138 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 relates to the technical field of multivariable analysis, and discloses a method and a device for improving the strength of flow-state solidified soil based on multivariable analysis, and the method comprises the following steps: collecting a material proportion parameter matrix, a stirring parameter matrix and an environment monitoring data matrix in flow-state solidified soil equipment, and executing feature extraction and feature integration; obtaining a target feature vector; performing intensity prediction of the time sequence on the target feature vector to obtain an intensity prediction value of the flow-state solidified soil; sensitivity analysis is carried out based on the intensity predicted value, and an intensity improvement potential index, a parameter adjustment stability index and an energy consumption influence index are obtained; according to the strength improvement potential index, the parameter adjustment stability index and the energy consumption influence index, adaptive parameter optimization is carried out through discount inverse reinforcement learning, and a target control parameter set is obtained. And the strength and the quality uniformity of the flow-state solidified soil are effectively improved while the operation stability is guaranteed.

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 relates to the technical field of lamination processes, in particular to a double-layer glue and three-layer film method for manufacturing a diamond sticker, which comprises the following steps of: setting the total thickness of a film layer based on a top PET film, a glue layer and a substrate PET film, distributing the total thickness to the boundary of the film surface and the glue layer, adjusting the thickness of the PET film, loading the interface thickness control variable of the glue layer, and adjusting the thickness of the glue layer. And generating initial parameters of the film layer structure. According to the method, the lamination loads of different areas of the film layer are decomposed through a finite element analysis algorithm, the loads of multiple areas are uniformly distributed on the surface of the film, the problem of local pressure bearing unbalance easily occurring in a traditional lamination process is avoided, and the thickness of the film layer and the thickness of the adhesive layer are reasonably distributed in the design stage; different functional layers can effectively bear stress in the pressing process, the stress concentration phenomenon between layers is reduced, the pressure is adjusted point by point through multivariate analysis, the overall stress distribution is optimized, and the uniformity and stability of the film layer under the pressure are guaranteed.

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.

The invention discloses a photoacoustic spectrum identification method based on a gas photoacoustic effect. The method comprises the following steps: S1, introducing a multi-component gas mixture into a closed photoacoustic cell; s2, based on the absorption characteristics of different gases, selecting corresponding laser wavelengths to irradiate the gas mixture in the photoacoustic cell; s3, collecting and recording a photoacoustic signal generated by the gas mixture through a high-sensitivity photoacoustic detector; s4, processing and separating the collected photoacoustic signals by using a multivariate analysis algorithm; s5, based on a separation result, identifying and quantifying the concentration of each component gas; according to the photoacoustic spectrum identification method based on the gas photoacoustic effect, separation and identification of signals are achieved, the problem that the equipment cost is greatly increased due to complexity of a light source is avoided, the application field of the photoacoustic spectrum technology is widened, and the method is suitable for popularization and application. The problem that separation is difficult due to overlapping of photoacoustic signals of different gases in multi-component gas mixture detection in the prior art is solved.

Provided is a PET discrimination method that is an objective method that offers comparatively simple differentiating operation and that makes it possible to differentiate between homogeneous polyester resins (including fiber). Infrared or near-infrared absorption spectra of respective comparison resins, of a group A of a plurality of comparison resins that are recycled PET resins and a group B of a plurality of comparisons resins that are virgin PET resins, are found. Spectral data in a predetermined wavenumber range is extracted from the absorption spectra, and an analysis data group obtained by multivariate analysis of the spectral data is accumulated as a database. Next, an unknown resin is taken to be a resin under test, and test data obtained by extracting spectral data in the predetermined wavenumber range from the resulting absorption spectra is found and cross-checked against the analysis data group of the database by multivariate analysis, to differentiate whether the resin under test is recycled PET resin or virgin PET resin.

In a data processing device, a multivariate analysis unit (18) performs multivariate analysis on a data set comprising a plurality of spectrograms obtained from a plurality of samples, and specifies the principal component of the data set as a multivariate analysis result. Each spectrogram has a first coordinate system. A distribution generation unit (20) generates a load distribution corresponding to the principal component as a multivariate analysis result. A drawing generation unit (22) generates a load drawing having a second coordinate system on the basis of the load distribution. The second coordinate system is the same as the first coordinate system.

In an aspect, there is provided a computer-implemented method for approximating product production in a bioreactor. The method comprises: providing a bioreactor containing live cells in a substrate, the bioreactor for cultivating, over a time period, a product derived from or of the cells during a manufacturing process; providing two or more sensors for measuring respective two or more different physical attributes of the substrate during the time period; receiving, at a processor, sensor data from the two or more sensors; and determining via the processor, based on a predetermined or recursive algorithm that correlates the sensor data and the product production by independent consideration of the sensor data of the two or more different physical attributes or by multivariate consideration of the sensor data of the two or more different physical attributes, an approximate amount of the product.

To provide a PET discrimination method that is an objective method in which discrimination operation is comparatively simple and that can discriminate homogeneous polyester resins (including fiber).SOLUTION: The discrimination method includes: obtaining infrared or near-infrared absorption spectra of respective comparison resins of a group A of a plurality of comparison resins that are recycled PET resins and a group B of a plurality of comparison resins that are virgin PET resins; extracting spectral data in a predetermined wavenumber range from the absorption spectra to accumulate an analysis data group obtained by multivariate analysis of the spectral data as a database; and taking an unknown resin to be a resin under test, obtaining test data obtained by extracting spectral data in the predetermined wavenumber range from the obtained absorption spectra, collating with the analysis data group of the database by the multivariate analysis to discriminate whether the resin under test is recycled PET resin or virgin PET resin.SELECTED DRAWING: Figure 6

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

A system and method of simulating and optimizing industrial and other processes includes a computer that performs multivariate analysis of input variables and output variables to generate a data model of the operation of the process. For industrial applications, the input variables include process variables and the output variables include result variables from the operation of the industrial process. The data model determines contributions to changes in the output or result variables by the respective input or process variables and is provided to a predictive algorithm to identify parameter values for input or process variables expected to have a most significant impact on the output or result variables during performance of the process. The outputs of the predictive algorithm are parameter values that are provided as input or process variables to the industrial process for simulation or performance optimization or product recommendations / optimizations.

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 provides a prediction or diagnosis system for identifying non-Hodgkin lymphoma and application, and belongs to the technical field of medical diagnosis. The prediction or diagnosis system comprises a Raman spectrum detection module of a to-be-detected sample and a Raman spectrum detection module of a negative control, the Raman spectrum detection module of the sample to be detected and the Raman spectrum detection module of the negative control take Raman spectrum data for detecting serum as indexes. Research finds that healthy people and non-Hodgkin lymphoma patients are distinguished through Raman spectrum data, non-Hodgkin lymphoma subtypes can be diagnosed through Raman data in combination with multivariate analysis, non-Hodgkin lymphoma and the non-Hodgkin lymphoma subtypes can be rapidly and noninvasively predicted or diagnosed, and the non-Hodgkin lymphoma subtypes and the non-Hodgkin lymphoma subtypes can be rapidly and noninvasively predicted or diagnosed. More choices are provided for rapid detection of non-Hodgkin lymphoma and subtypes thereof, and useful guidance is provided for application to treatment of DLBCL and CLL.

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.

Method and device for testing a multivariate model for generating a value of an analyte concentration using spectroscopic techniques, the method comprising a) providing a multivariate model for generating an output value of an analyte concentration based on a spectroscopic measurement; b) inputting to the model a spectrum of known concentration of the analyte to be measured; c) inputting a spectrum of an interferent; and d) processing the received spectra of the analyte to be measured and the interferent and generating an output value for the analyte to be measured using the model. It is further described a method for producing such a multivariate analysis model, the method comprising spectrally spiking an input spectrum to the model.

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 a concentration measuring device for obtaining a concentration of a target component in a liquid sample highly accurately, and includes: an optical measuring unit 2 that measures a spectroscopic spectrum of the liquid sample; an electrochemical measuring unit 3 that electrochemically measures a property value of the liquid sample; and a concentration calculating unit 41 that calculates the concentration of the target component by performing a multivariate analysis that uses the spectroscopic spectrum and the property value as explanatory variables.

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.

The invention discloses a multivariate evaluation method, system and device based on multi-scene education information analysis. The method comprises the following steps: acquiring classroom interaction audio data, after-class voice comment data, roll book image data, non-teaching scene voice data and third-party operation data; through voice preprocessing and image preprocessing, classroom analysis data, after-class analysis data, subject information analysis data and non-teaching scene analysis data are obtained; combining the after-class analysis data and the subject information analysis data to obtain stage feedback data; performing matching analysis on the classroom interaction audio data, the after-class voice comment data and the non-teaching scene voice data, and obtaining behavior analysis data through evaluation analysis; and performing optimization and correction by combining with the third-party operation behavior data, and further obtaining student multivariate evaluation data through multivariate analysis. According to the method, the problem that the evaluation method has defects in comprehensiveness and accuracy is solved, and meanwhile, the manual workload is reduced through automatic evaluation.

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.

The invention relates to the technical field of gasoline detection, in particular to a method and a device for detecting methanol and ethanol in automotive gasoline based on a near infrared spectrum, and provides a near infrared spectrum detection method for alcohol concentration in gasoline without multivariate analysis modeling. Spectral components of a methanol component and an ethanol component are obtained by performing spectral projection on a near infrared spectrum of an alcohol substance-containing gasoline solution, and a standard curve is established by using the alcohol spectral components and corresponding concentrations of a standard solution with known mass concentrations, so that the influence of coexistence of methanol and ethanol on the predictive capacity of the standard curve is eliminated, and the predictive capacity of the methanol and ethanol-containing gasoline solution is predicted. The problem of serious overlapping of near infrared spectrum bands of different components is solved, and the solving process is simple.

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.

An image analysis apparatus includes one or more processors configured to execute (a) acquiring, from a measurement device, spectral images for a plurality of wavelengths, obtained by imaging a measurement target, (b) acquiring a target range in each of the spectral images, (c) performing multivariate analysis of each pixel based on a gradation value of the pixel for each wavelength in the target range, (d) generating an analysis image including an analysis result of the multivariate analysis for each pixel in the target range, and (e) storing the generated analysis image into a memory.