17 results about "Quantile regression model" patented technology

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The application discloses a kind of static stability margin tail risk prediction method, device and medium of power system, belong to risk prediction technical field.Its method includes: using quantile regression model to carry out multi-quantile prediction to wind power, photovoltaic and other new energy output, obtains the cumulative distribution function of each new energy node output;Further, the discrete probability density function of new energy output is constructed by discretization and difference method, to avoid the modeling error caused by continuous distribution assumption;On this basis, combined with thermal power output configuration and static stability margin based on converter dynamic parameters, the mapping relationship between new energy random injection and system static stability margin is established, to realize the quantitative prediction of stability margin probability distribution and its tail risk.The application can accurately reflect the distribution characteristics of receiving-end power system static stability margin in the sense of probability, especially the stability margin change law under low-probability, high-risk working condition.

The invention relates to the technical field of remote sensing and geographic information, and discloses a method for identifying the influence of mining intensity on a mining industry urban society-ecosystem, which comprises the following steps: extracting year-by-year mining industry activity land vector data of a mining industry centralized development area, and establishing a space density field model; noctilucent remote sensing data and GDP statistical data are fused, and a social economic development index is constructed; integrating four key ecological factors of greenness, humidity, dryness and heat, and constructing comprehensive indexes for evaluating the regional ecological environment quality; constructing a mining city society-ecosystem analysis framework to systematically quantify an interaction relationship between social economy and ecological environment components; and analyzing the influence of the mining strength index on the interaction relationship by adopting a panel quantile regression model. According to the method, quantitative evaluation of the social-ecological system interaction relationship is realized, and the threshold feature of the influence of the mining intensity index on the system is revealed.

The invention discloses a micro-grid operation random optimization method based on source load probability prediction. The method comprises the following steps: firstly, performing energy-sensitive self-organizing segmentation on source-load historical time sequence data, and extracting morphological fingerprint features; and adopting an improved affinity propagation clustering algorithm fused with a power system operation constraint penalty mechanism to identify a typical operation mode. Secondly, a quantile regression model based on a gated pulse neural P system is established in each mode for probability prediction, and a probability scene library with weights is generated; and finally, constructing a two-stage stochastic optimization model with the goal of minimizing the expected operation cost, and solving by adopting a Benders decomposition algorithm to obtain a fixed equipment plan and a flexible operation strategy. According to the method, through refined mode recognition and probability modeling, on the basis of fully considering the uncertainty of the source load, robust optimization of economic operation of the micro-grid is realized, and the expected operation cost of the system is effectively reduced.

The application discloses a logistics transportation cost accounting method, system, device and medium, and relates to the field of cost accounting. In the method, pickup stage characteristics, transportation stage characteristics, delivery stage characteristics and manual operation characteristics are constructed according to order demand information and dynamic cost data; a first predicted cost of the pickup stage, a second predicted cost of the transportation stage and a third predicted cost of the delivery stage are predicted, and a manual operation cost is calculated; a preliminary total predicted cost is obtained according to the above costs; a preset quantile regression model is used to predict a cost median, a first target quantile cost and a second target quantile cost, a price interval is determined according to the first target quantile cost, the second target quantile cost and a preset buffer coefficient, and if the preliminary total predicted cost is in the price interval, the preliminary total predicted cost is used as a final total cost. By implementing the technical scheme provided in the application, the logistics transportation cost can be more accurately accounted.

This invention discloses a digital twin-based water environment monitoring station operation and maintenance simulation system, belonging to the field of environmental monitoring and operation and maintenance management technology. The system includes a multi-source sensing unit for the station, a digital twin modeling engine, an operation and maintenance simulation decision-making platform, and a station execution feedback unit, constructing a three-layer collaborative architecture. A high-fidelity digital twin is constructed through multi-source data fusion and generative 3D reconstruction. A two-stage anomaly detection model accurately identifies equipment and water quality anomalies, and an improved quantile regression model is used to predict future trends, ultimately generating an optimal operation and maintenance strategy and forming a closed loop. This invention solves the problems of passive operation and maintenance and extensive resource allocation in existing systems, upgrading the operation and maintenance mode from passive repair to proactive predictive maintenance, improving operation and maintenance efficiency, and reducing operation and maintenance costs.

The application provides a new energy power generation prediction method and system based on numerical weather prediction, and relates to the technical field of new energy power generation. First, the numerical weather prediction spatial grid multi-element data of a target area is obtained, feature construction is performed, and time domain features and space domain features are obtained. The time domain features and the space domain features are spliced to obtain space-time features. A preset point prediction model and a quantile regression model are trained to obtain trained point prediction models and quantile regression models. Finally, the space-time features are input into the trained point prediction models and quantile regression models, and new energy power generation prediction results are output. The application improves the accuracy of new energy power generation prediction by extracting the space-time features of the numerical weather prediction spatial grid multi-element data.

This invention discloses an intelligent early warning dosing method, system, and medium based on t-Copula and quantile regression, belonging to the technical field of water treatment. Data acquisition involves collecting historical operational data and constructing a joint fluctuation risk feature using a t-Copula model based on the volatility sequence of influent variables. An input feature set is constructed based on the joint fluctuation risk and influent variables, with effluent turbidity as the target variable, to build a quantile regression model. A multi-objective optimization function is constructed based on the joint fluctuation risk feature and quantile prediction results to obtain the optimal dosing dosage. This invention uses a t-Copula model to jointly model the volatility of multiple influent water quality parameters, quantifying the tail risk of multi-parameter coordinated fluctuations. This overcomes the shortcomings of existing technologies that ignore the correlation between parameters and cannot capture joint extreme risks, demonstrating good practicality.

The invention relates to the technical field of new energy battery detection, and provides a method and a system for predicting the remaining life of a decommissioned battery based on multi-electric parameter fusion, and the method comprises the steps: firstly, extracting a multi-dimensional health factor closely related to a battery aging mechanism from a specific voltage window through a standardized data collection process; and a temperature compensation function and a current adaptive calibration mechanism are introduced to normalize the features, so that the problem of feature drift caused by ambient temperature and charging current fluctuation is effectively overcome, and the generalization ability of the model is remarkably improved. On this basis, a dual-guarantee model fusing anomaly detection and uncertainty prediction is constructed: abnormal battery identification and interception are carried out by using a mahalanobis distance, and invalid prediction is completely eradicated; and synchronously outputting a point estimation value and a confidence interval of the residual life by adopting a quantile regression model, and realizing quantification of prediction uncertainty. The invention provides a high-reliability evaluation tool for echelon utilization of the retired battery.

The application provides a non-standard rock sample compressive strength correction method, comprising: collecting rock samples generated under different formation conditions from different oil and gas blocks, measuring the height and diameter of each sample, determining the corresponding sample type, and simultaneously obtaining the lithology of each sample, and measuring the compressive strength of each sample by setting a conventional experiment; the height, diameter, confining pressure, and compressive strength are dimensionally standardized, and the sample data is randomly divided into a training set and a test set with consistent sample type and lithology distribution according to a ratio of 7:3; a quantile regression model based on GBDT is constructed using the training set, the hyperparameters of the quantile regression model are optimized using a particle swarm optimization algorithm, the test set is used to verify the quantile regression model to output the compressive strength correction values of multiple key quantiles, and the uncertainty range of the correction result is quantified. The method can accurately establish a unified standard strength benchmark that adapts to different height-diameter ratios and different lithologies, and greatly improves the correction accuracy.

This application discloses a load forecasting method for rural transformer substations. The method includes: collecting load data corresponding to transformer substations in rural areas; inputting the load data corresponding to the transformer substations into a preset quantile regression model for regression analysis to construct a first forecast interval for each transformer substation; selecting a corresponding target hierarchical calibration amount from a preset conformal forecasting mechanism based on the quarterly and line attributes associated with the load data, and calibrating the first forecast interval based on the target hierarchical calibration amount to obtain a calibration interval for each transformer substation; aggregating the calibration intervals corresponding to all transformer substations in rural areas to obtain an aggregated interval for rural areas, thereby obtaining the forecast result. The embodiments of this application can improve the reliability of the predicted calibration interval for a single transformer substation and the predicted aggregated interval for the entire rural area.

The invention relates to the technical field of intelligent traffic and itinerary planning, in particular to an intelligent risk insight system based on flight event flow prediction and itinerary re-planning. Comprising a journey planning unit, a flight event flow prediction and journey planning unit and a client. The route planning unit fuses real-time traffic and airport operation data, and provides route planning and time estimation before boarding for users. The core flight event flow prediction and route planning unit is used for predicting flight time probability distribution by utilizing a quantile regression model through multi-source data fusion and real-time feature engineering, predicting a next key event by utilizing a survival analysis model, and carrying out quantification and interpretable attribution on risks; an intelligent re-planning scheme with a trigger condition is generated based on multi-objective optimization; and the final result is encapsulated and then presented through the client. According to the invention, active perception, accurate prediction and intelligent decision-making assistance of flight trip full-chain risks are realized.

This invention discloses a method, device, and medium for predicting the tail risk of static stability margin in power systems, belonging to the field of risk prediction technology. The method includes: using a quantile regression model to perform multi-quantile prediction of the output of new energy sources such as wind power and photovoltaics, obtaining the cumulative distribution function of the output of each new energy node; further constructing a discrete probability density function of the new energy output through discretization and differencing to avoid modeling errors caused by the assumption of continuous distribution; based on this, combining the thermal power output configuration and the static stability margin based on converter dynamic parameters, establishing a mapping relationship between the random injection of new energy and the static stability margin of the system, realizing the quantitative prediction of the stability margin probability distribution and its tail risk. This invention can accurately reflect the distribution characteristics of the static stability margin of the receiving-end power system in a probabilistic sense, especially the stability margin variation law under low-probability, high-risk operating conditions.

The application discloses a kind of calculation methods of motor efficiency stray loss, comprising the following steps: a) establishing dependent variable is quantile regression model of stray loss: b) set quantile value, realize the estimation of the parameter vector corresponding to different quantiles by minimizing the residual of stray loss real value and estimated value, obtain the fitting curve cluster of stray loss sample data;C) the goodness-of-fit test is carried out to the curve in step b), and the combination of the best curve or curve segment of fitting effect is screened out, to determine the motor efficiency stray loss thereof.The application starts from quantile regression method, obtains different curves by selecting different quantiles, selects the curve or curve segment most consistent with fitting standard by the test of goodness-of-fit, can reduce the influence of abnormal point on curve.

This invention provides a method and apparatus for predicting renewable energy power in extreme scenarios based on dual-input features and nonparametric quantile regression. First, historical power data and historical meteorological data are acquired. Then, based on the historical meteorological data and the physical response mechanism of renewable energy output under extreme weather conditions, a dual-input feature vector is constructed. Finally, the historical power data and the dual-input feature vector are input into a nonparametric quantile regression model to obtain the probability prediction result for the extreme scenario. This invention constructs a dual-input feature vector based on the physical response mechanism of renewable energy output under extreme weather conditions and combines it with a nonparametric quantile regression model with bandwidth parameter optimization for nonlinear fitting. This accurately characterizes the complex coupling relationship between meteorology and power output and achieves risk quantification, thereby effectively improving the prediction accuracy and reliability of renewable energy power in extreme scenarios.

A CFSv2 ground solar radiation revision method and system based on quantile regression, the revision method comprises the following steps: data acquisition and pretreatment step, selecting typical sites, collecting hourly solar radiation observation data for quality control, and carrying out data cleaning; the space-time matching step includes time alignment, space alignment; the quantile regression revision model establishment and training step includes model establishment, model training and parameter optimization; the revision process step, through the quantile regression model to the mode data, to obtain the corrected irradiance prediction value; the model effect evaluation step, using the root mean square error to compare and analyze the mode data before and after revision, and evaluate the revision effect. The present application can revise the ground solar radiation data, reduce the daily radiation prediction error, significantly improve the accuracy and reliability of the long-term trend prediction of solar energy resources, especially improve the prediction accuracy under extreme weather conditions.

The invention discloses a logistics transportation cost accounting method, system and device and a medium, and relates to the field of cost accounting. In the method, according to order demand information and dynamic cost data, goods picking stage features, transportation stage features, delivery stage features and manual operation features are constructed; predicting a first predicted cost of a delivery stage, a second predicted cost of a transportation stage and a third predicted cost of a delivery stage, and calculating a manual operation cost; obtaining a preliminary predicted total cost according to the above cost; and predicting a cost median, a first target quantile cost and a second target quantile cost by using a preset quantile regression model, determining a price interval according to the first target quantile cost, the second target quantile cost and a preset buffer coefficient, and if the preliminary prediction total cost is in the price interval, using the preliminary prediction total cost as a final total cost. By implementing the technical scheme provided by the invention, the logistics transportation cost can be more accurately calculated.