30 results about "Principal component regression" patented technology

The invention discloses a response-driven dominant oscillation mode energy characteristic influence factor analysis method, and relates to the technical field of electric power. The method comprises the following steps: extracting a dominant subsynchronous oscillation mode component of a grid-connected point current and a direct-driven wind power plant outlet voltage; fitting the dominant subsynchronous oscillation mode component according to the transient energy function of the grid-connected port of the direct-driven wind power plant to obtain energy flow power; according to a system operation principle, determining influence factors influencing the dominant subsynchronous oscillation energy characteristics; the influence factors serve as independent variables, the energy flow power serves as dependent variables, and the objective weight and the subjective weight of each influence factor on the energy flow power are calculated through a principal component regression method and an improved analytic hierarchy process; according to the objective weight and the subjective weight of each influence factor to the energy flow power, calculating the comprehensive weight of the influence factor to the energy flow power; and according to the comprehensive weight of each influence factor on the energy flow power, determining the dominant influence factor of the dominant oscillation mode energy characteristic.

The invention provides an economic prediction method and system based on a principal component regression model. According to the method, the advantages of principal component analysis and multiple regression are fused, and the defects existing in statistical method prediction are overcome by using the principal component analysis method, so that influence factors of economic development are better mined, and economic data are accurately predicted.

The present application provides a method and device for constructing a sensory quality prediction model for tobacco leaf raw materials. The method of the present application includes: obtaining tobacco leaf samples and naturally drying the tobacco leaf samples and then crushing them to obtain a sample set; using a prediction model of a near-infrared big data platform based on near-infrared spectroscopy to obtain the content of preset types of tobacco leaf chemical components for each sample in the sample set; obtaining the sensory quality of the sample set; applying grey correlation and principal component regression algorithms to the content of tobacco leaf chemical components and the sensory quality of the sample set, and establishing a sensory index prediction model based on chemical components. The technical solution provided by the present application can more accurately judge the sensory quality of tobacco leaves, avoid subjective differences in the process of manual smoking and judging, and has important application significance for accurately detecting tobacco leaf quality.

The present invention discloses a carbon dioxide concentration prediction method based on a semi-supervised deep probability model for on-line detection of the carbon dioxide content in a carbon dioxide absorption tower. Based on the conventional probabilistic principal component regression model, the present invention simultaneously introduces the ideas of semi-supervised learning and deep learning, expands the basic probabilistic principal component regression model into the structure of a semi-supervised deep learning model, that is, proposes a new soft sensing method based on a semi-supervised deep probabilistic principal component regression model for on-line detection of the carbon dioxide concentration in a carbon dioxide absorption tower. Compared with the conventional principal component regression model, the method of the present invention can not only effectively utilize a large amount of cheap unlabeled sample information, but also deeply extract the information hidden in the process data, thereby improving the actual prediction effect of the carbon dioxide concentration.

The present application provides a method and system for optimizing and analyzing manufacturing information of reducers. The method includes: crawling the real-time Internet information of reducer raw materials, the historical Internet information of reducer raw materials, the real-time Internet information of reducer parts, the historical Internet information of reducer parts, and the customer demand information of reducers; extracting the data features of the information; establishing three single prediction models, namely, artificial neural network model, support vector machine model, and principal component regression analysis prediction model; according to the virtual prediction method, three single models are used to predict the manufacturing capacity of reducers, and compared with the actual manufacturing capacity of reducers; according to the daily prediction error of each model, the prediction decision theory is used to complete the model screening, and the models with poor prediction effect are eliminated. The present application realizes crawling the real-time and historical information data of online raw materials, parts, customer needs, etc. of reducers, realizes intelligent decision-making optimization analysis, and realizes the intelligent manufacturing of reducers.

The application relates to the technical field of power energy consumption prediction, in particular to a power energy consumption prediction method and system based on multidimensional data and a medium, the method comprising the following steps: collecting power consumption data and various influence data of a power grid in real time; determining redundancy imbalance degrees of the various influence data in a time sequence interval; calculating mutual information dependency degrees of the various influence data in the time sequence interval; identifying each mutation point of the power consumption data in the time sequence interval; determining prediction effective contributions of the various influence data in the time sequence interval; combining the redundancy imbalance degrees and the prediction effective contributions to correct regression coefficients in a prediction process of the power consumption data of the power grid by using a PCR principal component regression algorithm. Therefore, the prediction accuracy of power energy consumption is improved.

The invention discloses a card reading method, system and device for a card reader, relates to the field of signal transmission technology, and is used to solve the problems of card reading recognition errors and low card reading efficiency caused by different initialization requirements of a card reader and a device. The method comprises utilizing a principal component regression method and a covariance analysis to analyze the initialization requirements of a card reader and a device, and judging whether a combination of a current card reader and a current device is to be marked in combination according to the analysis results in combination with environmental factors. When the environmental factors are large or the combination of the card reader and the device is not compatible, the card reader and the device are tested for card reading and the test data are collected. The card reading accuracy is calculated according to the test data, and the error rate is comprehensively reported. The method further analyzes and judges whether a combination of the current card reader and the device is to be marked in combination using a logistic regression method, thereby improving the accuracy of card reading and recording the combination of the card reader and the device, facilitating future query and matching, and reducing costs and increasing efficiency.

The present invention provides a method for detecting the content of lead in lubricating oil, and relates to the technical field of lead content detection in lubricating oil. The method comprises: using a rotating disk motor emission spectrometer to collect spectra of standard oils with known lead content under preset collection conditions, and collecting each standard oil multiple times; performing first-order differential and smoothing processing on the collected standard oil spectra, and performing multivariate scattering calibration; using a principal component regression method to establish a correlation model between the lead content and the standard oil spectra under specific parameters; using a rotating disk motor emission spectrometer to collect spectra of lubricating oil samples under the same collection conditions as the standard oil; inputting the lubricating oil sample spectra into the correlation model to obtain the content of lead in the lubricating oil sample. In this way, the effective information of many spectral lines of the lead element can be fully utilized, and the detection limit of the lead element of the rotating disk motor emission spectrometer can be effectively improved.

A device and method for detecting the gas content of a two-phase flow of oil and gas using a halogen lamp light source are disclosed. The method includes the following steps: Step 1: Turn on the halogen lamp light source and inject the prepared two-phase flow of oil and gas to be tested into the pipeline through a three-way valve according to the pre-calibrated gas content. Step 2: Turn on the peristaltic pump to uniformly fill the pipeline with the two-phase flow of oil and gas to be tested, then turn off the peristaltic pump. A first collimating lens is connected to the halogen lamp light source via a first optical fiber to receive the incident light emitted by the halogen lamp light source. A spectrometer is connected to the second collimating lens via a second optical fiber to receive the outgoing light of the two-phase flow of oil and gas passing through the flow cell. The spectrometer records the spectral information in a computer. Step 3: Repeat steps 1 and 2 until the spectral information of all calibrated gas contents is obtained. Step 4: Establish a prediction model using the spectral information. Specifically, a principal component regression (PCR) model for detecting the gas content of the two-phase flow of oil and gas is established to predict the gas content of the two-phase flow.

The invention belongs to the technical field of nondestructive testing, and relates to a method for detecting the contents of total sugar, crude protein and total flavone in folium cortex eucommiae by near infrared spectroscopy, which comprises the following steps: 1) collecting a folium cortex eucommiae correction set sample with sufficient representativeness, and measuring the contents of the crude protein, the total sugar and the total flavone; 2) collecting near infrared spectrum data of the eucommia ulmoides leaves in a range of 12000-4000 cm <-1 >; and 3) establishing a quantitative analysis model by combining the total sugar, crude protein and total flavone content values of the eucommia ulmoides leaves and the near infrared spectrum data of the pretreated eucommia ulmoides leaves, and substituting the near infrared spectrum data of unknown eucommia ulmoides leaf samples to verify the accuracy. Establishing a crude protein quantitative analysis model through a principal component regression modeling method; selecting a modeling wave band through a genetic algorithm, and establishing a total sugar quantitative analysis model through a random forest algorithm; a modeling wave band is selected through a genetic algorithm, and a general flavone quantitative analysis model is established through a partial least square method. According to the method, the folium cortex eucommiae components are rapidly and accurately detected, and technical support is provided for precise production, management, tending and the like of folium cortex eucommiae.

The present application relates to a kind of zirconium oxide humidity sensor low pressure compensation method, the method includes the following steps, step 1: first need to the current calibration of sensor, ensure the consistency of sensor, the current of sensor in different states is measured and calibrated to target current;Step 2: measure current pressure or user input current pressure P, standard atmospheric pressure atm is calculated to obtain oxygen vector [K,B];Step 3: the coherence of humidity and oxygen is analyzed using PLSR algorithm, and the principal component regression of PLSR algorithm is obtained to exist interference parameter, and more parameter is used to fit PLS model coefficient [A,B,C,D,E] with pressure;Step 4: oxygen concentration and humidity are calculated by the vector obtained in step 2 and 3;Step 5: in different pressure environment, the same coefficient can also be measured accurately oxygen concentration and humidity;Step 6: when sensor batch changes, only current calibration is needed, and the vector of step 2,3 is also applicable.

The invention relates to the technical field of electric power consumption prediction, in particular to an electric power consumption prediction method and system based on multi-dimensional data and a medium, and the method comprises the steps: collecting power consumption data of a power grid and various influence data in real time; determining the redundancy unbalance degree of each influence data in the time sequence interval; calculating mutual information dependency degrees of various influence data in the time sequence interval; identifying each abrupt change point of the electricity consumption data in the time sequence interval, and determining the prediction effective contribution of each influence data in the time sequence interval; and in combination with the redundancy unbalance degree and the prediction effective contribution, correcting a regression coefficient in a process of predicting the power consumption data of the power grid by using a PCR principal component regression algorithm. Therefore, the prediction precision of the power consumption is improved.

The invention discloses a chemical reaction organic matter real-time detection system based on a near-infrared instrument, which comprises the following steps: collecting a spectral signal of a reaction sample under irradiation of near-infrared light, carrying out standardization treatment on original data, extracting nonlinear characteristics by adopting an improved kernel principal component regression method, and calculating a nonlinear characteristic of the reaction sample; a Dirichlet process hybrid model is combined to realize adaptive clustering, then a posterior probability weight strategy is constructed to realize dynamic prediction of the concentration of the organic matter, finally detection output data with time stamps are generated, and real-time monitoring and closed-loop modeling control of organic matter components in chemical reaction are realized. The method is used for supporting component monitoring and control in the chemical process.

The invention relates to an automatic acquisition multi-channel battery detection comprehensive test system, and belongs to the technical field of battery comprehensive test. The system comprises a data acquisition module, a capacitance analysis module, a feature selection module, a model estimation module and a charge correction module. The data acquisition module is used for the battery pack to obtain full-life-cycle dynamic capacitance data of the battery pack through the battery detection model; the capacitance analysis module analyzes through a dynamic capacitance method according to the full-life-cycle dynamic capacitance data of the battery pack to obtain estimation model characteristics; the feature selection module obtains closely related features of the aging of the battery through a Pearson correlation analysis method according to the features of the estimation model; the model estimation module analyzes an SOH estimation model through principal component regression according to the closely related characteristics of battery aging to obtain a battery pack health state; and the charge correction module obtains the charge state of the battery pack through a charge correction model according to the health state of the battery pack. Accurate estimation of the health state of the battery pack is realized, and the detection efficiency of the battery management system is improved.

The present invention provides a method for determining ammonia-containing heavy metal wastewater, including: providing standard copper-ammine complex solutions of different concentrations, measuring them respectively using an ultraviolet-visible spectrophotometer to generate a first ultraviolet-visible spectrum; collecting first spectral data of the first ultraviolet-visible spectrum in a band of 500 to 800 nm, and establishing a concentration prediction model based on the spectral data; measuring the copper-ammine complex solution sample to be tested under the same measurement conditions as the standard copper-ammine complex solution to generate a second ultraviolet-visible spectrum; collecting second spectral data of a band of 500 to 800 nm in the second ultraviolet-visible spectrum; standardizing the second spectral data in turn, performing principal component regression analysis, and determining the concentration of the copper-ammine complex solution sample to be tested based on the concentration prediction model. Compared with the prior art, the method for detecting copper-ammine complex ions in water bodies realizes rapid and accurate monitoring of the actual copper-ammine complex ion concentration in each treatment link.

The invention discloses a coal ash sintering temperature prediction method based on principal component regression, comprising: 1) collecting coal ash sintering test historical data; 2) using useless information variable elimination method to select variables, remove variables with useless information on coal ash sintering temperature, reduce the influence of irrelevant information on the prediction model, improve the prediction accuracy of the model, and reduce the number of variables to improve the efficiency of operation; 3) establishing a principal component regression model, taking coal quality test data as the input variable of the model, and coal ash sintering temperature as the output variable of the model, establishing a one-to-one mapping relationship between the two, and using the obtained optimal number of principal components to establish a principal component regression model, and the established prediction model is 4) substituting the coal quality test data into the model to obtain a predicted value. The invention establishes a prediction model according to the full analysis data of coal types and the coal ash component analysis data to predict the initial sintering temperature of other coal ashes.

The invention relates to a cable degradation degree detection method and device based on principal component regression. The method comprises the following steps: acquiring a plurality of harmonic data in currents of a plurality of circuits, and performing data conversion on the plurality of harmonic data to obtain a plurality of harmonic data in a target format; performing data preprocessing on the harmonic data in the target format to obtain preprocessed target harmonic data; performing principal component analysis on the target harmonic data to obtain a data matrix corresponding to the target harmonic data; inputting the data matrix corresponding to the target harmonic data into a pre-trained target principal component regression model to obtain a cable degradation degree prediction value of the circuit corresponding to the target harmonic data; determining the degradation state of the cable of the circuit according to the cable degradation degree prediction value of the circuit; and determining a circuit cable maintenance suggestion according to the degradation state of the circuit cable. Stable and accurate degradation degree detection can be performed on the cable at least through a principal component regression model and a principal component analysis method.

The invention relates to the technical field of precision control of numerical control machine tools, and discloses an improved principal component regression machine tool thermal error modeling method based on density clustering, which comprises the following steps of: performing dimension reduction processing on original temperature data through principal component analysis to obtain a plurality of principal components; establishing a plurality of principal component regression models based on the principal components, wherein each model corresponds to a different principal component number; performing clustering analysis on prediction results of the plurality of principal component regression models by using a density clustering algorithm, and identifying and eliminating abnormal prediction values; and performing comprehensive processing on the prediction result after the abnormal value is removed to obtain a final thermal error prediction model. The principal component regression model prediction results corresponding to different principal component numbers are screened and optimized by combining a density clustering algorithm, abnormal prediction values are effectively eliminated, and reliable prediction results are integrated, so that a thermal error prediction model with higher precision is constructed.

The invention relates to a method for constructing a pork water-retaining property prediction model based on a magnetic resonance imaging technology. The method comprises the following steps: (1) preparing a sample; (2) sample T2 weighted image acquisition; (3) measuring water-retaining property parameters of the sample; (4) importing data; and (5) model construction and evaluation. According to the method, on the basis of the MRI technology, fresh pork and frozen and unfrozen pork are taken as research objects to be subjected to T2 weighted image acquisition, then after feature data are extracted from key information in the images, a pork water-retaining property prediction model is established through principal component regression analysis (PCR) and a partial least squares regression (PLSR), and the pork water-retaining property prediction model is obtained. And a scientific solution is provided for rapid and accurate detection of the water-retaining property of the pork.

The invention provides a method for constructing a prediction model for early warning of acquired myasthenia related to sepsis based on an electrophysiological index, a prediction method and application, and the construction method comprises the steps: determining the electrophysiological index most related to an APACHE II score and an SOFA score based on factor analysis and principal component regression analysis; the method comprises the following steps: acquiring personal information, electrophysiological indexes, inflammatory response and score, MRC score and EMG, RNS and DMS measurement data of a sample SIRS patient; constructing different attribute sets according to data categories, and constructing a similarity fusion network model based on the attribute sets; and based on a similarity measurement matrix output by the similarity fusion network model, carrying out clustering analysis according to a spectral clustering algorithm, outputting a first classification category and a confusion matrix, and based on the confusion matrix, taking the similarity fusion network model meeting accuracy and precision requirements as an early prediction model of acquired myasthenia related to sepsis. The prediction model can predict the type of the patient, and is beneficial to early intervention and patient rehabilitation.

The invention relates to the technical field of equipment system evaluation, in particular to a system contribution rate calculation method based on principal component regression analysis, which can solve the problems that the existing capability index cannot effectively reflect the influence degree of the capability index on the system effect and lacks the actual application value to a certain extent. The method comprises the following steps: designing task scenarios and extracting capability indexes; forming a plurality of scenario simulation samples through the designed task scenario, performing simulation operation on all the simulation samples for multiple times by using a simulation platform, and evaluating the system efficiency corresponding to each simulation sample; establishing an incidence matrix of system efficiency and capability indexes by using data output by simulation evaluation; calculating the principal components of the capability indexes by using a principal component analysis method, and eliminating the correlation influence between the capability indexes; the correlation degree of the capability indexes and the system efficiency is calculated through a multiple regression analysis method, and in a multiple regression model, the coefficient of each capability index is the system contribution rate of the capability index.

The invention provides a short-term power load interval prediction method based on kernel principal component regression analysis. The method comprises the following steps: acquiring historical power load data and corresponding historical power load influence data; clustering the historical power load influence data, and determining the historical power load influence data corresponding to each power consumption scene according to a clustering result; for the historical power load influence data corresponding to each power consumption scene, performing dimension reduction based on kernel principal component analysis, and extracting main power load influence characteristics corresponding to each power consumption scene; constructing a load interval prediction model based on the main power load influence characteristics corresponding to each power consumption scene and the corresponding historical power load data; and performing interval prediction on the short-term power load of the power system according to the load interval prediction model corresponding to each power consumption scene. The method can provide an interval prediction result for short-term power load prediction of the power system, overcomes the uncertainty of the prediction result, and gives consideration to the prediction efficiency.

The invention relates to a quality-related fault detection method based on a deep decomposition echo state network, which comprises the following steps of: dividing into process variable data and quality variable data, and standardizing by using a mean value and a variance of the process variable data and the quality variable data; the method comprises the following steps: constructing a first decomposition network to extract residual information, decomposing a process variable matrix U by using a principal component regression method, sending the decomposed process variable matrix U into a deep echo state network to extract potential residual information, calculating residual vectors # imgabs0 # and # imgabs1 # to construct a second decomposition network to extract dynamic information, and extracting dynamic characteristics X by using an echo state network method. Decomposing the X by using a principal component regression method, further sending the X into a deep echo state network to extract dynamic information in the X, and respectively calculating score matrixes Tre and Tun of the X; fusing and constructing a quality-related statistic # imgabs2 # and a quality-independent statistic # imgabs3 #, and calculating the control limits of the quality-related statistic # imgabs2 # and the quality-independent statistic # imgabs3 And collecting real-time process data, performing standardization processing, calculating a residual vector and a score vector of online data, and judging whether a fault affects a quality variable or not according to a statistical magnitude detection result. According to the fault detection method, the echo state network and the principal component regression method are combined, and the detection performance of industrial process data quality related faults is improved.

The invention belongs to the technical field of soil heavy metal spectrum determination, and particularly relates to a method for rapidly determining the content of low-concentration heavy metal in grassland soil around a mining area based on visible light-near infrared spectroscopy. In order to realize rapid determination of the heavy metal content based on hyperspectral data, three statistical methods of partial least squares regression (PLSR), principal component regression (PCR) and support vector machine regression (SVMR) and 16 preprocessing combinations are researched, developed and explored, the optimal combination is screened out, the determination mechanism of the heavy metal content is analyzed by the methods, and the determination result of the heavy metal content is obtained. The correlation between the heavy metal content in the soil around the mining area and spectral analysis is defined, and meanwhile, different pretreatment methods and statistical methods are compared, so that an important scientific support is provided for heavy metal pollution research. According to the method, variable information can be enhanced, model errors are reduced, and the accuracy and stability of the model are improved.

The invention discloses a greenhouse environment regulation and control method and system based on a crop growth model and an agricultural rule base. The method comprises the steps that an environment monitoring station is used in an experimental greenhouse scene to collect initial data and preprocess the initial data to generate environment data and growth data; establishing a crop growth model based on a principal component regression model by using the environment data and the growth data; and establishing an agricultural rule base of environment regulation and control rules according to the growth model to realize greenhouse environment regulation and control. Aiming at the problems that the optimal crop growth environment is difficult to keep in the greenhouse and the utilization efficiency of greenhouse facilities is low, the starting power and time of each facility in the greenhouse are reasonably controlled, and the crop planting efficiency and the fruit quality of the greenhouse facilities are remarkably improved.

The application discloses a large white pig genome prediction method based on improved genome feature principal component regression, which is defined as GF_PCR. The method divides the traditional PCR method into two parts, performs simple regression on the genome feature part, and performs PCR analysis on the remaining SNPs. When the genome feature contains more causal mutations, the model can obtain higher genome prediction accuracy. At the same time, the genome prediction accuracy of the GF_PCR model for the 100Kg body weight day age trait is higher than that of the PCR model, and the reason is that the proportion of the causal mutation in the preselected genome feature is larger, and the GF_PCR further improves the genome prediction accuracy.

The invention discloses a carbon dioxide concentration prediction method based on a semi-supervised depth probability model. The carbon dioxide concentration prediction method is used for online detection of the content of carbon dioxide in a carbon dioxide absorption tower. According to the invention, on the basis of a conventional probability principal component regression model, thoughts of semi-supervised learning and deep learning are introduced at the same time, and a basic probability principal component regression model is expanded into a structure of a semi-supervised deep learning model, namely, a new soft measurement method based on a semi-supervised deep probability principal component regression model is provided. The method is used for online detection of carbon dioxide concentration in a carbon dioxide absorption tower. Compared with a conventional principal component regression model, the method not only can effectively utilize a large amount of cheap label-free sample information, but also can deeply extract implied information in process data, so that the actual prediction effect of the carbon dioxide concentration is improved.

The present application relates to the field of big data and can be used in the financial field. It provides a method, device, equipment, medium and program product for weight analysis of resource acquisition influencing factors. The present application can improve the accuracy of the analysis results of resource acquisition influencing factors. The method includes: obtaining data corresponding to multiple objects under multiple resource acquisition influencing factors as independent variable data; obtaining data corresponding to multiple influencing factor evaluation dimensions based on the independent variable data as dependent variable data; establishing a multiple regression analysis equation based on the independent variable data and the dependent variable data; using the stepwise regression method and regression model according to the multiple regression analysis equation to screen out a combination of resource acquisition influencing factors that pass the significance test; using the principal component regression analysis method to obtain the weights of each resource acquisition influencing factor in the combination of resource acquisition influencing factors; and obtaining the weight analysis results of resource acquisition influencing factors based on the weights of each resource acquisition influencing factor.

The present invention provides a hardware security evaluation method for block ciphers based on template principal component regression. By means of principal component analysis, this method projects the original data from a high-dimensional space to a low-dimensional space through orthogonal transformation for linearly correlated variables, converting them into linearly uncorrelated variables. That is, it can solve the problem of irreversible singular matrices that may occur in multiple linear regression analysis without sacrificing the estimation performance of regression, and to a certain extent reduce the computational cost of regression analysis. Experimental results show that it has excellent performance in solving the problem of irreversible singular matrices that may occur in multiple linear regression analysis methods. The template constructed by template principal component regression has better universal value, evaluation convergence completion degree and evaluation computational complexity, providing a good evaluation means for detecting the security of block cipher devices. The present invention has the advantages of higher evaluation efficiency, lower computational complexity and better universality.

The application discloses a kind of urban space net red phenomenon determination method, it is related to urban space technical field, including the following steps: collection short video check-in data, obtain the online traffic and offline traffic of multiple net red check-in points;Based on the ratio of offline traffic and online traffic, obtain the traffic conversion rate of multiple net red check-in points in the preset research range;The surrounding area of net red check-in point is researched, and the influence factor of urban space net red phenomenon is determined and corresponding data is obtained;Based on the traffic conversion rate of net red check-in point and each influence factor, linear regression model is established respectively, and multiple regression analysis equation is obtained;The weight of each influence factor is obtained by using principal component regression analysis method, and weight analysis result is obtained.The application can analyze the correlation between urban net red phenomenon and influence factor, help to develop urban space potential, and help to promote the development of urban space net red phenomenon by optimizing space quality targetedly.