76 results about "Multivariate analysis" patented technology

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention provides an intelligent control method and system for a lithium carbonate preparation process, and the method comprises the steps: collecting the temperature field, ion concentration, feeding flow velocity and turbulence intensity data of a reaction kettle through a sensor array, and generating a state feature vector through multivariate analysis; inputting the feature vector into a depth prediction model to output a particle size distribution probability cloud picture and an impurity trend; inputting the probability cloud picture and the impurity trend into a multi-target optimizer to generate a temperature compensation value, a feeding correction coefficient and a stirring adjustment instruction; inputting the instruction into an anti-interference execution module and converting the instruction into an equipment driving signal; actual crystal data are obtained through an online particle size analyzer, and the prediction model is updated; and outputting a closed-loop optimization instruction to the execution terminal through the digital twin platform. The product consistency and the production stability are synchronously improved through full-process closed-loop intelligent control, and reliable technical support is provided for continuous production of battery-grade lithium carbonate.

The invention discloses a method for efficiently screening saline-alkaline tolerant rice. According to the method, controllable indoor saline-alkali environment simulation conditions are constructed, various salts are mixed in proportion to prepare solutions with different salinity gradients, the pH value is adjusted, and temperature, humidity and illumination parameters are accurately controlled; the method comprises the following steps: disinfecting rice seeds, carrying out a germination test, transplanting rice seedlings, and regularly measuring growth indexes; in the treatment process, physiological indexes such as malondialdehyde content, antioxidant enzyme activity, proline content and the like and the expression level of salt and alkali tolerance related genes are detected, key indexes are screened by applying statistics and multivariate analysis methods, and an evaluation system is established for comprehensive score screening. The method can quickly perform saline-alkali stress treatment on a large number of rice seeds or seedlings, is not limited by natural environment, shortens the screening period, can accurately simulate different saline-alkali soil environments, scientifically evaluates the saline-alkali tolerance of rice from multiple dimensions, and provides an efficient and reliable technical means for breeding saline-alkali tolerant rice varieties.

The invention discloses a steam pipeline pressure control method, which comprises the following steps of: acquiring operation information of each key position of a pipe network in real time by constructing a data acquisition system and deploying an execution unit; a multivariable analysis method is utilized, topological structure characteristics of a steam pipeline are combined, a load change model of a steam consumption unit is constructed, the pressure change process caused by operation of different devices is recognized in advance, and a long-term pressure control strategy is made; meanwhile, an accumulative deviation curve is constructed based on a prediction and actual measurement residual evolution sequence, the potential fault risk of the system is identified in an auxiliary manner, and structured alarm output is formed, so that the safety guarantee capability of pipe network operation is enhanced. The steam pipeline pressure control method is suitable for the application scene that the overall steam use requirement is stable, but the local high-frequency disturbance behavior exists, the tiny pressure fluctuation trend can be accurately predicted under the high-disturbance working conditions such as frequent starting and stopping, and it is guaranteed that the steam pressure is stable.

The invention relates to the technical field of intelligent optimization, and discloses a 3D printing powder proportion optimization method and system based on artificial intelligence. The method comprises the following steps: collecting historical data corresponding to a powder material based on a printing process of a 3D printed finished product, and performing multivariate analysis on the historical data to generate a plurality of powder compositions; the material simulation model is used for carrying out property simulation on any powder composition, outputting material property data and extracting a powder ratio meeting the printing requirement; the finished product prediction model performs finished product prediction on any powder ratio, outputs a finished product prediction result, performs matching optimization on the finished product prediction result and ideal finished product data, and screens out an optimal powder ratio and a corresponding optimal printing process; and performance testing is conducted on the actual printing finished product, if the actual printing finished product does not meet the requirement, the step is executed repeatedly, and a new optimal powder proportion and a new optimal printing technology are screened out again. According to the method, the precision of 3D printing powder proportion optimization is improved.

The invention relates to the technical field of electric power engineering, in particular to a photovoltaic power station electric energy quality real-time monitoring system which comprises a data acquisition module, a data processing module, an electric energy quality evaluation module, a fault early warning module, a self-adaptive control module and a visualization module. And the data processing module adopts a multivariate analysis method to identify abnormity and generate a problem report. The electric energy quality evaluation module evaluates the health state of the photovoltaic power station based on a standard electric energy quality index in combination with a data processing result, and the fault early warning module automatically triggers an alarm mechanism to inform an administrator or a maintainer of timely processing. And the self-adaptive control module intelligently adjusts the operation strategy of the power station according to the score and the early warning information so as to optimize the electric energy quality. According to the power quality monitoring system, the accuracy, the response speed and the optimization efficiency of power quality monitoring are effectively improved, and stable operation and efficient power generation of a photovoltaic power station are facilitated.

The invention discloses a coal mine filling mining geological risk assessment method based on data mining, relates to the technical field of geological risk assessment, and adopts a feature influence factor Wj calculation method to normalize the contribution rate of each feature so as to ensure that the influence of each feature in a model is balanced. In multivariate analysis, part of features may be dominant during calculation due to different dimensions or numerical ranges. According to the method, through feature influence factor Wj normalization, all features have consistent influence weights on a relative scale. A multivariable regression model is adopted to analyze the geological data set DW, a stratum settlement prediction model is established, stratum settlement prediction SP is obtained, and nonlinear features of stratum settlement can be accurately captured. According to the method, through a data driving mode, tedious parameter hypothesis is avoided, the prediction flexibility is improved, reasonable filling and mining measures are adopted, geological disasters such as surface collapse and roadway deformation are reduced, and the mine safety management level is improved.

The invention relates to the technical field of mine restoration, and discloses an intelligent surface mine environment monitoring and treatment system, which comprises an environment monitoring and multi-modal data acquisition module, a data processing module and a data processing module, the data acquisition module is used for acquiring various types of environmental data such as air quality, noise, water pollution, soil erosion and geological disasters through a sensor network, an unmanned aerial vehicle, satellite remote sensing and other detection devices, and performing local data processing; and the environment optimization and decision-making module based on edge computing and deep learning is configured to deploy computing resources at a data acquisition end close to a mining area by utilizing an edge computing technology and perform real-time processing and complex multivariate analysis on the multi-source environment data through a deep learning algorithm. Through multi-modal data acquisition and an environment optimization and decision module based on edge calculation and deep learning, multi-dimensional environment data from a mining area can be rapidly processed.

A method and a system for ensuring product quality in a process for producing a product from a material in a manufacturing plant via a software performed by a computer comprising the following steps of Establishing a data pipeline including a data transformation originated from different source systems, like an ERP, LIMS, or Data Historian, to the computer, wherein the data pipeline provides updated datasets to maintain up-to-date parameter information; Preparing the datasets for a multivariate analysis calculation and performing the multivariate analysis calculation via the software, wherein all relevant parameters, like quality parameter, process parameter, sub-suppliers' quality parameters, are included in the calculation; Visualizing the result data of the multivariate analysis calculation as a 2D plot on a display connected to the computer, wherein the software identifies and marks new production batches and reference batches, time series plots for the relevant parameters and principal components, and selected top parameters which contribute to a principal components' deviation from the new production batch; and Performing quality processes and workflows to improve the production process and product quality by using the visualized result data.

To provide an estimation method of a recirculation rate enabling estimation of a recirculation rate without being limited by a preparation location of a blood marker or an arrangement location of detection means of the blood marker.SOLUTION: An estimation method of a recirculation rate includes the steps of: acquiring data representing an amount of change in blood concentration after recirculation when a blood marker is added to blood to be extracorporeally circulated, data representing a relation between an amount of change in blood concentration after recirculation and blood concentration, and data representing a relation between the amount of change in blood concentration after recirculation and a blood flow rate; creating a model by multivariate analysis or machine learning to obtain a regression equation or a conversion model, with the amount of change in blood concentration after recirculation, blood concentration and a blood flow rate used as an explanatory variables and the recirculation rate as an objective variable; acquiring data representing the amount of change in blood concentration, blood concentration and a blood flow rate of blood flowing through an arterial line or a venous line; and inputting treatment data to the regression equation or the conversion model to estimate the recirculation rate.SELECTED DRAWING: Figure 3A

This invention provides a vehicle maneuverability assessment method and system for off-road maneuvers, comprising: Step S1: identifying obstacles in an off-road environment and interpreting off-road geomechanical parameters; Step S2: inverting off-road geomechanical parameters for a coupled vehicle; Step S3: modeling vehicle dynamics based on vehicle-ground interaction; and Step S4: assessing vehicle maneuverability in an off-road environment using multivariate analysis and predicting maneuverability speed based on vehicle performance parameters and a dynamic model. This invention provides a more accurate assessment of vehicle maneuverability in off-road environments, comprehensively considering the vehicle's geometric and dynamic properties, such as turning radius, height, width, and weight.

The invention relates to an exhaled gas metabolism marker for evaluating attack severity of a bronchial asthma patient and a screening method of the exhaled gas metabolism marker. The marker is acetic acid. The screening method of the exhaled gas metabolism marker for evaluating the severity of the attack of the bronchial asthma patients comprises the following steps: respectively collecting exhaled gas of the bronchial asthma patients with mild continuous attack and moderate continuous attack as analysis samples; analyzing each exhaled gas sample by using HPPI-TOFMS to obtain an original mass spectrum of each exhaled gas sample; preprocessing the original mass spectrum of each expired gas sample to obtain data containing relative intensity information of metabolites; carrying out univariate and multivariate analysis on the data containing the relative intensity information of the metabolites, and screening out differential metabolites; a metabolite with the largest difference is screened out through binary logistic regression analysis, and the metabolite is the marker. The method is simple to operate, can efficiently and objectively evaluate the severity of the attack of the bronchial asthma patient, and has important clinical application value.

The invention discloses a vancomycin antibacterial effect prediction method based on multivariate analysis, and relates to the technical field of medical data analysis and prediction.The vancomycin antibacterial effect prediction method comprises the steps that S1, prediction data of a target patient is collected, the prediction data comprises static baseline data acquired at the beginning of treatment and dynamic time sequence data acquired at a preset time interval in the treatment process; and S2, preprocessing the prediction data, wherein the preprocessing comprises performing data cleaning on missing values and abnormal values in the static baseline data and the dynamic time sequence data. According to the method, the static baseline data at the beginning of the treatment and the dynamic time sequence data in the treatment process are fused, and the time sequence evolution rule of the dynamic index is captured by using the long and short-term memory network, so that the treatment failure probability and the kidney injury risk probability are dynamically predicted. Meanwhile, supervised learning samples are generated through a sliding window, targeted data preprocessing and risk early warning and trend chart visualization are carried out, the prediction result can be updated in real time, and the prediction precision is improved.

The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes. The system provides intuitive, menu-driven functionality for inputting, processing and utilizing vast amounts of information, including company and case-specific information, attorney discretionary evaluation and input, document templates and monitoring and reporting requirements. A threat algorithm cross-correlates via multivariate analysis critical data points with customized company-specific information and business logic to generate a numeric threat score as an output. The system also predicts the expected settlement range with a high degree of precision for a specific case as the output of one or a combination of multiple artificial intelligence (AI) / machine learning algorithms. The system then generates a unique case strategy (which includes an expected outcome, including projected outcome, Case Plan and estimated attorneys' fees and costs). System logic learns from thousands of examples of similar cases and directs highly effective and efficient execution of a specific Case Plan providing each attorney and staff member assigned a role in a case with a prioritized task list that is updated in real time as other users complete tasks and litigation priorities change. For each user, the system integrates assigned tasks across multiple cases and multiple client portfolios into a single, prioritized task list. The execution features become increasingly valuable given the volume and complexity of projects across multiple organizations and users, allowing users to focus on their area of expertise and avoid the wasted time and distraction of trying to determine what they should be working on or recording notes to communicate what they have been doing since the system automatically performs these functions and directs the various users' next steps while monitoring their progress against the plan. Integration of administrative functions, including automated time tracking, billing and reporting without the need to rely on users to update this information, further increases quality and efficiency of legal services. Key objectives of the system of the present invention include eliminating gaps between business objectives and litigation strategy and optimizing communication and coordination of case-related tasks and priorities in a fully automated fashion, which are all achieved by leveraging data and technology to enable a fundamentally different approach that becomes smarter and more precise via machine learning and predictive modeling.

Disclosed herein is an article of smart clothing which automatically recognizes body configuration and motion. This smart clothing includes inertial motion units which are distal relative to selected joints and also sets of strain sensors which span those joints. The type of energy transmitted through a strain sensor can be electrical energy or light energy. Combined multivariate analysis of data from both inertial motion units and strain sensors provides more accurate recognition of body configuration and motion than data from either alone.

A phase analyzer includes a data acquisition unit that acquires a plurality of pieces of spectrum imaging data in which positions on a sample are associated with spectra which are based on signals from the sample; a first acquisition unit that acquires a first representative spectrum group for each piece of spectrum imaging data by performing multivariate analysis on each of the plurality of pieces of spectrum imaging data; a second acquisition unit that acquires a second representative spectrum group by performing multivariate analysis on the plurality of first representative spectrum groups acquired by the first acquisition unit; and a phase analysis unit that performs phase analysis on each of the plurality of pieces of spectrum imaging data by using the second representative spectrum group.

A control method includes: monitoring a statistic obtained by performing a multivariate analysis on a plurality of parameters; extracting, from the plurality of parameters, a predetermined number of higher-order parameters in terms of an influence degree on fluctuation of the statistic; generating a plurality of experimental patterns according to an experimental design method; acquiring a measurement result of a specific parameter indicating quality of a product when at least one device is controlled according to each of the plurality of experimental patterns; setting a new target value of the predetermined number of higher-order parameters in order to stabilize a value of the specific parameter within a management range based on the measurement result; and controlling the at least one device such that the predetermined number of higher-order parameters approaches the new target value.

The invention provides a seminal fluid storage strategy optimization system and method based on a hybrid neural network architecture, and the system comprises a data collection module which is used for obtaining temperature position vibration data, associating an offline sperm motility attenuation rate, calculating a spatial-temporal correlation degree, and constructing a coupling tensor; the time sequence prediction module is used for the LSTM to receive the temperature, the gradient, the correlation degree and the sensitive factors and predict the future temperature trend, and gsperm gating is used for filtering biological noise; the multivariable analysis module is used for carrying out Transform four-head attention analysis, focusing local activity coupling in a bag by V-head, and activating and punishing a supercooled region by cold-head; the strategy generation module is used for fusing the predicted temperature, the vitality weight, the supercooling penalty and the sensitive factor and softmax to output liquid nitrogen supply flow and the activity attenuation probability; and the closed-loop correction module is used for monthly counting the off-line survival rate, calculating a correction parameter omega, correcting the correlation degree gain and the basic flow, and realizing model locking without retraining.

The invention discloses a physical-data double-drive fused rapid prediction model for the solid fraction of a meshing point in double-roll thin-strip casting and application of the rapid prediction model. The model comprises the following steps: smelting target steel in a protective atmosphere, sampling 5-10 minutes before tapping, pretreating a sample to be detected, and then carrying out identification and component analysis; setting working parameters of the continuous casting machine, carrying out a throwing experiment, collecting real-time data of the working parameters of the continuous casting machine, and carrying out standardization processing; establishing a three-dimensional space model, and combining with a multivariate analysis module to obtain a multi-field coupling physical model; and performing optimization fitting on the multi-field coupling physical model to reduce errors of the two, fusing model data after optimization fitting and experimental data, extracting feature parameters, inputting the feature parameters into a long-short-term memory network algorithm to perform meshing point temperature prediction, converging a result to a precision threshold, and performing generalization processing to obtain the meshing point temperature prediction method. Through testing, the response rate of the model can reach 70 ms, and the technical requirement for online regulation and control of double-roller thin-strip continuous casting can be met.

Provided is an information processing apparatus that performs processing for presenting a relationship between variables in multivariate analysis. The information processing apparatus includes a detection unit that detects a combination of two variables having a characteristic relationship in multivariate analysis, and a presentation unit that presents information regarding the characteristic relationship between the two variables. The detection unit detects whether or not there is a characteristic relationship including at least one of a positive correlation, a negative correlation, or a non-linear relationship as the entire variables on the basis of the relationship between the explanatory variable and the explained variable for each of the two consecutive categories of the explanatory variable, and further quantifies the relationship between the explanatory variable and the explained variable as the entire variables.

The present invention relates to apparatus for non-invasive blood glucose monitoring, which includes: a signal transmitting module that transmits a terahertz wireless signal modulated into a PRBS pattern at a terahertz carrier frequency to a body part; a signal receiving module that demodulates the terahertz wireless signal reflected from the body part to measure performance parameters of the modulation pattern; and a sensing module that performs multivariate analysis using the performance parameters of the modulation pattern measured through the signal receiving module and quantifies features related to blood glucose through the multivariate analysis to measure blood glucose concentration.

The present invention enables efficient efforts for energy saving by analyzing electric power consumption data of a plurality of electric power supply facilities, the invention comprising: an electric power consumption data acquisition unit that acquires electric power consumption data of each of a plurality of electric power supply facilities for supplying electric power to a plurality of energy demand facilities; an electric power supply pattern model generation unit that performs multivariate analysis on the electric power consumption data, and generates an electric power supply pattern profile in which the plurality of electric power supply facilities are respectively classified into a plurality of electric power fluctuation patterns and / or an electric power supply pattern operation state sequence indicating operation states over time of the electric power supply pattern profile; and an electric power load amount calculation unit that, on the basis of the electric power supply pattern profile and / or the electric power supply pattern operation state sequence, calculates the electric power load amount of a plurality of energy demand facilities or the plurality of electric power supply facilities corresponding to the plurality of electric power fluctuation patterns, or the electric power load amount over time corresponding to the plurality of electric power fluctuation patterns.

The invention discloses a method for detecting serum bile acid abnormal data in PD cognitive function evaluation, and relates to the technical field of biological data detection. The method comprises the following steps: on the basis of a serum bile acid detection data set, determining first N bile acids with the highest correlation with data abnormality by combining a multivariate analysis method to form a key bile acid set; the method comprises the following steps: by taking normal serum bile acid detection data as training data, constructing corresponding N univariate prediction channels for N bile acids in a key bile acid set; performing data reconstruction to obtain an anomaly analysis data set; establishing a distribution relationship between the anomaly analysis data set and N single-variable prediction channels, inputting the N anomaly analysis data clusters into the N single-variable prediction channels for iterative prediction, and obtaining N single-variable prediction results; and calculating a variable deviation coefficient, and if the variable deviation coefficient exceeds a preset deviation threshold, marking the data as abnormal data. According to the method, the accuracy of marking the abnormal data of the serum bile acid is effectively improved.

Provide a system. 【Solution means】 A device for receiving voice information and video information, A device for analyzing the voice information with natural language analysis technology and extracting speech features, A device for extracting non-verbal information from the video information by multivariate analysis, A device for generating an individualized training plan based on the analyzed speech features and non-verbal information, A device for providing the generated training plan to the user, A device for collecting training activity information executed by the user, A device for evaluating the collected training activity information and creating a progress report, A system including a device for sharing the progress report with medical staff.

[Problem] To visually comprehend an evaluation factor which is preferable to be improved preferentially when compared with same or similar business styles in order to improve customer repeat visit intent, repeat purchase intent, contract intent, continuation intent, or recommendation intent. [Solution] An information providing device according to the present invention includes a research data acquisition means for acquiring evaluation data including an evaluation value of an evaluation item for a relevant product or service, a multivariate analysis means for calculating a contribution degree of an explanatory variable to an objective variable by performing multivariate analysis with repeat visit intent or the like as the objective variable and the evaluation value as the explanatory variable on the basis of the evaluation data, a reference value setting means for setting a reference value for the explanatory variable, a priority analysis means for analyzing an engagement priority on the basis of the reference value and the contribution degree, and an analysis result output means for displaying an analysis result by the priority analysis means on an information terminal.

This invention belongs to the field of traditional Chinese medicine (TCM) extraction technology, and particularly relates to a method and system for cross-scale dynamic control of TCM extraction processes. The method involves collecting process parameters and corresponding quality attributes of several batches of TCM preparations under small-scale, pilot-scale, and large-scale production conditions during extraction. A multivariate analysis model, a dynamic control standard identification model, a cross-scale nonlinear prediction model, and a multi-scale coupled dynamic simulation model are then trained sequentially. Real-time process parameters are input into the trained cross-scale nonlinear prediction model and multi-scale coupled dynamic simulation model to obtain predicted real-time key quality attributes. This invention achieves real-time prediction and dynamic control of key quality attributes through the coupling of the multivariate analysis model, the dynamic control standard identification model, the cross-scale nonlinear prediction model, and the multi-scale coupled dynamic simulation model, enabling timely adjustment of process parameters to stabilize production and ensure the stability of TCM extraction processes at large-scale production levels.

The application relates to the technical field of intelligent optimization, and discloses a 3D printing powder proportioning optimization method and system based on artificial intelligence. The method comprises the following steps: collecting historical data corresponding to powder materials based on the printing process of a 3D printing product, performing multivariate analysis on the historical data, and generating a plurality of powder compositions; a material simulation model simulates the properties of any powder composition, outputs material property data, and extracts a powder proportioning scheme meeting printing requirements; a product prediction model predicts the product of any powder proportioning scheme, outputs a product prediction result, matches the product prediction result with ideal product data, and selects the best powder proportioning scheme and the corresponding best printing process; and the performance of an actual printing product is tested, and if the actual printing product does not meet the requirements, the step is repeatedly executed, and a new best powder proportioning scheme and a new best printing process are selected again. The application improves the precision of 3D printing powder proportioning optimization.

An information processing device (100) for processing information relating to the status of a production line (2) including a plurality of steps calculates statistics per unit time by performing multivariate analysis on a plurality of variables indicating the status of the plurality of steps. An information processing device (100) generates an image signal indicating an image including a state monitoring screen (201). A state monitoring screen (201) displays a first graph (201A), which displays a time sequence of statistical magnitude with a first axis as a time and a second axis as a statistical magnitude, and a graph (201B), which indicates an implementation period of a job category to which a job pertaining to at least one of a plurality of steps belongs, so as to align the time axes. An information processing device (100) outputs an image signal to a terminal device (70) that is a display device.

This invention discloses a method and system for identifying and assessing soil heavy metal pollution sources based on machine learning. The method constructs a feature dataset by collecting data on heavy metal element content and spatial environmental attributes of soil in a target area; it decomposes the input matrix using multivariate analysis and k-means clustering to obtain a source spectrum matrix and a source contribution matrix, thus identifying pollution sources and analyzing their contributions; further, it establishes a background contribution model by combining conservative elements and time series information to extract the background contribution value of each pollution source within the target time period; it calculates single-factor and comprehensive risk indices based on pollution source reconstruction and background correction, and generates soil heavy metal pollution risk level information by combining dynamic risk grading thresholds; finally, it generates a soil heavy metal pollution risk distribution map of the target area through spatial interpolation and zoning processing, simultaneously achieving quantitative analysis of pollution sources, background value correction, risk index calculation, and spatial visualization.

The invention provides a method for rapidly screening aroma-producing bacillus to improve the quality of cigar tobacco leaves, and belongs to the technical field of agricultural microorganisms. On the basis of bacillus 16S rDNA (ribosomal deoxyribonucleic acid) sequence analysis and fermentation liquor smell evaluation, volatile aroma components of strain fermentation liquor and fermented cigar tobacco powder are analyzed by using HS-SPME-GC-MS (headspace solid-phase microextraction-gas chromatography-mass spectrometry) and an electronic nose, and excellent aroma-producing strains are screened by combining a multivariate analysis method. And the fermentation effect on the cigar tobacco leaves is verified by simulating fermentation. Results show that seven kinds of 50 bacillus strains are identified, and the smell difference of fermentation liquor smell evaluation is large. 177 volatile components are identified in strain fermentation liquor, 7 strains have relatively high aroma producing characteristics, and 2, 3-butanedione, 2-nonyl alcohol and isoamyl alcohol are common aroma components of the strains. Two strains with the highest volatile components and large difference in the fermented tobacco powder are screened out through PCA and clustering analysis, 1-F-2 can generate high-content alcohol, pyrazine, ketone and other components, and DMMJ-4 can generate high-content alcohol, pyridine, ketone and other components. The two strains are used for fermentation, so that the content of volatile aroma components of the cigar tobacco leaves is obviously improved, and the flavor characteristics and the quality characteristics are also improved to different extents. Therefore, the invention can provide reference for screening and application of cigar tobacco leaf microbial fermentation strains.