186 results about "Multivariable linear regression" patented technology

The invention relates to the technical field of automatic medical treatment, provides an infusion process control scheme, and particularly relates to an infusion port risk prediction method and device and electronic equipment. According to the method, clinical-related medical data is constructed and screened through a multiple linear regression method to obtain clinical infusion risk-related data, and the clinical infusion risk-related data is coded and subjected to weight updating to obtain an input sequence; and performing risk prediction on the input sequence based on the prediction model to obtain a final risk prediction result.

The invention relates to the technical field of welding, and particularly discloses an anti-fatigue welding process for a long-distance pipeline supporting steel structure. The process comprises the steps that a font groove structure is determined, and pre-welding polishing pretreatment and layered welding are conducted; fatigue samples of weld toe areas with different arc radiuses are collected, crack feature coordinates and stress concentration coefficients are extracted, failure modes are divided based on clustering, the arc radiuses are verified as main control factors of fatigue failure through multiple linear regression, and a core control range is determined by combining process capability indexes; carrying out fatigue verification on the basis of core parameter extension radius gradient, carrying out process compatibility matching in combination with a controllable interval and a thermal deformation interval of equipment, and outputting a radius candidate value through a response surface method; on-site construction constraints are collected, laboratory path space compatibility is analyzed, a formed track is optimized in a segmented mode, and constraint compensation factors are embedded. The anti-fatigue performance of the weld toe area of the supporting steel structure can be improved, and quality guarantee is provided for welding of the long-distance pipeline supporting steel structure.

The invention discloses a method for determining pesticides in water by liquid chromatography and tandem mass spectrometry, which comprises the following steps: acquiring retention time data of different pesticide compounds in a liquid chromatography system, recording retention behavior characteristics of each pesticide compound under optimized chromatographic conditions by adjusting the composition proportion of a mobile phase and a gradient elution program, and determining the retention behavior characteristics of each pesticide compound under optimized chromatographic conditions. Obtaining a standardized retention time spectrum library data set; establishing a quantitative relation model between molecular structure parameters and chromatographic retention time by adopting a multiple linear regression algorithm through key parameters such as molecular weight, polar surface area and lipid-water partition coefficient in the molecular structure parameter matrix, and obtaining a structure-retention correlation prediction model; and constructing a classification prediction model between molecular structure parameters and environmental durability by adopting a support vector machine algorithm through activity index data in the biological activity prediction result, and determining the environmental durability grade of the pesticide compound according to molecular stability parameters and degradation half-life characteristic values.

The invention relates to a wind noise modeling method and system based on machine learning and application, and belongs to the field of marine acoustics and environmental noise modeling, and the method comprises the steps of data preprocessing and feature extraction, hybrid network model building, hybrid network model training and verification. According to the method, a hybrid network model of multiple linear regression and a multi-layer perceptron is constructed based on a known physical mechanism of wind noise and two main noise generation mechanisms of surface turbulence and bubble oscillation, a linear relation and a non-linear relation are modeled respectively, outputs of the two models are integrated through a weighted fusion strategy, and a multi-layer perceptron model is constructed. Smooth transition modeling from a low-wind-speed linear relation to a high-wind-speed nonlinear relation is achieved.

The invention relates to a mechanical evaluation system based on rotation and inclination of a bracket-free appliance, and belongs to the technical field of orthodontic equipment. The system comprises four core units: an integral dental arch constraint unit simulates oral temperature through a constant-temperature water bath assembly, adjusts model postures through a camera holder type adjustable dental arch base, and constructs a precise constraint environment; the clinical displacement driving unit is based on an anchorage center bearing lever structure, and positioning-driving-data synchronization is achieved through adjustable anchorage positioning, multi-direction servo driving and displacement closed-loop calibration. The single-tooth mechanical sensing unit collects dynamic mechanical data, and interference is removed through Kalman filtering; and the data processing unit corrects attenuation deviation by using multiple linear regression in combination with environmental parameters, generates a mechanical attenuation curve, calculates a time attenuation percentage, and outputs a mechanical evaluation report by comparing with a clinical suitable force application interval. The evaluation accuracy and clinical suitability of the method provide a scientific basis for the optimization of an orthodontic scheme.

The invention relates to a large underground cavern group crustal stress field inversion method under a complex geological condition, and provides a method for inversing a crustal stress field of a large underground cavern group by taking a river valley center line or a ridge line as an inversion numerical model boundary to solve the problems that underground cavern group crustal stress measured data is discrete and difficult to accurately predict under the background of deep river valley terrain and multi-fault development. And selecting actual measurement points which are far away from the fault and are in three-dimensional distribution. The method comprises the following steps: acquiring a displacement boundary and a gravitational acceleration magnitude of a three-dimensional numerical model through limited measured data by adopting stepwise multiple linear regression and evolutionary neural network joint inversion, positively loading the displacement boundary and the gravitational acceleration magnitude to a model boundary to obtain a regional crustal stress field, and extracting measured point data for preliminary verification; and simulating caving excavation, and analyzing whether the stress concentration area is consistent with the observed stress type damage position or not. The method integrates the advantages of the two methods, more optimal solutions can be selected, the problem that precision and calculated amount are difficult to consider in stress field prediction of the complex structure motion area is solved, and the method is suitable for crustal stress field prediction of the complex structure area.

The invention discloses a short-time voltage fluctuation suppression method, device and equipment based on partition fluctuation evaluation, and relates to the technical field of power system control. The method comprises the following steps: under a minute-level time scale, taking the comprehensive minimization of active power loss, voltage fluctuation in adjacent time periods and reactive power regulation cost as a target, constructing a multi-period reactive power voltage optimization control model, and obtaining a reference voltage and a reference reactive power control quantity; a power system is partitioned through a modularity-based reactive voltage control robust partitioning algorithm, and influence areas which are mainly influenced by new energy fluctuation are identified on the basis of partitioning. And constructing a partition fluctuation evaluation index and setting a partition optimization event triggering mechanism. And under a short-time scale, constructing a subarea reactive voltage optimization control model based on quadratic programming by utilizing a subarea space decoupling technology, and executing subarea coordination optimization. And correcting the partition reactive voltage optimization control model through a model correction method based on multiple linear regression.

The invention belongs to the technical field of advertisement putting, and discloses an accurate customer obtaining and advertisement putting system and method based on big data. The method comprises the following steps: S1, multi-source data acquisition: acquiring Internet public data, enterprise private domain data and third-party compliance data to form a comprehensive data pool; s2, data structured storage: the collected data are classified and stored, and an index is established. According to the method, through the collaborative application of multiple technologies such as integrated distributed crawlers, generative adversarial networks, Kalman filtering, edge calculation, multiple linear regression and Lasso regularization, full-link optimization from data acquisition to effect landing is realized, the core problems of inaccurate customer acquisition and poor advertisement effect in the prior art are solved, and the service life of the advertisement is prolonged. Manual intervention cost is reduced through an automatic mechanism, the comprehensive goals of improving customer obtaining precision, optimizing advertisement effect, reducing marketing cost and guaranteeing long-term effectiveness are finally achieved, and a more systematic and more efficient technical support is provided for enterprise digital marketing.

The invention relates to the technical field of leather wastewater treatment, and provides an ultraviolet-visible spectrum-based leather wastewater ozone dosage and COD (Chemical Oxygen Demand) prediction method. The method comprises the following steps: firstly, determining the types of organic matters in a tannery wastewater sample, and determining the distribution characteristics of the organic matters in different fluorescent regions through three-dimensional fluorescence spectrum analysis, so as to discriminate the types of organic pollutants; secondly, screening key wavelengths, and determining characteristic absorption wavelengths representing different organic matters through an ultraviolet-visible light spectrum; then carrying out an ozone oxidation experiment, and synchronously measuring a COD value and an ultraviolet absorption spectrum under different ozone addition amounts; based on experimental data, a multiple linear regression and exponential regression prediction model of ozone dosage and a COD prediction model based on partial least squares regression are established. According to the invention, accurate control of ozone dosage and real-time monitoring of COD are realized, the treatment efficiency is improved, the operation cost is reduced, the adaptability to water quality fluctuation is enhanced, and the method has a wide engineering application prospect.

The invention relates to the technical field of petroleum and natural gas drilling engineering, and particularly discloses a multi-field coupled complex fractured formation fracture width distribution inversion method, which is characterized in that a drilling fluid leakage model considering a fluid-solid coupling effect is constructed based on a porous elastic mechanics theory, and a roughness correction factor is introduced to correct a traditional cubic law; the application of the Darcy law in the matrix is perfected, a matrix and crack coupled drilling fluid leakage mathematical model is established, the influence rule of key parameters such as fluid viscosity, pressure difference and crack width on the drilling fluid leakage amount is obtained through numerical simulation, and on the basis, the drilling fluid leakage amount is calculated. According to the method, the crack width multiple linear regression inversion equation with the fluid viscosity, the pressure difference and the accumulated leakage as independent variables is established, the inversion equation has high prediction precision and practicability and can be used for rapidly and accurately inversing the crack width on site, and theoretical and technical supports are provided for multi-scale fractured formation leakage mechanism understanding and leakage prevention and control optimization.

The invention discloses an electric equipment line risk online monitoring method based on multi-source data fusion, and particularly relates to the field of electric line monitoring, and the method comprises the steps: collecting the electric, insulation, physical and environmental data of a line according to a preset frequency, and carrying out the standardization processing to obtain corresponding standardization parameters; dividing the standardized parameters into input and output dimensions, calculating output dimension parameter weights by using an entropy weight method, and constructing a theoretical value model of each output dimension by using multiple linear regression; respectively constructing electrical, insulating and physical risk functions based on the standardized parameters and theoretical values; constructing a collaborative coupling matrix in combination with each risk function value and historical data, and calculating a total risk value through a nonlinear fusion logic construction function; defining a dynamic threshold value based on each risk value, judging an abnormal point when the threshold value is exceeded, and triggering iterative optimization of the model; the fault probability is calculated by combining the total risk value and the abnormal point data, and a three-level early warning mechanism is set to judge the line risk; according to the scheme, multi-dimensional risk collaborative monitoring is realized, and the risk identification accuracy and real-time performance are improved.

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

The invention relates to the technical field of photovoltaic energy utilization, and discloses an intelligent photovoltaic hot water system-oriented temperature control management and energy scheduling system, which comprises a photovoltaic cell panel electric energy collection unit, a heat storage material electric energy storage unit and a self-adaptive temperature control and energy scheduling unit, the photovoltaic cell panel electric energy collection unit is used for collecting solar energy and storing electric energy when illumination is sufficient; the heat storage material electric energy storage unit is used for storing redundant heat in a phase change material through the corrugated plate flat plate solar air collector; the self-adaptive temperature control and energy scheduling unit is used for preferentially utilizing the photovoltaic cell panel electric energy collection unit to heat and store electricity when the illumination is sufficient; when the illumination is insufficient. By introducing the heat demand prediction and decision tree energy scheduling model based on multiple linear regression, the use path of photovoltaic power generation, energy storage and power grid energy supply can be dynamically optimized according to the real-time environmental parameters, the photovoltaic electric energy utilization rate is maximized, and invalid heating and energy waste are reduced.

The invention relates to the technical field of combustion vibration prediction, in particular to an Agent-driven multiple linear regression prediction and residual error abnormal parameter alarm method, and provides the following scheme: real-time operation data is input into a pre-trained linear regression model through an intelligent agent, a combustion vibration prediction value is generated, and the combustion vibration prediction value is calculated; and the operation condition of the gas turbine is analyzed by calculating the residual error between the actual vibration value and the predicted vibration value. Related parameters are extracted through a data processing classification layer, nonlinear feature linearization is achieved by combining a physical model fine tuning kernel function, and the performance of a regression model is optimized. In addition, residual analysis and long-term change trend recognition abnormal modes are adopted, and accurate monitoring and abnormal alarm of combustion vibration are achieved. The method improves the prediction precision and calculation efficiency, and guarantees the stable operation of the gas turbine.

The present application relates to a kind of based on bracketless appliance rotation and oblique mechanical evaluation system, belong to oral orthodontic equipment technical field.The system includes four big core units: whole dental arch constraint unit simulates oral temperature by constant temperature water bath component, camera holder type adjustable dental arch base adjusts model posture, constructs accurate constraint environment;Clinical displacement driving unit is based on anchorage center bearing lever structure, through adjustable anchorage positioning, multidirectional servo driving and displacement closed-loop calibration, realize positioning-driving-data synchronization;Single tooth mechanical sensing unit collects dynamic mechanical data, and interference is removed by Kalman filtering;Data processing unit combines environmental parameters, and uses multiple linear regression to correct attenuation bias, generates mechanical attenuation curve and calculates time attenuation percentage, compares clinical suitable force interval and outputs mechanical evaluation report.The evaluation accuracy and clinical adaptability of the present application provide scientific basis for oral orthodontic scheme optimization.

The invention discloses a closed-loop intelligent compensation method for FC-BGA substrate expansion and contraction, and the method comprises the steps: building a sample database for key measurement sites in the complete manufacturing process of an FC-BGA substrate, storing the expansion and contraction data and product processing information of each key measurement site, screening key impact factors, and constructing a multiple linear regression prediction model; inputting the processing information of the new production order into the multiple linear regression prediction model to obtain an expansion and shrinkage prediction value of the key measurement site, and generating a compensation instruction which can be identified by downstream processing equipment through an intelligent compensation rule base; sending the compensation instruction to an alignment system of downstream processing equipment through an equipment interface to form an alignment compensation reference; and after the downstream processing equipment executes target spot grabbing operation, the alignment system automatically fuses the alignment compensation reference and the real-time target grabbing data to generate a pre-compensated processing coordinate system to process the substrate. The method can solve the problems that an existing FC-BGA substrate expansion and contraction control method depends on manpower, is low in efficiency and cannot achieve real-time closed-loop control.

The invention relates to the technical field of radionuclide data analysis, in particular to a half-life period parameter generation method and device, equipment and a storage medium. Analyzing the sampling data set based on a random forest regression model to obtain a first half-life period correction coefficient; analyzing the sampling data set based on a multiple linear regression method, a preset humidity correction function and a preset storage time length correction function to obtain a second half-life period correction coefficient; according to a preset dynamic coupling relation updating model, the first half-life period correction coefficient and the second half-life period correction coefficient, generating a real-time half-life period parameter; by integrating multi-source heterogeneous data support model training, real-time half-life parameters are generated, a reliable tool is provided for food safety supervision and the like, industry development is assisted, and economic and social values are achieved.

The invention belongs to the technical field of numerical control machine tools, and relates to a thermal error compensation method and system for a large bridge type gantry machining center, and the system comprises a comprehensive data collection module, a key point determination module, a thermal error modeling module, a compensation execution module and a compensation optimization module. The comprehensive data acquisition module is used for synchronously acquiring three-way thermal displacement change data of a tool nose point and key point temperature data; the key point determination module is used for screening a representative key point temperature set required by modeling according to correlation and a classification principle; the thermal error modeling module is used for establishing a temperature difference-based multiple linear regression compensation model by taking a reference temperature as a reference; the compensation execution module is used for converting the compensation model into a real-time PLC program, and calculating and outputting a compensation amount to a servo system; and the compensation optimization module iteratively adjusts the compensation correction coefficient K through a dichotomy method, and verifies and optimizes the compensation effect. The problems that an existing thermal error compensation method is single in compensation object, difficult to debug and difficult to apply in an engineering mode can be effectively solved.

The invention discloses a paleo-gradient constraint reservoir characterization attribute for deep fan delta sedimentary facies characterization. The paleo-gradient constraint reservoir characterization attribute comprises the following steps: 1) carrying out multiple seismic attributes of a target stratum and carrying out normalization processing; 2) seismic attribute optimization is carried out by using the correlation with the sandstone thickness; 3) fitting a multiple linear regression relationship between the sandstone thickness and the optimized seismic attributes; 4) drawing a whole-area sandstone thickness prediction contour map; 5) solving an ancient slope on the ancient landform map; 6) drawing a whole-region ancient slope contour map; 7) acquiring a relation inflection point between the sandstone thickness and the paleo-slope in the research area; 8) predicting sandstone thickness and paleo-gradient by using seismic attributes to establish paleo-gradient constrained reservoir characterization attributes; and 9) calculating all-region paleo-gradient constraint reservoir representation attributes, and drawing a contour map as a basis for depicting the deep fan delta sedimentary facies. The method solves the problem that seismic attribute representation sedimentary facies lacks environmental information and the problem of multiplicity of solutions of deep fan delta seismic reflection representation sedimentary facies.

The invention relates to the field of health information management, in particular to a health data interpolation and optimization method combining flag bit judgment and a physique constraint mechanism. The method comprises the following steps of: analyzing an added flag bit, determining an abnormal data fragment, performing data interpolation on the abnormal fragment based on a GM (1, 1) model and multiple linear regression, then determining the constitution and the corresponding range thereof, and correcting the interpolation data by taking the constitution range as a constraint condition. The method specifically comprises the following steps: analyzing four flag bits to determine abnormal data segments; processing the relevance between single-dimensional data interpolation and multiple linear regression processing dimensions based on a GM (1, 1) model; and determining the physique and the corresponding data range according to the data, and correcting the interpolation data by taking the physique range as a constraint condition. The invention aims to solve the problem of how to improve the accuracy of a health data interpolation result.

The invention discloses a carbon sequestration and sink increase effect monitoring method based on multi-region ecological restoration, and relates to the technical field of ecological restoration. The method comprises the following steps: dividing microhabitat units according to landform features and collecting microflora data; establishing a multiple linear regression equation according to the microflora data to obtain a first community feature coefficient, inputting the microflora data into a random forest model to obtain community feature importance, and combining the community feature importance with the first community feature coefficient through a dynamic weighted fusion method to obtain a second community feature coefficient; calculating the second community characteristic coefficient through an overproduction effect analysis method to obtain a community cooperative gain rate and determine a restoration planting type; clustering the landform data through a KMEANS method to obtain a first microhabitat landform cluster, and correcting by restoring a planting type to obtain a second microhabitat landform cluster; and collecting and repairing the carbon sink strength, comparing the carbon sink strength with an intra-cluster carbon sink strength threshold value, and if the carbon sink strength is lower than the threshold value, performing early warning to complete monitoring of the carbon sink strength.

The application discloses a kind of construction methods of evaluating corn southern corn rust resistance ability model, method for evaluating the ability of resistance to southern corn rust, it is related to plant molecular breeding technical field.The construction method of the model is universal for the construction of a variety of corn southern corn rust resistance ability model, the model construction method is provided, it is favorable for people to screen excellent line of extremely resistant or broad-spectrum resistance.The application also creates the method for evaluating the ability of resistance to southern corn rust of plant, the value of dependent variable is predicted by multiple linear regression equation, and the influence degree of each independent variable on dependent variable (resistance to southern corn rust) is evaluated.In addition, the application mines a rust-resistant candidate gene based on the above-mentioned method for evaluating the ability of corn southern corn rust resistance and the size of site effect, and has good application prospect for corn rust resistance.

The invention discloses a two-way current and voltage transmitting output method of a dynamic torque sensor, relates to the technical field of dynamic torque sensors, and aims to solve the problem that a deviation exists between a current and voltage signal transmitted and output in the prior art and an actual torque value. The method comprises the following steps; the method comprises the following steps: S1, data acquisition and preprocessing: performing multiple experiments under different working conditions through a dynamic torque sensor, acquiring torque values and corresponding current and voltage output values as sample data, and preprocessing the sample data, including abnormal value removal and data normalization operation; and S2, constructing and training a double-path transmitting model, constructing the double-path transmitting model based on the preprocessed sample data, establishing a mapping relation between a torque value and a double-path current and voltage output value by the double-path transmitting model by adopting a multiple linear regression algorithm, and dividing the preprocessed sample data into a training set and a test set. The method has the advantage that the current and voltage transmitting output precision is improved.

The application discloses a general optimization method for 3D printing sugar-reduced food, comprising the following steps: S1. Defining and measuring core parameters: including porosity P of a 3D printing food matrix, odor concentration C of an odor active component accounting for a total mass proportion of the food matrix, sucrose reduction amount Suc compared with a traditional full-sugar food, and a sensory evaluation index S of a sensory evaluator on a sweetness similarity degree between the sugar-reduced food and the traditional full-sugar food; S2. Constructing a quantitative model: under the condition of setting the sucrose reduction amount Suc value in advance, a plurality of different P, C and S corresponding data are obtained through experimental design, and based on the data, a quantitative model of the sweetness similarity S about the porosity P and the odor concentration C is obtained by using a multiple linear regression method; and the problem that the prior art cannot clearly guide how to set the printing parameters and the formula to obtain the best comprehensive sensory experience under the premise of ensuring a certain sugar reduction ratio is solved.

An electric power spot transaction decision support system based on multi-dimensional market analysis relates to the field of electric power spot transaction, and comprises a display layer which supports complex interaction through a Web end, large-screen cockpit command center monitoring and small program mobile end access, and realizes interaction result multi-terminal output. The application layer is communicated with the display layer, comprises a market analysis module and is used for processing multi-dimensional external market data to generate standardized background data; a user side load prediction module generates a user load day-ahead prediction result through an ARIMAX model based on historical power consumption and external factors, predicts monthly power by combining multiple linear regression and other models, and dynamically allocates weights by adopting an error reciprocal normalization method; and the day-ahead price prediction module cleans and preprocesses data such as historical electricity prices, and outputs a result through multi-model price comparison evaluation. The bottom layer is integrated with a scheduling management system through a data interaction interface, is in butt joint with a power grid operation environment and supports efficient decision-making of energy business.

This invention discloses a method for optimizing flue gas NO based on thermal parameters. x The emission control method includes the following steps: online measurement of thermal parameters for each stage of pellet roasting; analysis of the thermal parameters for each stage and their relationship with the NO inlet of the SCR denitrification system. x The correlation between concentrations was determined to establish the relationship between NO at the SCR denitrification inlet and each process stage. x Significantly correlated thermal parameters with concentration; constructing and solving for NO at the SCR denitrification inlet. x A multiple linear regression mathematical model for NO concentration was used to predict the NO concentration at the SCR denitrification inlet. x The trend of NO concentration change; identify the relationship with the SCR denitrification inlet NO. x The single factor most relevant to the concentration was established, and the relationship between the single factor and the NO inlet of the SCR denitrification system was established. x A linear relationship between concentrations was established to adjust NO x A univariate linear regression mathematical model for NO concentration is used to regulate NO. x Concentration. This invention can predict NO concentration at the SCR denitrification inlet. x The concentration trend can be observed, and the NO inlet of the SCR denitrification system can be precisely controlled. x Concentration, effectively achieving NO concentration in pellet roasting x Emissions are controlled throughout the process to achieve the goals of energy conservation and environmental protection.

The invention relates to the technical field of mass concrete hydration heat control, and particularly discloses a mass concrete hydration heat cooling water pipe arrangement method and system, and the method comprises the steps: building a hydration heat model based on the historical parameters of mass concrete; selecting characteristic parameters in the historical parameters, and generating a high-temperature area volume according to the hydration heat model; performing multiple linear regression modeling according to the mapping relation between the characteristic parameters and the high-temperature area volume to obtain a temperature field; generating a cooling water pipe layout scheme according to the temperature field; inputting the temperature field into a trained prediction model to obtain the arrangement spacing, the arrangement layer number and the flow of the cooling water pipe; combining the cooling water pipe arrangement scheme, the arrangement spacing, the arrangement layer number and the flow to obtain an optimal cooling water pipe arrangement scheme; and the cooling water pipes of the mass concrete are arranged according to the optimal cooling water pipe arrangement scheme, and arrangement is completed. According to the invention, temperature field distribution can be accurately predicted, and a scientific basis is provided for cooling water pipe layout.

The invention discloses a method for predicting the turbidity tendency of beer caused by micromolecular dextrin, and relates to the technical field of beer quality detection. According to the method disclosed by the invention, micromolecular dextrin is determined as a core turbid substance through systematic analysis on beer components. According to the method, colloidal substances are separated from beer to be detected through membrane filtration and centrifugation, the essence of starch substances is verified through an enzymolysis experiment, structural characterization is conducted through Fourier transform infrared spectroscopy, nuclear magnetic resonance and other experiments, and correlation between the colloidal substances and turbidity is verified through FTIR, an electron microscope and DSC (differential scanning calorimetry). Based on the concentration of micromolecule dextrin, the concentration of tannic acid and the DPPH free radical scavenging rate, a multiple linear regression prediction equation is established, accurate prediction of the turbidity tendency of beer is achieved, and technical support is provided for beer production process optimization and quality control.

The application provides a noise annoyance modeling method for an audio injection system, which comprises the following steps: firstly, according to a target sound, a corresponding control sound is selected to perform time domain superposition, so as to obtain a plurality of superimposed sound samples; then, a subjective evaluation experiment and data processing are performed on the obtained superimposed sound samples; then, psychoacoustic parameters of the target sound, the control sound and the superimposed sound are calculated, and the difference between the corresponding psychoacoustic parameters of the target sound and the control sound is calculated; the difference between the calculated psychoacoustic parameters of the target sound and the control sound is taken as an independent variable, and the corresponding superimposed sound annoyance score value is taken as a dependent variable; the correlation between the independent variable and the dependent variable, the correlation between the independent variables and the collinearity between the independent variables are calculated; according to the calculation results, the modeling independent variable is determined; the determined modeling independent variable and the superimposed sound annoyance score value are taken as the dependent variable, and a multiple linear regression is adopted to realize the audio injection superimposed sound annoyance modeling. The application improves the precision and parameter correlation of the model, and further improves the objective evaluation of the audio injection superimposed sound annoyance.

The method, system, and equipment for screening and optimizing reservoir gas injection parameters based on response surface methodology include: determining the gas injection parameters affecting reservoir gas drive recovery and assigning initial weights; selecting basic spatial sample points using the central composite method; establishing a response surface function; constructing a response surface function model; validating the response surface model and verifying its accuracy; obtaining the gas injection parameter variable set; obtaining the distribution results of the gas injection parameter variable set converted into weight coefficients; and obtaining the quadratic response distribution of the weight coefficients through quadratic response surface analysis to obtain the discrimination criteria for gas drive in fractured-vuggy carbonate reservoirs. The reservoir gas injection parameter screening method provided by this invention uses response surface analysis combined with multiple linear regression to screen reservoir gas injection parameters. It uses graphical technology to display the functional relationships, providing intuitive graphs to clearly identify the optimization region, allowing researchers to observe and select the optimal conditions in experimental design.