236 results about "Prediction interval" patented technology

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.

The invention relates to the technical field of land resource monitoring, in particular to a land resource dynamic monitoring and early warning method and system based on multi-source remote sensing data fusion, and the method comprises the steps: employing an unmanned plane to periodically collect optical images, SAR echoes and LiDAR point clouds, constructing a ground three-dimensional digital model, and carrying out the land parcel division; performing fusion to form a multi-dimensional feature vector, establishing an LSTM land parcel feature evolution model, and predicting a change rate interval of each feature in a current period based on a historical sequence; constructing a time sequence difference change detection algorithm, calculating a land parcel change rate, and screening potential abnormal land parcels by taking a prediction interval as an anomaly judgment threshold value; a double-branch convolutional neural network is adopted to identify crop states, growth stages and construction violation behaviors, abnormity is judged and determined, and confidence is given; spatial clustering is carried out on determined abnormal land parcels, accurate boundaries are obtained in combination with a three-dimensional model, multi-level early warning information is generated, and the decision-making efficiency and response speed of land resource monitoring are improved.

The invention provides a photovoltaic output prediction method and a distributed optical storage transformer area active / reactive support capability optimization method, and belongs to the technical field of power system automation. Comprising the following steps: constructing a prediction model based on meteorological information, extracting multi-scale features of meteorological data, reducing dimensions through kernel principal component analysis, predicting photovoltaic power through a long-short-term memory network, quantifying the uncertainty of the photovoltaic power, and forming a prediction interval. Based on a photovoltaic power prediction result, a robust nonlinear programming model is established, the sum of absolute values of active / reactive power transmitted to a main power grid by a light storage area is maximized as a target, and linear and nonlinear constraints such as energy storage operation, power balance, equipment capacity and power factors are considered at the same time. The robustness under the photovoltaic output uncertainty is ensured through a budget uncertainty set; the model is solved by adopting a sequential quadratic programming algorithm. According to the method, the photovoltaic power prediction precision is effectively improved, and organic combination of high-precision prediction and robust nonlinear optimization is realized.

The invention discloses a wind power multi-scale decomposition prediction method. At present, single-point prediction is not comprehensive and accurate enough, and cannot adapt to quantitative accurate requirements of a wind power plant and a power grid dispatching mechanism in risk management. The method comprises the following steps of: forming an original wind power sequence from actually acquired wind power data, sequentially performing feature selection and data decomposition processing to form multi-scale modal data, and constructing a depth prediction model according to the multi-scale modal data; a probability prediction interval determination process is completed in the residual error distribution mode depth prediction model through adaptive bandwidth kernel density estimation; after actually obtained wind power data form an original wind power sequence, an initial model is established, feature selection processing is performed on the initial model, that is, weighted marginal contribution is calculated for each feature of the initial model according to all involved feature subsets by using an SHAP algorithm based on a Shapley value in a game theory, and the weighted marginal contribution of each feature of the initial model is calculated; and completing a feature data acquisition process of accurately quantifying interdependence and interaction effect between features.

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 property energy consumption abnormity monitoring method and system based on big data analysis, and relates to the technical field of energy consumption abnormity management. According to the property energy consumption abnormity monitoring method based on big data analysis, in a specified energy consumption monitoring time period, an energy consumption power group of a specified property service area is firstly obtained, and the energy consumption power group is compared with an energy consumption prediction interval to screen energy consumption abnormity; according to the comparison result, energy consumption abnormal data in the abnormal energy consumption time period are repeatedly checked, transient normal fluctuation caused by normal working condition switching of equipment or sudden change of the external environment is eliminated, and repeated checking comprises peak value abnormal detection and recheck verification; and finally, after repeated inspection is completed, association verification is carried out in combination with the abnormal energy consumption feature set. According to the method, a scientific basis is provided for property energy consumption management, accurate positioning of energy consumption abnormal points is facilitated, the problem of abnormal identification misjudgment caused by transient change in energy consumption monitoring in the prior art is effectively solved, and energy conservation, consumption reduction and cost optimization are realized.

The invention provides a video abnormal behavior positioning method and device based on a multi-modal large model, and a medium, and relates to the field of computer vision. The method is applied to computer equipment and comprises the following steps: acquiring a to-be-processed video, and determining a plurality of query videos and a plurality of support videos; obtaining a thinking chain text according to the visual language model; obtaining a first robust feature representation and a second robust feature representation according to a semantic time sequence pyramid encoder; obtaining subtitle text fusion features according to a text encoder; and further performing alignment processing to obtain an alignment similarity matrix so as to determine a prediction interval of the query video, determine an abnormal behavior positioning result of the to-be-processed video and determine a dynamic model fine tuning sample. The video abnormal behavior positioning method and device solve the technical problems that in the prior art, visual information is excessively depended on, deep semantic understanding of the action context and the target relation is lacked, misjudgment is easily generated in the alignment process, the positioning precision is insufficient, and the video abnormal behavior positioning efficiency is low.

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 relates to the technical field of power system automation, and discloses a power distribution network operation risk assessment regulation and control method based on a graph attention network and reinforcement learning, and the method comprises the steps: mapping a topological structure of a power distribution network into a graph structure; constructing a node feature matrix and an adjacent matrix based on the node features and the edge features; inputting the node characteristic matrix and the adjacent matrix into a short-circuit current prediction model based on a graph attention network (GAT), and outputting short-circuit current prediction values of a bus and a line in the power distribution network and corresponding short-circuit current prediction intervals after the distributed power supply is connected to the grid; constructing a reward function aiming at reducing the short-circuit current risk and guaranteeing the load power supply, and training a strategy network and a value function through a SoftActor-Critic algorithm so as to obtain a reinforcement learning model; and collecting the state of the power distribution network in real time in a preset scheduling period to realize dynamic regulation and control of the short-circuit current risk of the power distribution network. According to the method, topology and electrical association is accurately captured, and the prediction precision is improved.

The invention provides a charging remaining time prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an initial temperature, an initial state of charge and an initial charging rate of a target battery at an initial moment; the initial temperature, the initial charging rate and the sum value of the initial charge state and the preset charge state step length are used as input, and the temperature of the target battery at the end point of each prediction interval is predicted through a temperature rise prediction network; the preset state-of-charge step length is used for indicating the state-of-charge variation of the adjacent prediction intervals; determining the charging rate of the target battery in each prediction interval from the mapping relation between the temperature and the charge state of the target battery and the charging rate; the preset charge state step length is compared with the charging rate of the target battery in the prediction interval, and the charging time of the target battery in each prediction interval is determined; and determining the sum value of the charging durations of all the prediction intervals as the charging remaining time of the target battery.

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.

The invention provides a sea attitude motion self-compensation method based on deep learning and model prediction, and the method comprises the steps: constructing a sea ship attitude prediction model based on deep learning, and predicting the attitude change of a ship in a future time period; designing a six-degree-of-freedom platform driving instruction resolving algorithm, and resolving a driving instruction of the six-degree-of-freedom platform according to the posture forecast value of the ship at the future moment; and based on a model prediction technology, designing an attitude compensation control model, and controlling the six-degree-of-freedom platform to realize attitude compensation according to the driving instruction. The prediction attitude compensation error in the prediction interval is used as a model prediction control instruction, a six-degree-of-freedom platform driving instruction most suitable for current instruction compensation is solved by adopting an optimization control algorithm, and accurate prediction of ship attitude change is realized through a deep learning technology. And the six-degree-of-freedom self-stabilization platform is efficiently controlled in combination with a model prediction control algorithm, so that the problems of response lag and insufficient control precision of an offshore self-stabilization platform in the prior art are effectively solved.

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 a photovoltaic power prediction method based on a multi-task neural network and physical constraints, and aims to solve the problem that a traditional prediction model is difficult to give consideration to physical rules and dynamic meteorological characteristics, and the method comprises the steps: carrying out the deep fusion of a data-driven feature extraction module, a physical constraint module and a time reasoning module; a multi-task neural network framework combined with physical information is constructed, and high-precision site-level prediction of photovoltaic power on the time scale of 4 hours and 15 minutes is achieved. Experimental results show that compared with a reference model, the prediction precision of the method is improved by 5.5%-38.6% within the prediction intervals of 4 hours and 15 minutes, the problem of uncertainty of photovoltaic power prediction under meteorological disturbance is effectively solved, and reliable technical support is provided for new energy grid-connected regulation and control.

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 provides a wind power prediction method based on marine meteorological characteristics and wake flow interference, and aims to solve the problem that the existing wind power prediction does not fully consider the thermal and dynamic characteristics of a marine boundary layer, the wake flow interference effect of a fan group and the attenuation of a salt mist environment. According to the method, power prediction is realized by constructing a multi-source marine meteorological characteristic field, a wake flow interference field and an environment attenuation compensation mechanism. Specifically, a meteorological characteristic matrix is generated by combining an integral norm of wave height change, a position and wind direction characteristic matrix is constructed based on a fan position and a prevailing wind direction, wake flow interference intensity is quantified through a Gaussian kernel function, and meanwhile, an attenuation compensation item containing an error function is designed to process a salt spray corrosion effect. Establishing a bimodal wind speed probability distribution model, and combining a space-time adaptive quantile regression output power prediction interval; the method effectively solves the problems of marine meteorological dynamic characterization, wake flow interference quantization and environmental attenuation compensation, and significantly improves the precision and reliability of offshore wind power prediction.

A battery system includes a battery configured to power a load, and a processing system comprising one or more processors. The processing system is configured to determine an electrical characteristic of the battery at a start of a prediction interval, predict a first predicted electrical characteristic of the battery in a first subsection of the prediction interval based at least in part on the electrical characteristic, predict a second predicted electrical characteristic of the battery in a second subsection of the prediction interval based at least in part on the electrical characteristic, the first predicted electrical characteristic, or both, and predict a spike power capability that the battery can support after an end of the prediction interval based at least in part on the second predicted electrical characteristic.

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.