42 results about "Coefficient of determination" patented technology

The invention discloses an oil and gas reservoir shale content logging curve intelligent prediction method, and belongs to the technical field of rock physics and logging curve processing and interpretation, and the method comprises the steps: in step 101, determining Gardner formula parameters through combining a determination coefficient and a grid optimization method, and determining a Gardner formula parameter; and then generating a virtual density data set capable of covering the actually measured density data set by using a Gardner formula. According to the method, the optimal parameters of the Gardner rock physical formula and the Larionov rock physical formula are determined by using the decision coefficient and the grid optimization method, so that the abnormal value of the actually measured logging data can be effectively corrected, and a fusion data set for training various machine learning models can be generated; a cross validation method, a correlation coefficient, a determination coefficient and a multi-dimensional SHAP method are comprehensively utilized to more comprehensively evaluate and explain a prediction result, compared with a traditional rock physics method, the shale content prediction precision is improved by 12%, a logging curve data set can be effectively corrected and expanded, and the prediction accuracy of the shale content is improved by 12%. And the accuracy and the reliability of predicting the shale content of the oil and gas reservoir can be obviously improved.

The invention discloses a determination method, an application method and a related system of a grain yield prediction model, and relates to the field of grain yield prediction, and the determination method comprises the steps: obtaining time-space dynamic data; preprocessing and normalizing climate data, management data and soil data in the spatial-temporal dynamic data to obtain a spatial-temporal data set; respectively inputting the spatio-temporal data set into a plurality of machine learning models to obtain outputs of the plurality of machine learning models; respectively calculating a root-mean-square error and a decision coefficient of each machine learning model based on grain annual output data corresponding to the spatio-temporal data set and the output of each machine learning model; abandoning the machine learning model with the determination coefficient smaller than a preset value to obtain a screened machine learning model; and based on the screened machine learning model and the corresponding root-mean-square error, constructing a weighted average grain yield prediction model. According to the invention, a scientific basis can be provided for grain production under different climate scenes and management strategies.

A welding width and fusion depth prediction method based on machine learning comprises the steps that S1, a data set containing welding parameters is read, and pre-operation is conducted on the data set; s2, constructing and training a machine learning model which comprises a neural network, a random forest regression device and a gradient lifting regression device, dividing the data set into a training set and a test set, and training the machine learning model by using the training set; s3, evaluating the prediction performance of the machine learning model, and using a mean square error (MSE), a mean absolute error (MAE) and a decision coefficient (R2) as evaluation indexes; and S4, using an early stop strategy to prevent overfitting, predicting the welding width and the fusion depth of the test set based on the trained model, and displaying the comparison between a prediction result and an actual value through a chart. The weld width prediction model is established on the basis of the BP neural network, the fusion depth prediction model is established on the basis of multiple integrated learning methods, the welding width and fusion depth are effectively predicted, and the prediction model has high precision and high practical value.

The invention relates to a Nusselt number prediction method, program and equipment based on a heat transfer associated hybrid model of a shell-and-tube heat exchanger and a storage medium, and belongs to the technical field of intelligent design and performance optimization of industrial heat exchange equipment. The objective of the invention is to solve the problems of low prediction precision, poor adaptability and lack of physical interpretability of a traditional empirical correlation under complex working conditions. Through an adaptive weight mechanism, the final hybrid model can automatically select an optimal prediction strategy according to different working conditions, and in four typical working condition cases, the average decision coefficient is up to 0.9930, and the average absolute percentage error (MAPE) is only 1.66%, which is significantly superior to that of a single model. According to the system, high-precision and high-robustness prediction of the heat transfer performance of the shell-and-tube heat exchanger is achieved, more importantly, the corrected correlation formula has clear physical significance and high interpretability, and a revolutionary tool is provided for intelligent design, real-time performance monitoring and energy efficiency optimization of the heat exchanger.

The invention discloses a combustion chamber outlet temperature modeling prediction method, and relates to the technical field of data processing, and the method comprises the steps: obtaining the sample outlet temperature data of a combustion chamber outlet, and the sample condition variable data influencing the sample outlet temperature data; training a plurality of machine learning models based on the sample outlet temperature data and the sample condition variable data; reserving the machine learning models of which the decision coefficients are greater than a preset threshold in the trained machine learning models to form a joint prediction model; wherein the predicted temperature output by the combined prediction model is a comprehensive value of the output temperature of each machine learning model included in the combined prediction model, so that the accuracy of predicting the outlet temperature of the combustion chamber is improved.

This invention discloses a cross-well shear wave velocity prediction method based on GR lithology bin alignment. Addressing the problems of large inter-well lithological differences, inconsistent engineering parameter distributions, and insufficient utilization of multi-source features in existing cross-well prediction methods, this method first collects drilling engineering parameters and logging data from multiple wells. After preprocessing, the data is divided into training and test sets using the well retention method. The optimal GR lithology bin alignment strategy is selected from the training set to achieve inter-well lithology alignment. Based on this, the absolute and relative deviation characteristics of engineering parameters are calculated, and drilling logging features such as P-wave transit time and density are integrated to construct a complete prediction feature set. Finally, the LightGBM regression algorithm is used to establish a cross-well shear wave velocity prediction model, which is optimized using an early stopping mechanism before outputting the prediction results. Practical application shows that the coefficient of determination R0 is significantly improved. 2 The accuracy reached 0.91, which is more than 18% higher than that of traditional modeling methods, and can effectively achieve high-precision and rapid prediction under the condition of no shear wave logging in new wells.

A method is disclosed. The method includes receiving interaction data related to a plurality of interactions in a time period, determining a multiple variable regression formula, and then determining a coefficient of determination associated with the multiple variable regression formula and the interaction data. The method further includes determining if the coefficient of determination satisfies a threshold or is maximized. If the coefficient of determination does not satisfy the threshold or is not maximized, then adjusting the slope coefficients. If the coefficient of determination does satisfy the threshold or is maximized, then using the multiple variable regression formula to determine risk associated with future interactions.

This invention discloses a machine learning-based method for predicting earthquake-induced sand liquefaction deformation, relating to the fields of earthquake engineering and machine learning. The method constructs a multi-source coupled earthquake dataset using engineering survey reports and monitoring data from historical earthquake cases. This dataset undergoes missing value processing, outlier detection, feature selection, and standardization to obtain an earthquake-induced sand liquefaction deformation dataset. Based on this dataset, a prediction model is constructed. The root mean square error, mean absolute error, and coefficient of determination of the model are calculated. Based on corresponding thresholds, the model is adjusted for hyperparameters using Bayesian optimization or encapsulated using Flask. New earthquake-induced sand liquefaction feature data is then processed and input into the encapsulated model to obtain the predicted earthquake-induced sand liquefaction deformation results. This method can effectively predict earthquake-induced sand liquefaction deformation, providing support for earthquake disaster prevention and engineering design.

The application provides a process parameter screening method and device for an anti-penetration high-entropy alloy, and relates to the field of armored materials.The method comprises the following steps: obtaining performance indexes based on theoretical and target shooting experiment analysis of an alloy material target plate under normal penetration, and taking heat treatment process parameters of solid solution and aging as characteristic factors to design an orthogonal experiment to obtain a data set; performing standardization processing on characteristic values in the data set, and performing transformation on the characteristics; performing redundancy judgment and selection on time characteristics and temperature characteristics before and after the transformation; establishing a machine learning model, and performing generalization performance evaluation on the model through a root mean square error and a determination coefficient; and establishing a multi-objective optimization method based on a Pareto front and a multi-dimensional joint probability distribution function to realize multi-objective fusion and screening of process parameters.The data set in the application is completely obtained from experiments, which reduces generalization errors generated when a machine learning model is built for a small data set, and can accurately screen required process parameters in a short time.

The invention discloses an asphalt mixture design method based on machine learning and continuous targeted prediction, relates to the technical field of road material design and engineering, and solves the problems that a traditional trial and error method is low in efficiency, and a machine learning model is insufficient in precision and weak in interpretability. The method comprises the following steps: S1, searching optimal hyper-parameters of a plurality of machine learning models of six targets of the asphalt mixture, training and storing the highest R2 machine learning model of each target, and determining the optimal model of each target according to a determination coefficient R2; s2, according to a continuous targeting prediction method, finely adjusting the input characteristics of the optimal model corresponding to the target with the low determination coefficient, and retraining the corresponding low-constant-coefficient target prediction model; s3, acquiring model feature importance of the low-decision-coefficient target prediction model, drawing an SHAP bee colony graph, and realizing input feature pruning of the low-decision-coefficient target prediction model; according to the method, the design indexes of the asphalt mixture are accurately predicted, and the defects of low efficiency and high cost of a traditional trial-and-error method are overcome.

A method is disclosed. The method includes receiving interaction data related to a plurality of interactions in a time period, determining a multiple variable regression formula, and then determining a coefficient of determination associated with the multiple variable regression formula and the interaction data. The method further includes determining if the coefficient of determination satisfies a threshold or is maximized. If the coefficient of determination does not satisfy the threshold or is not maximized, then adjusting the slope coefficients. If the coefficient of determination does satisfy the threshold or is maximized, then using the multiple variable regression formula to determine risk associated with future interactions.

The invention discloses a drainage basin water resource evolution feature recognition method based on an SSA-HELM algorithm. The method comprises the steps of obtaining hydrometeorological time series data such as drainage basin runoff, precipitation and air temperature; dividing the data into a training set and a test set, performing normalization processing, and inputting the data into a hierarchical extreme learning machine (HELM) model optimized by adopting a sparrow search algorithm (SSA) for training; evaluating the precision of the model by using the decision coefficient R and the root mean square error RMSE, and judging whether a preset requirement is met or not; and if the precision reaches the standard, predicting the future water resource amount by using the trained SSA-HELM model, carrying out reverse normalization processing on the prediction result, and identifying the long-term evolution trend and periodic characteristics of the water resource pattern in combination with a Mann-Kendall trend test method and self-correlation analysis. According to the method, the input weight and the hidden layer bias of the HELM model are optimized through the SSA algorithm, the convergence speed, the generalization performance and the prediction stability of the model are improved, local optimum is effectively avoided, water resource changes under complex climate changes can be accurately predicted, and a scientific basis is provided for water resource management and planning.

The invention relates to a method for predicting the solubility of CO2 in ionic liquid based on multi-model fusion and interpretable machine learning, and the method comprises the steps: building a stacked integrated model based on a MindSpark deep learning framework, an integrated random forest and a gradient lifting regression algorithm, carrying out the automatic optimization of hyper-parameters through a Bayesian multi-objective optimization algorithm, and explaining a model prediction result through a SHAP value method. According to the method, the determination coefficient on a test set reaches 0.9823, the mean square error is 0.0010, the mean absolute error is 0.0210, and the root-mean-square error is 0.0323. The method solves the problems of tedious operation, time consumption and labor consumption in the process of measuring the CO2 solubility of the ionic liquid through experiments, overcomes the defects of insufficient precision, poor generalization ability and weak interpretability of a traditional prediction method, can rapidly and accurately predict the CO2 solubility of the ionic liquid, and provides theoretical guidance for molecular design.

[Problem] To calibrate a force measurement device so that a wide range of force can be accurately measured. [Solution] This calibration method comprises: a step for acquiring a plurality of data points; a step for assuming one of the plurality of data points as a virtual inflection point CP'; a step for calculating a first virtual function and a first determination coefficient R1 2 on the basis of a low-force data point where the magnitude of the force is less than or equal to a virtual inflection value Fk; a step for calculating a second virtual function and a second determination coefficient R2 2 on the basis of a high-force data point where the magnitude of the force is greater than or equal to the virtual inflection value Fk; a step for calculating a sum of the first determination coefficient and the second determination coefficient: R1 2 + R2 2; and a step for defining the virtual inflection value CP' where the sum of the first determination coefficient and the second determination coefficient: R1 2 + R2 2 reaches a maximum as an inflection value CP, defining the first virtual function as a first function f1, and defining the second virtual function as a second function f2 to calculate a Z direction sensitivity characteristic function.

The invention discloses a small sample surface roughness prediction method under a cross-working-condition background, and belongs to the technical field of machining. According to the method, cutting signals (force and vibration) and technological parameters (main shaft rotating speed, feeding speed and cutting depth) are collected in real time through a three-axis force sensor and a vibration sensor, and a multi-dimensional feature vector is constructed in combination with a multi-level feature extraction strategy (time domain statistics, frequency band energy spectrum and eight-layer wavelet packet time frequency entropy). A multi-head attention mechanism (MHA) is innovatively adopted to dynamically allocate feature weights, and the key feature characterization capability is improved. And a meta-learning domain generalization (MLDG) training framework is further proposed, a data set is divided by using target working condition information, a source domain data set is divided into a pre-training set and a fine tuning set, a real domain offset scene is simulated, and the robustness of small sample prediction of the model under cross-working conditions is improved. Experiments show that in a TB6 titanium alloy milling test, the surface roughness prediction determination coefficient R2 is 0.962, the mean absolute error MAE is 0.016 [mu] m, the root-mean-square error RMSE is 0.020 [mu] m, and the method has high prediction precision and reliability.

This patent relates to an acute intraperitoneal toxicity prediction method based on MACCS bonds and a machine learning framework. This method collects and processes toxicity data of chemical compounds, calculates molecular descriptors using MACCS bonds, and constructs a gradient boosting decision tree model for toxicity prediction through feature selection and removal of highly correlated features. The model's hyperparameters are optimized using a grid search method, and the interpretability of the model is improved using SHAP value analysis. This achieves high-precision and highly interpretable toxicity prediction. The method achieves a coefficient of determination of 0.914, a mean squared error of 0.047, a root mean square error of 0.218, and a mean absolute error of 0.148 on the test set. This invention significantly improves the accuracy of toxicity prediction and has strong generalization ability, making it widely applicable to toxicity screening in new drug development, reducing reliance on traditional animal experiments, and lowering R&D costs and time.

A system and method for detecting driver of variance are disclosed. The method includes receiving dependent variable(s) (y) and a set of independent variables (Xn). Next, the method includes computing a correlation (Rxx) between at least two of the independent variables and then calculating a partial effect (β) of each of the independent variables on the dependent variable(s) (y). The method includes estimating a row relative weight as a percentage of coefficient of determination R2 based on a sum of squared values of the calculated partial effect of the independent variables. The method includes determining a distance from median of x-coordinate (DFM x) and y-coordinate (DFM y) of the set of independent variables. The method includes detecting and displaying at least one driver of variance calculated via a weighted Euclidean distance calculated based on the DFM x and DFM y, and the estimated row relative weight.

This invention relates to the field of hydraulic control compensation, and more particularly to a machine tool hydraulic control intelligent compensation method and system based on machine learning. The method includes: fitting a global wear decay rate to obtain a coefficient of determination; calculating the wear equivalent time of the grinding process to be compensated, and constructing a process feature vector for the grinding process to be compensated; training a regression prediction model based on the process feature vectors of historical grinding processes, inputting the process feature vector of the grinding process to be compensated into the trained regression prediction model to output a predicted value of the removal rate of the grinding process to be compensated; correcting the predicted value of the removal rate based on the coefficient of determination to obtain a corrected predicted rate of the grinding process to be compensated; calculating the hydraulic pressure compensation amount of the grinding process to be compensated; and generating control commands based on the hydraulic pressure compensation amount to complete the intelligent hydraulic compensation. The technical solution of this invention can improve the accuracy of hydraulic compensation results.

The present application belongs to the field of intelligent control and prediction technology of metallurgical industry process, and discloses a kind of electric arc furnace smelting end point parameter adaptive hybrid intelligent prediction method and system. Obtain electric arc furnace smelting process data;Build a double-driven prediction system containing mechanism model and data-driven model;Computer mechanism model prediction value and actual measurement value Determination coefficient, statistical effective historical data amount;Build a machine learning prediction model, and select modeling algorithm according to effective historical data amount;Based on determination coefficient and effective historical data amount, select hybrid prediction strategy;According to the hybrid prediction strategy, predict the output end point carbon content prediction value and end point temperature prediction value;Carry out prediction deviation threshold value judgment and execute control, adjust electric arc electric power, oxygen flow, feeding speed or cooling water flow.The present application realizes the accurate prediction and dynamic optimization control of electric arc furnace end point parameters, significantly improves the smelting quality, energy utilization rate and production stability.

The application discloses a method for two-stage prediction and optimization of process performance of sulfur circulation coupled denitrification phosphorus removal, comprising: first, preparing data sets for sulfate reduction amount and phosphorus removal rate respectively; in the first stage, taking anaerobic stage process parameters as input, selecting machine learning algorithm, cross-validation method and search method to realize prediction of sulfate reduction amount; in the second stage, taking anoxic stage process parameters and sulfate reduction amount prediction value as input, selecting machine learning algorithm, cross-validation method and search method to realize prediction of phosphorus removal rate; in the model training process, the model is evaluated through the coefficient of determination R 2 and the root mean square error RMSE, and the optimal model is selected according to the evaluation result. According to the optimal model, a scheduling platform is built, and performance prediction and optimization control of the sulfur circulation coupled denitrification phosphorus removal system are realized according to the scheduling platform.

The present invention provides a new and unique methodology for quantifying the impact of different oversize events on copper recovery, e.g., using historical PST and process data. For example, from historical PST data, an average deviation from a high limit of the control mesh was calculated and then used to train different machine learning models to predict the decrease in the recovery due to oversize events. The best machine learning model for this method was a Random Forest Regressor with a mean absolute error of 0.78 percent point and coefficient of determination (r2) equal to 0.901.

The application discloses a kind of verification methods of tellurium zinc cadmium (CZT) irradiation damage simulation accuracy, the method based on two software packages of SRIM software simulates the irradiation damage of CZT material, uses machine learning modeling to obtain regression curve to verify the accuracy of simulation result, improves simulation calculation speed.In the ion projection range distribution data set, the fitting effect of the linear regression model of machine learning is better than that of logarithmic regression model, and the determination coefficient of linear regression in the projection range fitting of H ion and He ion is greater than 0.98, while the determination coefficient of logarithmic regression model is only greater than 0.60.The method of the application has important significance and application prospect in the fields of energy, public safety, military, nuclear industry, nuclear medicine, scientific research and aerospace, energy acquisition, safety monitoring and radiation protection.

This invention relates to the field of vancomycin pharmacokinetic technology, specifically to the design of a vancomycin clearance prediction scheme based on a VPB ensemble model. This method, based on an ensemble learning strategy, constructs a blending ensemble model to predict vancomycin clearance in adult Chinese patients. First, a variational autoencoder is used to amplify the original sample data to increase data diversity. Then, a particle swarm optimization algorithm is introduced to optimize the parameters of multiple base learners, and the prediction results of the optimized base learners are used as new feature inputs. Finally, a support vector regression machine is used as a meta-learner to integrate and model the above features, forming the final vancomycin clearance prediction model. The VPB model constructed in this invention achieves a determination coefficient R² exceeding 0.9 on both the test and training sets, demonstrating superior prediction accuracy compared to population pharmacokinetic models.

The application discloses a kind of high-efficiency adaptive complex industrial parameter prediction methods based on regression application score, belong to industrial parameter prediction field.The application proposes the feature screening algorithm based on regression application score, the value of each feature is comprehensively evaluated by integrating three key indicators: hit rate within 3% error, determination coefficient R 2 And time-consuming score, and then the regression application score is obtained, the actual contribution of each feature to the model prediction performance can be accurately evaluated, and fast and effective feature selection is realized;Meanwhile, the way of iterative updating model is proposed, the model is trained using data for a period of time to predict data for a short period of time later, the model is retrained periodically using newly generated data to predict data for a period of time later, solving the problem of model timeliness, so that it can accurately predict for a long time.

This paper presents a method for predicting dissolved gas concentration in transformer oil based on EVO-VMD-LSSVM. It combines the Energy Valley Optimization (EVO) algorithm, Variable Mode Decomposition (VMD), and Least Squares Support Vector Machine (LSSVM). The VMD method decomposes the original dissolved gas concentration signal into several Intrinsic Mode Functions (IMFs) with different frequency characteristics, extracting multi-scale features of the signal. Multiple factors, including transformer load, operating oil temperature, ambient temperature, and operating time, are incorporated into the model. Based on the correlation of feature parameters, an LSSVM prediction model is established for each IMF, and the EVO algorithm is used to optimize the LSSVM model parameters, improving prediction accuracy. The prediction results of each IMF are reconstructed to obtain the final predicted dissolved gas concentration value. The prediction performance is significantly better than traditional methods, with evaluation indicators such as mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²) all showing high prediction accuracy and robustness.

The invention discloses a method for predicting the high-cycle fatigue life of a titanium alloy after multi-step thermal deformation based on machine learning, and belongs to the technical field of titanium alloy fatigue life prediction.The method comprises the steps that 1, a plurality of samples are cut from a Ti-6Al-4V titanium alloy after diffusion bonding to be subjected to a multi-step thermal deformation test, and fatigue samples are prepared; 2, performing high-cycle fatigue experiments under different loading conditions to obtain high-cycle fatigue data of the material; 3, performing normalization processing on the data, dividing a data set into a training set and a test set, and improving the total data amount by adopting data enhancement; 4, obtaining a machine learning model based on a training set learning result; and 5, evaluating the model by adopting a determination coefficient R2, an average absolute relative error AARE, a root mean square error RMSE and an average absolute error MAE to finish model establishment, and predicting the high-cycle fatigue life of the titanium alloy after multi-step thermal deformation by utilizing the model. According to the method, the high-cycle fatigue life of the Ti-6Al-4V titanium alloy after multi-step thermal deformation is accurately predicted through a machine learning model.

The present invention discloses an LSTM-CAM-Transformer flood forecasting model for long- and short-term dependent feature fusion. The LSTM-CAM-Transformer flood forecasting model of the present invention is trained based on the calibration period data by using the root mean square propagation method. This model couples LSTM and Transformer through a cross-attention mechanism, and fuses the short-term feature extraction ability of LSTM and the long-term dependence modeling ability of Transformer to construct. And the accuracy of the LSTM-CAM-Transformer flood forecasting model is verified based on the validation period data by using the coefficient of determination, and the trained LSTM-CAM-Transformer flood forecasting model is used for flood prediction. The present invention realizes the joint recognition of long- and short-term flood features, and improves the accuracy and reliability of flood forecasting.

The application discloses a landslide displacement prediction method based on multi-agent collaborative optimization and progressive optimization, which comprises the following steps: obtaining and preprocessing landslide monitoring data to obtain displacement, rainfall and groundwater level sequences; taking a dynamic weighted composite function as a target, simultaneously optimizing a penalty factor and a decomposition layer number of a variational mode decomposition based on a multi-agent collaborative optimization algorithm, and outputting a candidate set; performing variational mode decomposition on each parameter combination in the candidate set to obtain intrinsic mode function components arranged in a low-to-high frequency order, taking a first component as a trend item, a second component as a periodic item, and the rest as a fluctuation item; constructing a combination prediction model for each candidate decomposition result, calculating a determination coefficient on a verification set, and selecting an optimal parameter combination; and performing landslide displacement prediction by using the optimal combination prediction model. The application makes the variational mode decomposition parameter selection directly serve the prediction accuracy through two-stage progressive optimization, and significantly improves the accuracy and generalization ability of landslide displacement prediction.

The invention relates to a tea polyphenol and gallic acid content virtual measurement method based on a machine learning model, and belongs to the technical field of tea component analysis. The method comprises the following steps: S1, obtaining each component value in a tea sample, and combining data of each component value of a plurality of tea samples to construct a sample set; s2, selecting a part of data in the sample set as a training sample set, and constructing a prediction model by adopting a machine learning model to carry out fitting analysis and prediction on the content of tea polyphenol and gallic acid so as to obtain a tea polyphenol and gallic acid prediction model; and S3, evaluating the prediction accuracy of the tea polyphenol and gallic acid of the tea polyphenol and gallic acid prediction model by adopting the evaluation sample set, the mean square error and the decision coefficient so as to obtain the tea polyphenol and gallic acid prediction model with the optimal parameter combination. Compared with an existing prediction model of tea polyphenol and gallic acid, the prediction model of tea polyphenol and gallic acid in the tea sample obtained by the method has the advantages that the prediction accuracy is improved, and the method has good application and popularization prospects.

The invention provides a material supplement analysis system and method based on a least square method and a DeltaV system. The system comprises a product library management module, a data interface module, a curve fitting module, a deviation calculation module, a supplement amount calculation module and a man-machine interaction module. The core of the method is that the curve fitting module carries out polynomial fitting for 1-9 times by adopting a least square method, and evaluates the fitting quality according to a decision coefficient R2; the system also has a fitting strategy switching function, and when R2 is lower than a dynamically adjusted threshold value, a linear function or a local weighted regression strategy can be switched; the supplement amount calculation module has a self-learning capability, and can optimize the model and recommend a scheme according to historical data; the system is further integrated with a predictive analysis module for trend early warning, and closed-loop automatic control can be achieved through a special control block in DeltaV. According to the invention, the crossing of material supplement from auxiliary calculation to intelligent decision making and execution is realized, and the production precision and the automation level are obviously improved.