20 results about "Multiple linear regression analysis" patented technology

The application relates to the technical field of data compression storage, and provides a high-throughput genome sequencing quality score data parallel compression method.The method comprises the following steps: segmenting an original gene sequencing file; performing random sampling and k-mer analysis on the sampling data to obtain statistical characteristic information and establish a parallel sequence partition model for binary classification; splicing two partition files obtained through binary classification according to splicing parameters; predicting the to-be-compressed file through a multivariate linear regression analysis prediction method to obtain compression rate gain and establish a parallel four-level run prediction mapping model for data redundancy elimination; and performing context modeling on two redundancy-eliminated subfiles through a multi-core processor cluster, and performing cascade compression in combination with arithmetic coding to obtain a final compressed file.Under the premise of significantly reducing quality score data compression time and peak memory overhead, the application also improves the quality score data compression rate, reduces the size of to-be-compressed storage files, and saves the construction cost of basic storage facilities.

This invention relates to the field of cement building materials technology, specifically disclosing a method for improving the strength of silicate clinker based on mineral and crystal composition analysis. The method involves collecting 3-day and 28-day compressive strength test data of silicate clinker and quantitatively analyzing the mineral and crystal composition data using XRD. A mineral composition and strength zoning statistical method is employed to obtain appropriate C3S or C2S content to maintain a relatively good overall level of 3-day and 28-day compressive strength of the clinker. Then, a stepwise multiple linear regression analysis is used to establish the relationship between the 3-day and 28-day compressive strength of the clinker, clinker liter weight, f-CaO, the content of other chemical components, and the mineral composition and crystal content of the clinker. Specific analytical methods are used to determine optimization measures, further optimizing and improving the clinker strength. Using the above method, the 3-day and 28-day compressive strength of silicate clinker can be improved simultaneously.

This invention discloses a method, model, and application for predicting the available cadmium content in soil, relating to the field of soil nutrient prediction technology. The method includes the following steps: (1) collecting multiple farmland soil samples and measuring key nutrient indicators; (2) recording, statistically analyzing, and plotting the measured data; (3) performing correlation analysis on all statistical data to determine indicators significantly correlated with available cadmium; (4) establishing a prediction model for available cadmium in soil using multiple linear regression analysis; and calculating the available cadmium content in the soil based on the prediction model. The prediction model and method provided by this invention are simple, have few variable parameters, and high prediction accuracy, effectively predicting the available cadmium content in soil and providing a reference for the scientific management of cadmium-polluted farmland.

The application is suitable for the technical field of NOx concentration prediction, and provides an intelligent ammonia injection denitration prediction system based on a time sequence neural network model, which comprises the following steps: S1, data acquisition; S2, data processing, the importance of the data is preliminarily calculated through simple multiple linear regression analysis; the application solves the problems that the current denitration ammonia injection system adopts fixed ammonia distribution, cannot adapt to the variability of denitration inlet flue gas NOx under the condition of unit flexibility peak shaving, and has problems such as excessive ammonia injection and insufficient ammonia injection amount, under the condition of meeting the unit outlet emission, excessive ammonia injection will cause the increase of consumables and the problems such as the blockage of the boiler air preheater, and too little ammonia injection amount cannot meet the NOx emission standard, the accuracy of improving the inlet NOx concentration is achieved, the average error of the predicted value and the reference value of the inlet NOx concentration is reduced, and the power plant is effectively helped to control the ammonia injection amount in real time according to the prediction result, so as to realize intelligent quantitative ammonia injection.

The invention discloses a preparation method of a radix astragali extracting solution with high antioxidant activity and the extracting solution, and aims to solve the problems of low extraction rate of active substances and inaccurate detection of antioxidant activity in the prior art. According to the method, five factors including ethanol content, extracting solution multiple and the like are taken as key variables, U10 * (108) uniform test design is adopted, the DPPH free radical scavenging rate is accurately determined by combining an electron paramagnetic resonance technology, and the contents of astragaloside, general flavone and polysaccharide are synchronously detected. A quantitative model is established through multivariate linear regression analysis, and the optimal process parameters are determined: the ethanol content is 80%, the extracting solution multiple is 4, the temperature is 85 DEG C, the time is 1.5 h, and the extraction time is 1 time. The DPPH free radical scavenging rate of the extracting solution prepared through the technology is larger than or equal to 92.75%, polysaccharide is larger than or equal to 12520 mg / L, total flavone is larger than or equal to 323.77 mg / L, astragaloside is larger than or equal to 257 mg / L, synergistic efficient extraction of multiple components is achieved, and the extracting solution is suitable for industrial production.

The invention relates to the technical field of metallogenic research of wollastonite ore, in particular to a method and a system for constructing a metallogenic mode of wollastonite ore based on space-time constraint. The method comprises the following steps: acquiring multi-source basic data in a target area by taking a contact zone of an invading body and carbonate rock and a peripheral buffer range as the target area; extracting an initial geographic feature data set from the multi-source basic data; the method comprises the following steps: performing time sequence stability analysis on observation characteristics of a mineral marginal zone corresponding to wollastonite contact excursion mineralization, and determining a target time scale and a spatial data block in combination with spatial continuity constraints of wollastonite contact excursion mineralization; based on the target time scale and the spatial data block, screening a space-time coupling feature data set; and performing multiple linear regression analysis on the time-space coupling characteristic data set, screening target characteristic parameters related to the wollastonite mineralization process, and constructing a wollastonite mineralization mode based on the target characteristic parameters. According to the method, the geological credibility of the metallogenic characteristics can be improved.

The present application is suitable for the field of mineral exploration, and provides a lithium geochemical anomaly identification method and system for a lithology complex area, which comprises the following steps: obtaining geochemical data of the area, and preprocessing the geochemical data of the area; determining PLSR independent variable indexes according to the preprocessed geochemical data; constructing a PLSR regression model according to the PLSR independent variable indexes; determining lithium geochemical background values of each sample point in the area according to the PLSR regression model; and identifying lithium geochemical anomalies in the area according to the predicted upper limit of the lithium geochemical background values. The present application uses PLSR to construct a regression model between lithium and lithology indicating elements, and then can determine the lithium geochemical background values of each sample point, effectively solves the multicollinearity problem in lithium multiple linear regression analysis, improves the calculation accuracy of the lithium geochemical background values of each sample point in the lithology complex area, and lays a solid foundation for lithium geochemical anomaly identification.

The invention provides a fitting function boundary-based three-dimensional crustal stress field inversion method, which belongs to the technical field of geotechnical engineering, and comprises the following steps: establishing a three-dimensional finite element model, and extracting lateral boundary coordinates for polynomial fitting to obtain a boundary elevation fitting function; calculating a lateral pressure value of each boundary point according to the fitting function; six independent working condition loads are applied to the model, and finite element calculation is carried out to obtain stress components of all measuring points; performing multiple linear regression analysis on the stress component by taking the actually measured stress as a target value to obtain a working condition coefficient; and performing weighted stacking on each working condition stress field to obtain an initial ground stress field after inversion. According to the method, by precisely fitting the lateral boundary terrain, the problem that boundary conditions are not accurately applied in a traditional method is solved, and the precision and reliability of ground stress inversion under the complex terrain condition are remarkably improved.

The application relates to a sidewalk tree screening method based on a forest ecological index, a regional environment model is established by using a DEM technology, tree body data of different types of sidewalk trees are measured and recorded, a plurality of tree body data sub-models are established, and simulation calculation is sequentially performed on the tree body data sub-models, a simulation result is analyzed by using a stepwise multiple linear regression, a regional environment model and a sidewalk tree type model are fused, a simulation model is established, the regional environment model is trained by using the tree body data sub-models, simulation model characteristics are obtained, a heat balance coefficient of the simulation model and a tree species growth characteristic value are calculated, a trend curve is estimated, the simulation model trend curve is compared with a reference target model trend curve, and high-quality plants suitable for the target region are determined. When the simulation model is constructed, the heat balance coefficient in the target region range and the growth characteristic value trend of the plants in the target region are considered, so that more stable and longer forest ecological effects can be achieved in the target region.

The invention discloses a cauliflower flower ball color accurate quantification and grading method, and belongs to the technical field of agricultural informatization and agricultural product quality detection. The method comprises the following steps: selecting a cauliflower sample; carrying out image acquisition on the cauliflower flower balls by using handheld multispectral imaging equipment; selecting a to-be-detected area on the surface of the ball-flower through an ROI extraction technology, and calculating a spectral reflectivity mean value; processing the spectral data by using principal component analysis and multiple linear regression analysis, establishing a membership function and calculating a quantitative model formula; carrying out multi-modal fusion on the multispectral image and the spectral data by adopting a convolutional neural network, and carrying out color classification and grading prediction; and training and verifying the convolutional neural network model. According to the method, through combination of multispectral imaging, machine learning and deep learning technologies, a set of broccoli ball-flower color analysis process is established: firstly, high-dimensional spectral data is obtained through multispectral imaging, then, colors are accurately quantified through a machine learning algorithm, and finally, color grading is realized through a deep learning model.

The embodiment of the application discloses a kind of grain trade data mining method and system based on cascading failure model, wherein, method includes: obtaining the grain trade data of multiple regions;Grain trade data is uniformly converted into kilocalorie equivalent and is carried out multiple linear regression analysis, and linear regression model is obtained;Remove the regression coefficient and driving factor of low significance in model and take antilog, and obtain the first data model for reflecting the degree of trade preference between regions;The second data model of original export distribution proportion is corrected, and the third data model for reflecting the distribution proportion is obtained;Cascade failure model is run based on third data model and grain trade directed graph, simulate the change of grain supply in each region under different parameters, and real-time display the diffusion process and important data in grain trade directed graph.This embodiment can fully mine effective information in trade data, and be applied in the simulation analysis of cascading failure model, provide better data support.

The application discloses a building material light reflection effect evaluation method and system, relates to the technical field of building material measurement, adds description information to the ceramic tile material after collecting different kinds of ceramic tile materials, generates a difference degree from a ceramic tile description information set, screens the ceramic tile material according to the difference degree, and collects reflectivity data of the obtained ceramic tile material in different test scenes; key factors affecting the reflectivity of the ceramic tile material are identified by using multiple linear regression analysis, and reflectivity data and corresponding image data of the ceramic tile material in an actual use environment are collected; corresponding measured reflectivity is output from the output of the trained reflectivity measurement model, building energy consumption data is output from a building energy consumption digital twin model, and if the predicted energy consumption exceeds the expectation, optimization is made on the selection of the ceramic tile material of the building. The application can reduce the difficulty of evaluating the reflection effect of the ceramic tile material for the building, improve the evaluation efficiency, and ensure the authenticity and reliability of the evaluation.

The application provides a method for predicting volatile substances in breast milk and mother's diet and application thereof, comprising: obtaining diet information of a mother, performing nutrition element calculation on the diet information, inputting a prediction model, and predicting the content of volatile substances in breast milk; obtaining volatile substances in breast milk, inputting the prediction model, and predicting the diet information of the mother; the prediction model is obtained by using single factor variance analysis and multivariate linear regression analysis; wherein the volatile substances in breast milk include one or more of caprylic acid, 4-octanone, ethyl caprylate, nonanal, ethylbenzene and decane; and the mother's diet includes one or more of protein, fat, dietary fiber, cholesterol and folic acid. The volatile components in breast milk are separated by using HS-SPME-Arrow for the first time, and combined with chemometrics, so that the change of volatile flavor substances in breast milk can be rapidly and accurately detected.

The application relates to an IT resource measurement billing generation method and device in cloud computing and computer equipment, wherein configuration and procurement billing data of heterogeneous hardware are acquired, multiple linear regression analysis is performed, nonlinear premium of hardware is stripped, objective CPU, memory and disk benchmark billing coefficients are obtained, for different shared resource types, corresponding resource consumption indexes are extracted and differentiated quantitative allocation algorithms are adopted, combined with the benchmark coefficients, allocation costs are calculated, defects of traditional simple arithmetic average allocation are overcome, shared resource billing truly reflects actual consumption of each application, meanwhile, running modes of the application are identified, corresponding resource consumption or configuration data are acquired according to the modes, matched accounting logic is adopted to calculate total billing of basic computing power, finally, the shared resource billing and the total billing of the basic computing power are combined to generate total billing, and all the applications are traversed, so that accurate measurement billing of each application is output.

The invention discloses a method for correcting a tunnel load of an aeolian sand stratum, belongs to the technical field of tunnel engineering support, and solves the problems that an existing calculation method is difficult to accurately reflect a real load distribution rule of the aeolian sand stratum and the calculation result is relatively large in deviation. The method comprises the steps of establishing a vertical stress calculation model considering the incomplete soil arching effect in an aeolian sand stratum, establishing a calculation formula of a span influence coefficient, establishing a calculation formula of a surrounding rock level influence coefficient, comprehensively considering the influence of the surrounding rock level and the tunnel span, and calculating the surrounding rock level influence coefficient based on multiple linear regression analysis. Establishing a final calculation formula of the wind-blown sand stratum tunnel surrounding rock load; according to the method, the wind-blown sand stratum tunnel load correction calculation method considering the surrounding rock level and span is established on the basis of the novel method theory, quantitative load solving based on the special physical characteristics of the wind-blown sand stratum is achieved, and a key technical support is provided for promoting scientific design and risk prevention and control of a wind-blown sand tunnel supporting structure.

The invention discloses a modeling method for a relationship between man-machine collaborative decision evaluation indexes. The method comprises the following steps: establishing a man-machine collaborative decision model; establishing a man-machine collaborative decision evaluation index system; carrying out a man-machine collaborative decision-making human factor experiment, and obtaining experiment data of each index; calculating the score of each man-machine collaborative decision evaluation index, and carrying out index correlation analysis to obtain a correlation coefficient between every two indexes; selecting strongly correlated man-machine collaborative decision evaluation indexes, and establishing a relation function among the evaluation indexes by using a multiple linear regression analysis method; and evaluating the accuracy of the relation function between the evaluation indexes by calculating an average absolute percentage error between a predicted value and an actual value of the relation function. According to the method, the quantitative relation between the man-machine collaborative decision evaluation indexes is determined, a complete man-machine collaborative decision evaluation index system is established and simplified, the quantitative evaluation problem of man-machine decision is effectively solved, and a quantitative standard is provided for improving the accuracy of man-machine decision.

The invention discloses a tire vulcanization time prediction method based on test data, which comprises the following steps: acquiring sample data of a plurality of tires, the sample data comprising tire structure parameters, vulcanization process parameters and actual foaming point time BP measured by a foaming test; based on the sample data, a vulcanization time prediction model is established through multiple linear regression analysis, and the prediction model is used for calculating and predicting foaming point time according to tire parameters; and calculating the predicted foaming point time of the target tire according to the parameters of the target tire by using the prediction model, and determining the vulcanization time of the tire based on the predicted foaming point time. According to the method, by establishing the multiple linear regression model of the tire parameters and the vulcanization time, rapid and accurate theoretical prediction of the tire vulcanization time is realized, and the problems of resource waste and low efficiency caused by dependence on a large number of tests in the prior art are effectively solved.

The invention relates to the technical field of functional food quality control and research and development, and provides a method for determining the quality requirement of intestinal improvement type functional food and application of the method. On the basis that digestion products are obtained through in-vitro simulated digestion, metabolite is remarkably up-regulated after digestion is locked through non-targeted metabonomics, and potential core metabolite is screened out in combination with multiple evidence chains such as disease target correlation, network topology analysis and molecular docking; a corresponding prototype compound is determined through further reverse deduction and in-vitro conversion verification, and a core function prototype compound is determined as a quality control marker through multiple linear regression analysis; finally, a dose-effect relationship is established according to multiple model indexes such as anti-inflammation / barrier, mucous membrane permeability and micro-ecological metabolism, the lowest effective concentration is determined, and a quality qualification standard which is driven by a functional threshold value, can be quantified and traced and is suitable for a complex compound system is formed according to the lowest effective concentration.

The application of the QSM method and biomarkers for iron deposition in AD diagnostic products was evaluated, providing quantitative evidence for abnormal iron deposition in specific deep brain nuclei within the AD spectrum, suggesting that iron deposition in deep nuclei may serve as a potential radiological biomarker for early detection and progression monitoring of AD. The methods included: (1) magnetic resonance imaging (MRI) data acquisition for all subjects; (2) QSM images were obtained by reconstructing the phase and amplitude images of 3D-GRE sequences using STIsuite software; (3) the ROI susceptibility values ​​of the four groups were compared, and correlation analysis was performed between ROI susceptibility values ​​and neuropsychological examinations, correlation analysis between ROI susceptibility values ​​and plasma biomarkers, and multiple linear regression analysis of factors affecting ROI susceptibility values; (4) two-way ANOVA was used to explore the effects of gender and APOE ε4 on putamen susceptibility values.

This invention relates to the field of wastewater treatment technology, providing a method for ozone dosing control based on real-time ultraviolet spectroscopy monitoring. The method includes a step S of determining whether ozone needs to be added and a step P of quantitatively controlling ozone dosing. In step S, water samples are continuously acquired, and absorbance data is obtained using an ultraviolet-visible full-wavelength scanner to establish a dataset. LDA linear discriminant analysis is then performed to determine the ozone dosing discrimination formula. Step P involves continuously monitoring water samples, screening data, determining ultraviolet absorption spectral values, calculating the total ozone dosage, and constructing a dataset E for multiple linear regression analysis to establish the quantitative control formula for ozone dosing. This invention achieves precise control of ozone dosing in secondary treated effluent through ultraviolet spectral data and chemometric methods, optimizing ozone use, saving energy, improving wastewater treatment efficiency, and reducing operating costs.