162 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 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 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.

The invention discloses a photovoltaic power interval prediction method based on a GRU-LSTM combined neural network, and belongs to the technical field of photovoltaic power interval prediction. The method comprises the following steps: S1, taking historical power generation data as original wind-solar power generation power prediction data, processing the data, and screening related meteorological characteristics by adopting a Pearson correlation coefficient; s2, a GRU-LSTM combination model is constructed, and related hyper-parameters are set; s3, taking the screened related meteorological features as input for training, calculating a photovoltaic point prediction result according to a weight coefficient, and performing related error evaluation; and S4, based on the photovoltaic power point prediction result, calculating a photovoltaic power interval prediction result by using a quantile regression technology, and detecting performance evaluation through a test set. According to the method, the minimum prediction error correlation index is taken as the target, the influence of different weathers on photovoltaic power processing is considered, the Pearson's correlation coefficient analysis is utilized to select more representative meteorological characteristics, and the combined model and the quantile regression technology are utilized to finally obtain the photovoltaic power interval prediction result.

The invention belongs to the field of power system dispatching, and particularly relates to a day-ahead-intra-day cooperative dispatching method for multi-time scale coordinated optimization of a photovoltaic new energy access-containing power grid. Firstly, typical power grid source storage load characteristics of a region are analyzed, and a mathematical model is established; secondly, in the day-ahead stage, a large number of typical scenes are generated through a Latin hypercube sampling scene generation method, an initial scheduling scheme is obtained at the same time, a conditional quantile regression technology is introduced according to load prediction and new energy power generation prediction, and a wind power prediction error confidence interval based on prediction duration is established; therefore, the uncertainty of the new energy output is described more accurately. In the intra-day stage, the scheduling scheme is dynamically adjusted in combination with real-time data, and it is ensured that the system runs in the optimal economical and safe state. And finally, an IEEE 33 node standard test circuit is selected for analysis.

The invention relates to the technical field of power system operation and control, and particularly discloses a time-sharing electric quantity prediction method based on a logarithmic load density growth curve, which comprises the following steps of: firstly, performing causal detection and dynamic time-delay optimization on historical load and multivariate external data through convergence cross mapping and mutual information technologies, and constructing a causal time-delay feature set; and the problems of multi-element coupling and time-delay effect quantization are solved. Secondly, fitting a load trend by using time-frequency decomposition in cooperation with a segmented logistic model, extracting dynamic parameters representing a growth rate and a saturation capacity, and endowing the model with a sensing ability for a load evolution stage; then, causal features, growth parameters and load components are deeply fused through cross-domain modulation and a gating mechanism, the nonlinear modulation effect of an external environment on a load mode is explicitly modeled, and finally, a probability interval is generated in combination with quantile regression and residual error correction. According to the scheme, accurate and probabilistic prediction of the time-sharing electric quantity in a complex scene is realized, and the scientificity of an agent electricity purchase decision is improved.

The invention discloses a power distribution network photovoltaic openable capacity dynamic evaluation method fusing voltage stability margin and neural network optimization, and aims to solve the problems that a traditional method does not fully consider dynamic stability and is low in calculation efficiency. Firstly, a prediction method combining kernel density estimation and quantile regression is adopted to accurately quantify the uncertainty of distributed photovoltaic output. The core innovation of the method is that on the basis of traditional security constraints, a static voltage stability margin (VDSM) is introduced as a key constraint condition, and a double-layer interval analysis model capable of guaranteeing the dynamic stability of a power grid is constructed. Secondly, in order to efficiently solve, the invention provides a framework of'neural network pre-screening + parallel optimization ': after a model is decomposed into optimistic sub-problems and pessimistic sub-problems through an interval decoupling technology, massive candidate solutions are quickly screened by utilizing a neural network model, so that a feasible solution space is greatly reduced, and the solution efficiency is improved; and carrying out parallel optimization solution on the sub-models in combination with an improved particle swarm optimization algorithm.

The invention relates to a photovoltaic power interval prediction method based on time sequence decomposition and conformal quantile regression, and the method comprises the steps: collecting historical power data, solar irradiance data, temperature data, relative humidity data and wind speed data of a photovoltaic power station, supplementing missing values in the photovoltaic power data through employing a linear interpolation method, and carrying out the prediction of the photovoltaic power interval. Secondly, normalizing all data by using a Z-Score standard method, dividing the processed data into a training set, a calibration set and a test set, constructing a time sequence decomposition model based on a NeuralProphet framework, obtaining a deterministic prediction model, training two conditional quantile models respectively by using a quantile regression method, and obtaining a time sequence decomposition model based on the NeuralProphet framework; the method comprises the following steps: determining a power interval upper limit and lower limit prediction model according with a preset confidence level, deploying the model in a calibration set, calculating to obtain an empirical quantile, inputting test data into the constructed model, determining a final prediction interval, and realizing interval prediction of ultra-short-term photovoltaic power.

The invention relates to the technical field of dual-polarization radars, and discloses a dual-polarization radar transmitting dual-channel consistency checking method and system, and the method comprises the steps: constructing a dual-polarization radar transmitting dual-channel structure equation model and radar operation state data; determining a dual-polarization radar transmitting dual-channel consistency analysis path diagram; using a daily quantile pairing method and radar operation state data to check the significant difference of the same attribute observation variables of the two channels of the antenna; under the condition that significant difference exists, calculating a dual-channel optimal path coefficient of the same attribute observation variable by adopting a daily quantile-to-daily quantile regression method and radar operation state data; and configuring the optimal path coefficient of the two channels based on the analysis path diagram, and comparing and analyzing the consistency of the two channels on different daily quantiles. According to the method, the dynamic consistency characteristic of dual-channel influence during radar operation can be tested.

The invention discloses a vehicle-mounted power supply short-circuit heat prediction method based on big data analysis, and the method comprises the following steps: collecting real-time operation data, and storing the data in a data cache, thereby obtaining a cache time sequence signal; filtering the cached time sequence signal, and extracting a basic feature sequence; constructing an RC network model based on the basic feature sequence, and converting a fractional order operator into an equivalent integer order model; performing parameter estimation on the equivalent integer order model to generate a thermal prediction model; taking the thermal prediction model and the basic feature sequence as joint input and output power loss features; inputting the power loss characteristics into a quantile regression forest model to form a heat prediction result; comparing the heat prediction result with a heat threshold value and a temperature rise threshold value, and outputting a control trigger signal; and calling parameters in a control strategy library according to the control trigger signal to generate a control strategy. According to the invention, vehicle-mounted power supply short-circuit heat prediction is realized.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention discloses an extreme high-temperature scene multi-energy complementary optimization scheduling method considering uncertainty, which mainly comprises the following steps of: 1, fusing bidirectional time convolution, a bidirectional long-short-term memory network, an attention mechanism and a quantile regression forest, realizing high-precision prediction and uncertainty modeling of wind speed, solar irradiation and load, and constructing a typical day scene set; 2, constructing a two-stage scheduling model fusing epsilon-constraint multi-objective optimization and opportunity constraint mixed integer programming, optimizing and adjusting margin day ahead, and rolling and correcting a scheduling path within the day; 3, three physical correction mechanisms of wind power air density correction, photovoltaic temperature response and hydroelectric evaporation-water level coupling are provided for extreme high-temperature disturbance; and 4, a prediction-optimization-feedback-correction closed-loop process is integrated and constructed, and the stability and response toughness of the system under extreme climate are improved. The method has the beneficial effect that the prediction precision, the scheduling flexibility and the operation toughness of the system under the extreme climate are remarkably improved.

Systems, devices, and methods are provided for training and / or inferencing capacity planning using a machine learning model. A first time series may be provided as an input to a machine learning model, which may be an Auto Regressive Integrated Moving Average (ARIMA)-based forecasting model. The machine learning model may be trained solve a conditional maximum likelihood problem by performing quantile regression. The machine learning model may forecast one or more innovations using Monte-Carlo simulations. The machine learning model may generate, as an output, a value that corresponds to an amount of computing resources that is predicted, over a second time series, to be sufficient to satisfy a threshold level of availability or quality.

The invention relates to the technical field of equipment life prediction, and provides an equipment life prediction method based on a mixed attention enhancement time sequence convolutional network, which comprises the following steps of: preprocessing original test data, extracting 10 types of time domain statistical characteristics from the preprocessed original test data, screening high-importance feature data as model input through a random forest algorithm; a life prediction model is constructed, an encoder adopts a stacked expansion causal convolutional layer and a self-attention layer, and a decoder fuses historical features and exogenous variables through cross attention; training a life prediction model by using a mixed attention enhancement time sequence convolutional network, wherein a composite loss function synchronously optimizes point prediction and multi-quantile regression loss; and inputting sensor data collected in real time into the trained model, outputting a prediction result, and generating a 95% confidence interval based on nonparametric probability prediction. The overall reliability and accuracy of equipment life prediction can be effectively improved.

The invention discloses a medical institution dynamic risk early warning method and device and a storage medium. Relates to the technical field of smart medical. According to the method, equipment parameters are collected in real time, and load self-adaptive dynamic threshold early warning is realized based on a quantile regression forest model; quantifying an influence score of the associated equipment by using a pre-constructed knowledge graph; and fault injection simulation is carried out by means of a digital twin model, the root cause is verified by comparing simulation data with real data, and finally accurate early warning information is pushed to a person in charge. According to the method, the problems of high false alarm and missing alarm rate and difficulty in multi-alarm root cause positioning of traditional fixed threshold early warning are solved, and accurate and automatic fault diagnosis and early warning are realized.

The invention discloses a fuel cell performance degradation prediction method based on deep learning, and the method comprises the steps: collecting the historical operation data of a fuel cell, carrying out the preprocessing of the data through a simulated annealing isolation forest algorithm and a Gaussian filtering algorithm, carrying out the feature selection through employing SHAP-TCN, extracting key information, and carrying out the prediction of the performance degradation of the fuel cell. According to the method, the prediction interval of performance degradation is constructed in combination with a DeformamleTST deep learning model and a quantile regression method, so that uncertainty in the prediction process is comprehensively captured, a partially enhanced optimizer algorithm is further improved through an adaptive regularization weighting method, and an improved IPRO is obtained to perform hyper-parameter tuning on the model. According to the method, the residual life prediction precision is improved, the problem of uncertainty in a traditional method is effectively solved, the method has high engineering application value, and theoretical basis and technical support are provided for predictive maintenance and management of the fuel cell.

The invention relates to the technical field of network monitoring, in particular to a computer network data flow monitoring system and method, and the system comprises a data flow collection module, a protocol layer analysis module, a time sequence feature extraction module, a defect compensation reconstruction module and a monitoring result output module. According to the method, a continuous difference track can be formed by extracting protocol fields and marking change time points in data stream acquisition, a cross-layer synchronization index is generated by calculating the interval and the sequence of adjacent fields, so that a cross-layer relationship is quantized in a dynamic process, quantile regression is adopted in feature processing to unify a numerical interval, and the accuracy of the cross-layer relationship is improved. Compared with the prior art, the method guarantees the comparability under different time scales, can rapidly identify an abnormal section when cross-layer synchronization is broken, deduces a potential mode in combination with feature values before and after abnormity, reconstructs defect information in a probability mode, enables the result to have the abnormity recognition precision and the flow monitoring integrity, and improves the network management efficiency and safety.

The invention discloses an environmental benefit and resource load evaluation method for an urban sewage treatment plant, and belongs to the field of sustainable evaluation. The implementation method comprises the following steps: training a random forest model by taking pollutant effluent concentration, energy consumption intensity and medicament consumption intensity of a national town sewage treatment plant as output variables and taking other historical operation information as input variables; on the basis, quantile regression is used for calculating quantiles of actual values of three operation indexes of each factory under the original processing condition; constructing an environmental benefit evaluation index system represented by pollutant removal efficiency and a resource load evaluation system represented by consumption efficiency of resources such as energy and chemicals; through obtaining environmental benefit and resource load grading benchmark values of the town sewage treatment plant, core functions and necessary input of each plant are accurately quantified, and real environmental contribution of the town sewage treatment industry is clarified, so that evaluation of sustainability of the town sewage treatment plant is more objective and fair, and sustainable development of urban environment infrastructures is facilitated.

The invention discloses a risk indicator construction method and system for new energy output prediction, and belongs to the technical field of data processing and power system management, and the method comprises the steps: obtaining the preprocessing data of a plurality of new energy stations, carrying out the spatial-temporal feature analysis, and generating a spatial-temporal feature set; obtaining meteorological prediction data, and performing medium and long term output prediction through an extreme gradient boosting tree based on the space-time feature set; comparing the output prediction result with historical real output data to obtain a prediction error, and performing probability coupling modeling and quantile regression prediction in combination with a meteorological element evolution sequence to generate a dynamic quantile value; and determining a dynamic risk interval, and constructing a graded early warning index. According to the method, probabilistic coupling modeling and quantile regression prediction are adopted, prediction errors are deeply associated with dynamic evolution of meteorological elements, predicted uncertain risks can be quantized and graded, and decision support is provided for optimal scheduling and risk management of a power grid.

The invention discloses a photovoltaic power prediction method based on an adaptive modified quantile regression neural network. Feature extraction is realized by constructing a double-flow hybrid neural network so as to improve prediction accuracy, a branch, combined with a multi-head attention mechanism, of the convolutional neural network is responsible for extracting long-term features, a branch of a bidirectional gating circulation unit focuses on identifying short-term fluctuation, and the double-flow hybrid neural network is combined with quantile regression. In order to solve the problems of quantile crossing and non-differentiable zero point of a loss function, a self-adaptive correction marble loss function is provided, and smooth function optimization is introduced to ensure monotone increasing of predicted quantiles and whole-domain differentiable of the loss function. According to the method, point prediction, interval prediction and probability density prediction can be realized, the prediction effect is verified through a multi-dimensional evaluation index, potential information of photovoltaic power is fully mined, and the method has practical engineering application value.

The invention relates to the technical field of energy consumption interval prediction, and particularly discloses a building energy consumption interval prediction method, system and equipment, and the method comprises the steps: collecting historical energy consumption data, meteorological data, building characteristic data and use mode data of a target building, and carrying out the hierarchical processing, thereby obtaining a hierarchical data set; performing wavelet transform decomposition on the historical energy consumption data, and decomposing the energy consumption time sequence into a trend term, a periodic term and a random term to obtain multi-scale decomposition features; constructing a feature evaluation model, calculating a contribution degree and dynamically allocating weights to obtain a weighted feature set; constructing an integrated prediction framework based on the weighted feature set, and generating a prediction interval through quantile regression; and performing adaptive adjustment according to the historical prediction deviation and the environmental factor change to obtain a final prediction interval. Through multi-scale feature processing, dynamic weight optimization and prediction interval adaptive calibration, the accuracy and reliability of the prediction interval are improved, and more accurate support is provided for building energy management.

The invention discloses a QR-Transform model-based wind power supply guarantee probability prediction method. The method comprises the following steps of: based on a Transform model architecture, fusing a multi-head attention mechanism and a quantile regression layer to establish a wind power probability prediction model; the model is trained through Pinball loss functions under different quantile levels, corresponding loss values are adopted to evaluate the performance of the model, and a multi-quantile prediction model is output; inputting gridding wind speed and wind direction features to a multi-quantile prediction model to obtain a wind power probability prediction result, constructing wind power discrete probability distribution, performing fitting by using a pChip interpolation method, and performing stacking according to time scales to obtain a multi-time scale cumulative distribution function set; combining a power grid power generation plan and a typical operation scene, determining a guarantee power section threshold of each cluster, constructing a power supply guarantee demand section, and calculating a cumulative probability, namely a power supply guarantee probability, of the cumulative distribution function curve at the section; and a power supply guarantee probability prediction evaluation index system is constructed based on an expected correction error ECE, a Brier score and a Pinball loss function, and comprehensive evaluation of a prediction result is realized.

The invention relates to the technical field of chemistry and material science, in particular to a regenerant aging failure behavior identification method and system based on differential spectrum extraction. The method comprises the following steps: acquiring infrared spectrum data of a regenerated asphalt sample and an aged asphalt sample at the same stage; carrying out pretreatment on the infrared spectrum data; constructing a characteristic difference spectrum library of the regenerant; extracting a characteristic peak area variation; and obtaining an identification result of the regenerant aging failure behavior by establishing a correlation model of the characteristic peak area variation and the macroscopic performance index. According to the method, high-sensitivity extraction of 3% low-dosage regenerant signals is realized by combining adaptive morphology-quantile regression baseline correction, peak entropy weighted normalization and differential spectrum data enhancement algorithms; a Gaussian-Lorentz mixed peak dividing model and an aging response index model are adopted, microscopic functional group change and macroscopic performance are associated, the aging process of the regenerant is accurately tracked, and a universal technical framework is provided for failure judgment and performance optimization of the regenerant.