191 results about "Quantile regression" patented technology

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention discloses an intelligent recommendation method for optimizing advertisement keyword combination through cross validation, and relates to the technical field of advertisement technology and search engine marketing, which comprises the following steps: constructing a heterogeneous data set through multi-modal data fusion, and layering according to data sparseness: training a Transform-XL time sequence model by adopting time cross validation of a dynamic K value in a high resource layer; a graph neural network association graph is introduced into a low resource layer, semantic expression of a long tail word is enhanced, a stratified sampling-transfer learning two-channel mechanism is designed, and the generalization ability is improved in combination with exposure frequency weighting and a parameter freezing strategy; developing a Bayesian fusion engine, and dynamically weighting a high / low resource layer prediction result by using an improved Materon kernel function Gaussian process; and generating a confidence interval based on neural quantile regression, and outputting an optimal keyword combination sequence under ROI-risk-diversity constraint in combination with multi-target Pareto optimization. According to the method, the cold start efficiency and the long-tail resource utilization rate are improved, and high-robustness decision support is provided for advertisement putting.

The invention discloses an industrial and commercial energy storage EMS scheduling system based on multi-objective optimization, and relates to the field of intelligent control of energy storage systems. The method is used for solving the problems of battery life attenuation, electricity price fluctuation and insufficient load abrupt change adaptability in energy storage scheduling. The service life management module analyzes the ion migration state to generate a dynamic power safety envelope line as a layered optimization hard constraint; the feature extraction module constructs an electricity price association network, predicts photovoltaic / load in combination with quantile regression and extracts net load abrupt change features; the hierarchical optimization module compresses the discharge depth and activates load regulation in demand risk, and synchronously optimizes economy, life loss and power grid constraint through taboo search multi-objective evolution; the execution arbitration module monitors the distance between the power and the envelope line in real time, and when the distance is lower than a threshold value, rolling optimization is started to improve the life weight and feed back the lithium precipitation characteristic to drive the envelope line to update, and closed-loop protection is formed.

The invention provides a construction method of a prediction model based on dynamic gating and cross-modal attention fusion, and belongs to the technical field of model construction. Comprising the following steps: constructing a feature coding layer, and respectively coding multi-source input data to obtain each modal feature vector; constructing a multi-source data interaction layer, and performing deep interaction on each modal feature vector; and finally, carrying out weighted fusion on the main modal features and the cross-modal interaction features based on a dynamic gating mechanism. Constructing a feature fusion layer, and performing time sequence pooling and full-connection fusion on the interacted multi-modal features to obtain a fusion feature vector; and constructing a quantile regression layer, and outputting a prediction result based on the fusion feature vector. According to the method, the prediction model is constructed by fusing the dynamic gating mechanism and the cross-modal attention mechanism, so that the problems of an existing prediction model in the aspects of deep fusion of multi-source heterogeneous data, cross-modal dynamic interaction and accurate quantification of tail risks are solved, and then efficient prediction of stock price collapse risks is realized.

The embodiment of the invention provides a barrel service life prediction method and device based on gradient enhancement and a quantile recursive network, and the method comprises the steps: collecting the multi-source sensor data of the whole life cycle of a barrel, and carrying out the preprocessing, and forming a standardized data set; constructing an XGBoost model, performing feature importance analysis on the standardized data set through the XGBoost model, screening key features based on an analysis result to obtain a data set after feature selection, and performing optimization training on the XGBoost model through gradient lifting and regularization methods; dividing the data set after feature selection into a training set and a test set; in combination with a quantile regression method, constructing a life prediction model based on a quantile recurrent neural network, training the life prediction model by using the training set, and training and optimizing model parameters through a bifurcated sequence to obtain an optimized barrel life prediction model; and inputting a test set into the model, outputting a residual service life prediction result containing a prediction value and a confidence interval, and evaluating the prediction performance.

The invention discloses a wind power prediction method, system and equipment based on multi-model fusion, relates to the technical field of wind power prediction, and realizes non-parametric probability prediction of short-term wind power prediction by combining a Pearson's correlation coefficient, a frost optimization algorithm optimization variational mode decomposition model, a CNN-ASSA-Informer model and quantile regression. Complex time series data are processed through an optimization algorithm, and the complex relation between different frequency components and wind power in the time series data is solved. And a corresponding prediction interval is constructed, so that the precision and confidence of wind power prediction are improved, and a more reliable wind power prediction result can be provided for regional wind power scheduling, renewable energy integration and an electric power market.

The invention discloses an uncertainty train arrival delay prediction method based on quantile regression and application, and the method comprises the steps: determining a research line, and collecting train operation historical data used for model training; constructing a quantile regression late prediction network model; performing model training by using the constructed neural network and historical data; and constructing uncertainty evaluation index evaluation to carry out model performance evaluation. According to the method, the neural network combining the CNN and the Transformer is used for extracting time-space characteristic information related to the delay of the train historical information, a quantile regression loss function is designed for prediction, predicted delay values under different quantile conditions are obtained, the range of arrival delay and probability distribution of the range are obtained, and the prediction accuracy of the delay is improved. Richer decision information is provided for a dispatcher to carry out delay adjustment, and adjustment scheme making under different application scenes can be better coped with.

The invention discloses a load demand interval probability prediction method considering abnormal meteorological conditions, and relates to the field of power load prediction. Obtaining multi-dimensional data and carrying out data fusion processing to obtain a multi-dimensional feature set; performing dimension reduction on the multi-dimensional feature set by using a principal component analysis method to extract all meteorological factors influencing load demand changes in historical meteorological data under an abnormal meteorological condition; analyzing the combined features of all meteorological factors to obtain all extreme meteorological scenes under abnormal meteorological conditions; all the extreme meteorological scenes are clustered to obtain different types of extreme meteorological scenes, and key factors influencing load demand changes under the different types of extreme meteorological scenes are identified; constructing time sequence models corresponding to different types of extreme meteorological scenes based on the key factors; and combining quantile regression with a time sequence model to construct an interval load prediction model. According to the invention, the prediction precision and stability of the load demand under the abnormal condition are improved.

The invention discloses a buried pipeline residual life prediction method, relates to the technical field of pipeline prediction in computer measurement, can realize multi-step prediction of the residual life of a buried pipeline, and is small in accumulated error and higher in precision. According to the buried pipeline residual life prediction method, a training set is input into a TFT neural network for network training, a test set is input into the trained TFT neural network for testing, TFT model parameters are stored, and training of the neural network is completed; the TFT neural network comprises a gating residual network GRN module used for processing a nonlinear correlation relation between input data and a prediction target, a variable selection network module used for carrying out important feature screening on static and dynamic variables, and a multi-head attention layer module used for learning a long-term dependency relation of a pipeline life sequence. And a long and short term memory network (LSTM) module, and a quantile regression module for outputting the residual life of the pipeline based on regression of different quantiles.

The invention relates to a cultural scenic spot people flow prediction and scheduling method based on machine learning, and belongs to the field of scenic spot management. 2, constructing and training a mixed attention model, wherein the constructed mixed attention model comprises a spatial-temporal feature extraction module and a mixed attention mechanism module; step 3, people flow prediction and result output: inputting real-time data into the trained mixed attention model to carry out people flow prediction, and outputting a future time period passenger flow volume value; a quantile regression technology is combined to output a confidence interval of the passenger flow volume; step 4, scheduling strategy implementation: constructing a dynamic thermodynamic diagram based on a prediction result, and marking crowd density levels of different areas in the thermodynamic diagram; early warning information is pushed in real time, and touring suggestions are sent to tourists in advance.

The invention discloses a power system operation reserve quantification method, system and device based on photovoltaic probability prediction and a medium. The method comprises the following steps: calculating an Euclidean distance between a photovoltaic predicted value of a point to be decided and a historical photovoltaic predicted value, and searching a photovoltaic power generation historical data set similar to the point to be decided; performing quantile regression based on the similar historical data set to obtain quantiles corresponding to a plurality of tail end quantile levels, fitting a probability density function of photovoltaic prediction deviation by using a Gaussian mixture model, and further calculating a risk loss expectation of a point to be decided; and constructing a standby cost function, considering the reliability constraint of the standby, taking the sum of the minimum standby reserved cost and the risk loss expected cost as a target function, and finally optimizing to obtain the standby capacity of the power system. According to the method, the change of the reserve price along with the capacity is considered, the photovoltaic probability prediction information can be fully utilized, and a more economical and reliable power system reserve quantification result is obtained.

The invention belongs to the technical field of livestock and poultry breeding environment intelligent control, and particularly relates to a meat duck breeding environment temperature regulation and control method and system integrating quantile regression prediction and reinforcement learning. The method comprises the following steps: collecting multi-dimensional data of a breeding environment and operation state information of environment regulation and control equipment, and performing feature construction to obtain a feature vector; using the multi-source data set to predict in-house temperature based on quantile regression to obtain predicted temperatures under different quantiles; modeling a breeding environment temperature regulation problem into a Markov decision process, and dynamically adjusting fan power and a wet curtain equipment state; and setting an online updating model step threshold, and performing actual deployment and application on the reinforcement learning decision model obtained by training and fusing quantile regression information. According to the method, the problems that the meat duck breeding environment temperature regulation and control technology still faces insufficient predictability, regulation and control lag, control strategy static performance, multi-target optimization deficiency and the like are solved.

The invention relates to the technical field of metal smelting, and particularly discloses a mechanism data co-driven metal smelting process key process index sensing method, which comprises the following steps of S01, carrying out mechanism modeling in a smelting process, and providing a mechanism model for key process indexes to predict a key process index preliminary interpolation; s02, key process index accumulative error compensation: after a key process index preliminary interpolation is obtained through a mechanism model, spatial feature extraction is carried out on collected operation variables by introducing a CNN model, accumulative errors output by the mechanism model are compensated, and a complete key process index historical data sequence is obtained; and S03, based on probabilistic prediction of the compensation sequence, carrying out time sequence modeling and bidirectional feature capture on the sequence subjected to error compensation, improving the prediction precision in combination with an attention mechanism, and realizing prediction of each confidence interval and single-value prediction by adopting a mixed quantile regression mode. According to the method, the problem that the accuracy and the reliability of process index sensing in the traditional metal smelting process are low is solved.