139 results about "Predictor variable" patented technology

The invention relates to the technical field of water environment risk monitoring, in particular to a method for evaluating the algal bloom risk of a water body. The method comprises the following steps: collecting historical monitoring data of a to-be-evaluated water body, wherein the historical monitoring data comprises blue-green algae abundance data, water quality data and hydrological data; analyzing the correlation between the cyanobacteria abundance or chlorophyll a concentration and the water quality and hydrological data of the to-be-evaluated water body; hydrological and water quality parameters with the highest correlation with the cyanobacteria abundance or chlorophyll a concentration are screened out; hydrology and water quality parameters of a water body to be evaluated are taken as predictive variables, and cyanobacteria abundance or chlorophyll a concentration is taken as a response variable to construct a Bayesian network model; the weight of each parameter in the Bayesian network model is calculated, and the algal bloom risk probability that the cyanobacteria abundance exceeds a specific threshold value under the given parameter condition is calculated according to the weights. According to the invention, the scene-based probability deduction of the stable period and the dynamic period is realized through the double-branch Bayesian network model, so that the accuracy and timeliness of algal bloom risk assessment are improved.

The invention discloses a soybean planting suitable area evaluation method fused with yield prediction, and relates to the technical field of data processing, and the method comprises the steps: collecting soybean planting related data; the method comprises the following steps: carrying out time sequence division on soybean growth seasons based on phenology, refining the whole growth process into seven typical periods, constructing a multi-temporal remote sensing vegetation index state and change trend factor set in a soybean planting area, respectively inputting two combination forms into a machine learning model, taking the yield as a predictive variable, and carrying out model selection through precision evaluation. Obtaining a grid-scale soybean yield spatial distribution diagram; and constructing a maximum entropy model by taking the identified high-yield grid units as existence point data, carrying out suitability modeling analysis by integrating multi-source environment variables, identifying key ecological environment factors influencing soybean high-yield suitability distribution through variable contribution analysis and a response curve, and drawing a soybean high-yield suitability spatial distribution diagram. According to the invention, the soybean high-yield suitable growth area is identified.

A method for safety management and risk assessment in a work environment. The method includes obtaining data from a plurality of sources. The method further includes preprocessing, using a computer processor, the obtained data, where the preprocessing includes cleaning and normalizing the obtained data. The method further includes determining, using the computer processor and a machine learning model, a plurality of predictive variables based on the preprocessed data. The method further includes determining, using the computer processor and the machine learning model, risk exposure prioritization score based on the plurality of predictive variables. The method further includes determining, using the computer processor and the machine learning model, a plurality of safety recommendations based on the risk exposure prioritization score. The method further includes performing, in response to the safety recommendations and the risk exposure prioritization score, a maintenance operation on an equipment in the work environment.

The invention relates to a method for providing, by means of a warning device (12), an adaptation notification, preferably a warning notification, to at least one ego road user (10) to be notified to adapt at least one driving function to at least one surroundings road user (32) located and / or moving in the surrounding region (2) of the road user, said warning device being provided with respective movement paths (PCL, PR) which are predicted for the ego road user and for the at least one surroundings road user (32) and along which the movements of the road users (10, 32) are expected. The warning device (12) determines, on the basis of the predicted movement paths (PCL, PR), at least one notification variable, which is characteristic of a provision of the adaptation notification and which is preferably characteristic of whether an adaptation notification (I1, A32) is to be provided and / or at what time the adaptation notification (I1, A32) is to be provided. According to the invention, the warning device (12) determines the notification variable on the basis of at least one swarm prediction variable, in particular at least one statistical swarm prediction variable, for determining the prediction quality of at least one of the predicted movement paths (PCL, PR), wherein the at least one swarm prediction variable is determined on the basis of global swarm movement data of a plurality of road users, said movement data being generated in a plurality of different traffic regions (2).

A method for predicting fatigue of a driver of a vehicle comprises obtaining (110) environmental perception data of the vehicle from at least one environmental perception sensor of the vehicle, wherein the environmental perception data represents at least one environment of the vehicle while driving. The method further comprises processing (120) the environmental perception data to determine (150) a prediction variable that reflects whether the driver of the vehicle is fatigued. The method further comprises providing (160) a warning based on the prediction variable.

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 the technical field of automobile design, in particular to an intelligent generation type design method of an anti-collision beam, which comprises the following steps: firstly, performing experimental analysis on the anti-collision beam to obtain a section image comprising the anti-collision beam and performance response data corresponding to the section image; performing image recognition and text extraction on the section image; fusing the recognized image and the extracted text by using a multi-modal multi-layer fusion model to obtain a multi-modal design variable; training the constructed conditional variation network by using the multi-modal design variables and the performance response data corresponding to the multi-modal design variables to obtain a conditional variation model; generating design variables by using the conditional variation model, and predicting performance response data corresponding to the variables; optimal performance response data are screened out from the obtained performance response data, and then the optimal design scheme of the anti-collision beam to be designed is obtained. According to the method, the optimal design scheme is determined by using the conditional variation network, and the efficiency and the precision of optimization design are effectively improved.

The invention relates to a training method of a respiratory tract infection disease progress and prognosis prediction model. The training method comprises the following steps: extracting mRNA from peripheral blood of a target patient, and carrying out transcriptome sequencing to obtain a sequencing result; based on the ferroptosis related gene set, comparing ferroptosis score differences of two groups of patients with community-acquired pneumonia and sepsis, and screening corresponding ferroptosis related genes with statistical significance from a sequencing result; screening out genes meeting preset conditions from the ferroptosis related genes based on LASSO regression; and establishing an RTI clinical outcome prediction model through logistic regression by taking whether the patient is sepsis or not as an outcome dichotomy variable and taking the screened gene expression quantity as a prediction variable. According to the invention, after the prediction model is subjected to machine learning screening such as LASSO and the like, the core feature with the highest prediction value is reserved, so that the risk of over-fitting of the model on training data is reduced.

The invention discloses a multivariate time sequence missing variable prediction method, program and system based on a denoising diffusion model, and a storage medium, and belongs to the field of deep learning. The invention provides a novel model named DiffMissing based on DDPM. The novel model comprises a forward diffusion module, a context condition encoder module, a denoising network module and a prediction module. According to the model, two view angles of variables and time are fused, and the consistency of the interiors of the variables and the time can be more accurately ensured, so that information in data is more comprehensively understood, and the capability of predicting missing variables is remarkably improved. The DiffMissing prediction model provided by the invention has a remarkable technical effect, and can effectively improve the function of reconstructing the missing variable and the function of predicting the variable of the time sequence data.

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.

The invention discloses a wind and light resource seasonal scale influence factor screening method, device and equipment and a medium, which are used for solving the problems that a correlation analysis method is generally adopted for screening influence factors influencing a certain climate event in the existing climatic science, and the screened factors have correlation and do not have causality, so that the screening efficiency is low. And a prediction model established according to the method is low in precision. The method comprises the following steps: acquiring a wind and light resource seasonal scale influence factor, and preprocessing the wind and light resource seasonal scale influence factor to obtain a preprocessed influence factor; calculating a causal effect variable of the preprocessing influence factor and a prediction variable; screening an influence factor set from the preprocessed influence factors according to the causal effect variables; constructing a Bayesian network model by adopting the influence factor set; and generating an optimal influence factor set according to the Bayesian network model.

The invention relates to the technical field of liver disease prediction, in particular to a metabolism-related fatty liver disease intelligent prediction method and system and a storage medium. The method comprises the following steps: collecting multi-source data, respectively obtaining basic demographic information, laboratory indexes and prediction indexes, and extracting quantitative and qualitative tongue picture parameters; performing variable screening on the tongue picture parameters and the clinical indexes, and determining key prediction variables; obtaining a key variable value according to the key prediction variable, obtaining a prediction result of the occurrence risk of the metabolism-related fatty liver disease, and outputting the prediction result; the system comprises a multi-source data acquisition module, a variable screening module and a prediction result acquisition module. By means of the mode, the intelligent tongue picture parameters and the clinical indexes are fused, and the effect of early prediction of the metabolism-related fatty liver diseases is achieved.

The invention relates to the technical field of psychological prediction, and particularly discloses a webpage calculator construction and evaluation method for anticipated sadness occurrence probability of a family caregiver of a cancer patient, and the method comprises the steps: collecting the related data of the family caregiver and the cancer patient, determining the diagnosis standard of anticipated sadness through potential profile analysis, and evaluating the anticipated sadness occurrence probability of the family caregiver. And screening key predictive variables by using LASSO-logistic regression, constructing a predictive model, and developing an online webpage calculator based on a shiny technology. According to the method, the expected sadness risk of the family caregiver can be rapidly evaluated, the method has good distinction degree, calibration degree and clinical practicability, and a scientific basis is provided for medical staff to identify high-risk family caregivers and provide targeted support.

The invention discloses a soil humidity inversion method based on an ADF model, and belongs to the field of soil humidity inversion. The method comprises the following steps: S1, selecting a soil humidity inversion area, and obtaining an in-situ SSM data set of the corresponding area; s2, selecting a prediction variable, extracting the prediction variable from the corresponding prediction data set, and combining the prediction variable with the in-situ SSM data set to form a sample data set; s3, constructing an ADF model, and performing training and verification based on the sample data set to estimate SSM according to the prediction variable; s4, when the ADF model outputs the SSM estimation value, determining a prediction variable which has the maximum influence on SSM estimation; and S5, evaluating the generalization ability of the ADF model based on the sample data set. According to the ADF model, prediction accuracy, generalization ability and interpretability are realized at the same time.

The invention relates to a diabetic macular edema treatment effect prediction system, method and equipment, and belongs to the technical field of biomedicine. Comprising the steps of obtaining and preprocessing clinical data of a patient before treatment to obtain effective data; performing feature screening on the data to obtain feature data; constructing a prediction model, and training the prediction model by using the feature data to obtain an optimal vision recovery prediction model and an optimal edema regression prediction model; according to the two types of prediction models, a combined prediction model is constructed by adopting logic weighted fusion; inputting clinical data of a patient to be treated into the two types of prediction models to obtain a prediction result; and according to a prediction result, carrying out interpretability analysis on the optimal prediction model and the combined prediction model to obtain key characteristic variables, and further obtaining the probability of vision recovery and edema regression of the to-be-treated patient after treatment. According to the method, comprehensive and multi-dimensional sources of prediction variables are ensured, and the limitation of only depending on image data or subjective evaluation is effectively overcome, so that the prediction precision is improved.

The invention 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. The method comprises the following steps: firstly, carrying out lithofacies research on a fluid inclusion sample in a target area to determine a metallogenic stage; analyzing and testing the fluid inclusion samples at different mineralization stages to obtain a plurality of fluid parameters; exploratory data analysis is conducted on the multiple fluid parameters, and key fluid parameters closely related to the content of the main metallogenic elements are screened out; modeling the numerical relationship between the key fluid parameters and the main metallogenic elements by adopting a machine learning or deep learning algorithm, and determining an optimal numerical model; processing the key fluid parameters by adopting different spatial interpolation algorithms, and establishing 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 the metallogenic geological elements, and a prospecting target area is determined; the fluid parameters are introduced as predictive variables, so that the precision of metallogenic prediction is improved.

The invention discloses a high-resolution cloud-to-ground lightning density regression method using a random forest and topographic factors, and belongs to the field of cloud-to-ground lightning density regression, and the method comprises the steps: dividing a target region into 0.001 degree * 0.001 degree geographic grids by collecting cloud-to-ground lightning data of a lightning positioning system and high-precision digital elevation model data, and carrying out statistics on the cloud-to-ground lightning density and carrying out filtering processing to eliminate noise. Meanwhile, terrain parameters are obtained and resampled to the spatial resolution consistent with the ground-to-ground lightning density data. And inputting the filtered cloud-to-ground lightning density as a target variable and topographic parameters and latitude and longitude coordinates thereof as predictive variables into a random forest regression model for training, and outputting a high-resolution cloud-to-ground lightning density distribution diagram. And evaluating the performance of the model through statistical indexes, and comparing the result with actually measured data for verification. According to the method, the traditional statistical resolution limit is broken through, the resolution improvement from the kilometer level to the hectometer level is realized, and support is provided for the refined lightning protection design of the power transmission line.

The invention provides a prospecting prediction method and device based on geoscience big data fuzzy feature analysis. The method comprises the following steps: establishing a geoscience big data spatial database of a target prospecting area; dividing the target prospecting area to generate a plurality of grid units; selecting a plurality of model units from the plurality of grid units, and selecting an initial prediction variable; respectively determining a fuzzy membership function of any initial prediction variable in each model unit; obtaining a fuzzy membership degree of an initial prediction variable of the model unit according to the fuzzy membership degree function; calculating a fuzzy matching coefficient between any initial prediction variable and any other initial prediction variable according to the fuzzy membership degree of the initial prediction variable of the model unit; determining a weight coefficient of each initial prediction variable according to the fuzzy matching coefficient; selecting a target prediction variable according to the weight coefficients of all the initial prediction variables; and calculating the metallogenic favorable degree of the target prospecting area according to the fuzzy membership function and the weight coefficient of the target prediction variable. The prediction precision can be improved.

The invention provides a water soluble organic nitrogen property prediction method based on spectrum fingerprint information and machine learning and application. The method specifically comprises the following steps: research area selection, sample collection, pretreatment and related index detection. Preliminarily verifying the data set, screening representative spectrum indexes, and constructing a spectrum fingerprint information matrix; dividing a training set and a test set, using a machine learning method to construct prediction models by using the training set, and using the test set to perform performance evaluation on the prediction models; a prediction model with better performance is selected, a Shapley method is used for calculating the SHAP value of each characteristic value, and the key characteristic value with the dominant model influence degree is selected as a prediction variable to indicate the property characteristics of the soluble organic nitrogen in the research area. According to the method provided by the invention, the properties of the complex soluble organic nitrogen can be conveniently and efficiently preliminarily judged, the understanding of the current situation of the water ecological environment is further improved, and a certain technical support is provided for formulating water ecological environment protection measures.

The invention provides a femoral head ischemic necrosis risk prediction method and device based on machine learning, and the method comprises the steps: collecting the related information of a patient, screening a mapping relation between an outcome variable and a prediction variable according to standardized text data and a C ox regression analysis method, and building a text machine learning model; carrying out image segmentation and feature labeling on the standardized image data, establishing a focus positioning model, and carrying out training and verification by using sample image data; and fusing the text machine learning model and the image recognition model to obtain a multi-modal femoral head ischemic necrosis risk prediction model, predicting an ischemic necrosis focus position and a staging diagnosis result of a patient, and generating a corresponding risk assessment report. Through a machine learning model, whether the patient is more likely to develop into ANFH or not can be predicted, and a personalized treatment strategy is formulated. In combination with iconography data, lifestyle information and clinical data, the A I can construct an accurate prediction model to help doctors to formulate a more effective treatment scheme.

The present disclosure relates to a server for calculating an order quantity on the basis of material usage and demand prediction of an unmanned store, and a control method therefor, wherein a machine learning model may be trained on the basis of material usage data of the unmanned store, and the order quantity may be calculated by calculating, on the basis of a plurality of first prediction variables and second prediction variables, a result value including the order quantity and an available sales period for each product of the unmanned store, by using the trained machine learning model.

The invention provides a construction method and equipment of a fracture risk prediction model, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring basic data of a T2DM osteoporosis patient as a training set sample; the training set samples are divided into a fracture group and a fracture-free group; performing statistical analysis on the basic data, representing continuous variables conforming to normal distribution by using mean + / -standard deviation, and comparing the difference between the two groups through independent sample t inspection; adopting median and quartile to describe continuous variables which do not conform to normal distribution; through single-factor and multi-factor analysis, screening fracture influence characteristics of the osteoporosis patient from the basic data; and constructing a prediction model of the fracture risk of the osteoporosis patient based on the prediction variables. A special group of postmenopausal female diabetes is used as a research object, difference characteristics are analyzed, a prediction model is constructed, and early prevention and treatment of osteoporosis fracture patients are achieved.

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.

Method for providing an adaptation notification, preferably a warning notification, to at least one ego road user (10) to be notified, for adapting at least one driving function to at least one surrounding road user (32) located and / or moving in a surrounding area (2) of the road user, by means of a warning device (12) which has predicted movement paths (P CL , P R ) are and / or are provided, along which a movement of the road users (10, 32) is to be expected, wherein the warning device (12) is based on the predicted movement paths (P CL , P R) determines at least one notification variable characteristic of providing the adaptation notification, which is preferably characteristic of whether an adaptation notification (I1, A32) is to be provided and / or at what point in time the adaptation notification (I1, A32) is to be provided. According to the invention, the warning device (12) determines the notification variable as a function of at least one, in particular statistical, swarm prediction variable for determining a prediction quality of at least one of the predicted movement paths (P CL , P R ), wherein the at least one swarm prediction variable is determined on the basis of global swarm movement data of a plurality of road users, which are generated in a plurality of mutually different traffic areas (2).

The present invention provides a prospecting prediction method and device based on fuzzy feature analysis of geoscience big data. The method includes: establishing a geoscience big data spatial database of a target prospecting area; dividing the target prospecting area to generate multiple grid cells; selecting multiple model cells from the multiple grid cells and selecting initial prediction variables; determining a fuzzy membership function for any initial prediction variable in each model cell; obtaining the fuzzy membership of the initial prediction variable of the model cell based on the fuzzy membership function; calculating a fuzzy matching coefficient between any initial prediction variable and any other initial prediction variable based on the fuzzy membership of the initial prediction variable of the model cell; determining a weight coefficient for each initial prediction variable based on the fuzzy matching coefficient; selecting a target prediction variable based on the weight coefficients of all initial prediction variables; and calculating the metallogenic favorability of the target prospecting area based on the fuzzy membership function and the weight coefficient of the target prediction variable. The present invention can improve prediction accuracy.

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.

The invention discloses a subsea tunnel immersed tube health prediction method based on a learning model, and the method comprises the steps: collecting various types of monitoring data, designing an index weight system of a tunnel health state, and determining the weight of each type of monitoring data according to expert experience scores; then data weighted fusion is carried out, monitoring data after weighted fusion are input into a deep learning model to serve as input and predictive variables, the deep learning model is trained, and the deep learning model is used for predicting future trend data according to historical monitoring data; and taking the trend data as new observation data, and inputting the new observation data into a Bayesian model to calculate the probability of future damage risks. According to the method, the Bayesian reasoning and the deep learning model are combined, the short-term health state change of the tunnel structure can be accurately predicted, the probability of damage occurrence is quantified through the Bayesian model, graded early warning prompt is carried out in combination with the health state risk index, and potential risks can be prevented in advance.

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 present invention discloses a method and system for predicting the time domain of urban carbon peak, wherein the method includes: S1: obtaining multiple characteristic factors that affect the urban carbon peak and building a hierarchical analysis framework for urban carbon emission prediction; S2: using the hierarchical analysis framework to screen the principal component factors and obtain historical observation data; S3: based on the historical observation data of the principal component factors, using the Pearson correlation coefficient to quantitatively characterize the relationship between the explanatory variables and the predictive variables, testing the significance of the correlation between the observed data and the predictive variables, and fitting the regression prediction relationship of the urban carbon emissions; S4: calculating the trend fitting data value of the explanatory variable, using the regression prediction relationship to predict the prediction interval of the urban carbon emissions within a predetermined time, finding the carbon peak according to the prediction interval, and performing a prediction analysis of the urban carbon peak time domain. The present invention can provide theoretical support through the prediction analysis of urban carbon emissions, thereby better carrying out subsequent decision-making and management.