203 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.

A method and apparatus for video coding are disclosed. According to the method for the decoder side, a first syntax, related to whether the current block is coded in a CCLM related mode, is parsed from a bitstream comprising the encoded data for the current block. If the first syntax indicates the current block being coded in the CCLM related mode, a second syntax is parsed from the bitstream, wherein the second syntax is related to whether a multiple model CCLM mode is used or whether one or more model parameters are explicitly signalled or implicitly derived. The model parameters for the second-colour block are determined if the first syntax indicates the current block being coded in a CCLM related mode. The encoded data associated with the second-colour block is then decoded using prediction data comprising the cross-colour predictor for the second-colour block.

The present invention discloses a method for dynamic energy dispatching of microgrids, microgrids and storage devices, including the following steps: S1, defining decision variables, decision variable types and setting constraints, establishing an objective function, and quantitatively simulating the optimal dispatching of microgrids with multiple constraints; S2, importing prediction variables, and using the characteristics of the source, network, load and storage of the next period of time obtained by the time series prediction algorithm based on the long short-term memory network of energy data; S3, running the precise algorithm of mixed integer programming / discrete optimization to obtain the feasible domain of all solutions that meet the constraints. The present invention can realize the flexible allocation of energy according to the dynamic changes of real-time power consumption, power generation and cost factors, and greatly reduce the unnecessary loss of energy; and by considering the equipment constraints in a refined manner, the service life of the equipment is greatly extended, and the frequency and cost of operation and maintenance are reduced; finally, relying on the optimization algorithm, the system operation efficiency and economic benefits are given equal weight, and the microgrid is promoted to operate stably, greenly and efficiently in all directions.

The invention provides a method and a device for constructing a female osteoporosis prediction model based on physical examination data. The construction method comprises the steps of obtaining a potential prediction variable set of female osteoporosis based on literature query; screening a sample set from the physical examination data; dividing the sample set into a training set and a verification set, and based on the preprocessed first training set, obtaining an intersection of variables screened by using a Boruta algorithm, an LASSO algorithm, a varSelRF algorithm and an SVM-RFE algorithm, and taking the intersection as a feature variable set of the osteoporosis prediction model; obtaining a second training set and optimizing the plurality of constructed initial prediction models by using the second training set to obtain a plurality of parameter tuning prediction models; and obtaining a test set, evaluating each parameter tuning prediction model by using the test set, and determining a female osteoporosis prediction model based on an evaluation result. The female osteoporosis can be predicted on the basis of eight conventional physical examination data, and the method has the advantages of high accuracy and medium sensitivity.

The subject matter of the invention is a system for earthquake predictions and forecasts. The system comprises a data collection module, where the data collection module contains at least one data channel. The system comprises further an integrated AI and forecast module, where the integrated AI and forecast module contains at least one autoencoder and a predictor. The second subject matter of the invention is a method for earthquake predictions and forecasts, which is conducted in the system according to the invention.

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 of encoding a motion information predictor index, comprising: generating a list of motion information predictor candidates; when an Affine Merge mode is used, selecting one of the motion information predictor candidates in the list as an Affine Merge mode predictor; when a non-Affine Merge mode is used, selecting one of the motion information predictor candidates in the list as a non-Affine Merge mode predictor; and generating a motion information predictor index for the selected motion information predictor candidate using CABAC coding, one or more bits of the motion information predictor index being bypass CABAC coded.

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).

Techniques are provided for dynamic prediction-based regression optimization. In one embodiment, the techniques involve determining, via a process model, a variable state of the process model, wherein the variable state includes a first input state variable, a first output state variable, and a first control parameter, generating, via a short-term prediction module, a first prediction of a first update of the variable state, generating, via a terminal value prediction module, a second prediction of a second update to the variable state, generating, via a control optimization module, a second control parameter based on the first prediction and the second prediction, and controlling, via a processor, a production process of the process model based on the second control parameter.

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.

Some embodiments of a method may include: obtaining a previously decoded point cloud frame as a reference point cloud frame; determining a predictor feature map based on the reference point cloud frame; obtaining a current feature map that represents the current point cloud frame given the predictor feature map as a condition; and reconstructing the current point cloud frame based on the current feature map and using the predictor feature map as a condition.

Systems and methods for creating predictor variables from unstructured data for prediction models are provided. A variable creation application receives unstructured data and processing the unstructured data to generate processed data. Based on the processed data, the variable creation application generates an attribute pool that contains multiple predictor variables generated by applying natural language processing (NLP) procedures on the processed data. The variable creation application further executes a prediction model on at least the predictor variables in the attribute pool to generate a prediction result. Based on the prediction result, the variable creation application evaluates the predictive power of each of the predictor variables and retains predictor variables that are predictive as input predictor variables for the prediction model.

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.

A method and apparatus for inter prediction in video coding system are disclosed. According to the method, one or more model parameters of one or more cross-color models for the second-color block are determined. Then, cross-color predictors for the second-color block are determined, wherein one cross-color predictor value for the second-color block is generated for each second-color pixel of the second-color block by applying said one or more cross-color models to corresponding reconstructed or predicted first-color pixels. The input data associated with the second-color block is encoded using prediction data comprising the cross-color predictors for the second-color block at the encoder side, or the input data associated with the second-color block is decoded using the prediction data comprising the cross-color predictors for the second-color block at the decoder side.

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 discloses a green electricity hydrogen ammonia flexible scheduling method and system considering wind and light output correction, and relates to the technical field of energy power, and the method comprises the steps: setting the uncertainty of wind and light output prediction, and correcting the wind and light output prediction; determining a wind and light output prediction value participating in a scheduling algorithm; equally dividing a time period in which a scheduling instruction and a predictive variable need to be given into M small sections; determining a decision variable and an optimization interval; performing iterative optimization on the target function; calculating the power of the electrolytic cell at all time points in the corresponding iteration period; for each iteration period, calculating constraint variables of all time points in the scheduling period; judging whether the constraint variable meets a constraint condition or not, and obtaining an alternative set; and taking a decision variable corresponding to the minimum target function from the alternative set as an optimization result. According to the method, segmented optimization is carried out on the time period in which the scheduling instruction needs to be given, the optimization speed of the algorithm is improved, green hydrogen production is safe and stable, and economical and reliable operation is achieved.

The invention provides a water quality prediction method, device and equipment and a medium. The method comprises the following steps: acquiring water quality prediction training data; dividing the water quality prediction training data into a prediction variable and an influence variable, performing principal component analysis processing on the influence variable to obtain a first processing result, and performing time sequence decomposition on the prediction variable to obtain a second processing result; inputting the first processing result and the second processing result into a combined model for training to obtain a water quality prediction model; the historical water quality data of the target water area is subjected to principal component analysis and time sequence decomposition and then input into the water quality prediction model for prediction, and a water quality prediction result of the target water area is obtained and used for evaluating the future water quality pollution degree of the target water area; according to the method, redundant information in the water quality data can be effectively processed, variable relevance is eliminated, time sequence changes of the water quality data are captured through the combined model, and therefore hysteresis is reduced, and the accuracy of water quality prediction is improved.

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.

Prediction circuitry generates a prediction associated with a prediction input address, for controlling a speculative action by a processor. The prediction circuitry comprises combiner circuitry to determine a combined prediction by applying a prediction combination function to a given address and sets of prediction information generated by a plurality of predictors corresponding to the given address. A combiner cache structure comprises combiner cache entries. A given combiner cache entry associated with an address indication indicates items of combined prediction information determined by the combiner circuitry for an address corresponding to the address indication and different combinations of possible values for the respective sets of prediction information. Combiner cache lookup circuitry looks up the combiner cache structure based on the prediction input address to identify a selected combiner cache entry, and generates the prediction based on a selected item of combined prediction information selected from the selected combiner cache entry based on the respective sets of prediction information generated by the predictors corresponding to the prediction input address.

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 provides a risk control scene-based multi-branch decision tree segmentation method and system, and the method comprises the steps: obtaining historical customer data from a credit institution, carrying out the processing and sampling of the data according to a business scene, and obtaining an original data set; based on the original data set, keeping the numerical variables unchanged, and processing character type and Boolean type predictive variables to obtain a processed data set; and based on the processed data set, performing multi-way tree segmentation on numerical variables, character type and Boolean type predictive variables according to a specified mode and a specified branch number. The data acquisition module is responsible for acquiring historical customer data from a credit institution and processing and sampling the data according to a business scene to obtain an original data set. The system comprises a data acquisition module, a data processing module and a segmentation execution module. Through the steps of data acquisition, preprocessing, modeling and the like, effective assessment and prediction of the customer credit risk are realized, and a scientific and reliable risk management means is provided for a credit institution.