138 results about "Uncertainty quantification" patented technology

This invention discloses a method and system for fitting safety monitoring data based on adaptive hybrid basis functions, belonging to the field of computer-aided data analysis technology. The method achieves fully automatic high-precision curve fitting through a six-layer adaptive architecture: it automatically analyzes the trend complexity, periodicity, noise level, and nonlinearity of the data using multi-dimensional feature recognition technology; it calculates the data complexity score based on the feature analysis results and intelligently selects basis function combinations from an extended basis function library containing 32 sub-functions across 8 categories; it constructs a dynamic weighted hybrid basis function model, achieving adaptive adjustment of model parameters through time-varying weight functions and coupling correction terms; and it employs a multi-model dynamic fusion mechanism, integrating multiple candidate models based on six-dimensional confidence evaluation. This invention innovatively introduces adaptive regularization technology and a hierarchical optimization strategy, effectively balancing fitting accuracy and generalization ability, and possesses advantages such as full automation, strong robustness, and complete uncertainty quantification.

The application belongs to the technical field of material performance prediction and uncertainty analysis, and proposes a beryllium aluminum alloy performance uncertainty quantification method based on Bayesian ensemble learning, which is innovative in constructing and training multiple independent performance prediction models to form the basis of ensemble learning. After the training of each model is completed, a performance prediction value can be output for a new input sample. After obtaining the prediction outputs of multiple independent models, a Bayesian fusion method is used to comprehensively process the prediction results on the probability level to obtain the fused performance prediction distribution; and the complete results of the beryllium aluminum alloy performance prediction are output in a clear, intuitive and convenient engineering application format. The application has the advantages that the robustness and generalization ability of the prediction results are improved, a decision basis is provided for material performance evaluation, and good adaptability is achieved for the case of limited data quantity, and important innovations are achieved in the aspects of material performance uncertainty quantification theory and engineering application.

This invention relates to a test device and verification method for the space-ground consistency of a flexible spacecraft control system based on uncertainty quantification, belonging to the fields of intelligent manufacturing equipment industry and space-ground consistency verification technology. To address the problem of space-ground consistency verification and evaluation for flexible spacecraft, this invention includes an air-floating robot and a flexible component. The air-floating robot simulates the central rigid body of the flexible spacecraft, and the flexible component simulates the deployed solar panels. One end of the flexible component is connected to the upper support column of the air-floating robot, while the other end is free and equipped with a weight. Fiber optic grating sensors are distributed on the flexible component to measure flexible vibrations. A piezoelectric ceramic plate is installed at the end of the flexible component connected to the air-floating robot for driving and controlling the vibration of the flexible component. This invention evaluates the space-ground consistency of the designed flexible spacecraft control test system and, based on uncertainty quantification analysis, provides the impact level of uncertainty sources on space-ground consistency.

The present application belongs to the field of geological exploration and artificial intelligence technology, and discloses a mineral resource prediction method and system based on a Bayesian physical information neural network. The method comprises the following steps: obtaining and preprocessing multi-source geological data of a study area; constructing a Bayesian neural network based on variational inference; constructing a physical constraint loss with a geological source term; performing three-stage progressive training based on a multi-objective loss function; and performing Monte Carlo sampling prediction and uncertainty quantification. The present application has strong physical interpretability: by embedding a steady-state diffusion equation with a geological source term, the model prediction result conforms to the geological law of ore-forming element migration and enrichment, and the source term clearly corresponds to two geological actions of fracture channel and ore-forming parent rock. The present application realizes the organic combination of data driving and physical driving by using multi-source information such as geochemical data, fracture structure, rock mass distribution and known mine point labels.

The application discloses a shield tunnel excavation disturbance data intelligent identification method based on dynamic time sequence prediction, which comprises the following steps: in the case of excavation disturbance, collecting various time sequence data of shield tunnel monitoring in real time, carrying out denoising, interpolation and normalization processing on original data, and inputting time window samples into a DFCM algorithm; calculating a membership matrix based on the distance between samples and cluster centers, and updating the membership in real time, and constructing an LSTM model of the membership vector output by the DFCM clustering algorithm; predicting the membership of the next time step, outputting a predicted difference vector, and determining a difference dynamic threshold value by using an adaptive threshold method, and when the predicted difference exceeds the threshold value, it is judged that the data is disturbed. The method can realize real-time identification and uncertainty quantification of disturbed data in a tunnel structure monitoring environment with strong interference and high noise, improve the accuracy and stability of underground structure health monitoring, and is suitable for urban shield tunnel operation monitoring and maintenance.

The present application relates to the field of industrial software, and provides a chemical process automatic generation method, system and medium. The chemical process automatic generation method comprises the following steps: receiving a natural language description about an industrial process, wherein the natural language description comprises design constraints of the industrial process; starting a multi-agent collaboration system, converting the natural language description into an intermediate representation of the design process through closed-loop interaction among the multi-agents, wherein the multi-agent collaboration system utilizes a semantic entropy index to monitor the logical determinacy of each generation node in the process of generating the design process and triggers a real-time correction mechanism according to the uncertainty quantification result; mapping the intermediate representation into executable instructions of a heterogeneous simulation engine through an adaptation interface to drive the heterogeneous simulation engine to perform physical simulation calculation; and asynchronously extracting the calculation result of the heterogeneous simulation engine to realize closed-loop verification of the design process.

This invention discloses a photovoltaic power generation prediction method based on multidimensional data analysis, specifically relating to the field of new energy power prediction technology. The method includes: multi-source data fusion and field construction: integrating multi-source heterogeneous meteorological data, and generating a spatiotemporally continuous comprehensive meteorological influence factor field through spatiotemporal alignment and dynamic weight fusion; adaptive scene matching prediction: performing multidimensional similarity matching between the current meteorological field sequence and a historical scene database, retrieving similar historical scenes, adaptively selecting and fine-tuning the prediction model online, and generating a preliminary power prediction sequence; and quantified uncertainty output: quantifying three types of uncertainty—input data, model selection, and real-time deviation—and fusing them using a Bayesian model averaging method. This invention improves the quality of prediction input data, enhances the adaptive prediction capability for complex weather, and provides prediction results with quantifiable uncertainty, effectively improving the accuracy and practicality of photovoltaic power prediction.

The application provides an acoustic array adaptive calibration correction system applied to an underwater moving target, relates to the technical field of marine equipment, and comprises a multi-source excitation and environment perception module, an intelligent array perception and diagnosis module, an adaptive position inversion and uncertainty quantification module and a closed-loop calibration execution and self-learning optimization module; the multi-source excitation and environment perception module is responsible for providing a reference signal required for calibration and establishing an association model of environment and array distortion, the intelligent array perception and diagnosis module realizes multi-modal data acquisition, array element health state monitoring and array geometry self-perception, the adaptive position inversion and uncertainty quantification module completes signal processing, position estimation and error quantification propagation, and the closed-loop calibration execution and self-learning optimization module executes a compensation strategy, verifies calibration effect and continuously optimizes system performance through online learning; the system solves the problems that cannot be handled by traditional methods, such as environment time variation, multi-sensor conflict and uncertainty quantification.

The present application relates to machine vision, in particular to a cross-camera target alignment and deduplication counting method based on timing consistency, obtaining a video stream of a camera, establishing a unified time reference system; independently executing target detection and tracking algorithms in each camera, and generating a local trajectory for each target; generating a candidate trajectory pair set according to the number of consecutive tracking frames of the target in a single camera and the time window of the trajectory appearing in different cameras; calculating a matching score for each candidate trajectory pair based on motion behavior consistency; globally optimizing and conflict resolving all candidate trajectory pairs, and outputting a cross-camera associated trajectory pair set; merging the corresponding cross-camera associated trajectory pairs into the same target by using an uncertainty quantification mechanism, directly performing deduplication counting, and simultaneously performing uncertainty marking; the present application can overcome the defect that accurate cross-camera target alignment and deduplication counting cannot be performed in an industrial conveyor belt scene with dynamic characteristics.

The present application relates to the field of environmental science and air pollution control technology, and discloses a method and device for dynamically estimating and quantifying the emission of volatile organic compounds of gas stations, which is based on the data of volatile organic compound emissions of gas stations and multi-source environmental data to construct data sets, input each data set into the corresponding initial prediction model to train the target prediction model, and calculate the single station emission prediction value through the target model; then, based on all single station prediction values, the input data error and model structure uncertainty are integrated and quantified, and finally the total emission of all gas stations and the corresponding uncertainty interval are calculated to realize the accurate prediction of the emission and the systematic quantification of the uncertainty, wherein through the multi-model training and uncertainty quantification, the present application not only realizes the accurate prediction of the emission of volatile organic compounds of gas stations and the calculation of the total emission, but also outputs the total emission uncertainty interval with reliable confidence level and quantifies the fluctuation risk of the prediction result.

This invention belongs to the field of robot localization and relates to an adaptive hierarchical robot localization method and apparatus for degraded scenarios. The method includes: real-time monitoring of the data quality acquired by heterogeneous sensors, mapping the states of the heterogeneous sensors to a unified quantization space, and outputting unique degradation level identifiers D1 to D4; for the D1 environment, uncertainty quantification is performed on feature matching, and the most contributing feature subset is selected; for the D2 environment, the degraded subspace is located through singular value decomposition; for the D3 environment, the expected error of the heterogeneous sensor engine is estimated through a performance prediction network, and continuous confidence weighting is used instead of hard switching; for the D4 environment, a cross-platform generalized pure inertial odometry is provided through a three-level architecture of pre-training-fine-tuning-online adaptation; and historical events are stored as empirical data. This achieves refined differentiation and directional processing of degraded scenarios and constructs an experience-driven closed-loop optimization mechanism.

The present disclosure discloses a synergistic control method and device for emissions, electronic equipment and storage medium. According to the present application, the precise prediction and uncertainty quantification of nitrogen oxide emission concentration are realized through a probability prediction model, and the interpretable emission control rules are extracted and synergistically fused with the preset traditional control strategy to optimize the control logic, thereby improving the control response efficiency under transient operating conditions. Therefore, the technical problem of control instruction lag caused by insufficient calculation efficiency and excessively high load change rate when using model predictive control for transient operating condition control in the prior art can be solved, and the peak breakthrough of nitrogen oxide emission limit value is further triggered. The technical effects of improving the timeliness and accuracy of nitrogen oxide emission control of coal-fired boilers, avoiding emission exceeding the standard, ensuring environmental protection compliance of the unit, and optimizing economic operation indicators are achieved.

The application discloses a goaf coal spontaneous combustion three-dimensional dynamic numerical simulation method during working face advancing, relates to the technical field of coal spontaneous combustion three-dimensional dynamic numerical simulation, and comprises the following steps: based on the region of the coded identification, combining the three-dimensional disturbance offset track and the ventilation reverse distribution characteristics in the structure of the boundary disturbance, generating the boundary form uncertainty description body, and determining the boundary form uncertainty grade under the condition of local collapse of the goaf boundary space structure in the advancing process according to the space offset amplitude, disturbance duration and coupling strength in the boundary form uncertainty description body. The application introduces multi-source disturbance identification, boundary form uncertainty quantification, oxygen diffusion dynamic adjustment and oxygen consumption linkage feedback mechanism, realizes the synchronous update of the oxygen diffusion parameters and the coal oxidation reaction process under the boundary mutation condition, and thus improves the accuracy and reliability of the goaf coal spontaneous combustion three-dimensional dynamic numerical simulation.

The present application belongs to the field of database query optimization task, and relates to an uncertainty quantification query cost estimation method under generalization of cross-database. The uncertainty of cost estimation can be quantified, and the method can be accurately and effectively generalized to the database which has not been learned. The method mainly comprises a cross-database representation module CDR and an uncertainty cost estimation module CEU. The CDR module encodes the minimum set of cross-database query plan features, and uses Tree-LSTM to ignore the database-specific features. Then, the CEU module estimates the cost of a given set of query plans in the database and outputs the estimated uncertainty. Compared with the traditional cost estimation, the method can quantify the uncertainty of cost estimation, and can be accurately and effectively generalized to the database which has not been learned.

This invention belongs to the field of underwater acoustic communication technology and provides a performance simulation verification method, system, and storage medium for underwater acoustic communication systems. The method includes constructing a multi-source heterogeneous input feature space, constructing and training a deep neural network surrogate model in machine learning, generative learning and online inversion of the spatiotemporal dynamic error field, embedded online compensation and closed-loop verification iteration, and uncertainty quantification and robust decision-making. Through the end-to-end collaborative design of multi-source data fusion, machine learning surrogate modeling, spatiotemporal dynamic error field generation, embedded closed-loop simulation, and robust decision-making, the dynamic error field can adapt to the spatiotemporal nonstationarity of the marine environment and communication scenario in real time, accurately generating fine-grained error compensation amounts. The embedded closed-loop mechanism realizes automated iteration of error learning, compensation, and model optimization, continuously improving simulation accuracy without manual intervention and significantly improving verification efficiency.

This invention belongs to the field of unmanned aerial vehicle (UAV) autonomous control technology, and relates to an autonomous return-to-home control method for UAVs under interference environments based on multi-source information. It acquires raw observation data from multiple sensors and the UAV's self-checked motion state in real time, constructs a navigation reliability time-series evaluation set for uncertainty quantification, generates a confidence space boundary characterizing the potential deviation range of the UAV's position and velocity within a future preset time window, and adaptively constructs the UAV's return-to-home channel by combining the return-to-home target point location and real-time environmental obstacle information. It also integrates the UAV's motion laws to establish an objective function with dynamically adjusted weights based on the uncertainty quantification results, solves for the return-to-home control command sequence with the future preset time window as the optimized time domain, and executes it cyclically until return-to-home. This achieves adaptive coordination between the return-to-home channel and control strategy under interference environments, helping to improve the environmental adaptability and mission reliability of the UAV during the return-to-home process in complex interference scenarios.

The application discloses a complex equipment-oriented artificial intelligence high-quality data set construction method, equipment and medium, the data set construction method firstly carries out preprocessing to the obtained multi-source heterogeneous original data, then obtains the uncertainty score of each data sample through uncertainty quantification, and then combines the uncertainty score and multiple data dimensions to construct a comprehensive evaluation index to determine the sample level quality score; in data enhancement, data enhancement conforming to the physical knowledge constraint is carried out on the high uncertainty, low quality score and sample sparse area to obtain a synthetic sample; then, after feature extraction and alignment of the original sample and the synthetic sample, a dynamic fusion strategy is executed to obtain a high-quality data set. The high-quality data set is helpful to improve the model robustness and generalization ability. The application further constructs a closed-loop feedback mechanism for high-quality data set application, realizes the association and coupling of data set construction and model demand, and enables data-model to evolve cooperatively.

The application provides a new energy mine truck thrust rod fatigue life prediction method, belonging to the field of federated learning and application technology. First, a multi-mine area multi-source fatigue feature dataset is constructed. Then, a double-layer neural network structure based on Bayesian modeling is designed, including a sparse prior generation module, a thrust rod time series feature encoding module and a thrust rod residual life Bayesian prediction module, to realize model parameter sparsification, time series feature expression and uncertainty quantification. Further, a graph structure based federated training and information aggregation mechanism is adopted, combined with global aggregation and local graph modeling strategy, to realize knowledge sharing and personalized adaptation between different mine areas. Finally, an online fine-tuning mechanism based on uncertainty driving is proposed, realizing rapid adaptive optimization of the global model in the new mine area environment through Bayesian inference. The method can significantly improve the accuracy, robustness and cross-domain generalization ability of thrust rod life prediction while ensuring data privacy.

This invention discloses a machine learning-based method for predicting the compressive strength of geopolymer-stabilized soil, belonging to the field of building material prediction technology. The method includes: constructing a Bayesian prediction model for geopolymer properties based on theoretical strength values, combining a physical constraint layer and a Bayesian probabilistic inference layer; training the Bayesian prediction model to generate a trained model; inputting the mix proportion parameter vector of the geopolymer-stabilized soil into the trained model to perform multiple Monte Carlo sampling predictions to obtain predicted strength values ​​and physical constraint strength values; and performing statistical analysis on the predicted strength values ​​to generate prediction confidence intervals. This invention, by generating prediction confidence intervals and physical contribution parameters, achieves a simultaneous characterization of the distribution characteristics of predicted strength and the degree of mechanistic influence, and realizes a unified expression of uncertainty quantification and mechanistic contribution within a machine learning framework.

This invention provides a method for optimizing minimum EEOI speed of ships based on a PI-BT network, considering ocean current uncertainties, belonging to the field of ship energy efficiency optimization and intelligent navigation technology. Based on measured ocean current data from shipping routes, this invention mines the characteristics and probability distribution of ocean current uncertainties through statistical testing and wavelet decomposition techniques; derives the mapping relationship between EEOI and main engine speed and ocean current velocity, establishing a minimum EEOI speed optimization model; identifies core sensitive parameters through sensitivity analysis; constructs a physically guided Bayesian Transformer (PI-BT) network, designing a dual-channel input embedding layer, a Bayesian Transformer encoder, an EEOI physical information constraint layer, and a multi-objective optimization output module; and constructs a multi-component total loss function to complete network training, achieving robust optimization of minimum EEOI speed of ships under ocean current uncertainties. This invention integrates temporal modeling, uncertainty quantification, and physical constraint capabilities, significantly improving the accuracy, robustness, and computational efficiency of the speed optimization scheme.

This invention discloses a machine learning-based method and system for predicting groundwater level changes, relating to the field of hydrogeology. The method includes: standardizing multi-source time-series data to obtain a standardized multivariate time-series data matrix; constructing a supervised learning sample set; inputting the groundwater level sequence from the supervised learning sample set into a physically-guided variational mode decomposition network; decomposing the groundwater level sequence into K intrinsic mode component sequences and a residual term sequence using a loss function with physical-driven consistency constraints; for each of the K intrinsic mode component sequences, dynamically assembling a differentiable simulator from a library of differentiable simplified physical simulators, and co-training these simulators with the goal of approximating each intrinsic mode component sequence and reconstructing the original water level sequence as a whole, resulting in K fully trained assembled differentiable simulators. This invention generates reliable, visualized prediction results through multi-simulator collaborative extrapolation and uncertainty quantification.

The application discloses a three-dimensional asymmetric excitation rotor uncertainty steady-state response analysis method and system, relates to the energy power technology field, and comprises the following steps: a three-dimensional finite element dynamics model of an excitation rotor with an axial slot is established; uncertainty quantification is carried out on the excitation rotor system, and a dynamics model of the excitation rotor system containing interval uncertainty is established; a Latin hypercube sampling method is adopted, and a Kriging surrogate model is used to solve the uncertainty steady-state response; considering the interval uncertainty, a dynamic response decoupling coordinate method is used to suppress false resonance peaks; a fast analysis method of the three-dimensional asymmetric rotor system uncertainty steady-state response is established by combining the Kriging and Latin hypercube sampling method-based surrogate model; and the three-dimensional asymmetric rotor system steady-state response is analyzed; and the method solves the phenomenon that the interval method is prone to false resonance peaks when used for uncertainty analysis of the asymmetric rotor steady-state response.

The application discloses a kind of multi-modal human perception data fusion analysis and risk prediction method and system for wisdom medical care, by constructing the physiological, behavior and environmental data of multi-source acquisition terminal network synchronous acquisition, by high-precision timestamp calibration and UWB spatial positioning, time-space registration is realized;Data is executed multi-level cleaning and uncertainty quantification, and preprocessed data with confidence is generated;Deep features are extracted using convolutional neural networks, graph neural networks, etc., respectively, and are dynamically fused into comprehensive feature vectors through cross-modal attention mechanism;The vector is input into a personalized analysis model based on user feedback continuous dynamic update, and early probability prediction and grading of health risk are realized using time series prediction algorithm;Finally, combined with user real-time location and scenario-based rule base, accurate personalized intervention suggestions are generated and output.The application realizes the closed loop of perception, analysis, prediction and intervention, and improves the accuracy and practicality of wisdom medical care health monitoring.

The application discloses an IGBT residual life prediction method based on multi-feature fusion and KPCA optimization. The method first collects IGBT collector current and voltage and other multi-source signals, extracts time domain, frequency domain, time-frequency domain and derived statistical domain features after pretreatment; then adopts a two-stage strategy of comprehensive evaluation index preliminary screening and mutual information regression fine screening to eliminate redundant features and retain high correlation features. On this basis, nonlinear dimension reduction is carried out by using kernel principal component analysis to construct a high-robustness health index, and the exponential weighted moving average and adaptive gradient detection are combined to accurately divide the degradation into three stages. Finally, the CNN-BiLSTM model is used to deeply mine the time sequence degradation features, and the MC Dropout algorithm is introduced to realize the accurate prediction and uncertainty quantification of the residual life. The application effectively solves the one-sidedness of single-source signal representation and the feature redundancy interference problem, and significantly improves the prediction accuracy and reliability.

This invention discloses an adaptive control method for bearing grinding processes based on reinforcement learning, comprising: S1, real-time acquisition of multi-source state data; S2, construction of a high-dimensional state vector; S3, construction of a historical grinding dataset; S4, construction of an improved IQL model incorporating an uncertainty perception mechanism, offline training using the historical dataset, outputting the Q-value distribution, and learning a safety policy at the safety policy layer; S5, online deployment of the model, generation of candidate commands, and evaluation of uncertainty; S6, judgment by the safety constraint layer: issuing candidate commands when uncertainty is low, and invoking the safety policy to generate safe commands when uncertainty is high; S7, execution of commands and storage of new data in the dataset, periodically optimizing the model. This invention improves the safety and reliability of online decision-making through uncertainty quantification and a safety policy safety net, achieving adaptive and precise control of complex grinding conditions.

This invention discloses a deep learning-based InSAR geological hazard deformation identification method, belonging to the field of geological hazard monitoring and prevention technology. The method includes the following steps: S1, multi-source data acquisition; S2, establishing a multi-source data spatiotemporal registration and pixel-level fusion model; S3, constructing a joint extraction network for multi-scale three-dimensional deformation features; S4, introducing elastic mechanical constraints to optimize the three-dimensional deformation field inversion; S5, fusing the physical model and deep learning output uncertainty quantification results, finally outputting the three-dimensional deformation of each pixel and its corresponding confidence assessment, forming a quantitative deformation result map for geological hazard risk assessment. This deep learning-based InSAR geological hazard deformation identification method employs surface deformation monitoring via synthetic aperture radar interferometry, multi-source remote sensing data fusion, and intelligent deformation inversion and risk assessment combining deep learning and physical constraints. It can be applied to the identification, monitoring, and risk assessment of geological hazards.

The application discloses a kind of road scene automatic labeling method, it is related to the field of automatic driving, comprising: obtaining multimodal sensor data, time synchronization camera image and laser radar point cloud;The multimodal sensor data is preprocessed;The camera image and laser radar point cloud after pre-processing are input into pre-labeling model, and pre-labeling result containing target category, three-dimensional space information and uncertainty score is generated;According to the uncertainty score, the pre-labeling result is sorted to obtain the final labeling result.High-efficiency multi-sensor fusion is realized, and more rich labeling of semantic and geometric information is generated;Through uncertainty quantification, the labeling efficiency is greatly improved, and the labeling quality is guaranteed.

The application provides a dangerous goods UN code dynamic judgment model construction method, medium and system, and belongs to the dangerous goods model construction technical field.In the application, global chemical unified classification and related data of dangerous chemicals are collected to construct a multi-modal original data set; cognitive uncertainty quantification is performed on a candidate set based on deep ensemble Bayesian inference, an accurate matching path is selected or an un-specified item degradation matching path is triggered according to the quantification result; the candidate set is coupled and pruned with a packaging category constraint, the loss weight parameter of an artificial intelligence model is adjusted according to a dynamic confidence adjustment function, and final numbering and transportation information are output. Therefore, the technical problem that in the prior art, the training sample long-tail distribution leads to serious shortage of low-frequency United Nations dangerous goods number category representation learning, and further makes the prediction accuracy of the judgment model for low-frequency categories significantly decrease is solved.

The application discloses a kind of based on big language model's molecular sieve vertical field intelligent system construction method, its characteristics are that the method includes: structure data standardization and literature information extraction, inject field knowledge and tool call specification by supervision fine tuning, carry out reinforcement learning training using GRPO group relative strategy optimization algorithm and multidimensional reward function, construct GNN graph neural network agent model, realize millisecond level property prediction and uncertainty quantification, and system deployment based on asynchronous scheduling and multistage cache etc. steps.Compared with the prior art, the application has the functions of knowledge question and answer, property prediction, reverse design and synthesis planning in the field of molecular sieve, the monomer big language model internalizes professional knowledge through deep field training, uses GNN agent model as the reward signal source of reinforcement learning to greatly improve the training efficiency, balances response speed and calculation accuracy using native Function Call capability, the method is innovative, the system is complete, and has a broad prospect of material research and application.

The present application relates to the technical field of electric vehicle charging and discharging control, and particularly relates to a V2G charging and discharging collaborative control method and system based on AI. By acquiring battery state, renewable energy output and user behavior information, uncertainty quantification is performed to generate a scenario set, a charging and discharging scheduling optimization target is constructed based on scenario probability and battery state, a two-stage solution is adopted to generate a benchmark charging and discharging plan and dynamically reschedule in view of real-time deviation, spare capacity and flexible resources are configured and risk constraints are set, the rescheduling result is converted into a control instruction to trigger operation, actual operation data is acquired to calculate deviation and feedback optimization scenario parameters and risk threshold values. The present application improves the robustness of the V2G system in dealing with uncertainty and scheduling economy.