270 results about "Predictor variable" patented technology

The invention discloses a multivariable source load data prediction method and system based on a pre-trained large language model, and the method comprises the steps: carrying out the textualized coding of a historical source load time sequence, forming a pre-trained large language model based on a multi-expert mixed fine tuning module through the mixed use of a low-rank matrix adaptation method and an adapter method, and obtaining a preliminary prediction time sequence; finally, filtering calibration is carried out, and a variable with higher correlation is selected as an advanced variable based on the cross correlation between the variable to be predicted and other variables; filtering is carried out on the advance variable data and the variable data to be predicted in the preliminary prediction time sequence, and then integration is carried out to obtain a calibration deviation of the preliminary prediction time sequence for calibration. Fine adjustment is carried out on the basis of a large language model, different types of source load sequential sequence prediction tasks can be completed under the conditions of small samples and multiple variables, prompting and guiding operation is carried out on the to-be-predicted variables through the lead-lag relation between the variables, and the source load prediction accuracy and reliability are improved.

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 invention discloses an underground water hardness impact factor analysis method based on random forest and SHAP, which comprises the following steps: collecting related data of groundwater total hardness in a research area and a research time period, and constructing a groundwater total hardness data set; constructing a predictive variable data set at the same time; taking the predictive variables as model input, training and optimizing a random forest model, and constructing a mapping relation between the predictive variables and the groundwater total hardness through the model; based on the optimized random forest model, predicting the groundwater total hardness of all point locations in the research area to obtain the change trend of the groundwater total hardness in time and the distribution and change characteristics of the groundwater total hardness in space; sHAP analysis is carried out on the trained random forest model, and the importance proportion of each prediction variable is determined. According to the method, the groundwater total hardness of the whole research area and the research period can be predicted, and the influence characteristics of the influence factors on the groundwater total hardness can be obtained.

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.

Methods for video decoding and encoding, apparatuses and non-transitory storage media are provided. In one decoding method, the decoder obtains a geometric partition mode (GPM) blending area index at a certain level associated with a picture, where the GPM blending area index indicates a blending area width from a set of blending area widths. The decoder obtains one or more weighting values that are used to blend prediction samples of a GPM mode based on the blending area width indicated by the GPM blending area index. Furthermore, the decoder obtains a GPM predictor based on the one or more weighting values.

The invention discloses an effluent total phosphorus prediction model construction method based on a pigeon flock optimization echo state network, and the method comprises the steps: obtaining sewage treatment data, and carrying out the data preprocessing; determining an auxiliary variable set and a prediction variable according to the preprocessed sewage treatment data; wherein the predictive variable is effluent total phosphorus; determining the number of input nodes and the number of output nodes of the echo state network according to the auxiliary variable set and the prediction variable; and determining optimal parameters of an echo state network reserve pool layer based on a self-adaptive improved pigeon inspired optimization algorithm in combination with a tabu search algorithm. According to the method, the global search capability of the pigeon inspired optimization algorithm and the local search capability of the tabu search algorithm are utilized, the optimization capability of the algorithm is further improved by improving an operator updating formula in the pigeon inspired optimization algorithm, and the parameters and the structure of the echo state network are optimized by applying a self-adaptive adjustment mechanism. The original algorithm gets rid of a local optimal solution, and the model prediction precision can be improved.

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 relates to a mineralization prediction method for synthesizing rare data based on a mineralization space structure. The method comprises the following steps: collecting and preprocessing geological data based on a prediction demand; based on the geological background of the to-be-studied area, constructing a metallogenic prediction model, determining a prediction variable and creating a prediction layer; performing mineralization space structure analysis, synthesizing a preset number of prediction variables of rare data by combining the prediction variables of the to-be-studied area, and further generating a plurality of training data sets; adopting a plurality of training data sets to respectively train the constructed metallogenic prediction model so as to predict the metallogenic probability of the to-be-researched area; and performing performance evaluation on the metallogenic prediction model, and delineating a prospecting target region based on a prediction result of the metallogenic prediction model with relatively good performance. According to the metallogenic prediction method disclosed by the invention, the nuclear density analysis is carried out on the space distribution of the known ore deposit, so that a certain number of positive samples can be synthesized under the condition of considering the geological connotation, the training of the metallogenic prediction model is facilitated, and the prospecting prediction precision is 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 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).

The invention discloses an aircraft trajectory prediction method in a multi-dimensional variable scene based on a multi-attention collaborative network, and belongs to the technical field of trajectory prediction. According to the method, the problem of poor prediction accuracy caused by difficulty in fully modeling a complex relationship between variables in an existing prediction method based on data driving in a multivariable scene is solved. The variable distillation attention network designed by the invention weakens the negative influence of non-predictive variables, and converts the current sequence into spatial semantics. A new KeLSTM network variant is also designed to calculate the interaction between variables, alleviate information conflicts, and distinguish the time influence of the variables on tasks. According to the method, the negative influence of irrelevant variables can be reduced, the contribution of important variables to prediction is emphasized, and potential interaction among past target variables, non-predictive variables and an upcoming target, especially subtle differences between the influences of the target and the non-predictive variables, is successfully captured. The method can be applied to aircraft trajectory prediction.

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.

The invention relates to the related technical field of the orthopedics department, in particular to an orthopedics department trauma patient VTE risk prediction model establishing method and device based on a column diagram. The method comprises the steps that original data of an orthopedic trauma patient are collected, and the original data comprise VTE risk factors and outcome indexes; based on the original data, a Logistic regression method is adopted, an outcome index is used as a prediction result, VTE risk factors are used as prediction variables, and a risk prediction model is constructed; a bootstrap resampling method is used to carry out internal verification on the risk prediction model, Spiegelhalter Z test and Hosmer-Lemeshop test are carried out, and the fitting degree of the prediction probability and the real probability distribution is judged; performing decision curve analysis, checking the net income range of the model, and constructing a dynamic column graph; acquiring current patient information and an adjustment intervention strategy for the patient; and displaying a corresponding prediction result under the current patient information and a corresponding prediction result under the adjustment intervention strategy based on the dynamic column graph.

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 disclosed device a controller that sets an iteration counter for a loop based on an iteration value read from a loop iteration instruction for the loop. The controller also updates the iteration counter based on a number of times a loop heading instruction for the loop is decoded. When the iteration counter reaches an end value, the controller selects a not taken identifier for the loop to be fetched, to avoid a branch misprediction. Various other methods, systems, and computer-readable media are also disclosed.

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.

The invention relates to a water body carbon emission long-term trend prediction method based on a Bayesian additive regression tree, and belongs to the field of water body carbon emission prediction. The method comprises the steps that water-gas interface carbon flux data and related hydrological and water environment data are collected, the data are preprocessed and subjected to dimension unification, and predictive variables are determined through correlation analysis and a structural equation model. Thirdly, constructing a prediction model by using a BART algorithm model, and verifying the performance of the model through a test set; in addition, the method further comprises the steps of utilizing a BART algorithm model to invert historical carbon emission conditions, and coupling with a global climate mode to predict carbon emission trends under different climate change scenes in the future. The method provides an effective way for evaluation and long-term trend prediction of the carbon emission level of the water body, and is helpful for deeply understanding and alleviating the influence of global climate change.

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 an alternating current optimal power flow solving method based on distributed machine learning. The alternating current optimal power flow solving method comprises the following steps: establishing an alternating current optimal power flow model; the method comprises the following steps: modeling a power system network into a weighted graph based on a complex network theory, and performing community division on the weighted graph by using a Louvain algorithm; the method comprises the following steps: dividing a power system network into a plurality of non-overlapping regions, and decomposing a characteristic variable and a prediction variable in each region; for each region, independently training a DNN model so as to learn a mapping relation between an input feature vector and an output feature vector; and the trained DNN model uses an input feature vector to predict voltage amplitudes and angles of non-zero injection nodes in a region, and the voltage amplitudes and angles of all the non-zero injection nodes are obtained by combining prediction results of all the DNN models. The AC-OPF problem can be efficiently solved, real-time power balance of the high-proportion new energy power system is facilitated, and stable and economical operation of the power system is maintained.

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.

Methods for video decoding and encoding, apparatuses and non-transitory storage media are provided. In one decoding method, the decoder obtains a plurality of prediction samples of neighboring reconstructed samples of a coding unit (CU). Additionally, the decoder reorders a plurality of geometric partition mode (GPM) split modes based on distortion costs between the plurality of prediction samples of the neighboring reconstructed samples associated with each GPM split mode and neighboring reconstructed samples of the CU according to an order table to obtain a reordered list of the plurality of GPM split modes. Furthermore, the decoder obtains a GPM split mode index and then obtains a GPM split mode based on the GPM split mode index and the reordered list of the plurality of GPM split modes. Moreover, the decoder obtains a GPM predictor based on the GPM split mode.

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.