169 results about "Uncertainty model" patented technology

The invention relates to the field of manufacturing execution systems, intelligent production scheduling and industrial data integration, in particular to a clothing regulation and control production method and system based on MES, by integrating production state data and clothing order data which are collected in real time, a production graph model is constructed, Bayesian variational inference is adopted to carry out uncertainty modeling to generate graph representation data, and the production state data and the clothing order data are integrated. And constructing a hypergraph model based on graph representation data, generating a scheduling command by using non-Euclidean embedding and agent collaborative decision, issuing the scheduling command to each production unit for execution, collecting execution data and monitoring abnormality, triggering dynamic rescheduling according to abnormality feedback, and realizing continuous closed-loop optimization of the garment production process. The production efficiency and the resource utilization rate are remarkably improved, and the production risk is reduced.

The invention relates to the technical field of medical image processing, and discloses a human body abdominal fat analysis method based on a medical image, which comprises the following steps: acquiring multi-modal medical image data such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and ultrasonic images, constructing a fusion database through multi-scale affine transformation alignment, de-noising by using a model based on a residual self-encoder, and enhancing a boundary in combination with Canny edge detection. A segmentation model is constructed based on an improved U-Net architecture, multi-modal features are fused, and a dynamic convolution kernel and a channel attention mechanism are used for optimization. A quantitative analysis model is established by adopting a multi-task joint learning framework, gradient conflicts are solved, and a lightweight sub-network is searched and generated. Carrying out uncertainty modeling on the segmentation model, and carrying out active learning annotation to optimize the performance. A multi-granularity feature fusion framework is designed, anatomical priori knowledge is embedded to construct an association graph, a structured analysis report is generated, abdominal fat can be accurately analyzed, and diagnosis and treatment of obesity-related diseases are assisted.

The invention relates to the technical field of information retrieval, in particular to an artificial intelligence digital science and technology platform based on big data analysis, which comprises a data uncertainty modeling module for receiving a big data stream, determining a confidence interval boundary for each data item to represent the uncertainty of the data item, generating a data item uncertainty parameter set, and combining each data item with the corresponding data item uncertainty parameter set, and then storing the data item and the corresponding data item uncertainty parameter set to form a structured probability data record. According to the method, by independently establishing an uncertain parameter structure for each data item in the data stream, tight binding of the data items and the corresponding uncertain parameters is realized, a structured probability data record is generated, the precision and reliability of the data in the query and calling process are guaranteed, and retrieval errors caused by isolated storage of the data items are avoided; through probability attribute grouping of a preset space and a numerical range and establishment of a multi-dimensional probability index structure, the search space and the filtering difficulty of probability data are reduced.

The invention relates to an uncertainty perception passive multi-target field adaptive image classification method, which is used for image recognition of autism spectrum disorder patients. According to the method, firstly, source domain model parameters are obtained and used for initializing a target model, then resting state functional magnetic resonance images of a plurality of imaging centers are preprocessed, and a plurality of target domains are constructed. On this basis, a current most representative target domain is selected through a minimum inter-domain difference strategy, an uncertainty modeling method based on evidence deep learning is adopted to train a target model, and class feature consistency is improved through domain contrast learning based on a class prototype in combination with a dynamically expanded auxiliary data set; and generating a pseudo tag to relieve the influence caused by tag noise. And finally, a trained target model is obtained through fine tuning optimization, and accurate classification of unknown images is realized. The method does not need to access source domain data, has the advantages of high robustness, high generalization ability and the like, and is suitable for actual cross-center medical image analysis scenes.

The invention belongs to the field of photovoltaic technology, and discloses a distributed photovoltaic and energy storage combined planning method based on deep learning, comprising the following steps: step 1, data collection: collecting a multi-source heterogeneous data set affecting distributed photovoltaic power generation and energy storage system planning; and 2, data processing: carrying out data cleaning, normalization, denoising and feature extraction operations on the multi-source heterogeneous data. According to the invention, a self-adaptive variational auto-encoder model is adopted to carry out deep feature extraction and uncertainty modeling on multi-source heterogeneous data, and a dynamic feature weighting module is introduced to carry out self-adaptive weighting according to the influence of different features of photovoltaic power generation, load demand and electricity price fluctuation, so that the feature characterization capability is optimized; potential nonlinear distribution characteristics in data are accurately captured, the fitting capability of high-dimensional data is effectively improved by combining KL divergence constraint and a dynamic weighting mechanism, and meanwhile, the robustness of the model in an extreme scene is enhanced.

The invention relates to the technical field of anti-collision systems, in particular to an offshore wind power construction safety pre-warning system based on AIS (automatic identification system) data, which comprises a ship dynamic uncertainty modeling module for acquiring AIS signal updating frequency and positioning precision marks of a target ship and generating a ship future position probability ellipse. According to the invention, the position coordinate, the speed value and the course angle of the ship are obtained in real time based on the AIS data, the reliability and the motion trend of ship position prediction are determined by combining the AIS signal updating frequency and the positioning precision mark, and the future position probability ellipse of the ship is accurately generated; a protection area is dynamically constructed, comprehensive calculation is carried out in combination with the spatial relation between a ship future position probability ellipse and the positions of surrounding fan pile foundations, and a hourly dynamic collision risk index sequence is obtained; and further according to the statistical deviation between the current motion state of the ship and the standard parameter of the function partition, combining with path difference analysis to obtain a navigation abnormity comprehensive index.

The invention discloses an optimization system and method for participation of an electric vehicle cluster in an electric power standby market, and is applied to the field of an electric power auxiliary service market, and the system comprises a market uncertainty modeling module which is used for constructing a joint uncertainty model of market price and standby calling probability, wherein the model comprises a probability distribution model and a conditional probability model; the risk quantitative evaluation module is used for evaluating risk exposure degrees of different decision-making schemes, including establishing a multi-period risk accumulation model, and comprehensively evaluating balance indexes of expected income, risk openness and opportunity cost; the robust optimization decision-making module constructs a decision-making model according to the market uncertainty modeling module and the risk quantitative evaluation module, and generates an optimal strategy that the electric vehicle cluster participates in the standby market; the adaptive learning optimization module is used for optimizing the decision model through reinforcement learning; according to the invention, risk-controllable revenue maximization can be realized, so that the electric vehicle cluster can efficiently participate in the electric power standby market.

The invention discloses a power electronic equipment radiation emission level evaluation method and device based on a reverberation chamber, a medium and equipment. The method comprises the following steps: acquiring a reverberation chamber radiation receiving power sample and a transmission coefficient sample; obtaining a radiation power calculation model of the to-be-tested power electronic equipment, constructing a radiation power function of the to-be-tested power electronic equipment, and obtaining a probability density function of a radiation power estimation value and an uncertainty model based on the radiation power function of the to-be-tested power electronic equipment; evaluating a first radiation emission level of the to-be-tested power electronic equipment based on the radiation power calculation model and the uncertainty model; the second radiation emission level is determined according to the radiation power function of the power electronic equipment to be measured, so that the statistical distribution characteristics of the electromagnetic field in the reverberation chamber can be accurately described, and the influence of the independent sampling number on the measurement uncertainty can be quantitatively analyzed; effective evaluation of statistical characteristics and measurement uncertainty of radiation emission of power electronic equipment is realized.

Embodiments of the present disclosure may include a method for processing a spectrum image measurement, the method including receiving a spectrum image measurement. In some embodiments, the spectrum image measurement may include a plurality of power-frequency data values as a function of time and time data. Embodiments may also include selecting a prediction result based at least in part on the time data and an uncertainty model. Embodiments may also include generating a residual error image by applying the prediction result to the power-frequency data. Embodiments may also include scanning the residual error image for an anomaly. In some embodiments, an anomaly may be based at least in part on a portion of the residual error image exceeding a threshold range between a prediction result value and the power-frequency measured data value.

The invention discloses an electric power and electric quantity balance analysis scene generation method and system considering source load uncertainty, and the method comprises the steps: carrying out the fitting of the distribution of new energy output and load in each time period through employing kernel density estimation according to the historical data of new energy and load, so as to obtain an uncertainty model of a source load prediction error; historical net load data is calculated based on source load historical data, a K-means clustering algorithm is adopted to cluster net load curves, typical net load curve types are obtained, and the occurrence probability of each type is calculated; on the basis of different types of net load curves, according to the new energy and load proportion superposition source load prediction error uncertainty, obtaining a typical scene of power system operation under the high-proportion new energy; and constructing an optimization model with the aim of minimizing the power and electric quantity balance gap of the local power grid / maximizing the new energy consumption, and analyzing the power and electric quantity balance problem of the local power grid researched under the access of the high-proportion new energy by taking the typical scene as input. The method can efficiently and reasonably construct the operation scene of the power system.

The invention relates to the technical field of cloud computing and multi-source data fusion, and discloses a multi-source data fusion method and system based on cloud computing, and the method comprises the steps: obtaining source scene data and target scene data, carrying out the modeling of the uncertainty distribution of a source scene through a Bayesian neural network, and obtaining the target scene data; network parameters with probability distribution are obtained; calculating the domain difference between the source scene and the target scene, and determining the distribution offset degree; uncertainty perception knowledge migration is executed, parameter probability distribution of a source scene model is migrated to a target scene, and the migration intensity is adaptively determined by domain differences; using the migration result to generate a fusion result with a confidence interval, and providing decision reliability evaluation; on the basis of actual feedback of the target scene, uncertainty model parameters are updated, and a migration strategy is optimized; according to the multi-source data fusion method for Bayesian uncertainty migration, knowledge migration can be carried out while uncertainty is reserved and adjusted.

The invention discloses a two-stage optimization scheduling method for a mine energy system. The method comprises the following steps: constructing a mine comprehensive energy system model; establishing a distributed photovoltaic output uncertainty model, quantitatively predicting error space-time correlation through a gamma function and a Gaussian mixture distribution model, and decomposing a regional error by using a node weight coefficient; a distributed ADMM coordination algorithm is designed according to a distributed photovoltaic output uncertainty model, spatial-temporal correlation compensation is realized through a local layer error transfer function and coordination layer global variable updating, and a voltage out-of-limit probability constraint is introduced; and designing a day-ahead-day two-stage optimization scheduling framework, taking the minimum sum of the power generation cost of the gas turbine in the mine integrated energy system model and the photovoltaic light abandoning penalty as a day-ahead stage target, and dynamically adjusting the power deviation through virtual energy storage in the day-ahead stage. The method can effectively solve the problem of mine energy system scheduling under distributed photovoltaic access, and is suitable for a mine comprehensive energy scene with high-proportion new energy access.

Method and device for ship collision avoidance decision-making in restricted waters based on uncertainty modeling. The method includes: combining a ship motion model with a random variable representing the uncertainty of ship maneuverability to construct a motion model considering the uncertainty of ship maneuverability; constructing a ship observation model considering observation uncertainty using a Gaussian distribution; simulating the steering and speed changes of the ship using a Gaussian distribution to construct an uncertainty model for the ship's compliance with rules; capturing the motion uncertainty of other ships during navigation through a deep Gaussian process model and constructing a prediction model for the trajectories of other ships considering motion uncertainty; constructing an optimization model for ship collision avoidance behavior decision-making based on the above uncertainty models and solving the optimal collision avoidance behavior decision for the ship during navigation. The present invention comprehensively considers various uncertainty factors encountered by ships during navigation in complex and dynamic restricted waters, and establishes an optimization model for ship collision avoidance behavior decision-making with strong adaptability to effectively make ship collision avoidance decisions.

The invention belongs to the technical field of power distribution network reconstruction, and particularly relates to a power distribution network multistage reconstruction method and device considering main-distribution linkage under source load uncertainty, and the method comprises the steps: constructing a photovoltaic output model and a load model, and forming a source load uncertainty model; constructing a source load scene, and performing optical load scene generation based on Latin hypercube sampling and Cholesky decomposition through correlation modeling; performing scene reduction; dividing time periods by adopting a fuzzy C-means clustering method; constructing a reconstruction level evaluation double-layer model with the goal of minimizing light abandoning and load loss cost; converting the reconstruction level evaluation double-layer model into a single-layer model through integrated correlation modeling, and setting constraint conditions; and solving the single-layer model to obtain a multi-stage dynamic reconstruction scheme of the power distribution network. According to the method, through multi-scene modeling, time period dynamic division and multi-level collaborative optimization, efficient and stable operation of the power distribution network is realized, and the light abandoning and load loss cost is remarkably reduced.

The invention discloses a mangrove forest protection effect intelligent evaluation and scene prediction system, and relates to the field of ecological environment information intelligence, and the system comprises a key driving factor recognition module which is used for determining a core variable influencing the protection effect and a dynamic response interval of the core variable based on a statistical analysis and nonlinear fitting method; the protection effect quantitative evaluation module is used for calculating a net improvement effect of protection intervention by comparing ecological index differences inside and outside the protection area; the causal effect analysis module is used for separating independent contributions of natural factors and artificial protection by adopting a dual machine learning framework; the space-time dynamic modeling module is used for performing uncertainty modeling on the space-time evolution of the ecological indexes by using a Bayesian hierarchical structure; and the multi-scene prediction module is used for realizing ecological response prediction under multi-factor driving and supporting input and simulation of user-defined scenes. According to the scheme, multi-dimensional and high-precision evaluation and future trend reliable prediction of mangrove forest protection effects can be realized.

The invention discloses a thermal hydraulic flow field prediction method based on an evidence physical information neural network, and the method comprises the specific steps: collecting different physical quantity data sets in a thermal hydraulic system, and carrying out the preprocessing of the data; constructing data loss; constructing physical loss; constructing an evidence uncertainty modeling mechanism, and dynamically allocating evidence weights according to the reliability of different data and physical constraints; constructing a total loss function by integrating the data error, the physical residual error and the evidence reliability; and outputting the trained neural network model and prediction. According to the invention, by introducing an evidence learning mechanism, the uncertainty of the model is dynamically estimated in the network training process, and the adaptability of the model to data with inconsistent noise sensitivity degrees is significantly improved; the uncertainty of the evidence is mapped into a physical quantity weighting coefficient, a uniform normalized weighting loss function is constructed, and the subjectivity and robustness problems caused by manual setting of hyper-parameters for each physical quantity in a traditional physical information neural network are effectively avoided.

The invention discloses a multi-disaster power distribution network recovery method and device considering source load uncertainty, a storage medium and computer equipment, and the method comprises the steps: obtaining meteorological data under a current disaster, determining the fault probability of each line through a preset line fault probability calculation formula based on the meteorological data, and determining a fault line; according to the fault line, identifying a to-be-restored power supply area from the target power distribution network, according to a preset load uncertainty model, determining a predicted load demand of each load node in the to-be-restored power supply area, and based on the predicted load demand, determining a rigid load demand and a flexible load demand of the to-be-restored power supply area; generating a plurality of predicted disaster scenes corresponding to the to-be-restored power supply area according to the meteorological data under the current disaster; according to the network topology of the to-be-restored power supply area, constructing an objective function and constraint conditions; and solving the target function in each predicted disaster scene to obtain a power supply recovery scheme in each predicted disaster scene.

The invention provides a polarization / inertia / vision intelligent navigation method based on model error learning, which belongs to the field of navigation and comprises the following steps: modeling a polarization / inertia / vision integrated navigation system, and establishing a system state equation by taking an inertial navigation error and a camera pose error as system state quantities; establishing a system measurement equation based on the information of the polarization sensor and the visual sensor; a neural network learning modeling error is constructed by considering uncertainty modeling errors of a system state equation and a measurement equation caused by system parameter errors and environmental interference, and the precision of a polarization / inertia / vision integrated navigation system model is improved; the multi-state constraint Kalman filtering method of the embedded neural network is established for solving the problems that in an actual environment, sensor noise statistical characteristics are unknown and time-varying due to environmental factors. According to the invention, the navigation precision of the polarization / inertia / vision integrated navigation system in a complex environment can be improved.

The invention discloses an interval type parameter uncertainty model correction method based on a Riemannian manifold and Gaussian process model, and belongs to the technical field of engineering parameter uncertainty quantification and model correction. According to the method, aiming at the defect that traditional interval analysis cannot represent parameter correlation, a convexly optimized minimum volume ellipsoid model is constructed, and a coupling relation between parameters is captured through a geometric learning framework; designing a Gaussian process regression agent model based on a logarithm Euclidean metric kernel function, and keeping symmetric positive definite matrix constraints by using a manifold kernel function; and providing a Riemann gradient optimization algorithm, and realizing parameter space unconstrained optimization through matrix logarithm mapping. The technical scheme comprises three core modules: an ellipsoid convex model parameterization module for realizing and explicit representation of parameter correlation, a manifold embedding agent model module for guaranteeing mathematical consistency of physical constraints, and a manifold gradient optimization module for improving high-dimensional parameter correction efficiency. According to the method, the problems that a traditional method depends on heuristic projection, the calculation efficiency is low, and constraint keeping is difficult are effectively solved, and a high-precision and interpretable uncertainty parameter correction tool is provided for a numerical model in engineering.

The invention discloses a self-adaptive micro-grid operation optimization method, system and device and a medium, and belongs to the technical field of micro-grid operation optimization, and the method comprises the steps: obtaining the operation state data of a micro-grid; performing depth feature coding and space-time attention processing on the micro-grid operation state data through a state sensing module to generate unified state representation; carrying out online strategy learning and optimization through a strategy evolution module based on unified state representation, and outputting a strategy optimization result; a new energy uncertainty model is adopted to generate multi-scene prediction data, the prediction data and a strategy optimization result are combined, and a prediction control problem is solved through a rolling optimization module; and according to a real-time risk assessment result, the decision weights of the strategy evolution module and the rolling optimization module are adjusted, and a micro-grid operation scheduling instruction is output. According to the method, a new state deep perception-dynamic strategy learning-robustness optimization normal form is constructed, and intelligentization and adaptive learning of the micro-grid are realized.

The invention belongs to the technical field of power system automation, and provides a power distribution network multi-source measurement data fusion and state sensing method and system.Multi-source measurement equipment is integrated through a data acquisition module, data quality is monitored, and time alignment and evidence theory fusion are achieved through a data fusion module; the power distribution network state estimation module outputs an accurate state in combination with dynamic weighting and a power supply uncertainty model, the fault diagnosis module locates a fault and identifies the type based on a Bayesian network, the risk assessment module analyzes the risk and then performs early warning, and the visualization module graphically displays information. After the system is started, multi-source data are automatically collected, state estimation is carried out after fusion processing, states are monitored in real time, faults are rapidly diagnosed and positioned, risks are synchronously assessed and early warned, and operation and maintenance personnel grasp conditions and process the conditions through a visual interface.

The invention provides a shared energy storage and power transmission line cooperative configuration method, and belongs to the technical field of intelligent power grids. According to the method, power system data is collected and preprocessed, an uncertainty model of new energy output and load is established, and multiple operation scenes are generated. And constructing a collaborative optimization configuration model taking system annual comprehensive cost minimization as a target function, and considering energy storage and power transmission line related constraint conditions. And solving the model by adopting an improved particle swarm optimization algorithm to obtain an energy storage configuration scheme (installation position, capacity configuration and charging and discharging strategies) and a power transmission line planning scheme (extension path, capacity planning and upgrading reconstruction measures). And comprehensively evaluating the scheme by using a multi-index comprehensive evaluation system. By adopting the shared energy storage and power transmission line collaborative configuration method, the new energy consumption capability can be effectively improved, the blockage of the power transmission line can be relieved, the economical efficiency and reliability of the system can be improved, and the method is suitable for planning and operation of a power system with large-scale new energy access.

The invention relates to the technical field of electric digital data processing, in particular to an economic evaluation method for a compressed air energy storage system participating in wind and light absorption, which comprises the following steps of: respectively constructing mathematical models, a wind power uncertainty model and a photovoltaic uncertainty model of a plurality of components in the compressed air energy storage system; the wind and light uncertainty of the compressed air energy storage system is obtained, and compressed air energy storage and the wind and light uncertainty in the compressed air energy storage system are regarded as game subjects to obtain an optimal scheduling strategy of the compressed air energy storage system. And performing economic evaluation on the compressed air energy storage system in combination with the optimal scheduling strategy, the mathematical model, the plurality of constraint terms and the robust optimization cost to obtain an economic evaluation result of the compressed air energy storage system participating in wind and light absorption. Therefore, the technical problem that the compressed air energy storage system is difficult to effectively assess from the perspective of wind and light absorption due to the fact that an uncertainty model is rough and does not comprehensively consider the destructive effect of wind and light on a power grid is solved.

The invention discloses an image point cloud registration method based on multi-modal uncertainty modeling and modal alignment, and relates to the technical field of computer vision. The method comprises the steps that image data and point cloud data are received and preprocessed; and extracting a backbone network by using a pre-trained multi-modal feature, performing feature extraction on the image data and the point cloud data to obtain image features and point cloud features, and performing interactive processing on the image features and the point cloud features by using a multi-layer self-attention module and a cross attention module. According to the method, the importance of image patches is quantified through an uncertainty modeling mechanism, the network pays more attention to key areas, noise interference is reduced, multi-scale features are extracted through the hierarchical matching module, scale differences caused by perspective scaling are effectively coped with in combination with dynamic adjustment, and the image patch quality is improved. And meanwhile, by designing a feature alignment module, consistent representation of the image and point cloud features is realized, and the registration effect is greatly improved under an existing evaluation system.

The present invention discloses a decision-making method for community reserve service considering double uncertainties of an electric vehicle group. The method includes: based on the proposed reserve service architecture, considering factors such as response willingness, operation characteristics, and travel constraints, adopting a combination of basic and call reserve incentives, designing a monthly reserve contract mechanism for the electric vehicle group to establish an uncertainty model of the EV group's grid-connected power; establishing an uncertainty model of the EV group's response rate; based on the monthly reserve contract mechanism for the electric vehicle group, the uncertainty model of the EV group's grid-connected power, and the established uncertainty model of the EV group's response rate, constructing a community reserve service decision-making model with the goal of maximizing revenue; combining the genetic algorithm and the mixed integer programming method to solve the community reserve service decision-making model, and obtaining the optimal declared reserve capacity of the community, the incentive price of the electric vehicle group, and the discharge strategies of the electric vehicle group and energy storage.

The invention provides a power grid dispatching reserve capacity calculation method considering new energy and load uncertainty and a related device, and aims to solve the problems of uncertainty coping, N-1 security constraint satisfaction and calculation efficiency under high-proportion new energy grid connection. The method comprises the following steps: establishing a wind power ellipsoid uncertainty model and a load ellipsoid constraint model containing a spatial smoothing effect; introducing a participation factor to construct a generator adaptive response model, and realizing fluctuation compensation; constructing a main objective function by using conventional operation constraints (power balance, climbing rate and the like), and solving an initial scheduling scheme; the N-1 fault robustness is verified through an auxiliary objective function, and the constraint is iteratively updated; and the optimal reserve capacity is solved by adopting Benders decomposition. The method can accurately quantify the multi-source uncertainty, improve the wind power consumption capability, ensure the safe and economical operation of the system, is high in calculation efficiency, and is suitable for the calculation of the reserve capacity of a novel power system.

The present application relates to the technical field of power system operation analysis and regulation, and specifically discloses a kind of VSC-HVDC system power flow regulation capability evaluation method, comprising the following steps: S1, the power flow regulation range mathematical model of establishing containing back-to-back flexible HVDC transmission system BTB;S2, establish new energy output uncertainty model;S3, construct power flow over-limit risk and risk adaptive power regulation mechanism;S4, generalized polynomial chaos matrix method is used to evaluate the two-way power flow regulation range;S5, output power flow regulation range evaluation results and visual index.The present application uses the above-mentioned VSC-HVDC system power flow regulation capability evaluation method, realizes the collaborative quantification of new energy randomness, operation risk and BTB regulation capacity, improves the accuracy and engineering practicability of the evaluation results, and provides a decision basis for power system planning and design, operation control.

This invention relates to the fields of tunnel engineering and 3D geological modeling technology, specifically to an intelligent modeling method and system for 3D geological uncertainty in tunnels based on transition probability geostatistics and sparse boreholes. The method includes: S1, integrating multi-source data to construct a 3D geological conceptual model; S2, statistically characterizing a one-dimensional transition probability matrix, calculating and fitting a spatial continuity and 3D anisotropic variability function model; S3, calculating the prior spatial probabilities and transition adjustment factors for various lithologies, obtaining the posterior lithology distribution of nodes through Bayesian intelligent updating, and initially assigning lithology categories to nodes through random sampling; S4, assigning the most probable lithology category to each grid node, calculating the variance of lithology values ​​across all implementations, and measuring model uncertainty; S5, outputting the optimal 3D uncertainty model for the tunnel; and S6, verification and evaluation. This invention can effectively achieve 3D heterogeneous modeling and explicit quantification of uncertainty under strong geological constraints.

This invention discloses a steerable chassis trajectory tracking control method based on composite time delay estimation and compensation, comprising the following steps: establishing a discretized vehicle dynamics model considering steering execution lag, clarifying the difference between steering lag and stochastic network delay, and providing a basis for compensation design; designing an adaptive prediction time-domain function, dynamically calculating the optimal prediction time domain based on the current vehicle speed, road curvature, and network delay state; designing a control quantity considering steering lag and constructing a time delay uncertainty model, using the polyhedral method to handle the parameter uncertainty caused by time delay; performing rolling optimization solution, dynamically adjusting the optimization weights based on the uncertainty model, the adaptive time domain, and an online weight adaptive mechanism of reinforcement learning to obtain the optimal control increment. This invention solves the problems in existing technologies that simplify complex internal time delays into a single, fixed model, failing to distinguish and process their physical causes, resulting in limited compensation accuracy, poor system dynamic adaptability, and insufficient robustness.

The invention belongs to the technical field of flight path envelope prediction, and relates to a stratospheric airship flight path envelope prediction method based on an uncertainty model, and the method comprises the steps: obtaining a work task of a stratospheric airship, and building a flight path envelope prediction scene of the stratospheric airship; forecast wind field data are obtained, wind field information in a working area during the stratospheric airship air staying period is determined, and a wind field uncertainty model is obtained; according to the working scene and the scene information of the stratospheric airship, a working strategy of the stratospheric airship is formulated; according to the working strategy of the stratospheric airship, the three-degree-of-freedom kinematics model, the aerodynamic model, the wind field uncertainty model and the flight path envelope prediction scene, generating a flight path point set of the stratospheric airship in different limit scenes; and carrying out contour extraction on the flight path point set of the stratospheric airship in different limit scenes to obtain a flight path envelope. The method can predict the flight path envelope of the stratospheric airship.