203 results about "Optimality model" patented technology

The invention discloses a deep learning and numerical mode seamless fusion-based short temporary rainfall forecasting method. The method comprises the steps of constructing a short temporary rainfall initial forecasting model based on a Vison Transform network, and constructing a deep learning and numerical mode seamless fusion-based short temporary rainfall correction forecasting model based on an improved U-Net network; and collecting various meteorological data in real time, preprocessing the data, inputting the preprocessed data into the trained short-temporary rainfall forecast optimal model to obtain 0-6h short-temporary rainfall forecast, fusing the short-temporary rainfall forecast with 2-6h short-temporary rainfall forecast in a real-time regional numerical mode, inputting the trained short-temporary rainfall optimal correction forecast model, and obtaining 0-6h seamless fused short-temporary rainfall forecast. The method solves the problems that in the prior art, deep learning extrapolation and numerical mode short and temporary rainfall fusion forecasting is discontinuous in time, inconsistent in spatial position and low in forecasting accuracy.

The invention relates to the technical field of underwater positioning, in particular to an AUV (Autonomous Underwater Vehicle) three-dimensional pose joint estimation method and system based on multi-modal layering, and the method comprises the steps: distributing a historical observation sequence to corresponding independent convolutional neural network branches according to modals, carrying out the local time sequence feature extraction through each branch, and carrying out the local time sequence feature extraction; outputting local time sequence characteristics of each mode; performing hierarchical feature fusion based on local time sequence features of each mode, inputting global fusion features into a double-branch regression head, respectively decoding through a position regression branch and an attitude regression branch, and outputting a three-dimensional position increment and an attitude increment; and constructing a loss function by using the three-dimensional position increment and the attitude increment, training to obtain an optimal model, and deploying the optimal model to an AUV platform, thereby breaking through the limitation that the traditional method only focuses on a two-dimensional plane or separately estimates the attitude, completely covering the full-space positioning demand of the AUV three-dimensional maneuvering task, and improving the integrity and consistency of the attitude estimation.

The invention discloses a COPD-FE risk prediction method based on disease and symptom combination, and is applied to the technical field of chronic obstructive pulmonary disease risk prediction. Comprising the following steps: acquiring chronic obstructive pulmonary frequent acute exacerbation influence factor data of a patient; the influence factors are screened through LASSO regression and an improved Boruta algorithm respectively; the LASSO independent influence factors and the Boruta independent influence factors are combined in different modes, a Logistic regression model and an XGBoost model are trained, and a plurality of COPD-FE risk prediction models are obtained; and evaluating the performance of all the COPD-FE risk prediction models, and selecting the COPD-FE risk prediction model meeting the requirement to predict the COPD-FE risk. According to the method, clinical data of patients are collected, a risk prediction model is constructed in combination with a feature selection method and machine learning, and an optimal model is screened out through comprehensive evaluation.

The invention discloses an intelligent rainfall forecasting method and system based on real-time assimilation driving of sky-air-ground multi-mode sensing data, and relates to the field of machine learning, and the method comprises the steps: carrying out the coordinate alignment of an obtained standardized multi-source meteorological data set, distributing a weight according to the time-space credibility of a data source, and carrying out the calculation of the time-space credibility of the data source; generating an enhanced radar image feature set implying multi-modal information, and inputting the enhanced radar image feature set into the double-branch-UNet-GAN collaborative optimization model for training to obtain an optimal model adaptive to multi-modal data; based on severe convective weather characteristics and business requirements, precision correction and lightweight processing are performed on a forecast result after test set adversarial optimization through a regional attention mechanism, visual display is realized through an APP and a webpage terminal, positioning query and early warning push functions are synchronously provided, and a closed loop from data to service is completed. According to the method, full-chain innovation is formed from data fusion, model performance, aging precision, resource adaptation to service landing, and the scientificity and application value of intelligent rainfall forecasting are remarkably improved.

The invention discloses an autoclave temperature offline simulation and online prediction method based on Hybrid KAN feature fusion. The autoclave temperature offline simulation and online prediction method comprises the steps that historical temperature time sequence data and static process parameter data of different batches are collected; performing feature screening and preprocessing, and constructing a time sequence data input sequence and a static process parameter input sequence; designing a Hybrid KAN neural network model, taking the time sequence data input sequence and the static process parameter input sequence as input, and outputting a predicted value of the temperature of the autoclave at the next moment through feature extraction and fusion; taking minimization of a loss function as a target, training the model, and obtaining an optimal model by using a structured pruning strategy; and deploying an optimal model, and realizing off-line simulation and on-line prediction of the temperature of the autoclave through rolling time window iteration multi-step prediction. The method is high in accuracy, wide in universality, small in model parameter scale, high in reasoning efficiency and suitable for autoclave temperature prediction in complex scenes.

According to the large language model reasoning optimization method and device, the storage medium and the computer equipment provided by the invention, the multi-dimensional monitoring data is acquired, and then the multi-dimensional monitoring data is input into the pre-trained environment decision network to obtain the optimal model configuration. The current resource state is evaluated according to the multi-dimensional monitoring data, and the optimal execution path is determined according to the resource state and the optimal model configuration. And finally, adjusting the target model based on the optimal model configuration, and performing model reasoning according to the optimal execution path in the reasoning process of the target model. In the process, the resource state is evaluated and the optimal model configuration is determined by collecting the multi-dimensional detection data, and then the optimal execution path is dynamically determined, so that the model structure and the calculation strategy can be dynamically adjusted according to the state during actual operation, and the response delay is effectively reduced and the resource utilization efficiency is improved while the reasoning precision is guaranteed.

The invention provides a laser powder bed melting method for preparing a high-toughness TC4 titanium alloy based on machine learning. The method comprises the steps that performance data of the TC4 titanium alloy prepared through different SLM process parameters are collected; constructing a hybrid expert (MoE) machine learning prediction model framework; classifying the TC4 data by adopting a K-means clustering algorithm, and taking a clustering center as an initial weight of an input layer and a first layer of the gating network; presetting a TC4 performance prediction model through the gating network and the plurality of expert networks after clustering information initialization; performing small-range iteration through large-step network search in combination with a Bayesian optimization method so as to determine an optimal model structure and complete model training; the trained performance prediction model and a genetic algorithm are used for carrying out backstepping to obtain SLM printing parameters of the high-toughness TC4 alloy; and SLM forming is carried out, and the high-toughness TC4 titanium alloy is prepared. According to the method, a calculation framework which is high in precision and suitable for exploring a high-unknown characteristic space is constructed, and far-reaching influences on titanium alloy additive manufacturing and process optimization of other alloy systems are achieved.

The invention discloses a U-Net-based multi-mode wind energy resource monthly scale prediction correction method and system, belongs to the technical field of wind energy resource evaluation and climate numerical value prediction crossing, and is used for correcting monthly scale wind speed output by a climate mode. According to the method, a climate state wind speed field is constructed by utilizing ERA5 reanalysis, and a monthly-scale 10m wind speed anomaly is calculated to serve as a correction reference; monthly-scale historical return data of a plurality of dynamic climate modes are obtained, 10m wind speed and multilayer meteorological elements are extracted, unified interpolation and standardization are carried out, and a sample set is formed; a U-Net correction model with a coding and decoding structure is constructed based on a sample set, feature combination and hyper-parameters are optimized through cross validation, and nonlinear mapping from a multi-mode forecast field to an ERA5 distance flat field is learned. And correcting future monthly scale forecast by using the optimal model, generating a wind speed product of which space structure and amplitude distribution are closer to observation, and providing high-credibility wind energy climate information for wind power planning, power generation planning and power grid dispatching.

The invention discloses a Taylor flow two-phase volume fraction prediction method based on CFD and machine learning, and belongs to the field of hydrodynamics. The method aims at solving the problems that traditional CFD is low in efficiency and weak in generalization, and pure machine learning lacks explanatory and physical consistency. According to the method, a mixed CFD-ML framework integrating an interFoam solver of OpenFOAM and machine learning is constructed. The method comprises the steps that firstly, a multi-working-condition calculation example is established based on OpenFOAM, and a training data set containing velocity fields Ux and Uy and a liquid phase volume fraction field alpha. Water is generated through interFoam; training a fourier neural operator (FNO)-based model by using a training solver to obtain an optimal model; and finally, loading the model by using a prediction loop solver, and quickly predicting the volume fraction field of the next time step in CFD time marching. The method improves the prediction efficiency and precision, guarantees the physical consistency, is suitable for the fields of ocean engineering, oil and gas transmission and the like, and can be expanded to a multiphase flow scene.

A Seq2Seq multi-step 4D flight path prediction method based on gating error correction comprises the following steps: data preparation: selecting data of a plurality of real flight paths of the same flight; data preprocessing, including data normalization and data set division; a TCN-GRU-GECM model is constructed, and the TCN-GRU Carrying out the training of the TCN-GRU-GECM model; inputting a test set data sample as an input sequence into the stored optimal model to obtain a predicted value of the tth time step; and S6, traversing all the input sequences, circularly executing the previous process, and finally realizing a multi-step 4D prediction task for the whole track. Error accumulation is effectively inhibited, an error window is maintained through a gating error correction module, an error trend is analyzed based on a mean value and a standard deviation of historical errors, a correction value is dynamically generated by utilizing a gating mechanism and is fed back to a decoder, and the problem of error propagation amplification in autoregression prediction is remarkably relieved.

The invention discloses a data mining technology-based construction method and application of an elderly diabetic hypoglycemia risk prediction model, and belongs to the technical field of medicines and computers. The method comprises the following steps: selecting elderly diabetic patients meeting standards, collecting demographic, disease and lifestyle factor data, dividing a training set and a verification set, comparing the performance of five data mining models, screening an optimal model, confirming the effectiveness of the model through external verification, and visualizing the result. The model can output a quantitative risk probability, is suitable for multi-scene real-time evaluation, can accurately identify high-risk groups, guides hierarchical intervention, reduces the incidence rate of hypoglycemia, and improves the management efficiency and safety of elderly diabetic patients.

The invention discloses a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

The invention provides a deep learning trajectory prediction method introducing a vehicle kinematics constraint, and aims to solve the problem that trajectory prediction lacks physical consistency. The invention relates to the field of automatic driving. The system comprises a deep learning prediction module, a physical model prediction module and a model optimization training module. The deep learning prediction module receives vehicle and map information, extracts features and interacts with each other by using a gating cycle unit GRU and a graph convolution sub-graph network, and outputs a first prediction trajectory. And the physical model prediction module uses a PID controller to calculate a control quantity according to the reference position state, and drives a kinematic model to generate a second prediction trajectory conforming to kinematic constraints through closed-loop deduction. And the model optimization training module constructs joint loss and back propagation optimization network parameters based on a prediction error and a physical consistency error between the two trajectories, and finally loads an optimal model output trajectory.

A method of training a machine learning model to predict seafloor ripple geometry that includes receiving one or more input values, each input value based on an observation associated with ocean wave and seafloor conditions, and preprocessing the one or more input values. The method includes generating a training data set based on the preprocessed data set, splitting the training data set into a plurality of folds, and training via stacked generalization the machine learning model by performing a cross validation of each fold of training data based on at least one deterministic equilibrium ripple predictor model and on at least one machine learning algorithm. The method may include generating via the trained machine learning model, a set of one or more seafloor ripple geometry predictions, and performing Bayesian regression on the set of one or more seafloor ripple predictions to generate a probabilistic distribution of predicted seafloor ripple geometry.

The invention relates to the technical field of grassland ecological monitoring, and discloses a grassland biomass estimation method based on multi-modal phenotype fusion and moisture content correction. The method comprises the following steps: setting a 0.5 m * 0.5 m quadrat in a grassland research area, synchronously acquiring a multi-spectral image of an unmanned aerial vehicle, multi-view structural data of a ZED 2i depth camera and ground actual measurement data, extracting structural features such as a multi-view vegetation projection volume index and 10 vegetation indexes after preprocessing, and constructing an XGBoost moisture content inversion model; and constructing a biomass initial model by using a pure structure, a pure spectrum and fusion characteristics, introducing a moisture content correction formula to correct deviation, and screening an optimal model through Monte Carlo cross validation. According to the method, multi-source data are effectively fused, moisture interference is stripped, the grassland biomass estimation precision is improved, and reliable technical support is provided for grassland ecological monitoring, carbon cycle evaluation and resource management.

The invention belongs to the technical field of non-intrusive load identification or decomposition, and discloses a non-intrusive load decomposition method fusing bidirectional Mamb-Attention and dynamic feature enhancement, which comprises the following steps: acquiring household electric appliance consumption data within a period of time, and preprocessing the electric appliance consumption data; according to the preprocessed electric appliance consumption data, dynamically selecting one-dimensional convolution or multi-scale depth separable convolution to perform feature enhancement, and outputting a feature vector; a bidirectional Mamb-Attention fusion model is constructed, and the feature vectors are processed; the constructed bidirectional Mamb-Attention fusion model is trained and tested, and an optimal model is obtained; and performing non-intrusive load decomposition on the electric appliance consumption data acquired in real time by using the optimal model. According to the method, through collaborative design of a bidirectional Mamba module and a multi-head attention mechanism, joint modeling of long-term dependence and local features is realized.

The invention provides a self-adaptive cardinality estimation method, system and device and a storage medium, and belongs to the field of database query optimization, and the method comprises the steps: constructing original connection sequence data and an optimal table sequence of a table based on a disclosed benchmark test data set as a data set, and converting the data in the data set into an original feature vector; the original feature vectors are input into a plurality of Transform models with different hyper-parameter settings for model training, a linear transformation module is included in front of each Transform model, and a plurality of cardinal number prediction results are obtained through training; and selecting a model with a minimum error as an optimal model according to a cardinality prediction result to obtain all original query feature vectors and corresponding optimal models, and learning a mapping relationship between the query features and the optimal models by utilizing a classifier. Obtaining a new SQL query input classifier, and distributing an optimal model based on the index data set; and processing the feature vector by using the optimal model to obtain a cardinality estimation result. The defect of single model query is changed, and the accuracy of a query result is improved based on feature classification query.

The invention discloses a metallocene polypropylene synthesis catalytic performance prediction method, which comprises the following steps: data collection: collecting experimental data of metallocene catalyst synthesis polypropylene, and establishing a data set containing input variables and output variables; data preprocessing: cleaning, standardizing and coding experimental data; feature engineering: carrying out feature correlation analysis on the preprocessed data; model construction: constructing independent prediction models for predicting catalyst activity, number-average molecular weight and molecular weight distribution by using a plurality of machine learning algorithms respectively; performing hyper-parameter optimization: performing hyper-parameter optimization on the prediction model by adopting a Bayesian optimization algorithm to obtain an optimal model parameter; model evaluation: evaluating the performance of the prediction model by adopting a cross validation method; and predicting: inputting process parameters and catalyst ligand parameters input by a user into the optimized and evaluated optimal prediction model, and outputting predicted values of catalyst activity, number-average molecular weight and molecular weight distribution.

The invention discloses an osmotic pressure analysis method and device fusing model optimization and physical inversion and a medium, and the method comprises the steps: calculating correlation coefficients of a specified reservoir water level and osmotic pressure under different time lags based on cross-correlation function analysis, and recognizing a differential nonlinear feature corresponding to the maximum correlation coefficient; aiming at the differentiated nonlinear characteristics of different dam types, constructing a plurality of corresponding diagnosis prediction models in parallel, and establishing a model selection decision rule to select an optimal diagnosis model; according to delay parameters obtained through cross-correlation analysis, a permeability coefficient is reversely deduced through a pore medium heat transfer diffusion theoretical formula; according to the regression slope and the theoretical value attenuation factor of the optimal model, a permeability coefficient is reversely deduced in combination with a correction diffusion equation, credibility evaluation and weighted fusion are performed on the permeability coefficient, and a comprehensive permeability coefficient is calculated; and diagnosing the soil type and the permeability characteristic grade. According to the method, the propagation delay characteristic of reservoir water level change in the pore medium can be accurately quantified, and the inversion precision of the permeability coefficient and the engineering applicability are improved.

The invention relates to a geological disaster prediction method and system based on an optimized PU-XGBoost model, and belongs to the technical field of geological disaster monitoring and risk assessment, and the method comprises the steps: obtaining multi-source data including InSAR deformation monitoring data and historical geological disaster point data, taking the historical geological disaster points and InSAR significant deformation points in the InSAR deformation monitoring data as a positive sample set P, and carrying out the prediction of the InSAR significant deformation points in the historical geological disaster points; a PU-Learning strategy of a Spy sample is introduced to construct a reliable negative sample system, an XGBoost hyper-parameter is optimized and searched in combination with introduction of a BTO optimization algorithm, an optimal hyper-parameter combination is obtained, and an optimal model is obtained through training of a positive sample set P and a negative sample set RN; and performing geological disaster susceptibility prediction and hierarchical mapping on the research area based on the optimal model, thereby realizing high-precision and high-reliability evaluation of the geological disaster susceptibility of the research area.

The invention discloses a material demand prediction method based on an ARIMA model and an LSTM model, and the method comprises the steps: constructing a time-serialized material demand data set, and carrying out the data preprocessing; time sequence analysis is carried out, linear trend modeling is carried out by using an ARIMA model, a preliminary prediction value is obtained, and a prediction residual error is calculated; constructing an LSTM neural network to perform nonlinear feature learning on the residual error, predicting a future residual error value, determining an optimal model by using an LSTM model evaluation index, and predicting future annual material consumption by using the optimal model; determining a calculation range of an annual purchase quantity, and establishing an annual recommendation purchase model; calculating an annual purchase quantity recommendation value of a plurality of years in the future, and forming an annual purchase schedule; and setting an early warning inventory value, and monitoring the inventory in real time. Compared with a traditional method depending on a single model, the method can more comprehensively capture tendency and volatility in material demands, and adapts to changes of material consumption modes in complex construction scenes.

The invention provides a large language model collaborative matching method and device, equipment and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that a corresponding multi-dimensional feature vector is generated according to a task request; the task request comprises a multi-modal task; based on a model capability map, a system real-time state and the multi-dimensional feature vector, performing weighted evaluation calculation according to a multi-objective optimization algorithm, and generating an optimal model matching strategy; based on the optimal model matching strategy, performing task splitting, resource allocation and model scheduling according to the multi-modal task; and after the multi-modal task is executed, adjusting the weight coefficient calculated by the weighted evaluation, and iteratively updating the model capability map. According to the method, the optimal matching of the model and the task is flexibly realized, and the task processing efficiency, accuracy and overall performance are improved at low cost.

This invention discloses a method, apparatus, equipment, and storage medium for estimating geostress parameters in the longitudinal profile of a deep-buried, long tunnel, relating to the field of geological surveying technology. The method treats the tunnel geostress field as a random field and the measured geostress as several realizations of the random field. After obtaining the measured values ​​of geostress parameters from multiple points on the tunnel's longitudinal profile, the method first uses these measured data to fit multiple experimental variogram models to obtain model coefficients. Then, through error analysis, the optimal model is selected. Finally, using this optimal model, spatial interpolation calculations of geostress are performed to obtain the geostress results along the tunnel axis. This method eliminates the need for detailed and accurate geological data and soil parameters, and can quickly and efficiently obtain the geostress distribution results along the tunnel axis that meet the requirements and accuracy of tunnel engineering demonstration and design stages, using only a small amount of measured data from a few points. This shortens the required time and has significant theoretical and practical value.

The invention belongs to the technical field of natural gas hydrate geological exploration and logging evaluation, and relates to a hydrate-containing sediment effective resistivity prediction and hydrate saturation inversion method based on a logic function form, and the method comprises the steps: obtaining rock physical basic data of a target reservoir or a to-be-tested sample; establishing an effective resistivity forward modeling model based on a logic function form; determining fixed parameters in the effective resistivity forward model and fitting parameters to be inverted according to geological features or experimental conditions of the target to be analyzed; an objective function is constructed, fitting parameters are optimized through an optimization algorithm, optimal model parameters are obtained, and a unary response equation of the effective resistivity about the hydrate saturation is established; and calculating the effective resistivity or hydrate saturation of the unmeasured points according to the determined response equation. According to the method, the description precision and prediction reliability of the resistivity response characteristics of the complex hydrate reservoir are remarkably improved, and a foundation is laid for reservoir saturation fine evaluation and efficient resource exploration and development.

The invention relates to the technical field of uncertainty probability modeling, and provides a limit load modeling method and device for a high-temperature adhesive, a computer readable storage medium and electronic device.The method comprises the steps that self-service sampling processing is conducted on an original limit load sample set of the high-temperature adhesive, and N self-service sample sets are obtained; n is an integer greater than 1; fitting each candidate probability distribution model by using each self-service sample set, and calculating a Bayesian information criterion value corresponding to each candidate probability distribution model; under each self-service sample set, selecting an optimal model based on a Bayesian information criterion value; and counting the frequency of each candidate probability distribution model selected as an optimal model under the N self-service sample sets, and determining the candidate probability distribution model with the highest frequency as an optimal fitting distribution model of the limit load data of the high-temperature adhesive. The invention provides a scientific and reliable method for high-temperature adhesive limit load distribution modeling.

This invention discloses a deep learning-based method for downscaling ERA5 precipitation products. First, ERA5 reanalysis data, GPM satellite data, and daily precipitation observation data from ground stations are acquired and preprocessed. The correlation between meteorological factors and ground-observed precipitation is measured using the Pearson correlation coefficient method, and meteorological factors with high correlation are selected as effective features. Data are cropped to the study area and normalized. A precipitation downscaling model is established, and a loss function and model parameters are designed. The optimal model is trained, and the downscaling results are evaluated. A two-branch structure is adopted, using GPM satellite data as labels. One branch uses low-resolution precipitation as input, and the other branch uses effective meteorological features as input for learning assistance. The trained model not only captures temporal and spatial data variations but also effectively captures extreme cases, effectively reducing ERA5 precipitation data from 25km to 10km.

The application discloses a disc shear process parameter multi-objective optimization method based on machine learning and NSGA-II, acquires existing disc shear cutting related data of a factory, and constructs a disc shear cutting quality evaluation data set; a multi-model prediction system containing ANN, XGBoost, LightGBM and CatBoost is established, combined with Bayesian optimization to realize automatic optimization of hyperparameters, and the optimal prediction model combination suitable for different defects is screened; based on the optimal model combination, an NSGA-II multi-objective optimization model is constructed, and under the constraint of industrial operation parameters, a Pareto process parameter solution set is obtained, and the optimal solution is screened. The method combining industrial measured data, multi-model prediction and multi-objective optimization algorithm is adopted to mine the internal coupling correlation between process parameters and cutting quality, realize the collaborative improvement of multiple defects, and has important guiding significance for improving the cutting quality of the strip steel and enhancing the production benefit.

The invention provides an aircraft slip-off time prediction method based on differential evolution and XGBoost, which comprises the following steps of: firstly, screening key slip-off time prediction data characteristics based on a sliding motion process of a civil aircraft in an airport scene in a departure process, acquiring corresponding data, and preprocessing to construct an aircraft slip-off time prediction data set; then, an XGBoost model is adopted as an aircraft slip-out time prediction model, and model hyper-parameters needing to be optimized and a search range of the model hyper-parameters needing to be optimized are selected; optimizing the selected XGBoost model hyper-parameters by using a differential evolution optimization algorithm to obtain an optimal model hyper-parameter set; and finally, training by using the optimal model hyper-parameter set to obtain an aircraft slip-off time prediction model. According to the method, the intelligent optimization algorithm is used for searching the optimal model hyper-parameters for the prediction model, so that the comprehensive prediction capability of the model on the aircraft slip-off time is improved.

This invention discloses a carbon emission prediction system and method for papermaking processes based on BO-GBDT. The system collects process parameters and energy consumption data during papermaking production, preprocesses the input data, constructs a feature set containing key influencing factors, and obtains a carbon emission dataset for model training. A Bayesian optimization algorithm is introduced to intelligently search for the optimal hyperparameters of four gradient boosting models, constructing a hyperparameter optimization space and setting an optimization objective function. Model performance is evaluated through cross-validation, and the optimal model is updated and selected. BO-GBDT is selected as the final prediction model. The trained model is then used to accurately predict carbon emissions from the papermaking process. By automatically optimizing the hyperparameters of the gradient boosting decision tree model using Bayesian optimization and combining it with multi-source data feature engineering, high-precision and high-efficiency prediction of carbon emissions from the papermaking process is achieved, providing an effective tool for carbon management in the production process.

The invention relates to the technical field of ecological risk evaluation test strategies, in particular to a method for predicting the content of a biological enrichment factor by utilizing QICAR modeling, which comprises the following steps of: by taking known BCF, metal physicochemical properties and soil physicochemical properties of a corresponding area as data, obtaining a predicted value of unknown heavy metal BCF of a target area by utilizing training of a QICAR coupled machine learning model; the method comprises the following steps of: selecting a plurality of rounds of cross validation to screen test set data, evaluating the performance of a model through a decision coefficient R2 and a mean absolute error MAE to obtain an optimal model, and explaining an evaluation result through feature analysis and Shapley weighting to obtain an accurate and scientific predicted value, so that effective support is provided for predicting the heavy metal enrichment capability of crops. And guarantee is provided for food safety.