53 results about "Multiple linear regression analysis" patented technology

Disclosed in the present invention are a method and system for predicting a hematoma expansion event of a stroke patient. The method comprises: collecting patient data information, and performing data preprocessing; using an XG-Boost model to calculate the SHAP average value and the contribution degrees of different types of data, and extracting a feature quantity with the highest contribution degree from each type of data; performing multivariate linear regression analysis to obtain the weight of the effect of each type of data on hematoma expansion, and weighting all the feature quantities on the basis of the weight to obtain weighted feature quantities; using an extreme learning machine to predict a hematoma expansion probability; and using a northern goshawk optimization algorithm to search predicted values for the boundary of occurrence of a hematoma expansion event, and verifying the prediction accuracy. The present invention can accurately analyze and predict the expansion risk of hematoma after hemorrhagic stroke, which is crucial to early diagnosis and timely treatment.

The invention discloses a reactive power rapid tracking optimization method and system for a wind power plant in the field of electric power systems. The method comprises the following steps: constructing a power data set comprising a power change rate, the power of the current time period and the actual power value of the next time period by collecting ultra-short-term power data of a wind turbine generator; performing two-dimensional segmentation on the data according to the power size and the change rate, fitting a power predicted value of a next time period based on multiple linear regression analysis, and calculating a confidence interval of the power predicted value; dynamically correcting a voltage margin by combining an active-voltage sensitivity matrix obtained by Jacobian matrix inversion and a power confidence interval; and inputting the actually measured power of each wind turbine generator into the trained SAC decision model to obtain an optimized reactive equipment control strategy, converting into a Markov decision process through an SAC reinforcement learning algorithm to carry out offline training and online rolling optimization to generate a reactor and converter group control strategy, and updating an iteration model. According to the invention, second-level reactive dynamic regulation is realized, the voltage stability is improved, and the network loss is reduced.

The invention relates to the field of food with dysphagia, in particular to a method for constructing a swallowing characteristic evaluation model of food with dysphagia. Comprising the following steps: firstly, determining swallowing characteristic indexes such as oral transfer time, epiglottis time, tensile length, oropharyngeal residual quantity, swallowing grade, residual volume of needle tubing, sensory swallowing evaluation score and the like of food with dysphagia; the physical property indexes such as yield stress, consistency coefficient, flow index, zero shear viscosity, apparent viscosity, friction coefficient, density and surface tension in rheological properties are subjected to correlation analysis on the swallowing property indexes and the physical property indexes, and then multiple linear regression analysis is carried out; according to the method, a physical characteristic index-based swallowing characteristic evaluation model is established, a predicted value obtained by the evaluation model is compared with a measured value for verification, and the model has very high fitting precision and can better describe the swallowing characteristics of food with dysphagia.

The application discloses a kind of based on cylindrical surface variable pose spray coating thickness prediction modeling method, it is related to signal processing technical field.Step one: establish three-dimensional model of spray gun cap and control domain based on cylindrical surface static spraying, carry out numerical simulation experiment and collect numerical simulation experimental data;Step two: data analysis, analyze the coating film thickness law under the variable pose spraying of different cylindrical diameter, obtain the adjustable range of variable pose spray gun;Step three: establish spray gun mathematical model, combined with the adjustable range of spray gun pose, carry out spraying experiment;Step four: collect experimental coating data, according to variable pose spray gun mathematical model, using Trust-Region optimization algorithm is fitted to experimental data, obtains cylindrical surface variable pose model to be identified parameter, carries out multiple linear regression analysis, obtains cylindrical surface variable pose spraying coating film thickness prediction model.

The invention discloses a sensitivity analysis method and system for influence factors of moisture redistribution of an aeration zone in a subsidence area, and relates to the technical field of protection and utilization of water resources in ecologically fragile mining areas in northwest. The method comprises the following steps: carrying out a soil-water characteristic curve test under a disturbance stress path in a coal mining collapse subsidence area; a VG soil-water characteristic curve model suitable for the coal mining collapse subsidence area is obtained; determining the moisture redistribution condition of the vadose zone of the settlement area under coal mining collapse disturbance under each working condition, wherein the moisture redistribution condition is represented by saturation; based on a range analysis method and a multiple linear regression analysis method, sensitivity analysis is carried out on all main influence factors influencing moisture redistribution of the aeration zone of the subsidence zone under coal mining collapse disturbance, and sensitivity of all the main influence factors is ranked. According to the method, the accurate water redistribution rule of the aeration zone in the coal mining collapse subsidence area can be obtained through calculation, the sensitivity of the influence factors of the water redistribution of the aeration zone is reasonably analyzed, and an important theoretical basis is provided for rapid repair of the coal mining collapse subsidence area.

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.

The present invention discloses a sand body seismic intelligent prediction method based on RF-GAM, comprising the following steps: S1: collecting drilling data and seismic data of a target block, extracting sand-to-formation ratio information from the drilling data, and extracting seismic attribute information from the seismic data; S2: analyzing the correlation between the two types of information and screening relevant factors of the sand-to-formation ratio; S3: using a multivariate linear regression analysis method to predict sand bodies and obtain preliminary sand body prediction results; S4: based on the preliminary sand body prediction results, screening locations where the relevant factors match the sand-to-formation ratio, and using the seismic attribute information at these locations as influencing factors of the sand-to-formation ratio; S5: using a random forest to screen and obtain important influencing factors; S6: using a generalized additive model to perform attribute fusion, and visualizing the results of the attribute fusion to obtain the final sand body prediction results. The present invention can predict more accurate sand body prediction results, providing technical support for oil and gas field exploration and development.

The present application belongs to the field of oil and gas field development, and particularly relates to a method for characterizing the characteristics of gravel around a horizontal well in a conglomerate reservoir. The method comprises the following steps: (1) using a conglomerate reservoir gravel characteristic identification and analysis software to process FMI imaging logging images and extract gravel parameters; (2) using a multiple linear regression analysis method to carry out sensitivity analysis of the gravel parameters and logging data, and to determine the statistical relationship between the gravel parameters and logging curves; and (3) establishing a model of the logging curves and the gravel parameters, and substituting the logging curve data to measure the gravel characteristics. The present application solves the problem of characterizing conglomerate reservoir gravel, and for the first time realizes the use of conventional logging curves to characterize the gravel parameters around a horizontal well section, thereby providing an optimization direction for multi-cluster volume fracturing technology in a horizontal well section.

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 sugarcane variety lodging resistance identification method which comprises the following steps: collecting stem base diameter data, stem middle diameter data, stem height data, stem hardness data, root system development degree data and leaf size and weight data; and establishing a lodging resistance evaluation model based on multiple linear regression analysis according to the stem base diameter data, the stem middle diameter data, the stem height data, the stem hardness data, the root system development degree data and the leaf size and weight data. According to the method, a plurality of key indexes influencing the lodging-resistant capability of the sugarcane plants are comprehensively considered, the key indexes comprise the morphological index, the mechanical index, the root system index and the like, and compared with a traditional identification method with a single index or few indexes, the lodging-resistant characteristics of the sugarcane plants can be more comprehensively reflected. By constructing the lodging-resistant capability evaluation model, the lodging-resistant capability evaluation result can be quickly obtained only by substituting the measured data into the model, so that the identification efficiency is greatly improved.

This application provides a method, apparatus, and device for obtaining bound water saturation. The method includes: acquiring reservoir parameters of a rock sample to be tested, and calculating the bound water saturation of the rock sample based on the reservoir parameters and a bound water saturation formula. The bound water saturation formula is pre-established based on the bound water saturation of multiple first rock samples and reservoir parameters, through multiple linear regression analysis. The bound water saturation of the multiple first rock samples is calculated based on the cumulative permeability contribution rate corresponding to the category to which the first rock sample belongs under porosity and pore structure index. Different cumulative permeability contribution rates are used to calculate the bound water saturation of different categories of rock samples, thereby ensuring the accuracy of the bound water saturation of the multiple first rock samples and the established bound water saturation formula. Therefore, the accurate bound water saturation of the rock sample to be tested can be obtained based on this formula.

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 provides a construction method of a precise medication model for mycophenolic acid drugs, a prediction device and electronic equipment. Belongs to the cross technical field of precision medicine and pharmacology. The construction method of the precise medication model for the mycophenolic acid drugs comprises the following steps: S1, acquiring clinical data of a patient; s2, collecting blood samples at different time points after the patient takes the medicine, and detecting the blood concentration of the mycophenolic acid medicine; s3, the non-atrioventricular model is used as a basic model, multiple linear regression analysis is carried out, a precise medication model for the mycophenolic acid drugs is established, and a mathematical model of the precise medication model for the mycophenolic acid drugs is shown as a formula (1). By the adoption of the accurate medication model for the mycophenolic acid drugs, the blood sampling frequency for monitoring the blood concentration of the mycophenolic acid drugs can be reduced, the workload of medical workers is reduced, the pain of patients is relieved, and the compliance of the patients is improved.

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 invention provides a method and a device for evaluating the performance consistency of a fuel cell, relates to the technical field of fuel cells, and solves the technical problem that the prior art is difficult to meet the requirement of the industry for comprehensively and accurately evaluating the performance of the fuel cell. The method comprises the following steps: acquiring measurement parameters of each fuel cell in an operation process through a sensor; the measurement parameters comprise battery performance measurement parameters and working condition environment parameters; analyzing the measurement parameters by adopting multiple linear regression analysis, and establishing a quantitative relation model corresponding to each fuel cell; the quantitative relation model is used for representing the incidence relation between the battery performance measurement parameters and the working condition environment parameters; and for each fuel cell, carrying out performance consistency evaluation on the cell performance prediction parameters obtained by calculation under the working condition environment parameters of the same working condition based on the corresponding quantitative relation model to obtain a performance consistency evaluation result. The method is used in the fuel cell evaluation process.

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 present invention discloses a sensitivity analysis method and system for factors affecting the redistribution of moisture in the vadose zone of a subsidence area, and relates to the technical field of water resource protection and utilization in ecologically fragile mining areas in Northwest China. The method comprises the steps of: conducting a soil-water characteristic curve test under a disturbance stress path in a coal mining subsidence subsidence area; obtaining a VG soil-water characteristic curve model suitable for the coal mining subsidence subsidence area; determining the redistribution of moisture in the vadose zone of the subsidence area under various working conditions characterized by saturation; performing a sensitivity analysis on the main influencing factors affecting the redistribution of moisture in the vadose zone of the subsidence area under coal mining subsidence disturbance based on the range analysis method and the multivariate linear regression analysis method, and ranking the sensitivity of the main influencing factors. The present invention can calculate the accurate law of moisture redistribution in the vadose zone of a coal mining subsidence subsidence area, and rationally analyze the sensitivity of factors affecting moisture redistribution in the vadose zone, thus providing an important theoretical basis for achieving rapid repair of coal mining subsidence subsidence areas.

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 invention provides a rice quality prediction method based on meteorological data and growth periods, and the method comprises the steps: obtaining historical meteorological data and historical quality data in a rice growth process, and dividing the growth process into a plurality of growth periods; constructing three quantitative evaluation models of temperature, rainfall and sunlight according to the meteorological data in each growth period, and calculating three meteorological evaluation indexes of temperature, rainfall and sunlight; performing multiple linear regression analysis on the meteorological evaluation index of each growth period and the corresponding historical quality data to obtain a quantitative relationship between each quality index in the quality data and the meteorological evaluation index; and establishing a rice quality prediction model based on the quantitative relationship between the quality index and the meteorological evaluation index, and providing decision support for rice quality improvement. The method has the beneficial effects that on the basis of key time periods and key meteorological factors which influence the quality, in combination with a weather forecast product, reasonable countermeasures are taken for agricultural departments, and guidance is provided for quality regulation and upgrading.

The invention provides a soybean and product beany flavor influence factor analysis method and system, and relates to the technical field of soybean product analysis, and the method comprises the following steps: selecting a plurality of groups of soybean products of the same variety and the same quality as samples, scoring the beany flavor of the samples, and forming a sensory score data set; collecting the concentration of volatile organic compounds in the sample, simultaneously obtaining planting environment data and processing condition data of the sample, and calculating a compound concentration index, an environment comprehensive index and a processing comprehensive index; analyzing the correlation between the compound concentration index, the environmental comprehensive index and the processing comprehensive index and the final sensory score by using a Pearson's correlation coefficient method; and establishing a regression model by adopting a multiple linear regression analysis method, changing each index one by one, observing the change degree of the final sensory score, and calculating a sensitivity index. According to the method, producers can make more accurate decisions in actual production, and the sensory quality and market competitiveness of products are improved.

The invention discloses an optimization method of an optical fiber composite overhead ground wire (OPGW) connection device based on LS-SVR, and relates to the field of overhead ground wires, and the optimization method comprises the following steps: carrying out an insulator surface flashover test on the optical fiber composite overhead ground wire connection device, and constructing a sample data set X; determining the optimal weight of the insulator characteristics through a multiple linear regression analysis method; constructing an evaluation model through a least square support vector regression algorithm, introducing a Gaussian kernel function, and training the evaluation model through a sample data set X; when a training result reaches an iteration termination condition, completing evaluation model training, and verifying a training effect through a test sample; performing performance prediction on the optical fiber composite overhead ground wire connection devices with different specifications and designs through the evaluation model; through the novel connection device, the electric energy loss is reduced, the overall energy efficiency of a power transmission line is improved, the insulator flashover voltage under different design parameters is quickly and accurately predicted, and the insulation reliability of the device in a complex high-voltage power transmission environment is enhanced.

The invention relates to a drainage basin river channel outlet section pollutant flux tracing method, which comprises the following steps of: firstly, decomposing pollutant flux into relatively stable point source flux and surface source flux which is obviously influenced by rainfall through an improved iterative algorithm by adopting a digital filtering technology; and then, performing further traceability analysis on point source and surface source flux obtained by decomposition by using absolute principal component score-multiple linear regression analysis, and identifying contribution proportions of different pollution sources such as an industrial point source, a living point source and an agricultural surface source. According to the method, by adjusting parameters of the digital filtering algorithm and the APCS-MLR analysis algorithm, mutual matching of results of the two algorithms is achieved, and the accuracy and reliability of traceability analysis are improved. The method is suitable for flux traceability of pollutant types such as permanganate index, ammonia nitrogen, total phosphorus and the like, and has important practical application value for drainage basin management and pollution control.

The invention discloses a target development area identification method for coal bed gas exploitation, which comprises the following steps of: drilling wells in a research area, selecting well logging parameters of all drilled wells in the research area, and respectively and sequentially performing principal component analysis and multiple linear regression analysis, determining the correlation between the coal body crushing Z index and each logging parameter; calculating a coal body crushing Z index in each drilling well, and analyzing a coal body structure distribution rule of each drilling well; a single-well coal seam evaluation ZZ index is established by increasing the weight step by step, and the overall deformation strength of each single-well coal seam is quantitatively represented; selecting evaluation parameters of each drilling well coal sample, establishing a multi-factor combination scoring model, calculating a comprehensive score corresponding to each drilling well coal sample, and screening out an optimal target mining area for hydraulic fracturing from the research area based on the comprehensive scores of all drilling wells and the positions of the drilling wells; the method can predict a development area with high coal bed gas content in a large range, and is high in identification accuracy.

The invention discloses a method for removing false positive mass spectrum characteristics in a triglyceride ion peak list based on a retention time grid, and the method comprises the steps: screening data points according to equal DB values, and drawing a retention time grid chart through a new segmented multiple linear regression analysis formula and a fitting formula. The error of interference substances such as triglyceride can be effectively reduced. According to the improved splitting decomposition mechanism of the triglyceride in the CID mode, confirmation on charged fragments generated by free fatty acid and derivatives is increased, misjudgment on an identification result is effectively avoided, and therefore false positive caused by interfering substances is reduced. According to the method, on the basis of identifying the types of the fatty acids connected with the triglyceride, all the fatty acids are summarized, so that a list of all possible triglyceride is obtained by a mathematical exhaustion method, the triglyceride ion peak extraction process in the previous steps is rechecked according to the data, and errors caused by an analysis algorithm and interfering substances are reduced again.

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.