36 results about "Mixed effects" patented technology

The invention relates to the technical field of data processing, and discloses a hydrocephalus correlation analysis method and system based on multi-dimensional data. The method comprises the steps that a feature matrix is generated by obtaining and standardizing hydrocephalus multi-dimensional data, after the matrix is discretized, an association rule mining algorithm is adopted to extract an association mode, a rule set is obtained in combination with knowledge graph verification, data clusters are divided based on mahalanobis distance clustering, mixed effect time sequence model fitting parameters are established for all the clusters, and a mixed effect time sequence model is obtained. And calculating a target sample attribution cluster and predicting a symptom improvement trajectory and a confidence interval. According to the method, automatic integration, credible association rule extraction, precise subtype division and individualized symptom trajectory prediction of the multi-dimensional data of the hydrocephalus patient are realized.

The invention relates to the technical field of industrial economy analysis and power data mining, and provides a power utilization mode change-based industrial transfer implementation effect evaluation method, which comprises the following steps of: acquiring power utilization time sequence data before and after transfer; extracting multi-dimensional features; the relative change rate is calculated, and direction unification is carried out according to index'lifting / descending '; constructing a comprehensive effect index; performing grade judgment by adopting ordered judgment / ensemble learning; and outputting a result and confidence. An index system covering the dimensions of load, efficiency, stability and the like is constructed by establishing a power utilization mode base line and comparison before and after transfer, and an effect quantification mechanism and an integrated learning model with unified directions are introduced, so that the industrial transfer effect is objectively and interpretably evaluated and graded; the confidence coefficient adopts geometric weighting of calibrated probability, stability, integrity and time span, and the method can be selectively combined with synthetic contrast / causal recognition to eliminate hybrid influences of seasons, prices, meteorology and the like, so that the evaluation robustness is improved.

This invention belongs to the field of high-performance thermoplastic composite granule preparation and process optimization technology. Specifically, it relates to a method and system for determining the optimal process for twin-screw extrusion preparation of carbon fiber / polyetheretherketone (PEEK) granules based on numerical simulation and multi-objective optimization. A three-dimensional model of the internal flow channel of the twin-screw extruder is established, and discretization is performed using a mesh stacking method. Multiple simulation conditions are constructed within a preset temperature and extrusion speed range, and process safety constraints are established. The analytic hierarchy process (AHP) – approximation of ideal solution ranking method is used for index standardization, weight allocation, and comprehensive scoring. With process temperature and extrusion speed as decision variables, a non-dominated sorting genetic algorithm is used for multi-objective global search to obtain the Pareto optimal solution set, which is then ranked and optimized to output recommended process parameter combinations. This invention's method can reduce the risk of thermal degradation and fiber damage while considering the mixing effect, and reduces the cost and cycle time of trial and error.

The invention discloses a fish spawning site position calculation method based on otolith microchemistry and river micronization landscape atlas, which comprises the following steps: otolith and water body sample pretreatment: taking out an otolith sample from a sample tube for slice preparation, and preparing a standard solution by using the water body sample; experimental analysis: measuring trace chemical elements, namely Sr, Ba, Mn, Mg and Ca, of otolith; data analysis: drawing a river micronized landscape map, constructing a mixed effect model to calculate otolith trace element values, constructing a random forest model based on the otolith trace element values, and performing spawning site position calculation on fish otolith core microchemistry; according to the method, a brand new biogeochemical framework is constructed, and the spawning site position of the fish is accurately calculated by combining otolith microchemistry, a river micronization landscape map and a machine learning technology.

The invention discloses an automatic filling detection control method and device for a reaction kettle. The method comprises the following steps: acquiring image data of material mixing states at different positions in a reaction kettle at intervals of preset time, preprocessing a color image to enhance the material particle feature identification degree, extracting features by using a machine learning algorithm, identifying feature units meeting a mixing uniformity standard through a classification model, and counting the number, and adopting a corresponding control method for the specific image features based on the statistical result and a preset rule. The device comprises an image acquisition module, an image preprocessing module, an image feature processing module and a control module which are respectively used for realizing image acquisition, preprocessing, feature processing and rule-based automatic control. Through the image detection and intelligent control technology, the problems that an existing reaction kettle is lagged in mixing effect monitoring and insufficient in automatic control are solved, the production efficiency and the mixing uniformity are improved, and the reaction kettle is suitable for the fields of new energy materials, chemical engineering and the like.

The invention relates to a method for evaluating the correlation of anti-seismic deformation capability of a bridge structural member, which comprises the following steps of: firstly, collecting bridge pier column engineering parameters such as reinforcement ratio, axial compression ratio and the like and an anti-seismic deformation capability quantitative index, and dividing into a training set and a test set after sample screening, feature optimization and data conversion; then, constructing a Gaussian process regression model fusing a mixed effect and a multi-output kernel function so as to synchronously describe association among multiple damage states; the optimal kernel function is screened through training, hyper-parameters are optimized, and finally based on a trained model, two types of correlation of'different damage states of the same component 'and'the same damage state of different components' are scientifically quantified. According to the design, the defects of a traditional experience hypothesis method are overcome, automatic and accurate quantification of the anti-seismic deformation capacity correlation is achieved through data driving, and a reliable result of convergence verification lays a solid foundation for bridge pre-seismic anti-seismic analysis, post-seismic damage rapid evaluation and anti-seismic design optimization.

Methods, systems, and devices for providing game classification are disclosed. In particular, example embodiments provide game classification with respect to a degree of chance or skill that influences game outcomes, for example with chance-based games and skill-based games. In an example method, a threshold training dataset is obtained to train a classification model to classify a given game as one of multiple game types that describe different degrees of chance or skill involved in the given game. From the threshold raining dataset, outcome factor distributions of games are determined and used to set trained thresholds of the classification model. The classification model can then be deployed in a computer service platform that receives queries from client devices.

This application belongs to the field of microgrid frequency control, specifically disclosing a model-free adaptive microgrid frequency control method to resist network attacks. The method includes the following steps: considering the mixed effects of denial-of-service attacks and spoofing attacks, a system output model incorporating attack coefficients is established, modeling the microgrid frequency control system as a discrete-time nonlinear system; dynamic linearization technology is used to transform the nonlinear system into an equivalent linear data model; for the linear data model, a partial pseudo-derivative estimation algorithm is designed, and a model-free adaptive control law incorporating a decay function is designed; the time-varying partial pseudo-derivative matrix is ​​iteratively updated using the partial pseudo-derivative estimation algorithm, and based on the updated partial pseudo-derivative matrix, the control input at the current moment is calculated using the model-free adaptive control law to control the microgrid frequency. This application can achieve effective frequency control even when the microgrid frequency control system model is unknown and faces mixed network attack threats.

The present invention belongs to the field of environmental microorganisms, and specifically relates to a method for constructing and testing a medicinal plant quality prediction model based on a mixed-effect model and rhizosphere microorganisms, comprising the following steps: (1) determining sampling points in a medicinal plant planting area; (2) collecting, processing, and quantifying quality indicators of plant samples; (3) collecting rhizosphere soil samples, extracting DNA, and conducting high-throughput sequencing; (4) constructing a training set and a test set; (5) screening associated microorganisms, model construction, and coefficient extraction based on the training set; and (6) model verification based on the test set. The present invention, through high-throughput sequencing, sample set splitting, and a mixed-effect model, establishes a medicinal plant quality prediction model using rhizosphere microorganisms for the first time, which can effectively predict the content of the main active substances in medicinal plants, laying a foundation for scientifically and efficiently predicting and improving the quality of medicinal plants during the medicinal plant planting process, and has great significance for the quality control and production of high-quality traditional Chinese medicines.

The invention discloses an unmanned aerial vehicle remote sensing-based single tree biomass estimation method for catalpa bungei of different genotypes. The method comprises the following steps: acquiring unmanned aerial vehicle LiDAR data and catalpa bungei biomass ground measurement data of catalpa bungei in a preset area; according to unmanned aerial vehicle LiDAR data and catalpa bungei biomass ground measurement data, a biomass inversion basic model is constructed and screened, a dummy variable model is introduced to optimize the basic model, and a catalpa bungei genotype is used as a random effect to construct an unmanned aerial vehicle LiDAR-based nonlinear mixed effect individual tree biomass estimation model; and inputting the unmanned aerial vehicle LiDAR data of different genotypes of catalpa bungei in the to-be-processed area into the obtained nonlinear mixed effect individual tree biomass estimation model based on the unmanned aerial vehicle LiDAR data to obtain the individual tree biomass of the catalpa bungei. By adopting the technical scheme provided by the invention, the single tree biomass of the catalpa bungei with different genotypes in a large range can be effectively estimated, and improved variety breeding of the catalpa bungei is assisted.

The application discloses a biological rhythm-light interaction regulation method based on comprehensive cognitive ability index under value more conditions and an intelligent light environment regulation system, and belongs to the technical field of biological rhythm regulation and intelligent environment control. The method comprises the following steps: constructing an individualized comprehensive cognitive ability index, and fitting a solidified mixed effect model parameter based on pre-experiment data; obtaining a real-time biological rhythm phase of a person, combining the solidified parameter to calculate a cognitive ability benchmark value of the person under no light intervention, and determining a cognitive ability gap amount to be compensated; and under the physical constraint of light parameters, optimization solving is carried out to obtain optimal light intensity and color temperature, and a light source is driven to perform adjustment to change a physical light environment, and adaptive optimization is realized through a closed-loop feedback mechanism. The application realizes the transformation from a group statistical model to an individual real-time control strategy, can implement precise light regulation according to people and time, and effectively maintains cognitive ability and rhythm health during value more work.

The application discloses a kind of infrastructure flood resilience evaluation methods, comprising: using flood simulation coupling model to carry out the whole process simulation of urban flood in multiple scenarios to target area;From the simulation process, the spatial attribute index of blue-green-gray infrastructure is extracted as the independent variable, the flood dynamic index of different flood stages is extracted as the dependent variable, to form a sample data set, and the independent variable is divided into multiple independent variable groups according to function;Standardization is carried out on the independent variable and the dependent variable respectively, and data enhancement is carried out;Bayesian mixed effect model is constructed, prior distribution is set for model parameters, posterior distribution of parameters is obtained based on the enhanced sample data set using sampling algorithm, and the evaluation result is output according to the posterior distribution.The application can clearly reveal the dynamic action law of blue-green-gray infrastructure in initial rising period, flood peak period and recession period, output more stable and reliable phased dynamic resilience evaluation result, and significantly improve the safety guarantee level of urban flood control and drainage.

The present application relates to the technical field of power system, specifically relates to a kind of multi-modal fusion and dynamic game driven power grid special cost intelligent review method, comprising the following steps: S1, the multi-source data of power grid special cost project is uniformly collected and processed, including field structured, check, deduplication, fill and exception identification, generate standardized review dataset;S2, based on attention PCA and density peak clustering Weighted feature space is constructed, and same kind cost category is divided;S3, establish log-normal mixed effect model and fuse multi-level factors, estimate reasonable unit price interval in combination with experience bayes method;S4, construct Markov decision process, optimize review strategy and output conclusion and adjustment suggestion.The present application, by constructing multi-source data processing, attention clustering modeling, mixed effect statistical analysis and dynamic game decision fusion Intelligent review system, realizes the high precision, automation and strategy explainability of power grid special cost review.

The application discloses a kind of based on segmented structural equation model's influence lake ecosystem key biological group identification method and system, it is related to environmental ecology technical field, comprehensively investigates the biological community diversity and ecological function characteristics of lake;Through the analysis of the predatory relationship between biological groups, the trophic structure of lake ecosystem is constructed;Comprehensive cluster analysis and function threshold method assesses the multifunctionality of lake;Based on the trophic structure relationship of food web, segmented structural equation model is constructed, each path in the model is fitted by mixed effect model, the path coefficient between trophic level richness and multifunctionality is analyzed, and its direct and indirect effects are calculated;Finally, the key biological group that has the greatest influence on lake ecological function is identified.The present application can accurately identify the key biological group that has a decisive influence on lake ecological function, provide a clear target for ecological protection and restoration, and has practical reference significance in actual restoration management measure formulation.

The present application relates to a kind of community old people health management system and method based on metabolic equivalent, through the comprehensive collection of multidimensional health data, and the processing of multi-source heterogeneous time series data, construct health database;Then adopt mixed effect model and gradient boosting decision tree model, with metabolic equivalent as core quantitative index, extract individual random effect as personalized correction factor by mixed effect model, output personalized metabolic equivalent prediction value combined with gradient boosting decision tree model, solve the problem of existing technology evaluation standard missing, group evaluation one-size-fits-all;Then through time domain, frequency domain, nonlinear feature extraction, the motion load characteristics are fully described, and the association between motion load and health risk is accurately captured by Transformer-LSTM hybrid network, which significantly improves the prediction accuracy of cardiovascular events and fall risk;Finally, through the collaborative optimization model of multi-agent reinforcement learning, dynamically generate and optimize multi-dimensional exercise programs according to the real-time health status of the user.

The present application belongs to the vertical earthquake evaluation technical field, disclose a kind of vertical earthquake action evaluation method based on random effect method, including S1, the ground motion record dataset of southwest region is established;S2, determine as basic input seismic parameter;S3, determine the model equation capable of fully reflecting the V / H spectrum value variation law of target area;S4, the model equation in S3 is carried out multiple nonlinear regression fitting using random effect method;S5, according to the vibration data set established in S1 as training set, combined with the mixed effect expression fitting in S4 coefficient in S3, i.e. the V / H spectrum value model of southwest region is obtained;The present application takes magnitude, fault type, site condition, distance, focal depth as variable, and adopts random effect method to propose V / H spectrum value model suitable for southwest region, to reasonably evaluate the vertical earthquake action of the region.

The invention discloses a method for estimating the diameter at breast height of single trees of the same tree species and different genotypes based on unmanned aerial vehicle remote sensing. The method comprises the following steps: acquiring unmanned aerial vehicle LiDAR data and ground actually-measured diameter at breast height data of single trees in a preset area; constructing a single tree diameter at breast height basic model based on the unmanned aerial vehicle LiDAR; screening a single tree diameter-at-breast-height inversion basic model, introducing a dummy variable model to optimize the basic model, and constructing a nonlinear mixed effect single tree diameter-at-breast-height estimation model based on unmanned aerial vehicle LiDAR data by taking different genotypes of the tree species as random effects; and inputting the unmanned aerial vehicle LiDAR tree height data and crown breadth data of different genotypes of the tree species in the to-be-processed region into the nonlinear mixed effect individual tree diameter at breast height estimation model based on the unmanned aerial vehicle LiDAR data to obtain the individual tree diameter at breast height of the tree species. By adopting the technical scheme, the diameter at breast height of different genotypes of the same tree species in a large range can be conveniently, quickly and effectively estimated, and efficient breeding of improved varieties is supported.

The present application relates to a kind of multi-detection equipment system error real-time alarm method and system, method includes step 1: four-dimensional tensor data construction and pre-processing;Step 2: equipment error modeling based on mixed effect model;Step 3: equipment mutual inspection and error matrix construction;Step 4: adaptive dynamic threshold calculation and alarm triggering;System includes the hardware layer for data acquisition, data layer, calculation layer, application layer.By constructing four-dimensional tensor integration multidimensional detection data, using mixed effect model accurately split equipment system error (bias, drift), based on equipment mutual inspection and form error matrix, combined with adaptive dynamic threshold, realize the real-time alarm of system error, whole process does not need physical calibration, only by data cross validation can guarantee detection accuracy.

The application discloses a method for estimating the biomass of different genotypes of Catalpa speciosa individual trees based on unmanned aerial vehicle remote sensing, which comprises the following steps: acquiring unmanned aerial vehicle LiDAR data and ground measurement data of the biomass of Catalpa speciosa in a preset area; constructing and screening a biomass inversion basic model according to the unmanned aerial vehicle LiDAR data and the ground measurement data of the biomass of Catalpa speciosa; introducing a dummy variable model to optimize the basic model, taking the genotypes of Catalpa speciosa as random effects, and constructing a nonlinear mixed effect individual tree biomass estimation model based on unmanned aerial vehicle LiDAR; and inputting the unmanned aerial vehicle LiDAR data of different genotypes of Catalpa speciosa in a region to be processed into the nonlinear mixed effect individual tree biomass estimation model based on unmanned aerial vehicle LiDAR data obtained above, so as to obtain the biomass of Catalpa speciosa individual trees. The technical scheme of the application can effectively estimate the biomass of different genotypes of Catalpa speciosa individual trees in a large range, and can assist in Catalpa speciosa breeding.

The invention relates to the technical field of power systems, in particular to a multi-modal fusion and dynamic game driven power grid special cost intelligent review method, which comprises the following steps: S1, uniformly collecting and processing multi-source data of a power grid special cost project, including field structuring, verification, duplicate removal, filling and anomaly recognition, and generating a standardized review data set; s2, constructing a weighted feature space based on attention PCA and density peak clustering, and dividing same-type cost categories; s3, establishing a log-normal mixed effect model, fusing multi-level factors, and combining an empirical Bayesian method to estimate a reasonable unit price interval; and S4, constructing a Markov decision process, optimizing a review strategy, and outputting a conclusion and an adjustment suggestion. According to the invention, by constructing an intelligent review system integrating multi-source data processing, attention clustering modeling, mixed effect statistical analysis and dynamic game decision, high precision, automation and strategy interpretability improvement of power grid special cost review are realized.

The invention relates to a hemophilia A perioperative period blood coagulation factor drug delivery model and application thereof, and relates to the technical field of biological information. The hemophilia A perioperative period blood coagulation factor administration model is constructed by adopting a nonlinear mixed effect modeling method based on a two-chamber model structure, and covariants of the hemophilia A perioperative period blood coagulation factor administration model comprise blood type, body weight, age, perioperative period anti-fibrinolytic drug use condition and perioperative period NSAID analgesic drug use condition NSAID. The model can be used for predicting an FVIII concentration-time curve of a clinical individual patient, and individual fine optimization of the administration dosage and the administration time is realized.

The application provides a distortion correction method and device, electronic equipment and storage medium, wherein the method comprises: determining actual coordinates corresponding to a plurality of calibration points according to target images obtained by photographing the plurality of calibration points by a target camera; determining principal point coordinates and focal length of the target camera according to the actual coordinates corresponding to the plurality of calibration points; constructing a mixed effect model of the target camera based on the actual coordinates corresponding to the plurality of calibration points, the principal point coordinates and the focal length of the target camera, wherein the mixed effect model comprises a fixed effect model and a random effect model; and performing distortion correction on an imaging area of the target camera through the mixed effect model. The mixed effect model constructed by the application can capture global trends of distortion and correct local residual distortion, thereby achieving high-precision correction of full-field distortion, improving adaptability and robustness of distortion characteristics of different cameras, and being able to adapt to significant differences in regional distortion of large-field cameras.

This application provides a structural simulation analysis method and apparatus based on carboxymethyl cellulose multi-component mixing. The method includes: constructing multiple multi-component mixing models with the same external dimensions but different internal structures; performing fluid dynamics simulation on each multi-component mixing model to simulate the mixing process of multiple fluids and obtaining the corresponding mixing results; and determining the multi-component mixing model with the optimal mixing effect based on comparative analysis of the mixing results. This application, by constructing and simulating multiple multi-component mixing models with different internal structures in parallel and comparing and analyzing their mixing results based on fluid dynamics principles, can quickly and objectively select the mixing model with the optimal mixing efficiency.

This invention discloses a wind speed interpolation prediction method based on a functional spatial variable coefficient model. By constructing a functional spatial variable coefficient mixed effect model, the method uses a spatially dependent Matérn distribution family spatial correlation function to characterize spatial correlation in the principal component scores, fully drawing on information from geographically adjacent locations to analyze the spatial correlation of wind speeds between wind turbines. The spatial variable coefficient model can more effectively describe this spatial variation pattern and eliminate large-scale biases caused by data aggregation, thereby improving the accuracy of wind speed prediction. In addition, this invention can also effectively perform interpolation prediction for wind speed at any spatial location within the range.

The invention provides a method, medium and system for judging the influence of climate soil on the comprehensive quality of tobacco, and belongs to the technical field of use of neural network models.The method includes the steps that firstly, tobacco leaf and soil sample records and related geographic information of sampling points arranged at multiple points are obtained; obtaining soil physicochemical property parameters, growing season climate parameters and tobacco leaf quality evaluation indexes, and calculating tobacco comprehensive quality indexes; constructing a climate soil element Bayesian network model and an environment element incidence matrix, and converting the environment element incidence matrix into a sparse incidence matrix through threshold screening; identifying a plurality of correlation structures among the environmental elements; analyzing the influence of climate and geographical factors on soil characteristics by using a tobacco soil element interaction pre-training model; key ecological factors are determined through a mixed effect model and variance decomposition; and establishing a segmented structure equation model and analyzing influence mechanism differences under different conditions according to soil pH value grouping, thereby realizing systematic analysis of the tobacco quality formation mechanism.

The present application relates to lithium ion battery health state prediction technical field, specifically for lithium ion battery health state prediction method based on mixed effect model, the prediction method includes the following steps: utilize multinomial distribution to describe the degradation path of different group lithium ion battery;Obtain the offline estimation of corresponding parameters by using the restricted maximum likelihood estimation, obtain the mixed prior information of SOH degradation signal of different lithium ion battery group on the overall level;Beneficial effect is: the lithium ion battery health state prediction method based on mixed effect model proposed in the application aims at the problem that can only be predicted in the average sense of the group, introduces the structure of mixed prior distribution, considers the clustering behavior of lithium ion battery, so that the mixed historical data information can be more fully utilized, the purpose of improving the SOH prediction precision is achieved, the problem that can only be predicted in the average sense of the group is solved.

The invention discloses a construction method of a blood pressure meteorological risk dynamic evaluation index. The method comprises the following steps: using a mixing effect and introducing a cross basis function; selecting a random forest model to calculate a variation mean value, and dividing the variation mean value by the total variation to obtain a weight value; calculating an excess blood pressure value by adopting a mixed effect model, dividing the excess blood pressure value by the maximum excess blood pressure value, and multiplying by 10 to obtain a blood pressure meteorological index; a blood pressure meteorological index integrating various meteorological factors is constructed, risk levels are divided, risk early warning is carried out, decision buffer time is created for different subjects, people with hypertension and related managers are helped to actively cope with the influence of meteorological changes on blood pressure, and the adaptability of the people with hypertension to climate changes is improved; meanwhile, by screening meteorological factors sensitive to the blood pressure and evaluating the influence of composite exposure of the meteorological factors on the blood pressure, the method has more practical significance compared with existing single meteorological factor analysis, the interpretability of the blood pressure-meteorological association is improved, and therefore the method can also be popularized to other health outcomes caused by bad meteorological factors.

The invention relates to a quantitative analysis method for a fish otolith shape variation source, and belongs to the technical field of fishery resource evaluation and ecological morphological analysis. A matrix F is decomposed through a principal component analysis method, so that hierarchical redundancy analysis is carried out on a morphological matrix, and the fish otolith shape variation source is obtained. Modeling the relationship between the form matrix and different explanation matrixes, evaluating the significance of each variable by adopting replacement test, calculating a variance expansion factor to diagnose collinearity, forming a simplified model, and quantifying the independent contribution rate of the feeding habits to the otolith shape variation through variation division based on the simplified model. According to the method, through the mixed effect model and variance analysis, the mixed factors are effectively controlled, reliable quantitative estimation of the feeding habit influence is provided, and the scientific value of a research result is greatly improved. The method is not only suitable for various fishes, but also can be expanded to morphological ecology research of other biological characteristics, and a brand new research normal form and a powerful technical tool are provided for fish ecology and fishery resource management.

The invention provides a prediction method of a multi-response mixed effect model and a related system. The prediction method comprises the following steps: determining a fixed effect coefficient and a covariance matrix of the model; determining predicted original input data and training data; extracting a preset number of sample data from the training data, and determining predicted to-be-verified input data according to a relationship between the sample data and the original input data; constructing a precision matrix and an indication vector according to the to-be-verified input data, and obtaining a random effect coefficient; and obtaining the prediction result according to the fixed effect coefficient, the covariance matrix and the random effect coefficient. Therefore, the random effect coefficient is estimated through conditional expectation, and the calculation challenge of direct inversion of a large matrix is avoided.

The invention provides a mixing effect evaluation method and prediction system based on a physical information neural network. The evaluation method comprises the following steps: S1, obtaining different material concentration spatial distribution characteristics and time sequence characteristics in a small sample machine corresponding to a to-be-estimated system; carrying out multivariate evaluation by utilizing the separation intensity, the separation scale and the overall uniformity; s2, outputting mixing characteristics of materials in a to-be-estimated system by the multivariate evaluation model; s3, establishing a dynamic differential equation to obtain basic time sequence data under different processes; s4, processing the data obtained in the step S3, and constructing a training data set; s5, constructing a mixed effect prediction model by adopting a physical information neural network; and S6, visually displaying the mixing uniformity change curve evolved along with time obtained by the mixing effect prediction model. According to the method, the physical significance of the mixing effect prediction model can be enhanced, the verification cost of the closed mixing system is reduced, and the design efficiency of the mixing system can be improved.