204 results about "Independent predictor" patented technology

A facility for estimating a value relating to a occurrence is described. The facility receives a first occurrence that specifies a first value for each of a plurality of independent variables that include a distinguished independent variable designated to be monotonically linked to a dependent variable. The facility subjects the first independent variable values specified by the received occurrence to a statistical model to obtain a first value of the dependent variable. The facility receives a second occurrence that specifies a second value for each of the plurality of independent variables, the second value of the distinguished independent variable varying from the first value of the distinguished independent variable in a first direction. The facility subjects the second independent variable values specified by the received occurrence to the statistical model to obtain a second value of the dependent variable, the second value of the dependent variable being guaranteed not to vary from the first value of the dependent variable in a second direction that is opposite the first direction.

The invention belongs to the crossing field of urban design and multidisciplinary, and particularly relates to a mountain historical block ownership perception evaluation method based on multi-modal data, public spaces of mountain historical blocks are divided into multiple types, an experimental path is selected from each public space, and multiple experimental parking points are selected from each experimental path; the evaluation index system comprises physiological data, psychological data and spatial data, and the spatial data comprises feature data and acoustic environment data; for each experiment path, obtaining feature data, recruiting a plurality of experimenters, collecting acoustic environment data and physiological data of each experimenter in real time, and collecting psychological data from the experimenters in real time in a questionnaire mode at an experiment stop point; independent variables are defined as spatial data, dependent variables are defined as psychological data and physiological data, based on experimental data, the relation between the independent variables and the dependent variables is learned through a random forest model, and bar charts of the influence degrees of the independent variables and the dependent variables are output; and the evaluation result is more accurate and persuasive.

The invention discloses a fracture pressure prediction and internal friction angle joint inversion method for a heterogeneous stratum. The invention belongs to the technical field of petroleum engineering and artificial intelligence cross application, well depth is used as an independent variable, the minimum / maximum horizontal stress, pore pressure and cohesion are combined to construct well depth mixed input, only multi-scale periodic basis function transformation is carried out on the well depth, and a linear well depth channel is reserved; establishing a rupture pressure depth mapping model, introducing an augmented Lagrange physical constraint containing dual variables and a dynamic threshold, adaptively adjusting a physical weight according to a physical residual error and a threshold difference value, realizing dynamic balance of a rupture criterion and data fitting, and searching and optimizing a model topology by using a multi-target network structure; under a limited sample, a fracture pressure profile and a physically reasonable internal friction angle can be obtained at the same time, and the prediction precision and the physical consistency are improved.

The invention relates to the field of gates, and provides a gate vibration online analysis early warning method and system, and the method comprises the steps: training the historical monitoring data of a gate through a machine learning algorithm, and constructing a mapping relation between an independent variable and a dependent variable; upstream and downstream related independent variable information of the gate is obtained, and the gate vibration state is calculated online according to the mapping relation; analyzing and calculating the natural vibration frequency of the gate based on the modal analysis model of the gate; and evaluating the safety state of the gate according to the gate vibration state and the natural vibration frequency of the gate. When the system is used, the problems of data islanding, prediction lag, model distortion and low decision efficiency in the traditional technology are solved, high-precision and full-period intelligent guarantee is provided for hydraulic engineering safety, the resonance accident risk is remarkably reduced, and gate operation and maintenance management is promoted to be upgraded to the standard and unmanned direction.

The invention discloses an open-web gravity dam deformation prediction and mechanism interpretation method, system, equipment and medium, and belongs to the technical field of dam and hydraulic structure health monitoring and state evaluation. Constructing the preprocessed monitoring data into corresponding feature vectors, dividing a time sequence data set, and performing XGBoost model training and hyper-parameter optimization to obtain a prediction model; calculating a corresponding SHAP value, and screening a key factor as an independent variable for model analysis; and selecting a control index to evaluate the performance of the model, and deploying the model to a dam safety monitoring system after verification so as to carry out real-time prediction and early warning. According to the method, while high-precision deformation prediction of the open-web gravity dam is realized, quantifiable mechanism explanation of the prediction result is provided, so that transparent and reliable decision support is provided for safety monitoring and early warning of the dam.

The invention belongs to the technical field of CFD (computational fluid dynamics) parameter calibration, and discloses a CFD parameter adaptive calibration method and system based on measured data and a double-agent model, and the method comprises the steps: obtaining a CFD input parameter sample, inputting the CFD input parameter sample into a CFD solver, and obtaining an initial simulation result; determining an error evaluation index according to the initial simulation result based on a target actual measurement data result; constructing a double-agent model based on a Kriging model and a radial basis function neural network by taking a CFD input parameter sample as an independent variable and an error evaluation index as a dependent variable; the double-agent model is trained, the trained double-agent model takes the error evaluation index as fitness, and CFD input parameter values are obtained based on a genetic algorithm; the CFD input parameter values are input into the CFD solver for a simulation experiment, a calibrated simulation result is output, the reliability and generalization ability of prediction are improved through a double-agent model, a high-fidelity simulation result is output through the CFD solver, and the number of times of calling the CFD solver is reduced while the calibration precision is guaranteed.

The invention relates to the technical field of gastrointestinal tumor chemotherapy risk assessment, and particularly discloses a gastrointestinal tumor chemotherapy risk scoring model and a construction method thereof, and the method comprises the steps: obtaining the individualized feature data and basic physiological data of a historical patient, and constructing an individualized difference library with a unique ID, and a core influence library; the method comprises the following steps: standardizing and coding double-library data, synchronizing clinical data containing treatment effect codes, forming a data dictionary, acquiring and coding current gastrointestinal tumor patient data, and matching the data dictionary to judge individual difference abnormity; when the first data set is abnormal, constructing a first data set, screening core independent variables through Logistic regression, and constructing a scoring model by using an LSTM (Long Short Term Memory) model; when no abnormity exists, redundant codes are removed to obtain a second data set, and modeling is conducted through the same method. Through double-library linkage and scene-divided modeling, the three-level toxicity risk prediction precision is improved, data support is provided for clinical chemotherapy dose adjustment and toxicity prevention, and the risk of excessive treatment or insufficient treatment is reduced.

The invention provides a method, a system and a kit for constructing a model for evaluating the possibility that a subject suffers from gastric cancer based on a Swis-prot database, electronic equipment applying the construction method and a computer readable medium storing computer program codes. The construction method comprises the steps of obtaining a plurality of independent variables, carrying out screening and data processing on the independent variables, further screening effective independent variables in a regression model training process, reducing the number of the independent variables, obtaining a relationship among the independent variables through correlation analysis, carrying out replacement and combination by adopting the independent variables with a replacement relationship, and obtaining a regression model. And obtaining a plurality of prediction models. The prediction models can obtain highly accurate prediction results only by using fewer effective independent variables, so that the prediction efficiency is high, the application range is wide, and the cost is low. The invention provides an efficient, reliable and economical solution for the field of gastric cancer risk assessment.

The invention relates to the technical field of petroleum and natural gas drilling engineering, and particularly discloses a multi-field coupled complex fractured formation fracture width distribution inversion method, which is characterized in that a drilling fluid leakage model considering a fluid-solid coupling effect is constructed based on a porous elastic mechanics theory, and a roughness correction factor is introduced to correct a traditional cubic law; the application of the Darcy law in the matrix is perfected, a matrix and crack coupled drilling fluid leakage mathematical model is established, the influence rule of key parameters such as fluid viscosity, pressure difference and crack width on the drilling fluid leakage amount is obtained through numerical simulation, and on the basis, the drilling fluid leakage amount is calculated. According to the method, the crack width multiple linear regression inversion equation with the fluid viscosity, the pressure difference and the accumulated leakage as independent variables is established, the inversion equation has high prediction precision and practicability and can be used for rapidly and accurately inversing the crack width on site, and theoretical and technical supports are provided for multi-scale fractured formation leakage mechanism understanding and leakage prevention and control optimization.

The invention discloses a PM2.5 high-precision prediction method capable of fusing multi-source data and improving spatial resolution. The PM2.5 high-precision prediction method comprises the following steps: constructing an independent variable data set and a dependent variable data set of a research area; a PS-MSViT model of the combined inversion model comprises a multi-scale fusion module and a Pixel Shuffle downscaling module, and weighted regression processing of dependent variable data is carried out in a geographic map according to a target spatial resolution by using a dependent variable data set to obtain the PM2.5 concentration under the target spatial resolution; the multi-scale fusion module carries out feature extraction and fusion processing on the independent variable data set to obtain a feature map F1, the feature map F1 is divided, flattened and coded, and the Pixel Shuffle downscaling module rearranges the space to obtain a feature map F3 under the target spatial resolution; and finally outputting the predicted PM2.5 concentrations of all the position points of the research area by combining the inversion model. According to the method, high-precision PM2.5 concentration data can be obtained, and the method is of great significance to precise monitoring of air quality, public health risk assessment and environmental protection.

This invention discloses a water treatment dosing control method and system based on quadratic programming, belonging to the field of water treatment process control and optimization technology. It collects historical water treatment operation data and constructs a mechanistic feature set, using the mechanistic feature set as the independent variable and turbidity reduction as the target variable. The turbidity reduction is used to represent the change in flocculation or sedimentation of suspended impurities in the water, and a full-variable regression model is constructed. Variables in the mechanistic feature set are screened, and the full-variable regression model is optimized using a stepwise regression method to obtain a simplified prediction model. Real-time influent water quality parameters are acquired and input into the simplified prediction model. The process of maximizing turbidity reduction in the simplified prediction model is transformed into minimizing a convex loss function, and the convex loss function is iteratively optimized using a hierarchical constrained projection gradient descent method to obtain the optimal dosing scheme. Through mechanism-driven modeling, convex function optimization, and hierarchical constrained projection, intelligent, efficient, and reliable control of the dosing process is achieved.

The invention provides a clearing price interval prediction method based on an LSTM network and a similar point matching algorithm, and the method comprises the following steps: carrying out the optimization of an independent variable feature set, taking a real-time clearing price as a target variable based on the historical data of an electricity market at an interval of 15 minutes, constructing a point prediction model based on the LSTM network, and carrying out the calculation of the point prediction model. On the basis of optimization of an independent variable feature set, a long-short-term memory neural network is adopted to construct a real-time clearing price point prediction model, high-precision point prediction, similar point matching and statistical feature extraction are realized, feature vectors of to-be-predicted time points are constructed to be used for matching similar time points in a historical data set, and a real-time clearing price point prediction model is obtained. And constructing upper and lower limits of the prediction interval. A real-time clearing price point prediction model is constructed based on an LSTM network, and an upper limit and a lower limit of a real-time clearing price prediction interval are formed in combination with a similar point matching algorithm and an interval feature statistical analysis method, so that uncertainty in an electricity market is quantified better, and more decision bases are provided for market participants.

The application discloses a method and system for rapidly predicting a creep stress index of a tin-based solder alloy by integrating a model, collects element compositions, test temperatures and creep stress index values of the tin-based solder alloy from literature, and adds experimental data as dataset samples; the element compositions and test temperatures of the tin-based solder alloy are sorted out and used as features for modeling; the dataset is randomly divided into a training set and a test set in a 4:1 ratio; the collected creep stress index values of the tin-based solder alloy are used as target variables, and the features are used as independent variables; based on the divided training set, the independent variables are scaled by using RobustScaler, three learners are trained and integrated to obtain an R-X-L integrated model; and the R-X-L integrated model is used to rapidly predict the creep stress index of the test set samples and four independent experimental samples. The prediction model for the creep stress index of the tin-based solder alloy is simple, fast, low-cost, pollution-free and the like based on reliable literature data and a modeling method.

The invention belongs to the technical field of medical information processing, and particularly discloses a regression model establishment method and system for detecting related indexes of myocardial cells of Wuzhishan pigs by using blood biochemical indexes, and the method comprises the following steps: collecting blood and heart tissue samples of the Wuzhishan pigs at different month ages, detecting the blood biochemical indexes including liver function, blood fat and blood sugar indexes, and establishing a regression model for detecting the related indexes of myocardial cells of the Wuzhishan pigs. Related indexes of myocardial cells; analyzing the index expression quantity difference, and screening significant difference indexes; then calculating the correlation between the remarkably different blood biochemical indexes and myocardial cell related indexes, and determining high-correlation blood biochemical indexes; and finally, constructing a first version of multiple regression model by respectively taking the myocardial enzyme index and the myocardial cell related index as dependent variables and the high-correlation blood biochemical index as an independent variable, removing outliers and updating the sample set on the basis of the first version of multiple regression model, and constructing a second version of multiple regression model by utilizing the new sample set, and on the basis of the second version of multiple regression model, constructing a third version of multiple regression model by adopting a stepwise regression method. According to the method, related indexes of myocardial cells can be accurately deduced through blood biochemical indexes, and support is provided for Wuzhishan pig heart disease research and model animal research and development.

The invention belongs to the technical field of biological medicine, and discloses a portal cholangiocarcinoma postoperative risk prediction model based on a gamma-glutamyltransferase (GGT) dynamic change track and a construction method thereof. The method comprises the following steps: (1) collecting clinical characteristic data and survival characteristic data (including survival time and survival state) of a patient suffering from porta hepatis cholangiocarcinoma subjected to radical resection; 2) on the basis of GGT detection results at different time points in a perioperative period, dividing patients into different GGT dynamic change track types by using a latent category hybrid model; and 3) taking the clinical characteristic data and the GGT dynamic change track type as independent variables, taking survival data as response variables, screening prediction factors through LASSO regression, and constructing a multi-factor Cox regression model, namely a postoperative risk prediction model. According to the method, the dynamic evolution rule of the GGT is introduced, so that the limitation of single-time static detection is made up, the quantitative evaluation of the survival risk is realized, and a scientific basis is provided for individualized treatment and follow-up visit management.

The application discloses a wiring error and electric leakage user robust positioning method and device and a storage medium, and relates to the technical field of electric leakage detection. The method comprises the following steps: establishing a regression model with residual current as the dependent variable and user load current as the independent variable, and performing initial solving; calculating residual error based on the initial fitting parameters obtained by solving, and determining initial weight coefficients; repeating the steps until the iteration termination condition is met; according to the current weight coefficients, solving the parameters of the regression model to obtain a candidate model; when the fitting accuracy of the candidate model is better than that of the current optimal model, updating the candidate model to the current optimal model, calculating new residual error, and updating the weight coefficients according to the new residual error; and determining the user with wiring error and electric leakage fault according to the parameters corresponding to each user load current in the current optimal model after iteration. The application realizes clearer and more reliable fault user identification in real engineering data, and reduces the misjudgment rate and the missed judgment rate.

The invention discloses application of a group of blood nutrition markers in prediction of central nervous system (CNS) injury function prognosis. According to the technical scheme, the method is characterized in that serum albumin, total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C) are determined as independent predictive factors of CNS injury prognosis for the first time through retrospective queue research (n = 404), and clinical decision critical values are determined to be 36.35 g / L, 3.60 mmol / L and 2.13 mmol / L respectively. The detection kit developed on the basis of the discovery can objectively and quantitatively evaluate the prognosis risk of the patient within 24 hours of admission, and is obviously superior to that of a traditional scoring system. According to the method, key technical support is provided for precise nutrition intervention and early rehabilitation strategy formulation of the CNS injury.

The invention discloses a method for predicting the market price trend of bulk commodities, and relates to the technical field of data analysis, and the method comprises the steps: taking the spot price of a target commodity as a prediction target quantity, determining an independent variable parameter which influences the prediction target quantity, and obtaining a historical data set; constructing a time sequence prediction model and a machine learning prediction model based on a prediction target quantity, and training the time sequence prediction model and the machine learning prediction model through the historical data set; minimizing a mean square error weight of combined prediction, and performing linear combination on the time sequence prediction model and the machine learning prediction model to form a target prediction model; and predicting the prediction target quantity based on the target prediction model. The method has the trend step advantage of a trend prediction model on price continuous change and the market trend analysis capability of a machine learning model. When daily, weekly and monthly prediction is carried out on multiple categories such as alloy, scrap steel and coke, the average accuracy rate of trend prediction is up to 70%.

Method for implementing a torque control using a neural network (NN), comprising: in response to a torque request (225), executing, by a processor (220), a torque prediction model (210) of the NN to receive a set of measured vehicle operating inputs (205) associated with the torque prediction; replacing a set of multiple independent variables of the torque prediction model (210) implemented by the NN with a simplified mathematical expression in a pseudo-NN containing a reduced set of variables including at least one independent variable to replace a previous set of multiple independent variables of the NN;Processing the set of measured vehicle operating inputs by the pseudo-NN based on the torque prediction model (210) using at least one independent variable and one or more constants that replace a set of substituted independent variables in a simplified mathematical expression of the pseudo-NN; and solving at least one root of the simplified mathematical expression of the pseudo-NN by obtaining a root value without having to rely on an inversion operation of a mathematical expression consisting of a whole set of independent variables to obtain a requested torque value.

This invention discloses a method for predicting sudden cardiac death based on forensic autopsy data using machine learning and its forensic applications, belonging to the fields of machine learning, statistics, and forensic identification. This invention provides a method for screening independent predictors of forensic diagnosis of sudden cardiac death using LASSO regression and logistic regression. Using 10-fold cross-validation, the method selects the minimum lambda(λ) to identify 14 risk factors. Logistic regression ultimately identifies 9 independent predictors, including age, heart weight, left ventricular wall thickness, right ventricular wall thickness, interventricular septum thickness, aortic valve circumference, mitral valve circumference, liver weight, and left kidney weight. A nomogram and a web-based calculator are constructed for predicting sudden cardiac death in forensic practice, thereby determining sudden cardiac death using objective indicators. This method has not been reported worldwide.

This invention provides a method for predicting the equilibrium adsorption capacity of an adsorbent material for an adsorbate at any initial pH value, belonging to the field of phosphorus removal technology for phosphogypsum lightweight aggregates. The prediction method of this invention includes the following steps: using a Langmuir isotherm adsorption model, obtaining the theoretical maximum adsorption capacity and affinity constant of the adsorbent material for the adsorbate at at least four different initial pH values; establishing empirical functions for the theoretical maximum adsorption capacity and affinity constant with the initial pH value as the independent variable, and substituting these functions into the Langmuir isotherm adsorption model to obtain a pH-corrected isotherm adsorption model; and based on the pH-corrected isotherm adsorption model, predicting the equilibrium adsorption capacity of the adsorbent material for the adsorbate at any initial pH value. The prediction method of this invention can predict the equilibrium adsorption capacity of the adsorbent material for the adsorbate at any initial pH value, eliminating the need for repeated experiments, saving time and effort, and reducing costs.

The application discloses an ecological environment monitoring method and system based on multi-target correlation. The method comprises the following steps: collecting original training data sets including independent variable data representing environmental variable characteristics and target variable data representing ecological environment; constructing a lag correlation matrix with the maximum sum of lag correlation coefficients of two target variables in the lag period as the target; analyzing the correlation between different target variables according to the correlation matrix, the partial correlation matrix and the lag correlation matrix, and generating enhanced features including lag correlation features, interaction features, graph embedding features and time window statistical features; splicing the enhanced features with the independent variable data to generate enhanced independent variable features; training a preset ecological environment monitoring model with the enhanced independent variable features as the input and the target variable data as the label, and the trained ecological environment monitoring model is used for monitoring the ecological quality of a target region. The application improves the prediction performance of ecological environment monitoring.

The invention provides an automatic driving scene generation method based on multi-target antagonism optimization, which comprises the following steps of: 1, constructing a noise generator, taking a current noisy scene xt, a high-definition map code m and a time step t as input, and taking predicted noise epsilon theta (xt, t) as output; step 2, designing a multi-target evaluator Ltotal; wherein L < total > is a weighting function of a plurality of potential functions; the independent variable of the potential function is obtained from an estimated denoising scene; step 3, reverse sampling of adversarial guidance is executed; and step 4, outputting a final scene x0. On the premise of strict scene authenticity, the key confrontation scene with the highest test value for the automatic driving system is efficiently and controllably generated, and the test coverage rate and efficiency are greatly improved.

Systems and methods for adaptively optimizing a performance function for a system. In one embodiment, an apparatus is configured to select an independent variable that determines an operating state of the system described by the performance function being discontinuous, establish a range of values for the independent variable from data from the system, select a set of random values within the range of values for the independent variable, and evaluate the performance function with the set of random values for the independent variable to provide extrema results for the performance function. The apparatus is further configured to optimize the performance function by selecting a value for the independent variable from the set of random values that provides an extremum result from the extrema results to produce an optimized performance function, and utilize the optimized performance function to control the system in response to real-time data from the system.

The application discloses a kind of painting workshop environment quality prediction method and prediction model establishment method, the present application selects volatile organic compound concentration as painting workshop environment quality reference index, selects volatile organic compound concentration as dependent variable, spraying time, spraying distance, air flow rate as independent variable, establishes the multiple linear regression model of painting workshop environment quality, establishes neural network model, the fitting value of multiple regression model is used as the input of neural network model together with independent variable, constructs multiple linear regression-neural network combination model, according to painting working condition, the air quality in workshop is predicted using combination model, when volatile organic compound concentration in painting workshop reaches certain threshold value, it is necessary to carry out alarm and forced ventilation.The present application can accurately and quickly calculate volatile organic compound concentration in workshop by painting workshop environment quality prediction model, so as to adjust ventilation volume in time, protect construction personnel, save energy consumption.

The invention provides a time-space geographically weighted regression method and device for counting data, equipment and a storage medium, and relates to the technical field of time-space information data analysis. The method comprises the following steps: acquiring multi-source space-time observation data of a target area; constructing a geographically weighted Poisson regression model or a geographically weighted logistic regression model fused with space-time non-stationarity based on a generalized linear model framework so as to fit a local space-time variation relationship between a numeric type or binary type response variable and an independent variable; solving a regression coefficient of the spatio-temporal change through an iterative algorithm; and finally, model output is converted into early warning information, a dredging scheme or a planning decision. According to the method, the data discrete characteristics and the process spatial-temporal heterogeneity are described in a unified manner, so that the problems of modeling error and insufficient precision caused by neglecting the spatial-temporal coupling effect and distribution mismatch when a traditional model is used for coping with spatial-temporal data with discrete distribution characteristics are solved; the accuracy and decision support capability of dynamic risk early warning and refined resource allocation in the fields of environmental monitoring, traffic safety, land planning and the like are remarkably improved.

The invention discloses a concrete uniaxial compressive strength prediction method based on multi-dimensional matrix energy, and the method comprises the steps: instantiating original nondestructive testing data into a three-dimensional measurement matrix group, extracting a multi-dimensional matrix energy feature set which can distinguish the macroscopic compactness and local non-uniformity of a material, and taking the multi-dimensional matrix energy feature set as an independent variable to drive a model for iterative optimization. Through the mode, the deviation degree of the secondary effect relative to the dominant effect is effectively measured, and a unified feature scale suitable for samples of different strength grades is constructed, so that the physical interpretability, the robustness and the prediction precision of a concrete strength prediction model are remarkably improved.

This specification provides a user-side load forecasting method and apparatus based on intelligent segmentation. The user-side load forecasting method based on intelligent segmentation includes: acquiring historical load data of the user side at each time point within a preset historical period; constructing independent variables based on the historical load data of any two adjacent time points, and constructing a drastic evaluation function based on the independent variables, fluctuation sensitivity coefficient, mean relative load change rate, fluctuation deviation coefficient, and standard deviation of relative load change rate; inputting the historical load data of each time point into the drastic evaluation function for processing, and determining the load fluctuation period within the preset historical period based on the processing result; dividing the historical load data of each time point into a fluctuating load dataset and a stable load dataset based on the determination result of the load fluctuation period; and training the prediction model to be trained using the fluctuating load dataset and the stable load dataset respectively to generate a fluctuating load prediction model and a stable load prediction model.

The invention provides an electricity consumption information data compression method and device, and belongs to the field of electric power data processing. The method comprises the following steps: analyzing distribution characteristics, variation trend and redundancy of electricity consumption information data, and taking the distribution characteristics, variation trend and redundancy as prediction errors of the electricity consumption information data; performing compression testing on multiple compression modes with different compression ratios based on the prediction error, and determining an allowable compression ratio of the power consumption information data; constructing a target function which takes a compression mode and a compression moment as independent variables and takes storage cost, transmission cost and calculation cost as dependent variables; and solving the target function by taking the compression ratio as a constraint and taking the minimum target function value as an optimization target to obtain a target compression mode and a target compression moment, and compressing the power consumption information data based on the target compression mode and the target compression moment. According to the invention, data quality can be guaranteed and effective utilization of resources can be realized.