178 results about "Prediction interval" patented technology

The invention provides a mining area surrounding soil heavy metal spatial distribution inversion method based on hyperspectral data, and the method comprises the steps: processing multi-source monitoring data of mining area surrounding soil, and obtaining a consistent reflectivity data set; performing soil spectrum purification based on the consistent reflectivity data set to obtain a pure soil signal; spectrum key features are screened out from the pure soil signals; obtaining a modeling data set in combination with the spectrum key features and the heavy metal concentration labels so as to construct a multi-task inversion model, and outputting each metal prediction interval and an over-standard risk probability graph; and outputting a multi-layer package based on the model, performing global and local interpretation and mechanism verification, and generating an interpretation report and traceable evidence. According to the method, full-link unification and purification can be achieved, domain deviation and mixed pollution are remarkably reduced, the accuracy and interpretability of feature screening can be considered, and the model generalization ability, prediction accuracy and space credibility are improved.

The invention discloses a power load prediction method, and the method comprises the steps: firstly solving an extreme event data sparsity problem through a generative adversarial network, and constructing an event time sequence library through a time sequence anomaly detection algorithm; then analyzing the causal relationship between the event and the load by applying a causal discovery algorithm, and converting prediction output into probability distribution by adopting a Bayesian neural network to quantify uncertainty; constructing a prediction model triggered by an event, and generating a multi-time scale probability prediction interval; and finally, generating a multi-scene prediction result through Monte Carlo simulation, quantifying the system recovery capability in combination with a toughness index, and integrating the system recovery capability to a decision support system to generate a risk response scheme. According to the method, the accuracy and robustness of load prediction under the extreme climate are remarkably improved, full-chain risk insight from early warning to recovery is realized, and prospective decision support is provided for safe operation of a power system.

The invention relates to the technical field of industrial equipment state monitoring and fault diagnosis, in particular to a trend fault prediction method based on cooperation of a dynamic mode and a threshold value. According to the method, a theoretical prediction interval dynamically changing along with a load is generated in real time by establishing nonlinear mapping between working conditions and key parameters, parameter drift interference caused by working condition fluctuation is effectively eliminated, a real-time health baseline of equipment is quantified in combination with maintenance records, and the width of an early warning threshold value is cooperatively adjusted according to feature similarity and the health level. Self-adaptive monitoring of different aging stages of the whole life cycle is realized, mode matching is performed by utilizing multi-dimensional feature vectors and fusing physical field information, deviation severity and form similarity are comprehensively evaluated through fuzzy reasoning, abnormity is locked in advance according to high feature goodness of fit when a numerical value does not seriously exceed a limit, and a real-time monitoring result is obtained. Early weak symptoms are accurately captured, abnormal sources are output, and the diagnosis precision under variable working conditions is remarkably improved.

The invention relates to the technical field of civil engineering, and discloses a road subgrade settlement prediction method and system based on multi-source data. The method comprises the following steps: acquiring and correcting a precipitation sequence and roadbed soil moisture distribution data; analyzing rainfall accumulation characteristics and carrying out penetration risk classification, thereby extracting a penetration depth estimation value, and utilizing finite element analysis to simulate a soil body saturation state change trend; quantifying a soil body supporting capacity reduction range, constructing and calibrating a settlement initiation probability calculation model, and obtaining settlement probability distribution; identifying a high-risk evolution area, integrating path weights and accumulated influence factors, and generating a settlement prediction interval subjected to multi-dimensional verification; and fusing the prediction interval and actual roadbed structure data through a geographic information system to generate a comprehensive evaluation report. According to the method, the whole-process accurate evaluation of the roadbed settlement risk from multi-source perception, mechanism simulation to space prediction is realized, and the accuracy and timeliness of the roadbed settlement risk prediction and the pertinence of engineering maintenance are improved.

The invention relates to a power fee accounting method based on intelligent management, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data, carrying out the preprocessing, and carrying out the collection, data standardization, time calibration and automatic tagging of the multi-source heterogeneous data, and obtaining a standard structured data stream; s2, constructing a dynamic accounting rule data set based on the standard structured data stream and transaction data, market rules, contract terms, regions and type price standards; s3, constructing an intelligent prediction model based on LSTM and ProphetAI models, automatically detecting reasonability and correcting a prediction interval according to the dynamic accounting rule data set, and obtaining a pre-accounting result set; s4, according to the pre-checking result set, in combination with the price standard and the amount fee strategy, configuring subsidy and refunding policies and parameter processing, automatically matching a settlement unit and a settlement rule, and flexibly applying different checking templates according to distributed, direct-purchase and non-direct-purchase power plants to obtain a checking intermediate table; and S5, generating a final settlement packet according to the accounting intermediate table and the user configuration parameters.

The invention discloses an automatic driving vehicle risk assessment method and device, electronic equipment, a readable storage medium and a computer program product. The method comprises the following steps: collecting historical motion state information and road structure information of a vehicle and surrounding vehicles; fusing the historical motion state information to obtain a fused context feature vector; performing trajectory prediction according to a pre-constructed vehicle trajectory prediction module and the context feature vector to obtain a surrounding vehicle prediction trajectory and a self-vehicle prediction trajectory; according to the surrounding vehicle prediction trajectory, the own vehicle prediction trajectory and the road structure information, calculating a quantitative perception risk time sequence of preset look-ahead time of the own vehicle in the prediction interval; and performing fusion perception risk calculation according to the quantitative perception risk time sequence to obtain a potential risk assessment result of the vehicle in the driving scene. According to the method, the problem of inaccurate evaluation result caused by difficult parameter adjustment in a multi-vehicle interaction scene of an existing risk evaluation method can be solved.

The invention discloses an intelligent fusion terminal power quality monitoring evaluation data processing method and system. The method comprises the following steps: dividing a plurality of intelligent fusion terminals into consensus evaluation groups according to a power grid topological structure, and allocating reputation values to the terminals; and each terminal periodically measures the electric energy quality parameter and generates a prediction interval of the group consensus value in the current evaluation period. And then, according to the actual measurement value and the reputation value of each terminal, a group consensus value is determined by calculating a weighted cumulative weight. And dynamically adjusting the reputation value of each terminal by comparing the group consensus value with the prediction interval of each terminal. And finally, outputting the group consensus value and the updated reputation value, and generating an electric energy quality comprehensive evaluation result based on the group consensus value. Meanwhile, each terminal adaptively updates a measurement error compensation model in the terminal according to the deviation between a self-measurement value and a group consensus value. According to the invention, the credibility of the power quality monitoring data and the accuracy of the evaluation result are effectively improved.

According to the data-driven electricity-to-water coefficient composite model and the prediction method provided by the invention, the spatial interpolation model, the neural network model and the regionalization prediction model are operated in parallel and are subjected to comparative analysis to form the composite model, so that the composite model can adapt to different characteristics of data, the prediction precision and generalization ability are remarkably improved, and the prediction efficiency is improved. The method is suitable for estimating the water conversion coefficient by electricity in different scenes. Wherein the EPC-STIN model (neural network class) and the regional prediction model are innovative architectures of converting electricity into water prediction models. Besides, a plurality of evaluation indexes are adopted, and the prediction result of the composite model is comprehensively evaluated through a comprehensive evaluation system, so that the prediction model with the optimal accuracy is output for different data sets, and it is ensured that a comprehensive score can reflect the balanced performance of the model in multiple aspects such as error accuracy and prediction interval reliability. And the scientificity of model selection is improved. Therefore, compared with the prior art, a more comprehensive evaluation basis is provided for electricity-to-water coefficient prediction, a user is helped to scientifically quantify the uncertainty of a prediction result, and the credibility of decision making is improved.

The invention discloses a new energy uncertainty interval fine modeling method based on ELM-Copula. Comprising the following steps: 1) data reading and preprocessing: obtaining a power prediction value and an actual output value by reading historical operation data of a new energy electric field, performing kernel density estimation on the per-unit power prediction value and the actual output value, and calculating an error absolute value according to the per-unit power prediction value and the actual output value; 2) constructing a dynamic Copula function model; 3) measuring goodness of fit of the model; 4) calculating a confidence interval of a prediction error, analyzing uncertainty of power generation power prediction, and giving a confidence interval of a prediction value; according to the method, a multi-type dynamic Copula model is introduced to construct a dynamic dependency structure between a prediction error and power, the ELM is applied to a post-processing stage of a dynamic Copula prediction interval, a correction coefficient is generated by learning historical deviation characteristics, and an original interval is shrunk, so that the prediction precision and practicability are improved on the premise that the coverage rate is not reduced.

The invention relates to the technical field of video compression, in particular to an AI-based video compression platform, which comprises a key target detection module, an image region division module, a coding level setting module, a parameter adjustment and analysis module and a coding result integration module. According to the method, key region extraction is completed by collecting pixel color combination and contour change information in a video frame image, background and non-background region boundaries are delimited, a region attribute labeling result is constructed, a region distribution classification relation mapping coding level is established, and reference frame and prediction interval configuration is extracted. Real-time adjustment of compression levels is realized by performing normalized combination analysis on textures and motion change trends of different regions, and region fragments under different compression levels are recombined and subjected to unified code stream processing, so that coding resources can be dynamically allocated on the basis of accurately identifying contents in a compression process; the problems of inaccurate target positioning, fixed compression configuration, delayed content response and the like in an existing compression system are effectively solved.

The invention discloses a video code rate dynamic allocation compression method based on content complexity prediction, which comprises the following steps of: S1, acquiring a video frame sequence, and preprocessing; s2, inputting the frame-level feature tensor into a gated residual convolutional network, and outputting a complexity prediction sequence; s3, constructing a frame priority queue, and calculating the complexity jump amplitude between adjacent frames; s4, a compression area is divided, and a corresponding code rate resource scale factor is allocated; s5, configuring a reference frame structure, a prediction interval and an initial quantization step size for each compression region, and calculating a region target bit number; s6, distributing regional bits to each frame in a compression coding process, and dynamically adjusting a frame-level quantization parameter and an entropy coding strategy; and S7, after compression is completed, reversely updating convolution prediction network parameters through bit distribution errors. According to the invention, fine code rate dynamic allocation and adaptive compression control based on content complexity are realized, and the video compression quality and bit utilization efficiency are effectively improved.

The invention discloses an interval generation and probability correction method and system for new energy power prediction, and belongs to the technical field of power system operation and control, and the method comprises the steps: generating a multi-dimensional environment feature code through numerical weather forecast data, and calling a basic point prediction model to obtain a point prediction result and basic probability distribution; inputting the multi-dimensional environment feature code and the point prediction result into a dual-channel dynamic interval generator to form a preliminary prediction interval, calculating a short-term error sequence based on the actual power of new energy power generation and historical prediction data, analyzing the trend characteristics of the short-term error sequence, and correcting the basic probability distribution according to the trend characteristics; and extracting a probability verification signal from the corrected probability distribution, feeding back the probability verification signal to an interval generator, carrying out optimization adjustment on the preliminary prediction interval, and outputting an optimized prediction interval. According to the technical scheme, the preliminary interval is generated by adopting a dual-channel mechanism, and feedback correction and closed-loop optimization are performed in combination with the short-term error trend, so that the adaptive capacity, accuracy and reliability of the prediction interval can be improved.

The invention relates to the technical field of intelligent traffic system (ITS) and urban public traffic scheduling control, in particular to an intelligent bus scheduling method based on multi-source data fusion, which comprises the following steps: S1, a central scheduling platform receives and stores multi-source data; s2, obtaining a fusion feature sequence; s3, sending to a calibrated demand prediction model to obtain a point prediction and demand interval; s4, constructing a transport capacity interval with an upper bound and a lower bound based on the vehicle availability, the driver shift, the maximum passenger capacity of the single vehicle and the road section speed distribution; s5, calculating overlapping ratio = intersection / prediction interval length; when the vehicle number is lower than the threshold value, distributed robust multi-target scheduling optimization of an ambiguity set defined by the Wasserstein radius is solved, the departure interval and the vehicle distribution number are updated, and the target is that the vehicle waiting time condition is weighted in a risk value and an empty driving rate, and the vehicle number, the employees, the departure interval and the insertable control point constraint are met; and S6, issuing an instruction and executing according to a vehicle positioning and control point arrival event. And unified modeling and robust triggering in the same space-time key interval are realized.

The invention discloses a multi-step time sequence prediction method and system based on double-segmentation conformal prediction. The method comprises the following steps: acquiring new input data; distributing the new input data to a corresponding clustering cluster to obtain a clustering result; extracting information from the recorder according to a clustering result to construct a prediction interval; adjusting the corresponding content according to the prediction interval; wherein a similar trend sequence is vertically classified and clustered, errors of adjacent time steps are horizontally and dynamically combined to optimize window division, over-estimation and under-estimation errors are asymmetrically processed to construct a precise confidence interval, the precise confidence interval is stored in the recorder, and an error set is dynamically updated. By implementing the method provided by the invention, the defects in the prior art can be overcome through a two-dimensional segmentation mechanism, more accurate uncertainty quantization is realized, and the adaptability and accuracy of multi-step time sequence prediction are improved.

The invention discloses an expressway toll and traffic flow prediction method based on a deep neural network, and the method comprises the following steps: collecting comprehensive traffic data, carrying out the data standardization processing, and generating structured time series data; classifying the time sequence data to obtain nodal time sequence data; extracting operation characteristics according to a set time window to obtain model input data; a deep neural network is constructed and calculation is carried out, and a prediction result is output; based on the prediction result, generating the income prediction of the traffic flow and the road fee in the future target time period, and outputting the initial quantile information corresponding to the prediction value at the same time; training the deep neural network to obtain a prediction model; a prediction model is adopted, sequential Mondrian configuration prediction is used, and a prediction interval is output; and writing the observation data and the prediction result into the calibration record of the corresponding group, and outputting the calibrated prediction result of the next prediction period. According to the invention, highway toll and traffic flow prediction is realized.

The invention relates to the technical field of hydroelectric generating set operation monitoring, in particular to a hydroelectric generating set vibration signal abnormity early warning method based on iTransform-Bootstrap, and the method comprises the following steps: collecting vibration signals of a hydroelectric generating set in a healthy operation stage, and sequentially carrying out the extreme value elimination, normalization and denoising preprocessing; an iTransform model is trained, vibration signal single-point prediction is realized through a dimensionality inversion attention layer of the model, and a residual sequence of a predicted value and a true value is calculated at the same time; processing the residual error sequence by adopting a Bootstrap nonparametric resampling method, and constructing a self-adaptive prediction interval; vibration signals collected in real time are preprocessed and then compared with the self-adaptive prediction interval, and if the real-time signals exceed the boundary, it is judged that the signals are abnormal and early warning is given out. Experimental verification shows that the prediction interval coverage rate reaches 100%, the average interval width is remarkably reduced, fault symptoms can be accurately recognized, the early warning reliability is effectively improved, and technical support is provided for safe operation and preventive maintenance of the hydropower station.

The invention discloses a high-temperature component reliability interval assessment method, device, equipment, medium and product, and relates to the technical field of reliability assessment, the method comprises the following steps: carrying out probability damage assessment on a high-temperature component to obtain stress and strain probability distribution of a dangerous point of the high-temperature component; constructing and training a plurality of physical information neural network models by taking the creep fatigue test conditions of the high-temperature part as input and the test life as output; according to the life prediction result of each physical information neural network model, constructing a life prediction interval and basic trust distribution of each model; carrying out fusion through a Dempster method to obtain a fusion life prediction interval; according to the fused life prediction interval and the stress and strain probability distribution, a reliability interval evaluation result of the life of the high-temperature component is obtained, the model uncertainty brought by the selected model is considered, and the method has the advantages of being high in prediction precision, high in engineering applicability and good in physical interpretation.

The invention discloses a multi-component gas parameter storage control method and system, and belongs to the technical field of automatic control, and the method comprises the steps: collecting initial environment parameters, obtaining a stacking form image of a stored material, recognizing stacking feature parameters, constructing a gradient pre-judgment model to obtain a gradient pre-judgment interval, and marking a high-risk region; constructing a layered node monitoring network, collecting local parameters of each monitoring node in real time, and identifying an abnormal deviation region in combination with a total environment mean value; coupling correlation is constructed, predictive gradient intervention is carried out, an optimal airflow path is calculated, a gas supply strategy is adjusted, a dynamic impedance adaptation strategy is utilized, a high-impedance area is identified, multi-target optimization is utilized, differential regulation is carried out, and a targeted regulation strategy is generated; and setting an update time interval and calculating a pre-judgment deviation rate to carry out deviation correction, thereby ensuring that local parameters are stabilized in a safe range, avoiding the out-of-control problem caused by spatial heterogeneity and dynamic coupling, and ensuring the stability of the whole storage effect.

The invention provides a joint robot singular system control method based on an interval type-2 fuzzy sliding mode, and the method comprises the steps: sensing and quantifying the dynamic drift of a singular region caused by the change of a tail end load in real time, and constructing a dynamic singular interval; inputting the dynamic singular interval into an interval type-2 fuzzy system, and outputting a disturbance prediction interval and a nominal disturbance prediction quantity; generating a dynamically adjusted sliding mode surface according to the uncertainty reflected by the disturbance prediction interval; fusing nominal dynamics feed-forward compensation, correction feedback based on a sliding mode surface and feed-forward disturbance compensation based on nominal disturbance predictive quantity to synthesize a final joint control moment; the final joint control torque is executed, the actual response is collected, closed-loop evaluation is formed, and online correction is conducted on the dynamic singular interval according to the closed-loop performance. The method can sense and actively adapt to dynamic drifting of the singular area in real time so that it can be guaranteed that when the robot passes through the time-varying singular area under the complex working condition, the dynamic drifting of the time-varying singular area can be accurately detected. And high-precision and high-stability trajectory tracking can still be realized.

The application relates to the technical field of charge and discharge control, in particular to a BBU charge and discharge state prediction and thermal management system based on AI. The system comprises the following steps: collecting basic data to form a feature vector; obtaining coupling heat release between the basic data based on energy conservation; constructing a sliding window, predicting the time in the prediction interval through the data in the sliding window to obtain the predicted values of different data, and then adjusting the loss function of the coupling heat release obtained based on the predicted values to obtain the predicted values of the data types; determining the comprehensive risk based on the predicted values, constructing a global risk assessment for each position; and distributing the total cooling resources of the battery units based on the global risk assessment to obtain the cooling resources of each space point, thereby completing thermal management. The application improves the prediction accuracy and the thermal management efficiency.

The application relates to a failure prediction maintenance method and device with a preventable strategy, and relates to the field of equipment prediction and maintenance technology. In view of the problems in the prior art that a traditional two-state reliability model is difficult to accurately describe the degradation degree, failure condition and maintenance state of a system in a service life, the calculation process is relatively single, and the result error is relatively large, the technical scheme is as follows: the failure prediction maintenance method with the preventable strategy comprises the following steps: collecting a probability distribution obeyed by the residual life of a system; collecting maintenance time and maintenance cost; establishing an optimization model; iteratively processing the model to obtain an optimal failure prediction interval and a residual life threshold value; when the optimal failure prediction interval is reached, the residual life of the system is predicted, the residual life is compared with the threshold value, and if the residual life is lower than the threshold value, the system is subjected to corrective maintenance, and if the residual life is higher than the threshold value, the system is subjected to preventive maintenance. The application is suitable for application in preventive maintenance work, and establishes a new research basis for the research of preventive maintenance work.

This invention discloses a method for predicting robot motion under time-varying delay conditions. The method determines the number of secondary predictors and their gain parameters based on the maximum time delay, the robot's initial joint position, and the initial joint velocity. The time-varying prediction interval of each secondary predictor is determined by the time-varying delay magnitude. The robot's state from time-delayed motion to the current motion interval is divided into motion sub-states, the same number as the number of secondary predictors. Secondary predictors are constructed, the same number as the number of motion sub-states. The secondary predictors are connected in series, with the time-varying delay robot joint position and velocity signals as input to the first secondary predictor, and the predicted values ​​output by the previous secondary predictor as input to each subsequent secondary predictor. The last secondary predictor outputs the predicted values ​​of the robot's actual joint position and actual joint velocity. This invention offers high prediction accuracy, wide applicability, ease of engineering application and promotion, and contributes to the development of robotics technology.

The invention belongs to the field of vehicle control, and particularly relates to a vehicle longitudinal stability control method based on optimal sliding rate fusion compensation prediction. According to the method, the mapping relation between the utilization adhesion coefficient and the sliding rate in the vehicle driving process is analyzed in combination with road test data of a vehicle under different road conditions in advance, and the change rule of the error of the optimal sliding rate and the maximum utilization adhesion coefficient of the vehicle under an interpolation method along with the real-time slope and the prediction interval is fitted. Then collecting the wheel speed information of the current vehicle, and predicting the optimal sliding rate and the maximum utilization adhesion coefficient of the current vehicle by adopting an interpolation method; and after slope-based correction and prediction area-based correction and weighted fusion are carried out on the torque increment, a designed cost function is combined, and the acceleration and the sliding rate of the vehicle can follow the expected torque increment and are executed by adopting a catfish Rhododendron optimization algorithm to solve and execute the torque increment. According to the method, the problems of insufficient prediction precision and relatively large delay of the optimal sliding rate in the longitudinal stability control of the existing electric vehicle are solved.

The invention discloses a TFT-LCD residual life probability prediction method fusing physical features and Gaussian process regression, and the method comprises the steps: S1, constructing a multi-source feature data set which comprises performance monitoring data, structure response feature data and environment stress data; s2, constructing a Gaussian process regression model with time and environmental stress as input and performance degradation amount as output, and initializing hyper-parameters of the model by using the multi-source feature data set; s3, adopting variational Bayesian inference to realize online updating and uncertainty quantification of model hyper-parameters; s4, based on the updated Gaussian process regression degradation model, obtaining predicted distribution of performance degradation values at future time points; and S5, based on the prediction distribution, generating a probability density function of the residual life and prediction intervals under different confidence levels through Monte Carlo simulation. The method provided by the invention overcomes the defect of insufficient prediction precision of a traditional method under a small sample condition, and can provide individualized life prediction with a confidence interval.

The invention discloses a method for predicting the machining limit of a free pipe bending device based on multi-body implicit interference inference. Comprising the following steps: firstly, obtaining the overall representation of a to-be-tested free elbow device, obtaining respective joint prediction intervals of curvature and torsion of a to-be-processed elbow by using an operator fusion network, and judging whether interference occurs between the to-be-processed elbow and the to-be-tested free elbow device under each rebound axis; finally, whether interference occurs between the bent pipe to be processed and the free bent pipe device to be detected or not is obtained. And whether interference occurs between the dies is determined by using the symbol distance value. The method has excellent generalization performance, has reliable pipe section-mold interference inference capability, and can realize more comprehensive processing limit prediction of the free pipe bending device.

The invention discloses a routing request method and device, electronic equipment and a storage medium. The method comprises the following steps: in response to a service request received in a current time window, acquiring flow index time sequence data of a historical service request and a service layer semantic feature and a network layer flow index feature of the service request in the current time window; determining a request traffic prediction result of the prediction interval according to the traffic index time sequence data of the historical service request; wherein the prediction interval refers to an interval of a preset duration after the current time window; determining a target computing node according to a request traffic prediction result of the prediction interval, a service layer semantic feature of the service request and a network layer traffic index feature; and routing the service request of the current time window according to the target computing node. According to the method, the limitation of static routing of a traditional gateway is broken through, intelligent upgrading of service semantic perception, future flow pre-judgment and dynamic resource allocation is realized, and the high-concurrency and low-delay requirements of a financial scene are accurately met.

The application provides a hydropower station reservoir flow prediction and scheduling optimization method, which separates different scale hydrological characteristics through multi-time scale processing of multi-source data, extracts reservoir inflow characteristics by cooperating with gray correlation analysis, avoids time structure aliasing, and makes the hydrological law clearer; the prediction interval is corrected by the deviation of the observed value of the reservoir inflow, the prediction result is dynamically adjusted with the actual hydrology, the error of the initial prediction is made up, the prediction accuracy is twice improved, and the problem of inaccurate reservoir inflow prediction is solved. In addition, the corrected prediction interval is used as an uncertainty set, a double-layer robust optimization model of safety margin constraint is combined with a confidence parameter adjustment, scheduling safety and benefit are balanced; different risk level strategies are generated by weighting and combining scheduling decision variables through a risk preference parameter, and the scientificity and stability of reservoir scheduling are improved.

The application relates to a multi-source covariate irrigation load short-period prediction method, a device and a medium, and relates to the fields of power system load prediction and intelligent analysis of agricultural irrigation energy. The application is to solve the problems that the existing irrigation load short-period prediction method has limited modeling capability for exogenous factors, cannot effectively utilize user difference information, and is prone to lag or drift in prediction. The application performs correlation evaluation on candidate multi-source covariates and historical global irrigation load, and then selects effective covariates according to the evaluation results; extracts a user feature vector based on the effective covariates; constructs a historical input sequence by combining the historical global irrigation load and the effective covariates, and constructs a future known covariate by combining the known future prior information in the prediction interval; constructs input data by combining the user feature vector, the historical input sequence and the future known covariate; inputs the input data into a load prediction model adopting a TimeXer framework, and outputs an irrigation load prediction result in the prediction interval.

The invention provides a construction deformation prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of data prediction, and the method comprises the steps: obtaining historical working condition parameters of a subway tunnel construction process; constructing a regularization item related to a sample point feature space; determining a decision tree splitting criterion based on a regularization item, recursively dividing each feature in the sample point feature space into non-overlapping hyperrectangular regions, and constructing a decision tree; calculating a posterior probability distribution parameter of each leaf node in the decision tree based on a Bayesian inference updating rule; defining a structure risk penalty function in combination with the posterior probability distribution parameters to perform post pruning on the decision tree to obtain an optimal decision tree; real-time geological exploration parameters and real-time shield tunneling machine tunneling parameters in the subway tunnel construction process are collected; and real-time geological exploration parameters and real-time shield tunneling machine tunneling parameters are input into the optimal decision tree, and a monitoring point ground settlement prediction interval is output, so that the deformation prediction accuracy under the complex geological condition is effectively improved.

The application discloses a photovoltaic ultra-short-term probability prediction method based on data decomposition and reconstruction, solves the problems of low prediction accuracy, weak generalization ability, poor prediction interval calibration, insufficient stability across data sets and low calculation efficiency of existing methods, and cannot meet the 5-minute ultra-short-term photovoltaic probability prediction engineering requirements. The method first collects and pre-processes photovoltaic power and meteorological data, uses the ACEEMDAN algorithm to decompose the photovoltaic power into fixed high-frequency and low-frequency dual components through four stages and regularizes; then the corresponding features of the three types of data are extracted through CNN, iTransformer and BiLSTM respectively, and the unified feature representation is obtained after the multi-head attention mechanism fusion, and then the EQN network is input; finally, the improved EQN loss function containing the width penalty term is used to train the model to convergence, and the probability prediction result is output. The method performs excellently in the deterministic and probability evaluation indexes, has strong stability across data sets, and can be directly applied to power grid dispatching and photovoltaic power generation system operation management.