51 results about "Predictor variable" patented technology

The invention discloses a wastewater treatment plant modeling method and system based on a physical information neural network. The method comprises the following steps: acquiring and inputting working condition parameters and inlet water quality data to a neural network model, and performing forward calculation to obtain predicted outlet water state variables and key kinetic parameters; determining a first deviation based on a difference value between the predicted effluent and an actual measurement value, and substituting the predicted variable and the parameter into an activated sludge mechanism differential equation set to calculate a residual error so as to obtain a second deviation; performing weighted summation on the two deviations to form a total optimization target, and performing iterative training according to the total optimization target to obtain a final model; different working condition parameters are substituted into the model for multiple times of forward deduction, and finally in combination with energy consumption data, the working condition with the lowest energy consumption meeting the effluent standard is determined as the optimal process control parameter. By implementing the technical scheme provided by the invention, the actual operation energy consumption meeting the effluent quality standard is reduced.

The invention relates to a multi-resolution geological data conversion method and system based on ensemble learning, and belongs to the technical field of geological information processing, and the conversion method comprises the steps: obtaining a low-resolution geological data set containing a target element, and a high-resolution geological data set not containing the target element; performing spatial grid aggregation processing on the high-resolution geological data set to generate a predictive variable matrix which is spatially aligned with the low-resolution geological data set; training a Stacking integrated regression model by taking the predictive variable matrix as input and a target element value in the low-resolution geological data set as an output target; inputting the high-resolution geological data set into the fusion prediction model, and outputting a preliminary prediction value of a target element; and performing spatial error correction on the target element preliminary prediction value based on the target element actual value of the low-resolution geological data set to generate corrected high-resolution target element data. According to the invention, multi-scale and multi-source heterogeneous geological data can be effectively integrated.

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.

A machine learning system and method for optical critical dimension measurement. From a training set of spectra and references, features are extracted and subjected to regression analysis to generate predictor variables. Using feature functions, inverse feature functions, a machine-learning predictor component and masks, a machine-learning optical critical dimension explainer is generated. A wafer is analyzed by metrology tools and the machine-learning predictor component calculates a critical dimension inference from measured spectra. Theoretical spectra are then generated by the predictor component based upon a modification of the critical dimension inference. The measured spectra are compared to the theoretical spectra and the fit of the measured spectra to the theoretical spectra is evaluated for acceptance. The results of the comparison and analysis is output in human readable form.

The invention relates to the technical field of Internet of Things, and provides an industrial production prediction model construction method and system. A standard industrial data set is subjected to periodic decomposition and trend smoothing processing according to a time period to generate a time sequence change sequence, and the time sequence change sequence is subjected to power load segmentation adjustment and feature extraction to obtain a production power load capacity sequence and a capacity feature set. Carrying out industrial state type identification and state probability calculation according to the production power load sequence and the capacity feature set to generate a state probability sequence, and integrating the production power load sequence and the capacity feature set according to the state probability sequence to obtain a prediction variable set of each industrial state type; and constructing an industrial production prediction model according to the state probability sequence and the prediction variable set. According to the method, the state condition regression model is constructed through temperature and calendar stripping, mixing fusion and state recognition, and the precision, robustness and interpretability of industrial output proximity prediction are improved.

This invention discloses a lightweight modeling method, apparatus, system, and storage medium for high-dimensional data meteorological forecasting tasks, belonging to the technical field of computation, extrapolation, or counting. The method constructs prediction vectors and target variables from the original high-dimensional data required for the target meteorological forecasting task. Through an improved variational autoencoder strategy, it reconstructs latent predictor variables that influence or potentially influence the target variables from the prediction vectors. It then selects latent variables highly correlated with the original predictor variables to construct a candidate predictor variable dataset. After removing candidate variables that are significantly uncorrelated with the target variables and those with strong linear correlations from the candidate predictor variable dataset, it performs key predictor variable selection and constructs a lightweight prediction model based on the key predictor variables. This invention significantly reduces the dimensionality of the model input data while maintaining prediction accuracy, and enhances the model's prediction accuracy by constructing predictor variables that influence the target variables and have clear physical meaning.

The application discloses a kind of wind turbine blade icing mass prediction methods based on data driving, method includes: based on preset value range, obtain sampling data;Simulation is carried out by CFD simulation software, and the corresponding icing mass data set under different parameter conditions is obtained;Icing mass data set obtained by simulation is compared with the data set of laboratory environment simulation result and is corrected;According to the icing mass data set obtained after correction, respectively to each variable Application polynomial fitting obtains the order relationship of the variable and icing mass after no-parameter system identification, obtains the function relationship of each parameter and icing mass;The variable value to be predicted is obtained by actual environmental information, and the variable value to be predicted is predicted according to the function relationship, and the icing prediction result is obtained.The application has the advantages of low cost, strong real-time, high precision, small data processing amount when using, etc., and can be widely applied in data processing technical field.

The invention relates to the technical field of biology, in particular to application of an SNP (Single Nucleotide Polymorphism) molecular marker combination to identification of Shouguang chickens and non-Shouguang chickens. The SNP molecular marker combination is a genotype result of 16 loci, the 16 SNP molecular marker combination is used as a predictive variable, a Shouguang chicken or a non-Shouguang chicken is used as a predictive value, and 70% of individuals in a sample to be detected are used as a training group to carry out 20 times of random sampling to train a support vector machine model. Results show that the prediction precision of the identification of the Shouguang chicken is 99% when the 16 SNP molecular markers are combined for identification. And a more powerful technical support is provided for identification and genetic breeding of the Shouguang chicken.

The application discloses a kind of dabigatran bleeding risk prediction methods based on machine learning, it is related to computer-aided drug risk management technical field, the method is by obtaining patient baseline and follow-up data, multiple imputation method is handled missing value;With HAS-BLED score as the basis, candidate variables are screened from potential risk factors using LassoCV algorithm, and anemia, insulin use and antifungal agent use are determined as new prediction variables through clinical correlation analysis to construct a set of prediction variables;Finally, a random forest or gradient boosting algorithm is used to train the model to output the bleeding risk assessment results of the individual to be tested.The application combines machine learning algorithm, improves the prediction accuracy of dabigatran bleeding risk in Chinese non-valvular atrial fibrillation patients, solves the problem of insufficient prediction accuracy of traditional HAS-BLED score, and provides a more reliable basis for clinical individualized anticoagulant therapy decision.

A computer implemented system for predicting a property or classification associated with document data. The system has a data extraction module configured to receive input document data and extract from the input document data a plurality of data sets, each data set having data of one of a plurality of data types. The system also has processing pathways each configured to process one of the plurality of data sets to generate a vector output representative of the data set processed by the processing pathway. The system has a vector concatenation layer configured to concatenate the vector outputs of each processing pathway to generate a concatenated vector, and a plurality of predictions heads. Each prediction head is configured to process the concatenated vector to generate a prediction variable indicative of a property or classification predicted to be associated with the input document data.

The invention relates to atmospheric chemical reaction calculation substitution and acceleration in an atmospheric chemical model, in particular to an atmospheric chemical reaction calculation substitution model and a construction method thereof, the model has a high-precision reappearance chemical reaction process, and in 19-day off-line prediction of 279 predictive variables of a case, the proportion of the predictive variable decision coefficient exceeding 0.99 is 96%; the model can realize continuous stable prediction for at least 10 days; compared with an original GEOS-Chem chemical integrator, the ChemKNet has the advantages that the time consumed by calculation on a single CPU can be saved by about 79%, only about 1% of the original calculation time is needed on a single GPU, and the calculation efficiency of large-scale simulation is greatly improved; the model supports coupling and continuous prediction; the model is provided with a full-chema chemical mechanism covering GEOS-Chem, and is provided with global simulation and a full-chema chemical mechanism covering GEOS-Chem.

The invention belongs to the technical field of artificial intelligence and power system crossing, and discloses an electricity price prediction method based on automatic hyper-parameter and feature optimization and a related device. The electricity price prediction method comprises the steps of predicting input data based on a selected time period, performing electricity price prediction by using an electricity price prediction model, and obtaining an electricity price prediction result of the selected time period; the electricity price prediction model is defined, constructed and trained according to the optimal configuration scheme; the obtaining process of the optimal configuration scheme comprises the steps of obtaining a target prediction variable used for model construction and selected input features useful for target prediction to form a structured data set, defining a mathematical optimization space based on the structured data set, and searching in the mathematical optimization space to obtain the optimal configuration scheme. According to the technical scheme disclosed by the invention, the technical problems of low efficiency, high dependence on artificial experience, suboptimal configuration combination, fragmentation of an optimization process, difficulty in maximization of prediction precision and the like in an existing electricity price prediction model construction process are solved.

A computer program product, a monitoring method for a production process in a production system, a production system provided with a monitoring device and a method for configuring the monitoring device, which is configured to monitor the production process in the production system for quality forecasting, wherein a process variable is detected and, based on this, a plurality of aggregation variables are determined, where a quality parameter is also detected, multiple aggregation variables and the quality parameter are combined to form a workpiece data set, a respective interdependence between a respective aggregation variable and the quality parameter is additionally determined and a forecasting variable is determined from among the aggregation variables based on the determined interdependence, the forecasting variable is specified as the variable of the production process to be monitored for the operation of the monitoring device.

Abstract In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions. Abstract 20 26 20 53 15 06 J ul 2 02 6 A b s t r a c t 2 0 2 6 2 0 5 3 1 5 0 6 J u l 2 0 2 6

The invention discloses a method for predicting the service life of a relay for an operating mechanism based on a grey theoretical model, and relates to the technical field of a relay for a high-voltage circuit breaker operating mechanism of a power system, and the method comprises the following steps: simulating a salt mist aging test parameter test system through an electromagnetic relay, and simulating the operation environment of the relay for the operating mechanism; an alternating salt spray test is carried out, and characteristic parameters of the relay are extracted; performing dimension reduction processing on the characteristic parameters of the relay by using a principal component analysis method, and extracting digital characteristics of the characteristic parameters; on the basis of the digital characteristics of the characteristic parameters, the characteristic parameter with the maximum discriminant function value is selected by utilizing Fisher discriminant criteria; and taking the characteristic parameter with the maximum discrimination function value as a prediction variable, constructing a gray theoretical model, predicting the service life of the relay, and completing the service life prediction of the relay for the operating mechanism based on the gray theoretical model. The problem that the existing relay life prediction precision is low is solved.

The application discloses a social media public participation degree prediction method, medium and computer equipment, wherein the method comprises the following steps: selecting a social media platform, obtaining a green travel related original data set on the social media platform, and obtaining high-quality samples after cleaning and screening; text preprocessing is performed on the high-quality samples to obtain effective samples, and a latent Dirichlet allocation model is selected for training and prediction, and the clustering effect of the latent Dirichlet allocation model is highly dependent on the selection of the number of themes; the number of themes is determined by comprehensively considering the perplexity and consistency evaluation indexes; text themes are extracted according to the theme recognition result of the latent Dirichlet allocation model, a prediction variable set is constructed by fusing multi-modal features, a social media public participation degree prediction model is established, and a participation degree prediction result and key driving factors are output. The application discloses the mechanism of the social media in spreading green travel, and provides a scientific and direct basis for customizing and optimizing a spreading strategy.

This invention discloses a method and system for predicting the risk of malignant cerebral edema after mechanical thrombectomy in ischemic stroke, aiming to address the pain points of existing prediction tools, such as insufficient accuracy, lack of clinical interpretability, and difficulty in directly supporting decision-making. The core solution involves: stably extracting key predictive variables from multi-source clinical and laboratory data through a multi-algorithm consensus feature selection mechanism (LASSO, Boruta algorithm); employing eight machine learning algorithms to select the best high-performance prediction model, and using repeated cross-validation and grid search for robust optimization; innovatively and deeply integrating the SHAP interpretability framework to achieve global, local, and individualized interpretation of the model predictions; and finally deploying it as an integrated clinical decision support system, outputting a visual report that combines risk probability, risk classification, and decision-making basis. This method significantly improves the early risk identification capability of malignant cerebral edema, and the model exhibits excellent discrimination and calibration.

This invention describes a method for predicting the risk of severe herpes zoster in cancer patients, comprising the following steps: constructing a scoring system to score the severity of herpes zoster and defining the severe outcome of herpes zoster; constructing an original dataset based on medical data of cancer patients diagnosed with herpes zoster after onset, using a certain sample size; constructing a Least Absolute Contraction and Selection Operator (LASSO) regression model, which introduces a norm penalty term on the basis of a logistic regression model, and performing feature filtering on the data in the original dataset to output core predictive variables; constructing an original multivariate logistic regression prediction model with the core predictive variables as independent variables and the severe outcome as the dependent variable; and recalibrating the original multivariate logistic regression prediction model. This method screens and determines the core predictive indicators, establishes a risk prediction model for severe herpes zoster, clarifies the coefficients and intercepts of each predictive variable, and possesses excellent discriminative ability and good calibration performance.

This invention provides a method for evaluating forest carbon storage potential based on site factors. It employs the Mitscherlich equation to construct the relationship between average diameter at breast height (DBH) and corresponding survey time intervals, thus inversely determining stand age. Based on the growth of average stand carbon storage, site quality grades are classified, and a graded growth model for carbon storage is established. After differentiation, stand age is used as a predictor variable to determine the annual growth rate of carbon storage. Based on a general linear regression model, a backward elimination method is used to screen out factors affecting carbon sequestration rates, quantifying the direction and extent of the influence of stand factors and environmental drivers on forest carbon storage potential. Furthermore, the differences in the influence of tree species on carbon sequestration potential are explored based on site factors. This invention establishes a linear relationship between annual carbon storage growth and stand factors and environmental drivers, providing a new method for quantitatively assessing the carbon sequestration potential of suitable afforestation sites at a future time under climate change, and providing technical support for formulating appropriate afforestation plans and carbon reduction policies.

This invention discloses a method for determining the suppression threshold of extreme pollutant concentrations based on an adaptive pHuber-hyperbolic safety loss function. The method includes: collecting eco-hydrological data from all stations within the study area; preprocessing the eco-hydrological data; performing feature selection on the preprocessed data; constructing an adaptive pHuber-hyperbolic safety loss function and training a LightGBM model; calculating the SHAP value using the trained LightGBM model, selecting the predictor variable with the highest importance as the supporting feature; performing regression fitting on the SHAP value of the supporting feature and its corresponding original data standard value, calculating the curvature of the fitted curve, and taking the point of maximum curvature as the optimal threshold for the supporting feature, expressed as the suppression threshold for extreme pollutant concentrations. This invention, by constructing an adaptive pHuber-hyperbolic safety loss function, can significantly enhance the accuracy and robustness of the model's prediction of extreme pollutant concentrations, adapting to different regions.

The present disclosure is to an Artificial Intelligence based Intellectual Property valuation system, including a valuation database that includes, as raw data, reference information, patent data, and economic statistical information, and, as extracted information processed from the raw data, statistical data and AI training dataset, a collection / refinement module that processes the raw data, computes and provides the statistical data required in a process of generating the AI training dataset or key variables, computes the AI training dataset, and stores the same, an AI module that, for outputting the key variables, trains AI models for the respective key variables, identifies, and, through the AI models, computes corresponding prediction-variable values using respective explanatory-variable values collected by the collection / refinement module to output respective key-variable values, and a valuation service module that computes a value of the target IP based on the key-variable values and generates a valuation report including the statistical data.

Systems and methods for automatically scoring and classifying characteristics of complex medications and emerging therapies are disclosed. In some embodiments, a disclosed method includes obtaining a request for classification determination of a medication, extracting, based on variables of a scoring model, relevant data of the medication from at least one database, and computing, using the scoring model, a score for the medication based on the relevant data. The method further includes generating, based on the score, a classification, and transmitting at least one of the classification or the medication score to a user.

The application discloses a metallogenic prediction method and device based on fluid parameters and fluid field modeling, a medium and a product, and relates to the field of metallogenic prediction. First, petrographic study is carried out on fluid inclusion samples of a target area to determine the metallogenic stage; the fluid inclusion samples of different metallogenic stages are analyzed and tested to obtain multiple fluid parameters; exploratory data analysis is performed on the multiple fluid parameters to screen out key fluid parameters closely related to the content of main ore-forming elements; a machine learning or deep learning algorithm is used to model the numerical relationship between the key fluid parameters and the main ore-forming elements to determine an optimal numerical model; the key fluid parameters are processed by using different spatial interpolation algorithms to establish a three-dimensional fluid field model; according to the three-dimensional fluid field model and the optimal numerical model, three-dimensional metallogenic prediction is carried out in combination with metallogenic geological elements to determine a prospecting target area; since the fluid parameters are introduced as prediction variables, the accuracy of metallogenic prediction is improved.

This application relates to a method and system for multi-resolution geological data conversion based on ensemble learning, belonging to the field of geological information processing technology. The conversion method includes: acquiring a low-resolution geological dataset containing target elements and a high-resolution geological dataset not containing target elements; performing spatial grid aggregation processing on the high-resolution geological dataset to generate a predictor variable matrix spatially aligned with the low-resolution geological dataset; training a Stacking ensemble regression model with the predictor variable matrix as input and the target element values ​​in the low-resolution geological dataset as the output target; inputting the high-resolution geological dataset into the fusion prediction model to output preliminary predicted values ​​of the target elements; and performing spatial error correction on the preliminary predicted values ​​of the target elements based on the actual values ​​of the target elements in the low-resolution geological dataset to generate corrected high-resolution target element data. This application can effectively integrate multi-scale, multi-source heterogeneous geological data.

This invention discloses a method and related apparatus for predicting the strength of cement-stabilized soil, relating to the field of building materials. The prediction method includes determining the normalized unconfined compressive strength index I of cement-stabilized soil based on the solidification test parameters and basic physical properties of the cement-stabilized soil sample; determining the unconfined compressive strength test value of the cement-stabilized soil; fitting I and the test value into a formula, calculating the values ​​of a and b, and substituting them into the formula to obtain the prediction model formula, which can be used to predict the unconfined compressive strength of cement-stabilized soil. Based on the physical properties of the solidified soil, the solidification test parameters, and the unconfined compressive strength test value, this application constructs a prediction model formula for the unconfined compressive strength of cement-stabilized soil with this strength index as the sole predictor variable, thereby achieving rapid prediction of the strength of cement-stabilized soil, reducing on-site testing costs, and improving construction efficiency and quality.

The application relates to a kind of whole society power consumption prediction method and system based on season-cumulative air temperature index.The method comprises the following steps: collecting daily air temperature data, daily whole society power consumption data, monthly air temperature data and monthly whole society power consumption data in historical data; the daily air temperature data is transformed by cumulative effect and season effect to obtain daily air temperature index; the monthly air temperature data is transformed by season effect to obtain monthly air temperature index; the lag period of monthly air temperature index and monthly whole society power consumption data is used as low-frequency prediction variable, and the daily air temperature index and daily whole society power consumption data are used as high-frequency prediction variable; monthly whole society power consumption is predicted by using low-frequency prediction variable and high-frequency prediction variable.The application proposes a mixed-frequency prediction model (MIDAS-MT-DT) of whole society power consumption based on "season-cumulative air temperature index", and the model has higher prediction accuracy compared with existing models.

The present application relates to the replacement and acceleration of atmospheric chemical reaction calculation in atmospheric chemical model, and particularly relates to a replacement model of atmospheric chemical reaction calculation and a construction method, the model has high precision in reproducing chemical reaction process, in the 19-day offline prediction of 279 prediction variables of the case, the proportion of prediction variable determination coefficient exceeding 0.99 is 96%; the model can realize at least 10-day continuous stable prediction; the model has significant acceleration of chemical calculation: compared with the original GEOS-Chem chemical integrator, the time consumption of ChemKNet calculation on single CPU can be saved by about 79%, and only about 1% of the original calculation time is needed on single GPU, greatly improving the calculation efficiency of large-scale simulation; the model supports coupling and continuous prediction; the model has full-chem chemical mechanism and global simulation covering GEOS-Chem.

The invention discloses an unmanned aerial vehicle fault evaluation method and system based on a chain neural network. The method comprises the following steps: constructing a variable database; correlation coefficients of the variable combinations are obtained through a Pearson's correlation coefficient method, and the variable combinations lower than a correlation coefficient threshold value are removed; constructing a chain neural network model, wherein the variable prediction sub-model n is a prediction model for predicting the variable n by using the variable having correlation with the variable n in the correlation matrix; data related to the data factor items in the unmanned aerial vehicle fault-free time is collected and input into the chain neural network model for prediction learning training; related data of the unmanned aerial vehicle and the data factor items are collected in real time and input into the variable prediction sub-model set, and the alarm number is counted to serve as an unmanned aerial vehicle fault evaluation result. According to the invention, fault occurrence probability evaluation in flight of the unmanned aerial vehicle is realized, timely emergency command of stopping and landing of the unmanned aerial vehicle is facilitated, fault inspection is performed timely, further expansion of the fault of the unmanned aerial vehicle is prevented, and flight safety of the unmanned aerial vehicle is improved.

The invention relates to the field of data mining and machine learning, in particular to a variable correlation analysis method based on machine learning prediction precision, which comprises the following steps: acquiring a data set containing a plurality of variables, and performing data preprocessing; variable pairing and target setting; performing association degree quantification based on a machine learning model; constructing a correlation matrix; and outputting the correlation matrix for visualization or downstream analysis tasks. The method has the advantages that a machine learning model is adopted as a core tool for correlation measurement, any complex nonlinear and non-monotonic relation between variables can be effectively captured, and inherent limitations of traditional correlation analysis methods such as a Pearson coefficient, a mutual information method and a maximum mutual information coefficient are broken through; the abstract correlation is defined as specific'prediction precision ', so that the analysis result has clear and intuitive practical significance, and'the prediction precision of predicting the variable B by adopting the variable A is 0.8' is easy to understand by field experts and be applied to actual decision making.

The invention discloses an epidural delivery analgesic lower head dystocia risk prediction method and system. The method comprises the following steps: retrospectively collecting clinical feature data of a parturient to be parturified under epidural delivery analgesia; the method comprises the following steps: determining classification tangency points of continuous variables according to clinical medicine consensus, literature review or statistical distribution, converting the continuous variables into ordered or disordered classification variables, performing code conversion on the collected classification variables, and constructing a structured data set; carrying out preliminary screening on all feature data by using single-factor logistic regression analysis and carrying out multi-collinearity diagnosis; the screened features are incorporated into a multi-factor logistic regression model for optimization to determine key prediction variables, and a logistic regression prediction model is constructed to derive a logistic regression equation; and converting the logistic regression model into a Nomogram column graph model, and outputting a prediction result of the occurrence risk probability of the head dystocia. According to the scheme, early risk prediction of epidural delivery analgesia lower head dystocia is realized, and an earlier decision window is provided for clinic.