is carried out, and time correlation is eliminated; and S3, monitoring a multivariable statistical process based on a residual vector. Aiming at the problem that modeling is interfered by spatial-temporal correlation of wind power plant-level multi-unit state data, the invention provides a multi-unit joint monitoring model, fault feature extraction is realized through multi-unit cross reference, and the spatial non-stationary trend among wind power plant units is eliminated; based on a vector autoregression (VAR) model, carrying out joint modeling on the data of the multiple units, eliminating time autocorrelation among the multiple units, and obtaining a stable and independent residual vector; and a multivariable residual control chart is established, the state of the wind power plant is jointly monitored, and a fault unit is accurately identified. The model guarantees the operation reliability of the wind turbine generator, and effectively reduces the operation and maintenance cost of a wind power plant.

The invention relates to the technical field of power grid operation optimization, in particular to a power grid operation optimization method, system, equipment and medium considering a multi-dimensional index, and the method comprises the steps: collecting the original operation data of a power grid in real time, and carrying out the preprocessing of the original operation data of the power grid, and obtaining the preprocessed operation data of the power grid; constructing a multi-dimensional performance evaluation index system according to characteristics and requirements of power grid operation; according to the multi-dimensional performance evaluation index system, comprehensively evaluating the preprocessed power grid operation data by using a multivariate statistical analysis method to obtain a power grid operation performance evaluation index; and based on the power grid operation performance evaluation index, optimizing and adjusting the power grid operation state by using an optimization algorithm. According to the method, comprehensive and accurate monitoring and optimization of the operation state of the power grid are realized by comprehensively considering the multi-dimensional indexes, the operation stability and efficiency of the power grid are improved, and the safety and reliability of power supply are ensured.

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention discloses an industrial process monitoring method based on a lightweight deep principal component analysis-auto-encoder model, and the method comprises the steps: collecting the related process variable data of an industrial process on line, carrying out the preprocessing of the collected data, inputting the preprocessed data into a trained lightweight deep principal component analysis-auto-encoder model, and carrying out the recognition of the related process variable data. The T2 statistical magnitude and the SPE statistical magnitude are obtained and compared with corresponding control limits, and a monitoring result is output; the lightweight deep principal component analysis-auto-encoder model uses a PCA dimension reduction module for dimension reduction to obtain the data score, inputs the score into the auto-encoder, and outputs the reconstruction data. According to the method, the calculation efficiency of the model is improved, compared with a traditional deep network model, the calculation speed is obviously improved, compared with a single multivariate statistical model, the strong feature extraction capacity of the deep hierarchical cascade model structure is incomparable to that of the single multivariate statistical model, and the problem of complex industrial process monitoring is effectively solved.

The invention discloses an electrolysis system fault intelligent detection system and method based on an explosion-proof robot dog, and relates to the technical field of electrolysis system intelligent detection, and the method comprises the steps: collecting electrolytic cell environment data and electrolytic cell operation state data in real time through the explosion-proof robot dog, and employing a Kalman filtering and timestamp synchronization method, carrying out filtering and de-noising processing and converting into a uniform timestamp; fusing the processed electrolytic cell environment data and the electrolytic cell operation state data through a multivariable statistical method to obtain electrolytic cell multi-source fusion state data, and performing real-time compensation by using a linear regression model; a random forest training method is adopted, and the compensated historical data of the multi-source fusion state of the electrolytic cell are utilized to train the isolated forest model to obtain a short circuit detection model; data are collected through the explosion-proof robot dog, and Kalman filtering and timestamp synchronous processing are applied, so that the safety of data collection is ensured, and the accuracy of data and the consistency of time dimensions are also ensured.

The invention relates to a cherry tomato storage period evaluation method and application, and belongs to the technical field of analysis and detection. The method for evaluating the storage period of the cherry tomatoes comprises the following steps: acquiring a cherry tomato sample; loading into a headspace bottle and incubating to obtain a headspace gas; carrying out headspace-gas chromatography-ion mobility spectrometry detection; vOCal analysis software is adopted for data collection; carrying out qualitative analysis on the volatile components by adopting a database; a cherry tomato classification model is constructed by adopting a multivariate statistics method, so that cherry tomato samples in different storage periods are classified. The evaluation method disclosed by the invention can be used for monitoring, classifying and judging the change of the volatile components of the cherry tomato samples under different storage conditions, and has the characteristics of high throughput, fast response, good repeatability, high accuracy and strong analysis capability.

The invention discloses a method for researching the specific distribution of a multi-component complex system in animal tissues based on mass spectrum imaging and multivariate statistical analysis, which comprises the following steps: treating the multi-component complex system, performing animal administration for a certain time, killing the animal, taking the animal tissues, slicing the animal tissues, and taking out the sliced animal tissues; and performing mass spectrum imaging analysis and multivariate statistical analysis to obtain a specific distribution result of the multi-component complex system in the animal tissue. On the basis of a frozen section technology and a mass spectrum imaging technology, a DESI-MSI analysis method of the whole mouse tissue section is established, and in-situ distribution characteristics of the active ingredients of the Anefortia divaricata on the brain tissue section are disclosed by combining multivariate statistical analysis.

The invention discloses a packaging machinery health state evaluation method and system based on multi-source data fusion, and relates to the technical field of multi-source data fusion, and the method comprises the steps: carrying out the fault root cause positioning according to a feature vector obtained through the decomposition of a main feature value, and obtaining a main evaluation dimension which causes the reduction of a comprehensive health degree index; according to the method, the evaluation dimension mapped by the component with the maximum absolute value in the feature vector corresponding to the main feature value is analyzed, accurate positioning of the fault root cause is achieved, the process effectively strips coupling correlation among dimension data on the basis of the multivariate statistical principle, the problems of misjudgment and missed judgment caused by judgment depending on experience of operation and maintenance personnel in the traditional technology are solved, and the accuracy of the fault root cause is improved. When the comprehensive health degree index of the equipment is reduced, the problem can be quickly locked from electrical, mechanical, process or environment dimensions, the troubleshooting time is greatly shortened, the maintenance response efficiency is improved, and a clear direction is provided for targeted equipment maintenance.

The invention discloses a method and system for automatically detecting unsafe behaviors of workers based on machine vision, and relates to the field of industrial safety, and the method comprises the steps: collecting and preprocessing the behavior data of the workers, the behavior data of the workers comprising position information, equipment state, certificate state and motion trail; performing unsafe behavior detection on the preprocessed operator behavior data to generate a behavior target detection frame; inputting the behavior target detection frame into a target tracking algorithm to perform multi-target tracking, and generating a personnel movement track data set; performing multivariate statistical analysis on the behavior feature vector by using a quantitative theory to generate an unsafe behavior reaction matrix; and evaluating a risk level in the unsafe behavior reaction matrix through a fuzzy analytic hierarchy process, and performing safety early warning notification according to the risk level. According to the method, multivariate statistical analysis is carried out on the behavior feature vectors through a quantification theory, and deep quantification and comprehensive evaluation of the unsafe behavior features of the workers are realized.

The present invention discloses a method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function, which relates to the technical fields of surface engineering and performance evaluation. The method includes collecting the electronic work function data and material characteristic data on the surface of ductile iron; extracting the characteristics of the electronic work function data to obtain the electronic work function characteristics; establishing a mathematical model based on the electronic work function characteristics to analyze the correlation between the electronic work function data and the material characteristic data; analyzing the relationship between the electronic work function characteristics and the wear resistance, and establishing a comprehensive evaluation model to evaluate the wear resistance of ductile iron. The present invention combines characterization techniques, complex data analysis methods and multivariate statistical models at the same time. It can not only deeply understand the connection between the electronic structure and macroscopic properties of materials, but also establish a comprehensive evaluation model to predict the wear resistance of ductile iron; this method guides the design of materials and the optimization of the production process.

The invention discloses a method and system for evaluating breast milk nutritional ingredient composition by using a multivariate statistical method and application, and belongs to the field of nutritional ingredient evaluation.The method comprises the following steps that the content of nutritional ingredients in to-be-evaluated breast milk or formula food is obtained to serve as an original data variable; carrying out normalization processing on original data variables to eliminate dimensional differences, and carrying out centralization processing; inputting the centralized data into a pre-constructed evaluation model to obtain a score output by the evaluation model, comparing the score with a preset median, and if the difference value is smaller, indicating that the result is closer to breast milk; wherein the evaluation model comprises four common factors obtained through principal component analysis and a scoring model obtained through loads and influence degrees in factor analysis. According to the method, macro nutrients and micronutrients of the breast milk are simplified by adopting a principal component multivariate statistical method, and main indexes are selected from numerous quality evaluation indexes to evaluate the nutritional ingredients of the breast milk or the formula food.

The invention provides a method for constructing an ankylosing spondylitis ossification progress prediction model based on metabonomics and artificial intelligence. The method comprises the following steps: carrying out metabonomics analysis and peak extraction to obtain a characteristic ion peak table, carrying out compound identification on characteristic ion peaks, and carrying out characteristic screening through a lasso regression method, a multivariate statistical method of unbiased variable selection and a BORUTA method to obtain candidate characteristics related to the ankylosing spondylitis ossification progress; carrying out data standardization processing, principal component analysis and orthogonal partial least-partial-square discriminant analysis on the characteristic ion peak table, and carrying out pathway enrichment analysis to obtain differential metabolites; an LR model, an RF model and an SVM model used for distinguishing the ossification progress speed of the AS patient are established based on known risk factors, candidate features and differential metabolites, the efficiency of the three models is verified and compared, and finally the ankylosing spondylitis ossification progress prediction models with the optimal efficiency are obtained and serve as the LR model and the RF model.

The invention discloses a method, a device and equipment for defining an ecological underground water level in a desertification region, and relates to the field of underground water level localization, and the method comprises the following steps: carrying out underground water dynamic simulation according to meteorological data, hydrological data, geological data and social economic data; correlation analysis is carried out according to underground water level change conditions and ecological data in different media in the desertification region, and key ecological variables are obtained; determining an ecological health threshold value according to underground water level change conditions and key ecological variables in different media in the desertification region; according to the meteorological data, the hydrological data and the geological data, determining key environment variables influencing ecological system response characteristics under different underground water level conditions; and determining the ecological underground water level by using a multivariate statistical method according to the underground water level change condition, the key ecological variable, the ecological health threshold value and the key environment variable in different media in the desertification region. According to the invention, the underground water range required for maintaining the health of the ecological system in the research area can be defined.

The invention relates to a characteristic marker combination for identifying honeysuckle tea varieties and an identification method thereof, and belongs to the technical field of variety tea identification. The characteristic marker combination comprises chlorogenic acid, swertioside, secoxyloganin, galuteolin, isochlorogenic acid A and isochlorogenic acid C. The invention further discloses a kit for detecting the secoxyloganin. The contents of the characteristic markers are simultaneously detected through a high performance liquid chromatography (HPLC) or liquid chromatography-mass spectrometry (LC-MS) technology, and different honeysuckle tea varieties can be rapidly and accurately identified by combining multivariate statistical analysis (such as principal component analysis or clustering analysis). The identification method provided by the invention has the characteristics of strong specificity, high stability and high sensitivity, is suitable for large-scale sample analysis, and can be widely applied to quality control, authenticity identification and market supervision of honeysuckle tea varieties.

The invention relates to the technical field of medical biology, and discloses a traditional Chinese medicine quality marker screening method and an angelica dahurica variety quality marker group, the traditional Chinese medicine quality marker screening method and the angelica dahurica variety quality marker group comprise the following steps: respectively crushing, extracting and detecting a newly bred angelica dahurica variety and a farm variety; carrying out pretreatment on mass spectrum data by adopting MZmine 2.5 software; and the coumarin components are screened by adopting a multi-stage mass loss window. According to the traditional Chinese medicine quality marker screening method and the angelica dahurica variety quality marker group, through integration of ultra-high performance liquid chromatography-tandem mass spectrometry and polygon quality defect filtering, extended virtual molecular formula library screening, characteristic molecular network, network annotation transmission and other calculation strategies, a plurality of coumarin components are successfully annotated; and multivariate statistical analysis is adopted, so that the quality marker of the newly cultivated angelica dahurica variety is obtained.

The invention relates to the technical field of industrial process soft measurement and quality control, and discloses a paper quality prediction method based on multivariate statistical latent variable fusion and space-time transformation, which comprises the following steps: acquiring space-time sequence data of a multi-source sensor in a papermaking process, constructing a space-time diagram structure reflecting a topological relation of equipment, and preprocessing. Then, multi-view latent variables are extracted through non-negative matrix factorization, independent component analysis and robust principal component analysis, attention fusion is conducted on the latent variables through an LV fusion module, and fusion latent variables are obtained; and inputting the fusion latent variable and original node data into a multi-scale convolution auto-encoder to obtain spatial feature embedding, and inputting the spatial feature embedding and the fusion latent variable into a space-time Transform module together to realize joint modeling of space correlation and time dependence. And finally, outputting a paper quality predicted value through a linear regression module. The method can achieve the accurate prediction of the paper quality under a high-dimensional and multi-noise working condition, and is suitable for online monitoring and modeling updating.

The present invention relates to the field of oil and gas exploration and development, and discloses a coal seam thickness prediction method, device and equipment based on well-seismic combination, which can obtain the real acoustic wave curve corresponding to the target layer segment and the constructed coal seam comprehensive identification factor curve. The real acoustic wave curve is subjected to wavelet transformation to extract the low-frequency component curve; the coal seam comprehensive identification factor curve is subjected to multivariate statistics to extract the high-frequency component curve. The low-frequency component curve and the high-frequency component curve are modulated to obtain a pseudo-acoustic wave curve. Based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method, the thickness distribution of the coal seam to be measured is determined. The present invention combines the coal seam comprehensive identification factor curve and the seismic wave impedance inversion method to predict the coal seam thickness, effectively solving the problem of the difficulty in predicting the coal seam thickness in the complex structural area of ​​the oil field with no wells or few wells, improving the efficiency and accuracy of coal seam thickness prediction in the complex structural area of ​​the oil field, and diversifying the coal seam thickness prediction method in the complex structural area of ​​the oil field.

The application discloses a method for identifying original tea tree varieties by using characteristic metabolites, and particularly relates to the field of identifying tea tree varieties, which comprises the steps of sample preparation, metabolite extraction, liquid chromatography-mass spectrometry detection, data correction processing, characteristic metabolite construction and multivariate statistical clustering determination, etc.; in view of the internal metabolic characteristics formed in the long-term natural domestication process of the floating Liang chestnut leaf population, a high-dimensional characteristic metabolite vector is constructed by using the stable numerical distribution mode of the fresh leaves of the floating Liang chestnut on the sixteen characteristic metabolites; after the peak intensity data of each sample is corrected, filtered and structured, the obtained characteristic vector is input into the Euclidean distance hierarchical clustering model, so that the metabolic composition difference between the samples to be identified and the standard floating Liang chestnut leaf population is unsupervised statistically distinguished.

The invention discloses a strong wine authenticity identification method fusing multivariate statistics and metabolic feature extraction, relates to the technical field of strong wine authenticity identification, and overcomes the limitation that a traditional method only depends on peak area or mass-to-charge ratio for distinguishing by incorporating non-dominant structure difference in a metabolic map into an analysis model. A traditional method is difficult to distinguish metabolic differences caused by different processes or environmental changes, but according to the method, through multi-dimensional non-targeted metabolic characteristic analysis, the influence of the processing processes on the metabolic structures of the wine products can be accurately captured, and the distinguishing precision of the wine products of the same variety, different processing batches, the same raw material and different processes is remarkably improved. According to the method disclosed by the invention, the comprehensive isomerism index FCY is constructed by combining the structural isomerism variance and the map complexity factor, so that the difficulty in judgment caused by complex structures and hidden changes of wine samples is effectively solved.

The invention discloses a pyrazine-flavor-based multi-mode intelligent identification method for a Maotai-flavor liquor process. The method comprises the following steps: pretreatment: pretreating Maotai-flavor liquor samples of different brewing processes; collecting sensory data of electronic noses and electronic tongues of Maotai-flavor baijiu of different brewing processes; establishing a liquid phase detection method for the characteristic components of the alkylpyrazine in the Maotai-flavor liquor; verifying a component analysis method through precision, stability and repeatability experiments; component quantitative analysis based on liquid chromatography fluorescence detection; performing clustering and difference evaluation on Maotai-flavor liquor samples of different brewing processes by adopting multivariate statistical analysis; establishing a Maotai-flavor liquor process identification model: utilizing VIPgt in multivariate statistics; establishing different machine learning multi-classification models for different brewing processes of Maotai-flavor liquor according to the common characteristics of 1, and performing multi-model comparison; and based on the optimal performance classification model, establishing an SHAP model of the Maotai-flavor liquor brewing process identification model, and globally explaining the identification model by adopting an SHAP algorithm.

The invention discloses a credit card user marketing business distribution method and system, and the method comprises the steps: carrying out the analysis of the basic data of a credit card user through employing a multivariate statistical analysis method, and obtaining a first target user; classifying the first target users according to the regional information to form a plurality of second target users, and distributing the second target users to corresponding regional banks; determining a marketing mode according to the credit card use information of the second target user; determining a marketing scheme of the second target user according to the credit card consumption information of the second target user; and pushing the corresponding marketing scheme to the second target user according to the marketing mode of the second target user, evaluating the marketing effect, and optimizing the influence strategy according to the evaluation result, so that the method effectively improves the efficiency and accuracy of credit card customer management. Effective marketing can be implemented for stock customers, and the consumption permeability is improved. Banks are helped to realize business development, market competitiveness is enhanced, customer experience is improved, and customer satisfaction and loyalty are improved.